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Spatiotemporal analysis for travel patterns of the personal mobility using an explainable machine learning approach: A case of Seoul, South Korea

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  • Sooyoung Lim
  • Gunhak Lee

Abstract

Shared bicycles are becoming increasingly popular as a primary mode of micro-mobility due to easy accessibility in urban areas and ability to integrate with other public transportation systems. Understanding the travel patterns and modal characteristics of shared bicycles is essential for developing sustainable and eco-friendly urban transportation systems. Although the recent advance of GeoAIs has enabled the exploration of complex, nonlinear relationships in shared mobility patterns, these studies often provide a temporally aggregated view. Consequently, they tend to overlook the dynamics of these nonlinear effects, specifically how the influence of built environment, demographic, and public transit factors shifts across different times of day and seasons. This study addresses this gap by emphasizing a LightGBM model enhanced with a robust SHapley Additive Explanation (SHAP) framework, explicitly capturing these nonlinear effects and distinguishing spatiotemporal dynamics. As an empirical case study, we examine the shared bicycle system in Seoul. The findings reveal distinct differences in travel patterns and influencing factors between weekdays and weekends. Also, it is empirically shown that shared bicycles can be served mainly as an alternative means of connecting the first- and last-mile gaps, effectively integrating with the public transportation network. This study contributes to clarify personal mobility patterns and inform sustainable transport policy by demonstrating shared bicycles’ urban potential.

Suggested Citation

  • Sooyoung Lim & Gunhak Lee, 2026. "Spatiotemporal analysis for travel patterns of the personal mobility using an explainable machine learning approach: A case of Seoul, South Korea," Environment and Planning B, , vol. 53(2), pages 363-376, February.
    • Handle: RePEc:sae:envirb:v:53:y:2026:i:2:p:363-376
    DOI: 10.1177/23998083251359763
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    1. Lv, Huitao & Li, Haojie & Chen, Yanlu & Feng, Tao, 2023. "An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 113(C).
    2. Corcoran, Jonathan & Li, Tiebei & Rohde, David & Charles-Edwards, Elin & Mateo-Babiano, Derlie, 2014. "Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events," Journal of Transport Geography, Elsevier, vol. 41(C), pages 292-305.
    3. Qiu, Waishan & Chang, Hector, 2021. "The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca," Journal of Transport Geography, Elsevier, vol. 96(C).
    4. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2016. "Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system," Journal of Transport Geography, Elsevier, vol. 54(C), pages 218-227.
    5. Kim, Minjun & Cho, Gi-Hyoug, 2021. "Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns," Journal of Transport Geography, Elsevier, vol. 93(C).
    6. 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).
    7. Shelat, Sanmay & Huisman, Raymond & van Oort, Niels, 2018. "Analysing the trip and user characteristics of the combined bicycle and transit mode," Research in Transportation Economics, Elsevier, vol. 69(C), pages 68-76.
    8. Xiaolu Zhou, 2015. "Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    9. Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    10. Majbah Uddin & Ho-Ling Hwang & Md Sami Hasnine, 2023. "An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City," Transportation Planning and Technology, Taylor & Francis Journals, vol. 46(4), pages 482-498, May.
    11. Rui Jiang & Mao-Bin Hu & Qing-Song Wu & Wei-Guo Song, 2017. "Traffic Dynamics of Bicycle Flow: Experiment and Modeling," Transportation Science, INFORMS, vol. 51(3), pages 998-1008, August.
    12. Faghih-Imani, Ahmadreza & Eluru, Naveen & El-Geneidy, Ahmed M. & Rabbat, Michael & Haq, Usama, 2014. "How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal," Journal of Transport Geography, Elsevier, vol. 41(C), pages 306-314.
    13. Wafic El-Assi & Mohamed Salah Mahmoud & Khandker Nurul Habib, 2017. "Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto," Transportation, Springer, vol. 44(3), pages 589-613, May.
    14. De Zhao & Ghim Ping Ong & Wei Wang & Wei Zhou, 2021. "Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    15. 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).
    16. Caigang, Zhuang & Shaoying, Li & Zhangzhi, Tan & Feng, Gao & Zhifeng, Wu, 2022. "Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level," Journal of Transport Geography, Elsevier, vol. 102(C).
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