Returners and explorers dichotomy in human mobility

TLDR

Massive digital traces of human whereabouts have provided novel insights into quantitative patterns of human mobility. The study aims to investigate how recurrent mobility affects individuals’ travel distance and to develop realistic models that capture the resulting returner and explorer classes. The authors used mobile phone and GPS data to develop realistic mobility models that distinguish returners from explorers. The study finds that despite high variability in travel distance, future locations are highly predictable, identifies two distinct classes—returners and explorers—and shows that these classes differ in their influence on spreading phenomena and correlate with social interactions.

Abstract

Abstract The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.