Intelligent Traffic Signal Management using Real-Time Data and Image Processing

Traffic congestion is a major challenge in urban areas, resulting in extended travel times, excess fuel consumption, and environmental pollution. Traditional traffic systems based on fixed timers lack adaptability. Our proposed system uses real-time image processing via YOLO algorithm on Raspberry Pi to dynamically control traffic lights. With 95% vehicle detection and 93% traffic density estimation accuracy, the system reduces congestion by up to 30% during peak hours, offering a cost- effective, scalable, and real-time solution.