Understanding user mobility is central to develop better transport systems that answer users’ needs. Users usually plan their travel according to their needs and preferences; however, different factors can influence their choices when traveling. In this work, we model users’ preferences, and we match against their actual transport use.We use data coming from a mobility platform developed for mobile devices, whose aim is to understand the value of users’ travel time.Our first goal is to characterize the perception that users have of their mobility, by analyzing their general preferences expressed be for etheir travel time. Our approach combines dimensionality reduction and clustering techniques to provide interpretable profiles of users. Then, we perform the same task after monitoring users’ travels, by doing a matching between users’ preferences and their actual behavior. Our results show that there are strong differences between users’ perception of their mobility and their actual behavior.