Machine Learning Approach For Intelligent Traffic Management

In urban areas, efficient traffic management is essential for minimizing delays and enhancing emergency response times. This project introduces a machine learning-based approach for intelligent traffic management at four-way signals, focusing on emergency vehicle identification and prioritization. The methodology involves several steps: identifying emergency vehicles using YOLOv8 and RCNN techniques on video data, counting and categorizing all vehicles at the intersection, and analyzing the videos to predict the optimal time required to clear traffic. Utilizing a video database from COCO, the system processes inputs to detect emergency vehicles and provides real-time alerts to prioritize their passage. If an emergency vehicle is identified, a message displays the vehicle's location and clears the route promptly. When no emergency vehicles are present, the system estimates the time needed to clear the intersection based on the total vehicle count, optimizing traffic flow. This approach balances routine traffic management with emergency response needs, aiming to reduce congestion and enhance overall traffic efficiency. The results indicate that intelligent traffic management using advanced techniques can significantly improve urban mobility and emergency response capabilities.

Keywords: Intelligent Traffic Management, Machine Learning, YOLOv8, RCNN, Emergency Vehicle Identification, Video Analysis, COCO Database, Traffic Optimization, Urban Mobility, Real-time Alerts.