Smart Traffic Control System with Ambulance Detection Using Signal Processing

In densely populated urban environments, the rapid and efficient passage of emergency vehicles, such as ambulances, is paramount to saving lives. This abstract introduces a cutting-edge approach to enhance traffic management and emergency response by leveraging signal processing techniques for real-time ambulance detection and priority routing. A timely response during emergencies, especially for ambulances, depends on effective traffic management. This study describes a smart traffic control system that can identify and rank ambulance vehicles in traffic using signal processing techniques. To analyze real-time traffic data from various sources, including cameras and sensors, the proposed system makes use of sophisticated signal processing algorithms. Rapid identification of emergency vehicles is made possible by ambulance detection, which is accomplished through image processing and pattern recognition. When an ambulance is detected, the traffic control system dynamically modifies the timing of the traffic signals to allow the ambulance to pass quickly through the busy roads. This clever traffic control system not only speeds up overall traffic flow efficiency and reduces response times for emergency vehicles. This proposed system includes the process of data collection and processing, ambulance detection, pattern recognition and classification, dynamic traffic control light adjustment. The proposed intelligent traffic control system improves overall traffic flow efficiency while also speeding up emergency vehicle response times. The effectiveness of the system in optimizing urban transportation systems for both emergency situations and routine traffic scenarios is shown by experimental results.