The development of urban rail transit is one of the best ways to solve congestion and practice "low carbon traffic". At present, China's urban rail transit has entered a rapid development period. Predicting flow of urban rail transit provides the basis for the construction scale of rail transit. Finds the relations between rail transit flow and macroscopical factors, such as GDP, population and so on, which is the basis for prediction model. First, factors of urban rail transit flow are proposed. Second, construct flow prediction model by support vector machine (SVM) .Finally, the comparison study between BP neural network prediction model and SVM model based on the case study of Shanghai rail traffic flow prediction shows that the model is feasible. This model can be used in flow prediction of urban rail transit.


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    Titel :

    Flow Prediction Research of Urban Rail Transit Based on Support Vector Machine


    Beteiligte:
    Li, Zhaolong (Autor:in) / Zhang, Qingnian (Autor:in) / Wang, Lei (Autor:in)

    Kongress:

    First International Conference on Transportation Information and Safety (ICTIS) ; 2011 ; Wuhan, China


    Erschienen in:

    ICTIS 2011 ; 2276-2282


    Erscheinungsdatum :

    16.06.2011




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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