A short-term traffic flow forecasting model is studied for Beijing Traffic Forecast System. From a practical view, a combined forecast model is considered, including Discrete Fourier Transform model, Autoregressive model and Neighborhood Regression model. In order to update weight real-timely, the Bayesian approach is utilized to adjust weights of each sub-model. A large amount of data test is carried out among all sub-models and combined model. It shows advantages of combined model.


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    Titel :

    Combined short-term traffic flow forecast model for Beijing Traffic Forecast System


    Beteiligte:
    Shen Dong, (Autor:in) / Liguang Sun, (Autor:in) / Tanghsien Chang, (Autor:in) / Huapu Lu, (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    222851 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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