Smart City Traffic Management

Welcome to the Smart City Traffic Management system. This project aims to leverage AI and machine learning techniques to optimize and manage traffic flow in a smart city environment.

Table of Contents

• Introduction
• Features
• Project Structure
• Setup and Installation
• Usage
• Configuration
• Development
• Testing
• Deployment
• Contributing
• License

Introduction

This project provides a comprehensive system to manage traffic in a smart city using various machine learning models and APIs. It includes functionalities such as data loading, preprocessing, model training, prediction, monitoring, and visualization through a web application.

Features

• Data Management: Load and preprocess traffic data.
• Machine Learning Models: Train and use supervised, unsupervised, and reinforcement learning models.
• API: Expose endpoints for predictions and data interactions.
• Monitoring: Track model performance and errors.
• Web Application: Visualize traffic data and model predictions on a dashboard.
• Security: Audit and scan for vulnerabilities.
• Continuous Integration/Continuous Deployment: Automated testing and deployment using GitHub Actions.

Project Structure

smart-city-traffic-management/
├── .env
├── .gitignore
├── .pre-commit-config.yaml
├── .github/
│   └── workflows/
│       ├── ci.yml
│       └── cd.yml
├── CHANGELOG.md
├── CONTRIBUTING.md
├── INSTALLATION.md
├── README.md
├── USAGE.md
├── configs/
│   ├── data_config.yaml
│   ├── deployment_config.yaml
│   ├── model_config.yaml
│   └── training_config.yaml
├── deployment/
│   ├── api/
│   │   ├── api_endpoints.go
│   │   ├── api_endpoints.py
│   │   └── requirements.txt
│   └── docker/
│       ├── Dockerfile
│       └── docker-compose.yml
├── docs/
│   ├── README.md
│   ├── CONTRIBUTING.md
│   ├── INSTALLATION.md
│   ├── USAGE.md
│   └── CHANGELOG.md
├── experiments/
│   ├── experiment_1.yaml
│   └── experiment_2.yaml
├── monitoring/
│   ├── logging_config.py
│   ├── error_tracking.py
│   ├── model_monitoring.py
│   └── performance_monitoring.py
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── deep_learning.ipynb
│   ├── nlp_analysis.ipynb
│   ├── reinforcement_learning.ipynb
│   ├── supervised_learning.ipynb
│   ├── unsupervised_learning.ipynb
│   └── experiments/
│       └── experiment_analysis.ipynb
├── reports/
│   └── final_report.pdf
├── security/
│   ├── security_audit.py
│   └── vulnerability_scan.py
├── src/
│   ├── data/
│   │   ├── data_loader.py
│   │   ├── data_preprocessing.py
│   │   ├── feature_engineering.py
│   │   └── data_schema.json
│   ├── models/
│   │   ├── supervised/
│   │   │   └── regression_model.py
│   │   ├── unsupervised/
│   │   │   └── clustering_model.py
│   │   ├── reinforcement/
│   │   │   └── rl_agent.py
│   │   └── nlp/
│   │       └── nlp_model.py
│   ├── main.py
│   └── utils/
│       ├── helpers.py
│       └── validators.py
├── setup.py
├── requirements_dev.txt
└── tests/
    ├── test_api_endpoints.py
    ├── test_data_loading.py
    ├── test_integration.py
    ├── test_model_evaluation.R
    ├── test_model_evaluation.py
    ├── test_model_training.py
    └── test_end_to_end.py
├── webapp/
│   ├── static/
│   │   ├── app.js
│   │   ├── styles.css
│   │   └── charts.js
│   ├── templates/
│   │   ├── index.html
│   │   └── dashboard.html
│   └── app.py

Setup and Installation

Prerequisites

• Python 3.8 or higher
• Docker
• Git

Installation

1. Clone the repository:

   git clone https://github.com/your-username/smart-city-traffic-management.git
   cd smart-city-traffic-management

2. Create and activate a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Setup environment variables:

   Create a .env file in the root directory and add your configurations (example provided).

5. Initialize the database:

   Ensure your database is set up as per configs/data_config.yaml.

6. Run migrations (if applicable):

   # Example for Flask-Migrate
   flask db upgrade

Usage

Running the API

1. Start the Flask API server:

   python deployment/api/api_endpoints.py

2. Access the API at http://localhost:8000.

Running the Web Application

1. Start the Flask web application:

   python webapp/app.py

2. Access the web application at http://localhost:5000.

Docker

1. Build and run the Docker container:

   docker-compose up --build

2. Access the services as configured.

Configuration

Configuration files are located in the configs/ directory.

• Data Configuration: configs/data_config.yaml
• Deployment Configuration: configs/deployment_config.yaml
• Model Configuration: configs/model_config.yaml
• Training Configuration: configs/training_config.yaml

Development

Setting Up Pre-commit Hooks

1. Install pre-commit:

   pip install pre-commit

2. Install the hooks:

   pre-commit install

Running Jupyter Notebooks

Jupyter notebooks for experimentation and analysis are located in the notebooks/ directory. To run them, start Jupyter:

```bash
jupyter notebook

Testing

Running Unit Tests

```bash
pytest

Running Security Audits

```bash
python security/security_audit.py
python security/vulnerability_scan.py

Deployment

Continuous Integration and Deployment

This project uses GitHub Actions for CI/CD. Workflows are defined in the .github/workflows/ directory.

• CI Workflow: .github/workflows/ci.yml
• CD Workflow: .github/workflows/cd.yml

Contributing

We welcome contributions! Please see the CONTRIBUTING.md for more details on how to get started.