Volume 2 Issue 2 | 2025 | View PDF
Paper Id:IJMSM-V2I2P106
doi: 10.71141/30485037/V2I2P106

AI-Driven Smart Traffic Management System: An Adaptive Approach Using YOLO and OpenCV

Vitthal B Kamble, Onkar N Mundhe, Chaitanya M Walunjkar, Gaurav A Kale

Citation:
Vitthal B Kamble, Onkar N Mundhe, Chaitanya M Walunjkar, Gaurav A Kale, "AI-Driven Smart Traffic Management System: An Adaptive Approach Using YOLO and OpenCV" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 2, pp. 66-72, 2025.

Abstract:
Intelligent traffic management systems are required due to the growing congestion in urban areas. Conventional fixed-time traffic lights frequently result in inefficiencies, such as long wait times and traffic jams at busy junctions.Using YOLO-based vehicle recognition and dynamic signal control, this work presents an AI-driven traffic light management system. In order to optimize traffic flow, the system analyzes vehicle density in several lanes using real-time image processing from IP cameras and makes informed decisions.The suggested approach prioritizes highly crowded lanes while maintaining signal distribution equity, improving traffic efficiency. Python, OpenCV, and Ultralytics YOLO are used for real-time detection in this fully software-based system. When compared to static signal systems, the results show better traffic flow management; deep learning models and reinforcement learning may be used to further improve the system.

Keywords: AI Traffic Management, YOLO, OpenCV, Dynamic Signal Control, Smart Cities, Real-Time Traffic Analysis.

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