Social inequalities in travel behaviour: trip distances in the context of residential self-selection and lifestyles
Introduction
There has been much research on the determinants of travel behaviour in general, and trip distances in particular. In recent years two interrelated strands of research have become prominent in this context. Firstly, the ‘classical’ socio-demographic differentiation of travel has been challenged by lifestyle-oriented approaches that claim to be more appropriate in individualised, affluent societal contexts (Ohnmacht et al., 2009, Scheiner and Kasper, 2003) where a majority of the population can afford to choose from various options in their consumer (and, more specifically, travel) behaviour. Typically, lifestyle approaches to travel include subjective attitudes, values, housing or leisure preferences and wishes, rather than just the mere objective circumstances of daily life, such as employment, age or gender roles.
Secondly, while these studies aim to socially differentiate travel, another line of research is more directed towards spatial differentiation and the built environment. Much recent research in this area focuses on the question of whether, and if so the extent to which spatial differences in travel behaviour may be attributed to the built environment, or whether such differences are rather to be attributed to individuals locating in environments that match their specific accessibility and travel preferences (self-selection hypothesis) (see Cao et al. (2009) for a recent review).
These two lines of research are closely interrelated, and this paper aims to enhance understanding of both the roles of lifestyles and residential self-selection by studying trip distances for three types of activities in the context of life situation, lifestyle, the built environment, and accessibility preferences. Trip distances are an extremely important measure of travel behaviour as they are closely related to transport externalities such as noise emissions, climate change, traffic accidents, and land consumption caused by transport infrastructure. What is more, trip distances may serve as proxies for activity spaces and thus capture social inequalities in the radius of daily spatial behaviour and participation in societal life.
This paper draws on empirical data collected in the region of Cologne. The data are analysed using structural equation modelling (SEM), a flexible technique which is increasingly being used in transport studies (Golob, 2003). Unlike most standard statistical methods, SEM is not limited to the analysis of explanatory variables on a single dependent variable. It can deal with several endogenous variables with interdependent relations with one other, as well as the inclusion of intervening variables. As such, SEM is an adequate tool for the investigation of complex, multi-stage interrelations between variables.
The next section briefly reviews recent related literature. This review is followed by a description of the data and the methodology. Subsequently, the results are presented. The last section draws some conclusions for policy and research.
Section snippets
Lifestyles and travel
Originating from market research, the theoretical background for research on lifestyles is provided by sociological debates on modernisation (Giddens, 1990) and individualisation (Beck, 1992). Observations of a growing ‘dis-embedding’ of individual action and social networks from spatio-temporal contexts, the decreasing relevance of traditional structures of social inequality, and the change from materialist to hedonist, ‘post-materialist’ values lead to the assumption of ‘new’ horizontal
Data and study areas
The data used in this paper were collected in a standardised household survey within the scope of the project StadtLeben while the analysis was undertaken in a follow-up project.
Model fit
There are a number of heuristic indicators to assess the goodness-of-fit of structural equation models. For most of these indicators there are decision rules available and they have been tested in methodological studies. Two of these indicators, along with the corresponding decision rule, are given in Table 2 for the models shown in the figures below and for the respective best model version (i.e., the ones that have been empirically fitted to the data, version 2). The fit values of the
Conclusion
This paper has presented structural equation models of travel behaviour focussing on objective as well as subjective determinants of trip distances for three travel purposes, i.e., for job, maintenance, and leisure trips. The findings show that the main factors relevant for distance behaviour vary greatly between different types of activity.
Overall, trip distances for all travel purposes studied are strongly influenced by social status. Accordingly, the size of activity spaces appears to be
Acknowledgement
This research was funded by the German Research Foundation (DFG) as part of the project ‘Wohnstandortwahl, Raum und Verkehr im Kontext von Lebensstil und Lebenslage’ (Choice of residential location, built environment and transport in the context of lifestyle and life situation, 2006–2008).
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