IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1299-d319056.html
   My bibliography  Save this article

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Author

Listed:
  • Zijia Wang

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Lei Cheng

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yongxing Li

    (School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

  • Zhiqiang Li

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

Suggested Citation

  • Zijia Wang & Lei Cheng & Yongxing Li & Zhiqiang Li, 2020. "Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1299-:d:319056
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1299/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Yanfeng & Szeto, W.Y. & Long, Jiancheng & Shui, C.S., 2016. "A multiple type bike repositioning problem," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 263-278.
    2. Yuan Li & Zhenjun Zhu & Xiucheng Guo, 2019. "Operating Characteristics of Dockless Bike-Sharing Systems near Metro Stations: Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    3. Li, Wei & Sun, Wen & Li, Guomin & Jin, Baihui & Wu, Wen & Cui, Pengfei & Zhao, Guohao, 2018. "Transmission mechanism between energy prices and carbon emissions using geographically weighted regression," Energy Policy, Elsevier, vol. 115(C), pages 434-442.
    4. Dziauddin, Mohd Faris, 2019. "Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR)," Research in Transportation Economics, Elsevier, vol. 74(C), pages 10-20.
    5. Gu, Tianqi & Kim, Inhi & Currie, Graham, 2019. "Measuring immediate impacts of a new mass transit system on an existing bike-share system in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 20-39.
    6. Tien Dung Tran & Nicolas Ovtracht & Bruno Faivre d'Arcier, 2015. "Modeling Bike Sharing System using Built Environment Factors," Post-Print halshs-01474166, HAL.
    7. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
    8. Caggiani, Leonardo & Camporeale, Rosalia & Marinelli, Mario & Ottomanelli, Michele, 2019. "User satisfaction based model for resource allocation in bike-sharing systems," Transport Policy, Elsevier, vol. 80(C), pages 117-126.
    9. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    10. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis," Environment and Planning A, , vol. 30(11), pages 1905-1927, November.
    11. Zhang, J. & Meng, M. & Wang, David, Z.W., 2019. "A dynamic pricing scheme with negative prices in dockless bike sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 201-224.
    12. 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.
    13. Zhang, Ying & Thomas, Tom & Brussel, Mark & van Maarseveen, Martin, 2017. "Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China," Journal of Transport Geography, Elsevier, vol. 58(C), pages 59-70.
    14. Xiaolu Zhou, 2015. "Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    15. Haider, Zulqarnain & Nikolaev, Alexander & Kang, Jee Eun & Kwon, Changhyun, 2018. "Inventory rebalancing through pricing in public bike sharing systems," European Journal of Operational Research, Elsevier, vol. 270(1), pages 103-117.
    16. Mateo-Babiano, Iderlina & Bean, Richard & Corcoran, Jonathan & Pojani, Dorina, 2016. "How does our natural and built environment affect the use of bicycle sharing?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 295-307.
    17. Wafic El-Assi & Mohamed Salah Mahmoud & Khandker Nurul Habib, 2017. "Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto," Transportation, Springer, vol. 44(3), pages 589-613, May.
    18. Cheng, Peng & OuYang, Zhe & Liu, Yang, 2019. "Understanding bike sharing use over time by employing extended technology continuance theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 433-443.
    19. Mingyang Du & Lin Cheng, 2018. "Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kumar Dey, Bibhas & Anowar, Sabreena & Eluru, Naveen, 2021. "A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 119-133.
    2. 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).
    3. 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).
    4. 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.
    5. Andreas Piter & Philipp Otto & Hamza Alkhatib, 2022. "The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1294-1318, July.
    6. Chung, Jaehoon & Yao, Enjian & Pan, Long & Ko, Joonho, 2024. "Understanding the route choice preferences of private and dock-based public bike users using GPS data in Seoul, South Korea," Journal of Transport Geography, Elsevier, vol. 116(C).
    7. Nikolaos-Fivos Galatoulas & Konstantinos N. Genikomsakis & Christos S. Ioakimidis, 2020. "Spatio-Temporal Trends of E-Bike Sharing System Deployment: A Review in Europe, North America and Asia," Sustainability, MDPI, vol. 12(11), pages 1-17, June.

    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. 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.
    2. Dongdong Feng & Lin Cheng & Mingyang Du, 2020. "Exploring the Impact of Dockless Bikeshare on Docked Bikeshare—A Case Study in London," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    3. Hui Bi & Zhirui Ye & He Zhu, 2024. "Mining bike sharing trip record data: a closer examination of the operating performance at station level," Transportation, Springer, vol. 51(3), pages 1015-1041, June.
    4. 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).
    5. Wang, Yacan & Li, Jingjing & Su, Duan & Zhou, Huiyu, 2023. "Spatial-temporal heterogeneity and built environment nonlinearity in inconsiderate parking of dockless bike-sharing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    6. Si, Hongyun & Su, Yangyue & Wu, Guangdong & Liu, Bingsheng & Zhao, Xianbo, 2020. "Understanding bike-sharing users’ willingness to participate in repairing damaged bicycles: Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 203-220.
    7. Hu, Yujie & Zhang, Yongping & Lamb, David & Zhang, Mingming & Jia, Peng, 2019. "Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US," Applied Energy, Elsevier, vol. 247(C), pages 1-12.
    8. Ji, Shujuan & Liu, Xiaojie & Wang, Yuanqing, 2024. "The role of road infrastructures in the usage of bikeshare and private bicycle," Transport Policy, Elsevier, vol. 149(C), pages 234-246.
    9. Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar, 2022. "Spatiotemporal evolving patterns of bike-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    10. 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.
    11. 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.
    12. 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.
    13. Liu, Hung-Chi & Lin, Jen-Jia, 2019. "Associations of built environments with spatiotemporal patterns of public bicycle use," Journal of Transport Geography, Elsevier, vol. 74(C), pages 299-312.
    14. Mix, Richard & Hurtubia, Ricardo & Raveau, Sebastián, 2022. "Optimal location of bike-sharing stations: A built environment and accessibility approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 126-142.
    15. Namkung, Ok Stella & Park, Jonghan & Ko, Joonho, 2023. "Public bike users’ annual travel distance: Findings from combined data of user survey and annual rental records," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    16. 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).
    17. Xinwei Ma & Ruiming Cao & Jianbiao Wang, 2019. "Effects of Psychological Factors on Modal Shift from Car to Dockless Bike Sharing: A Case Study of Nanjing, China," IJERPH, MDPI, vol. 16(18), pages 1-16, September.
    18. Andreas Piter & Philipp Otto & Hamza Alkhatib, 2022. "The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1294-1318, July.
    19. 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).
    20. Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

    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:gam:jsusta:v:12:y:2020:i:4:p:1299-:d:319056. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.