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Modelling the scaling properties of human mobility

Abstract

Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.

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Figure 1: Empirical results versus the predictions of the individual-mobility model.
Figure 2: Schematic description of the individual-mobility model.
Figure 3: Testing the hypotheses behind the individual-mobility model.
Figure 4: Population heterogeneity and ultraslow growth of the radius of gyration.

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References

  1. Vespignani, A. Predicting the behavior of techno-social systems. Science 325, 425–428 (2009).

    Article  ADS  MathSciNet  Google Scholar 

  2. Colizza, V., Barrat, A., Barthélemy, M. & Vespignani, A. Predictability and epidemic pathways in global outbreaks of infectious diseases: The SARS case study. BMC Med. 5, 34 (2007).

    Article  Google Scholar 

  3. Balcan, D. et al. Seasonal transmission potential and activity peaks of the new influenza A(H1N1): A Monte Carlo likelihood analysis based on human mobility. BMC Med. 7, 45 (2009).

    Article  Google Scholar 

  4. Eubank, S. et al. Modelling disease outbreaks in realistic urban social networks. Nature 429, 180–184 (2004).

    Article  ADS  Google Scholar 

  5. Toroczkai, Z. & Guclu, H. Proximity networks and epidemics. J. Phys. A 378, 68–75 (2007).

    Google Scholar 

  6. Makse, H. A., Havlin, S. & Stanley, H. E. Modelling urban growth patterns. Nature 377, 608–612 (1995).

    Article  ADS  Google Scholar 

  7. Hufnagel, L., Brockmann, D. & Geisel, T. Forecast and control of epidemics in a globalized worlds. Proc. Natl Acad. Sci. USA 101, 15124–15129 (2004).

    Article  ADS  Google Scholar 

  8. Rozenfeld, H. D. et al. Laws of population growth. Proc. Natl Acad. Sci. USA 105, 18702–18707 (2008).

    Article  ADS  Google Scholar 

  9. Krings, G., Calabrese, F., Ratti, C. & Blondel, V. D. Urban gravity: A model for inter-city telecommunication flows. J. Stat. Mech.-Theor. Exp. L07003 (2009).

  10. Ratti, C. & Richens, P. Raster analysis of urban form. Environ. Plan. B 31, 297–309 (2004).

    Article  Google Scholar 

  11. Gabaix, X., Gopikrishnan, P., Plerou, V. & Stanley, H. E. A theory of power-law distributions in financial market fluctuations. Nature 423, 267–270 (2003).

    Article  ADS  Google Scholar 

  12. Wang, P., González, M. C., Hidalgo, C. A. & Barabási, A. L. Understanding the spreading patterns of mobile phone viruses. Science 324, 1071–1076 (2009).

    Article  ADS  Google Scholar 

  13. Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439, 462–465 (2006).

    Article  ADS  Google Scholar 

  14. González, M. C., Hidalgo, C. A. & Barabási, A. L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008).

    Article  ADS  Google Scholar 

  15. Montroll, E. W. & Weiss, G. H. Random walks on lattices II. J. Math. Phys. 6, 167–181 (1965).

    Article  ADS  MathSciNet  Google Scholar 

  16. Weiss, G. H. Aspects and Applications of the Random Walk (North-Holland, 1994).

    MATH  Google Scholar 

  17. Metzler, R. & Klafter, J. The random walk’s guide to anomalous diffusion: A fractional dynamic approach. Phys. Rep. 339, 1–77 (2000).

    Article  ADS  MathSciNet  Google Scholar 

  18. Ben-Avraham, D. & Havlin, S. Diffusion and Reactions in Fractals and Disordered Systems (Cambridge Univ. Press, 2000).

    Book  Google Scholar 

  19. Redner, S. A Guide to First-Passage Processes (Cambridge Univ. Press, 2001).

    Book  Google Scholar 

  20. Ciliberti, S., Caldarelli, G., De los Rios, P., Pietronero, L. & Zhang, Y. C. Discretized diffusion processes. Phys. Rev. Lett. 85, 4848–4851 (2000).

    Article  ADS  Google Scholar 

  21. Larralde, H., Trunfio, P., Havlin, S., Stanley, H. E. & Weiss, G. H. Territory covered by N diffusing particles. Nature 355, 423–426 (1992).

    Article  ADS  Google Scholar 

  22. Larralde, H., Trunfio, P., Havlin, S., Stanley, H. E. & Weiss, G. H. Number of distinct sites visited by N random walkers. Phys. Rev. A 45, 7128–7138 (1992).

