Abstract—Traffic congestion in urban areas has escalated into a critical socio-economic challenge, contributing to increased travel delays, fuel wastage, air pollution, and emergency re- sponse failures. Traditional fixed-timer traffic signals fail to adapt to dynamic traffic patterns, resulting in inefficient junc- tion management. This paper presents a comprehensive Smart Traffic Management System (STMS) that integrates real-time vehicle detection using custom-trained YOLOv8, density-based adaptive signal timing, emergency vehicle priority via audio- based siren detection, and a full-stack web dashboard using Flask and OpenCV. The system processes live video streams from intersection cameras, calculates lane-wise vehicle density, dynamically allocates green time, and instantly grants priority upon detecting emergency vehicle sirens. A real-time analytics dashboard provides live heatmaps, density graphs, and perfor- mance metrics. Experimental evaluation on Indian urban traffic datasets demonstrates a mean Average Precision (mAP@50) of 0.974, inference speed of 28–32 FPS on CPU, and a 42.1% reduction in average waiting time compared to fixed-timer systems. The proposed system offers a scalable, cost-effective solution for intelligent traffic management in smart cities.

—Smart Traffic Management, YOLOv8, Adaptive Signal Control, Emergency Vehicle Detection, Vehicle Density Estimation, Computer Vision, Deep Learning, Flask Web Appli- cation, Real-time Systems, Audio Processing, MFCC, OpenCV, Indian Traffic Dataset, Si