Returners and explorers dichotomy in human mobility

By Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, Albert-László Barabási. Nature Communications, 2015

517 citations recorded by the source corpus.

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. Human mobility studies established that a high degree of predictability coexists with the variability of individual mobility. Here, the authors, by looking at mobile phone and GPS data, identify and model the emergence of two classes of mobility patterns in humans, returners and explorers.

Subfields: Computer Networks and Communications, Computer Science Applications, Transportation

View Paper: https://www.semanticscholar.org/paper/1fbdacf86f4157b978281bf1f7ff7670a76d826b

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Citations in the corpus

Human mobility: Models and applications (2017, background): “nurture)? The same can be said about regularities whereby human behavior is divided between those that are explorers or returners in their mobility pattern [89].”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, background): “Human mobility revealed from CDR data illustrates a pattern of preferential returns to previously visited locations and explorations of new places as a general and universal feature in human mobility [15]–[17].”
A Survey on Deep Learning for Human Mobility (2020, background): “Several studies subsequently improved EPR by adding increasingly sophisticated spatial or social mechanisms [7, 12, 42, 140, 191].”