🚦 AI-Powered Traffic Management System

An intelligent traffic management system that uses reinforcement learning to optimize traffic light timing based on real-time traffic conditions. The system integrates with the TomTom API to fetch live traffic data and simulates vehicle movement through a city's road network.

📌 Latest Updates (July 2024)

• Enhanced Simulation: Improved vehicle movement and traffic signal control
• Robust Error Handling: Added comprehensive error handling and logging
• Compatibility Fixes: Resolved OSMnx version compatibility issues
• Performance Optimizations: Improved simulation performance and stability
• Detailed Logging: Added timestamped logging for better debugging

🌟 Features

• Real-time Traffic Monitoring: Displays live traffic data through a city's road network
• AI-Powered Traffic Signals: Uses Q-learning to optimize traffic light timing
• Interactive Dashboard: Streamlit-based web interface for monitoring and control
• Detailed Simulation: Realistic vehicle movement and traffic flow modeling
• Comprehensive Logging: Timestamped logs for debugging and analysis
• Error Resilience: Robust error handling and recovery mechanisms
• Modular Architecture: Easy to extend and customize

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Internet connection (for fetching real-time traffic data)
• TomTom API Key (free tier available)
• Git (for version control)

Installation

1. Clone the repository:

   git clone https://github.com/06navdeep06/realtime-traffic-sensor.git
   cd realtime-traffic-sensor

2. Create and activate a virtual environment (recommended):

   # On Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # On macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

   For development, also install:

   pip install -r requirements-dev.txt

Development Setup

1. Install pre-commit hooks for code quality:

   pre-commit install

2. Run tests:

   python -m pytest tests/

3. Run the debug script to test the simulation:

   python debug_simulation.py

Environment Setup

1. Copy the example environment file and update with your API key:

   copy .env.example .env

2. Edit the .env file and add your TomTom API key:

   TOMTOM_API_KEY=your_api_key_here
   DEBUG=False
   

   Note: Make sure to add .env to your .gitignore to keep your API key secure.

🏃‍♂️ Running the Application

Web Dashboard

1. Start the Streamlit dashboard:

   streamlit run dashboard.py

2. Open your browser and navigate to the URL shown in the terminal (usually http://localhost:8501)

3. In the sidebar:

   • Specify the city to monitor (e.g., "Patan, Nepal")
   • Adjust the number of vehicles to display (10-500)
   • Click "Start Real-time Traffic Monitoring"

Running Simulations

1. For a quick test of the simulation:

   python simple_simulation_test.py

2. For detailed debugging:

   python debug_simulation.py

   This will generate a timestamped log file with detailed simulation information.

🛠 Project Structure

• dashboard.py: Streamlit web interface for the traffic simulation
• simulation.py: Core simulation logic and vehicle management
• traffic_signal.py: Traffic signal controller with Q-learning agent
• rl_agent.py: Implementation of the Q-learning algorithm
• road_network.py: Handles road network data and real-time traffic updates
• visualization.py: Visualization utilities for the road network and traffic
• debug_simulation.py: Debugging tool for the simulation engine
• simple_simulation_test.py: Basic test script for the simulation
• test_*.py: Unit tests for various components

🤖 How It Works

The system provides real-time traffic monitoring and analysis:

1. Road Network Modeling: Uses OSMnx to fetch and model real-world road networks
2. Real-time Data Processing: Continuously fetches and processes live traffic data
3. Traffic Analysis: Uses machine learning to analyze traffic patterns and congestion
4. Interactive Visualization: Provides real-time visualization of traffic flow and conditions

📊 Performance Metrics

The dashboard displays several key metrics:

• Simulation step count
• Number of active vehicles
• Average trip time (in simulation steps)
• Current state of each traffic signal

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TomTom for their excellent mapping and traffic APIs
• OSMnx for powerful street network analysis
• Streamlit for the intuitive web interface
• The open-source community for invaluable tools and libraries

📬 Contact

For questions or feedback, please contact [email protected]