diff --git a/.github/workflows/branch-docs.yml b/.github/workflows/branch-docs.yml
new file mode 100644
index 000000000..b197c02bd
--- /dev/null
+++ b/.github/workflows/branch-docs.yml
@@ -0,0 +1,55 @@
+name: ActivitySim Branch Docs
+# This workflow is provided as a service for forks to build branch-specific documentation.
+
+on: push
+
+jobs:
+  docbuild:
+    if: "contains(github.event.head_commit.message, '[makedocs]') && (github.repository_owner != 'ActivitySim') && (github.ref_name != 'develop')"
+    # develop branch docs are built at the end of the core test workflow, regardless of repository owner or commit message flags
+    name: ubuntu-latest py3.9
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0  # get all tags, lets setuptools_scm do its thing
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+      - name: Install dependencies
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          miniforge-variant: Mambaforge
+          miniforge-version: latest
+          use-mamba: true
+          environment-file: conda-environments/docbuild.yml
+          python-version: 3.9
+          activate-environment: docbuild
+          auto-activate-base: false
+          auto-update-conda: false
+      - name: Install activitysim
+        run: |
+          python -m pip install .
+      - name: Conda checkup
+        run: |
+          conda info -a
+          conda list
+          echo REPOSITORY ${{ github.repository }}
+          echo REF ${{ github.ref }}
+          echo REF_NAME ${{ github.ref_name }}
+      - name: Build the docs
+        run: |
+          cd docs
+          make clean
+          make html
+      - name: Push to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v3.8.0
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          # Token is created automatically by Github Actions, no other config needed
+          publish_dir: ./docs/_build/html
+          destination_dir: ${{ github.ref_name }}
diff --git a/.github/workflows/config-testpypi-version.py b/.github/workflows/config-testpypi-version.py
new file mode 100644
index 000000000..c4359d29e
--- /dev/null
+++ b/.github/workflows/config-testpypi-version.py
@@ -0,0 +1,50 @@
+import argparse
+import copy
+import pathlib
+
+import tomli
+import tomli_w
+
+
+def split_path(path, sep="/"):
+    if isinstance(path, str):
+        return [part for part in path.split(sep) if part]
+    else:
+        return path
+
+
+def extract(mapping, path, sep="/"):
+    parts = split_path(path, sep=sep)
+    cur = mapping
+    for part in parts:
+        cur = cur[part]
+
+    return cur
+
+
+def update(mapping, path, value, sep="/"):
+    new = copy.deepcopy(mapping)
+
+    parts = split_path(path, sep=sep)
+    parent = extract(new, parts[:-1])
+    parent[parts[-1]] = value
+
+    return new
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("path", type=pathlib.Path)
+args = parser.parse_args()
+
+content = args.path.read_text()
+decoded = tomli.loads(content)
+with_local_scheme = update(
+    decoded, "tool.setuptools_scm.local_scheme", "no-local-version", sep="."
+)
+# work around a bug in setuptools / setuptools-scm
+with_setuptools_pin = copy.deepcopy(with_local_scheme)
+requires = extract(with_setuptools_pin, "build-system.requires", sep=".")
+requires[0] = "setuptools>=42,<60"
+
+new_content = tomli_w.dumps(with_setuptools_pin)
+args.path.write_text(new_content)
diff --git a/.github/workflows/core_tests.yml b/.github/workflows/core_tests.yml
new file mode 100644
index 000000000..ef4b4acaf
--- /dev/null
+++ b/.github/workflows/core_tests.yml
@@ -0,0 +1,361 @@
+name: Core Testing
+
+on:
+  push:
+    branches:
+      - '*'
+
+  pull_request:
+    branches:
+      - '*'
+
+env:
+  CACHE_NUMBER: 1  # increase to reset cache manually
+
+jobs:
+  foundation:
+
+    strategy:
+      matrix:
+        python-version: [3.9]
+    defaults:
+      run:
+        shell: bash -l {0}
+    name: linux-64-py${{ matrix.python-version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Mambaforge
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+            miniforge-variant: Mambaforge
+            miniforge-version: latest
+            activate-environment: asim-test
+            use-mamba: true
+            python-version: ${{ matrix.python-version }}
+
+      - name: Set cache date for year and month
+        run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
+
+      - uses: actions/cache@v2
+        with:
+          path: /usr/share/miniconda3/envs/asim-test
+          key: linux-64-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+        id: cache
+
+      - name: Update environment
+        run: mamba env update -n asim-test -f conda-environments/github-actions-tests.yml
+        if: steps.cache.outputs.cache-hit != 'true'
+
+      - name: Install activitysim
+        # installing without dependencies is faster, we trust that all needed dependencies
+        # are in the conda environment defined above.  Also, this avoids pip getting
+        # confused and reinstalling tables (pytables).
+        run: |
+          python -m pip install -e . --no-deps
+
+      - name: Conda checkup
+        run: |
+          mamba info -a
+          mamba list
+
+      - name: Lint with Black
+        run: |
+          # stop the build if there are problems
+          black --check --diff .
+
+      - name: Test activitysim.core
+        run: |
+          python -m pytest --pyargs activitysim.core
+
+      - name: Test activitysim.abm.models
+        run: |
+          python -m pytest --pyargs activitysim.abm.models
+
+      - name: Test activitysim.abm.test
+        run: |
+          python -m pytest --pyargs activitysim.abm.test
+
+      - name: Test activitysim.cli
+        run: |
+          python -m pytest --pyargs activitysim.cli
+
+  cross-platform:
+    # also test foundation cross platforms, but do not require a successful
+    # completion before starting regional model tests
+    needs: foundation
+    strategy:
+      matrix:
+        include:
+          - os: macos-latest
+            label: macOS
+            prefix: /Users/runner/miniconda3/envs/asim-test
+            python-version: 3.9
+
+          - os: windows-latest
+            label: win-64
+            prefix: C:\Miniconda3\envs\asim-test
+            python-version: 3.9
+
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    name: ${{ matrix.label }}
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Mambaforge
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+            miniforge-variant: Mambaforge
+            miniforge-version: latest
+            activate-environment: asim-test
+            use-mamba: true
+            python-version: ${{ matrix.python-version }}
+
+      - name: Set cache date for year and month
+        run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
+
+      - uses: actions/cache@v2
+        with:
+          path: ${{ matrix.prefix }}
+          key: ${{ matrix.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+        id: cache
+
+      - name: Update environment
+        run: mamba env update -n asim-test -f conda-environments/github-actions-tests.yml
+        if: steps.cache.outputs.cache-hit != 'true'
+
+      - name: Install activitysim
+        # installing without dependencies is faster, we trust that all needed dependencies
+        # are in the conda environment defined above.  Also, this avoids pip getting
+        # confused and reinstalling tables (pytables).
+        run: |
+          python -m pip install -e . --no-deps
+
+      - name: Conda checkup
+        run: |
+          mamba info -a
+          mamba list
+
+      - name: Lint with Black
+        run: |
+          # stop the build if there are problems
+          black --check --diff .
+
+      - name: Test activitysim.core
+        run: |
+          python -m pytest --pyargs activitysim.core
+
+      - name: Test activitysim.abm.models
+        run: |
+          python -m pytest --pyargs activitysim.abm.models
+
+      - name: Test activitysim.abm.test
+        run: |
+          python -m pytest --pyargs activitysim.abm.test
+
+      - name: Test activitysim.cli
+        run: |
+          python -m pytest --pyargs activitysim.cli
+
+  regional_models:
+    needs: foundation
+    env:
+      mamba-env-prefix: /usr/share/miniconda3/envs/asim-test
+      python-version: 3.9
+      label: linux-64
+    strategy:
+      matrix:
+        region:
+          - prototype_mtc
+          - prototype_arc
+          - placeholder_psrc
+          - prototype_marin
+          - prototype_mtc_extended
+          - placeholder_sandag
+          - prototype_sandag_xborder
+          - prototype_semcog
+          - prototype_mwcog
+          - placeholder_multiple_zone
+      fail-fast: false
+    defaults:
+      run:
+        shell: bash -l {0}
+    name: ${{ matrix.region }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Mambaforge
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          miniforge-variant: Mambaforge
+          miniforge-version: latest
+          activate-environment: asim-test
+          use-mamba: true
+          python-version: ${{ env.python-version }}
+
+      - name: Set cache date for year and month
+        run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
+
+      - uses: actions/cache@v2
+        with:
+          path: ${{ env.mamba-env-prefix }}
+          key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+        id: cache
+
+      - name: Update environment
+        run: mamba env update -n asim-test -f conda-environments/github-actions-tests.yml
+        if: steps.cache.outputs.cache-hit != 'true'
+
+      - name: Install activitysim
+        # installing without dependencies is faster, we trust that all needed dependencies
+        # are in the conda environment defined above.  Also, this avoids pip getting
+        # confused and reinstalling tables (pytables).
+        run: |
+          python -m pip install -e . --no-deps
+
+      - name: Conda checkup
+        run: |
+          mamba info -a
+          mamba list
+
+      # TODO: Cache sharrow compiled flows?  The contents of __pycache__ appear to
+      #       be ignored, so this is not working as expected right now
+      #
+      #  - name: Define Inputs
+      #    run: echo "REGION_DEFS=activitysim/examples/${{ matrix.region }}/** " >> $GITHUB_ENV
+      #
+      #  - name: Get a random number
+      #    run: echo "RANDOM_SUFFIX=${RANDOM}${RANDOM}" >> $GITHUB_ENV
+      #
+      #  - uses: actions/cache@v2
+      #    # store the regional model's cache directory in github actions cache
+      #    # this will (almost) never hit on primary key due to the random number
+      #    # but will pull the most recent cache from restore-keys... and then
+      #    # update the cache with additional compiled flows as needed.
+      #    # Hoping this will result in fewer re-compiles on tests and faster
+      #    # testing overall
+      #    with:
+      #      path: activitysim/examples/${{ matrix.region }}/test/output/cache
+      #      key: ${{ matrix.region }}-${{ env.label }}-${{ hashFiles(env.REGION_DEFS) }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}-${{ env.RANDOM_SUFFIX }}
+      #      restore-keys: |
+      #        ${{ matrix.region }}-${{ env.label }}-${{ hashFiles(env.REGION_DEFS) }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+      #    id: cache-example
+
+      - name: Test ${{ matrix.region }}
+        run: |
+          python -m pytest activitysim/examples/${{ matrix.region }}/test --durations=0
+
+  estimation_mode:
+    needs: foundation
+    env:
+      mamba-env-prefix: /usr/share/miniconda3/envs/asim-test
+      python-version: 3.9
+      label: linux-64
+    defaults:
+      run:
+        shell: bash -l {0}
+    name: estimation_mode_test
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Mambaforge
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          miniforge-variant: Mambaforge
+          miniforge-version: latest
+          activate-environment: asim-test
+          use-mamba: true
+          python-version: ${{ env.python-version }}
+
+      - name: Set cache date for year and month
+        run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV
+
+      - uses: actions/cache@v2
+        with:
+          path: ${{ env.mamba-env-prefix }}
+          key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
+        id: cache
+
+      - name: Update environment
+        run: mamba env update -n asim-test -f conda-environments/github-actions-tests.yml
+        if: steps.cache.outputs.cache-hit != 'true'
+
+      - name: Install Larch
+        run: mamba install "larch>=5.5.3"
+
+      - name: Install activitysim
+        # installing without dependencies is faster, we trust that all needed dependencies
+        # are in the conda environment defined above.  Also, this avoids pip getting
+        # confused and reinstalling tables (pytables).
+        run: |
+          python -m pip install -e . --no-deps
+
+      - name: Conda checkup
+        run: |
+          mamba info -a
+          mamba list
+
+      - name: Test Estimation Mode
+        run: |
+          python -m pytest activitysim/estimation/test/test_larch_estimation.py --durations=0
+
+  develop-docbuild:
+    needs: foundation
+    if: github.ref_name == 'develop'
+    name: develop-docbuild
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0  # get all tags, lets setuptools_scm do its thing
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+      - name: Install dependencies
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          miniforge-variant: Mambaforge
+          miniforge-version: latest
+          use-mamba: true
+          environment-file: conda-environments/docbuild.yml
+          python-version: 3.9
+          activate-environment: docbuild
+          auto-activate-base: false
+          auto-update-conda: false
+      - name: Install activitysim
+        run: |
+          python -m pip install .
+      - name: Conda checkup
+        run: |
+          conda info -a
+          conda list
+          echo ${{ github.repository }}
+          echo ${{ github.ref_name }}
+      - name: localize version switcher
+        run: |
+          python .github/workflows/localize-base-urls.py docs/_static/switcher.json
+          git update-index --assume-unchanged docs/_static/switcher.json
+          cat docs/_static/switcher.json
+      - name: Build the docs
+        run: |
+          cd docs
+          make clean
+          make html
+      - name: Push to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v3.8.0
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          # Token is created automatically by Github Actions, no other config needed
+          publish_dir: ./docs/_build/html
+          destination_dir: develop
diff --git a/.github/workflows/deployment.yml b/.github/workflows/deployment.yml
new file mode 100644
index 000000000..3145ab066
--- /dev/null
+++ b/.github/workflows/deployment.yml
@@ -0,0 +1,92 @@
+name: ActivitySim to PyPI
+
+on:
+  release:
+    types:
+      - published
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  build-artifacts:
+    runs-on: ubuntu-latest
+    if: github.repository == 'ActivitySim/activitysim'
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v4
+        name: Install Python
+        with:
+          python-version: 3.9
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build setuptools setuptools-scm wheel twine check-manifest
+      - name: Build tarball and wheels
+        run: |
+          git clean -xdf
+          git restore -SW .
+          python -m build --sdist --wheel .
+      - name: Check built artifacts
+        run: |
+          python -m twine check dist/*
+          pwd
+          if [ -f dist/activitysim-0.0.0.tar.gz ]; then
+            echo "❌ INVALID VERSION NUMBER"
+            exit 1
+          else
+            echo "✅ Looks good"
+          fi
+      - uses: actions/upload-artifact@v3
+        with:
+          name: releases
+          path: dist
+
+  test-built-dist:
+    needs: build-artifacts
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/setup-python@v4
+        name: Install Python
+        with:
+          python-version: 3.9
+      - uses: actions/download-artifact@v3
+        with:
+          name: releases
+          path: dist
+      - name: List contents of built dist
+        run: |
+          ls -ltrh
+          ls -ltrh dist
+      - name: Verify the built dist/wheel is valid
+        if: github.event_name == 'push'
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install dist/activitysim*.whl
+          python -m activitysim --version
+      - name: Publish package to TestPyPI
+        if: github.event_name == 'push'
+        uses: pypa/gh-action-pypi-publish@v1.5.1
+        with:
+          user: __token__
+          password: ${{ secrets.TESTPYPI_TOKEN }}
+          repository_url: https://test.pypi.org/legacy/
+          verbose: true
+
+  upload-to-pypi:
+    needs: test-built-dist
+    if: github.event_name == 'release'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/download-artifact@v3
+        with:
+          name: releases
+          path: dist
+      - name: Publish package to PyPI
+        uses: pypa/gh-action-pypi-publish@v1.5.1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_TOKEN }}
+          verbose: true
\ No newline at end of file
diff --git a/.github/workflows/localize-base-urls.py b/.github/workflows/localize-base-urls.py
new file mode 100644
index 000000000..a32be5e43
--- /dev/null
+++ b/.github/workflows/localize-base-urls.py
@@ -0,0 +1,18 @@
+import argparse
+import os
+import pathlib
+
+GITHUB_REPOSITORY_OWNER = os.environ.get(
+    "GITHUB_REPOSITORY_OWNER", "ActivitySim"
+).lower()
+
+parser = argparse.ArgumentParser()
+parser.add_argument("path", type=pathlib.Path)
+args = parser.parse_args()
+
+if GITHUB_REPOSITORY_OWNER != "activitysim":
+    content = args.path.read_text()
+    new_content = content.replace(
+        "activitysim.github.io", f"{GITHUB_REPOSITORY_OWNER}.github.io"
+    )
+    args.path.write_text(new_content)
diff --git a/.github/workflows/test-deploy.yml b/.github/workflows/test-deploy.yml
new file mode 100644
index 000000000..111a472d3
--- /dev/null
+++ b/.github/workflows/test-deploy.yml
@@ -0,0 +1,81 @@
+name: Test to PyPI
+
+on:
+  push:
+    branches:
+      - 'main'
+      - 'develop'
+
+# no need for concurrency limits
+
+jobs:
+  build-artifacts:
+    runs-on: ubuntu-latest
+    if: github.repository == 'ActivitySim/activitysim'
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v4
+        name: Install Python
+        with:
+          python-version: "3.9"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build setuptools setuptools-scm wheel twine check-manifest
+          python -m pip install tomli tomli_w
+      - name: Disable local versions
+        run: |
+          python .github/workflows/config-testpypi-version.py pyproject.toml
+          git update-index --assume-unchanged pyproject.toml
+          cat pyproject.toml
+      - name: Build tarball and wheels
+        run: |
+          git clean -xdf
+          python -m build --sdist --wheel .
+      - name: Check built artifacts
+        run: |
+          python -m twine check dist/*
+          pwd
+          if [ -f dist/activitysim-0.0.0.tar.gz ]; then
+            echo "❌ INVALID VERSION NUMBER"
+            exit 1
+          else
+            echo "✅ Looks good"
+          fi
+      - uses: actions/upload-artifact@v3
+        with:
+          name: releases
+          path: dist
+
+  test-built-dist:
+    needs: build-artifacts
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/setup-python@v4
+        name: Install Python
+        with:
+          python-version: "3.9"
+      - uses: actions/download-artifact@v3
+        with:
+          name: releases
+          path: dist
+      - name: List contents of built dist
+        run: |
+          ls -ltrh
+          ls -ltrh dist
+      - name: Verify the built dist/wheel is valid
+        if: github.event_name == 'push'
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install dist/activitysim*.whl
+          python -m activitysim --version
+      - name: Publish package to TestPyPI
+        if: github.event_name == 'push'
+        uses: pypa/gh-action-pypi-publish@v1.5.1
+        with:
+          user: __token__
+          password: ${{ secrets.TESTPYPI_TOKEN }}
+          repository_url: https://test.pypi.org/legacy/
+          verbose: true
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 6db55f67e..f89439651 100644
--- a/.gitignore
+++ b/.gitignore
@@ -73,3 +73,5 @@ _test_est
 *_local.*
 
 **/output/
+**/_generated_version.py
+docs/**/_generated
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 000000000..4556a45a8
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,26 @@
+repos:
+
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.3.0
+  hooks:
+  - id: check-yaml
+    exclude: logging.yaml  # TODO don't exclude, will require fixing logging
+  - id: end-of-file-fixer
+    exclude: .*\.ipynb
+  - id: trailing-whitespace
+
+- repo: https://github.com/pycqa/isort
+  rev: 5.10.1
+  hooks:
+  - id: isort
+    args: ["--profile", "black", "--filter-files"]
+
+- repo: https://github.com/psf/black
+  rev: 22.6.0
+  hooks:
+    - id: black
+
+- repo: https://github.com/PyCQA/flake8
+  rev: 5.0.4
+  hooks:
+    - id: flake8
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index a94e2b7d1..000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,104 +0,0 @@
-os: linux
-language: python
-
-env:
-  global:
-  - TEST_ENV=activitysim-test
-
-jobs:
-  include:
-    # Add new TEST_SUITE jobs as needed via Travis build matrix expansion
-    # define an alternative TEST_ENV to add special dependencies for particular tests
-    # Test suites are arranged in groups; all tests in a group must pass before
-    # the next stage group can begin.
-
-    - stage: Core Functionality
-      env: TEST_SUITE=activitysim/abm/models
-    - env: TEST_SUITE=activitysim/abm/test
-    - env: TEST_SUITE=activitysim/cli
-    - env: TEST_SUITE=activitysim/core
-
-    - stage: Examples
-      name: "Prototype MTC"
-      env: TEST_SUITE=activitysim/examples/prototype_mtc/test
-    - name: "Prototype MTC (Extended)"
-      env: TEST_SUITE=activitysim/examples/prototype_mtc_extended/test
-    - name: "Multizone Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_multiple_zone/test
-    - name: "Marin Example"
-      env: TEST_SUITE=activitysim/examples/prototype_marin/test
-    - name: "ARC Example"
-      env: TEST_SUITE=activitysim/examples/prototype_arc/test
-    - name: "SEMCOG Example"
-      env: TEST_SUITE=activitysim/examples/prototype_semcog/test
-    - name: "PSRC Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_psrc/test
-    - name: "SANDAG Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_sandag/test
-    - name: "SANDAG Cross-Border Example"
-      env: TEST_SUITE=activitysim/examples/prototype_sandag_xborder/test
-
-    - stage: Estimation Mode
-      name: "Larch Test"
-      env: TEST_SUITE=activitysim/estimation/test/test_larch_estimation.py TEST_ENV="activitysim-test-larch"
-
-    - stage: Deployment
-      name: Documentation
-      env: TEST_ENV="activitysim-test-larch"
-      script:
-        - coveralls
-        # Build docs
-        - mamba install sphinx numpydoc
-        - conda install -c conda-forge sphinx_rtd_theme==0.5.2
-        - cd docs
-        - make clean
-        - make html
-        - touch _build/html/.nojekyll
-      deploy:
-        - provider: pages
-          local_dir: docs/_build/html
-          skip_cleanup: true
-          github_token: $GH_TOKEN  # Set in the settings page of the repository, as a secure variable
-          keep_history: true
-          on:
-            branch: master
-    - name: "PyPI Deployment"
-      script: skip     # do not want to rerun any tests
-      deploy:
-        - provider: pypi
-          username: "__token__"
-          password: "$PYPI_AUTH_TOKEN"  # Set in the settings page of the repository, as a secure variable
-          skip_existing: true
-          on:
-            branch: [ master ]
-
-python:
-  - '3.9'
-
-install:
-- wget -O Mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh
-- bash Mambaforge.sh -b -p $HOME/miniconda
-- source "$HOME/miniconda/etc/profile.d/conda.sh"
-- hash -r
-- conda config --set always_yes yes --set changeps1 no
-- mamba update -q mamba
-- mamba info -a
-# write travis python version into selected test environment definition file
-- sed -e "s/\${TRAVIS_PYTHON_VERSION}/${TRAVIS_PYTHON_VERSION}/" conda-environments/${TEST_ENV}.yml > conda-env.yml
-# create test environment in one pass
-- mamba env create -n asimtest --file conda-env.yml
-- conda activate asimtest
-- pip install .
-- pip freeze
-
-script:
-# This is the "default" script used for each test suite, unless overridden with a "script" in the jobs above.
-# build 2 and 3 zone test data twice since the Python test code on Linux sees these as different locations
-- python activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py
-- python activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py
-- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py
-- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py
-- black --check --diff activitysim
-# run specific TEST_SUITE job on travis to avoid job max time
-- travis_wait 50 py.test $TEST_SUITE --cov activitysim --cov-report term-missing --durations=0
-# coveralls # disable coveralls service, which errors frequently
diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md
index 0c5196226..e8f2a4263 100644
--- a/HOW_TO_RELEASE.md
+++ b/HOW_TO_RELEASE.md
@@ -17,7 +17,7 @@
     cd activitysim
     ```
 
-00. Per project policy, code on the master branch should have been released,
+00. Per project policy, code on the main branch should have been released,
     but if you are *preparing* a release then the code should be on the `develop`
     branch.  Switch to that branch now, and make sure it is synced to the 
     version on GitHub:
@@ -119,7 +119,7 @@
     ```
 
 00. For non-development releases, open a pull request to merge the proposed 
-    release into master. The following command will open a web browser for 
+    release into main. The following command will open a web browser for 
     you to create the pull request.
     ```sh
     gh pr create --web
@@ -127,7 +127,7 @@
     After creating the PR, confirm with the ActivitySim PMC that the release
     is ready before actually merging it.
     
-    Once final approval is granted, merge the PR into master.  The presence
+    Once final approval is granted, merge the PR into main.  The presence
     of the git tags added earlier will trigger automated build steps to
     prepare and deploy the release to pypi and conda-forge.
     
@@ -137,7 +137,7 @@
     ```
     For a development pre-release, include the `--prerelease` argument.
     As the project's policy is that only formally released code is merged
-    to the master branch, any pre-release should also be built against a 
+    to the main branch, any pre-release should also be built against a 
     non-default branch.  For example, to pre-release from the `develop`
     branch:
     ```sh
diff --git a/README.md b/README.md
index 8c8d2e32d..2df8fe152 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 ActivitySim
 ===========
 
-[![Build Status](https://travis-ci.com/ActivitySim/activitysim.svg?branch=master)](https://travis-ci.org/github/ActivitySim/activitysim)[![Coverage Status](https://coveralls.io/repos/github/ActivitySim/activitysim/badge.svg?branch=master)](https://coveralls.io/github/ActivitySim/activitysim?branch=master)
+[![Build Status](https://travis-ci.com/ActivitySim/activitysim.svg?branch=main)](https://travis-ci.org/github/ActivitySim/activitysim)[![Coverage Status](https://coveralls.io/repos/github/ActivitySim/activitysim/badge.svg?branch=main)](https://coveralls.io/github/ActivitySim/activitysim?branch=main)
 
 The mission of the ActivitySim project is to create and maintain advanced, open-source, 
 activity-based travel behavior modeling software based on best software development 
diff --git a/activitysim/__init__.py b/activitysim/__init__.py
index 497c4499e..57b8b7ab3 100644
--- a/activitysim/__init__.py
+++ b/activitysim/__init__.py
@@ -1,6 +1,18 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
-
-__version__ = "1.1.0"
 __doc__ = "Activity-Based Travel Modeling"
+
+try:
+    from ._generated_version import __version__, __version_tuple__
+except ImportError:
+    # Package is not "installed", parse git tag at runtime
+    from importlib.metadata import PackageNotFoundError, version
+
+    try:
+        __version__ = version(__package__)
+    except PackageNotFoundError:
+        # package is not installed
+        __version__ = "999"
+
+    __version_tuple__ = __version__.split(".")
diff --git a/activitysim/__main__.py b/activitysim/__main__.py
new file mode 100644
index 000000000..c7112daf9
--- /dev/null
+++ b/activitysim/__main__.py
@@ -0,0 +1,37 @@
+# This file allows running ActivitySim as a Python module from the command line.
+# For example:
+#
+#   python -m activitysim run ...
+#
+# This style of calling ActivitySim permits developers to more easily ensure
+# they are calling the correct version that has been installed with the
+# particular Python executable invoked, especially for debugging.  It also can
+# be configured to invoke differ configurations automatically (like engaging the
+# threadstopper options to prevent multithread thrashing). It is probably not
+# needed by typical users with only one installed version.
+
+import os
+import sys
+
+
+def main():
+    # clean up message formatting
+    if sys.argv and sys.argv[0].endswith("__main__.py"):
+        sys.argv[0] = "activitysim"
+
+    # threadstopper
+    if "--fast" not in sys.argv:
+        os.environ["MKL_NUM_THREADS"] = "1"
+        os.environ["OMP_NUM_THREADS"] = "1"
+        os.environ["OPENBLAS_NUM_THREADS"] = "1"
+        os.environ["NUMBA_NUM_THREADS"] = "1"
+        os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
+        os.environ["NUMEXPR_NUM_THREADS"] = "1"
+
+    from .cli.main import main
+
+    main()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py
index bce5a91e2..7ef4d43af 100644
--- a/activitysim/abm/models/joint_tour_participation.py
+++ b/activitysim/abm/models/joint_tour_participation.py
@@ -147,7 +147,7 @@ def participants_chooser(probs, choosers, spec, trace_label):
         indicating that the participant has been chosen to participate in the tour
     trace_label : str
 
-    Returns - same as logit.make_choices
+    Returns
     -------
     choices, rands
         choices, rands as returned by logit.make_choices (in same order as probs)
diff --git a/activitysim/benchmarking/asv.conf.json b/activitysim/benchmarking/asv.conf.json
index ca1360579..1b1148955 100644
--- a/activitysim/benchmarking/asv.conf.json
+++ b/activitysim/benchmarking/asv.conf.json
@@ -30,7 +30,7 @@
 
     // List of branches to benchmark. If not provided, defaults to "master"
     // (for git) or "default" (for mercurial).
-    // "branches": ["master"], // for git
+    // "branches": ["main"], // for git
     // "branches": ["default"],    // for mercurial
 
     // The DVCS being used.  If not set, it will be automatically
diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py
index 27c933470..547c8f064 100644
--- a/activitysim/core/chunk.py
+++ b/activitysim/core/chunk.py
@@ -1143,7 +1143,7 @@ def adaptive_chunked_choosers_and_alts(
     rows_per_chunk : int
 
     Yields
-    -------
+    ------
     i : int
         one-based index of current chunk
     num_chunks : int
diff --git a/activitysim/core/configuration.py b/activitysim/core/configuration.py
new file mode 100644
index 000000000..3174653b2
--- /dev/null
+++ b/activitysim/core/configuration.py
@@ -0,0 +1,291 @@
+from typing import Union
+
+try:
+    from pydantic import BaseModel as PydanticBase
+except ModuleNotFoundError:
+
+    class PydanticBase:
+        pass
+
+
+class InputTable(PydanticBase):
+    """
+    The features that define an input table to be read by ActivitySim.
+    """
+
+    tablename: str
+    """Name of the injected table"""
+
+    filename: str = None
+    """
+    Name of the CSV or HDF5 file to read.
+
+    If not provided, defaults to `input_store`
+    """
+
+    index_col: str = None
+    """table column to use for the index"""
+
+    rename_columns: dict[str, str] = None
+    """dictionary of column name mappings"""
+
+    keep_columns: list[str] = None
+    """
+    columns to keep once read in to memory.
+
+    Save only the columns needed for modeling or analysis to save on memory
+    and file I/O
+    """
+
+    h5_tablename: str = None
+    """table name if reading from HDF5 and different from `tablename`"""
+
+
+class Settings(PydanticBase):
+    """
+    The overall settings for the ActivitySim model system.
+
+    The input for these settings is typically stored in one main YAML file,
+    usually called ``settings.yaml``.
+
+    Note that this implementation is presently used only for generating
+    documentation, but future work may migrate the settings implementation to
+    actually use this pydantic code to validate the settings before running
+    the model.
+    """
+
+    models: list[str]
+    """
+    list of model steps to run - auto ownership, tour frequency, etc.
+
+    See :ref:`model_steps` for more details about each step.
+    """
+
+    resume_after: str = None
+    """to resume running the data pipeline after the last successful checkpoint"""
+
+    input_table_list: list[InputTable]
+    """list of table names, indices, and column re-maps for each table in `input_store`"""
+
+    input_store: str = None
+    """HDF5 inputs file"""
+
+    create_input_store: bool = False
+    """
+    Write the inputs as read in back to an HDF5 store.
+
+    If enabled, this writes the store to the outputs folder to use for subsequent
+    model runs, as reading HDF5 can be faster than reading CSV files."""
+
+    households_sample_size: int = None
+    """
+    Number of households to sample and simulate
+
+    If omitted or set to 0, ActivitySim will simulate all households.
+    """
+    trace_hh_id: Union[int, list] = None
+    """
+    Trace household id(s)
+
+    If omitted, no tracing is written out
+    """
+
+    trace_od: list[int] = None
+    """
+    Trace origin, destination pair in accessibility calculation
+
+    If omitted, no tracing is written out.
+    """
+
+    chunk_training_mode: str = None
+    """
+    The method to use for chunk training.
+
+    Valid values include {disabled, training, production, adaptive}.
+    See :ref:`chunk_size` for more details.
+    """
+
+    chunk_size: int = None
+    """
+    Approximate amount of RAM to allocate to ActivitySim for batch processing.
+
+    See :ref:`chunk_size` for more details.
+    """
+
+    chunk_method: str = None
+    """
+    Memory use measure to use for chunking.
+
+    See :ref:`chunk_size`.
+    """
+
+    checkpoints: Union[bool, list] = True
+    """
+    When to write checkpoint (intermediate table states) to disk.
+
+    If True, checkpoints are written at each step. If False, no intermediate
+    checkpoints will be written before the end of run.  Or, provide an explicit
+    list of models to checkpoint.
+    """
+
+    check_for_variability: bool = False
+    """
+    Debugging feature to find broken model specifications.
+
+    Enabling this check does not alter valid results but slows down model runs.
+    """
+
+    log_alt_losers: bool = False
+    """
+    Write out expressions when all alternatives are unavailable.
+
+    This can be useful for model development to catch errors in specifications.
+    Enabling this check does not alter valid results but slows down model runs.
+    """
+
+    use_shadow_pricing: bool = False
+    """turn shadow_pricing on and off for work and school location"""
+
+    output_tables: list[str] = None
+    """list of output tables to write to CSV or HDF5"""
+
+    want_dest_choice_sample_tables: bool = False
+    """turn writing of sample_tables on and off for all models"""
+
+    cleanup_pipeline_after_run: bool = False
+    """
+    Cleans up pipeline after successful run.
+
+    This will clean up pipeline only after successful runs, by creating a
+    single-checkpoint pipeline file, and deleting any subprocess pipelines.
+    """
+
+    sharrow: Union[bool, str] = False
+    """
+    Set the sharrow operating mode.
+
+    .. versionadded:: 1.2
+
+    * `false` - Do not use sharrow.  This is the default if no value is given.
+    * `true` - Use sharrow optimizations when possible, but fall back to
+      legacy `pandas.eval` systems when any error is encountered.  This is the
+      preferred mode for running with sharrow if reliability is more important
+      than performance.
+    * `require` - Use sharrow optimizations, and raise an error if they fail
+      unexpectedly.  This is the preferred mode for running with sharrow
+      if performance is a concern.
+    * `test` - Run every relevant calculation using both sharrow and legacy
+      systems, and compare them to ensure the results match.  This is the slowest
+      mode of operation, but useful for development and debugging.
+    """
+
+
+class ZarrDigitalEncoding(PydanticBase):
+    """Digital encoding instructions for skim tables.
+
+    .. versionadded:: 1.2
+    """
+
+    regex: str
+    """A regular expression for matching skim matrix names.
+
+    All skims with names that match under typical regular expression rules
+    for Python will be processed together.
+    """
+
+    joint_dict: str
+    """The name of the joint dictionary for this group.
+
+    This must be a unique name for this set of skims, and a new array
+    will be added to the Dataset with this name.  It will be an integer-
+    type array indicating the position of each element in the jointly
+    encoded dictionary."""
+
+
+class TAZ_Settings(PydanticBase):
+    """
+    Complex settings for TAZ skims that are not just OMX file(s).
+
+    .. versionadded:: 1.2
+    """
+
+    omx: str = None
+    """The filename of the data stored in OMX format.
+
+    This is treated as a fallback for the raw input data, if ZARR format data
+    is not available.
+    """
+
+    zarr: str = None
+    """The filename of the data stored in ZARR format.
+
+    Reading ZARR data can be much faster than reading OMX format data, so if
+    this filename is given, the ZARR file format is preferred if it exists. If
+    it does not exist, then OMX data is read in and then ZARR data is written
+    out for future usage.
+
+    .. versionadded:: 1.2
+    """
+
+    zarr_digital_encoding: list[ZarrDigitalEncoding] = None
+    """
+    A list of encodings to apply before saving skims in ZARR format.
+
+    .. versionadded:: 1.2
+    """
+
+
+class NetworkSettings(PydanticBase):
+    """
+    Network level of service and skims settings
+
+    The input for these settings is typically stored in one YAML file,
+    usually called ``network_los.yaml``.
+    """
+
+    zone_system: int
+    """Which zone system type is used.
+
+    * 1 - TAZ only.
+    * 2 - MAZ and TAZ.
+    * 3 - MAZ, TAZ, and TAP
+    """
+
+    taz_skims: Union[str, TAZ_Settings] = None
+    """Instructions for how to load and pre-process skim matrices.
+
+    If given as a string, it is interpreted as the location for OMX file(s),
+    either as a single file or as a glob-matching pattern for multiple files.
+    The time period for the matrix must be represented at the end of the matrix
+    name and be seperated by a double_underscore (e.g. `BUS_IVT__AM` indicates base
+    skim BUS_IVT with a time period of AM.
+
+    Alternatively, this can be given as a nested dictionary defined via the
+    TAZ_Settings class, which allows for ZARR transformation and pre-processing.
+    """
+
+    skim_time_periods: dict
+    """time period upper bound values and labels
+
+    * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+    * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes)
+    * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+    * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+    """
+
+    read_skim_cache: bool = False
+    """Read cached skims (using numpy memmap) from output directory.
+
+    Reading from memmap is much faster than omx, but the memmap is a huge
+    uncompressed file.
+    """
+
+    write_skim_cache: bool = False
+    """Write memmapped cached skims to output directory.
+
+    This is needed if you want to use the cached skims to speed up subsequent
+    runs.
+    """
+
+    cache_dir: str = None
+    """alternate dir to read/write cache files (defaults to output_dir)"""
diff --git a/activitysim/core/logit.py b/activitysim/core/logit.py
index 652965e39..784e073f8 100644
--- a/activitysim/core/logit.py
+++ b/activitysim/core/logit.py
@@ -423,7 +423,7 @@ def _each_nest(spec, parent_nest, post_order):
         (post-order means we yield the alternatives sub-tree before current node.)
 
     Yields
-    -------
+    ------
         spec_node : dict
             Nest tree spec dict for this node subtree
         nest : Nest
@@ -493,7 +493,7 @@ def each_nest(nest_spec, type=None, post_order=False):
         (post-order means we yield the alternatives sub-tree before current node.)
 
     Yields
-    -------
+    ------
         nest : Nest
             Nest object with info about the current node (nest or leaf)
     """
diff --git a/activitysim/estimation/larch/general.py b/activitysim/estimation/larch/general.py
index 2639db3e0..1844d5964 100644
--- a/activitysim/estimation/larch/general.py
+++ b/activitysim/estimation/larch/general.py
@@ -48,7 +48,7 @@ def cv_to_ca(alt_values, dtype="float64", required_labels=None):
     x_ca_tall = alt_values.stack()
 
     # Set the last level index name to 'altid'
-    x_ca_tall.index.rename("altid", -1, inplace=True)
+    x_ca_tall.index.rename("altid", level=-1, inplace=True)
     c_, v_, a_ = x_ca_tall.index.names
 
     # case and alt id's should be integers.
diff --git a/activitysim/estimation/test/test_larch_estimation.py b/activitysim/estimation/test/test_larch_estimation.py
index 0a427b539..b90a0ee70 100644
--- a/activitysim/estimation/test/test_larch_estimation.py
+++ b/activitysim/estimation/test/test_larch_estimation.py
@@ -42,7 +42,9 @@ def est_data():
     os.chdir(cwd)
 
 
-def _regression_check(dataframe_regression, df, basename=None):
+def _regression_check(dataframe_regression, df, basename=None, rtol=None):
+    if rtol is None:
+        rtol = 0.1
     dataframe_regression.check(
         df.select_dtypes("number")
         .drop(columns=["holdfast"], errors="ignore")
@@ -50,8 +52,8 @@ def _regression_check(dataframe_regression, df, basename=None):
         # pandas 1.3 handles int8 dtypes as actual numbers, so holdfast needs to be dropped manually
         # we're dropping it not adding to the regression check so older pandas will also work.
         basename=basename,
-        default_tolerance=dict(atol=1e-6, rtol=0.1)
-        # set a little loose, as there is sometimes a little variance in these
+        default_tolerance=dict(atol=1e-6, rtol=rtol)
+        # can set a little loose, as there is sometimes a little variance in these
         # results when switching backend implementations. We're checking all
         # the parameters and the log likelihood, so modest variance in individual
         # parameters, especially those with high std errors, is not problematic.
@@ -89,16 +91,23 @@ def test_simple_simulate(est_data, num_regression, dataframe_regression, name, m
 
 
 @pytest.mark.parametrize(
-    "name,method",
+    "name,method,rtol",
     [
-        ("workplace_location", "SLSQP"),
-        ("school_location", "SLSQP"),
-        ("non_mandatory_tour_destination", "SLSQP"),
-        ("atwork_subtour_destination", "BHHH"),
-        ("trip_destination", "SLSQP"),
+        ("workplace_location", "SLSQP", None),
+        ("school_location", "SLSQP", None),
+        ("non_mandatory_tour_destination", "SLSQP", None),
+        ("atwork_subtour_destination", "BHHH", None),
+        ("trip_destination", "SLSQP", 0.12),
+        # trip_destination model has unusual parameter variance on a couple
+        # parameters when switching platforms, possibly related to default data
+        # types and high standard errors.  Most parameters and the overall
+        # log likelihoods behave fine, suggesting it is just a numerical
+        # precision issue.
     ],
 )
-def test_location_model(est_data, num_regression, dataframe_regression, name, method):
+def test_location_model(
+    est_data, num_regression, dataframe_regression, name, method, rtol
+):
     from activitysim.estimation.larch import component_model, update_size_spec
 
     m, data = component_model(name, return_data=True)
@@ -109,7 +118,7 @@ def test_location_model(est_data, num_regression, dataframe_regression, name, me
         {"loglike_prior": loglike_prior, "loglike_converge": r.loglike},
         basename=f"test_loc_{name}_loglike",
     )
-    _regression_check(dataframe_regression, m.pf)
+    _regression_check(dataframe_regression, m.pf, rtol=rtol)
     size_spec = update_size_spec(
         m,
         data,
@@ -136,7 +145,7 @@ def test_location_model(est_data, num_regression, dataframe_regression, name, me
     ],
 )
 def test_scheduling_model(est_data, num_regression, dataframe_regression, name, method):
-    from activitysim.estimation.larch import component_model, update_size_spec
+    from activitysim.estimation.larch import component_model
 
     m, data = component_model(name, return_data=True)
     m.load_data()
@@ -255,7 +264,7 @@ def test_tour_and_subtour_mode_choice(est_data, num_regression, dataframe_regres
 
     m = tour_mode_choice_model()
     s = atwork_subtour_mode_choice_model()
-    m.extend(s)  # join the atwork subtour model to the master group
+    m.extend(s)  # join the atwork subtour model to the main group
     m.load_data()
     m.doctor(repair_ch_av="-")
     loglike_prior = m.loglike()
diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv
similarity index 100%
rename from activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_.csv
rename to activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv
diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv
similarity index 100%
rename from activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_.csv
rename to activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv
diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv
similarity index 100%
rename from activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_.csv
rename to activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv
diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_0_12_.csv
similarity index 100%
rename from activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_.csv
rename to activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_0_12_.csv
diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv
similarity index 100%
rename from activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_.csv
rename to activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv
diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml
index f9b7b50a1..5c29a9924 100644
--- a/activitysim/examples/example_manifest.yaml
+++ b/activitysim/examples/example_manifest.yaml
@@ -693,6 +693,9 @@
       placeholder_psrc
     - placeholder_sandag/configs_2_zone
     - placeholder_sandag/data_2
+    - placeholder_sandag/data_3/maz_to_maz_bike.csv
+      data_2/maz_to_maz_bike.csv
+      # the original data for data_2 was corrupted, but there's no need to store this file twice in the repo
     - placeholder_sandag/output_2
 
 - name: placeholder_sandag_2_zone_full
@@ -870,7 +873,7 @@
       2d481ec20f69204fc02a259d2d7c4e3d955d6a83b13d7bae920c9c7f8e28c517
     - prototype_sandag_xborder/data/persons_xborder.csv  # this matches the local tours_xborder, see below
     - https://raw.githubusercontent.com/activitysim/activitysim_resources/master/sandag_xborder/tap_lines.csv
-      data/tap_lines.csv 
+      data/tap_lines.csv
       750745a33f39963f0c3e4efa6135ff89fb7dd49f58f17cdbd90d62e7057fea01
     - https://raw.githubusercontent.com/activitysim/activitysim_resources/master/sandag_xborder/taps.csv
       data/taps.csv
@@ -894,3 +897,19 @@
     - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_xborder/traffic_skims_xborder_PM.omx
       data/traffic_skims_xborder_PM.omx
       87544b59488c4ca654ae8d9756dfb3a8226b85faa21e631339d86fae5dc60feb
+
+- name: prototype_mwcog
+  description: 53 zone test example for the MWCOG region
+  # activitysim create -e prototype_mwcog -d test_prototype_mwcog
+  # cd test_prototype_mwcog
+  # python simulation.py -c configs -o output -d data
+  # cd ..
+  include:
+    - prototype_mwcog/extensions
+    - prototype_mwcog/data
+    - prototype_mwcog/configs
+    - prototype_mwcog/configs_mp
+    - prototype_mwcog/output
+    - prototype_mwcog/README.MD
+    - prototype_mwcog/simulation.py
+ 
\ No newline at end of file
diff --git a/activitysim/examples/placeholder_multiple_zone/README.MD b/activitysim/examples/placeholder_multiple_zone/README.MD
index e0b6c252f..b1fc79f2f 100644
--- a/activitysim/examples/placeholder_multiple_zone/README.MD
+++ b/activitysim/examples/placeholder_multiple_zone/README.MD
@@ -1,4 +1,4 @@
 
 ### Multiple zone system setup examples
 
-See the [examples manifest](https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml) for more information.
+See the [examples manifest](https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml) for more information.
diff --git a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py
index 99f7568b8..c0f19d3b6 100644
--- a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py
+++ b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py
@@ -27,10 +27,13 @@ def mtc_example_path(dirname):
 
 
 def build_data():
-    # FIXME this irks travis
-    # subprocess.check_call(['coverage', 'run', example_path('scripts/two_zone_example_data.py')])
-    # subprocess.check_call(['coverage', 'run', example_path('scripts/three_zone_example_data.py')])
-    pass
+    if os.environ.get("TRAVIS") != "true":
+        subprocess.check_call(
+            ["coverage", "run", example_path("scripts/two_zone_example_data.py")]
+        )
+        subprocess.check_call(
+            ["coverage", "run", example_path("scripts/three_zone_example_data.py")]
+        )
 
 
 @pytest.fixture(scope="module")
@@ -52,7 +55,7 @@ def regress(zone):
         tours_df.to_csv(
             test_path(f"regress/final_tours_{zone}_zone_last_run.csv"), index=False
         )
-        print(f"regress tours")
+        print("regress tours")
         pdt.assert_frame_equal(tours_df, regress_tours_df, rtol=1e-03)
 
         # regress trips
@@ -63,7 +66,7 @@ def regress(zone):
         trips_df.to_csv(
             test_path(f"regress/final_trips_{zone}_zone_last_run.csv"), index=False
         )
-        print(f"regress trips")
+        print("regress trips")
         pdt.assert_frame_equal(trips_df, regress_trips_df, rtol=1e-03)
 
     file_path = os.path.join(os.path.dirname(__file__), "simulation.py")
@@ -74,7 +77,7 @@ def regress(zone):
         "-c",
         example_path(f"configs_{zone}_zone"),
         "-c",
-        mtc_example_path(f"configs"),
+        mtc_example_path("configs"),
         "-d",
         example_path(f"data_{zone}"),
         "-o",
diff --git a/activitysim/examples/placeholder_sandag/data_2/maz_to_maz_bike.csv b/activitysim/examples/placeholder_sandag/data_2/maz_to_maz_bike.csv
deleted file mode 100644
index 56ab0e117..000000000
--- a/activitysim/examples/placeholder_sandag/data_2/maz_to_maz_bike.csv
+++ /dev/null
@@ -1,2645 +0,0 @@
-OMAZ,DMAZ,DISTBIKE
-15299,15299,0.088166667
-15299,15298,0.7705
-15291,15281,1.050333333
-15291,15280,0.971333333
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-15297,14940,2.408333333
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-15283,15283,0.051666667
-15283,15293,0.859
-15283,15292,1.092333333
-15283,15291,0.727166667
-15283,15297,1.3485
-15283,15296,1.335333333
-15283,15295,1.642
-15283,15294,1.114
-15283,15299,0.631333333
-15283,15298,0.587833333
-15284,15278,0.253666667
-15284,15281,0.602
-15295,14940,2.135833333
-15284,15280,0.462833333
-15284,15279,0.279833333
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-15284,15283,0.3425
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-15284,15292,0.874
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-15284,15298,0.416
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-15297,15298,1.195
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diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv
index c58aab933..4b0273f67 100644
--- a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv
+++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv
@@ -1,79 +1,80 @@
 tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose
 1359025,33146,work,1,1,1,1,mandatory,1,1100.0,560.0,12593,13.0,5.0,18.0,13.0,,,SHARED2FREE,0.3653801991721616,no_subtours,,2out_0in,work
-1359066,33147,work,1,1,1,1,mandatory,1,1070.0,560.0,12593,59.0,8.0,13.0,5.0,,,SHARED3FREE,0.0994117679786244,no_subtours,,0out_3in,work
-1494647,36454,shopping,2,1,1,2,non_mandatory,1,723.0,580.0,13797,135.0,14.0,14.0,0.0,,11.54675919468374,DRIVEALONEFREE,0.1529407042214395,,,0out_1in,shopping
-1494648,36454,shopping,2,2,2,2,non_mandatory,1,729.0,580.0,13797,151.0,15.0,21.0,6.0,,11.568558678347042,WALK,-0.1460596676048219,,,0out_2in,shopping
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-52734819,1286215,eat,1,1,1,1,atwork,1,1077.0,606.0,437625,88.0,10.0,13.0,3.0,,17.962217038040098,DRIVEALONEFREE,-1.0786066119567437,,52734854.0,0out_0in,atwork
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-52915705,1290626,work,1,1,1,1,mandatory,1,1070.0,1100.0,442036,64.0,8.0,18.0,10.0,,,WALK_LRF,-0.4685879450126991,eat,,1out_0in,work
-76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,975.0,960.0,721960,151.0,15.0,21.0,6.0,,12.829462211744756,SHARED3FREE,0.2299060311424891,,,0out_0in,othdiscr
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-81048476,1976792,eat,1,1,1,1,atwork,1,540.0,517.0,761445,70.0,9.0,9.0,0.0,,18.0619307578632,DRIVEALONEFREE,0.3349711049350423,,81048511.0,0out_0in,atwork
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+52638611,1283868,maint,1,1,1,1,atwork,1,713.0,923.0,435278,154.0,16.0,16.0,0.0,,18.360719272260976,DRIVEALONEFREE,-0.2710302783470972,,52638627.0,0out_0in,atwork
+52638627,1283868,work,1,1,1,1,mandatory,1,923.0,537.0,435278,79.0,9.0,18.0,9.0,,,DRIVEALONEFREE,-0.43545366426747517,maint,,0out_1in,work
+52641825,1283946,work,1,1,1,1,mandatory,1,1005.0,523.0,435356,48.0,7.0,18.0,11.0,,,TNC_SINGLE,-0.15324519179056317,no_subtours,,0out_0in,work
+52668557,1284598,work,1,1,1,1,mandatory,1,551.0,562.0,436008,80.0,9.0,19.0,10.0,,,DRIVEALONEFREE,0.5908314098573031,no_subtours,,0out_0in,work
+52734819,1286215,eat,1,1,1,1,atwork,1,1077.0,606.0,437625,88.0,10.0,13.0,3.0,,18.129659652290968,DRIVEALONEFREE,-1.0786066119567435,,52734854.0,0out_0in,atwork
+52734854,1286215,work,1,1,1,1,mandatory,1,606.0,656.0,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.34770108350806966,eat,,0out_2in,work
+52897544,1290184,business,1,1,1,1,atwork,1,763.0,952.0,441594,99.0,11.0,11.0,0.0,,17.608830334830582,DRIVEALONEFREE,-0.2638632561642185,,52897583.0,0out_0in,atwork
+52897550,1290184,eatout,1,1,4,4,non_mandatory,1,946.0,1096.0,441594,181.0,20.0,21.0,1.0,,11.792316582973942,DRIVEALONEFREE,-0.27383050892956645,,,0out_1in,eatout
+52897569,1290184,othdiscr,3,1,1,4,non_mandatory,1,684.0,1096.0,441594,19.0,6.0,6.0,0.0,,12.786690476320405,DRIVEALONEFREE,0.009380538645919477,,,0out_0in,othdiscr
+52897570,1290184,othdiscr,3,2,2,4,non_mandatory,1,762.0,1096.0,441594,185.0,21.0,22.0,1.0,,12.837735379300371,DRIVEALONEFREE,-0.10046309164294116,,,0out_0in,othdiscr
+52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,725.0,1096.0,441594,189.0,23.0,23.0,0.0,,12.816550040191554,DRIVEALONEFREE,0.10969889039862483,,,1out_0in,othdiscr
+52897583,1290184,work,1,1,1,1,mandatory,1,952.0,1096.0,441594,81.0,9.0,20.0,11.0,,,DRIVEALONEFREE,-0.27927777887955096,business1,,0out_1in,work
+52915670,1290626,eat,1,1,1,1,atwork,1,1070.0,1070.0,442036,86.0,10.0,11.0,1.0,,19.46157380226947,WALK,0.547115313877139,,52915705.0,0out_1in,atwork
+52915705,1290626,work,1,1,1,1,mandatory,1,1070.0,1100.0,442036,64.0,8.0,18.0,10.0,,,WALK_LRF,-0.47850756825328533,eat,,1out_0in,work
+76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,975.0,960.0,721960,151.0,15.0,21.0,6.0,,12.772851804931955,SHARED3FREE,0.21983705793331118,,,0out_0in,othdiscr
+76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,980.0,960.0,721960,104.0,11.0,16.0,5.0,,12.8503916377897,SHARED2FREE,0.22336856671451474,,,0out_1in,othdiscr
+80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,564.0,509.0,760593,62.0,8.0,16.0,8.0,,13.379667308006209,SHARED3FREE,1.164517752717716,,,0out_0in,othdiscr
+80946591,1974307,eat,1,1,1,1,atwork,1,853.0,696.0,760593,113.0,12.0,13.0,1.0,,18.752242924634118,TNC_SINGLE,0.2835771146749119,,80946626.0,0out_0in,atwork
+80946626,1974307,work,1,1,1,1,mandatory,1,696.0,509.0,760593,79.0,9.0,18.0,9.0,,,WALK,0.32952252919909103,eat,,1out_0in,work
+80946637,1974308,escort,1,1,1,1,non_mandatory,1,716.0,509.0,760593,0.0,5.0,5.0,0.0,,12.669005002293558,SHARED3FREE,-0.8421136445916944,,,0out_0in,escort
+81048440,1976791,escort,1,1,1,1,non_mandatory,1,908.0,613.0,761445,124.0,13.0,13.0,0.0,,12.820903024777094,SHARED2FREE,-0.9700895599836439,,,0out_0in,escort
+81048476,1976792,eat,1,1,1,1,atwork,1,517.0,517.0,761445,70.0,9.0,9.0,0.0,,17.917187712862642,DRIVEALONEFREE,1.0395075013303896,,81048511.0,0out_0in,atwork
+81048508,1976792,social,1,1,1,1,non_mandatory,1,831.0,613.0,761445,140.0,14.0,19.0,5.0,,12.06743909439602,WALK,0.46364302229357707,,,0out_0in,social
+81048511,1976792,work,1,1,1,1,mandatory,1,517.0,613.0,761445,7.0,5.0,12.0,7.0,,,DRIVEALONEFREE,0.03163142089216641,eat,,0out_0in,work
+81130344,1978788,social,1,1,1,1,non_mandatory,1,739.0,961.0,762159,66.0,8.0,20.0,12.0,,11.906202943037743,SHARED3FREE,0.6662525964318254,,,0out_0in,social
+81130399,1978790,escort,1,1,1,1,non_mandatory,1,992.0,961.0,762159,54.0,8.0,8.0,0.0,,12.577144975000508,SHARED2FREE,-0.8831308547168382,,,0out_0in,escort
+81130429,1978790,work,1,1,1,1,mandatory,1,681.0,961.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,-0.01697772512269618,no_subtours,,0out_0in,work
 81130470,1978791,work,1,1,1,1,mandatory,1,1070.0,961.0,762159,47.0,7.0,17.0,10.0,,,SHARED2FREE,-0.928003496988582,no_subtours,,0out_0in,work
-102419958,2498047,school,1,1,1,1,mandatory,1,851.0,730.0,922602,77.0,9.0,16.0,7.0,,,WALK,2.0017504625659783,,,0out_0in,school
-102420007,2498048,work,1,1,1,1,mandatory,1,722.0,730.0,922602,46.0,7.0,16.0,9.0,,,WALK,2.055671714033067,no_subtours,,0out_0in,work
-102420048,2498049,work,1,1,1,1,mandatory,1,755.0,730.0,922602,29.0,6.0,16.0,10.0,,,WALK,1.157766391073298,no_subtours,,0out_0in,work
-107509903,2622192,school,1,1,1,1,mandatory,1,997.0,1025.0,952720,44.0,7.0,14.0,7.0,,,SHARED3FREE,-0.1774843622416207,,,0out_0in,school
-107509922,2622193,escort,1,1,1,2,non_mandatory,1,773.0,1025.0,952720,19.0,6.0,6.0,0.0,,12.864427008033694,SHARED2FREE,-0.7023079893536955,,,0out_0in,escort
-107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,550.0,1025.0,952720,61.0,8.0,15.0,7.0,,12.464179851550291,DRIVEALONEFREE,-0.0718658205254766,,,0out_0in,othmaint
-107509987,2622194,shopping,1,1,1,1,non_mandatory,1,989.0,1025.0,952720,72.0,9.0,11.0,2.0,,11.89419969827446,WALK,0.5420719730248779,,,0out_0in,shopping
-107510034,2622195,work,1,1,1,1,mandatory,1,1021.0,1025.0,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.5725252120237364,no_subtours,,0out_0in,work
-116640406,2844887,work,1,1,1,1,mandatory,1,821.0,846.0,1028031,109.0,11.0,21.0,10.0,,,DRIVEALONEFREE,0.35881282856704,no_subtours,,0out_1in,work
-120287676,2933845,school,1,1,1,1,mandatory,1,563.0,574.0,1048898,121.0,12.0,21.0,9.0,,,WALK,0.0674642264245942,,,0out_0in,school
-120287717,2933846,school,1,1,1,1,mandatory,1,515.0,574.0,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.10324431187674,,,0out_0in,school
-120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,623.0,574.0,1048898,42.0,7.0,12.0,5.0,,12.842166431288351,DRIVEALONEFREE,0.1313944840147255,,,0out_1in,othdiscr
-120287807,2933848,work,1,1,1,1,mandatory,1,502.0,574.0,1048898,31.0,6.0,18.0,12.0,,,DRIVEALONEFREE,0.2481327510805355,no_subtours,,0out_1in,work
-131881533,3216622,school,1,1,1,1,mandatory,1,1074.0,1076.0,1148260,136.0,14.0,15.0,1.0,,,WALK,-0.5000951875907141,,,0out_2in,univ
+102419958,2498047,school,1,1,1,1,mandatory,1,737.0,730.0,922602,77.0,9.0,16.0,7.0,,,WALK,2.674742093358264,,,0out_0in,school
+102420007,2498048,work,1,1,1,1,mandatory,1,722.0,730.0,922602,46.0,7.0,16.0,9.0,,,WALK,2.04119139378735,no_subtours,,0out_0in,work
+102420048,2498049,work,1,1,1,1,mandatory,1,755.0,730.0,922602,29.0,6.0,16.0,10.0,,,WALK,1.1684000946902757,no_subtours,,0out_0in,work
+107509903,2622192,school,1,1,1,1,mandatory,1,1004.0,1025.0,952720,44.0,7.0,14.0,7.0,,,WALK,0.2818642371778841,,,0out_0in,school
+107509922,2622193,escort,1,1,1,2,non_mandatory,1,773.0,1025.0,952720,19.0,6.0,6.0,0.0,,12.838724343519536,SHARED2FREE,-0.7024293828826976,,,0out_0in,escort
+107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,550.0,1025.0,952720,61.0,8.0,15.0,7.0,,12.457187924154038,DRIVEALONEFREE,-0.07186582052547667,,,0out_0in,othmaint
+107509987,2622194,shopping,1,1,1,1,non_mandatory,1,989.0,1025.0,952720,72.0,9.0,11.0,2.0,,11.894071981116028,WALK,0.5415545535543075,,,0out_0in,shopping
+107510034,2622195,work,1,1,1,1,mandatory,1,1021.0,1025.0,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.5761659299416622,no_subtours,,0out_0in,work
+116640406,2844887,work,1,1,1,1,mandatory,1,796.0,846.0,1028031,109.0,11.0,21.0,10.0,,,DRIVEALONEFREE,0.4117327764905702,no_subtours,,0out_1in,work
+120287676,2933845,school,1,1,1,1,mandatory,1,563.0,574.0,1048898,121.0,12.0,21.0,9.0,,,WALK,0.07010835444210718,,,0out_0in,school
+120287717,2933846,school,1,1,1,1,mandatory,1,515.0,574.0,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.0988307662510817,,,0out_0in,school
+120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,623.0,574.0,1048898,42.0,7.0,12.0,5.0,,12.852488990704565,DRIVEALONEFREE,0.12407659114596234,,,0out_1in,othdiscr
+120287807,2933848,work,1,1,1,1,mandatory,1,502.0,574.0,1048898,31.0,6.0,18.0,12.0,,,DRIVEALONEFREE,0.2516333086253729,no_subtours,,0out_1in,work
+131881533,3216622,school,1,1,1,1,mandatory,1,1074.0,1076.0,1148260,136.0,14.0,15.0,1.0,,,WALK,-0.4986891348518952,,,0out_2in,univ
diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv
index f22f1c787..466427db8 100644
--- a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv
+++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv
@@ -1,173 +1,176 @@
 trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum
-10872201,33146,12593,work,1,True,3,919,560,1359025,escort,13.785227555497102,5,SHARED2FREE,0.1010691700000322
-10872202,33146,12593,work,2,True,3,997,919,1359025,escort,14.800826647980395,7,WALK,0.9197060691621816
+10872201,33146,12593,work,1,True,3,919,560,1359025,escort,13.797406374403845,5,SHARED2FREE,0.10106917000003225
+10872202,33146,12593,work,2,True,3,997,919,1359025,escort,14.823334388201712,7,WALK,0.9197060691621817
 10872203,33146,12593,work,3,True,3,1100,997,1359025,work,,7,WALK,0.82889632223554
-10872205,33146,12593,work,1,False,1,560,1100,1359025,home,,18,SHARED2FREE,0.1467435629678679
-10872529,33147,12593,work,1,True,1,1070,560,1359066,work,,8,SHARED3FREE,-0.4079095023060143
-10872533,33147,12593,work,1,False,4,1070,1070,1359066,escort,13.64047730651845,12,WALK,1.909059896050356
-10872534,33147,12593,work,2,False,4,1070,1070,1359066,social,14.024445122110302,13,WALK,1.9166764346188128
-10872535,33147,12593,work,3,False,4,1070,1070,1359066,escort,13.382025653482817,13,WALK,1.9166764346188128
-10872536,33147,12593,work,4,False,4,560,1070,1359066,home,,13,DRIVEALONEFREE,-0.3973368955091486
-11957177,36454,13797,shopping,1,True,1,723,580,1494647,shopping,,14,DRIVEALONEFREE,0.179596286394362
-11957181,36454,13797,shopping,1,False,2,803,723,1494647,shopping,12.311513280490422,14,DRIVEALONEFREE,0.2409830104958434
-11957182,36454,13797,shopping,2,False,2,580,803,1494647,home,,14,TNC_SINGLE,0.0928838246517932
-11957185,36454,13797,shopping,1,True,1,729,580,1494648,shopping,,15,WALK,-1.7031016034742517
-11957189,36454,13797,shopping,1,False,3,553,729,1494648,shopping,7.163338517484656,20,WALK,-2.305136215683256
-11957190,36454,13797,shopping,2,False,3,627,553,1494648,shopping,12.132294962728002,21,WALK,0.058274110853962
-11957191,36454,13797,shopping,3,False,3,580,627,1494648,home,,21,WALK,0.2535949467857011
-11957441,36455,13797,othdiscr,1,True,1,587,580,1494680,othdiscr,,16,SHARED2FREE,1.0398658929316622
-11957445,36455,13797,othdiscr,1,False,3,756,587,1494680,othmaint,15.249247256839514,21,SHARED2FREE,0.7096157747159312
-11957446,36455,13797,othdiscr,2,False,3,648,756,1494680,social,15.113132471205256,21,SHARED2FREE,0.9397952540243504
-11957447,36455,13797,othdiscr,3,False,3,580,648,1494680,home,,21,SHARED2FREE,0.9782981839701124
+10872205,33146,12593,work,1,False,1,560,1100,1359025,home,,18,SHARED2FREE,0.14674356296786795
+10872529,33147,12593,work,1,True,1,1070,560,1359066,work,,8,SHARED3FREE,-0.40790950230601436
+10872533,33147,12593,work,1,False,4,1070,1070,1359066,escort,13.642079871849612,12,WALK,1.9090598960503562
+10872534,33147,12593,work,2,False,4,1070,1070,1359066,social,14.027127795427717,13,WALK,1.9166764346188128
+10872535,33147,12593,work,3,False,4,1070,1070,1359066,escort,13.381200357328245,13,WALK,1.9166764346188128
+10872536,33147,12593,work,4,False,4,560,1070,1359066,home,,13,DRIVEALONEFREE,-0.39733689550914864
+11957177,36454,13797,shopping,1,True,1,725,580,1494647,shopping,,13,SHARED2FREE,1.2453258408734116
+11957181,36454,13797,shopping,1,False,2,761,725,1494647,shopping,15.764611995142664,17,SHARED2FREE,1.1709751969914468
+11957182,36454,13797,shopping,2,False,2,580,761,1494647,home,,17,WALK,1.3392441607126495
+11957185,36454,13797,shopping,1,True,1,729,580,1494648,shopping,,18,WALK,-1.7031209563587821
+11957189,36454,13797,shopping,1,False,3,553,729,1494648,shopping,7.264665283489851,18,WALK,-2.3051362156832558
+11957190,36454,13797,shopping,2,False,3,627,553,1494648,shopping,12.179490253590181,18,WALK,0.058269234389169706
+11957191,36454,13797,shopping,3,False,3,580,627,1494648,home,,18,WALK,0.2535934392499022
+11957441,36455,13797,othdiscr,1,True,1,621,580,1494680,othdiscr,,16,DRIVEALONEFREE,0.18231279579933599
+11957445,36455,13797,othdiscr,1,False,3,988,621,1494680,othmaint,12.894038982208713,21,DRIVEALONEFREE,0.2197183118455912
+11957446,36455,13797,othdiscr,2,False,3,715,988,1494680,social,12.71003546323969,21,DRIVEALONEFREE,0.34189784527709266
+11957447,36455,13797,othdiscr,3,False,3,580,715,1494680,home,,21,DRIVEALONEFREE,0.30871306064437815
 11957553,36455,13797,work,1,True,1,555,580,1494694,work,,8,WALK,0.3634416376281795
 11957557,36455,13797,work,1,False,1,580,555,1494694,home,,12,WALK,0.3634848237755095
-13679289,41705,15777,eatout,1,True,1,1070,645,1709911,eatout,,9,DRIVEALONEFREE,0.3322582945146777
+13679289,41705,15777,eatout,1,True,1,1070,645,1709911,eatout,,9,DRIVEALONEFREE,0.33225829451467775
 13679293,41705,15777,eatout,1,False,1,645,1070,1709911,home,,15,SHARED2FREE,0.2798579035187319
-13679569,41706,15777,atwork,1,True,1,1070,712,1709946,atwork,,12,WALK,1.7140165672695504
-13679573,41706,15777,atwork,1,False,1,712,1070,1709946,work,,16,SHARED2FREE,1.656392941325861
-13679881,41706,15777,work,1,True,1,712,645,1709985,work,,8,DRIVEALONEFREE,0.1746524100077627
-13679885,41706,15777,work,1,False,2,1058,712,1709985,escort,12.880657454713988,18,DRIVEALONEFREE,-0.266796751882562
+13679569,41706,15777,atwork,1,True,1,988,763,1709946,atwork,,12,WALK,-1.7814320077964532
+13679573,41706,15777,atwork,1,False,1,763,988,1709946,work,,16,WALK,-1.781485908054844
+13679881,41706,15777,work,1,True,1,763,645,1709985,work,,8,DRIVEALONEFREE,0.07885919089299016
+13679885,41706,15777,work,1,False,2,1058,763,1709985,escort,12.95511617078327,18,DRIVEALONEFREE,-0.2304178431510166
 13679886,41706,15777,work,2,False,2,645,1058,1709985,home,,19,DRIVEALONEFREE,-0.2819678461804502
 16411585,50035,18261,eatout,1,True,1,986,757,2051448,eatout,,16,BIKE,-0.7708542602773313
-16411589,50035,18261,eatout,1,False,2,676,986,2051448,escort,11.34839696405484,17,BIKE,-0.3634178122038118
-16411590,50035,18261,eatout,2,False,2,757,676,2051448,home,,18,BIKE,0.4283597212315027
+16411589,50035,18261,eatout,1,False,2,676,986,2051448,escort,11.368855031767414,17,BIKE,-0.5723144319848409
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 423180670,1290184,441594,work,2,False,2,1096,1039,52897583,home,,20,DRIVEALONEFREE,-0.2200680365507812
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 611033041,1862905,721960,othdiscr,1,True,1,975,960,76379130,othdiscr,,15,SHARED2FREE,0.8489306664543732
 611033045,1862905,721960,othdiscr,1,False,1,960,975,76379130,home,,21,DRIVEALONEFREE,0.8562669493898725
 611033369,1862906,721960,othdiscr,1,True,1,980,960,76379171,othdiscr,,11,SHARED2FREE,0.8131565075329917
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 647572573,1974306,760593,othdiscr,1,False,1,509,564,80946571,home,,16,SHARED3FREE,0.9905529532027648
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+647573101,1974308,760593,escort,1,False,1,509,716,80946637,home,,5,DRIVEALONEFREE,0.8953863338640417
 648387521,1976791,761445,escort,1,True,1,908,613,81048440,escort,,13,DRIVEALONEFREE,0.4328362297251925
-648387525,1976791,761445,escort,1,False,1,613,908,81048440,home,,13,SHARED2FREE,0.4285280536748749
-648387809,1976792,761445,atwork,1,True,1,540,517,81048476,atwork,,9,DRIVEALONEFREE,-0.0203662912199864
-648387813,1976792,761445,atwork,1,False,1,517,540,81048476,work,,9,DRIVEALONEFREE,-0.0130514311108918
-648388065,1976792,761445,social,1,True,1,919,613,81048508,social,,14,SHARED2FREE,1.0075161151372274
-648388069,1976792,761445,social,1,False,1,613,919,81048508,home,,19,SHARED3FREE,1.0047231263030936
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+648387525,1976791,761445,escort,1,False,1,613,908,81048440,home,,13,SHARED2FREE,0.42852805367487495
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+648388065,1976792,761445,social,1,True,1,831,613,81048508,social,,14,WALK,-1.2078033165324606
+648388069,1976792,761445,social,1,False,1,613,831,81048508,home,,19,WALK,-1.207728371470949
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 648388093,1976792,761445,work,1,False,1,613,517,81048511,home,,12,DRIVEALONEFREE,-0.1608731955485122
 649042753,1978788,762159,social,1,True,1,739,961,81130344,social,,8,SHARED3FREE,0.873733496035867
 649042757,1978788,762159,social,1,False,1,961,739,81130344,home,,20,SHARED3FREE,0.8702085615611009
 649043193,1978790,762159,escort,1,True,1,992,961,81130399,escort,,8,SHARED2FREE,0.5349665222974443
 649043197,1978790,762159,escort,1,False,1,961,992,81130399,home,,8,SHARED2FREE,0.5205938591259834
-649043433,1978790,762159,work,1,True,1,681,961,81130429,work,,8,DRIVEALONEFREE,-0.237249322548973
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 649043437,1978790,762159,work,1,False,1,961,681,81130429,home,,17,DRIVEALONEFREE,-0.2292289864885827
 649043761,1978791,762159,work,1,True,1,1070,961,81130470,work,,7,DRIVEALONEFREE,-0.2212113455808548
-649043765,1978791,762159,work,1,False,1,961,1070,81130470,home,,17,SHARED2FREE,-0.4493643936269187
-819359665,2498047,922602,school,1,True,1,851,730,102419958,school,,9,WALK,-0.7129519836886864
-819359669,2498047,922602,school,1,False,1,730,851,102419958,home,,16,WALK,-0.7125079143265478
+649043765,1978791,762159,work,1,False,1,961,1070,81130470,home,,17,SHARED2FREE,-0.44936439362691877
+819359665,2498047,922602,school,1,True,1,737,730,102419958,school,,9,WALK,-0.03448765862389232
+819359669,2498047,922602,school,1,False,1,730,737,102419958,home,,16,WALK,-0.034053789427079574
 819360057,2498048,922602,work,1,True,1,722,730,102420007,work,,7,WALK,-0.5757010163806285
 819360061,2498048,922602,work,1,False,1,730,722,102420007,home,,16,WALK,-0.5758022637976002
-819360385,2498049,922602,work,1,True,1,755,730,102420048,work,,6,WALK,-2.133440153293813
+819360385,2498049,922602,work,1,True,1,755,730,102420048,work,,6,WALK,-2.1334401532938134
 819360389,2498049,922602,work,1,False,1,730,755,102420048,home,,16,WALK,-2.146424135711287
-860079225,2622192,952720,school,1,True,1,997,1025,107509903,school,,7,WALK,-0.8381416189260318
-860079229,2622192,952720,school,1,False,1,1025,997,107509903,home,,14,SHARED3FREE,-0.8387619711156924
+860079225,2622192,952720,school,1,True,1,1004,1025,107509903,school,,7,WALK,-0.2623158965920474
+860079229,2622192,952720,school,1,False,1,1025,1004,107509903,home,,14,WALK,-0.2623158965920474
 860079377,2622193,952720,escort,1,True,1,773,1025,107509922,escort,,6,DRIVEALONEFREE,0.5629145959926543
 860079381,2622193,952720,escort,1,False,1,1025,773,107509922,home,,6,SHARED2FREE,0.5769090131439206
 860079529,2622193,952720,othmaint,1,True,1,550,1025,107509941,othmaint,,8,DRIVEALONEFREE,0.4954921391516582
@@ -176,20 +179,20 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest
 860079901,2622194,952720,shopping,1,False,1,1025,989,107509987,home,,11,WALK,0.7145047811622831
 860080273,2622195,952720,work,1,True,1,1021,1025,107510034,work,,8,DRIVEALONEFREE,0.1571601323326824
 860080277,2622195,952720,work,1,False,1,1025,1021,107510034,home,,17,DRIVEALONEFREE,0.1570916696297328
-933123249,2844887,1028031,work,1,True,1,821,846,116640406,work,,11,DRIVEALONEFREE,0.0804499098545225
-933123253,2844887,1028031,work,1,False,2,781,821,116640406,eatout,11.902872231959044,20,DRIVEALONEFREE,0.075942323791996
-933123254,2844887,1028031,work,2,False,2,846,781,116640406,home,,21,DRIVEALONEFREE,0.1665747787316702
+933123249,2844887,1028031,work,1,True,1,796,846,116640406,work,,11,DRIVEALONEFREE,0.12762065173808665
+933123253,2844887,1028031,work,1,False,2,756,796,116640406,eatout,12.25646044961711,20,DRIVEALONEFREE,-0.03647710212827898
+933123254,2844887,1028031,work,2,False,2,846,756,116640406,home,,21,DRIVEALONEFREE,-0.04899108523921566
 962301409,2933845,1048898,school,1,True,1,563,574,120287676,school,,12,WALK,0.6010774898562959
 962301413,2933845,1048898,school,1,False,1,574,563,120287676,home,,21,WALK,0.601110793064989
 962301737,2933846,1048898,school,1,True,1,515,574,120287717,school,,8,WALK,-2.077872684673323
 962301741,2933846,1048898,school,1,False,1,574,515,120287717,home,,16,SHARED2FREE,-2.083095264404366
-962302017,2933847,1048898,othdiscr,1,True,1,623,574,120287752,othdiscr,,7,TNC_SINGLE,0.1653638612245029
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+962302017,2933847,1048898,othdiscr,1,True,1,623,574,120287752,othdiscr,,7,TNC_SINGLE,0.16536386122450294
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 962302022,2933847,1048898,othdiscr,2,False,2,574,877,120287752,home,,12,DRIVEALONEFREE,0.0157047931957291
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-962302462,2933848,1048898,work,2,False,2,574,728,120287807,home,,18,DRIVEALONEFREE,-0.1967678489672951
+962302457,2933848,1048898,work,1,True,1,502,574,120287807,work,,6,DRIVEALONEFREE,-0.05432573151273244
+962302461,2933848,1048898,work,1,False,2,728,502,120287807,escort,12.16357348271453,17,DRIVEALONEFREE,-0.0901887950029015
+962302462,2933848,1048898,work,2,False,2,574,728,120287807,home,,18,DRIVEALONEFREE,-0.19676784896729518
 1055052265,3216622,1148260,univ,1,True,1,1074,1076,131881533,univ,,14,WALK,-1.3446315964343616
-1055052269,3216622,1148260,univ,1,False,3,1074,1074,131881533,univ,9.853996342181825,15,WALK,-0.5067482781368078
-1055052270,3216622,1148260,univ,2,False,3,1070,1074,131881533,escort,12.166528210851974,15,WALK,-0.7310853193957327
+1055052269,3216622,1148260,univ,1,False,3,1074,1074,131881533,univ,9.853683224081658,15,WALK,-0.5067482781368078
+1055052270,3216622,1148260,univ,2,False,3,1070,1074,131881533,escort,12.185525189473289,15,WALK,-0.7310853193957327
 1055052271,3216622,1148260,univ,3,False,3,1076,1070,131881533,home,,15,WALK,1.4364023617430983
diff --git a/activitysim/examples/placeholder_sandag/test/test_sandag.py b/activitysim/examples/placeholder_sandag/test/test_sandag.py
index e97ad885d..58db5d2d3 100644
--- a/activitysim/examples/placeholder_sandag/test/test_sandag.py
+++ b/activitysim/examples/placeholder_sandag/test/test_sandag.py
@@ -1,6 +1,7 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 import os
+import shutil
 import subprocess
 
 import pandas as pd
@@ -32,7 +33,10 @@ def psrc_example_path(dirname):
 
 
 def build_data():
-    pass
+    shutil.copy(
+        example_path(os.path.join("data_3", "maz_to_maz_bike.csv")),
+        example_path(os.path.join("data_2", "maz_to_maz_bike.csv")),
+    )
 
 
 @pytest.fixture(scope="module")
diff --git a/activitysim/examples/prototype_marin/README.MD b/activitysim/examples/prototype_marin/README.MD
index e0b6c252f..b1fc79f2f 100644
--- a/activitysim/examples/prototype_marin/README.MD
+++ b/activitysim/examples/prototype_marin/README.MD
@@ -1,4 +1,4 @@
 
