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

Huitao Lv, Haojie Li, Yanlu Chen and Tao Feng

Journal of Transport Geography, 2023, vol. 113, issue C

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.

Keywords: Origin-destination (OD) level; Competition effect; Bike-sharing; Underground; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0966692323001886

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:113:y:2023:i:c:s0966692323001886

DOI: 10.1016/j.jtrangeo.2023.103716

Access Statistics for this article

Journal of Transport Geography is currently edited by Frank Witlox

More articles in Journal of Transport Geography from Elsevier
Bibliographic data for series maintained by Catherine Liu ().