AI Traffic Management System

Abstract

There are major difficulties that occur due to congestion of traffic inside the city regarding the availability of essential services like the police service, the fire department, and the ambulances. These services end up being stranded at the regular junctions, leading to an increase in the timeframe. This puts the lives of individuals at risk. The current arrangement of traffic light systems is rigid in programming. This paper proposes an AI-Based Emergency Vehicle Priority Traffic Management System that integrates Computer Vision, GPS tracking, and Internet of Things (IoT) technologies. The data from the CCTV is fed into the YOLOv8 system, which is responsible for the detection of the approaching emergency vehicle. The GPS tracker system provides information about the location and velocity of the approaching emergency vehicle. This information is then analyzed against the localized density of the intersection so as to determine the degree of congestion present. On the basis of all this information, the controller adjusts the normal functioning of the lights to ensure that there is no hindrance to creating a green corridor for the emergency vehicle. Practical tests validate that this setup optimizes signal allocation and dramatically cuts down travel windows, offering a highly adaptable blueprint for contemporary smart-city architecture.