EDGEWISE TRAFFIC: SMART CONGESTION MONITORING AND CONTROL WITH CANNY DETECTION

Abstract

Urban traffic congestion is a growing challenge that affects commute times, fuel consumption, and environmental quality. This study proposes EdgeWise Traffic, a smart traffic control system that utilizes density-based analysis and the Canny edge detection algorithm to monitor and manage vehicular flow in real time. By capturing video feeds from intersections and applying Canny edge detection, the system identifies vehicles, measures traffic density, and detects congestion hotspots. The collected data is then processed to dynamically adjust traffic signal timings, optimize traffic flow, and reduce waiting times. Experimental results demonstrate that the proposed approach improves traffic throughput, decreases delays, and provides accurate real-time congestion information. The research highlights the potential of integrating computer vision techniques with density-based traffic management strategies to create intelligent, responsive, and scalable solutions for modern urban transportation systems.