Advertisement

Predicting User’s Movement with a Combination of Self-Organizing Map and Markov Model

  • Sang-Jun Han
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

In the development of location-based services, various location-sensing techniques and experimental/commercial services have been used. We propose a novel method of predicting the user’s future movements in order to develop advanced location-based services. The user’s movement trajectory is modeled using a combination of recurrent self-organizing maps (RSOM) and the Markov model. Future movement is predicted based on past movement trajectories. To verify the proposed method, a GPS dataset was collected on the Yonsei University campus. The results were promising enough to confirm that the application works flexibly even in ambiguous situations.

Keywords

Markov Model Ubiquitous Computing Trajectory Model Movement Prediction Bayesian Network Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ashbrook, D., Starner, T.: Learning Significant Locations and Predicting User Movement with GPS. In: Proceedings of IEEE Sixth International Symposium on Wearable Computing, Seattle, WA (October 2002)Google Scholar
  2. 2.
    Stilp, L.: Carrier and End-User Applications for Wireless Location Systems. In: Proceedings of SPIE, vol. 2602, pp. 119–126 (1996)Google Scholar
  3. 3.
    Pousman, Z., Iachello, G., Fithian, R., Moghazy, J., Stasko, J.: Design Iterations for a Location-Aware Event Planner. Personal and Ubiquitous Computing 8(2), 117–225 (2004)CrossRefGoogle Scholar
  4. 4.
    Benford, S., Anastasi, R., Flintham, M., Drozd, A., Crabtree, A., Greenhalgh, C., Tandavanitj, N., Adams, M., Row-Farr, J.: Coping with uncertainty in a location-based game. IEEE Pervasive Computing 2(3), 34–41 (2003)CrossRefGoogle Scholar
  5. 5.
    Cheok, A.D., Goh, K.H., Liu, W., Farbiz, F., Fong, S.W., Teo, S.L., Li, Y., Yang, X.: Human Pacman: A Mobile, Wide-area Entertainment System based on Physical, Social, and Ubiquitous Computing. Personal and Ubiquitous Computing 8(2), 71–81 (2004)CrossRefGoogle Scholar
  6. 6.
    Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  7. 7.
    Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low- Level Sensors. In: Proceedings of the Fifth International Conference on Ubiquitous Computing, Seattle, WA, October 2003, pp. 73–89 (2003)Google Scholar
  8. 8.
    Sparacino, F.: Sto(ry)chastics: A Bayesian Network Architecture for User Modeling and Computational Storytelling for Interactive Spaces. In: Proceedings of the Fifth International Conference on Ubiquitous Computing, Seattle, WA, October 2003, pp. 54–72 (2003)Google Scholar
  9. 9.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  10. 10.
    Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal Sequence Processing using Recurrent SOM. In: Proceedings of Second International Conference on Knowledge- Based Intelligent Engineering Systems, Adelaide, Australia, April 1998, vol. 1, pp. 290–297 (1998)Google Scholar
  11. 11.
    Winston, W.L.: Operations Research: Applications and Algorithms. Duxbury, Belmont (1994)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Jun Han
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations