Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters.


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    Titel :

    Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences


    Weitere Titelangaben:

    J. Transp. Eng., Part A: Systems


    Beteiligte:
    Shuai, Chunyan (Autor:in) / Wang, WenCong (Autor:in) / Xu, Geng (Autor:in) / He, Min (Autor:in) / Lee, Jaeyoung (Autor:in)


    Erscheinungsdatum :

    2022-06-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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