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@article{189210,
        author = {Shantanu Game and Ayush Gaikwad and Tarun Rathod and Shreya Phagade},
        title = {An AI-Based Adaptive Traffic Signal Management System Using YOLOv5 for Real-Time Vehicle Detection and Emergency Priority Control},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {8081-8088},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189210},
        abstract = {Fixed-time traffic signal systems fail to adapt to changing traffic conditions at urban intersections, leading to unnecessary delays and ineffective emergency handling. This paper proposes an AI-based adaptive traffic signal management system that adjusts signal timings using real-time vehicle density. A custom-trained YOLOv5 model is used to detect and count vehicles on each traffic lane from camera input. Based on the vehicle count, a decision logic module dynamically controls green signal duration to reduce waiting time. The system also provides emergency vehicle priority by temporarily overriding normal signal operation after a short warning phase. The proposed approach is implemented as a functional prototype and demonstrates reliable vehicle detection and adaptive signal control.},
        keywords = {Adaptive traffic control, computer vision, emergency vehicle priority, intelligent transportation system, YOLOv5},
        month = {December},
        }