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Decision-makers in cities worldwide have the responsibility to contribute to the reduction of greenhouse gas emissions in urban transport. Therefore, effective measures and policies that allow for a change in people’s mobility towards sustainable mobility must be derived. To understand how different people respond to measures and policies, and to increase the effectiveness of such policies, individual mobility needs and mobility determinants have to be considered. For this, the definition of individual mobility styles as holistic descriptions considering travel behavior, attitudes, as well as life stages is useful. This study presents a segmentation approach that identifies eight urban mobility styles by using data from a multidimensional survey conducted in Berlin and San Francisco. We applied a cluster analysis with both behavioral and attitudinal characteristics as segmentation criteria. By analyzing the characteristics, we identified a mobility style—the Environmentally Oriented Multimodals—that is environmentally oriented, but not yet all people in this cluster are sustainable in their mobility. Thus, they are the group with the highest potential to accept and use sustainable mobility. Additionally, we found that within the Environmentally Oriented Multimodals, the change from one life stage to another is also likely to be accompanied by a car acquisition.

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Car use in modern cities with a well-developed public transit is more sophisticated to explain only through hard factors such as sociodemographic characteristics. In cities, it is especially important to consider motives for car use. Therefore, we examined two modern cities with a high modal share of non-motorized modes and public transit to answer the question: How do the affective and instrumental motives influence car use in such cities? The used data set was collected in Berlin and San Francisco. To investigate the role of motives, we applied an ordered hybrid choice model (OHCM) with a probit kernel. Based on the OHCM we explained more than 14% of the overall heterogeneity and gave further insights to the decision-making process. The affective motive had a strong influence on car use frequency, whereby the instrumental aspects did not matter. Furthermore, an effect resulting from age could not be determined for the affective motives in these cities. Results suggest people are more likely to use cars for affective motives despite the city’s adversities. For these people it is difficult to achieve a shift to alternative means of transport. The only way to intervene here is through regulatory intervention.

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