The metro system has been a mainstream of public transportation as it can alleviate the pressure of urban traffic. Before designing and constructing a new metro station, it is crucial to understand its daily ridership. In this study, a combined random forest has been proposed for prediction. The combined random forest fuses random forest and geographical random forest, which can combine their advantages to effectively reduce variance and bias respectively. To demonstrate the performance, three prediction accuracy indicators and Moran’s I test were employed. These models were implemented and validated on real-world metro station ridership data in Shenzhen, China. The results demonstrate the superior performance of the combined random forest based on the accuracy indicators. The reason is considered that the combined random forest accounts for the spatial heterogeneity according to the Moran’s I test.


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    Titel :

    Prediction Analysis of Daily Ridership at Station Level for New Metro Stations


    Beteiligte:
    Li, Pengshun (Autor:in) / Zhang, Yi (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    Erscheinungsdatum :

    14.12.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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