Theories on the reciprocal relationship between land-use and transport address changes in locational decisions and travel behavior of private actors (households and firms) due to alternations in the transport system, respectively land-use system. Although the impact of land-use system on travel behaviour (transport system) has been the subject of much research (for reviews, see, e.g. Handy, 2002; Stead and Marshall, 2001; Crane, 2000; Wegener and Fürst, 1999), there is still no consensus about the strength of this relationship. This may be caused by different types of variables whether or not included in the research. Initially only land-use variables were taken into account, but nowadays socio-economical variables are also incorporated. Still, within ‘homogeneous groups’, there may be attitudes, lifestyles, perceptions, and preferences which can have an impact on land-use and/or travel behaviour. Academic literature on the latter remains scarce and the main focus of the existing behavioural literature is the impact of residential location on travel choices, especially modal choice. However, little work has thus far been done on other dimensions of travel choices (e.g., distance, time) and other location types (e.g., commercial, industrial, recreational). Less is known about the reverse relationship, e.g. the impact of the transport system on location decisions of households and firms (land-use system). A difference in time-scale can be the reason for this. Transformations in land-use occur much slower (years) compared to transformations in travel behaviour (days, weeks, months). In this paper we explore several possibilities to fill in some of the gaps in our knowledge on the land-use/transport system. Understanding the two-way interaction between land-use and travel behaviour involves having (i) data on land-use patterns, socio-economic background of individuals and their attitudes, perceptions and preferences towards land-use and travel; and (ii) a methodology, dealing with potential multiple directions of causality. The first issue can be achieved by combining empirical, revealed and stated preference research. The second methodological question can be solved using structural equation modelling (SEM). This is a modelling technique which can handle a large number of endogenous and exogenous variables. Because of the multiple directions of causality that can be explored, SEM can help us to define the relationship between revealed preference data and stated preference data.
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Paper provided by European Regional Science Association in its series ERSA conference papers with number
ersa05p601.
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