Abstract
Location-awareness and prediction of future locations is an important problem in pervasive and mobile computing. In cellular systems (e.g., GSM) the serving cell is easily available as an indication of the user location, without any additional hardware or network services. With this location data and other context variables we can determine places that are important to the user, such as work and home. We devise online algorithms that learn routes between important locations and predict the next location when the user is moving. We incrementally build clusters of cell sequences to represent physical routes. Predictions are based on destination probabilities derived from these clusters. Other context variables such as the current time can be integrated into the model. We evaluate the model with real location data, and show that it achieves good prediction accuracy with relatively little memory, making the algorithms suitable for online use in mobile environments.
Chapter PDF
Similar content being viewed by others
References
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 275–286 (2003)
Marmasse, N., Schmandt, C.: A user-centered location model. Personal and Ubiquitous Computing 6, 318–321 (2002)
Harrington, A., Cahill, V.: Route profiling: putting context to work. In: Proceedings of the 2004 ACM symposium on Applied computing (SAC 2004), pp. 1567–1573. ACM Press, New York (2004)
Laasonen, K., Raento, M., Toivonen, H.: Adaptive on-device location recognition. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 287–304. Springer, Heidelberg (2004)
Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting places from traces of locations. In: WMASH 2004: Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots, pp. 110–118. ACM Press, New York (2004)
Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)
Yang, J., Wang, W.: CLUSEQ: efficient and effective sequence clustering. In: Proceedings of the 19th International Conference on Data Engineering, pp. 101–112. IEEE Computer Society, Los Alamitos (2003)
Mannila, H., Moen, P.: Similarity between event types in sequences. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 271–280. Springer, Heidelberg (1999)
Gusfield, D.: Algorithms on strings, trees, and sequences. Cambridge University Press, Cambridge (1997)
Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone: a prototyping platform for context-aware mobile applications. IEEE Pervasive Computing 4, 51–59 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Laasonen, K. (2005). Clustering and Prediction of Mobile User Routes from Cellular Data. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_59
Download citation
DOI: https://doi.org/10.1007/11564126_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29244-9
Online ISBN: 978-3-540-31665-7
eBook Packages: Computer ScienceComputer Science (R0)