Real-Time Traffic Monitoring and Signal Timing Optimization

Traffic congestion in urban areas is a growing challenge, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal systems operate on fixed timing schedules, failing to adapt to real-time traffic conditions. This research proposes an AI-based smart traffic management system that dynamically adjusts traffic signal timings using CCTV cameras and deep learning models. The system continuously monitors stopped vehicles at intersections using YOLO-based object detection and assigns adaptive green light durations based on vehicle density. The proposed solution is tested using a custom-trained YOLO model, YOLOv8, and YOLOv5, comparing performance metrics such as mAP, precision, and recall. Experimental results demonstrate that the proposed model achieves improved traffic flow efficiency by reducing waiting times and prioritizing lanes with higher congestion. The system is scalable for smart cities and can integrate IoT-based sensors and real-time traffic analytics for further optimization