Advertisement

Daily Life Mobility of a Student: From Position Data to Human Mobility Model through Expectation Maximization Clustering

  • Hyunuk Kim
  • Ha Yoon Song
Part of the Communications in Computer and Information Science book series (CCIS, volume 263)

Abstract

There has been large number of research results to describe human mobility for various purposes. It has been researched that a person’s mobility pattern can be predicted with the probability up to 93%, even though various factors and parameters can affect the human mobility pattern. In this research we tried to build a bridge between positioning data and human mobility pattern. Human mobility trails of a person can be presented in forms of positioning data sets. Positioning data from GPS or WPS and so on are somewhat accurate and usually in a tuple form of <time, latitude, longitude> while these form of data is barely interpreted by human perception. Humans can precept location information as street names, building names or shapes, etc. The error prone accuracy of positioning data leads a problem of clustering in order to figure out the point of frequent places for human mobility. These places and human mobility trails can be identified by clustering techniques, and we used Expectation Maximization clustering technique with the use of probability models derived from Levy Walk researches. We believe our research can be a starting point to model a human mobility pattern for further use.

Keywords

Human Mobility Expectation Maximization Clustering Global Positioning System 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Colizza, V., Vespignani, A.: Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. Journal of Theoretical Biology 251(3), 450–467 (2008)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ni, S., Weng, W.: Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks. Physical Review E 79(1) (2009)Google Scholar
  3. 3.
    Wang, P., Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding the spreading patterns of mobile phone viruses. Science 22 324(5930), 171–1076 (2009)Google Scholar
  4. 4.
    Wiehe, S.E., Hoch, S.C., Liu, G.C., Carroll, A.E.: Adolescent travel patterns: pilot data indicating distance from home varies by time of day and day of week. Journal of Adolescent Health 42(4), 418–420 (2008)CrossRefGoogle Scholar
  5. 5.
    Sarah, W., Aaron, C., Gilbert, L., Kelly, H., Shawn, H., Jeffery, W., Dennis, F.J.: Using GPS-enabled cell phones to track the travel patterns of adolescents. International Journal of Health Geographics 7(1) (2008)Google Scholar
  6. 6.
    Bai, F., Sadagopam, N., Helmy, A.: Important: A framework to systematically analyze the Impact of Mobility on Performance of Routing Protocol for Ad hoc Networks. In: Twenty-second Annual Joint Conference of The IEEE Computer And Communications Societies, vol. 2, pp. 825–835 (2003)Google Scholar
  7. 7.
    Zhou, B., Xu, K., Gerla, M.: Group and swarm mobility models for ad hoc network scenarios using virtual tracks. In: IEEE Military Communications Conference, vol. 1, pp. 289–294 (2004)Google Scholar
  8. 8.
    Bailenson, J.N., Shum, M.S., Uttal, D.H.: Road climbing: Principles governing asymmetric route choice on maps. Environmental Psychology 18(3), 251–264 (1998)CrossRefGoogle Scholar
  9. 9.
    Ghosh, J.: Sociological orbit aware location approximation and routing (SOLAR) in MANET. Ad Hoc Networks 5(2), 189–209 (2007)CrossRefGoogle Scholar
  10. 10.
    Williams, S.A.: A group force mobility model. In: 9th Communications and Networking Simulation Symposium (2006)Google Scholar
  11. 11.
    Hartley, H.O.: Maximum Likelihood Estimation from Incomplete Data. Biometrics 14(2), 174–194 (1958)CrossRefzbMATHGoogle Scholar
  12. 12.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Song, C., Zehui, Q., Nicholas, B., Albert-Laszio, B.: Limits of Predictability in Human Mobility. Science 19 327(5968), 1018–1021 (2010)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Gonzalez, M.C., Hidalgo, A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature (2008)Google Scholar
  15. 15.
  16. 16.
    Kim, W., Song, H.Y.: Optimization Conditions of OCSVM for Erroneous GPS Data Filtering. Will be Presented in MULGRAB 2011 IPS (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hyunuk Kim
    • 1
  • Ha Yoon Song
    • 1
  1. 1.Department of Computer EngineeringHongik UniversitySeoulKorea

Personalised recommendations