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Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

Author

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  • Xinwei Ma

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 210096, China)

  • Yanjie Ji

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 210096, China)

  • Yuchuan Jin

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 210096, China)

  • Jianbiao Wang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 210096, China)

  • Mingjia He

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Dongnandaxue Road 2, Nanjing 210096, China)

Abstract

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

Suggested Citation

  • Xinwei Ma & Yanjie Ji & Yuchuan Jin & Jianbiao Wang & Mingjia He, 2018. "Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model," Sustainability, MDPI, vol. 10(11), pages 1-12, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3949-:d:179238
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    References listed on IDEAS

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    2. Milad Malekzadeh & Jed A. Long, 2024. "Mobility deviation index: incorporating geographical context into analysis of human mobility," Journal of Geographical Systems, Springer, vol. 26(3), pages 427-448, July.
    3. Hu, Songhua & Chen, Mingyang & Jiang, Yuan & Sun, Wei & Xiong, Chenfeng, 2022. "Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems," Journal of Transport Geography, Elsevier, vol. 98(C).
    4. 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).
    5. 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.
    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. Cheng, Long & Wang, Kailai & De Vos, Jonas & Huang, Jie & Witlox, Frank, 2022. "Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 175-187.
    8. Pfiester, Laura Mali & Thompson, Russell G. & Zhang, Lele, 2021. "Spatiotemporal exploration of Melbourne pedestrian demand," Journal of Transport Geography, Elsevier, vol. 95(C).
    9. 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.
    10. Zaouche, Mounia & Bode, Nikolai W.F., 2023. "Bayesian spatio-temporal models for mapping urban pedestrian traffic," Journal of Transport Geography, Elsevier, vol. 111(C).
    11. Chen, Qun & Pan, Xiaoyi & Liu, Fang & Xiong, Yong & Li, Zhitao & Tang, Jinjun, 2022. "Reposition optimization in free-floating bike-sharing system: A case study in Shenzhen City," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    12. Rui Zhao & Linchuan Yang & Xinrong Liang & Yuanyuan Guo & Yi Lu & Yixuan Zhang & Xinyun Ren, 2019. "Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    13. Qian, Xiaodong & Xiao, Runhua (Ivan) & Joby, Raina & Jaller, Miguel, 2024. "Optimizing bikeshare service to connect affordable housing units with transit service," Journal of Transport Geography, Elsevier, vol. 117(C).

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