Addressing transit mode location bias in built environment-transit mode use research
Introduction
The built environment (BE) influences transit demand by determining the locations to which individuals travel and the ease with which they can be accessed by transit (Cervero et al., 2002; Litman and Steele, 2017; Mitchell and Rapkin, 1954). However little research explores how the BE affects ridership for different transit modes (e.g. heavy/light rail and bus transit). Research in this area faces a major methodological problem: ‘mode location bias’. Transit modes have cost and capacity characteristics and a development history which means some modes always tend to operate in areas of cities with specific BE characteristics.This represents a sample bias when trying to understand how ridership of each transit modes relates to all BE features. A new methodology is needed to objectively explore how each BE feature might affect ridership for each mode, whatever spatial areas they service.
This study develops and applies two new methods to determine how BE attributes link to ridership of individual transit modes accounting for mode location bias. In doing this, the research seeks to establish if different BE factors are associated with ridership for train, tram and bus after mode location bias is addressed.
The remainder of this paper is structured as follows; previous research literature on BE and transit use is outlined including research on mode location bias and how bias is generallyaddressed in the literature. Methodology is then described including the two proposed methods addressing bias. Results are described and then discussed. Conclusions outline findings and implications for research and practice.
Section snippets
Built environment, transit use and transit modes
Empirical research finds an important role for the BE in predicting transit usage (Boulange et al., 2017; Ewing and Cervero, 2010). However almost all studies in the field explore how BE influences transit as a whole. Yet transit modes are distinctive with respect to speed, reliability and space. The priority afforded to different transit modes is a defining characteristic (Kittelson and Associates Inc. et al., 2013). Buses typically have no fixed right of way infrastructure and flexible
Research setting
This study aims to determine whether the BE attributes that predict ridership differ by mode after adjusting for mode location bias. The sample for this study was derived from metropolitan Melbourne's network of almost 20,000 transit stops (Fig. 1) (Public Transport Victoria, 2018b). Melbourne, Australia, has been the subject of studies that explore the relationship between the BE and travel behaviour (Boulange et al., 2017; Jeffrey et al., 2019). Melbourne is also a city with a diverse modal
Results
Descriptive statistics for all facilities in Melbourne are shown in Fig. 1. Descriptives for each sample are available in the online data associated with this paper (3).
Discussion
Table 7 provides an overview of the key results by bias mitigation method and also compares the results with the analysis undertaken without the use of any bias mitigation. Results with alpha levels meeting a critical threshold of 0.05 are considered to have statistical significance and are highlighted bold. Results which do not meet the significance threshold but which have a standardized effects size exceeding 0.1 are included in the table, to show effects that may have practical significance.
Conclusions
This is the first study that compared BE associations with demand for different transit modes while accounting for systematic bias in the location characteristics of modes. Testing unbiased samples makes the main finding of this study generalizable: the BE affects ridership of modes differently, irrespective of the type of location each mode services.
Linear regression of transit ridership for train, tram and bus in Melbourne produce different results when modes are modelled individually, rather
Notes
1 - Results pertaining to the ‘Co-located’ sample were presented as a poster at the 2020 Transportation Research Board Annual Meetingtion Aston et al., 2020.
2 - The data and data aggregation procedures pertaining to this paper are available on Bridges at https://doi.org/10.26180/5d9994f4704ea.
3 - Supplementary results and tables are available on GitHub at https://github.com/Laura-k-a/BE-TU_Melb_Clusters and indexed in supplementary Table 8 associated with this paper.
Acknowledgments
We would like to acknowledge funding provided by the Victorian Department of Transport and the Commonwealth Government's Research and Training Program to support the research activities of Laura Aston, the lead author of this paper.
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