Predicting user's movement with a combination of self-organizing map and Markov model

Sang Jun Han, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Pages884-893
Number of pages10
ISBN (Print)3540388710, 9783540388715
Publication statusPublished - 2006 Jan 1
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: 2006 Sep 102006 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4132 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Neural Networks, ICANN 2006
CountryGreece
CityAthens
Period06/9/1006/9/14

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Han, S. J., & Cho, S. B. (2006). Predicting user's movement with a combination of self-organizing map and Markov model. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings (pp. 884-893). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4132 LNCS - II). Springer Verlag.