AI-Based Ambulance Priority Traffic Management System

Traffic jams at road intersections can cause delays for Ambulance. These delays make it hard to get emergency help to people fast. Most traffic signal systems use fixed timing. They do not react quickly when an Ambulance is coming. This paper presents an Ambulance Priority Traffic Management System. It uses computer vision, deep learning and automatic decision-making to help Ambulance move faster and to monitor traffic better at intersections. The system works at a four-way intersection with four traffic zones: North-West, North-East South-West and South-East. It uses the You Only Look Once (YOLO) object detection algorithm to analyze traffic images and video frames. A special YOLO model is trained to detect Ambulance. A general YOLO model identifies vehicles like cars, buses, trucks and motorcycles. The system draws bounding boxes and class labels around each vehicle. It also finds where each vehicle is located in the traffic scene. When Ambulance is detected the system finds the center point of Ambulance’s bounding box. It then identifies which traffic zone Ambulance is in. After that the traffic light for that zone turns green. The lights for the three zones turn red in the prototype interface. This direction-based control gives priority to Ambulance. It stops traffic from moving at the same time. Once Ambulance has passed the system could later be designed to return the signals to normal using a control strategy. In addition to giving priority to Ambulance the system counts cars, buses, trucks and motorcycles. This helps create a picture of traffic. The counts are used to label traffic density as low, medium or high. This data can help monitor traffic conditions. It also lays the groundwork for adaptive signal-control strategies. The prototype was tested using traffic images and video clips. It showed its ability to detect vehicles choose the direction count vehicles and display signal changes. Detailed testing is needed to check how accurately the system detects vehicles, how fast it responds and how well it works in real traffic. The system shows how object detection and direction-specific signal control can be combined in an emergency traffic-management prototype. Future improvements might include live CCTV feeds, vehicle tracking, Ambulance siren detection, GPS location, predictive traffic analysis and multi junction coordination, across intersections. These upgrades could help build an efficient smart traffic management system.