https://doi.org/10.1140/epjds/s13688-021-00261-2
Regular Article
Characteristics of human mobility patterns revealed by high-frequency cell-phone position data
1
College of Computer and Cyber Security, Hebei Normal University, 050024, Shijiazhuang, P.R. China
2
Hebei Key Laboratory of Network and Information Security, 050024, Shijiazhuang, P.R. China
3
Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, 050024, Shijiazhuang, P.R. China
4
School of Systems Science, Beijing Normal University, 100875, Beijing, P.R. China
5
Department of Science and Environmental Studies, The Education University of Hong Kong, Hong Kong, P.R. China
Received:
28
January
2020
Accepted:
11
January
2021
Published online:
19
January
2021
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 on sparsely sampled position data. In this work, we re-examined human mobility patterns via comprehensive cell-phone position data recorded at a high frequency up to every second. We constructed human mobility networks and found that 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 inconsistent 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.
Key words: Human mobility / Mobile phone / High frequency data
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-021-00261-2.
© The Author(s) 2021
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