The University of Southampton
University of Southampton Institutional Repository

Neighbourhood characteristics and bicycle commuting in the Greater London area

Neighbourhood characteristics and bicycle commuting in the Greater London area
Neighbourhood characteristics and bicycle commuting in the Greater London area
As the need to encourage modal shift from motorised vehicle use to active modes becomes greater, it is important to understand the key factors influencing the decision of how to travel. This paper explores the association between bicycle commuting and a range of sociodemographic and built and natural environment characteristics across wards and boroughs in Greater London, UK, with an aim to identify the key factors which influence participation. We employed a Bayesian multilevel heteroskedastic model with heterogeneity in variance, which can address dependencies in the data and unobserved heterogeneity more fully. This allowed us to account for unobserved/unmeasured covariates such as collective attitudes and the existence of cycling cultures that may differ between Greater London boroughs. We found that the propensity for bicycle commuting increases with an increase in the employment rate, the populations of white British and mixed white and black Caribbean, the proportion of terraced houses, and cycle network density. Conversely, we found that the propensity for bicycle commuting decreases with an increase in the absence of academic qualifications, the area of non-domestic buildings, the population of Indians and Pakistanis, and the number of cars per household. Our analysis also revealed important between-borough variations in the effect of key explanatory variables. Notably, the effects of the populations of Indians, Pakistanis, and mixed white and black Caribbean, and the number of cars per household all vary across Greater London boroughs. Finally, by allowing for heterogeneity in variance, we found that rates of bicycle commuting are more dispersed in Inner London and as the number of cars per household increases. Our analysis highlights the importance of cycling infrastructure in promoting bicycle commuting.
Sociodemographic, bicycle commuting, boroughs in Greater London, cycling infrastructure, heteroskedastic model, Active travel, Neighbourhood characteristics, Cycling network, Bicycle commuting, Bayesian multilevel heteroscedastic model
0967-070X
152-161
McCreery-Phillips, Samuel
fd11c75b-ca53-4fc2-98c0-c26f55cebdb5
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
McCreery-Phillips, Samuel
fd11c75b-ca53-4fc2-98c0-c26f55cebdb5
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9

McCreery-Phillips, Samuel and Heydari, Shahram (2023) Neighbourhood characteristics and bicycle commuting in the Greater London area. Transport Policy, 142, 152-161. (doi:10.1016/j.tranpol.2023.08.007).

Record type: Article

Abstract

As the need to encourage modal shift from motorised vehicle use to active modes becomes greater, it is important to understand the key factors influencing the decision of how to travel. This paper explores the association between bicycle commuting and a range of sociodemographic and built and natural environment characteristics across wards and boroughs in Greater London, UK, with an aim to identify the key factors which influence participation. We employed a Bayesian multilevel heteroskedastic model with heterogeneity in variance, which can address dependencies in the data and unobserved heterogeneity more fully. This allowed us to account for unobserved/unmeasured covariates such as collective attitudes and the existence of cycling cultures that may differ between Greater London boroughs. We found that the propensity for bicycle commuting increases with an increase in the employment rate, the populations of white British and mixed white and black Caribbean, the proportion of terraced houses, and cycle network density. Conversely, we found that the propensity for bicycle commuting decreases with an increase in the absence of academic qualifications, the area of non-domestic buildings, the population of Indians and Pakistanis, and the number of cars per household. Our analysis also revealed important between-borough variations in the effect of key explanatory variables. Notably, the effects of the populations of Indians, Pakistanis, and mixed white and black Caribbean, and the number of cars per household all vary across Greater London boroughs. Finally, by allowing for heterogeneity in variance, we found that rates of bicycle commuting are more dispersed in Inner London and as the number of cars per household increases. Our analysis highlights the importance of cycling infrastructure in promoting bicycle commuting.

Text
bike commuting London Transport Policy - Accepted Manuscript
Restricted to Repository staff only until 14 August 2025.
Request a copy
Text
1-s2.0-S0967070X23002160-main - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)

More information

Accepted/In Press date: 13 August 2023
e-pub ahead of print date: 14 August 2023
Published date: October 2023
Additional Information: Publisher Copyright: © 2023 The Authors
Keywords: Sociodemographic, bicycle commuting, boroughs in Greater London, cycling infrastructure, heteroskedastic model, Active travel, Neighbourhood characteristics, Cycling network, Bicycle commuting, Bayesian multilevel heteroscedastic model

Identifiers

Local EPrints ID: 485980
URI: http://eprints.soton.ac.uk/id/eprint/485980
ISSN: 0967-070X
PURE UUID: 673c5c63-52cd-4bc1-a603-018f0c53dd0a

Catalogue record

Date deposited: 04 Jan 2024 18:27
Last modified: 17 Mar 2024 06:40

Export record

Altmetrics

Contributors

Author: Samuel McCreery-Phillips
Author: Shahram Heydari

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×