AI Based Traffic Control System

This project explores the development of an AI-based traffic control system aimed at enhancing urban traffic management. By utilizing machine learning algorithms and real-time data from various sources, including traffic cameras, sensors, and GPS devices, the system analyzes traffic patterns and predicts congestion. We are using several AI and ML models, including Reinforcement Learning, Deep Learning, Neural Network and also Computer Vision. The integration of vehicle to infrastructure communication further enhances responsiveness, allowing for timely updates to drivers. Simulation results indicate significant reductions in travel time and improved traffic efficiency, demonstrating the potential of AI technologies to optimize urban transportation systems and support sustainable development. Key Words: Reinforcement Learning, Neural Network, Computer Vision, YOLO.