Predictability of individuals' mobility with high-resolution positioning data

Abstract

The ability to foresee the next moves of a user is crucial to ubiquitous computing. Disregarding major differences in individuals' routines, recent ground-breaking analysis on mobile phone data suggests high predictability in mobility. By nature, however, mobile phone data offer very low spatial and temporal resolutions. It remains largely unknown how the predictability changes with respect to different spatial/temporal scales. Using high-resolution GPS data, this paper investigates the scaling effects on predictability. Given specified spatial-temporal scales, recorded trajectories are encoded into long strings of distinct locations, and several information-theoretic measures of predictability are derived. Somewhat surprisingly, high predictability is still present at very high spatial/temporal resolutions. Moreover, the predictability is independent of the overall mobility area covered. This suggests highly regular mobility behaviors. Moreover, by varying the scales over a wide range, an invariance is observed which suggests that certain trade-offs between the predicting accuracy and spatial-temporal resolution are unavoidable. As many applications in ubiquitous computing concern mobility, these findings should have direct implications.