Automated Traffic Lights This project implements an automated traffic light system designed to optimize vehicle flow at a roundabout. By dynamically adjusting signal durations based on traffic volume in each lane, it prioritizes heavily congested areas to reduce waiting times and enhance overall efficiency. The model serves as a simplified approach for simulating traffic management scenarios.

Features Dynamic signal adjustment based on traffic volume. Prioritization of congested lanes to improve throughput. Simplified implementation for easy testing and adaptation. Installation Ensure you have Python 3.x installed on your system. Clone the repository: bash Copy code git clone https://github.com/kirogi97/automated-traffic-lights.git cd automated-traffic-lights Install required dependencies: bash Copy code pip install -r requirements.txt Usage Run the traffic light optimization model: bash Copy code python main.py Customize traffic parameters in the code to simulate different scenarios. Future Improvements Integration with real-world data: Incorporate live traffic data from sensors or cameras. Multi-lane support: Extend the model to handle multiple lanes and intersections. Advanced algorithms: Use machine learning models for more adaptive and predictive traffic management. Visualization tools: Add graphical interfaces to display simulations and outputs. Contributing Contributions are welcome! Feel free to fork the repository, make your changes, and submit a pull request. For major changes, please open an issue to discuss your ideas.

Acknowledgments This project was inspired by the need for smarter traffic management solutions. Special thanks to the open-source community for providing tools and libraries that made this implementation possible.

License This project is open-source and available under the MIT License.