IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v104y2022ics0966692322001764.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692322001764
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2022.103453?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    2. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    3. Tuncer, Sylvaine & Laurier, Eric & Brown, Barry & Licoppe, Christian, 2020. "Notes on the practices and appearances of e-scooter users in public space," Journal of Transport Geography, Elsevier, vol. 85(C).
    4. Sanders, Rebecca L. & Branion-Calles, Michael & Nelson, Trisalyn A., 2020. "To scoot or not to scoot: Findings from a recent survey about the benefits and barriers of using E-scooters for riders and non-riders," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 217-227.
    5. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    6. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    7. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    8. Younes, Hannah & Zou, Zhenpeng & Wu, Jiahui & Baiocchi, Giovanni, 2020. "Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 308-320.
    9. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    10. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    11. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    12. Laa, Barbara & Leth, Ulrich, 2020. "Survey of E-scooter users in Vienna: Who they are and how they ride," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. Yang, Hongtai & Huo, Jinghai & Bao, Yongxing & Li, Xuan & Yang, Linchuan & Cherry, Christopher R., 2021. "Impact of e-scooter sharing on bike sharing in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 23-36.
    14. Yang, Haoran & Zhang, Qinran & Helbich, Marco & Lu, Yi & He, Dongsheng & Ettema, Dick & Chen, Long, 2022. "Examining non-linear associations between built environments around workplace and adults’ walking behaviour in Shanghai, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 234-246.
    15. Shang, Wen-Long & Chen, Jinyu & Bi, Huibo & Sui, Yi & Chen, Yanyan & Yu, Haitao, 2021. "Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis," Applied Energy, Elsevier, vol. 285(C).
    16. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
    17. Huo, Jinghai & Yang, Hongtai & Li, Chaojing & Zheng, Rong & Yang, Linchuan & Wen, Yi, 2021. "Influence of the built environment on E-scooter sharing ridership: A tale of five cities," Journal of Transport Geography, Elsevier, vol. 93(C).
    18. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    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. 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. 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.
    3. 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).
    4. Jin, Scarlett T. & Wang, Lei & Sui, Daniel, 2023. "How the built environment affects E-scooter sharing link flows: A machine learning approach," Journal of Transport Geography, Elsevier, vol. 112(C).
    5. 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).
    6. Sun, Shan & Guo, Liang & Yang, Shuo & Cao, Jason, 2024. "Exploring the contributions of Ebike ownership, transit access, and the built environment to car ownership in a developing city," Journal of Transport Geography, Elsevier, vol. 116(C).

    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. Jin, Scarlett T. & Wang, Lei & Sui, Daniel, 2023. "How the built environment affects E-scooter sharing link flows: A machine learning approach," Journal of Transport Geography, Elsevier, vol. 112(C).
    2. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    3. Maximilian Heumann & Tobias Kraschewski & Tim Brauner & Lukas Tilch & Michael H. Breitner, 2021. "A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage," Sustainability, MDPI, vol. 13(22), pages 1-24, 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).
    5. Yang, Hongtai & Huo, Jinghai & Bao, Yongxing & Li, Xuan & Yang, Linchuan & Cherry, Christopher R., 2021. "Impact of e-scooter sharing on bike sharing in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 23-36.
    6. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    7. Yin, Chun & Cao, Jason & Sun, Bindong & Liu, Jiahang, 2023. "Exploring built environment correlates of walking for different purposes: Evidence for substitution," Journal of Transport Geography, Elsevier, vol. 106(C).
    8. Krauss, Konstantin & Gnann, Till & Burgert, Tobias & Axhausen, Kay W., 2024. "Faster, greener, scooter? An assessment of shared e-scooter usage based on real-world driving data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    9. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    10. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    11. Gao, Kun & Yang, Ying & Gil, Jorge & Qu, Xiaobo, 2023. "Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility," Journal of Transport Geography, Elsevier, vol. 110(C).
    12. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    13. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    14. Tao, Tao & Cao, Jason, 2023. "Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode," Journal of Transport Geography, Elsevier, vol. 109(C).
    15. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    16. Huo, Jinghai & Yang, Hongtai & Li, Chaojing & Zheng, Rong & Yang, Linchuan & Wen, Yi, 2021. "Influence of the built environment on E-scooter sharing ridership: A tale of five cities," Journal of Transport Geography, Elsevier, vol. 93(C).
    17. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
    18. Yang, Jiawen & Cao, Jason & Zhou, Yufei, 2021. "Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 74-88.
    19. Samira Dibaj & Aryan Hosseinzadeh & Miloš N. Mladenović & Robert Kluger, 2021. "Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas," Sustainability, MDPI, vol. 13(21), pages 1-27, October.
    20. Li, Wu & Zhao, Shengchuan & Ma, Jingwen & Nielsen, Otto Anker & Jiang, Yu, 2023. "Book-ahead ride-hailing trip and its determinants: Findings from large-scale trip records in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(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:eee:jotrge:v:104:y:2022:i:c:s0966692322001764. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.