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Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

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  • Gao, Kun
  • Yang, Ying
  • Gil, Jorge
  • Qu, Xiaobo

Abstract

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jotrge:v:110:y:2023:i:c:s0966692323000765
    DOI: 10.1016/j.jtrangeo.2023.103604
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    as
    1. 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.
    2. Arias-Molinares, Daniela & Romanillos, Gustavo & García-Palomares, Juan Carlos & Gutiérrez, Javier, 2021. "Exploring the spatio-temporal dynamics of moped-style scooter sharing services in urban areas," Journal of Transport Geography, Elsevier, vol. 96(C).
    3. 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).
    4. Roya Etminani-Ghasrodashti & Shima Hamidi, 2019. "Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    5. McKenzie, Grant, 2019. "Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C," Journal of Transport Geography, Elsevier, vol. 78(C), pages 19-28.
    6. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    7. Steffen Andreas Schüle & Gabriele Bolte, 2015. "Interactive and Independent Associations between the Socioeconomic and Objective Built Environment on the Neighbourhood Level and Individual Health: A Systematic Review of Multilevel Studies," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-31, April.
    8. Gilbert Laporte & Frédéric Meunier & Roberto Wolfler Calvo, 2018. "Shared mobility systems: an updated survey," Annals of Operations Research, Springer, vol. 271(1), pages 105-126, December.
    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. 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.
    11. 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).
    12. Cheng, Long & Huang, Jie & Jin, Tanhua & Chen, Wendong & Li, Aoyong & Witlox, Frank, 2023. "Comparison of station-based and free-floating bikeshare systems as feeder modes to the metro," Journal of Transport Geography, Elsevier, vol. 107(C).
    13. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    14. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
    15. 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.
    16. 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.
    17. David L. McCollum & Charlie Wilson & Michela Bevione & Samuel Carrara & Oreane Y. Edelenbosch & Johannes Emmerling & Céline Guivarch & Panagiotis Karkatsoulis & Ilkka Keppo & Volker Krey & Zhenhong Li, 2018. "Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles," Nature Energy, Nature, vol. 3(8), pages 664-673, August.
    18. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    19. Pengfei Lin & Jiancheng Weng & Quan Liang & Dimitrios Alivanistos & Siyong Ma, 2020. "Impact of Weather Conditions and Built Environment on Public Bikesharing Trips in Beijing," Networks and Spatial Economics, Springer, vol. 20(1), pages 1-17, March.
    20. Hu, Songhua & Xiong, Chenfeng & Liu, Zhanqin & Zhang, Lei, 2021. "Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic," Journal of Transport Geography, Elsevier, vol. 91(C).
    21. Becker, Henrik & Balac, Milos & Ciari, Francesco & Axhausen, Kay W., 2020. "Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 228-243.
    22. 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).
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