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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    Title :

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


    Contributors:
    Siyuan Zhang (author) / Shijun Yu (author) / Shejun Deng (author) / Qinghui Nie (author) / Pengpeng Zhang (author) / Chen Chen (author)


    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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