Intelligent Traffic Management System Using Computer Vision Techniques

Abstract

Rapid urbanization and increasing vehicle density have led to severe traffic congestion, longer travel times, and higher accident rates in modern cities. Conventional traffic management systems, which rely on fixed timing signals and manual monitoring, are often inadequate for handling dynamic traffic conditions. This study presents an intelligent traffic management system using computer vision techniques to enable real-time traffic monitoring, analysis, and control. The proposed methodology utilizes surveillance cameras installed at intersections to capture continuous video streams, which are processed using advanced image processing and deep learning models. Object detection algorithms such as You Only Look Once (YOLO) and Faster Region-based Convolutional Neural Networks (Faster R-CNN) are employed to identify and classify vehicles, pedestrians, and traffic density levels. The extracted data is used to estimate queue lengths, traffic flow rates, and congestion patterns at different lanes. A dynamic signal control algorithm is then implemented to optimize traffic light timings based on real-time traffic conditions, reducing unnecessary waiting times and improving overall traffic efficiency. The system performance is evaluated using metrics such as vehicle detection accuracy, processing time, congestion reduction rate, and average vehicle delay. The results demonstrate that the proposed computer vision-based system significantly improves traffic flow efficiency compared to traditional fixed-time traffic signal systems. It effectively reduces congestion at busy intersections and enhances road safety by minimizing signal violations and human intervention. The study concludes that intelligent traffic management systems powered by computer vision offer a scalable and cost-effective solution for modern urban transportation challenges.