 ### Multiple zone system setup examples
 
-See the [examples manifest](https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml) for more information.
+See the [examples manifest](https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml) for more information.
diff --git a/activitysim/examples/prototype_mwcog/.gitignore b/activitysim/examples/prototype_mwcog/.gitignore
new file mode 100644
index 000000000..443919a09
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/.gitignore
@@ -0,0 +1,2 @@
+data_*/
+output_*/
diff --git a/activitysim/examples/prototype_mwcog/README.MD b/activitysim/examples/prototype_mwcog/README.MD
new file mode 100644
index 000000000..962ee831b
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/README.MD
@@ -0,0 +1,3 @@
+### MWCOG Example
+
+This is an example of the Metropolitan Washington Council of Governments ActivitySim model. This is a 53 zone portion of the model, centered on the National Mall. 
diff --git a/activitysim/examples/prototype_mwcog/configs/_dummy_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/_dummy_coefficients.csv
new file mode 100644
index 000000000..2b8df5025
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/_dummy_coefficients.csv
@@ -0,0 +1,2 @@
+coefficient_name,value,constrain
+coef_one,1,T
diff --git a/activitysim/examples/prototype_mwcog/configs/accessibility.csv b/activitysim/examples/prototype_mwcog/configs/accessibility.csv
new file mode 100644
index 000000000..c7dcc7782
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/accessibility.csv
@@ -0,0 +1,59 @@
+Description,Target,Expression
+#,,
+#,, auto peak
+#,,
+#,, assume peak occurs in AM for outbound and PM for inbound
+peak round trip time,_auPkTime,"skim_od[('SOV_TIME', 'AM')] + skim_do[('SOV_TIME', 'PM')]"
+decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile)
+auto peak retail,auPkRetail,df.RETEMP * _decay
+auto peak total,auPkTotal,df.TOTEMP * _decay
+#,,
+#,, auto off-peak
+#,,
+#,, assume midday occurs entirely in the midday period
+off-peak round trip time,_auOpTime,"skim_od[('SOV_TIME', 'MD')] + skim_do[('SOV_TIME', 'MD')]"
+decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile)
+auto off-peak retail,auOpRetail,df.RETEMP * _decay
+auto off-peak total,auOpTotal,df.TOTEMP * _decay
+#,,
+#,, transit peak
+#,,
+#,, assume peak outbound transit occurs in AM
+o-d peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'AM')]"
+o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'AM')] + skim_od[('WK_TRN_WK_XWAIT', 'AM')] + skim_od[('WK_TRN_WK_WACC_EGR', 'AM')] + skim_od[('WK_TRN_WK_WAUX', 'AM')]"
+o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+#,, assume peak inbound transit occurs in PM
+d-o peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'PM')]"
+d-o peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'PM')] + skim_od[('WK_TRN_WK_XWAIT', 'PM')] + skim_od[('WK_TRN_WK_WACC_EGR', 'PM')] + skim_od[('WK_TRN_WK_WAUX', 'PM')]"
+d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do
+round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0)
+decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit)
+transit peak retail,trPkRetail,df.RETEMP * _decay
+transit peak total,trPkTotal,df.TOTEMP * _decay
+#,,
+#,, transit off-peak
+#,,
+#,, assume off-peak outbound transit occurs in the MD time period
+o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'MD')]"
+o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'MD')] + skim_od[('WK_TRN_WK_XWAIT', 'MD')] + skim_od[('WK_TRN_WK_WACC_EGR', 'MD')] + skim_od[('WK_TRN_WK_WAUX', 'MD')]"
+o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+#,, assume off-peak inbound transit occurs in the MD time period
+d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WK_TRN_WK_TOTIVT', 'MD')]"
+d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WK_TRN_WK_IWAIT', 'MD')] + skim_do[('WK_TRN_WK_XWAIT', 'MD')] + skim_do[('WK_TRN_WK_WACC_EGR', 'MD')] + skim_do[('WK_TRN_WK_WAUX', 'MD')]"
+d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+peak transit time,_trOpTime,_trOpTime_od + _trOpTime_do
+#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job
+#round trip path is available,_rt_available,(_trOpTime > 0)
+round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0)
+decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit)
+transit off-peak retail,trOpRetail,df.RETEMP * _decay
+transit off-peak total,trOpTotal,df.TOTEMP * _decay
+#,,
+#,, non motorized
+#,,
+non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK']
+round trip path is available,_rt_available,_nmDist <= maximum_walk_distance
+decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk)
+retail accessibility,nmRetail,df.RETEMP * _decay
+total accessibility,nmTotal,df.TOTEMP * _decay
diff --git a/activitysim/examples/prototype_mwcog/configs/accessibility.yaml b/activitysim/examples/prototype_mwcog/configs/accessibility.yaml
new file mode 100644
index 000000000..b52c69ee1
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/accessibility.yaml
@@ -0,0 +1,13 @@
+
+# columns from land_use table to add to df
+land_use_columns: ['RETEMP', 'TOTEMP']
+
+CONSTANTS:
+  # dispersion parameters
+  dispersion_parameter_automobile: -0.05
+  dispersion_parameter_transit: -0.05
+  dispersion_parameter_walk: -1.00
+  # maximum walk distance in miles
+  maximum_walk_distance: 3.0
+  # perceived minute of in-vehicle time for every minute of out-of-vehicle time
+  out_of_vehicle_time_weight: 2.0
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_households.csv b/activitysim/examples/prototype_mwcog/configs/annotate_households.csv
new file mode 100644
index 000000000..c1383d174
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_households.csv
@@ -0,0 +1,42 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+#,,FIXME households.income can be negative  - so we clip?
+income,income,households.hhincadj.fillna(0)
+income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0)
+income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 50, 100, 150, np.inf], labels=[1, 2, 3, 4]).astype(int)"
+#,,
+,_MIN_VOT,setting('min_value_of_time')
+,_MAX_VOT,setting('max_value_of_time')
+,_MU,setting('distributed_vot_mu')
+,_SIGMA,setting('distributed_vot_sigma')
+median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})"
+hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)"
+#,,
+#num_workers was renamed in import,,
+,num_workers,"_PERSON_COUNT('(ESR==1)|(ESR==2)|(ESR==4)|(ESR==5)', persons, households)"
+number of non_workers,num_non_workers,households.hhsize - num_workers
+#,,
+#,,we assume that everyone 16 and older is a potential driver
+number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)"
+num_adults,num_adults,"_PERSON_COUNT('18 <= age', persons, households)"
+num_children,num_children,"_PERSON_COUNT('18 > age', persons, households)"
+num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)"
+num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)"
+num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)"
+num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)"
+num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)"
+non_family,non_family,households.HHT.isin(HHT_NONFAMILY)
+family,family,households.HHT.isin(HHT_FAMILY)
+home_is_urban,home_is_urban,"reindex(land_use.AREATYPE, households.home_zone_id) < setting('urban_threshold')"
+home_is_rural,home_is_rural,"reindex(land_use.AREATYPE, households.home_zone_id) > setting('rural_threshold')"
+#,, default for work and school location logsums before auto_ownership model is run
+,auto_ownership,households.auto_ownership
+TAZ column to match settings file,TAZ,households.home_zone_id
+number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)"
+number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)"
+number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)"
+number of part time workers,num_partTime_workers,"_PERSON_COUNT('is_parttime_worker', persons, households)"
+number of retired adults in the household,_num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)"
+Retired Adults Only Households,retired_adults_only_hh,(households.hhsize > 0) & (households.hhsize == _num_retired_adults)
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_households_cdap.csv b/activitysim/examples/prototype_mwcog/configs/annotate_households_cdap.csv
new file mode 100644
index 000000000..44b4fdcbf
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_households_cdap.csv
@@ -0,0 +1,9 @@
+Description,Target,Expression
+#,, annotate households table after cdap model has run
+num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults
+num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers
+participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0)
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_households_workplace.csv b/activitysim/examples/prototype_mwcog/configs/annotate_households_workplace.csv
new file mode 100644
index 000000000..1b53e91da
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_households_workplace.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate households table after workplace_location model has run
+#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio
+,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0)
+#,,handle persons with no locatcion
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_landuse.csv b/activitysim/examples/prototype_mwcog/configs/annotate_landuse.csv
new file mode 100644
index 000000000..8cb7aab08
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_landuse.csv
@@ -0,0 +1,9 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.HH / (land_use.LANDAREA * 640)
+employment_density,employment_density,land_use.TOTEMP / (land_use.LANDAREA * 640)
+,employment_density,employment_density.fillna(0)
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
+topology (assume flat),TOPOLOGY,1
+zero out land use for zero area,Park_Acres,"np.where(land_use.LANDAREA==0,0,land_use.Park_Acres)"
+zero out land use for zero area,GC_Acres,"np.where(land_use.LANDAREA==0,0,land_use.GC_Acres)"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons.csv
new file mode 100644
index 000000000..df047862c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons.csv
@@ -0,0 +1,68 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_0_to_5,age_0_to_5,"persons.age.between(0,5)"
+age_6_to_12,age_6_to_12,"persons.age.between(6,12)"
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+young,young,persons.age <= 25
+old,old,persons.age >= 65
+male,male,persons.SEX == 1
+female,female,persons.SEX == 2
+,esr,persons.ESR.fillna(0)
+,wkhp,persons.WKHP.fillna(0)
+,wkw,persons.WKW.fillna(0)
+,schg,persons.SCHG.fillna(0)
+,mil,persons.MIL.fillna(0)
+employment status type,pemploy,np.zeros(len(persons))
+,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)"
+,pemploy,"np.where((persons['age'] >= 16) & ((esr == 3) | (esr == 6)), PEMPLOY_NOT, pemploy)"
+,pemploy,"np.where((persons.age>=16)&((esr != 3)&(esr != 6))&(wkhp >= 35) & (wkw >= 1) & (wkw <= 4), PEMPLOY_FULL, pemploy)"
+student category,pstudent,np.zeros(len(persons))
+,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
+,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((schg < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
+,pstudent,"np.where((schg < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((schg >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where((schg >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (schg >=1) & (schg<=14),  PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where(pstudent == 0, 3, pstudent)"
+person type,ptype,np.zeros(len(persons))
+,ptype,"np.where((pemploy == 1),  PTYPE_FULL, PTYPE_NONWORK)"
+,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] >= 6) & (persons['age'] <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)"
+,ptype,"np.where((pstudent == 2)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] < 6)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] >= 16)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_DRIVING, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] >= 6) & (persons['age'] < 16)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)"
+presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, ptype == PTYPE_NONWORK)"
+presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, ptype == PTYPE_RETIRED)"
+presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, ptype == PTYPE_PRESCHOOL)"
+presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, ptype == PTYPE_DRIVING)"
+presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, ptype == PTYPE_SCHOOL)"
+presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, pemploy==PEMPLOY_FULL)"
+presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, pemploy==PEMPLOY_PART)"
+presence of university student other than self in household,has_university,"other_than(persons.household_id, ptype == PTYPE_UNIVERSITY)"
+student_is_employed,student_is_employed,"np.where(((ptype == PTYPE_UNIVERSITY) | (ptype == PTYPE_DRIVING)) & ((pemploy == PEMPLOY_FULL) | (pemploy == PEMPLOY_PART)), True, False)"
+nonstudent_to_school,nonstudent_to_school,"np.where(((ptype ==  PTYPE_FULL) | (ptype ==   PTYPE_PART) | (ptype ==  PTYPE_NONWORK) | (ptype ==  PTYPE_RETIRED))  & ((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent ==  PSTUDENT_UNIVERSITY)), True, False)"
+is_student,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY), True, False)"
+preschool age can go to preschool,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY) & (persons.age > GRADE_SCHOOL_MIN_AGE), True, is_student)"
+is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE)
+is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE)
+is_university,is_university,pstudent == PSTUDENT_UNIVERSITY
+school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)"
+school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)"
+school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
+#,,
+is_worker,is_worker,"np.where((pemploy == PEMPLOY_FULL) |( pemploy == PEMPLOY_PART), True, False)"
+#,,
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
+person number,PNUM,persons.per_num
+income,income,"reindex(households.hhincadj, persons.household_id)"
+income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0)
+income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 50, 100, 150, np.inf], labels=[1, 2, 3, 4]).astype(int)"
+is_fulltime_worker,is_fulltime_worker,"((age_16_p) & (wkhp >=35) & (wkw>=1) & (wkw<=4) & (~esr.isin([3,6])))"
+is_parttime_worker,is_parttime_worker,"((age_16_p) & (~esr.isin([3,6])) & (is_fulltime_worker == False))"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_after_hh.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_after_hh.csv
new file mode 100644
index 000000000..d3fe7f160
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_after_hh.csv
@@ -0,0 +1,23 @@
+Description,Target,Expression
+#,, annotate persons table after annotate_households
+#,, adults get full hh_value_of_time and children get 60%
+,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
+,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"
+,_hh_income,"reindex(households.hhincadj, persons.household_id)"
+,_num_adults,"reindex(households.num_adults, persons.household_id)"
+,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)"
+,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)"
+,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)"
+Income less than 25K,is_income_less25K,(_hh_income)<25000
+Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000)
+Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000)
+Income greater than 60K,is_income_greater60K,((_hh_income)>=60000)
+Income greater than 120K,is_income_greater120K,((_hh_income)>=120000)
+Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0
+all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers)
+Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0
+,_has_young_children,"reindex(households.num_young_children, persons.household_id)"
+,_has_children_6_to_12,"reindex(households.num_children_6_to_12, persons.household_id)"
+has_young_children,has_young_children,_has_young_children>0
+has_children_6_to_12,has_children_6_to_12,_has_children_6_to_12>0
+hh_child,hh_child,"reindex(households.num_children, persons.household_id)"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_cdap.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_cdap.csv
new file mode 100644
index 000000000..2ad5e56a6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_cdap.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate persons table after cdap model has run
+travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME
+under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])"
+has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))"
+has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_jtp.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_jtp.csv
new file mode 100644
index 000000000..a72c86605
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_jtp.csv
@@ -0,0 +1,3 @@
+Description,Target,Expression
+#,, annotate persons table after joint_tour_participation model has run
+num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_mtf.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_mtf.csv
new file mode 100644
index 000000000..4f8f5a8b0
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_mtf.csv
@@ -0,0 +1,10 @@
+Description,Target,Expression
+#,, annotate persons table after mandatory_tour_frequency model has run
+,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)"
+,_Q,"lambda s: ""'{}'"".format(s)"
+work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker
+work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student
+number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)"
+number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)"
+presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))"
+presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_nmtf.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_nmtf.csv
new file mode 100644
index 000000000..11f8b111e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_nmtf.csv
@@ -0,0 +1,15 @@
+Description,Target,Expression
+#,, annotate persons table after non_mandatory_tour_frequency model has run
+num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours
+total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours
+"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours
+number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) *  (num_shop_maint_tours - 1)"
+total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours
+number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) *  (num_soc_discr_tours - 1)"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_school.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_school.csv
new file mode 100644
index 000000000..553b124c3
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_school.csv
@@ -0,0 +1,7 @@
+Description,Target,Expression
+#,, annotate persons table after school_location model has run
+,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)"
+#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD
+temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))"
+temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))"
+free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)"
diff --git a/activitysim/examples/prototype_mwcog/configs/annotate_persons_workplace.csv b/activitysim/examples/prototype_mwcog/configs/annotate_persons_workplace.csv
new file mode 100644
index 000000000..707b30ab3
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/annotate_persons_workplace.csv
@@ -0,0 +1,32 @@
+Description,Target,Expression
+#,, annotate persons table after workplace_location model has run
+,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)"
+workplace_in_cbd,workplace_in_cbd,"reindex(land_use.AREATYPE, persons.workplace_zone_id) < setting('cbd_threshold')"
+work_zone_area_type,work_zone_area_type,"reindex(land_use.AREATYPE, persons.workplace_zone_id)"
+#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD
+#,,roundtrip_auto_time_to_work
+,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))"
+,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))"
+,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)"
+#,,_roundtrip_walk_time_to_work
+,_MAX_TIME_TO_WORK,999
+,_WALK_SPEED_MPH,3
+,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH"
+,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH"
+,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0)
+,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)"
+#,,_roundtrip_transit_time_to_work
+,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_TOTIVT', 'MD'))"
+,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_TOTIVT', 'MD'))"
+,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0)
+,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_IWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_IWAIT', 'MD'))"
+,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_XWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_XWAIT', 'MD'))"
+,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_WAUX', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_WAUX', 'MD'))"
+,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_WACC_EGR', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_WACC_EGR', 'MD'))"
+,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WK_TRN_WK_WACC_EGR', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WK_TRN_WK_WACC_EGR', 'MD'))"
+,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr
+#,,work_auto_savings_ratio
+,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)"
+,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)"
+#,,auto savings over walk or transit capped at 120 and normalized to unity
+,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)"
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.csv
new file mode 100644
index 000000000..76162b48b
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.csv
@@ -0,0 +1,18 @@
+Label,Description,Expression,atwork
+local_dist,local_dist,_DIST@skims['DIST'],1
+util_dist,util_dist,@_DIST,coef_dist_atwork
+util_dist_squared,"Distance squared, capped at 30 miles",@(_DIST)**2,coef_dist_squared_atwork
+util_dist_cubed,"Distance cubed, capped at 30 miles",@(_DIST)**3,coef_dist_cubed_atwork
+util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork
+util_dist_female,"Distance,female",@(df['female']==True) * _DIST,coef_dist_female_atwork
+util_dist_zero_auto,"Distance,zero auto HH",@(df['auto_ownership']==0) * _DIST,coef_dist_zero_auto_atwork
+util_dist_calibration_0_1,util_dist_calibration_0_1,@((_DIST>0) & (_DIST<=1)),coef_calib_0_1_dist_atwork
+util_dist_calibration_1_3,util_dist_calibration_1_3,@((_DIST>1) & (_DIST<=3)),coef_calib_1_3_dist_atwork
+util_dist_calibration_3_6,util_dist_calibration_3_6,@((_DIST>3) & (_DIST<=6)),coef_calib_3_6_dist_atwork
+util_dist_calibration_6_10,util_dist_calibration_6_10,@((_DIST>6) & (_DIST<=10)),coef_calib_6_10_dist_atwork
+util_dist_calibration_10_15,util_dist_calibration_10_15,@((_DIST>10) & (_DIST<=15)),coef_calib_10_15_dist_atwork
+util_dist_calibration_15_pl,util_dist_calibration_15_pl,@(_DIST>15),coef_calib_15_pl_dist_atwork
+util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork
+util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0
+util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum_atwork
+util_sample_of_alternatives_correction_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_sample_of_alternatives_correction_factor
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.yaml b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.yaml
new file mode 100644
index 000000000..c60ae4377
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination.yaml
@@ -0,0 +1,33 @@
+
+SPEC: atwork_subtour_destination.csv
+SAMPLE_SPEC:  atwork_subtour_destination_sample.csv
+COEFFICIENTS: atwork_subtour_destination_coeffs.csv
+
+
+SAMPLE_SIZE: 30
+
+SIMULATE_CHOOSER_COLUMNS:
+  - person_id
+  - income_segment
+  - workplace_zone_id
+  - female
+  - auto_ownership
+
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+
+# model-specific logsum-related settings
+CHOOSER_ORIG_COL_NAME: workplace_zone_id
+ORIG_ZONE_ID: workplace_zone_id
+ALT_DEST_COL_NAME: alt_dest
+IN_PERIOD: 21
+OUT_PERIOD: 19
+
+# optional (comment out if not desired)
+DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum
+
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
+DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample
+
+SIZE_TERM_SELECTOR: atwork
+
+SEGMENTS: [atwork]
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_coeffs.csv
new file mode 100644
index 000000000..d0d5cb5f4
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_coeffs.csv
@@ -0,0 +1,17 @@
+coefficient_name,value,constrain
+coef_dist_atwork,0.487260901,F
+coef_dist_squared_atwork,-0.007463552,F
+coef_dist_cubed_atwork,2.78E-05,F
+coef_dist_logged_atwork,-5.322821417,F
+coef_dist_female_atwork,-0.033246111,F
+coef_dist_zero_auto_atwork,-0.135191524,F
+coef_calib_0_1_dist_atwork,-0.755322501,T
+coef_calib_1_3_dist_atwork,-0.283854352,
+coef_calib_3_6_dist_atwork,0.964729369,
+coef_calib_6_10_dist_atwork,1.476441427,
+coef_calib_10_15_dist_atwork,1.189974447,
+coef_calib_15_pl_dist_atwork,0.626309631,
+coef_size_variable_atwork,1,T
+coef_no_attractions_atwork_size_variable_is_0,-999,T
+coef_mode_choice_logsum_atwork,0.424,F
+coef_sample_of_alternatives_correction_factor,1,T
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_sample.csv
new file mode 100644
index 000000000..4ef4969ec
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_destination_sample.csv
@@ -0,0 +1,16 @@
+Label,Description,Expression,atwork
+local_dist,local_dist,_DIST@skims['DIST'],1
+util_dist,util_dist,@_DIST,coef_dist_atwork
+util_dist_squared,util_dist_squared,@(_DIST)**2,coef_dist_squared_atwork
+util_dist_cubed,util_dist_cubed,@(_DIST)**3,coef_dist_cubed_atwork
+util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork
+util_dist_female,"Distance,female",@(df['female']==True) * _DIST,coef_dist_female_atwork
+util_dist_zero_auto,"Distance,zero auto HH",@(df['auto_ownership']==0) * _DIST,coef_dist_zero_auto_atwork
+util_dist_calibration_0_1,util_dist_calibration_0_1,@((_DIST>0) & (_DIST<=1)),coef_calib_0_1_dist_atwork
+util_dist_calibration_1_3,util_dist_calibration_1_3,@((_DIST>1) & (_DIST<=3)),coef_calib_1_3_dist_atwork
+util_dist_calibration_3_6,util_dist_calibration_3_6,@((_DIST>3) & (_DIST<=6)),coef_calib_3_6_dist_atwork
+util_dist_calibration_6_10,util_dist_calibration_6_10,@((_DIST>6) & (_DIST<=10)),coef_calib_6_10_dist_atwork
+util_dist_calibration_10_15,util_dist_calibration_10_15,@((_DIST>10) & (_DIST<=15)),coef_calib_10_15_dist_atwork
+util_dist_calibration_15_pl,util_dist_calibration_15_pl,@(_DIST>15),coef_calib_15_pl_dist_atwork
+util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork
+util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv
new file mode 100644
index 000000000..a4eec1a88
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv
@@ -0,0 +1,23 @@
+Label,Expression,no_subtours,eat,business1,maint,business2,eat_business
+util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business
+util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business
+util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business
+util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business
+util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business
+util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business
+util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business
+util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business
+util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business
+util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business
+util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business
+util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business
+util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business
+util_log_of_the_work_tour_duration,@np.log((df.duration/2.25)+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business
+util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business
+util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business
+util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business
+util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business
+util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business
+util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business
+util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business
+util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.yaml
new file mode 100644
index 000000000..1b5d27101
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.yaml
@@ -0,0 +1,11 @@
+
+SPEC: atwork_subtour_frequency.csv
+COEFFICIENTS: atwork_subtour_frequency_coeffs.csv
+
+preprocessor:
+  SPEC: atwork_subtour_frequency_annotate_tours_preprocessor
+  DF: df
+  TABLES:
+    - land_use
+    - tours
+    - joint_tour_participants
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_alternatives.csv
new file mode 100644
index 000000000..ba9941919
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_alternatives.csv
@@ -0,0 +1,8 @@
+#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate
+alt,eat,business,maint
+no_subtours,0,0,0
+eat,1,0,0
+business1,0,1,0
+maint,0,0,1
+business2,0,2,0
+eat_business,1,1,0
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv
new file mode 100644
index 000000000..ec500df50
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv
@@ -0,0 +1,9 @@
+Description,Target,Expression
+,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours
+#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)"
+#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)"
+joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')"
+,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)"
+,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)"
+#,,
+,work_tour_is_SOV,df.tour_mode.isin(['DRIVEALONE'])
diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_coeffs.csv
new file mode 100644
index 000000000..c59a4e2ee
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency_coeffs.csv
@@ -0,0 +1,133 @@
+coefficient_name,value,constrain
+coefficient_dummy_for_full_time_worker_business1,-7.375,F
+coefficient_dummy_for_full_time_worker_business2,-14.28,F
+coefficient_dummy_for_full_time_worker_eat,-7.28,F
+coefficient_dummy_for_full_time_worker_eat_business,-14.79,F
+coefficient_dummy_for_full_time_worker_maint,-8.093,F
+coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F
+coefficient_dummy_for_non_full_time_worker_business1,-8.319,F
+coefficient_dummy_for_non_full_time_worker_business2,-14.28,F
+coefficient_dummy_for_non_full_time_worker_eat,-8.604,F
+coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F
+coefficient_dummy_for_non_full_time_worker_maint,-8.214,F
+coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F
+coefficient_dummy_for_non_workers_business1,-5,T
+coefficient_dummy_for_non_workers_business2,-5,T
+coefficient_dummy_for_non_workers_eat,0,T
+coefficient_dummy_for_non_workers_eat_business,-5,T
+coefficient_dummy_for_non_workers_maint,-5,T
+coefficient_dummy_for_non_workers_no_subtours,0,T
+coefficient_medium_hh_income_dummy_business1,0.5555,F
+coefficient_medium_hh_income_dummy_business2,1.111,F
+coefficient_medium_hh_income_dummy_eat,0.61,F
+coefficient_medium_hh_income_dummy_eat_business,1.1655,F
+coefficient_medium_hh_income_dummy_maint,0.1527,F
+coefficient_medium_hh_income_dummy_no_subtours,0,T
+coefficient_high_hh_income_dummy_business1,1.066,F
+coefficient_high_hh_income_dummy_business2,2.132,F
+coefficient_high_hh_income_dummy_eat,0.8693,F
+coefficient_high_hh_income_dummy_eat_business,1.9353,F
+coefficient_high_hh_income_dummy_maint,0.1651,F
+coefficient_high_hh_income_dummy_no_subtours,0,T
+coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F
+coefficient_zero_cars_owned_by_hh_dummy_business2,0,T
+coefficient_zero_cars_owned_by_hh_dummy_eat,0,T
+coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F
+coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F
+coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T
+coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F
+coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F
+coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F
+coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F
+coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F
+coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T
+coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F
+coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F
+coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F
+coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F
+coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F
+coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T
+coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F
+coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F
+coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F
+coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F
+coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F
+coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F
+coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F
+coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T
+coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F
+coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F
+coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F
+coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F
+coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F
+coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T
+coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F
+coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F
+coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F
+coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F
+coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F
+coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T
+coefficient_log_of_the_work_tour_duration_business1,1.142,F
+coefficient_log_of_the_work_tour_duration_business2,2.284,F
+coefficient_log_of_the_work_tour_duration_eat,1.55,F
+coefficient_log_of_the_work_tour_duration_eat_business,2.692,F
+coefficient_log_of_the_work_tour_duration_maint,1.659,F
+coefficient_log_of_the_work_tour_duration_no_subtours,0,T
+coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F
+coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F
+coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F
+coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F
+coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F
+coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T
+coefficient_two_work_tours_by_person_business1,0.3753,F
+coefficient_two_work_tours_by_person_business2,0.7506,F
+coefficient_two_work_tours_by_person_eat,-0.9862,F
+coefficient_two_work_tours_by_person_eat_business,-0.6109,F
+coefficient_two_work_tours_by_person_maint,-0.2312,F
+coefficient_two_work_tours_by_person_no_subtours,0,T
+coefficient_workplace_urban_area_dummy_business1,-0.2235,F
+coefficient_workplace_urban_area_dummy_business2,-0.447,F
+coefficient_workplace_urban_area_dummy_eat,-0.4182,F
+coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F
+coefficient_workplace_urban_area_dummy_maint,-0.1479,F
+coefficient_workplace_urban_area_dummy_no_subtours,0,T
+coefficient_workplace_suburban_area_dummy_business1,-0.1102,F
+coefficient_workplace_suburban_area_dummy_business2,-0.2204,F
+coefficient_workplace_suburban_area_dummy_eat,-0.2916,F
+coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F
+coefficient_workplace_suburban_area_dummy_maint,0,T
+coefficient_workplace_suburban_area_dummy_no_subtours,0,T
+coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F
+coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F
+coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F
+coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F
+coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F
+coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T
+coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T
+coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T
+coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F
+coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F
+coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F
+coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F
+coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T
+coefficient_at_work_sub_tour_alternative_specific_constant_business1,-0.5372,F
+coefficient_at_work_sub_tour_alternative_specific_constant_business2,-2.1337,F
+coefficient_at_work_sub_tour_alternative_specific_constant_eat,0.8576,F
+coefficient_at_work_sub_tour_alternative_specific_constant_eat_business,-0.9721,F
+coefficient_at_work_sub_tour_alternative_specific_constant_maint,-0.6198,F
+coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,0,T
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/auto_ownership.csv b/activitysim/examples/prototype_mwcog/configs/auto_ownership.csv
new file mode 100644
index 000000000..e9ad2c58e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/auto_ownership.csv
@@ -0,0 +1,25 @@
+Label,Description,Expression,cars0,cars1,cars2,cars3,cars4
+util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2
+util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3
+util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up
+util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17
+util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24
+util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34
+util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4
+util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17
+util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3
+util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k
+util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up
+util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up
+util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers
+util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers
+util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers
+util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers
+util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc
+util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers
+util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers
+util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers
+util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers
+util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor
+util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor
+util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker
diff --git a/activitysim/examples/prototype_mwcog/configs/auto_ownership.yaml b/activitysim/examples/prototype_mwcog/configs/auto_ownership.yaml
new file mode 100644
index 000000000..25f3249e6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/auto_ownership.yaml
@@ -0,0 +1,6 @@
+
+SPEC: auto_ownership.csv
+COEFFICIENTS: auto_ownership_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
diff --git a/activitysim/examples/prototype_mwcog/configs/auto_ownership_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/auto_ownership_coeffs.csv
new file mode 100644
index 000000000..a173d345c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/auto_ownership_coeffs.csv
@@ -0,0 +1,68 @@
+coefficient_name,value,constrain
+coef_cars1_drivers_2,0,T
+coef_cars1_drivers_3,0,T
+coef_cars1_persons_16_17,0,T
+coef_cars234_asc_marin,0,T
+coef_cars1_persons_25_34,0,T
+coef_cars1_num_workers_clip_3,0,T
+coef_cars1_hh_income_30_up,0,T
+coef_cars1_density_0_10_no_workers,0,T
+coef_cars1_density_10_up_workers,-0.000691606,F
+coef_retail_non_motor,-0.03,T
+coef_cars4_asc,-2.261638434,F
+coef_cars3_asc,-0.673762698,F
+coef_cars34_persons_16_17,-1.013171309,F
+coef_cars2_asc,0.565146715,F
+coef_cars34_persons_18_24,-0.226330826,F
+coef_cars2_persons_18_24,-0.277986366,F
+coef_cars2_persons_16_17,-0.849990546,F
+coef_cars34_persons_25_34,-0.507269783,F
+coef_cars1_asc_county,-0.566,F
+coef_retail_transit_workers,-0.265994069,F
+coef_cars2_persons_25_34,-0.284585751,F
+coef_cars2_asc_county,-0.4429,F
+coef_cars1_persons_18_24,0.312283469,F
+coef_cars34_density_0_10_no_workers,-1.061191084,F
+coef_retail_transit_no_workers,-0.461089186,F
+coef_cars1_asc_marin,-0.2434,F
+coef_cars34_asc_county,-0.2372,F
+coef_cars2_density_0_10_no_workers,-0.433456601,F
+coef_cars34_density_10_up_no_workers,-0.1766,T
+coef_cars2_density_10_up_no_workers,0.223494727,F
+coef_cars2_density_10_up_workers,-0.1106,F
+coef_cars1_density_10_up_no_workers,-0.627879618,F
+coef_cars2_hh_income_30_up,0.013802348,F
+coef_cars3_hh_income_30_up,0.017568266,F
+coef_cars4_hh_income_30_up,0.020241067,F
+coef_cars1_presence_children_5_17,0.220192925,F
+coef_cars1_hh_income_0_30k,0.087548177,F
+coef_cars2_hh_income_0_30k,0.113631992,F
+coef_cars3_hh_income_0_30k,0.109196449,F
+coef_cars4_hh_income_0_30k,0.123715611,F
+coef_retail_auto_no_workers,-0.27201648,F
+coef_cars34_asc_san_francisco,0.1458,F
+coef_retail_auto_workers,-0.344050452,F
+coef_cars2_presence_children_5_17,0.326754993,F
+coef_cars2_num_workers_clip_3,0.713126665,F
+coef_cars1_presence_children_0_4,-0.134103303,F
+coef_cars1_asc_san_francisco,0.4259,F
+coef_cars2_asc_san_francisco,0.4683,F
+coef_cars1_auto_time_saving_per_worker,1.313894714,F
+coef_cars34_presence_children_5_17,0.092889725,F
+coef_cars3_auto_time_saving_per_worker,0.983048236,F
+coef_cars2_auto_time_saving_per_worker,1.090225147,F
+coef_cars3_num_workers_clip_3,1.000861535,F
+coef_cars234_presence_children_0_4,0.049938988,F
+coef_cars4_auto_time_saving_per_worker,0.899014851,F
+coef_cars4_num_workers_clip_3,1.079450658,F
+coef_cars1_asc,3.525287666,F
+coef_cars1_drivers_4_up,-1.451519292,F
+coef_cars4_drivers_2,2.405668371,F
+coef_cars2_drivers_2,2.817372734,F
+coef_cars3_drivers_2,2.680131012,F
+coef_cars2_drivers_3,2.581972363,F
+coef_cars4_drivers_3,3.964857311,F
+coef_cars3_drivers_3,4.277120564,F
+coef_cars2_drivers_4_up,1.518205653,F
+coef_cars3_drivers_4_up,3.49162912,F
+coef_cars4_drivers_4_up,4.509213244,F
diff --git a/activitysim/examples/prototype_mwcog/configs/cdap.yaml b/activitysim/examples/prototype_mwcog/configs/cdap.yaml
new file mode 100644
index 000000000..9ce3a1d2b
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/cdap.yaml
@@ -0,0 +1,34 @@
+COEFFICIENTS: _dummy_coefficients.csv
+INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv
+
+FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv
+
+CONSTANTS:
+  FULL: 1
+  PART: 2
+  UNIVERSITY: 3
+  NONWORK: 4
+  RETIRED: 5
+  DRIVING: 6
+  SCHOOL: 7
+  PRESCHOOL: 8
+
+PERSON_TYPE_MAP:
+  WORKER:
+    - 1
+    - 2
+  CHILD:
+    - 6
+    - 7
+    - 8
+
+annotate_persons:
+  SPEC: annotate_persons_cdap
+  DF: persons
+
+
+annotate_households:
+  SPEC: annotate_households_cdap
+  DF: households
+  TABLES:
+    - persons
diff --git a/activitysim/examples/prototype_mwcog/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/prototype_mwcog/configs/cdap_fixed_relative_proportions.csv
new file mode 100644
index 000000000..788f398b6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/cdap_fixed_relative_proportions.csv
@@ -0,0 +1,9 @@
+Description,Expression,M,N,H
+Full-time worker,ptype == 1,0.79647,0.09368,0.10985
+Part-time worker,ptype == 2,0.61678,0.25757,0.12565
+University student,ptype == 3,0.69229,0.15641,0.1513
+Non-working adult,ptype == 4,0,0.67169,0.32831
+Retired,ptype == 5,0,0.54295,0.45705
+Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387
+Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342
+Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432
diff --git a/activitysim/examples/prototype_mwcog/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/prototype_mwcog/configs/cdap_indiv_and_hhsize1.csv
new file mode 100644
index 000000000..3d4ad55bc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/cdap_indiv_and_hhsize1.csv
@@ -0,0 +1,70 @@
+Description,Expression,M,N,H
+Full-time worker alternative-specific constants,ptype == 1,1.378734579,0.622662391,
+Part-time worker alternative-specific constants,ptype == 2,-0.718823738,0.636032467,
+University student alternative-specific constants,ptype == 3,2.353595176,0.609709846,
+Non-working adult alternative-specific constants,ptype == 4,-999,0.594645386,
+Retired alternative-specific constants,ptype == 5,-999,0.408202071,
+Driving-age child who is in school alternative-specific constants,ptype == 6,2.330918685,-0.599119112,
+Pre-driving-age child who is in school alternative-specific constants,ptype == 7,3.295863529,0.57142434,
+Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),-0.2943,,
+Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),-0.7141,-0.672,
+Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,1.052531189,-0.837567776,
+# corrected tm1 age bug,,,,
+Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),-0.4515,,
+Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),0.6107,,
+#,,,,
+Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),0.2091,,
+Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,0.7666
+Full-time worker interaction with female gender,(ptype == 1) & (SEX == 2),-0.1259,,
+Non-working adult interaction with female gender,(ptype == 4) & (SEX == 2),-0.743,,
+Retired interaction with female,(ptype == 5) & (SEX == 2),0.4769,,
+Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),0.6515,0.8168,
+Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),2.992,1.056,
+Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,0.2991,
+Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,0.5039
+Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,0.8965
+Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,0.5496
+Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,0.6475
+Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,0.5862
+Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,0.5061
+Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,0.5313
+Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,0.533
+Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,0.3232
+Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.4032
+Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,0.4207,-0.3534
+Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5602
+Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,-0.7188
+Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,1.307
+Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5031
+Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,-2.046
+Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5708
+Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,-0.6186
+Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,0.1212,,
+Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,0.2004,,
+Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,0.2314,,
+Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,0.2792,,
+Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,0.07207,
+Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,0.07207,
+University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,0.07207,
+Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,0.08233,
+Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,0.08233,
+Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,0.08233,
+# commented out because not used in mtctm1,,,,
+# Full-time worker interaction with usual work location is home,(ptype == 1) * usualWorkLocationIsHome,-1.758,,0.1813
+# Part-time worker interaction with usual work location is home,(ptype == 2) * usualWorkLocationIsHome,-1.758,,0.1813
+# Full-time worker interaction with no usual work location,(ptype == 1) * noUsualWorkLocation,-0.5935,,
+# Part-time worker interaction with no usual work location,(ptype == 2) * noUsualWorkLocation,-0.5935,,
+# Driving-age child who is in school interaction with no usual school location,(ptype == 6) * noUsualWorkLocation,-0.866,,
+# Pre-driving age child who is in school interaction with no usual school location,(ptype == 7) * noUsualWorkLocation,-0.866,,
+#tm1 scenario test,,,,
+#Simulate telecommuting by reducing mandatory patterns,(ptype == 1) * (income_in_thousands >= 50),-0.142930042,,
+Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,0.526,0.496
+Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,1.387,1.584
+Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,1.848,1.711
+#Turning Mandatory pattern off for FT and PT who work from home (sandag),,,,
+"Full time worker, works from home",(ptype == 1) & (work_from_home),-999,,
+"Part time worker, works from home",(ptype == 2) & (work_from_home),-999,,
+Part-time worker cdap calibration constants,ptype == 2,-0.692991088,-0.557605681,
+Non-working adult cdap calibration constants,ptype == 4,-999,-1.465030036,
+Retired cdap calibration constants,ptype == 5,-999,-0.734499235,
+Preschoolers cdap calibration constants,ptype == 8,-3.624275591,0.705578326,
diff --git a/activitysim/examples/prototype_mwcog/configs/cdap_interaction_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/cdap_interaction_coefficients.csv
new file mode 100644
index 000000000..f2d3b025c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/cdap_interaction_coefficients.csv
@@ -0,0 +1,140 @@
+activity,interaction_ptypes,coefficient
+# 2-way interactions,,
+H,11,1.626
+H,12,0.7407
+H,13,1.183
+H,14,0.9436
+H,15,1.298
+H,16,2.064
+H,17,1.501
+H,18,0.9912
+H,22,0.8911
+H,23,1.642
+H,24,0.7057
+H,25,0.463
+H,26,3.057
+H,27,0.7685
+H,28,1.07
+H,33,1.018
+H,34,1.781
+H,35,0.4835
+H,36,1.546
+H,37,1.552
+H,38,1.34
+H,44,1.352
+H,45,1.209
+H,46,0.5243
+H,47,0.8112
+H,48,1.167
+H,55,1.407
+H,56,0.8632
+H,57,0.8632
+H,58,0.8632
+H,66,2.198
+H,67,0.977
+H,68,1.467
+H,77,2.8
+H,78,1.434
+H,88,1.378
+M,11,0.141
+M,12,0.08845
+M,13,0.4273
+M,16,0.3842
+M,17,0.2623
+M,18,0.5118
+M,22,1.135
+M,23,0.173
+M,26,1.103
+M,27,0.3079
+M,28,0.5074
+M,33,0.8726
+M,36,-0.0021
+M,37,0.2975
+M,38,0.2254
+M,66,0.4794
+M,67,0.5151
+M,68,0.5516
+M,77,0.9731
+M,78,0.5961
+M,88,1.651
+N,11,1.123
+N,12,0.4947
+N,13,0.5523
+N,14,0.02186
+N,15,0.3115
+N,16,0.4095
+N,17,0.6008
+N,18,0.751
+N,22,1.032
+N,23,0.3355
+N,24,0.7477
+N,25,0.09831
+N,26,0.495
+N,27,0.8984
+N,28,1.452
+N,33,1.054
+N,34,0.193
+N,35,0.4065
+N,36,1.62
+N,37,0.5165
+N,38,0.8973
+N,44,0.6984
+N,45,0.1864
+N,46,0.6801
+N,47,0.5646
+N,48,1.164
+N,55,0.7291
+N,56,0.2919
+N,57,0.2919
+N,58,0.2919
+N,66,1.512
+N,67,1.422
+N,68,1.273
+N,77,1.553
+N,78,0.6184
+N,88,0.8771
+# 3-way interactions,,
+H,124,0.9573
+H,122,0.9573
+H,144,0.9573
+H,126,0.2939
+H,146,0.2939
+H,222,0.9881
+H,224,0.9881
+H,244,0.9881
+H,226,0.4374
+H,246,0.4374
+H,446,0.4374
+H,266,0.4747
+H,466,0.4747
+M,111,0.3133
+M,112,0.3495
+M,114,0.3495
+M,666,-0.3906
+N,112,0.4637
+N,114,0.4637
+N,124,0.3491
+N,122,0.3491
+N,144,0.3491
+N,166,0.3553
+N,222,-1.386
+N,224,-1.386
+N,444,-1.386
+N,246,-0.8571
+N,226,-0.8571
+N,446,-0.8571
+# cdap_final_rules,,
+M,5,-999
+M,4,-999
+# cdap_all_people,,
+M,***,-0.0671
+N,***,-0.3653
+H,***,-1.1810
+M,****,-0.6104
+N,****,-1.3460
+H,****,-3.7330
+M,*****,-1.5280
+N,*****,-3.4530
+H,*****,-8.6210
+
+
diff --git a/activitysim/examples/prototype_mwcog/configs/constants.yaml b/activitysim/examples/prototype_mwcog/configs/constants.yaml
new file mode 100644
index 000000000..41ccfdb73
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/constants.yaml
@@ -0,0 +1,144 @@
+## ActivitySim
+## See full license in LICENSE.txt.
+
+
+#HHT_NONE: 0
+#HHT_FAMILY_MARRIED: 1
+#HHT_FAMILY_MALE: 2
+#HHT_FAMILY_FEMALE: 3
+#HHT_NONFAMILY_MALE_ALONE: 4
+#HHT_NONFAMILY_MALE_NOTALONE: 5
+#HHT_NONFAMILY_FEMALE_ALONE: 6
+#HHT_NONFAMILY_FEMALE_NOTALONE: 7
+
+# convenience for expression files
+HHT_NONFAMILY: [4, 5, 6, 7]
+HHT_FAMILY: [1, 2, 3]
+
+PSTUDENT_GRADE_OR_HIGH: 1
+PSTUDENT_UNIVERSITY: 2
+PSTUDENT_NOT: 3
+
+GRADE_SCHOOL_MAX_AGE: 14
+GRADE_SCHOOL_MIN_AGE: 5
+
+SCHOOL_SEGMENT_NONE: 0
+SCHOOL_SEGMENT_GRADE: 1
+SCHOOL_SEGMENT_HIGH: 2
+SCHOOL_SEGMENT_UNIV: 3
+
+INCOME_SEGMENT_LOW: 1
+INCOME_SEGMENT_MED: 2
+INCOME_SEGMENT_HIGH: 3
+INCOME_SEGMENT_VERYHIGH: 4
+
+PEMPLOY_FULL: 1
+PEMPLOY_PART: 2
+PEMPLOY_NOT: 3
+PEMPLOY_CHILD: 4
+
+PTYPE_FULL: &ptype_full 1
+PTYPE_PART: &ptype_part 2
+PTYPE_UNIVERSITY: &ptype_university 3
+PTYPE_NONWORK: &ptype_nonwork 4
+PTYPE_RETIRED: &ptype_retired 5
+PTYPE_DRIVING: &ptype_driving 6
+PTYPE_SCHOOL: &ptype_school 7
+PTYPE_PRESCHOOL:  &ptype_preschool 8
+
+# these appear as column headers in non_mandatory_tour_frequency.csv
+PTYPE_NAME:
+  *ptype_full: PTYPE_FULL
+  *ptype_part: PTYPE_PART
+  *ptype_university: PTYPE_UNIVERSITY
+  *ptype_nonwork: PTYPE_NONWORK
+  *ptype_retired: PTYPE_RETIRED
+  *ptype_driving: PTYPE_DRIVING
+  *ptype_school: PTYPE_SCHOOL
+  *ptype_preschool: PTYPE_PRESCHOOL
+
+
+CDAP_ACTIVITY_MANDATORY: M
+CDAP_ACTIVITY_NONMANDATORY: N
+CDAP_ACTIVITY_HOME: H
+
+# Mode Choice
+#CONSTANTS:
+#valueOfTime: 8.00
+# AAA:https://exchange.aaa.com/wp-content/uploads/2018/09/18-0090_2018-Your-Driving-Costs-Brochure_FNL-Lo-5-2.pdf
+costPerMile: 19.26
+costShareSr2: 1.75
+costShareSr3: 2.50
+waitThresh: 10.00
+shortWalk: 0.333
+longWalk: 0.667
+walkSpeed: 3.00
+bikeThresh: 6.00
+bikeSpeed: 12.00
+# RIDEHAIL Settings
+Taxi_baseFare: 2.20
+Taxi_costPerMile: 2.30
+Taxi_costPerMinute: 0.10
+
+TNC_single_baseFare: 2.20
+TNC_single_costPerMile: 1.33
+TNC_single_costPerMinute: 0.24
+TNC_single_costMinimum: 7.20
+TNC_single_waitTime_mean:
+  1: 3.0
+  2: 6.3
+  3: 8.4
+  4: 8.5
+  5: 10.3
+TNC_single_waitTime_sd:
+  1: 0
+  2: 0
+  3: 0
+  4: 0
+  5: 0
+TNC_shared_baseFare: 2.20
+TNC_shared_costPerMile: 0.53
+TNC_shared_costPerMinute: 0.10
+TNC_shared_costMinimum: 3.00
+TNC_shared_IVTFactor: 1.5
+TNC_shared_waitTime_mean:
+  1: 5.0
+  2: 8.0
+  3: 11.0
+  4: 15.0
+  5: 15.0
+TNC_shared_waitTime_sd:
+  1: 0
+  2: 0
+  3: 0
+  4: 0
+  5: 0
+min_waitTime: 0
+max_waitTime: 50
+ivt_cost_multiplier: 0.6  
+topology_bike_multiplier: 20
+topology_trn_multiplier: 2.20
+topology_walk_multiplier: 15
+ridehail_wait_time_multiplier: 1.5
+dacc_ratio_multiplier: 0
+origin_density_index_multiplier: -15
+origin_density_index_max: -15
+#FIXME: Check these 
+ivt_LB_multiplier: 1.0
+ivt_XB_multiplier: 0.9
+ivt_BR_multiplier: 0.9
+ivt_LR_multiplier: 0.9
+ivt_MR_multiplier: 0.8
+ivt_CR_multiplier: 0.7
+ivt_com_multiplier: 0.80
+ivt_hvy_multiplier: 0.80
+short_i_wait_multiplier: 2
+long_i_wait_multiplier: 1
+wacc_multiplier: 2
+wegr_multiplier: 2
+waux_multiplier: 2
+dtim_multiplier: 2
+xwait_multiplier: 2
+dacc_ratio: 0
+drvtrn_distpen_0_multiplier: 270
+drvtrn_distpen_max: 15
diff --git a/activitysim/examples/prototype_mwcog/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_mwcog/configs/destination_choice_size_terms.csv
new file mode 100644
index 000000000..9706d64a3
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/destination_choice_size_terms.csv
@@ -0,0 +1,26 @@
+model_selector,segment,HH,TOTEMP,INDEMP,RETEMP,OTHEMP,OFFEMP,K_8,G9_12,COLLEGE,Park_Acres,GC_Acres
+workplace,work_low,0,0,1,2.3131,2.2566,1,0,0,0,0,0
+workplace,work_med,0,0,0.2828,0.5745,1.3069,1,0,0,0,0,0
+workplace,work_high,0,0,0.3528,0.2593,0.81,1,0,0,0,0,0
+workplace,work_veryhigh,0,0,0.1523,0.1345,0.81,1,0,0,0,0,0
+school,university,0,0,0,0,0,0,0,0,1,0,0
+school,gradeschool,0,0,0,0,0,0,1,0,0,0,0
+school,highschool,0,0,0,0,0,0,0,1,0,0,0
+non_mandatory,escort,0.3564,0,0.4562,0.2672,0.7392,0.0876,1,0.5518,0,0,0
+non_mandatory,shopping,0,0,0,1,0,0.6418,0,0,0,0,0
+non_mandatory,eatout,0.0286,0,0,1,0.0877,0,0,0,0,0,0
+non_mandatory,othmaint,0.1122,0,0.0183,1,0.5016,0.2158,0,0,0,0,0
+non_mandatory,social,1,0,0,0.3833,0,0,0,0,0,0,0
+non_mandatory,othdiscr,0.3429,0,0,0.268,1,0.268,0,0,0,0.3429,1.6185
+atwork,atwork,0,0,0,1,0,0.5399,0,0,0,0,0
+trip,work,0,1,1,1,1,1,0,0,0,0,0
+trip,escort,0.001,0,0,0.225,0,0,0.465,0.166,0,0,0
+trip,shopping,0.001,0,0,0.999,0,0,0,0,0,0,0
+trip,eatout,0,0,0,0.742,0,0,0,0,0,0,0
+trip,othmaint,0.001,0,0,0.481,0,0,0,0,0,0,0
+trip,social,0.001,0,0,0.521,0,0,0,0,0,0,0
+trip,othdiscr,0.252,0,0,0.212,0.165,0.165,0,0.098,0,0,0
+trip,univ,0,0,0,0,0,0,0,0,1,0,0
+# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,0,0,0
+#trip,gradeschool,0,0,0,0,0,0,0,0,0,0,0
+#trip,highschool,0,0,0,0,0,0,1,1,0,0,0
diff --git a/activitysim/examples/prototype_mwcog/configs/free_parking.csv b/activitysim/examples/prototype_mwcog/configs/free_parking.csv
new file mode 100644
index 000000000..2505a9bf7
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/free_parking.csv
@@ -0,0 +1,6 @@
+Label,Description,Expression,free,pay
+util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0
+util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0
+util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0
+util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0
+util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership<df.num_workers,coef_fewer_autos_than_workers,0
diff --git a/activitysim/examples/prototype_mwcog/configs/free_parking.yaml b/activitysim/examples/prototype_mwcog/configs/free_parking.yaml
new file mode 100644
index 000000000..d8086e8df
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/free_parking.yaml
@@ -0,0 +1,14 @@
+
+SPEC: free_parking.csv
+COEFFICIENTS: free_parking_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
+FREE_PARKING_ALT: 0
+
+# preprocessor:
+#   SPEC: free_parking_annotate_persons_preprocessor
+#   DF: persons
+#   TABLES:
+#     - land_use
diff --git a/activitysim/examples/prototype_mwcog/configs/free_parking_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/free_parking_annotate_persons_preprocessor.csv
new file mode 100644
index 000000000..677599949
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/free_parking_annotate_persons_preprocessor.csv
@@ -0,0 +1,2 @@
+Description,Target,Expression
+,workplace_county_id,"reindex(land_use.COUNTY, persons.workplace_zone_id)"
diff --git a/activitysim/examples/prototype_mwcog/configs/free_parking_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/free_parking_coeffs.csv
new file mode 100644
index 000000000..dc15f7d03
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/free_parking_coeffs.csv
@@ -0,0 +1,9 @@
+coefficient_name,value,constrain
+coef_asc_san_francisco,-2.6403,F
+coef_asc_santa_clara,0.2118,F
+coef_asc_alameda,-0.1092,F
+coef_income_very_high,0.23,F
+coef_income_high,0.23,F
+coef_hh_size_4_up,0.253,F
+coef_more_autos_than_workers,0.231,F
+coef_fewer_autos_than_workers,-1.479,F
diff --git a/activitysim/examples/prototype_mwcog/configs/initialize_households.yaml b/activitysim/examples/prototype_mwcog/configs/initialize_households.yaml
new file mode 100644
index 000000000..fdd962cb2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/initialize_households.yaml
@@ -0,0 +1,21 @@
+
+annotate_tables:
+  - tablename: persons
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
+  - tablename: persons
+    annotate:
+      SPEC: annotate_persons_after_hh
+      DF: persons
+      TABLES:
+        - households
diff --git a/activitysim/examples/prototype_mwcog/configs/initialize_landuse.yaml b/activitysim/examples/prototype_mwcog/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..84b5baaca
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/initialize_landuse.yaml
@@ -0,0 +1,7 @@
+
+
+annotate_tables:
+  - tablename: land_use
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.csv
new file mode 100644
index 000000000..de03e3dcc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.csv
@@ -0,0 +1,22 @@
+Label,Description,Expression,adults,children,mixed
+util_asc,Alternative-specific constant,1,,coef_asc_children,coef_asc_mixed
+util_tour_purpose_is_eating_out,Joint tour purpose is eating out (dummy),tour_type=='eat',,coef_tour_purpose_is_eating_out_children,coef_tour_purpose_is_eating_out_mixed
+util_tour_purpose_is_discretionary,Joint tour purpose is discretionary (dummy),tour_type=='disc',coef_tour_purpose_is_discretionary_adults,coef_tour_purpose_is_discretionary_children,
+util_number_of_full_time_workers,Number of Full-Time Workers in the household,num_full_max3,coef_number_of_full_time_workers_adults,,coef_number_of_full_time_workers_mixed
+util_number_of_part_time_workers,Number of Part-Time Workers in the household,num_part_max3,coef_number_of_part_time_workers_adults,,coef_number_of_part_time_workers_mixed
+util_number_of_university_students,Number of University students in the household,num_univ_max3,coef_number_of_university_students,,
+util_number_of_non_workers,Number of Non-Workers in the household,num_nonwork_max3,coef_number_of_non_workers_adults,,coef_number_of_non_workers_mixed
+util_number_of_children_too_young_for_school,Number of Children too Young for School in the household,num_preschool_max3,,coef_number_of_children_too_young_for_school_children,coef_number_of_children_too_young_for_school_mixed
+util_number_of_pre_driving_age_children,Number of Pre-driving Age Children in the household,num_school_max3,,coef_number_of_pre_driving_age_children_children,coef_number_of_pre_driving_age_children_mixed
+util_number_of_driving_age_children,Number of Driving-age Children in the household,num_driving_max3,,coef_number_of_driving_age_children_children,coef_number_of_driving_age_children_mixed
+util_low_income_households,Low income households (dummy),income_in_thousands<30,coef_low_income_households_adults,,coef_low_income_households_mixed
+util_medium_income_households,Medium income households (dummy),(income_in_thousands>=30) & (income_in_thousands<60),coef_medium_income_households,,
+util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed
+util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,,
+util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed
+util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,,
+util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows,
+util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows
+util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,,
+util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable,
+util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.yaml b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.yaml
new file mode 100644
index 000000000..a699bb337
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition.yaml
@@ -0,0 +1,11 @@
+LOGIT_TYPE: MNL
+
+SPEC: joint_tour_composition.csv
+COEFFICIENTS: joint_tour_composition_coeffs.csv
+
+preprocessor:
+  SPEC: joint_tour_composition_annotate_households_preprocessor
+  DF: households
+#  TABLES:
+#    - persons
+#    - accessibility
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_annotate_households_preprocessor.csv
new file mode 100644
index 000000000..3267e3793
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_annotate_households_preprocessor.csv
@@ -0,0 +1,22 @@
+Description,Target,Expression
+#,,
+,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)"
+,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25
+,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25
+,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25
+logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult)
+logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child)
+logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child)
+#,,
+,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)"
+,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)"
+,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)"
+,num_full_max3,"_num_full.clip(0,3)"
+,num_part_max3,"_num_part.clip(0,3)"
+,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)"
+,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)"
+,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)"
+,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)"
+,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)"
+#,,
+,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part)
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_coeffs.csv
new file mode 100644
index 000000000..4d929c2b5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_composition_coeffs.csv
@@ -0,0 +1,32 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_asc_children,5.3517,F
+coef_asc_mixed,5.6290,fF
+coef_tour_purpose_is_eating_out_children,-0.9678,F
+coef_tour_purpose_is_eating_out_mixed,-0.8027,F
+coef_tour_purpose_is_discretionary_adults,0.7648,F
+coef_tour_purpose_is_discretionary_children,0.5101,F
+coef_number_of_full_time_workers_adults,1.024,F
+coef_number_of_full_time_workers_mixed,0.3624,F
+coef_number_of_part_time_workers_adults,0.5412,F
+coef_number_of_part_time_workers_mixed,0.3164,F
+coef_number_of_university_students,0.8245,F
+coef_number_of_non_workers_adults,0.6263,F
+coef_number_of_non_workers_mixed,-0.3724,F
+coef_number_of_children_too_young_for_school_children,0.7306,F
+coef_number_of_children_too_young_for_school_mixed,0.7906,F
+coef_number_of_pre_driving_age_children_children,0.7306,F
+coef_number_of_pre_driving_age_children_mixed,0.3532,F
+coef_number_of_driving_age_children_children,-0.2667,F
+coef_number_of_driving_age_children_mixed,-0.9399,F
+coef_low_income_households_adults,1.248,F
+coef_low_income_households_mixed,0.5755,F
+coef_medium_income_households,0.8369,F
+coef_household_has_more_cars_than_workers_adults,1.386,F
+coef_household_has_more_cars_than_workers_mixed,0.751,F
+coef_household_in_urban_area,0.5741,F
+coef_household_in_suburban_area_adults,0.5105,F
+coef_household_in_suburban_area_mixed,0.1283,F
+coef_log_max_overlap_of_adults_time_windows,1.192,F
+coef_log_max_overlap_of_childrens_time_windows,1.841,F
+coef_log_max_overlap_of_time_windows,1.958,F
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.csv
new file mode 100644
index 000000000..96cd1c29f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.csv
@@ -0,0 +1,17 @@
+Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr
+local_dist,_DIST@skims['DIST'],1,1,1,1,1,1
+util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,0,coef_dist_othmaint,0,coef_dist_othdiscr
+util_dist_squared,"@(_DIST).clip(0,20)**2",0,coef_dist_squared_shopping,0,0,0,coef_dist_squared_othdiscr
+util_dist_cubed,"@(_DIST).clip(0,20)**3",0,coef_dist_cubed_shopping,0,0,0,coef_dist_cubed_othdiscr
+util_dist_squared,"@(_DIST).clip(0,15)**2",0,0,0,coef_dist_squared_othmaint,0,0
+util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr
+util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,0,coef_dist_hh_child_othdiscr
+util_dist_zero_auto,@(df['auto_ownership']==0) * _DIST,0,0,0,0,0,coef_dist_zero_auto_othdiscr
+util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_very_high,@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,0,0,coef_dist_veryhigh_inc_eatout,0,coef_dist_veryhigh_inc_social,coef_dist_veryhigh_inc_othdiscr
+util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,coef_dist_joint_tour_othmaint,0,coef_dist_joint_tour_othdiscr
+Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1
+No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999
+Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr
+Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.yaml b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.yaml
new file mode 100644
index 000000000..5fe0c0636
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination.yaml
@@ -0,0 +1,54 @@
+
+SAMPLE_SPEC: joint_tour_destination_sample.csv
+SPEC: joint_tour_destination.csv
+COEFFICIENTS: joint_tour_destination_coeffs.csv
+
+SEGMENTS:
+  - shopping
+  - othmaint
+  - othdiscr
+  - eatout
+  - social
+
+
+SAMPLE_SIZE: 30
+
+# we can't use use household income_segment as this will also be set for non-workers
+CHOOSER_SEGMENT_COLUMN_NAME: tour_type
+
+SIMULATE_CHOOSER_COLUMNS:
+  - tour_type
+  - TAZ
+  - person_id
+  - income_segment
+  - num_children
+  - hh_child
+  - auto_ownership
+
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+
+# model-specific logsum-related settings
+CHOOSER_ORIG_COL_NAME: TAZ
+ALT_DEST_COL_NAME: alt_dest
+IN_PERIOD: 15
+OUT_PERIOD: 19
+
+SIZE_TERM_SELECTOR: non_mandatory
+
+# optional (comment out if not desired)
+DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum
+
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
+DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample
+
+SEGMENT_IDS:
+  work_low: 1
+  work_med: 2
+  work_high: 3
+  work_veryhigh: 4
+
+CONSTANTS:
+  WORK_LOW_SEGMENT_ID: 1
+  WORK_MED_SEGMENT_ID: 2
+  WORK_HIGH_SEGMENT_ID: 3
+  WORK_VERYHIGH_SEGMENT_ID: 4
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_coeffs.csv
new file mode 100644
index 000000000..0c4e5fee2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_coeffs.csv
@@ -0,0 +1,39 @@
+coefficient_name,value,constrain
+coef_mode_logsum_escort,0.676,F
+coef_dist_escort,0.046736802,F
+coef_dist_squared_escort,0,F
+coef_dist_cubed_escort,0,F
+coef_dist_logged_escort,-2.403808965,F
+coef_dist_hh_child_escort,-0.078449805,F
+coef_mode_logsum_shopping,0.676,F
+coef_dist_shopping,-0.045383662,F
+coef_dist_squared_shopping,-0.001019621,F
+coef_dist_cubed_shopping,0,F
+coef_dist_logged_shopping,-2.184394753,F
+coef_dist_joint_tour_shopping,0,F
+coef_mode_logsum_eatout,0.79,F
+coef_dist_eatout,0,F
+coef_dist_squared_eatout,0,F
+coef_dist_cubed_eatout,0,F
+coef_dist_logged_eatout,-1.904643053,F
+coef_dist_hh_child_eatout,-0.034067726,F
+coef_dist_veryhigh_inc_eatout,-0.03079323,F
+coef_mode_logsum_othmaint,0.676,F
+coef_dist_othmaint,-0.04055464,F
+coef_dist_squared_othmaint,-0.001372519,F
+coef_dist_cubed_othmaint,0,F
+coef_dist_logged_othmaint,-1.270318612,F
+coef_dist_joint_tour_othmaint,0.020235681,F
+coef_mode_logsum_social,0.79,F
+coef_dist_social,0,F
+coef_dist_logged_social,-1.408912476,F
+coef_dist_veryhigh_inc_social,-0.018940702,F
+coef_mode_logsum_othdiscr,0.79,F
+coef_dist_othdiscr,0.016951123,F
+coef_dist_squared_othdiscr,-0.00183013,F
+coef_dist_cubed_othdiscr,0,F
+coef_dist_logged_othdiscr,-1.692232511,F
+coef_dist_hh_child_othdiscr,-0.016330734,F
+coef_dist_zero_auto_othdiscr,0.064871626,F
+coef_dist_veryhigh_inc_othdiscr,-0.008650825,F
+coef_dist_joint_tour_othdiscr,0.013751855,F
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_sample.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_sample.csv
new file mode 100644
index 000000000..4052c5040
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_destination_sample.csv
@@ -0,0 +1,15 @@
+Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr
+local_dist,_DIST@skims['DIST'],1,1,1,1,1,1
+util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,0,coef_dist_othmaint,0,coef_dist_othdiscr
+util_dist_squared,"@(_DIST).clip(0,20)**2",0,coef_dist_squared_shopping,0,0,0,coef_dist_squared_othdiscr
+util_dist_cubed,"@(_DIST).clip(0,20)**3",0,coef_dist_cubed_shopping,0,0,0,coef_dist_cubed_othdiscr
+util_dist_squared,"@(_DIST).clip(0,15)**2",0,0,0,coef_dist_squared_othmaint,0,0
+util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr
+util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,0,coef_dist_hh_child_othdiscr
+util_dist_zero_auto,@(df['auto_ownership']==0) * _DIST,0,0,0,0,0,coef_dist_zero_auto_othdiscr
+util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_very_high,@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,0,0,coef_dist_veryhigh_inc_eatout,0,coef_dist_veryhigh_inc_social,coef_dist_veryhigh_inc_othdiscr
+util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,coef_dist_joint_tour_othmaint,0,coef_dist_joint_tour_othdiscr
+Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1
+No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.csv
new file mode 100644
index 000000000..48d46a8fb
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.csv
@@ -0,0 +1,78 @@
+Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD
+util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours
+util_alternative_specific_constants_calibration,alternative_specific_constants_calibration,1,asc_calib_0_tours,asc_calib_1_Shop,asc_calib_1_Main,asc_calib_1_Eat,asc_calib_1_Visit,asc_calib_1_Disc,asc_calib_2_SS,asc_calib_2_SM,asc_calib_2_SE,asc_calib_2_SV,asc_calib_2_SD,asc_calib_2_MM,asc_calib_2_ME,asc_calib_2_MV,asc_calib_2_MD,asc_calib_2_EE,asc_calib_2_EV,asc_calib_2_ED,asc_calib_2_VV,asc_calib_2_VD,asc_calib_2_DD
+#_zero_tours,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,,
+util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,,
+util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,,
+util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,,
+util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,,
+util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,,
+#_shopping,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,,
+util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,,
+util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,,
+util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,,
+util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,,
+util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,,
+util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,,
+util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,,
+util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,,
+util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,,
+util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,,
+util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,,
+#_Maintenance,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,,
+util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,,
+util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,,
+util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,,
+util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,,
+util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,,
+util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,,
+util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,,
+util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,,
+util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,,
+util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,,
+util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,,
+util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,,
+#_eatout,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,,
+util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,,
+util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,,
+util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,,
+util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,,
+util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,,
+util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,,
+util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,,
+util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,,
+util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,,
+util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,,
+util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,,
+util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,,
+#_visiting,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting,
+util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting,
+util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting,
+util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting,
+util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting,
+util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting,
+util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting,
+util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting,
+util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting,
+util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting,
+#_discretionary,,,,,,,,,,,,,,,,,,,,,,,
+util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc
+util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc
+util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc
+util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc
+util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc
+util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc
+util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc
+util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc
+util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc
+util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc
+util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc
+util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc
+util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc
+util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc
+util_zeroAutomobiles_dis,zeroAutomobiles_disc,auto_ownership == 0,,,,,,coef_zeroAutomobiles_disc,,,,,coef_zeroAutomobiles_disc,,,,coef_zeroAutomobiles_disc,,,coef_zeroAutomobiles_disc,,coef_zeroAutomobiles_disc,2 * coef_zeroAutomobiles_disc
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.yaml
new file mode 100644
index 000000000..61e1f1bdc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency.yaml
@@ -0,0 +1,11 @@
+LOGIT_TYPE: MNL
+
+SPEC: joint_tour_frequency.csv
+COEFFICIENTS: joint_tour_frequency_coeffs.csv
+
+preprocessor:
+  SPEC: joint_tour_frequency_annotate_households_preprocessor
+  DF: households
+  TABLES:
+    #- persons
+    - accessibility
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_alternatives.csv
new file mode 100644
index 000000000..7bf93731f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_alternatives.csv
@@ -0,0 +1,23 @@
+#,,,,,alt file for building joint tours
+alt,shopping,othmaint,eatout,social,othdiscr
+0_tours,0,0,0,0,0
+1_Shop,1,0,0,0,0
+1_Main,0,1,0,0,0
+1_Eat,0,0,1,0,0
+1_Visit,0,0,0,1,0
+1_Disc,0,0,0,0,1
+2_SS,2,0,0,0,0
+2_SM,1,1,0,0,0
+2_SE,1,0,1,0,0
+2_SV,1,0,0,1,0
+2_SD,1,0,0,0,1
+2_MM,0,2,0,0,0
+2_ME,0,1,1,0,0
+2_MV,0,1,0,1,0
+2_MD,0,1,0,0,1
+2_EE,0,0,2,0,0
+2_EV,0,0,1,1,0
+2_ED,0,0,1,0,1
+2_VV,0,0,0,2,0
+2_VD,0,0,0,1,1
+2_DD,0,0,0,0,2
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv
new file mode 100644
index 000000000..c5580ed71
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv
@@ -0,0 +1,32 @@
+Description,Target,Expression
+,_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype, activity, households, persons: persons.query('ptype == %s and cdap_activity==\'%s\'' % (ptype, activity)).groupby('household_id').size().reindex(households.index).fillna(0)"
+,_PEMPLOY_CDAP_PATTERN_COUNT,"lambda pemploy, activity, households, persons: persons.query('pemploy == %s and cdap_activity==\'%s\'' % (pemploy, activity)).groupby('household_id').size().reindex(households.index).fillna(0)"
+,_2_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype1, ptype2, activity, households, persons: persons.query('(ptype == %s or ptype == %s) and cdap_activity==\'%s\'' % (ptype1, ptype2, activity)).groupby('household_id').size().reindex(households.index).fillna(0)"
+#,,
+,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)"
+,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25
+,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25
+,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25
+#,,
+,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)"
+,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)"
+,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)"
+,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)"
+,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)"
+,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)"
+,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)"
+,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)"
+,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)"
+,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)"
+,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)"
+,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)"
+,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)"
+,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)"
+,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)"
+,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000)
+,income_greater_than_100,households.income > 100000
+,income_missing,0
+logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult)
+logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child)
+logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child)
+nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.TAZ)"
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_coeffs.csv
new file mode 100644
index 000000000..2f7f80365
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_frequency_coeffs.csv
@@ -0,0 +1,106 @@
+coefficient_name,value,constrain
+# asc,,
+coef_asc_0_tours,0,T
+coef_asc_1_Shop,-6.0149,F
+coef_asc_1_Main,-5.7389,F
+coef_asc_1_Eat,-6.3757,F
+coef_asc_1_Visit,-5.8818,F
+coef_asc_1_Disc,-5.4806,F
+coef_asc_2_tours,-14.4576,F
+# zero_tours,,
+coef_fullTimeHomeMaxThree_zero_tours,1.175,F
+coef_partTimeHomeMaxThree_zero_tours,1.447,F
+coef_nonWorkerHomeMaxThree_zero_tours,1.514,F
+coef_retireeHomeMaxThree_zero_tours,0.6053,F
+coef_universityHomeMaxThree_univ_and_driving_zero_tours,0.5685,F
+coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,0.53,F
+# shopping,,
+coef_fullTimeNonMandMaxThree_shopping,0.2052,F
+coef_partTimeNonMandMaxThree_shopping,0.1866,F
+coef_nonWorkerNonMandMaxThree_shopping,0.7078,F
+coef_retireeNonMandMaxThree_shopping,0.941,F
+coef_universityNonMandMaxThree_shopping,0.7648,F
+coef_preDrivingNonMandMaxThree_shopping,0.5474,F
+coef_fullTimeMandMaxThree_shopping,-0.2424,F
+coef_logTimeWindowOverlapAdult_shopping,0.5945,F
+coef_logTimeWindowOverlapChild_shopping,0.1416,F
+coef_logTimeWindowOverlapAdultChild_shopping,0.1086,F
+coef_fewerCarsThanDrivers_shopping,0.2523,F
+coef_moreCarsThanWorkers_shopping,-0.3027,F
+# maintenance,,
+coef_fullTimeNonMandMaxThree_maint,0.3173,F
+coef_partTimeNonMandMaxThree_maint,0.2452,F
+coef_nonWorkerNonMandMaxThree_maint,0.4643,F
+coef_retireeNonMandMaxThree_maint,0.905,F
+coef_universityNonMandMaxThree_maint,0.2643,F
+coef_preDrivingNonMandMaxThree_maint,0.6482,F
+coef_fullTimeMandMaxThree_maint,-0.3009,F
+coef_drivingAgeStuMandMaxThree_maint,-0.3237,F
+coef_preDrivingAgeMandMaxThree_maint,0.2299,F
+coef_logTimeWindowOverlapAdult_maint,0.3714,F
+coef_logTimeWindowOverlapChild_maint,0.176,F
+coef_logTimeWindowOverlapAdultChild_maint,0.2443,F
+coef_fewerCarsThanDrivers_maint,0.461,F
+# eatout,,
+coef_fullTimeNonMandMaxThree_eatout,0.2275,F
+coef_partTimeNonMandMaxThree_eatout,0.3765,F
+coef_nonWorkerNonMandMaxThree_eatout,0.182,F
+coef_retireeNonMandMaxThree_eatout,0.4264,F
+coef_universityNonMandMaxThree_eatout,0.4097,F
+coef_preDrivingNonMandMaxThree_eatout,0.3851,F
+coef_logTimeWindowOverlapAdult_eatout,0.4856,F
+coef_logTimeWindowOverlapAdultChild_eatout,0.0921,F
+coef_incomeBetween50And100_eatout,0.2977,F
+coef_incomeGreaterThan100_eatout,0.4492,F
+coef_incomeMissing_dummy_always_zero_eatout,0.278,F
+coef_moreCarsThanWorkers_eatout,0.3825,F
+coef_walkRetailAccessibility_eatout,0.062,F
+# visiting,,
+coef_fullTimeNonMandMaxThree_visiting,0.6445,F
+coef_partTimeNonMandMaxThree_visiting,0.1332,F
+coef_nonWorkerNonMandMaxThree_visiting,0.5475,F
+coef_retireeNonMandMaxThree_visiting,0.5579,F
+coef_universityNonMandMaxThree_visiting,0.2809,F
+coef_preDrivingNonMandMaxThree_visiting,0.6008,F
+coef_timeWindowOverlapAdult_visiting,0.0596,F
+coef_timeWindowOverlapChild_visiting,0.0092,F
+coef_timeWindowOverlapAdultChild_visiting,0.0256,F
+coef_zeroAutomobiles_visiting,-0.98,F
+# discretionary,,
+coef_fullTimeNonMandMaxThree_disc,0.1275,F
+coef_partTimeNonMandMaxThree_disc,0.4979,F
+coef_nonWorkerNonMandMaxThree_disc,0.2871,F
+coef_retireeNonMandMaxThree_disc,0.6136,F
+coef_universityNonMandMaxThree_disc,0.7546,F
+coef_preDrivingNonMandMaxThree_disc,0.5331,F
+coef_drivingAgeStuMandMaxThree_disc,0.1932,F
+coef_preDrivingAgeMandMaxThree_disc,0.3862,F
+coef_logTimeWindowOverlapAdult_disc,0.3428,F
+coef_logTimeWindowOverlapChild_disc,0.1162,F
+coef_logTimeWindowOverlapAdultChild_disc,0.2212,F
+coef_incomeBetween50And100_disc,0.3167,F
+coef_incomeGreaterThan100_disc,0.486,F
+coef_incomeMissing_dummy_always_zero_disc,0.3723,F
+coef_zeroAutomobiles_disc,-0.909,F
+#Phase1_Calibration,,
+asc_calib_0_tours,0,T
+asc_calib_1_Shop,1.053698044,F
+asc_calib_1_Main,0.732431708,F
+asc_calib_1_Eat,0.877259241,F
+asc_calib_1_Visit,0.436534239,F
+asc_calib_1_Disc,0.619623918,F
+asc_calib_2_SS,2.404591356,F
+asc_calib_2_SM,2.998240224,F
+asc_calib_2_SE,2.042554293,F
+asc_calib_2_SV,0.054676458,F
+asc_calib_2_SD,2.467068755,F
+asc_calib_2_MM,3.614067275,F
+asc_calib_2_ME,4.359003674,F
+asc_calib_2_MV,5.998710535,F
+asc_calib_2_MD,5.114616381,F
+asc_calib_2_EE,3.295790528,F
+asc_calib_2_EV,5.235744732,F
+asc_calib_2_ED,2.359825593,F
+asc_calib_2_VV,1.285350946,F
+asc_calib_2_VD,4.250918308,F
+asc_calib_2_DD,2.958796827,F
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.csv
new file mode 100644
index 000000000..cd692d8d2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.csv
@@ -0,0 +1,67 @@
+Label,Description,Expression,participate,not_participate
+util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not
+util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not
+util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party,
+util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party,
+util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party,
+util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party,
+util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party,
+util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party,
+util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party,
+util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party,
+util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party,
+util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party,
+#,,,,
+util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not
+util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not
+util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours,
+util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours,
+util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours,
+util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours,
+util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours,
+util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours,
+util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours,
+util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours,
+util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours,
+util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours,
+util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours,
+util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours,
+#,,,,
+util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party,
+util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party,
+util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party,
+util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party,
+util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party,
+util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party,
+util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party,
+util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party,
+util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party,
+util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party,
+util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party,
+util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party,
+#,,,,
+util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party,
+util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party,
+util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party,
+util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party,
+util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only,
+util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed,
+util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only,
+util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed,
+util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party,
+util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party,
+util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party,
+util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed,
+#,,,,
+util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,
+util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed,
+util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed,
+util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child,
+#,,,,
+util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable,
+util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable,
+util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable,
+util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable
+util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable
+util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable
+util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.yaml b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.yaml
new file mode 100644
index 000000000..59941e832
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation.yaml
@@ -0,0 +1,20 @@
+
+SPEC: joint_tour_participation.csv
+COEFFICIENTS: joint_tour_participation_coeffs.csv
+
+LOGIT_TYPE: MNL
+
+#max_participation_choice_iterations: 5000
+
+preprocessor:
+  SPEC: joint_tour_participation_annotate_participants_preprocessor
+  DF: participants
+#  TABLES:
+#    - persons
+#    - accessibility
+
+annotate_persons:
+  SPEC: annotate_persons_jtp
+  DF: persons
+  TABLES:
+    - joint_tour_participants
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv
new file mode 100644
index 000000000..cb78bff30
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv
@@ -0,0 +1,24 @@
+Description,Target,Expression
+,_P_OVERLAPS,person_time_window_overlap(persons)
+,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)/2.25"
+,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)/2.25"
+,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)/2.25"
+logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult)
+logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child)
+logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child)
+#,,
+,person_is_full,participants.ptype == PTYPE_FULL
+,person_is_part,participants.ptype == PTYPE_PART
+,person_is_univ,participants.ptype == PTYPE_UNIVERSITY
+,person_is_nonwork,participants.ptype == PTYPE_NONWORK
+,person_is_driving,participants.ptype == PTYPE_DRIVING
+,person_is_school,participants.ptype == PTYPE_SCHOOL
+,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL
+,tour_type_is_eat,participants.tour_type=='eat'
+,tour_type_is_disc,participants.tour_type=='disc'
+,tour_composition_is_adults,participants.composition=='adults'
+,tour_composition_is_children,participants.composition=='children'
+,tour_composition_is_mixed,participants.composition=='mixed'
+,home_is_suburban,~(participants.home_is_urban | participants.home_is_rural)
+,high_income,participants.income_in_thousands > 60
+,more_cars_than_workers,participants.auto_ownership > participants.num_workers
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_coeffs.csv
new file mode 100644
index 000000000..455f08be9
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_participation_coeffs.csv
@@ -0,0 +1,68 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_full_time_worker_mixed_party,-3.566,F
+coef_full_time_worker_mixed_party_not,0.5,T
+coef_part_time_worker_adults_only_party,-3.566,F
+coef_part_time_worker_adults_only_party_not,0.5,T
+coef_part_time_worker_mixed_party,-0.3655,F
+coef_university_student_mixed_party,-3.041,F
+coef_non_worker_adults_only_party,-3.164,F
+coef_non_worker_mixed_party,0.7152,F
+coef_child_too_young_for_school_children_only_party,-2.786,F
+coef_child_too_young_for_school_mixed_party,-1.893,F
+coef_pre_driving_age_student_children_only_party,-0.7217,F
+coef_pre_driving_age_student_mixed_party,-1.752,F
+coef_driving_age_student_children_only_party,-1.822,F
+coef_driving_age_student_mixed_party,-1.353,F
+#,,
+coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,F
+coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,T
+coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392,F
+coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,T
+coef_part_time_worker_specific_to_eating_out_joint_tours,2.188,F
+coef_part_time_worker_specific_to_discretionary_joint_tours,0.285,F
+coef_university_student_specific_to_eating_out_joint_tours,-0.82,F
+coef_university_student_specific_to_discretionary_joint_tours,0,T
+coef_non_worker_specific_to_eating_out_joint_tours,0.1617,F
+coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,F
+coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.6589,F
+coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.1284,F
+coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,F
+coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.6626,F
+coef_driving_age_student_specific_to_eating_out_joint_tours,2.344,F
+coef_driving_age_student_specific_to_discretionary_joint_tours,-0.6675,F
+#,,
+coef_household_in_urban_area_adult_adult_only_party,0,T
+coef_household_in_urban_area_adult_mixed_party,-0.137,F
+coef_household_in_urban_area_child_child_only_party,1.21,F
+coef_household_in_urban_area_child_mixed_party,0.6265,F
+coef_household_in_suburban_area_adult_adult_only_party,0,T
+coef_household_in_suburban_area_adult_mixed_party,-0.06007,F
+coef_household_in_suburban_area_child_child_only_party,0,T
+coef_household_in_suburban_area_child_mixed_party,0,T
+coef_adult_more_automobiles_than_workers_adult_only_party,-0.2133,F
+coef_adult_more_automobiles_than_workers_mixed_party,-0.6031,F
+coef_child_more_automobiles_than_workers_child_only_party,-0.4214,F
+coef_child_more_automobiles_than_workers_mixed_party,-0.3776,F
+#,,
+coef_dummy_for_high_income_for_adult_in_adult_party,-0.1682,F
+coef_dummy_for_high_income_for_adult_in_mixed_party,-0.02613,F
+coef_dummy_for_high_income_for_child_in_children_party,-0.5619,F
+coef_dummy_for_high_income_for_child_in_mixed_party,-0.1528,F
+coef_adult_number_of_joint_tours_adult_only,-0.3242,F
+coef_adult_number_of_joint_tours_mixed,-0.3584,F
+coef_child_number_of_joint_tours_child_only,0.1047,F
+coef_child_number_of_joint_tours_mixed,-0.5089,F
+coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,T
+coef_adult_number_of_other_adults_in_the_household_mixed_party,0,T
+coef_child_number_of_other_children_in_the_household_child_only_party,0,T
+coef_child_number_of_other_children_in_the_household_mixed,0,T
+#,,
+coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,0.8436,F
+coef_adult_log_of_max_window_overlap_with_a_child_mixed,2.189,F
+coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,F
+coef_child_log_of_max_window_overlap_with_a_child_child,1.296,F
+
+
+
+
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling.yaml
new file mode 100644
index 000000000..e81afe238
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling.yaml
@@ -0,0 +1,12 @@
+LOGIT_TYPE: MNL
+
+SPEC: tour_scheduling_joint.csv
+COEFFICIENTS: tour_scheduling_joint_coeffs.csv
+
+preprocessor:
+  SPEC: joint_tour_scheduling_annotate_tours_preprocessor
+  DF: joint_tours
+  TABLES:
+    - land_use
+    - households
+    - joint_tour_participants
diff --git a/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv
new file mode 100644
index 000000000..594b79624
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv
@@ -0,0 +1,2 @@
+Description,Target,Expression
+,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_DIST', 'MD'))"
diff --git a/activitysim/examples/prototype_mwcog/configs/logging.yaml b/activitysim/examples/prototype_mwcog/configs/logging.yaml
new file mode 100644
index 000000000..6dfca578a
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/logging.yaml
@@ -0,0 +1,53 @@
+# Config for logging
+# ------------------
+# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema
+
+logging:
+  version: 1
+  disable_existing_loggers: true
+
+
+  # Configuring the default (root) logger is highly recommended
+  root:
+    level: NOTSET
+    handlers: [console]
+
+  loggers:
+
+    activitysim:
+      level: DEBUG
+      handlers: [console, logfile]
+      propagate: false
+
+    orca:
+      level: WARN
+      handlers: [console, logfile]
+      propagate: false
+
+  handlers:
+
+    logfile:
+      class: logging.FileHandler
+      filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log']
+      mode: w
+      formatter: fileFormatter
+      level: NOTSET
+
+    console:
+      class: logging.StreamHandler
+      stream: ext://sys.stdout
+      formatter: simpleFormatter
+      level: NOTSET
+
+  formatters:
+
+    simpleFormatter:
+      class: logging.Formatter
+      # format: '%(levelname)s - %(name)s - %(message)s'
+      format: '%(levelname)s - %(message)s'
+      datefmt: '%d/%m/%Y %H:%M:%S'
+
+    fileFormatter:
+      class: logging.Formatter
+      format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
+      datefmt: '%d/%m/%Y %H:%M:%S'
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv
new file mode 100644
index 000000000..848bbf77a
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv
@@ -0,0 +1,101 @@
+Label,Description,Expression,work1,work2,school1,school2,work_and_school
+util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,,
+util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,,
+util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc
+util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,,
+util_retired,Retired alternative-specific constants,ptype == 5,,,,,
+util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc
+util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc,
+util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school
+util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school
+util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school
+util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,,
+util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,,
+util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school
+util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2,
+util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school
+util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school
+util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school
+util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,
+util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,,
+util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,,
+util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,,
+util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,,
+util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,,
+util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2,
+util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2,
+util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2,
+util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school
+util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school
+util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school
+util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school
+util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2
+util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2
+util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2
+util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,
+util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,
+util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school
+util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school
+util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,
+util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed
+util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed
+util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school
+util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school
+util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,,
+util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,,
+util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school
+util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school
+util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school
+util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,
+util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,
+util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school
+util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,
+util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2,
+util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2,
+util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2,
+util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school
+util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school
+util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school
+util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,
+util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,
+util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school
+util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,
+util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,,
+util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,,
+util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school
+util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school
+util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school
+util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,
+util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,
+util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school
+util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,
+util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1
+util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1
+util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2
+util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,,
+util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,,
+util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2
+util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,,
+util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school
+util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school
+util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school
+util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,
+util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,
+util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school
+util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,
+util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school
+util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school
+util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school
+util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,
+util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,
+util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school
+util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,
+util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable,
+util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable,
+util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable
+util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable
+util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,,
+util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable
+util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable
+util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable
+util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.yaml
new file mode 100644
index 000000000..de8e115fd
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.yaml
@@ -0,0 +1,10 @@
+
+SPEC: mandatory_tour_frequency.csv
+COEFFICIENTS: mandatory_tour_frequency_coeffs.csv
+
+annotate_persons:
+  SPEC: annotate_persons_mtf
+  DF: persons
+  TABLES:
+    - tours
+
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_alternatives.csv
new file mode 100644
index 000000000..025decbb1
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_alternatives.csv
@@ -0,0 +1,7 @@
+#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate
+alt,work,school
+work1,1,0
+work2,2,0
+school1,0,1
+school2,0,2
+work_and_school,1,1
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_coeffs.csv
new file mode 100644
index 000000000..c0909e681
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency_coeffs.csv
@@ -0,0 +1,54 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_ft_worker_work2_asc,-3.3781,F
+coef_pt_worker_work2_asc,-3.0476,F
+coef_univ_work1_asc,-2.630262534,F
+coef_univ_work2_asc,-4.5,F
+coef_univ_school2_asc,-3.841285259,F
+coef_univ_work_and_school_asc,-4.529863127,F
+coef_driving_age_child_school2_asc,-3.136,F
+coef_driving_age_child_work_and_school_asc,-4.4362,F
+coef_pre_driving_age_child_school2_asc,-3.9703,F
+coef_female_work1,0.1737,F
+coef_female_work2,-0.2255,F
+coef_female_school1,0.1592,F
+coef_female_school2,0.114,F
+coef_female_work_and_school,-0.3442,F
+coef_female_univ_work1,0.1737,F
+coef_under_35_work1,-0.4629,F
+coef_under_35_work2,-0.1375,F
+coef_under_35_school1,0.7218,F
+coef_under_35_school2,1.275,F
+coef_under_35_work_and_school,0.9761,F
+coef_can_walk_to_work_work2,0.5268,F
+coef_can_walk_to_work_school2,0.7114,F
+coef_can_walk_to_work_and_school,0.1391,F
+coef_round_trip_auto_time_to_work_work2,-0.0035,F
+coef_round_trip_auto_time_to_work_school2,-0.0034,F
+coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F
+coef_student_employed,3.014,F
+coef_non_student_goes_to_school,3.883,F
+coef_no_cars_in_hh_work2,-1.306,F
+coef_no_cars_in_hh_school2,-1.413,F
+coef_no_cars_in_hh_work_and_school,-1.302,F
+coef_few_cars_than_drivers_school2,-0.5759,F
+coef_num_preschool_in_hh_work1,0.2191,F
+coef_num_preschool_in_hh_work2,-0.1478,F
+coef_num_preschool_in_hh_school1,-0.1335,F
+coef_num_preschool_in_hh_school2,-0.5577,F
+coef_num_preschool_in_hh_work_and_school,-0.1251,F
+coef_num_non_workers_in_hh_school1,0.2574,F
+coef_hh_income_gt_50k_work,-0.0528,F
+coef_hh_income_gt_50k_school1,0.0347,F
+coef_hh_income_gt_50k_worker_work_and_school,0.0347,F
+coef_hh_income_gt_50k_student_work_and_school,-0.0528,F
+coef_non_family_hh_category1,-0.25,F
+coef_non_family_hh_category2,-0.1792,F
+coef_num_under_16_not_at_school_work2,0.1804,
+coef_num_under_16_not_at_school_school2,0.0866,
+coef_num_under_16_not_at_school_work_and_school,-0.1955,
+coef_home_urban_work1,-0.2831,
+coef_home_urban_work2,0.2308,
+coef_home_urban_school1,-0.1361,
+coef_home_urban_school2,0.317,
+coef_home_urban_work_and_school,-0.3509,
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml
new file mode 100644
index 000000000..120959d36
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml
@@ -0,0 +1,57 @@
+
+SIMULATE_CHOOSER_COLUMNS:
+  - age
+  - female
+  - ptype
+  - is_university
+  - is_income_less25K
+  - is_income_25K_to_60K
+  - is_income_60K_to_120K
+  - is_income_greater120K
+  - is_pre_drive_child_in_HH
+  - is_non_worker_in_HH
+  - auto_ownership
+  - is_all_adults_full_time_workers
+  - distance_to_school
+  - roundtrip_auto_time_to_work
+  - roundtrip_auto_time_to_school
+  - free_parking_at_work
+  - workplace_zone_id
+  - school_zone_id
+  - home_zone_id
+  - TAZ
+  
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+
+TOUR_SPEC_SEGMENTS:
+  work: work
+  school: school
+  univ: univ
+
+ALTS_PREPROCESSOR:
+  work:
+    SPEC: mandatory_tour_scheduling_annotate_tours_preprocessor.csv
+    DF: alt_tdd
+
+SPEC_SEGMENTS:
+  work:
+    'SPEC': tour_scheduling_work.csv
+    'COEFFICIENTS': tour_scheduling_work_coeffs.csv
+  school:
+    'SPEC': tour_scheduling_school.csv
+    'COEFFICIENTS': tour_scheduling_school_coeffs.csv
+  univ:
+    'SPEC': tour_scheduling_university.csv
+    'COEFFICIENTS': tour_scheduling_university_coeffs.csv
+
+#SPEC:
+#  work: tour_scheduling_work.csv
+#  school: tour_scheduling_school.csv
+#  univ: tour_scheduling_university.csv
+
+#CHOOSER_ORIG_COL_NAME: TAZ
+
+DESTINATION_FOR_TOUR_PURPOSE:
+  work: workplace_zone_id
+  school: school_zone_id
+  univ: school_zone_id
diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv
new file mode 100644
index 000000000..6c2089653
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv
@@ -0,0 +1,4 @@
+Description,Target,Expression
+departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)"
+arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)"
+duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))"
diff --git a/activitysim/examples/prototype_mwcog/configs/network_los.yaml b/activitysim/examples/prototype_mwcog/configs/network_los.yaml
new file mode 100644
index 000000000..3e4862903
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/network_los.yaml
@@ -0,0 +1,19 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: False
+
+#alternate dir to read/write skim cache (defaults to output_dir)
+#cache_dir: data/cache
+
+zone_system: 1
+
+taz_skims: skims.omx
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 30
+    periods: [0, 6, 12, 24, 32, 48]
+    labels: ['NT', 'AM', 'MD', 'PM', 'NT']
+
+#NT is repeated twice as it's on both sides of the day. 
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.csv
new file mode 100644
index 000000000..090e480b8
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.csv
@@ -0,0 +1,21 @@
+Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr
+local_dist,_DIST@skims['DIST'],1,1,1,1,1,1
+util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,0,coef_dist_othmaint,0,coef_dist_othdiscr
+util_dist_squared,"@(_DIST).clip(0,20)**2",0,coef_dist_squared_shopping,0,0,0,coef_dist_squared_othdiscr
+util_dist_cubed,"@(_DIST).clip(0,20)**3",0,coef_dist_cubed_shopping,0,0,0,coef_dist_cubed_othdiscr
+util_dist_squared,"@(_DIST).clip(0,15)**2",0,0,0,coef_dist_squared_othmaint,0,0
+util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr
+util_dist_young,@(df['young']==True) * _DIST,0,coef_dist_young_shopping,coef_dist_young_eatout,coef_dist_young_othmaint,0,coef_dist_young_othdiscr
+util_dist_old,@(df['old']==True) * _DIST,0,0,0,0,0,0
+util_dist_female,@(df['female']==True) * _DIST,0,coef_dist_female_shopping,0,0,coef_dist_female_social,coef_dist_female_othdiscr
+util_dist_part_time,@(df['pemploy']==2) * _DIST,0,0,coef_dist_part_time_eatout,coef_dist_part_time_othmaint,0,0
+util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,0,0,0
+util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,0,coef_dist_hh_child_othdiscr
+util_dist_zero_auto,@(df['auto_ownership']==0) * _DIST,0,0,0,0,0,coef_dist_zero_auto_othdiscr
+util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_very_high,@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,0,0,coef_dist_veryhigh_inc_eatout,0,coef_dist_veryhigh_inc_social,coef_dist_veryhigh_inc_othdiscr
+Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1
+No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999
+Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr
+Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.yaml
new file mode 100644
index 000000000..4f9f144a8
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination.yaml
@@ -0,0 +1,79 @@
+SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv
+SPEC: non_mandatory_tour_destination.csv
+COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv
+
+SAMPLE_SIZE: 30
+
+SIZE_TERM_SELECTOR: non_mandatory
+
+# we can't use use household income_segment as this will also be set for non-workers
+CHOOSER_SEGMENT_COLUMN_NAME: tour_type
+
+# optional (comment out if not desired)
+DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum
+
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
+DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample
+
+
+SEGMENTS:
+  - shopping
+  - othmaint
+  - othdiscr
+  - eatout
+  - social
+  - escort
+
+SIMULATE_CHOOSER_COLUMNS:
+  - tour_type
+  - TAZ
+  - person_id
+  - income_segment
+  - pemploy
+  - is_student
+  - age_0_to_5
+  - age_6_to_12
+  - hh_child
+  - young
+  - old
+  - female
+  - auto_ownership
+
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+
+# model-specific logsum-related settings
+CHOOSER_ORIG_COL_NAME: TAZ
+ALT_DEST_COL_NAME: alt_dest
+IN_PERIOD:
+  shopping: 19
+  othmaint: 19
+  othdiscr: 36
+  eatout: 36
+  social: 36
+  escort: 11  
+OUT_PERIOD:
+  shopping: 15
+  othmaint: 15
+  othdiscr: 31
+  eatout: 31
+  social: 31
+  escort: 10  
+  
+SEGMENT_IDS:
+  work_low: 1
+  work_med: 2
+  work_high: 3
+  work_veryhigh: 4
+
+CONSTANTS:
+  WORK_LOW_SEGMENT_ID: 1
+  WORK_MED_SEGMENT_ID: 2
+  WORK_HIGH_SEGMENT_ID: 3
+  WORK_VERYHIGH_SEGMENT_ID: 4
+  
+
+preprocessor:
+  SPEC: non_mandatory_tour_destination_annotate_tours_preprocessor
+  DF: tours
+  TABLES:
+    - persons
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_coeffs.csv
new file mode 100644
index 000000000..76136fd93
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_coeffs.csv
@@ -0,0 +1,45 @@
+coefficient_name,value,constrain
+coef_mode_logsum_escort,0.676,F
+coef_dist_escort,0.046736802,F
+coef_dist_squared_escort,0,F
+coef_dist_cubed_escort,0,F
+coef_dist_logged_escort,-2.403808965,F
+coef_dist_hh_child_escort,-0.078449805,F
+coef_mode_logsum_shopping,0.676,F
+coef_dist_shopping,-0.046575944,F
+coef_dist_squared_shopping,-0.000953534,F
+coef_dist_cubed_shopping,0,F
+coef_dist_logged_shopping,-2.297550828,F
+coef_dist_young_shopping,0.032022587,F
+coef_dist_female_shopping,0.02130081,F
+coef_mode_logsum_eatout,0.79,F
+coef_dist_eatout,0,F
+coef_dist_squared_eatout,0,F
+coef_dist_cubed_eatout,0,F
+coef_dist_logged_eatout,-1.904643053,F
+coef_dist_young_eatout,0.023697451,F
+coef_dist_part_time_eatout,0.024375725,F
+coef_dist_hh_child_eatout,-0.034067726,F
+coef_dist_veryhigh_inc_eatout,-0.03079323,F
+coef_mode_logsum_othmaint,0.676,F
+coef_dist_othmaint,-0.04955464,F
+coef_dist_squared_othmaint,-0.001372519,F
+coef_dist_cubed_othmaint,0,F
+coef_dist_logged_othmaint,-1.270318612,F
+coef_dist_part_time_othmaint,-0.01938104,F
+coef_dist_young_othmaint,-0.049119954,F
+coef_mode_logsum_social,0.79,F
+coef_dist_social,0,F
+coef_dist_logged_social,-1.408912476,F
+coef_dist_female_social,-0.008330037,F
+coef_dist_veryhigh_inc_social,-0.018940702,F
+coef_mode_logsum_othdiscr,0.79,F
+coef_dist_othdiscr,0.015757703,F
+coef_dist_squared_othdiscr,-0.001806674,F
+coef_dist_cubed_othdiscr,0,F
+coef_dist_logged_othdiscr,-1.688427559,F
+coef_dist_young_othdiscr,0.023693922,F
+coef_dist_female_othdiscr,-0.008809842,F
+coef_dist_hh_child_othdiscr,-0.016829222,F
+coef_dist_zero_auto_othdiscr,0.065231488,F
+coef_dist_veryhigh_inc_othdiscr,-0.008247062,F
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_sample.csv
new file mode 100644
index 000000000..33d3f6f4e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_destination_sample.csv
@@ -0,0 +1,19 @@
+Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr
+local_dist,_DIST@skims['DIST'],1,1,1,1,1,1
+util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,0,coef_dist_othmaint,0,coef_dist_othdiscr
+util_dist_squared,"@(_DIST).clip(0,20)**2",0,coef_dist_squared_shopping,0,0,0,coef_dist_squared_othdiscr
+util_dist_cubed,"@(_DIST).clip(0,20)**3",0,coef_dist_cubed_shopping,0,0,0,coef_dist_cubed_othdiscr
+util_dist_squared,"@(_DIST).clip(0,15)**2",0,0,0,coef_dist_squared_othmaint,0,0
+util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr
+util_dist_young,@(df['young']==True) * _DIST,0,coef_dist_young_shopping,coef_dist_young_eatout,coef_dist_young_othmaint,0,coef_dist_young_othdiscr
+util_dist_old,@(df['old']==True) * _DIST,0,0,0,0,0,0
+util_dist_female,@(df['female']==True) * _DIST,0,coef_dist_female_shopping,0,0,coef_dist_female_social,coef_dist_female_othdiscr
+util_dist_part_time,@(df['pemploy']==2) * _DIST,0,0,coef_dist_part_time_eatout,coef_dist_part_time_othmaint,0,0
+util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,0,0,0
+util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,0,coef_dist_hh_child_othdiscr
+util_dist_zero_auto,@(df['auto_ownership']==0) * _DIST,0,0,0,0,0,coef_dist_zero_auto_othdiscr
+util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,0,0,0
+util_dist_very_high,@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,0,0,coef_dist_veryhigh_inc_eatout,0,coef_dist_veryhigh_inc_social,coef_dist_veryhigh_inc_othdiscr
+Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1
+No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv
new file mode 100644
index 000000000..6abd71a70
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv
@@ -0,0 +1,223 @@
+Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL
+util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour
+util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour
+util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour
+util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour
+util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour
+util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour
+util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours  = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours
+util_total_number_of_tours_is_0_prior_tours,Total Number of Tours  = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours
+util_total_number_of_tours_is_1,Total Number of Tours  = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1
+util_total_number_of_tours_is_2,Total Number of Tours  = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2
+util_total_number_of_tours_is_3,Total Number of Tours  = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3
+util_total_number_of_tours_is_4,Total Number of Tours  = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4
+util_total_number_of_tours_is_5,Total Number of Tours  = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5
+util_total_number_of_tours_is_6_plus,Total Number of Tours  = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus
+util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0
+util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1
+util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2
+util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3
+util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4
+util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus
+util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0
+util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1
+util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2
+util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3
+util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4
+util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours >  4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus
+util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours
+util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours
+util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours
+util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours
+util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours
+util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0
+util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1
+util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2
+util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3
+util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4
+util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours >  4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus
+util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1
+util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2
+util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3
+util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4
+util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours >  4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus
+util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1
+util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2
+util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3
+util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4
+util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours >  4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus
+util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1
+util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2
+util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3
+util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4
+util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours >  4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus
+util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour
+util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour
+util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour
+util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour
+util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour
+util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour
+util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour
+util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour
+util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour
+util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour
+util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour
+util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour
+util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour
+util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour
+util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour
+util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1
+util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2
+util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3
+util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4
+util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5
+util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour
+util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour
+util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour
+util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour
+util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour
+util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1
+util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2
+util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3
+util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4
+util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours >  4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus
+util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1
+util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2
+util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3
+util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4
+util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus
+util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1
+util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2
+util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3
+util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4
+util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus
+util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1
+util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2
+util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3
+util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4
+util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5
+util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1
+util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2
+util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3
+util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4
+util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5
+util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1
+util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2
+util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3
+util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4
+util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5
+util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1
+util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2
+util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3
+util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4
+util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5
+util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour
+util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour
+util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour
+util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour
+util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour
+util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour
+util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour
+util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour
+util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour
+util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour
+util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour
+util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour
+util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour
+util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour
+util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour
+util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour
+util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour
+util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour
+util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour
+util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour
+util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour
+util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour
+util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour
+util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour
+util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour
+util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour
+util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour
+util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour
+util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour
+util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour
+util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour
+util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour
+util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour
+util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour
+util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour
+util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour
+util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour
+util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour
+util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour
+util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour
+util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour
+util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour
+util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker  (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour
+util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour
+util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour
+util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour
+util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour
+util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour
+util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour
+util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour
+util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1
+util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2
+util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3
+util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4
+util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus
+util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1
+util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2
+util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3
+util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4
+util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus
+util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1
+util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2
+util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3
+util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4
+util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus
+util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting
+util_transit_access_to_retail_and_escorting,Transit Access to Retail  & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting
+util_auto_access_to_retail_and_escorting,Auto Access to Retail  & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting
+util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping
+util_transit_access_to_retail_and_shopping,Transit Access to Retail  & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping
+util_auto_access_to_retail_and_shopping,Auto Access to Retail  & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping
+util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance
+util_transit_access_to_retail_and_maintenance,Transit Access to Retail  & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance
+util_auto_access_to_retail_and_maintenance,Auto Access to Retail  & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance
+util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out
+util_transit_access_to_retail_and_eating_out,Transit Access to Retail  & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out
+util_auto_access_to_retail_and_eating_out,Auto Access to Retail  & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out
+util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary
+util_transit_access_to_retail_and_discretionary,Transit Access to Retail  & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary
+util_auto_access_to_retail_and_discretionary,Auto Access to Retail  & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary
+util_urban_and_tour_frequency_is_1,Urban AREATYPE & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1
+util_urban_and_tour_frequency_is_2,Urban AREATYPE & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2
+util_urban_and_tour_frequency_is_3,Urban AREATYPE & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3
+util_urban_and_tour_frequency_is_4,Urban AREATYPE & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4
+util_urban_and_tour_frequency_is_5_plus,Urban AREATYPE & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus
+util_urban_and_escorting_tour,Urban AREATYPE & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour
+util_urban_and_shopping_tour,Urban AREATYPE &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour
+util_urban_and_maintenance_tour,Urban AREATYPE & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour
+util_urban_and_eatingout_tour,Urban AREATYPE & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour
+util_urban_and_discretionary_tour,Urban AREATYPE & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour
+util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant
+util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant
+util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant
+util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant
+util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant
+util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant
+util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant
+util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour
+util_calibration_mand_gt0_nonmandtours_1,Mandatory tours > 0 and Non-mandatory tours = 1 Calibration Constant,(num_mand > 0) & (tot_tours == 1),coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0,coef_calib_nonmandtours_1_mand_gt0
+util_calibration_mand_gt0_nonmandtours_2,Mandatory tours > 0 and Non-mandatory tours = 2 Calibration Constant,(num_mand > 0) & (tot_tours == 2),coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0,coef_calib_nonmandtours_2_mand_gt0
+util_calibration_mand_gt0_nonmandtours_gt3,Mandatory tours > 0 and Non-mandatory tours = 3+ Calibration Constant,(num_mand > 0) & (tot_tours >= 3),coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0,coef_calib_nonmandtours_gt3_mand_gt0
+util_calibration_mand_0_nonmandtours_2,Mandatory tours = 0 and Non-mandatory tours = 2 Calibration Constant,(num_mand == 0)&(tot_tours == 2),coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0
+util_calibration_mand_0_nonmandtours_3,Mandatory tours = 0 and Non-mandatory tours = 3 Calibration Constant,(num_mand == 0)&(tot_tours == 3),coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0
+util_calibration_mand_0_nonmandtours_gt4,Mandatory tours = 0 and Non-mandatory tours = 4+ Calibration Constant,(num_mand == 0)&(tot_tours >= 4),coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0
+util_tc_1dayperweek_1,Person that telecommutes 1 day per week,telecommute_frequency=='1_day_week' & tot_tours == 1,coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,0,0,0,0
+util_tc_23dayperweek_1,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='2_3_days_week' & tot_tours == 1,coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,0,0,0,0
+util_tc_4pdayperweek_1,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='4_days_week' & tot_tours == 1,coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,0,0,0,0
+util_tc_1dayperweek_2p,Person that telecommutes 1 day per week,telecommute_frequency=='1_day_week' & tot_tours > 1,coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,0,0,0,0
+util_tc_23dayperweek_2p,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='2_3_days_week' & tot_tours > 1,coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,0,0,0,0
+util_tc_4pdayperweek_2p,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='4_days_week' & tot_tours > 1,coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,0,0,0,0
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.yaml
new file mode 100644
index 000000000..239b5a38a
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.yaml
@@ -0,0 +1,42 @@
+
+SEGMENT_COL: ptype
+SPEC: non_mandatory_tour_frequency.csv
+
+SPEC_SEGMENTS:
+  - NAME: PTYPE_FULL
+    PTYPE: 1
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv
+  - NAME: PTYPE_PART
+    PTYPE: 2
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv
+  - NAME: PTYPE_UNIVERSITY
+    PTYPE: 3
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv
+  - NAME: PTYPE_NONWORK
+    PTYPE: 4
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv
+  - NAME: PTYPE_RETIRED
+    PTYPE: 5
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv
+  - NAME: PTYPE_DRIVING
+    PTYPE: 6
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv
+  - NAME: PTYPE_SCHOOL
+    PTYPE: 7
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv
+  - NAME: PTYPE_PRESCHOOL
+    PTYPE: 8
+    COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv
+
+annotate_persons:
+  SPEC: annotate_persons_nmtf
+  DF: persons
+  TABLES:
+    - tours
+
+preprocessor:
+  SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor
+  DF: persons
+  TABLES:
+    - tours
+#    - accessibility
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_alternatives.csv
new file mode 100644
index 000000000..b9765aa75
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_alternatives.csv
@@ -0,0 +1,97 @@
+escort,shopping,othmaint,othdiscr,eatout,social
+0,0,0,0,0,0
+0,0,0,1,0,0
+0,0,0,0,0,1
+0,0,0,1,0,1
+0,0,0,0,1,0
+0,0,0,1,1,0
+0,0,0,0,1,1
+0,0,0,1,1,1
+0,0,1,0,0,0
+0,0,1,1,0,0
+0,0,1,0,0,1
+0,0,1,1,0,1
+0,0,1,0,1,0
+0,0,1,1,1,0
+0,0,1,0,1,1
+0,0,1,1,1,1
+0,1,0,0,0,0
+0,1,0,1,0,0
+0,1,0,0,0,1
+0,1,0,1,0,1
+0,1,0,0,1,0
+0,1,0,1,1,0
+0,1,0,0,1,1
+0,1,0,1,1,1
+0,1,1,0,0,0
+0,1,1,1,0,0
+0,1,1,0,0,1
+0,1,1,1,0,1
+0,1,1,0,1,0
+0,1,1,1,1,0
+0,1,1,0,1,1
+0,1,1,1,1,1
+1,0,0,0,0,0
+1,0,0,1,0,0
+1,0,0,0,0,1
+1,0,0,1,0,1
+1,0,0,0,1,0
+1,0,0,1,1,0
+1,0,0,0,1,1
+1,0,0,1,1,1
+1,0,1,0,0,0
+1,0,1,1,0,0
+1,0,1,0,0,1
+1,0,1,1,0,1
+1,0,1,0,1,0
+1,0,1,1,1,0
+1,0,1,0,1,1
+1,0,1,1,1,1
+1,1,0,0,0,0
+1,1,0,1,0,0
+1,1,0,0,0,1
+1,1,0,1,0,1
+1,1,0,0,1,0
+1,1,0,1,1,0
+1,1,0,0,1,1
+1,1,0,1,1,1
+1,1,1,0,0,0
+1,1,1,1,0,0
+1,1,1,0,0,1
+1,1,1,1,0,1
+1,1,1,0,1,0
+1,1,1,1,1,0
+1,1,1,0,1,1
+1,1,1,1,1,1
+2,0,0,0,0,0
+2,0,0,1,0,0
+2,0,0,0,0,1
+2,0,0,1,0,1
+2,0,0,0,1,0
+2,0,0,1,1,0
+2,0,0,0,1,1
+2,0,0,1,1,1
+2,0,1,0,0,0
+2,0,1,1,0,0
+2,0,1,0,0,1
+2,0,1,1,0,1
+2,0,1,0,1,0
+2,0,1,1,1,0
+2,0,1,0,1,1
+2,0,1,1,1,1
+2,1,0,0,0,0
+2,1,0,1,0,0
+2,1,0,0,0,1
+2,1,0,1,0,1
+2,1,0,0,1,0
+2,1,0,1,1,0
+2,1,0,0,1,1
+2,1,0,1,1,1
+2,1,1,0,0,0
+2,1,1,1,0,0
+2,1,1,0,0,1
+2,1,1,1,0,1
+2,1,1,0,1,0
+2,1,1,1,1,0
+2,1,1,0,1,1
+2,1,1,1,1,1
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv
new file mode 100644
index 000000000..fc6616aba
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv
@@ -0,0 +1,21 @@
+Description,Target,Expression
+#,,
+,max_window,person_max_window(persons)/2.25
+,log_max_window,np.log1p(max_window)
+,medium_low_income,(persons.income_in_thousands > 20) & (persons.income_in_thousands <= 50)
+,medium_high_income,(persons.income_in_thousands > 50) & (persons.income_in_thousands <= 100)
+,high_income,(persons.income_in_thousands > 100)
+,no_cars,(persons.auto_ownership == 0)
+,car_sufficiency,persons.auto_ownership-persons.num_workers
+#,,
+# UEC file comments says these are joint tour counts per persons but code is for household counts,,
+,_JOINT_TOURS,tours[tours.tour_category=='joint']
+,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), persons.household_id)"
+,num_hh_joint_shop_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='shopping'].groupby('household_id').size(), persons.household_id)"
+,num_hh_joint_eatout_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='eatout'].groupby('household_id').size(), persons.household_id)"
+,num_hh_joint_maint_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='maint'].groupby('household_id').size(), persons.household_id)"
+,num_hh_joint_social_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='social'].groupby('household_id').size(), persons.household_id)"
+,num_hh_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='othdiscr'].groupby('household_id').size(), persons.household_id)"
+# non_mandatory tour frequency extension,,
+,has_mandatory_tour,(persons.num_mand > 0) * 1
+,has_joint_tour,(persons.num_joint_tours > 0) * 1
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv
new file mode 100644
index 000000000..8f120c97d
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv
@@ -0,0 +1,217 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-7.1506,F
+coef_total_number_of_tours_is_2,-11.1214,F
+coef_total_number_of_tours_is_3,-13.175,F
+coef_total_number_of_tours_is_4,-999,T
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.234,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.9231,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-6.5835,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,-0.2162,F
+coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3587,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2701,F
+coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_shopping_tours,0,T
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.3298,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,1.3759,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,3.2808,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,3.2808,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,3.2808,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_2,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_3,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_4,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T
+coef_high_income_group_and_tour_frequency_is_1,0,T
+coef_high_income_group_and_tour_frequency_is_2,0,T
+coef_high_income_group_and_tour_frequency_is_3,0,T
+coef_high_income_group_and_tour_frequency_is_4,0,T
+coef_high_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumlow_income_group_and_shopping_tour,0,T
+coef_mediumhigh_income_group_and_shopping_tour,0.2443,F
+coef_high_income_group_and_shopping_tour,0.2443,F
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0.3982,F
+coef_high_income_group_and_maintenance_tour,0.3982,F
+coef_mediumlow_income_group_and_eating_out_tour,0,T
+coef_mediumhigh_income_group_and_eating_out_tour,0.4916,F
+coef_high_income_group_and_eating_out_tour,0.4916,F
+coef_mediumlow_income_group_and_discretionary_tour,0.9169,F
+coef_mediumhigh_income_group_and_discretionary_tour,1.405,F
+coef_high_income_group_and_discretionary_tour,2.327,F
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,0.2858,F
+coef_high_income_group_and_visiting_tour,0.2858,F
+coef_female_and_tour_frequency_is_1,0,T
+coef_female_and_tour_frequency_is_2,0,T
+coef_female_and_tour_frequency_is_3,0,T
+coef_female_and_tour_frequency_is_4,0,T
+coef_female_and_tour_frequency_is_5,0,T
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0,T
+coef_zero_car_ownership_and_tour_frequency_is_1,-0.6369,F
+coef_zero_car_ownership_and_tour_frequency_is_2,-0.6369,F
+coef_zero_car_ownership_and_tour_frequency_is_3,-0.6369,F
+coef_zero_car_ownership_and_tour_frequency_is_4,-0.6369,F
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.6369,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.6369,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.6369,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.6369,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.6369,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.6369,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.2902,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.0352,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.0352,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.0352,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.0352,F
+coef_presence_of_non_worker_and_tour_frequency_is_1,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_2,-0.6571,F
+coef_presence_of_non_worker_and_tour_frequency_is_3,-1.4044,F
+coef_presence_of_non_worker_and_tour_frequency_is_4,-1.4044,F
+coef_presence_of_non_worker_and_tour_frequency_is_5,-1.4044,F
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.3219,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-1.0874,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-1.0874,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-1.0874,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-1.0874,F
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,0,T
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,0,T
+coef_presence_of_part_time_worker_and_shopping_tour,0,T
+coef_presence_of_non_worker_and_shopping_tour,0,T
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,0,T
+coef_presence_of_part_time_worker_and_eating_out_tour,0,T
+coef_presence_of_non_worker_and_eating_out_tour,0,T
+coef_presence_of_retiree_and_eating_out_tour,0,T
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,-0.6377,F
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-1.5698,F
+coef_presence_of_pre_school_kid_and_eating_out_tour,-0.2987,F
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,0,T
+coef_presence_of_part_time_worker_and_discretionary_tour,0,T
+coef_presence_of_non_worker_and_discretionary_tour,0,T
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,-1.2834,F
+coef_presence_of_driving_school_kid_and_discretionary_tour,-0.9202,F
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_2,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_3,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_4,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0.1004,F
+coef_auto_access_to_retail_and_tour_frequency_is_2,0.1004,F
+coef_auto_access_to_retail_and_tour_frequency_is_3,0.1004,F
+coef_auto_access_to_retail_and_tour_frequency_is_4,0.1004,F
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0.1004,F
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0,T
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,0,T
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,1.0394,F
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,-0.4934,F
+coef_2_plus_escort_tours_constant,1.4155,F
+coef_1_plus_shopping_tours_constant,0.532,F
+coef_1_plus_maintenance_tours_constant,-0.4344,F
+coef_1_plus_eating_out_tours_constant,-0.0242,F
+coef_1_plus_visting_tours_constant,0.2367,F
+coef_1_plus_other_discretionary_tours_constant,-0.2602,F
+coef_0_auto_household_and_escorting_tour,-2,
+coef_calib_nonmandtours_1_mand_gt0,-0.583554487,T
+coef_calib_nonmandtours_2_mand_gt0,-5.8999778,T
+coef_calib_nonmandtours_gt3_mand_gt0,-0.207059818,T
+coef_calib_nonmandtours_2_mand_0,0.087018342,T
+coef_calib_nonmandtours_3_mand_0,-0.142844665,T
+coef_calib_nonmandtours_gt4_mand_0,-3.578617789,T
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv
new file mode 100644
index 000000000..b641b5f2d
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv
@@ -0,0 +1,223 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-7.3572,F
+coef_total_number_of_tours_is_2,-10.647,F
+coef_total_number_of_tours_is_3,-13.5005,F
+coef_total_number_of_tours_is_4,-16.3965,F
+coef_total_number_of_tours_is_5,-19.6843,F
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8887,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.3343,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.3343,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.3343,F
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_2,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_3,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_4,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T
+coef_number_of_joint_shopping_tours,0,T
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,-0.5866,F
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.2562,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,1.2868,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,1.3993,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,1.3993,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.3993,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0.4981,F
+coef_mediumlow_income_group_and_tour_frequency_is_2,0.8345,F
+coef_mediumlow_income_group_and_tour_frequency_is_3,1.0213,F
+coef_mediumlow_income_group_and_tour_frequency_is_4,1.0213,F
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0213,F
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4981,F
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8345,F
+coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0213,F
+coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0213,F
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0213,F
+coef_high_income_group_and_tour_frequency_is_1,0.5189,F
+coef_high_income_group_and_tour_frequency_is_2,1.1336,F
+coef_high_income_group_and_tour_frequency_is_3,1.3899,F
+coef_high_income_group_and_tour_frequency_is_4,1.3899,F
+coef_high_income_group_and_tour_frequency_is_5_plus,1.3899,F
+coef_mediumlow_income_group_and_shopping_tour,0,T
+coef_mediumhigh_income_group_and_shopping_tour,0,T
+coef_high_income_group_and_shopping_tour,0,T
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0,T
+coef_high_income_group_and_maintenance_tour,0,T
+coef_mediumlow_income_group_and_eating_out_tour,0,T
+coef_mediumhigh_income_group_and_eating_out_tour,0.5581,F
+coef_high_income_group_and_eating_out_tour,0.5581,F
+coef_mediumlow_income_group_and_discretionary_tour,0,T
+coef_mediumhigh_income_group_and_discretionary_tour,0.2565,F
+coef_high_income_group_and_discretionary_tour,0.2565,F
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,-0.2423,F
+coef_high_income_group_and_visiting_tour,-0.2423,F
+coef_female_and_tour_frequency_is_1,-0.0766,F
+coef_female_and_tour_frequency_is_2,-0.1062,F
+coef_female_and_tour_frequency_is_3,-0.3274,F
+coef_female_and_tour_frequency_is_4,-0.3274,F
+coef_female_and_tour_frequency_is_5,-0.3274,F
+coef_female_and_escorting_tour,0.1824,F
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0,T
+coef_zero_car_ownership_and_tour_frequency_is_1,-0.3486,F
+coef_zero_car_ownership_and_tour_frequency_is_2,-0.3486,F
+coef_zero_car_ownership_and_tour_frequency_is_3,-0.3486,F
+coef_zero_car_ownership_and_tour_frequency_is_4,-0.3486,F
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.3486,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.1304,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.1304,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.1304,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.1304,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.1304,F
+coef_presence_of_non_worker_and_tour_frequency_is_1,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_2,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_3,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_4,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_5,0,T
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,-0.4815,F
+coef_presence_of_retiree_and_escorting_tour,-0.808,F
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0.3601,F
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3974,F
+coef_presence_of_pre_school_kid_and_escorting_tour,0.6842,F
+coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.2746,F
+coef_at_home_pre_school_kid_and_escorting_tour,-1.5675,F
+coef_presence_of_full_time_worker_and_shopping_tour,-0.3059,F
+coef_presence_of_part_time_worker_and_shopping_tour,-0.1541,F
+coef_presence_of_non_worker_and_shopping_tour,-0.416,F
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,-0.208,F
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,-0.1685,F
+coef_presence_of_part_time_worker_and_maintenance_tour,-0.1584,F
+coef_presence_of_non_worker_and_maintenance_tour,-0.3237,F
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,-0.3571,F
+coef_presence_of_part_time_worker_and_eating_out_tour,0,T
+coef_presence_of_non_worker_and_eating_out_tour,-0.2014,F
+coef_presence_of_retiree_and_eating_out_tour,-0.5708,F
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,-0.4225,F
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,-0.667,F
+coef_presence_of_part_time_worker_and_discretionary_tour,-0.2102,F
+coef_presence_of_non_worker_and_discretionary_tour,-0.4281,F
+coef_presence_of_retiree_and_discretionary_tour,-0.9104,F
+coef_presence_of_university_student_and_discretionary_tour,-0.8551,F
+coef_presence_of_driving_school_kid_and_discretionary_tour,-0.3963,F
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.3959,F
+coef_presence_of_pre_school_kid_and_discretionary_tour,-0.5081,F
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,-0.4703,F
+coef_at_home_pre_school_kid_and_discretionary_tour,-0.4703,F
+coef_walk_access_to_retail_and_tour_frequency_is_1,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_2,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_3,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_4,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_1,0.0226,F
+coef_transit_access_to_retail_and_tour_frequency_is_2,0.0226,F
+coef_transit_access_to_retail_and_tour_frequency_is_3,0.0226,F
+coef_transit_access_to_retail_and_tour_frequency_is_4,0.0226,F
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0226,F
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0.0451,F
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0.033,F
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0.1067,F
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0.0749,F
+coef_walk_access_to_retail_and_eating_out,0.145,F
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0.0567,F
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0.0844,F
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,-0.4316,F
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,0.0298,F
+coef_2_plus_escort_tours_constant,0.7402,F
+coef_1_plus_shopping_tours_constant,0.4774,F
+coef_1_plus_maintenance_tours_constant,0.1202,F
+coef_1_plus_eating_out_tours_constant,0.0097,F
+coef_1_plus_visting_tours_constant,0.0522,F
+coef_1_plus_other_discretionary_tours_constant,0.7412,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,-0.036689277,T
+coef_calib_nonmandtours_2_mand_gt0,-0.634072285,T
+coef_calib_nonmandtours_gt3_mand_gt0,-1.583991937,T
+coef_calib_nonmandtours_2_mand_0,-0.8239916,T
+coef_calib_nonmandtours_3_mand_0,-0.826594126,T
+coef_calib_nonmandtours_gt4_mand_0,-0.248973454,T
+coef_tc_1dayperweek_1,-0.005,F
+coef_tc_23dayperweek_1,0.142,F
+coef_tc_4pdayperweek_1,-0.406,F
+coef_tc_1dayperweek_2p,-0.348,F
+coef_tc_23dayperweek_2p,-0.041,F
+coef_tc_4pdayperweek_2p,-0.447,F
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv
new file mode 100644
index 000000000..3ad11daf2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv
@@ -0,0 +1,217 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-8.9791,F
+coef_total_number_of_tours_is_2,-12.0248,F
+coef_total_number_of_tours_is_3,-14.8516,F
+coef_total_number_of_tours_is_4,-17.7037,F
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.6766,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.0518,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,-0.1699,F
+coef_number_of_joint_tours_and_tour_frequency_is_2,-0.4285,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-0.6551,F
+coef_number_of_joint_tours_and_tour_frequency_is_4,-1.0411,F
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-1.0411,F
+coef_number_of_joint_shopping_tours,-0.2391,F
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,-0.7727,F
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.7637,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,1.7928,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,1.7928,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,1.7928,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.7928,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0.5709,F
+coef_mediumlow_income_group_and_tour_frequency_is_2,0.8315,F
+coef_mediumlow_income_group_and_tour_frequency_is_3,0.8315,F
+coef_mediumlow_income_group_and_tour_frequency_is_4,0.8315,F
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.8315,F
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0.7426,F
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8546,F
+coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0792,F
+coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0792,F
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0792,F
+coef_high_income_group_and_tour_frequency_is_1,1.0633,F
+coef_high_income_group_and_tour_frequency_is_2,1.0633,F
+coef_high_income_group_and_tour_frequency_is_3,1.7742,F
+coef_high_income_group_and_tour_frequency_is_4,2.3941,F
+coef_high_income_group_and_tour_frequency_is_5_plus,2.3941,F
+coef_mediumlow_income_group_and_shopping_tour,0.7734,F
+coef_mediumhigh_income_group_and_shopping_tour,0.8906,F
+coef_high_income_group_and_shopping_tour,0.9776,F
