RESEARCH ARTICLE


Projecting the Impact of New Public Transport Networks on Mode Shift



Richard Ryall1, *, Matthew Sullivan-Kilgour1
1 Ironbark Sustainability, Suite 8, Level 2, 70-80 Wellington St, Collingwood, VIC 3066, Australia


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Creative Commons License
© 2023 Ryall and Sullivan-Kilgour

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Ironbark Sustainability, Suite 8, Level 2, 70-80 Wellington St, Collingwood, VIC 3066, Australia; E-mail: richard@realaction.com.au


Abstract

Background:

Although many studies have identified the key factors influencing travel mode, they have typically centred around survey data, which has several limitations. In this research, actual transport data (GPS) has been provided by Google Environment Insights Explorer (EIE) for 169 municipalities in Australia across 2018 and 2019.

Objective:

A key outcome of this paper is to project the independent impact of new public transport networks (rail, bus and tram) on mode shift away from vehicles for each municipality and estimate the total distance travelled.

Methods:

This study uses a combination of linear regression and logit transformations to predict the proportion of automobile transport relative to all other transport modes.

Results:

The results suggest that South Australia would benefit from a metropolitan northeast rail line, New South Wales would benefit from a metropolitan southwest tram line, and Victoria would benefit from a metropolitan southeast bus service.

Conclusion:

Although the analysis is somewhat crude, it utilises open-access data and thus could be easily replicated for any country globally, which could be greatly beneficial, especially for countries with low socio-demographic backgrounds.

Keywords: Mode shift, Public transport, Predictions, Big data, GPS, EIE.