Supplementary presentation to support poster presented at Transportation Research Board Annual Meeting, 2020.</p>

Version two contains results updated to reflect corrections to bus ridership data.

Presentation details:

Session: 1695, Current Topics in Public Transportation
Session Location: Hall A / Convention Center
Session Time: Wednesday, Jan 15, 2020 8:00AM 9:45AM
Paper: 20-01322 - Exploring Variation in Built Environment Predictors of Ridership by Transit Mode

Presentation Abstract:

Many studies have identified links between the built environment and transit use. However, little is known about whether the built environment predictors of bus, train, tram and other transit services are different. Studies to date typically analyze modes in aggregate, by combining bus, train, and tram; or analyze each mode separately. Findings from these studies demonstrate the built environment attributes that are relevant for apportioning trips to transit. However, they do not differentiate demand for competing modes. This study aims to investigate if built environment impacts on transit ridership vary according to mode, by analyzing two types of co-located (matched) transit modes (train-bus and tram-bus) in Melbourne. Multivariate multiple linear regression models were estimated to identify the relationships between different indicators of the built environment with patronage of each mode.

This research indicates built environment impacts on ridership vary in type and relative importance according to mode. Tram and bus shared three significant predictors out of six. Access to employment and land use diversity were strong predictors of tram use but not bus, while bus depended on more sociodemographic and service factors than tram. In the train-bus sample, bus ridership was predicted by five built environment variables and train by four, but only one variable was a common predictor to both. These differences provide evidence that built environment impacts on transit cannot be generalized for all modes. This suggests that ridership models differentiated for modes could produce more accurate forecasts.

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Document information

Published on 02/12/19
Accepted on 02/12/19
Submitted on 02/12/19

Volume 2019, 2019
DOI: 10.26180/5de6e60d8aab7
Licence: CC BY-NC-SA license

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