IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v174y2025ics0967070x25003683.html

Investigating the influencing factors of cooperation between shared e-bikes and subway systems: A multivariate data analysis

Author

Listed:
  • Wei, Daqin
  • Zhang, Hongyang
  • Tang, Wei
  • Gong, Jinrui
  • Mei, Zhenyu

Abstract

Shared electric bikes (shared e-bikes) are used to connect the public transportation and have gained popularity in numerous big cities over the past decades. However, the factors influencing the integrated use of shared e-bikes and subway remain unclear. Utilizing extensive individual trip data and built environment variables as independent factors, this study established a moderated multiple regression (MMR) model to explore the cooperation relationship between shared e-bikes and subway. The findings reveal that higher land-use diversity promote the combined use of shared e-bikes and subway by reducing long-distance bike trip. In contrast, higher road density facilitates the use of shared e-bikes for entire trips, thereby discouraging their use as a means of connecting to subways. Furthermore, the study uncovers disparities in public transportation usage across different socioeconomic classes. Additionally, shared e-bike's origin distance to the nearest subway station (ODS) and destination distance to the nearest subway station (DDS) were found to have an interaction effect on the cooperation level. Shared e-bike users whose travel purpose can be met within the pedestrian catchment area around subway stations are more likely to use e-bikes to solve the last mile problem. Temporal heterogeneity examination shows that subway proximity has higher influence on people's choice to integrate shared e-bikes with subways on weekdays than holidays. There findings provide references for shared e-bikes’ deployment and public transit planning.

