The invention discloses a traffic flow prediction method based on a deep learning nerve network structure. Various kinds of traffic flow data are collected, a depth automatic encoder model is utilized for training the collected various kinds of traffic flow data, the depth automatic encoder model is adjusted in the training process, and finally, the adjusted depth automatic encoder model is used for predicting a short-period traffic flow. By adopting the method, deeper excavation analysis is carried out on traffic flow data, so that the short-period prediction of the traffic flow is more accurate, and the performance is better.


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    Title :

    Traffic flow prediction method based on deep learning nerve network structure


    Contributors:
    HUANG BUTIAN (author) / FANG JIULIN (author)

    Publication date :

    2015-12-16


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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