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@article{195211,
        author = {Saad Khalil KIlledar and Siddharth Amit Kamble and Shivraj Sunil Kativale and Archana S. Gaikwad and Rashmi S. Pande and Deepak P. Jagtap},
        title = {Real-Time Urban Traffic Density Estimation Using AI-Driven Vehicle Detection for Smart City Traffic Control},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7195-7199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195211},
        abstract = {Traffic Congestion is Major problem in most of Modern Cities due to increasing number of vehicles, especially during the peak hour. The existing traffic signals use a fixed time signals where the time is fixed for each vehicle we cannot adjust the time signal automatically by detecting vehicle, our project mainly develops a smart traffic signal using YOLO (You Only Look Once), it mainly detects the vehicle and count the number of vehicle and it will store real time data and if the road has more traffic it will detect the vehicle till the last second of red signal and according to it will increase green time signal so that traffic congestion cannot occur and also the road where 2 vehicles on road where traffic signal is present it will detect it and according to it will provide green time signal and the areas where traffic signal are not there it will detects the vehicle and it will provide better routes for heavy vehicle which make the traffic less and traffic control system place a sign board that heavy vehicles are not a loud on particular road this system helps to provide a smart traffic management.},
        keywords = {Traffic Management, Artificial Intelligence- Based, YOLO Algorithm, Vehicle Counting, Smart Traffic System, Computer Vision.},
        month = {March},
        }