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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11875581_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)