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YOLO ENHANCED: REAL-TIME TRAFFIC OPTIMIZATION WITH AI-POWERED SIGNAL ALLOCATION
Abstract
In cities experiencing a rapid surge in vehicle numbers, outpacing the growth of available traffic infrastructure, congestion becomes a formidable challenge, exacerbated by vehicle accidents. This issue significantly impacts various facets of modern society, including economic development, traffic safety, greenhouse gas emissions, time efficiency, and public health. Traditional traffic signals struggle to adapt to changing traffic patterns. To address this challenge, we introduce a modern traffic management system powered by YOLO v8. This system enables responsive traffic lights that adjust in real-time based on current traffic conditions. Our proposed system aims to utilize live images from the cameras at traffic junctions for traffic density calculation using image processing. It also focuses on the algorithm for switching the traffic lights based on the vehicle density to reduce congestion, thereby providing faster transit to people and reducing pollution.
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