Short-term traffic flow prediction, which is useful to improve traffic congestion and road efficiency, has been a hot issue in the field of transportation. However, only considering Euclidean space, conventional methods are always unable to make good use of the spatial-temporal correlation of traffic flow data which is usually a topological structure. In this paper, a deep learning model, GCN-LSTM (graph convolutional network-LSTM), was proposed with encoder and decoder structure. GCN-LSTM will simultaneously capture the spatial and temporal characteristic of traffic flow by embedding GCN into the structure of LSTM. Training with the traffic flow data of previous T moments and adjacent section, GCN-LSTM effectively perform short-term traffic flow prediction. Experiments on real data demonstrate that our method, considering both of spatial and temporal features, has a more powerful representation ability and higher prediction accuracy compared with LSTM.


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    Titel :

    Short-Term Traffic Flow Prediction Based on Graph Convolutional Network Embedded LSTM


    Beteiligte:
    Huang, Yanguo (Autor:in) / Zhang, Shuo (Autor:in) / Wen, Junlin (Autor:in) / Chen, Xinqiang (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2020 ; 2020 ; Seattle, Washington (Conference Cancelled)



    Erscheinungsdatum :

    2020-08-31




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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