Real-time traffic optimization using Deep Learning and Reinforcement Learning.
This project aims to develop an AI-powered adaptive traffic signal system that dynamically adjusts signal timings based on real-time traffic density analysis using CCTV footage, Deep Learning (YOLOv8), and Reinforcement Learning.
- Real-time vehicle detection and counting from multiple CCTV video feeds.
- Adaptive signal timing optimization based on traffic density using Reinforcement Learning.
- Proactive congestion forecasting using LSTM deep learning models.
- Multi-modal data integration (weather, time of day, historical data).
- Night-time traffic monitoring using advanced low-light models.
Clone the repository and install the required libraries:
git clone https://github.com/your-username/traffic-signal-ai
cd traffic-signal-ai
pip install -r requirements.txt
Run the real-time detection and traffic control engine:
python main.py --source your_video_feed_path
We welcome contributions! Please fork the repository, make your changes, and create a pull request. For major changes, please open an issue first to discuss what you would like to change.