  Human mobility is an important characteristic of human behavior, but since
tracking personalized position to high temporal and spatial resolution is
difficult, most studies on human mobility patterns rely largely on mathematical
models. Seminal models which assume frequently visited locations tend to be
re-visited, reproduce a wide range of statistical features including collective
mobility fluxes and numerous scaling laws. However, these models cannot be
verified at a time-scale relevant to our daily travel patterns as most
available data do not provide the necessary temporal resolution. In this work,
we re-examined human mobility mechanisms via comprehensive cell-phone position
data recorded at a high frequency up to every second. We found that the next
location visited by users is not their most frequently visited ones in many
cases. Instead, individuals exhibit origin-dependent, path-preferential
patterns in their short time-scale mobility. These behaviors are prominent when
the temporal resolution of the data is high, and are thus overlooked in most
previous studies. Incorporating measured quantities from our high frequency
data into conventional human mobility models shows contradictory statistical
results. We finally revealed that the individual preferential transition
mechanism characterized by the first-order Markov process can quantitatively
reproduce the observed travel patterns at both individual and population levels
at all relevant time-scales.
