SMARTEDGEFLOW: OPTIMIZED TRAFFIC CONTROL WITH CANNY EDGE DETECTION

Abstract

Urban traffic congestion remains a significant challenge in modern cities, often exacerbated by traditional traffic management systems that rely on fixed timers or manual control. To address this issue, we propose SmartEdgeFlow, an intelligent traffic control system that leverages Canny Edge Detection to dynamically assess vehicle density and optimize signal timings in real time. SmartEdgeFlow employs digital image processing techniques to analyze live traffic camera feeds. By detecting edges within the captured images, the system estimates the number of vehicles present at an intersection. The density of these edges correlates with traffic volume, enabling the system to adjust signal durations accordingly—allocating more green time to lanes with higher vehicle density and reducing it where traffic is lighter. This approach offers several advantages over conventional methods: Real-Time Adaptability: The system continuously processes live images, allowing for immediate adjustments to traffic signals based on current conditions. Enhanced Efficiency: By optimizing signal timings in response to actual traffic volumes, SmartEdgeFlow reduces waiting times and improves overall traffic flow. Scalability: The modular nature of the system allows for easy integration into existing traffic infrastructure without the need for extensive hardware modifications.