Self-Adaptive Traffic Density Based Management System

Overpopulation is one of the largest issues within the globe today. Speaking of records and the boom in the number of humans, approach a growth in the range of cars on the roads. Thus, road traffic control is an important problem faced in many metropolitan cities nowadays. There are many problems associated with the population on the road in many towns, especially for emergency vehicles. Lack of efficient management on-site results in loss of life due to ambulance delays, having gotten stuck in traffic jams. In this study, a method is presented for managing traffic using the YOLO algorithm to process images in real time. Our system incorporates ESP32 cameras with FTDI (Future Technology Device International Ltd.) chips to capture and analyze images of traffic intersections. The intelligent system adjusts traffic lights dynamically by considering real-time density data derived from image analysis. To cover all four lanes, a servo motor is employed to rotate the camera. The main aim of this project is to efficiently manage traffic flow and alleviate congestion by self-adapting signal timings based on traffic density.