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0,T
+coef_high_income_group_and_maintenance_tour,0,T
+coef_mediumlow_income_group_and_eating_out_tour,0.2766,F
+coef_mediumhigh_income_group_and_eating_out_tour,0.4631,F
+coef_high_income_group_and_eating_out_tour,0.7086,F
+coef_mediumlow_income_group_and_discretionary_tour,0.1707,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.5009,F
+coef_high_income_group_and_discretionary_tour,0.8846,F
+coef_mediumlow_income_group_and_visiting_tour,-0.267,F
+coef_mediumhigh_income_group_and_visiting_tour,-0.267,F
+coef_high_income_group_and_visiting_tour,-0.9449,F
+coef_female_and_tour_frequency_is_1,0.3902,F
+coef_female_and_tour_frequency_is_2,0.5323,F
+coef_female_and_tour_frequency_is_3,0.7452,F
+coef_female_and_tour_frequency_is_4,1.1294,F
+coef_female_and_tour_frequency_is_5,1.1294,F
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,-0.2464,F
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0,T
+coef_zero_car_ownership_and_tour_frequency_is_1,-0.3623,F
+coef_zero_car_ownership_and_tour_frequency_is_2,-1.272,F
+coef_zero_car_ownership_and_tour_frequency_is_3,-1.9307,F
+coef_zero_car_ownership_and_tour_frequency_is_4,-1.9307,F
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,-1.9307,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.3623,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.272,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,-1.9307,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,-1.9307,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.9307,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7738,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.7738,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.7738,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.7738,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.7738,F
+coef_presence_of_non_worker_and_tour_frequency_is_1,-0.3763,F
+coef_presence_of_non_worker_and_tour_frequency_is_2,-0.719,F
+coef_presence_of_non_worker_and_tour_frequency_is_3,-1.0229,F
+coef_presence_of_non_worker_and_tour_frequency_is_4,-1.0229,F
+coef_presence_of_non_worker_and_tour_frequency_is_5,-1.0229,F
+coef_presence_of_retiree_and_tour_frequency_is_1,-0.464,F
+coef_presence_of_retiree_and_tour_frequency_is_2,-0.4795,F
+coef_presence_of_retiree_and_tour_frequency_is_3,-0.4795,F
+coef_presence_of_retiree_and_tour_frequency_is_4,-0.4795,F
+coef_presence_of_retiree_and_tour_frequency_is_5,-0.4795,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.7161,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.7161,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.7161,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.7161,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.7161,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0.1486,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0.484,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0.484,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0.484,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.484,F
+coef_presence_of_full_time_worker_and_escorting_tour,0.3947,F
+coef_presence_of_part_time_worker_and_escorting_tour,-0.5861,F
+coef_presence_of_non_worker_and_escorting_tour,0,T
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3773,F
+coef_presence_of_pre_school_kid_and_escorting_tour,0.7194,F
+coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.148,F
+coef_at_home_pre_school_kid_and_escorting_tour,-0.1373,F
+coef_presence_of_full_time_worker_and_shopping_tour,0,T
+coef_presence_of_part_time_worker_and_shopping_tour,0,T
+coef_presence_of_non_worker_and_shopping_tour,0,T
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,-0.4667,F
+coef_presence_of_part_time_worker_and_eating_out_tour,0,T
+coef_presence_of_non_worker_and_eating_out_tour,-0.4976,F
+coef_presence_of_retiree_and_eating_out_tour,-0.6911,F
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,-0.3926,F
+coef_at_home_pre_school_kid_and_eating_out_tour,-0.3926,F
+coef_presence_of_full_time_worker_and_discretionary_tour,-0.3545,F
+coef_presence_of_part_time_worker_and_discretionary_tour,-0.3545,F
+coef_presence_of_non_worker_and_discretionary_tour,0,T
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,0,T
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0.0713,F
+coef_walk_access_to_retail_and_tour_frequency_is_2,0.1256,F
+coef_walk_access_to_retail_and_tour_frequency_is_3,0.1508,F
+coef_walk_access_to_retail_and_tour_frequency_is_4,0.1508,F
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.1508,F
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0.0598,F
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0.0956,F
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0.0772,F
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,0,T
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,-0.0629,F
+coef_2_plus_escort_tours_constant,0.9273,F
+coef_1_plus_shopping_tours_constant,0.4683,F
+coef_1_plus_maintenance_tours_constant,-0.0653,F
+coef_1_plus_eating_out_tours_constant,-0.1429,F
+coef_1_plus_visting_tours_constant,-0.1272,F
+coef_1_plus_other_discretionary_tours_constant,0.3334,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_gt0,0,T
+coef_calib_nonmandtours_gt3_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_0,-0.295195996,T
+coef_calib_nonmandtours_3_mand_0,-0.883739518,T
+coef_calib_nonmandtours_gt4_mand_0,-1.073545808,T
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv
new file mode 100644
index 000000000..d27e5d1f1
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv
@@ -0,0 +1,223 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-7.6391,F
+coef_total_number_of_tours_is_2,-10.4557,F
+coef_total_number_of_tours_is_3,-14.0176,F
+coef_total_number_of_tours_is_4,-16.9717,F
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.239,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.8208,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.5923,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.5923,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.5923,F
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1986,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-1.1986,F
+coef_number_of_joint_tours_and_tour_frequency_is_4,-1.1986,F
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_shopping_tours,0,T
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.5748,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,2.0026,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,2.0026,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,2.0026,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.0026,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0.5981,F
+coef_mediumlow_income_group_and_tour_frequency_is_2,0.9178,F
+coef_mediumlow_income_group_and_tour_frequency_is_3,1.7539,F
+coef_mediumlow_income_group_and_tour_frequency_is_4,1.7539,F
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.7539,F
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0.8682,F
+coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5362,F
+coef_mediumhigh_income_group_and_tour_frequency_is_3,1.9331,F
+coef_mediumhigh_income_group_and_tour_frequency_is_4,1.9331,F
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.9331,F
+coef_high_income_group_and_tour_frequency_is_1,0.8682,F
+coef_high_income_group_and_tour_frequency_is_2,1.5362,F
+coef_high_income_group_and_tour_frequency_is_3,1.9331,F
+coef_high_income_group_and_tour_frequency_is_4,1.9331,F
+coef_high_income_group_and_tour_frequency_is_5_plus,1.9331,F
+coef_mediumlow_income_group_and_shopping_tour,0.4421,F
+coef_mediumhigh_income_group_and_shopping_tour,0.4421,F
+coef_high_income_group_and_shopping_tour,0.7066,F
+coef_mediumlow_income_group_and_maintenance_tour,0.6763,F
+coef_mediumhigh_income_group_and_maintenance_tour,0.6763,F
+coef_high_income_group_and_maintenance_tour,0.6763,F
+coef_mediumlow_income_group_and_eating_out_tour,0,T
+coef_mediumhigh_income_group_and_eating_out_tour,0,T
+coef_high_income_group_and_eating_out_tour,0,T
+coef_mediumlow_income_group_and_discretionary_tour,0.296,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.296,F
+coef_high_income_group_and_discretionary_tour,0.296,F
+coef_mediumlow_income_group_and_visiting_tour,-0.6868,F
+coef_mediumhigh_income_group_and_visiting_tour,-0.6868,F
+coef_high_income_group_and_visiting_tour,-0.6868,F
+coef_female_and_tour_frequency_is_1,0,T
+coef_female_and_tour_frequency_is_2,0,T
+coef_female_and_tour_frequency_is_3,0,T
+coef_female_and_tour_frequency_is_4,0,T
+coef_female_and_tour_frequency_is_5,0,T
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0.4524,F
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0.3072,F
+coef_zero_car_ownership_and_tour_frequency_is_1,-0.5498,F
+coef_zero_car_ownership_and_tour_frequency_is_2,-0.5498,F
+coef_zero_car_ownership_and_tour_frequency_is_3,-0.5498,F
+coef_zero_car_ownership_and_tour_frequency_is_4,-0.5498,F
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.5498,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.5498,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.5498,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.5498,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.5498,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.5498,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_1,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_2,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_3,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_4,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_5,0,T
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.1559,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.5681,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.5681,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.5681,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.5681,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,-0.5263,F
+coef_presence_of_retiree_and_escorting_tour,-0.7516,F
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0.4164,F
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.5795,F
+coef_presence_of_pre_school_kid_and_escorting_tour,0.5414,F
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,0,T
+coef_presence_of_part_time_worker_and_shopping_tour,0,T
+coef_presence_of_non_worker_and_shopping_tour,0,T
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,-0.3131,F
+coef_presence_of_part_time_worker_and_maintenance_tour,-0.5621,F
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,0,T
+coef_presence_of_part_time_worker_and_eating_out_tour,0,T
+coef_presence_of_non_worker_and_eating_out_tour,-0.6545,F
+coef_presence_of_retiree_and_eating_out_tour,-1.389,F
+coef_presence_of_university_student_and_eating_out_tour,-1.4318,F
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,0,T
+coef_presence_of_part_time_worker_and_discretionary_tour,0,T
+coef_presence_of_non_worker_and_discretionary_tour,-1.0371,F
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,0,T
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0.0899,F
+coef_walk_access_to_retail_and_tour_frequency_is_2,0.1447,F
+coef_walk_access_to_retail_and_tour_frequency_is_3,0.3479,F
+coef_walk_access_to_retail_and_tour_frequency_is_4,0.3479,F
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.3479,F
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0,T
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,-0.3929,F
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,0.5272,F
+coef_2_plus_escort_tours_constant,1.5987,F
+coef_1_plus_shopping_tours_constant,0.7569,F
+coef_1_plus_maintenance_tours_constant,0.5533,F
+coef_1_plus_eating_out_tours_constant,0.6914,F
+coef_1_plus_visting_tours_constant,0.1405,F
+coef_1_plus_other_discretionary_tours_constant,0.7989,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,-0.960395864,T
+coef_calib_nonmandtours_2_mand_gt0,-3.237898437,T
+coef_calib_nonmandtours_gt3_mand_gt0,-4.730005533,T
+coef_calib_nonmandtours_2_mand_0,-1.289386462,T
+coef_calib_nonmandtours_3_mand_0,-2.244022623,T
+coef_calib_nonmandtours_gt4_mand_0,-2.739584736,T
+coef_tc_1dayperweek_1,-0.005,F
+coef_tc_23dayperweek_1,0.142,F
+coef_tc_4pdayperweek_1,-0.406,F
+coef_tc_1dayperweek_2p,-0.348,F
+coef_tc_23dayperweek_2p,-0.041,F
+coef_tc_4pdayperweek_2p,-0.447,F
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv
new file mode 100644
index 000000000..d0ed359e2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv
@@ -0,0 +1,217 @@
+coefficient_name,value,constrain
+coef_escorting_tour,2.491,F
+coef_discretionary_tour,0.903,F
+coef_shopping_tour,0,T
+coef_maintenance_tour,1.022,F
+coef_visiting_or_social_tour,0.769,F
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-5.759,F
+coef_total_number_of_tours_is_2,-11.517,F
+coef_total_number_of_tours_is_3,-17.276,F
+coef_total_number_of_tours_is_4,-23.035,F
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_2,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_3,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_4,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T
+coef_number_of_joint_shopping_tours,0,T
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_2,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_3,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_4,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_1,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_2,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_3,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_4,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T
+coef_high_income_group_and_tour_frequency_is_1,0,T
+coef_high_income_group_and_tour_frequency_is_2,0,T
+coef_high_income_group_and_tour_frequency_is_3,0,T
+coef_high_income_group_and_tour_frequency_is_4,0,T
+coef_high_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumlow_income_group_and_shopping_tour,0,T
+coef_mediumhigh_income_group_and_shopping_tour,0,T
+coef_high_income_group_and_shopping_tour,0,T
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0,T
+coef_high_income_group_and_maintenance_tour,0,T
+coef_mediumlow_income_group_and_eating_out_tour,0,T
+coef_mediumhigh_income_group_and_eating_out_tour,0,T
+coef_high_income_group_and_eating_out_tour,0,T
+coef_mediumlow_income_group_and_discretionary_tour,0,T
+coef_mediumhigh_income_group_and_discretionary_tour,0,T
+coef_high_income_group_and_discretionary_tour,0,T
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,0,T
+coef_high_income_group_and_visiting_tour,0,T
+coef_female_and_tour_frequency_is_1,0,T
+coef_female_and_tour_frequency_is_2,0,T
+coef_female_and_tour_frequency_is_3,0,T
+coef_female_and_tour_frequency_is_4,0,T
+coef_female_and_tour_frequency_is_5,0,T
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0,T
+coef_zero_car_ownership_and_tour_frequency_is_1,0,T
+coef_zero_car_ownership_and_tour_frequency_is_2,0,T
+coef_zero_car_ownership_and_tour_frequency_is_3,0,T
+coef_zero_car_ownership_and_tour_frequency_is_4,0,T
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_1,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_2,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_3,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_4,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_5,0,T
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_full_time_worker_and_escorting_tour,-0.893,F
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,0.89,F
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,0,T
+coef_presence_of_part_time_worker_and_shopping_tour,1.155,F
+coef_presence_of_non_worker_and_shopping_tour,0.808,F
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,0,T
+coef_presence_of_part_time_worker_and_eating_out_tour,1.037,F
+coef_presence_of_non_worker_and_eating_out_tour,1.157,F
+coef_presence_of_retiree_and_eating_out_tour,0,T
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,0,T
+coef_presence_of_part_time_worker_and_discretionary_tour,0,T
+coef_presence_of_non_worker_and_discretionary_tour,0.791,F
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,0,T
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_2,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_3,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_4,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0,T
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,0,T
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,0.3622,F
+coef_2_plus_escort_tours_constant,2.2219,F
+coef_1_plus_shopping_tours_constant,1.6919,F
+coef_1_plus_maintenance_tours_constant,0.6788,F
+coef_1_plus_eating_out_tours_constant,0.9612,F
+coef_1_plus_visting_tours_constant,0.4424,F
+coef_1_plus_other_discretionary_tours_constant,1.4935,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_gt0,0,T
+coef_calib_nonmandtours_gt3_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_0,-0.228121764,T
+coef_calib_nonmandtours_3_mand_0,-0.38783141,T
+coef_calib_nonmandtours_gt4_mand_0,1.093947549,T
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv
new file mode 100644
index 000000000..0be070740
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv
@@ -0,0 +1,217 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-8.5684,F
+coef_total_number_of_tours_is_2,-12.7416,F
+coef_total_number_of_tours_is_3,-15.0978,F
+coef_total_number_of_tours_is_4,-19.5439,F
+coef_total_number_of_tours_is_5,-20.7897,F
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-5.0196,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0196,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_2,-0.95,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-7.143,F
+coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_shopping_tours,-0.8072,F
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.8357,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,2.2707,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,4.4023,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,4.4023,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,4.4023,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_2,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_3,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_4,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T
+coef_high_income_group_and_tour_frequency_is_1,0,T
+coef_high_income_group_and_tour_frequency_is_2,0,T
+coef_high_income_group_and_tour_frequency_is_3,0,T
+coef_high_income_group_and_tour_frequency_is_4,0,T
+coef_high_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumlow_income_group_and_shopping_tour,1.0949,F
+coef_mediumhigh_income_group_and_shopping_tour,1.0949,F
+coef_high_income_group_and_shopping_tour,1.0949,F
+coef_mediumlow_income_group_and_maintenance_tour,0.7648,F
+coef_mediumhigh_income_group_and_maintenance_tour,0.7648,F
+coef_high_income_group_and_maintenance_tour,1.3795,F
+coef_mediumlow_income_group_and_eating_out_tour,0.9769,F
+coef_mediumhigh_income_group_and_eating_out_tour,1.181,F
+coef_high_income_group_and_eating_out_tour,1.4842,F
+coef_mediumlow_income_group_and_discretionary_tour,1.0095,F
+coef_mediumhigh_income_group_and_discretionary_tour,1.0095,F
+coef_high_income_group_and_discretionary_tour,1.0095,F
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,-0.4368,F
+coef_high_income_group_and_visiting_tour,-0.5137,F
+coef_female_and_tour_frequency_is_1,-0.9348,F
+coef_female_and_tour_frequency_is_2,-1.3028,F
+coef_female_and_tour_frequency_is_3,-2.266,F
+coef_female_and_tour_frequency_is_4,-2.266,F
+coef_female_and_tour_frequency_is_5,-2.266,F
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0.9688,F
+coef_female_and_maintenance_tour,0.7424,F
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0.4954,F
+coef_zero_car_ownership_and_tour_frequency_is_1,0,T
+coef_zero_car_ownership_and_tour_frequency_is_2,0,T
+coef_zero_car_ownership_and_tour_frequency_is_3,0,T
+coef_zero_car_ownership_and_tour_frequency_is_4,0,T
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7965,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.1302,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.1302,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.1302,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.1302,F
+coef_presence_of_non_worker_and_tour_frequency_is_1,0.224,F
+coef_presence_of_non_worker_and_tour_frequency_is_2,0.2436,F
+coef_presence_of_non_worker_and_tour_frequency_is_3,0.62,F
+coef_presence_of_non_worker_and_tour_frequency_is_4,3.3742,F
+coef_presence_of_non_worker_and_tour_frequency_is_5,3.3742,F
+coef_presence_of_retiree_and_tour_frequency_is_1,-0.4458,F
+coef_presence_of_retiree_and_tour_frequency_is_2,-0.5315,F
+coef_presence_of_retiree_and_tour_frequency_is_3,-0.5315,F
+coef_presence_of_retiree_and_tour_frequency_is_4,-0.5315,F
+coef_presence_of_retiree_and_tour_frequency_is_5,-0.5315,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,0,T
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4903,F
+coef_presence_of_pre_school_kid_and_escorting_tour,0.5027,F
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,-0.3609,F
+coef_presence_of_part_time_worker_and_shopping_tour,0,T
+coef_presence_of_non_worker_and_shopping_tour,0,T
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,-0.788,F
+coef_presence_of_part_time_worker_and_eating_out_tour,-0.788,F
+coef_presence_of_non_worker_and_eating_out_tour,-0.788,F
+coef_presence_of_retiree_and_eating_out_tour,-0.9282,F
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,-0.4835,F
+coef_presence_of_part_time_worker_and_discretionary_tour,0,T
+coef_presence_of_non_worker_and_discretionary_tour,-0.5603,F
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,0,T
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0.0616,F
+coef_walk_access_to_retail_and_tour_frequency_is_2,0.0616,F
+coef_walk_access_to_retail_and_tour_frequency_is_3,0.0616,F
+coef_walk_access_to_retail_and_tour_frequency_is_4,0.0616,F
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.0616,F
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0,T
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,0,T
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,-0.3992,F
+coef_2_plus_escort_tours_constant,0.5175,F
+coef_1_plus_shopping_tours_constant,0.5947,F
+coef_1_plus_maintenance_tours_constant,0.1046,F
+coef_1_plus_eating_out_tours_constant,0.0245,F
+coef_1_plus_visting_tours_constant,0.2789,F
+coef_1_plus_other_discretionary_tours_constant,0.4282,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_gt0,0,T
+coef_calib_nonmandtours_gt3_mand_gt0,0,T
+coef_calib_nonmandtours_2_mand_0,-0.601049675,T
+coef_calib_nonmandtours_3_mand_0,-1.047585076,T
+coef_calib_nonmandtours_gt4_mand_0,-0.995317712,T
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv
new file mode 100644
index 000000000..106322e29
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv
@@ -0,0 +1,217 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-7.4863,F
+coef_total_number_of_tours_is_2,-10.718,F
+coef_total_number_of_tours_is_3,-13.7884,F
+coef_total_number_of_tours_is_4,-999,T
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,-1.0331,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7445,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.7445,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,-0.6149,F
+coef_number_of_joint_tours_and_tour_frequency_is_2,-0.6149,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_shopping_tours,0,T
+coef_number_of_joint_maintenance_tours,-1.3476,F
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_0,0,T
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.5603,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,1.5603,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,1.5603,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,1.5603,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5603,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,1.0873,F
+coef_mediumlow_income_group_and_tour_frequency_is_2,1.0873,F
+coef_mediumlow_income_group_and_tour_frequency_is_3,1.0873,F
+coef_mediumlow_income_group_and_tour_frequency_is_4,1.0873,F
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0873,F
+coef_mediumhigh_income_group_and_tour_frequency_is_1,1.5197,F
+coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5197,F
+coef_mediumhigh_income_group_and_tour_frequency_is_3,1.5197,F
+coef_mediumhigh_income_group_and_tour_frequency_is_4,1.5197,F
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.5197,F
+coef_high_income_group_and_tour_frequency_is_1,2.0175,F
+coef_high_income_group_and_tour_frequency_is_2,2.0175,F
+coef_high_income_group_and_tour_frequency_is_3,2.0175,F
+coef_high_income_group_and_tour_frequency_is_4,2.0175,F
+coef_high_income_group_and_tour_frequency_is_5_plus,2.0175,F
+coef_mediumlow_income_group_and_shopping_tour,-0.6506,F
+coef_mediumhigh_income_group_and_shopping_tour,-0.6506,F
+coef_high_income_group_and_shopping_tour,-0.6506,F
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0,T
+coef_high_income_group_and_maintenance_tour,0,T
+coef_mediumlow_income_group_and_eating_out_tour,-0.701,F
+coef_mediumhigh_income_group_and_eating_out_tour,-0.701,F
+coef_high_income_group_and_eating_out_tour,-0.701,F
+coef_mediumlow_income_group_and_discretionary_tour,0,T
+coef_mediumhigh_income_group_and_discretionary_tour,0,T
+coef_high_income_group_and_discretionary_tour,0,T
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,0,T
+coef_high_income_group_and_visiting_tour,0,T
+coef_female_and_tour_frequency_is_1,0,T
+coef_female_and_tour_frequency_is_2,0,T
+coef_female_and_tour_frequency_is_3,0,T
+coef_female_and_tour_frequency_is_4,0,T
+coef_female_and_tour_frequency_is_5,0,T
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,0,T
+coef_female_and_discretionary_tour,0,T
+coef_zero_car_ownership_and_tour_frequency_is_1,0,T
+coef_zero_car_ownership_and_tour_frequency_is_2,0,T
+coef_zero_car_ownership_and_tour_frequency_is_3,0,T
+coef_zero_car_ownership_and_tour_frequency_is_4,0,T
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_1,0.2177,F
+coef_presence_of_non_worker_and_tour_frequency_is_2,0.2177,F
+coef_presence_of_non_worker_and_tour_frequency_is_3,0.2177,F
+coef_presence_of_non_worker_and_tour_frequency_is_4,0.2177,F
+coef_presence_of_non_worker_and_tour_frequency_is_5,0.2177,F
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.4439,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.4439,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.4439,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.4439,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.4439,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.2264,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-0.2264,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-0.2264,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-0.2264,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-0.2264,F
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,0,T
+coef_presence_of_non_worker_and_escorting_tour,0,T
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,0,T
+coef_presence_of_part_time_worker_and_shopping_tour,0,T
+coef_presence_of_non_worker_and_shopping_tour,-0.645,F
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0.9365,F
+coef_presence_of_pre_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,0,T
+coef_presence_of_part_time_worker_and_eating_out_tour,0,T
+coef_presence_of_non_worker_and_eating_out_tour,-1.3074,F
+coef_presence_of_retiree_and_eating_out_tour,0,T
+coef_presence_of_university_student_and_eating_out_tour,0,T
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,0.7526,F
+coef_presence_of_part_time_worker_and_discretionary_tour,0.3721,F
+coef_presence_of_non_worker_and_discretionary_tour,0,T
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,0,T
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_2,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_3,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_4,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_1,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_2,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_3,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_4,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0.0629,F
+coef_walk_access_to_retail_and_shopping,0,T
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0,T
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0.0738,F
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0,T
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0,T
+coef_urban_and_tour_frequency_is_1,0,T
+coef_urban_and_tour_frequency_is_2,0,T
+coef_urban_and_tour_frequency_is_3,0,T
+coef_urban_and_tour_frequency_is_4,0,T
+coef_urban_and_tour_frequency_is_5_plus,0,T
+coef_urban_and_escorting_tour,0.4352,F
+coef_urban_and_shopping_tour,0,T
+coef_urban_and_maintenance_tour,0,T
+coef_urban_and_eatingout_tour,0,T
+coef_urban_and_discretionary_tour,0,T
+coef_1_escort_tour_constant,-0.7551,F
+coef_2_plus_escort_tours_constant,-0.0086,F
+coef_1_plus_shopping_tours_constant,0.4783,F
+coef_1_plus_maintenance_tours_constant,-0.506,F
+coef_1_plus_eating_out_tours_constant,1.1145,F
+coef_1_plus_visting_tours_constant,-0.4006,F
+coef_1_plus_other_discretionary_tours_constant,0.4634,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,-0.156305899,T
+coef_calib_nonmandtours_2_mand_gt0,-1.611733765,T
+coef_calib_nonmandtours_gt3_mand_gt0,0.625561203,T
+coef_calib_nonmandtours_2_mand_0,-1.688433324,T
+coef_calib_nonmandtours_3_mand_0,-0.631744091,T
+coef_calib_nonmandtours_gt4_mand_0,3.754622037,T
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv
new file mode 100644
index 000000000..75f38dba4
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv
@@ -0,0 +1,223 @@
+coefficient_name,value,constrain
+coef_escorting_tour,0,T
+coef_discretionary_tour,0,T
+coef_shopping_tour,0,T
+coef_maintenance_tour,0,T
+coef_visiting_or_social_tour,0,T
+coef_eating_out_tour,0,T
+coef_total_number_of_tours_is_0_no_prior_tours,-999,T
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-6.2138,F
+coef_total_number_of_tours_is_2,-8.908,F
+coef_total_number_of_tours_is_3,-12.3261,F
+coef_total_number_of_tours_is_4,-15.8114,F
+coef_total_number_of_tours_is_5,-999,T
+coef_total_number_of_tours_is_6_plus,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.1852,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8753,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.6158,F
+coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_0,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_1,0,T
+coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3153,F
+coef_number_of_joint_tours_and_tour_frequency_is_3,-0.7351,F
+coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T
+coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T
+coef_number_of_joint_shopping_tours,-0.713,F
+coef_number_of_joint_maintenance_tours,0,T
+coef_number_of_joint_eating_out_tours,0,T
+coef_number_of_joint_visit_tours,0,T
+coef_number_of_joint_discretionary_tours,0.6713,F
+coef_logged_maximum_residual_window_tour_frequency_is_0,1.1858,F
+coef_logged_maximum_residual_window_tour_frequency_is_1,1.4842,F
+coef_logged_maximum_residual_window_tour_frequency_is_2,1.4842,F
+coef_logged_maximum_residual_window_tour_frequency_is_3,1.4842,F
+coef_logged_maximum_residual_window_tour_frequency_is_4,1.4842,F
+coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.4842,F
+coef_mediumlow_income_group_and_tour_frequency_is_1,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_2,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_3,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_4,0,T
+coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T
+coef_mediumhigh_income_group_and_tour_frequency_is_1,0.1109,F
+coef_mediumhigh_income_group_and_tour_frequency_is_2,0.3914,F
+coef_mediumhigh_income_group_and_tour_frequency_is_3,0.6137,F
+coef_mediumhigh_income_group_and_tour_frequency_is_4,0.6137,F
+coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0.6137,F
+coef_high_income_group_and_tour_frequency_is_1,0.3986,F
+coef_high_income_group_and_tour_frequency_is_2,0.8009,F
+coef_high_income_group_and_tour_frequency_is_3,0.8254,F
+coef_high_income_group_and_tour_frequency_is_4,0.8254,F
+coef_high_income_group_and_tour_frequency_is_5_plus,0.8254,F
+coef_mediumlow_income_group_and_shopping_tour,0.5693,F
+coef_mediumhigh_income_group_and_shopping_tour,0.5693,F
+coef_high_income_group_and_shopping_tour,0.5693,F
+coef_mediumlow_income_group_and_maintenance_tour,0,T
+coef_mediumhigh_income_group_and_maintenance_tour,0,T
+coef_high_income_group_and_maintenance_tour,0,T
+coef_mediumlow_income_group_and_eating_out_tour,0,T
+coef_mediumhigh_income_group_and_eating_out_tour,-0.7207,F
+coef_high_income_group_and_eating_out_tour,-0.7207,F
+coef_mediumlow_income_group_and_discretionary_tour,0,T
+coef_mediumhigh_income_group_and_discretionary_tour,0,T
+coef_high_income_group_and_discretionary_tour,0,T
+coef_mediumlow_income_group_and_visiting_tour,0,T
+coef_mediumhigh_income_group_and_visiting_tour,-0.3694,F
+coef_high_income_group_and_visiting_tour,-0.3694,F
+coef_female_and_tour_frequency_is_1,0.0973,F
+coef_female_and_tour_frequency_is_2,0.2361,F
+coef_female_and_tour_frequency_is_3,1.9002,F
+coef_female_and_tour_frequency_is_4,1.9002,F
+coef_female_and_tour_frequency_is_5,1.9002,F
+coef_female_and_escorting_tour,0,T
+coef_female_and_shopping_tour,0,T
+coef_female_and_maintenance_tour,0,T
+coef_female_and_eatingout_tour,-0.6568,F
+coef_female_and_discretionary_tour,-0.3266,F
+coef_zero_car_ownership_and_tour_frequency_is_1,-0.581,F
+coef_zero_car_ownership_and_tour_frequency_is_2,-0.581,F
+coef_zero_car_ownership_and_tour_frequency_is_3,-0.581,F
+coef_zero_car_ownership_and_tour_frequency_is_4,-0.581,F
+coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.581,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.581,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.581,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.581,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.581,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.581,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T
+coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T
+coef_presence_of_non_worker_and_tour_frequency_is_1,-0.8506,F
+coef_presence_of_non_worker_and_tour_frequency_is_2,-1.1804,F
+coef_presence_of_non_worker_and_tour_frequency_is_3,-1.1804,F
+coef_presence_of_non_worker_and_tour_frequency_is_4,-1.1804,F
+coef_presence_of_non_worker_and_tour_frequency_is_5,-1.1804,F
+coef_presence_of_retiree_and_tour_frequency_is_1,0,T
+coef_presence_of_retiree_and_tour_frequency_is_2,0,T
+coef_presence_of_retiree_and_tour_frequency_is_3,0,T
+coef_presence_of_retiree_and_tour_frequency_is_4,0,T
+coef_presence_of_retiree_and_tour_frequency_is_5,0,T
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.9961,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-1.9096,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-2.8469,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-2.8469,F
+coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-2.8469,F
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T
+coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T
+coef_presence_of_full_time_worker_and_escorting_tour,0,T
+coef_presence_of_part_time_worker_and_escorting_tour,-1.8213,F
+coef_presence_of_non_worker_and_escorting_tour,0,T
+coef_presence_of_retiree_and_escorting_tour,0,T
+coef_presence_of_university_student_and_escorting_tour,0,T
+coef_presence_of_driving_school_kid_and_escorting_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_escorting_tour,0.9489,F
+coef_presence_of_pre_school_kid_and_escorting_tour,2.1465,F
+coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T
+coef_at_home_pre_school_kid_and_escorting_tour,0,T
+coef_presence_of_full_time_worker_and_shopping_tour,-0.7728,F
+coef_presence_of_part_time_worker_and_shopping_tour,-0.5199,F
+coef_presence_of_non_worker_and_shopping_tour,0,T
+coef_presence_of_retiree_and_shopping_tour,0,T
+coef_presence_of_university_student_and_shopping_tour,0,T
+coef_presence_of_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T
+coef_presence_of_pre_school_kid_and_shopping_tour,1.3135,F
+coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T
+coef_at_home_pre_school_kid_and_shopping_tour,0,T
+coef_presence_of_full_time_worker_and_maintenance_tour,0,T
+coef_presence_of_part_time_worker_and_maintenance_tour,0,T
+coef_presence_of_non_worker_and_maintenance_tour,0,T
+coef_presence_of_retiree_and_maintenance_tour,0,T
+coef_presence_of_university_student_and_maintenance_tour,0,T
+coef_presence_of_driving_school_kid_and_maintenance_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.3863,F
+coef_presence_of_pre_school_kid_and_maintenance_tour,0.9694,F
+coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T
+coef_at_home_pre_school_kid_and_maintenance_tour,0,T
+coef_presence_of_full_time_worker_and_eating_out_tour,-0.5251,F
+coef_presence_of_part_time_worker_and_eating_out_tour,-1.9795,F
+coef_presence_of_non_worker_and_eating_out_tour,0,T
+coef_presence_of_retiree_and_eating_out_tour,0,T
+coef_presence_of_university_student_and_eating_out_tour,-0.6529,F
+coef_presence_of_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_presence_of_pre_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T
+coef_at_home_pre_school_kid_and_eating_out_tour,0,T
+coef_presence_of_full_time_worker_and_discretionary_tour,-0.4833,F
+coef_presence_of_part_time_worker_and_discretionary_tour,0,T
+coef_presence_of_non_worker_and_discretionary_tour,0.9781,F
+coef_presence_of_retiree_and_discretionary_tour,0,T
+coef_presence_of_university_student_and_discretionary_tour,-0.6542,F
+coef_presence_of_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_presence_of_pre_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T
+coef_at_home_pre_school_kid_and_discretionary_tour,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_1,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_2,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_3,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_4,0,T
+coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_transit_access_to_retail_and_tour_frequency_is_1,0.0664,F
+coef_transit_access_to_retail_and_tour_frequency_is_2,0.0664,F
+coef_transit_access_to_retail_and_tour_frequency_is_3,0.0664,F
+coef_transit_access_to_retail_and_tour_frequency_is_4,0.0664,F
+coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0664,F
+coef_auto_access_to_retail_and_tour_frequency_is_1,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_2,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_3,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_4,0,T
+coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T
+coef_walk_access_to_retail_and_escorting,0,T
+coef_transit_access_to_retail_and_escorting,0,T
+coef_auto_access_to_retail_and_escorting,0,T
+coef_walk_access_to_retail_and_shopping,0.0972,F
+coef_transit_access_to_retail_and_shopping,0,T
+coef_auto_access_to_retail_and_shopping,0,T
+coef_walk_access_to_retail_and_maintenance,0,T
+coef_transit_access_to_retail_and_maintenance,0.0314,F
+coef_auto_access_to_retail_and_maintenance,0,T
+coef_walk_access_to_retail_and_eating_out,0,T
+coef_transit_access_to_retail_and_eating_out,0,T
+coef_auto_access_to_retail_and_eating_out,0.1018,F
+coef_walk_access_to_retail_and_discretionary,0,T
+coef_transit_access_to_retail_and_discretionary,0,T
+coef_auto_access_to_retail_and_discretionary,0.094,F
+coef_urban_and_tour_frequency_is_1,-1.1648,F
+coef_urban_and_tour_frequency_is_2,-2.3177,F
+coef_urban_and_tour_frequency_is_3,-2.5027,F
+coef_urban_and_tour_frequency_is_4,-2.5027,F
+coef_urban_and_tour_frequency_is_5_plus,-2.5027,F
+coef_urban_and_escorting_tour,0.8516,F
+coef_urban_and_shopping_tour,0.533,F
+coef_urban_and_maintenance_tour,1.0316,F
+coef_urban_and_eatingout_tour,0.68,F
+coef_urban_and_discretionary_tour,0.9563,F
+coef_1_escort_tour_constant,1.7028,F
+coef_2_plus_escort_tours_constant,2.8379,F
+coef_1_plus_shopping_tours_constant,1.8403,F
+coef_1_plus_maintenance_tours_constant,0.3348,F
+coef_1_plus_eating_out_tours_constant,2.0723,F
+coef_1_plus_visting_tours_constant,1.2172,F
+coef_1_plus_other_discretionary_tours_constant,1.3389,F
+coef_0_auto_household_and_escorting_tour,-2,T
+coef_calib_nonmandtours_1_mand_gt0,1.112189691,T
+coef_calib_nonmandtours_2_mand_gt0,0.319534579,T
+coef_calib_nonmandtours_gt3_mand_gt0,0.738362555,T
+coef_calib_nonmandtours_2_mand_0,-1.075998448,T
+coef_calib_nonmandtours_3_mand_0,-2.067563956,T
+coef_calib_nonmandtours_gt4_mand_0,-3.294051601,T
+coef_tc_1dayperweek_1,-0.005,F
+coef_tc_23dayperweek_1,0.142,F
+coef_tc_4pdayperweek_1,-0.406,F
+coef_tc_1dayperweek_2p,-0.348,F
+coef_tc_23dayperweek_2p,-0.041,F
+coef_tc_4pdayperweek_2p,-0.447,F
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_extension_probs.csv
new file mode 100644
index 000000000..ec78c4c8e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency_extension_probs.csv
@@ -0,0 +1,193 @@
+ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours
+1,0,0,1,0.829545455,1,1
+2,0,0,1,0.769230769,1,1
+3,0,0,1,0.893939394,1,1
+4,0,0,1,0.75,1,1
+5,0,0,1,0.842105263,1,1
+6,0,0,1,0.714285714,1,1
+7,0,0,1,0.814814815,1,1
+8,0,0,1,0.75,1,1
+1,1,0,1,0.789473684,1,1
+2,1,0,1,0.6,1,1
+3,1,0,1,1,1,1
+4,1,0,1,1,1,1
+5,1,0,1,0.825910931,1,1
+6,1,0,1,0.837209302,1,1
+7,1,0,1,0.6,1,1
+8,1,0,1,1,1,1
+1,0,1,1,0.842105263,1,1
+2,0,1,1,1,1,1
+3,0,1,1,1,1,1
+4,0,1,1,1,1,1
+5,0,1,1,1,1,1
+6,0,1,1,1,1,1
+7,0,1,1,1,1,1
+8,0,1,1,1,1,1
+1,1,1,1,1,1,1
+2,1,1,1,1,1,1
+3,1,1,1,1,1,1
+4,1,1,1,1,1,1
+5,1,1,1,0.777777778,1,1
+6,1,1,1,1,1,1
+7,1,1,1,1,1,1
+8,1,1,1,1,1,1
+1,0,0,2,0.892694064,0.99086758,1
+2,0,0,2,0.84057971,0.992753623,1
+3,0,0,2,0.971014493,1,1
+4,0,0,2,0.96969697,1,1
+5,0,0,2,0.870056497,0.994350282,1
+6,0,0,2,0.866666667,1,1
+7,0,0,2,0.971014493,1,1
+8,0,0,2,0.931034483,1,1
+1,1,0,2,0.885057471,1,1
+2,1,0,2,0.727272727,1,1
+3,1,0,2,0.971428571,1,1
+4,1,0,2,1,1,1
+5,1,0,2,0.895977809,0.993065187,1
+6,1,0,2,0.885185185,1,1
+7,1,0,2,1,1,1
+8,1,0,2,1,1,1
+1,0,1,2,0.910087719,0.993421053,1
+2,0,1,2,0.88,1,1
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diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml
new file mode 100644
index 000000000..34b5a9a8e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml
@@ -0,0 +1,93 @@
+SPEC: tour_scheduling_nonmandatory.csv
+COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv
+
+LOGIT_TYPE: MNL
+
+preprocessor:
+  SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor
+  DF: non_mandatory_tours
+  TABLES:
+    - land_use
+    - joint_tour_participants
+
+SIMULATE_CHOOSER_COLUMNS:
+
+  - age
+  - female
+  - adult
+  - ptype
+  - has_pre_school_child_with_mandatory
+  - has_driving_age_child_with_mandatory
+  - retired_adults_only_hh
+  - is_income_less25K
+  - is_income_25K_to_60K
+  - is_income_60K_to_120K
+  - auto_ownership
+  - num_children
+  - num_adults
+  - num_mand
+  - num_joint_tours
+  - num_escort_tours
+  - num_non_escort_tours
+  - num_add_shop_maint_tours
+  - num_add_soc_discr_tours
+
+#LOGSUM_SETTINGS: tour_mode_choice.yaml
+
+# map : <tour_segment>: <spec_segment>
+# segmentation of tours for processing may differ from segmentation of spec
+# vectorize_tour_scheduling iterates over <tour_segment>
+#   using <spec_segment> to choose spec for scheduling alt chice
+#   using <tour_segment> for logsums (if enabled)
+# tour_mode_choice segmentation for logsums is: eatout,escort,othdiscr,othmaint,shopping,social,school,univ,work,atwork
+TOUR_SPEC_SEGMENTS:
+  escort: escort
+  shopping: shopping
+  eatout: eatout
+  othdiscr: othdiscr
+  othmaint: othmaint
+  social: social
+
+# spec keyed by <spec_segment>
+SPEC_SEGMENTS:
+  escort:
+    'SPEC': tour_scheduling_nonmandatory_escort.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_escort_coefficients.csv
+  shopping:
+    'SPEC': tour_scheduling_nonmandatory_shopping.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_shopping_coefficients.csv
+  eatout:
+    'SPEC': tour_scheduling_nonmandatory_eatout.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_eatout_coefficients.csv
+  othdiscr:
+    'SPEC': tour_scheduling_nonmandatory_othdiscr.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_othdiscr_coefficients.csv
+  othmaint:
+    'SPEC': tour_scheduling_nonmandatory_othmaint.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_othmaint_coefficients.csv
+  social:
+    'SPEC': tour_scheduling_nonmandatory_social.csv
+    'COEFFICIENTS': tour_scheduling_nonmandatory_social_coefficients.csv
+
+## alts preprocessor keyed by <spec_segment>
+#ALTS_PREPROCESSOR:
+#  escort:
+#    SPEC: non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#  shopping:
+#    SPEC: non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#  eatout:
+#    SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#  othdiscr:
+#    SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#  othmaint:
+#    SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#  social:
+#    SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv
+#    DF: alt_tdd
+#
+#DESTINATION_FOR_TOUR_PURPOSE: destination
diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv
new file mode 100644
index 000000000..616a627d2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,,
+number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)"
+#,,
+,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, ('SOV_DIST', 'MD'))"
diff --git a/activitysim/examples/prototype_mwcog/configs/school_location.csv b/activitysim/examples/prototype_mwcog/configs/school_location.csv
new file mode 100644
index 000000000..8db3f8056
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/school_location.csv
@@ -0,0 +1,22 @@
+Label,Description,Expression,university,highschool,gradeschool
+local_dist,,_DIST@skims['DIST'],1,1,1
+util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0
+util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0
+util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0
+util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0
+util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0
+util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1
+util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1
+util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999
+util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum_uni,coef_mode_logsum,coef_mode_logsum
+util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1
+util_dist,Distance,@_DIST,0,coef_dist,coef_dist
+util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared
+util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed
+util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged
+util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time
+util_dist_child_0_5,"Distance,child 0  to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5
+util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12
+util_dist_low,"Distance,low income (<50)",@(df['income_segment']==INC_LOW_SEGMENT_ID) * _DIST,0,coef_dist_low_inc,coef_dist_low_inc
+util_dist_very_high,"Distance,very high income (>150)",@(df['income_segment']==INC_VERYHIGH_SEGMENT_ID) * _DIST,0,coef_dist_very_high_inc,coef_dist_very_high_inc
+util_dist_calib,Distance calibration Gen3,@_DIST,0,coef_dist_calib,coef_dist_calib
diff --git a/activitysim/examples/prototype_mwcog/configs/school_location.yaml b/activitysim/examples/prototype_mwcog/configs/school_location.yaml
new file mode 100644
index 000000000..4a75198e0
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/school_location.yaml
@@ -0,0 +1,70 @@
+SAMPLE_SIZE: 30
+
+SIMULATE_CHOOSER_COLUMNS:
+  - home_zone_id
+  - school_segment
+  - household_id
+  - is_student
+  - age_0_to_5
+  - age_6_to_12
+  - pemploy
+  - income_segment
+  - auto_ownership
+
+# model-specific logsum-related settings
+CHOOSER_ORIG_COL_NAME: home_zone_id
+ALT_DEST_COL_NAME: alt_dest
+IN_PERIOD: 26
+OUT_PERIOD: 9
+
+DEST_CHOICE_COLUMN_NAME: school_zone_id
+
+SAMPLE_SPEC: school_location_sample.csv
+SPEC: school_location.csv
+COEFFICIENTS: school_location_coeffs.csv
+
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+LOGSUM_PREPROCESSOR: nontour_preprocessor
+
+LOGSUM_TOUR_PURPOSE:
+  university: univ
+  highschool: school
+  gradeschool: school
+
+annotate_persons:
+  SPEC: annotate_persons_school
+  DF: persons
+
+# - shadow pricing
+
+# required by initialize_households when creating school_destination_size table
+CHOOSER_TABLE_NAME: persons
+
+# size_terms model_selector
+MODEL_SELECTOR: school
+
+# chooser column with segment_id for this segment type
+CHOOSER_SEGMENT_COLUMN_NAME: school_segment
+
+# boolean column to filter choosers (True means keep)
+CHOOSER_FILTER_COLUMN_NAME: is_student
+
+
+# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this
+SEGMENT_IDS:
+  university: 3
+  highschool: 2
+  gradeschool: 1
+  
+CONSTANTS:
+  INC_LOW_SEGMENT_ID: 1
+  INC_MED_SEGMENT_ID: 2
+  INC_HIGH_SEGMENT_ID: 3
+  INC_VERYHIGH_SEGMENT_ID: 4
+
+# model adds these tables (informational - not added if commented out)
+SHADOW_PRICE_TABLE: school_shadow_prices
+MODELED_SIZE_TABLE: school_modeled_size
+
+# not loaded if commented out
+SAVED_SHADOW_PRICE_TABLE_NAME: final_school_shadow_prices.csv
diff --git a/activitysim/examples/prototype_mwcog/configs/school_location_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/school_location_coeffs.csv
new file mode 100644
index 000000000..a6041e3d5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/school_location_coeffs.csv
@@ -0,0 +1,18 @@
+coefficient_name,value,constrain
+coef_univ_dist_0_1,-3.2451,F
+coef_univ_dist_1_2,-2.7011,F
+coef_univ_dist_2_5,-0.5707,F
+coef_univ_dist_5_15,-0.5002,F
+coef_univ_dist_15_up,-0.073,F
+coef_mode_logsum_uni,0.5358,F
+coef_dist,-0.075971485,F
+coef_dist_squared,0,F
+coef_dist_cubed,0,F
+coef_dist_logged,-2.073546963,F
+coef_dist_part_time,-0.049133509,F
+coef_dist_child_0_5,0,F
+coef_dist_child_6_12,-0.051582555,F
+coef_dist_low_inc,-0.028471563,F
+coef_dist_very_high_inc,-0.018662186,F
+coef_mode_logsum,0.3,F
+coef_dist_calib,-0.035,F
diff --git a/activitysim/examples/prototype_mwcog/configs/school_location_sample.csv b/activitysim/examples/prototype_mwcog/configs/school_location_sample.csv
new file mode 100644
index 000000000..a7d0120ab
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/school_location_sample.csv
@@ -0,0 +1,17 @@
+Label,Description,Expression,university,highschool,gradeschool
+local_dist,,_DIST@skims['DIST'],1,1,1
+util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0
+util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0
+util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0
+util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0
+util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0
+util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1
+util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1
+util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999
+util_dist,Distance,@_DIST,0,coef_dist,coef_dist
+util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared
+util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed
+util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged
+util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time
+util_dist_child_0_5,"Distance,child 0  to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5
+util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12
diff --git a/activitysim/examples/prototype_mwcog/configs/settings.yaml b/activitysim/examples/prototype_mwcog/configs/settings.yaml
new file mode 100644
index 000000000..4f8110237
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/settings.yaml
@@ -0,0 +1,155 @@
+# input tables
+#
+# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use)
+# as well as for created tables (tours, joint_tour_participants, trips)
+# e.g. the households table must have an index column 'household_id' and the foreign key to households in the
+# persons table is also household_id. This naming convention allows activitysim to intuit the relationship
+# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips
+# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by
+# household_id.
+#
+# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id
+# you cannot change the well-known name of the index by modifying this directive. However, if your input file
+# has a different id column name, you can rename it to the required index name with the rename_columns directive.
+# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings.
+#
+# input tables
+input_table_list:
+  #
+  # households (table index 'household_id')
+  #
+  - tablename: households
+    filename: combined_synthetic_hh_2018.csv
+    index_col: household_id
+    rename_columns:
+      NP: hhsize
+      VEH: auto_ownership
+      TAZ: home_zone_id
+  #
+  # persons (table index 'person_id')
+  #
+  - tablename: persons
+    filename: combined_synthetic_per_2018.csv
+    index_col: person_id
+    rename_columns:
+      AGEP: age
+      hh_id: household_id
+  #
+  # land_use (table index 'zone_id')
+  #
+  - tablename: land_use
+    filename: LU_taz3722_rnd91a_2018_adj_enrollment.csv
+    index_col: zone_id
+    rename_columns:
+      TAZ: zone_id
+
+# convert input CSVs to HDF5 format and save to outputs directory
+# create_input_store: True
+
+# number of households to simulate
+households_sample_size:  100
+# simulate all households
+#households_sample_size: 0
+
+#household_ids: override_household_ids.csv
+
+chunk_size: 0
+
+# set false to disable variability check in simple_simulate and interaction_simulate
+check_for_variability: False
+
+# - shadow pricing global switches
+
+# turn shadow_pricing on and off for all models (e.g. school and work)
+# shadow pricing is deprecated for less than full samples
+# see shadow_pricing.yaml for additional settings
+use_shadow_pricing: False
+
+
+# - tracing
+
+# trace household id; comment out or leave empty for no trace
+# households with all tour types
+#  [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154]
+#trace_hh_id: 94944
+
+# trace origin, destination in accessibility calculation; comment out or leave empty for no trace
+# trace_od: [5, 11]
+trace_od:
+
+
+models:
+  - initialize_landuse
+  - compute_accessibility
+  - initialize_households
+  - school_location
+  - workplace_location
+  - work_from_home
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - auto_ownership_simulate
+  - free_parking
+  - telecommute_frequency
+  - cdap_simulate
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling
+  - joint_tour_frequency
+  - joint_tour_composition
+  - joint_tour_participation
+  - joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - non_mandatory_tour_destination
+  - non_mandatory_tour_scheduling
+  - tour_mode_choice_simulate
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_purpose_and_destination
+  - trip_scheduling
+  - trip_mode_choice
+  - write_data_dictionary
+  - track_skim_usage
+  - write_tables
+  - write_trip_matrices
+
+# to resume after last successful checkpoint, specify resume_after: _
+resume_after:
+
+output_tables:
+  h5_store: False
+  action: include
+  prefix: final_
+  tables:
+    - checkpoints
+    - accessibility
+    - land_use
+    - households
+    - persons
+    - tours
+    - trips
+    - joint_tour_participants
+
+# area_types less than this are considered urban
+urban_threshold: 4
+cbd_threshold: 2
+rural_threshold: 6
+
+# - value of time
+
+# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot)
+
+min_value_of_time: 1
+max_value_of_time: 50
+distributed_vot_mu: 0.684
+distributed_vot_sigma: 0.85
+
+household_median_value_of_time:
+  1: 6.01
+  2: 8.81
+  3: 10.44
+  4: 12.86
diff --git a/activitysim/examples/prototype_mwcog/configs/shadow_pricing.yaml b/activitysim/examples/prototype_mwcog/configs/shadow_pricing.yaml
new file mode 100644
index 000000000..b61ec4192
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/shadow_pricing.yaml
@@ -0,0 +1,34 @@
+shadow_pricing_models:
+  school: school_location
+  workplace: workplace_location
+
+# global switch to enable/disable loading of saved shadow prices
+# (ignored if global use_shadow_pricing switch is False)
+LOAD_SAVED_SHADOW_PRICES: True
+
+# number of shadow price iterations for cold start
+MAX_ITERATIONS: 10
+
+# number of shadow price iterations for warm start (after loading saved shadow_prices)
+MAX_ITERATIONS_SAVED: 1
+
+# ignore criteria for zones smaller than size_threshold
+SIZE_THRESHOLD: 10
+
+# zone passes if modeled is within percent_tolerance of  predicted_size
+PERCENT_TOLERANCE: 5
+
+# max percentage of zones allowed to fail
+FAIL_THRESHOLD: 10
+
+# CTRAMP or daysim
+SHADOW_PRICE_METHOD: ctramp
+#SHADOW_PRICE_METHOD: daysim
+
+# ctramp-style shadow_pricing_method parameters
+DAMPING_FACTOR: 1
+
+# daysim-style shadow_pricing_method parameters
+# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above?
+DAYSIM_ABSOLUTE_TOLERANCE: 50
+DAYSIM_PERCENT_TOLERANCE: 10
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/stop_frequency.yaml
new file mode 100644
index 000000000..bc953e8fc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency.yaml
@@ -0,0 +1,83 @@
+LOGIT_TYPE: MNL
+
+preprocessor:
+  SPEC: stop_frequency_annotate_tours_preprocessor
+  DF: tours_merged
+  TABLES:
+    - persons
+    - land_use
+    - accessibility
+
+SEGMENT_COL: primary_purpose
+
+SPEC_SEGMENTS:
+  - primary_purpose: work
+    SPEC: stop_frequency_work.csv
+    COEFFICIENTS: stop_frequency_coefficients_work.csv
+  - primary_purpose: school
+    SPEC: stop_frequency_school.csv
+    COEFFICIENTS: stop_frequency_coefficients_school.csv
+  - primary_purpose: univ
+    SPEC: stop_frequency_univ.csv
+    COEFFICIENTS: stop_frequency_coefficients_univ.csv
+  - primary_purpose: social
+    SPEC: stop_frequency_social.csv
+    COEFFICIENTS: stop_frequency_coefficients_social.csv
+  - primary_purpose: shopping
+    SPEC: stop_frequency_shopping.csv
+    COEFFICIENTS: stop_frequency_coefficients_shopping.csv
+  - primary_purpose: eatout
+    SPEC: stop_frequency_eatout.csv
+    COEFFICIENTS: stop_frequency_coefficients_eatout.csv
+  - primary_purpose: escort
+    SPEC: stop_frequency_escort.csv
+    COEFFICIENTS: stop_frequency_coefficients_escort.csv
+  - primary_purpose: othmaint
+    SPEC: stop_frequency_othmaint.csv
+    COEFFICIENTS: stop_frequency_coefficients_othmaint.csv
+  - primary_purpose: othdiscr
+    SPEC: stop_frequency_othdiscr.csv
+    COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv
+  - primary_purpose: atwork
+    SPEC: stop_frequency_atwork.csv
+    COEFFICIENTS: stop_frequency_coefficients_atwork.csv
+
+CONSTANTS:
+  TRANSIT_MODES:
+    - WALK_AB
+    - WALK_BM
+    - WALK_MR
+    - WALK_CR
+    - PNR_AB
+    - PNR_BM
+    - PNR_MR
+    - PNR_CR
+    - KNR_AB
+    - KNR_BM
+    - KNR_MR
+    - KNR_CR
+
+  DRIVE_TO_TRANSIT_MODES:
+    - PNR_AB
+    - PNR_BM
+    - PNR_MR
+    - PNR_CR
+    - KNR_AB
+    - KNR_BM
+    - KNR_MR
+    - KNR_CR
+  NONMOTORIZED_MODES:
+    - WALK
+    - BIKE
+  SHOP_TOUR: shopping
+  MAINT_TOUR: othmaint
+  SCHOOL_TOUR: school
+  EATOUT_TOUR: eatout
+  SOCIAL_TOUR: social
+  num_atwork_subtours_map:
+    no_subtours: 0
+    eat: 1
+    business1: 1
+    maint: 1
+    business2: 2
+    eat_business: 2
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_alternatives.csv
new file mode 100644
index 000000000..72f49a7c7
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_alternatives.csv
@@ -0,0 +1,18 @@
+#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate
+alt,out,in
+0out_0in,0,0
+0out_1in,0,1
+0out_2in,0,2
+0out_3in,0,3
+1out_0in,1,0
+1out_1in,1,1
+1out_2in,1,2
+1out_3in,1,3
+2out_0in,2,0
+2out_1in,2,1
+2out_2in,2,2
+2out_3in,2,3
+3out_0in,3,0
+3out_1in,3,1
+3out_2in,3,2
+3out_3in,3,3
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_annotate_tours_preprocessor.csv
new file mode 100644
index 000000000..f69e1a027
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_annotate_tours_preprocessor.csv
@@ -0,0 +1,47 @@
+Description,Target,Expression
+#,,
+# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,,
+# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,,
+# so the 'school' tour_type can treat univ and non-univ school tours differently,,
+,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')"
+,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')"
+#,,
+,distance_in_miles,od_skims['DIST']
+#,,
+,is_joint,df.tour_category=='joint'
+,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()"
+,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)"
+,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)"
+,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)"
+Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)"
+Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)"
+Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)"
+,more_cars_than_workers,df.auto_ownership >= (num_full + num_part)
+,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES)
+,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES)
+,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES)
+#,,
+#num_work_tours already defined,,
+school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)"
+,num_univ_tours,(df.is_university) * num_school_tours
+#num_escort_tours already defined,,
+# indiv tour counts should not include joint tours by point_person,,
+,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)"
+,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)"
+,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)"
+,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)"
+#,,
+Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)"
+#,,
+Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)"
+Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)"
+tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM')
+AccesibilityAtOrigin fallback,hhacc,0
+AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))"
+AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)"
+AccesibilityADestination fallback,pracc,0
+AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)"
+AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)"
+AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))"
+AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))"
+,destination_area_type,"reindex(land_use.AREATYPE, df.destination)"
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_atwork.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_atwork.csv
new file mode 100644
index 000000000..dbd1c61c5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_atwork.csv
@@ -0,0 +1,13 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh_,Middle to Low Income HH ,income_in_thousands<50000,,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person
+util_subtour_departure_less_than_or_equal_to_11am,Subtour departure less than or equal to 11AM,start<12,,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am
+util_subtour_return_time_greater_or_equal_to_2pm,Subtour return time greater or equal to 2PM,end>16,,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm
+util_subtour_duration_in_hours_integer_,Subtour duration in hours (integer),end-start,,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_
+util_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,dummy for subtour origin (tour destination) at Exurban  or Rual (AreaTypes = 6 or 7),destination_area_type >5,,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_
+util_primary_destination_accessibility_log_of_it_,Primary Destination Accessibility (LOG of it),pracc,,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_
+util_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,subtour distance in miles (from tour destination to subtour primary destination one way),distance_in_miles,,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+util_Phase1_Calibration,Phase1_Calibration,1,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_atwork.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_atwork.csv
new file mode 100644
index 000000000..5e1a09627
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_atwork.csv
@@ -0,0 +1,32 @@
+Description,value,coefficient_name
+Middle to Low Income HH ,0.45,coef_middle_to_low_income_hh_
+Number of eating tours tours undertaken by the person,-0.28,coef_number_of_eating_tours_tours_undertaken_by_the_person
+Subtour departure less than or equal to 11AM,0.31,coef_subtour_departure_less_than_or_equal_to_11am
+Subtour return time greater or equal to 2PM,0.34,coef_subtour_return_time_greater_or_equal_to_2pm
+Subtour duration in hours (integer),0.56,coef_subtour_duration_in_hours_integer_
+dummy for subtour origin (tour destination) at Exurban  or Rual (AreaTypes = 6 or 7),0.27,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_
+Primary Destination Accessibility (LOG of it),0.18,coef_primary_destination_accessibility_log_of_it_
+subtour distance in miles (from tour destination to subtour primary destination one way),0.02,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_
+Alternative specific constant for return  stops,-3.671,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops,-5.388,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops,-6.21,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for outbound stops,-3.896,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for the total number of stops,2.127,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for outbound stops,-5.709,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops,-7.361,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-2.63434376,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,-2.784737668,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-0.489626558,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,-3.622571928,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,-0.718600931,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,-2.115702598,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-4.459844297,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,-3.657486874,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,0.273537757,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,0.252608976,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-0.50657628,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,-5.084387708,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,0.390308574,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,2.069420944,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,2.886598497,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_eatout.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_eatout.csv
new file mode 100644
index 000000000..203b54ba4
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_eatout.csv
@@ -0,0 +1,93 @@
+Description,value,coefficient_name
+Number of Vehicles,-0.19,coef_number_of_vehicles
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.73,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.28,coef_number_of_work_tours_undertaken_by_the_person
+Number of shop tours undertaken by the person,-0.24,coef_number_of_shop_tours_undertaken_by_the_person
+At least one kid and one adult participate in the tour,0.37,coef_at_least_one_kid_and_one_adult_participate_in_the_tour
+Arrival later than 17:00.,-0.45,coef_arrival_later_than_17_00_
+Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,1.31,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_
+dummy for distance in miles,-0.01,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-1.761,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Dummy for a return visiting tour,-0.64,coef_dummy_for_a_return_visiting_tour
+Alternative specific constant for return  stops,-3.697,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-4.717,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Number of persons participating in the tour.Outgoing stops interaction,-0.46,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction
+Alternative specific constant for outbound stops,-2.19,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Dummy for an outbound visiting tour,-0.69,coef_dummy_for_an_outbound_visiting_tour
+Dummy for a visiting tour with both outbound and return leg,0.44,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg
+Alternative specific constant for the total number of stops,0.94,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-4.516,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,2.026,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-5.255,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-0.128986631,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.188202401,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,0.418146682,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.313324465,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,0.680527273,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,1.928344879,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-0.120616813,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,1.321299113,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,1.707738852,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,1.476132289,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-0.328929277,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,1.676875153,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,0.767585618,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,1.885405325,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,1.528508267,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_escort.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_escort.csv
new file mode 100644
index 000000000..4f131a0a2
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_escort.csv
@@ -0,0 +1,88 @@
+Description,value,coefficient_name
+Number of HH Persons,-0.24,coef_number_of_hh_persons
+Number of Students in HH,0.19,coef_number_of_students_in_hh
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.91,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.29,coef_number_of_work_tours_undertaken_by_the_person
+Number of escort tours tours undertaken by the person,-0.15,coef_number_of_escort_tours_tours_undertaken_by_the_person
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.59,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+dummy for distance less than 5 Miles,0.32,coef_dummy_for_distance_less_than_5_miles
+dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-0.968,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Alternative specific constant for return  stops,-2.41,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-3.024,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Alternative specific constant for outbound stops,-2.173,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-4.294,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,-1.807,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-4.758,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-0.456326296,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,-0.317345391,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-0.79256653,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.030779564,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,-0.082115923,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,-0.236742759,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-0.922620733,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,0.345406346,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,0.713277244,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,-2.170823478,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,1.928143697,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,0.624920575,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,1.181749655,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,1.977311333,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,3.381035483,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othdiscr.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othdiscr.csv
new file mode 100644
index 000000000..4a861dfa6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othdiscr.csv
@@ -0,0 +1,90 @@
+Description,value,coefficient_name
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-2.4578,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.6153,coef_number_of_work_tours_undertaken_by_the_person
+Number of shool tours tours undertaken by the person,-0.8176,coef_number_of_shool_tours_tours_undertaken_by_the_person
+Number of shop tours undertaken by the person,-0.629,coef_number_of_shop_tours_undertaken_by_the_person
+Number of maintenace tours tours undertaken by the person,-0.3715,coef_number_of_maintenace_tours_tours_undertaken_by_the_person
+Arrival later than 17:00.,-0.6383,coef_arrival_later_than_17_00_
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.8335,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+dummy for distance less than 10 Miles ,0.3756,coef_dummy_for_distance_less_than_10_miles_
+dummy for distance in miles,-0.0225,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-0.921,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Alternative specific constant for return  stops,-2.336,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-2.927,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Alternative specific constant for outbound stops,-1.581,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Alternative specific constant for the total number of stops,0.863,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-3.323,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,0.939,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-4.623,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,0.290792835,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.472319542,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,0.76294318,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.694317783,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,0.515942097,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,0.691734937,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-0.047773074,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,1.232476585,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,1.267702806,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,0.047365121,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-1.077060366,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,2.338918384,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,-0.480740616,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,1.10284077,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,2.15563482,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othmaint.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othmaint.csv
new file mode 100644
index 000000000..95fd9b89c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_othmaint.csv
@@ -0,0 +1,98 @@
+Description,value,coefficient_name
+Middle to Low Income HH ,0.17,coef_middle_to_low_income_hh_
+Mid to High Income HH,0.23,coef_mid_to_high_income_hh
+High Income HH,0.24,coef_high_income_hh
+Number of HH Persons,-0.31,coef_number_of_hh_persons
+Number of Students in HH,0.21,coef_number_of_students_in_hh
+Presence of Kids between 0 and 4 (including) years old,0.74,coef_presence_of_kids_between_0_and_4_including_years_old
+Dummy for female,0.3012,coef_dummy_for_female
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.4329,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.364,coef_number_of_work_tours_undertaken_by_the_person
+Number of university tours tours undertaken by the person,-0.6252,coef_number_of_university_tours_tours_undertaken_by_the_person
+Number of shool tours tours undertaken by the person,-1.4135,coef_number_of_shool_tours_tours_undertaken_by_the_person
+Number of shop tours undertaken by the person,-0.1428,coef_number_of_shop_tours_undertaken_by_the_person
+Number of maintenace tours undertaken by the houshold,-0.0468,coef_number_of_maintenace_tours_undertaken_by_the_houshold
+Number of persons participating in the tour.Return stops interaction,0.4904,coef_number_of_persons_participating_in_the_tour_return_stops_interaction
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.5134,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+dummy for distance less than 20 Miles ,-0.408,coef_dummy_for_distance_less_than_20_miles_
+dummy for distance in miles,0.0273,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-0.585,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Alternative specific constant for return  stops,-1.48,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-2.462,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Alternative specific constant for outbound stops,-1.761,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Alternative specific constant for the total number of stops,0.414,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-3.661,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,0.488,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-5.426,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,0.205739686,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.210426665,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,0.646983288,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.794725126,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,0.796895947,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,0.311667481,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,1.123540292,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,1.103785395,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,0.470492284,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,0.633549942,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,1.884190334,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,1.422429266,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,2.390980042,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,0.863350747,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,1.268073696,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_school.csv
new file mode 100644
index 000000000..0a093aa2c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_school.csv
@@ -0,0 +1,36 @@
+Description,value,coefficient_name
+Number of HH Persons,-0.506,coef_number_of_hh_persons
+Presence of kids between 5 and 15 (including) years old,0.3299,coef_presence_of_kids_between_5_and_15_including_years_old
+Number of Cars > Number of Workers,0.5331,coef_number_of_cars_number_of_workers
+Dummy for female,0.4099,coef_dummy_for_female
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.8163,coef_dummy_for_walking_to_all_stops
+Number of escort tours tours undertaken by the person,1.2365,coef_number_of_escort_tours_tours_undertaken_by_the_person
+Arrival later than 17:00.,1.8377,coef_arrival_later_than_17_00_
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.9549,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+dummy for distance in miles,0.0438,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-1.206,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops,-2.672,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops,-3.364,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for outbound stops,-2.123,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for the total number of stops,0.701,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for outbound stops,-3.798,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for the total number of stops,1.135,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for outbound stops,-5.85,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-2.254972707,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,-2.011200307,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-2.140534054,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,-2.6157216,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,-2.05970613,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,-1.802894203,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-2.177625735,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,-2.918162565,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,-3.083316079,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,-3.893518401,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-1.086994529,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,-5.180196121,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,-4.548639536,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,-3.323713083,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,-0.129535785,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_shopping.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_shopping.csv
new file mode 100644
index 000000000..8524ebe28
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_shopping.csv
@@ -0,0 +1,93 @@
+Description,value,coefficient_name
+Number of HH Persons,-0.1522,coef_number_of_hh_persons
+Num kids between 5 and 15 (including) years old,0.0482,coef_num_kids_between_5_and_15_including_years_old
+Dummy for female,0.1721,coef_dummy_for_female
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.4908,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.548,coef_number_of_work_tours_undertaken_by_the_person
+Number of university tours tours undertaken by the person,-0.6709,coef_number_of_university_tours_tours_undertaken_by_the_person
+Number of maintenace tours tours undertaken by the person,-0.1977,coef_number_of_maintenace_tours_tours_undertaken_by_the_person
+Number of shop tours undertaken by the houshold,-0.0733,coef_number_of_shop_tours_undertaken_by_the_houshold
+Dummy for only adults participate in the tour,0.1902,coef_dummy_for_only_adults_participate_in_the_tour
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.9056,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+dummy for distance less than 5 Miles,0.3768,coef_dummy_for_distance_less_than_5_miles
+dummy for distance in miles,0.0289,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-1.179,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Alternative specific constant for return  stops,-2.305,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-3.024,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Alternative specific constant for outbound stops,-1.339,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Alternative specific constant for the total number of stops,0.252,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-3.11,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,0.514,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-4.487,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,0.017121958,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.034817267,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-0.130330682,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.209231,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,0.02414933,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,0.461897472,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,0.158579421,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,0.850102453,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,1.341252395,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,1.18586399,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-0.041491927,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,1.607200672,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,1.911336809,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,1.065391227,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,1.37891856,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_social.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_social.csv
new file mode 100644
index 000000000..664ed1e5e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_social.csv
@@ -0,0 +1,93 @@
+Description,value,coefficient_name
+Number of Vehicles,-0.19,coef_number_of_vehicles
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.73,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.28,coef_number_of_work_tours_undertaken_by_the_person
+Number of shop tours undertaken by the person,-0.24,coef_number_of_shop_tours_undertaken_by_the_person
+At least one kid and one adult participate in the tour,0.37,coef_at_least_one_kid_and_one_adult_participate_in_the_tour
+Arrival later than 17:00.,-0.45,coef_arrival_later_than_17_00_
+Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,1.31,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_
+dummy for distance in miles,-0.01,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-1.12,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in
+Dummy for a return visiting tour,-0.64,coef_dummy_for_a_return_visiting_tour
+Alternative specific constant for return  stops,-2.764,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in
+Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in
+Alternative specific constant for return  stops,-3.451,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for return  stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+Number of persons participating in the tour.Outgoing stops interaction,-0.46,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction
+Alternative specific constant for outbound stops,-1.081,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in
+Dummy for an outbound visiting tour,-0.69,coef_dummy_for_an_outbound_visiting_tour
+Dummy for a visiting tour with both outbound and return leg,0.44,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg
+Alternative specific constant for the total number of stops,0.496,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in
+Alternative specific constant for outbound stops,-2.874,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in
+Alternative specific constant for the total number of stops,0.882,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+Alternative specific constant for outbound stops,-4.552,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-0.22936999,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.529587765,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,0.538219032,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,0.218718688,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,-0.318021224,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,1.030756549,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-2.51155013,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,1.386390249,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,-1.163443209,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,0.220714165,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,0.895315224,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,2.965417908,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,0.472314942,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,3.172643171,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,3.347592,coef_calibration_for_3out_3in
+,-0.126,coef_telecommute1_2
+,-0.301,coef_telecommute1_3
+,-0.301,coef_telecommute1_4
+,-0.126,coef_telecommute1_5
+,-0.301,coef_telecommute1_6
+,-0.301,coef_telecommute1_7
+,-0.611,coef_telecommute1_8
+,-0.301,coef_telecommute1_9
+,-0.301,coef_telecommute1_10
+,-0.611,coef_telecommute1_11
+,-0.611,coef_telecommute1_12
+,-0.301,coef_telecommute1_13
+,-0.611,coef_telecommute1_14
+,-0.611,coef_telecommute1_15
+,-0.611,coef_telecommute1_16
+,-0.149,coef_telecommute23_2
+,-0.768,coef_telecommute23_3
+,-0.768,coef_telecommute23_4
+,-0.768,coef_telecommute23_5
+,-0.768,coef_telecommute23_6
+,-0.768,coef_telecommute23_7
+,-0.909,coef_telecommute23_8
+,-0.768,coef_telecommute23_9
+,-0.768,coef_telecommute23_10
+,-0.909,coef_telecommute23_11
+,-0.909,coef_telecommute23_12
+,-0.768,coef_telecommute23_13
+,-0.909,coef_telecommute23_14
+,-0.909,coef_telecommute23_15
+,-0.909,coef_telecommute23_16
+,-0.158,coef_telecommute4p_2
+,-0.473,coef_telecommute4p_3
+,-0.473,coef_telecommute4p_4
+,-0.473,coef_telecommute4p_5
+,-0.473,coef_telecommute4p_6
+,-0.473,coef_telecommute4p_7
+,-1.47,coef_telecommute4p_8
+,-0.473,coef_telecommute4p_9
+,-0.473,coef_telecommute4p_10
+,-1.47,coef_telecommute4p_11
+,-1.47,coef_telecommute4p_12
+,-0.473,coef_telecommute4p_13
+,-1.47,coef_telecommute4p_14
+,-1.47,coef_telecommute4p_15
+,-1.47,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_univ.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_univ.csv
new file mode 100644
index 000000000..ae51954ae
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_univ.csv
@@ -0,0 +1,35 @@
+Description,value,coefficient_name
+Number of HH Persons,-0.2827,coef_number_of_hh_persons
+Presence of kids between 5 and 15 (including) years old,0.6823,coef_presence_of_kids_between_5_and_15_including_years_old
+Number of Vehicles,0.1703,coef_number_of_vehicles
+Dummy for female,0.7349,coef_dummy_for_female
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Number of escort tours tours undertaken by the person,0.9018,coef_number_of_escort_tours_tours_undertaken_by_the_person
+Arrival later than 17:00.,0.389,coef_arrival_later_than_17_00_
+Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.8434,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+HH accesibility for inbound tours. Interaction,0.2481,coef_hh_accesibility_for_inbound_tours_interaction
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-2.003,coef_alternative_specific_constant_for_return_stops_0out_1in
+Alternative specific constant for return  stops,-3.51,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops,-3.677,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for outbound stops,-2.628,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for the total number of stops,1.272,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in
+Alternative specific constant for outbound stops,-3.741,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for the total number of stops,1.871,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for outbound stops,-4.981,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,-0.957893279,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.013364686,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-1.128041828,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,-0.815453021,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,0.36813072,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,1.560091494,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-1.363437784,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,-0.87970088,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,-1.280333926,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,-1.562370933,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-0.135706787,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,-1.287689684,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,-1.046385509,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,-1.344388242,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,-2.396963195,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_work.csv
new file mode 100644
index 000000000..446c24d97
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_coefficients_work.csv
@@ -0,0 +1,49 @@
+Description,value,coefficient_name
+Middle to Low Income HH,0.17,coef_middle_to_low_income_hh
+Mid to High Income HH,0.23,coef_mid_to_high_income_hh
+High Income HH,0.24,coef_high_income_hh
+Number of HH Persons,-0.31,coef_number_of_hh_persons
+Number of Students in HH,0.21,coef_number_of_students_in_hh
+Presence of Kids between 0 and 4 (including) years old,0.74,coef_presence_of_kids_between_0_and_4_including_years_old
+Num kids between 5 and 15 (including) years old,0.08,coef_num_kids_between_5_and_15_including_years_old
+Presence of kids between 5 and 15 (including) years old,0.26,coef_presence_of_kids_between_5_and_15_including_years_old
+Number of Adults (>= 16 years old),0.03,coef_number_of_adults_16_years_old_
+Number of Cars > Number of Workers,0.16,coef_number_of_cars_number_of_workers
+Dummy for female,0.22,coef_dummy_for_female
+Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit
+Dummy for walking to all stops,-1.54,coef_dummy_for_walking_to_all_stops
+Number of work tours undertaken by the person,-0.15,coef_number_of_work_tours_undertaken_by_the_person
+Number of university tours tours undertaken by the person,-0.48,coef_number_of_university_tours_tours_undertaken_by_the_person
+Number of school tours tours undertaken by the person,-1.55,coef_number_of_school_tours_tours_undertaken_by_the_person
+Number of escort tours tours undertaken by the person,0.2,coef_number_of_escort_tours_tours_undertaken_by_the_person
+Number of shop tours undertaken by the houshold,-0.05,coef_number_of_shop_tours_undertaken_by_the_houshold
+AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,-1.93,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours
+Evening Arrival (>=19:00) Interacted with return tours,0.31,coef_evening_arrival_19_00_interacted_with_return_tours
+Dummy for the duration of the tour being equal or greater than or equal to 11 hours,0.6,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours
+dummy for distance less than 20 Miles,-0.22,coef_dummy_for_distance_less_than_20_miles
+dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles
+No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit
+Alternative specific constant for return  stops,-0.445,coef_alternative_specific_constant_for_return_stops_0out_1in
+Number of subtours in the tour,0.19,coef_number_of_subtours_in_the_tour
+Alternative specific constant for return  stops,-1.775,coef_alternative_specific_constant_for_return_stops_0out_2in
+Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in
+Alternative specific constant for return  stops,-2.139,coef_alternative_specific_constant_for_return_stops_0out_3in
+Alternative specific constant for outbound stops,-0.833,coef_alternative_specific_constant_for_outbound_stops_1out_0in
+Alternative specific constant for outbound stops,-2.613,coef_alternative_specific_constant_for_outbound_stops_2out_0in
+Alternative specific constant for the total number of stops,0.695,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+Alternative specific constant for outbound stops,-3.934,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+Calibrated coefficient for stops 0 out and 1 in,0.282545956,coef_calibration_for_0out_1in
+Calibrated coefficient for stops 0 out and 2 in,0.378884046,coef_calibration_for_0out_2in
+Calibrated coefficient for stops 0 out and 3 in,-0.14156218,coef_calibration_for_0out_3in
+Calibrated coefficient for stops 1 out and 0 in,-0.201468584,coef_calibration_for_1out_0in
+Calibrated coefficient for stops 1 out and 1 in,-0.150830082,coef_calibration_for_1out_1in
+Calibrated coefficient for stops 1 out and 2 in,-0.038370256,coef_calibration_for_1out_2in
+Calibrated coefficient for stops 1 out and 3 in,-0.458167576,coef_calibration_for_1out_3in
+Calibrated coefficient for stops 2 out and 0 in,-0.238615836,coef_calibration_for_2out_0in
+Calibrated coefficient for stops 2 out and 1 in,-0.366936647,coef_calibration_for_2out_1in
+Calibrated coefficient for stops 2 out and 2 in,0.964901262,coef_calibration_for_2out_2in
+Calibrated coefficient for stops 2 out and 3 in,-0.052575958,coef_calibration_for_2out_3in
+Calibrated coefficient for stops 3 out and 0 in,0.102180805,coef_calibration_for_3out_0in
+Calibrated coefficient for stops 3 out and 1 in,-1.078935365,coef_calibration_for_3out_1in
+Calibrated coefficient for stops 3 out and 2 in,-0.962167532,coef_calibration_for_3out_2in
+Calibrated coefficient for stops 3 out and 3 in,-0.165201068,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_eatout.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_eatout.csv
new file mode 100644
index 000000000..479dd8ba7
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_eatout.csv
@@ -0,0 +1,56 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,,
+util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles
+util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,,
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction
+util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_at_least_one_kid_and_one_adult_participate_in_the_tour,At least one kid and one adult participate in the tour,composition=='mixed',,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,,
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_dummy_for_an_outbound_visiting_tour,Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour
+util_dummy_for_a_return_visiting_tour,Dummy for a return visiting tour,primary_purpose == 'social',,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour
+util_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_escort.csv
new file mode 100644
index 000000000..aab34960b
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_escort.csv
@@ -0,0 +1,50 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,female,,,,,,,,,,,,,,,,
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,,
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_5_miles,dummy for distance less than 5 Miles,(distance_in_miles < 5),,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_othdiscr.csv
new file mode 100644
index 000000000..098c7b074
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_othdiscr.csv
@@ -0,0 +1,52 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,,
+util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,,
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_10_miles_,dummy for distance less than 10 Miles ,(distance_in_miles < 10),,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_othmaint.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_othmaint.csv
new file mode 100644
index 000000000..6259d5513
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_othmaint.csv
@@ -0,0 +1,53 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old
+util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,~is_joint & female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person
+util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_undertaken_by_the_houshold,Number of maintenace tours undertaken by the houshold,num_hh_maint_tours,,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold
+util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,,
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_school.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_school.csv
new file mode 100644
index 000000000..af33fb5d8
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_school.csv
@@ -0,0 +1,44 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,,
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_15_miles,dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,,
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,1,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_shopping.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_shopping.csv
new file mode 100644
index 000000000..b7c022569
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_shopping.csv
@@ -0,0 +1,53 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,~is_joint & female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person
+util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold
+util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_dummy_for_only_adults_participate_in_the_tour,Dummy for only adults participate in the tour,composition=='adults',,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,,
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_5_miles,dummy for distance less than 5 Miles,(distance_in_miles < 5),,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_social.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_social.csv
new file mode 100644
index 000000000..e83689f52
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_social.csv
@@ -0,0 +1,56 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,,
+util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles
+util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,,
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction
+util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,
+util_at_least_one_kid_and_one_adult_participate_in_the_tour,At least one kid and one adult participate in the tour,composition=='mixed',,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,,
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in
+util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return  stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in
+util_dummy_for_an_outbound_visiting_tour,Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour
+util_dummy_for_a_return_visiting_tour,Dummy for a return visiting tour,primary_purpose == 'social',,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour
+util_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg
+util_Phase1_Calibration,Phase1_Calibration,~is_joint,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+util_telecommute1,(num_work_tours == 0) & (telecommute_frequency=='1_day_week'),,coef_telecommute1_2,coef_telecommute1_3,coef_telecommute1_4,coef_telecommute1_5,coef_telecommute1_6,coef_telecommute1_7,coef_telecommute1_8,coef_telecommute1_9,coef_telecommute1_10,coef_telecommute1_11,coef_telecommute1_12,coef_telecommute1_13,coef_telecommute1_14,coef_telecommute1_15,coef_telecommute1_16
+util_telecommute23,(num_work_tours == 0) & (telecommute_frequency=='2_3_days_week'),,coef_telecommute23_2,coef_telecommute23_3,coef_telecommute23_4,coef_telecommute23_5,coef_telecommute23_6,coef_telecommute23_7,coef_telecommute23_8,coef_telecommute23_9,coef_telecommute23_10,coef_telecommute23_11,coef_telecommute23_12,coef_telecommute23_13,coef_telecommute23_14,coef_telecommute23_15,coef_telecommute23_16
+util_telecommute4p,(num_work_tours == 0) & (telecommute_frequency=='4_days_week'),,coef_telecommute4p_2,coef_telecommute4p_3,coef_telecommute4p_4,coef_telecommute4p_5,coef_telecommute4p_6,coef_telecommute4p_7,coef_telecommute4p_8,coef_telecommute4p_9,coef_telecommute4p_10,coef_telecommute4p_11,coef_telecommute4p_12,coef_telecommute4p_13,coef_telecommute4p_14,coef_telecommute4p_15,coef_telecommute4p_16
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_univ.csv
new file mode 100644
index 000000000..f0854f242
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_univ.csv
@@ -0,0 +1,45 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,
+util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles
+util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,,
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,,
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,,
+util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,,
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,,
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,,
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_Phase1_Calibration,Phase1_Calibration,1,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
+#util_Major University Calibration,Major University Calibration,is_major_univ_student,,-0.199248949,0.652388897,0.625258509,-1.850257358,-1.049976118,-0.922350027,0.617946267,-1.566065939,-0.401448626,-0.508496898,-0.026438159,-3.878745091,-5,0.876560807,-1.094489763
diff --git a/activitysim/examples/prototype_mwcog/configs/stop_frequency_work.csv b/activitysim/examples/prototype_mwcog/configs/stop_frequency_work.csv
new file mode 100644
index 000000000..abd7a4129
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/stop_frequency_work.csv
@@ -0,0 +1,45 @@
+Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in
+util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh
+util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh
+util_high_income_hh,High Income HH,(income_in_thousands>=100000),,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh
+util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons
+util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,,
+util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh
+util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,
+util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old
+util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old
+util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old
+util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_
+util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,
+util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers
+util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,
+util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female
+util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit
+util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops
+util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person
+util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person
+util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person
+util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person
+util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,,
+util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,,
+util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,,
+util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,,
+util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold
+util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours
+util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,,
+util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,,
+util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,,
+util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,
+util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles
+util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles
+util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit
+util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in
+util_alternative_specific_constant_for_return_stops,Alternative specific constant for return  stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in
+util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in
+util_number_of_subtours_in_the_tour,Number of subtours in the tour,num_atwork_subtours,,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour
+util_Phase1_Calibration,Phase1_Calibration,1,,coef_calibration_for_0out_1in,coef_calibration_for_0out_2in,coef_calibration_for_0out_3in,coef_calibration_for_1out_0in,coef_calibration_for_1out_1in,coef_calibration_for_1out_2in,coef_calibration_for_1out_3in,coef_calibration_for_2out_0in,coef_calibration_for_2out_1in,coef_calibration_for_2out_2in,coef_calibration_for_2out_3in,coef_calibration_for_3out_0in,coef_calibration_for_3out_1in,coef_calibration_for_3out_2in,coef_calibration_for_3out_3in
diff --git a/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.csv b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.csv
new file mode 100644
index 000000000..d001f013f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.csv
@@ -0,0 +1,21 @@
+Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week
+#util_Services,Services,@df.occup==52,0,coef_Services_1day,coef_Services_23day,coef_Services_4day
+#util_SalesOffice,SalesOffice,@df.occup==53,0,coef_SalesOffice_1day,coef_SalesOffice_23day,coef_SalesOffice_4day
+#util_ResourceConstruct,ResourceConstruct,@df.occup==54,0,coef_ResourceConstruct_1day,coef_ResourceConstruct_23day,coef_ResourceConstruct_4day
+#util_TransportMat,TransportMat,@df.occup==55,0,coef_TransportMat_1day,coef_TransportMat_23day,coef_TransportMat_4day
+util_HasChildren0to5,Has children 0 to 5 years old,@df.has_young_children,0,coef_HasChildren0to5_1day,coef_HasChildren0to5_23day,coef_HasChildren0to5_4day
+util_HasChildren6to12,Has children 6 to 12 years old,@df.has_children_6_to_12,0,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day
+util_OneAdultInHH,One adult in hh,@df.num_adults==1,0,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day
+util_2plusAdultsInHH,2 or more adults in hh,@df.num_adults==2,0,coef_2plusAdultsInHH_1day,coef_2plusAdultsInHH_23day,coef_2plusAdultsInHH_4day
+util_Female,female,@df.SEX==2,0,coef_Female_1day,coef_Female_23day,coef_Female_4day
+util_PartTimeWorker,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_PartTimeWorker_1day,coef_PartTimeWorker_23day,coef_PartTimeWorker_4day
+util_CollegeStudent,College student,@df.ptype==PTYPE_UNIVERSITY,0,coef_CollegeStudent_1day,coef_CollegeStudent_23day,coef_CollegeStudent_4day
+util_PaysToPark,Pays to park,@~df.free_parking_at_work,0,coef_PaysToPark_1day,coef_PaysToPark_23day,coef_PaysToPark_4day
+util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",0,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day
+util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",0,coef_Income100to150k_1day,coef_Income100to150k_23day,coef_Income100to150k_4day
+util_Income150kplus,Income 150k+,@df.income > 150000,0,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day
+util_0Autos,0 Autos,@df.auto_ownership==0,0,coef_0Autos_1day,coef_0Autos_23day,coef_0Autos_4day
+util_1Auto,1 Auto,@df.auto_ownership==1,0,coef_1Auto_1day,coef_1Auto_23day,coef_1Auto_4day
+util_3plusAutos,3+ Autos,@df.auto_ownership>=3,0,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day
+util_DistanceToWork,Distance to work,@df.distance_to_work,0,coef_DistanceToWork_1day,coef_DistanceToWork_23day,coef_DistanceToWork_4day
+util_temp_calib,temporary calibration,1,0,coef_temp_calib_1day,coef_temp_calib_23day,coef_temp_calib_4day
diff --git a/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.yaml b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.yaml
new file mode 100644
index 000000000..162066ef6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency.yaml
@@ -0,0 +1,9 @@
+
+# borrowed from free parking model
+
+SPEC: telecommute_frequency.csv
+COEFFICIENTS: telecommute_frequency_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
diff --git a/activitysim/examples/prototype_mwcog/configs/telecommute_frequency_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency_coeffs.csv
new file mode 100644
index 000000000..1a7acb4fc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/telecommute_frequency_coeffs.csv
@@ -0,0 +1,61 @@
+coefficient_name,value,constrain
+coef_Services_1day,-1.624,F
+coef_SalesOffice_1day,-0.62,F
+coef_ResourceConstruct_1day,-1.57,F
+coef_TransportMat_1day,-14.747,F
+coef_HasChildren0to5_1day,0,F
+coef_HasChildren6to12_1day,0,F
+coef_OneAdultInHH_1day,0.177,F
+coef_2plusAdultsInHH_1day,0,F
+coef_Female_1day,0,F
+coef_PartTimeWorker_1day,0,F
+coef_CollegeStudent_1day,0,F
+coef_PaysToPark_1day,0.457,F
+coef_Income60to100k_1day,0.56,F
+coef_Income100to150k_1day,0.644,F
+coef_Income150kplus_1day,0.92,F
+coef_0Autos_1day,0,F
+coef_1Auto_1day,0,F
+coef_3plusAutos_1day,0,F
+coef_DistanceToWork_1day,0.016,F
+coef_Services_23day,-0.651,F
+coef_SalesOffice_23day,-0.738,F
+coef_ResourceConstruct_23day,0,F
+coef_TransportMat_23day,0,F
+coef_HasChildren0to5_23day,0,F
+coef_HasChildren6to12_23day,0.517,F
+coef_OneAdultInHH_23day,0,F
+coef_2plusAdultsInHH_23day,0,F
+coef_Female_23day,0,F
+coef_PartTimeWorker_23day,0.425,F
+coef_CollegeStudent_23day,0.6,F
+coef_PaysToPark_23day,0,F
+coef_Income60to100k_23day,0.389,F
+coef_Income100to150k_23day,0.193,F
+coef_Income150kplus_23day,0.765,F
+coef_0Autos_23day,0.407,F
+coef_1Auto_23day,0,F
+coef_3plusAutos_23day,-0.73,F
+coef_DistanceToWork_23day,0,F
+coef_Services_4day,0,F
+coef_SalesOffice_4day,-0.894,F
+coef_ResourceConstruct_4day,0,F
+coef_TransportMat_4day,0,F
+coef_HasChildren0to5_4day,-0.864,F
+coef_HasChildren6to12_4day,-0.81,F
+coef_OneAdultInHH_4day,-0.043,F
+coef_2plusAdultsInHH_4day,0,F
+coef_Female_4day,0,F
+coef_PartTimeWorker_4day,1.112,F
+coef_CollegeStudent_4day,0,F
+coef_PaysToPark_4day,0,F
+coef_Income60to100k_4day,0,F
+coef_Income100to150k_4day,0,F
+coef_Income150kplus_4day,0,F
+coef_0Autos_4day,0,F
+coef_1Auto_4day,0,F
+coef_3plusAutos_4day,0,F
+coef_DistanceToWork_4day,0,F
+coef_temp_calib_1day,-3.464358858,F
+coef_temp_calib_23day,-3.046318883,F
+coef_temp_calib_4day,-3.518320974,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..30ff417ca
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,1177 @@
+start,end
+1,1
+1,2
+1,3
+1,4
+1,5
+1,6
+1,7
+1,8
+1,9
+1,10
+1,11
+1,12
+1,13
+1,14
+1,15
+1,16
+1,17
+1,18
+1,19
+1,20
+1,21
+1,22
+1,23
+1,24
+1,25
+1,26
+1,27
+1,28
+1,29
+1,30
+1,31
+1,32
+1,33
+1,34
+1,35
+1,36
+1,37
+1,38
+1,39
+1,40
+1,41
+1,42
+1,43
+1,44
+1,45
+1,46
+1,47
+1,48
+2,2
+2,3
+2,4
+2,5
+2,6
+2,7
+2,8
+2,9
+2,10
+2,11
+2,12
+2,13
+2,14
+2,15
+2,16
+2,17
+2,18
+2,19
+2,20
+2,21
+2,22
+2,23
+2,24
+2,25
+2,26
+2,27
+2,28
+2,29
+2,30
+2,31
+2,32
+2,33
+2,34
+2,35
+2,36
+2,37
+2,38
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diff --git a/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_segments.csv b/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_segments.csv
new file mode 100644
index 000000000..eafafb016
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_departure_and_duration_segments.csv
@@ -0,0 +1,50 @@
+tour_purpose,time_period,start,end
+work,AM,9,10
+work,MD,13,24
+work,PM,25,27
+work,NT,33,33
+#,,,
+school,AM,9,10
+school,MD,13,24
+school,PM,25,27
+school,NT,33,33
+#,,,
+univ,AM,9,10
+univ,MD,13,24
+univ,PM,25,27
+univ,NT,33,33
+#,,,
+shopping,AM,9,10
+shopping,MD,13,24
+shopping,PM,25,27
+shopping,NT,33,33
+#,,,
+escort,AM,9,10
+escort,MD,13,24
+escort,PM,25,27
+escort,NT,33,33
+#,,,
+othmaint,AM,9,10
+othmaint,MD,13,24
+othmaint,PM,25,27
+othmaint,NT,33,33
+#,,,
+othdiscr,AM,9,10
+othdiscr,MD,13,24
+othdiscr,PM,25,27
+othdiscr,NT,33,33
+#,,,
+social,AM,9,10
+social,MD,13,24
+social,PM,25,27
+social,NT,33,33
+#,,,
+eatout,AM,9,10
+eatout,MD,13,24
+eatout,PM,25,27
+eatout,NT,33,33
+#,,,
+atwork,AM,9,10
+atwork,MD,13,24
+atwork,PM,25,27
+atwork,NT,33,33
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.csv b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.csv
new file mode 100644
index 000000000..678329e71
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.csv
@@ -0,0 +1,335 @@
+Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_AB,WALK_BM,WALK_MR,WALK_CR,PNR_AB,PNR_BM,PNR_MR,PNR_CR,KNR_AB,KNR_BM,KNR_MR,KNR_CR,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED
+#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,,,,,
+Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_forJoint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * df.terminal_time,coef_walk,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_cost,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@df.daily_parking_cost * 1.22,coef_cost,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Total_toll_,DRIVEALONE - Total toll ,@(odt_skims['SOV_TOLL'] + dot_skims['SOV_TOLL'])*1.22,coef_cost,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,,,,,
+#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,,,,,
+util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,coef_ivt,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * df.terminal_time,,coef_walk,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Operating_cost,SHARED2 - Operating cost,@costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,coef_cost,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Parking_cost,SHARED2 - Parking cost,@df.daily_parking_cost / costShareSr2 * 1.22,,coef_cost,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Total_toll,SHARED2 - Total toll,@((odt_skims['HOV2_TOLL'] + dot_skims['HOV2_TOLL']) / costShareSr2) * 1.22,,coef_cost,,,,,,,,,,,,,,,,,,,
+util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age16p_sr,,,,,,,,,,,,,,,,,,,
+#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,,,,,
+util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,,,,,
+util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,coef_ivt,,,,,,,,,,,,,,,,,,
+util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * df.terminal_time,,,coef_walk,,,,,,,,,,,,,,,,,,
+util_SHARED3_Operating_cost,SHARED3 - Operating cost,@costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,coef_cost,,,,,,,,,,,,,,,,,,
+util_SHARED3_Parking_cost,SHARED3 - Parking cost,@df.daily_parking_cost / costShareSr3 * 1.22,,,coef_cost,,,,,,,,,,,,,,,,,,
+util_SHARED3_Total_toll,SHARED3 - Total toll,@((odt_skims['HOV3_TOLL'] + dot_skims['HOV3_TOLL']) / costShareSr3) * 1.22,,,coef_cost,,,,,,,,,,,,,,,,,,
+util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,,
+util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,,
+util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,,
+#util_WALK,#Walk,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,-999,,,,,,,,,,,,,,,,,
+util_WALK_Time,WALK - Time,@(od_skims['DISTWALK'] + od_skims.reverse('DISTWALK'))*60/walkSpeed,,,,coef_walk,,,,,,,,,,,,,,,,,
+util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@df.density_index,,,,coef_density_walk,,,,,,,,,,,,,,,,,
+util_WALK_Topology,WALK - Topology,@df.dest_topology,,,,coef_topology_walk,,,,,,,,,,,,,,,,,
+#util_BIKE,#Bike,,,,,,,,,,,,,,,,,,,,,,
+util_BIKE_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,-999,,,,,,,,,,,,,,,,
+util_BIKE_Unavailable_if_didn't_BIKE_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,,,,,
+util_BIKE_Time,BIKE - Time,@(od_skims['DISTBIKE'] + od_skims.reverse('DISTBIKE'))*60/bikeSpeed,,,,,coef_bike,,,,,,,,,,,,,,,,
+util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@df.density_index,,,,,coef_density_bike,,,,,,,,,,,,,,,,
+util_BIKE_Topology,BIKE - Topology,@df.dest_topology,,,,,coef_topology_bike,,,,,,,,,,,,,,,,
+#util_WALK_to_AB,#Walk to AB,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_AB_Unavailable,WALK_AB - Unavailable,walk_ab_available == False,,,,,,-999,,,,,,,,,,,,,,,
+util_WALK_AB_In_vehicle_time,WALK_AB - In-vehicle time,@(odt_skims['WK_AB_WK_TOTIVT'] + dot_skims['WK_AB_WK_TOTIVT']),,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_In_vehicle_time_on_XB,WALK_AB - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['WK_AB_WK_IVTXB'] + dot_skims['WK_AB_WK_IVTXB']),,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_iwait_time,WALK_AB - wait time,@(odt_skims['WK_AB_WK_IWAIT'] + dot_skims['WK_AB_WK_IWAIT']),,,,,,coef_iwait,,,,,,,,,,,,,,,
+util_WALK_AB_transfer_wait_time,WALK_AB - transfer wait time,@(odt_skims['WK_AB_WK_XWAIT'] + dot_skims['WK_AB_WK_XWAIT']),,,,,,coef_xwait,,,,,,,,,,,,,,,
+util_WALK_AB_number_of_transfers,WALK_AB - number of transfers,@((odt_skims['WK_AB_WK_XFERS']).clip(0) + (dot_skims['WK_AB_WK_XFERS']).clip(0)),,,,,,coef_xfers_wlktrn,,,,,,,,,,,,,,,
+util_WALK_AB_Walk_time,WALK_AB - Walk time,@(odt_skims['WK_AB_WK_WACC_EGR'] + dot_skims['WK_AB_WK_WACC_EGR']),,,,,,coef_wlk_acc,,,,,,,,,,,,,,,
+util_WALK_AB_Walk_other_time,WALK_AB - Walk other time,@(odt_skims['WK_AB_WK_WAUX'] + dot_skims['WK_AB_WK_WAUX']),,,,,,coef_wlk_aux,,,,,,,,,,,,,,,
+util_WALK_AB_Fare,WALK_AB - Fare,@(odt_skims['WK_AB_WK_FARE'] + dot_skims['WK_AB_WK_FARE']) * 1.22,,,,,,coef_cost,,,,,,,,,,,,,,,
+util_WALK_AB_Destination_zone_densityIndex,WALK_AB - Destination zone densityIndex,@df.dest_density_index,,,,,,coef_density_wlktrn,,,,,,,,,,,,,,,
+util_WALK_AB_Topology,WALK_AB - Topology,@df.dest_topology,,,,,,coef_topology_wlktrn,,,,,,,,,,,,,,,
+util_WALK_AB_Person_is_less_than_10_years_old,WALK_AB - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_wlktrn,,,,,,,,,,,,,,,
+#util_WALK_to_BM,#Walk to BM,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_BM_Unavailable,WALK_BM - Unavailable,walk_bm_available == False,,,,,,,-999,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time,WALK_BM - In-vehicle time,@(odt_skims['WK_BM_WK_TOTIVT'] + dot_skims['WK_BM_WK_TOTIVT']),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_LB,WALK_BM - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['WK_BM_WK_IVTLB'] + dot_skims['WK_BM_WK_IVTLB']),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_XB,WALK_BM - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['WK_BM_WK_IVTXB'] + dot_skims['WK_BM_WK_IVTXB']),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_MR,WALK_BM - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['WK_BM_WK_IVTMR'] + dot_skims['WK_BM_WK_IVTMR']),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_LR,WALK_BM - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['WK_BM_WK_IVTLR'] + dot_skims['WK_BM_WK_IVTLR']),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_iwait_time,WALK_BM - Short iwait time,@(odt_skims['WK_BM_WK_IWAIT'] + dot_skims['WK_BM_WK_IWAIT']),,,,,,,coef_iwait,,,,,,,,,,,,,,
+util_WALK_BM_transfer_wait_time,WALK_BM - transfer wait time,@(odt_skims['WK_BM_WK_XWAIT'] + dot_skims['WK_BM_WK_XWAIT']),,,,,,,coef_xwait,,,,,,,,,,,,,,
+util_WALK_BM_number_of_transfers,WALK_BM - number of transfers,@((odt_skims['WK_BM_WK_XFERS']).clip(0) + (dot_skims['WK_BM_WK_XFERS']).clip(0)),,,,,,,coef_xfers_wlktrn,,,,,,,,,,,,,,
+util_WALK_BM_Walk_time,WALK_BM - Walk  time,@(odt_skims['WK_BM_WK_WACC_EGR'] + dot_skims['WK_BM_WK_WACC_EGR']),,,,,,,coef_wlk_acc,,,,,,,,,,,,,,
+util_WALK_BM_Walk_other_time,WALK_BM - Walk other time,@(odt_skims['WK_BM_WK_WAUX'] + dot_skims['WK_BM_WK_WAUX']),,,,,,,coef_wlk_aux,,,,,,,,,,,,,,
+util_WALK_BM_Fare,WALK_BM - Fare,@(odt_skims['WK_BM_WK_FARE'] + dot_skims['WK_BM_WK_FARE']) * 1.22,,,,,,,coef_cost,,,,,,,,,,,,,,
+util_WALK_BM_Destination_zone_densityIndex,WALK_BM - Destination zone densityIndex,@df.dest_density_index,,,,,,,coef_density_wlktrn,,,,,,,,,,,,,,
+util_WALK_BM_Topology,WALK_BM - Topology,@df.dest_topology,,,,,,,coef_topology_wlktrn,,,,,,,,,,,,,,
+util_WALK_BM_Person_is_less_than_10_years_old,WALK_BM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_wlktrn,,,,,,,,,,,,,,
+#util_WALK_to_MR,#Walk to MR,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_MR_Unavailable,WALK_MR - Unavailable,walk_mr_available == False,,,,,,,,-999,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time,WALK_MR - In-vehicle time,@(odt_skims['WK_MR_WK_TOTIVT'] + dot_skims['WK_MR_WK_TOTIVT']),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time_on_MR,WALK_MR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['WK_MR_WK_IVTMR'] + dot_skims['WK_MR_WK_IVTMR']),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time_on_LR,WALK_MR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['WK_MR_WK_IVTLR'] + dot_skims['WK_MR_WK_IVTLR']),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_iwait_time,WALK_MR - iwait time,@(odt_skims['WK_MR_WK_IWAIT'] + dot_skims['WK_MR_WK_IWAIT']),,,,,,,,coef_iwait,,,,,,,,,,,,,
+util_WALK_MR_transfer_wait_time,WALK_MR - transfer wait time,@(odt_skims['WK_MR_WK_XWAIT'] + dot_skims['WK_MR_WK_XWAIT']),,,,,,,,coef_xwait,,,,,,,,,,,,,
+util_WALK_MR_number_of_transfers,WALK_MR - number of transfers,@((odt_skims['WK_MR_WK_XFERS']).clip(0) + (dot_skims['WK_MR_WK_XFERS']).clip(0)),,,,,,,,coef_xfers_wlktrn,,,,,,,,,,,,,
+util_WALK_MR_Walk_time,WALK_MR - Walk access time,@(odt_skims['WK_MR_WK_WACC_EGR'] + dot_skims['WK_MR_WK_WACC_EGR']),,,,,,,,coef_wlk_acc,,,,,,,,,,,,,
+util_WALK_MR_Walk_other_time,WALK_MR - Walk other time,@(odt_skims['WK_MR_WK_WAUX'] + dot_skims['WK_MR_WK_WAUX']),,,,,,,,coef_wlk_aux,,,,,,,,,,,,,
+util_WALK_MR_Fare,WALK_MR - Fare,@(odt_skims['WK_MR_WK_FARE'] + dot_skims['WK_MR_WK_FARE']) * 1.22,,,,,,,,coef_cost,,,,,,,,,,,,,
+util_WALK_MR_Destination_zone_densityIndex,WALK_MR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,coef_density_wlktrn,,,,,,,,,,,,,
+util_WALK_MR_Topology,WALK_MR - Topology,@df.dest_topology,,,,,,,,coef_topology_wlktrn,,,,,,,,,,,,,
+util_WALK_MR_Person_is_less_than_10_years_old,WALK_MR - Person is less than 10 years old,@(df.age <= 10),,,,,,,,coef_age010_wlktrn,,,,,,,,,,,,,
+#util_WALK_to_CR,#Walk to CR,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_CR_Unavailable,WALK_CR - Unavailable,walk_cr_available == False,,,,,,,,,-999,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time,WALK_CR - In-vehicle time,@(odt_skims['WK_CR_WK_TOTIVT'] + dot_skims['WK_CR_WK_TOTIVT']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_CR,WALK_CR - In-vehicle time on CR,@(ivt_CR_multiplier - 1) * (odt_skims['WK_CR_WK_IVTCR'] + dot_skims['WK_CR_WK_IVTCR']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_LB,WALK_CR - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['WK_CR_WK_IVTLB'] + dot_skims['WK_CR_WK_IVTLB']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_XB,WALK_CR - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['WK_CR_WK_IVTXB'] + dot_skims['WK_CR_WK_IVTXB']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_MR,WALK_CR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['WK_CR_WK_IVTMR'] + dot_skims['WK_CR_WK_IVTMR']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_BR,WALK_CR - In-vehicle time on BR,@(ivt_BR_multiplier - 1) * (odt_skims['WK_CR_WK_IVTBR'] + dot_skims['WK_CR_WK_IVTBR']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_LR,WALK_CR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['WK_CR_WK_IVTLR'] + dot_skims['WK_CR_WK_IVTLR']),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_iwait_time,WALK_CR - iwait time,@(odt_skims['WK_CR_WK_IWAIT'] + dot_skims['WK_CR_WK_IWAIT']),,,,,,,,,coef_iwait,,,,,,,,,,,,
+util_WALK_CR_transfer_wait_time,WALK_CR - transfer wait time,@(odt_skims['WK_CR_WK_XWAIT'] + dot_skims['WK_CR_WK_XWAIT']),,,,,,,,,coef_xwait,,,,,,,,,,,,
+util_WALK_CR_number_of_transfers,WALK_CR - number of transfers,@((odt_skims['WK_CR_WK_XFERS']).clip(0) + (dot_skims['WK_CR_WK_XFERS']).clip(0)),,,,,,,,,coef_xfers_wlktrn,,,,,,,,,,,,
+util_WALK_CR_Walk_time,WALK_CR - Walk time,@(odt_skims['WK_CR_WK_WACC_EGR'] + dot_skims['WK_CR_WK_WACC_EGR']),,,,,,,,,coef_wlk_acc,,,,,,,,,,,,
+util_WALK_CR_Walk_other_time,WALK_CR - Walk other time,@(odt_skims['WK_CR_WK_WAUX'] + dot_skims['WK_CR_WK_WAUX']),,,,,,,,,coef_wlk_aux,,,,,,,,,,,,
+util_WALK_CR_Fare,WALK_CR - Fare,@(odt_skims['WK_CR_WK_FARE'] + dot_skims['WK_CR_WK_FARE']) * 1.22,,,,,,,,,coef_cost,,,,,,,,,,,,
+util_WALK_CR_Destination_zone_densityIndex,WALK_CR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,coef_density_wlktrn,,,,,,,,,,,,
+util_WALK_CR_Topology,WALK_CR - Topology,@df.dest_topology,,,,,,,,,coef_topology_wlktrn,,,,,,,,,,,,
+util_WALK_CR_Person_is_less_than_10_years_old,WALK_CR - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_wlktrn,,,,,,,,,,,,
+#util_PNR_to_AB,#PNR to AB,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_AB_Unavailable,PNR_AB - Unavailable,pnr_ab_available == False,,,,,,,,,,-999,,,,,,,,,,,
+util_PNR_AB_Unavailable_for_persons_less_than_16,PNR_AB - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,,,,,
+util_PNR_AB_In_vehicle_time,PNR_AB - In-vehicle time,@(odt_skims['DR_AB_WK_TOTIVT'] + dot_skims['WK_AB_DR_TOTIVT']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_In_vehicle_time_on_LB,PNR_AB - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['DR_AB_WK_IVTLB'] + dot_skims['WK_AB_DR_IVTLB']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_In_vehicle_time_on_XB,PNR_AB - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['DR_AB_WK_IVTXB'] + dot_skims['WK_AB_DR_IVTXB']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_iwait_time,PNR_AB - iwait time,@(odt_skims['DR_AB_WK_IWAIT'] + dot_skims['WK_AB_DR_IWAIT']),,,,,,,,,,coef_iwait,,,,,,,,,,,
+util_PNR_AB_transfer_wait_time,PNR_AB - transfer wait time,@(odt_skims['DR_AB_WK_XWAIT'] + dot_skims['WK_AB_DR_XWAIT']),,,,,,,,,,coef_xwait,,,,,,,,,,,
+util_PNR_AB_number_of_transfers,PNR_AB - number of transfers,@((odt_skims['DR_AB_WK_XFERS']).clip(0) + (dot_skims['WK_AB_DR_XFERS']).clip(0)),,,,,,,,,,coef_xfers_pnr,,,,,,,,,,,
+util_PNR_AB_PNR_time,PNR_AB - PNR time,@(odt_skims['DR_AB_WK_DTIME'] + dot_skims['WK_AB_DR_DTIME']),,,,,,,,,,coef_drvacc_pnr,,,,,,,,,,,
+util_PNR_AB_Walk_time,PNR_AB - Walk time,@(odt_skims['DR_AB_WK_WACC_EGR'] + dot_skims['WK_AB_DR_WACC_EGR']),,,,,,,,,,coef_wlk_acc,,,,,,,,,,,
+util_PNR_AB_Walk_other_time,PNR_AB - Walk other time,@(odt_skims['DR_AB_WK_WAUX'] + dot_skims['WK_AB_DR_WAUX']),,,,,,,,,,coef_wlk_aux,,,,,,,,,,,
+util_PNR_AB_Fare_and_operating_cost,PNR_AB - Fare and operating cost,@((odt_skims['DR_AB_WK_FARE'] + dot_skims['WK_AB_DR_FARE'])  * 1.22 + ((odt_skims['DR_AB_WK_DDIST']+dot_skims['WK_AB_DR_DDIST']) * costPerMile)),,,,,,,,,,coef_cost,,,,,,,,,,,
+util_PNR_AB_Ratio_of_PNR_access_distance_to_OD_distance,PNR_AB - Ratio of PNR access distance to OD distance,@((odt_skims['DR_AB_WK_DDIST']+ dot_skims['WK_AB_DR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,coef_drvratio_pnr,,,,,,,,,,,
+util_PNR_AB_Destination_zone_densityIndex,PNR_AB - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,coef_density_pnr,,,,,,,,,,,
+util_PNR_AB_Topology,PNR_AB - Topology,@df.dest_topology,,,,,,,,,,coef_topology_pnr,,,,,,,,,,,
+#util_PNR_to_BM,#PNR to BM,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_BM_Unavailable,PNR_BM - Unavailable,pnr_bm_available == False,,,,,,,,,,,-999,,,,,,,,,,
+util_PNR_BM_Unavailable_for_persons_less_than_16,PNR_BM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,,,,,
+util_PNR_BM_In_vehicle_time,PNR_BM - In-vehicle time,@(odt_skims['DR_BM_WK_TOTIVT'] + dot_skims['WK_BM_DR_TOTIVT']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_LB,PNR_BM - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['DR_BM_WK_IVTLB'] + dot_skims['WK_BM_DR_IVTLB']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_XB,PNR_BM - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['DR_BM_WK_IVTXB'] + dot_skims['WK_BM_DR_IVTXB']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_MR,PNR_BM - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['DR_BM_WK_IVTMR'] + dot_skims['WK_BM_DR_IVTMR']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_LR,PNR_BM - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['DR_BM_WK_IVTLR'] + dot_skims['WK_BM_DR_IVTLR']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_iwait_time,PNR_BM - iwait time,@(odt_skims['DR_BM_WK_IWAIT'] + dot_skims['WK_BM_DR_IWAIT']),,,,,,,,,,,coef_iwait,,,,,,,,,,
+util_PNR_BM_transfer_wait_time,PNR_BM - transfer wait time,@(odt_skims['DR_BM_WK_XWAIT'] + dot_skims['WK_BM_DR_XWAIT']),,,,,,,,,,,coef_xwait,,,,,,,,,,
+util_PNR_BM_number_of_transfers,PNR_BM - number of transfers,@((odt_skims['DR_BM_WK_XFERS']).clip(0) + (dot_skims['WK_BM_DR_XFERS']).clip(0)),,,,,,,,,,,coef_xfers_pnr,,,,,,,,,,
+util_PNR_BM_PNR_time,PNR_BM - PNR time,@(odt_skims['DR_BM_WK_DTIME'] + dot_skims['WK_BM_DR_DTIME']),,,,,,,,,,,coef_drvacc_pnr,,,,,,,,,,
+util_PNR_BM_Walk_time,PNR_BM - Walk time,@(odt_skims['DR_BM_WK_WACC_EGR'] + dot_skims['WK_BM_DR_WACC_EGR']),,,,,,,,,,,coef_wlk_acc,,,,,,,,,,
+util_PNR_BM_Walk_other_time,PNR_BM - Walk other time,@(odt_skims['DR_BM_WK_WAUX'] + dot_skims['WK_BM_DR_WAUX']),,,,,,,,,,,coef_wlk_aux,,,,,,,,,,
+util_PNR_BM_Fare_and_operating_cost,PNR_BM - Fare and operating cost,@((odt_skims['DR_BM_WK_FARE'] + dot_skims['WK_BM_DR_FARE'])  * 1.22 + ((odt_skims['DR_BM_WK_DDIST']+dot_skims['WK_BM_DR_DDIST']) * costPerMile)),,,,,,,,,,,coef_cost,,,,,,,,,,
+util_PNR_BM_Ratio_of_PNR_access_distance_to_OD_distance,PNR_BM - Ratio of PNR access distance to OD distance,@((odt_skims['DR_BM_WK_DDIST']+ dot_skims['WK_BM_DR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,coef_drvratio_pnr,,,,,,,,,,
+util_PNR_BM_Destination_zone_densityIndex,PNR_BM - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,coef_density_pnr,,,,,,,,,,
+util_PNR_BM_Topology,PNR_BM - Topology,@df.dest_topology,,,,,,,,,,,coef_topology_pnr,,,,,,,,,,
+#util_PNR_to_MR,#PNR to MR,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_MR_Unavailable,PNR_MR - Unavailable,pnr_mr_available == False,,,,,,,,,,,,-999,,,,,,,,,
+util_PNR_MR_Unavailable_for_persons_less_than_16,PNR_MR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,,,,,
+util_PNR_MR_In_vehicle_time,PNR_MR - In-vehicle time,@(odt_skims['DR_MR_WK_TOTIVT'] + dot_skims['WK_MR_DR_TOTIVT']),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_MR,PNR_MR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['DR_MR_WK_IVTMR'] + dot_skims['WK_MR_DR_IVTMR']),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_LR,PNR_MR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['DR_MR_WK_IVTLR'] + dot_skims['WK_MR_DR_IVTLR']),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_iwait_time,PNR_MR - iwait time,@(odt_skims['DR_MR_WK_IWAIT'] + dot_skims['WK_MR_DR_IWAIT']),,,,,,,,,,,,coef_iwait,,,,,,,,,
+util_PNR_MR_transfer_wait_time,PNR_MR - transfer wait time,@(odt_skims['DR_MR_WK_XWAIT'] + dot_skims['WK_MR_DR_XWAIT']),,,,,,,,,,,,coef_xwait,,,,,,,,,
+util_PNR_MR_number_of_transfers,PNR_MR - number of transfers,@((odt_skims['DR_MR_WK_XFERS']).clip(0) + (dot_skims['WK_MR_DR_XFERS']).clip(0)),,,,,,,,,,,,coef_xfers_pnr,,,,,,,,,
+util_PNR_MR_PNR_time,PNR_MR - PNR time,@(odt_skims['DR_MR_WK_DTIME'] + dot_skims['WK_MR_DR_DTIME']),,,,,,,,,,,,coef_drvacc_pnr,,,,,,,,,
+util_PNR_MR_Walk_time,PNR_MR - Walk time,@(odt_skims['DR_MR_WK_WACC_EGR'] + dot_skims['WK_MR_DR_WACC_EGR']),,,,,,,,,,,,coef_wlk_acc,,,,,,,,,
+util_PNR_MR_Walk_other_time,PNR_MR - Walk other time,@(odt_skims['DR_MR_WK_WAUX'] + dot_skims['WK_MR_DR_WAUX']),,,,,,,,,,,,coef_wlk_aux,,,,,,,,,
+util_PNR_MR_Fare_and_operating_cost,PNR_MR - Fare and operating cost,@((odt_skims['DR_MR_WK_FARE'] + dot_skims['WK_MR_DR_FARE'])  * 1.22 + ((odt_skims['DR_MR_WK_DDIST']+dot_skims['WK_MR_DR_DDIST']) * costPerMile)),,,,,,,,,,,,coef_cost,,,,,,,,,
+util_PNR_MR_Ratio_of_PNR_access_distance_to_OD_distance,PNR_MR - Ratio of PNR access distance to OD distance,@((odt_skims['DR_MR_WK_DDIST']+ dot_skims['WK_MR_DR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,coef_drvratio_pnr,,,,,,,,,
+util_PNR_MR_Destination_zone_densityIndex,PNR_MR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,coef_density_pnr,,,,,,,,,
+util_PNR_MR_Topology,PNR_MR - Topology,@df.dest_topology,,,,,,,,,,,,coef_topology_pnr,,,,,,,,,
+#util_PNR_to_CR,#PNR to CR,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_CR_Unavailable,PNR_CR - Unavailable,pnr_cr_available == False,,,,,,,,,,,,,-999,,,,,,,,
+util_PNR_CR_Unavailable_for_persons_less_than_16,PNR_CR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,,,,,
+util_PNR_CR_In_vehicle_time,PNR_CR - In-vehicle time,@(odt_skims['DR_CR_WK_TOTIVT'] + dot_skims['WK_CR_DR_TOTIVT']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_CR,PNR_CR - In-vehicle time on CR,@(ivt_CR_multiplier - 1) * (odt_skims['DR_CR_WK_IVTCR'] + dot_skims['WK_CR_DR_IVTCR']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_LB,PNR_CR - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['DR_CR_WK_IVTLB'] + dot_skims['WK_CR_DR_IVTLB']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_XB,PNR_CR - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['DR_CR_WK_IVTXB'] + dot_skims['WK_CR_DR_IVTXB']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_MR,PNR_CR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['DR_CR_WK_IVTMR'] + dot_skims['WK_CR_DR_IVTMR']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_BR,PNR_CR - In-vehicle time on BR,@(ivt_BR_multiplier - 1) * (odt_skims['DR_CR_WK_IVTBR'] + dot_skims['WK_CR_DR_IVTBR']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_LR,PNR_CR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['DR_CR_WK_IVTLR'] + dot_skims['WK_CR_DR_IVTLR']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_iwait_time,PNR_CR - iwait time,@(odt_skims['DR_CR_WK_IWAIT'] + dot_skims['WK_CR_DR_IWAIT']),,,,,,,,,,,,,coef_iwait,,,,,,,,
+util_PNR_CR_transfer_wait_time,PNR_CR - transfer wait time,@(odt_skims['DR_CR_WK_XWAIT'] + dot_skims['WK_CR_DR_XWAIT']),,,,,,,,,,,,,coef_xwait,,,,,,,,
+util_PNR_CR_number_of_transfers,PNR_CR - number of transfers,@((odt_skims['DR_CR_WK_XFERS']).clip(0) + (dot_skims['WK_CR_DR_XFERS']).clip(0)),,,,,,,,,,,,,coef_xfers_pnr,,,,,,,,
+util_PNR_CR_PNR_time,PNR_CR - PNR time,@(odt_skims['DR_CR_WK_DTIME'] + dot_skims['WK_CR_DR_DTIME']),,,,,,,,,,,,,coef_drvacc_pnr,,,,,,,,
+util_PNR_CR_Walk_time,PNR_CR - Walk time,@(odt_skims['DR_CR_WK_WACC_EGR'] + dot_skims['WK_CR_DR_WACC_EGR']),,,,,,,,,,,,,coef_wlk_acc,,,,,,,,
+util_PNR_CR_Walk_other_time,PNR_CR - Walk other time,@(odt_skims['DR_CR_WK_WAUX'] + dot_skims['WK_CR_DR_WAUX']),,,,,,,,,,,,,coef_wlk_aux,,,,,,,,
+util_PNR_CR_Fare_and_operating_cost,PNR_CR - Fare and operating cost,@((odt_skims['DR_CR_WK_FARE'] + dot_skims['WK_CR_DR_FARE'])  * 1.22 + ((odt_skims['DR_CR_WK_DDIST']+dot_skims['WK_CR_DR_DDIST']) * costPerMile)),,,,,,,,,,,,,coef_cost,,,,,,,,
+util_PNR_CR_Ratio_of_PNR_access_distance_to_OD_distance,PNR_CR - Ratio of PNR access distance to OD distance,@((odt_skims['DR_CR_WK_DDIST']+ dot_skims['WK_CR_DR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,coef_drvratio_pnr,,,,,,,,
+util_PNR_CR_Destination_zone_densityIndex,PNR_CR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,,coef_density_pnr,,,,,,,,
+util_PNR_CR_Topology,PNR_CR - Topology,@df.dest_topology,,,,,,,,,,,,,coef_topology_pnr,,,,,,,,
+#util_KNR_to_AB,#KNR to AB,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_AB_Unavailable,KNR_AB - Unavailable,knr_ab_available == False,,,,,,,,,,,,,,-999,,,,,,,
+util_KNR_AB_Unavailable_for_persons_less_than_16,KNR_AB - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,,
+util_KNR_AB_In_vehicle_time,KNR_AB - In-vehicle time,@(odt_skims['KR_AB_WK_TOTIVT'] + dot_skims['WK_AB_KR_TOTIVT']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_In_vehicle_time_on_LB,KNR_AB - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['KR_AB_WK_IVTLB'] + dot_skims['WK_AB_KR_IVTLB']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_In_vehicle_time_on_XB,KNR_AB - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['KR_AB_WK_IVTXB'] + dot_skims['WK_AB_KR_IVTXB']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_iwait_time,KNR_AB - iwait time,@(odt_skims['KR_AB_WK_IWAIT'] + dot_skims['WK_AB_KR_IWAIT']),,,,,,,,,,,,,,coef_iwait,,,,,,,
+util_KNR_AB_transfer_wait_time,KNR_AB - transfer wait time,@(odt_skims['KR_AB_WK_XWAIT'] + dot_skims['WK_AB_KR_XWAIT']),,,,,,,,,,,,,,coef_xwait,,,,,,,
+util_KNR_AB_number_of_transfers,KNR_AB - number of transfers,@((odt_skims['KR_AB_WK_XFERS']).clip(0) + (dot_skims['WK_AB_KR_XFERS']).clip(0)),,,,,,,,,,,,,,coef_xfers_knr,,,,,,,
+util_KNR_AB_KNR_time,KNR_AB - KNR time,@(odt_skims['KR_AB_WK_DTIME'] + dot_skims['WK_AB_KR_DTIME']),,,,,,,,,,,,,,coef_drvacc_knr,,,,,,,
+util_KNR_AB_Walk_time,KNR_AB - Walk time,@(odt_skims['KR_AB_WK_WACC_EGR'] + dot_skims['WK_AB_KR_WACC_EGR']),,,,,,,,,,,,,,coef_wlk_acc,,,,,,,
+util_KNR_AB_Walk_other_time,KNR_AB - Walk other time,@(odt_skims['KR_AB_WK_WAUX'] + dot_skims['WK_AB_KR_WAUX']),,,,,,,,,,,,,,coef_wlk_aux,,,,,,,
+util_KNR_AB_Fare_and_operating_cost,KNR_AB - Fare and operating cost,@((odt_skims['KR_AB_WK_FARE'] + dot_skims['WK_AB_KR_FARE'])  * 1.22 + ((odt_skims['KR_AB_WK_DDIST']+dot_skims['WK_AB_KR_DDIST']) * costPerMile)),,,,,,,,,,,,,,coef_cost,,,,,,,
+util_KNR_AB_Ratio_of_KNR_access_distance_to_OD_distance,KNR_AB - Ratio of KNR access distance to OD distance,@((odt_skims['KR_AB_WK_DDIST']+ dot_skims['WK_AB_KR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,coef_drvratio_knr,,,,,,,
+util_KNR_AB_Destination_zone_densityIndex,KNR_AB - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,,,coef_density_knr,,,,,,,
+util_KNR_AB_Topology,KNR_AB - Topology,@df.dest_topology,,,,,,,,,,,,,,coef_topology_knr,,,,,,,
+#util_KNR_to_BM,#KNR to BM,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_BM_Unavailable,KNR_BM - Unavailable,knr_bm_available == False,,,,,,,,,,,,,,,-999,,,,,,
+util_KNR_BM_Unavailable_for_persons_less_than_16,KNR_BM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,,
+util_KNR_BM_In_vehicle_time,KNR_BM - In-vehicle time,@(odt_skims['KR_BM_WK_TOTIVT'] + dot_skims['WK_BM_KR_TOTIVT']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_LB,KNR_BM - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['KR_BM_WK_IVTLB'] + dot_skims['WK_BM_KR_IVTLB']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_XB,KNR_BM - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['KR_BM_WK_IVTXB'] + dot_skims['WK_BM_KR_IVTXB']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_MR,KNR_BM - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['KR_BM_WK_IVTMR'] + dot_skims['WK_BM_KR_IVTMR']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_LR,KNR_BM - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['KR_BM_WK_IVTLR'] + dot_skims['WK_BM_KR_IVTLR']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_iwait_time,KNR_BM - iwait time,@(odt_skims['KR_BM_WK_IWAIT'] + dot_skims['WK_BM_KR_IWAIT']),,,,,,,,,,,,,,,coef_iwait,,,,,,
+util_KNR_BM_transfer_wait_time,KNR_BM - transfer wait time,@(odt_skims['KR_BM_WK_XWAIT'] + dot_skims['WK_BM_KR_XWAIT']),,,,,,,,,,,,,,,coef_xwait,,,,,,
+util_KNR_BM_number_of_transfers,KNR_BM - number of transfers,@((odt_skims['KR_BM_WK_XFERS']).clip(0) + (dot_skims['WK_BM_KR_XFERS']).clip(0)),,,,,,,,,,,,,,,coef_xfers_knr,,,,,,
+util_KNR_BM_KNR_time,KNR_BM - KNR time,@(odt_skims['KR_BM_WK_DTIME'] + dot_skims['WK_BM_KR_DTIME']),,,,,,,,,,,,,,,coef_drvacc_knr,,,,,,
+util_KNR_BM_Walk_time,KNR_BM - Walk time,@(odt_skims['KR_BM_WK_WACC_EGR'] + dot_skims['WK_BM_KR_WACC_EGR']),,,,,,,,,,,,,,,coef_wlk_acc,,,,,,
+util_KNR_BM_Walk_other_time,KNR_BM - Walk other time,@(odt_skims['KR_BM_WK_WAUX'] + dot_skims['WK_BM_KR_WAUX']),,,,,,,,,,,,,,,coef_wlk_aux,,,,,,
+util_KNR_BM_Fare_and_operating_cost,KNR_BM - Fare and operating cost,@((odt_skims['KR_BM_WK_FARE'] + dot_skims['WK_BM_KR_FARE'])  * 1.22 + ((odt_skims['KR_BM_WK_DDIST']+dot_skims['WK_BM_KR_DDIST']) * costPerMile)),,,,,,,,,,,,,,,coef_cost,,,,,,
+util_KNR_BM_Ratio_of_KNR_access_distance_to_OD_distance,KNR_BM - Ratio of KNR access distance to OD distance,@((odt_skims['KR_BM_WK_DDIST']+ dot_skims['WK_BM_KR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,coef_drvratio_knr,,,,,,
+util_KNR_BM_Destination_zone_densityIndex,KNR_BM - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,,,,coef_density_knr,,,,,,
+util_KNR_BM_Topology,KNR_BM - Topology,@df.dest_topology,,,,,,,,,,,,,,,coef_topology_knr,,,,,,
+#util_KNR_to_MR,#KNR to MR,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_MR_Unavailable,KNR_MR - Unavailable,knr_mr_available == False,,,,,,,,,,,,,,,,-999,,,,,
+util_KNR_MR_Unavailable_for_persons_less_than_16,KNR_MR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,,
+util_KNR_MR_In_vehicle_time,KNR_MR - In-vehicle time,@(odt_skims['KR_MR_WK_TOTIVT'] + dot_skims['WK_MR_KR_TOTIVT']),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_MR,KNR_MR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['KR_MR_WK_IVTMR'] + dot_skims['WK_MR_KR_IVTMR']),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_LR,KNR_MR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['KR_MR_WK_IVTLR'] + dot_skims['WK_MR_KR_IVTLR']),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_iwait_time,KNR_MR - iwait time,@(odt_skims['KR_MR_WK_IWAIT'] + dot_skims['WK_MR_KR_IWAIT']),,,,,,,,,,,,,,,,coef_iwait,,,,,
+util_KNR_MR_transfer_wait_time,KNR_MR - transfer wait time,@(odt_skims['KR_MR_WK_XWAIT'] + dot_skims['WK_MR_KR_XWAIT']),,,,,,,,,,,,,,,,coef_xwait,,,,,
+util_KNR_MR_number_of_transfers,KNR_MR - number of transfers,@((odt_skims['KR_MR_WK_XFERS']).clip(0) + (dot_skims['WK_MR_KR_XFERS']).clip(0)),,,,,,,,,,,,,,,,coef_xfers_knr,,,,,
+util_KNR_MR_KNR_time,KNR_MR - KNR time,@dtim_multiplier * (odt_skims['KR_MR_WK_DTIME'] + dot_skims['WK_MR_KR_DTIME']),,,,,,,,,,,,,,,,coef_drvacc_knr,,,,,
+util_KNR_MR_Walk_time,KNR_MR - Walk time,@(odt_skims['KR_MR_WK_WACC_EGR'] + dot_skims['WK_MR_KR_WACC_EGR']),,,,,,,,,,,,,,,,coef_wlk_acc,,,,,
+util_KNR_MR_Walk_other_time,KNR_MR - Walk other time,@(odt_skims['KR_MR_WK_WAUX'] + dot_skims['WK_MR_KR_WAUX']),,,,,,,,,,,,,,,,coef_wlk_aux,,,,,
+util_KNR_MR_Fare_and_operating_cost,KNR_MR - Fare and operating cost,@((odt_skims['KR_MR_WK_FARE'] + dot_skims['WK_MR_KR_FARE']) * 1.22 + ((odt_skims['KR_MR_WK_DDIST']+dot_skims['WK_MR_KR_DDIST']) * costPerMile)),,,,,,,,,,,,,,,,coef_cost,,,,,
+util_KNR_MR_Ratio_of_KNR_access_distance_to_OD_distance,KNR_MR - Ratio of KNR access distance to OD distance,@((odt_skims['KR_MR_WK_DDIST']+ dot_skims['WK_MR_KR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,coef_drvratio_knr,,,,,
+util_KNR_MR_Destination_zone_densityIndex,KNR_MR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,,,,,coef_density_knr,,,,,
+util_KNR_MR_Topology,KNR_MR - Topology,@df.dest_topology,,,,,,,,,,,,,,,,coef_topology_knr,,,,,
+#util_KNR_to_CR,#KNR to CR,#DR AND KR SKIMS ARE THE SAME FOR CR,,,,,,,,,,,,,,,,,,,,,
+util_KNR_CR_Unavailable,KNR_CR - Unavailable,knr_cr_available == False,,,,,,,,,,,,,,,,,-999,,,,
+util_KNR_CR_Unavailable_for_persons_less_than_16,KNR_CR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,,
+util_KNR_CR_In_vehicle_time,KNR_CR - In-vehicle time,@(odt_skims['KR_CR_WK_TOTIVT'] + dot_skims['WK_CR_KR_TOTIVT']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_CR,KNR_CR - In-vehicle time on CR,@(ivt_CR_multiplier - 1) * (odt_skims['KR_CR_WK_IVTCR'] + dot_skims['WK_CR_KR_IVTCR']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_LB,KNR_CR - In-vehicle time on LB,@(ivt_LB_multiplier - 1) * (odt_skims['KR_CR_WK_IVTLB'] + dot_skims['WK_CR_KR_IVTLB']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_XB,KNR_CR - In-vehicle time on XB,@(ivt_XB_multiplier - 1) * (odt_skims['KR_CR_WK_IVTXB'] + dot_skims['WK_CR_KR_IVTXB']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_MR,KNR_CR - In-vehicle time on MR,@(ivt_MR_multiplier - 1) * (odt_skims['KR_CR_WK_IVTMR'] + dot_skims['WK_CR_KR_IVTMR']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_BR,KNR_CR - In-vehicle time on BR,@(ivt_BR_multiplier - 1) * (odt_skims['KR_CR_WK_IVTBR'] + dot_skims['WK_CR_KR_IVTBR']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_LR,KNR_CR - In-vehicle time on LR,@(ivt_LR_multiplier - 1) * (odt_skims['KR_CR_WK_IVTLR'] + dot_skims['WK_CR_KR_IVTLR']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_iwait_time,KNR_CR - iwait time,@(odt_skims['KR_CR_WK_IWAIT'] + dot_skims['WK_CR_KR_IWAIT']),,,,,,,,,,,,,,,,,coef_iwait,,,,
+util_KNR_CR_transfer_wait_time,KNR_CR - transfer wait time,@(odt_skims['KR_CR_WK_XWAIT'] + dot_skims['WK_CR_KR_XWAIT']),,,,,,,,,,,,,,,,,coef_xwait,,,,
+util_KNR_CR_number_of_transfers,KNR_CR - number of transfers,@((odt_skims['KR_CR_WK_XFERS']).clip(0) + (dot_skims['WK_CR_KR_XFERS']).clip(0)),,,,,,,,,,,,,,,,,coef_xfers_knr,,,,
+util_KNR_CR_KNR_time,KNR_CR - KNR time,@(odt_skims['KR_CR_WK_DTIME'] + dot_skims['WK_CR_KR_DTIME']),,,,,,,,,,,,,,,,,coef_drvacc_knr,,,,
+util_KNR_CR_Walk_time,KNR_CR - Walk time,@(odt_skims['KR_CR_WK_WACC_EGR'] + dot_skims['WK_CR_KR_WACC_EGR']),,,,,,,,,,,,,,,,,coef_wlk_acc,,,,
+util_KNR_CR_Walk_other_time,KNR_CR - Walk other time,@(odt_skims['KR_CR_WK_WAUX'] + dot_skims['WK_CR_KR_WAUX']),,,,,,,,,,,,,,,,,coef_wlk_aux,,,,
+util_KNR_CR_Fare_and_operating_cost,KNR_CR - Fare and operating cost,@((odt_skims['KR_CR_WK_FARE'] + dot_skims['WK_CR_KR_FARE'])  * 1.22 + ((odt_skims['KR_CR_WK_DDIST']+dot_skims['WK_CR_KR_DDIST']) * costPerMile)),,,,,,,,,,,,,,,,,coef_cost,,,,
+util_KNR_CR_Ratio_of_KNR_access_distance_to_OD_distance,KNR_CR - Ratio of KNR access distance to OD distance,@((odt_skims['KR_CR_WK_DDIST']+ dot_skims['WK_CR_KR_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,,coef_drvratio_knr,,,,
+util_KNR_CR_Destination_zone_densityIndex,KNR_CR - Destination zone densityIndex,@df.dest_density_index,,,,,,,,,,,,,,,,,coef_density_knr,,,,
+util_KNR_CR_Topology,KNR_CR - Topology,@df.dest_topology,,,,,,,,,,,,,,,,,coef_topology_knr,,,,
+#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,,,,,
+School_Bus_Unavailable_if_NOT_school_tour,School Bus - Unavailable if NOT school tour,~is_school,,,,,,,,,,,,,,,,,,-999,,,
+School_Bus_In_vehicle_time_(HOV3+_skims)_20_mph,School Bus - In-vehicle time (HOV3+ skims) - 20 mph,@(odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST'])*3,,,,,,,,,,,,,,,,,,coef_ivt,,,
+School_Bus_WALK_Time,School Bus - Walk Time,10,,,,,,,,,,,,,,,,,,coef_wlk_acc,,,
+School_Bus_Wait_Time,School Bus - Wait Time,10,,,,,,,,,,,,,,,,,,coef_iwait,,,
+#,Taxi,,,,,,,,,,,,,,,,,,,,,,
+util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,,,,coef_ivt,,
+util_Taxi_Wait_time,Taxi - Wait time,@df.totalWaitTaxi,,,,,,,,,,,,,,,,,,,coef_iwait,,
+util_Taxi_Total_toll,Taxi - Total toll,@(odt_skims['HOV2_TOLL'] + dot_skims['HOV2_TOLL']) * 1.22,,,,,,,,,,,,,,,,,,,coef_cost,,
+util_Taxi_Fare,Taxi - Fare,@(Taxi_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * Taxi_costPerMile +(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_cost,,
+#,TNC Single,,,,,,,,,,,,,,,,,,,,,,
+util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,,,,,coef_ivt,
+util_TNC_Single_Wait_time,TNC Single - Wait time,@df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,,,,coef_iwait,
+util_TNC_Single_Total_toll,TNC Single - Total toll,@(odt_skims['HOV2_TOLL'] + dot_skims['HOV2_TOLL']) * 1.22,,,,,,,,,,,,,,,,,,,,coef_cost,
+util_TNC_Single_Cost,TNC Single - Cost,"@np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_cost,
+#,TNC Shared,,,,,,,,,,,,,,,,,,,,,,
+util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt
+util_TNC_Shared_Wait_time,TNC Shared - Wait time,@df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,,,,coef_iwait
+util_TNC_Shared_Total_toll,TNC Shared - Total toll,@(odt_skims['HOV2_TOLL'] + dot_skims['HOV2_TOLL']) * 1.22,,,,,,,,,,,,,,,,,,,,,coef_cost
+util_TNC_Shared_Cost,TNC Shared - Cost,"@np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV3_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_cost
+#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,walk_ASC_no_auto,,,,,,,,,,,,,,,,,
+util_WALK_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_deficient,,,,,,,,,,,,,,,,,
+util_WALK_ASC_Auto_sufficient,Walk ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,,,,,
+util_BIKE_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,bike_ASC_no_auto,,,,,,,,,,,,,,,,
+util_BIKE_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_deficient,,,,,,,,,,,,,,,,
+util_BIKE_ASC_Auto_sufficient,Bike ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,,,,,
+util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,sr2_ASC_no_auto,,,,,,,,,,,,,,,,,,,
+util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,,,,
+util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,,,,
+util_Shared_ride_3+_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr3p_ASC_no_auto,,,,,,,,,,,,,,,,,,
+util_Shared_ride_3+_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,,,,,
+util_Shared_ride_3+_Auto_sufficient,Shared ride 3+ - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,,,,,
+util_WALK_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,,,,,,
+util_WALK_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,,,,,,
+util_WALK_to_Transit_Auto_sufficient,Walk to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,,,,,,
+util_PNR_to_Transit_Zero_auto,PNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,,,,,,,,
+util_PNR_to_Transit_Auto_deficient,PNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,,,,,,,,
+util_PNR_to_Transit_Auto_sufficient,PNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,,,,,,,,
+util_KNR_to_Transit_Zero_auto,KNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,,,,
+util_KNR_to_Transit_Auto_deficient,KNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,,,,
+util_KNR_to_Transit_Auto_sufficient,KNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,,,,
+util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_no_auto,,
+util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,,
+util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,,
+util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto,
+util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient,
+util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient,
+util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto
+util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient
+util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient
+#util_Joint_tour_ASCs,#joint tour ASCs,,,,,,,,,,,,,,,,,,,,,,
+util_Joint_WALK_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,,,,,
+util_Joint_WALK_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,,,,,
+util_Joint_WALK_ASC_Auto_sufficient,Joint - Walk ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,,,,,
+util_Joint_BIKE_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,,,,,
+util_Joint_BIKE_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,,,,,
+util_Joint_BIKE_ASC_Auto_sufficient,Joint - Bike ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_3+_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_3+_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,,,,,
+util_Joint_Shared_ride_3+_Auto_sufficient,Joint - Shared ride 3+ - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,,,,,
+util_Joint_WALK_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,,,,,,
+util_Joint_WALK_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,,,,,,
+util_Joint_WALK_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,,,,,,
+util_Joint_PNR_to_Transit_Zero_auto,Joint - PNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,,,,,,,,
+util_Joint_PNR_to_Transit_Auto_deficient,Joint - PNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,,,,,,,,
+util_Joint_PNR_to_Transit_Auto_sufficient,Joint - PNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,,,,,,,,
+util_Joint_KNR_to_Transit_Zero_auto,Joint - KNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,,,,
+util_Joint_KNR_to_Transit_Auto_deficient,Joint - KNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,,,,
+util_Joint_KNR_to_Transit_Auto_sufficient,Joint - KNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,,,,
+util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,,
+util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,,
+util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,,
+util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto,
+util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient,
+util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient,
+util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto
+util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient
+util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient
+util_WALK_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,,,,,,
+Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,,,
+Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,coef_distpen_drvtrn,,,,
+util_schoolbus_ASC_no_auto,School Bus No Auto ASC,@(df.auto_ownership == 0),,,,,,,,,,,,,,,,,,schoolbus_ASC_no_auto,,,
