Neighbourhood design impact on travel behavior: A comparison of US and UK experience

Dr. Paulus T. Aditjandra, Professor Corinne A. Mulley, Professor John D. Nelson, 2010, in MIT Journal of Planning

doi:nan

Location	UK
Population	General
Sample size	716
Factor analysis type	exploratory factor analysis, oblimin rotation
Stepwise regression	nan
Removal of insignificant variables	nan
Reviewed by	AR

Abstract

This paper presents evidence from the UK in respect of the impact of neighbourhood design on travel behaviour using a neighbourhood, micro scale, case-study approach. Whilst there is an extensive American literature on this subject, this is limited in applicability to European or British practice since the urban form variables, such as street layout and levels of car use in all areas, have a different scale. Neighbourhood design and travel attitude characteristics were modelled using factor analysis and the causation relationship was established using reported vehicle miles driven (VMD) as the dependent variable in a subsequent regression analysis. Despite modest differences in VMD between UK and US, there are significant differences in its explanation. The most important predictors for the UK study are the socio-economic variables, followed by travel attitudes, neighbourhood characteristics preferences and land-use type in contrast to the US experience which identifies travel attitude as the biggest predictor of VMD. Many studies in this field suffer from the criticism that respondents select their area of residence because of specific neighbourhood characteristics and this gives rise to a 'self-selection' issue. This study addresses this by the collection and analysis of quasi-longitudinal data from respondents who moved home in the previous eight years. This analysis shows that travel accessibility is sensitive to changes in walking and public transport use, suggesting that residents of British neighbourhoods are more aware of public transport than their US counterparts and more likely to use sustainable, low carbon means of travel.

Factors

Models