The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.


    Access

    Download


    Export, share and cite



    Title :

    Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain


    Contributors:
    Xiang Li (author) / Qipeng Yan (author) / Yixiong Tang (author) / Chen Luo (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    The analysis of spatio-temporal characteristics and determinants of dockless bike-sharing and metro integration

    Ma, Junze / Zheng, Changjiang / Yu, Miao et al. | Taylor & Francis Verlag | 2024


    Dockless bike-sharing systems: what are the implications?

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

    Free access

    Dynamic incentive schemes for managing dockless bike-sharing systems

    Jin, Huan / Liu, Shaoxuan / So, Kut C. et al. | Elsevier | 2021


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

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

    Free access