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)


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.


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.


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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

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