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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    Titel :

    Short-Term Traffic Flow Prediction using DTW-BiGRU Model


    Beteiligte:
    Zou, Hanyi (Autor:in) / Liu, Heng (Autor:in) / Zhou, Tianli (Autor:in) / Jiashun, Li (Autor:in) / Zhan, Yang (Autor:in)


    Erscheinungsdatum :

    16.10.2020


    Format / Umfang :

    825278 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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