Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.


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    Titel :

    Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System


    Beteiligte:
    Siyuan Zhang (Autor:in) / Shijun Yu (Autor:in) / Shejun Deng (Autor:in) / Qinghui Nie (Autor:in) / Pengpeng Zhang (Autor:in) / Chen Chen (Autor:in)


    Erscheinungsdatum :

    2019




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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