Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
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Section: New Results

Identifying and profiling novelty-seeking behavior in human mobility

Participants : Licia Amichi, Aline Carneiro Viana, Mark Corvella [Boston Univ.] , Antonio F. Loureiro [Federal U. of Minas Gerais] .

The prediction of individuals' dynamics has attracted significant community attention and has implication for many fields: e.g. epidemic spreading, urban planning, recommendation systems. Current prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior.

Many prediction models have been proposed to forecast individuals trajectories. However, they all show limited bounded predictive performance. Regardless of the applied methods (e.g., Markov chains, Naive Bayes, neural networks), the type of prediction (i.e., next-cell or next place) or the used data sets (e.g., GPS, CDR, surveys), accuracy of prediction never reaches the coveted 100%. The reasons for such limitations in the accuracy are manyfold: the lack of ground truth data, human beings' complex nature and behavior, as well the exploration phenomenon (i.e., visits to never seen before places). In this work, we focus on the exploration problem, which has rarely been tackled in the literature but indeed, represents a real issue. By construction, most prediction models attempt to forecast future locations from the set of known places, which hinders predicting new unseen places and by consequence, reduces the predictive performance.

Thus, when considering the exploration problem, previous studies either did not provide any consideration of the exploration factors of individuals, or divided the population based on properties that are not always consistent, or assumed that all individuals have the same propensity to explore. Our main goal in this work is to understand the exploration phenomenon and answer the following question: What type of visits characterize the mobility of individuals? Using newly designed metrics capturing spatiotemporal properties of human mobility – i.e., known/new and recurrent/intermittent visits – our strategy identifies three groups of individuals according to their degree of exploration: scouters, routineers, and regulars. In the future, we plan to deeply investigate the mobility behavior of individuals in each profile and to assign to each individual an exploration factor describing her susceptibility to explore.

This work was published at the Student workshop of ACM CONEXT 2019 [9]. An extended version is being prepared for submission to an int. conference.