Advertisement

Modeling and Understanding Intrinsic Characteristics of Human Mobility

  • Jameson L. Toole
  • Yves-Alexandre de Montjoye
  • Marta C. González
  • Alex (Sandy) Pentland
Part of the Computational Social Sciences book series (CSS)

Abstract

Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. So far, modeling these processes has been hindered by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we, as human, are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.

Keywords

Mobile Phone Geographic Information System Gravity Model Mobility Model Route Choice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., et al. (2012, October). Quantifying the impact of human mobility on malaria. Science, 338(6104), 267–270.CrossRefADSGoogle Scholar
  2. 2.
    Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2006, February). The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America, 103(7), 2015–2020.CrossRefADSGoogle Scholar
  3. 3.
    Bettencourt, L. M. A. (2013). The origins of scaling in cities. Science, 340, 1438–1441.MathSciNetCrossRefADSGoogle Scholar
  4. 4.
    Pan, W., Ghoshal, G., Krumme, C., Cebrian, M., & Pentland, A. (2013) Urban characteristics attributable to density-driven tie formation. Nature Communications, 4, 1961.ADSGoogle Scholar
  5. 5.
    Cottrill, C. D. A., Pereira, F. C. A., Zhao, F. A., Dias, I. F. B., Lim, H. B. C., Ben-Akiva, M. E. D., et al. (2013) Future mobility survey. Transportation Research Record, 2354, 59–67.CrossRefGoogle Scholar
  6. 6.
    Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge: MIT Press.Google Scholar
  7. 7.
    Hall, R. W. (Ed.) (1999). Handbook of transportation science. International series in operations research & management science (Vol. 23). Boston: Springer.Google Scholar
  8. 8.
    de Dios Ortúzar, J., & Willumsen, L. G. (2011). Modelling transport. Chichester: Wiley.CrossRefGoogle Scholar
  9. 9.
    de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Nature Scientific Reports, 3, 1376.Google Scholar
  10. 10.
    de Montjoye, Y.-A., Shmueli, E., Wang, S. S., & Pentland, A. S. (2014). OpenPDS: Protecting the privacy of metadata through SafeAnswers. PLoS ONE, 9, e98790.CrossRefADSGoogle Scholar
  11. 11.
    Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., et al. (2009). Computational social science. Science, 323(5915), 721–723.CrossRefGoogle Scholar
  12. 12.
    de Montjoye, Y. A., Smoreda, Z., Trinquart, R., Ziemlicki, C., & Blondel, V. D. (2014, July). D4D-Senegal: The second mobile phone data for development challenge.Google Scholar
  13. 13.
    Aharony, N., Pan, W., Ip, C., Khayal, I., & Pentland, A. (2011). Social fMRI: Investigating and shaping social mechanisms in the real world. In Pervasive and mobile computing (Vol. 7, pp. 643–659).Google Scholar
  14. 14.
    Eagle, N., & Pentland, A. S. (2009). Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63, 1057–1066.CrossRefGoogle Scholar
  15. 15.
    Kosta, E., Graux, H., & Dumortier, J. (2014). Collection and storage of personal data: A critical view on current practices in the transportation sector. In Privacy technologies and policy SE - 10 (Vol. 8319, pp. 157–176).Google Scholar
  16. 16.
    Ranjan, G., Zang, H., Zhang, Z.-L., & Bolot, J. (2012). Are call detail records biased for sampling human mobility? ACM SIGMOBILE Mobile Computing and Communications Review, 16(3), 33–44. http://dl.acm.org/citation.cfm?id=2412101.
  17. 17.
    González, M. C., Hidalgo, C. A., & Barabási, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.CrossRefADSGoogle Scholar
  18. 18.
    Song, C., Koren, T., Wang, P., & Barabási, A.-L. (2010, September). Modelling the scaling properties of human mobility. Nature Physics, 6(10), 818–823CrossRefADSGoogle Scholar
  19. 19.
    Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) Limits of predictability in human mobility. Science, 327(5968), 1018–1021.MathSciNetCrossRefADSzbMATHGoogle Scholar
  20. 20.
    Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439, 462–465.CrossRefADSGoogle Scholar
  21. 21.
    Cheng, Z., Caverlee, J., Lee, K., & Sui, D. Z. (2011). Exploring millions of footprints in location sharing services. In ICWSM (pp. 81–88).Google Scholar
  22. 22.
    Kim, H. S. (2003, January). QoS provisioning in cellular networks based on mobility prediction techniques. IEEE communications magazine, 41(1), 86–92.CrossRefGoogle Scholar
  23. 23.
    Liu, T., Bahl, P., Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16, 922–935.CrossRefGoogle Scholar
  24. 24.
    Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., & Toledo, S. (2009). VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In Proceedings of the 7th ACM conference on embedded networked sensor systems - SenSys ’09 (pp. 85–98).Google Scholar
  25. 25.
    Krumm, J., Horvitz, E., Dourish, P., & Friday, A. (2006). Predestination: Inferring destinations from partial trajectories. UbiComp 2006: Ubiquitous Computing, 4206, 243–260.Google Scholar
  26. 26.
    Minkyong, K., Kotz, D., & Songkuk, K. (2006). Extracting a mobility model from real user traces. In Proceedings - IEEE INFOCOM.Google Scholar
  27. 27.
    Lee, K., Hong, S., Kim, S. J., Rhee, I., & Chong, S. (2009). SLAW: a new mobility model for human walks. In IEEE INFOCOM 2009.Google Scholar
  28. 28.
    Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD 11 (p. 1082). New York: ACM Press.Google Scholar
  29. 29.
    De Domenico, M. (2012). Interdependence and predictability of human mobility and social interactions. Journal Pervasive and Mobile Computing, 9, 798–807.CrossRefGoogle Scholar
  30. 30.
    Scellato, S., Musolesi, M., Mascolo, C., Latora, V., & Campbell, A. T. (2011). NextPlace: A spatio-temporal prediction framework for pervasive systems. In Pervasive computing, Lecture notes in computer science (Vol. 6696, pp. 152–169). Heidelberg: Springer.Google Scholar
  31. 31.
    Eagle, N., & Pentland, A. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10, 255–268.CrossRefGoogle Scholar
  32. 32.
    Sadilek, A., & Krumm, J. (2012). Far out: Predicting long-term human mobility. In AAAI (pp. 814–820).Google Scholar
  33. 33.
    Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of the Royal Society, Interface the Royal Society, 10(84), 20130246.Google Scholar
  34. 34.
    Sun, L., Axhausen, K. W., Lee, D.-H., & Huang, X. (2013, August). Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences, 110(34), 13774–13779.CrossRefADSGoogle Scholar
  35. 35.
    Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 8–12.CrossRefGoogle Scholar
  36. 36.
    Wang, P., Hunter, T., Bayen, A. M., Schechtner, K., & González, M. C. (2012, January). Understanding road usage patterns in urban areas. Scientific Reports, 2, 1001.ADSGoogle Scholar
  37. 37.
    McNally, M. G. (2008, November). The four step model. Irvine: Center for Activity Systems Analysis.Google Scholar
  38. 38.
    Hansen, W. G. (1959, May). How accessibility shapes land use. Journal of the American Institute of Planners, 25(2), 73–76.CrossRefGoogle Scholar
  39. 39.
    Yang, Y., Herrera, C., Eagle, N., & González, M. C. (2014, January). Limits of predictability in commuting flows in the absence of data for calibration. Scientific Reports, 4, 5662.ADSGoogle Scholar
  40. 40.
    Iqbal, Md. S., Choudhury, C. F., Wang, P., & González, M. C. (2014, March). Development of origin-destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40, 63–74.Google Scholar
  41. 41.
    Spiess, H. (1990, May). Technical note-conical volume-delay functions. Transportation Science, 24(2), 153–158.CrossRefGoogle Scholar
  42. 42.
    Samaranayake, S., Blandin, S., & Bayen, A. (2011). Learning the dependency structure of highway networks for traffic forecast. In Proceedings of the IEEE conference on decision and control (pp. 5983–5988).Google Scholar
  43. 43.
    Herring, R., Nasr, T. A., Khalek, A. A., & Bayen, A. (2010). Using mobile phones to forecast arterial traffic through statistical learning. Electrical Engineering, 59, 1–22.Google Scholar
  44. 44.
    Herrera, J. C., Work, D. B., Herring, R., Ban, X., Jacobson, Q., & Bayen, A. M. (2010). Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transportation Research Part C: Emerging Technologies, 18, 568–583.CrossRefGoogle Scholar
  45. 45.
    Jariyasunant, J. (2012). Improving traveler information and collecting behavior data with smartphones. PhD thesis.Google Scholar
  46. 46.
    Wang, J., Mao, Y., Li, J., Xiong, Z., & Wang, W.-X. (2015). Predictability of road traffic and congestion in urban areas. PLoS One, 10(4), e0121825. doi: 10.1371/journal.pone.0121825. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121825.
  47. 47.
    Wang, P., Liu, L., Li, X., Li, G., & González, M. C. (2014, January). Empirical study of long-range connections in a road network offers new ingredient for navigation optimization models. New Journal of Physics, 16(1), 013012.CrossRefADSGoogle Scholar
  48. 48.