Suggested Citation

  • Wei, Daqin & Zhang, Hongyang & Tang, Wei & Gong, Jinrui & Mei, Zhenyu, 2025. "Investigating the influencing factors of cooperation between shared e-bikes and subway systems: A multivariate data analysis," Transport Policy, Elsevier, vol. 174(C).
  • Handle: RePEc:eee:trapol:v:174:y:2025:i:c:s0967070x25003683
    DOI: 10.1016/j.tranpol.2025.103825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X25003683
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2025.103825?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    2. Dillon T. Fitch & Hossain Mohiuddin & Susan L. Handy, 2021. "Examining the Effects of the Sacramento Dockless E-Bike Share on Bicycling and Driving," Sustainability, MDPI, vol. 13(1), pages 1-26, January.
    3. Tomasz Bieliński & Łukasz Dopierała & Maciej Tarkowski & Agnieszka Ważna, 2020. "Lessons from Implementing a Metropolitan Electric Bike Sharing System," Energies, MDPI, vol. 13(23), pages 1-21, November.
    4. Ahmed El-Geneidy & Michael Grimsrud & Rania Wasfi & Paul Tétreault & Julien Surprenant-Legault, 2014. "New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas," Transportation, Springer, vol. 41(1), pages 193-210, January.
    5. Yang Liu & Rui Tang & Zhuangbin Shi & Mingwei He & Long Cheng, 2025. "Shared mobility choices in metro connectivity: shared bikes versus shared e-bikes," Transportation, Springer, vol. 52(6), pages 2187-2213, December.
    6. Ying Ni & Jiaqi Chen, 2020. "Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    7. Kong, Hui & Chao, Hao & Fu, Wenyan & Lin, Diao & Zhang, Yongping, 2025. "Relationship between shared micromobility and public transit: The differences between shared bikes and shared E-bikes," Journal of Transport Geography, Elsevier, vol. 123(C).
    8. Yun, Meiping & Huang, Wenxu & Zhang, Cen & Yan, Xi & Zheng, Jun, 2024. "Quantitative analysis of the relationships between dockless bike sharing and public transport: A trip-level perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    9. Ma, Xinwei & Ji, Yanjie & Yang, Mingyuan & Jin, Yuchuan & Tan, Xu, 2018. "Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data," Transport Policy, Elsevier, vol. 71(C), pages 57-69.
    10. Ziedan, Abubakr & Darling, Wesley & Brakewood, Candace & Erhardt, Greg & Watkins, Kari, 2021. "The impacts of shared e-scooters on bus ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 20-34.
    11. Huang, Ganxiang & Wang, Hongyan & Xu, Di, 2024. "Understanding the complementary effect of bike-sharing on public transit: A case study of subway line expansion in Xiamen, China," Journal of Transport Geography, Elsevier, vol. 121(C).
    12. Cairns, S. & Behrendt, F. & Raffo, D. & Beaumont, C. & Kiefer, C., 2017. "Electrically-assisted bikes: Potential impacts on travel behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 327-342.
    13. Clare Field & Ihnji Jon, 2021. "E-Scooters: A New Smart Mobility Option? The Case of Brisbane, Australia," Planning Theory & Practice, Taylor & Francis Journals, vol. 22(3), pages 368-396, May.
    14. Cherry, Christopher R. & Weinert, Jonathan X. & Yang, Xinmiao, 2009. "Comparative Environmental Impacts of Electric Bikes in China," Institute of Transportation Studies, Working Paper Series qt16k918sh, Institute of Transportation Studies, UC Davis.
    15. Guidon, Sergio & Reck, Daniel J. & Axhausen, Kay, 2020. "Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests," Journal of Transport Geography, Elsevier, vol. 84(C).
    16. Zhao, Pengjun & Li, Shengxiao, 2017. "Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 46-60.
    17. Huo, Jinghai & Yang, Hongtai & Li, Chaojing & Zheng, Rong & Yang, Linchuan & Wen, Yi, 2021. "Influence of the built environment on E-scooter sharing ridership: A tale of five cities," Journal of Transport Geography, Elsevier, vol. 93(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhan, Zilin & Guo, Yuanyuan & Noland, Robert B. & He, Sylvia Y. & Wang, Yacan, 2023. "Analysis of links between dockless bikeshare and metro trips in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    2. Lidong Zhu & Mujahid Ali & Elżbieta Macioszek & Mahdi Aghaabbasi & Amin Jan, 2022. "Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    3. Zhang, Yongping & Fu, Wenyan & Yan, Jiaxin & Chao, Hao & Kong, Hui, 2025. "The effect of dockless e-bike-sharing on reducing carbon emissions: A dynamic investigation of two small Chinese cities between 2020 and 2022," Journal of Transport Geography, Elsevier, vol. 128(C).
    4. Kimpton, Anthony & Loginova, Julia & Pojani, Dorina & Bean, Richard & Sigler, Thomas & Corcoran, Jonathan, 2022. "Weather to scoot? How weather shapes shared e-scooter ridership patterns," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Chen Xie & Dexin Yu & Ciyun Lin & Xiaoyu Zheng & Bo Peng, 2022. "Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data," Sustainability, MDPI, vol. 14(10), pages 1-24, May.
    6. Li, Wenxiang & Chen, Shawen & Dong, Jieshuang & Wu, Jingxian, 2021. "Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros," Journal of Transport Geography, Elsevier, vol. 92(C).
    7. Yao, Di & Zhang, Chunqin & Li, Jinpei, 2025. "How does bike-sharing prompt the modal shift from cars to public transit?," Transport Policy, Elsevier, vol. 174(C).
    8. Wu, Xueying & Lu, Yi & Gong, Yongxi & Kang, Yuhao & Yang, Linchuan & Gou, Zhonghua, 2021. "The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space," Journal of Transport Geography, Elsevier, vol. 97(C).
    9. Ma, Xinwei & Xie, Ruiyuan & Meng, Yu & Guo, Longxiao & Li, Zibiao, 2025. "Effects of catchment measurement on the associations between determinants and metro-ridesourcing integration," Journal of Transport Geography, Elsevier, vol. 128(C).
    10. Liu, Yang & Li, Liqiong & Liu, Kai & He, Mingwei & Shi, Zhuangbin, 2025. "Investigating user preferences for dockless bike- and electric bike-sharing through tracking usage patterns," Transport Policy, Elsevier, vol. 169(C), pages 41-55.
    11. Jenkins, Michael & Lustosa, Lucio & Chia, Victoria & Wildish, Sarah & Tan, Maria & Hoornweg, Daniel & Lloyd, Meghann & Dogra, Shilpa, 2022. "What do we know about pedal assist E-bikes? A scoping review to inform future directions," Transport Policy, Elsevier, vol. 128(C), pages 25-37.
    12. Zhu, Yisong & Yang, Ziqi & Feng, Xi & Cheng, Cheng & Guo, Yuntao & Li, Qiumeng & Wu, Tianhao & Li, Xinghua & Witlox, Frank, 2025. "Comparing built environment effects on bike-sharing and electric bike-sharing usage: a spatiotemporal machine learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 200(C).
    13. Shah, Nitesh R. & Ziedan, Abubakr & Brakewood, Candace & Cherry, Christopher R., 2023. "Shared e-scooter service providers with large fleet size have a competitive advantage: Findings from e-scooter demand and supply analysis of Nashville, Tennessee," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    14. Ying Ni & Jiaqi Chen, 2020. "Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    15. Yoo, Sunbin & Kumagai, Junya & Kim, Sung Hoo & Managi, Shunsuke, 2026. "Shared e-mopeds and equity implications: Insights from trip-level data in Fukuoka, Japan," Journal of Transport Geography, Elsevier, vol. 130(C).
    16. Bi, Hui & Gao, Hui & Li, Aoyong & Ye, Zhirui, 2024. "Using topic modeling to unravel the nuanced effects of built environment on bicycle-metro integrated usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    17. Krauss, Konstantin & Gnann, Till & Burgert, Tobias & Axhausen, Kay W., 2024. "Faster, greener, scooter? An assessment of shared e-scooter usage based on real-world driving data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    18. Kyoungok Kim, 2024. "Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul," Transportation, Springer, vol. 51(4), pages 1373-1407, August.
    19. Gan, Zuoxian & Yang, Min & Zeng, Qingcheng & Timmermans, Harry J.P., 2021. "Associations between built environment, perceived walkability/bikeability and metro transfer patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 171-187.
    20. Liu, Yixiao & Tian, Zihao & Pan, Baoran & Zhang, Wenbin & Liu, Yunqi & Tian, Lixin, 2022. "A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing," Applied Energy, Elsevier, vol. 315(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:trapol:v:174:y:2025:i:c:s0967070x25003683. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.