+util_schoolbus_ASC_auto_deficient,School Bus Auto Deficient ASC,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,schoolbus_ASC_auto_deficient,,,
+util_schoolbus_ASC_auto_sufficient,School Bus Auto Sufficient ASC,@((df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,schoolbus_ASC_auto_sufficient,,,
+util_transit_type_ASC,Transit technology ASC,1,,,,,,,walk_BM_ASC,walk_MR_ASC,walk_CR_ASC,,PNR_BM_ASC,PNR_MR_ASC,PNR_CR_ASC,,KNR_BM_ASC,KNR_MR_ASC,KNR_CR_ASC,,,,
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.yaml
new file mode 100644
index 000000000..ace2fa287
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice.yaml
@@ -0,0 +1,114 @@
+LOGIT_TYPE: NL
+#LOGIT_TYPE: MNL
+
+NESTS:
+  name: root
+  coefficient: coef_nest_root
+  alternatives:
+      - name: AUTO
+        coefficient: coef_nest_AUTO
+        alternatives:
+            - DRIVEALONE
+            - SHARED2
+            - SHARED3
+      - name: NONMOTORIZED
+        coefficient: coef_nest_NONMOTORIZED
+        alternatives:
+          - WALK
+          - BIKE
+      - name: TRANSIT
+        coefficient:  coef_nest_TRANSIT
+        alternatives:
+            - name: WALKACCESS
+              coefficient: coef_nest_TRANSIT_WALKACCESS
+              alternatives:
+              - WALK_AB
+              - WALK_BM
+              - WALK_MR
+              - WALK_CR
+            - name: PNRACCESS
+              coefficient: coef_nest_TRANSIT_PNRACCESS
+              alternatives:
+              - PNR_AB
+              - PNR_BM
+              - PNR_MR
+              - PNR_CR
+            - name: KNRACCESS
+              coefficient: coef_nest_TRANSIT_KNRACCESS
+              alternatives:
+              - KNR_AB
+              - KNR_BM
+              - KNR_MR
+              - KNR_CR
+      - name: SCHOOL_BUS
+        coefficient: coef_nest_SCHOOL_BUS
+        alternatives:
+          - SCHOOLBUS
+      - name: RIDEHAIL
+        coefficient: coef_nest_RIDEHAIL
+        alternatives:
+          - TAXI
+          - TNC_SINGLE
+          - TNC_SHARED
+
+SPEC: tour_mode_choice.csv
+COEFFICIENTS: tour_mode_choice_coeffs.csv
+COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv
+
+CONSTANTS:   
+  #costPerMile: 19.26 
+  #costShareSr2: 1.75
+  #costShareSr3: 2.50
+  #waitThresh: 10.00
+  walkThresh: 1.50
+  shortWalk: 0.333
+  longWalk: 0.667
+  walkSpeed: 3.00
+  bikeThresh: 6.00
+  bikeSpeed: 12.00
+  maxCbdAreaTypeThresh: 2
+  indivTour: 1.00000
+  walkThresh: 1.50
+  Taxi_waitTime_mean:
+    1: 5.5
+    2: 9.5
+    3: 13.3
+    4: 17.3
+    5: 26.5
+  Taxi_waitTime_sd:
+    1: 0
+    2: 0
+    3: 0
+    4: 0
+    5: 0
+    
+# so far, we can use the same spec as for non-joint tours
+preprocessor:
+  SPEC: tour_mode_choice_annotate_choosers_preprocessor
+  DF: choosers
+  TABLES:
+    - land_use
+    - tours
+
+nontour_preprocessor:
+  SPEC: tour_mode_choice_annotate_choosers_preprocessor
+  DF: choosers
+  TABLES:
+    - land_use
+
+# to reduce memory needs filter chooser table to these fields
+LOGSUM_CHOOSER_COLUMNS:
+  - tour_type
+  - hhsize
+  - density_index
+  - age
+  - age_16_p
+  - age_16_to_19
+  - auto_ownership
+  - number_of_participants
+  - tour_category
+  - num_workers
+  - value_of_time
+  - free_parking_at_work
+
+MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv
new file mode 100644
index 000000000..cffc75890
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv
@@ -0,0 +1,88 @@
+Description,Target,Expression
+#,,
+local,_DF_IS_TOUR,'tour_type' in df.columns
+,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1
+,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False
+#,,
+ local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns
+,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''"
+,work_tour_is_drive,_parent_tour_mode.isin(['DRIVEALONE'])
+,work_tour_is_bike,_parent_tour_mode=='BIKE'
+,work_tour_is_SOV,_parent_tour_mode.isin(['DRIVEALONE'])
+#,,
+,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False
+,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False
+,is_indiv,~is_joint
+,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False
+,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False
+,is_school,(df.tour_type=='school') & (df.is_university==False)  if _DF_IS_TOUR else False
+#,,
+#,c_cost,(0.60 * c_ivt) / df.value_of_time
+#,,
+,ivot,1.0/df.value_of_time
+,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])"
+,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])"
+,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])"
+# FIXME no transit subzones so all zones short walk to transit,,
+,_walk_transit_origin,True
+,_walk_transit_destination,True
+,walk_transit_available,_walk_transit_origin & _walk_transit_destination
+,pnr_transit_available,_walk_transit_destination & (df.auto_ownership > 0)
+,knr_transit_available,_walk_transit_origin & _walk_transit_destination
+,origin_walk_time,shortWalk*60/walkSpeed
+,destination_walk_time,shortWalk*60/walkSpeed
+# RIDEHAIL,,
+,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.LANDAREA*640, df[orig_col_name]) / 640)"
+,dest_density_measure,"(reindex(land_use.TOTPOP, df[dest_col_name]) + reindex(land_use.TOTEMP, df[dest_col_name])) / (reindex(land_use.LANDAREA*640, df[dest_col_name]) / 640)"
+,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
+,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
+,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})"
+,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})"
+,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})"
+,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})"
+# ,, Note that the mean and standard deviation are not the values for the distribution itself  but of the underlying normal distribution it is derived from
+,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})"
+,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})"
+,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})"
+,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})"
+,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})"
+,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})"
+,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})"
+,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})"
+,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)"
+,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime
+,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime
+,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime
+#,,
+,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False
+,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])"
+,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])"
+,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)"
+,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)"
+,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration/2, _hourly_offpeak_parking_cost * df.duration/2)"
+#,,
+,distance,od_skims['DIST']
+,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0)
+,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0
+,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0)
+,walk_ab_available,walk_transit_available & (odt_skims['WK_AB_WK_IVTLB']>0) & (dot_skims['WK_AB_WK_IVTLB']>0)
+,walk_bm_available,walk_transit_available & (odt_skims['WK_BM_WK_IVTMR']>0) & (dot_skims['WK_BM_WK_IVTMR']>0)
+,walk_mr_available,walk_transit_available & (odt_skims['WK_MR_WK_IVTMR']>0) & (dot_skims['WK_MR_WK_IVTMR']>0)
+,walk_cr_available,walk_transit_available & (odt_skims['WK_CR_WK_IVTCR']>0) & (dot_skims['WK_CR_WK_IVTCR']>0)
+,pnr_ab_available,pnr_transit_available & (odt_skims['DR_AB_WK_IVTLB']>0) & (dot_skims['WK_AB_DR_IVTLB']>0)
+,pnr_bm_available,pnr_transit_available & (odt_skims['DR_BM_WK_IVTMR']>0) & (dot_skims['WK_BM_DR_IVTMR']>0)
+,pnr_mr_available,pnr_transit_available & (odt_skims['DR_MR_WK_IVTMR']>0) & (dot_skims['WK_MR_DR_IVTMR']>0)
+,pnr_cr_available,pnr_transit_available & (odt_skims['DR_CR_WK_IVTCR']>0) & (dot_skims['WK_CR_DR_IVTCR']>0)
+,knr_ab_available,knr_transit_available & (odt_skims['KR_AB_WK_IVTLB']>0) & (dot_skims['WK_AB_KR_IVTLB']>0)
+,knr_bm_available,knr_transit_available & (odt_skims['KR_BM_WK_IVTMR']>0) & (dot_skims['WK_BM_KR_IVTMR']>0)
+,knr_mr_available,knr_transit_available & (odt_skims['KR_MR_WK_IVTMR']>0) & (dot_skims['WK_MR_KR_IVTMR']>0)
+,knr_cr_available,knr_transit_available & (odt_skims['KR_CR_WK_IVTCR']>0) & (dot_skims['WK_CR_KR_IVTCR']>0)
+#,,
+destination in central business district,destination_in_cbd,"(reindex(land_use.AREATYPE, df[dest_col_name]) < setting('cbd_threshold')) * 1"
+#,,FIXME diagnostic
+#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST'])
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs.csv
new file mode 100644
index 000000000..17a55c327
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs.csv
@@ -0,0 +1,454 @@
+coefficient_name,value,constrain
+coef_ivt_work,-0.025,F
+coef_cost_work,-0.000659341,F
+coef_walk_work,-0.0344,F
+coef_bike_work,-0.0367,F
+coef_wlk_acc_work,-0.0457,F
+coef_wlk_aux_work,-0.05,F
+coef_drvacc_pnr_work,-0.05,F
+coef_drvacc_knr_work,-0.05,F
+coef_iwait_work,-0.0372,F
+coef_xwait_work,-0.0382,F
+coef_xfers_wlktrn_work,0.0,T
+coef_xfers_pnr_work,0.0,T
+coef_xfers_knr_work,0.0,T
+walk_transit_CBD_ASC_work,0.479,F
+drive_transit_CBD_ASC_work,1.17,F
+coef_density_walk_work,0.058,F
+coef_density_bike_work,0.0145,F
+coef_density_wlktrn_work,0.0,T
+coef_distpen_drvtrn_work,0.0,T
+coef_drvratio_knr_work,0.0,T
+coef_drvratio_pnr_work,0.0,T
+coef_age010_wlktrn_work,0.0,T
+coef_age1619_da_work,-0.205,F
+coef_age16p_sr_work,-0.454,F
+coef_hhsize1_sr_work,-1.5,F
+coef_hhsize2_sr_work,-0.895,F
+coef_topology_bike_work,0.0,T
+coef_topology_knr_work,0.0,T
+coef_topology_pnr_work,0.0,T
+coef_topology_walk_work,0.0,T
+coef_topology_wlktrn_work,0.0,T
+walk_MR_ASC_work,1.01,F
+walk_BM_ASC_work,0.149,F
+walk_CR_ASC_work,1.2,F
+PNR_MR_ASC_work,-0.8759999999999999,F
+PNR_BM_ASC_work,-1.89,F
+PNR_CR_ASC_work,2.08,F
+KNR_MR_ASC_work,0.43,F
+KNR_BM_ASC_work,0.5579999999999999,F
+KNR_CR_ASC_work,-8.88,F
+coef_ivt_school_univ,-0.01,T
+coef_cost_school_univ,-0.001,F
+coef_walk_school_univ,-0.0138,F
+coef_bike_school_univ,-0.0147,F
+coef_wlk_acc_school_univ,-0.0183,F
+coef_wlk_aux_school_univ,-0.0183,F
+coef_drvacc_pnr_school_univ,-0.0182,F
+coef_drvacc_knr_school_univ,-0.0182,F
+coef_iwait_school_univ,-0.0149,F
+coef_xwait_school_univ,-0.0153,F
+coef_xfers_wlktrn_school_univ,0.0,T
+coef_xfers_pnr_school_univ,0.0,T
+coef_xfers_knr_school_univ,0.0,T
+walk_transit_CBD_ASC_school_univ,0.1916,F
+drive_transit_CBD_ASC_school_univ,0.468,F
+coef_density_walk_school_univ,0.0232,F
+coef_density_bike_school_univ,0.0058,F
+coef_density_wlktrn_school_univ,0.0,T
+coef_distpen_drvtrn_school_univ,0.0,T
+coef_drvratio_knr_school_univ,0.0,T
+coef_drvratio_pnr_school_univ,0.0,T
+coef_age010_wlktrn_school_univ,-0.7140000000000001,F
+coef_age1619_da_school_univ,0.066,F
+coef_age16p_sr_school_univ,-0.407,F
+coef_hhsize1_sr_school_univ,-2.65,F
+coef_hhsize2_sr_school_univ,-0.6759999999999999,F
+coef_topology_bike_school_univ,0.0,T
+coef_topology_knr_school_univ,0.0,T
+coef_topology_pnr_school_univ,0.0,T
+coef_topology_walk_school_univ,0.0,T
+coef_topology_wlktrn_school_univ,0.0,T
+walk_MR_ASC_school_univ,1.11,F
+walk_BM_ASC_school_univ,0.537,F
+walk_CR_ASC_school_univ,1.36,F
+PNR_MR_ASC_school_univ,-999.0,T
+PNR_BM_ASC_school_univ,-999.0,T
+PNR_CR_ASC_school_univ,-999.0,T
+KNR_MR_ASC_school_univ,-999.0,T
+KNR_BM_ASC_school_univ,-999.0,T
+KNR_CR_ASC_school_univ,-999.0,T
+coef_ivt_nonmandatory,-0.0213,F
+coef_cost_nonmandatory,-0.00106,F
+coef_walk_nonmandatory,-0.0488,F
+coef_bike_nonmandatory,-0.0522,F
+coef_wlk_acc_nonmandatory,-0.0371,F
+coef_wlk_aux_nonmandatory,-0.0426,T
+coef_drvacc_pnr_nonmandatory,-0.0426,T
+coef_drvacc_knr_nonmandatory,-0.0426,T
+coef_iwait_nonmandatory,-0.03195,T
+coef_xwait_nonmandatory,-0.0426,T
+coef_xfers_wlktrn_nonmandatory,0.0,T
+coef_xfers_pnr_nonmandatory,0.0,T
+coef_xfers_knr_nonmandatory,0.0,T
+walk_transit_CBD_ASC_nonmandatory,0.272,F
+drive_transit_CBD_ASC_nonmandatory,0.0,F
+coef_density_walk_nonmandatory,0.0579,F
+coef_density_bike_nonmandatory,0.041,F
+coef_density_wlktrn_nonmandatory,0.0,T
+coef_distpen_drvtrn_nonmandatory,0.0,T
+coef_drvratio_knr_nonmandatory,0.0,T
+coef_drvratio_pnr_nonmandatory,0.0,T
+coef_age010_wlktrn_nonmandatory,-1.49,F
+coef_age1619_da_nonmandatory,-1.23,F
+coef_age16p_sr_nonmandatory,0.79,F
+coef_hhsize1_sr_nonmandatory,-1.13,F
+coef_hhsize2_sr_nonmandatory,-0.757,F
+coef_topology_bike_nonmandatory,0.0,T
+coef_topology_knr_nonmandatory,0.0,T
+coef_topology_pnr_nonmandatory,0.0,T
+coef_topology_walk_nonmandatory,0.0,T
+coef_topology_wlktrn_nonmandatory,0.0,T
+walk_MR_ASC_nonmandatory,0.0,F
+walk_BM_ASC_nonmandatory,-0.979,F
+walk_CR_ASC_nonmandatory,2.01,F
+PNR_MR_ASC_nonmandatory,-999.0,F
+PNR_BM_ASC_nonmandatory,-999.0,F
+PNR_CR_ASC_nonmandatory,-999.0,F
+KNR_MR_ASC_nonmandatory,-999.0,F
+KNR_BM_ASC_nonmandatory,-999.0,F
+KNR_CR_ASC_nonmandatory,-999.0,F
+coef_ivt_atwork,-0.0361,F
+coef_cost_atwork,-0.000434,F
+coef_walk_atwork,-0.0709,F
+coef_bike_atwork,-0.0722,T
+coef_wlk_acc_atwork,-0.0722,F
+coef_wlk_aux_atwork,-0.0722,T
+coef_drvacc_pnr_atwork,0.0,T
+coef_drvacc_knr_atwork,0.0,F
+coef_iwait_atwork,-0.1,F
+coef_xwait_atwork,-0.1,T
+coef_xfers_wlktrn_atwork,0.0,T
+coef_xfers_pnr_atwork,0.0,T
+coef_xfers_knr_atwork,0.0,T
+walk_transit_CBD_ASC_atwork,0.58,F
+drive_transit_CBD_ASC_atwork,0.0,T
+coef_density_walk_atwork,0.0437,F
+coef_density_bike_atwork,0.0,T
+coef_density_wlktrn_atwork,0.0,T
+coef_distpen_drvtrn_atwork,0.0,T
+coef_drvratio_knr_atwork,0.0,T
+coef_drvratio_pnr_atwork,0.0,T
+coef_age010_wlktrn_atwork,0.0,T
+coef_age1619_da_atwork,0.0,T
+coef_age16p_sr_atwork,0.0,T
+coef_hhsize1_sr_atwork,0.0,T
+coef_hhsize2_sr_atwork,0.0,T
+coef_topology_bike_atwork,0.0,T
+coef_topology_knr_atwork,0.0,T
+coef_topology_pnr_atwork,0.0,T
+coef_topology_walk_atwork,0.0,T
+coef_topology_wlktrn_atwork,0.0,T
+walk_MR_ASC_atwork,1.99,F
+walk_BM_ASC_atwork,-14.3,F
+walk_CR_ASC_atwork,-2.63,F
+PNR_MR_ASC_atwork,-999.0,T
+PNR_BM_ASC_atwork,-999.0,T
+PNR_CR_ASC_atwork,-999.0,T
+KNR_MR_ASC_atwork,-999.0,T
+KNR_BM_ASC_atwork,-999.0,T
+KNR_CR_ASC_atwork,-999.0,T
+coef_nest_AUTO,0.72,F
+coef_nest_AUTO_DRIVEALONE,0.35,F
+coef_nest_AUTO_SHAREDRIDE2,0.35,F
+coef_nest_AUTO_SHAREDRIDE3,0.35,F
+coef_nest_NONMOTORIZED,0.72,F
+coef_nest_RIDEHAIL,0.36,F
+coef_nest_root,1.0,T
+coef_nest_SCHOOL_BUS,0.72,F
+coef_nest_TRANSIT,0.72,F
+coef_nest_TRANSIT_KNRACCESS,0.5,F
+coef_nest_TRANSIT_PNRACCESS,0.5,F
+coef_nest_TRANSIT_WALKACCESS,0.5,F
+bike_ASC_auto_deficient_discretionary,-2.314622563152318,F
+bike_ASC_auto_sufficient_discretionary,-3.16592714693174,F
+bike_ASC_no_auto_discretionary,1.8206473922492574,F
+joint_bike_ASC_auto_deficient_discretionary,-6.684326329341298,F
+joint_bike_ASC_auto_sufficient_discretionary,-4.501341297960985,F
+joint_bike_ASC_no_auto_discretionary,-8.32,F
+joint_knr_transit_ASC_auto_deficient_discretionary,-18.9,F
+joint_knr_transit_ASC_auto_sufficient_discretionary,-9.8,F
+joint_knr_transit_ASC_no_auto_discretionary,-5.28,F
+joint_pnr_transit_ASC_auto_deficient_discretionary,-8.392032157640141,F
+joint_pnr_transit_ASC_auto_sufficient_discretionary,-15.7,F
+joint_pnr_transit_ASC_no_auto_discretionary,0.0,F
+joint_sr2_ASC_auto_deficient_discretionary,4.47397710397656,F
+joint_sr2_ASC_auto_sufficient_discretionary,0.596089371356585,F
+joint_sr2_ASC_no_auto_discretionary,-0.3348269658286712,F
+joint_sr3p_ASC_auto_deficient_discretionary,-8.759597659763918,F
+joint_sr3p_ASC_auto_sufficient_discretionary,-2.1453451696139387,F
+joint_sr3p_ASC_no_auto_discretionary,25.531137193393857,F
+joint_taxi_ASC_auto_deficient_discretionary,-5.899240823980289,F
+joint_taxi_ASC_auto_sufficient_discretionary,-4.418718421723628,F
+joint_taxi_ASC_no_auto_discretionary,-4.2064399470153795,F
+joint_tnc_shared_ASC_auto_deficient_discretionary,-6.479240823980289,F
+joint_tnc_shared_ASC_auto_sufficient_discretionary,-4.418718421723628,F
+joint_tnc_shared_ASC_no_auto_discretionary,-4.936439947015377,F
+joint_tnc_single_ASC_auto_deficient_discretionary,-7.879240823980287,F
+joint_tnc_single_ASC_auto_sufficient_discretionary,-4.418718421723628,F
+joint_tnc_single_ASC_no_auto_discretionary,-5.126439947015377,F
+joint_walk_ASC_auto_deficient_discretionary,-2.6423977462101016,F
+joint_walk_ASC_auto_sufficient_discretionary,0.27930973040681684,F
+joint_walk_ASC_no_auto_discretionary,29.06807239954025,F
+joint_walk_transit_ASC_auto_deficient_discretionary,-7.489016223032866,F
+joint_walk_transit_ASC_auto_sufficient_discretionary,-3.5462229327295973,F
+joint_walk_transit_ASC_no_auto_discretionary,27.13232401017065,F
+knr_transit_ASC_auto_deficient_discretionary,-19.0,F
+knr_transit_ASC_auto_sufficient_discretionary,-2.36489842946777,F
+knr_transit_ASC_no_auto_discretionary,2.4500588375695913,F
+pnr_transit_ASC_auto_deficient_discretionary,-18.3,F
+pnr_transit_ASC_auto_sufficient_discretionary,-3.033477671851898,F
+pnr_transit_ASC_no_auto_discretionary,0.0,F
+schoolbus_ASC_not_school,-999.0,F
+sr2_ASC_auto_deficient_discretionary,-0.9806021413713831,F
+sr2_ASC_auto_sufficient_discretionary,-1.5358224919444443,F
+sr2_ASC_no_auto_discretionary,1.7032603135320528,F
+sr3p_ASC_auto_deficient_discretionary,-1.515123236684089,F
+sr3p_ASC_auto_sufficient_discretionary,-1.6169797400620591,F
+sr3p_ASC_no_auto_discretionary,1.4029597077211255,F
+taxi_ASC_auto_deficient_discretionary,-1.8061232402875835,F
+taxi_ASC_auto_sufficient_discretionary,-2.7196310783821733,F
+taxi_ASC_no_auto_discretionary,3.366136911788236,F
+tnc_shared_ASC_auto_deficient_discretionary,-2.066123240287584,F
+tnc_shared_ASC_auto_sufficient_discretionary,-4.289631078382175,F
+tnc_shared_ASC_no_auto_discretionary,1.7761369117882366,F
+tnc_single_ASC_auto_deficient_discretionary,-0.8861232402875836,F
+tnc_single_ASC_auto_sufficient_discretionary,-2.6396310783821733,F
+tnc_single_ASC_no_auto_discretionary,3.696136911788236,F
+walk_ASC_auto_deficient_discretionary,1.2396747160555504,F
+walk_ASC_auto_sufficient_discretionary,0.14620669993715266,F
+walk_ASC_no_auto_discretionary,5.430660236876429,F
+walk_transit_ASC_auto_deficient_discretionary,0.09537642212091921,F
+walk_transit_ASC_auto_sufficient_discretionary,-1.8612136472259984,F
+walk_transit_ASC_no_auto_discretionary,5.16027718326133,F
+bike_ASC_auto_deficient_maintenance,-3.377536003532798,F
+bike_ASC_auto_sufficient_maintenance,-3.776552123413538,F
+bike_ASC_no_auto_maintenance,2.487381459660064,F
+joint_bike_ASC_auto_deficient_maintenance,-49.41432632934129,F
+joint_bike_ASC_auto_sufficient_maintenance,-4.671341297960987,F
+joint_bike_ASC_no_auto_maintenance,-10.6,F
+joint_knr_transit_ASC_auto_deficient_maintenance,-18.9,F
+joint_knr_transit_ASC_auto_sufficient_maintenance,-11.7,F
+joint_knr_transit_ASC_no_auto_maintenance,-33.4,F
+joint_pnr_transit_ASC_auto_deficient_maintenance,-27.492032157640157,F
+joint_pnr_transit_ASC_auto_sufficient_maintenance,-14.399999999999999,F
+joint_pnr_transit_ASC_no_auto_maintenance,0.0,F
+joint_sr2_ASC_auto_deficient_maintenance,4.47397710397656,F
+joint_sr2_ASC_auto_sufficient_maintenance,0.596089371356585,F
+joint_sr2_ASC_no_auto_maintenance,-0.3348269658286712,F
+joint_sr3p_ASC_auto_deficient_maintenance,-11.899597659763911,F
+joint_sr3p_ASC_auto_sufficient_maintenance,-2.6353451696139376,F
+joint_sr3p_ASC_no_auto_maintenance,0.7511371933938734,F
+joint_taxi_ASC_auto_deficient_maintenance,-22.909240823980294,F
+joint_taxi_ASC_auto_sufficient_maintenance,-4.418718421723628,F
+joint_taxi_ASC_no_auto_maintenance,1.4535600529846209,F
+joint_tnc_shared_ASC_auto_deficient_maintenance,-20.409240823980284,F
+joint_tnc_shared_ASC_auto_sufficient_maintenance,-4.418718421723628,F
+joint_tnc_shared_ASC_no_auto_maintenance,-22.046439947015372,F
+joint_tnc_single_ASC_auto_deficient_maintenance,-20.30924082398029,F
+joint_tnc_single_ASC_auto_sufficient_maintenance,-4.418718421723628,F
+joint_tnc_single_ASC_no_auto_maintenance,-0.6764399470153786,F
+joint_walk_ASC_auto_deficient_maintenance,-4.172397746210102,F
+joint_walk_ASC_auto_sufficient_maintenance,-0.08069026959318316,F
+joint_walk_ASC_no_auto_maintenance,5.248072399540233,F
+joint_walk_transit_ASC_auto_deficient_maintenance,-22.809016223032856,F
+joint_walk_transit_ASC_auto_sufficient_maintenance,-2.3262229327295967,F
+joint_walk_transit_ASC_no_auto_maintenance,3.3623240101706475,F
+knr_transit_ASC_auto_deficient_maintenance,-18.6,F
+knr_transit_ASC_auto_sufficient_maintenance,-13.1,F
+knr_transit_ASC_no_auto_maintenance,-15.7,F
+pnr_transit_ASC_auto_deficient_maintenance,-15.600000000000001,F
+pnr_transit_ASC_auto_sufficient_maintenance,-17.3,F
+pnr_transit_ASC_no_auto_maintenance,0.0,F
+sr2_ASC_auto_deficient_maintenance,-0.770836558232873,F
+sr2_ASC_auto_sufficient_maintenance,-1.4126398075072164,F
+sr2_ASC_no_auto_maintenance,2.8368705489342365,F
+sr3p_ASC_auto_deficient_maintenance,-1.0418043251252822,F
+sr3p_ASC_auto_sufficient_maintenance,-1.5096457960498852,F
+sr3p_ASC_no_auto_maintenance,3.1958843831466086,F
+taxi_ASC_auto_deficient_maintenance,-3.050813822837973,F
+taxi_ASC_auto_sufficient_maintenance,-3.4535730013724333,F
+taxi_ASC_no_auto_maintenance,4.779640150510731,F
+tnc_shared_ASC_auto_deficient_maintenance,-3.960813822837972,F
+tnc_shared_ASC_auto_sufficient_maintenance,-208.72357300137233,F
+tnc_shared_ASC_no_auto_maintenance,1.3096401505107311,F
+tnc_single_ASC_auto_deficient_maintenance,-3.6008138228379702,F
+tnc_single_ASC_auto_sufficient_maintenance,-5.21357300137243,F
+tnc_single_ASC_no_auto_maintenance,3.7896401505107287,F
+walk_ASC_auto_deficient_maintenance,0.961103491281816,F
+walk_ASC_auto_sufficient_maintenance,0.3086378353701181,F
+walk_ASC_no_auto_maintenance,6.71332037504109,F
+walk_transit_ASC_auto_deficient_maintenance,-0.9816456943716195,F
+walk_transit_ASC_auto_sufficient_maintenance,-1.7154122348374308,F
+walk_transit_ASC_no_auto_maintenance,6.493934015624426,F
+bike_ASC_auto_deficient_school,-0.3587962142802607,F
+bike_ASC_auto_sufficient_school,-2.2481434287968605,F
+bike_ASC_no_auto_school,-6.61,F
+joint_bike_ASC_auto_deficient_all,0.5839388785109728,F
+joint_bike_ASC_auto_sufficient_all,2.2382650098327144,F
+joint_bike_ASC_no_auto_all,0.0,F
+joint_knr_transit_ASC_auto_deficient_all,28.0,F
+joint_knr_transit_ASC_auto_sufficient_all,28.0,F
+joint_knr_transit_ASC_no_auto_all,0.0,F
+joint_pnr_transit_ASC_auto_deficient_all,14.307967842359856,F
+joint_pnr_transit_ASC_auto_sufficient_all,28.0,F
+joint_pnr_transit_ASC_no_auto_all,0.0,F
+joint_sr2_ASC_auto_deficient_all,8.856717099261305,F
+joint_sr2_ASC_auto_sufficient_all,1.866557830927722,F
+joint_sr2_ASC_no_auto_all,-1.3136054748830173,F
+joint_sr3p_ASC_auto_deficient_all,-12.744213154583111,F
+joint_sr3p_ASC_auto_sufficient_all,1.1793670542694572,F
+joint_sr3p_ASC_no_auto_all,7.188133036662488,F
+joint_taxi_ASC_auto_deficient_all,-6.309240823980289,F
+joint_taxi_ASC_auto_sufficient_all,-4.418718421723628,F
+joint_taxi_ASC_no_auto_all,-2.946439947015379,F
+joint_tnc_shared_ASC_auto_deficient_all,-6.309240823980289,F
+joint_tnc_shared_ASC_auto_sufficient_all,-4.418718421723628,F
+joint_tnc_shared_ASC_no_auto_all,-2.946439947015379,F
+joint_tnc_single_ASC_auto_deficient_all,-6.309240823980289,F
+joint_tnc_single_ASC_auto_sufficient_all,-4.418718421723628,F
+joint_tnc_single_ASC_no_auto_all,-2.946439947015379,F
+joint_walk_ASC_auto_deficient_all,-8.649695786770533,F
+joint_walk_ASC_auto_sufficient_all,-0.846896869850848,F
+joint_walk_ASC_no_auto_all,2.530309112287652,F
+joint_walk_transit_ASC_auto_deficient_all,-22.25316841330574,F
+joint_walk_transit_ASC_auto_sufficient_all,-9.232201799737867,F
+joint_walk_transit_ASC_no_auto_all,-3.56379435293537,F
+knr_transit_ASC_auto_deficient_school,-0.7771297774010263,F
+knr_transit_ASC_auto_sufficient_school,-0.3479379885195866,F
+knr_transit_ASC_no_auto_school,4.416351644923216,F
+pnr_transit_ASC_auto_deficient_school,-17.6,F
+pnr_transit_ASC_auto_sufficient_school,-3.0833071983095968,F
+pnr_transit_ASC_no_auto_school,0.0,F
+schoolbus_ASC_auto_deficient_school,1.4496683582704122,F
+schoolbus_ASC_auto_sufficient_school,1.4373341048849948,F
+schoolbus_ASC_no_auto_school,3.598171871862293,F
+sr2_ASC_auto_deficient_school,1.1400834084843785,F
+sr2_ASC_auto_sufficient_school,0.3723534041153868,F
+sr2_ASC_no_auto_school,-4.82,F
+sr3p_ASC_auto_deficient_school,1.3751525207907713,F
+sr3p_ASC_auto_sufficient_school,0.807684697254822,F
+sr3p_ASC_no_auto_school,0.5483992541943021,F
+taxi_ASC_auto_deficient_school,-21.15823481107509,F
+taxi_ASC_auto_sufficient_school,-2.4103716060177884,F
+taxi_ASC_no_auto_school_univ,0.0,F
+tnc_shared_ASC_auto_deficient_school,-20.25823481107509,F
+tnc_shared_ASC_auto_sufficient_school,-20.760371606017802,F
+tnc_shared_ASC_no_auto_school,0.8154516592775722,F
+tnc_single_ASC_auto_deficient_school,-2.538234811075092,F
+tnc_single_ASC_auto_sufficient_school,-3.3003716060177894,F
+tnc_single_ASC_no_auto_school,0.8154516592775722,F
+walk_ASC_auto_deficient_school,1.9700826773800473,F
+walk_ASC_auto_sufficient_school,0.9069008474873451,F
+walk_ASC_no_auto_school,4.44605462232996,F
+walk_transit_ASC_auto_deficient_school,0.608197830087687,F
+walk_transit_ASC_auto_sufficient_school,-0.5348068368653319,F
+walk_transit_ASC_no_auto_school,4.555462342810206,F
+bike_ASC_auto_deficient_univ,-2.983656896539415,F
+bike_ASC_auto_sufficient_univ,-2.9847326070220013,F
+bike_ASC_no_auto_univ,-0.25960529412546857,F
+knr_transit_ASC_auto_deficient_univ,-2.027091996792729,F
+knr_transit_ASC_auto_sufficient_univ,-3.5700988463934267,F
+knr_transit_ASC_no_auto_univ,-6.0,F
+pnr_transit_ASC_auto_deficient_univ,-0.624872727953999,F
+pnr_transit_ASC_auto_sufficient_univ,-3.5367931821403795,F
+pnr_transit_ASC_no_auto_univ,0.0,F
+sr2_ASC_auto_deficient_univ,0.3770160010136651,F
+sr2_ASC_auto_sufficient_univ,-0.09113888340291802,F
+sr2_ASC_no_auto_univ,0.676637641704824,F
+sr3p_ASC_auto_deficient_univ,-0.15358517526001764,F
+sr3p_ASC_auto_sufficient_univ,-0.14430611838574922,F
+sr3p_ASC_no_auto_univ,1.4803358638547641,F
+taxi_ASC_auto_deficient_univ,-2.147566732660339,F
+taxi_ASC_auto_sufficient_univ,-2.987735993361906,F
+tnc_shared_ASC_auto_deficient_univ,-2.147566732660339,F
+tnc_shared_ASC_auto_sufficient_univ,-2.987735993361906,F
+tnc_shared_ASC_no_auto_univ,1.5140890468266357,F
+tnc_single_ASC_auto_deficient_univ,-2.147566732660339,F
+tnc_single_ASC_auto_sufficient_univ,-2.987735993361906,F
+tnc_single_ASC_no_auto_univ,1.5140890468266357,F
+walk_ASC_auto_deficient_univ,-1.179511441787613,F
+walk_ASC_auto_sufficient_univ,-0.62287570112084,F
+walk_ASC_no_auto_univ,2.339757435025446,F
+walk_transit_ASC_auto_deficient_univ,-0.17454653043031698,F
+walk_transit_ASC_auto_sufficient_univ,-0.9495168757760516,F
+walk_transit_ASC_no_auto_univ,2.175197417493062,F
+bike_ASC_auto_deficient_work,-0.3546072404991606,F
+bike_ASC_auto_sufficient_work,-3.1638889609274634,F
+bike_ASC_no_auto_work,2.7801931794592605,F
+knr_transit_ASC_auto_deficient_work,-1.0765656262524248,F
+knr_transit_ASC_auto_sufficient_work,-3.9684689024532487,F
+knr_transit_ASC_no_auto_work,2.21305651338722,F
+pnr_transit_ASC_auto_deficient_work,-0.09995602533313773,F
+pnr_transit_ASC_auto_sufficient_work,-1.1852550721052821,F
+pnr_transit_ASC_no_auto_work,0.0,F
+sr2_ASC_auto_deficient_work,-0.27426164764014943,F
+sr2_ASC_auto_sufficient_work,-0.4398130630584324,F
+sr2_ASC_no_auto_work,0.6258725935957756,F
+sr3p_ASC_auto_deficient_work,-0.9248068312058698,F
+sr3p_ASC_auto_sufficient_work,-1.1074544445404517,F
+sr3p_ASC_no_auto_work,0.9884803116660514,F
+taxi_ASC_auto_deficient_work,-0.45047836938742336,F
+taxi_ASC_auto_sufficient_work,-2.6947142945014066,F
+taxi_ASC_no_auto_work,2.8060963105426344,F
+tnc_shared_ASC_auto_deficient_work,-1.6704783693874226,F
+tnc_shared_ASC_auto_sufficient_work,-4.384714294501407,F
+tnc_shared_ASC_no_auto_work,1.8060963105426353,F
+tnc_single_ASC_auto_deficient_work,-0.0904783693874232,F
+tnc_single_ASC_auto_sufficient_work,-3.2747142945014067,F
+tnc_single_ASC_no_auto_work,3.5060963105426355,F
+walk_ASC_auto_deficient_work,2.740454435923793,F
+walk_ASC_auto_sufficient_work,-0.026829625626013584,F
+walk_ASC_no_auto_work,4.9093958356590734,F
+walk_transit_ASC_auto_deficient_work,1.6265981598135546,F
+walk_transit_ASC_auto_sufficient_work,-1.2277772965975742,F
+walk_transit_ASC_no_auto_work,5.648304016804462,F
+bike_ASC_auto_deficient_atwork,6.813220683803054,F
+bike_ASC_auto_sufficient_atwork,-1.6328617899578455,F
+bike_ASC_no_auto_atwork,-15.773940012738107,F
+knr_transit_ASC_auto_deficient_atwork,-24.5,F
+knr_transit_ASC_auto_sufficient_atwork,-2.19190489598871,F
+knr_transit_ASC_no_auto_atwork,-48.7,F
+pnr_transit_ASC_auto_deficient_atwork,-4.69,F
+pnr_transit_ASC_auto_sufficient_atwork,-5.759358027052075,F
+pnr_transit_ASC_no_auto_atwork,0.0,F
+sr2_ASC_auto_deficient_atwork,-1.1422376987882268,F
+sr2_ASC_auto_sufficient_atwork,-2.1215879415511036,F
+sr2_ASC_no_auto_atwork,-18.166720634032373,F
+sr3p_ASC_auto_deficient_atwork,-1.5999360630401558,F
+sr3p_ASC_auto_sufficient_atwork,-2.6237767822576106,F
+sr3p_ASC_no_auto_atwork,-18.663765831776058,F
+taxi_ASC_auto_deficient_atwork,-0.929831757998666,F
+taxi_ASC_auto_sufficient_atwork,-1.9249125548560715,F
+taxi_ASC_no_auto_atwork,-16.96419195282012,F
+tnc_shared_ASC_auto_deficient_atwork,-19.76983175799867,F
+tnc_shared_ASC_auto_sufficient_atwork,-2.9449125548560713,F
+tnc_shared_ASC_no_auto_atwork,-47.46419195282012,F
+tnc_single_ASC_auto_deficient_atwork,-31.669831757998658,F
+tnc_single_ASC_auto_sufficient_atwork,-3.034912554856072,F
+tnc_single_ASC_no_auto_atwork,-16.46419195282012,F
+walk_ASC_auto_deficient_atwork,2.6758140375284643,F
+walk_ASC_auto_sufficient_atwork,0.2733957575113875,F
+walk_ASC_no_auto_atwork,-11.143550534379164,F
+walk_transit_ASC_auto_deficient_atwork,-0.8345491422957563,F
+walk_transit_ASC_auto_sufficient_atwork,-3.6768955022907024,F
+walk_transit_ASC_no_auto_atwork,-15.379013613731004,F
+coef_density_knr_atwork,0.0,T
+coef_density_knr_nonmandatory,0.0,T
+coef_density_knr_school_univ,0.0,T
+coef_density_knr_work,0.0,T
+coef_density_pnr_atwork,0.0,T
+coef_density_pnr_nonmandatory,0.0,T
+coef_density_pnr_school_univ,0.0,T
+coef_density_pnr_work,0.0,T
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs_template.csv
new file mode 100644
index 000000000..02b3d48bd
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_mode_choice_coeffs_template.csv
@@ -0,0 +1,118 @@
+coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork
+coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root
+coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO
+coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE
+coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2
+coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3
+coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED
+coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT
+coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS
+coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS
+coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS
+coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS
+coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL
+coef_ivt,coef_ivt_nonmandatory,coef_ivt_nonmandatory,coef_ivt_nonmandatory,coef_ivt_nonmandatory,coef_ivt_school_univ,coef_ivt_nonmandatory,coef_ivt_nonmandatory,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork
+coef_cost,coef_cost_nonmandatory,coef_cost_nonmandatory,coef_cost_nonmandatory,coef_cost_nonmandatory,coef_cost_school_univ,coef_cost_nonmandatory,coef_cost_nonmandatory,coef_cost_school_univ,coef_cost_work,coef_cost_atwork
+coef_walk,coef_walk_nonmandatory,coef_walk_nonmandatory,coef_walk_nonmandatory,coef_walk_nonmandatory,coef_walk_school_univ,coef_walk_nonmandatory,coef_walk_nonmandatory,coef_walk_school_univ,coef_walk_work,coef_walk_atwork
+coef_wlk_acc,coef_wlk_acc_nonmandatory,coef_wlk_acc_nonmandatory,coef_wlk_acc_nonmandatory,coef_wlk_acc_nonmandatory,coef_wlk_acc_school_univ,coef_wlk_acc_nonmandatory,coef_wlk_acc_nonmandatory,coef_wlk_acc_school_univ,coef_wlk_acc_work,coef_wlk_acc_atwork
+coef_wlk_aux,coef_wlk_aux_nonmandatory,coef_wlk_aux_nonmandatory,coef_wlk_aux_nonmandatory,coef_wlk_aux_nonmandatory,coef_wlk_aux_school_univ,coef_wlk_aux_nonmandatory,coef_wlk_aux_nonmandatory,coef_wlk_aux_school_univ,coef_wlk_aux_work,coef_wlk_aux_atwork
+coef_bike,coef_bike_nonmandatory,coef_bike_nonmandatory,coef_bike_nonmandatory,coef_bike_nonmandatory,coef_bike_school_univ,coef_bike_nonmandatory,coef_bike_nonmandatory,coef_bike_school_univ,coef_bike_work,coef_bike_atwork
+coef_iwait,coef_iwait_nonmandatory,coef_iwait_nonmandatory,coef_iwait_nonmandatory,coef_iwait_nonmandatory,coef_iwait_school_univ,coef_iwait_nonmandatory,coef_iwait_nonmandatory,coef_iwait_school_univ,coef_iwait_work,coef_iwait_atwork
+coef_xwait,coef_xwait_nonmandatory,coef_xwait_nonmandatory,coef_xwait_nonmandatory,coef_xwait_nonmandatory,coef_xwait_school_univ,coef_xwait_nonmandatory,coef_xwait_nonmandatory,coef_xwait_school_univ,coef_xwait_work,coef_xwait_atwork
+coef_xfers_wlktrn,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_school_univ,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_nonmandatory,coef_xfers_wlktrn_school_univ,coef_xfers_wlktrn_work,coef_xfers_wlktrn_atwork
+coef_xfers_pnr,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_school_univ,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_nonmandatory,coef_xfers_pnr_school_univ,coef_xfers_pnr_work,coef_xfers_pnr_atwork
+coef_xfers_knr,coef_xfers_knr_nonmandatory,coef_xfers_knr_nonmandatory,coef_xfers_knr_nonmandatory,coef_xfers_knr_nonmandatory,coef_xfers_knr_school_univ,coef_xfers_knr_nonmandatory,coef_xfers_knr_nonmandatory,coef_xfers_knr_school_univ,coef_xfers_knr_work,coef_xfers_knr_atwork
+coef_drvacc_knr,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_school_univ,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_nonmandatory,coef_drvacc_knr_school_univ,coef_drvacc_knr_work,coef_drvacc_knr_atwork
+coef_drvacc_pnr,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_school_univ,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_nonmandatory,coef_drvacc_pnr_school_univ,coef_drvacc_pnr_work,coef_drvacc_pnr_atwork
+coef_drvratio_pnr,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_school_univ,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_nonmandatory,coef_drvratio_pnr_school_univ,coef_drvratio_pnr_work,coef_drvratio_pnr_atwork
+coef_drvratio_knr,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_school_univ,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_nonmandatory,coef_drvratio_knr_school_univ,coef_drvratio_knr_work,coef_drvratio_knr_atwork
+coef_distpen_drvtrn,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_school_univ,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_nonmandatory,coef_distpen_drvtrn_school_univ,coef_distpen_drvtrn_work,coef_distpen_drvtrn_atwork
+coef_density_walk,coef_density_walk_nonmandatory,coef_density_walk_nonmandatory,coef_density_walk_nonmandatory,coef_density_walk_nonmandatory,coef_density_walk_school_univ,coef_density_walk_nonmandatory,coef_density_walk_nonmandatory,coef_density_walk_school_univ,coef_density_walk_work,coef_density_walk_atwork
+coef_density_bike,coef_density_bike_nonmandatory,coef_density_bike_nonmandatory,coef_density_bike_nonmandatory,coef_density_bike_nonmandatory,coef_density_bike_school_univ,coef_density_bike_nonmandatory,coef_density_bike_nonmandatory,coef_density_bike_school_univ,coef_density_bike_work,coef_density_bike_atwork
+coef_density_wlktrn,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_school_univ,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_nonmandatory,coef_density_wlktrn_school_univ,coef_density_wlktrn_work,coef_density_wlktrn_atwork
+coef_density_pnr,coef_density_pnr_nonmandatory,coef_density_pnr_nonmandatory,coef_density_pnr_nonmandatory,coef_density_pnr_nonmandatory,coef_density_pnr_school_univ,coef_density_pnr_nonmandatory,coef_density_pnr_nonmandatory,coef_density_pnr_school_univ,coef_density_pnr_work,coef_density_pnr_atwork
+coef_density_knr,coef_density_knr_nonmandatory,coef_density_knr_nonmandatory,coef_density_knr_nonmandatory,coef_density_knr_nonmandatory,coef_density_knr_school_univ,coef_density_knr_nonmandatory,coef_density_knr_nonmandatory,coef_density_knr_school_univ,coef_density_knr_work,coef_density_knr_atwork
+coef_topology_walk,coef_topology_walk_nonmandatory,coef_topology_walk_nonmandatory,coef_topology_walk_nonmandatory,coef_topology_walk_nonmandatory,coef_topology_walk_school_univ,coef_topology_walk_nonmandatory,coef_topology_walk_nonmandatory,coef_topology_walk_school_univ,coef_topology_walk_work,coef_topology_walk_atwork
+coef_topology_bike,coef_topology_bike_nonmandatory,coef_topology_bike_nonmandatory,coef_topology_bike_nonmandatory,coef_topology_bike_nonmandatory,coef_topology_bike_school_univ,coef_topology_bike_nonmandatory,coef_topology_bike_nonmandatory,coef_topology_bike_school_univ,coef_topology_bike_work,coef_topology_bike_atwork
+coef_topology_wlktrn,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_school_univ,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_nonmandatory,coef_topology_wlktrn_school_univ,coef_topology_wlktrn_work,coef_topology_wlktrn_atwork
+coef_topology_pnr,coef_topology_pnr_nonmandatory,coef_topology_pnr_nonmandatory,coef_topology_pnr_nonmandatory,coef_topology_pnr_nonmandatory,coef_topology_pnr_school_univ,coef_topology_pnr_nonmandatory,coef_topology_pnr_nonmandatory,coef_topology_pnr_school_univ,coef_topology_pnr_work,coef_topology_pnr_atwork
+coef_topology_knr,coef_topology_knr_nonmandatory,coef_topology_knr_nonmandatory,coef_topology_knr_nonmandatory,coef_topology_knr_nonmandatory,coef_topology_knr_school_univ,coef_topology_knr_nonmandatory,coef_topology_knr_nonmandatory,coef_topology_knr_school_univ,coef_topology_knr_work,coef_topology_knr_atwork
+coef_age1619_da,coef_age1619_da_nonmandatory,coef_age1619_da_nonmandatory,coef_age1619_da_nonmandatory,coef_age1619_da_nonmandatory,coef_age1619_da_school_univ,coef_age1619_da_nonmandatory,coef_age1619_da_nonmandatory,coef_age1619_da_school_univ,coef_age1619_da_work,coef_age1619_da_atwork
+coef_age010_wlktrn,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_school_univ,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_nonmandatory,coef_age010_wlktrn_school_univ,coef_age010_wlktrn_work,coef_age010_wlktrn_atwork
+coef_age16p_sr,coef_age16p_sr_nonmandatory,coef_age16p_sr_nonmandatory,coef_age16p_sr_nonmandatory,coef_age16p_sr_nonmandatory,coef_age16p_sr_school_univ,coef_age16p_sr_nonmandatory,coef_age16p_sr_nonmandatory,coef_age16p_sr_school_univ,coef_age16p_sr_work,coef_age16p_sr_atwork
+coef_hhsize1_sr,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_school_univ,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_nonmandatory,coef_hhsize1_sr_school_univ,coef_hhsize1_sr_work,coef_hhsize1_sr_atwork
+coef_hhsize2_sr,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_school_univ,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_nonmandatory,coef_hhsize2_sr_school_univ,coef_hhsize2_sr_work,coef_hhsize2_sr_atwork
+walk_ASC_no_auto,walk_ASC_no_auto_discretionary,walk_ASC_no_auto_maintenance,walk_ASC_no_auto_discretionary,walk_ASC_no_auto_maintenance,walk_ASC_no_auto_school,walk_ASC_no_auto_maintenance,walk_ASC_no_auto_discretionary,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork
+walk_ASC_auto_deficient,walk_ASC_auto_deficient_discretionary,walk_ASC_auto_deficient_maintenance,walk_ASC_auto_deficient_discretionary,walk_ASC_auto_deficient_maintenance,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_maintenance,walk_ASC_auto_deficient_discretionary,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork
+walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_discretionary,walk_ASC_auto_sufficient_maintenance,walk_ASC_auto_sufficient_discretionary,walk_ASC_auto_sufficient_maintenance,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_maintenance,walk_ASC_auto_sufficient_discretionary,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork
+bike_ASC_no_auto,bike_ASC_no_auto_discretionary,bike_ASC_no_auto_maintenance,bike_ASC_no_auto_discretionary,bike_ASC_no_auto_maintenance,bike_ASC_no_auto_school,bike_ASC_no_auto_maintenance,bike_ASC_no_auto_discretionary,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork
+bike_ASC_auto_deficient,bike_ASC_auto_deficient_discretionary,bike_ASC_auto_deficient_maintenance,bike_ASC_auto_deficient_discretionary,bike_ASC_auto_deficient_maintenance,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_maintenance,bike_ASC_auto_deficient_discretionary,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork
+bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_discretionary,bike_ASC_auto_sufficient_maintenance,bike_ASC_auto_sufficient_discretionary,bike_ASC_auto_sufficient_maintenance,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_maintenance,bike_ASC_auto_sufficient_discretionary,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork
+sr2_ASC_no_auto,sr2_ASC_no_auto_discretionary,sr2_ASC_no_auto_maintenance,sr2_ASC_no_auto_discretionary,sr2_ASC_no_auto_maintenance,sr2_ASC_no_auto_school,sr2_ASC_no_auto_maintenance,sr2_ASC_no_auto_discretionary,sr2_ASC_no_auto_univ,sr2_ASC_no_auto_work,sr2_ASC_no_auto_atwork
+sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_discretionary,sr2_ASC_auto_deficient_maintenance,sr2_ASC_auto_deficient_discretionary,sr2_ASC_auto_deficient_maintenance,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_maintenance,sr2_ASC_auto_deficient_discretionary,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork
+sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_discretionary,sr2_ASC_auto_sufficient_maintenance,sr2_ASC_auto_sufficient_discretionary,sr2_ASC_auto_sufficient_maintenance,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_maintenance,sr2_ASC_auto_sufficient_discretionary,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork
+sr3p_ASC_no_auto,sr3p_ASC_no_auto_discretionary,sr3p_ASC_no_auto_maintenance,sr3p_ASC_no_auto_discretionary,sr3p_ASC_no_auto_maintenance,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_maintenance,sr3p_ASC_no_auto_discretionary,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork
+sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_discretionary,sr3p_ASC_auto_deficient_maintenance,sr3p_ASC_auto_deficient_discretionary,sr3p_ASC_auto_deficient_maintenance,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_maintenance,sr3p_ASC_auto_deficient_discretionary,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork
+sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_discretionary,sr3p_ASC_auto_sufficient_maintenance,sr3p_ASC_auto_sufficient_discretionary,sr3p_ASC_auto_sufficient_maintenance,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_maintenance,sr3p_ASC_auto_sufficient_discretionary,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork
+walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_discretionary,walk_transit_ASC_no_auto_maintenance,walk_transit_ASC_no_auto_discretionary,walk_transit_ASC_no_auto_maintenance,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_maintenance,walk_transit_ASC_no_auto_discretionary,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork
+walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_discretionary,walk_transit_ASC_auto_deficient_maintenance,walk_transit_ASC_auto_deficient_discretionary,walk_transit_ASC_auto_deficient_maintenance,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_maintenance,walk_transit_ASC_auto_deficient_discretionary,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork
+walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_discretionary,walk_transit_ASC_auto_sufficient_maintenance,walk_transit_ASC_auto_sufficient_discretionary,walk_transit_ASC_auto_sufficient_maintenance,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_maintenance,walk_transit_ASC_auto_sufficient_discretionary,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork
+pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto_discretionary,pnr_transit_ASC_no_auto_maintenance,pnr_transit_ASC_no_auto_discretionary,pnr_transit_ASC_no_auto_maintenance,pnr_transit_ASC_no_auto_school,pnr_transit_ASC_no_auto_maintenance,pnr_transit_ASC_no_auto_discretionary,pnr_transit_ASC_no_auto_univ,pnr_transit_ASC_no_auto_work,pnr_transit_ASC_no_auto_atwork
+pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient_discretionary,pnr_transit_ASC_auto_deficient_maintenance,pnr_transit_ASC_auto_deficient_discretionary,pnr_transit_ASC_auto_deficient_maintenance,pnr_transit_ASC_auto_deficient_school,pnr_transit_ASC_auto_deficient_maintenance,pnr_transit_ASC_auto_deficient_discretionary,pnr_transit_ASC_auto_deficient_univ,pnr_transit_ASC_auto_deficient_work,pnr_transit_ASC_auto_deficient_atwork
+pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient_discretionary,pnr_transit_ASC_auto_sufficient_maintenance,pnr_transit_ASC_auto_sufficient_discretionary,pnr_transit_ASC_auto_sufficient_maintenance,pnr_transit_ASC_auto_sufficient_school,pnr_transit_ASC_auto_sufficient_maintenance,pnr_transit_ASC_auto_sufficient_discretionary,pnr_transit_ASC_auto_sufficient_univ,pnr_transit_ASC_auto_sufficient_work,pnr_transit_ASC_auto_sufficient_atwork
+knr_transit_ASC_no_auto,knr_transit_ASC_no_auto_discretionary,knr_transit_ASC_no_auto_maintenance,knr_transit_ASC_no_auto_discretionary,knr_transit_ASC_no_auto_maintenance,knr_transit_ASC_no_auto_school,knr_transit_ASC_no_auto_maintenance,knr_transit_ASC_no_auto_discretionary,knr_transit_ASC_no_auto_univ,knr_transit_ASC_no_auto_work,knr_transit_ASC_no_auto_atwork
+knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient_discretionary,knr_transit_ASC_auto_deficient_maintenance,knr_transit_ASC_auto_deficient_discretionary,knr_transit_ASC_auto_deficient_maintenance,knr_transit_ASC_auto_deficient_school,knr_transit_ASC_auto_deficient_maintenance,knr_transit_ASC_auto_deficient_discretionary,knr_transit_ASC_auto_deficient_univ,knr_transit_ASC_auto_deficient_work,knr_transit_ASC_auto_deficient_atwork
+knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient_discretionary,knr_transit_ASC_auto_sufficient_maintenance,knr_transit_ASC_auto_sufficient_discretionary,knr_transit_ASC_auto_sufficient_maintenance,knr_transit_ASC_auto_sufficient_school,knr_transit_ASC_auto_sufficient_maintenance,knr_transit_ASC_auto_sufficient_discretionary,knr_transit_ASC_auto_sufficient_univ,knr_transit_ASC_auto_sufficient_work,knr_transit_ASC_auto_sufficient_atwork
+taxi_ASC_no_auto,taxi_ASC_no_auto_discretionary,taxi_ASC_no_auto_maintenance,taxi_ASC_no_auto_discretionary,taxi_ASC_no_auto_maintenance,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_maintenance,taxi_ASC_no_auto_discretionary,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork
+taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_discretionary,taxi_ASC_auto_deficient_maintenance,taxi_ASC_auto_deficient_discretionary,taxi_ASC_auto_deficient_maintenance,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_maintenance,taxi_ASC_auto_deficient_discretionary,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork
+taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_discretionary,taxi_ASC_auto_sufficient_maintenance,taxi_ASC_auto_sufficient_discretionary,taxi_ASC_auto_sufficient_maintenance,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_maintenance,taxi_ASC_auto_sufficient_discretionary,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork
+tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_discretionary,tnc_single_ASC_no_auto_maintenance,tnc_single_ASC_no_auto_discretionary,tnc_single_ASC_no_auto_maintenance,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_maintenance,tnc_single_ASC_no_auto_discretionary,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork
+tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_discretionary,tnc_single_ASC_auto_deficient_maintenance,tnc_single_ASC_auto_deficient_discretionary,tnc_single_ASC_auto_deficient_maintenance,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_maintenance,tnc_single_ASC_auto_deficient_discretionary,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork
+tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_discretionary,tnc_single_ASC_auto_sufficient_maintenance,tnc_single_ASC_auto_sufficient_discretionary,tnc_single_ASC_auto_sufficient_maintenance,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_maintenance,tnc_single_ASC_auto_sufficient_discretionary,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork
+tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_discretionary,tnc_shared_ASC_no_auto_maintenance,tnc_shared_ASC_no_auto_discretionary,tnc_shared_ASC_no_auto_maintenance,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_maintenance,tnc_shared_ASC_no_auto_discretionary,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork
+tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_discretionary,tnc_shared_ASC_auto_deficient_maintenance,tnc_shared_ASC_auto_deficient_discretionary,tnc_shared_ASC_auto_deficient_maintenance,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_maintenance,tnc_shared_ASC_auto_deficient_discretionary,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork
+tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_discretionary,tnc_shared_ASC_auto_sufficient_maintenance,tnc_shared_ASC_auto_sufficient_discretionary,tnc_shared_ASC_auto_sufficient_maintenance,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_maintenance,tnc_shared_ASC_auto_sufficient_discretionary,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork
+joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_discretionary,joint_walk_ASC_no_auto_maintenance,joint_walk_ASC_no_auto_discretionary,joint_walk_ASC_no_auto_maintenance,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_maintenance,joint_walk_ASC_no_auto_discretionary,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all
+joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_discretionary,joint_walk_ASC_auto_deficient_maintenance,joint_walk_ASC_auto_deficient_discretionary,joint_walk_ASC_auto_deficient_maintenance,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_maintenance,joint_walk_ASC_auto_deficient_discretionary,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all
+joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_discretionary,joint_walk_ASC_auto_sufficient_maintenance,joint_walk_ASC_auto_sufficient_discretionary,joint_walk_ASC_auto_sufficient_maintenance,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_maintenance,joint_walk_ASC_auto_sufficient_discretionary,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all
+joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_discretionary,joint_bike_ASC_no_auto_maintenance,joint_bike_ASC_no_auto_discretionary,joint_bike_ASC_no_auto_maintenance,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_maintenance,joint_bike_ASC_no_auto_discretionary,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all
+joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_discretionary,joint_bike_ASC_auto_deficient_maintenance,joint_bike_ASC_auto_deficient_discretionary,joint_bike_ASC_auto_deficient_maintenance,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_maintenance,joint_bike_ASC_auto_deficient_discretionary,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all
+joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_discretionary,joint_bike_ASC_auto_sufficient_maintenance,joint_bike_ASC_auto_sufficient_discretionary,joint_bike_ASC_auto_sufficient_maintenance,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_maintenance,joint_bike_ASC_auto_sufficient_discretionary,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all
+joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_discretionary,joint_sr2_ASC_no_auto_maintenance,joint_sr2_ASC_no_auto_discretionary,joint_sr2_ASC_no_auto_maintenance,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_maintenance,joint_sr2_ASC_no_auto_discretionary,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all
+joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_discretionary,joint_sr2_ASC_auto_deficient_maintenance,joint_sr2_ASC_auto_deficient_discretionary,joint_sr2_ASC_auto_deficient_maintenance,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_maintenance,joint_sr2_ASC_auto_deficient_discretionary,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all
+joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_discretionary,joint_sr2_ASC_auto_sufficient_maintenance,joint_sr2_ASC_auto_sufficient_discretionary,joint_sr2_ASC_auto_sufficient_maintenance,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_maintenance,joint_sr2_ASC_auto_sufficient_discretionary,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all
+joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_discretionary,joint_sr3p_ASC_no_auto_maintenance,joint_sr3p_ASC_no_auto_discretionary,joint_sr3p_ASC_no_auto_maintenance,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_maintenance,joint_sr3p_ASC_no_auto_discretionary,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all
+joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_discretionary,joint_sr3p_ASC_auto_deficient_maintenance,joint_sr3p_ASC_auto_deficient_discretionary,joint_sr3p_ASC_auto_deficient_maintenance,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_maintenance,joint_sr3p_ASC_auto_deficient_discretionary,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all
+joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_discretionary,joint_sr3p_ASC_auto_sufficient_maintenance,joint_sr3p_ASC_auto_sufficient_discretionary,joint_sr3p_ASC_auto_sufficient_maintenance,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_maintenance,joint_sr3p_ASC_auto_sufficient_discretionary,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all
+joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_discretionary,joint_walk_transit_ASC_no_auto_maintenance,joint_walk_transit_ASC_no_auto_discretionary,joint_walk_transit_ASC_no_auto_maintenance,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_maintenance,joint_walk_transit_ASC_no_auto_discretionary,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all
+joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_discretionary,joint_walk_transit_ASC_auto_deficient_maintenance,joint_walk_transit_ASC_auto_deficient_discretionary,joint_walk_transit_ASC_auto_deficient_maintenance,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_maintenance,joint_walk_transit_ASC_auto_deficient_discretionary,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all
+joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_discretionary,joint_walk_transit_ASC_auto_sufficient_maintenance,joint_walk_transit_ASC_auto_sufficient_discretionary,joint_walk_transit_ASC_auto_sufficient_maintenance,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_maintenance,joint_walk_transit_ASC_auto_sufficient_discretionary,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all
+joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto_discretionary,joint_pnr_transit_ASC_no_auto_maintenance,joint_pnr_transit_ASC_no_auto_discretionary,joint_pnr_transit_ASC_no_auto_maintenance,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_maintenance,joint_pnr_transit_ASC_no_auto_discretionary,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all
+joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient_discretionary,joint_pnr_transit_ASC_auto_deficient_maintenance,joint_pnr_transit_ASC_auto_deficient_discretionary,joint_pnr_transit_ASC_auto_deficient_maintenance,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_maintenance,joint_pnr_transit_ASC_auto_deficient_discretionary,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all
+joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient_discretionary,joint_pnr_transit_ASC_auto_sufficient_maintenance,joint_pnr_transit_ASC_auto_sufficient_discretionary,joint_pnr_transit_ASC_auto_sufficient_maintenance,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_maintenance,joint_pnr_transit_ASC_auto_sufficient_discretionary,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all
+joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto_discretionary,joint_knr_transit_ASC_no_auto_maintenance,joint_knr_transit_ASC_no_auto_discretionary,joint_knr_transit_ASC_no_auto_maintenance,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_maintenance,joint_knr_transit_ASC_no_auto_discretionary,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all
+joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient_discretionary,joint_knr_transit_ASC_auto_deficient_maintenance,joint_knr_transit_ASC_auto_deficient_discretionary,joint_knr_transit_ASC_auto_deficient_maintenance,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_maintenance,joint_knr_transit_ASC_auto_deficient_discretionary,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all
+joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient_discretionary,joint_knr_transit_ASC_auto_sufficient_maintenance,joint_knr_transit_ASC_auto_sufficient_discretionary,joint_knr_transit_ASC_auto_sufficient_maintenance,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_maintenance,joint_knr_transit_ASC_auto_sufficient_discretionary,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all
+joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_discretionary,joint_taxi_ASC_no_auto_maintenance,joint_taxi_ASC_no_auto_discretionary,joint_taxi_ASC_no_auto_maintenance,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_maintenance,joint_taxi_ASC_no_auto_discretionary,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all
+joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_discretionary,joint_taxi_ASC_auto_deficient_maintenance,joint_taxi_ASC_auto_deficient_discretionary,joint_taxi_ASC_auto_deficient_maintenance,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_maintenance,joint_taxi_ASC_auto_deficient_discretionary,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all
+joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_discretionary,joint_taxi_ASC_auto_sufficient_maintenance,joint_taxi_ASC_auto_sufficient_discretionary,joint_taxi_ASC_auto_sufficient_maintenance,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_maintenance,joint_taxi_ASC_auto_sufficient_discretionary,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all
+joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_discretionary,joint_tnc_single_ASC_no_auto_maintenance,joint_tnc_single_ASC_no_auto_discretionary,joint_tnc_single_ASC_no_auto_maintenance,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_maintenance,joint_tnc_single_ASC_no_auto_discretionary,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all
+joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_discretionary,joint_tnc_single_ASC_auto_deficient_maintenance,joint_tnc_single_ASC_auto_deficient_discretionary,joint_tnc_single_ASC_auto_deficient_maintenance,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_maintenance,joint_tnc_single_ASC_auto_deficient_discretionary,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all
+joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_discretionary,joint_tnc_single_ASC_auto_sufficient_maintenance,joint_tnc_single_ASC_auto_sufficient_discretionary,joint_tnc_single_ASC_auto_sufficient_maintenance,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_maintenance,joint_tnc_single_ASC_auto_sufficient_discretionary,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all
+joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_discretionary,joint_tnc_shared_ASC_no_auto_maintenance,joint_tnc_shared_ASC_no_auto_discretionary,joint_tnc_shared_ASC_no_auto_maintenance,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_maintenance,joint_tnc_shared_ASC_no_auto_discretionary,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all
+joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_discretionary,joint_tnc_shared_ASC_auto_deficient_maintenance,joint_tnc_shared_ASC_auto_deficient_discretionary,joint_tnc_shared_ASC_auto_deficient_maintenance,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_maintenance,joint_tnc_shared_ASC_auto_deficient_discretionary,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all
+joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_discretionary,joint_tnc_shared_ASC_auto_sufficient_maintenance,joint_tnc_shared_ASC_auto_sufficient_discretionary,joint_tnc_shared_ASC_auto_sufficient_maintenance,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_maintenance,joint_tnc_shared_ASC_auto_sufficient_discretionary,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all
+walk_transit_CBD_ASC,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_nonmandatory,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork
+drive_transit_CBD_ASC,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_nonmandatory,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork
+schoolbus_ASC_no_auto,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_no_auto_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school
+schoolbus_ASC_auto_deficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_deficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school
+schoolbus_ASC_auto_sufficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_sufficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school
+walk_BM_ASC,walk_BM_ASC_nonmandatory,walk_BM_ASC_nonmandatory,walk_BM_ASC_nonmandatory,walk_BM_ASC_nonmandatory,walk_BM_ASC_school_univ,walk_BM_ASC_nonmandatory,walk_BM_ASC_nonmandatory,walk_BM_ASC_school_univ,walk_BM_ASC_work,walk_BM_ASC_atwork
+walk_MR_ASC,walk_MR_ASC_nonmandatory,walk_MR_ASC_nonmandatory,walk_MR_ASC_nonmandatory,walk_MR_ASC_nonmandatory,walk_MR_ASC_school_univ,walk_MR_ASC_nonmandatory,walk_MR_ASC_nonmandatory,walk_MR_ASC_school_univ,walk_MR_ASC_work,walk_MR_ASC_atwork
+walk_CR_ASC,walk_CR_ASC_nonmandatory,walk_CR_ASC_nonmandatory,walk_CR_ASC_nonmandatory,walk_CR_ASC_nonmandatory,walk_CR_ASC_school_univ,walk_CR_ASC_nonmandatory,walk_CR_ASC_nonmandatory,walk_CR_ASC_school_univ,walk_CR_ASC_work,walk_CR_ASC_atwork
+PNR_BM_ASC,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_school_univ,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_nonmandatory,PNR_BM_ASC_school_univ,PNR_BM_ASC_work,PNR_BM_ASC_atwork
+PNR_MR_ASC,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_school_univ,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_nonmandatory,PNR_MR_ASC_school_univ,PNR_MR_ASC_work,PNR_MR_ASC_atwork
+PNR_CR_ASC,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_school_univ,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_nonmandatory,PNR_CR_ASC_school_univ,PNR_CR_ASC_work,PNR_CR_ASC_atwork
+KNR_BM_ASC,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_school_univ,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_nonmandatory,KNR_BM_ASC_school_univ,KNR_BM_ASC_work,KNR_BM_ASC_atwork
+KNR_MR_ASC,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_school_univ,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_nonmandatory,KNR_MR_ASC_school_univ,KNR_MR_ASC_work,KNR_MR_ASC_atwork
+KNR_CR_ASC,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_school_univ,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_nonmandatory,KNR_CR_ASC_school_univ,KNR_CR_ASC_work,KNR_CR_ASC_atwork
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv
new file mode 100644
index 000000000..93d8fc472
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv
@@ -0,0 +1,50 @@
+Label,Description,Expression,Coefficient
+util_Female_Departure_after_1230_pm_Linear,Female - Departure after 12:30 pm - Linear,"@np.where(((df.female) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear
+util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm  - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear
+util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours
+util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours
+#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours
+#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours
+util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm  - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear
+util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear
+util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours
+util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm  - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear
+util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear
+util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util__Departure_constants,# Departure constants,,coef__Departure_constants
+util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear
+util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM
+util_1100_AM_1130_AM,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM
+util_1130_AM_1200_PM,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM
+util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM
+util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM
+util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM
+util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root
+util__Arrival_constants,# Arrival constants,,coef__Arrival_constants
+util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear
+util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM
+util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM
+util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM
+util_0100_PM_0130_PM,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM
+util_0130_PM_0200_PM,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM
+util_0200_PM_0230_PM,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM
+util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM
+util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear
+util__Duration_constants,# Duration constants,,coef__Duration_constants
+util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs
+util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs
+util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs
+util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs
+util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs
+util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs
+util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root
+util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18
+util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19
+util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20
+util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.yaml b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.yaml
new file mode 100644
index 000000000..f04f93b8e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.yaml
@@ -0,0 +1,16 @@
+
+SPEC: tour_scheduling_atwork.csv
+COEFFICIENTS: tour_scheduling_atwork_coeffs.csv
+
+preprocessor:
+   SPEC: tour_scheduling_atwork_preprocessor
+   DF: df
+#  TABLES:
+#    - land_use
+#    - tours
+
+SIMULATE_CHOOSER_COLUMNS:
+  - od_distance
+  
+CONSTANTS:
+  time_cap: 30
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_coeffs.csv
new file mode 100644
index 000000000..0e7ab1acb
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_coeffs.csv
@@ -0,0 +1,47 @@
+coefficient_name,value,constrain
+coef_Female_Departure_after_1230_pm_Linear,0.05574558,F
+coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F
+coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F
+coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F
+coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F
+coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F
+coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F
+coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F
+coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F
+coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F
+coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F
+coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F
+coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F
+coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F
+coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F
+coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F
+coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F
+coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F
+coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F
+coef_start_Before_1100_AM,-2.176744062,F
+coef_start_1100_AM_1130_AM,-1.190017952,F
+coef_start_1130_AM_1200_PM,-0.198229872,F
+coef_start_1200_AM_1230_PM,0,T
+coef_start_1230_PM_0100_PM,-0.084950396,F
+coef_start_After_0100_PM,-0.205562723,F
+coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F
+coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F
+coef_end_Before_1200_PM,0.279351638,F
+coef_end_1200_AM_1230_PM,-0.045281832,F
+coef_end_1230_PM_0100_PM,0.214070736,F
+coef_end_0100_PM_0130_PM,0,T
+coef_end_0130_PM_0200_PM,-0.69742748,F
+coef_end_0200_PM_0230_PM,-1.284283533,F
+coef_end_After_0230_PM,-2.119733896,F
+coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F
+coef_0_hrs,-0.969734874,F
+coef_0p5_hrs,0,T
+coef_1_hrs,0.177457256,F
+coef_1p5hrs,-0.171124657,F
+coef_2_hrs,-0.4678094,F
+coef_Longer_than_2_hrs,-0.523935526,F
+coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F
+coef_Calibration_Constant_Departure_eq_18,-0.045958531,F
+coef_Calibration_Constant_Departure_eq_19,-0.099009925,F
+coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F
+coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_preprocessor.csv
new file mode 100644
index 000000000..26ac917ae
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork_preprocessor.csv
@@ -0,0 +1,3 @@
+Description,Target,Expression
+#,,
+local scalar distance skim,od_distance,"od_skims[('SOV_DIST', 'MD')]"
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv
new file mode 100644
index 000000000..a9f0a5186
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv
@@ -0,0 +1,315 @@
+Label,Description,Expression,Coefficient
+,,,
+#ESCORT,#ESCORT,,ESCORT
+util_escort_Mode_Choice_Logsum,ESCORT - Mode Choice Logsum,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort')), df.mode_choice_logsum, 0)",coef_escort_Mode_Choice_Logsum
+util_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,ESCORT - Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0))),0)",coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period
+util_escort_Distance_to_destination_Duration_greater_than_0p5_hours,ESCORT - Distance to destination - Duration greater than 0.5 hours,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours
+util_escort_Fulltime_worker_Departure_after_8_00_am_Linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear
+util_escort_Fulltime_worker_Departure_after_3_00_am_Linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear
+util_escort_Fulltime_worker_Duration_lt_0p5_hrs,ESCORT - Full-time worker - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Fulltime_worker_Duration_lt_0p5_hrs
+util_escort_Fulltime_worker_Duration_gt_0p5_hrs,ESCORT - Full-time worker - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Fulltime_worker_Duration_gt_0p5_hrs
+util_escort_University_student_Duration_lt_0p5_hrs,ESCORT - University student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_University_student_Duration_lt_0p5_hrs
+util_escort_Nondriving_age_student_Duration_gt_0p5_hrs,ESCORT - Non-driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs
+util_escort_Driving_age_student_Duration_lt_0p5_hrs,ESCORT - Driving age student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Driving_age_student_Duration_lt_0p5_hrs
+util_escort_Driving_age_student_Duration_gt_0p5_hrs,ESCORT - Driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Driving_age_student_Duration_gt_0p5_hrs
+util_escort_Preschool_kid_Duration_gt_0p5_hrs,ESCORT - Pre-school kid - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Preschool_kid_Duration_gt_0p5_hrs
+util_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs
+util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM
+util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM
+util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM
+util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM
+util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM
+util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM
+util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM
+util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM
+util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start >10)), (np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM
+util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM
+util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM
+util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM
+util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM
+util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM
+util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) &( df.end>25)), (np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM
+util_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs
+#FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run,#FIXME - Number of non-escort tours is not known until the non-mandatory frequency model is run,,FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run
+#util_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,#ESCORT - Number of Individual Tours (excluding escorting) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type != 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs
+util_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,ESCORT - Number of joint tours - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs
+util_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear
+util_escort_Departure_Constant_Before_07_00_AM,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<9)),coef_escort_Departure_Constant_Before_07_00_AM
+util_escort_Departure_Constant_07_00_AM_07_30_AM,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Departure_Constant_07_00_AM_07_30_AM
+util_escort_Departure_Constant_07_30_AM_08_00_AM,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Departure_Constant_07_30_AM_08_00_AM
+util_escort_Departure_Constant_08_00_AM_08_30_AM,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==11)),coef_escort_Departure_Constant_08_00_AM_08_30_AM
+util_escort_Departure_Constant_08_30_AM_09_00_AM,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==12)),coef_escort_Departure_Constant_08_30_AM_09_00_AM
+util_escort_Departure_Constant_After_09_00_AM,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>12)),coef_escort_Departure_Constant_After_09_00_AM
+util_escort_Departure_Constant_01_30_PM_02_00_PM,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==22)),coef_escort_Departure_Constant_01_30_PM_02_00_PM
+util_escort_Departure_Constant_02_00_PM_02_30_PM,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Departure_Constant_02_00_PM_02_30_PM
+util_escort_Departure_Constant_02_30_PM_03_00_PM,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Departure_Constant_02_30_PM_03_00_PM
+util_escort_Departure_Constant_03_00_PM_03_30_PM,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==25)),coef_escort_Departure_Constant_03_00_PM_03_30_PM
+util_escort_Departure_Constant_After_03_30_PM,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>25)),coef_escort_Departure_Constant_After_03_30_PM
+util_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear
+util_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear
+util_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear
+util_escort_Arrival_Constant_Before_07_00_AM,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<9)),coef_escort_Arrival_Constant_Before_07_00_AM
+util_escort_Arrival_Constant_07_00_AM_07_30_AM,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==9)),coef_escort_Arrival_Constant_07_00_AM_07_30_AM
+util_escort_Arrival_Constant_07_30_AM_08_00_AM,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==10)),coef_escort_Arrival_Constant_07_30_AM_08_00_AM
+util_escort_Arrival_Constant_08_00_AM_08_30_AM,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==11)),coef_escort_Arrival_Constant_08_00_AM_08_30_AM
+util_escort_Arrival_Constant_08_30_AM_09_00_AM,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==12)),coef_escort_Arrival_Constant_08_30_AM_09_00_AM
+util_escort_Arrival_Constant_After_09_00_AM,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>12)),coef_escort_Arrival_Constant_After_09_00_AM
+util_escort_Arrival_Constant_02_30_PM_03_00_PM,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==24)),coef_escort_Arrival_Constant_02_30_PM_03_00_PM
+util_escort_Arrival_Constant_03_00_PM_03_30_PM,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==25)),coef_escort_Arrival_Constant_03_00_PM_03_30_PM
+util_escort_Arrival_Constant_03_30_PM_04_00_PM,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==26)),coef_escort_Arrival_Constant_03_30_PM_04_00_PM
+util_escort_Arrival_Constant_04_00_PM_04_30_PM,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==27)),coef_escort_Arrival_Constant_04_00_PM_04_30_PM
+util_escort_Arrival_Constant_After_04_30_PM,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>27)),coef_escort_Arrival_Constant_After_04_30_PM
+util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear
+util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear
+util_escort_Duration_Constant_0_hrs,ESCORT - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Duration_Constant_0_hrs
+util_escort_Duration_Constant_0p5_hrs,ESCORT - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Duration_Constant_0p5_hrs
+util_escort_Duration_Constant_1_hrs,ESCORT - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==2)),coef_escort_Duration_Constant_1_hrs
+util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==3)),coef_escort_Duration_Constant_1p5hrs
+util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs
+util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs
+util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1
+util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2
+util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9
+util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10
+util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23
+util_escort_Calibration_Constant_Departure_eq_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Calibration_Constant_Departure_eq_24
+#SHOPPING,#SHOPPING,,SHOPPING
+util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM  - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear
+util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear
+util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs
+util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr
+util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs
+util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs
+util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr
+util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr
+util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs
+util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,27), 0)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr
+util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs
+util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2)), ((np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance)), 0)",coef_shop_Distance_Duration_gt_1p5_hr
+util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear
+util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root
+util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM
+util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM
+util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM
+util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM
+util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM
+util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM
+util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear
+util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
+util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear
+util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM
+util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM
+util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM
+util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM
+util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM
+util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM
+util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM
+util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM
+util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM
+util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM
+util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear
+util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs
+util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs
+util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs
+util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs
+util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs
+util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs
+util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear
+util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root
+util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1
+util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2
+util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3
+#MAINTENANCE,#MAINTENANCE,,MAINTENANCE
+util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before 10:00 AM  - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear
+util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear
+util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs
+util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr
+util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs
+util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs
+util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr
+util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr
+util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs
+util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr
+util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr
+util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs
+util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr
+util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear
+util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root
+util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM
+util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM
+util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM
+util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM
+util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM
+util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM
+util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM
+util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM
+util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear
+util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared
+util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear
+util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM
+util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM
+util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM
+util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM
+util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM
+util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM
+util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM
+util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM
+util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear
+util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs
+util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs
+util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs
+util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs  - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear
+util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1  hrs  - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root
+util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1
+util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2
+util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3
+util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4
+util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5
+#EATOUT,#EAT-OUT,,EATOUT
+util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs
+util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs
+util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs
+util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs
+util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs
+util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour  - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs
+util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs
+util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM
+util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM
+util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM
+util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear
+util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM
+util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM
+util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM
+util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM
+util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM
+util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM
+util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear
+util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM
+util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM
+util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM
+util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM
+util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear
+util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM
+util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM
+util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM
+util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM
+util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM
+util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM
+util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear
+util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,12), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear
+util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours
+util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous
+util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour
+util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours
+util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more
+util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,13), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear
+util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1
+util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2
+util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3
+util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4
+util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure  = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1
+util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure  = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2
+util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure  = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3
+util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure  = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17
+util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure  = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18
+util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure  = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19
+util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure  = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20
+util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure  = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21
+#SOCIAL,#SOCIAL,,SOCIAL
+util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear
+util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear
+util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear
+util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear
+util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear
+util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs
+util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr
+util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr
+util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) * (df.origin_to_destination_distance)), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear
+util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0) * (df.origin_to_destination_distance)), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear
+util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear
+util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM
+util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM
+util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear
+util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM
+util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM
+util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM
+util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM
+util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM
+util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM
+util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint')  & (df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear
+util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM
+util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM
+util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM
+util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM
+util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear
+util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM
+util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM
+util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM
+util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM
+util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM
+util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM
+util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear
+util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear
+util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours
+util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours
+util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours
+util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more
+util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear
+util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1
+util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2
+util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3
+util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4
+util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5
+util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6
+util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7
+util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8
+util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9
+#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY
+util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear
+util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear
+util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear
+util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear
+util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear
+util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs
+util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr
+util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr
+util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear
+util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear
+util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear
+util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_
+util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM
+util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM
+util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM
+util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM
+util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM
+util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM
+util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear
+util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM
+util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM
+util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM
+util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM
+util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM
+util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM
+util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear
+util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear
+util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM
+util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM
+util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM
+util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM
+util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM
+util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM
+util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM
+util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear
+util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours
+util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous
+util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour
+util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours
+util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours
+util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more
+util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,47), 0)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear
+util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4
+util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5
+util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29
+util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30
+util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31
+util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint_coeffs.csv
new file mode 100644
index 000000000..1717ce523
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint_coeffs.csv
@@ -0,0 +1,307 @@
+coefficient_name,value,constrain
+coef_escort_Mode_Choice_Logsum,1.173173034,F
+coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F
+coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F
+coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F
+coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F
+coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F
+coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F
+coef_escort_University_student_Duration_lt_0p5_hrs,-0.426802718,F
+coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F
+coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.554146191,F
+coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F
+coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F
+coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.477582648,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.204065502,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.091614107,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F
+coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F
+coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F
+coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F
+coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F
+coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F
+coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.039005148,F
+coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F
+coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.117680977,F
+coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F
+coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F
+coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F
+coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.469240002,F
+coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F
+coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F
+coef_escort_Departure_Constant_07_30_AM_08_00_AM,0,T
+coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.214617667,F
+coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F
+coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F
+coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F
+coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F
+coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F
+coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F
+coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F
+coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F
+coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F
+coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F
+coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F
+coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F
+coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F
+coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0,T
+coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.683756801,F
+coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F
+coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F
+coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F
+coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.396838392,F
+coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F
+coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F
+coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F
+coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F
+coef_escort_Duration_Constant_0_hrs,-0.173757322,F
+coef_escort_Duration_Constant_0p5_hrs,0,T
+coef_escort_Duration_Constant_1_hrs,-0.431287743,F
+coef_escort_Duration_Constant_1p5hrs,-0.700473959,F
+coef_escort_Duration_Constant_2_hrs,-1.071871358,F
+coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F
+coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F
+coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F
+coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F
+coef_escort_Calibration_Constant_Departure_eq_10,0.215386864,F
+coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F
+coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F
+coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,-0.190727375,F
+coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,-0.029551313,F
+coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F
+coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F
+coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F
+coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F
+coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F
+coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F
+coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F
+coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F
+coef_shop_Distance_Duration_lt_1p5_hrs,-0.214802537,F
+coef_shop_Distance_Duration_gt_1p5_hr,0.007991656,F
+coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.959875456,F
+coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,1.112594898,F
+coef_shop_Departure_Constant_Before_09_00_AM,-0.446394064,F
+coef_shop_Departure_Constant_09_00_AM_09_30_AM,-0.021669265,F
+coef_shop_Departure_Constant_09_30_AM_10_00_AM,-0.282978638,F
+coef_shop_Departure_Constant_10_00_AM_10_30_AM,0,T
+coef_shop_Departure_Constant_10_30_AM_11_00_AM,-0.309421311,F
+coef_shop_Departure_Constant_After_11_00_AM,-0.541073357,F
+coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,-0.072013428,F
+coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.000653398,F
+coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.18376635,F
+coef_shop_Arrival_Constant_Before_12_30_PM,-0.716195343,F
+coef_shop_Arrival_Constant_12_30_PM_03_00_PM,-0.502714001,F
+coef_shop_Arrival_Constant_03_00_PM_03_30_PM,-0.167868872,F
+coef_shop_Arrival_Constant_03_30_PM_04_00_PM,-0.156786941,F
+coef_shop_Arrival_Constant_04_00_PM_04_30_PM,0,T
+coef_shop_Arrival_Constant_04_30_PM_05_00_PM,-0.057314044,F
+coef_shop_Arrival_Constant_05_00_PM_05_30_PM,-0.580040851,F
+coef_shop_Arrival_Constant_05_30_PM_07_00_PM,-0.32239566,F
+coef_shop_Arrival_Constant_07_00_PM_09_30_PM,-0.347828147,F
+coef_shop_Arrival_Constant_After_09_30_PM,-1.123574723,F
+coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.499770654,F
+coef_shop_Duration_Constant_0_hrs,-0.131743185,F
+coef_shop_Duration_Constant_0p5_hrs,0.888857137,F
+coef_shop_Duration_Constant_1_hrs,0,T
+coef_shop_Duration_Constant_1p5hrs,-0.333413031,F
+coef_shop_Duration_Constant_2_hrs,-0.850897912,F
+coef_shop_Duration_Constant_Longer_than_2_hrs,-1.203783479,F
+coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.293581223,F
+coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,-0.215759138,F
+coef_shop_Calibration_Constant_Duration_eq_1,-0.138450424,F
+coef_shop_Calibration_Constant_Duration_eq_2,-0.092704403,F
+coef_shop_Calibration_Constant_Duration_eq_3,-0.087738073,F
+coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.139150288,F
+coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,-0.065786345,F
+coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F
+coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F
+coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F
+coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F
+coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F
+coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F
+coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F
+coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0,T
+coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F
+coef_maint_Distance_Duration_lt_1p5_hrs,-0.214802537,F
+coef_maint_Distance_Duration_gt_1p5_hr,0.007991656,F
+coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,-0.864112609,F
+coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.504598473,F
+coef_maint_Departure_Constant_Before_08_00_AM,-0.383711788,F
+coef_maint_Departure_Constant_08_00_AM_08_30_AM,-0.076771517,F
+coef_maint_Departure_Constant_08_30_AM_09_00_AM,-0.169259979,F
+coef_maint_Departure_Constant_09_00_AM_09_30_AM,-0.051785379,F
+coef_maint_Departure_Constant_09_30_AM_10_00_AM,-0.214942451,F
+coef_maint_Departure_Constant_10_00_AM_10_30_AM,0,T
+coef_maint_Departure_Constant_10_30_AM_11_00_AM,-0.427568963,F
+coef_maint_Departure_Constant_After_11_00_AM,-0.520863411,F
+coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.042879095,F
+coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.003157293,F
+coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,-0.226803619,F
+coef_maint_Arrival_Constant_Before_10_30_AM,-0.223212258,F
+coef_maint_Arrival_Constant_10_30_AM_11_00_AM,0,T
+coef_maint_Arrival_Constant_11_00_AM_11_30_AM,-0.128382637,F
+coef_maint_Arrival_Constant_11_30_AM_01_30_PM,0.167977332,F
+coef_maint_Arrival_Constant_01_30_PM_02_30_PM,-0.149495878,F
+coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.087679934,F
+coef_maint_Arrival_Constant_04_00_PM_04_30_PM,0.121707557,F
+coef_maint_Arrival_Constant_After_04_30_PM,0.106745013,F
+coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.232610927,F
+coef_maint_Duration_Constant_0_hrs,-0.483549396,F
+coef_maint_Duration_Constant_0p5_hrs,0,T
+coef_maint_Duration_Constant_Longer_than_0p5_hrs,-1.450618319,F
+coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,-0.275082922,F
+coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.208434683,F
+coef_maint_Calibration_Constant_Duration_eq_1,-0.124602605,F
+coef_maint_Calibration_Constant_Duration_eq_2,-0.103637715,F
+coef_maint_Calibration_Constant_Duration_eq_3,-0.225442145,F
+coef_maint_Calibration_Constant_Duration_eq_4,-0.145273012,F
+coef_maint_Calibration_Constant_Duration_eq_5,-0.019241539,F
+coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.134981987,F
+coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.017860742,F
+coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,1.002485807,F
+coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,0.499822018,F
+coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.259409942,F
+coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.785123348,F
+coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.626003709,F
+coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.531539506,F
+coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.673838195,F
+coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.422292261,F
+coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.033290717,F
+coef_eatout_Departure_Constant_Before_05_30_PM,-0.561079452,F
+coef_eatout_Departure_Constant_05_30_PM_06_00_PM,-0.178719161,F
+coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0,T
+coef_eatout_Departure_Constant_06_30_PM_07_00_PM,-0.282095841,F
+coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.299748613,F
+coef_eatout_Departure_Constant_After_07_30_PM,-0.845300559,F
+coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.667843486,F
+coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,2.002032369,F
+coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,2.115334472,F
+coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,1.647879687,F
+coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,1.525310078,F
+coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,-0.152980854,F
+coef_eatout_Arrival_Constant_Before_7_00_PM,-0.41165142,F
+coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.384557379,F
+coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.044050359,F
+coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T
+coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.239939049,F
+coef_eatout_Arrival_Constant_After_09_00_PM,-0.248639696,F
+coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204771082,F
+coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.3357414,F
+coef_eatout_Duration_Constant_0_hours,-4.268996522,F
+coef_eatout_Duration_Constant_0p5_hous,-1.323297693,F
+coef_eatout_Duration_Constant_1_hour,0,T
+coef_eatout_Duration_Constant_1p5_hours,-0.195669185,F
+coef_eatout_Duration_Constant_2_hours_or_more,-0.523723192,F
+coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,-0.649331488,F
+coef_eatout_Calibration_Constant_Duration_eq_1,-0.333697861,F
+coef_eatout_Calibration_Constant_Duration_eq_2,-0.245716,F
+coef_eatout_Calibration_Constant_Duration_eq_3,0.052708833,F
+coef_eatout_Calibration_Constant_Duration_eq_4,0.041571499,F
+coef_eatout_Calibration_Constant_Departure_eq_1,-10,F
+coef_eatout_Calibration_Constant_Departure_eq_2,-10,F
+coef_eatout_Calibration_Constant_Departure_eq_3,-10,F
+coef_eatout_Calibration_Constant_Departure_eq_17,0.706568704,F
+coef_eatout_Calibration_Constant_Departure_eq_18,0.634353544,F
+coef_eatout_Calibration_Constant_Departure_eq_19,0.584387268,F
+coef_eatout_Calibration_Constant_Departure_eq_20,0.469777884,F
+coef_eatout_Calibration_Constant_Departure_eq_21,0.39548931,F
+coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F
+coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F
+coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F
+coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F
+coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F
+coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F
+coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F
+coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F
+coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F
+coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F
+coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.529943196,F
+coef_social_Departure_Constant_Before_09_00_AM,-0.198438086,F
+coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.137620628,F
+coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.142078961,F
+coef_social_Departure_Constant_Before_05_30_PM,-0.390965052,F
+coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.453580491,F
+coef_social_Departure_Constant_06_00_PM_06_30_PM,0,T
+coef_social_Departure_Constant_06_30_PM_07_00_PM,-0.088537991,F
+coef_social_Departure_Constant_07_00_PM_07_30_PM,0.052983115,F
+coef_social_Departure_Constant_After_07_30_PM,-0.649629162,F
+coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.09574499,F
+coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.37674882,F
+coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.583355461,F
+coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.727855233,F
+coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.249551955,F
+coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.053771388,F
+coef_social_Arrival_Constant_Before_8_30_PM,0.308763611,F
+coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,-0.208797698,F
+coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,-0.336319511,F
+coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0,T
+coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,-0.055707591,F
+coef_social_Arrival_Constant_After_10_30_PM,-0.612356296,F
+coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-0.348479901,F
+coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,0.619073863,F
+coef_social_Duration_Constant_Less_than_2_hours,-0.584024011,F
+coef_social_Duration_Constant_2_hours,-0.271552271,F
+coef_social_Duration_Constant_2p5_hours,0,T
+coef_social_Duration_Constant_3_hours_or_more,0.042083404,F
+coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,-0.13049452,F
+coef_social_Calibration_Constant_Duration_eq_1,-1.346772472,F
+coef_social_Calibration_Constant_Duration_eq_2,0.377121689,F
+coef_social_Calibration_Constant_Duration_eq_3,0.179818928,F
+coef_social_Calibration_Constant_Duration_eq_4,-0.283418619,F
+coef_social_Calibration_Constant_Duration_eq_5,-0.103541313,F
+coef_social_Calibration_Constant_Duration_eq_6,-0.03704707,F
+coef_social_Calibration_Constant_Duration_eq_7,-0.062437167,F
+coef_social_Calibration_Constant_Duration_eq_8,0.047640282,F
+coef_social_Calibration_Constant_Duration_eq_9,0.284369793,F
+coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F
+coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F
+coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F
+coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F
+coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F
+coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F
+coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F
+coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F
+coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F
+coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F
+coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.742176805,F
+coef_disc_Departure_Constant_Before_7_30_AM_,-1.323901585,F
+coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,-0.695441631,F
+coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,-0.269903336,F
+coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,-0.093709211,F
+coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,0.265634082,F
+coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,0.287521134,F
+coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,0.396547817,F
+coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-0.245885745,F
+coef_disc_Departure_Constant_Before_05_00_PM,-1.344482349,F
+coef_disc_Departure_Constant_05_00_PM_05_30_PM,-0.622632748,F
+coef_disc_Departure_Constant_05_30_PM_06_00_PM,-0.456718676,F
+coef_disc_Departure_Constant_06_00_PM_06_30_PM,-0.206896106,F
+coef_disc_Departure_Constant_06_30_PM_07_00_PM,0,T
+coef_disc_Departure_Constant_After_07_00_PM,-0.46439343,F
+coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,-0.291998986,F
+coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.148649188,F
+coef_disc_Arrival_Constant_Before_6_30_PM,0.668775963,F
+coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,-0.053520826,F
+coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,0.099726391,F
+coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,0.063414092,F
+coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,0,T
+coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,-0.18610847,F
+coef_disc_Arrival_Constant_After_9_00_PM,-0.423207857,F
+coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.525545923,F
+coef_disc_Duration_Constant_0_hours,-0.944257762,F
+coef_disc_Duration_Constant_0p5_hous,-0.117695955,F
+coef_disc_Duration_Constant_1_hour,0.438403665,F
+coef_disc_Duration_Constant_1p5_hours,-0.002500048,F
+coef_disc_Duration_Constant_2_hours,0,T
+coef_disc_Duration_Constant_2p5_hours_or_more,0.239192556,F
+coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.108260689,F
+coef_disc_Calibration_Constant_Duration_eq_4,-0.132674257,F
+coef_disc_Calibration_Constant_Duration_eq_5,-0.013371871,F
+coef_disc_Calibration_Constant_Departure_eq_29,0.232927977,F
+coef_disc_Calibration_Constant_Departure_eq_30,0.306104612,F
+coef_disc_Calibration_Constant_Departure_eq_31,0.285520678,F
+coef_disc_Calibration_Constant_Departure_eq_32,0.115886631,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout.csv
new file mode 100644
index 000000000..6c02794aa
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout.csv
@@ -0,0 +1,60 @@
+Label,Description,Expression,Coefficient
+#EAT-OUT,#EAT-OUT,,#EAT-OUT
+util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr
+util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr
+util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr
+util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr
+util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr
+util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear
+util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr
+util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr
+util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm
+util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr
+util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am
+util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am
+util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am
+util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm
+util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm
+util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm
+util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm
+util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear
+util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.start<31)),coef_eatout_departure_constant_before_6_pm
+util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm
+util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm
+util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm
+util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.start>33)),coef_eatout_departure_constant_after_7_30_pm
+util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear
+util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am
+util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm
+util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm
+util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm
+util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm
+util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm
+util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear
+util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm
+util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm
+util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm
+util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm
+util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm
+util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm
+util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.end>36)),coef_eatout_arrival_constant_after_9_pm
+util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear
+util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.duration==0)),coef_eatout_duration_constant_0_hour
+util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.duration==1)),coef_eatout_duration_constant_30_minutes
+util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.duration==2)),coef_eatout_duration_constant_1_hour
+util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes
+util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.duration==4)),coef_eatout_duration_constant_2_hours
+util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more
+util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear
+util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_eatout_calibration_constant_duration_1
+util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_eatout_calibration_constant_duration_2
+util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.duration==2)),coef_eatout_calibration_constant_duration_3
+util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.duration==3)),coef_eatout_calibration_constant_duration_4
+util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure  = 1,@((df.start == 1)),coef_eatout_calibration_constant_departure_1
+util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure  = 2,@((df.start == 2)),coef_eatout_calibration_constant_departure_2
+util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure  = 3,@((df.start == 3)),coef_eatout_calibration_constant_departure_3
+util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure  = 17,@((df.start ==17)),coef_eatout_calibration_constant_departure_17
+util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure  = 18,@((df.start ==18)),coef_eatout_calibration_constant_departure_18
+util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure  = 19,@((df.start ==19)),coef_eatout_calibration_constant_departure_19
+util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure  = 20,@((df.start ==20)),coef_eatout_calibration_constant_departure_20
+util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure  = 21,@((df.start ==21)),coef_eatout_calibration_constant_departure_21
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv
new file mode 100644
index 000000000..d94961976
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv
@@ -0,0 +1,59 @@
+coefficient_name,value,constrain
+coef_eatout_distance_to_destination_duration_less_than_1_hr,-0.134981987,F
+coef_eatout_distance_to_destination_duration_greater_than_1_hr,0.017860742,F
+coef_eatout_low_income_duration_less_than_1_hr,1.002485807,F
+coef_eatout_medium_income_duration_less_than_1_hr,0.499822018,F
+coef_eatout_zeroauto_HH_duration_greater_than_1_hr,0.259409942,F
+coef_eatout_university_student_departure_after_7_pm_linear,0.293827759,F
+coef_eatout_female_duration_less_than_1_hr,-0.399414247,F
+coef_eatout_female_duration_greater_than_1_hr,0.064593482,F
+coef_eatout_time_pressure_departure_before_6_30_pm,0.083673557,F
+coef_eatout_time_pressure_duration_less_than_1_hr,1.69632588,F
+coef_eatout_departure_constant_7_30_am_to_9_am,1.222417262,F
+coef_eatout_departure_constant_10_30_am_to_11_am,0.519559134,F
+coef_eatout_departure_constant_11_am_to_11_30_am,1.191543552,F
+coef_eatout_departure_constant_11_30_am_to_12_pm,1.66870995,F
+coef_eatout_departure_constant_12_pm_to_12_30_pm,1.164106986,F
+coef_eatout_departure_constant_12_30_pm_to_1_pm,1.057346496,F
+coef_eatout_departure_constant_1_pm_to_1_30_pm,0.728959087,F
+coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.477439748,F
+coef_eatout_departure_constant_before_6_pm,-1.21554671,F
+coef_eatout_departure_constant_6_pm_to_6_30_pm,-0.425984037,F
+coef_eatout_departure_constant_6_30_pm_to_7_pm,0,T
+coef_eatout_departure_constant_7_pm_to_7_30_pm,-0.227800647,F
+coef_eatout_departure_constant_after_7_30_pm,-0.293904097,F
+coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.55440861,F
+coef_eatout_arrival_constant_9_30_am_to_11_am,0.486337344,F
+coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.629299404,F
+coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.938528731,F
+coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.584420106,F
+coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.842550215,F
+coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.298486505,F
+coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.125034982,F
+coef_eatout_arrival_constant_before_6_30_pm,-0.029062996,F
+coef_eatout_arrival_constant_6_30_pm_to_7_pm,-0.509075598,F
+coef_eatout_arrival_constant_7_pm_to_7_30_pm,-0.124885931,F
+coef_eatout_arrival_constant_7_30_pm_to_8_pm,-0.605455664,F
+coef_eatout_arrival_constant_8_pm_to_8_30_pm,0,T
+coef_eatout_arrival_constant_8_30_pm_to_9_pm,-0.657382835,F
+coef_eatout_arrival_constant_after_9_pm,-0.813629712,F
+coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.204722406,F
+coef_eatout_duration_constant_0_hour,-11.72660422,F
+coef_eatout_duration_constant_30_minutes,-5.08873115,F
+coef_eatout_duration_constant_1_hour,-0.125521065,F
+coef_eatout_duration_constant_1_hour_30_minutes,0,T
+coef_eatout_duration_constant_2_hours,-0.124807752,F
+coef_eatout_duration_constant_2_hour_30_minutes_or_more,-0.156019835,F
+coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.3357414,F
+coef_eatout_calibration_constant_duration_1,-0.333697861,F
+coef_eatout_calibration_constant_duration_2,-0.245716,F
+coef_eatout_calibration_constant_duration_3,0.052708833,F
+coef_eatout_calibration_constant_duration_4,0.041571499,F
+coef_eatout_calibration_constant_departure_1,-10,F
+coef_eatout_calibration_constant_departure_2,-10,F
+coef_eatout_calibration_constant_departure_3,-10,F
+coef_eatout_calibration_constant_departure_17,0.706568704,F
+coef_eatout_calibration_constant_departure_18,0.634353544,F
+coef_eatout_calibration_constant_departure_19,0.584387268,F
+coef_eatout_calibration_constant_departure_20,0.469777884,F
+coef_eatout_calibration_constant_departure_21,0.39548931,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort.csv
new file mode 100644
index 000000000..b07727992
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort.csv
@@ -0,0 +1,76 @@
+Label,Description,Expression,Coefficient
+# ESCORT,,,
+util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum
+"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,#
+util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes
+util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes
+util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear
+"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,,
+util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear
+util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes
+util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes
+util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes
+util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes
+util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes
+util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes
+"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,,
+util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes
+util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes
+util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am
+util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am
+util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm
+util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm
+util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am
+util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am
+util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm
+util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm
+util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am
+util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm
+util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am
+util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm
+util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am
+util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am
+util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm
+util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes
+util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes
+util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes
+util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear
+util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.start<9)),coef_escort_departure_constant_before_7_am
+util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am
+util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am
+util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am
+util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am
+util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.start>12)),coef_escort_departure_constant_after_9_am
+util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm
+util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm
+util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm
+util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm
+util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.start>25)),coef_escort_departure_constant_after_3_30_pm
+util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear
+util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear
+util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear
+util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.end<9)),coef_escort_arrival_constant_before_7_am
+util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am
+util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am
+util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am
+util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am
+util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.end>12)),coef_escort_arrival_constant_after_9_am
+util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm
+util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm
+util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm
+util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm
+util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_escort_arrival_constant_after_4_30_pm
+util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear
+util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear
+util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.duration==0)),coef_escort_duration_constant_0_hour
+util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.duration==1)),coef_escort_duration_constant_30_minutes
+util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.duration==2)),coef_escort_duration_constant_1_hour
+util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes
+util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.duration==4)),coef_escort_duration_constant_2_hours
+util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_escort_duration_constant_longer_than_2_hours
+util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_escort_calibration_constant_duration_1
+util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_escort_calibration_constant_duration_2
+util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.start==9)),coef_escort_calibration_constant_departure_9
+util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.start==10)),coef_escort_calibration_constant_departure_10
+util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.start==23)),coef_escort_calibration_constant_departure_23
+util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.start==24)),coef_escort_calibration_constant_departure_24
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv
new file mode 100644
index 000000000..76a5b7c13
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv
@@ -0,0 +1,72 @@
+coefficient_name,value,constrain
+coef_escort_mode_choice_logsum,1.173173034,F
+coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.335017673,F
+coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.005298165,F
+coef_escort_fulltime_worker_departure_after_8_am_linear,-0.037980109,F
+coef_escort_fulltime_worker_departure_after_3_am_linear,0.163254125,F
+coef_escort_fulltime_worker_duration_less_than_30_minutes,-0.275077482,F
+coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.051530545,F
+coef_escort_university_student_duration_less_than_30_minutes,-0.426802718,F
+coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.240582361,F
+coef_escort_driving_age_student_duration_less_than_30_minutes,-0.554146191,F
+coef_escort_driving_age_student_duration_greater_than_30_minutes,0.299387708,F
+coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.195482563,F
+coef_escort_med_high_income_duration_greater_than_30_minutes,-0.029281467,F
+coef_escort_households_with_no_kids_departure_before_7_30_am,0.589083327,F
+coef_escort_households_with_no_kids_departure_after_8_00_am,0.086690827,F
+coef_escort_households_with_no_kids_departure_before_2_30_pm,0.477582648,F
+coef_escort_households_with_no_kids_departure_after_3_00_pm,-0.204065502,F
+coef_escort_households_with_no_kids_arrival_before_8_am,-0.360039254,F
+coef_escort_households_with_no_kids_arrival_after_8_30_am,0.091614107,F
+coef_escort_households_with_no_kids_arrival_before_3_pm,0.432854268,F
+coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.131037275,F
+coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.109700265,F
+coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.224568648,F
+coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-0.357416434,F
+coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.629285298,F
+coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.039005148,F
+coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,-0.06556611,F
+coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.117680977,F
+coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,-0.057322708,F
+coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.062899692,F
+coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,-0.048533895,F
+coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.469240002,F
+coef_escort_departure_constant_before_7_am,-2.070292862,F
+coef_escort_departure_constant_7_am_to_7_30_am,-0.642734296,F
+coef_escort_departure_constant_7_30_am_to_8_am,0,T
+coef_escort_departure_constant_8_am_to_8_30_am,-0.214617667,F
+coef_escort_departure_constant_8_30_am_to_9_am,-0.147266606,F
+coef_escort_departure_constant_after_9_am,-1.356686422,F
+coef_escort_departure_constant_1_30_pm_to_2_pm,0.368092381,F
+coef_escort_departure_constant_2_pm_to_2_30_pm,1.166803383,F
+coef_escort_departure_constant_2_30_pm_to_3_pm,1.28466083,F
+coef_escort_departure_constant_3_pm_to_3_30_pm,0.581891245,F
+coef_escort_departure_constant_after_3_30_pm,0.834510243,F
+coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.175257649,F
+coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,-0.019161202,F
+coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.44978138,F
+coef_escort_arrival_constant_before_7_am,0.549584585,F
+coef_escort_arrival_constant_7_am_to_7_30_am,0.488181278,F
+coef_escort_arrival_constant_7_30_am_to_8_am,0.236447651,F
+coef_escort_arrival_constant_8_am_to_8_30_am,0,T
+coef_escort_arrival_constant_8_30_am_to_9_am,-0.683756801,F
+coef_escort_arrival_constant_after_9_am,-1.428888485,F
+coef_escort_arrival_constant_2_30_pm_to_3_pm,1.311480662,F
+coef_escort_arrival_constant_3_pm_to_3_30_pm,1.316883154,F
+coef_escort_arrival_constant_3_30_pm_to_4_pm,1.396838392,F
+coef_escort_arrival_constant_4_pm_to_4_30_pm,1.03146139,F
+coef_escort_arrival_constant_after_4_30_pm,0.907344583,F
+coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.148408887,F
+coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.389082896,F
+coef_escort_duration_constant_0_hour,-0.173757322,F
+coef_escort_duration_constant_30_minutes,0,T
+coef_escort_duration_constant_1_hour,-0.431287743,F
+coef_escort_duration_constant_1_hour_30_minutes,-0.700473959,F
+coef_escort_duration_constant_2_hours,-1.071871358,F
+coef_escort_duration_constant_longer_than_2_hours,-1.691098421,F
+coef_escort_calibration_constant_duration_1,-0.047200214,F
+coef_escort_calibration_constant_duration_2,0.035611332,F
+coef_escort_calibration_constant_departure_9,0.106814756,F
+coef_escort_calibration_constant_departure_10,0.215386864,F
+coef_escort_calibration_constant_departure_23,-0.255087318,F
+coef_escort_calibration_constant_departure_24,-0.296870428,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr.csv
new file mode 100644
index 000000000..69a5e039a
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr.csv
@@ -0,0 +1,55 @@
+Label,Description,Expression,Coefficient
+#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY
+util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear
+util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear
+util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear
+util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear
+util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear
+util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear
+util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear
+util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear
+"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,,
+util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear
+util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear
+util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr
+util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr
+util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr
+util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear
+util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.start<10)),coef_discretionary_departure_constant_before_7_30_am
+util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am
+util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am
+util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am
+util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am
+util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am
+util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am
+util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear
+util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.start<29)),coef_discretionary_departure_constant_before_5_pm
+util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm
+util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm
+util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm
+util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm
+util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.start>32)),coef_discretionary_departure_constant_after_7_pm
+util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear
+util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear
+util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm
+util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm
+util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm
+util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm
+util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm
+util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm
+util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_discretionary_arrival_constant_after_9_pm
+util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear
+util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.duration==0)),coef_discretionary_duration_constant_0_hour
+util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.duration==1)),coef_discretionary_duration_constant_30_minutes
+util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.duration==2)),coef_discretionary_duration_constant_1_hour
+util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes
+util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.duration==4)),coef_discretionary_duration_constant_2_hours
+util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes
+util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more
+util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear
+util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.duration==3)),coef_discretionary_calibration_constant_duration_4
+util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.duration==4)),coef_discretionary_calibration_constant_duration_5
+util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.start==29)),coef_discretionary_calibration_constant_departure_29
+util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.start==30)),coef_discretionary_calibration_constant_departure_30
+util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.start==31)),coef_discretionary_calibration_constant_departure_31
+util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.start==32)),coef_discretionary_calibration_constant_departure_32
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv
new file mode 100644
index 000000000..d9a4e607c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv
@@ -0,0 +1,53 @@
+coefficient_name,value,constrain
+coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F
+coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F
+coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F
+coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,-0.312282762,F
+coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F
+coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F
+coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F
+coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F
+coef_discretionary_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F
+coef_discretionary_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F
+coef_discretionary_time_pressure_duration_less_than_1_hr,-0.229264474,F
+coef_discretionary_time_pressure_duration_greater_than_1_hr,0.219325112,F
+coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F
+coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.742176805,F
+coef_discretionary_departure_constant_before_7_30_am,-1.323901585,F
+coef_discretionary_departure_constant_7_30_am_to_8_am,-0.695441631,F
+coef_discretionary_departure_constant_8_am_to_8_30_am,-0.269903336,F
+coef_discretionary_departure_constant_8_30_am_to_9_am,-0.093709211,F
+coef_discretionary_departure_constant_9_am_to_9_30_am,0.265634082,F
+coef_discretionary_departure_constant_9_30_am_to_10_am,0.287521134,F
+coef_discretionary_departure_constant_10_am_to_10_30_am,0.396547817,F
+coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,-0.245885745,F
+coef_discretionary_departure_constant_before_5_pm,-1.344482349,F
+coef_discretionary_departure_constant_5_pm_to_5_30_pm,-0.622632748,F
+coef_discretionary_departure_constant_5_30_pm_to_6_pm,-0.456718676,F
+coef_discretionary_departure_constant_6_pm_to_6_30_pm,-0.206896106,F
+coef_discretionary_departure_constant_6_30_pm_to_7_pm,0,T
+coef_discretionary_departure_constant_after_7_pm,-0.46439343,F
+coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-0.291998986,F
+coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.148649188,F
+coef_discretionary_arrival_constant_before_6_30_pm,0.668775963,F
+coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-0.053520826,F
+coef_discretionary_arrival_constant_7_pm_to_7_30_pm,0.099726391,F
+coef_discretionary_arrival_constant_7_30_pm_to_8_pm,0.063414092,F
+coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0,T
+coef_discretionary_arrival_constant_8_30_pm_to_9_pm,-0.18610847,F
+coef_discretionary_arrival_constant_after_9_pm,-0.423207857,F
+coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.525545923,F
+coef_discretionary_duration_constant_0_hour,0.436988282,F
+coef_discretionary_duration_constant_30_minutes,1.35967577,F
+coef_discretionary_duration_constant_1_hour,1.692672999,F
+coef_discretionary_duration_constant_1_hr_30_minutes,1.118932964,F
+coef_discretionary_duration_constant_2_hours,0.771255733,F
+coef_discretionary_duration_constant_2_hr_30_minutes,0,T
+coef_discretionary_duration_constant_3_hours_or_more,-0.631242175,F
+coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.700935645,F
+coef_discretionary_calibration_constant_duration_4,-0.132674257,F
+coef_discretionary_calibration_constant_duration_5,-0.013371871,F
+coef_discretionary_calibration_constant_departure_29,0.232927977,F
+coef_discretionary_calibration_constant_departure_30,0.306104612,F
+coef_discretionary_calibration_constant_departure_31,0.285520678,F
+coef_discretionary_calibration_constant_departure_32,0.115886631,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv
new file mode 100644
index 000000000..04f26223e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv
@@ -0,0 +1,54 @@
+Label,Description,Expression,Coefficient
+#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE
+util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes
+util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes
+util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes
+util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes
+util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes
+util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes
+util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes
+util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes
+util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes
+util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes
+util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes
+util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes
+util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes
+util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes
+util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes
+util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes
+util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes
+util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes
+util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes
+util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes
+util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear
+util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root
+util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am
+util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am
+util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am
+util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am
+util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am
+util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am
+util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am
+util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am
+util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear
+util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
+util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear
+util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am
+util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am
+util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am
+util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm
+util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm
+util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm
+util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm
+util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm
+util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear
+util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr
+util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes
+util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes
+util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr  - Linear,"@np.where(((df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear
+util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1  hr  - Square Root,"@np.where(((df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root
+util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1
+util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2
+util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3
+util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4
+util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv
new file mode 100644
index 000000000..991068579
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv
@@ -0,0 +1,53 @@
+coefficient_name,value,constrain
+coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F
+coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F
+coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F
+coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F
+coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F
+coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F
+coef_maintenance_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F
+coef_maintenance_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F
+coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F
+coef_maintenance_female_duration_less_than_1_hour_30_minutes,-0.417208254,F
+coef_maintenance_female_duration_greater_than_1_hour_30_minutes,0.045801918,F
+coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F
+coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F
+coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,0,T
+coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F
+coef_maintenance_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F
+coef_maintenance_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F
+coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F
+coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F
+coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F
+coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.864112609,F
+coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.504598473,F
+coef_maintenance_departure_constant_before_8_am,-0.383711788,F
+coef_maintenance_departure_constant_8_am_to_8_30_am,-0.076771517,F
+coef_maintenance_departure_constant_8_30_am_to_9_00_am,-0.169259979,F
+coef_maintenance_departure_constant_9_am_to_9_30_am,-0.051785379,F
+coef_maintenance_departure_constant_9_30_am_to_10_am,-0.214942451,F
+coef_maintenance_departure_constant_10_am_to_10_30_am,0,T
+coef_maintenance_departure_constant_10_30_am_to_11_am,-0.427568963,F
+coef_maintenance_departure_constant_after_11_am,-0.520863411,F
+coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.042879095,F
+coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.003157293,F
+coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.226803619,F
+coef_maintenance_arrival_constant_before_10_30_am,-0.223212258,F
+coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T
+coef_maintenance_arrival_constant_11_am_to_11_30_am,-0.128382637,F
+coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.167977332,F
+coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,-0.149495878,F
+coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.087679934,F
+coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.121707557,F
+coef_maintenance_arrival_constant_after_4_30_pm,0.106745013,F
+coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.232610927,F
+coef_maintenance_duration_constant_0_hr,-0.483549396,F
+coef_maintenance_duration_constant_30_minutes,0,T
+coef_maintenance_duration_constant_longer_than_30_minutes,-1.450618319,F
+coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.275082922,F
+coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,0.208434683,F
+coef_maintenance_calibration_constant_duration_1,-0.124602605,F
+coef_maintenance_calibration_constant_duration_2,-0.103637715,F
+coef_maintenance_calibration_constant_duration_3,-0.225442145,F
+coef_maintenance_calibration_constant_duration_4,-0.145273012,F
+coef_maintenance_calibration_constant_duration_5,-0.019241539,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv
new file mode 100644
index 000000000..11211303e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv
@@ -0,0 +1,54 @@
+Label,Description,Expression,Coefficient
+#SHOPPING,,,#SHOPPING
+util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes
+util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes
+util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes
+util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes
+util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes
+util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes
+util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes
+util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes
+util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes
+util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes
+util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes
+util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes
+util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes
+util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes
+util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes
+util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes
+util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes
+util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes
+util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)),  (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes
+util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear
+util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root
+util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am
+util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am
+util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am
+util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am
+util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am
+util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am
+util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear
+util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared
+util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear
+util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm
+util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm
+util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm
+util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm
+util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm
+util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm
+util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm
+util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm
+util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm
+util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm
+util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear
+util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr
+util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes
+util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr
+util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes
+util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs
+util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs
+util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear
+util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root
+util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1
+util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2
+util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv
new file mode 100644
index 000000000..fac50e210
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv
@@ -0,0 +1,53 @@
+coefficient_name,value,constrain
+coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F
+coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F
+coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F
+coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F
+coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F
+coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F
+coef_shopping_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F
+coef_shopping_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F
+coef_shopping_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F
+coef_shopping_female_duration_less_than_1_hour_30_minutes,-0.417208254,F
+coef_shopping_female_duration_greater_than_1_hour_30_minutes,0.045801918,F
+coef_shopping_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F
+coef_shopping_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F
+coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F
+coef_shopping_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F
+coef_shopping_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F
+coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F
+coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F
+coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F
+coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.959875456,F
+coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,1.112594898,F
+coef_shopping_departure_constant_before_9_am,-0.446394064,F
+coef_shopping_departure_constant_9_am_to_9_30_am,-0.021669265,F
+coef_shopping_departure_constant_9_30_am_to_10_am,-0.282978638,F
+coef_shopping_departure_constant_10_am_to_10_30_am,0,T
+coef_shopping_departure_constant_10_30_am_to_11_00_am,-0.309421311,F
+coef_shopping_departure_constant_after_11_am,-0.541073357,F
+coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,-0.072013428,F
+coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.000653398,F
+coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,-0.18376635,F
+coef_shopping_arrival_constant_before_12_30_pm,-0.716195343,F
+coef_shopping_arrival_constant_12_30_pm_to_3_pm,-0.502714001,F
+coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.167868872,F
+coef_shopping_arrival_constant_3_30_pm_to_4_pm,-0.156786941,F
+coef_shopping_arrival_constant_4_pm_to_4_30_pm,0,T
+coef_shopping_arrival_constant_4_30_pm_to_5_pm,-0.057314044,F
+coef_shopping_arrival_constant_5_pm_to_5_30_pm,-0.580040851,F
+coef_shopping_arrival_constant_5_30_pm_to_7_pm,-0.32239566,F
+coef_shopping_arrival_constant_7_pm_to_9_30_pm,-0.347828147,F
+coef_shopping_arrival_constant_after_9_30_pm,-1.123574723,F
+coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.499770654,F
+coef_shopping_duration_constant_0_hr,-0.131743185,F
+coef_shopping_duration_constant_30_minutes,0.888857137,F
+coef_shopping_duration_constant_1_hr,0,T
+coef_shopping_duration_constant_1_hour_30_minutes,-0.333413031,F
+coef_shopping_duration_constant_2_hrs,-0.850897912,F
+coef_shopping_duration_constant_longer_than_2_hrs,-1.203783479,F
+coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.293581223,F
+coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,-0.215759138,F
+coef_shopping_calibration_constant_duration_1,-0.138450424,F
+coef_shopping_calibration_constant_duration_2,-0.092704403,F
+coef_shopping_calibration_constant_duration_3,-0.087738073,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social.csv
new file mode 100644
index 000000000..174da53c8
