An approach to short-term traffic flow prediction based on empirical mode decomposition (EMD) and artificial neural network (ANN) is proposed. The traffic flow is decomposed into different modes by EMD, and these different modes are predicted by appropriate ANNs. The predictive traffic flow is obtained by adding up all predictive values. This method is used to predict traffic flow with the actual measurement data. The results show that the proposed method has high predictive accuracy, and is more successful than the outcome of directly using ANN prediction.


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    Titel :

    Short-Term Traffic Flow Prediction Based on EMD and Artificial Neural Network


    Beteiligte:
    Luo, Xianglong (Autor:in) / Niu, Guohong (Autor:in) / Wu, Qianjiao (Autor:in)

    Kongress:

    Ninth International Conference of Chinese Transportation Professionals (ICCTP) ; 2009 ; Harbin, China


    Erschienen in:

    Erscheinungsdatum :

    23.07.2009




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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