Real-time and accurate traffic flow prediction is one of the key technologies of Intelligent Transportation System (ITS). Traditional traffic flow prediction models cannot extract the spatio-temporal characteristics of road traffic flow well. To solve the problem, this paper proposes a short-term traffic flow prediction model based on Dynamic Time Warping (DTW) algorithm and Bi-directional Gated Recurrent Unit (BiGRU) network. The DWT algorithm is used to extract the spatial features of the traffic flow, and the extracted spatio-temporal feature matrix is input to BiGRU network for temporal features extraction. Experimental verification shows that the predicted root mean square error (RMSE) of the DTW-BiGRU model is 20.46% and 21.03% smaller than the Long Short-Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network, respectively. The results show that the DTW-BiGRU model is an effective short-term traffic flow prediction model.


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    Title :

    Short-Term Traffic Flow Prediction using DTW-BiGRU Model


    Contributors:
    Zou, Hanyi (author) / Liu, Heng (author) / Zhou, Tianli (author) / Jiashun, Li (author) / Zhan, Yang (author)


    Publication date :

    2020-10-16


    Size :

    825278 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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