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social.csv
@@ -0,0 +1,54 @@
+Label,Description,Expression,Coefficient
+#SOCIAL,#SOCIAL,,#SOCIAL
+util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear
+util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear
+util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear
+util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear
+util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear
+util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear
+util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear
+util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear
+"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,,
+util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_1_hr_linear
+util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_1_hr_linear
+util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr
+util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr
+util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr
+util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@((df.start<12)) * ((np.minimum(12-df.start,48)*(df.start<12)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear
+util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am
+util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am
+util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear
+util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm
+util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm
+util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm
+util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm
+util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm
+util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm
+util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear
+util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm
+util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm
+util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm
+util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm
+util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear
+util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm
+util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm
+util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm
+util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm
+util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm
+util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm
+util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear
+util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear
+util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours
+util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours
+util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes
+util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more
+util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear
+util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1
+util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2
+util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3
+util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4
+util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5
+util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6
+util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7
+util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8
+util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv
new file mode 100644
index 000000000..9819e0e69
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv
@@ -0,0 +1,52 @@
+coefficient_name,value,constrain
+coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F
+coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F
+coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F
+coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,-0.312282762,F
+coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F
+coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F
+coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F
+coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F
+coef_social_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F
+coef_social_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F
+coef_social_time_pressure_duration_less_than_1_hr,-0.229264474,F
+coef_social_time_pressure_duration_greater_than_1_hr,0.219325112,F
+coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F
+coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.529943196,F
+coef_social_departure_constant_before_9_am,-0.198438086,F
+coef_social_departure_constant_9_am_to_9_30_am,0.137620628,F
+coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,-0.142078961,F
+coef_social_departure_constant_before_5_30_pm,-0.390965052,F
+coef_social_departure_constant_5_30_pm_to_6_pm,-0.453580491,F
+coef_social_departure_constant_6_pm_to_6_30_pm,0,T
+coef_social_departure_constant_6_30_pm_to_7_pm,-0.088537991,F
+coef_social_departure_constant_7_pm_to_7_30_pm,0.052983115,F
+coef_social_departure_constant_after_7_30_pm,-0.649629162,F
+coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.09574499,F
+coef_social_arrival_constant_3_pm_to_3_30_pm,0.37674882,F
+coef_social_arrival_constant_3_30_pm_to_4_pm,0.583355461,F
+coef_social_arrival_constant_4_pm_to_4_30_pm,0.727855233,F
+coef_social_arrival_constant_5_pm_to_6_pm,0.249551955,F
+coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,0.053771388,F
+coef_social_arrival_constant_before_8_30_pm,0.308763611,F
+coef_social_arrival_constant_8_30_pm_to_9_pm,-0.208797698,F
+coef_social_arrival_constant_9_pm_to_9_30_pm,-0.336319511,F
+coef_social_arrival_constant_9_30_pm_to_10_pm,0,T
+coef_social_arrival_constant_10_pm_to_10_30_pm,-0.055707591,F
+coef_social_arrival_constant_after_10_30_pm,-0.612356296,F
+coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.348479901,F
+coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.614440191,F
+coef_social_duration_constant_less_than_3_hours,0.353666691,F
+coef_social_duration_constant_3_hours,0,T
+coef_social_duration_constant_3_hrs_30_minutes,-0.691218836,F
+coef_social_duration_constant_4_hours_or_more,-1.344375328,F
+coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-0.786970714,F
+coef_social_calibration_constant_duration_1,-1.346772472,F
+coef_social_calibration_constant_duration_2,0.377121689,F
+coef_social_calibration_constant_duration_3,0.179818928,F
+coef_social_calibration_constant_duration_4,-0.283418619,F
+coef_social_calibration_constant_duration_5,-0.103541313,F
+coef_social_calibration_constant_duration_6,-0.03704707,F
+coef_social_calibration_constant_duration_7,-0.062437167,F
+coef_social_calibration_constant_duration_8,0.047640282,F
+coef_social_calibration_constant_duration_9,0.284369793,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv
new file mode 100644
index 000000000..434942040
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv
@@ -0,0 +1,59 @@
+Label,Description,Expression,Coefficient
+util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum
+util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am  - Linear,"@((df.is_income_less25K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_before_730_am__Linear
+util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_after_800_am_Linear
+util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_8hrs
+util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_gt_8hrs
+util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am  - Linear,"@((df.is_income_25K_to_60K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear
+util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_0_to_5_yrs_Departure_Before_730_am
+util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_Before_730_am
+util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_After_800_am
+util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_16_to_17_yrs_Departure_After_800_am
+util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_lt_8hrs
+util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_gt_8hrs
+util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_lt_8hrs
+util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_gt_8hrs
+util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_16_to_17_yrs_Duration_gt_8hrs
+util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am  - Linear,"@(df.start<10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear
+util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start>10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear
+util_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration < 8hrs,"@(df.end<27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs
+util_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end>27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs
+util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am  - Linear,"@((df.is_all_adults_full_time_workers) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear
+util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear
+util_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end<27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs
+util_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end>27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs
+util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours
+util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours
+util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am
+util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours
+util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours
+util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours
+util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours
+util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM
+util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7
+util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8
+util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9
+util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10
+util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start>10),coef_Departure_Constant_After_0800_AM
+util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * ((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear
+util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11)) * (((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root
+util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM
+util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_
+util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_
+util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_
+util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_
+util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_
+util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29
+util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_
+util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM
+util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * ((np.minimum(24-df.end,6)*(df.end<24)) + (np.minimum(df.end-31,12)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear
+util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * ((np.minimum(13-df.duration,48)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear
+util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs
+util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours
+util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours
+util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours
+util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours
+util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours
+util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs
+util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear
+util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school_coeffs.csv
new file mode 100644
index 000000000..589b4ce61
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school_coeffs.csv
@@ -0,0 +1,59 @@
+coefficient_name,value,constrain
+coef_Mode_Choice_Logsum,0.524017431,F
+coef_Low_income_lt25000_Departure_before_730_am__Linear,0.134574548,F
+coef_Low_income_lt25000_Departure_after_800_am_Linear,-0.075554725,F
+coef_Low_income_lt25000_Duration_lt_8hrs,-0.150039779,F
+coef_Low_income_lt25000_Duration_gt_8hrs,-0.043562413,F
+coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.102594589,F
+coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.178916721,F
+coef_Age_13_to_15_yrs_Departure_Before_730_am,-0.164708594,F
+coef_Age_13_to_15_yrs_Departure_After_800_am,0.169641715,F
+coef_Age_16_to_17_yrs_Departure_After_800_am,0.077527892,F
+coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.254486164,F
+coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.14409403,F
+coef_Age_13_to_15_yrs_Duration_lt_8hrs,-0.211129273,F
+coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.102348303,F
+coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.1184616,F
+coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.011813391,F
+coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.0088956,F
+coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,-0.011793416,F
+coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,0.001485453,F
+coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.1625279,F
+coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.161840551,F
+coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,-0.233061473,F
+coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,0.08462748,F
+coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.154332088,F
+coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,-0.62871831,F
+coef_Subsequent_tour_is_school_tour_Departure_after_800_am,-0.41618671,F
+coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.261423274,F
+coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,-0.263857404,F
+coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.537535787,F
+coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.545479806,F
+coef_Departure_Constant_Before_0600_AM,-10.04417122,F
+coef_Departure_Constant_0600_AM_to_0630_AM_7,-3.792318538,F
+coef_Departure_Constant_0630_AM_to_0700_AM_8,-1.941704371,F
+coef_Departure_Constant_0700_AM_to_0730_AM_9,-0.558080224,F
+coef_Departure_Constant_0730_AM_to_0800_AM_10,0,T
+coef_Departure_Constant_After_0800_AM,-0.280439854,F
+coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.293697164,F
+coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-1.220165702,F
+coef_Arrival_Constant_Before_0230_PM,0.720751128,F
+coef_Arrival_Constant_0230_PM_0300_PM_24_,1.605012317,F
+coef_Arrival_Constant_0300_PM_0330_PM_25_,0.463502951,F
+coef_Arrival_Constant_0330_PM_0400_PM_26_,0.196107179,F
+coef_Arrival_Constant_0400_PM_0430_PM_27_,0,T
+coef_Arrival_Constant_0430_PM_0500_PM_28_,-0.389421484,F
+coef_Arrival_Constant_0500_PM_0530_PM_29,-1.412720271,F
+coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.938567609,F
+coef_Arrival_Constant_After_0600_PM,-2.246103785,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.552223894,F
+coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.249724903,F
+coef_Duration_Constant_Shorter_than_7_hrs,-2.791243553,F
+coef_Duration_Constant_7_hours,-1.679006455,F
+coef_Duration_Constant_7p5_hours,-0.555288612,F
+coef_Duration_Constant_8_hours,0,T
+coef_Duration_Constant_8p5_hours,-0.139412248,F
+coef_Duration_Constant_9_hours,-0.509620713,F
+coef_Duration_Constant_Longer_than_9_hrs,-0.561449384,F
+coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.379484906,F
+coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,-0.028814477,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv
new file mode 100644
index 000000000..160311a16
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv
@@ -0,0 +1,42 @@
+Label,Description,Expression,Coefficient
+util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum
+util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am  - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear
+util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs
+util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear
+util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs
+util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear
+util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41)  & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear
+util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear
+util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am  - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear
+util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear
+util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear
+util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear
+util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs -  Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root
+util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am
+util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1)  == 'work')) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours
+util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1)  == 'work')) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours
+util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1)  == 'school')) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am
+util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1)  == 'school')) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours
+util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours
+util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours
+util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours
+util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear
+util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM
+util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM
+util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM
+util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM
+util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM
+util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root
+util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear
+util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM
+util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM
+util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM
+util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM
+util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM
+util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear
+util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root
+util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root
+util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less
+util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours
+util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more
+util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university_coeffs.csv
new file mode 100644
index 000000000..1472dfb9f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university_coeffs.csv
@@ -0,0 +1,42 @@
+coefficient_name,value,constrain
+coef_Mode_Choice_Logsum,0.384091138,F
+coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F
+coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F
+coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F
+coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F
+coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F
+coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F
+coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F
+coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F
+coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F
+coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F
+coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F
+coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F
+coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F
+coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F
+coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F
+coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F
+coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F
+coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F
+coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F
+coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F
+coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F
+coef_Departure_Constant_Before_0730_AM,-0.296228472,F
+coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F
+coef_Departure_Constant_0800_AM_0830_AM,0,T
+coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F
+coef_Departure_Constant_After_0900_AM,-0.536008149,F
+coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F
+coef_Arrival_Constant_Before_0300_PM,-0.962572172,F
+coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F
+coef_Arrival_Constant_0330_PM_0400_PM,0,T
+coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F
+coef_Arrival_Constant_After_0430_PM,-0.66545038,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F
+coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F
+coef_Duration_Constant_4p5_hours_or_less,0.03106769,F
+coef_Duration_Constant_5_hours,0,T
+coef_Duration_Constant_5p5_hours_or_more,0.343447232,F
+coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work.csv
new file mode 100644
index 000000000..e180d0c15
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work.csv
@@ -0,0 +1,100 @@
+Label,Description,Expression,Coefficient
+util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum
+util_Female_Departure_before_7_am,Female - Departure before 7:00 am  - Linear,@((df.female) & (df.start<9)) * df.departureLinearShift1,coef_Female_Departure_before_7_am
+util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm
+util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am  - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am
+util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am
+util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm
+util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am  - Linear,@((df.is_income_less25K) & (df.start<9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am
+util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start>9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am
+util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm
+util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am  - Linear,@((df.is_income_25K_to_60K) & (df.start<9)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am
+util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end>30)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm
+util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am  - Linear,@((df.is_income_60K_to_120K) & (df.start<9)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am
+util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start<9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am
+util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start>9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am
+util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) &  (df.start>9)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am
+util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start<9)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am
+util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start>9)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am
+util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm
+util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm
+util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm
+util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm
+util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm
+util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am  - Linear,@((df.auto_ownership == 0) & (df.start<9)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am
+util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end>30)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm
+util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am  - Linear,@((df.ptype==2) & (df.start<9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am
+util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start>9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am
+util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm  - Linear,@((df.ptype==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm
+util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end>30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm
+util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start>9)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am
+util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm  - Linear,@((df.ptype==3) & (df.end<30)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm
+util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end>30)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm
+#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am  - Linear,@((df.work_segment==5) & (df.start<9)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am
+#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start>9)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am
+#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm  - Linear,@((df.work_segment==5)& (df.end<30)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm
+#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am  - Linear,@((df.work_segment==2) & (df.start<9)) * df.departureLinearShift1,coef_Service_Departure_before_7_am
+#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start>9)) * df.departureLinearShift1,coef_Service_Departure_after_7_am
+#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm  - Linear,@((df.work_segment==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm
+#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am  - Linear,@((df.work_segment==3) & (df.start<9)) * df.departureLinearShift1,coef_Health_Departure_before_7_am
+#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end>30)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm
+#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start>9)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am
+#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm  - Linear,@((df.work_segment==4) & (df.end<30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm
+#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end>30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm
+util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am  - Linear,@(df.start<9) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am
+util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start>9) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am
+util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end<30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm
+util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm  - Linear,@(df.end>30) * df.arrivalLinearShift1  * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm
+util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am  - Linear,@((df.is_non_worker_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am
+util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm  - Linear,@((df.is_non_worker_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm
+util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am  - Linear,@((df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am
+util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am
+util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm  - Linear,@((df.is_pre_drive_child_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm
+util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end>30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm
+util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start<9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am
+util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start>9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am
+util_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours
+util_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours
+util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm
+util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm
+util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours
+util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours
+#,#Departure Constants,,coef_Departure_Constants
+util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@(df.start<6) * ((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am
+util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM
+util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7
+util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8
+util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9
+util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10
+util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11
+util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12
+util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM
+util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@(df.start>13) * (((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root
+#,#Arrival Constants,,coef_Arrival_Constants
+util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@(df.end<25) * ((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm
+util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM
+util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26
+util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27
+util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28
+util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29
+util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30
+util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31
+util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32
+util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33
+util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34
+util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM
+util_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 6:30 pm - Square root,"@(df.end>35) * (((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root
+#,#Duration Constants,,coef_Duration_Constants
+util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@(df.duration<16) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs
+util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs
+util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours
+util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours
+util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours
+util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours
+util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours
+util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours
+util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours
+util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours
+util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs
+util_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,Duration Constant: Shift for every 30 minutes more than 10 hrs - Linear,"@(df.duration>25) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs
+util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0
diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work_coeffs.csv
new file mode 100644
index 000000000..19a85e817
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_work_coeffs.csv
@@ -0,0 +1,97 @@
+coefficient_name,value,constrain
+coef_Mode_Choice_Logsum,0.2279,T
+coef_Female_Departure_before_7_am,-0.12935306,F
+coef_Female_Arrival_after_6_pm,-0.041312616,F
+coef_Female_with_preschool_child_Departure_before_7_am,-0.129130464,F
+coef_Female_with_preschool_child_Departure_after_7_am,-0.031947595,F
+coef_Female_with_preschool_child_Arrival_after_6_pm,-0.048859218,F
+coef_Low_income_lt_25000_Departure_before_7_am,0.232768373,F
+coef_Low_income_lt_25000_Departure_after_7_am,0.014908169,F
+coef_Low_income_lt_25000_Arrival_after_6_pm,0.039105101,F
+coef_Med_income_25k_to_60k_Departure_before_7_am,0.123945957,F
+coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.020965221,F
+coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.09890939,F
+coef_Age_16_to_18_yrs_Departure_Before_7_am,-0.459593556,F
+coef_Age_16_to_18_yrs_Departure_After_7_am,0.060951693,F
+coef_Age_19_to_24_yrs_Departure_After_7_am,0.031477187,F
+coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.11723451,F
+coef_Age_65_plus_yrs_Departure_After_7_am,0.051923956,F
+coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.032734453,F
+coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.027623617,F
+coef_Age_56_to_64_yrs_Arrival_after_6_pm,-0.049130187,F
+coef_Age_65_plus_yrs_Arrival_before_5_pm,0.056774635,F
+coef_Age_65_plus_yrs_Arrival_after_6_pm,-0.077532684,F
+coef_Zero_auto_HH_Departure_before_7_am,0.396983749,F
+coef_Zero_auto_HH_Arrival_after_6_pm,0.050665232,F
+coef_Parttime_worker_Departure_before_7_am,-0.264760988,F
+coef_Parttime_worker_Departure_after_7_am,0.126626287,F
+coef_Parttime_worker_Arrival_before_5_pm,0.175158545,F
+coef_Parttime_worker_Arrival_after_6_pm,-0.054124518,F
+coef_University_student_Departure_after_7_am,0.024758204,F
+coef_University_student_Arrival_before_5_pm,0.035389739,F
+coef_University_student_Arrival_after_6_pm,0.06173996,F
+coef_Blue_collar_Departure_before_7_am,0.327242475,F
+coef_Blue_collar_Departure_after_7_am,0.047214248,F
+coef_Blue_collar_Arrival_before_5_pm,0.04197056,F
+coef_Service_Departure_before_7_am,0.117783508,F
+coef_Service_Departure_after_7_am,0.081611629,F
+coef_Service_Arrival_before_5_pm,0,T
+coef_Health_Departure_before_7_am,0.135275931,F
+coef_Health_Arrival_after_6_pm,0.062010123,F
+coef_Retail_and_food_Departure_after_7_am,0.076302969,F
+coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F
+coef_Retail_and_food_Arrival_after_6_pm,0.027069194,F
+coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.011511462,F
+coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.003821379,F
+coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.00549578,F
+coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.002253695,F
+coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.069957209,F
+coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,-0.019807228,F
+coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.084564489,F
+coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,-0.023894467,F
+coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.018983499,F
+coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.032091123,F
+coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,0.145890035,F
+coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.214531877,F
+coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.3069241,F
+coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.526297898,F
+coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,-0.221304523,F
+coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.176348812,F
+coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.064893097,F
+coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.656942049,F
+coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-1.151564775,F
+coef_Departure_Constant_Before_06_AM,-2.197677208,F
+coef_Departure_Constant_06_AM_06_AM_7,-1.314098638,F
+coef_Departure_Constant_06_AM_07_AM_8,-0.558766028,F
+coef_Departure_Constant_07_AM_07_AM_9,0,T
+coef_Departure_Constant_07_AM_08_AM_10,-0.036957515,F
+coef_Departure_Constant_08_AM_08_AM_11,-0.285560423,F
+coef_Departure_Constant_08_AM_09_AM_12,-0.555478447,F
+coef_Departure_Constant_After_09_AM,-0.865125273,F
+coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.435746145,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.191607342,F
+coef_Arrival_Constant_Before_03_PM,-0.289333093,F
+coef_Arrival_Constant_03_PM_04_PM_26,-0.273555837,F
+coef_Arrival_Constant_04_PM_04_PM_27,-0.142653706,F
+coef_Arrival_Constant_04_PM_05_PM_28,-0.124814807,F
+coef_Arrival_Constant_05_PM_05_PM_29,0.004265544,F
+coef_Arrival_Constant_05_PM_06_PM_30,0,T
+coef_Arrival_Constant_06_PM_06_PM_31,-0.060515031,F
+coef_Arrival_Constant_06_PM_7_PM_32,-0.236621114,F
+coef_Arrival_Constant_7_PM_7_PM_33,-0.577646614,F
+coef_Arrival_Constant_7_PM_8_PM_34,-0.815994515,F
+coef_Arrival_Constant_After_08_PM,-0.854151925,F
+coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,-0.469720787,F
+coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.074266981,F
+coef_Duration_Constant_Shorter_than_8p5_hrs,-0.748584335,F
+coef_Duration_Constant_8p5_hours,-0.654814097,F
+coef_Duration_Constant_9_hours,-0.372064236,F
+coef_Duration_Constant_9p5_hours,-0.144226124,F
+coef_Duration_Constant_10_hours,0.013153356,F
+coef_Duration_Constant_10p5_hours,0,T
+coef_Duration_Constant_11_hours,-0.115847245,F
+coef_Duration_Constant_11p5_hours,-0.288506368,F
+coef_Duration_Constant_12_hours,-0.524241874,F
+coef_Duration_Constant_Longer_than_12_hrs,-0.598634071,F
+coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,-0.293607565,F
+coef_Calibration_constant_Duration_0,-10,F
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.csv b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.csv
new file mode 100644
index 000000000..9f0f2f0f5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.csv
@@ -0,0 +1,21 @@
+Label,Description,Expression,no_pass,pass
+util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft_pass
+util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt_pass
+util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un_pass
+util_nw,Non-working adult,@df.ptype==PTYPE_NONWORK,0,coef_nw_pass
+util_rt,Retired,@df.ptype==PTYPE_RETIRED,0,coef_rt_pass
+util_inc1,0-$9k,"@df.income.between(0, 9000)",0,coef_inc1_pass
+util_inc2,$10-$24k,"@df.income.between(10000, 24000)",0,coef_inc2_pass
+util_inc3,$25-$34k,"@df.income.between(25000,34000)",0,coef_inc3_pass
+util_inc4,$35-$49k,"@df.income.between(35000,49000)",0,coef_inc4_pass
+util_inc10,$250k+,@df.income >= 250000,0,coef_inc10_pass
+#util_na20,NAICS 20 (mining/utilities/construct),@df.industry_naics=='20',0,coef_na20_pas
+#util_na30,NAICS 30 (manufacturing,@df.industry_naics=='30',0,coef_na30_pas
+#util_na50,NAICS 50 (Info/Fin/insur/real estate/prof/sci/tech/manage/admin),@df.industry_naics=='50',0,coef_na50_pas
+#util_na70,NAICS 70 (Entertain/accom),@df.industry_naics=='70',0,coef_na70_pas
+#util_na80,NAICS 80 (Other services),@df.industry_naics=='80',0,coef_na80_pas
+#util_publ,NAICS 90 (Public admin),@df.industry_naics=='90',0,coef_publ_pas
+#approximate measure for the time being,,,,
+util_wrkamt,Auto minus transit time (work),@df.work_auto_savings * -1,0,coef_wrkamt_pas
+util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pas
+utils_pass_asc,Constant,1,0,coef_pass_asc
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.yaml b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.yaml
new file mode 100644
index 000000000..5cb754b4a
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership.yaml
@@ -0,0 +1,7 @@
+
+SPEC: transit_pass_ownership.csv
+COEFFICIENTS: transit_pass_ownership_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership_coeffs.csv
new file mode 100644
index 000000000..aa756b4c0
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_ownership_coeffs.csv
@@ -0,0 +1,20 @@
+coefficient_name,coefficient_name,value,constrain
+coef_ft_pass,Full-time worker,2.034739841,F
+coef_pt_pass,Part-time worker,1.934451611,F
+coef_un_pass,University/College,2.38777737,F
+coef_nw_pass,Non-working adult,1.520740664,F
+coef_rt_pass,Retired,1.963879497,F
+coef_inc1_pass,0-$9k,0.609753946,F
+coef_inc2_pass,$10-$24k,0.421989203,F
+coef_inc3_pass,$25-$34k,0.2119704,F
+coef_inc4_pass,$35-$49k,0.139201682,F
+coef_inc10_pass,$250k+,-0.169998314,F
+coef_wrkamt_pas,Auto minus transit time (work),0.001934995,F
+coef_na20_pas,NAICS 20  (mining/utilities/construct),-0.324742563,F
+coef_na30_pas,NAICS 30 (manufacturing),-0.473249951,F
+coef_na50_pas,NAICS 50 (Info/Fin/insur/real estate/prof/sci/tech/manage/admin),0.2731,F
+coef_na70_pas,NAICS 70 (Entertain/accom),0.219432837,F
+coef_na80_pas,NAICS 80 (Other services),-0.585251651,F
+coef_publ_pas,NAICS 90 (Public admin),0.30410587,F
+coef_subs_pas,Subsidy offered,1.967569275,F
+coef_pass_asc,Constant,-3.932862618,F
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.csv b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.csv
new file mode 100644
index 000000000..1a3d6a2aa
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.csv
@@ -0,0 +1,12 @@
+Label,Description,Expression,no_subsidy,subsidy
+util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft
+util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt
+util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un
+#util_na70,NAICS 70 (Entertain/accom),@df.industry_naics=='70',0,coef_na70
+#util_na80,NAICS 80 (Other services),@df.industry_naics=='80',0,coef_na80
+#util_publ,NAICS 90 (Public admin),@df.industry_naics=='90',0,coef_publ
+#approximate measure for the time being,,,,
+util_tr_hh_emp,Household transit accessibility,@df.trOpRetail,0,coef_tr_hh_emp
+#approximate measure for the time being,,,,
+util_pkcost,Daily parking cost (dollars),@df.PRKCST+df.OPRKCST,0,coef_pkcost
+utils_sub_asc,Constant,1,0,coef_sub_asc
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.yaml b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.yaml
new file mode 100644
index 000000000..5f5244134
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy.yaml
@@ -0,0 +1,7 @@
+
+SPEC: transit_pass_subsidy.csv
+COEFFICIENTS: transit_pass_subsidy_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
diff --git a/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy_coeffs.csv
new file mode 100644
index 000000000..8e4b511e0
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/transit_pass_subsidy_coeffs.csv
@@ -0,0 +1,10 @@
+coefficient_name,coefficient_name,value,constrain
+coef_ft,Full-time worker,1.882665664,F
+coef_pt,Part-time worker,1.360746182,F
+coef_un,University/College,2.026985147,F
+coef_na70,NAICS 70 (Entertain/accom),-0.659568416,F
+coef_na80,NAICS 80 (Other services),-0.352588858,F
+coef_publ,NAICS 90 (Public admin),0.864991571,F
+coef_tr_hh_emp,Transit accessibility to households,0.049608562,F
+coef_pkcost,Daily parking cost (dollars),0.060145818,F
+coef_sub_asc,Constant,-5.138015944,F
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination.csv
new file mode 100644
index 000000000..3f950aa96
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_destination.csv
@@ -0,0 +1,17 @@
+Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork
+util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one
+util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+#util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,,
+util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork
+util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork
+util_distance_joint,distance (calibration adjustment joint),@df.is_joint * (od_skims['DIST'] + dp_skims['DIST']),,,,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,
+util_prox_home_outbound,stop proximity to home (outbound),@df.outbound * od_skims['DIST'],coef_prox_home_outbound_work,,,,,,,,,
+util_prox_home_inbound,stop proximity to home (inbound),@~df.outbound * dp_skims['DIST'],coef_prox_home_inbound_work,,,,,,,,,
+util_prox_dest_outbound,stop proximity to main destination (outbound),@df.outbound * dp_skims['DIST'],coef_prox_dest_outbound_work,,,,,,,,,
+util_prox_dest_inbound,stop proximity to main destination (inbound),@~df.outbound * od_skims['DIST'],0,,,,,,,,,
+#,,,,,,,,,,,,
+util_sample_of_alternatives_correction_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one
+util_mode_choice_logsum_os,Mode choice logsum from origin to stop,od_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum
+util_stop_not_accessible_by_this_tour_mode,Can't access stop zone by this tour mode,(od_logsum < -100),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_mode_choice_logsum_sd,Mode choice logsum from stop to destination,dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum
+util_dest_not_accessible_by_this_tour_mode,Can't access destination zone by this tour mode,(dp_logsum < -100),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination.yaml b/activitysim/examples/prototype_mwcog/configs/trip_destination.yaml
new file mode 100644
index 000000000..8bac788cc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_destination.yaml
@@ -0,0 +1,44 @@
+SAMPLE_SPEC: trip_destination_sample.csv
+SPEC: trip_destination.csv
+COEFFICIENTS: trip_destination_coefficients.csv
+
+SAMPLE_SIZE: 30
+
+DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv
+DESTINATION_SPEC: trip_destination.csv
+
+LOGSUM_SETTINGS: trip_mode_choice.yaml
+
+# optional (comment out if not desired)
+DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum
+
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
+DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample
+
+# model-specific logsum-related settings
+TRIP_ORIGIN: origin
+ALT_DEST_COL_NAME: dest_taz
+PRIMARY_DEST: destination
+
+# tour_mode is already in trips table, so we don't need it from tours
+# (it is assigned in trip_destination_annotate_trips_preprocessor )
+REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS:
+  - tour_mode
+
+CONSTANTS:
+    max_walk_distance: 3
+    max_bike_distance: 8
+
+preprocessor:
+  SPEC: trip_destination_annotate_trips_preprocessor
+  DF: trips
+  TABLES:
+    - tours
+    - persons
+
+# drop failed trips and cleanup failed trip leg_mates for consistency
+# (i.e. adjust trip_count, trip_num, first for missing failed trips)
+CLEANUP: False
+
+# this setting is used by testing code to force failed trip_destination
+# fail_some_trips_for_testing: False
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv
new file mode 100644
index 000000000..0217a6855
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv
@@ -0,0 +1,9 @@
+Description,Target,Expression
+#,,
+,tour_mode,"reindex(tours.tour_mode, df.tour_id)"
+,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))"
+,trip_period,network_los.skim_time_period_label(_tod)
+,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'"
+#,,not needed as school is not chosen as an intermediate trip destination
+#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)"
+#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))"
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_coefficients.csv
new file mode 100644
index 000000000..db732ac07
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_destination_coefficients.csv
@@ -0,0 +1,19 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_one,1,T
+coef_mode_choice_logsum,1.821,F
+coef_distance_joint,-0.1238,F
+coef_util_distance_work_outbound,-0.049725916,F
+coef_util_distance_work_inbound,0.147813279,F
+coef_util_distance_univ,-0.0613,F
+coef_util_distance_school,-0.1056,F
+coef_util_distance_escort,-0.1491,F
+coef_util_distance_shopping,-0.1192,F
+coef_util_distance_eatout,-0.1029,F
+coef_util_distance_othmaint,-0.205,F
+coef_util_distance_social,-0.1329,F
+coef_util_distance_othdiscr,-0.146172224,F
+coef_util_distance_atwork,-0.167334597,F
+coef_prox_home_outbound_work,-0.38,F
+coef_prox_home_inbound_work,-0.15,F
+coef_prox_dest_outbound_work,-0.26,F
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv
new file mode 100644
index 000000000..6a0a53480
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv
@@ -0,0 +1,18 @@
+Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork
+,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1
+,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1
+Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+#If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,,
+size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1
+no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,,
+distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597
+distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597
+distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0
+stop proximity to home (outbound),@df.outbound * _od_DIST,-0.38,0,0,0,0,0,0,0,0,0
+stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.15,0,0,0,0,0,0,0,0,0
+stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,,
+stop proximity to main destination (inbound),@~df.outbound * _od_DIST,0,,,,,,,,,
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv
new file mode 100644
index 000000000..fa8def35f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv
@@ -0,0 +1,455 @@
+Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_AB,WALK_BM,WALK_MR,WALK_CR,PNR_AB,PNR_BM,PNR_MR,PNR_CR,KNR_AB,KNR_BM,KNR_MR,KNR_CR,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED
+#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,coef_unavailable,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,coef_unavailable,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_for_joint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,coef_unavailable,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,coef_unavailable,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,,
+util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),coef_age1619_da,,,,,,,,,,,,,,,,,,,,
+#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,coef_unavailable,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,coef_unavailable,,,,,,,,,,,,,,,,,,,
+util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@odt_skims['HOV2_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Terminal_time,SHARED2 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,coef_ivt,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,,,,,
+util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr,,,,,,,,,,,,,,,,,,,
+util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age1619_da,,,,,,,,,,,,,,,,,,,
+#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,,,,,
+util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,coef_unavailable,,,,,,,,,,,,,,,,,,
+util_SHARED3_Unavailable_based_joint_tour_mode,SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,,,,,
+util_SHARED3_Unavailable_if_tour_mode_is_shared_2,SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,,,,,
+util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@odt_skims['HOV3_TIME'],,,coef_ivt,,,,,,,,,,,,,,,,,,
+util_SHARED3_Terminal_time,SHARED3 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,,
+util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,coef_ivt,,,,,,,,,,,,,,,,,,
+util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot *  df.total_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,,,,,
+util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,,
+util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,,
+util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,,
+#Walk,#Walk,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,,,,,
+util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,,,,,
+util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,,,,,
+util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,coef_ivt,,,,,,,,,,,,,,,,,
+#Bike,#Bike,,,,,,,,,,,,,,,,,,,,,,
+util_BIKE_Unavailable_if_tour_mode_is_not_bike,BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,coef_unavailable,,,,,,,,,,,,,,,,
+util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,coef_unavailable,,,,,,,,,,,,,,,,
+util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,,,,,
+util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,,,,,
+util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,coef_ivt,,,,,,,,,,,,,,,,
+util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,coef_ivt,,,,,,,,,,,,,,,,
+#Walk_to_AB,#Walk to AB,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_AB_Unavailable,WALK_AB - Unavailable,walk_ab_available == False,,,,,,coef_unavailable,,,,,,,,,,,,,,,
+util_WALK_AB_In_vehicle_time,WALK_AB - In-vehicle time,@odt_skims['WK_AB_WK_TOTIVT'],,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_In_vehicle_time_on_XB,WALK_AB - In-vehicle time on XB,@(coef_ivt_xb_multiplier - 1) * odt_skims['WK_AB_WK_IVTXB'],,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Short_iwait_time,WALK_AB - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WK_AB_WK_IWAIT']).clip(upper=waitThresh),,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Long_iwait_time,WALK_AB - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WK_AB_WK_IWAIT']-waitThresh).clip(0),,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_transfer_wait_time,WALK_AB - transfer wait time,@coef_xwait_multiplier * odt_skims['WK_AB_WK_XWAIT'],,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_number_of_transfers,WALK_AB - number of transfers,@xfers_wlk_multiplier * (odt_skims['WK_AB_WK_XFERS']).clip(0),,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Walk_access_time,WALK_AB - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Walk_egress_time,WALK_AB - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Walk_other_time,WALK_AB - Walk other time,@coef_waux_multiplier * odt_skims['WK_AB_WK_WAUX'],,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Fare,WALK_AB - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WK_AB_WK_FARE'],,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Destination_zone_densityIndex,WALK_AB - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Topology,WALK_AB - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,coef_ivt,,,,,,,,,,,,,,,
+util_WALK_AB_Person_is_less_than_10_years_old,WALK_AB - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn,,,,,,,,,,,,,,,
+#Walk_to_BM,#Walk to BM,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_BM_Unavailable,WALK_BM - Unavailable,walk_bm_available == False,,,,,,,coef_unavailable,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time,WALK_BM - In-vehicle time,@odt_skims['WK_BM_WK_TOTIVT'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_XB,WALK_BM - In-vehicle time on XB,@(coef_ivt_xb_multiplier - 1) * odt_skims['WK_BM_WK_IVTXB'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_MR,WALK_BM - In-vehicle time on MR,@(coef_ivt_mr_multiplier - 1) * odt_skims['WK_BM_WK_IVTMR'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_In_vehicle_time_on_LR,WALK_BM - In-vehicle time on LR,@(coef_ivt_lr_multiplier - 1) * odt_skims['WK_BM_WK_IVTLR'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Short_iwait_time,WALK_BM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WK_BM_WK_IWAIT']).clip(upper=waitThresh),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Long_iwait_time,WALK_BM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WK_BM_WK_IWAIT']-waitThresh).clip(0),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_transfer_wait_time,WALK_BM - transfer wait time,@coef_xwait_multiplier * odt_skims['WK_BM_WK_XWAIT'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_number_of_transfers,WALK_BM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WK_BM_WK_XFERS']).clip(0),,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Walk_access_time,WALK_BM - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Walk_egress_time,WALK_BM - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Walk_otherLight_rail/Ferry_time,WALK_BM - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WK_BM_WK_WAUX'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Fare,WALK_BM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WK_BM_WK_FARE'],,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Destination_zone_densityIndex,WALK_BM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Topology,WALK_BM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,,,,,
+util_WALK_BM_Person_is_less_than_10_years_old,WALK_BM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn,,,,,,,,,,,,,,
+#Walk_to_MR,#Walk to MR,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_MR_Unavailable,WALK_MR - Unavailable,walk_mr_available == False,,,,,,,,coef_unavailable,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time,WALK_MR - In-vehicle time,@odt_skims['WK_MR_WK_TOTIVT'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time_on_MR,WALK_MR - In-vehicle time on MR,@(coef_ivt_mr_multiplier - 1) * odt_skims['WK_MR_WK_IVTMR'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_In_vehicle_time_on_LR,WALK_MR - In-vehicle time on LR,@(coef_ivt_lr_multiplier - 1) * odt_skims['WK_MR_WK_IVTLR'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Short_iwait_time,WALK_MR - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WK_MR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Long_iwait_time,WALK_MR - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WK_MR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_transfer_wait_time,WALK_MR - transfer wait time,@coef_xwait_multiplier * odt_skims['WK_MR_WK_XWAIT'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_number_of_transfers,WALK_MR - number of transfers,@xfers_wlk_multiplier * (odt_skims['WK_MR_WK_XFERS']).clip(0),,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Walk_access_time,WALK_MR - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Walk_egress_time,WALK_MR - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Walk_other_time,WALK_MR - Walk other time,@coef_waux_multiplier * odt_skims['WK_MR_WK_WAUX'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Fare,WALK_MR - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WK_MR_WK_FARE'],,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Destination_zone_densityIndex,WALK_MR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Topology,WALK_MR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,,,,,
+util_WALK_MR_Person_is_less_than_10_years_old,WALK_MR - Person is less than 10 years old,@(df.age <= 10),,,,,,,,coef_age010_trn,,,,,,,,,,,,,
+#Walk_to_CR,#Walk to CR,,,,,,,,,,,,,,,,,,,,,,
+util_WALK_CR_Unavailable,WALK_CR - Unavailable,walk_cr_available == False,,,,,,,,,coef_unavailable,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time,WALK_CR - In-vehicle time,@odt_skims['WK_CR_WK_TOTIVT'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_XB,WALK_CR - In-vehicle time on XB,@(coef_ivt_xb_multiplier - 1) * odt_skims['WK_CR_WK_IVTXB'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_MR,WALK_CR - In-vehicle time on MR,@(coef_ivt_mr_multiplier - 1) * odt_skims['WK_CR_WK_IVTMR'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_BR,WALK_CR - In-vehicle time on BR,@(coef_ivt_br_multiplier - 1) * odt_skims['WK_CR_WK_IVTBR'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_LR,WALK_CR - In-vehicle time on LR,@(coef_ivt_lr_multiplier - 1) * odt_skims['WK_CR_WK_IVTLR'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_In_vehicle_time_on_CR,WALK_CR - In-vehicle time on CR,@(coef_ivt_cr_multiplier - 1) * odt_skims['WK_CR_WK_IVTCR'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Short_iwait_time,WALK_CR - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WK_CR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Long_iwait_time,WALK_CR - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WK_CR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_transfer_wait_time,WALK_CR - transfer wait time,@coef_xwait_multiplier * odt_skims['WK_CR_WK_XWAIT'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_number_of_transfers,WALK_CR - number of transfers,@xfers_wlk_multiplier * (odt_skims['WK_CR_WK_XFERS']).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Walk_access_time,WALK_CR - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Walk_egress_time,WALK_CR - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Walk_other_time,WALK_CR - Walk other time,@coef_waux_multiplier * odt_skims['WK_CR_WK_WAUX'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Fare,WALK_CR - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WK_CR_WK_FARE'],,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Destination_zone_densityIndex,WALK_CR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Topology,WALK_CR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,,,,,
+util_WALK_CR_Person_is_less_than_10_years_old,WALK_CR - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,,,,,,,,,,
+#PNR_to_AB,#PNR to AB,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_AB_Unavailable_for_zero_auto_households,PNR_AB - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,coef_unavailable,,,,,,,,,,,
+util_PNR_AB_Unavailable_for_persons_less_than_16,PNR_AB - Unavailable for persons less than 16,age < 16,,,,,,,,,,coef_unavailable,,,,,,,,,,,
+util_PNR_AB_Destination_zone_densityIndex,PNR_AB - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_Topology,PNR_AB - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_Person_is_less_than_10_years_old,PNR_AB - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Unavailable,PNR_AB outbound - Unavailable,outbound & ~pnr_ab_available_outbound,,,,,,,,,,coef_unavailable,,,,,,,,,,,
+util_PNR_AB_outbound_In_vehicle_time,PNR_AB outbound - In-vehicle time,@df.outbound * odt_skims['DR_AB_WK_TOTIVT'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_In_vehicle_time_on_XB,PNR_AB outbound - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['DR_AB_WK_IVTXB'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Short_iwait_time,PNR_AB outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DR_AB_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Long_iwait_time,PNR_AB outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DR_AB_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_transfer_wait_time,PNR_AB outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DR_AB_WK_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_number_of_transfers,PNR_AB outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['DR_AB_WK_XFERS']).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Drive_time,PNR_AB outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DR_AB_WK_DTIME'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Walk_egress_time,PNR_AB outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Walk_other_time,PNR_AB outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DR_AB_WK_WAUX'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Fare_and_operating_cost,PNR_AB outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DR_AB_WK_FARE'] + costPerMile*odt_skims['DR_AB_WK_DDIST']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_AB outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DR_AB_WK_DDIST'])/ (od_skims['DIST']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Unavailable,PNR_AB inbound - Unavailable,inbound & ~pnr_ab_available_inbound,,,,,,,,,,coef_unavailable,,,,,,,,,,,
+util_PNR_AB_inbound_In_vehicle_time,PNR_AB inbound - In-vehicle time,@df.inbound * odt_skims['WK_AB_DR_TOTIVT'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Short_iwait_time,PNR_AB inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_AB_DR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Long_iwait_time,PNR_AB inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_AB_DR_IWAIT']-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_transfer_wait_time,PNR_AB inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_AB_DR_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_number_of_transfers,PNR_AB inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WK_AB_DR_XFERS']).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Drive_time,PNR_AB inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_AB_DR_DTIME'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Walk_access_time,PNR_AB inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Walk_other_time,PNR_AB inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_AB_DR_WAUX'],,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Fare_and_operating_cost,PNR_AB inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_AB_DR_FARE'] + costPerMile*odt_skims['WK_AB_DR_DDIST']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+util_PNR_AB_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_AB inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WK_AB_DR_DDIST'])/ (od_skims['DIST']),,,,,,,,,,coef_ivt,,,,,,,,,,,
+#Drive_to_BM,#Drive to BM,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_BM_Unavailable_for_zero_auto_households,PNR_BM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,coef_unavailable,,,,,,,,,,
+util_PNR_BM_Unavailable_for_persons_less_than_16,PNR_BM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,coef_unavailable,,,,,,,,,,
+util_PNR_BM__Destination_zone_densityIndex,PNR_BM  - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM__Topology,PNR_BM  - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM__Person_is_less_than_10_years_old,PNR_BM  - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Unavailable,PNR_BM outbound - Unavailable,outbound & ~pnr_bm_available_outbound,,,,,,,,,,,coef_unavailable,,,,,,,,,,
+util_PNR_BM_outbound_In_vehicle_time,PNR_BM outbound - In-vehicle time,@df.outbound * odt_skims['DR_BM_WK_TOTIVT'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_XB,PNR_BM - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['DR_BM_WK_IVTXB'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_MR,PNR_BM - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['DR_BM_WK_IVTMR'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_LR,PNR_BM - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['DR_BM_WK_IVTLR'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Short_iwait_time,PNR_BM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DR_BM_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Long_iwait_time,PNR_BM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DR_BM_WK_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_transfer_wait_time,PNR_BM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DR_BM_WK_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_number_of_transfers,PNR_BM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DR_BM_WK_XFERS']).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Drive_time,PNR_BM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DR_BM_WK_DTIME'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Walk_egress_time,PNR_BM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Walk_other_time,PNR_BM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DR_BM_WK_WAUX'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Fare_and_operating_cost,PNR_BM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DR_BM_WK_FARE'] + costPerMile * odt_skims['DR_BM_WK_DDIST']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_BM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DR_BM_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Unavailable,PNR_BM inbound - Unavailable,inbound & ~pnr_bm_available_inbound,,,,,,,,,,,coef_unavailable,,,,,,,,,,
+util_PNR_BM_inbound_In_vehicle_time,PNR_BM inbound - In-vehicle time,@df.inbound *  odt_skims['WK_BM_DR_TOTIVT'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_XB,PNR_BM - In-vehicle time on XB,@df.inbound * (coef_ivt_xb_multiplier - 1) * odt_skims['WK_BM_DR_IVTXB'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_MR,PNR_BM - In-vehicle time on MR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_BM_DR_IVTMR'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_In_vehicle_time_on_LR,PNR_BM - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_BM_DR_IVTLR'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Short_iwait_time,PNR_BM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_BM_DR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Long_iwait_time,PNR_BM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_BM_DR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_transfer_wait_time,PNR_BM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_BM_DR_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_number_of_transfers,PNR_BM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_BM_DR_XFERS']).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Drive_time,PNR_BM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_BM_DR_DTIME'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Walk_access_time,PNR_BM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Walk_other_time,PNR_BM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_BM_DR_WAUX'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Fare_and_operating_cost,PNR_BM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_BM_DR_FARE'] + costPerMile * odt_skims['WK_BM_DR_DDIST']),,,,,,,,,,,coef_ivt,,,,,,,,,,
+util_PNR_BM_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_BM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_BM_DR_DDIST'])/ od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,,,,,
+#PNR_to_MR,#PNR to MR,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_MR_Unavailable_for_zero_auto_households,PNR_MR - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,coef_unavailable,,,,,,,,,
+util_PNR_MR_Unavailable_for_persons_less_than_16,PNR_MR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,coef_unavailable,,,,,,,,,
+util_PNR_MR_Destination_zone_densityIndex,PNR_MR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_Topology,PNR_MR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_Person_is_less_than_10_years_old,PNR_MR - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Unavailable,PNR_MR outbound - Unavailable,outbound & ~pnr_mr_available_outbound,,,,,,,,,,,,coef_unavailable,,,,,,,,,
+util_PNR_MR_outbound_In_vehicle_time,PNR_MR outbound - In-vehicle time,@df.outbound *  odt_skims['DR_MR_WK_TOTIVT'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_MR,PNR_MR - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['DR_MR_WK_IVTMR'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_LR,PNR_MR - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['DR_MR_WK_IVTLR'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Short_iwait_time,PNR_MR outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DR_MR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Long_iwait_time,PNR_MR outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DR_MR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_transfer_wait_time,PNR_MR outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DR_MR_WK_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_number_of_transfers,PNR_MR outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DR_MR_WK_XFERS']).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Drive_time,PNR_MR outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DR_MR_WK_DTIME'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Walk_egress_ime,PNR_MR outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Walk_other_time,PNR_MR outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DR_MR_WK_WAUX'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Fare_and_operating_cost,PNR_MR outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DR_MR_WK_FARE'] + costPerMile * odt_skims['DR_MR_WK_DDIST']),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MR outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DR_MR_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Unavailable,PNR_MR inbound - Unavailable,inbound & ~pnr_mr_available_inbound,,,,,,,,,,,,coef_unavailable,,,,,,,,,
+util_PNR_MR_inbound_In_vehicle_time,PNR_MR inbound - In-vehicle time,@df.inbound *  odt_skims['WK_MR_DR_TOTIVT'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_MR,PNR_MR - In-vehicle time on MR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_MR_DR_IVTMR'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_In_vehicle_time_on_LR,PNR_MR - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_MR_DR_IVTLR'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Short_iwait_time,PNR_MR inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_MR_DR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Long_iwait_time,PNR_MR inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_MR_DR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_transfer_wait_time,PNR_MR inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_MR_DR_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_number_of_transfers,PNR_MR inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_MR_DR_XFERS']).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Drive_time,PNR_MR inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_MR_DR_DTIME'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Walk_access_time,PNR_MR inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Walk_other_time,PNR_MR inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_MR_DR_WAUX'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Fare_and_operating_cost,PNR_MR inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_MR_DR_FARE'] + costPerMile * odt_skims['WK_MR_DR_DDIST']),,,,,,,,,,,,coef_ivt,,,,,,,,,
+util_PNR_MR_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MR inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_MR_DR_DDIST']) / od_skims['DIST'],,,,,,,,,,,,coef_ivt,,,,,,,,,
+#PNR_to_CR,#PNR to CR,,,,,,,,,,,,,,,,,,,,,,
+util_PNR_CR_Unavailable_for_zero_auto_households,PNR_CR - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,coef_unavailable,,,,,,,,
+util_PNR_CR_Unavailable_for_persons_less_than_16,PNR_CR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,coef_unavailable,,,,,,,,
+util_PNR_CR_Destination_zone_densityIndex,PNR_CR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_Topology,PNR_CR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_Person_is_less_than_10_years_old,PNR_CR - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Unavailable,PNR_CR outbound - Unavailable,outbound & ~pnr_cr_available_outbound,,,,,,,,,,,,,coef_unavailable,,,,,,,,
+util_PNR_CR_outbound_In_vehicle_time,PNR_CR outbound - In-vehicle time,@df.outbound * odt_skims['DR_CR_WK_TOTIVT'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_XB,PNR_CR - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['DR_CR_WK_IVTXB'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_MR,PNR_CR - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['DR_CR_WK_IVTMR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_BR,PNR_CR - In-vehicle time on BR,@df.outbound * (coef_ivt_br_multiplier - 1) * odt_skims['DR_CR_WK_IVTBR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_CR,PNR_CR - In-vehicle time on CR,@df.outbound * (coef_ivt_cr_multiplier - 1) * odt_skims['DR_CR_WK_IVTCR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_LR,PNR_CR - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['DR_CR_WK_IVTLR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Short_iwait_time,PNR_CR outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DR_CR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Long_iwait_time,PNR_CR outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DR_CR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_transfer_wait_time,PNR_CR outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DR_CR_WK_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_number_of_transfers,PNR_CR outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DR_CR_WK_XFERS']).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Drive_time,PNR_CR outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DR_CR_WK_DTIME'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Walk_egress_ime,PNR_CR outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Walk_other_time,PNR_CR outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DR_CR_WK_WAUX'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Fare_and_operating_cost,PNR_CR outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DR_CR_WK_FARE'] + costPerMile * odt_skims['DR_CR_WK_DDIST']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_CR outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DR_CR_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Unavailable,PNR_CR inbound - Unavailable,inbound & ~pnr_cr_available_inbound,,,,,,,,,,,,,coef_unavailable,,,,,,,,
+util_PNR_CR_inbound_In_vehicle_time,PNR_CR inbound - In-vehicle time,@df.inbound * odt_skims['WK_CR_DR_TOTIVT'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_XB,PNR_CR - In-vehicle time on XB,@df.inbound * (coef_ivt_xb_multiplier - 1) * odt_skims['WK_CR_DR_IVTXB'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_MR,PNR_CR - In-vehicle time on MR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_CR_DR_IVTMR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_BR,PNR_CR - In-vehicle time on BR,@df.inbound * (coef_ivt_br_multiplier - 1) * odt_skims['WK_CR_DR_IVTBR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_CR,PNR_CR - In-vehicle time on CR,@df.inbound * (coef_ivt_cr_multiplier - 1) * odt_skims['WK_CR_DR_IVTCR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_In_vehicle_time_on_LR,PNR_CR - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_CR_DR_IVTLR'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Short_iwait_time,PNR_CR inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_CR_DR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Long_iwait_time,PNR_CR inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_CR_DR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_transfer_wait_time,PNR_CR inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_CR_DR_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_number_of_transfers,PNR_CR inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_CR_DR_XFERS']).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Drive_time,PNR_CR inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_CR_DR_DTIME'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Walk_access_time,PNR_CR inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Walk_other_time,PNR_CR inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_CR_DR_WAUX'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Fare_and_operating_cost,PNR_CR inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_CR_DR_FARE'] + costPerMile * odt_skims['WK_CR_DR_DDIST']),,,,,,,,,,,,,coef_ivt,,,,,,,,
+util_PNR_CR_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_CR inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_CR_DR_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,,,,,
+#KNR_to_AB,#KNR to AB,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_AB_Unavailable_for_persons_less_than_16,KNR_AB - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,coef_unavailable,,,,,,,
+util_KNR_AB_Destination_zone_densityIndex,KNR_AB - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_Topology,KNR_AB - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_Person_is_less_than_10_years_old,KNR_AB - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Unavailable,KNR_AB outbound - Unavailable,outbound & ~knr_ab_available_outbound,,,,,,,,,,,,,,coef_unavailable,,,,,,,
+util_KNR_AB_outbound_In_vehicle_time,KNR_AB outbound - In-vehicle time,@df.outbound * odt_skims['KR_AB_WK_TOTIVT'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_In_vehicle_time_on_XB,KNR_AB outbound - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['KR_AB_WK_IVTXB'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Short_iwait_time,KNR_AB outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KR_AB_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Long_iwait_time,KNR_AB outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KR_AB_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_transfer_wait_time,KNR_AB outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KR_AB_WK_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_number_of_transfers,KNR_AB outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['KR_AB_WK_XFERS']).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Drive_time,KNR_AB outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KR_AB_WK_DTIME'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Walk_egress_time,KNR_AB outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Walk_other_time,KNR_AB outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KR_AB_WK_WAUX'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Fare_and_operating_cost,KNR_AB outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KR_AB_WK_FARE'] + costPerMile*odt_skims['KR_AB_WK_DDIST']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_AB outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KR_AB_WK_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Unavailable,KNR_AB inbound - Unavailable,inbound & ~knr_ab_available_inbound,,,,,,,,,,,,,,coef_unavailable,,,,,,,
+util_KNR_AB_inbound_In_vehicle_time,KNR_AB inbound - In-vehicle time,@df.inbound * odt_skims['WK_AB_KR_TOTIVT'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Short_iwait_time,KNR_AB inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_AB_KR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Long_iwait_time,KNR_AB inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_AB_KR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_transfer_wait_time,KNR_AB inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_AB_KR_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_number_of_transfers,KNR_AB inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WK_AB_KR_XFERS']).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Drive_time,KNR_AB inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_AB_KR_DTIME'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Walk_access_time,KNR_AB inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Walk_other_time,KNR_AB inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_AB_KR_WAUX'],,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Fare_and_operating_cost,KNR_AB inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_AB_KR_FARE'] + costPerMile*odt_skims['WK_AB_KR_DDIST']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+util_KNR_AB_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_AB inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WK_AB_KR_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,,,coef_ivt,,,,,,,
+#Drive_to_BM,#Drive to BM,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_BM_Unavailable_for_persons_less_than_16,KNR_BM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,coef_unavailable,,,,,,
+util_KNR_BM__Destination_zone_densityIndex,KNR_BM  - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM__Topology,KNR_BM  - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM__Person_is_less_than_10_years_old,KNR_BM  - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Unavailable,KNR_BM outbound - Unavailable,outbound & ~knr_bm_available_outbound,,,,,,,,,,,,,,,coef_unavailable,,,,,,
+util_KNR_BM_outbound_In_vehicle_time,KNR_BM outbound - In-vehicle time,@df.outbound * odt_skims['KR_BM_WK_TOTIVT'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_XB,KNR_BM - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['KR_BM_WK_IVTXB'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_MR,KNR_BM - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['KR_BM_WK_IVTMR'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_LR,KNR_BM - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['KR_BM_WK_IVTLR'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Short_iwait_time,KNR_BM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KR_BM_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Long_iwait_time,KNR_BM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KR_BM_WK_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_transfer_wait_time,KNR_BM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KR_BM_WK_XWAIT'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_number_of_transfers,KNR_BM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KR_BM_WK_XFERS']).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Drive_time,KNR_BM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KR_BM_WK_DTIME'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Walk_egress_time,KNR_BM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Walk_other_time,KNR_BM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KR_BM_WK_WAUX'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Fare_and_operating_cost,KNR_BM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KR_BM_WK_FARE'] + costPerMile * odt_skims['KR_BM_WK_DDIST']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_BM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KR_BM_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Unavailable,KNR_BM inbound - Unavailable,inbound & ~knr_bm_available_inbound,,,,,,,,,,,,,,,coef_unavailable,,,,,,
+util_KNR_BM_inbound_In_vehicle_time,KNR_BM inbound - In-vehicle time,@df.inbound * odt_skims['WK_BM_KR_TOTIVT'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_XB,KNR_BM - In-vehicle time on XB,@df.inbound * (coef_ivt_xb_multiplier - 1) * odt_skims['WK_BM_KR_IVTXB'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_MR,KNR_BM - In-vehicle time on MR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_BM_KR_IVTMR'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_In_vehicle_time_on_LR,KNR_BM - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_BM_KR_IVTLR'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Short_iwait_time,KNR_BM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_BM_KR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Long_iwait_time,KNR_BM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_BM_KR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_transfer_wait_time,KNR_BM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_BM_KR_XWAIT'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_number_of_transfers,KNR_BM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_BM_KR_XFERS']).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Drive_time,KNR_BM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_BM_KR_DTIME'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Walk_access_time,KNR_BM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Walk_other_time,KNR_BM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_BM_KR_WAUX'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Fare_and_operating_cost,KNR_BM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_BM_KR_FARE'] + costPerMile * odt_skims['WK_BM_KR_DDIST']),,,,,,,,,,,,,,,coef_ivt,,,,,,
+util_KNR_BM_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_BM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_BM_KR_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,,,coef_ivt,,,,,,
+#KNR_to_MR,#KNR to MR,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_MR_Unavailable_for_persons_less_than_16,KNR_MR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,coef_unavailable,,,,,
+util_KNR_MR_Destination_zone_densityIndex,KNR_MR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_Topology,KNR_MR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_Person_is_less_than_10_years_old,KNR_MR - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Unavailable,KNR_MR outbound - Unavailable,outbound & ~knr_mr_available_outbound,,,,,,,,,,,,,,,,coef_unavailable,,,,,
+util_KNR_MR_outbound_In_vehicle_time,KNR_MR outbound - In-vehicle time,@df.outbound *  odt_skims['KR_MR_WK_TOTIVT'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_MR,KNR_MR - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['KR_MR_WK_IVTMR'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_LR,KNR_MR - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['KR_MR_WK_IVTLR'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Short_iwait_time,KNR_MR outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KR_MR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Long_iwait_time,KNR_MR outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KR_MR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_transfer_wait_time,KNR_MR outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KR_MR_WK_XWAIT'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_number_of_transfers,KNR_MR outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KR_MR_WK_XFERS']).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Drive_time,KNR_MR outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KR_MR_WK_DTIME'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Walk_egress_ime,KNR_MR outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Walk_other_time,KNR_MR outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KR_MR_WK_WAUX'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Fare_and_operating_cost,KNR_MR outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KR_MR_WK_FARE'] + costPerMile * odt_skims['KR_MR_WK_DDIST']),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MR outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KR_MR_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Unavailable,KNR_MR inbound - Unavailable,inbound & ~knr_mr_available_inbound,,,,,,,,,,,,,,,,coef_unavailable,,,,,
+util_KNR_MR_inbound_In_vehicle_time,KNR_MR inbound - In-vehicle time,@df.inbound * odt_skims['WK_MR_KR_TOTIVT'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_MR,KNR_MR - In-vehicle time on MR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_MR_KR_IVTMR'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_In_vehicle_time_on_LR,KNR_MR - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_MR_KR_IVTLR'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Short_iwait_time,KNR_MR inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_MR_KR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Long_iwait_time,KNR_MR inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_MR_KR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_transfer_wait_time,KNR_MR inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_MR_KR_XWAIT'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_number_of_transfers,KNR_MR inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_MR_KR_XFERS']).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Drive_time,KNR_MR inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_MR_KR_DTIME'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Walk_access_time,KNR_MR inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Walk_other_time,KNR_MR inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_MR_KR_WAUX'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Fare_and_operating_cost,KNR_MR inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_MR_KR_FARE'] + costPerMile * odt_skims['WK_MR_KR_DDIST']),,,,,,,,,,,,,,,,coef_ivt,,,,,
+util_KNR_MR_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MR inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_MR_KR_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,,
+#KNR_to_CR,#KNR to CR,,,,,,,,,,,,,,,,,,,,,,
+util_KNR_CR_Unavailable_for_persons_less_than_16,KNR_CR - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,coef_unavailable,,,,
+util_KNR_CR_Destination_zone_densityIndex,KNR_CR - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_Topology,KNR_CR - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_Person_is_less_than_10_years_old,KNR_CR - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Unavailable,KNR_CR outbound - Unavailable,outbound & ~knr_cr_available_outbound,,,,,,,,,,,,,,,,,coef_unavailable,,,,
+util_KNR_CR_outbound_In_vehicle_time,KNR_CR outbound - In-vehicle time,@df.outbound * odt_skims['DR_CR_WK_TOTIVT'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_XB,KNR_CR - In-vehicle time on XB,@df.outbound * (coef_ivt_xb_multiplier - 1) * odt_skims['DR_CR_WK_IVTXB'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_MR,KNR_CR - In-vehicle time on MR,@df.outbound * (coef_ivt_mr_multiplier - 1) * odt_skims['DR_CR_WK_IVTMR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_BR,KNR_CR - In-vehicle time on BR,@df.outbound * (coef_ivt_br_multiplier - 1) * odt_skims['DR_CR_WK_IVTBR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_LR,KNR_CR - In-vehicle time on LR,@df.outbound * (coef_ivt_lr_multiplier - 1) * odt_skims['DR_CR_WK_IVTLR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_CR,KNR_CR - In-vehicle time on CR,@df.outbound * (coef_ivt_cr_multiplier - 1) * odt_skims['DR_CR_WK_IVTCR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Short_iwait_time,KNR_CR outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DR_CR_WK_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Long_iwait_time,KNR_CR outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DR_CR_WK_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_transfer_wait_time,KNR_CR outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DR_CR_WK_XWAIT'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_number_of_transfers,KNR_CR outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DR_CR_WK_XFERS']).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Drive_time,KNR_CR outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DR_CR_WK_DTIME'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Walk_egress_ime,KNR_CR outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Walk_other_time,KNR_CR outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DR_CR_WK_WAUX'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Fare_and_operating_cost,KNR_CR outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DR_CR_WK_FARE'] + costPerMile * odt_skims['DR_CR_WK_DDIST']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_CR outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DR_CR_WK_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Unavailable,KNR_CR inbound - Unavailable,inbound & ~knr_cr_available_inbound,,,,,,,,,,,,,,,,,coef_unavailable,,,,
+util_KNR_CR_inbound_In_vehicle_time,KNR_CR inbound - In-vehicle time,@df.inbound * odt_skims['WK_CR_DR_TOTIVT'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_XB,KNR_CR - In-vehicle time on XB,@df.inbound * (coef_ivt_xb_multiplier - 1) * odt_skims['WK_CR_DR_IVTXB'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_CR,KNR_CR - In-vehicle time on CR,@df.inbound * (coef_ivt_mr_multiplier - 1) * odt_skims['WK_CR_DR_IVTCR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_BR,KNR_CR - In-vehicle time on BR,@df.inbound * (coef_ivt_br_multiplier - 1) * odt_skims['WK_CR_DR_IVTBR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_LR,KNR_CR - In-vehicle time on LR,@df.inbound * (coef_ivt_lr_multiplier - 1) * odt_skims['WK_CR_DR_IVTLR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_In_vehicle_time_on_CR,KNR_CR - In-vehicle time on CR,@df.inbound * (coef_ivt_cr_multiplier - 1) * odt_skims['WK_CR_DR_IVTCR'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Short_iwait_time,KNR_CR inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WK_CR_DR_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Long_iwait_time,KNR_CR inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WK_CR_DR_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_transfer_wait_time,KNR_CR inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WK_CR_DR_XWAIT'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_number_of_transfers,KNR_CR inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WK_CR_DR_XFERS']).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Drive_time,KNR_CR inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WK_CR_DR_DTIME'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Walk_access_time,KNR_CR inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Walk_other_time,KNR_CR inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WK_CR_DR_WAUX'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Fare_and_operating_cost,KNR_CR inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WK_CR_DR_FARE'] + costPerMile * odt_skims['WK_CR_DR_DDIST']),,,,,,,,,,,,,,,,,coef_ivt,,,,
+util_KNR_CR_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_CR inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WK_CR_DR_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,,
+#Taxi,#Taxi,,,,,,,,,,,,,,,,,,,,,,
+util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,,,,coef_ivt,,
+util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.origTaxiWaitTime,,,,,,,,,,,,,,,,,,,coef_ivt,,
+#Taxi_Tolls,#Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,,
+#Taxi_Bridge_toll,#Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,,
+util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,,
+#TNC_Single,#TNC Single,,,,,,,,,,,,,,,,,,,,,,
+util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,,,,coef_ivt,
+util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,,,,coef_ivt,
+#TNC_Single_Tolls,#TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,,,coef_ivt,
+#TNC_Single_Bridge_toll,#TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt,
+util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt,
+#TNC_Shared,#TNC Shared,,,,,,,,,,,,,,,,,,,,,,
+util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt
+util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,,,,coef_ivt
+#TNC_Shared_Tolls,#TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,,,,coef_ivt
+#TNC_Shared_Bridge_toll,#TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt
+util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt
+#,#,,,,,,,,,,,,,,,,,,,,,,
+util_Auto_tour_mode_availability,Auto tour mode availability,tour_mode_is_auto,,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,
+util_SOV_tour_mode_availability,SOV tour mode availability,tour_mode_is_SOV,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_sr2_tour_mode_availability,SR2 tour mode availability,tour_mode_is_sr2,,,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_sr3p_tour_mode_availability,SR3+ tour mode availability,tour_mode_is_sr3p,,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_Walk_tour_mode_availability,Walk tour mode availability,tour_mode_is_walk,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_Bike_tour_mode_availability,Bike tour mode availability,tour_mode_is_bike,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_Walk_to_Transit_tour_mode_availability,Walk to Transit tour mode availability,tour_mode_is_walk_transit,coef_unavailable,,,,coef_unavailable,,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,
+util_Drive_to_Transit_tour_mode_availability,Drive to Transit tour mode availability,tour_mode_is_drive_transit,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,,,,,,,coef_unavailable,,,
+util_School_bus_tour_mode_availability,School bus tour mode availability,tour_mode_is_school_bus,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable
+util_Ride_Hail_tour_mode_availability,Ride Hail tour mode availability,tour_mode_is_ride_hail,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,
+#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,,
+util_Drive_Alone_tour_mode_ASC_shared_ride_2,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,sov_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_Drive_Alone_tour_mode_ASC_shared_ride_3+,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr3p,,,,,,,,,,,,,,,,,,
+util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,sov_ASC_walk,,,,,,,,,,,,,,,,,
+util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh
+util_Shared_Ride_2_tour_mode_ASC_drive_alone,Shared Ride 2 tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),sr2_ASC_sov,,,,,,,,,,,,,,,,,,,,
+util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,sr2_ASC_walk,,,,,,,,,,,,,,,,,
+util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh
+util_Shared_Ride_3+_tour_mode_ASC_drive_alone,Shared Ride 3+ tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),sr3p_ASC_sr2,,,,,,,,,,,,,,,,,,,,
+util_Shared_Ride_3+_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,sr3p_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_Shared_Ride_3+_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,sr3p_ASC_walk,,,,,,,,,,,,,,,,,
+util_Shared_Ride_3+_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh
+util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh
+util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv &  (df.i_tour_mode == I_BIKE_MODE),,,,bike_ASC_walk,,,,,,,,,,,,,,,,,
+util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh
+util_Walk_transit_tour_mode_ASC_shared_ride_2,Walk-transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,walk_transit_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_Walk_transit_tour_mode_ASC_shared_ride_3+,Walk-transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,walk_transit_ASC_sr3p,,,,,,,,,,,,,,,,,,
+util_Walk_transit_tour_mode_ASC_walk,Walk-transit tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,walk_transit_ASC_walk,,,,,,,,,,,,,,,,,
+util_Walk_transit_tour_mode_ASC_ride_hail,Walk-transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh
+util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh
+util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,,
+util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single,
+util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared
+util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_joint_auto_tour_mode_ASC_shared_ride_3+,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr3p,,,,,,,,,,,,,,,,,,
+util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,joint_auto_ASC_walk,,,,,,,,,,,,,,,,,
+util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh
+util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,joint_bike_ASC_walk,,,,,,,,,,,,,,,,,
+util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh
+util_joint_Walk_transit_tour_mode_ASC_shared_ride_2,joint - Walk-transit tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_joint_Walk_transit_tour_mode_ASC_shared_ride_3+,joint - Walk-transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,,,,,
+util_joint_Walk_transit_tour_mode_ASC_walk,joint - Walk-transit tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,,,,,
+util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,,,,,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh
+util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh
+util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,,
+util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single,
+util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared
+#,#,,,,,,,,,,,,,,,,,,,,,,
+util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,coef_unavailable,,,,,,,,,,,,,,,,,
+util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,coef_unavailable,,,,,,,,,,,,,,,,
+#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,,,,,
+util_School_Bus_Unavailable_if_primary_purpose_NOT_school,School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,,,,coef_unavailable,,,
+util_School_Bus_Unavailable_Tour_Mode_SOV,School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,,,,coef_unavailable,,,
+util_School_Bus_Unavailable_Tour_Mode_Transit,School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,,,,coef_unavailable,,,
+util_School_Bus_In_vehicle_time_(20_miles_per_hour),School Bus - In-vehicle time (20 miles per hour),@odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,,,,1,,,
+util_School_Bus_Walk_Time,School Bus - Walk Time,@coef_wacc_multiplier*10,,,,,,,,,,,,,,,,,,1,,,
+util_School_Bus_Wait_Time,School Bus - Wait Time,@coef_short_iwait_multiplier*10,,,,,,,,,,,,,,,,,,1,,,
+util_School_Bus_tour_mode_ASC_shared_ride_2,School Bus tour mode ASC -- shared ride 2,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,schoolbus_ASC_sr2,,,,,,,,,,,,,,,,,,,
+util_School_Bus_tour_mode_ASC_shared_ride_3+,School Bus tour mode ASC -- shared ride 3+,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,schoolbus_ASC_sr3p,,,,,,,,,,,,,,,,,,
+util_School_Bus_tour_mode_ASC_walk,School Bus tour mode ASC -- walk,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,schoolbus_ASC_walk,,,,,,,,,,,,,,,,,
+util_School_Bus_tour_mode_ASC_ride_hail,School Bus tour mode ASC -- ride hail,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,,,,,,,,,,,,,,,,schoolbus_ASC_rh,schoolbus_ASC_rh,schoolbus_ASC_rh
+util_Origin_density_index,Origin density index,@(origin_density_applied*df.origin_density_index).clip(origin_density_index_max) if origin_density_applied else 0,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,coef_ivt,coef_ivt
+util_TNC_shared_adjustment,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.yaml
new file mode 100644
index 000000000..79a06b74d
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.yaml
@@ -0,0 +1,148 @@
+# trip_mode_choice.yaml
+
+SPEC: trip_mode_choice.csv
+COEFFICIENTS: trip_mode_choice_coefficients.csv
+COEFFICIENT_TEMPLATE: trip_mode_choice_coefficients_template.csv
+
+LOGIT_TYPE: NL
+
+NESTS:
+  name: root
+  coefficient: 1.00
+  alternatives:
+      - name: AUTO
+        coefficient: 0.72
+        alternatives:
+            - DRIVEALONE
+            - SHARED2
+            - SHARED3
+      - name: NONMOTORIZED
+        coefficient: 0.72
+        alternatives:
+          - WALK
+          - BIKE
+      - name: TRANSIT
+        coefficient:  0.72
+        alternatives:
+            - name: WALKACCESS
+              coefficient: 0.5
+              alternatives:
+              - WALK_AB
+              - WALK_BM
+              - WALK_MR
+              - WALK_CR
+            - name: PNRACCESS
+              coefficient: 0.5
+              alternatives:
+              - PNR_AB
+              - PNR_BM
+              - PNR_MR
+              - PNR_CR
+            - name: KNRACCESS
+              coefficient: 0.5
+              alternatives:
+              - KNR_AB
+              - KNR_BM
+              - KNR_MR
+              - KNR_CR
+      - name: SCHOOL_BUS
+        coefficient: 0.72
+        alternatives:
+          - SCHOOLBUS
+      - name: RIDEHAIL
+        coefficient: 0.36
+        alternatives:
+          - TAXI
+          - TNC_SINGLE
+          - TNC_SHARED
+
+CONSTANTS:
+  orig_col_name: origin
+  dest_col_name: destination
+  walkThresh: 1.00
+  walktimelong_multiplier: 5 
+  biketimelong_multiplier: 5
+  xfers_wlk_multiplier: 5
+  xfers_drv_multiplier: 15
+  density_index_multiplier: -5
+  I_MODE_MAP:
+    DRIVEALONE: 1
+    SHARED2: 2
+    SHARED3: 3
+    WALK: 4
+    BIKE: 5
+    WALK_AB: 6
+    WALK_BM: 7
+    WALK_MR: 8
+    WALK_CR: 9
+    PNR_AB: 10
+    PNR_BM: 11
+    PNR_MR: 12
+    PNR_CR: 13
+    KNR_AB: 14
+    KNR_BM: 15
+    KNR_MR: 16
+    KNR_CR: 17
+    SCHOOLBUS: 18
+    TAXI: 19
+    TNC_SINGLE: 20
+    TNC_SHARED: 21
+  I_SOV_MODES: [1]
+  I_SR2_MODES: [2]
+  I_SR3P_MODES: [3]
+  I_AUTO_MODES: [1, 2, 3]
+  I_WALK_MODE: 4
+  I_BIKE_MODE: 5
+  I_WALK_TRANSIT_MODES: [6,7,8,9]
+  I_DRIVE_TRANSIT_MODES: [10,11,12,13,14,15,16,17]
+  I_PNR_TRANSIT_MODES: [10,11,12,13]
+  I_KNR_TRANSIT_MODES: [14,15,16,17]
+  I_AB_MODES: [6,10,14]
+  I_BM_MODES: [7,11,15]
+  I_MR_MODES: [8,12,16]
+  I_CR_MODES: [9,13,17]  
+  I_SCHOOLBUS_MODE: [18]
+  I_RIDE_HAIL_MODES: [19, 20, 21]
+  Taxi_waitTime_mean:
+    1: 5.5
+    2: 9.5
+    3: 13.3
+    4: 17.3
+    5: 26.5
+  Taxi_waitTime_sd:
+    1: 0
+    2: 0
+    3: 0
+    4: 0
+    5: 0
+  
+
+# so far, we can use the same spec as for non-joint tours
+preprocessor:
+  SPEC: trip_mode_choice_annotate_trips_preprocessor
+  DF: df
+  TABLES:
+    - land_use
+    - tours
+
+#  - SPEC: trip_mode_choice_annotate_trips_preprocessor2
+#    DF: df
+#    TABLES:
+#      - land_use
+
+# to reduce memory needs filter chooser table to these fields
+TOURS_MERGED_CHOOSER_COLUMNS:
+  - hhsize
+  - age
+  - auto_ownership
+  - number_of_participants
+  - tour_category
+  - parent_tour_id
+  - tour_mode
+  - duration
+  - value_of_time
+  - tour_type
+  - free_parking_at_work
+
+
+MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv
new file mode 100644
index 000000000..33d0c0de9
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv
@@ -0,0 +1,101 @@
+Description,Target,Expression,
+,is_joint,(df.number_of_participants > 1),
+,is_indiv,(df.number_of_participants == 1),
+,is_atwork_subtour,~df.parent_tour_id.isnull(),
+,is_school,"(df.primary_purpose=='school') & (df.purpose.isin(['school', 'Home']))",
+,c_cost,(0.60 * coef_ivt) / df.value_of_time,
+,ivot,1.0 / df.value_of_time,
+#,,,
+#atwork subtours,,,
+#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,,,
+,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')",
+,work_tour_is_SOV,parent_tour_mode.isin(['DRIVEALONE']),
+,work_tour_is_bike,parent_tour_mode=='BIKE',
+#,,,
+,i_tour_mode,df.tour_mode.map(I_MODE_MAP),
+,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES),
+,tour_mode_is_sr2,i_tour_mode.isin(I_SR2_MODES),
+,tour_mode_is_sr3p,i_tour_mode.isin(I_SR3P_MODES),
+,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES),
+,tour_mode_is_walk,i_tour_mode.isin([I_WALK_MODE]),
+,tour_mode_is_bike,i_tour_mode.isin([I_BIKE_MODE]),
+,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES),
+,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES),
+,tour_mode_not_drive_transit,~tour_mode_is_drive_transit,
+,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES),
+,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES),
+,tour_mode_is_ab,i_tour_mode.isin(I_AB_MODES),
+,tour_mode_is_bm,i_tour_mode.isin(I_BM_MODES),
+,tour_mode_is_mr,i_tour_mode.isin(I_MR_MODES),
+,tour_mode_is_cr,i_tour_mode.isin(I_CR_MODES),
+,tour_mode_is_school_bus,i_tour_mode.isin(I_SCHOOLBUS_MODE),
+,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES),
+#,,,
+,inbound,~df.outbound,
+,first_trip,df.trip_num == 1,
+,last_trip,df.trip_num == df.trip_count,
+origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))",
+dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))",
+,total_terminal_time,_origin_terminal_time + _dest_terminal_time,
+#,,,
+,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work,
+,dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[DESTINATION])",
+,origin_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[ORIGIN])",
+,origin_duration,"np.where(first_trip, np.where(inbound,df.duration/2 * ~free_parking_available,0), 1)",
+,dest_duration,"np.where(last_trip, np.where(inbound, df.duration/2 * ~free_parking_available, 0), 1)",
+,origin_parking_cost,origin_duration*origin_hourly_peak_parking_cost,
+,dest_parking_cost,dest_duration*dest_hourly_peak_parking_cost,
+,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0,
+,trip_topology,"np.where(df.outbound, reindex(land_use.TOPOLOGY, df[DESTINATION]), reindex(land_use.TOPOLOGY, df[ORIGIN]))",
+,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))",
+,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))",
+# FIXME no transit subzones so all zones short walk to transit,,,
+,_walk_transit_origin,True,
+,_walk_transit_destination,True,
+,walk_transit_available,_walk_transit_origin & _walk_transit_destination & (tour_mode_not_drive_transit),
+,pnr_transit_available,tour_mode_is_pnr_transit,
+,knr_transit_available,tour_mode_is_knr_transit,
+,origin_walk_time,shortWalk*60/walkSpeed,
+,destination_walk_time,shortWalk*60/walkSpeed,
+# RIDEHAIL,,,
+,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.LANDAREA*640, df[orig_col_name]) / 640)",
+,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)",
+,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})",
+,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})",
+# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from
+,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)",
+,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})",
+,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})",
+,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)",
+,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})",
+,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})",
+,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)",
+#,,,
+,sov_available,(odt_skims['SOV_TIME']>0) & tour_mode_not_drive_transit,
+,hov2_available,(odt_skims['HOV2_TIME']>0) & tour_mode_not_drive_transit,
+,hov3_available,(odt_skims['HOV3_TIME']>0) & tour_mode_not_drive_transit,
+,walk_ab_available,walk_transit_available & (odt_skims['WK_AB_WK_IVTLB']>0) & tour_mode_is_ab,
+,walk_bm_available,walk_transit_available & (odt_skims['WK_BM_WK_IVTMR']>0) & tour_mode_is_bm,
+,walk_mr_available,walk_transit_available & (odt_skims['WK_MR_WK_IVTMR']>0) & tour_mode_is_mr,
+,walk_cr_available,walk_transit_available & (odt_skims['WK_CR_WK_IVTCR']>0) & tour_mode_is_cr,
+,pnr_ab_available_outbound,pnr_transit_available & df.outbound & (odt_skims['DR_AB_WK_IVTLB']>0) & tour_mode_is_ab,
+,pnr_ab_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['WK_AB_DR_IVTLB']>0) & tour_mode_is_ab,
+,pnr_bm_available_outbound,pnr_transit_available & df.outbound & (odt_skims['DR_BM_WK_IVTMR']>0) & tour_mode_is_bm,
+,pnr_bm_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['WK_BM_DR_IVTMR']>0) & tour_mode_is_bm,
+,pnr_mr_available_outbound,pnr_transit_available & df.outbound & (odt_skims['DR_MR_WK_IVTMR']>0) & tour_mode_is_mr,
+,pnr_mr_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['WK_MR_DR_IVTMR']>0) & tour_mode_is_mr,
+,pnr_cr_available_outbound,pnr_transit_available & df.outbound & (odt_skims['DR_CR_WK_IVTCR']>0) & tour_mode_is_cr,
+,pnr_cr_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['WK_CR_DR_IVTCR']>0) & tour_mode_is_cr,
+,knr_ab_available_outbound,knr_transit_available & df.outbound & (odt_skims['KR_AB_WK_IVTLB']>0) & tour_mode_is_ab,
+,knr_ab_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['WK_AB_KR_IVTLB']>0) & tour_mode_is_ab,
+,knr_bm_available_outbound,knr_transit_available & df.outbound & (odt_skims['KR_BM_WK_IVTMR']>0) & tour_mode_is_bm,
+,knr_bm_available_inbound,knr_transit_available & ~df.outbound &  (odt_skims['WK_BM_KR_IVTMR']>0) & tour_mode_is_bm,
+,knr_mr_available_outbound,knr_transit_available & df.outbound & (odt_skims['KR_MR_WK_IVTMR']>0) & tour_mode_is_mr,
+,knr_mr_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['WK_MR_KR_IVTMR']>0) & tour_mode_is_mr,
+,knr_cr_available_outbound,knr_transit_available & df.outbound & (odt_skims['KR_CR_WK_IVTCR']>0) & tour_mode_is_cr,
+,knr_cr_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['WK_CR_KR_IVTCR']>0) & tour_mode_is_cr,
+#,od_dist_walk,od_skims['DISTWALK'],
+#,do_dist_walk,od_skims.reverse('DISTWALK'),
+#,max_dist_walk,od_skims.max('DISTWALK'),
+#,dist_bike,od_skims['DISTBIKE'],
+#,dist_only,od_skims['DIST'],
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients.csv b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients.csv
new file mode 100644
index 000000000..911b55be3
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients.csv
@@ -0,0 +1,557 @@
+coefficient_name,value,constrain
+coef_unavailable,-999.0,T
+coef_one,1.0,T
+coef_nest_root,1.0,T
+coef_nest_AUTO,0.72,T
+coef_nest_NONMOTORIZED,0.72,T
+coef_nest_TRANSIT,0.72,T
+coef_nest_WALKACCESS,0.5,T
+coef_nest_PNRACCESS,0.5,T
+coef_nest_KNRACCESS,0.5,T
+coef_nest_RIDEHAIL,0.36,T
+#,,
+coef_ivt_othmaint_social,-0.02,F
+coef_ivt_work,-0.02,F
+coef_ivt_univ_school,-0.03,F
+coef_ivt_escort_shopping_eatout_othdiscr_atwork,-0.03,F
+#,,
+coef_age1619_da,0.0,F
+coef_age010_trn,0.0,F
+coef_age16p_sr,0.0,F
+coef_hhsize1_sr,-0.73,F
+coef_hhsize2_sr,0.0,F
+coef_walktimeshort_atwork,1.35,F
+coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_biketimeshort_atwork,2.7,F
+coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,4.0,T
+coef_dtim_atwork,1.35,F
+coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_ivt_xb_multiplier,1.0,T
+coef_ivt_mr_multiplier,0.9,T
+coef_ivt_br_multiplier,0.9,T
+coef_ivt_lr_multiplier,0.8,T
+coef_ivt_cr_multiplier,0.7,T
+coef_long_iwait_atwork,0.67,F
+coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,1.0,T
+coef_short_iwait_atwork,1.35,F
+coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_wacc_atwork,1.35,F
+coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_waux_atwork,1.35,F
+coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_wegr_atwork,1.35,F
+coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+coef_xwait_atwork,1.35,F
+coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T
+#,,
+coef_bike_ASC_walk_atwork,-5.05,
+coef_bike_ASC_walk_eatout,-3.7,
+coef_bike_ASC_walk_escort,-15.52,
+coef_bike_ASC_walk_othdiscr,-2.36,
+coef_bike_ASC_walk_othmaint,-2.57,
+coef_bike_ASC_walk_school,-3.24,
+coef_bike_ASC_walk_shopping,-3.02,
+coef_bike_ASC_walk_social,-14.24,
+coef_bike_ASC_walk_univ,-2.49,
+coef_bike_ASC_walk_work,-2.83,
+coef_bike_ASC_rh_atwork,-7.0,
+coef_bike_ASC_rh_eatout,-7.0,
+coef_bike_ASC_rh_escort,-7.0,
+coef_bike_ASC_rh_othdiscr,-7.0,
+coef_bike_ASC_rh_othmaint,-7.0,
+coef_bike_ASC_rh_parking,-7.0,
+coef_bike_ASC_rh_school,-7.0,
+coef_bike_ASC_rh_shopping,-7.0,
+coef_bike_ASC_rh_social,-7.0,
+coef_bike_ASC_rh_univ,-7.0,
+coef_bike_ASC_rh_work,-7.0,
+coef_joint_bike_ASC_walk_atwork,0.0,
+coef_joint_bike_ASC_walk_eatout,-3.0,
+coef_joint_bike_ASC_walk_escort,-3.0,
+coef_joint_bike_ASC_walk_othdiscr,-3.0,
+coef_joint_bike_ASC_walk_othmaint,-3.0,
+coef_joint_bike_ASC_walk_school,0.0,
+coef_joint_bike_ASC_walk_shopping,-3.0,
+coef_joint_bike_ASC_walk_social,-3.0,
+coef_joint_bike_ASC_walk_univ,0.0,
+coef_joint_bike_ASC_walk_work,0.0,
+coef_joint_bike_ASC_rh_atwork,0.0,
+coef_joint_bike_ASC_rh_eatout,-12.31,
+coef_joint_bike_ASC_rh_escort,0.0,
+coef_joint_bike_ASC_rh_othdiscr,-12.31,
+coef_joint_bike_ASC_rh_othmaint,-12.31,
+coef_joint_bike_ASC_rh_parking,0.0,
+coef_joint_bike_ASC_rh_school,0.0,
+coef_joint_bike_ASC_rh_shopping,-12.31,
+coef_joint_bike_ASC_rh_social,-12.31,
+coef_joint_bike_ASC_rh_univ,0.0,
+coef_joint_bike_ASC_rh_work,0.0,
+coef_joint_auto_ASC_sr2_atwork,0.0,
+coef_joint_auto_ASC_sr2_eatout,-0.0634,
+coef_joint_auto_ASC_sr2_escort,0.0,
+coef_joint_auto_ASC_sr2_othdiscr,-0.0634,
+coef_joint_auto_ASC_sr2_othmaint,-0.0634,
+coef_joint_auto_ASC_sr2_school,0.0,
+coef_joint_auto_ASC_sr2_shopping,0.5875,
+coef_joint_auto_ASC_sr2_social,-0.0634,
+coef_joint_auto_ASC_sr2_univ,0.0,
+coef_joint_auto_ASC_sr2_work,0.0,
+coef_joint_auto_ASC_sr3p_atwork,0.0,
+coef_joint_auto_ASC_sr3p_eatout,-0.1625,
+coef_joint_auto_ASC_sr3p_escort,0.0,
+coef_joint_auto_ASC_sr3p_othdiscr,-0.1625,
+coef_joint_auto_ASC_sr3p_othmaint,-0.1625,
+coef_joint_auto_ASC_sr3p_school,0.0,
+coef_joint_auto_ASC_sr3p_shopping,-19.0454,
+coef_joint_auto_ASC_sr3p_social,-0.1625,
+coef_joint_auto_ASC_sr3p_univ,0.0,
+coef_joint_auto_ASC_sr3p_work,0.0,
+coef_joint_auto_ASC_walk_atwork,0.0,
+coef_joint_auto_ASC_walk_eatout,-0.7144,
+coef_joint_auto_ASC_walk_escort,0.0,
+coef_joint_auto_ASC_walk_othdiscr,-0.7144,
+coef_joint_auto_ASC_walk_othmaint,-0.7144,
+coef_joint_auto_ASC_walk_school,0.0,
+coef_joint_auto_ASC_walk_shopping,-23.4688,
+coef_joint_auto_ASC_walk_social,-0.7144,
+coef_joint_auto_ASC_walk_univ,0.0,
+coef_joint_auto_ASC_walk_work,0.0,
+coef_joint_auto_ASC_rh_atwork,0.0,
+coef_joint_auto_ASC_rh_eatout,-7.0,
+coef_joint_auto_ASC_rh_escort,0.0,
+coef_joint_auto_ASC_rh_othdiscr,-7.0,
+coef_joint_auto_ASC_rh_othmaint,-7.0,
+coef_joint_auto_ASC_rh_school,0.0,
+coef_joint_auto_ASC_rh_shopping,-7.0,
+coef_joint_auto_ASC_rh_social,-7.0,
+coef_joint_auto_ASC_rh_univ,0.0,
+coef_joint_auto_ASC_rh_work,0.0,
+coef_joint_walk_transit_ASC_sr2_atwork,0.07382298328759998,
+coef_joint_walk_transit_ASC_sr2_eatout,-2.31,
+coef_joint_walk_transit_ASC_sr2_escort,-2.2361770167123995,
+coef_joint_walk_transit_ASC_sr2_othdiscr,-2.2361770167123995,
+coef_joint_walk_transit_ASC_sr2_othmaint,-2.2361770167123995,
+coef_joint_walk_transit_ASC_sr2_school,0.07382298328759998,
+coef_joint_walk_transit_ASC_sr2_shopping,-2.2361770167123995,
+coef_joint_walk_transit_ASC_sr2_social,-2.31,
+coef_joint_walk_transit_ASC_sr2_univ,0.07382298328759998,
+coef_joint_walk_transit_ASC_sr2_work,0.07382298328759998,
+coef_joint_walk_transit_ASC_sr3p_atwork,-10.0,
+coef_joint_walk_transit_ASC_sr3p_eatout,-3.88,
+coef_joint_walk_transit_ASC_sr3p_escort,-13.88,
+coef_joint_walk_transit_ASC_sr3p_othdiscr,-13.88,
+coef_joint_walk_transit_ASC_sr3p_othmaint,-13.88,
+coef_joint_walk_transit_ASC_sr3p_school,-10.0,
+coef_joint_walk_transit_ASC_sr3p_shopping,-13.88,
+coef_joint_walk_transit_ASC_sr3p_social,-3.88,
+coef_joint_walk_transit_ASC_sr3p_univ,-10.0,
+coef_joint_walk_transit_ASC_sr3p_work,-10.0,
+coef_joint_walk_transit_ASC_walk_atwork,-0.5954623418574255,
+coef_joint_walk_transit_ASC_walk_eatout,1.58,
+coef_joint_walk_transit_ASC_walk_escort,0.9845376581425747,
+coef_joint_walk_transit_ASC_walk_othdiscr,0.9845376581425747,
+coef_joint_walk_transit_ASC_walk_othmaint,0.9845376581425747,
+coef_joint_walk_transit_ASC_walk_school,-0.5954623418574255,
+coef_joint_walk_transit_ASC_walk_shopping,0.9845376581425747,
+coef_joint_walk_transit_ASC_walk_social,1.58,
+coef_joint_walk_transit_ASC_walk_univ,-0.5954623418574255,
+coef_joint_walk_transit_ASC_walk_work,-0.5954623418574255,
+coef_joint_walk_transit_ASC_rh_atwork,0.0,
+coef_joint_walk_transit_ASC_rh_eatout,1.23,
+coef_joint_walk_transit_ASC_rh_escort,0.0,
+coef_joint_walk_transit_ASC_rh_othdiscr,1.23,
+coef_joint_walk_transit_ASC_rh_othmaint,1.23,
+coef_joint_walk_transit_ASC_rh_parking,0.0,
+coef_joint_walk_transit_ASC_rh_school,0.0,
+coef_joint_walk_transit_ASC_rh_shopping,1.23,
+coef_joint_walk_transit_ASC_rh_social,1.23,
+coef_joint_walk_transit_ASC_rh_univ,0.0,
+coef_joint_walk_transit_ASC_rh_work,0.0,
+coef_joint_drive_transit_ASC_rh_atwork,0.0,
+coef_joint_drive_transit_ASC_rh_eatout,4.61,
+coef_joint_drive_transit_ASC_rh_escort,0.0,
+coef_joint_drive_transit_ASC_rh_othdiscr,4.61,
+coef_joint_drive_transit_ASC_rh_othmaint,4.61,
+coef_joint_drive_transit_ASC_rh_parking,0.0,
+coef_joint_drive_transit_ASC_rh_school,0.0,
+coef_joint_drive_transit_ASC_rh_shopping,4.61,
+coef_joint_drive_transit_ASC_rh_social,4.61,
+coef_joint_drive_transit_ASC_rh_univ,0.0,
+coef_joint_drive_transit_ASC_rh_work,0.0,
+coef_schoolbus_ASC_sr2_atwork,0.0,
+coef_schoolbus_ASC_sr2_eatout,0.0,
+coef_schoolbus_ASC_sr2_escort,0.0,
+coef_schoolbus_ASC_sr2_othdiscr,0.0,
+coef_schoolbus_ASC_sr2_othmaint,0.0,
+coef_schoolbus_ASC_sr2_school,-6.566636768886607,
+coef_schoolbus_ASC_sr2_shopping,0.0,
+coef_schoolbus_ASC_sr2_social,0.0,
+coef_schoolbus_ASC_sr2_univ,0.0,
+coef_schoolbus_ASC_sr2_work,0.0,
+coef_schoolbus_ASC_sr3p_atwork,0.0,
+coef_schoolbus_ASC_sr3p_eatout,0.0,
+coef_schoolbus_ASC_sr3p_escort,0.0,
+coef_schoolbus_ASC_sr3p_othdiscr,0.0,
+coef_schoolbus_ASC_sr3p_othmaint,0.0,
+coef_schoolbus_ASC_sr3p_school,-6.52605763616968,
+coef_schoolbus_ASC_sr3p_shopping,0.0,
+coef_schoolbus_ASC_sr3p_social,0.0,
+coef_schoolbus_ASC_sr3p_univ,0.0,
+coef_schoolbus_ASC_sr3p_work,0.0,
+coef_schoolbus_ASC_walk_atwork,0.0,
+coef_schoolbus_ASC_walk_eatout,0.0,
+coef_schoolbus_ASC_walk_escort,0.0,
+coef_schoolbus_ASC_walk_othdiscr,0.0,
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+coef_schoolbus_ASC_walk_school,-6.382554226620955,
+coef_schoolbus_ASC_walk_shopping,0.0,
+coef_schoolbus_ASC_walk_social,0.0,
+coef_schoolbus_ASC_walk_univ,0.0,
+coef_schoolbus_ASC_walk_work,0.0,
+coef_schoolbus_ASC_rh_atwork,0.0,
+coef_schoolbus_ASC_rh_othdisc,0.0,
+coef_schoolbus_ASC_rh_social,0.0,
+coef_schoolbus_ASC_rh_othmaint,0.0,
+coef_schoolbus_ASC_rh_eatout,0.0,
+coef_schoolbus_ASC_rh_shopping,0.0,
+coef_schoolbus_ASC_rh_escort,-2.28,
+coef_schoolbus_ASC_rh_school,-5.0,
+coef_schoolbus_ASC_rh_univ,0.0,
+coef_schoolbus_ASC_rh_work,0.0,
+coef_sov_ASC_sr2_atwork,-999.0,
+coef_sov_ASC_sr2_eatout,-999.0,
+coef_sov_ASC_sr2_escort,0.0,
+coef_sov_ASC_sr2_othdiscr,-999.0,
+coef_sov_ASC_sr2_othmaint,-999.0,
+coef_sov_ASC_sr2_parking,-999.0,
+coef_sov_ASC_sr2_school,-999.0,
+coef_sov_ASC_sr2_shopping,-999.0,
+coef_sov_ASC_sr2_social,-999.0,
+coef_sov_ASC_sr2_univ,-999.0,
+coef_sov_ASC_sr2_work,-999.0,
+coef_sov_ASC_sr3p_atwork,-999.0,
+coef_sov_ASC_sr3p_eatout,-999.0,
+coef_sov_ASC_sr3p_escort,0.0,
+coef_sov_ASC_sr3p_othdiscr,-999.0,
+coef_sov_ASC_sr3p_othmaint,-999.0,
+coef_sov_ASC_sr3p_school,-999.0,
+coef_sov_ASC_sr3p_shopping,-999.0,
+coef_sov_ASC_sr3p_social,-999.0,
+coef_sov_ASC_sr3p_univ,-999.0,
+coef_sov_ASC_sr3p_work,-999.0,
+coef_sov_ASC_walk_atwork,-13.03,
+coef_sov_ASC_walk_eatout,-11.13,
+coef_sov_ASC_walk_escort,-10.41,
+coef_sov_ASC_walk_othdiscr,-11.34,
+coef_sov_ASC_walk_othmaint,-11.71,
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+coef_sov_ASC_walk_shopping,-11.88,
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+coef_sov_ASC_walk_univ,-10.33,
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+coef_sov_ASC_rh_atwork,-7.0,
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+coef_sov_ASC_rh_othdiscr,0.0,
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+coef_sov_ASC_rh_parking,-6.65,
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+coef_sov_ASC_rh_univ,-6.65,
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+coef_sr2_ASC_sov_atwork,-0.4988466896476158,
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+coef_sr2_ASC_sov_escort,-0.2175035032905785,
+coef_sr2_ASC_sov_othdiscr,-0.5234703277804155,
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+coef_sr2_ASC_sov_school,0.41591282919463496,
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+coef_sr2_ASC_sov_social,-0.5234703277804155,
+coef_sr2_ASC_sov_univ,-0.903569738115396,
+coef_sr2_ASC_sov_work,-0.010011487339503113,
+coef_sr2_ASC_walk_atwork,-14.4,
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+coef_sr2_ASC_walk_escort,-13.470000000000002,
+coef_sr2_ASC_walk_othdiscr,-10.690000000000001,
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+coef_sr2_ASC_walk_univ,-9.83,
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+coef_sr2_ASC_rh_parking,-6.69,
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+coef_sr2_ASC_rh_univ,-6.69,
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+coef_sr3p_ASC_sov_atwork,5.062326073251256,
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+coef_sr3p_ASC_sov_univ,-3.0858954190275263,
+coef_sr3p_ASC_sov_work,3.5876358231539403,
+coef_sr3p_ASC_sr2_atwork,-2.080782638559888,
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+coef_sr3p_ASC_walk_univ,-9.98,
+coef_sr3p_ASC_walk_work,-10.73,
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+coef_walk_transit_ASC_sr2_atwork,-8.0,
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+coef_walk_transit_ASC_walk_work,0.45108415805734536,
+coef_walk_transit_ASC_rh_atwork,-3.85,
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+coef_walk_transit_ASC_rh_work,-3.74,
+coef_drive_transit_ASC_rh_atwork,-6.95,
+coef_drive_transit_ASC_rh_eatout,3.57,
+coef_drive_transit_ASC_rh_escort,0.69,
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+coef_drive_transit_ASC_rh_shopping,0.69,
+coef_drive_transit_ASC_rh_social,3.57,
+coef_drive_transit_ASC_rh_univ,-0.63,
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+coef_joint_walk_ASC_rh_atwork,0.0,
+coef_joint_walk_ASC_rh_eatout,-3.04,
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+coef_walk_ASC_rh_atwork,-7.0,
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+coef_walk_ASC_rh_univ,-7.0,
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+coef_auto_ASC_rh_atwork,0.0,
+coef_auto_ASC_rh_eatout,-7.0,
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+coef_auto_ASC_rh_univ,0.0,
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+coef_ride_hail_ASC_sr2_atwork,-12.601900928873786,
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+coef_ride_hail_ASC_sr3p_social,-15.049601054356177,
+coef_ride_hail_ASC_sr3p_univ,-16.405932727661213,
+coef_ride_hail_ASC_sr3p_work,-15.300582211216001,
+coef_ride_hail_ASC_walk_atwork,-2.7032741603658432,
+coef_ride_hail_ASC_walk_eatout,1.8707796557281626,
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+coef_ride_hail_ASC_walk_othdiscr,1.8707796557281626,
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+coef_ride_hail_ASC_walk_parking,-10.85,
+coef_ride_hail_ASC_walk_school,-11.24,
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+coef_ride_hail_ASC_walk_social,1.8707796557281626,
+coef_ride_hail_ASC_walk_univ,-8.315651083482015,
+coef_ride_hail_ASC_walk_work,11.274653599340692,
+coef_ride_hail_ASC_walk_transit_atwork,-7.0,
+coef_ride_hail_ASC_walk_transit_eatout,-7.0,
+coef_ride_hail_ASC_walk_transit_escort,-7.0,
+coef_ride_hail_ASC_walk_transit_othdiscr,-7.0,
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+coef_ride_hail_ASC_walk_transit_social,-7.0,
+coef_ride_hail_ASC_walk_transit_univ,-7.0,
+coef_ride_hail_ASC_walk_transit_work,-10.87,
+coef_ride_hail_ASC_taxi_atwork,6.801151893636805,
+coef_ride_hail_ASC_taxi_eatout,5.016880709175902,
+coef_ride_hail_ASC_taxi_escort,5.749519895198072,
+coef_ride_hail_ASC_taxi_othdiscr,5.016880709175902,
+coef_ride_hail_ASC_taxi_othmaint,5.749519895198072,
+coef_ride_hail_ASC_taxi_parking,-1.6,
+coef_ride_hail_ASC_taxi_school,4.537307920198721,
+coef_ride_hail_ASC_taxi_shopping,5.749519895198072,
+coef_ride_hail_ASC_taxi_social,5.016880709175902,
+coef_ride_hail_ASC_taxi_univ,6.276571222444565,
+coef_ride_hail_ASC_taxi_work,6.743937159221356,
+coef_ride_hail_ASC_tnc_single_atwork,5.437200737804858,
+coef_ride_hail_ASC_tnc_single_eatout,6.242409580427641,
+coef_ride_hail_ASC_tnc_single_escort,6.411533193972196,
+coef_ride_hail_ASC_tnc_single_othdiscr,6.242409580427641,
+coef_ride_hail_ASC_tnc_single_othmaint,6.411533193972196,
+coef_ride_hail_ASC_tnc_single_parking,0.11,
+coef_ride_hail_ASC_tnc_single_school,3.8633980460214343,
+coef_ride_hail_ASC_tnc_single_shopping,6.411533193972196,
+coef_ride_hail_ASC_tnc_single_social,6.242409580427641,
+coef_ride_hail_ASC_tnc_single_univ,5.529890080456208,
+coef_ride_hail_ASC_tnc_single_work,8.561417006634297,
+coef_ride_hail_ASC_tnc_shared,0.0,
+coef_joint_ride_hail_ASC_sr2_atwork,1.3253957469297355,
+coef_joint_ride_hail_ASC_sr2_eatout,-3.3,
+coef_joint_ride_hail_ASC_sr2_escort,2.245395746929735,
+coef_joint_ride_hail_ASC_sr2_othdiscr,2.2853957469297352,
+coef_joint_ride_hail_ASC_sr2_othmaint,2.245395746929735,
+coef_joint_ride_hail_ASC_sr2_parking,-2.58,
+coef_joint_ride_hail_ASC_sr2_school,2.5253957469297355,
+coef_joint_ride_hail_ASC_sr2_shopping,2.245395746929735,
+coef_joint_ride_hail_ASC_sr2_social,-3.3,
+coef_joint_ride_hail_ASC_sr2_univ,3.005395746929736,
+coef_joint_ride_hail_ASC_sr2_work,1.6753957469297354,
+coef_joint_ride_hail_ASC_sr3p_atwork,-25.65862562346605,
+coef_joint_ride_hail_ASC_sr3p_eatout,-4.06,
+coef_joint_ride_hail_ASC_sr3p_escort,-25.65862562346605,
+coef_joint_ride_hail_ASC_sr3p_othdiscr,-22.71862562346605,
+coef_joint_ride_hail_ASC_sr3p_othmaint,-25.65862562346605,
+coef_joint_ride_hail_ASC_sr3p_parking,-3.19,
+coef_joint_ride_hail_ASC_sr3p_school,-22.748625623466047,
+coef_joint_ride_hail_ASC_sr3p_shopping,-25.65862562346605,
+coef_joint_ride_hail_ASC_sr3p_social,-4.06,
+coef_joint_ride_hail_ASC_sr3p_univ,-21.848625623466056,
+coef_joint_ride_hail_ASC_sr3p_work,-29.528625623466056,
+coef_joint_ride_hail_ASC_walk_atwork,3.820817277110533,
+coef_joint_ride_hail_ASC_walk_eatout,-1.33,
+coef_joint_ride_hail_ASC_walk_escort,-1.4991827228894663,
+coef_joint_ride_hail_ASC_walk_othdiscr,4.170817277110533,
+coef_joint_ride_hail_ASC_walk_othmaint,-1.4991827228894663,
+coef_joint_ride_hail_ASC_walk_parking,-10.85,
+coef_joint_ride_hail_ASC_walk_school,4.260817277110533,
+coef_joint_ride_hail_ASC_walk_shopping,-1.4991827228894663,
+coef_joint_ride_hail_ASC_walk_social,-1.33,
+coef_joint_ride_hail_ASC_walk_univ,-5.34918272288947,
+coef_joint_ride_hail_ASC_walk_work,7.000817277110533,
+coef_joint_ride_hail_ASC_walk_transit_atwork,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_eatout,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_escort,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_othdiscr,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_othmaint,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_parking,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_school,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_shopping,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_social,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_univ,-7.0,
+coef_joint_ride_hail_ASC_walk_transit_work,-10.87,
+coef_joint_ride_hail_ASC_taxi_atwork,8.803997422549498,
+coef_joint_ride_hail_ASC_taxi_eatout,-7.0,
+coef_joint_ride_hail_ASC_taxi_escort,8.803997422549498,
+coef_joint_ride_hail_ASC_taxi_othdiscr,1.8039974225494988,
+coef_joint_ride_hail_ASC_taxi_othmaint,1.8039974225494988,
+coef_joint_ride_hail_ASC_taxi_parking,0.0,
+coef_joint_ride_hail_ASC_taxi_school,8.803997422549498,
+coef_joint_ride_hail_ASC_taxi_shopping,1.8039974225494988,
+coef_joint_ride_hail_ASC_taxi_social,-7.0,
+coef_joint_ride_hail_ASC_taxi_univ,8.803997422549498,
+coef_joint_ride_hail_ASC_taxi_work,8.803997422549498,
+coef_joint_ride_hail_ASC_tnc_single_atwork,5.793160946034619,
+coef_joint_ride_hail_ASC_tnc_single_eatout,-4.73,
+coef_joint_ride_hail_ASC_tnc_single_escort,5.793160946034619,
+coef_joint_ride_hail_ASC_tnc_single_othdiscr,1.0631609460346199,
+coef_joint_ride_hail_ASC_tnc_single_othmaint,1.0631609460346199,
+coef_joint_ride_hail_ASC_tnc_single_parking,0.0,
+coef_joint_ride_hail_ASC_tnc_single_school,5.793160946034619,
+coef_joint_ride_hail_ASC_tnc_single_shopping,1.0631609460346199,
+coef_joint_ride_hail_ASC_tnc_single_social,-4.73,
+coef_joint_ride_hail_ASC_tnc_single_univ,5.793160946034619,
+coef_joint_ride_hail_ASC_tnc_single_work,5.793160946034619,
+coef_joint_ride_hail_ASC_tnc_shared,0.0,
+walk_express_penalty,10.0,T
+adjust_tnc_shared,30.0,T
+coef_origin_density_applied_work_univ_school,0.0,T
+coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,1.0,T
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients_template.csv b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients_template.csv
new file mode 100644
index 000000000..61acc0f61
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice_coefficients_template.csv
@@ -0,0 +1,88 @@
+coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork
+#same for all segments,,,,,,,,,,
+coef_unavailable,,,,,,,,,,
+coef_one,,,,,,,,,,
+coef_nest_root,,,,,,,,,,
+coef_nest_AUTO,,,,,,,,,,
+coef_nest_NONMOTORIZED,,,,,,,,,,
+coef_nest_TRANSIT,,,,,,,,,,
+coef_nest_WALKACCESS,,,,,,,,,,
+coef_nest_PNRACCESS,,,,,,,,,,
+coef_nest_KNRACCESS,,,,,,,,,,
+coef_nest_RIDEHAIL,,,,,,,,,,
+#,,,,,,,,,,
+coef_ivt,coef_ivt_work,coef_ivt_univ_school,coef_ivt_univ_school,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_othmaint_social,coef_ivt_othmaint_social,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork
+coef_age1619_da,,,,,,,,,,
+coef_age010_trn,,,,,,,,,,
+coef_age16p_sr,,,,,,,,,,
+coef_hhsize1_sr,,,,,,,,,,
+coef_hhsize2_sr,,,,,,,,,,
+#multipliers that differ by purpose and were apparently estimated only for atwork,,,,,,,,,,
+coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier,coef_ivt_xb_multiplier
+coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier,coef_ivt_mr_multiplier
+coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier,coef_ivt_br_multiplier
+coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier,coef_ivt_lr_multiplier
+coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier,coef_ivt_cr_multiplier
+coef_short_iwait_multiplier,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_atwork
+coef_long_iwait_multiplier,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_atwork
+coef_wacc_multiplier,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_atwork
+coef_wegr_multiplier,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_atwork
+coef_waux_multiplier,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_atwork
+coef_dtim_multiplier,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_atwork
+coef_xwait_multiplier,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_atwork
+coef_walktimeshort_multiplier,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_atwork
+coef_biketimeshort_multiplier,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_atwork
+#ASCs,,,,,,,,,,
+sov_ASC_sr2,coef_sov_ASC_sr2_work,coef_sov_ASC_sr2_univ,coef_sov_ASC_sr2_school,coef_sov_ASC_sr2_escort,coef_sov_ASC_sr2_shopping,coef_sov_ASC_sr2_eatout,coef_sov_ASC_sr2_othmaint,coef_sov_ASC_sr2_social,coef_sov_ASC_sr2_othdiscr,coef_sov_ASC_sr2_atwork
+sov_ASC_sr3p,coef_sov_ASC_sr3p_work,coef_sov_ASC_sr3p_univ,coef_sov_ASC_sr3p_school,coef_sov_ASC_sr3p_escort,coef_sov_ASC_sr3p_shopping,coef_sov_ASC_sr3p_eatout,coef_sov_ASC_sr3p_othmaint,coef_sov_ASC_sr3p_social,coef_sov_ASC_sr3p_othdiscr,coef_sov_ASC_sr3p_atwork
+sov_ASC_walk,coef_sov_ASC_walk_work,coef_sov_ASC_walk_univ,coef_sov_ASC_walk_school,coef_sov_ASC_walk_escort,coef_sov_ASC_walk_shopping,coef_sov_ASC_walk_eatout,coef_sov_ASC_walk_othmaint,coef_sov_ASC_walk_social,coef_sov_ASC_walk_othdiscr,coef_sov_ASC_walk_atwork
+sov_ASC_rh,coef_sov_ASC_rh_work,coef_sov_ASC_rh_univ,coef_sov_ASC_rh_school,coef_sov_ASC_rh_escort,coef_sov_ASC_rh_shopping,coef_sov_ASC_rh_eatout,coef_sov_ASC_rh_othmaint,coef_sov_ASC_rh_social,coef_sov_ASC_rh_othdiscr,coef_sov_ASC_rh_atwork
+sr2_ASC_sov,coef_sr2_ASC_sov_work,coef_sr2_ASC_sov_univ,coef_sr2_ASC_sov_school,coef_sr2_ASC_sov_escort,coef_sr2_ASC_sov_shopping,coef_sr2_ASC_sov_eatout,coef_sr2_ASC_sov_othmaint,coef_sr2_ASC_sov_social,coef_sr2_ASC_sov_othdiscr,coef_sr2_ASC_sov_atwork
+sr2_ASC_walk,coef_sr2_ASC_walk_work,coef_sr2_ASC_walk_univ,coef_sr2_ASC_walk_school,coef_sr2_ASC_walk_escort,coef_sr2_ASC_walk_shopping,coef_sr2_ASC_walk_eatout,coef_sr2_ASC_walk_othmaint,coef_sr2_ASC_walk_social,coef_sr2_ASC_walk_othdiscr,coef_sr2_ASC_walk_atwork
+sr2_ASC_rh,coef_sr2_ASC_rh_work,coef_sr2_ASC_rh_univ,coef_sr2_ASC_rh_school,coef_sr2_ASC_rh_escort,coef_sr2_ASC_rh_shopping,coef_sr2_ASC_rh_eatout,coef_sr2_ASC_rh_othmaint,coef_sr2_ASC_rh_social,coef_sr2_ASC_rh_othdiscr,coef_sr2_ASC_rh_atwork
+sr3p_ASC_sov,coef_sr3p_ASC_sov_work,coef_sr3p_ASC_sov_univ,coef_sr3p_ASC_sov_school,coef_sr3p_ASC_sov_escort,coef_sr3p_ASC_sov_shopping,coef_sr3p_ASC_sov_eatout,coef_sr3p_ASC_sov_othmaint,coef_sr3p_ASC_sov_social,coef_sr3p_ASC_sov_othdiscr,coef_sr3p_ASC_sov_atwork
+sr3p_ASC_sr2,coef_sr3p_ASC_sr2_work,coef_sr3p_ASC_sr2_univ,coef_sr3p_ASC_sr2_school,coef_sr3p_ASC_sr2_escort,coef_sr3p_ASC_sr2_shopping,coef_sr3p_ASC_sr2_eatout,coef_sr3p_ASC_sr2_othmaint,coef_sr3p_ASC_sr2_social,coef_sr3p_ASC_sr2_othdiscr,coef_sr3p_ASC_sr2_atwork
+sr3p_ASC_walk,coef_sr3p_ASC_walk_work,coef_sr3p_ASC_walk_univ,coef_sr3p_ASC_walk_school,coef_sr3p_ASC_walk_escort,coef_sr3p_ASC_walk_shopping,coef_sr3p_ASC_walk_eatout,coef_sr3p_ASC_walk_othmaint,coef_sr3p_ASC_walk_social,coef_sr3p_ASC_walk_othdiscr,coef_sr3p_ASC_walk_atwork
+sr3p_ASC_rh,coef_sr3p_ASC_rh_work,coef_sr3p_ASC_rh_univ,coef_sr3p_ASC_rh_school,coef_sr3p_ASC_rh_escort,coef_sr3p_ASC_rh_shopping,coef_sr3p_ASC_rh_eatout,coef_sr3p_ASC_rh_othmaint,coef_sr3p_ASC_rh_social,coef_sr3p_ASC_rh_othdiscr,coef_sr3p_ASC_rh_atwork
+walk_ASC_rh,coef_walk_ASC_rh_work,coef_walk_ASC_rh_univ,coef_walk_ASC_rh_school,coef_walk_ASC_rh_escort,coef_walk_ASC_rh_shopping,coef_walk_ASC_rh_eatout,coef_walk_ASC_rh_othmaint,coef_walk_ASC_rh_social,coef_walk_ASC_rh_othdiscr,coef_walk_ASC_rh_atwork
+bike_ASC_walk,coef_bike_ASC_walk_work,coef_bike_ASC_walk_univ,coef_bike_ASC_walk_school,coef_bike_ASC_walk_escort,coef_bike_ASC_walk_shopping,coef_bike_ASC_walk_eatout,coef_bike_ASC_walk_othmaint,coef_bike_ASC_walk_social,coef_bike_ASC_walk_othdiscr,coef_bike_ASC_walk_atwork
+bike_ASC_rh,coef_bike_ASC_rh_work,coef_bike_ASC_rh_univ,coef_bike_ASC_rh_school,coef_bike_ASC_rh_escort,coef_bike_ASC_rh_shopping,coef_bike_ASC_rh_eatout,coef_bike_ASC_rh_othmaint,coef_bike_ASC_rh_social,coef_bike_ASC_rh_othdiscr,coef_bike_ASC_rh_atwork
+walk_transit_ASC_sr2,coef_walk_transit_ASC_sr2_work,coef_walk_transit_ASC_sr2_univ,coef_walk_transit_ASC_sr2_school,coef_walk_transit_ASC_sr2_escort,coef_walk_transit_ASC_sr2_shopping,coef_walk_transit_ASC_sr2_eatout,coef_walk_transit_ASC_sr2_othmaint,coef_walk_transit_ASC_sr2_social,coef_walk_transit_ASC_sr2_othdiscr,coef_walk_transit_ASC_sr2_atwork
+walk_transit_ASC_sr3p,coef_walk_transit_ASC_sr3p_work,coef_walk_transit_ASC_sr3p_univ,coef_walk_transit_ASC_sr3p_school,coef_walk_transit_ASC_sr3p_escort,coef_walk_transit_ASC_sr3p_shopping,coef_walk_transit_ASC_sr3p_eatout,coef_walk_transit_ASC_sr3p_othmaint,coef_walk_transit_ASC_sr3p_social,coef_walk_transit_ASC_sr3p_othdiscr,coef_walk_transit_ASC_sr3p_atwork
+walk_transit_ASC_walk,coef_walk_transit_ASC_walk_work,coef_walk_transit_ASC_walk_univ,coef_walk_transit_ASC_walk_school,coef_walk_transit_ASC_walk_escort,coef_walk_transit_ASC_walk_shopping,coef_walk_transit_ASC_walk_eatout,coef_walk_transit_ASC_walk_othmaint,coef_walk_transit_ASC_walk_social,coef_walk_transit_ASC_walk_othdiscr,coef_walk_transit_ASC_walk_atwork
+walk_transit_ASC_rh,coef_walk_transit_ASC_rh_work,coef_walk_transit_ASC_rh_univ,coef_walk_transit_ASC_rh_school,coef_walk_transit_ASC_rh_escort,coef_walk_transit_ASC_rh_shopping,coef_walk_transit_ASC_rh_eatout,coef_walk_transit_ASC_rh_othmaint,coef_walk_transit_ASC_rh_social,coef_walk_transit_ASC_rh_othdiscr,coef_walk_transit_ASC_rh_atwork
+schoolbus_ASC_sr2,coef_schoolbus_ASC_sr2_work,coef_schoolbus_ASC_sr2_univ,coef_schoolbus_ASC_sr2_school,coef_schoolbus_ASC_sr2_escort,coef_schoolbus_ASC_sr2_shopping,coef_schoolbus_ASC_sr2_eatout,coef_schoolbus_ASC_sr2_othmaint,coef_schoolbus_ASC_sr2_social,coef_schoolbus_ASC_sr2_othdiscr,coef_schoolbus_ASC_sr2_atwork
+schoolbus_ASC_sr3p,coef_schoolbus_ASC_sr3p_work,coef_schoolbus_ASC_sr3p_univ,coef_schoolbus_ASC_sr3p_school,coef_schoolbus_ASC_sr3p_escort,coef_schoolbus_ASC_sr3p_shopping,coef_schoolbus_ASC_sr3p_eatout,coef_schoolbus_ASC_sr3p_othmaint,coef_schoolbus_ASC_sr3p_social,coef_schoolbus_ASC_sr3p_othdiscr,coef_schoolbus_ASC_sr3p_atwork
+schoolbus_ASC_walk,coef_schoolbus_ASC_walk_work,coef_schoolbus_ASC_walk_univ,coef_schoolbus_ASC_walk_school,coef_schoolbus_ASC_walk_escort,coef_schoolbus_ASC_walk_shopping,coef_schoolbus_ASC_walk_eatout,coef_schoolbus_ASC_walk_othmaint,coef_schoolbus_ASC_walk_social,coef_schoolbus_ASC_walk_othdiscr,coef_schoolbus_ASC_walk_atwork
+schoolbus_ASC_rh,coef_schoolbus_ASC_rh_work,coef_schoolbus_ASC_rh_univ,coef_schoolbus_ASC_rh_school,coef_schoolbus_ASC_rh_escort,coef_schoolbus_ASC_rh_shopping,coef_schoolbus_ASC_rh_eatout,coef_schoolbus_ASC_rh_othmaint,coef_schoolbus_ASC_rh_social,coef_schoolbus_ASC_rh_othdisc,coef_schoolbus_ASC_rh_atwork
+#,,,,,,,,,,
+drive_transit_ASC_rh,coef_drive_transit_ASC_rh_work,coef_drive_transit_ASC_rh_univ,coef_drive_transit_ASC_rh_school,coef_drive_transit_ASC_rh_escort,coef_drive_transit_ASC_rh_shopping,coef_drive_transit_ASC_rh_eatout,coef_drive_transit_ASC_rh_othmaint,coef_drive_transit_ASC_rh_social,coef_drive_transit_ASC_rh_othdiscr,coef_drive_transit_ASC_rh_atwork
+ride_hail_ASC_sr2,coef_ride_hail_ASC_sr2_work,coef_ride_hail_ASC_sr2_univ,coef_ride_hail_ASC_sr2_school,coef_ride_hail_ASC_sr2_escort,coef_ride_hail_ASC_sr2_shopping,coef_ride_hail_ASC_sr2_eatout,coef_ride_hail_ASC_sr2_othmaint,coef_ride_hail_ASC_sr2_social,coef_ride_hail_ASC_sr2_othdiscr,coef_ride_hail_ASC_sr2_atwork
+ride_hail_ASC_sr3p,coef_ride_hail_ASC_sr3p_work,coef_ride_hail_ASC_sr3p_univ,coef_ride_hail_ASC_sr3p_school,coef_ride_hail_ASC_sr3p_escort,coef_ride_hail_ASC_sr3p_shopping,coef_ride_hail_ASC_sr3p_eatout,coef_ride_hail_ASC_sr3p_othmaint,coef_ride_hail_ASC_sr3p_social,coef_ride_hail_ASC_sr3p_othdiscr,coef_ride_hail_ASC_sr3p_atwork
+ride_hail_ASC_walk,coef_ride_hail_ASC_walk_work,coef_ride_hail_ASC_walk_univ,coef_ride_hail_ASC_walk_school,coef_ride_hail_ASC_walk_escort,coef_ride_hail_ASC_walk_shopping,coef_ride_hail_ASC_walk_eatout,coef_ride_hail_ASC_walk_othmaint,coef_ride_hail_ASC_walk_social,coef_ride_hail_ASC_walk_othdiscr,coef_ride_hail_ASC_walk_atwork
+ride_hail_ASC_walk_transit,coef_ride_hail_ASC_walk_transit_work,coef_ride_hail_ASC_walk_transit_univ,coef_ride_hail_ASC_walk_transit_school,coef_ride_hail_ASC_walk_transit_escort,coef_ride_hail_ASC_walk_transit_shopping,coef_ride_hail_ASC_walk_transit_eatout,coef_ride_hail_ASC_walk_transit_othmaint,coef_ride_hail_ASC_walk_transit_social,coef_ride_hail_ASC_walk_transit_othdiscr,coef_ride_hail_ASC_walk_transit_atwork
+ride_hail_ASC_taxi,coef_ride_hail_ASC_taxi_work,coef_ride_hail_ASC_taxi_univ,coef_ride_hail_ASC_taxi_school,coef_ride_hail_ASC_taxi_escort,coef_ride_hail_ASC_taxi_shopping,coef_ride_hail_ASC_taxi_eatout,coef_ride_hail_ASC_taxi_othmaint,coef_ride_hail_ASC_taxi_social,coef_ride_hail_ASC_taxi_othdiscr,coef_ride_hail_ASC_taxi_atwork
+ride_hail_ASC_tnc_single,coef_ride_hail_ASC_tnc_single_work,coef_ride_hail_ASC_tnc_single_univ,coef_ride_hail_ASC_tnc_single_school,coef_ride_hail_ASC_tnc_single_escort,coef_ride_hail_ASC_tnc_single_shopping,coef_ride_hail_ASC_tnc_single_eatout,coef_ride_hail_ASC_tnc_single_othmaint,coef_ride_hail_ASC_tnc_single_social,coef_ride_hail_ASC_tnc_single_othdiscr,coef_ride_hail_ASC_tnc_single_atwork
+ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared
+joint_auto_ASC_sr2,coef_joint_auto_ASC_sr2_work,coef_joint_auto_ASC_sr2_univ,coef_joint_auto_ASC_sr2_school,coef_joint_auto_ASC_sr2_escort,coef_joint_auto_ASC_sr2_shopping,coef_joint_auto_ASC_sr2_eatout,coef_joint_auto_ASC_sr2_othmaint,coef_joint_auto_ASC_sr2_social,coef_joint_auto_ASC_sr2_othdiscr,coef_joint_auto_ASC_sr2_atwork
+joint_auto_ASC_sr3p,coef_joint_auto_ASC_sr3p_work,coef_joint_auto_ASC_sr3p_univ,coef_joint_auto_ASC_sr3p_school,coef_joint_auto_ASC_sr3p_escort,coef_joint_auto_ASC_sr3p_shopping,coef_joint_auto_ASC_sr3p_eatout,coef_joint_auto_ASC_sr3p_othmaint,coef_joint_auto_ASC_sr3p_social,coef_joint_auto_ASC_sr3p_othdiscr,coef_joint_auto_ASC_sr3p_atwork
+joint_auto_ASC_walk,coef_joint_auto_ASC_walk_work,coef_joint_auto_ASC_walk_univ,coef_joint_auto_ASC_walk_school,coef_joint_auto_ASC_walk_escort,coef_joint_auto_ASC_walk_shopping,coef_joint_auto_ASC_walk_eatout,coef_joint_auto_ASC_walk_othmaint,coef_joint_auto_ASC_walk_social,coef_joint_auto_ASC_walk_othdiscr,coef_joint_auto_ASC_walk_atwork
+joint_auto_ASC_rh,coef_joint_auto_ASC_rh_work,coef_joint_auto_ASC_rh_univ,coef_joint_auto_ASC_rh_school,coef_joint_auto_ASC_rh_escort,coef_joint_auto_ASC_rh_shopping,coef_joint_auto_ASC_rh_eatout,coef_joint_auto_ASC_rh_othmaint,coef_joint_auto_ASC_rh_social,coef_joint_auto_ASC_rh_othdiscr,coef_joint_auto_ASC_rh_atwork
+joint_walk_ASC_rh,coef_joint_walk_ASC_rh_work,coef_joint_walk_ASC_rh_univ,coef_joint_walk_ASC_rh_school,coef_joint_walk_ASC_rh_escort,coef_joint_walk_ASC_rh_shopping,coef_joint_walk_ASC_rh_eatout,coef_joint_walk_ASC_rh_othmaint,coef_joint_walk_ASC_rh_social,coef_joint_walk_ASC_rh_othdiscr,coef_joint_walk_ASC_rh_atwork
+joint_bike_ASC_walk,coef_joint_bike_ASC_walk_work,coef_joint_bike_ASC_walk_univ,coef_joint_bike_ASC_walk_school,coef_joint_bike_ASC_walk_escort,coef_joint_bike_ASC_walk_shopping,coef_joint_bike_ASC_walk_eatout,coef_joint_bike_ASC_walk_othmaint,coef_joint_bike_ASC_walk_social,coef_joint_bike_ASC_walk_othdiscr,coef_joint_bike_ASC_walk_atwork
+joint_bike_ASC_rh,coef_joint_bike_ASC_rh_work,coef_joint_bike_ASC_rh_univ,coef_joint_bike_ASC_rh_school,coef_joint_bike_ASC_rh_escort,coef_joint_bike_ASC_rh_shopping,coef_joint_bike_ASC_rh_eatout,coef_joint_bike_ASC_rh_othmaint,coef_joint_bike_ASC_rh_social,coef_joint_bike_ASC_rh_othdiscr,coef_joint_bike_ASC_rh_atwork
+joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2_work,coef_joint_walk_transit_ASC_sr2_univ,coef_joint_walk_transit_ASC_sr2_school,coef_joint_walk_transit_ASC_sr2_escort,coef_joint_walk_transit_ASC_sr2_shopping,coef_joint_walk_transit_ASC_sr2_eatout,coef_joint_walk_transit_ASC_sr2_othmaint,coef_joint_walk_transit_ASC_sr2_social,coef_joint_walk_transit_ASC_sr2_othdiscr,coef_joint_walk_transit_ASC_sr2_atwork
+joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p_work,coef_joint_walk_transit_ASC_sr3p_univ,coef_joint_walk_transit_ASC_sr3p_school,coef_joint_walk_transit_ASC_sr3p_escort,coef_joint_walk_transit_ASC_sr3p_shopping,coef_joint_walk_transit_ASC_sr3p_eatout,coef_joint_walk_transit_ASC_sr3p_othmaint,coef_joint_walk_transit_ASC_sr3p_social,coef_joint_walk_transit_ASC_sr3p_othdiscr,coef_joint_walk_transit_ASC_sr3p_atwork
+joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk_work,coef_joint_walk_transit_ASC_walk_univ,coef_joint_walk_transit_ASC_walk_school,coef_joint_walk_transit_ASC_walk_escort,coef_joint_walk_transit_ASC_walk_shopping,coef_joint_walk_transit_ASC_walk_eatout,coef_joint_walk_transit_ASC_walk_othmaint,coef_joint_walk_transit_ASC_walk_social,coef_joint_walk_transit_ASC_walk_othdiscr,coef_joint_walk_transit_ASC_walk_atwork
+joint_walk_transit_ASC_rh,coef_joint_walk_transit_ASC_rh_work,coef_joint_walk_transit_ASC_rh_univ,coef_joint_walk_transit_ASC_rh_school,coef_joint_walk_transit_ASC_rh_escort,coef_joint_walk_transit_ASC_rh_shopping,coef_joint_walk_transit_ASC_rh_eatout,coef_joint_walk_transit_ASC_rh_othmaint,coef_joint_walk_transit_ASC_rh_social,coef_joint_walk_transit_ASC_rh_othdiscr,coef_joint_walk_transit_ASC_rh_atwork
+joint_drive_transit_ASC_rh,coef_joint_drive_transit_ASC_rh_work,coef_joint_drive_transit_ASC_rh_univ,coef_joint_drive_transit_ASC_rh_school,coef_joint_drive_transit_ASC_rh_escort,coef_joint_drive_transit_ASC_rh_shopping,coef_joint_drive_transit_ASC_rh_eatout,coef_joint_drive_transit_ASC_rh_othmaint,coef_joint_drive_transit_ASC_rh_social,coef_joint_drive_transit_ASC_rh_othdiscr,coef_joint_drive_transit_ASC_rh_atwork
+joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2_work,coef_joint_ride_hail_ASC_sr2_univ,coef_joint_ride_hail_ASC_sr2_school,coef_joint_ride_hail_ASC_sr2_escort,coef_joint_ride_hail_ASC_sr2_shopping,coef_joint_ride_hail_ASC_sr2_eatout,coef_joint_ride_hail_ASC_sr2_othmaint,coef_joint_ride_hail_ASC_sr2_social,coef_joint_ride_hail_ASC_sr2_othdiscr,coef_joint_ride_hail_ASC_sr2_atwork
+joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p_work,coef_joint_ride_hail_ASC_sr3p_univ,coef_joint_ride_hail_ASC_sr3p_school,coef_joint_ride_hail_ASC_sr3p_escort,coef_joint_ride_hail_ASC_sr3p_shopping,coef_joint_ride_hail_ASC_sr3p_eatout,coef_joint_ride_hail_ASC_sr3p_othmaint,coef_joint_ride_hail_ASC_sr3p_social,coef_joint_ride_hail_ASC_sr3p_othdiscr,coef_joint_ride_hail_ASC_sr3p_atwork
+joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk_work,coef_joint_ride_hail_ASC_walk_univ,coef_joint_ride_hail_ASC_walk_school,coef_joint_ride_hail_ASC_walk_escort,coef_joint_ride_hail_ASC_walk_shopping,coef_joint_ride_hail_ASC_walk_eatout,coef_joint_ride_hail_ASC_walk_othmaint,coef_joint_ride_hail_ASC_walk_social,coef_joint_ride_hail_ASC_walk_othdiscr,coef_joint_ride_hail_ASC_walk_atwork
+joint_ride_hail_ASC_walk_transit,coef_joint_ride_hail_ASC_walk_transit_work,coef_joint_ride_hail_ASC_walk_transit_univ,coef_joint_ride_hail_ASC_walk_transit_school,coef_joint_ride_hail_ASC_walk_transit_escort,coef_joint_ride_hail_ASC_walk_transit_shopping,coef_joint_ride_hail_ASC_walk_transit_eatout,coef_joint_ride_hail_ASC_walk_transit_othmaint,coef_joint_ride_hail_ASC_walk_transit_social,coef_joint_ride_hail_ASC_walk_transit_othdiscr,coef_joint_ride_hail_ASC_walk_transit_atwork
+joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi_work,coef_joint_ride_hail_ASC_taxi_univ,coef_joint_ride_hail_ASC_taxi_school,coef_joint_ride_hail_ASC_taxi_escort,coef_joint_ride_hail_ASC_taxi_shopping,coef_joint_ride_hail_ASC_taxi_eatout,coef_joint_ride_hail_ASC_taxi_othmaint,coef_joint_ride_hail_ASC_taxi_social,coef_joint_ride_hail_ASC_taxi_othdiscr,coef_joint_ride_hail_ASC_taxi_atwork
+joint_ride_hail_ASC_tnc_single,coef_joint_ride_hail_ASC_tnc_single_work,coef_joint_ride_hail_ASC_tnc_single_univ,coef_joint_ride_hail_ASC_tnc_single_school,coef_joint_ride_hail_ASC_tnc_single_escort,coef_joint_ride_hail_ASC_tnc_single_shopping,coef_joint_ride_hail_ASC_tnc_single_eatout,coef_joint_ride_hail_ASC_tnc_single_othmaint,coef_joint_ride_hail_ASC_tnc_single_social,coef_joint_ride_hail_ASC_tnc_single_othdiscr,coef_joint_ride_hail_ASC_tnc_single_atwork
+joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared
+walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty
+adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared
+origin_density_applied,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_purpose.yaml b/activitysim/examples/prototype_mwcog/configs/trip_purpose.yaml
new file mode 100644
index 000000000..10668a618
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_purpose.yaml
@@ -0,0 +1,7 @@
+
+preprocessor:
+  SPEC: trip_purpose_annotate_trips_preprocessor
+  DF: trips
+  TABLES:
+    - persons
+    - tours
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_purpose_and_destination.yaml b/activitysim/examples/prototype_mwcog/configs/trip_purpose_and_destination.yaml
new file mode 100644
index 000000000..76f592348
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_purpose_and_destination.yaml
@@ -0,0 +1,6 @@
+
+MAX_ITERATIONS: 5
+
+# drop failed trips and cleanup failed trip leg_mates for consistency
+# (i.e. adjust trip_count, trip_num, first for missing failed trips)
+CLEANUP: True
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_purpose_annotate_trips_preprocessor.csv
new file mode 100644
index 000000000..782116aa9
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_purpose_annotate_trips_preprocessor.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,,
+,ptype,"reindex(persons.ptype, df.person_id)"
+,person_type,ptype.map(PTYPE_NAME)
+,start,"reindex_i(tours.start, df.tour_id)"
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_purpose_probs.csv b/activitysim/examples/prototype_mwcog/configs/trip_purpose_probs.csv
new file mode 100644
index 000000000..3cd1aaa27
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_purpose_probs.csv
@@ -0,0 +1,132 @@
+primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr
+work,TRUE,1,18,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066
+work,TRUE,1,18,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027
+work,TRUE,1,18,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115
+work,TRUE,19,48,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046
+work,TRUE,19,48,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057
+work,TRUE,19,48,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015
+work,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2
+univ,TRUE,1,48,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0
+univ,TRUE,1,48,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0
+univ,TRUE,1,48,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05
+school,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058
+school,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201
+school,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041
+escort,TRUE,1,48,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06
+escort,TRUE,1,48,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061
+escort,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034
+escort,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.444,0.216,0.084,0.108,0.118,0.03
+escort,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.37,0.204,0.192,0.03,0.068,0.136
+escort,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.586,0.227,0,0.072,0.115,0
