The accuracy of traffic flow prediction is significantly degraded by data anomalies and data noise. To solve this problem, a hybrid traffic flow prediction model based on iForest-VMD-GRU is proposed in this paper. Firstly, we detect the outlier anomalies in the original traffic speed sequence through iForest and use interpolation to complete the normal traffic speed sequence. Then, to reduce the interference of noisy data and improve the model prediction accuracy, we decompose the normal traffic speed sequence into basic trend components and multiple random fluctuation components through VMD. Finally, GRU is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. The empirical analysis shows that the proposed model in this paper can significantly improve the prediction performance.


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    Titel :

    Highway travel speed prediction based on ETC toll data


    Beteiligte:
    Liu, Wei (Autor:in) / Zhao, Min (Autor:in)

    Kongress:

    Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022) ; 2022 ; Guangzhou,China


    Erschienen in:

    Proc. SPIE ; 12302


    Erscheinungsdatum :

    23.11.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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