This paper is about traffic flow short term prediction and monitoring based on magnetic sensors measurements. For these purposes, the advantages and drawbacks of feed-forward and real time recurrent learning neural networks are investigated. Structures determination, weights initialization, networks training and automatic incidents detection are discussed.


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    Titel :

    Feed-forward and RTRL neural networks for the macroscopic traffic flow prediction and monitoring: the potential of each other


    Beteiligte:
    Messai, N. (Autor:in) / Thomas, P. (Autor:in) / El Moudni, A. (Autor:in) / Leclercq, E. (Autor:in) / Druaux, F. (Autor:in) / Lefebvre, D. (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    374873 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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