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 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 before their 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 substantial differences between users' perception of their mobility and their actual behavior: users overestimate their preferences for specific mobility modes, that in general, yield a lower return in terms of the worthwhileness of their trip.
Mobility and transport, by their nature, involve crowds and require the coordination of multiple stakeholders - such as policy-makers, planners, transport operators, and the travelers themselves. However, traditional approaches have been focused on time savings, proposing to users solutions that include the shortest or fastest paths. We argue that this approach towards travel time value is not centered on a traveler's perspective. To date, very few works have mined data from crowds of travelers to test the efficacy and efficiency of novel mobility paradigms. In this paper, we build upon a different paradigm of worthwhile time in which travelers can use their travel time for other activities; we present a new dataset, which contains data about travelers and their journeys, collected from a dedicated mobile application. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive and negative experience factors. To showcase this new dataset's potential, we also present a use case, which compares corresponding trip legs with different transport modes, studying experience factors that negatively impact users using cycling and public transport as alternatives to cars. We conclude by discussing other application domains and research opportunities enabled by the dataset.
Mobility and transport, by their nature, involve crowds and require the coordination of multiple stakeholders -such as policy-makers, planners, transport operators, and the travelers themselves. However, traditional approaches have been focused on time savings, proposing to users solutions that include the shortest or fastest paths. We argue that this approach towards travel time value is not centered on a traveler's perspective. To date, very few works have mined data from crowds of travelers to test the efficacy and efficiency of novel mobility paradigms. In this paper, we build upon a different paradigm of "worthwhile time" in which travelers can use their travel time for other activities; we present a new dataset, which contains data about travelers and their journeys, collected from a dedicated mobile application. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive and negative experience factors. To showcase this new dataset's potential, we also present a use case, which compares corresponding trip legs with different transport modes, studying experience factors that negatively impact users using cycling and public transport as alternatives to cars. We conclude by discussing other application domains and research opportunities enabled by the dataset.
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