+escort,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.37,0.183,0.29,0.064,0.013,0.08
+escort,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.531,0.064,0,0.131,0.196,0.078
+shopping,TRUE,1,48,PTYPE_FULL,0,0,0,0.102,0.456,0.226,0.11,0.06,0.046
+shopping,TRUE,1,48,PTYPE_PART,0,0,0,0.182,0.291,0.311,0.108,0.031,0.077
+shopping,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.13,0.262,0.36,0.124,0.06,0.064
+shopping,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.144,0.336,0.274,0.122,0.068,0.056
+shopping,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.058,0.357,0.418,0.05,0.047,0.07
+shopping,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.076,0.193,0.298,0.047,0.13,0.256
+shopping,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.121,0.142,0.232,0.291,0.03,0.184
+shopping,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.138,0.292,0.301,0.187,0.064,0.018
+othmaint,TRUE,1,48,PTYPE_FULL,0,0,0,0.201,0.252,0.366,0.117,0.032,0.032
+othmaint,TRUE,1,48,PTYPE_PART,0,0,0,0.27,0.259,0.325,0.109,0,0.037
+othmaint,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.489,0.13,0.167,0.025,0.15,0.039
+othmaint,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.279,0.229,0.344,0.078,0.039,0.031
+othmaint,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.224,0.139,0.321,0.098,0.064,0.154
+othmaint,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.135,0,0.259,0.083,0.523,0
+othmaint,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.191,0.408,0.344,0.041,0.008,0.008
+othmaint,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.143,0.301,0.464,0.017,0.029,0.046
+eatout,TRUE,1,48,PTYPE_FULL,0,0,0,0.144,0.283,0.202,0.036,0.129,0.206
+eatout,TRUE,1,48,PTYPE_PART,0,0,0,0.169,0.374,0.179,0.013,0.135,0.13
+eatout,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.32,0.085,0.111,0,0.153,0.331
+eatout,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.201,0.224,0.269,0.063,0.082,0.161
+eatout,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.142,0.237,0.237,0.034,0.123,0.227
+eatout,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.175,0.289,0.346,0,0.105,0.085
+eatout,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.124,0.135,0.135,0.04,0.048,0.518
+eatout,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.055,0.329,0.165,0.061,0,0.39
+social,TRUE,1,48,PTYPE_FULL,0,0,0,0.186,0.382,0.144,0.122,0.126,0.04
+social,TRUE,1,48,PTYPE_PART,0,0,0,0.175,0.153,0.167,0.147,0.183,0.175
+social,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0,0.212,0.091,0.432,0.234,0.031
+social,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.311,0.392,0.149,0.071,0.058,0.019
+social,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.12,0.407,0.203,0.151,0.102,0.017
+social,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0,0,0,0.415,0.585
+social,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.322,0.11,0.05,0,0.378,0.14
+social,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.294,0,0.159,0,0.547,0
+othdiscr,TRUE,1,48,PTYPE_FULL,0,0,0,0.236,0.169,0.143,0.19,0.093,0.169
+othdiscr,TRUE,1,48,PTYPE_PART,0,0,0,0.223,0.208,0.181,0.193,0.129,0.066
+othdiscr,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.135,0.123,0.061,0.342,0.123,0.216
+othdiscr,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.263,0.295,0.148,0.088,0.082,0.124
+othdiscr,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.225,0.056,0.389,0.16,0.091,0.079
+othdiscr,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.311,0.126,0.051,0.018,0.142,0.352
+othdiscr,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.222,0.112,0.172,0.173,0.141,0.18
+othdiscr,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.271,0.108,0.393,0.146,0.043,0.039
+atwork,TRUE,1,48,PTYPE_FULL,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014
+atwork,TRUE,1,48,PTYPE_PART,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014
+atwork,TRUE,1,48,PTYPE_UNIVERSITY,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014
+atwork,TRUE,1,48,PTYPE_DRIVING,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014
+#,,,,,,,,,,,,,
+work,FALSE,1,30,PTYPE_FULL,0.175,0,0,0.14,0.27,0.162,0.134,0.05,0.069
+work,FALSE,1,30,PTYPE_PART,0.097,0,0,0.252,0.211,0.192,0.159,0.089,0
+work,FALSE,1,30,PTYPE_UNIVERSITY,0.134,0,0,0.329,0.114,0.212,0.169,0.042,0
+work,FALSE,31,48,PTYPE_FULL,0.151,0.011,0,0.201,0.28,0.127,0.103,0.035,0.092
+work,FALSE,31,48,PTYPE_PART,0.11,0,0,0.243,0.281,0.13,0.119,0.036,0.081
+work,FALSE,31,48,PTYPE_UNIVERSITY,0.058,0.127,0,0.224,0.269,0.079,0.072,0.108,0.063
+work,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2
+univ,FALSE,1,48,PTYPE_FULL,0.352,0.032,0,0.032,0.146,0.114,0.177,0.028,0.119
+univ,FALSE,1,48,PTYPE_PART,0,0,0,0.822,0.178,0,0,0,0
+univ,FALSE,1,48,PTYPE_UNIVERSITY,0.054,0.025,0,0.194,0.209,0.179,0.159,0.067,0.113
+school,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.301,0.117,0.098,0.169,0.186,0.129
+school,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.166,0.158,0.147,0.122,0.133,0.274
+school,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.38,0.148,0.089,0.146,0.102,0.135
+escort,FALSE,1,48,PTYPE_FULL,0,0,0,0.343,0.235,0.114,0.222,0.039,0.047
+escort,FALSE,1,48,PTYPE_PART,0,0,0,0.24,0.298,0.128,0.157,0.045,0.132
+escort,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.195,0.319,0.287,0.02,0.027,0.152
+escort,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.28,0.325,0.169,0.103,0.05,0.073
+escort,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.31,0.317,0.073,0.111,0.112,0.077
+escort,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.489,0,0.148,0.363,0
+escort,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.188,0.259,0.129,0.202,0.06,0.162
+escort,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.413,0.215,0.118,0.211,0.019,0.024
+shopping,FALSE,1,48,PTYPE_FULL,0,0,0,0.091,0.526,0.159,0.152,0.047,0.025
+shopping,FALSE,1,48,PTYPE_PART,0,0,0,0.104,0.553,0.156,0.105,0.037,0.045
+shopping,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.1,0.43,0.064,0.344,0.003,0.059
+shopping,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.528,0.158,0.122,0.059,0.023
+shopping,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.052,0.549,0.159,0.123,0.06,0.057
+shopping,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.118,0.707,0,0.041,0.134,0
+shopping,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.015,0.19,0.256,0.157,0.179,0.203
+shopping,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.206,0.172,0.22,0.202,0.158,0.042
+othmaint,FALSE,1,48,PTYPE_FULL,0,0,0,0.171,0.364,0.215,0.159,0.029,0.062
+othmaint,FALSE,1,48,PTYPE_PART,0,0,0,0.228,0.365,0.17,0.13,0.041,0.066
+othmaint,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.046,0.345,0.192,0.298,0.06,0.059
+othmaint,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.17,0.423,0.158,0.171,0.064,0.014
+othmaint,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.099,0.391,0.213,0.241,0.036,0.02
+othmaint,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.031,0.356,0.075,0.458,0.031,0.049
+othmaint,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.181,0.255,0.142,0.313,0,0.109
+othmaint,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.164,0.249,0.338,0.053,0.006,0.19
+eatout,FALSE,1,48,PTYPE_FULL,0,0,0,0.106,0.44,0.112,0.041,0.128,0.173
+eatout,FALSE,1,48,PTYPE_PART,0,0,0,0.168,0.331,0.225,0.023,0.063,0.19
+eatout,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.165,0.334,0.104,0.088,0.135,0.174
+eatout,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.148,0.547,0.092,0.056,0.055,0.102
+eatout,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.166,0.414,0.169,0.02,0.166,0.065
+eatout,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.195,0.332,0.114,0.114,0,0.245
+eatout,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.072,0.356,0.053,0.019,0.169,0.331
+eatout,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.01,0.286,0.045,0.117,0.064,0.478
+social,FALSE,1,48,PTYPE_FULL,0,0,0,0.12,0.286,0.123,0.19,0.255,0.026
+social,FALSE,1,48,PTYPE_PART,0,0,0,0.106,0.122,0.039,0.553,0.047,0.133
+social,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.105,0.274,0.176,0,0.206,0.239
+social,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.313,0.326,0.13,0.062,0.075,0.094
+social,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.097,0.338,0.067,0.156,0.328,0.014
+social,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0,0.368,0.15,0.482,0
+social,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.058,0.162,0.085,0.281,0.125,0.289
+social,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.23,0.028,0.072,0.23,0.44,0
+othdiscr,FALSE,1,48,PTYPE_FULL,0,0,0,0.108,0.319,0.132,0.27,0.112,0.059
+othdiscr,FALSE,1,48,PTYPE_PART,0,0,0,0.102,0.346,0.154,0.181,0.087,0.13
+othdiscr,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.116,0.374,0.124,0.162,0.033,0.191
+othdiscr,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.389,0.19,0.19,0.067,0.054
+othdiscr,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.111,0.284,0.186,0.197,0.111,0.111
+othdiscr,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.277,0.304,0.057,0.205,0.157,0
+othdiscr,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.114,0.204,0.148,0.291,0.089,0.154
+othdiscr,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.335,0.133,0.111,0.282,0.052,0.087
+atwork,FALSE,1,48,PTYPE_FULL,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018
+atwork,FALSE,1,48,PTYPE_PART,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018
+atwork,FALSE,1,48,PTYPE_UNIVERSITY,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018
+atwork,FALSE,1,48,PTYPE_DRIVING,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/trip_scheduling.yaml
new file mode 100644
index 000000000..a006e7436
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling.yaml
@@ -0,0 +1,10 @@
+
+# int to add to probs column index to get time period it represents.
+# e.g. depart_alt_base = 5 means first column (column 0) represents period 5
+DEPART_ALT_BASE: 1
+
+MAX_ITERATIONS: 100
+
+#FAILFIX: drop_and_cleanup
+FAILFIX: choose_most_initial
+
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs.csv b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs.csv
new file mode 100644
index 000000000..93c9bcdb1
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs.csv
@@ -0,0 +1,3457 @@
+primary_purpose,outbound,tour_hour,trip_num,HR1,HR2,HR3,HR4,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23,HR24,HR25,HR26,HR27,HR28,HR29,HR30,HR31,HR32,HR33,HR34,HR35,HR36,HR37,HR38,HR39,HR40,HR41,HR42,HR43,HR44,HR45,HR46,HR47,HR48
+work,TRUE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+work,TRUE,1,2,0.4070175438596491,0.5929824561403508,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+work,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+work,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+work,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+work,TRUE,2,2,0.0,0.30238823771032364,0.6976117622896764,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+othmaint,TRUE,1,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,2,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,2,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,2,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,3,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,3,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,3,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,4,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,4,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,4,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,5,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,5,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,5,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,6,2,0.0,0.0,0.0,0.0,0.0,0.0,0.13467336683417086,0.16733668341708544,0.2,0.23266331658291456,0.26532663316582916,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,6,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,6,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,7,1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,7,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5112781954887218,0.48872180451127817,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,8,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,8,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.22945641665611993,0.2721685894560219,0.18431075398095653,0.09645291850589112,0.06813750032462951,0.039822082143367916,0.011506663962106308,0.011075933225556875,0.01064520248900744,0.010214471752458007,0.009783741015908572,0.009353010279359138,0.008922279542809705,0.008491548806260272,0.008060818069710837,0.007630087333161403,0.007199356596611969,0.0067686258600625345,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,8,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.24052843928648374,0.12605540820438518,0.11273712233144474,0.09941883645850427,0.08610055058556383,0.07278226471262338,0.05946397883968292,0.04614569296674248,0.032827407093802025,0.01950912122086156,0.018361525854928527,0.017213930488995496,0.01606633512306246,0.014918739757129427,0.013771144391196396,0.012623549025263362,0.011475953659330329,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,8,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2124360877453406,0.045522018802572985,0.1012699983506515,0.09450766947055914,0.08774534059046676,0.08098301171037439,0.07422068283028203,0.06745835395018968,0.060696025070097304,0.053933696190004946,0.04717136730991259,0.04040903842982022,0.03364670954972786,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,9,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,9,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3805979752214715,0.27317378481264054,0.07766776394381054,0.059313970164385166,0.059973014277322774,0.029986507138661387,0.04975783052678978,0.06952915391491817,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+othmaint,TRUE,17,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.38047752780054894,0.3461245116418672,0.2733979605575838,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,17,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05011941418897653,0.06073580025378388,0.31662686193700784,0.5725179236202318,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,18,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,18,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.37331268616818764,0.40561847014356567,0.17248666574633137,0.048582177941915466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,18,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.20569888790785956,0.10401228216018026,0.10266238411770522,0.10131248607523015,0.09996258803275508,0.09861268999028004,0.09726279194780497,0.09591289390532992,0.09456299586285485,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,18,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.664163607217234,0.335836392782766,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,19,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,19,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23232446208669766,0.5671796844914477,0.029481542644875798,0.056613107252841216,0.052554054279995994,0.03813373450804587,0.023713414736095752,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,19,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10094818281562304,0.10711739739697572,0.25564081266132377,0.16697248190097347,0.07830415114062311,0.07160389903271412,0.06490364692480514,0.058203394816896147,0.05150314270898716,0.04480289060107818,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,19,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14101123595505619,0.12415730337078652,0.18258426966292135,0.15853932584269664,0.13449438202247194,0.11044943820224722,0.08640449438202248,0.06235955056179776,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,20,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,20,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4751939521243822,0.2470285230990692,0.16314208394543114,0.0739901726534752,0.04064526817764238,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,20,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09797920858395902,0.307727292145175,0.4767184489701152,0.11757505030075081,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,20,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,21,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3192731142746874,0.5694813829472318,0.1112455027780807,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21704394141145142,0.255659121171771,0.2609853528628495,0.2663115845539281,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5956908832827897,0.340159368104585,0.06414974861262529,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6693051890941073,0.3306948109058927,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8549222797927462,0.14507772020725387,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.35222051007011906,0.5276555537502944,0.1201239361795865,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.43365501172963294,0.3070802947133541,0.240698423941677,0.01856626961533597,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.37272727272727274,0.2909090909090909,0.20909090909090908,0.12727272727272726,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4703672978558499,0.25149187352576885,0.1893162554047642,0.08882457321361692,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10092592592592593,0.15046296296296297,0.2,0.24953703703703703,0.2990740740740741,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,24,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10092592592592593,0.15046296296296297,0.2,0.24953703703703703,0.2990740740740741,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,25,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6711690125246024,0.27538993387397864,0.05344105360141894,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,26,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.24708353838897273,0.42820359820351384,0.14918292889589033,0.1755299345116232,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,26,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,26,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,27,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,27,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.34185572109552254,0.3708153046551616,0.2193814263014925,0.06794754794782339,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,27,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23592303410127327,0.24075594020498559,0.24558884630869796,0.27773217938504324,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11595518510014498,0.707374962040246,0.17666985285960912,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5847246357614936,0.32110861497807414,0.0941667492604323,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,29,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,30,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,30,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17041195462534542,0.4939281347489742,0.3356599106256804,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,30,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5436578919259509,0.4563421080740492,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,30,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,31,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,31,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3197278911564626,0.6802721088435374,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,31,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,31,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,32,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,32,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41670607916996244,0.5832939208300376,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,32,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,32,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,33,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,33,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12651043235845735,0.5545971774306326,0.29116318921384754,0.027729200997062435,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,33,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41011000093229244,0.30337000031076417,0.1966299996892358,0.08988999906770749,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,33,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,34,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,34,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,34,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,35,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,35,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,35,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,35,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,36,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,36,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,36,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,36,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,37,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,37,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,37,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,37,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,38,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,38,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,38,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,38,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,39,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,39,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,39,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,39,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,40,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,40,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,40,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,40,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,41,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,41,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,41,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,41,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,42,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,42,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,42,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,42,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,43,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,43,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,43,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,43,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,44,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,TRUE,44,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,TRUE,44,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,TRUE,44,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,TRUE,45,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,TRUE,45,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,TRUE,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,TRUE,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,TRUE,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,TRUE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,TRUE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,TRUE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,TRUE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,TRUE,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,TRUE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,FALSE,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,FALSE,1,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,FALSE,1,3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,1,4,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,2,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,2,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,2,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,3,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,3,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,3,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,4,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,4,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,4,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,5,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,5,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,5,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,6,2,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,6,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,6,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,7,1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,7,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,8,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,8,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,8,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,8,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,9,1,0.0,0.0,0.0,0.0,0.0,0.0,0.17157712305025996,0.09878682842287695,0.729636048526863,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,9,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,9,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,9,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,10,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17811505489861143,0.8218849451013885,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,10,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,10,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,10,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,11,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2345053784493712,0.7654946215506289,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,11,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17833173537871524,0.21188878235858102,0.2454458293384468,0.36433365292425696,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,11,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,11,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,12,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5759286927203697,0.4240713072796303,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,12,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05721550757563484,0.9427844924243651,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,12,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,12,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,13,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.054405921978130795,0.03134134247827413,0.4129049471458381,0.501347788397757,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,13,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.31289958631064313,0.6765701391500565,0.01053027453930049,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,13,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,13,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,14,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4576059702299425,0.5423940297700576,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,14,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10875666956557833,0.8562537558861758,0.03498957454824597,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,14,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04919498722090051,0.6612415935922464,0.28956341918685313,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,14,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,15,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009496949154471256,0.03133388319755484,0.45974250502534686,0.499426662622627,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,15,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11719383785522011,0.22090205060702517,0.6374762085329129,0.024427903004841843,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,15,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8131118512048484,0.1868881487951516,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,15,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,16,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006418732155486201,0.010211619338273501,0.014004506521060802,0.04570506345880864,0.4202625509418612,0.5033975275845097,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,16,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03075915400644836,0.023365126601052118,0.2726917307110133,0.20623366344733057,0.3714547956401702,0.09549552959398545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,16,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08584783440784594,0.25833786790202967,0.5415931633993366,0.11422113429078773,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,16,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2690961314283053,0.23925270517994324,0.2918357173074033,0.1998154460843482,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,17,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.011197853396584374,0.035691748017189,0.3699455673249998,0.5831648312612269,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,17,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.036020879223057835,0.2331531862764629,0.6243660845023324,0.10645984999814681,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,17,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.025741986825419592,0.05075167924194251,0.03417970234661434,0.48438507466969616,0.4049415569163273,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,17,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17881381340802874,0.2844765213309548,0.39013922925388084,0.14657043600713568,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,18,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03169585857468679,0.040914423826167945,0.5321741888642274,0.3952155287349179,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,18,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.015208137289406783,0.1985506812783663,0.38155654776770526,0.3021003403544287,0.10258429331009283,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,18,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.15824507452474512,0.21812158920978383,0.4796749408395773,0.1439583954258938,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,18,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3297810610518174,0.39505130201959604,0.27516763692858653,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,19,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006163195465132538,0.02453438250022884,0.04290556953532514,0.39947644481884986,0.5269204076804636,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,19,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.011748674799365779,0.03817687660611117,0.06460507841285655,0.09103328021960194,0.3329122829279582,0.39605735872699827,0.06546644830710802,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,19,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03524706988963434,0.04396825087907656,0.052689431868518787,0.3140008440216324,0.28651289155695536,0.25712109104274267,0.01046042074143987,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,19,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08960945365188602,0.3056083076558206,0.41528218241405346,0.18950005627823993,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,20,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0022072308281968736,0.03038754108007774,0.05856785133195861,0.3970748264984346,0.5117625502613323,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,20,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.036303155815626834,0.1489878496930755,0.3712141306399957,0.3925801951320498,0.050914668719252175,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,20,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04187017652009959,0.11508967108775642,0.46415056253050874,0.3181196808845463,0.06076990897708899,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,20,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04074533762514755,0.19946439740350366,0.33609182714019825,0.2947467393608004,0.1289516984703502,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.002387506897892367,0.006589519038182933,0.047631368341788764,0.4014932966652584,0.5418983090568776,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,21,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14828645020642328,0.08097162576512176,0.30291353517234415,0.31589145307851635,0.15193693577759437,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07046513158888228,0.03541538785922938,0.07209129790498021,0.2617543328242292,0.1820397584050817,0.2868996700964267,0.09133442132117052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.15141556844708173,0.05307662275212325,0.1166639113260351,0.10013241120978163,0.08010877324269816,0.15788532057304355,0.16620090434076004,0.17451648810847656,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006918750452214692,0.07585544944291994,0.42546001758618096,0.4917657825186845,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.043764122841007835,0.042103633772715955,0.04044314470442408,0.0387826556361322,0.03712216656784033,0.07491382842494568,0.3612041887944431,0.3076489236219947,0.05401733563649623,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.015629350347859446,0.14332549529227637,0.21566636067587372,0.529886479930062,0.09549231375392854,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5175924133064806,0.31489279391188785,0.11219317451729502,0.055321618264336396,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00479918603055065,0.011158107521030261,0.01091814821950273,0.06853747799442335,0.46446052080762656,0.4401265594268665,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006205419534836946,0.02756818086779292,0.04893094220074889,0.02118312785175313,0.19232975985506726,0.2491177986219223,0.3443747491517244,0.1102900219161541,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01674138777235162,0.05487633659090426,0.09301128540945691,0.4022903343385845,0.16314186861032057,0.18871479156207777,0.08122399571630418,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21998260416150126,0.20037222716504707,0.2733635955649312,0.2581135286710696,0.04816804443745077,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006297234854220209,0.008396313138960278,0.010495391423700349,0.010769184243449054,0.011042977063197758,0.05644086251826307,0.4487689373567565,0.4477890994014528,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020956030675818114,0.016500922480389424,0.05722505226710666,0.09794918205382389,0.10997476329579829,0.36167539410777605,0.2875545183174073,0.04816413680188031,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10538720538720539,0.10067340067340068,0.15185185185185185,0.1814814814814815,0.3074074074074074,0.08148148148148149,0.07171717171717172,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,24,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17177522349936145,0.20114942528735633,0.2305236270753512,0.10983397190293742,0.13218390804597702,0.1545338441890166,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,25,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0037391030313108577,0.0037391030313108577,0.0037391030313108577,0.005423383676045478,0.05868033766255418,0.527366808332266,0.3973121612352018,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08618665362215573,0.06885435137858172,0.05152204913500769,0.09700548570781722,0.25870654646562935,0.3398018010548185,0.09792311263598978,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04054397191446974,0.1008766010793697,0.19922931894395066,0.28864085965599323,0.3045696243167972,0.06613962408941948,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1686295292062769,0.07747843233801913,0.10368437268764323,0.028484717771330558,0.1936960808450478,0.263768486562521,0.16425838058916148,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007643386269844774,0.008047187808629027,0.008450989347413278,0.008854790886197532,0.009258592424981784,0.009662393963766036,0.04491138455707116,0.46560103385699353,0.437570240885103,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,26,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007123508096350515,0.01014789646971,0.013172284843069487,0.016196673216428974,0.01922106158978846,0.022245449963147947,0.02526983833650743,0.028294226709866914,0.0313186150832264,0.029657796068038107,0.021131891061688803,0.05983589507226333,0.051115032873754566,0.30245371282725797,0.3248430517952447,0.03797306599365627,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,26,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06256585641589274,0.042685116994020285,0.022804377572147823,0.017070168073728003,0.011335958575308181,0.2362564110768993,0.23886840699968234,0.20595158952439147,0.16246211476792966,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,26,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06886727427703847,0.15455705774000023,0.24024684120296197,0.07710780282300887,0.0430483425930985,0.12739608427150373,0.15957254503996696,0.1292040520524214,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,27,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.022962734414014023,0.10001512068617083,0.4485759984982655,0.42844614640154965,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,27,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.017447195259677143,0.009057621417169967,0.06294589429305998,0.11344677671441836,0.32182592498958795,0.3839679121428937,0.09130867518319294,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,27,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03385332634013409,0.02426196560573383,0.13129623999418463,0.03938887199825539,0.04423315992603755,0.04907744785381972,0.05392173578160188,0.16124404013648297,0.3161074871426311,0.14661572522111885,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11587783901894477,0.1055697937774487,0.09526174853595262,0.08495370329445653,0.07464565805296046,0.1102448885110794,0.13599551940417823,0.16174615029727704,0.11570469910770231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.005221068070407514,0.010646883908281989,0.016072699746156462,0.021498515584030937,0.026924331421905413,0.04937151216281614,0.39801390254905894,0.4722510865573426,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014017262863907608,0.01807375463277868,0.022130246401649747,0.026186738170520817,0.03024322993939189,0.18371984018356516,0.40987045811703043,0.2728544454037251,0.02290402428743073,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04615356219189043,0.037656672081249516,0.029159781970608596,0.020662891859967683,0.12230131501884864,0.2239397381777296,0.197303015539239,0.2627654589693454,0.060057564191121016,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0390953503317447,0.027341192715664597,0.10871519604474432,0.19008919937382404,0.2714632027029038,0.058965386640870504,0.0707804708620476,0.1975209529832592,0.0360290483449412,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.001954476320348524,0.002577642683358198,0.06198187265906605,0.4570089618521332,0.4764770464850941,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00666934935393186,0.011260568112073358,0.015851786870214858,0.02116793490595764,0.05126637172167803,0.19703228644898063,0.24982288995169394,0.4032010084305432,0.0437278042049265,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0187258954385437,0.018946777560361387,0.01916765968217907,0.019388541803996757,0.01960942392581444,0.019830306047632126,0.02005118816944981,0.020272070291267492,0.0383966755093075,0.0565212807273475,0.10428090395592685,0.06656521205513431,0.19791038114864534,0.09792440733917347,0.276052779728467,0.006356496616753366,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,29,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.10750046661210608,0.14518553393509362,0.18287060125808113,0.22055566858106868,0.11174820541440815,0.2200498060696132,0.01208971812962895,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,30,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.019922882349248744,0.048220807063389715,0.4857263537324635,0.44612995685489815,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,30,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020179751434316565,0.019081144937339926,0.01032094157327641,0.05589958264992929,0.16181803343646517,0.2839555946369509,0.3362538150778383,0.1124911362538834,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,30,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02292480221349966,0.028227501790921313,0.03353020136834296,0.038832900945764616,0.04413560052318626,0.08227218738302682,0.0835435376799551,0.20803722382530998,0.1994672043062447,0.2590288399637485,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,30,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06327863983653105,0.05913891573507574,0.054999191633620444,0.05085946753216514,0.04671974343070984,0.04258001932925454,0.08042892539970302,0.068562077172557,0.12478311220100982,0.07333225551149393,0.09817060012022573,0.23714705209765372,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,31,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0033107920978842674,0.0029212871451920006,0.002531782192499734,0.019318316933750197,0.0518752369610731,0.45340044434792576,0.46664214032167484,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,31,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024085559461470146,0.05868360067684162,0.13980826548288824,0.03526338816182093,0.14725219075980786,0.1705222305718117,0.33735738428977136,0.08702738059558826,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,31,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.015724615313145503,0.03158026908723389,0.04743592286132227,0.22996636503915469,0.030968378709256526,0.11898292253613431,0.04205635813362483,0.10772893925783619,0.09906507647281666,0.14754930702168198,0.12894184556779312,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,31,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.019495546131780526,0.05226465558732652,0.31171163030608384,0.04901480032011027,0.039866380347113144,0.030717960374116015,0.021569540401118883,0.1870742830943195,0.13024684011444862,0.07341939713457774,0.08461896618900483,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,32,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006334954420403081,0.006066670831560874,0.005798387242718667,0.00553010365387646,0.005261820065034253,0.0049935364761920465,0.004725252887349839,0.005296437302304215,0.03749046796336905,0.11310126665197308,0.4452914211632271,0.36010968134199134,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,32,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01672321231785461,0.017504670837380526,0.018286129356906444,0.01906758787643236,0.022943622133280907,0.026819656390129454,0.030695690646977993,0.03457172490382654,0.03844775916067509,0.0030312770655366527,0.049877377877565375,0.19176992069165988,0.21679980300805626,0.30257569766170567,0.01088587007201232,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,32,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04237946281320558,0.058576062655560146,0.0747726624979147,0.09096926234026928,0.10716586218262385,0.12336246202497841,0.0067372912875999155,0.07989404348762093,0.20738146966789944,0.13790694046592308,0.036126755185027705,0.03472772539137704,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,32,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13316950343294867,0.02662703160865349,0.07962712576403116,0.7605763391943666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,33,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00747958343980217,0.010107545188921851,0.05579364944284862,0.44389499802948357,0.4827242238989438,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,33,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06486825181918536,0.04192043014498887,0.07893122546562482,0.11594202078626079,0.2514583826345173,0.26354136619064356,0.18333832295877933,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,33,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.20085673273362226,0.1097955666542387,0.018734400574855134,0.10806595187028757,0.19739750316572,0.1544191037172138,0.03728899435530776,0.1734417469287546,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,33,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1039761303496193,0.07342811211038301,0.2640705703831648,0.45471302865594654,0.10381215850088632,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05257807889046262,0.044512703682022724,0.03644732847358283,0.5578407969704411,0.30862109198349075,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,34,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.016547226292703682,0.036185692662066296,0.0558241590314289,0.0754626254007915,0.09510109177015412,0.11473955813951674,0.021275005233476158,0.17710987262739983,0.1840197033869904,0.22373506545547245,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,34,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04516620147170769,0.04992387718853083,0.05468155290535397,0.05943922862217711,0.06419690433900026,0.2231667089571175,0.3821365135752347,0.12128901294087795,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,34,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,35,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014440082708191122,0.0894081787682167,0.4131979356800636,0.4829538028435287,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,35,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03932277444019661,0.04216275259421081,0.045002730748225014,0.04784270890223922,0.0665210267613326,0.08519934462042598,0.07209175314036045,0.07733478973238667,0.2748225013653741,0.21103222282905518,0.03866739486619333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,35,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.043612574959113214,0.05451571869889151,0.06541886243866982,0.07250590586952572,0.0795929493003816,0.06360167181537343,0.047610394330365256,0.03161911684535708,0.05015446120298019,0.2129747410503362,0.1515536979829184,0.1268399055060876,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,35,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17116060961313012,0.13677217663149668,0.10238374364986323,0.06799531066822978,0.08792497069167642,0.10785463071512308,0.32590855803048063,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,36,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009097204249164685,0.010317236702587822,0.011537269156010957,0.012757301609434094,0.013977334062857229,0.015197366516280364,0.0164173989697035,0.017637431423126636,0.01885746387654977,0.020077496329972907,0.021297528783396046,0.022517561236819177,0.023737593690242316,0.024957626143665448,0.049227760282097056,0.07349789442052869,0.46975283792075634,0.16913769462680694,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,36,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.015810579317945227,0.022340218977816306,0.02886985863768738,0.03539949829755846,0.04192913795742953,0.04845877761730061,0.05498841727717169,0.06151805693704276,0.06804769659691384,0.1419079963564807,0.08171397576961974,0.021519955182758784,0.045235824159676634,0.06895169313659448,0.0516039741627379,0.211704339615266,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,36,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.24162692856645557,0.048722043381856435,0.060134730469418994,0.07154741755698156,0.08296010464454412,0.17478241717188894,0.13002299654506885,0.08526357591824876,0.10493978574553695,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,36,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0625,0.08092948717948718,0.09935897435897435,0.11778846153846155,0.12115384615384615,0.12451923076923077,0.12788461538461537,0.13125,0.1346153846153846,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,37,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020433245040586478,0.025481795752953684,0.030530346465320886,0.03557889717768809,0.0406274478900553,0.045675998602422496,0.0507245493147897,0.013456039416971583,0.04136486191143116,0.25557195985018105,0.4405548585775996,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,37,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07688427809565408,0.07380972927435588,0.07073518045305766,0.06766063163175944,0.06458608281046123,0.044568174103125295,0.024550265395789358,0.09669027602033962,0.16883028664488992,0.09744566880174854,0.026061050958607165,0.027666260619101084,0.029271470279595008,0.028327229302833875,0.07119576964778915,0.03171764596089272,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,37,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.022829049613487525,0.027295602798735084,0.04739509213234911,0.06749458146596313,0.08759407079957715,0.10769356013319116,0.1277930494668052,0.1478925388004192,0.1173297350606234,0.0867669313208276,0.0562041275810318,0.02564132384123599,0.03457055360899653,0.04349978337675708,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+othmaint,FALSE,38,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.019392524569386855,0.15160956441468698,0.28382660425998707,0.41604364410528727,0.12912766265065187,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,38,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0707785892876667,0.06734611012638637,0.06391363096510604,0.0604811518038257,0.05704867264254535,0.05361619348126501,0.05018371431998468,0.04675123515870434,0.043318755997424005,0.039886276836143666,0.04757676812154391,0.05526725940694417,0.06295775069234441,0.06373983455187665,0.03835737097976106,0.012974907407645465,0.1658017782208325,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,38,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6822916666666666,0.3177083333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,38,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,39,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11645050870873863,0.30243360723410556,0.5811158840571558,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,39,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1076743949500231,0.10531440066074418,0.10295440637146529,0.10059441208218639,0.0982344177929075,0.06256969286172452,0.16174265604755786,0.26091561923339124,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,39,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.029141342883675866,0.03985986442419322,0.050578385964710565,0.061296907505227906,0.07201542904574526,0.08273395058626261,0.09345247212677994,0.1041709936672973,0.11278461588268106,0.12139823809806481,0.13001186031344858,0.10255593950191284,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,39,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,40,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1046481538962782,0.5073846117687856,0.38796723433493635,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,40,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.055019129167425754,0.05361025080463958,0.0522013724418534,0.05079249407906722,0.04938361571628105,0.04797473735349486,0.04656585899070869,0.045156980627922505,0.043748102265136334,0.04233922390235015,0.04093034553956397,0.03952146717677779,0.03811258881399162,0.036703710451205435,0.035294832088419256,0.03388595372563308,0.044452541446529416,0.05110220440881763,0.057751867371105846,0.06440153033339406,0.07105119329568227,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,40,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.056374836662310995,0.055342652274648886,0.05431046788698679,0.05327828349932469,0.052246099111662586,0.05121391472400048,0.05018173033633838,0.04914954594867628,0.04811736156101418,0.04708517717335207,0.04605299278568997,0.04502080839802787,0.043988624010365775,0.042956439622703665,0.04192425523504156,0.04089207084737946,0.03985988645971735,0.038827702072055256,0.037614336382303534,0.036400970692551805,0.035187605002800076,0.03397423931304835,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,40,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.35555555555555557,0.3333333333333333,0.3111111111111111,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,41,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.719626168224299,0.2803738317757009,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,41,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,41,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,41,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,42,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,42,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,42,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,42,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,43,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,43,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,43,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,43,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,44,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6153846153846154,0.38461538461538464,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,44,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,44,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,44,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othmaint,FALSE,45,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,FALSE,45,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,FALSE,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,FALSE,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othmaint,FALSE,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,FALSE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,FALSE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,FALSE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othmaint,FALSE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,FALSE,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,FALSE,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,FALSE,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othmaint,FALSE,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,FALSE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,FALSE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othmaint,FALSE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,1,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,2,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,2,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,2,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,3,2,0.0,0.0,0.0,0.6106255783686745,0.3893744216313256,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,3,3,0.0,0.0,0.0,0.0,0.0,0.15505118561213216,0.21835039520404403,0.2816496047959559,0.34494881438786784,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,3,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,4,2,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,4,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,4,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,5,2,0.0,0.0,0.0,0.0,0.0,0.4262948207171315,0.5737051792828686,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,5,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,5,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,6,2,0.0,0.0,0.0,0.0,0.0,0.2860279480421333,0.054234459984848785,0.06573873937557428,0.07724301876629978,0.08874729815702528,0.1722519446103813,0.2557565910637373,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,6,3,0.0,0.0,0.0,0.0,0.0,0.0,0.046985096987287284,0.05731673310011368,0.06764836921294008,0.07798000532576646,0.08831164143859287,0.09864327755141926,0.11549745416923961,0.13235163078705994,0.1492058074048803,0.16605998402270064,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,6,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21176470588235294,0.20588235294117646,0.2,0.19411764705882353,0.18823529411764706,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,7,1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,7,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23954372623574147,0.3333333333333333,0.4271229404309252,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+othdiscr,TRUE,32,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,33,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,33,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06767881055800092,0.3813708503265754,0.22691931593327902,0.32403102318214466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,33,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,33,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,34,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6380530973451327,0.268141592920354,0.09380530973451327,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,34,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,34,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,35,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,35,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14743589743589744,0.4451566951566951,0.2841880341880342,0.12321937321937322,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,35,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,35,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,36,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,36,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7200647249190939,0.27993527508090615,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,36,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,36,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,37,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,37,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,37,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,37,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,38,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,38,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,38,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,38,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,39,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,39,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,39,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,39,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,40,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,40,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,40,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,40,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,41,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+othdiscr,TRUE,41,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,41,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,42,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,42,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,42,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,42,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,43,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,43,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,43,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,43,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,44,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+othdiscr,TRUE,44,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othdiscr,TRUE,44,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othdiscr,TRUE,44,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othdiscr,TRUE,45,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+othdiscr,TRUE,45,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othdiscr,TRUE,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othdiscr,TRUE,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othdiscr,TRUE,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+othdiscr,TRUE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othdiscr,TRUE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othdiscr,TRUE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othdiscr,TRUE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+othdiscr,TRUE,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,TRUE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+othdiscr,FALSE,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.18280901659648252,0.1591115514821237,0.1354140863677648,0.11171662125340599,0.08801915613904714,0.0643216910246883,0.040624225910329444,0.10106514738667327,0.11691850383948475
+othdiscr,FALSE,1,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,1,3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,1,4,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,2,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,2,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,2,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,3,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,3,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,3,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,4,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,4,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,4,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,5,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,5,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,5,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,5,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,6,1,0.0,0.0,0.0,0.0,0.26366120218579236,0.7363387978142076,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,6,2,0.0,0.0,0.0,0.0,0.45255474452554745,0.5474452554744526,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,6,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,6,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,7,1,0.0,0.0,0.0,0.0,0.0,0.3142296178774695,0.6857703821225305,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,7,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,8,1,0.0,0.0,0.0,0.0,0.0,0.0429313931043178,0.3261064760541849,0.6309621308414973,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,8,2,0.0,0.0,0.0,0.0,0.0,0.0,0.4,0.6,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,8,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,8,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,9,1,0.0,0.0,0.0,0.0,0.0,0.0,0.021990539405707908,0.22387711276175049,0.7541323478325416,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,9,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,9,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,9,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,10,1,0.0,0.0,0.0,0.0,0.0,0.0,0.03012928197401491,0.1981181055933148,0.36610692921261473,0.4056456832200556,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,10,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6807228915662651,0.3192771084337349,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,10,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,10,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,11,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01704301029524212,0.3006444200553927,0.6823125696493652,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,11,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5697383451913457,0.43026165480865425,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,11,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,11,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,12,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.28317327704265993,0.7168267229573401,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+othdiscr,FALSE,12,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.18840579710144928,0.8115942028985508,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,3,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,3,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,4,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,4,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,4,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,5,2,0.0,0.0,0.0,0.0,0.0,0.32530521300350207,0.6746947869964979,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,5,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,5,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,6,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,6,3,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,6,4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,7,1,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,7,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,8,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,8,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16133594717176264,0.8386640528282373,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,8,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3716094032549729,0.29053646775165765,0.20946353224834238,0.12839059674502712,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,8,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.22325581395348837,0.2006201550387597,0.177984496124031,0.15534883720930234,0.13271317829457366,0.11007751937984496,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,9,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,9,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3492703833452329,0.3493404024351693,0.2169098722182557,0.08447934200134212,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,9,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2304636088150651,0.19333987400955782,0.18168850683760704,0.1700371396656563,0.15838577249370556,0.06608509817840819,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,9,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07015954779374785,0.11472791620755199,0.15929628462135612,0.20386465303516027,0.2484330214489644,0.20351857689321942,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,10,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,10,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.32443532605422365,0.1391025107722742,0.29622483772958097,0.1788207210578341,0.06141660438608724,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,10,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4110191363212417,0.33333333333333337,0.25564753034542503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,10,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,11,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,11,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.32593146577810334,0.6740685342218966,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,11,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5075987841945289,0.2735562310030395,0.2188449848024316,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,11,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,21,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2670454545454545,0.2832167832167832,0.24431818181818182,0.20541958041958042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.044128434852690274,0.6046290075156545,0.012951754260812404,0.06285767769221332,0.11276360112361421,0.16266952455501513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.28593895598123414,0.4605260203187126,0.23802034800625532,0.01551467569379802,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.33153153153153153,0.40540540540540543,0.26306306306306304,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4548289006036915,0.47446309245579665,0.07070800694051184,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.04129554655870445,0.04723346828609987,0.05317139001349528,0.05910931174089069,0.0650472334682861,0.0709851551956815,0.07692307692307693,0.08286099865047233,0.08879892037786775,0.09473684210526316,0.10067476383265858,0.10661268556005399,0.1125506072874494,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,24,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,25,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.054107212902882076,0.9458927870971179,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,26,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3711370262390671,0.6288629737609329,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,26,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8489795918367347,0.1510204081632653,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,26,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4515463917525773,0.5484536082474227,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,27,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,27,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12702387727016334,0.6595189391096051,0.1740044056076928,0.03945277801253869,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,27,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23204349210467443,0.7326632435021537,0.03529326439317191,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2958714051004921,0.39826277992053616,0.30586581497897175,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.19014411901441186,0.3714551371455137,0.2587943592127693,0.1461335812800248,0.03347280334728033,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.49148418491484186,0.3333333333333333,0.17518248175182483,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,30,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,30,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7161026621422972,0.2838973378577027,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,30,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3047722342733189,0.210412147505423,0.4848156182212581,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,31,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,31,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.48328025477707004,0.51671974522293,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,31,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.19793459552495699,0.17957544463568562,0.16121629374641422,0.14285714285714288,0.1244979919678715,0.10613884107860011,0.08777969018932875,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,32,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,33,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,34,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,34,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,35,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,36,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,TRUE,39,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,39,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,39,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,40,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,40,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,40,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,40,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,41,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,41,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,41,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,41,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,42,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,42,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,42,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,42,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,43,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+escort,TRUE,43,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+escort,TRUE,43,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+escort,TRUE,43,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+escort,TRUE,44,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
+escort,TRUE,44,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+escort,TRUE,44,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+escort,TRUE,44,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+escort,TRUE,45,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
+escort,TRUE,45,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+escort,TRUE,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+escort,TRUE,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+escort,TRUE,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+escort,TRUE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+escort,TRUE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+escort,TRUE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+escort,TRUE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
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+escort,FALSE,1,3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,1,4,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,2,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,2,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,2,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,3,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,3,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,3,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,4,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,4,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,4,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,5,1,0.0,0.0,0.0,0.6006983847737727,0.39930161522622726,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,5,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,5,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,5,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,6,1,0.0,0.0,0.0,0.13595532896334062,0.3333333333333333,0.530711337703326,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,6,2,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,6,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,6,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,7,1,0.0,0.0,0.0,0.0,0.06088301590415379,0.580827005609531,0.3582899784863151,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,7,2,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,7,3,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,7,4,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,8,1,0.0,0.0,0.0,0.0,0.0,0.03070277302439106,0.15735171175000418,0.8119455152256048,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,8,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,10,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02031866961208096,0.8293998494834712,0.15028148090444785,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,11,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.005467952287358367,0.3932988533915064,0.6012331943211352,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,11,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,12,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02109151135940575,0.3933132010363253,0.5855952876042689,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,12,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09147309190592988,0.673495888039244,0.23503102005482615,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,12,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,13,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01624223828418801,0.4101922097568738,0.5735655519589382,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,13,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.28391326722082966,0.3123719134940599,0.4037148192851104,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,13,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5763508222396241,0.19028974158183243,0.23335943617854346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,13,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,14,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.013430411594508808,0.010530381216131502,0.5267956345854725,0.44924357260388725,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,14,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.049824803246607374,0.04213754788855938,0.09594833539489535,0.7515009553958484,0.06058835807408959,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,14,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.258779316408547,0.741220683591453,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,14,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,15,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05879470421187831,0.31259118453853146,0.6286141112495902,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,15,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.051435469985595554,0.3486872725862786,0.48849286035587075,0.1113843970722552,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,15,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037771847427532354,0.13481644004903856,0.827411712523429,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,15,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,16,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09635461235666609,0.4495978093445148,0.4540475782988191,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,17,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05539529871995381,0.1579938209632178,0.2605923432064817,0.5260185371103466,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,19,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0036945481978282136,0.4661950909003097,0.530110360901862,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,19,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,20,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02357962776441745,0.2884780136801235,0.687942358555459,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,20,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.17492998532684886,0.19750408351794232,0.2200781817090358,0.2991339319296219,0.10835381751655127,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,20,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39373586090553453,0.2979119536351782,0.20208804636482183,0.10626413909446548,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,20,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.32068139596193773,0.6793186040380622,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+escort,FALSE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4217345134902667,0.5782654865097333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.013777500244661015,0.41001200758460066,0.5762104921707383,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0513724061086017,0.304891142739344,0.6437364511520542,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1550174250087541,0.17750871250437705,0.2,0.22249128749562294,0.2449825749912459,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3721192368874413,0.6278807631125587,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07714123652864435,0.316505955757232,0.3102665910380034,0.29608621667612023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09773755656108597,0.6298642533936651,0.27239819004524884,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6453407510431154,0.3333333333333333,0.02132591562355123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01973736974780499,0.0461207067337297,0.35824206401557673,0.5758998595028886,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.287923833795009,0.11972942313668433,0.5923467430683067,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11147236919279421,0.19133389586092023,0.2711954225290462,0.3510569491971723,0.07494136322006693,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,24,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.28064609291267384,0.23505432241427096,0.1894625519158681,0.14387078141746523,0.09827901091906237,0.05268724042065951,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,25,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02962314113856744,0.23647145533566843,0.7339054035257642,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.18109584893640832,0.18407191549180724,0.18704798204720616,0.3424409165615694,0.10534333696300875,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+escort,FALSE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14470554589978357,0.3207354202875812,0.24946088244589645,0.17818634460421173,0.10691180676252704,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+eatout,TRUE,33,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+eatout,TRUE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+eatout,FALSE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+eatout,FALSE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+eatout,FALSE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
+eatout,FALSE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5673575129533679,0.2538860103626943,0.17875647668393782,0.0
+eatout,FALSE,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.0,0.0,0.0,0.0,0.0,0.0
+eatout,FALSE,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+eatout,FALSE,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+eatout,FALSE,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23923444976076555,0.3333333333333333,0.4274322169059011
+eatout,FALSE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+eatout,FALSE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+eatout,FALSE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home.csv b/activitysim/examples/prototype_mwcog/configs/work_from_home.csv
new file mode 100644
index 000000000..b6f9853df
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/work_from_home.csv
@@ -0,0 +1,15 @@
+Label,Description,Expression,work_at_home,work_away_from_home
+util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0
+util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0
+util_female_worker,Female Worker,@df.SEX==2,coef_female_worker,0
+util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.SEX==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0
+util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_logsum,coef_access_to_workplaces,0
+util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0
+util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0
+util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0
+util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0
+util_age_35_to_45,Age Group - 35 yrs to 45 yrs,"@df.age.between(35, 45)",coef_age_35_to_45,0
+util_age_45_to_55,Age Group - 45 yrs to 55 yrs,"@df.age.between(45, 55)",coef_age_45_to_55,0
+util_age_55_to_65,Age Group - 55 yrs to 65 yrs,"@df.age.between(55, 65)",coef_age_55_to_65,0
+util_age_65_plus,Age Group - Older than 65yrs,@df.age > 65,coef_age_65_plus,0
+util_calibration,ABM2 calibration - work from home,1,coef_calibration,0
diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml b/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml
new file mode 100644
index 000000000..c4f265ced
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml
@@ -0,0 +1,12 @@
+
+# borrowed from free parking model
+
+SPEC: work_from_home.csv
+COEFFICIENTS: work_from_home_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
+WORK_FROM_HOME_ALT: 0
+
+DEST_CHOICE_COLUMN_NAME: workplace_zone_id
diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/work_from_home_coeffs.csv
new file mode 100644
index 000000000..a3868e981
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/work_from_home_coeffs.csv
@@ -0,0 +1,15 @@
+coefficient_name,value,constrain
+coef_work_from_home_constant,0.438361,T
+coef_full_time_worker,-0.811935,F
+coef_female_worker,-0.347009,F
+coef_female_worker_preschool_child,0.572657,F
+coef_access_to_workplaces,-0.140426,F
+coef_non_working_adult_in_hh,-0.372481,F
+coef_education_ba_plus,0.284678,F
+coef_low_income,-0.393068,F
+coef_age_lt_35,-0.573545,F
+coef_age_35_to_45,0,F
+coef_age_45_to_55,0.21438,F
+coef_age_55_to_65,0.451657,F
+coef_age_65_plus,0.583518,F
+coef_calibration,-0.0162,T
diff --git a/activitysim/examples/prototype_mwcog/configs/workplace_location.csv b/activitysim/examples/prototype_mwcog/configs/workplace_location.csv
new file mode 100644
index 000000000..1fef3349c
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/workplace_location.csv
@@ -0,0 +1,20 @@
+Label,Description,Expression,coefficient
+local_dist,,_DIST@skims['DIST'],1
+util_dist,Distance,@_DIST,coef_dist
+util_dist_squared,"Distance squared, capped at 30 miles","@(_DIST).clip(0,30)**2",coef_dist_squared
+util_dist_cubed,"Distance cubed, capped at 30 miles","@(_DIST).clip(0,30)**3",coef_dist_cubed
+util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged
+util_dist_low,"Distance,low income (<50)",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc
+util_dist_med,"Distance,med income (50-100)",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc
+util_dist_very_high,"Distance,very high income (>150)",@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,coef_dist_very_high_inc
+util_dist_female,"Distance,female",@(df['female']==True) * _DIST,coef_dist_female
+util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time
+util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student
+util_dist_young,"Distance,young person (<25)",@(df['young']==True) * _DIST,coef_dist_young
+util_dist_old,"Distance,young person (>65)",@(df['old']==True) * _DIST,coef_dist_old
+util_dist_zero_auto,"Distance,zero auto HH",@(df['auto_ownership']==0) * _DIST,coef_dist_zero_auto
+util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1
+util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1
+util_no_attractions,No attractions,@df['size_term']==0,-999
+util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum
+util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1
diff --git a/activitysim/examples/prototype_mwcog/configs/workplace_location.yaml b/activitysim/examples/prototype_mwcog/configs/workplace_location.yaml
new file mode 100644
index 000000000..dba0fa9e9
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/workplace_location.yaml
@@ -0,0 +1,81 @@
+SAMPLE_SIZE: 30
+
+SIMULATE_CHOOSER_COLUMNS:
+  - income_segment
+  - home_zone_id
+  - is_student
+  - pemploy
+  - young
+  - old
+  - female
+  - auto_ownership
+
+SAMPLE_SPEC: workplace_location_sample.csv
+SPEC: workplace_location.csv
+COEFFICIENTS: workplace_location_coeffs.csv
+
+LOGSUM_SETTINGS: tour_mode_choice.yaml
+LOGSUM_PREPROCESSOR: nontour_preprocessor
+LOGSUM_TOUR_PURPOSE: work
+
+# model-specific logsum-related settings
+CHOOSER_ORIG_COL_NAME: home_zone_id
+ALT_DEST_COL_NAME: alt_dest
+IN_PERIOD: 30
+OUT_PERIOD: 9
+
+DEST_CHOICE_COLUMN_NAME: workplace_zone_id
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table
+DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum
+
+# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
+DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample
+
+annotate_persons:
+  SPEC: annotate_persons_workplace
+  DF: persons
+  TABLES:
+    - land_use
+
+annotate_households:
+  SPEC: annotate_households_workplace
+  DF: households
+  TABLES:
+    - persons
+
+# - shadow pricing
+
+
+# income_segment is in households, but we want to count persons
+CHOOSER_TABLE_NAME: persons_merged
+
+# size_terms model_selector
+MODEL_SELECTOR: workplace
+
+# we can't use use household income_segment as this will also be set for non-workers
+CHOOSER_SEGMENT_COLUMN_NAME: income_segment
+
+# boolean column to filter choosers (True means keep)
+CHOOSER_FILTER_COLUMN_NAME: is_worker
+
+# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this
+# FIXME - these are not needed for this model and should be re/factored out
+SEGMENT_IDS:
+  work_low: 1
+  work_med: 2
+  work_high: 3
+  work_veryhigh: 4
+
+CONSTANTS:
+  WORK_LOW_SEGMENT_ID: 1
+  WORK_MED_SEGMENT_ID: 2
+  WORK_HIGH_SEGMENT_ID: 3
+  WORK_VERYHIGH_SEGMENT_ID: 4
+
+
+# model adds these tables (informational - not added if commented out)
+SHADOW_PRICE_TABLE: workplace_shadow_prices
+MODELED_SIZE_TABLE: workplace_modeled_size
+
+# not loaded if commented out
+SAVED_SHADOW_PRICE_TABLE_NAME: final_workplace_shadow_prices.csv
diff --git a/activitysim/examples/prototype_mwcog/configs/workplace_location_coeffs.csv b/activitysim/examples/prototype_mwcog/configs/workplace_location_coeffs.csv
new file mode 100644
index 000000000..521f88ce5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/workplace_location_coeffs.csv
@@ -0,0 +1,15 @@
+coefficient_name,value,constrain
+coef_dist,-0.045010687,F
+coef_dist_squared,0.002654513,F
+coef_dist_cubed,-7.39E-05,F
+coef_dist_logged,-0.855216378,F
+coef_dist_low_inc,-0.018844727,F
+coef_dist_med_inc,-0.004762151,F
+coef_dist_very_high_inc,0.005722937,F
+coef_dist_female,-0.010731711,F
+coef_dist_part_time,-0.016178149,F
+coef_dist_student,-0.025985743,F
+coef_mode_logsum,0.25,F
+coef_dist_young,-0.036726831,F
+coef_dist_old,-0.013668916,F
+coef_dist_zero_auto,-0.033718425,F
diff --git a/activitysim/examples/prototype_mwcog/configs/workplace_location_sample.csv b/activitysim/examples/prototype_mwcog/configs/workplace_location_sample.csv
new file mode 100644
index 000000000..8cae71719
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/workplace_location_sample.csv
@@ -0,0 +1,17 @@
+Label,Description,Expression,coefficient
+local_dist,,_DIST@skims['DIST'],1
+util_dist,Distance,@_DIST,coef_dist
+util_dist_squared,"Distance squared, capped at 30 miles","@(_DIST).clip(0,30)**2",coef_dist_squared
+util_dist_cubed,"Distance cubed, capped at 30 miles","@(_DIST).clip(0,30)**3",coef_dist_cubed
+util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged
+util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc
+util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc
+util_dist_very_high,"Distance,very high income (>150)",@(df['income_segment']==WORK_VERYHIGH_SEGMENT_ID) * _DIST,coef_dist_very_high_inc
+util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time
+util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student
+util_dist_young,"Distance,young person (<25)",@(df['young']==True) * _DIST,coef_dist_young
+util_dist_old,"Distance,young person (>65)",@(df['old']==True) * _DIST,coef_dist_old
+util_dist_zero_auto,"Distance,zero auto HH",@(df['auto_ownership']==0) * _DIST,coef_dist_zero_auto
+util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1
+util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1
+util_no_attractions,No attractions,@df['size_term']==0,-999
diff --git a/activitysim/examples/prototype_mwcog/configs/write_trip_matrices.yaml b/activitysim/examples/prototype_mwcog/configs/write_trip_matrices.yaml
new file mode 100644
index 000000000..4ce9b1e4f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/write_trip_matrices.yaml
@@ -0,0 +1,259 @@
+# read trips table post preprocessor and run expressions to code
+# additional data fields, with one data fields for each matrix specified below
+
+preprocessor:
+  SPEC: write_trip_matrices_annotate_trips_preprocessor
+  DF: trips
+  TABLES:
+    - tours
+
+# divide trip counts by household expansion factor
+HH_EXPANSION_WEIGHT_COL: sample_rate  # added when households read in
+
+# save preprocessed trips table to pipeline if desired
+SAVE_TRIPS_TABLE: False
+
+MATRICES:
+  - file_name: auto_trips_am.omx
+    tables:
+      - name: DRIVEALONE
+        data_field: DRIVEALONE_AM
+      - name: SHARED2
+        data_field: SHARED2_AM
+      - name: SHARED3
+        data_field: SHARED3_AM
+  - file_name: nm_trips_am.omx
+    tables:
+      - name: WALK
+        data_field: WALK_AM
+      - name: BIKE
+        data_field: BIKE_AM
+  - file_name: trn_trips_am.omx
+    tables:
+      - name: WALK_AB
+        data_field: WALK_AB_AM
+      - name: WALK_BM
+        data_field: WALK_BM_AM
+      - name: WALK_MR
+        data_field: WALK_MR_AM
+      - name: WALK_CR
+        data_field: WALK_CR_AM
+      - name: PNR_AB
+        data_field: PNR_AB_AM
+      - name: PNR_BM
+        data_field: PNR_BM_AM
+      - name: PNR_MR
+        data_field: PNR_MR_AM
+      - name: PNR_CR
+        data_field: PNR_CR_AM
+      - name: KNR_AB
+        data_field: KNR_AB_AM
+      - name: KNR_BM
+        data_field: KNR_BM_AM
+      - name: KNR_MR
+        data_field: KNR_MR_AM
+      - name: PNRE_AB
+        data_field: PNRE_AB_AM
+      - name: PNRE_BM
+        data_field: PNRE_BM_AM
+      - name: PNRE_MR
+        data_field: PNRE_MR_AM
+      - name: PNRE_CR
+        data_field: PNRE_CR_AM
+      - name: KNRE_AB
+        data_field: KNRE_AB_AM
+      - name: KNRE_BM
+        data_field: KNRE_BM_AM
+      - name: KNRE_MR
+        data_field: KNRE_MR_AM
+  - file_name: sb_trips_am.omx
+    tables:
+      - name: SCHOOLBUS
+        data_field: SCHOOLBUS_AM
+  - file_name: auto_trips_md.omx
+    tables:
+      - name: DRIVEALONE
+        data_field: DRIVEALONE_MD
+      - name: SHARED2
+        data_field: SHARED2_MD
+      - name: SHARED3
+        data_field: SHARED3_MD
+  - file_name: nm_trips_md.omx
+    tables:
+      - name: WALK
+        data_field: WALK_MD
+      - name: BIKE
+        data_field: BIKE_MD
+  - file_name: trn_trips_md.omx
+    tables:
+      - name: WALK_AB
+        data_field: WALK_AB_MD
+      - name: WALK_BM
+        data_field: WALK_BM_MD
+      - name: WALK_MR
+        data_field: WALK_MR_MD
+      - name: WALK_CR
+        data_field: WALK_CR_MD
+      - name: PNR_AB
+        data_field: PNR_AB_MD
+      - name: PNR_BM
+        data_field: PNR_BM_MD
+      - name: PNR_MR
+        data_field: PNR_MR_MD
+      - name: PNR_CR
+        data_field: PNR_CR_MD
+      - name: KNR_AB
+        data_field: KNR_AB_MD
+      - name: KNR_BM
+        data_field: KNR_BM_MD
+      - name: KNR_MR
+        data_field: KNR_MR_MD
+      - name: PNRE_AB
+        data_field: PNRE_AB_MD
+      - name: PNRE_BM
+        data_field: PNRE_BM_MD
+      - name: PNRE_MR
+        data_field: PNRE_MR_MD
+      - name: PNRE_CR
+        data_field: PNRE_CR_MD
+      - name: KNRE_AB
+        data_field: KNRE_AB_MD
+      - name: KNRE_BM
+        data_field: KNRE_BM_MD
+      - name: KNRE_MR
+        data_field: KNRE_MR_MD
+  - file_name: sb_trips_md.omx
+    tables:
+      - name: SCHOOLBUS
+        data_field: SCHOOLBUS_MD
+  - file_name: auto_trips_pm.omx
+    tables:
+      - name: DRIVEALONE
+        data_field: DRIVEALONE_PM
+      - name: SHARED2
+        data_field: SHARED2_PM
+      - name: SHARED3
+        data_field: SHARED3_PM
+  - file_name: nm_trips_pm.omx
+    tables:
+      - name: WALK
+        data_field: WALK_PM
+      - name: BIKE
+        data_field: BIKE_PM
+  - file_name: trn_trips_pm.omx
+    tables:
+      - name: WALK_AB
+        data_field: WALK_AB_PM
+      - name: WALK_BM
+        data_field: WALK_BM_PM
+      - name: WALK_MR
+        data_field: WALK_MR_PM
+      - name: WALK_CR
+        data_field: WALK_CR_PM
+      - name: PNR_AB
+        data_field: PNR_AB_PM
+      - name: PNR_BM
+        data_field: PNR_BM_PM
+      - name: PNR_MR
+        data_field: PNR_MR_PM
+      - name: PNR_CR
+        data_field: PNR_CR_PM
+      - name: KNR_AB
+        data_field: KNR_AB_PM
+      - name: KNR_BM
+        data_field: KNR_BM_PM
+      - name: KNR_MR
+        data_field: KNR_MR_PM
+      - name: PNRE_AB
+        data_field: PNRE_AB_PM
+      - name: PNRE_BM
+        data_field: PNRE_BM_PM
+      - name: PNRE_MR
+        data_field: PNRE_MR_PM
+      - name: PNRE_CR
+        data_field: PNRE_CR_PM
+      - name: KNRE_AB
+        data_field: KNRE_AB_PM
+      - name: KNRE_BM
+        data_field: KNRE_BM_PM
+      - name: KNRE_MR
+        data_field: KNRE_MR_PM
+  - file_name: sb_trips_pm.omx
+    tables:
+      - name: SCHOOLBUS
+        data_field: SCHOOLBUS_PM
+  - file_name: auto_trips_nt.omx
+    tables:
+      - name: DRIVEALONE
+        data_field: DRIVEALONE_NT
+      - name: SHARED2
+        data_field: SHARED2_NT
+      - name: SHARED3
+        data_field: SHARED3_NT
+  - file_name: nm_trips_nt.omx
+    tables:
+      - name: WALK
+        data_field: WALK_NT
+      - name: BIKE
+        data_field: BIKE_NT
+  - file_name: trn_trips_nt.omx
+    tables:
+      - name: WALK_AB
+        data_field: WALK_AB_NT
+      - name: WALK_BM
+        data_field: WALK_BM_NT
+      - name: WALK_MR
+        data_field: WALK_MR_NT
+      - name: WALK_CR
+        data_field: WALK_CR_NT
+      - name: PNR_AB
+        data_field: PNR_AB_NT
+      - name: PNR_BM
+        data_field: PNR_BM_NT
+      - name: PNR_MR
+        data_field: PNR_MR_NT
+      - name: PNR_CR
+        data_field: PNR_CR_NT
+      - name: KNR_AB
+        data_field: KNR_AB_NT
+      - name: KNR_BM
+        data_field: KNR_BM_NT
+      - name: KNR_MR
+        data_field: KNR_MR_NT
+      - name: PNRE_AB
+        data_field: PNRE_AB_NT
+      - name: PNRE_BM
+        data_field: PNRE_BM_NT
+      - name: PNRE_MR
+        data_field: PNRE_MR_NT
+      - name: PNRE_CR
+        data_field: PNRE_CR_NT
+      - name: KNRE_AB
+        data_field: KNRE_AB_NT
+      - name: KNRE_BM
+        data_field: KNRE_BM_NT
+      - name: KNRE_MR
+        data_field: KNRE_MR_NT
+  - file_name: sb_trips_nt.omx
+    tables:
+      - name: SCHOOLBUS
+        data_field: SCHOOLBUS_NT
+
+CONSTANTS:
+  time_periods:
+    AM:
+      first_hour: 7
+      last_hour: 12
+    MD:
+      first_hour: 13
+      last_hour: 24
+    PM:
+      first_hour: 25
+      last_hour: 32
+    NT:
+      first_hour: 33
+      last_hour: 6
+
+  # SHARED2 and SHARED2 Occupancies
+  OCC_SHARED2: 2.0
+  OCC_SHARED3: 3.33
diff --git a/activitysim/examples/prototype_mwcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv
new file mode 100644
index 000000000..f56185ddc
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv
@@ -0,0 +1,109 @@
+Description,Target,Expression
+# add additional fields,,
+,tour_participants,trips.tour_id.map(tours.number_of_participants)
+,distance,od_skims['DIST']
+# code time periods,,
+,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])"
+,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])"
+,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])"
+,is_nt,(trips.depart >= time_periods['NT']['first_hour']) | (trips.depart <= time_periods['NT']['last_hour'])
+# am trips,,
+,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants
+,SHARED2_AM,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_am) * tour_participants / OCC_SHARED2"
+,SHARED3_AM,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_am) * tour_participants / OCC_SHARED3"
+,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants
+,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants
+,WALK_AB_AM,((trips.trip_mode == 'WALK_AB') & is_am) * tour_participants
+,WALK_BM_AM,((trips.trip_mode == 'WALK_BM') & is_am) * tour_participants
+,WALK_MR_AM,((trips.trip_mode == 'WALK_MR') & is_am) * tour_participants
+,WALK_CR_AM,((trips.trip_mode == 'WALK_CR') & is_am) * tour_participants
+,PNR_AB_AM,((trips.trip_mode == 'PNR_AB') & is_am & trips.outbound) * tour_participants
+,PNR_BM_AM,((trips.trip_mode == 'PNR_BM') & is_am & trips.outbound) * tour_participants
+,PNR_MR_AM,((trips.trip_mode == 'PNR_MR') & is_am & trips.outbound) * tour_participants
+,PNR_CR_AM,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_am & trips.outbound) * tour_participants"
+,KNR_AB_AM,((trips.trip_mode == 'KNR_AB') & is_am & trips.outbound) * tour_participants
+,KNR_BM_AM,((trips.trip_mode == 'KNR_BM') & is_am & trips.outbound) * tour_participants
+,KNR_MR_AM,((trips.trip_mode == 'KNR_MR') & is_am & trips.outbound) * tour_participants
+,PNRE_AB_AM,((trips.trip_mode == 'PNR_AB') & is_am & ~trips.outbound) * tour_participants
+,PNRE_BM_AM,((trips.trip_mode == 'PNR_BM') & is_am & ~trips.outbound) * tour_participants
+,PNRE_MR_AM,((trips.trip_mode == 'PNR_MR') & is_am & ~trips.outbound) * tour_participants
+,PNRE_CR_AM,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_am & ~trips.outbound) * tour_participants"
+,KNRE_AB_AM,((trips.trip_mode == 'KNR_AB') & is_am & ~trips.outbound) * tour_participants
+,KNRE_BM_AM,((trips.trip_mode == 'KNR_BM') & is_am & ~trips.outbound) * tour_participants
+,KNRE_MR_AM,((trips.trip_mode == 'KNR_MR') & is_am & ~trips.outbound) * tour_participants
+,SCHOOLBUS_AM,((trips.trip_mode == 'SCHOOLBUS') & is_am) * tour_participants
+# md trips,,
+,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants
+,SHARED2_MD,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_md) * tour_participants / OCC_SHARED2"
+,SHARED3_MD,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_md) * tour_participants / OCC_SHARED3"
+,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants
+,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants
+,WALK_AB_MD,((trips.trip_mode == 'WALK_AB') & is_md) * tour_participants
+,WALK_BM_MD,((trips.trip_mode == 'WALK_BM') & is_md) * tour_participants
+,WALK_MR_MD,((trips.trip_mode == 'WALK_MR') & is_md) * tour_participants
+,WALK_CR_MD,((trips.trip_mode == 'WALK_CR') & is_md) * tour_participants
+,PNR_AB_MD,((trips.trip_mode == 'PNR_AB') & is_md & trips.outbound) * tour_participants
+,PNR_BM_MD,((trips.trip_mode == 'PNR_BM') & is_md & trips.outbound) * tour_participants
+,PNR_MR_MD,((trips.trip_mode == 'PNR_MR') & is_md & trips.outbound) * tour_participants
+,PNR_CR_MD,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_md & trips.outbound) * tour_participants"
+,KNR_AB_MD,((trips.trip_mode == 'KNR_AB') & is_md & trips.outbound) * tour_participants
+,KNR_BM_MD,((trips.trip_mode == 'KNR_BM') & is_md & trips.outbound) * tour_participants
+,KNR_MR_MD,((trips.trip_mode == 'KNR_MR') & is_md & trips.outbound) * tour_participants
+,PNRE_AB_MD,((trips.trip_mode == 'PNR_AB') & is_md & ~trips.outbound) * tour_participants
+,PNRE_BM_MD,((trips.trip_mode == 'PNR_BM') & is_md & ~trips.outbound) * tour_participants
+,PNRE_MR_MD,((trips.trip_mode == 'PNR_MR') & is_md & ~trips.outbound) * tour_participants
+,PNRE_CR_MD,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_md & ~trips.outbound) * tour_participants"
+,KNRE_AB_MD,((trips.trip_mode == 'KNR_AB') & is_md & ~trips.outbound) * tour_participants
+,KNRE_BM_MD,((trips.trip_mode == 'KNR_BM') & is_md & ~trips.outbound) * tour_participants
+,KNRE_MR_MD,((trips.trip_mode == 'KNR_MR') & is_md & ~trips.outbound) * tour_participants
+,SCHOOLBUS_MD,((trips.trip_mode == 'SCHOOLBUS') & is_md) * tour_participants
+# pm trips,,
+,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants
+,SHARED2_PM,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_pm) * tour_participants / OCC_SHARED2"
+,SHARED3_PM,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_pm) * tour_participants / OCC_SHARED3"
+,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants
+,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants
+,WALK_AB_PM,((trips.trip_mode == 'WALK_AB') & is_pm) * tour_participants
+,WALK_BM_PM,((trips.trip_mode == 'WALK_BM') & is_pm) * tour_participants
+,WALK_MR_PM,((trips.trip_mode == 'WALK_MR') & is_pm) * tour_participants
+,WALK_CR_PM,((trips.trip_mode == 'WALK_CR') & is_pm) * tour_participants
+,PNR_AB_PM,((trips.trip_mode == 'PNR_AB') & is_pm & trips.outbound) * tour_participants
+,PNR_BM_PM,((trips.trip_mode == 'PNR_BM') & is_pm & trips.outbound) * tour_participants
+,PNR_MR_PM,((trips.trip_mode == 'PNR_MR') & is_pm & trips.outbound) * tour_participants
+,PNR_CR_PM,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_pm & trips.outbound) * tour_participants"
+,KNR_AB_PM,((trips.trip_mode == 'KNR_AB') & is_pm & trips.outbound) * tour_participants
+,KNR_BM_PM,((trips.trip_mode == 'KNR_BM') & is_pm & trips.outbound) * tour_participants
+,KNR_MR_PM,((trips.trip_mode == 'KNR_MR') & is_pm & trips.outbound) * tour_participants
+,PNRE_AB_PM,((trips.trip_mode == 'PNR_AB') & is_pm & ~trips.outbound) * tour_participants
+,PNRE_BM_PM,((trips.trip_mode == 'PNR_BM') & is_pm & ~trips.outbound) * tour_participants
+,PNRE_MR_PM,((trips.trip_mode == 'PNR_MR') & is_pm & ~trips.outbound) * tour_participants
+,PNRE_CR_PM,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_pm & ~trips.outbound) * tour_participants"
+,KNRE_AB_PM,((trips.trip_mode == 'KNR_AB') & is_pm & ~trips.outbound) * tour_participants
+,KNRE_BM_PM,((trips.trip_mode == 'KNR_BM') & is_pm & ~trips.outbound) * tour_participants
+,KNRE_MR_PM,((trips.trip_mode == 'KNR_MR') & is_pm & ~trips.outbound) * tour_participants
+,SCHOOLBUS_PM,((trips.trip_mode == 'SCHOOLBUS') & is_pm) * tour_participants
+# nt trips,,
+,DRIVEALONE_NT,((trips.trip_mode == 'DRIVEALONE') & is_nt) * tour_participants
+,SHARED2_NT,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_nt) * tour_participants / OCC_SHARED2"
+,SHARED3_NT,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_nt) * tour_participants / OCC_SHARED3"
+,WALK_NT,((trips.trip_mode == 'WALK') & is_nt) * tour_participants
+,BIKE_NT,((trips.trip_mode == 'BIKE') & is_nt) * tour_participants
+,WALK_AB_NT,((trips.trip_mode == 'WALK_AB') & is_nt) * tour_participants
+,WALK_BM_NT,((trips.trip_mode == 'WALK_BM') & is_nt) * tour_participants
+,WALK_MR_NT,((trips.trip_mode == 'WALK_MR') & is_nt) * tour_participants
+,WALK_CR_NT,((trips.trip_mode == 'WALK_CR') & is_nt) * tour_participants
+,PNR_AB_NT,((trips.trip_mode == 'PNR_AB') & is_nt & trips.outbound) * tour_participants
+,PNR_BM_NT,((trips.trip_mode == 'PNR_BM') & is_nt & trips.outbound) * tour_participants
+,PNR_MR_NT,((trips.trip_mode == 'PNR_MR') & is_nt & trips.outbound) * tour_participants
+,PNR_CR_NT,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_nt & trips.outbound) * tour_participants"
+,KNR_AB_NT,((trips.trip_mode == 'KNR_AB') & is_nt & trips.outbound) * tour_participants
+,KNR_BM_NT,((trips.trip_mode == 'KNR_BM') & is_nt & trips.outbound) * tour_participants
+,KNR_MR_NT,((trips.trip_mode == 'KNR_MR') & is_nt & trips.outbound) * tour_participants
+,PNRE_AB_NT,((trips.trip_mode == 'PNR_AB') & is_nt & ~trips.outbound) * tour_participants
+,PNRE_BM_NT,((trips.trip_mode == 'PNR_BM') & is_nt & ~trips.outbound) * tour_participants
+,PNRE_MR_NT,((trips.trip_mode == 'PNR_MR') & is_nt & ~trips.outbound) * tour_participants
+,PNRE_CR_NT,"((trips.trip_mode.isin(['PNR_CR', 'KNR_CR'])) & is_nt & ~trips.outbound) * tour_participants"
+,KNRE_AB_NT,((trips.trip_mode == 'KNR_AB') & is_nt & ~trips.outbound) * tour_participants
+,KNRE_BM_NT,((trips.trip_mode == 'KNR_BM') & is_nt & ~trips.outbound) * tour_participants
+,KNRE_MR_NT,((trips.trip_mode == 'KNR_MR') & is_nt & ~trips.outbound) * tour_participants
+,SCHOOLBUS_NT,((trips.trip_mode == 'SCHOOLBUS') & is_nt) * tour_participants
diff --git a/activitysim/examples/prototype_mwcog/configs_mp/logging.yaml b/activitysim/examples/prototype_mwcog/configs_mp/logging.yaml
new file mode 100644
index 000000000..e932009c5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs_mp/logging.yaml
@@ -0,0 +1,57 @@
+# Config for logging
+# ------------------
+# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema
+
+logging:
+  version: 1
+  disable_existing_loggers: true
+
+
+  # Configuring the default (root) logger is highly recommended
+  root:
+    level: DEBUG
+    handlers: [console, logfile]
+
+  loggers:
+
+    activitysim:
+      level: DEBUG
+      handlers: [console, logfile]
+      propagate: false
+
+    orca:
+      level: WARNING
+      handlers: [console, logfile]
+      propagate: false
+
+  handlers:
+
+    logfile:
+      class: logging.FileHandler
+      filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log']
+      mode: w
+      formatter: fileFormatter
+      level: NOTSET
+
+    console:
+      class: logging.StreamHandler
+      stream: ext://sys.stdout
+      formatter: simpleFormatter
+      #level: NOTSET
+      level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET]
+
+  formatters:
+
+    simpleFormatter:
+      class: logging.Formatter
+      #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s'
+      format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [
+        '%(processName)-10s %(levelname)s - %(name)s - %(message)s',
+        '%(levelname)s - %(name)s - %(message)s']
+      datefmt: '%d/%m/%Y %H:%M:%S'
+
+    fileFormatter:
+      class: logging.Formatter
+      format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
+      datefmt: '%d/%m/%Y %H:%M:%S'
+
diff --git a/activitysim/examples/prototype_mwcog/configs_mp/settings.yaml b/activitysim/examples/prototype_mwcog/configs_mp/settings.yaml
new file mode 100644
index 000000000..04aa781d7
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs_mp/settings.yaml
@@ -0,0 +1,92 @@
+# Configs File with Sample Rate set by Model Runner 
+inherit_settings: True
+# raise error if any sub-process fails without waiting for others to complete
+fail_fast: True
+# - ------------------------- dev config
+multiprocess: True
+strict: False
+mem_tick: 30
+use_shadow_pricing: False
+## - example sample
+households_sample_size: 0
+chunk_size: 200_000_000_000
+#chunk_size: 0
+chunk_training_mode: production
+num_processes: 20
+# - tracing
+#trace_hh_id: 1205209
+trace_hh_id: 1086014
+trace_od:
+#trace_hh_id: 1482966
+#trace_od: [5, 11]
+# to resume after last successful checkpoint, specify resume_after: _
+resume_after: 
+models:
+  ### mp_initialize step
+  - initialize_landuse
+  - compute_accessibility
+  - initialize_households
+  ### mp_households step
+  - school_location
+  - workplace_location
+  - work_from_home
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - auto_ownership_simulate
+  - free_parking
+  - telecommute_frequency
+  - cdap_simulate
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling
+  - joint_tour_frequency
+  - joint_tour_composition
+  - joint_tour_participation
+  - joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - non_mandatory_tour_destination
+  - non_mandatory_tour_scheduling
+  - tour_mode_choice_simulate
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_purpose_and_destination
+  - trip_scheduling
+  - trip_mode_choice
+  ### mp_summarize step
+  - write_data_dictionary
+  - write_tables
+  - write_trip_matrices
+multiprocess_steps:
+  - name: mp_initialize
+    begin: initialize_landuse
+  - name: mp_households
+    begin: school_location
+    slice:
+      tables:
+        - households
+        - persons
+  - name: mp_summarize
+    begin: write_data_dictionary
+output_tables:
+  action: include
+  prefix: final_
+  tables:
+    - checkpoints
+    - accessibility
+    - land_use
+    - households
+    - persons
+    - tours
+    - trips
+    - school_shadow_prices
+    - workplace_shadow_prices
+    - joint_tour_participants
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+#read_skim_cache: True
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+#write_skim_cache: True
diff --git a/activitysim/examples/prototype_mwcog/configs_mp/settings_source.yaml b/activitysim/examples/prototype_mwcog/configs_mp/settings_source.yaml
new file mode 100644
index 000000000..63db1d86e
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs_mp/settings_source.yaml
@@ -0,0 +1,95 @@
+inherit_settings: True
+
+# raise error if any sub-process fails without waiting for others to complete
+fail_fast: True
+
+# - ------------------------- dev config
+multiprocess: True
+strict: False
+mem_tick: 30
+use_shadow_pricing: False
+
+
+## - example sample
+households_sample_size: %sample_size%
+chunk_size: 200_000_000_000
+#chunk_size: 0
+chunk_training_mode: %training_mode%
+num_processes: 20
+# - tracing
+#trace_hh_id: 1205209
+trace_hh_id: 1086014
+trace_od:
+#trace_hh_id: 1482966
+#trace_od: [5, 11]
+# to resume after last successful checkpoint, specify resume_after: _
+resume_after: 
+models:
+  ### mp_initialize step
+  - initialize_landuse
+  - compute_accessibility
+  - initialize_households
+  ### mp_households step
+  - school_location
+  - workplace_location
+  - work_from_home
+  - transit_pass_subsidy
+  - transit_pass_ownership
+  - auto_ownership_simulate
+  - free_parking
+  - telecommute_frequency
+  - cdap_simulate
+  - mandatory_tour_frequency
+  - mandatory_tour_scheduling
+  - joint_tour_frequency
+  - joint_tour_composition
+  - joint_tour_participation
+  - joint_tour_destination
+  - joint_tour_scheduling
+  - non_mandatory_tour_frequency
+  - non_mandatory_tour_destination
+  - non_mandatory_tour_scheduling
+  - tour_mode_choice_simulate
+  - atwork_subtour_frequency
+  - atwork_subtour_destination
+  - atwork_subtour_scheduling
+  - atwork_subtour_mode_choice
+  - stop_frequency
+  - trip_purpose
+  - trip_destination
+  - trip_purpose_and_destination
+  - trip_scheduling
+  - trip_mode_choice
+  ### mp_summarize step
+  - write_data_dictionary
+  - write_tables
+  - write_trip_matrices
+multiprocess_steps:
+  - name: mp_initialize
+    begin: initialize_landuse
+  - name: mp_households
+    begin: school_location
+    slice:
+      tables:
+        - households
+        - persons
+  - name: mp_summarize
+    begin: write_data_dictionary
+output_tables:
+  action: include
+  prefix: final_
+  tables:
+    - checkpoints
+    - accessibility
+    - land_use
+    - households
+    - persons
+    - tours
+    - trips
+    - school_shadow_prices
+    - workplace_shadow_prices
+    - joint_tour_participants
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+#read_skim_cache: True
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+#write_skim_cache: True
diff --git a/activitysim/examples/prototype_mwcog/configs_mp/shadow_pricing.yaml b/activitysim/examples/prototype_mwcog/configs_mp/shadow_pricing.yaml
new file mode 100644
index 000000000..fda98f4d4
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs_mp/shadow_pricing.yaml
@@ -0,0 +1,37 @@
+
+inherit_settings: True
+
+shadow_pricing_models:
+  school: school_location
+  workplace: workplace_location
+
+# global switch to enable/disable loading of saved shadow prices
+# (ignored if global use_shadow_pricing switch is False)
+LOAD_SAVED_SHADOW_PRICES: False
+
+# number of shadow price iterations for cold start
+MAX_ITERATIONS: 10
+
+# number of shadow price iterations for warm start (after loading saved shadow_prices)
+MAX_ITERATIONS_SAVED: 1
+
+# ignore criteria for zones smaller than size_threshold
+SIZE_THRESHOLD: 10
+
+# zone passes if modeled is within percent_tolerance of  predicted_size
+PERCENT_TOLERANCE: 5
+
+# max percentage of zones allowed to fail
+FAIL_THRESHOLD: 1
+
+# CTRAMP or daysim
+SHADOW_PRICE_METHOD: ctramp
+#SHADOW_PRICE_METHOD: daysim
+
+# ctramp-style shadow_pricing_method parameters
+DAMPING_FACTOR: 1
+
+# daysim-style shadow_pricing_method parameters
+# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above?
+DAYSIM_ABSOLUTE_TOLERANCE: 50
+DAYSIM_PERCENT_TOLERANCE: 10
diff --git a/activitysim/examples/prototype_mwcog/data/LU_taz3722_rnd91a_2018_adj_enrollment.csv b/activitysim/examples/prototype_mwcog/data/LU_taz3722_rnd91a_2018_adj_enrollment.csv
new file mode 100644
index 000000000..db0b013fb
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/data/LU_taz3722_rnd91a_2018_adj_enrollment.csv
@@ -0,0 +1,54 @@
+TAZ,HH,HHPOP,GQPOP,TOTPOP,TOTEMP,INDEMP,RETEMP,OFFEMP,OTHEMP,JURCODE,LANDAREA,HHINCIDX,ADISTTOX,TAZXCRD,TAZYCRD,K_8,G9_12,COLLEGE,Park_Acres,GC_Acres,PRKCST,OPRKCST,TERMINAL,AREATYPE
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diff --git a/activitysim/examples/prototype_mwcog/data/combined_synthetic_hh_2018.csv b/activitysim/examples/prototype_mwcog/data/combined_synthetic_hh_2018.csv
new file mode 100644
index 000000000..9a1b15db5
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/data/combined_synthetic_hh_2018.csv
@@ -0,0 +1,29973 @@
+household_id,puma_geoid,TAZ,TYPE,NP,VEH,HHT,hhincadj,workers,has_children
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diff --git a/activitysim/examples/prototype_mwcog/data/combined_synthetic_per_2018.csv b/activitysim/examples/prototype_mwcog/data/combined_synthetic_per_2018.csv
new file mode 100644
index 000000000..26eda2b11
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/data/combined_synthetic_per_2018.csv
@@ -0,0 +1,40138 @@
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diff --git a/activitysim/examples/prototype_mwcog/data/land_use.csv b/activitysim/examples/prototype_mwcog/data/land_use.csv
new file mode 100644
index 000000000..a50d63838
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/data/land_use.csv
@@ -0,0 +1,54 @@
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+56,467,830,1370,2200,9812,563,1487,7359,403,0,0.0652,2,28.66,1298820,449535,0,0,5479,3.030183,0.0,118.875,200,5,1,11.191526073619633,235.14187116564418,10.68306779203773,1
+57,1092,1535,1679,3214,1847,40,266,1405,136,0,0.0531,5,28.85,1297625,449235,0,0,3088,0.34132,0.0,118.25,200,5,1,32.132768361581924,54.34910546139359,20.19367919831297,1
+58,320,442,1948,2390,724,43,179,471,31,0,0.0539,2,28.87,1298550,448272,0,592,7444,1.075076,0.0,118.5,200,5,1,9.276437847866418,20.987940630797773,6.433085250819241,1
+59,752,1131,0,1131,4750,26,69,3873,783,0,0.0881,5,29.15,1297439,447302,0,0,0,10.990833,0.0,113.5,200,5,1,13.33711691259932,84.24375709421112,11.51423215827822,1
+60,489,688,8,696,2981,301,500,2038,143,0,0.0743,17,29.12,1296503,448319,0,0,0,31.479772,0.0,114.75,200,5,1,10.283479138627186,62.68926648721399,8.834308735518052,1
diff --git a/activitysim/examples/prototype_mwcog/data/skims.omx b/activitysim/examples/prototype_mwcog/data/skims.omx
new file mode 100644
index 000000000..b60981484
Binary files /dev/null and b/activitysim/examples/prototype_mwcog/data/skims.omx differ
diff --git a/activitysim/examples/prototype_mwcog/data/taz_skims.omx b/activitysim/examples/prototype_mwcog/data/taz_skims.omx
new file mode 100644
index 000000000..03305a1fb
Binary files /dev/null and b/activitysim/examples/prototype_mwcog/data/taz_skims.omx differ
diff --git a/activitysim/examples/prototype_mwcog/extensions/__init__.py b/activitysim/examples/prototype_mwcog/extensions/__init__.py
new file mode 100644
index 000000000..a5b766f31
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/extensions/__init__.py
@@ -0,0 +1,4 @@
+from . import work_from_home
+from . import telecommute_frequency
+from . import transit_pass_ownership
+from . import transit_pass_subsidy
diff --git a/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py b/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py
new file mode 100644
index 000000000..c37081491
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py
@@ -0,0 +1,105 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import logging
+
+import pandas as pd
+
+from activitysim.core import tracing
+from activitysim.core import config
+from activitysim.core import pipeline
+from activitysim.core import simulate
+from activitysim.core import inject
+from activitysim.core import expressions
+
+from activitysim.abm.models.util import estimation
+
+logger = logging.getLogger(__name__)
+
+
+@inject.step()
+def telecommute_frequency(persons_merged, persons, chunk_size, trace_hh_id):
+    """
+    This model predicts the frequency of telecommute for a person (worker) who
+    does not works from home. The alternatives of this model are 'No Telecommute',
+    '1 day per week', '2 to 3 days per week' and '4 days per week'. This model
+    reflects the choices of people who prefer a combination of working from home and
+    office during a week.
+
+    The main interface to the telecommute frequency model is the telecommute_frequency() function.
+    This function is registered as an orca step in the example Pipeline.
+    """
+
+    trace_label = "telecommute_frequency"
+    model_settings_file_name = "telecommute_frequency.yaml"
+
+    choosers = persons_merged.to_frame()
+    choosers = choosers[choosers.workplace_zone_id > -1]
+
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+    estimator = estimation.manager.begin_estimation("telecommute_frequency")
+
+    constants = config.get_model_constants(model_settings)
+
+    # - preprocessor
+    preprocessor_settings = model_settings.get("preprocessor", None)
+    if preprocessor_settings:
+
+        locals_d = {}
+        if constants is not None:
+            locals_d.update(constants)
+
+        expressions.assign_columns(
+            df=choosers,
+            model_settings=preprocessor_settings,
+            locals_dict=locals_d,
+            trace_label=trace_label,
+        )
+
+    model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
+    coefficients_df = simulate.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
+
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    if estimator:
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_spec(model_settings)
+        estimator.write_coefficients(coefficients_df)
+        estimator.write_choosers(choosers)
+
+    choices = simulate.simple_simulate(
+        choosers=choosers,
+        spec=model_spec,
+        nest_spec=nest_spec,
+        locals_d=constants,
+        chunk_size=chunk_size,
+        trace_label=trace_label,
+        trace_choice_name="telecommute_frequency",
+        estimator=estimator,
+    )
+
+    choices = pd.Series(model_spec.columns[choices.values], index=choices.index)
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(
+            choices, "persons", "telecommute_frequency"
+        )
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    persons = persons.to_frame()
+    persons["telecommute_frequency"] = (
+        choices.reindex(persons.index).fillna("").astype(str)
+    )
+
+    pipeline.replace_table("persons", persons)
+
+    tracing.print_summary(
+        "telecommute_frequency", persons.telecommute_frequency, value_counts=True
+    )
+
+    if trace_hh_id:
+        tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
diff --git a/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py b/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py
new file mode 100644
index 000000000..ebe43953f
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py
@@ -0,0 +1,92 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import logging
+
+import numpy as np
+
+from activitysim.core import tracing
+from activitysim.core import config
+from activitysim.core import pipeline
+from activitysim.core import simulate
+from activitysim.core import inject
+from activitysim.core import expressions
+
+from activitysim.abm.models.util import estimation
+
+logger = logging.getLogger("activitysim")
+
+
+@inject.step()
+def transit_pass_ownership(persons_merged, persons, chunk_size, trace_hh_id):
+    """
+    Transit pass ownership model.
+    """
+
+    trace_label = "transit_pass_ownership"
+    model_settings_file_name = "transit_pass_ownership.yaml"
+
+    choosers = persons_merged.to_frame()
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+    estimator = estimation.manager.begin_estimation("transit_pass_ownership")
+
+    constants = config.get_model_constants(model_settings)
+
+    # - preprocessor
+    preprocessor_settings = model_settings.get("preprocessor", None)
+    if preprocessor_settings:
+
+        locals_d = {}
+        if constants is not None:
+            locals_d.update(constants)
+
+        expressions.assign_columns(
+            df=choosers,
+            model_settings=preprocessor_settings,
+            locals_dict=locals_d,
+            trace_label=trace_label,
+        )
+
+    model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
+    coefficients_df = simulate.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
+
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    if estimator:
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_spec(model_settings)
+        estimator.write_coefficients(coefficients_df)
+        estimator.write_choosers(choosers)
+
+    choices = simulate.simple_simulate(
+        choosers=choosers,
+        spec=model_spec,
+        nest_spec=nest_spec,
+        locals_d=constants,
+        chunk_size=chunk_size,
+        trace_label=trace_label,
+        trace_choice_name="transit_pass_ownership",
+        estimator=estimator,
+    )
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(
+            choices, "persons", "transit_pass_ownership"
+        )
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    persons = persons.to_frame()
+    persons["transit_pass_ownership"] = choices.reindex(persons.index)
+
+    pipeline.replace_table("persons", persons)
+
+    tracing.print_summary(
+        "transit_pass_ownership", persons.transit_pass_ownership, value_counts=True
+    )
+
+    if trace_hh_id:
+        tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
diff --git a/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py b/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py
new file mode 100644
index 000000000..e902753cb
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py
@@ -0,0 +1,92 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import logging
+
+import numpy as np
+
+from activitysim.core import tracing
+from activitysim.core import config
+from activitysim.core import pipeline
+from activitysim.core import simulate
+from activitysim.core import inject
+from activitysim.core import expressions
+
+from activitysim.abm.models.util import estimation
+
+logger = logging.getLogger("activitysim")
+
+
+@inject.step()
+def transit_pass_subsidy(persons_merged, persons, chunk_size, trace_hh_id):
+    """
+    Transit pass subsidy model.
+    """
+
+    trace_label = "transit_pass_subsidy"
+    model_settings_file_name = "transit_pass_subsidy.yaml"
+
+    choosers = persons_merged.to_frame()
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+    estimator = estimation.manager.begin_estimation("transit_pass_subsidy")
+
+    constants = config.get_model_constants(model_settings)
+
+    # - preprocessor
+    preprocessor_settings = model_settings.get("preprocessor", None)
+    if preprocessor_settings:
+
+        locals_d = {}
+        if constants is not None:
+            locals_d.update(constants)
+
+        expressions.assign_columns(
+            df=choosers,
+            model_settings=preprocessor_settings,
+            locals_dict=locals_d,
+            trace_label=trace_label,
+        )
+
+    model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
+    coefficients_df = simulate.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
+
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    if estimator:
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_spec(model_settings)
+        estimator.write_coefficients(coefficients_df)
+        estimator.write_choosers(choosers)
+
+    choices = simulate.simple_simulate(
+        choosers=choosers,
+        spec=model_spec,
+        nest_spec=nest_spec,
+        locals_d=constants,
+        chunk_size=chunk_size,
+        trace_label=trace_label,
+        trace_choice_name="transit_pass_subsidy",
+        estimator=estimator,
+    )
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(
+            choices, "persons", "transit_pass_subsidy"
+        )
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    persons = persons.to_frame()
+    persons["transit_pass_subsidy"] = choices.reindex(persons.index)
+
+    pipeline.replace_table("persons", persons)
+
+    tracing.print_summary(
+        "transit_pass_subsidy", persons.transit_pass_subsidy, value_counts=True
+    )
+
+    if trace_hh_id:
+        tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
diff --git a/activitysim/examples/prototype_mwcog/extensions/work_from_home.py b/activitysim/examples/prototype_mwcog/extensions/work_from_home.py
new file mode 100644
index 000000000..82d6618d6
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/extensions/work_from_home.py
@@ -0,0 +1,104 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import logging
+
+import numpy as np
+
+from activitysim.core import tracing
+from activitysim.core import config
+from activitysim.core import pipeline
+from activitysim.core import simulate
+from activitysim.core import inject
+from activitysim.core import expressions
+
+from activitysim.abm.models.util import estimation
+
+logger = logging.getLogger(__name__)
+
+
+@inject.step()
+def work_from_home(persons_merged, persons, chunk_size, trace_hh_id):
+    """
+    This model predicts the whether a person (worker) works from home. The output
+    from this model is TRUE (if works from home) or FALSE (works away from home).
+    The workplace location choice is overridden for workers who work from home
+    and set to -1.
+
+    The main interface to the work from home model is the work_from_home() function.
+    This function is registered as an orca step in the example Pipeline.
+    """
+
+    trace_label = "work_from_home"
+    model_settings_file_name = "work_from_home.yaml"
+
+    choosers = persons_merged.to_frame()
+    choosers = choosers[choosers.workplace_zone_id > -1]
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+    estimator = estimation.manager.begin_estimation("work_from_home")
+
+    constants = config.get_model_constants(model_settings)
+
+    # - preprocessor
+    preprocessor_settings = model_settings.get("preprocessor", None)
+    if preprocessor_settings:
+
+        locals_d = {}
+        if constants is not None:
+            locals_d.update(constants)
+
+        expressions.assign_columns(
+            df=choosers,
+            model_settings=preprocessor_settings,
+            locals_dict=locals_d,
+            trace_label=trace_label,
+        )
+
+    model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
+    coefficients_df = simulate.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
+
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    if estimator:
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_spec(model_settings)
+        estimator.write_coefficients(coefficients_df)
+        estimator.write_choosers(choosers)
+
+    choices = simulate.simple_simulate(
+        choosers=choosers,
+        spec=model_spec,
+        nest_spec=nest_spec,
+        locals_d=constants,
+        chunk_size=chunk_size,
+        trace_label=trace_label,
+        trace_choice_name="work_from_home",
+        estimator=estimator,
+    )
+
+    work_from_home_alt = model_settings["WORK_FROM_HOME_ALT"]
+    choices = choices == work_from_home_alt
+
+    dest_choice_column_name = model_settings["DEST_CHOICE_COLUMN_NAME"]
+    print(dest_choice_column_name)
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(choices, "persons", "work_from_home")
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    persons = persons.to_frame()
+    persons["work_from_home"] = choices.reindex(persons.index).fillna(0).astype(bool)
+    persons[dest_choice_column_name] = np.where(
+        persons.work_from_home is True, -1, persons[dest_choice_column_name]
+    )
+
+    pipeline.replace_table("persons", persons)
+
+    tracing.print_summary("work_from_home", persons.work_from_home, value_counts=True)
+
+    if trace_hh_id:
+        tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
diff --git a/activitysim/examples/prototype_mwcog/scripts/mwcog_crop.py b/activitysim/examples/prototype_mwcog/scripts/mwcog_crop.py
new file mode 100644
index 000000000..3ba678f22
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/scripts/mwcog_crop.py
@@ -0,0 +1,399 @@
+# crop marin tvpb example data processing to one county
+# Ben Stabler, ben.stabler@rsginc.com, 09/17/20
+
+import os
+import pandas as pd
+import openmatrix as omx
+import argparse
+import numpy as np
+
+MAZ_OFFSET = 0
+
+MAZ_LIST = [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
+    24,
+    25,
+    26,
+    27,
+    28,
+    29,
+    30,
+    31,
+    32,
+    33,
+    34,
+    35,
+    36,
+    37,
+    38,
+    39,
+    40,
+    41,
+    42,
+    43,
+    44,
+    45,
+    46,
+    47,
+    49,
+    50,
+    53,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    60,
+    181,
+    182,
+    184,
+    185,
+    186,
+    187,
+    188,
+    190,
+    191,
+    192,
+    193,
+    194,
+    195,
+    196,
+    197,
+    198,
+    199,
+    200,
+    201,
+    202,
+    203,
+    204,
+    205,
+    206,
+    207,
+    208,
+    209,
+    278,
+    279,
+    280,
+    281,
+    282,
+    283,
+    284,
+    285,
+    286,
+    287,
+    288,
+    289,
+    290,
+    291,
+    292,
+    298,
+    365,
+    370,
+    371,
+    372,
+    373,
+    374,
+    375,
+    376,
+    377,
+    378,
+    379,
+    380,
+    381,
+    383,
+    384,
+]
+
+
+segments = {"test": {"TAZ": np.array(MAZ_LIST)}}
+
+parser = argparse.ArgumentParser(description="crop raw_data")
+parser.add_argument(
+    "segment_name",
+    metavar="segment_name",
+    type=str,
+    nargs=1,
+    help=f"geography segmentation (e.g. full)",
+)
+
+parser.add_argument(
+    "-c",
+    "--check_geography",
+    default=False,
+    action="store_true",
+    help="check consistency of MAZ, TAZ, TAP zone_ids and foreign keys & write orphan_households file",
+)
+
+args = parser.parse_args()
+
+
+segment_name = args.segment_name[0]
+check_geography = args.check_geography
+
+assert segment_name in segments.keys(), f"Unknown seg: {segment_name}"
+
+input_dir = "./data_raw"
+output_dir = f"./data_{segment_name}"
+
+
+print(f"segment_name {segment_name}")
+
+print(f"input_dir {input_dir}")
+print(f"output_dir {output_dir}")
+
+print(f"check_geography {check_geography}")
+
+if not os.path.isdir(output_dir):
+    print(f"creating output directory {output_dir}")
+    os.mkdir(output_dir)
+
+
+def input_path(file_name):
+    return os.path.join(input_dir, file_name)
+
+
+def output_path(file_name):
+    return os.path.join(output_dir, file_name)
+
+
+def patch_maz(df, maz_offset):
+    for c in df.columns:
+        if c in ["maz", "OMAZ", "DMAZ", "mgra", "orig_mgra", "dest_mgra"]:
+            df[c] += maz_offset
+    return df
+
+
+def read_csv(file_name):
+    df = pd.read_csv(input_path(file_name))
+    print(f"read {file_name} {df.shape}")
+    return df
+
+
+def to_csv(df, file_name):
+    df.to_csv(output_path(file_name), index=False)
+    print(f"write {file_name} {df.shape}")
+
+
+def crop_omx(omx_file_name, zones, num_outfiles=1):
+    skim_data_type = np.float32
+    omx_in = omx.open_file(input_path(f"{omx_file_name}.omx"))
+    print(f"omx_in shape {omx_in.shape()}")
+
+    offset_map = None
+    for mapping_name in omx_in.listMappings():
+        _offset_map = np.asanyarray(omx_in.mapentries(mapping_name))
+        if (
+            offset_map is not None
+            or not (_offset_map == np.arange(1, len(_offset_map) + 1)).all()
+        ):
+            assert offset_map is None or (offset_map == _offset_map).all()
+            offset_map = _offset_map
+
+    if offset_map is not None:
+        om = pd.Series(offset_map)
+        om = om[om.isin(zones.values)]
+        nm = om[~om.isin(zones.values)]
+        indexes = om.index.values
+
+    else:
+        indexes = zones.index.tolist()  # index of TAZ in skim (zero-based, no mapping)
+    labels = zones.values  # TAZ zone_ids in omx index order
+
+    # create
+    if num_outfiles == 1:
+        omx_out = [omx.open_file(output_path(f"{omx_file_name}.omx"), "w")]
+    else:
+        omx_out = [
+            omx.open_file(output_path(f"{omx_file_name}{i + 1}.omx"), "w")
+            for i in range(num_outfiles)
+        ]
+
+    for omx_file in omx_out:
+        omx_file.create_mapping("zone_number", labels)
+
+    iskim = 0
+    for mat_name in omx_in.list_matrices():
+        # make sure we have a vanilla numpy array, not a CArray
+        m = np.asanyarray(omx_in[mat_name]).astype(skim_data_type)
+        m = m[indexes, :][:, indexes]
+        print(f"{mat_name} {m.shape}")
+
+        omx_file = omx_out[iskim % num_outfiles]
+        omx_file[mat_name] = m
+        iskim += 1
+
+    omx_in.close()
+
+    for omx_file in omx_out:
+        omx_file.close()
+
+
+# non-standard input file names
+
+LAND_USE = "land_use.csv"
+HOUSEHOLDS = "households.csv"
+PERSONS = "persons.csv"
+MAZ_TAZ = "maz.csv"
+TAP_MAZ = "tap.csv"
+TAZ = "taz.csv"
+SUBZONE = "subzoneData.csv"
+
+
+if check_geography:
+
+    # ######## check for orphan_households not in any maz in land_use
+    land_use = read_csv(LAND_USE)
+    land_use = land_use[["maz", "taz"]]
+    land_use = land_use.sort_values(["taz", "maz"])
+
+    households = read_csv(HOUSEHOLDS)
+    orphan_households = households[~households.maz.isin(land_use.maz)]
+    print(f"{len(orphan_households)} orphan_households")
+
+    # write orphan_households to INPUT directory (since it doesn't belong in output)
+    if len(orphan_households) > 0:
+        file_name = "orphan_households.csv"
+        print(
+            f"writing {file_name} {orphan_households.shape} to {input_path(file_name)}"
+        )
+        orphan_households.to_csv(input_path(file_name), index=False)
+
+    # ######## check that land_use and maz and taz tables have same MAZs and TAZs
+
+    # could just build maz and taz files, but want to make sure PSRC data is right
+
+    land_use = read_csv(LAND_USE)
+    # assert land_use.set_index('MAZ').index.is_monotonic_increasing
+
+    land_use = land_use.sort_values("maz")
+    maz = read_csv(MAZ_TAZ).sort_values("MAZ")
+
+    # ### FATAL ###
+    if not land_use.maz.isin(maz.MAZ).all():
+        print(
+            f"land_use.MAZ not in maz.MAZ\n{land_use.maz[~land_use.maz.isin(maz.maz)]}"
+        )
+        # raise RuntimeError(f"land_use.MAZ not in maz.MAZ")
+
+    if not maz.MAZ.isin(land_use.maz).all():
+        print(f"maz.MAZ not in land_use.MAZ\n{maz.maz[~maz.maz.isin(land_use.maz)]}")
+
+    # ### FATAL ###
+    if not land_use.taz.isin(maz.TAZ).all():
+        print(
+            f"land_use.TAZ not in maz.TAZ\n{land_use.taz[~land_use.taz.isin(maz.taz)]}"
+        )
+        raise RuntimeError(f"land_use.TAZ not in maz.TAZ")
+
+    if not maz.TAZ.isin(land_use.taz).all():
+        print(f"maz.TAZ not in land_use.TAZ\n{maz.taz[~maz.taz.isin(land_use.taz)]}")
+
+
+# land_use
+land_use = read_csv(LAND_USE)
+ur_land_use = land_use.copy()
+
+slicer = segments[segment_name]
+print(land_use.columns)
+for slice_col, slice_values in slicer.items():
+    # print(f"slice {slice_col}: {slice_values}")
+    land_use = land_use[land_use[slice_col].isin(slice_values)]
+
+print(f"land_use shape after slicing {land_use.shape}")
+to_csv(land_use, "land_use.csv")
+
+
+# TAZ
+taz = pd.DataFrame({"TAZ": sorted(ur_land_use.TAZ.unique())})
+taz = taz[taz.TAZ.isin(land_use["TAZ"])]
+to_csv(taz, TAZ)
+
+# maz_taz
+if os.path.exists(MAZ_TAZ):
+    maz_taz = read_csv(MAZ_TAZ).sort_values("MAZ")
+    maz_taz = maz_taz[maz_taz.MAZ.isin(land_use.maz)]
+    to_csv(maz_taz, MAZ_TAZ)
+
+# tap
+if os.path.exists(TAP_MAZ):
+    taps = read_csv(TAP_MAZ)
+    taps = taps[["TAP", "MAZ"]].sort_values(by="TAP").reset_index(drop=True)
+    taps = taps[taps["MAZ"].isin(land_use["maz"])]
+    to_csv(taps, "tap.csv")
+
+# maz to tap
+if os.path.exists("maz_to_tap_walk.csv"):
+    maz_tap_walk = read_csv("maz_to_tap_walk.csv").sort_values(["MAZ", "TAP"])
+    maz_tap_walk = maz_tap_walk[
+        maz_tap_walk["MAZ"].isin(land_use["maz"])
+        & maz_tap_walk["TAP"].isin(taps["TAP"])
+    ]
+    to_csv(maz_tap_walk, "maz_to_tap_walk.csv")
+if os.path.exists("maz_to_tap_drive.csv"):
+    taz_tap_drive = read_csv("maz_to_tap_drive.csv").sort_values(["MAZ", "TAP"])
+    taz_tap_drive = taz_tap_drive[
+        taz_tap_drive["MAZ"].isin(land_use["maz"])
+        & taz_tap_drive["TAP"].isin(taps["TAP"])
+    ]
+    to_csv(taz_tap_drive, "maz_to_tap_drive.csv")
+
+# maz to maz
+if os.path.exists("maz_to_maz_walk.csv"):
+    maz_maz_walk = read_csv("maz_to_maz_walk.csv").sort_values(["OMAZ", "DMAZ"])
+    maz_maz_walk = maz_maz_walk[
+        maz_maz_walk["OMAZ"].isin(land_use["maz"])
+        & maz_maz_walk["DMAZ"].isin(land_use["maz"])
+    ]
+    to_csv(maz_maz_walk, "maz_to_maz_walk.csv")
+
+if os.path.exists("maz_to_maz_bike.csv"):
+    maz_maz_bike = read_csv("maz_to_maz_bike.csv").sort_values(["OMAZ", "DMAZ"])
+    maz_maz_bike = maz_maz_bike[
+        maz_maz_bike["OMAZ"].isin(land_use["maz"])
+        & maz_maz_bike["DMAZ"].isin(land_use["maz"])
+    ]
+    to_csv(maz_maz_bike, "maz_to_maz_bike.csv")
+
+# tap_lines
+if os.path.exists("tapLines.csv"):
+    tap_lines = read_csv("tapLines.csv")
+    tap_lines = tap_lines[tap_lines["TAP"].isin(taps["TAP"])]
+    to_csv(tap_lines, "tapLines.csv")
+
+# households
+households = read_csv(HOUSEHOLDS)
+households = households[households["TAZ"].isin(land_use["TAZ"])]
+to_csv(households, "households.csv")
+
+# persons
+persons = read_csv(PERSONS)
+persons = persons[persons["household_id"].isin(households["household_id"])]
+to_csv(persons, "persons.csv")
+
+# Subzone (added ASR)
+if os.path.exists(SUBZONE):
+    subzone = read_csv(SUBZONE)
+    subzone = subzone[subzone["subzone09"].isin(land_use["maz"])]
+    to_csv(subzone, "subzone.csv")
+
+# skims
+crop_omx("taz_skims", taz.TAZ, num_outfiles=(4 if segment_name == "full" else 1))
diff --git a/activitysim/examples/prototype_mwcog/simulation.py b/activitysim/examples/prototype_mwcog/simulation.py
new file mode 100644
index 000000000..3126777b8
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/simulation.py
@@ -0,0 +1,17 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+
+import sys
+import argparse
+
+from activitysim.cli.run import add_run_args, run
+
+import extensions
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    add_run_args(parser)
+    args = parser.parse_args()
+
+    sys.exit(run(args))
diff --git a/activitysim/examples/prototype_mwcog/test/configs/settings.yaml b/activitysim/examples/prototype_mwcog/test/configs/settings.yaml
new file mode 100644
index 000000000..12820a341
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/configs/settings.yaml
@@ -0,0 +1,39 @@
+inherit_settings: True
+
+strict: True
+
+# number of households to simulate
+households_sample_size:  10
+# simulate all households
+# households_sample_size: 0
+
+chunk_size: 0
+
+# - shadow pricing global switches
+# turn shadow_pricing on and off for all models (e.g. school and work)
+# shadow pricing is deprecated for less than full samples
+# see shadow_pricing.yaml for additional settings
+use_shadow_pricing: False
+
+# turn writing of sample_tables on and off for all models
+# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings)
+want_dest_choice_sample_tables: False
+
+# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only)
+want_dest_choice_presampling: True
+
+# - tracing
+# trace household id; comment out or leave empty for no trace
+trace_hh_id:
+
+# trace origin, destination in accessibility calculation; comment out or leave empty for no trace
+# trace_od: [5, 11]
+trace_od:
+
+output_tables:
+  h5_store: False
+  action: include
+  prefix: final_
+  sort: True
+  tables:
+    - trips
diff --git a/activitysim/examples/prototype_mwcog/test/output/.gitignore b/activitysim/examples/prototype_mwcog/test/output/.gitignore
new file mode 100644
index 000000000..bf5bf15e3
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/output/.gitignore
@@ -0,0 +1,7 @@
+*.csv
+*.log
+*.prof
+*.h5
+*.txt
+*.yaml
+*.omx
diff --git a/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore b/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore
new file mode 100644
index 000000000..3dd2e62f9
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore
@@ -0,0 +1,2 @@
+*.mmap
+*.feather
diff --git a/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore b/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore
new file mode 100644
index 000000000..8edb80678
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore
@@ -0,0 +1,3 @@
+*.csv
+*.log
+*.txt
diff --git a/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv b/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv
new file mode 100644
index 000000000..81d05d0dd
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv
@@ -0,0 +1,50 @@
+trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum
+52153233,159003,1590,school,1,True,1,27,25,6519154,school,,11,WALK,3.3669236886282277
+52153237,159003,1590,school,1,False,1,25,27,6519154,home,,27,WALK,3.486923685946018
+52153889,159005,1590,school,1,True,1,27,25,6519236,school,,7,WALK,3.3669236886282277
+52153893,159005,1590,school,1,False,1,25,27,6519236,home,,27,WALK,3.486923685946018
+52154545,159007,1590,school,1,True,1,19,25,6519318,school,,11,WALK,3.1673109534958903
+52154549,159007,1590,school,1,False,1,25,19,6519318,home,,27,WALK,3.1673109534958903
+53136529,162001,1620,othdiscr,1,True,1,25,25,6642066,othdiscr,,13,WALK,2.0354872001527298
+53136533,162001,1620,othdiscr,1,False,1,25,25,6642066,home,,17,WALK,2.0354872001527298
+53136593,162001,1620,shopping,1,True,1,24,25,6642074,shopping,,20,WALK,0.5873669693786134
+53136597,162001,1620,shopping,1,False,1,25,24,6642074,home,,23,WALK,0.5873669693786134
+53136689,162002,1620,atwork,1,True,1,38,38,6642086,atwork,,22,WALK,-0.611999989181757
+53136693,162002,1620,atwork,1,False,1,38,38,6642086,work,,23,WALK,-0.611999989181757
+53136969,162002,1620,work,1,True,1,38,25,6642121,work,,15,KNR_MR,-0.8976646759623489
+53136973,162002,1620,work,1,False,1,25,38,6642121,home,,38,KNR_MR,-0.8795523663195886
+53137017,162003,1620,atwork,1,True,1,40,26,6642127,atwork,,19,WALK,0.8548156977447776
+53137021,162003,1620,atwork,1,False,1,26,40,6642127,work,,19,WALK,0.8548156977447776
+53137297,162003,1620,work,1,True,1,26,25,6642162,work,,13,WALK,-0.16346785221758944
+53137301,162003,1620,work,1,False,1,25,26,6642162,home,,30,WALK,-0.16346785221758944
+53530241,163201,1632,work,1,True,1,24,25,6691280,work,,9,PNR_MR,-0.4392097253621265
+53530245,163201,1632,work,1,False,1,25,24,6691280,home,,33,PNR_MR,-0.2591827350249029
+53530329,163202,1632,escort,1,True,1,39,25,6691291,escort,,28,DRIVEALONE,-0.06197964360005489
+53530333,163202,1632,escort,1,False,1,25,39,6691291,home,,29,SHARED2,-0.1701462043767353
+53530337,163202,1632,escort,1,True,1,23,25,6691292,escort,,24,WALK,-0.92999997921288
+53530341,163202,1632,escort,1,False,1,25,23,6691292,home,,25,WALK,-0.92999997921288
+53530849,163203,1632,shopping,1,True,1,22,25,6691356,shopping,,19,WALK,-1.409999968484044
+53530853,163203,1632,shopping,1,False,1,25,22,6691356,home,,20,WALK,-1.289999971166253
+278275841,848401,8484,work,1,True,1,1,42,34784480,work,,16,DRIVEALONE,-0.3047444691565592
+278275845,848401,8484,work,1,False,1,42,1,34784480,home,,30,DRIVEALONE,-0.3737130471632631
+381661281,1163601,11636,shopping,1,True,1,25,47,47707660,shopping,,39,SHARED2,0.13405080458662966
+381661285,1163601,11636,shopping,1,False,2,37,25,47707660,shopping,12.938418648448124,40,SHARED2,-0.19334951027125485
+381661286,1163601,11636,shopping,2,False,2,47,37,47707660,home,,40,SHARED2,0.11558833086308477
+381661441,1163601,11636,work,1,True,1,55,47,47707680,work,,14,DRIVEALONE,-0.2776450324248023
+381661445,1163601,11636,work,1,False,3,49,55,47707680,shopping,8.755660261900594,37,DRIVEALONE,-1.318377909968898
+381661446,1163601,11636,work,2,False,3,26,49,47707680,shopping,11.445671730580356,37,DRIVEALONE,-0.628015657476555
+381661447,1163601,11636,work,3,False,3,47,26,47707680,home,,38,DRIVEALONE,-1.096460443761022
+381661769,1163602,11636,work,1,True,1,40,47,47707721,work,,11,PNR_AB,-0.2324912003637291
+381661773,1163602,11636,work,1,False,1,47,40,47707721,home,,24,PNR_AB,-0.24617547380648477
+415969977,1268201,12682,eatout,1,True,1,39,47,51996247,eatout,,19,WALK,-1.3714739979449653
+415969981,1268201,12682,eatout,1,False,1,47,39,51996247,home,,21,WALK,-1.3714739979449653
+415970241,1268201,12682,work,1,True,1,12,47,51996280,work,,6,DRIVEALONE,-0.3338142112103269
+415970245,1268201,12682,work,1,False,1,47,12,51996280,home,,15,DRIVEALONE,-0.339542247430106
+468417329,1428101,14281,othdiscr,1,True,2,44,49,58552166,escort,13.862092230717861,25,WALK,2.83170898894978
+468417330,1428101,14281,othdiscr,2,True,2,38,44,58552166,othdiscr,,26,WALK,-0.689999984577298
+468417333,1428101,14281,othdiscr,1,False,4,54,38,58552166,shopping,16.463054848061834,25,WALK,-1.409999968484044
+468417334,1428101,14281,othdiscr,2,False,4,54,54,58552166,othdiscr,26.955410749877714,26,WALK,4.63959178556302
+468417335,1428101,14281,othdiscr,3,False,4,45,54,58552166,othmaint,26.28617562432176,26,WALK,4.039591798974065
+468417336,1428101,14281,othdiscr,4,False,4,49,45,58552166,home,,28,WALK,2.895014407185858
+650752641,1984001,19840,work,1,True,1,28,57,81344080,work,,9,WALK,0.21761578208744412
+650752645,1984001,19840,work,1,False,1,57,28,81344080,home,,13,WALK_MR,0.22380439703983218
diff --git a/activitysim/examples/prototype_mwcog/test/test_mwcog.py b/activitysim/examples/prototype_mwcog/test/test_mwcog.py
new file mode 100644
index 000000000..bd2307768
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/test/test_mwcog.py
@@ -0,0 +1,60 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import os
+import subprocess
+
+import pandas as pd
+import pandas.testing as pdt
+import pkg_resources
+
+from activitysim.core import inject
+
+
+def teardown_function(func):
+    inject.clear_cache()
+    inject.reinject_decorated_tables()
+
+
+def test_mwcog():
+    def example_path(dirname):
+        resource = os.path.join("examples", "prototype_mwcog", dirname)
+        return pkg_resources.resource_filename("activitysim", resource)
+
+    def test_path(dirname):
+        return os.path.join(os.path.dirname(__file__), dirname)
+
+    def regress():
+        regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv"))
+        final_trips_df = pd.read_csv(test_path("output/final_trips.csv"))
+
+        # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose,
+        # destination,origin,destination_logsum,depart,trip_mode,mode_choice_logsum
+        # compare_cols = []
+        pdt.assert_frame_equal(final_trips_df, regress_trips_df)
+
+    file_path = os.path.join(os.path.dirname(__file__), ".." + os.sep + "simulation.py")
+
+    subprocess.run(
+        [
+            "coverage",
+            "run",
+            "-a",
+            file_path,
+            "-c",
+            test_path("configs"),
+            "-c",
+            example_path("configs"),
+            "-d",
+            example_path("data"),
+            "-o",
+            test_path("output"),
+        ],
+        check=True,
+    )
+
+    regress()
+
+
+if __name__ == "__main__":
+
+    test_mwcog()
diff --git a/conda-environments/activitysim-test-larch.yml b/conda-environments/activitysim-test-larch.yml
index 43c61e244..5f4b1f687 100644
--- a/conda-environments/activitysim-test-larch.yml
+++ b/conda-environments/activitysim-test-larch.yml
@@ -1,4 +1,4 @@
-# Environment for testing
+# Environment for testing estimation in travis
 # usage: $ conda env create --file=activitysim-test.yml
 name: asimtest
 channels:
@@ -6,25 +6,27 @@ channels:
 dependencies:
 - python=${TRAVIS_PYTHON_VERSION}
 - pip
+- black
+- coveralls
+- cytoolz >= 0.8.1
+- isort
+- larch >=5.5.3
+- nbmake
+- numba >= 0.55.2
 - numpy >= 1.16.1,<=1.21
-- pandas >= 1.1.0
-- pyarrow >= 2.0
 - openmatrix >= 0.3.4.1
-- pyyaml >= 5.1
-- pytables >= 3.5.1
-- cytoolz >= 0.8.1
-- psutil >= 4.1
-- requests >= 2.7
-- numba >= 0.51.2
 - orca >= 1.6
-- larch >=5.5.3
-- xarray
-- sharrow
+- pandas >= 1.1.0
+- psutil >= 4.1
+- pyarrow >= 2.0
+- pypyr >= 5.3
+- pytables >= 3.5.1,<3.7  # orca's constraint
 - pytest
 - pytest-cov
-- coveralls
-- black
 - pytest-regressions
-- xarray
-- sharrow
+- pyyaml >= 5.1
+- requests >= 2.7
+- sharrow >= 2.2.4
 - simwrapper > 1.7
+- xarray >= 0.21
+- zarr
diff --git a/conda-environments/activitysim-test.yml b/conda-environments/activitysim-test.yml
index 8c91b1667..bcd8e7d40 100644
--- a/conda-environments/activitysim-test.yml
+++ b/conda-environments/activitysim-test.yml
@@ -1,25 +1,30 @@
-# Environment for testing
+# Environment for testing in travis
 # usage: $ conda env create --file=activitysim-test.yml
 name: asimtest
 channels:
 - conda-forge
 dependencies:
 - python=${TRAVIS_PYTHON_VERSION}
-- pip
+- black
+- coveralls
+- cytoolz >= 0.8.1
+- isort
+- nbmake
+- numba >= 0.55.2
 - numpy >= 1.16.1,<=1.21
-- pandas >= 1.1.0
-- pyarrow >= 2.0
 - openmatrix >= 0.3.4.1
-- pyyaml >= 5.1
-- pytables >= 3.5.1
-- cytoolz >= 0.8.1
-- psutil >= 4.1
-- requests >= 2.7
-- numba >= 0.51.2
 - orca >= 1.6
+- pandas >= 1.1.0
+- psutil >= 4.1
+- pyarrow >= 2.0
+- pypyr >= 5.3
+- pytables >= 3.5.1,<3.7  # orca's constraint
 - pytest
 - pytest-cov
-- coveralls
-- black
 - pytest-regressions
-- simwrapper > 1.7
\ No newline at end of file
+- pyyaml >= 5.1
+- requests >= 2.7
+- sharrow >= 2.2
+- simwrapper > 1.7
+- xarray >= 0.21
+- zarr
diff --git a/conda-environments/docbuild.yml b/conda-environments/docbuild.yml
new file mode 100644
index 000000000..a4617fcf6
--- /dev/null
+++ b/conda-environments/docbuild.yml
@@ -0,0 +1,46 @@
+# Environment for building docs
+name: docbuild
+channels:
+- conda-forge
+dependencies:
+- python=3.9
+- pip
+- black
+- bump2version
+- coveralls
+- cytoolz >= 0.8.1
+- descartes
+- geopandas
+- gh
+- git
+- jupyter-book
+- jupyterlab
+- larch >=5.5.3
+- matplotlib
+- myst-parser
+- numba >= 0.51.2
+- numpy >= 1.16.1,<=1.21
+- numpydoc
+- openmatrix >= 0.3.4.1
+- orca >= 1.6
+- pandas >= 1.1.0
+- psutil >= 4.1
+- pyarrow >= 2.0
+- pydantic
+- pypyr >= 5.3
+- pytables >=3.5.1,<3.7
+- pytest
+- pytest-cov
+- pytest-regressions
+- pyyaml >= 5.1
+- requests >= 2.7
+- sharrow >= 2.2.4
+- simwrapper > 1.7
+- sphinx-argparse
+- sphinx_rtd_theme
+- xarray >= 0.21
+- zarr
+
+- pip:
+  - autodoc_pydantic
+  - -e ..
diff --git a/conda-environments/github-actions-tests.yml b/conda-environments/github-actions-tests.yml
new file mode 100644
index 000000000..01108bdc4
--- /dev/null
+++ b/conda-environments/github-actions-tests.yml
@@ -0,0 +1,29 @@
+# Environment for testing in GitHub Actions
+name: asim-test
+channels:
+- conda-forge
+dependencies:
+- pip
+- black
+- coveralls
+- cytoolz >= 0.8.1
+- isort
+- nbmake
+- numba >= 0.55.2
+- numpy >= 1.16.1,<=1.21
+- openmatrix >= 0.3.4.1
+- orca >= 1.6
+- pandas >= 1.1.0
+- psutil >= 4.1
+- pyarrow >= 2.0
+- pypyr >= 5.3
+- pytables >= 3.5.1,<3.7  # orca's constraint
+- pytest
+- pytest-cov
+- pytest-regressions
+- pyyaml >= 5.1
+- requests >= 2.7
+- sharrow >= 2.2.4
+- simwrapper > 1.7
+- xarray >= 0.21
+- zarr
diff --git a/docs/_static/switcher.json b/docs/_static/switcher.json
new file mode 100644
index 000000000..2176747a6
--- /dev/null
+++ b/docs/_static/switcher.json
@@ -0,0 +1,16 @@
+[
+    {
+        "name": "Latest Dev",
+        "version": "1.2.dev",
+        "url": "https://activitysim.github.io/activitysim/develop/index.html"
+    },
+    {
+        "name": "1.1 (Released)",
+        "version": "1.1.1",
+        "url": "https://activitysim.github.io/activitysim/v1.1.1/index.html"
+    },
+    {
+        "version": "1.0.4",
+        "url": "https://activitysim.github.io/activitysim/v1.0.4/index.html"
+    }
+]
diff --git a/docs/_static/theme_overrides.css b/docs/_static/theme_overrides.css
index 7c1a52022..a78755ebc 100644
--- a/docs/_static/theme_overrides.css
+++ b/docs/_static/theme_overrides.css
@@ -1,10 +1,9 @@
-/* override table width restrictions */
-.wy-table-responsive table td, .wy-table-responsive table th {
-    white-space: normal;
+a.navbar-brand {
+    font-size: 20pt;
+    font-weight: bold;
 }
 
