The forecast of railway freight volume has important influence on effective allocation of railway resource. In the paper, we introduced a novel non-linear regression method: random forest regression (RFR), to quantitatively estimate China railway freight volume. Through analyzing the monthly data on railway freight volume between 2001 and 2013 by RFR model, we get a series of predicted results and the results show that Mean Absolute Error and Mean Relative Error are respectively 736.15 million tons and 3.32%. The RFR model has the characteristics of high precision of prediction, strong generalization ability, good robust performance and less adjustable parameters.


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    Titel :

    Forecast of China railway freight volume by random forest regression model


    Beteiligte:
    Junning Gao, (Autor:in) / Xiaochun Lu, (Autor:in)


    Erscheinungsdatum :

    2015-07-01


    Format / Umfang :

    320314 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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