Smart Traffic Management Web System Using AI and Real-Time Analytics

Abstract: Urban traffic congestion has become a major challenge in rapidly growing cities, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic control systems are mostly static and do not adapt to real-time conditions, which results in inefficient traffic flow. This paper presents a Smart Traffic Management Web System that integrates real-time monitoring, artificial intelligence, and adaptive signal control to improve overall traffic efficiency.
The proposed system uses computer vision techniques based on YOLO and OpenCV to detect vehicles from video input and estimate traffic density. A dynamic signal control mechanism adjusts traffic light timing based on congestion levels. The system also provides a web-based dashboard for monitoring traffic conditions, analytics visualization, and emergency vehicle prioritization. Firebase is used as the backend for real-time data storage and synchronization.