Exploring the nonlinear effects of built environment characteristics on customized bus service

Jiangbo Wang, Xinyu (Jason) Cao, Kai Liu, De Wang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The high failure rate of demand-responsive transit (DRT) systems suggests that DRT services are viable only in selected areas. However, few studies have quantitatively examined how built environment characteristics affect DRT use. Applying gradient boosting decision trees to the data of customized bus service (CBS, a type of DRT) in Dalian, we investigate the nonlinear association between the built environment and CBS use, controlling for demographics and service features. Local accessibility at the residence and workplace are the most important correlates of CBS use, followed by the proximity of workplace to bus stop. Some built environment variables (including distance from workplace to transit stops, distance from residence to business centers, and population density) influence CBS use differently from traditional transit use observed in the literature. Furthermore, built environment variables show salient threshold associations with CBS use, guiding planners to design the CBS system efficiently.

Original languageEnglish (US)
Article number103523
JournalTransportation Research Part D: Transport and Environment
Volume114
DOIs
StatePublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Built environment
  • Demand-responsive transit
  • Gradient boosting
  • Machine learning
  • Travel behavior

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