Lane-Wise Traffic Intelligence Using Deep Vision Systems for Signal Optimization

Abstract

Urban traffic congestion remains a critical challenge affecting commute times, fuel efficiency, and air quality. This project presents a data-driven approach to traffic flow optimization by dynamically adjusting traffic signal timings based on real-time vehicle density across multiple lanes. Utilizing computer vision techniques, such as YOLO-based vehicle detection, the system captures live video feeds from intersections to estimate vehicle count per lane. The signal timings are then optimized to prioritize lanes with higher traffic density, thereby reducing overall waiting times and improving traffic throughput. Experimental results from a simulated environment demonstrate significant improvements in traffic flow efficiency and reduced signal idle times. The proposed solution offers a scalable and adaptive framework for smart city traffic management systems.