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Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Author

Listed:
  • Fan Yang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Fan Ding

    (Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA)

  • Xu Qu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Bin Ran

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

Suggested Citation

  • Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3220-:d:238709
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    Cited by:

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    2. Wang, Xudong & Cheng, Zhanhong & Trépanier, Martin & Sun, Lijun, 2021. "Modeling bike-sharing demand using a regression model with spatially varying coefficients," Journal of Transport Geography, Elsevier, vol. 93(C).
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    4. Shuo Zhang & Li Chen & Yingzi Li, 2021. "Shared Bicycle Distribution Connected to Subway Line Considering Citizens’ Morning Peak Social Characteristics for Urban Low-Carbon Development," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
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    8. Daozhi Zhao & Di Wang, 2019. "The Research of Tripartite Collaborative Governance on Disorderly Parking of Shared Bicycles Based on the Theory of Planned Behavior and Motivation Theories—A Case of Beijing, China," Sustainability, MDPI, vol. 11(19), pages 1-21, September.

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