Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual's mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictors.
A recent study REF proposes methods to identify and predict departures from routine in individual mobility using information-theoretic metrics, such as the instantaneous entropy, and developing a Bayesian framework that explicitly models the tendency of individuals to break from routine.
Breaking the habit: Measuring and predicting departures from routine in individual human mobility
Pervasive Mob. Comput.
Pervasive Mob. Comput.
Computer Science