Real-Time Traffic Monitoring and Ambulance Prioritization Using YOLOv9 and Deep Learning

Enhancing urban traffic management, as an important exercise, would entail putting viable solutions in place through which high-density traffic, as encountered, can cause congestion, delays, and emergencies. In this paper, an automated traffic monitoring system designed to permit real-time applications utilizes computer vision and deep learning for the task of vehicle detection and classification while giving priority to emergency vehicles. Continuous transmission of traffic data from the webcam is done utilizing the YOLOv9 model, which indeed is the proposal of choice because of speed and accuracy in dynamic environments like roads and streets, hence enabling vehicle detection and counts to be achieved with performance guarantees across different lanes. An embedded deep reinforcement learning system with spatiotemporal convolutional neural networks forms the background work of this program, wherein various lanes connect the dynamic timing of lights through an efficient intelligent lane allocation mechanism with congestion reduction aimed at maximizing response time for emergency vehicles. Thus, the DRL agent helps train itself through the historical records and also takes real-time feedback to try and improve overall flow and prioritization towards emergency vehicles with advances in Traffic Light Synchronization using Computer Vision. Real-time information comprising standard parameters like the total lane counts, average speed, and traffic flow data that might assist the operators would then be furnished. Once any lane is identified as the emergency lane, it instantly receives a “high-priority” tag, permitting it clearance for ambulances and similar vehicles by coordinating traffic light signals or that of closely hanging traffic systems. Overall, such a system introduces priority of emergency venues as efficient and improves urban traffic flow, dealing with current traffic issues rather well.