This paper proposes a research framework for investigating the travel patterns of dockless bike-sharing and accomplishing the large-scale bike rebalancing at the city level. A case study involving Shanghai combines GPS-based bike-sharing usage data and road network data. First, the spatiotemporal mobility patterns are analyzed visually; then community detection is used to divide the study area into management sub-areas according to the mobility characteristics of bike-sharing users; in addition, a clustering algorithm is used to identify virtual stations. On this basis, a heuristic algorithm is used to generate a rebalancing scheme that enables multiple visits to a given station. The results show that Shanghai can be divided into 28 bike-sharing management sub-areas. Static rebalancing based on the identified management sub-areas reduces the number and driving distance of rebalancing vehicles in use, which is a better outcome than that with a method based on administrative divisions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Exploring travel patterns and static rebalancing strategies for dockless bike-sharing systems from multi-source data: a framework and case study


    Contributors:
    Lu, Chen (author) / Gao, Linjie (author) / Huang, Yuqiao (author)

    Published in:

    Transportation Letters ; 15 , 4 ; 336-349


    Publication date :

    2023-04-21


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Dockless bike-sharing systems: what are the implications?

    Chen, Zheyan / van Lierop, Dea / Ettema, Dick | Taylor & Francis Verlag | 2020

    Free access


    Forecasting usage and bike distribution of dockless bike-sharing using journey data

    Hua, Mingzhuang / Chen, Jingxu / Chen, Xuewu et al. | IET | 2020

    Free access