Abstract
Although various location-sensing techniques and services have been developed, most of the conventional location-based services provide only static service. They do not consider user’s preference but only a current location. Considering the trajectory might help to understand the user’s intention and to provide a proper service. We propose a novel method that predicts user’s mobility to provide service corresponding to the intention. The user’s movement trajectory is analyzed by two stage modeling of recurrent self-organizing maps (RSOM) and Markov models. Using a GPS data set collected on the campus of Yonsei University, we have verified the usefulness of the proposed method.
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References
Chan, J., Seneviratne, A.: A practical user mobility prediction algorithm for supporting adaptive QOS in wireless networks. In: Proc. IEEE Int’l Conf. Networks (ICON 1999), pp. 104–111 (1999)
Soh, W., Kim, H.: QOS provisioning in cellular networks based on mobility prediction techniques. IEEE Comm. Magazine 41(1), 86–92 (2003)
Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: Proc. Sixth Int’l Symp. Wearable Computers (ISWC 2002), October 2002, pp. 101–108 (2002)
Marmasse, N., Schmandt, C.: A user-centered location model. Personal and ubiquitous computing 6(5-6), 318–321 (2002)
Kettani, D., Moulin, B.: A spatial model based on the notions of spatial conceptual map and of object’s influence areas. In: Proc. Conf. Spatial Information Theory (COSIT 1999), August 1999, pp. 401–416 (1999)
Nancy, S., Ahmed, K.: A mobility prediction architecture based on contextual knowledge and spatial conceptual maps. IEEE Transactions on Mobile Computing 4(6), 537–551 (2005)
Liu, G., Maguire, G.: A class of mobile motion prediction algorithms for wireless mobile computing and communication. ACM Int’l J. Wireless Networks 1(2), 113–121 (1996)
Tabbane, S.: An alternative strategy for location tracking. IEEE J. select. Areas Commus. 13, 880–892 (1995)
Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal sequence processing using recurrent SOM. In: Proc. of 2nd Int. Conf. on Knowledge-based Intelligent Engineering System, Adelaide, Australia, April, vol. 1, pp. 290–297 (1998)
Winston, W.: Operations Research: Applications and Algorithm. Duxbury, Belmont, CA (1994)
Nancy, S., Ahmed, K.: A mobility prediction architecture based on contextual knowledge and spatial conceptual maps. IEEE Transactions on Mobile Computing 4(6) (November 2005)
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Park, MH., Hong, JH., Cho, SB. (2006). Two-Stage User Mobility Modeling for Intention Prediction for Location-Based Services. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_65
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DOI: https://doi.org/10.1007/11875581_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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