Addressing transit mode location bias in built environment-transit mode use research

Laura Aston, Graham Currie, Md Kamruzzaman, Alexa Delbosc, Nicholas Fournier, David Teller

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Many studies have identified links between the built environment (BE) and transit use. However, little is known about whether the BE predictors of bus, train, tram and other transit modes are different. Studies to date typically analyze modes in combination; or analyze one mode at a time. A major barrier to comparing BE impacts on modes is the difference in the types of locations that tend to be serviced by each mode. A method is needed to account for this ‘mode location bias’ in order to draw robust comparison of the predictors of each mode. This study addresses this gap using data from Melbourne, Australia where three types of public transport modes (train, tram, bus) operate in tandem. Two approaches are applied to mitigate mode location bias: a) Co-located sampling – estimating ridership of different modes that are located in the same place; and b) Stratified BE sampling – observations are sampled from subcategories with similar BE characteristics. Regression analyses using both methods show that the BE variables impacting ridership vary by mode. Results from both samples suggest there are two common BE factors between tram and train, and between tram and bus; and three common BE factors between train and bus. The remaining BE predictors – three for train and tram and one for bus - are unique to each mode. The study's design makes it possible to confirm this finding is valid irrespective of the type of locations serviced by modes. This suggests planning and forecasting should consider the specific associations of different modes to their surrounding land use to accurately predict and match transit supply and demand. The Stratified sampling approach is recommended for treating location bias in future mode comparison, because it explains more ridership variability and offers a transferrable approach to generating representative samples.

Original languageEnglish
Article number102786
Number of pages14
JournalJournal of Transport Geography
Volume87
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Built environment
  • Forecasting
  • Matching
  • Multimodal
  • Public transport
  • Transit

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