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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:

    Conference:

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


    Published in:

    Publication date :

    2009-07-23




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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

    Luo, X. / Niu, G. / Wu, Q. | British Library Conference Proceedings | 2009


    Short term traffic flow prediction in heterogeneous condition using artificial neural network

    Kranti Kumar / Manoranjan Parida / Vinod Kumar Katiyar | DOAJ | 2015

    Free access


    Short-Term Traffic Flow Prediction Based on Genetic BP Neural Network

    Liu, Y. / Hu, W. / Xin, S. et al. | British Library Conference Proceedings | 2010