Intelligent Traffic Management System: Leveraging AI for Efficient Intersection Control

Traffic jams at intersections result in delays, irate drivers, and pollution. The growing number of cars on city streets makes it urgent for traffic management to improve. Traditional methods for handling traffic cannot keep up with traffic changes, resulting in congestion, wide delays and potential hazards. To solve these problems, this study suggests a thorough traffic management system that makes use of computer vision, reinforcement learning and adaptive control strategies. For real-time detection and counting of vehicles at intersections, convolutional neural networks (CNN) and temporal convolutional networks (TCN) are put to use. The number of vehicles is given as input which allows a Q-learning algorithm to manage traffic signal timing. The signals allocate shorter periods to vehicles when there are fewer automobiles and become more generous with time as the number increases. The system adapts itself to maintain its efficiency over time as it tracks changes in traffic. Here, the goal is to use technology to improve urban transportation in a way that is safer, more efficient and supports the environment. Both passengers and the environment benefit from the system’s ability to enhance traffic flow and reduce congestion.