After analyzing the influencing factors of railway freight volume, the WD-WNN forecast method of railway freight volume based on grey correlation analysis is proposed, in order to improve the prediction accuracy of regional railway freight volume. The gray correlation analysis method is used to analyze the correlation between the freight volume and its influencing factors, and WNN input variables are selected by the gray correlation. Then the influencing factors original sequence are denoised by WD technology to improve the smoothness of the input variables. Finally, the freight volumes are trained and predicted by the WNN prediction model. The analysis of the freight volume of Qinghai Province in China from 1990 to 2017 shows that the GRA-WD-WNN method has a faster convergence speed and higher prediction accuracy, and the average relative error of the prediction results is only 4.30%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Railway Freight Volume Forecast Based on GRA-WD-WNN


    Beteiligte:
    Tian, Wan qi (Autor:in) / Zhao, Peng (Autor:in) / Qiao, Ke (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    3142451 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Railway freight volume forecast based on Hybird Algorithms

    Tang, Shijie / Zhang, Donglei | IEEE | 2022


    Railway Freight Volume Forecast Based on GRA-BP Model

    Wang, Wenyan / Wang, Yanbin | Springer Verlag | 2024

    Freier Zugriff



    The Application of Combined Forecast Method in Predicting Freight Volume of Railway

    Huang, F. / Shen, Y. / Tao, S. et al. | British Library Conference Proceedings | 2007