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.
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Han, SJ., Cho, SB. (2006). Predicting User’s Movement with a Combination of Self-Organizing Map and Markov Model. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_92
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DOI: https://doi.org/10.1007/11840930_92
Publisher Name: Springer, Berlin, Heidelberg
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