Highlights Apply the Random Forest model to predict the customer demand in bike-sharing system. Propose a hub-first-route-second bike repositioning strategy based on the predicted demand. Develop a solution method incorporating the heuristic algorithm (ABC) and the exact method.

    Abstract This paper addresses a static bike repositioning problem by embedding a short-term demand forecasting process, the Random Forest (RF) model, to account for the demand dynamics in the daytime. To tackle the heterogeneous repositioning fleets, a novel repositioning operation strategy constructed on the hub-and-spoke network framework is proposed. The repositioning optimization model is formulated using mixed-integer programming. An artificial bee colony algorithm, integrated with a commercial solver, is applied to address computational complexity. Experimental results show that the RF can achieve a high forecasting accuracy, and the proposed repositioning strategy can efficiently decrease the users’ dissatisfaction.


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    Title :

    A static bike repositioning model in a hub-and-spoke network framework


    Contributors:
    Huang, Di (author) / Chen, Xinyuan (author) / Liu, Zhiyuan (author) / Lyu, Cheng (author) / Wang, Shuaian (author) / Chen, Xuewu (author)


    Publication date :

    2020-07-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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