Traffic Light Management Based on Vehicle Density using Deep Learning Approaches

Traffic congestion wastes time, creates pollution, uses fuel, and stresses people. In crowded areas, conventional traffic control might not be effective. This paper offers a computer vision and artificial intelligence based adaptive traffic monitoring and control system. Using CCTV cameras and the YOLOv9 object detection approach, our system maximizes traffic signal timing depending on real-time vehicle density. A microcontroller-based integrated lane logic component evaluates all conceivable lane configurations to find the optimal traffic flow pattern for emergency vehicles and pedestrian safety. Without increasing infrastructure, this system reduces emissions, traffic, fuel consumption, and emergency response times. Traffic is controlled by smart vehicle monitoring, dynamic lane density evaluation, and priority-driven lane optimization.