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@article{194498,
        author = {Anurag Birari and Adwait Babar and Aarya Ghadage and Pavan Gorde},
        title = {Adaptive Traffic Signal Management System using Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4284-4287},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194498},
        abstract = {Traffic congestion has become a major issue in urban areas due to the increasing number of vehicles on roads. Conventional traffic signal systems generally operate on fixed timing mechanisms that do not consider real time traffic conditions. As a result, some roads remain congested while others remain empty, causing unnecessary delays, increased fuel consumption, and environmental pollution.
This project proposes an Adaptive Traffic Signal Management System using Computer Vision and Deep Learning techniques. The system uses traffic video input to detect and count vehicles using the YOLO (You Only Look Once) object detection model and OpenCV for image processing. Vehicles such as cars, buses, trucks, and motorcycles are automatically detected and counted from video frames. Based on the detected vehicle density, the system dynamically calculates the optimal green signal duration for efficient traffic management.
The proposed approach aims to reduce traffic congestion, improve traffic flow efficiency, and support the development of intelligent transportation systems. This paper presents the system architecture, methodology, key features, challenges, and future improvements of the proposed adaptive traffic signal control system.},
        keywords = {Traffic management, Computer vision, YOLO, OpenCV, Vehicle detection, Smart traffic signals, Intelligent transportation systems.},
        month = {March},
        }