Real-Time AI-Driven Traffic Management Using YOLOv8n For Adaptive Signal Control

This research paper presents a detailed exploration of an AI-based traffic management system leveraging the YOLOv8n object detection model. The system aims to improve traffic flow, reduce congestion, and enhance overall road safety through real-time analysis of traffic conditions. The paper covers various aspects, including the system architecture, the implementation details of YOLOv8n for vehicle detection and tracking, the integration of detected data into a traffic management platform, and the experimental results demonstrating the system\\\'s performance and effectiveness. The study also addresses challenges in deploying AI-based traffic management systems and suggests potential solutions for future research and development. The proposed system is trained using the COCO dataset along with custom traffic video data to ensure robustness under different environmental conditions. Performance evaluation is carried out using standard metrics such as precision, recall, and detection accuracy. Experimental results show that the model achieves a precision of 0.92, recall of 0.89, and overall detection accuracy of 91%, while effectively estimating traffic density in real-time scenarios. These results demonstrate the system's capability to support adaptive signal timing and significantly improve traffic efficiency.