-.wy-table-responsive {
-    margin-bottom: 24px;
-    max-width: 100%;
-    overflow: visible;
+div.sd-card-header {
+    font-weight: 800;
+    var(--pst-font-family-base);
 }
diff --git a/docs/_templates/version-date.html b/docs/_templates/version-date.html
new file mode 100644
index 000000000..8b6a6f04d
--- /dev/null
+++ b/docs/_templates/version-date.html
@@ -0,0 +1,4 @@
+<!-- This will display the version of the docs -->
+<p class="last-updated">
+ActivitySim {{ release }}, documentation last updated {{ last_updated }}.<br/>
+</p>
\ No newline at end of file
diff --git a/docs/benchmarking.rst b/docs/benchmarking.rst
index bf3b85ddf..83b91b810 100644
--- a/docs/benchmarking.rst
+++ b/docs/benchmarking.rst
@@ -109,9 +109,9 @@ This will run the benchmarks only on the "HEAD" commit of the main activitysim g
 repository.  To run on some other historical commit[s] from the git history, you can
 specify an individual commit or a range, in the same way you would do so for the
 `git log` command. For example, to run benchmarks on the commits to develop since
-it was branched off master, run::
+it was branched off main, run::
 
-    activitysim benchmark run master..develop
+    activitysim benchmark run main..develop
 
