Real-Time Traffic Management System Using Machine Learning and Image Processing

Abstract

In major and rapidly developing urban regions across the world, traffic congestion is unavoidable. Congestion during rush hour is an unavoidable consequence, especially in large metropolitan. In the current circumstance, the traditional technique works well only if the count is low, as the density of vehicles on one side of the lane road grows if the traffic is heavier on one side of the road than on the other; otherwise, the approach fails. As a result, our goal is to create a traffic system that can switch signals as well as track and handle signals in real time. Signal switching will be done in this project based on real-time image detection and accuracy of vehicular traffic. Another goal is to put in place a traffic management system that gives priority to the lane where the ambulance arrives that offers ambulance detection.