AI-Based Intelligent Traffic Signal Control for Emergency Vehicle Prioritization Using Deep Learning

Rapid urbanization has significantly increased road congestion, making efficient traffic management a critical requirement for modern cities. Traditional traffic control systems often fail to prioritize emergency vehicles such as ambulances, fire trucks, and police cars, resulting in delayed response times during critical situations. This research presents an intelligent traffic management framework that uses deep learning and video analytics to detect emergency vehicles and dynamically control traffic signals at four-way intersections. The proposed system employs advanced object detection models including YOLO and Region-Based Convolutional Neural Networks (RCNN) to identify vehicles from real-time traffic camera feeds. By analyzing traffic density and detecting emergency vehicles, the system automatically adjusts signal timings to provide priority clearance. Experimental analysis demonstrates that the proposed approach improves detection accuracy while reducing traffic congestion and emergency response delays. The system provides a scalable solution for smart city infrastructure by integrating computer vision and intelligent traffic signal control.