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Nonlinear and threshold effects of the built environment on e-scooter sharing ridership

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  • Yang, Hongtai
  • Zheng, Rong
  • Li, Xuan
  • Huo, Jinghai
  • Yang, Linchuan
  • Zhu, Tong

Abstract

Understanding the relationship between the built environment and e-scooter sharing (ESS) usage is important because it could help planners determine the high-demand area and design an effective investment plan to promote the use of micromobility. Previous studies usually assume that the relationship is linear, which may lead to inaccurate ridership prediction and ineffective policy. Thus, this study explores the nonlinear and threshold effects of the built environment on ESS ridership in Los Angeles using the gradient boosting decision tree. Fourteen built environment and ten demographic variables are selected as independent variables. We find that the built environment accounts for 91.66% of the total relative importance. The ten most important variables are intersection density, road density, public transit station density, restaurant density, employment density, distance to the center, population density, proportion of park area, parking density, and bike lane density. The nonlinear and threshold effects of the built environment on ESS ridership are determined. By using two spatial analysis units (census tract and census block group) and four temporal analysis units (spring, summer, autumn, and winter), the modifiable areal unit problem and the modifiable temporal unit problem are revealed.

Suggested Citation

  • Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:jotrge:v:104:y:2022:i:c:s0966692322001764
    DOI: 10.1016/j.jtrangeo.2022.103453
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    References listed on IDEAS

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    1. Karimpour, Abolfazl & Hosseinzadeh, Aryan & Kluger, Robert, 2023. "A data-driven approach to estimating dockless electric scooter service areas," Journal of Transport Geography, Elsevier, vol. 109(C).
    2. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Theodora Sorkou & Panagiotis G. Tzouras & Katerina Koliou & Lambros Mitropoulos & Christos Karolemeas & Konstantinos Kepaptsoglou, 2022. "An Approach to Model the Willingness to Use of E-Scooter Sharing Services in Different Urban Road Environments," Sustainability, MDPI, vol. 14(23), pages 1-15, November.
    4. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).

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