Advance Emergency and Traffic Management System

This paper introduces a comprehensive AI-based traffic management system designed to dynamically optimize traffic light timings at urban intersections through the integration of real-time vehicle detection and adaptive optimization techniques. Employing the YOLOv4 Tiny model for precise vehicle detection from video inputs, the system offers an innovative approach to address the complexities of urban traffic flow. Coupled with a genetic algorithm tailored for traffic optimization, it adjusts green light durations based on current traffic density, significantly reducing congestion, lowering environmental impact, and enhancing road safety. The study presents an in-depth analysis of the system’s implementation, discussing the frontend user interface developed with React for handling video uploads and displaying results, and the backend processing managed by Flask for data orchestration and optimization. Empirical results from a deployment across several intersections in an urban setting have demonstrated an average reduction of 25% in wait times during peak hours, a 12% decrease in CO2 emissions, and a 15% drop in minor traffic accidents. These outcomes underscore the system’s efficacy in real-world scenarios, providing a scalable solution for traffic management that aligns with smart city objectives.