Towards a More Efficient Traffic Management System: Implementing a Density-Sensitive Canny Edge Detection Algorithm for Real-Time Congestion Mitigation

In the ever-expanding urban landscape, traffic congestion remains a formidable challenge, necessitating the development of intelligent, adaptive traffic management frameworks. This research endeavors to revolutionize conventional traffic control methodologies by leveraging a hybridized approach amalgamating the Canny Edge Detection algorithm with the YOLO-based object detection framework to optimize vehicular flow dynamically. Traditional methodologies often fall short due to inaccuracies in vehicle density estimation and failure to distinguish emergency vehicles from regular traffic, leading to suboptimal signal allocation. Our proposed system overcomes these limitations by integrating real-time image processing, wherein traffic density is computed through a comparative white-pixel analysis between real-time images and a reference image of an uncongested road. YOLO’s bounding box regression enables accurate vehicle classification, thereby prioritizing emergency vehicles irrespective of congestion levels. The Canny Edge Detection algorithm refines this process by accentuating vehicular boundaries, eliminating extraneous features, and ensuring high-precision traffic density computation. This study follows a structured methodology encompassing image acquisition, grayscale transformation, Gaussian filtering for noise reduction, and multi-threshold edge detection. The processed data dynamically calibrates signal durations, mitigating unnecessary vehicular stagnation and significantly enhancing throughput efficiency. Experimental results substantiate the efficacy of this approach, demonstrating notable reductions in average wait times and congestion levels while ensuring uninterrupted passage for emergency vehicles. These findings hold profound implications for the advancement of intelligent transportation systems, paving the way for scalable, adaptive traffic control architectures that can be generalized across diverse urban landscapes. The integration of density-sensitive edge detection and real-time object recognition into traffic signal algorithms signifies a paradigm shift, offering a robust, data-driven alternative to static, pre-configured traffic control mechanisms. This research serves as a foundational framework for further explorations into AI-driven urban mobility solutions.