MoTiV: a Dataset of European User Mobility for Behavioral-Data

Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.