Abstract
Policy makers in urban areas are subjected to increasing pressure to find sustainable solutions to congestion and transportation. A detailed understanding of the motivations of car owners is required to enable the development of policies that are both socially fair and take effective measures. The objective of this study is to provide a more granular differentiation of car owners using psychographic profiles in three basic dimensions (privacy, autonomy, and car excitement). These profiles are also examined in relation to general travel behavior in everyday and long-distance travel. Data was collected in Munich and Berlin (Germany) and a latent class analysis was applied to segment respondents into latent profile classes. On this basis, six different profile classes were identified. In addition to the Car Independents profile class which does not have strong orientations toward the car, several profile classes were also identified with high concerns about “privacy” in relation to social distances in public transit. The information and analysis presented enables a deeper understanding of the motivations of the different target profile classes and discusses the need for tailored, socially fair measures to reduce car ownership and use within these groups.
Highlights
Policy makers in urban areas are subjected to increasing pressure to find sustainable solutions to congestion and transportation
Once specific groups are identified, it is possible to assess potential reactions to various policy restrictions, support measures or marketing strategies, and develop appropriate and expedient approaches. To support this analysis and understanding, this study addresses the following key research questions: How can differentiations be made between car users based on their psychographic profiles? What prevents car users from considering other means of transport, such as public transit, as their mode of choice? These questions are approached methodically by applying latent class analysis (LCA), where dependencies between observable variables, for example, attitudes toward cars, are attempted to be explained by unobservable underlying classes [7]
LCA based on attitudinal data was used to examine the two study objectives: an investigation of underlying
Summary
Policy makers in urban areas are subjected to increasing pressure to find sustainable solutions to congestion and transportation. One method for explaining differences in relation to car use in cities is to use ‘‘segmentation approaches’’ to identify groups of people with specific characteristics These approaches have been used by other researchers to identify travel-related segments, such as specific car users [2, 3]. Since individuals derive benefits from the use of their cars that they do not derive from public transit, a greater understanding of these benefits is needed to identify more efficient strategies to influence car owners to change from. To support this analysis and understanding, this study addresses the following key research questions: How can differentiations be made between car users based on their psychographic profiles? To support this analysis and understanding, this study addresses the following key research questions: How can differentiations be made between car users based on their psychographic profiles? What prevents car users from considering other means of transport, such as public transit, as their mode of choice? These questions are approached methodically by applying latent class analysis (LCA), where dependencies between observable variables, for example, attitudes toward cars, are attempted to be explained by unobservable underlying classes [7]
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Schedule a callMore From: Transportation Research Record: Journal of the Transportation Research Board
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