A time-varying weighted combination model is proposed to predict the short-term traffic flow in the paper. The changed weights will much minimize the influence of random factors and generalize the application conditions. Firstly, the training data are classified into several categories according to the traffic situations, and LS-SVM is used to train the single local prediction model of each category; Secondly, the occurring possibility of different traffic situations is made as the weight of the corresponding local prediction model; and then the model is constructed with the linear weighted combination strategy. Finally, the experiment with the actual speed data shows that the method is effective.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Short-Term Traffic Flow Combination Prediction Model with Adaptive Weights


    Beteiligte:
    Sun, Chuanxia (Autor:in) / Sun, Xiaoliang (Autor:in) / Yin, Peixuan (Autor:in) / Zhang, Shenyuan (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 324-331


    Erscheinungsdatum :

    12.08.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Short-Term Traffic Flow Combination Prediction Model with Adaptive Weights

    Sun, Chuanxia / Sun, Xiaoliang / Yin, Peixuan et al. | TIBKAT | 2020




    Event-Based Short-Term Traffic Flow Prediction Model

    Head, K. L. / National Research Council / Transportation Research Board | British Library Conference Proceedings | 1995


    Event-Based Short-Term Traffic Flow Prediction Model

    Head, K.Larry | Online Contents | 1995