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Understanding individual human mobility patterns

Nature volume 453, pages 779–782 (2008)Cite this article

An Addendum to this article was published on 12 March 2009

Abstract

Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

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Figure 1: Basic human mobility patterns.
Figure 2: The bounded nature of human trajectories.
Figure 3: The shape of human trajectories.

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Acknowledgements

We thank D. Brockmann, T. Geisel, J. Park, S. Redner, Z. Toroczkai, A. Vespignani and P. Wang for discussions and comments on the manuscript. This work was supported by the James S. McDonnell Foundation 21st Century Initiative in Studying Complex Systems, the National Science Foundation within the DDDAS (CNS-0540348), ITR (DMR-0426737) and IIS-0513650 programs, and the US Office of Naval Research Award N00014-07-C. Data analysis was performed on the Notre Dame Biocomplexity Cluster supported in part by the NSF MRI grant number DBI-0420980. C.A.H. acknowledges support from the Kellogg Institute at Notre Dame.

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Authors and Affiliations

  1. Center for Complex Network Research and Department of Physics, Biology and Computer Science, Northeastern University, Boston, Massachusetts 02115, USA,

    Marta C. González, César A. Hidalgo & Albert-László Barabási

  2. Center for Complex Network Research and Department of Physics and Computer Science, University of Notre Dame, Notre Dame, Indiana 46556, USA,

    César A. Hidalgo & Albert-László Barabási

  3. Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA ,

    Albert-László Barabási

Authors
  1. Marta C. González
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  2. César A. Hidalgo
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  3. Albert-László Barabási
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Corresponding author

Correspondence to Albert-László Barabási.

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Supplementary information

The file contains Supplementary Data with Supplementary Figures S1-S9 and additional references. (PDF 1065 kb)

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González, M., Hidalgo, C. & Barabási, AL. Understanding individual human mobility patterns. Nature 453, 779–782 (2008). https://doi.org/10.1038/nature06958

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