Enhancing Public Safety and Traffic Management: A Machine Learning Solution for Missing Vehicles and Smart Signaling

This paper presents a system for missing vehicle tracking and smart traffic signaling using machine learning, embedded systems, RFID, and OpenCV. The missing vehicle tracking component uses RFID tags and readers at strategic locations to identify and track vehicles. Upon a theft report, the system activates tracking, utilizing RFID data and potentially integrating with other tracking mechanisms (e.g., GPS if available) to locate the vehicle. The smart signaling system employs OpenCV and cameras to analyze real-time traffic flow. Machine learning algorithms process this visual data to predict congestion and dynamically adjust signal timings for optimized traffic flow. The system integrates these two aspects: in the event of a stolen vehicle being identified via RFID, the smart signaling system can prioritize traffic flow in the vehicle's direction, aiding in recovery. This combined approach aims to reduce vehicle theft, improve traffic management, enhance safety, and contribute to cost savings through optimized traffic flow and faster vehicle recovery.