 or to run only on the latest commit in develop, run::
 
@@ -209,7 +209,7 @@ Publishing to Github Pages
 Publishing the standard airspeed velocity content to GitHub pages is a built-in
 feature of the command line tool, available to users who have write-access to the
 asim-benchmarks GitHub repository.  Be sure you have all the relevant branches
-tracked locally (especially master and develop) and then run::
+tracked locally (especially main and develop) and then run::
 
     activitysim benchmark gh-pages
 
diff --git a/docs/cli.rst b/docs/cli.rst
index c309971b2..c9bcbfeb4 100644
--- a/docs/cli.rst
+++ b/docs/cli.rst
@@ -8,7 +8,7 @@ ActivitySim includes a :ref:`cli` for creating examples and running the model.
 more information.
 
 .. note::
-   The `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_ 
+   The `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
    contains example commands to create and run several versions of the examples.
 
 Create
diff --git a/docs/conf.py b/docs/conf.py
index 082acb46a..9149b0e37 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -15,13 +15,9 @@
 import os
 import sys
 
-import sphinx_rtd_theme
-
 # -- Get Package Version --------------------------------------------------
 import activitysim
 
-print("package version: " + activitysim.__version__)
-
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
@@ -40,15 +36,27 @@
     "sphinx.ext.mathjax",
     "numpydoc",
     "sphinx.ext.autosummary",
+    "myst_parser",
+    "sphinx_design",
+    "sphinxarg.ext",
+    "sphinxcontrib.autodoc_pydantic",
 ]
 
+myst_enable_extensions = ["colon_fence"]
 numpydoc_show_class_members = False
+autosummary_generate = True
+autodoc_pydantic_model_signature_prefix = "settings"
+autodoc_pydantic_model_show_json = False
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
 # The suffix of source filenames.
-source_suffix = ".rst"
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".txt": "markdown",
+    ".md": "markdown",
+}
 
 # The encoding of source files.
 # source_encoding = 'utf-8-sig'
@@ -65,10 +73,13 @@
 # built documents.
 #
 # The short X.Y version.
-version = activitysim.__version__
+version = ".".join(str(i) for i in activitysim.__version_tuple__[:3])
 # The full version, including alpha/beta/rc tags.
 release = activitysim.__version__
 
+print("package version: " + version)
+print("package release: " + release)
+
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
 # language = None
@@ -112,19 +123,31 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = "sphinx_rtd_theme"
+html_theme = "pydata_sphinx_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
-# html_theme_options = {}
+GITHUB_REPOSITORY_OWNER = os.environ.get(
+    "GITHUB_REPOSITORY_OWNER", "ActivitySim"
+).lower()
+print("github repo owner: " + GITHUB_REPOSITORY_OWNER)
+
+html_theme_options = {
+    "footer_items": ["version-date", "sphinx-version"],
+    "switcher": {
+        "json_url": f"https://{GITHUB_REPOSITORY_OWNER}.github.io/activitysim/switcher.json",
+        "version_match": version,
+    },
+    "navbar_end": ["version-switcher"],
+}
 
 # Add any paths that contain custom themes here, relative to this directory.
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+# html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 # The name for this set of Sphinx documents.  If None, it defaults to
 # "<project> v<release> documentation".
-# html_title = None
+html_title = f"ActivitySim {release}"
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 # html_short_title = None
@@ -150,7 +173,7 @@
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
-# html_last_updated_fmt = '%b %d, %Y'
+html_last_updated_fmt = "%b %d, %Y"
 
 # If true, SmartyPants will be used to convert quotes and dashes to
 # typographically correct entities.
@@ -282,8 +305,6 @@
 # -- Table width fix for Read the Docs Sphinx theme -----------------------
 html_static_path = ["_static"]
 
-html_context = {
-    "css_files": [
-        "_static/theme_overrides.css",  # override wide tables in RTD theme
-    ],
-}
+html_css_files = [
+    "theme_overrides.css",
+]
diff --git a/docs/core.rst b/docs/core.rst
index bd4a3a30f..d2af1c54a 100644
--- a/docs/core.rst
+++ b/docs/core.rst
@@ -223,7 +223,7 @@ Expressions
 The expressions class is often used for pre- and post-processor table annotation, which read a CSV file of expression, calculate
 a number of additional table fields, and join the fields to the target table.  An example table annotation expressions
 file is found in the example configuration files for households for the CDAP model -
-`annotate_households_cdap.csv <https://github.com/activitysim/activitysim/blob/master/example/configs/annotate_households_cdap.csv>`__.
+`annotate_households_cdap.csv <https://github.com/activitysim/activitysim/blob/main/example/configs/annotate_households_cdap.csv>`__.
 
 .. automodule:: activitysim.core.expressions
    :members:
diff --git a/docs/dev-guide/index.rst b/docs/dev-guide/index.rst
new file mode 100644
index 000000000..e18240afa
--- /dev/null
+++ b/docs/dev-guide/index.rst
@@ -0,0 +1,29 @@
+
+.. _devguide:
+
+Developers
+==========
+
+The mission of the ActivitySim project is to create and maintain advanced, open-source,
+activity-based travel behavior modeling software based on best software development
+practices for distribution at no charge to the public.
+
+The ActivitySim project is led by a consortium of Metropolitan Planning Organizations
+(MPOs) and other transportation planning agencies, which provides technical direction
+and resources to support project development. New member agencies are welcome to join
+the consortium. All member agencies help make decisions about development priorities
+and benefit from contributions of other agency partners.  Additional information about
+the development and management of the ActivitySim is on the `project site <http://www.activitysim.org>`__.
+
+
+Contents
+--------
+
+.. toctree::
+   :maxdepth: 3
+
+   install
+   ../development
+   ../models
+   ../core
+   ../benchmarking
diff --git a/docs/dev-guide/install.md b/docs/dev-guide/install.md
new file mode 100644
index 000000000..755a6dd29
--- /dev/null
+++ b/docs/dev-guide/install.md
@@ -0,0 +1,70 @@
+(developer-installation)=
+# Developer Installation
+
+Installing ActivitySim as a developer is almost as easy as just using it,
+but making some tweaks to the processes enables live code development and
+testing.
+
+## Package Manager
+
+ActivitySim has a lot of dependencies.  It's easiest and fastest to install
+them using a package manager like conda. There's a faster version called
+[Mambaforge](https://github.com/conda-forge/miniforge#mambaforge).
+Depending on your security settings, you might need to install in a
+container like docker, instructions for that are coming soon.
+
+Note that if you are installing `mamba`, you only should install `mamba`
+in the *base* environment. If you install `mamba` itself in other environments,
+it will not function correctly.  If you've got an existing conda installation
+and you want to install mamba into it, you can install mamba into the *base*
+environment like this:
+
+```sh
+conda update conda -n base
+conda install -n base -c conda-forge mamba
+```
+
+While you are at it, if you are a Jupyter user you might want to also install
+`nb_conda_kernels` in your base conda environment alongside any other `jupyter`
+libraries:
+
+```sh
+mamba install -n base nb_conda_kernels -c conda-forge
+```
+
+This will ensure your development environments are selectable as kernels in
+Jupyter Notebook/Lab/Etc.
+
+## Environment
+
+It's convenient to start from a completely clean conda environment
+and git repository. Assuming you have `mamba` installed, and you
+want to install in a new directory called "workspace" run:
+
+```sh
+mkdir workspace
+cd workspace
+mamba env create -p ASIM-ENV --file https://raw.githubusercontent.com/camsys/activitysim/sharrow-black/conda-environments/activitysim-dev-base.yml
+conda activate ./ASIM-ENV
+git clone https://github.com/ActivitySim/sharrow.git
+python -m pip install -e ./sharrow
+git clone https://github.com/camsys/activitysim.git
+cd activitysim
+git switch sharrow-black
+cd ..
+python -m pip install -e ./activitysim
+```
+
+Note the above commands will create an environment with all the
+necessary dependencies, clone both ActivitySim and sharrow from GitHub,
+and `pip install` each of these libraries in editable mode, which
+will allow your code changes to be reflected when running ActivitySim
+in this environment.
+
+Now your environment should be ready to use.  Happy coding!
+
+```{important}
+If you add to the ActivitySim dependencies, make sure to also update
+the environments in `conda-environments`, which are used for testing
+and development.
+```
diff --git a/docs/development.rst b/docs/development.rst
index 98edd7bae..fef5684c3 100644
--- a/docs/development.rst
+++ b/docs/development.rst
@@ -63,13 +63,13 @@ and network skim matrices.  With this design, the Python code, which can be thou
 engine, and the specific model calculations, such as the utilities, are separate.  This helps to avoid 
 modifying the actual Python code when making changes to the models, such as during model calibration. An 
 example of model expressions is found in the example auto ownership model specification file - 
-`auto_ownership.csv <https://github.com/activitysim/activitysim/blob/master/example/configs/auto_ownership.csv>`__. 
+`auto_ownership.csv <https://github.com/activitysim/activitysim/blob/main/example/configs/auto_ownership.csv>`__.
 Refer to the :ref:`expressions` section for more detail.
 
 Many of the models have pre- and post-processor table annotators, which read a CSV file of expression, calculate 
 required additional table fields, and join the fields to the target tables.  An example table annotation expressions 
 file is found in the example configuration files for households for the CDAP model - 
-`annotate_households_cdap.csv <https://github.com/activitysim/activitysim/blob/master/example/configs/annotate_households_cdap.csv>`__. 
+`annotate_households_cdap.csv <https://github.com/activitysim/activitysim/blob/main/example/configs/annotate_households_cdap.csv>`__.
 Refer to :ref:`table_annotation` for more information and the :func:`activitysim.abm.models.util.expressions.assign_columns` function.
 
 Choice Models
@@ -153,7 +153,7 @@ Working Together in the Repository
 We use `GitHub Flow <https://guides.github.com/introduction/flow>`__.  The key points to 
 our GitHub workflow are:
 
-* The ``master`` branch contains the latest release version of the ActivitySim resources
+* The ``main`` branch contains the latest release version of the ActivitySim resources
 * The ``develop`` branch contains new features or revisions planned for the next release.
   Generally, developers should not work directly in the ``develop`` branch.
 * Work to implement new features or other revisions is done in an issue/feature branch
@@ -166,8 +166,8 @@ our GitHub workflow are:
   ``develop`` branch.
 * If tests pass for the ``develop`` branch, new features are suitably documented, and on approval of
   `a lazy majority of the PMC <https://github.com/ActivitySim/activitysim/wiki/Governance#actions>`__,
-  a repository administrator can approve a manual pull request to merge ``develop`` into ``master``,
-  and otherwise make a `product release <https://github.com/ActivitySim/activitysim/blob/master/HOW_TO_RELEASE.md>`__.
+  a repository administrator can approve a manual pull request to merge ``develop`` into ``main``,
+  and otherwise make a `product release <https://github.com/ActivitySim/activitysim/blob/main/HOW_TO_RELEASE.md>`__.
 
 
 Versioning
@@ -211,7 +211,7 @@ To run the tests locally, first make sure the required packages are installed.
     py.test
 
 These same tests are run by Travis with each push to the repository.  These tests need to pass in order
-to merge the revisions into master.
+to merge the revisions into main.
 
 In some cases, test targets need to be updated to match the new results produced by the code since these 
 are now the correct results.  In order to update the test targets, first determine which tests are 
@@ -255,7 +255,7 @@ Releases
 ~~~~~~~~
 
 With the agreement of the PMC, a project administrator will handle making releases, following the detailed
-steps outlined in the `HOW_TO_RELEASE <https://github.com/ActivitySim/activitysim/blob/master/HOW_TO_RELEASE.md>`__
+steps outlined in the `HOW_TO_RELEASE <https://github.com/ActivitySim/activitysim/blob/main/HOW_TO_RELEASE.md>`__
 document.
 
 
@@ -268,7 +268,7 @@ License
 ~~~~~~~
 
 ActivitySim is provided "as is."  See the 
-`License <https://github.com/ActivitySim/activitysim/blob/master/LICENSE.txt>`__ for more information.
+`License <https://github.com/ActivitySim/activitysim/blob/main/LICENSE.txt>`__ for more information.
 
 Contribution Review Criteria
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -354,7 +354,7 @@ Furthermore, multiple versions of these examples can exist, and be used for vari
   prototype_mtc and includes additional settings for reading in survey files and producing estimation data bundles.
 
 Regardless of the type or version, all functioning examples are described in a common list stored in
-`example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_.
+`example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_.
 Each item included in this file represents one example, and each includes the following tags:
 
 * *name*: A unique name for the example, used to identify the example when using the `activitysim create` command. The
@@ -385,7 +385,7 @@ The test plan for test examples versus agency examples is different:
 Both types of examples are stored in the ActivitySim repositories for version control and collaborative maintenance.
 There are two storage locations:
 
-* The `activitysim package example folder <https://github.com/ActivitySim/activitysim/tree/master/activitysim/examples>`_,
+* The `activitysim package example folder <https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples>`_,
   which stores the test and agency example setup files, cropped data and cropping script, regression test script,
   expected results, and a change log to track any revisions to the example to get it working for testing.  These
   resources are the resources automatically tested by the TravisCI test system with each revision to the software.
@@ -397,7 +397,7 @@ There are two storage locations:
 This two-part solution allows for the main activitysim repo to remain relatively lightweight, while providing an
 organized and accessible storage solution for the full scale example data.  The ActivitySim command line interface for
 creating and running examples makes uses the
-`example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_
+`example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 to maintain the dictionary of the examples and how to get and run them.
 
 Running the Test System
@@ -406,9 +406,9 @@ Running the Test System
 The automatic TravisCI test system runs the test examples and the cropped agency examples.  Examples of the testing 
 resources for each agency example that need to be up-to-date are:
 
-* `scripts folder (including crop script) <https://github.com/ActivitySim/activitysim/tree/master/activitysim/examples/prototype_semcog/scripts>`_
-* `test folder (including test script) <https://github.com/ActivitySim/activitysim/tree/master/activitysim/examples/prototype_semcog/test>`_
-* `regress folder (including expected outputs) <https://github.com/ActivitySim/activitysim/tree/master/activitysim/examples/prototype_semcog/test/regress>`_
+* `scripts folder (including crop script) <https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/prototype_semcog/scripts>`_
+* `test folder (including test script) <https://github.com/ActivitySim/activitysim/main/main/activitysim/examples/prototype_semcog/test>`_
+* `regress folder (including expected outputs) <https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/prototype_semcog/test/regress>`_
 
 For the time being, running the full scale examples is done manually since it involves getting and running several large examples that take many hours to run.  The entire system could be fully automated, and either run in the cloud or on a local server.
 
diff --git a/docs/estimation.rst b/docs/estimation.rst
index 72f8bdb99..814575e6c 100644
--- a/docs/estimation.rst
+++ b/docs/estimation.rst
@@ -5,7 +5,7 @@ Estimation
 ----------
 
 ActivitySim includes the ability to re-estimate submodels using choice model estimation tools
-such as `larch <https://larch.newman.me/>`__.  To do so, ActivitySim adopts the concept of an estimation 
+such as `larch <https://larch.newman.me/>`__.  To do so, ActivitySim adopts the concept of an estimation
 data bundle (EDB), which is a collection of the necessary data to re-estimate a submodel.  For example, for the auto ownership submodel,
 the EDB consists of the following files:
 
@@ -16,13 +16,13 @@ the EDB consists of the following files:
 
 ActivitySim also includes Jupyter :ref:`estimation_example_notebooks` for estimating submodels with larch, as well as an ``activitysim.estimation.larch`` submodule that transforms EDBs into larch models.  Additional estimation software translators can be added later if desired.
 
-The combination of writing an EDB for a submodel + a larch estimation notebook means users can easily re-estimate submodels. This 
+The combination of writing an EDB for a submodel + a larch estimation notebook means users can easily re-estimate submodels. This
 combination of functionality means:
 
 * There is no duplication of model specifications. ActivitySim owns the specification and larch pivots off of it.  Users code model specifications and utility expressions in ActivitySim so as to facilitate ease of use and eliminate inconsistencies and errors between the code used to estimate the models and the code used to apply the models.
 * The EDB includes all the data and model structure information and the ``activitysim.estimation.larch`` submodule used by the example notebooks transforms the EDB to larch's data model for estimation.
 * Users are able to add zones, alternatives, new chooser data, new taz data, new modes, new coefficients, revise utilities, and revise nesting structures in ActivitySim and larch responds accordingly.
-* Eventually it may be desirable for ActivitySim to automatically write larch estimators (or other types of estimators), but for now the integration is loosely coupled rather than tightly coupled in order to provide flexibility. 
+* Eventually it may be desirable for ActivitySim to automatically write larch estimators (or other types of estimators), but for now the integration is loosely coupled rather than tightly coupled in order to provide flexibility.
 
 Workflow
 ~~~~~~~~
@@ -96,51 +96,51 @@ ActivitySim includes a `Jupyter Notebook <https://jupyter.org>`__ recipe book wi
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | Example                             | Notebook                                                                                                                                                                            |
 +=====================================+=====================================================================================================================================================================================+
-| Estimation mode overview            | `01_estimation_mode.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/01_estimation_mode.ipynb>`_                     |
+| Estimation mode overview            | `01_estimation_mode.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/01_estimation_mode.ipynb>`_                       |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| School location                     | `02_school_location.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb>`_                     |
+| School location                     | `02_school_location.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb>`_                       |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Work location                       | `03_work_location.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb>`_                         |
+| Work location                       | `03_work_location.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb>`_                           |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Auto ownership                      | `04_auto_ownership.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb>`_                       |
+| Auto ownership                      | `04_auto_ownership.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb>`_                         |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Free parking                        | `05_free_parking.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb>`_                           |
+| Free parking                        | `05_free_parking.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb>`_                             |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| CDAP                                | `06_cdap.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb>`_                                           |
+| CDAP                                | `06_cdap.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb>`_                                             |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Mandatory tour frequency            | `07_mand_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb>`_                       |
+| Mandatory tour frequency            | `07_mand_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb>`_                         |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Work tour scheduling                | `08_work_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb>`_           |
+| Work tour scheduling                | `08_work_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb>`_             |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| School tour scheduling              | `09_school_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb>`_       |
+| School tour scheduling              | `09_school_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb>`_         |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Joint tour frequency                | `10_joint_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb>`_                     |
+| Joint tour frequency                | `10_joint_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb>`_                       |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Jointatory tour composition         | `11_joint_tour_composition.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb>`_       |
+| Jointatory tour composition         | `11_joint_tour_composition.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb>`_         |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Jointatory tour participation       | `12_joint_tour_participation.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb>`_   |
+| Jointatory tour participation       | `12_joint_tour_participation.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb>`_     |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Joint nonmandatory tour destination | `13_joint_nonmand_tour_dest.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb>`_     |
+| Joint nonmandatory tour destination | `13_joint_nonmand_tour_dest.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb>`_       |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Joint tour scheduling               | `14_joint_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb>`_         |
+| Joint tour scheduling               | `14_joint_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb>`_           |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Non mandatory tour frequency        | `15_non_mand_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb>`_               |
+| Non mandatory tour frequency        | `15_non_mand_tour_freq.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb>`_                 |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Non mandatory tour scheduling       | `16_nonmand_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb>`_     |
+| Non mandatory tour scheduling       | `16_nonmand_tour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb>`_       |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Tour mode choice                    | `17_tour_mode_choice.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb>`_                   |
+| Tour mode choice                    | `17_tour_mode_choice.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb>`_                     |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Atwork subtour frequency            | `18_atwork_subtour_freq.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb>`_             |
+| Atwork subtour frequency            | `18_atwork_subtour_freq.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb>`_               |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Atwork subtour destination          | `19_atwork_subtour_dest.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb>`_             |
+| Atwork subtour destination          | `19_atwork_subtour_dest.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb>`_               |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Atwork subtour scheduling           | `20_atwork_subtour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb>`_ |
+| Atwork subtour scheduling           | `20_atwork_subtour_scheduling.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb>`_   |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Stop frequency                      | `21_stop_frequency.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb>`_                       |
+| Stop frequency                      | `21_stop_frequency.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb>`_                         |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Trip destination                    | `22_trip_dest.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb>`_                                 |
+| Trip destination                    | `22_trip_dest.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb>`_                                   |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Trip mode choice                    | `23_trip_mode_choice.ipynb <https://github.com/activitysim/activitysim/blob/master/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb>`_                   |
+| Trip mode choice                    | `23_trip_mode_choice.ipynb <https://github.com/activitysim/activitysim/blob/main/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb>`_                     |
 +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 
 
@@ -178,4 +178,4 @@ Models API
    :members:
 
 .. automodule:: activitysim.estimation.larch.stop_frequency
-   :members:
\ No newline at end of file
+   :members:
diff --git a/docs/examples.rst b/docs/examples.rst
index 1b2c56ade..cf2431efc 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -56,9 +56,11 @@ The current examples are:
 +---------------------------------+-----------------------------------------------------------+--------------+----------------------+
 | :ref:`prototype_sandag_xborder` | SANDAG agency example                                     | 3            | In development       |
 +---------------------------------+-----------------------------------------------------------+--------------+----------------------+
+| :ref:`prototype_mwcog`          | MWCOG agency example                                      | 2            | In development       |
++---------------------------------+-----------------------------------------------------------+--------------+----------------------+
 
 .. note::
-   The `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_
+   The `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
    contains example commands to create and run several versions of the examples.  See also :ref:`adding_agency_examples` for more
    information on agency example models.
 
@@ -506,60 +508,11 @@ Area types
   skim inputs for a subset of user-defined zones.
 
 .. index:: configuration
-.. _configuration:
 
 Configuration
 ^^^^^^^^^^^^^
 
-The ``configs`` folder contains settings, expressions files, and other files required for specifying
-model utilities and form.  The first place to start in the ``configs`` folder is ``settings.yaml``, which
-is the main settings file for the model run.  This file includes:
-
-* ``models`` - list of model steps to run - auto ownership, tour frequency, etc. - see :ref:`model_steps`
-* ``resume_after`` - to resume running the data pipeline after the last successful checkpoint
-* ``input_store`` - HDF5 inputs file
-* ``input_table_list`` - list of table names, indices, and column re-maps for each table in `input_store`
-
-    * ``tablename`` - name of the injected table
-    * ``filename`` - name of the CSV or HDF5 file to read (optional, defaults to `input_store`)
-    * ``index_col`` - table column to use for the index
-    * ``rename_columns`` - dictionary of column name mappings
-    * ``keep_columns`` - columns to keep once read in to memory to save on memory needs and file I/O
-    * ``h5_tablename`` - table name if reading from HDF5 and different from `tablename`
-
-* ``create_input_store`` - write new `input_data.h5` file to outputs folder using CSVs from `input_table_list` to use for subsequent model runs
-* ``households_sample_size`` - number of households to sample and simulate; comment out to simulate all households
-* ``trace_hh_id`` - trace household id; comment out for no trace
-* ``trace_od`` - trace origin, destination pair in accessibility calculation; comment out for no trace
-* ``chunk_training_mode`` - disabled, training, production, or adaptive, see :ref:`chunk_size`.
-* ``chunk_size`` - approximate amount of RAM in GBs to allocate to ActivitySim for batch processing choosers, see :ref:`chunk_size`.
-* ``chunk_method`` - memory use measure such as hybrid_uss, see :ref:`chunk_size`.
-* ``checkpoints`` - if True, checkpoints are written at each step; if False, no intermediate checkpoints will be written before the end of run; also supports an explicit list of models to checkpoint
-* ``check_for_variability`` - disable check for variability in an expression result debugging feature in order to speed-up runtime
-* ``log_alt_losers`` - if True, log (i.e. write out) expressions for debugging that return prohibitive utility values that exclude all alternatives.  This feature slows down the model run and so it is recommended for debugging purposes only.
-* ``use_shadow_pricing`` - turn shadow_pricing on and off for work and school location
-* ``output_tables`` - list of output tables to write to CSV or HDF5
-* ``want_dest_choice_sample_tables`` - turn writing of sample_tables on and off for all models
-* ``cleanup_pipeline_after_run`` - if true, cleans up pipeline after successful run by creating a single-checkpoint pipeline file and deletes any subprocess pipelines
-* global variables that can be used in expressions tables and Python code such as:
-
-    * ``urban_threshold`` - urban threshold area type max value
-    * ``county_map`` - mapping of county codes to county names
-    * ``household_median_value_of_time`` - various household and person value-of-time model settings
-
-Also in the ``configs`` folder is ``network_los.yaml``, which includes network LOS / skims settings such as:
-
-* ``zone_system`` - 1 (taz), 2 (maz and taz), or 3 (maz, taz, tap)
-* ``taz_skims`` - skim matrices in one OMX file.  The time period for the matrix must be represented at the end of the matrix name and be seperated by a double_underscore (e.g. BUS_IVT__AM indicates base skim BUS_IVT with a time period of AM.
-* ``skim_time_periods`` - time period upper bound values and labels
-
-    * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
-    * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes)
-    * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
-    * ``labels`` - Labels to define names for aggregate periods for skims and assignment
-* ``read_skim_cache`` - read cached skims (using numpy memmap) from output directory (memmap is faster than omx)
-* ``write_skim_cache`` - write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
-* ``cache_dir`` - alternate dir to read/write cache files (defaults to output_dir)
+This section has been moved to :ref:`configuration`.
 
 .. _sub-model-spec-files:
 
@@ -849,7 +802,7 @@ The example should run in a few minutes since it runs a small sample of househol
 
 .. note::
 
-  A customizable run script for power users can be found in the `Github repo <https://github.com/ActivitySim/activitysim/tree/master/other_resources/scripts>`__.
+  A customizable run script for power users can be found in the `Github repo <https://github.com/ActivitySim/activitysim/tree/main/other_resources/scripts>`__.
   This script takes many of the same arguments as the ``activitysim run`` command, including paths to
   ``--config``, ``--data``, and ``--output`` directories. The script looks for these folders in the current
   working directory by default.
@@ -1518,7 +1471,7 @@ Example ARC Sub-Model Specification Files
 Example
 ~~~~~~~
 
-See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 for running prototype_arc.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
 
 .. _prototype_semcog :
@@ -1566,7 +1519,7 @@ Example SEMCOG Sub-Model Specification Files
 Example
 ~~~~~~~
 
-See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 for running prototype_semcog.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
 
 
@@ -1586,7 +1539,7 @@ prototype_mtc model design.  It uses PSRC zones, land use, synthetic population,
 Example
 ~~~~~~~
 
-See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 for running placeholder_psrc.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
 
 .. _placeholder_sandag :
@@ -1605,7 +1558,7 @@ prototype_mtc model design.  It uses SANDAG zones, land use, synthetic populatio
 Example
 ~~~~~~~
 
-See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_ 
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 for running placeholder_sandag.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
 
 .. _prototype_sandag_xborder :
@@ -1630,5 +1583,18 @@ model is required to assign tour origins and destinations simultaneously.
 Example
 ~~~~~~~
 
-See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/example_manifest.yaml>`_ 
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
 for running prototype_sandag_xborder.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
+
+.. _prototype_mwcog :
+
+prototype_mwcog
+---------------
+
+The prototype_mwcog is a one zone system (TAZs only).
+
+Example
+~~~~~~~
+
+See example commands in `example_manifest.yaml <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_manifest.yaml>`_
+for running prototype_mwcog.  For optimal performance, configure multiprocessing and chunk_size based on machine hardware.
diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index e0201b1d9..00db3e792 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -15,13 +15,21 @@ Installation
 ------------
 
 1. It is recommended that you install and use a *conda* package manager
-for your system. One easy way to do so is by using `Anaconda 64bit Python 3 <https://www.anaconda.com/distribution/>`__,
+for your system. One easy way to do so is by using
+`Mambaforge <https://github.com/conda-forge/miniforge#mambaforge>`__.
+Mamba is a free open source cross-platform package manager that runs on
+Windows, OS X and Linux and is fully compatible with conda packages.  It is
+also usually substantially faster than conda itself.
+
+Alternatively, if you prefer a package installer backed by corporate tech
+support available (for a fee) as necessary, you can install
+`Anaconda 64bit Python 3 <https://www.anaconda.com/distribution/>`__,
 although you should consult the `terms of service <https://www.anaconda.com/terms-of-service>`__
 for this product and ensure you qualify since businesses and
-governments with over 200 employees do not qualify for free usage.  If you prefer
-a completely free open source *conda* tool, you can download and install the
-appropriate version of `Miniforge <https://github.com/conda-forge/miniforge#miniforge3>`__.  If desired, 
-select 'add miniforge3 to the system PATH environment variable' so typing 'conda' on the command line will work.
+governments with over 200 employees do not qualify for free usage.
+If you're using `conda` instead of `mamba`, just replace every call to
+`mamba` below with `conda`, as they share the same user interface and most
+command formats.
 
 2. If you access the internet from behind a firewall, then you may need to
 configure your proxy server. To do so, create a `.condarc` file in your
@@ -37,7 +45,7 @@ home installation folder, such as:
 3. Create a conda environment (basically a Python install just for this project)
 using conda Prompt (on Windows) or the terminal (macOS or Linux)::
 
-  conda create -n asim python=3.9 activitysim -c conda-forge --override-channels
+  mamba create -n asim python=3.9 activitysim -c conda-forge --override-channels
 
 This command will create the environment and install all the dependencies
 required for running ActivitySim.  It is only necessary to create the environment
@@ -45,35 +53,35 @@ once per machine, you do not need to (re)create the environment for each session
 If you would also like to install other tools or optional dependencies, it is
 possible to do so by adding additional libraries to this command.  For example::
 
-  conda create -n asim python=3.9 activitysim jupyterlab larch -c conda-forge --override-channels
+  mamba create -n asim python=3.9 activitysim jupyterlab larch -c conda-forge --override-channels
 
 This example installs a specific version of Python, version 3.9.  A similar
 approach can be used to install specific versions of other libraries as well,
 including ActivitySim, itself. For example::
 
-  conda create -n asim python=3.9 activitysim=1.0.2 -c conda-forge --override-channels
+  mamba create -n asim python=3.9 activitysim=1.0.2 -c conda-forge --override-channels
 
 Additional libraries can also be installed later.  You may want to consider these
 tools for certain development tasks::
 
   # packages for testing
-  conda install pytest pytest-cov coveralls pycodestyle pytest-regressions -c conda-forge --override-channels -n asim
+  mamba install pytest pytest-cov coveralls pycodestyle pytest-regressions -c conda-forge --override-channels -n asim
 
   # packages for building documentation
-  conda install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim
+  mamba install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim
 
   # packages for estimation integration
-  conda install larch -c conda-forge --override-channels -n asim
+  mamba install larch -c conda-forge --override-channels -n asim
 
   # packages for example notebooks
-  conda install jupyterlab matplotlib geopandas descartes -c conda-forge --override-channels -n asim
+  mamba install jupyterlab matplotlib geopandas descartes -c conda-forge --override-channels -n asim
 
 To create an environment containing all these optional dependencies at once, you
 can run the shortcut command
 
 ::
 
-  conda env create activitysim/ASIM -n asim
+  mamba env create activitysim/ASIM -n asim
 
 4. To use the **asim** environment, you need to activate it
 
@@ -82,7 +90,9 @@ can run the shortcut command
   conda activate asim
 
 The activation of the correct environment needs to be done every time you
-start a new session (e.g. opening a new conda Prompt window).
+start a new session (e.g. opening a new conda Prompt window).  Note that
+the *activate* and *deactivate* commands to start and stop using environments
+are called as `conda` even if you are otherwise using `mamba`.
 
 Alternative Installation Methods
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -93,7 +103,7 @@ required dependencies installed correctly.  If you can use conda for
 the dependencies, you can get most of the libraries you need from there::
 
   # required packages for running ActivitySim
-  conda install cytoolz numpy pandas psutil pyarrow numba pytables pyyaml openmatrix requests -c conda-forge
+  mamba install cytoolz numpy pandas psutil pyarrow numba pytables pyyaml openmatrix requests -c conda-forge
 
   # required for ActivitySim version 1.0.1 and earlier
   pip install zbox
@@ -102,14 +112,14 @@ And then simply install just activitysim with pip.
 
 ::
 
-  pip install activitysim
+  python -m pip install activitysim
 
 If you are using a firewall you may need to add ``--trusted-host pypi.python.org --proxy=myproxy.org:8080`` to this command.
 
 For development work, can also install ActivitySim directly from source. Clone
 the ActivitySim repository, and then from within that directory run::
 
-  pip install . -e
+  python -m pip install . -e
 
 The "-e" will install in editable mode, so any changes you make to the ActivitySim
 code will also be reflected in your installation.
@@ -118,7 +128,7 @@ Installing from source is easier if you have all the necessary dependencies alre
 installed in a development conda environment.  Developers can create an
 environment that has all the optional dependencies preinstalled by running::
 
-  conda env create activitysim/ASIM-DEV
+  mamba env create activitysim/ASIM-DEV
 
 If you prefer to use a different environment name than `ASIM-DEV`, just
 append `--name OTHERNAME` to the command. Then all that's left to do is install
@@ -179,11 +189,11 @@ ActivitySim includes a `Jupyter Notebook <https://jupyter.org>`__ recipe book wi
 * Type ``jupyter notebook`` to launch the web-based notebook manager
 * Navigate to the ``examples/prototype_mtc/notebooks`` folder and select a notebook to learn more:
 
-  * `Getting started <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/prototype_mtc/notebooks/getting_started.ipynb/>`__
-  * `Summarizing results <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/prototype_mtc/notebooks/summarizing_results.ipynb/>`__
-  * `Testing a change in auto ownership <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb/>`__
-  * `Adding TNCs <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/prototype_mtc/notebooks/adding_tncs.ipynb/>`__
-  * `Memory usage <https://github.com/ActivitySim/activitysim/blob/master/activitysim/examples/prototype_mtc/notebooks/memory_usage.ipynb/>`__
+  * `Getting started <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/getting_started.ipynb/>`__
+  * `Summarizing results <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/summarizing_results.ipynb/>`__
+  * `Testing a change in auto ownership <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb/>`__
+  * `Adding TNCs <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/adding_tncs.ipynb/>`__
+  * `Memory usage <https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/memory_usage.ipynb/>`__
 
 Hardware
 --------
diff --git a/docs/index.rst b/docs/index.rst
index 1a3be5035..421d776e7 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,43 +1,43 @@
-.. ActivitySim documentation master file, created by
-   sphinx-quickstart on Tue May 26 14:13:47 2016.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
+===========
 ActivitySim
 ===========
 
-The mission of the ActivitySim project is to create and maintain advanced, open-source, 
-activity-based travel behavior modeling software based on best software development 
+The mission of the ActivitySim project is to create and maintain advanced, open-source,
+activity-based travel behavior modeling software based on best software development
 practices for distribution at no charge to the public.
 
-The ActivitySim project is led by a consortium of Metropolitan Planning Organizations 
-(MPOs) and other transportation planning agencies, which provides technical direction 
-and resources to support project development. New member agencies are welcome to join 
-the consortium. All member agencies help make decisions about development priorities 
-and benefit from contributions of other agency partners.  Additional information about 
+The ActivitySim project is led by a consortium of Metropolitan Planning Organizations
+(MPOs) and other transportation planning agencies, which provides technical direction
+and resources to support project development. New member agencies are welcome to join
+the consortium. All member agencies help make decisions about development priorities
+and benefit from contributions of other agency partners.  Additional information about
 the development and management of the ActivitySim is on the `project site <http://www.activitysim.org>`__.
 
+.. grid:: 2
 
-Contents
---------
+    .. grid-item-card::
 
-.. toctree::
-   :maxdepth: 3
+        :fa:`book` |nbsp| |nbsp| :ref:`User's Guide <userguide>`
+
+        ^^^
+
+        Start here to learn about using ActivitySim, including how to install,
+        the software, and how to configure and run models.
 
-   gettingstarted
-   examples
-   models
-   core
-   cli
-   estimation
-   howitworks
-   development
-   benchmarking
+    .. grid-item-card::
 
+        :fa:`terminal` |nbsp| |nbsp| :ref:`Developer's Guide <devguide>`
+
+        ^^^
+
+        Start here to learn about developing ActivitySim, including creating
+        model components, or changing the codebase.
+
+.. toctree::
+   :hidden:
 
-Indices and tables
-==================
+   users-guide/index
+   dev-guide/index
 
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+.. |nbsp| unicode:: 0xA0
+   :trim:
diff --git a/docs/users-guide/configuration.rst b/docs/users-guide/configuration.rst
new file mode 100644
index 000000000..e726301e6
--- /dev/null
+++ b/docs/users-guide/configuration.rst
@@ -0,0 +1,24 @@
+
+.. index:: configuration
+.. _configuration:
+
+=============
+Configuration
+=============
+
+The ``configs`` folder for a model implementation contains settings, expressions
+files, and other files required for specifying model utilities and form.  Each
+component will have one or more files that control the operation of that
+component.
+
+.. currentmodule:: activitysim.core.configuration
+
+.. autosummary::
+    :toctree: _generated
+    :recursive:
+
+    Settings
+    InputTable
+    NetworkSettings
+    TAZ_Settings
+    ZarrDigitalEncoding
diff --git a/docs/users-guide/index.rst b/docs/users-guide/index.rst
new file mode 100644
index 000000000..266d01aa4
--- /dev/null
+++ b/docs/users-guide/index.rst
@@ -0,0 +1,31 @@
+.. ActivitySim documentation users guide
+
+.. _userguide:
+
+Users Guide
+===========
+
+The mission of the ActivitySim project is to create and maintain advanced, open-source,
+activity-based travel behavior modeling software based on best software development
+practices for distribution at no charge to the public.
+
+The ActivitySim project is led by a consortium of Metropolitan Planning Organizations
+(MPOs) and other transportation planning agencies, which provides technical direction
+and resources to support project development. New member agencies are welcome to join
+the consortium. All member agencies help make decisions about development priorities
+and benefit from contributions of other agency partners.  Additional information about
+the development and management of the ActivitySim is on the `project site <http://www.activitysim.org>`__.
+
+
+Contents
+--------
+
+.. toctree::
+   :maxdepth: 3
+
+   ../gettingstarted
+   ../examples
+   configuration
+   ../cli
+   ../estimation
+   ../howitworks
diff --git a/ez_setup.py b/ez_setup.py
deleted file mode 100644
index 6df0f8fde..000000000
--- a/ez_setup.py
+++ /dev/null
@@ -1,374 +0,0 @@
-#!/usr/bin/env python
-"""Bootstrap setuptools installation
-
-To use setuptools in your package's setup.py, include this
-file in the same directory and add this to the top of your setup.py::
-
-    from ez_setup import use_setuptools
-    use_setuptools()
-
-To require a specific version of setuptools, set a download
-mirror, or use an alternate download directory, simply supply
-the appropriate options to ``use_setuptools()``.
-
-This file can also be run as a script to install or upgrade setuptools.
-"""
-import contextlib
-import optparse
-import os
-import platform
-import shutil
-import subprocess
-import sys
-import tempfile
-import textwrap
-import zipfile
-from distutils import log
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib2 import urlopen
-
-try:
-    from site import USER_SITE
-except ImportError:
-    USER_SITE = None
-
-DEFAULT_VERSION = "5.1"
-DEFAULT_URL = "https://pypi.python.org/packages/source/s/setuptools/"
-
-
-def _python_cmd(*args):
-    """
-    Return True if the command succeeded.
-    """
-    args = (sys.executable,) + args
-    return subprocess.call(args) == 0
-
-
-def _install(archive_filename, install_args=()):
-    with archive_context(archive_filename):
-        # installing
-        log.warn("Installing Setuptools")
-        if not _python_cmd("setup.py", "install", *install_args):
-            log.warn("Something went wrong during the installation.")
-            log.warn("See the error message above.")
-            # exitcode will be 2
-            return 2
-
-
-def _build_egg(egg, archive_filename, to_dir):
-    with archive_context(archive_filename):
-        # building an egg
-        log.warn("Building a Setuptools egg in %s", to_dir)
-        _python_cmd("setup.py", "-q", "bdist_egg", "--dist-dir", to_dir)
-    # returning the result
-    log.warn(egg)
-    if not os.path.exists(egg):
-        raise IOError("Could not build the egg.")
-
-
-class ContextualZipFile(zipfile.ZipFile):
-    """
-    Supplement ZipFile class to support context manager for Python 2.6
-    """
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type, value, traceback):
-        self.close()
-
-    def __new__(cls, *args, **kwargs):
-        """
-        Construct a ZipFile or ContextualZipFile as appropriate
-        """
-        if hasattr(zipfile.ZipFile, "__exit__"):
-            return zipfile.ZipFile(*args, **kwargs)
-        return super(ContextualZipFile, cls).__new__(cls)
-
-
-@contextlib.contextmanager
-def archive_context(filename):
-    # extracting the archive
-    tmpdir = tempfile.mkdtemp()
-    log.warn("Extracting in %s", tmpdir)
-    old_wd = os.getcwd()
-    try:
-        os.chdir(tmpdir)
-        with ContextualZipFile(filename) as archive:
-            archive.extractall()
-
-        # going in the directory
-        subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0])
-        os.chdir(subdir)
-        log.warn("Now working in %s", subdir)
-        yield
-
-    finally:
-        os.chdir(old_wd)
-        shutil.rmtree(tmpdir)
-
-
-def _do_download(version, download_base, to_dir, download_delay):
-    egg = os.path.join(
-        to_dir,
-        "setuptools-%s-py%d.%d.egg"
-        % (version, sys.version_info[0], sys.version_info[1]),
-    )
-    if not os.path.exists(egg):
-        archive = download_setuptools(version, download_base, to_dir, download_delay)
-        _build_egg(egg, archive, to_dir)
-    sys.path.insert(0, egg)
-
-    # Remove previously-imported pkg_resources if present (see
-    # https://bitbucket.org/pypa/setuptools/pull-request/7/ for details).
-    if "pkg_resources" in sys.modules:
-        del sys.modules["pkg_resources"]
-
-    import setuptools
-
-    setuptools.bootstrap_install_from = egg
-
-
-def use_setuptools(
-    version=DEFAULT_VERSION,
-    download_base=DEFAULT_URL,
-    to_dir=os.curdir,
-    download_delay=15,
-):
-    to_dir = os.path.abspath(to_dir)
-    rep_modules = "pkg_resources", "setuptools"
-    imported = set(sys.modules).intersection(rep_modules)
-    try:
-        import pkg_resources
-    except ImportError:
-        return _do_download(version, download_base, to_dir, download_delay)
-    try:
-        pkg_resources.require("setuptools>=" + version)
-        return
-    except pkg_resources.DistributionNotFound:
-        return _do_download(version, download_base, to_dir, download_delay)
-    except pkg_resources.VersionConflict as VC_err:
-        if imported:
-            msg = textwrap.dedent(
-                """
-                The required version of setuptools (>={version}) is not available,
-                and can't be installed while this script is running. Please
-                install a more recent version first, using
-                'easy_install -U setuptools'.
-
-                (Currently using {VC_err.args[0]!r})
-                """
-            ).format(VC_err=VC_err, version=version)
-            sys.stderr.write(msg)
-            sys.exit(2)
-
-        # otherwise, reload ok
-        del pkg_resources, sys.modules["pkg_resources"]
-        return _do_download(version, download_base, to_dir, download_delay)
-
-
-def _clean_check(cmd, target):
-    """
-    Run the command to download target. If the command fails, clean up before
-    re-raising the error.
-    """
-    try:
-        subprocess.check_call(cmd)
-    except subprocess.CalledProcessError:
-        if os.access(target, os.F_OK):
-            os.unlink(target)
-        raise
-
-
-def download_file_powershell(url, target):
-    """
-    Download the file at url to target using Powershell (which will validate
-    trust). Raise an exception if the command cannot complete.
-    """
-    target = os.path.abspath(target)
-    ps_cmd = (
-        "[System.Net.WebRequest]::DefaultWebProxy.Credentials = "
-        "[System.Net.CredentialCache]::DefaultCredentials; "
-        "(new-object System.Net.WebClient).DownloadFile(%(url)r, %(target)r)" % vars()
-    )
-    cmd = [
-        "powershell",
-        "-Command",
-        ps_cmd,
-    ]
-    _clean_check(cmd, target)
-
-
-def has_powershell():
-    if platform.system() != "Windows":
-        return False
-    cmd = ["powershell", "-Command", "echo test"]
-    with open(os.path.devnull, "wb") as devnull:
-        try:
-            subprocess.check_call(cmd, stdout=devnull, stderr=devnull)
-        except Exception:
-            return False
-    return True
-
-
-download_file_powershell.viable = has_powershell
-
-
-def download_file_curl(url, target):
-    cmd = ["curl", url, "--silent", "--output", target]
-    _clean_check(cmd, target)
-
-
-def has_curl():
-    cmd = ["curl", "--version"]
-    with open(os.path.devnull, "wb") as devnull:
-        try:
-            subprocess.check_call(cmd, stdout=devnull, stderr=devnull)
-        except Exception:
-            return False
-    return True
-
-
-download_file_curl.viable = has_curl
-
-
-def download_file_wget(url, target):
-    cmd = ["wget", url, "--quiet", "--output-document", target]
-    _clean_check(cmd, target)
-
-
-def has_wget():
-    cmd = ["wget", "--version"]
-    with open(os.path.devnull, "wb") as devnull:
-        try:
-            subprocess.check_call(cmd, stdout=devnull, stderr=devnull)
-        except Exception:
-            return False
-    return True
-
-
-download_file_wget.viable = has_wget
-
-
-def download_file_insecure(url, target):
-    """
-    Use Python to download the file, even though it cannot authenticate the
-    connection.
-    """
-    src = urlopen(url)
-    try:
-        # Read all the data in one block.
-        data = src.read()
-    finally:
-        src.close()
-
-    # Write all the data in one block to avoid creating a partial file.
-    with open(target, "wb") as dst:
-        dst.write(data)
-
-
-download_file_insecure.viable = lambda: True
-
-
-def get_best_downloader():
-    downloaders = (
-        download_file_powershell,
-        download_file_curl,
-        download_file_wget,
-        download_file_insecure,
-    )
-    viable_downloaders = (dl for dl in downloaders if dl.viable())
-    return next(viable_downloaders, None)
-
-
-def download_setuptools(
-    version=DEFAULT_VERSION,
-    download_base=DEFAULT_URL,
-    to_dir=os.curdir,
-    delay=15,
-    downloader_factory=get_best_downloader,
-):
-    """
-    Download setuptools from a specified location and return its filename
-
-    `version` should be a valid setuptools version number that is available
-    as an egg for download under the `download_base` URL (which should end
-    with a '/'). `to_dir` is the directory where the egg will be downloaded.
-    `delay` is the number of seconds to pause before an actual download
-    attempt.
-
-    ``downloader_factory`` should be a function taking no arguments and
-    returning a function for downloading a URL to a target.
-    """
-    # making sure we use the absolute path
-    to_dir = os.path.abspath(to_dir)
-    zip_name = "setuptools-%s.zip" % version
-    url = download_base + zip_name
-    saveto = os.path.join(to_dir, zip_name)
-    if not os.path.exists(saveto):  # Avoid repeated downloads
-        log.warn("Downloading %s", url)
-        downloader = downloader_factory()
-        downloader(url, saveto)
-    return os.path.realpath(saveto)
-
-
-def _build_install_args(options):
-    """
-    Build the arguments to 'python setup.py install' on the setuptools package
-    """
-    return ["--user"] if options.user_install else []
-
-
-def _parse_args():
-    """
-    Parse the command line for options
-    """
-    parser = optparse.OptionParser()
-    parser.add_option(
-        "--user",
-        dest="user_install",
-        action="store_true",
-        default=False,
-        help="install in user site package (requires Python 2.6 or later)",
-    )
-    parser.add_option(
-        "--download-base",
-        dest="download_base",
-        metavar="URL",
-        default=DEFAULT_URL,
-        help="alternative URL from where to download the setuptools package",
-    )
-    parser.add_option(
-        "--insecure",
-        dest="downloader_factory",
-        action="store_const",
-        const=lambda: download_file_insecure,
-        default=get_best_downloader,
-        help="Use internal, non-validating downloader",
-    )
-    parser.add_option(
-        "--version",
-        help="Specify which version to download",
-        default=DEFAULT_VERSION,
-    )
-    options, args = parser.parse_args()
-    # positional arguments are ignored
-    return options
-
-
-def main():
-    """Install or upgrade setuptools and EasyInstall"""
-    options = _parse_args()
-    archive = download_setuptools(
-        version=options.version,
-        download_base=options.download_base,
-        downloader_factory=options.downloader_factory,
-    )
-    return _install(archive, _build_install_args(options))
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..11e9d1c35
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,34 @@
+[build-system]
+requires = [
+    "setuptools>=42,<64",
+    "wheel",
+    "setuptools_scm[toml]>=7.0",
+]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools_scm]
+fallback_version = "999"
+write_to = "activitysim/_generated_version.py"
+version_scheme = "python-simplified-semver"
+
+[tool.isort]
+profile = "black"
+skip_gitignore = true
+float_to_top = true
+default_section = "THIRDPARTY"
+known_first_party = "activitysim"
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = "-v --nbmake --disable-warnings --ignore=activitysim/estimation/test"
+testpaths = [
+    "activitysim/abm/test",
+    "activitysim/cli/test",
+    "activitysim/core/test",
+    "activitysim/abm/models/util/test",
+]
+filterwarnings = [
+    "ignore::tables.NaturalNameWarning",
+]
+log_cli = true
+tb = "native"
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index a8b6e7123..000000000
--- a/pytest.ini
+++ /dev/null
@@ -1,6 +0,0 @@
-[pytest]
-filterwarnings =
-    ignore::tables.NaturalNameWarning
-addopts = --ignore=activitysim/estimation/test
-log_cli = true
-tb = native
diff --git a/setup.cfg b/setup.cfg
index 7fc5ef362..bf02916f3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,3 +1,46 @@
-[pycodestyle]
-max-line-length = 120
-#exclude = ./other_resources/,./sandbox/
+[metadata]
+name = activitysim
+description = Activity-Based Travel Modeling
+long_description = file: README.md
+license = BSD 3-Clause License
+classifiers =
+    Programming Language :: Python :: 3
+
+[flake8]
+exclude =
+    .git,
+    __pycache__,
+    docs/_build,
+    docs/conf.py,
+    **/__sharrowcache__,
+    other_resources/,
+    sandbox/
+max-line-length = 160
+extend-ignore = E203, E731, W503, F841, F541, F811
+
+[options]
+packages = find:
+zip_safe = False
+include_package_data = True
+python_requires = >=3.9
+install_requires =
+    cytoolz >= 0.8.1
+    numba >= 0.55.2
+    numpy >= 1.16.1
+    openmatrix >= 0.3.4.1
+    orca >= 1.6
+    pandas >= 1.1.0
+    psutil >= 4.1
+    pyarrow >= 2.0
+    pypyr >= 5.3
+    pyyaml >= 5.1
+    requests >= 2.7
+    sharrow >= 2.2.4
+    simwrapper > 1.7
+    tables >= 3.5.1
+    xarray >= 0.21
+
+[options.entry_points]
+console_scripts =
+    activitysim = activitysim.cli.main:main
+    activitysim_checksums = activitysim.cli.create:display_sha256_checksums
diff --git a/setup.py b/setup.py
index 6b93a3b81..def5be7a6 100644
--- a/setup.py
+++ b/setup.py
@@ -1,44 +1,4 @@
-from ez_setup import use_setuptools
+#!/usr/bin/env python
+from setuptools import setup
 
-use_setuptools()  # nopep8
-
-import os
-import re
-
-from setuptools import find_packages, setup
-
-with open(os.path.join("activitysim", "__init__.py")) as f:
-    info = re.search(r"__.*", f.read(), re.S)
-    exec(info[0])
-
-setup(
-    name="activitysim",
-    version=__version__,
-    description=__doc__,
-    author="contributing authors",
-    author_email="ben.stabler@rsginc.com",
-    license="BSD-3",
-    url="https://github.com/activitysim/activitysim",
-    classifiers=[
-        "Development Status :: 5 - Production/Stable",
-        "Programming Language :: Python :: 3.8",
-        "License :: OSI Approved :: BSD License",
-    ],
-    packages=find_packages(exclude=["*.tests"]),
-    include_package_data=True,
-    entry_points={"console_scripts": ["activitysim=activitysim.cli.main:main"]},
-    install_requires=[
-        "pyarrow >= 2.0",
-        "numpy >= 1.16.1,<=1.21",
-        "openmatrix >= 0.3.4.1",
-        "pandas >= 1.1.0",
-        "pyyaml >= 5.1",
-        "tables >= 3.5.1",
-        "cytoolz >= 0.8.1",
-        "psutil >= 4.1",
-        "requests >= 2.7",
-        "numba >= 0.51.2",
-        "orca >= 1.6",
-        "simwrapper > 1.7",
-    ],
-)
+setup(use_scm_version={"fallback_version": "1999"})