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A detailed knowledge of potential travelers' behavior and underlying psychological factors is essential to estimate the potential of mobility-related services and improve transportation systems.The definition of mobility types allows the assignment of individuals to the respective groups of people with similar mobility needs.Previous research has mainly focused on one dimension only, either attitudes or travel behavior, for identifying distinct mobility types with cluster analysis.Considering both dimensions allows to uncover dissonances and consistencies between attitudes and behavior.Further, only a few studies compare mobility types in an international setting.In our study, we try to identify two-dimensional urban mobility types and compare them between cities in different cultural contexts.Therefore, we develop an integrated clustering approach and support it by machine learning algorithms in pre-and post-analysis.To combine attitudes and behavior in different urban mobility types, we use data from a standardized survey, conducted in Berlin, Shanghai and San Francisco.This survey is based on the concept of a travel skeleton that allows us to collect typical weekly travel behavior as well as psychological constructs.Based on the clustering processes, we identify 11 distinct urban mobility types.The results show clusters with dissonances between attitudes and behavior (e.g., Cluster 10 "Car-Enthusiasts with high Norms") and clusters with consistent characteristics (e.g., Cluster 4 "Convinced Bicycle and Public Transportation Users").Further, the comparison between the cities highlights city specifics.Berlin and Shanghai are more similar in terms of occurring mobility types and thus mobility needs than San Francisco.
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