How commuters’ motivations to drive relate to propensity to carpool: evidence from the United Kingdom and the United States
How commuters’ motivations to drive relate to propensity to carpool: evidence from the United Kingdom and the United States
This paper examines how commuter motivations to drive relate to propensities to carpool, using two sequential studies: Study 1 determines the key dimensions of commuters’ motivations for driving using secondary data (N = 432) from staff and postgraduate student commuters at a United Kingdom university. We code the contents of the respondents’ self-reported reasons for driving (i.e. the instrumental rationalities attributed to driving with respect to varying purposes) to identify keywords representing motivation to drive; we then analyse the keyword data using multidimensional scaling (MDS). Study 2 examines how the dimensions discovered through study 1 relate to propensity to carpool, using structural equation modelling (SEM). Data for Study 2 are the survey responses of commuters (N = 1028) based in the United States. The MDS reveals four key dimensions of motivation to drive. These capture instrumental rationalities for driving within four situational domains which we term: (1) Family; (2) Public transport impractical changes; (3) Rigid schedule; and (4) Non-urban areas. The SEM results show that the regression coefficients on propensity to carpool of Public transport impractical changes and Rigid schedule are significant and negative; the Family domain has a positive but non-significant regression coefficient. Regarding demographics, men’s mean values on all four domain variables are significantly higher than those for women, except for the Family domain where the mean value for women is higher; meanwhile, age predicts decreasing propensity to carpool. Additionally, the situational domains of Public transport impractical changes, Rigid schedule and Non-urban areas significantly positively correlate. Consequently, overall, the results imply that addressing commuters’ instrumental rationalities for driving, namely increasing schedule flexibility and providing more direct or quicker public transport, could indirectly encourage commuters to carpool. The study makes an original contribution by estimating the causal relationship between commuters’ motivation to drive and propensity to carpool.
Propensity to Carpool; Motivations to drive; Carpooling; Commuting; Multidimensional Scaling; Structural Equation Modelling.
128-148
Neoh, Jun Guan
2bbc9ad6-b5ad-477f-b62e-6db65c153687
Chipulu, Maxwell
12545803-0d1f-4a37-b2d2-f0d21165205e
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616
Tewkesbury, Adam
814a6ae9-5741-4978-b942-d062875ae1e4
April 2018
Neoh, Jun Guan
2bbc9ad6-b5ad-477f-b62e-6db65c153687
Chipulu, Maxwell
12545803-0d1f-4a37-b2d2-f0d21165205e
Marshall, Alasdair
93aa95a2-c707-4807-8eaa-1de3b994b616
Tewkesbury, Adam
814a6ae9-5741-4978-b942-d062875ae1e4
Neoh, Jun Guan, Chipulu, Maxwell, Marshall, Alasdair and Tewkesbury, Adam
(2018)
How commuters’ motivations to drive relate to propensity to carpool: evidence from the United Kingdom and the United States.
Transportation Research Part A: Policy and Practice, 110, .
(doi:10.1016/j.tra.2018.02.013).
Abstract
This paper examines how commuter motivations to drive relate to propensities to carpool, using two sequential studies: Study 1 determines the key dimensions of commuters’ motivations for driving using secondary data (N = 432) from staff and postgraduate student commuters at a United Kingdom university. We code the contents of the respondents’ self-reported reasons for driving (i.e. the instrumental rationalities attributed to driving with respect to varying purposes) to identify keywords representing motivation to drive; we then analyse the keyword data using multidimensional scaling (MDS). Study 2 examines how the dimensions discovered through study 1 relate to propensity to carpool, using structural equation modelling (SEM). Data for Study 2 are the survey responses of commuters (N = 1028) based in the United States. The MDS reveals four key dimensions of motivation to drive. These capture instrumental rationalities for driving within four situational domains which we term: (1) Family; (2) Public transport impractical changes; (3) Rigid schedule; and (4) Non-urban areas. The SEM results show that the regression coefficients on propensity to carpool of Public transport impractical changes and Rigid schedule are significant and negative; the Family domain has a positive but non-significant regression coefficient. Regarding demographics, men’s mean values on all four domain variables are significantly higher than those for women, except for the Family domain where the mean value for women is higher; meanwhile, age predicts decreasing propensity to carpool. Additionally, the situational domains of Public transport impractical changes, Rigid schedule and Non-urban areas significantly positively correlate. Consequently, overall, the results imply that addressing commuters’ instrumental rationalities for driving, namely increasing schedule flexibility and providing more direct or quicker public transport, could indirectly encourage commuters to carpool. The study makes an original contribution by estimating the causal relationship between commuters’ motivation to drive and propensity to carpool.
Text
Accepted Manuscript How motivations to drive relate to commuters propensity to carpool_R2
- Accepted Manuscript
More information
Accepted/In Press date: 19 February 2018
e-pub ahead of print date: 20 March 2018
Published date: April 2018
Keywords:
Propensity to Carpool; Motivations to drive; Carpooling; Commuting; Multidimensional Scaling; Structural Equation Modelling.
Identifiers
Local EPrints ID: 418096
URI: http://eprints.soton.ac.uk/id/eprint/418096
ISSN: 0965-8564
PURE UUID: 33ebc886-947d-4b0f-bc8d-76629701a6af
Catalogue record
Date deposited: 22 Feb 2018 17:30
Last modified: 16 Mar 2024 06:14
Export record
Altmetrics
Contributors
Author:
Jun Guan Neoh
Author:
Maxwell Chipulu
Author:
Adam Tewkesbury
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