Smart Traffic Management System For Emergency Vehicles Using Deep Learning

Road congestion is the main cause for slower speed, long waiting time for the emergency vehicles. The project proposes a Smart Traffic Management System for ambulances to reduce road congestion, improve response time, and reduce waiting times. It uses modern communication, computation, and sensing technologies to minimize response time, travel time, and waiting time. The system uses Dijkstra’s shortest path algorithm to calculate optimal routes based on real-time traffic conditions, road closures, and congestion, ensuring the ambulance is always on the most efficient route. The Smart Traffic Management System uses the YOLO deep learning algorithm to detect and track ambulances in real-time, enabling rapid and accurate vehicle recognition in complex traffic scenarios. Once detected, the system controls traffic signals along the ambulance’s route, prioritizing green lights when approaching intersections. This intelligent traffic signal control balances the needs of the emergency vehicle with traffic flow, minimizing delays and improving response times. The system’s combination of YOLO-based vehicle detection, Dijkstra path optimization, and intelligent traffic signal control makes it a valuable tool for modern urban emergency services, reducing ambulance response times.