    Article  ADS  Google Scholar 

  23. Yuste, S. B., Klafter, J. & Lindenberg, K. Number of distinct sites visited by a subdiffusive random walker. Phys. Rev. E 77, 032101 (2008).

    Article  ADS  Google Scholar 

  24. Gillis, J. E. & Weiss, G. H. Expected number of distinct sites visited by a random walk with an infinite variance. J. Math. Phys. 11, 1307–1312 (1970).

    Article  ADS  MathSciNet  Google Scholar 

  25. Mantegna, R. N. & Stanley, H. E. Stochastic process with ultraslow convergence to a Gaussian: The truncated Levy flight. Phys. Rev. Lett. 73, 2946–2949 (1994).

    Article  ADS  MathSciNet  Google Scholar 

  26. Sinai, Y. G. The limiting behavior of a one-dimensional random walk in random medium. Theor. Probl. Appl. 27, 256–268 (1982).

    Article  Google Scholar 

  27. Schiessel, H., Sokolov, I. M. & Blumen, A. Dynamics of a polyampholyte hooked around an obstacle. Phys. Rev. E 56, R2390–R2393 (1997).

    Article  ADS  Google Scholar 

  28. Drager, J. & Klafter, J. Strong anomaly in diffusion generated by iterated maps. Phys. Rev. Lett. 84, 5998–6001 (2000).

    Article  ADS  Google Scholar 

  29. Viswanathan, G. M. et al. Optimizing the success of random searches. Nature 401, 911–914 (1999).

    Article  ADS  Google Scholar 

  30. Santos, M. C. et al. Origin of power-law distributions in deterministic walks: The influence of landscape geometry. Phys. Rev. E 75, 061114 (2007).

    Article  ADS  Google Scholar 

  31. Lomholt, M. A., Koren, T., Metzler, R. & Klafter, J. Levy strategies in intermittent search processes are advantageous. Proc. Natl Acad. Sci. USA 105, 11055–11059 (2008).

    Article  ADS  Google Scholar 

  32. Raposo, E. P., Buldyrev, S. V., da Luz, M. G. E., Viswanathan, G. M. & Stanley, H. E. Levy flights and random searches. J. Phys. A 42, 434003 (2009).

    Article  ADS  MathSciNet  Google Scholar 

  33. Viswanathan, G. M., Raposo, E. P. & da Luz, M. G. E. Levy flights and superdiffusion in the context of biological encounters and random searches. Phys. Life Rev. 5, 133–150 (2008).

    Article  ADS  Google Scholar 

  34. Eggenberger, F. & Polya, G. Uber die Statistik verketteter vorgange. Z. Angew. Math. Mech. 1, 279–289 (1923).

    Article  Google Scholar 

  35. Yule, G. U. A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Phil. Trans. R. Soc. Lond. B 213, 21–87 (1925).

    Article  ADS  Google Scholar 

  36. Simon, H. A. On a class of skew distribution functions. Biometrika 42, 425–440 (1955).

    Article  MathSciNet  Google Scholar 

  37. Price, D. d. S. A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inform. Sci. 27, 292–306 (1976).

    Article  Google Scholar 

  38. Barabási, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  ADS  MathSciNet  Google Scholar 

  39. Song, C., Qu, Z., Blumm, N. & Barabási, A. L. Limits of predictability in human mobility. Science 327, 1018–1021 (2010).

    Article  ADS  MathSciNet  Google Scholar 

  40. Brown, C. T., Liebovitch, L. S. & Glendon, R. Lévy flights in Dobe Ju/’hoansi foraging patterns. Hum. Ecol. 35, 129–138 (2007).

    Article  Google Scholar 

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Acknowledgements

We thank M. Gonzales, D. Wang, J. Bagrow and Z. Qu for discussions and comments on the manuscript. This work was supported by the James S. McDonnell Foundation 21st Century Initiative in Studying Complex Systems; NSF within the Information Technology Research (DMR-0426737), and IIS-0513650 programmes; the Defense Threat Reduction Agency Award HDTRA1-08-1-0027 and the Network Science Collaborative Technology Alliance sponsored by the US Army Research Laboratory under Agreement Number W911NF-09-2-0053.

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C.S., T.K. and A-L.B. conceived and executed the research; C.S. and T.K. ran the numerical simulations. C.S., T.K. and P.W. analysed the empirical data.

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Correspondence to Albert-László Barabási.

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The authors declare no competing financial interests.

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Song, C., Koren, T., Wang, P. et al. Modelling the scaling properties of human mobility. Nature Phys 6, 818–823 (2010). https://doi.org/10.1038/nphys1760

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