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The influence of the built environment on travel behaviour and the role of intervening variables such as socio-demographics and travel-related attitudes have long been debated in the literature. To date, most empirical studies have applied cross-sectional designs to investigate their bidirectional relationships. However, these designs provide limited evidence for causality. This study represents one of the first attempts to employ a longitudinal design on these relationships. We applied cross lagged panel structural equation models to a two-wave longitudinal dataset to assess the directions and strengths of the relationships between the built environment, travel behaviour and travel-related attitudes. Results show that the residential built environment has a small but significant influence on car use and travel attitudes. In addition, the built environment influenced travel-related attitudes indicating that people tend to adjust their attitudes to their built environment. This provides some support for land use policies that aim to influence travel behaviour.

This chapter contributes to the improvement of GPS-based travel surveying by introducing a combined method of GPS, GIS and web-based user interaction, which has been applied in large- scale fieldwork in the netherlands. With over 1000 participants, as far as we know, this is the first time that a GPS-based method that measures travel mode choice as well as the location and type of destinations that are visited has been used on such a large scale. The chapter focuses in particular on the identification of travel modes and destinations, which is still an under-researched issue. Our approach concentrates on the issue of deriving and validating the purpose of trip destinations and travel modes, while also allowing reliable multi-day data collection. The method consists of an interpretation process and a validation process. The interpretation process uses spatial data (e.g. railways, shops) and characteristics of the respondents (e.g. home address, possession of cars) to interpret data from the logs. The travel behaviour data that result from this interpretation round can be adjusted and added to by the respondents in a validation application. The link between both processes is interactive; when new individual characteristics (e.g. the address of a friend’s house) are entered by the respondents, these characteristics will be used for further interpretation of the data. The remainder of this chapter is structured as follows. The following section gives an overview of the advantages and drawbacks of current GPS-based data collection methods that are suitable for measuring choice of travel mode and/or trip destinations. The subsequent section describes the GPS-based method that we developed and in section four the value of our method is evaluated by presenting the results of the fieldwork we recently undertook. The results are compared with results from an internet survey that was carried out at an earlier date and also with the Dutch Travel Survey (DTS) that uses paper diaries. The chapter ends with conclusions on the use of GPS-based methods for the collection of travel behaviour data and a discussion of future possibilities.

Long-term, large-scale empirical studies on the simultaneous development of transport infrastructure and the built environment are scarce. This paper provides a long-term study of the development of the railway network and its impact on the built-up area—and vice versa—using the case study of the Randstad in the Netherlands between 1850 and 2010. The analysis is both qualitative and quantitative. We describe the shares of the built-up area in concentric buffers of 1-kilometer intervals from railway stations and estimate binomial logit models to predict the likelihood of new stations being built based on the amount of the preceding and subsequent built-up area and the likelihood that a new station might have encouraged further growth. Results show that during the early days stations followed existing urbanization patterns. But as time went by, new stations were more likely to be located in undeveloped areas and less likely to be located within the established built-up areas, which were already serviced by existing stations. Moreover, they prompted further growth, increasing the likelihood of more urbanization in their vicinity.

Numerous studies of the interaction between land use and transport have been carried out in the past. In this paper we evaluate a selection of Dutch studies. Before reviewing the studies, we present a conceptual model for passenger transport (section 2), give an overview of the way in which land-use influences travel behaviour (section 3). We also give an overview of transport in Dutch land-use policy plans (section 4) and a categorisation of research (section 5). We then review empirical studies (section 6) and model-simulation studies (section 7) of the impact of land-use on transport. This is followed by a synthesis of the results and look at the transferability of results to other countries (section 8), a discussion of the policy implications of our findings (section 9) and some suggestions for further research (section 10).