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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    Title :

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


    Contributors:
    Shen Dong, (author) / Liguang Sun, (author) / Tanghsien Chang, (author) / Huapu Lu, (author)


    Publication date :

    2011-10-01


    Size :

    222851 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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