Optimizing Urban Traffic with Image Processing: A New Era of Management

Urban traffic management is a crucial concern in today's cities due to increased vehicle density and the complexity of transport networks. Effective real-time vehicle tracking systems are critical for reducing traffic congestion, improving road safety, and optimizing traffic flow. The suggested system uses real-time video feeds from cameras strategically deployed throughout metropolitan traffic networks. YOLOv5 recognizes and localizes vehicles inside video frames, while Alex Net V3 classifies the observed vehicles as automobiles, busses, trucks, and motorcyclists. The system continuously refreshes traffic data, providing information about traffic density, flow patterns, and potential bottlenecks. This data is integrated into traffic management systems to enable adaptive signal control and dynamic route planning. This greatly increased the precision and efficiency of urban traffic management, providing a scalable solution to the mounting issues of urbanization.