The ever increasing vehicle count in the city causes increase in traffic congestion, wastage of time, accidents, and pollution. Currently, different types of traffic management techniques exist with their own advantages and drawbacks. In this paper, we aim to enhance the efficiency of traffic management by taking real-time traffic information and predict future traffic density at any intersection. Our goal is to manage real-time traffic at crossroads by detecting vehicles using object detection and image processing as well as predicting the traffic density using machine learning algorithms. Using cameras installed at a junction we can obtain the number of vehicles using object detection in real-time. Also, with the assistance of machine learning we can predict the real-time traffic considering previous traffic conditions at the same junction. The results obtained proved the efficiency of the proposed technique.


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    Titel :

    Intelligent Traffic Signal System Using Machine Learning


    Beteiligte:
    Deshmukh, Shivtej (Autor:in) / Badvar, Nikhil (Autor:in) / Borate, Chetan (Autor:in) / Jadhav, Dipti (Autor:in)


    Erscheinungsdatum :

    2022-08-26


    Format / Umfang :

    1511091 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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