The model for real-time traffic flow prediction of intersections based on wavelet neural network is proposed. The main characteristics of this model are that the laws of traffic flow can be learned dynamically using wavelet network. Therefore, there is no complicated computation for the model learning process and it can also learn from historical data constantly. The experimental results show the effectiveness of the model with such properties as simple structure of network and fast convergence.


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    Title :

    Traffic Flow Prediction of Intersections Using Wavelet Neural Network


    Contributors:
    Li, Sheng (author)

    Conference:

    Seventh International Conference on Applications of Advanced Technologies in Transportation (AATT) ; 2002 ; Boston Marriot, Cambridge, Massachusetts, United States



    Publication date :

    2002-07-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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