An AI-Centric Approach to Real-Time Traffic Signal Control and Congestion Mitigation

Rapid urbanization and constant growth in vehicular traffic have resulted in serious congestion, rise in travel time, fuel consumption, and air pollution in urban environments. The traditional straffic management system functions based on fixed-time signal control and manual monitoring, which makes it inefficient in dynamic traffic situations. This paper presents an AI-based traffic management system that utilizes real-time visual data and machine learning algorithms to accomplish adaptive traffic signal control and smart route management. The proposed system uses computer vision models to identify and categorize vehicles from live camera inputs, calculate traffic density at road intersections, and adjust signal timing dynamically according to congestion levels. Moreover, a route optimization module using shortest-path algorithms is incorporated to redirect vehicles along less congested routes, thus optimizing overall traffic flow. Simulation experiments conducted on urban traffic conditions show a substantial decrease in average waiting time and enhanced traffic throughput compared to traditional fixed-time traffic signal control systems. The proposed system offers a scalable and efficient solution for real-time traffic management in smart city settings. Keywords— Artificial Intelligence, Traffic Management System, Computer Vision, Adaptive Traffic Signal Control, Traffic Density Estimation, Route Optimization, Smart Cities