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Temporal understanding of human mobility: A multi-time scale analysis

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  • Tongtong Liu
  • Zheng Yang
  • Yi Zhao
  • Chenshu Wu
  • Zimu Zhou
  • Yunhao Liu

Abstract

The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at a large-scale (3,542,235 individuals) and finer-granularity (7.5min). Next, we investigated statistical variations and biases of mobility models caused by different time scales (from 7.5min to 32h), and found that the time scale does influence the mobility model, which indicates a deep coupling of human mobility and time. We further show that mobility behaviors like transportation modes contribute to the diversity of human mobility, by exploring several novel and refined features (e.g., motion speed, duration, and trajectory distance). Particularly, we point out that 2-hour sampling adopted in previous works is insufficient to study detailed motion behaviors. Our work not only offers a macroscopic and microscopic view of spatial-temporal human mobility, but also applies previously unavailable features, both of which are beneficial to the studies on phenomena driven by human mobility.

Suggested Citation

  • Tongtong Liu & Zheng Yang & Yi Zhao & Chenshu Wu & Zimu Zhou & Yunhao Liu, 2018. "Temporal understanding of human mobility: A multi-time scale analysis," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
  • Handle: RePEc:plo:pone00:0207697
    DOI: 10.1371/journal.pone.0207697
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    References listed on IDEAS

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    1. David W. Sims & Emily J. Southall & Nicolas E. Humphries & Graeme C. Hays & Corey J. A. Bradshaw & Jonathan W. Pitchford & Alex James & Mohammed Z. Ahmed & Andrew S. Brierley & Mark A. Hindell & David, 2008. "Scaling laws of marine predator search behaviour," Nature, Nature, vol. 451(7182), pages 1098-1102, February.
    2. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    3. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
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    Cited by:

    1. Fabio Vanni & David Lambert, 2021. "On the regularity of human mobility patterns at times of a pandemic," SciencePo Working papers Main hal-04103882, HAL.
    2. Fan Yang & Zhenxing Yao & Fan Ding & Huachun Tan & Bin Ran, 2019. "Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    3. Fabio Vanni & David Lambert, 2021. "On the regularity of human mobility patterns at times of a pandemic," Papers 2104.08975, arXiv.org.
    4. Fabio Vanni & David Lambert, 2021. "On the regularity of human mobility patterns at times of a pandemic," Working Papers hal-04103882, HAL.

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