    Lorenzo, G. D., Sbodio, M. L., Calabrese, F., Berlingerio, M., Nair, R., & Pinelli, F. (2014, January). AllAboard. In Proceedings of the 19th international conference on Intelligent User Interfaces - IUI ’14 (pp. 335–340). New York: ACM Press.CrossRefGoogle Scholar
  49. 49.
    Ching, A. M. L. (2012). A user-flocksourced bus intelligence system for Dhaka. Diss. Massachusetts Institute of Technology.Google Scholar
  50. 50.
    Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S. H., & Ratti, C. (2014). Quantifying the benefits of vehicle pooling with shareability networks. Proceedings of the National Academy of Sciences 111(37), 13290–13294. http://www.pnas.org/content/111/37/13290.short.
  51. 51.
    Nicolaides, C., Cueto-Felgueroso, L., González, M. C., & Juanes, R. (2012). A metric of influential spreading during contagion dynamics through the air transportation network. PLoS ONE, 7, e40961.CrossRefADSGoogle Scholar
  52. 52.
    Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., & Tomkins, A. (2005). Geographic routing in social networks. Proceedings of the National Academy of Sciences of the United States of America, 102(33), 11623–11628.CrossRefADSGoogle Scholar
  53. 53.
    Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. No. w19843. National Bureau of Economic Research. http://qje.oxfordjournals.org/content/early/2014/10/16/qje.qju022.full#cited-by.
  54. 54.
    Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., & Buckee, C. O. (2013, April). The impact of biases in mobile phone ownership on estimates of human mobility. Journal of the Royal Society, Interface / the Royal Society, 10(81), 20120986.Google Scholar
  55. 55.
    Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J. L., & Vespignani, A. (2009, December). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences of the United States of America, 106(51), 21484–21489.CrossRefADSGoogle Scholar
  56. 56.
    Meloni, S., Perra, N., Arenas, A., Gómez, S., Moreno, Y., & Vespignani, A. (2011, January). Modeling human mobility responses to the large-scale spreading of infectious diseases. Scientific Reports, 1, 62.CrossRefADSGoogle Scholar
  57. 57.
    Backstrom, L., Sun, E., & Marlow, C. (2010). Find me if you can: Improving geographical prediction with social and spatial proximity. In Proceedings of the 19th international conference on World wide web (pp. 61–70).Google Scholar
  58. 58.
    Grabowicz, P. A., Ramasco, J. J., Gonçalves, B., & Eguiluz, V. M. (2013). Entangling mobility and interactions in social media. PLoS One, 9(3), e92196. http://dx.plos.org/10.1371/journal.pone.0092196.
  59. 59.
    Toole, J. L., Cha, M., & González, M. C. (2012). Modeling the adoption of innovations in the presence of geographic and media influences. PLoS ONE, 7(1), e29528.CrossRefADSGoogle Scholar
  60. 60.
    Herrera-Yagüe, C., Schneider, C. M., Smoreda, Z., Couronné, T., Zufiria, P. J., & González, M. C. (2014). The elliptic model for communication fluxes. Journal of Statistical Mechanics: Theory and Experiment, 2014(4), P04022.CrossRefGoogle Scholar
  61. 61.
    van den Berg, P., Arentze, T. A., & Timmermans, H. J. P. (2010). Size and composition of ego-centered social networks and their effect on geographic distance and contact frequency. Transportation Research Record, 2135, 1–9.CrossRefGoogle Scholar
  62. 62.
    Kim, S. (1989). Labor specialization and the extent of the market. Journal of Political Economy, 97, 692–705.CrossRefGoogle Scholar
  63. 63.
    Freedman, M. L. (2008). Job hopping, earnings dynamics, and industrial agglomeration in the software publishing industry. Journal of Urban Economics, 64, 590–600.CrossRefGoogle Scholar
  64. 64.
    Yankow, J. J. (2006). Why do cities pay more? An empirical examination of some competing theories of the urban wage premium. Journal of Urban Economics, 60, 139–161.CrossRefGoogle Scholar
  65. 65.
    Bettencourt, L. M. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007, April). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences of the United States of America, 104(17), 7301–7306.CrossRefADSGoogle Scholar
  66. 66.
    Gurley, T., & Bruce, D. (2005). The effects of car access on employment outcomes for welfare recipients. Journal of Urban Economics, 58, 250–272.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jameson L. Toole
    • 1
  • Yves-Alexandre de Montjoye
    • 2
  • Marta C. González
    • 3
  • Alex (Sandy) Pentland
    • 2
  1. 1.Engineering Systems DivisionMITCambridgeUSA
  2. 2.Media Lab, MITCambridgeUSA
  3. 3.Department of Civil and Environmental EngineeringMITCambridgeUSA

Personalised recommendations