Traffic congestion in urban areas significantly affects the movement of emergency vehicles such as ambulances, often leading to delays that can result in life-threatening situations. Traditional traffic management systems operate on fixed timing mechanisms and fail to respond to real-time traffic conditions. This study proposes a smart traffic light system that utilizes the YOLO (You Only Look Once) deep learning algorithm to detect ambulances and dynamically control traffic signals. The system processes input images to identify ambulances based on trained models and automatically prioritizes the corresponding lane by turning the signal green. The implementation is carried out using Python, OpenCV, and a graphical user interface for simulation. The performance of the system demonstrates high detection accuracy and fast response time, ensuring efficient traffic flow during emergencies. The proposed system provides a scalable and cost-effective solution for intelligent traffic management and highlights the potential of artificial intelligence in real-world applications.

KEYWORDS

Machine Learning, Computer Vision, YOLO, Traffic Management, Ambulance Detection, Python