Smart Traffic Management for Congestion Control and Emergency Vehicle Priority

In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic management. The system combines Radio Frequency Identification (RFID) sensors, cameras, and microphones to enhance the responsiveness of ambulance drivers and alleviate traffic congestion. The RFID sensors are strategically placed along the ambulance route to facilitate seamless communication between the ambulance and traffic signals. When an ambulance approaches, the RFID sensors trigger pre-programmed traffic signal adjustments, such as extending green lights or halting conflicting traffic flow, to expedite the ambulance's passage. Simultaneously, the camera-based detection system identifies the presence of ambulances in traffic and assesses congestion levels in real-time. Utilizing computer vision algorithms, the system analyzes live camera feeds to detect ambulance vehicles and evaluate traffic density and movement patterns. This information enables dynamic rerouting of ambulances to less congested routes, optimizing response times and minimizing delays. Furthermore, a microphone array is employed to detect the distinct audio signature of ambulance sirens. By leveraging sound analysis techniques, the system accurately identifies the approach of an ambulance and triggers additional traffic management measures, such as prioritizing ambulance lanes or temporarily rerouting vehicles to clear a path. Integration of these sensor technologies into a unified control system offers a comprehensive solution for improving ambulance navigation through urban traffic. Through proactive traffic signal adjustments, dynamic route optimization, and real-time siren detection, the proposed system enhances overall emergency response effectiveness while reducing the risk of traffic-related delays and accidents. Moreover, the system's adaptability and scalability make it suitable for deployment in diverse urban environments, contributing to safer and more efficient emergency services.