This study discusses about traffic prediction, which is possible in intelligent transportation systems. This involves making predictions based on data from the previous year and data from the most recent years, which eventually yields accuracy and mean square error. For those who need to check the current traffic situation, this prediction will be useful. The traffic statistics is based on a 1 hour time gap. From this prediction, live traffic numbers are examined. So, while the user is also driving, this will be simpler to examine. The core objective of this proposed system is to identify the future traffic based on the video analysis. The proposed system uses video analysis and ELM based neural network. The proposed system is also useful for central and state government for maintaining smooth traffic flow.


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    Titel :

    Prediction of Road Traffic using an ELM-based Neural Network


    Beteiligte:
    Ali Fathima, R Syed (Autor:in) / Sumathi, R (Autor:in)


    Erscheinungsdatum :

    01.03.2023


    Format / Umfang :

    412977 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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