Smart traffic management system

Abstract

This project aims to design and develop a smart traffic management system for a four-way intersection to improve overall traffic efficiency, emergency response, and road safety. The suggested system dynamically manages traffic lights based on the number of vehicles in each lane and only allows a green signal to the lane with the most vehicles. It contains a camerabased approach using a pre-trained YOLO model to detect vehicle congestion, classify emergency vehicles, and give signal priority even if no vehicle is present in the other lanes. To increase the reliability of the system, a hybrid backup architecture is used. If the camerabased detection fails, the microcontroller-based IIoT sensor network is automatically activated, and congestion and emergency vehicle detection continue. Furthermore, a special microcontroller continuously monitors wrong-way vehicle movement and raises an alarm when a wrong-way vehicle is detected, thereby enhancing traffic safety. The proposed system features AI, embedded systems, and multiple sensors as redundant features, making it robust, scalable, and compatible with modern intelligent transportation systems.