Mobility profiling
Author(s) -
Licia Amichi,
Aline Carneiro Viana,
Mark Crovella,
António Loureiro
Publication year - 2019
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-7006-6
DOI - 10.1145/3360468.3366771
Subject(s) - profiling (computer programming) , computer science , data science , population , artificial intelligence , machine learning , sociology , demography , operating system
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. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal properties of visits -- i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal) -- and classifies individuals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal exploration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
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