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@article{186209,
        author = {Pawar Omkar Sanjay and Shinde Mahesh Ashok and Wabale Dheeraj Gitaram and Munale Satyam Suresh and Prof. Kanchan Jadhav},
        title = {Computer Vision Enabled Dynamic Traffic Light Control for Urban Mobility},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {938-942},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186209},
        abstract = {— Urban traffic has become a burning issue throughout the world with high travel durations, high fuel usage, and pollution of the environment. Conventional traffic lights work at a set time frame whereby they do not rely on the actual traffic density, which makes them ineffective in regulating traffic. The proposal is known as Computer Vision Enabled Dynamic Traffic Light Control in Urban Mobility and proposes an intelligent system that aims to use computer vision and artificial intelligence to optimize traffic on-the-fly. The offered model relies on live video feeds on a camera placed at intersections to identify and count the number of vehicles in each lane based on image processing and object detection algorithms, including YOLO and motion tracking on OpenCV. This system then uses real time to regulate the amount of time the green and red lights spend on the road based on the traffic density to maintain a smooth flow of traffic and minimizing on the amount of time spent in the red light. The system can also detect the emergency vehicles and pass them preferentially. The suggested solution contributes to the efficiency of the road system, decreases air pollution, and contributes to the creation of intelligent cities. The system provides a solution to the contemporary challenges of transportation in the city through constant learning and flexibility, which is innovative and long-lasting.},
        keywords = {— Computer Vision, Dynamic Traffic Light Control, Artificial Intelligence, Smart City, Urban Mobility, Machine Learning, Real-Time Traffic Management, Image Processing, YOLO Algorithm, Intelligent Transportation System (ITS).},
        month = {November},
        }