A comprehensive, intelligent traffic control system that uses computer vision and machine learning to optimize traffic flow, reduce congestion, and improve urban transportation.

Watch the demo video

The Traffic Control System is a modular solution designed to manage traffic lights efficiently by analyzing real-time traffic conditions. It employs computer vision for vehicle detection, machine learning for traffic prediction, and inter-traffic light communication for coordinated traffic management.

• Vehicle Detection: Uses computer vision to detect and count vehicles in real-time
• Traffic Density Analysis: Calculates traffic density and congestion levels
• Dynamic Traffic Light Control: Adjusts signal timing based on current traffic conditions
• Green Wave Coordination: Creates coordinated "green waves" for improved traffic flow
• Emergency Vehicle Priority: Automatically creates green corridors for emergency vehicles
• Public Transport Integration: Gives buses and trams priority at intersections
• Pedestrian Safety: Responsive to pedestrian crossing requests
• Weather Integration: Adjusts traffic patterns based on weather conditions
• Machine Learning Prediction: Predicts traffic patterns using historical data
• Interactive Dashboard: Web-based visualization of traffic data and statistics
• Simulation Mode: Test the system without physical hardware

┌────────────────────────────────────────────────────────────────────┐
│                          System Integrator                          │
└───────────────────────────┬────────────────────────────────────────┘
                                │
                                ▼
┌────────────────────────────────────────────────────────────────────┐
│                           Event Manager                             │
└───────┬─────────┬──────────┬─────────┬───────────┬─────────┬───────┘
        │         │          │         │           │         │
        ▼         ▼          ▼         ▼           ▼         ▼
┌──────────┐ ┌─────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│Green Wave│ │Emergency│ │ Public │ │Pedestr.│ │Weather │ │  V2I   │
│Controller│ │ Control │ │Transp. │ │Control │ │Control │ │Control │
└──────────┘ └─────────┘ └────────┘ └────────┘ └────────┘ └────────┘
        │         │          │         │           │         │
        └─────────┴──────────┴─────────┴───────────┴─────────┘
                                │
                                ▼
┌──────────┐ ┌─────────┐ ┌────────────┐ ┌────────────┐ ┌───────────┐
│ ML Pred. │ │Analytics│ │ Dashboard  │ │Event Logger│ │Simulation │
│Controller│ │ Control │ │ Controller │ │ Controller │ │Controller │
└──────────┘ └─────────┘ └────────────┘ └────────────┘ └───────────┘

• Python 3.8 or higher
• Git (optional, for cloning the repository)

git clone https://github.com/ErfanZohrabi/traffic-control-system.git
cd traffic-control-system

# Linux/macOS
python -m venv venv
source venv/bin/activate

# Windows
python -m venv venv
.\venv\Scripts\activate

pip install -r requirements.txt
pip install -e .

We've created a script that automatically fixes common issues:

python fix_traffic_system.py

We've created a simplified demo that works without TensorFlow or OpenCV:

python simple_demo.py

This will:

• Start the traffic control system
• Simulate traffic patterns for 30 seconds
• Display events in the console

The dashboard provides a visual interface to monitor the traffic system:

python test_dashboard.py

This will:

• Start the traffic control system
• Launch a web dashboard at http://localhost:8080
• Simulate traffic patterns for demonstration

For a comprehensive demo of all traffic control features:

python demo.py

For a quick test of the core functionality:

python test_minimal.py

To test the RESTful API for external system integration:

python test_api.py

For Windows users, we've created helper scripts:

# Using PowerShell
.\run_traffic.ps1

# Or using batch files
.\run_dashboard.bat
.\run_demo.bat

See WINDOWS_SETUP.md for detailed Windows setup instructions.

The system can be configured using JSON or YAML files:

# Export default configuration
python main.py --export-config config.yaml

# Run with custom configuration
python main.py --config config.yaml

• ModuleNotFoundError: Make sure you've installed the package in development mode with pip install -e .
• Address already in use: Another process is using port 8080, modify the dashboard port or close the other application
• TensorFlow Import Error: Make sure TensorFlow is installed with pip install tensorflow
• System exits immediately: Check logs in the logs/ directory for error messages

For Windows-specific issues, see WINDOWS_SETUP.md.

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

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

For any inquiries, please contact erfanzohrabi.ez@gmail.com.

Erfan Zohrabi

• GitHub: ErfanZohrabi
• LinkedIn: in/erfan-zohrabi