Optimization Conditions of OCSVM for Erroneous GPS Data Filtering

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


The topics on human mobility model have long been researched by various academic and industrial fields. It has been proven that human mobility has specific patterns and can be predicted up to the probability of 93%, since the mobility of a person cannot be random while peoples have their own frequent visiting places such as home, office, haunt restaurants, and so on. The positioning data of a human can be obtained by GPS or similar positioning system, however, it contains inherited environmental errors. In this paper we will present filtering method of erroneous GPS data of human mobility. With the use of One Class Support Vector Machine (OCSVM), we adapted Radial Basis Function (RBF) as kernel function. Experimental values of the critical parameter γ for RBF has been found for optimal filtering.


Human Mobility Global Positioning System One Class Support Vector Machine Radial Basis Function Parameter Optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    Steg, L.: Car use: Lust and must. Instrumental, symbolic and affective motives for car use. Transportation Research Part A: Policy and Practice 39(2-3), 147–162 (2005)Google Scholar
  3. 3.
    Garling, T., Fujii, S., Boe, O.: Empirical tests of a model of determinants of script-based driving choice. Transportation Research Part F: Traffic Psychology and Behaviour 4(2), 89–102 (2001)CrossRefGoogle Scholar
  4. 4.
    Steg, L., Vlek, C., Slotegraaf, G.: Instrumental-reasoned and symbolic affective motives for using a motor car. Transportation research part F: Traffic psychology and behaviour 4(3), 151–169 (2001)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, M.C., Hidalgo, A., Barabasi, A.-L.: Understanding individual human mobility patterns: Nature (2008)Google Scholar
  6. 6.
    Verplanken, B., Aarts, K., Knippenberg, A.V.: Habit, information acquisition, and the process of making travel mode choice. European Journal of Social Psychology 27(5), 539–560 (1997)CrossRefGoogle Scholar
  7. 7.
    Fujii, S., Garling, T.: Development of script-based travel mode choice after forced change. Transportation Research Part F: Traffic Psychology and Behaviour 6(2), 117–124 (2003)CrossRefGoogle Scholar
  8. 8.
    Schlkopf, B., Platt, J., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
  10. 10.
    Kim, W., Song, H.Y.: A Study on Novelty Detection of GPS Data Using Human Mobility and OCSVM(One-class SVM). In: Proceedings of the Korea Information Processing Society Conference, pp. 1060–1063 (2011)Google Scholar
  11. 11.
    GPS Real Trajectory, University of Illinois at Chicago,
  12. 12.
    Kim, H., Song, H.Y.: Daily Life Mobility of A Student: From Position Data to Human Mobility Model through Expectation Maximization Clustering. Will be Presented in the Proceedings of MULGRAB (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

Personalised recommendations