Examining the factors infuencing microtransit users’ next ride decisions using Bayesian networks

Jiajing He, Tai-Yu Ma

Research output: Contribution to journalArticlepeer-review

Abstract

The progress of microtransit services across the world has been slower than expected due to institutional, operational, and financial barriers. However, how users' ride experiences and system attributes affects their future ride decisions remain an important issue for successful deployment. A Bayesian network approach is proposed to infer users’ next ride decisions on a microtransit service based on historical ride data from Kussbus, a pilot microtransit system operating in the Belgium–Luxembourg cross-border areas in 2018. The results indicate that the proposed Bayesian network approach could reveal a plausible causal relationship between different dependent factors compared to the classical multinomial logit modeling approach. By examining public transport coverage in the study area, we find that Kussbus complements the existing public transport and provides an effective alternative to personal car use.
Original languageEnglish
Article number47
JournalEuropean Transport Research Review
Volume14
Issue number47
DOIs
Publication statusPublished - 27 Oct 2022

Bibliographical note

Funding Information:
The work was supported by the Luxembourg National Research Fund (C20/SC/14703944).

Funding Information:
We thank the Utopian Future Technologies S.A. for providing Kussbus ride data for this research.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Microtransit
  • Demand-responsive transport
  • On-demand mobility
  • Bayesian network

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