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An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China

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  • Li, Aoyong
  • Zhao, Pengxiang
  • Huang, Yizhe
  • Gao, Kun
  • Axhausen, Kay W.

Abstract

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R2 = 0.774) than the OLS regression model (Adjusted R2 = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.

Suggested Citation

  • Li, Aoyong & Zhao, Pengxiang & Huang, Yizhe & Gao, Kun & Axhausen, Kay W., 2020. "An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jotrge:v:88:y:2020:i:c:s0966692320302623
    DOI: 10.1016/j.jtrangeo.2020.102828
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    References listed on IDEAS

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    3. Ross-Perez, Antonio & Walton, Neil & Pinto, Nuno, 2022. "Identifying trip purpose from a dockless bike-sharing system in Manchester," Journal of Transport Geography, Elsevier, vol. 99(C).
    4. Zhang, Ziru & Krishnakumari, Panchamy & Schulte, Frederik & van Oort, Niels, 2023. "Improving the service of E-bike sharing by demand pattern analysis: A data-driven approach," Research in Transportation Economics, Elsevier, vol. 101(C).
    5. Giuffrida, Nadia & Pilla, Francesco & Carroll, Páraic, 2023. "The social sustainability of cycling: Assessing equity in the accessibility of bike-sharing services," Journal of Transport Geography, Elsevier, vol. 106(C).
    6. Mohiuddin, Hossain & Fitch, Dillon & Handy, Susan, 2022. "Examining Market Segmentation to Increase Bike-Share Use: The Case of the Greater Sacramento Region," Institute of Transportation Studies, Working Paper Series qt71h6g0td, Institute of Transportation Studies, UC Davis.
    7. Li, Chunzhi & Xiao, Wei & Zhang, Dayong & Ji, Qiang, 2021. "Low-carbon transformation of cities: Understanding the demand for dockless bike sharing in China," Energy Policy, Elsevier, vol. 159(C).
    8. 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).
    9. Zhitao Li & Yuzhen Shang & Guanwei Zhao & Muzhuang Yang, 2022. "Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    10. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    11. Yiling Deng & Pengjun Zhao, 2023. "The determinants of shared bike use in China," Transportation, Springer, vol. 50(1), pages 1-23, February.

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