Traffic Monitoring System using YOLOv3 Model

Abstract

An intelligent city’s traffic management system is one of its key components. Large cities frequently have traffic issues. To monitor and regulate traffic lights automatically and lessen traffic congestion, modern technical solutions are required. Police, ambulances, fire trucks, and other emergency vehicles have trouble getting through traffic. The number of automobiles on the road is growing daily. Controlling roads, motorways, and highways have become a critical issue due to the growing urban population and, therefore, the number of cars. The methods employed for traffic control are the primary cause of the current traffic issue. The lack of attention to actual traffic scenarios in today’s traffic management systems results in ineffective traffic management systems. Road traffic is frequently congested due to the metropolitan cities’ fast population expansion and urban mobility. To address a variety of challenges with controlling trafficon roadways and to assist authorities in effective planning. Using the Advanced Traffic Management System, an advanced branch of the Intelligent Transportation System, which employs the Deep Learning algorithm. To avoid severe traffic congestion, this task was carried out utilizing deep learning. Problems like traffic accidents, congestion, disputes, and bottlenecks have been brought on by the growing number of cars at our road crossings. Now, the only way to overcome these issues is to provide effective traffic control at junctions, which may be done by installing automated volume-based traffic signal systems there to allow for smooth and efficient circulation of cars through the intersections.