This paper establishes a prediction model of traffic flow, where three cycle dependent components are used to model three characteristics of traffic data, respectively. CNN is used to extract spatial features, and the combination of LSTM and attention mechanism is used to dynamically capture the influence of historical period on target period. Finally, the results are obtained by weighted integration of each component. Its prediction result is more accurate, which can provide reference for governance of urban transportation industry under the background of big data.


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    Titel :

    Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example


    Beteiligte:
    Yi Zhang (Autor:in) / Shuwang Yang (Autor:in) / Hang Zhang (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Urban traffic congestion

    Orchard, D.F. | Engineering Index Backfile | 1959


    Alleviating urban traffic congestion

    Arnott, Richard / Rave, Tilmann / Schöb, Ronnie et al. | TIBKAT | 2005


    Urban traffic-congestion problem

    MacDonald, H.A. | Engineering Index Backfile | 1946


    Alleviating urban traffic congestion

    Arnott, Richard / Rave, Tilmann / Schöb, Ronnie et al. | SLUB | 2005