  Human travelling behaviours are markedly regular, to a large extent,
predictable, and mostly driven by biological necessities (\eg sleeping, eating)
and social constructs (\eg school schedules, synchronisation of labour). Not
surprisingly, such predictability is influenced by an array of factors ranging
in scale from individual (\eg preference, choices) and social (\eg household,
groups) all the way to global scale (\eg mobility restrictions in a pandemic).
In this work, we explore how spatio-temporal patterns in individual-level
mobility, which we refer to as \emph{predictability states}, carry a large
degree of information regarding the nature of the regularities in mobility. Our
findings indicate the existence of contextual and activity signatures in
predictability states, pointing towards the potential for more sophisticated,
data-driven approaches to short-term, higher-order mobility predictions beyond
frequentist/probabilistic methods.
