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An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning

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  • Lv, Huitao
  • Li, Haojie
  • Chen, Yanlu
  • Feng, Tao

Abstract

Bike-sharing offers a convenient transportation option, enhancing the potential for direct competition with underground transportation, especially for short-distance trips. However, research on bike-sharing trips primarily focuses on survey data or aggregated data at the station-level. Few attempts have been made to understand the competition between bike-sharing and underground at the origin-destination (OD) level. This study aims to explore the competitiveness of bike-sharing to the underground at short-distance level using actual OD-level bike-sharing and underground ridership data collected in London. Light Gradient Boosting Machine and SHapley additive explanations models are employed for the analysis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:113:y:2023:i:c:s0966692323001886
    DOI: 10.1016/j.jtrangeo.2023.103716
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