International Journal of Science, Engineering and TechnologyAuthors: Mr. Rahul Desai, Mr Domendra Kumar Verma, Ms Purnima Dutta
Abstract: Rapid urbanization and increasing vehicle density have made traffic management a major challenge in smart cities, particularly during emergency situations where immediate road access is essential. This paper presents an intelligent emergency traffic handling system that utilizes deep learning, IoT devices, and mobile-based communication to support faster movement of emergency vehicles. The framework uses a YOLO-powered detection algorithm integrated with live video surveillance to recognize ambulances, police vehicles, and fire engines in real time. Once an emergency vehicle is detected, a wireless communication mechanism transfers the information to an IoT-enabled traffic controller that automatically changes traffic signals to ensure priority movement. Simultaneously, an audio announcement module alerts nearby commuters to clear the route. To strengthen coordination, a mobile application sends instant notifications and live location updates to traffic personnel. The system also maintains cloud-based records for monitoring and future traffic analysis. The proposed approach improves emergency response efficiency, minimizes delays caused by congestion, and supports the development of scalable and intelligent urban transportation infrastructure.
DOI: http://doi.org/10.5281/zenodo.20175447
