TrafficOS 2.0: An AI-Driven Smart Traffic Management System

Traffic congestion is a major issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic management systems rely on fixed signal timings, which are inefficient in handling dynamic traffic conditions. This paper presents TrafficOS 2.0, an AI-driven smart traffic management system designed to optimize traffic flow using real-time computer vision and deep learning techniques. The system utilizes the YOLOv8 model for accurate and high-speed vehicle detection from live video feeds captured through CCTV or IP cameras. Using OpenCV for image processing and FastAPI for backend operations, the system performs lane-wise vehicle density analysis and dynamically adjusts traffic signals based on real-time conditions. WebSocket communication enables low-latency data transmission between the backend and a React-based dashboard for live monitoring and visualization. Additionally, performance optimization techniques such as frame skipping and resolution scaling are implemented to ensure efficient processing. The system also integrates with SUMO traffic simulation to evaluate and validate its performance in realistic scenarios. Experimental results indicate that TrafficOS 2.0 significantly improves traffic flow efficiency and reduces congestion compared to conventional systems, making it a scalable and effective solution for smart city applications.