This contribution presents theoretical considerations concerning the connections between life situation, lifestyle, choice of residential location and travel behaviour, as well as empirical results of structural equation models. The analyses are based on data resulting from a survey in seven study areas in the region of Cologne. The results indicate that lifestyles influence mode choice, although slightly, even when life situation is controlled for. The influence of life situation on mode choice exceeds the influence of lifestyle. The influence that lifestyle, and in part also life situation, has on mode choice is primarily mediated by specific location attitudes and location decisions that influence mode choice, respectively. Here objective spatial conditions as well as subjective location attitudes are important. Copyright Springer Science+Business Media, LLC 2007Lifestyle, Linear structural equation modelling, Residential location choice, Residential self-selection, Travel behaviour, Travel mode choice,
There is a lot of research on spatial differences in travel behaviour, specifically on travel distances. This research suggests that the distances travelled by the inhabitants of municipalities with lower population and neighbourhoods with lower density and less mixed land use are longer than those travelled by the inhabitants of cities with higher population, high density, and greater mixed land use. However, related studies focus mainly on daily travel. In this paper we study travel distances in daily trips based on random day trip diaries and long-distance trips for private and business purposes based on retrospective questions in the same questionnaire, asking about "longer journeys with overnight stay" within three months of the survey. We use Heckman models and ordinary least squares regressions to study the effects of municipality size classes based on population, population density, and land-use mix, while controlling for sociodemographics. We find distances travelled on long-distance trips and daily trips to be affected by sociodemographics in much the same way, while spatial effects affect distances travelled on daily and long-distance trips mostly in different directions. Residents of small municipalities and low-density neighbourhoods make fewer and/or shorter long-distance journeys than those living in large cities and high-density neighbourhoods, but the latter travel shorter distances in their daily lives.
This paper focuses on mode use in long-distance travel. Long-distance travel is responsible for more than 50 percent of climate impact. Nevertheless, it is usually excluded from analyses that examine travel behavior. Whereas studies on daily travel prove that the rural population covers longer distances in daily travel, recent studies (e.g., Holz-Rau, Scheiner, and Sicks 2014; Brand and Preston 2010) show a different picture in long-distance travel. Here, the urban population undertakes more long-distance trips, especially by air. The aim of this paper is to analyze the mode use in long-distance travel in different spatial settings by using multivariate regression models. The (underlying) data derive from a nationwide survey with a sample size of 60,713 respondents, Mobility in Germany 2008 (MiD). A broad range of socio-demographic and socioeconomic characteristics are thereby included as control variables. The results show that even when household income, car accessibility, and education level are considered, the urban population undertakes more long-distance trips, in particular by train and by air. These differences are found in business as well as in private travel.
It has long been argued in feminist studies that women's daily lives are more complex than men's. This is largely due to the gendered division of work, according to which women juggle more varied obligations, including employment, household work and caregiving. Complex activity patterns in turn encourage women to organise their trips in a more efficient manner in trip chains. This paper studies the complexity of activity patterns (measured by Shannon entropy) and trip chaining patterns from a gender specific perspective. The data used is the German Mobility Panel 1994-2012 which records respondents' trips over the period of a week. The outcome variables are regressed on sociodemographics, residential and workplace spatial context attributes, cohort and period effects. Gender differences in the effects of variables are tested using interaction terms. The results suggest that women's patterns are more complex than men's. Some effects differed distinctly between men and women, suggesting that men and women are differently affected by circumstances impacting the complexity of their lives, most notably by having children and by having a partner.
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