As cities grow in population and the number of vehicles increases, traffic congestion has become a major problem. Traffic jams lead to delays, driver stress, higher fuel consumption, and increased air pollution. Megacities are particularly affected. To manage traffic effectively, it's essential to calculate road traffic density in real-time. Optimizing traffic control is crucial for this. The system uses live images from traffic cameras and AI-based image processing to measure vehicle density and adjust traffic signals accordingly, reducing congestion, speeding up transit, and lowering pollution.


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    Titel :

    YOLO-based Traffic Signal Optimization for Intelligent Traffic Flow Management


    Beteiligte:
    C, Joesam Dinesh (Autor:in) / S, Shrinidhi (Autor:in) / Amaran, Sibi (Autor:in) / K, Sree Kumar (Autor:in) / U, Karthikeyan (Autor:in)


    Erscheinungsdatum :

    03.10.2024


    Format / Umfang :

    449265 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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