Advertisement

A HMM-Based Location Prediction Framework with Location Recognizer Combining k-Nearest Neighbor and Multiple Decision Trees

  • Yong-Joong Kim
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

Knowing user’s current or next location is very important task for context-aware services in mobile environment. Many researchers have tried to predict user location using their own methods. However, they focused mainly the performance of method, and only few were considered development of real working system on mobile devices. In this paper, we present a location prediction framework, and develop a personalized destination prediction system based on this framework using smartphone. The framework consists of two methods of recognizing user location based on the combined method of k-nearest neighbor (kNN) and decision tree, and predicting user destination based on the hidden Markov model (HMM). The destination prediction system is composed of four parts including mobile sensor log collector, location recognition module, location prediction module, and system management module. Experiments on real datasets of five persons showed that our method achieved average prediction accuracy above 87%.

Keywords

Location recognition Location prediction Location extraction Location- based services 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alvarez-Garcia, J.A., Ortega, J.A., Gonzalez-Abril, L., Velasco, F.: Trip Destination Prediction Based on Past GPS Log Using a Hidden Markov Model. Expert Systems with Applications 37(12), 8166–8171 (2010)CrossRefGoogle Scholar
  2. 2.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  3. 3.
    Calabrese, F., Lorenzo, G.D., Ratti, C.: Human Mobility Prediction based on Individual and Collective Geographical Preferences. In: Proceedings of the 13th IEEE Intelligent Transportation Systems, pp. 312–317 (2010)Google Scholar
  4. 4.
    Caruana, R., Niculescu-Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161–168 (2006)Google Scholar
  5. 5.
    Chen, G., Kotz, D.: A Survey of Context-Aware Computing Research. Technical Report TR2000-381, Dartmouth (November 2000)Google Scholar
  6. 6.
    Hamerly, G., Elkan, C.: Learning the k in k-means. In: Advanced in Neural Information Processing Systems, vol. 16 (2003)Google Scholar
  7. 7.
    Hightower, J., Consolvo, S., LaMarca, A., Smith, I., Hughes, J.: Learning and Recognizing the Places We Go. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 159–176. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting Places from Traces of Locations. In: Proceedings of the 2nd International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, pp. 110–118 (2005)Google Scholar
  9. 9.
    Krumm, J., Horvitz, E.: Predestination: Inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Lee, Y.S., Cho, S.B.: An Efficient Energy Management System for Android Phone Using Bayesian Networks. In: Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops, pp. 102–107 (2012)Google Scholar
  11. 11.
    Lim, T.S., Loh, W.Y., Shih, Y.S.: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 40(3), 203–228 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Löwe, R., Mandl, P., Weber, M.: Context Directory: A Context-Aware Service for Mobile Context-Aware Computing Applications by the Example of Google Android. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops 2012, pp. 76–81 (2012)Google Scholar
  13. 13.
    Mathew, W., Raposo, R., Martins, B.: Predicting future locations with hidden Markov models. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 911–918 (2012)Google Scholar
  14. 14.
    Monreale, A., Pinelli, F., Trasarti, R.: WhereNext: a Location Predictor on Trajectory Pattern Mining. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, pp. 637–646 (2009)Google Scholar
  15. 15.
    Morzy, M.: Mining Frequent Trajectories of Moving Objects for Location Prediction. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 667–680. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Noh, H.Y., Lee, J.H., Oh, S.W., Hwang, K.S., Cho, S.B.: Exploiting Indoor Location and Mobile Information for Context-Awareness Service. Information Processing and Management 48(1), 1–12 (2012)CrossRefGoogle Scholar
  17. 17.
    Park, H.S., Oh, K., Cho, S.B.: Bayesian Network-Based High-Level Context Recognition for Mobile Context Sharing in Cyber-Physical System. International Journal of Distributed Sensor Networks (2011)Google Scholar
  18. 18.
    Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of Indoor Movements Using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 211–222. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Comparison of Different Methods for Next Location Prediction. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 909–918. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc., San Francisco (1993)Google Scholar
  21. 21.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  22. 22.
    Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T.: NextPlace: A spatio-temporal prediction framework for pervasive systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 152–169. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Simmons, R., Browning, B., Zhang, Y., Sadekar, V.: Learning to Predict Driver Route and Destination Intent. In: Proceedings of the IEEE Intelligent Transportation Systems Conference 2006, pp. 127–132 (2006)Google Scholar
  24. 24.
    Yavas, G., Katsaros, D., Ulusoy, Ö., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering 54(2), 121–146 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yong-Joong Kim
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations