An Intelligent Traffic Control And Ambulance Detection

Abstract

Rapid urbanization and the increasing number of vehicles on roads have led to severe traffic congestion, making efficient traffic management a critical challenge. This paper presents an intelligent traffic control and ambulance detection system that leverages Artificial Intelligence (AI) and Internet of Things (IoT) technologies to optimize traffic flow and prioritize emergency vehicles. The proposed system utilizes real-time video processing and machine learning algorithms to monitor traffic density at intersections and dynamically adjust signal timings. Additionally, an ambulance detection module based on image recognition and sound analysis identifies approaching emergency vehicles and automatically triggers traffic signal preemption to create a clear path. The integration of wireless communication enables coordination between traffic signals and emergency services, reducing response time and potentially saving lives. Experimental analysis demonstrates that the system significantly reduces traffic congestion and improves emergency vehicle transit efficiency compared to conventional traffic control methods. This approach provides a scalable and cost-effective solution for smart city traffic management.