The exponential growth in urban populations and the consequent surge in vehicular traffic have rendered traditional traffic management systems inadequate. These systems, reliant on static timing mechanisms, are unable to adapt to real-time traffic conditions, resulting in inefficiencies, congestion, and delayed emergency responses. This paper presents an innovative Traffic Management System (TMS) that leverages artificial intelligence (AI) and image recognition technologies to dynamically manage traffic flow. The system aims to optimize signal timings, prioritize emergency vehicles, and promptly respond to traffic incidents, thereby enhancing overall transportation efficiency and urban mobility.
