Real time and accurate predictions of intersections help set scientific traffic signal programs, expand road capacity and improve traffic conditions. The paper establishes a short-term forecasting model of intersection channel imports according to the Levenberg-Marquardt (LM) neural network algorithm, which is based on the analysis of intersection traffic volume time and spatial correlation, combining LM neural network distributed processing, self-organizing, adaptive, self-learning, and other good characteristics. Using MATLAB to forecast short-term traffic volume of intersection imports with the prediction model and some specific examples, the empirical results show that the prediction model has preferable prediction accuracy.


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    Titel :

    Forecasting the Intersection Traffic Volume Based on the Levenberg-Marquardt Algorithm


    Beteiligte:
    Wu, Fang (Autor:in) / Zhang, Junfeng (Autor:in) / Ma, Changxi (Autor:in)

    Kongress:

    16th COTA International Conference of Transportation Professionals ; 2016 ; Shanghai, China


    Erschienen in:

    CICTP 2016 ; 337-345


    Erscheinungsdatum :

    01.07.2016




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch