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Demographic and Behavioural Factors Affecting Public Support for Pedestrianisation in City Centres: The Case of Edinburgh, UK

Semple, Torran; Fountas, Grigorios

Authors

Torran Semple



Abstract

This paper provides an integrated analytical framework to investigate the demographic and behavioural factors that significantly influence public support for pedestrianisation. Pedestrianisation is often introduced by local authorities with the intention of improving air quality, the walkability of streets, road safety and opportunities for the local economy, however, issues remain regarding how accessible pedestrianised areas are for individuals who have conditions that limit their mobility. Using data from a survey, conducted during 2020 in Edinburgh (UK), public perceptions towards pedestrianisation were investigated through statistical testing and the development of random forest and ordered probit models. The random forest approach can help identify the relative importance of explanatory variables, whereas the ordered probit models can unveil the demographic and behavioural determinants of public support. To account for the potential effect of unobserved heterogeneity within respondents’ perceptions, random parameters were also considered in the ordered probit modelling framework. Initial results showed that residents are generally supportive of most issues surrounding pedestrianisation. Random parameters ordered probit modelling identified mode of travel and trip frequency as significant factors affecting key aspects of public support, such that active travellers were significantly more likely to support pedestrianisation, while those who rarely visit Edinburgh city centre were more likely to oppose pedestrianisation. Overall, a variety of independent analyses and modelling approaches suggest common influences on opinion, including behavioural patterns relating to transport modal choice and trip frequency, while disability was also found to have considerable effect on support as a fixed and random parameter. The statistical models are evaluated in terms of goodness-of-fit measures, before policy implications are discussed.

Journal Article Type Article
Acceptance Date Dec 24, 2021
Online Publication Date Mar 4, 2022
Publication Date 2023-03
Deposit Date Jan 28, 2022
Publicly Available Date Mar 5, 2023
Journal International Journal of Transportation Science and Technology
Print ISSN 2046-0430
Publisher Multi-Science Publishing
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Pages 103-118
DOI https://doi.org/10.1016/j.ijtst.2021.12.001
Keywords Pedestrianisation; Public perceptions; Random forest; Random parameters ordered probit; Unobserved heterogeneity
Public URL http://researchrepository.napier.ac.uk/Output/2838761

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