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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    Title :

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


    Contributors:
    Huang, Yanguo (author) / Zhang, Shuo (author) / Wen, Junlin (author) / Chen, Xinqiang (author)

    Conference:

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



    Publication date :

    2020-08-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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