AI-Based Traffic Management System for Urban Areas: Real-Time Ambulance Detection and Signal Optimization Using Top-View Images

Urban traffic congestion has become one of the major challenges in modern cities, especially affecting
the movement of emergency vehicles such as ambulances. Delays in ambulance transportation during
medical emergencies can lead to serious consequences, including loss of life. Traditional traffic
management systems mainly depend on fixed traffic signal timings and manual monitoring, which are
not efficient in handling real-time emergency situations. Existing GPS-based ambulance prioritization
systems also face limitations such as dependency on network connectivity, infrastructure cost, and lack
of visual verification. To overcome these issues, this project proposes an AI-Based Traffic Management
System for Urban Areas using top-view traffic image analysis and intelligent signal optimization.
Once an ambulance is detected, the system dynamically controls traffic signals using IoT-enabled signal
management to provide green signal priority and reduce waiting time at intersections. In addition,
Dijkstra’s shortest path algorithm is used to calculate the fastest and most efficient route for ambulance
movement by considering real-time traffic conditions. GSM and mobile communication modules
further support coordination between ambulances, traffic control centers, and drivers.