IRTX MATSim to COPERT connector

Introduction

This model is a connector between the upstream MATSim model and the downstream COPERT model for emissions analysis.

The purpose of this connector is to process a given MATSim simulation output, and aggregate the obtained vehicle traces to COPERT-compatible vehicle type information and prepare the input file for COPERT.

Requirements

Software requirements

The converter is packaged in a Python script. All dependencies to run the model have been collected in a conda environment, which is available in the LEAD repository as environment.yml.

Input / Output

Input

To run the model, a finished MATSim simulation must be available. Specifically, the MATSim output directory should contain a file called trips.csv describing all the agent movements recorded from the simulation.

Output

The output of the model is an Excel sheet that can be transmitted for emissions calculation to the COPERT model (copert.xlsx).

Configuration

The conversion process needs to be configured using a configuration file (configuration.json) in JSON format. It is structured as follows:

{
  "peaks": [
    { "start": 21600, "end": 32400 },
    { "start": 61200, "end": 68400 }
  ],
  "temperature": {
    "min": [
      1.1, 1.4, 4.2, 7.2, 11.2, 15.0, 17.0, 16.6, 12.8, 9.6, 4.9, 2.0
    ],
    "max": [
      7.1, 9.0, 13.8, 17.4, 21.5, 25.6, 28.2, 28.0, 23.1, 17.7, 11.4, 7.7
    ]
  },
  "humidity": [
    84.0, 80.0, 74.0, 71.0, 72.0, 70.0, 65.0, 70.0, 76.0, 82.0, 84.0, 86.0
  ],
  "mode_mapping": {}
}

First, it is defined what is understood as a peak period in the simulation given as a start time and an end time in terms of seconds from midnight. After, maximum and minimum temperatures for the twelve months of the year (starting January) are given, as well as the mean humidity in percentage points. Finally, mappings from the simulated MATSim modes to the COPERT emission classes are given as follows:

{
  "mode_mapping": {
    "freight:van": {
      "category": "Light Commercial Vehicles",
      "fuel": "Diesel",
      "segment": "N1-III",
      "euro_standard": "Euro 5",
      "fuel_tank_size": 100,
      "sampling_rate": 0.05
    }
  }
}

Here, an example for the freight:van mode is given. It is mapped to a light vehicle emissions class for Diesel with a specified fuel tank size. Note that JSprit vehicle types (when used as input to the MATSim simulation) are translated into freight:{vehicle_type} while the other relevant contribution to emissions is the standard car mode.

Attention, only modes that are defined in the configuration are considered in the analysis.

Additionally, it is possible to define the sampling rate per mode which should correspond to the sampling rate of the MATSim simulation. The relevant KPIs like distance and vehicle count is scaled accordingly by the inverse of the sampling rate.

Running the model

The model is defined in a Jupyter notebook called Convert.ipynb. To run it, call it through the papermill command line utility (which is installed as a dependency in the conda environment) as described below:

papermill "Convert.ipynb" /dev/null \
  -pconfiguration_path /path/to/configuration.json \
  -ptrips_path /path/to/trips.csv \
  -poutput_path /path/to/output/copert.xlsx \
  -pyear 2022

The mandatory parameters are detailed in the following table:

Parameter	Values	Description
configuration_path	String	Path to the conversion configuration file (see above)
trips_path	String	Path to the MATSim output trips file
output_path	String	Path where the COPERT data will be saved

The following optional parameter is available:

Parameter	Values	Description
year	Integer (default 2022)	Year for which the information is written in the COPERT file

Standard scenarios

For the Lyon living lab, a configuration file has already been prepared in data/configuration_lyon.json. It can be used to prepare COPERT data for the three main scenarios (Baseline 2022, UCC 2022, UCC 2030) as follows:

papermill "Convert.ipynb" /dev/null \
  -pconfiguration_path /irtx-matsim-copert-connector/data/configuration_lyon.json \
  -ptrips_path /irtx-matsim/output/output_{scenario}/trips.csv \
  -poutput_path /irtx-matsim-copert-connector/output/copert_{scenario}.xlsx \
  -pyear {year}

Here, {scenario} = baseline_2022 | ucc_2022 | ucc_2030 and year = 2022 | 2030 according to the respective scenario.