AI DRIVEN TRAFFIC MANAGEMENT SYSTEM USING COMPUTER VISION AND ML

Abstract

Traffic congestion and road safety remain significant challenges in urban planning. Traditional traffic management systems rely on predefined rules and sensor-based inputs, which often lack real-time adaptability. This research proposes an AI-driven traffic management system integrating computer vision and machine learning to enhance traffic efficiency and safety. The system utilizes real-time video analytics, object detection, and predictive modeling to dynamically optimize traffic flow, reduce congestion, and enhance road safety. Experimental results demonstrate improved traffic monitoring and decision-making capabilities compared to conventional systems.