An alternative approach to the limits of predictability in human mobility

TL;DR

This paper investigates the fundamental limits of predicting human mobility, showing that traditional methods overestimate predictability due to stationarity, and proposes an alternative approach focusing on predicting the next location independent of time scales.

Contribution

It introduces a new method for estimating human mobility predictability that accounts for stationarity effects, providing more realistic upper limits.

Findings

Predictability of next location is about 71%.

Traditional methods overestimate predictability due to stationarity.

Proposed approach is independent of temporal scales.

Abstract

Next place prediction algorithms are invaluable tools, capable of increasing the efficiency of a wide variety of tasks, ranging from reducing the spreading of diseases to better resource management in areas such as urban planning. In this work we estimate upper and lower limits on the predictability of human mobility to help assess the performance of competing algorithms. We do this using GPS traces from 604 individuals participating in a multi year long experiment, The Copenhagen Networks study. Earlier works, focusing on the prediction of a participant's whereabouts in the next time bin, have found very high upper limits (>90%). We show that these upper limits are highly dependent on the choice of a spatiotemporal scales and mostly reflect stationarity, i.e. the fact that people tend to not move during small changes in time. This leads us to propose an alternative approach, which aims to predict the next location, rather than the location in the next bin. Our approach is independent of the temporal scale and introduces a natural length scale. By removing the effects of stationarity we show that the predictability of the next location is significantly lower (~71%) than the predictability of the location in the next bin.