P REDICTABILITY STATES IN HUMAN MOBILITY Diogo Pacheco BioComplex Laboratory Department of Computer Science University of Exeter d.pacheco@exeter.ac.uk Marcos Oliveira BioComplex Laboratory Department of Computer Science University of Exeter, UK m.a.oliveira@exeter.ac.uk Zexun Chen Business School University of Edinburgh, UK zexun.chen@ed.ac.uk Hugo Barbosa BioComplex Laboratory Department of Computer Science University of Exeter, UK h.barbosa@exeter.ac.uk Brooke Foucault Welles Northeastern University Boston, MA, USA b.welles@neu.edu Gourab Ghoshal Department of Physics and Astronomy University of Rochester, NY, USA gghoshal@pas.rochester.edu Ronaldo Menezes BioComplex Laboratory Department of Computer Science University of Exeter, UK r.menezes@exeter.ac.uk January 6, 2022 ABSTRACT Spatio-temporal constraints coupled with social constructs have the potential to create fluid predictabil- ity to human mobility patterns. Accordingly, predictability in human mobility is non-monotonic and varies according to this spatio-socio-temporal context. Here, we propose that the predictability in human mobility is a state and not a static trait of individuals. First, we show that time (of the week) explains people’s whereabouts more than the sequences of locations they visit. Then, we show that not only does predictability depend on time but also the type of activity an individual is engaged in, thus establishing the importance of contexts in human mobility. 1 Introduction Human beings are routine-oriented to the extent that lack of predictability in daily mobility patterns is linked to high levels of stress [1, 2]. This change-averse behaviour leads to people having well-defined routines, which allows for high predictability in daily mobility patterns. Human trajectories have been shown to exhibit regularities at multiple scales, despite the inherent complexity that exists in the choices humans can make for the routes of their daily travels. Indeed, the analysis of large populations via mobile phone data has suggested the possibility of predicting up to 93% of human movement [3]. The predictability of human mobility, however, tells us only part of the story, since it neglects spatio-temporal constraints and the social embedding behind mobility regularities. Therefore, this work demonstrates that mobility predictability should be seen as a transient state, rather than a trait of individuals. The understanding of mechanisms governing human travelling behaviour is crucial to a variety of domains such as epidemic modelling [4], traffic management [5], and national security [6], to name but a few [7]. The modelling of human predictability as a state dependent on activity being performed (spatio-social) and the time of such activity (temporal) can lead to better decisions within the aforementioned domains as it offers a finer, more detailed, view of human dynamics. arXiv:2201.01376v1 [physics.soc-ph] 4 Jan 2022