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Human mobility is an important characteristic of human behavior, but sincetracking personalized position to high temporal and spatial resolution isdifficult, most studies on human mobility patterns rely largely on mathematicalmodels. Seminal models which assume frequently visited locations tend to bere-visited, reproduce a wide range of statistical features including collectivemobility fluxes and numerous scaling laws. However, these models cannot beverified at a time-scale relevant to our daily travel patterns as mostavailable data do not provide the necessary temporal resolution. In this work,we re-examined human mobility mechanisms via comprehensive cell-phone positiondata recorded at a high frequency up to every second. We found that the nextlocation visited by users is not their most frequently visited ones in manycases. Instead, individuals exhibit origin-dependent, path-preferentialpatterns in their short time-scale mobility. These behaviors are prominent whenthe temporal resolution of the data is high, and are thus overlooked in mostprevious studies. Incorporating measured quantities from our high frequencydata into conventional human mobility models shows contradictory statisticalresults. We finally revealed that the individual preferential transitionmechanism characterized by the first-order Markov process can quantitativelyreproduce the observed travel patterns at both individual and population levelsat all relevant time-scales.

Characteristics of human mobility patterns revealed by high-frequency cell-phone position data