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Human Mobility-Pattern Discovery and Next-Place Prediction from GPS Data

  • Faina Khoroshevsky
  • Boaz Lerner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10183)

Abstract

We provide a novel algorithm for the discovery of mobility patterns and prediction of users’ destination locations, both in terms of geographic coordinates and semantic meaning. We did not use any semantic data voluntarily provided by a user, and there was no sharing of data among the users. An advantage of our algorithm is that it allows a trade-off between prediction accuracy and information. Experimental validation was conducted on a GPS dataset collected in the Microsoft Research Asia GeoLife project by 168 users in a period of over five years.

Keywords

GeoLife Human behavior Location extraction Mobility pattern Next place prediction Positioning technology Semantic information Stay point Trajectory data 

References

  1. 1.
    Openstreetmap. http://wiki.openstreetmap.org/wiki/About. Accessed 01 Jan 2016
  2. 2.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquit. Comput. 7(5), 275–286 (2003)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)CrossRefGoogle Scholar
  5. 5.
    Do, T.M.T., Gatica-Perez, D.: Contextual conditional models for smartphone-based human mobility prediction. In: Proceeding of the 2012 ACM Conference on Ubiquitous Computing, pp. 163–172 (2012)Google Scholar
  6. 6.
    Do, T.M.T., Gatica-Perez, D.: Where and what: using smartphones to predict next locations and applications in daily life. Pervasive Mob. Comput. 12, 79–91 (2014)CrossRefGoogle Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  8. 8.
    Etter, V., Kafsi, M., Kazemi, E., Grossglauser, M., Thiran, P.: Where to go from here? Mobility prediction from instantaneous information. Pervasive Mob. Comput. 9(6), 784–797 (2013)CrossRefGoogle Scholar
  9. 9.
    Eugster, M.J.A., Schlesinger, T.: osmar: OpenStreetMap and R. The R Journal 5(1), 53–63 (2013)Google Scholar
  10. 10.
    Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Next place prediction using mobility Markov chains. In: Proceeding of the 1st Workshop on Measurement, Privacy, and Mobility, p. 3. ACM (2012)Google Scholar
  11. 11.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hung, S.H., Shih, C.S., Shieh, J.P., Lee, C.P., Huang, Y.H.: Executing mobile applications on the cloud: framework and issues. Comput. Math. Appl. 63(2), 573–587 (2012)CrossRefGoogle Scholar
  13. 13.
    Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)CrossRefGoogle Scholar
  14. 14.
    Kim, M., Kotz, D., Kim, S.: Extracting a mobility model from real user traces. In: INFOCOM, vol. 6, pp. 1–13 (2006)Google Scholar
  15. 15.
    Lei, P.R., Shen, T.J., Peng, W.C., Su, J.: Exploring spatial-temporal trajectory model for location prediction. In: IEEE 12th International Conference on Mobile Data Management, vol. 1, pp. 58–67 (2011)Google Scholar
  16. 16.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceeding of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, no. 34. ACM (2008)Google Scholar
  18. 18.
    Mwemezi, J.J., Huang, Y.: Optimal facility location on spherical surfaces: algorithm and application. NY Sci. J. 4(7), 21–28 (2011)Google Scholar
  19. 19.
    Oshiro, T.M., Perez, P.S., Baranauskas, J.A.: How many trees in a random forest? In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 154–168. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31537-4_13 CrossRefGoogle Scholar
  20. 20.
    Prabhala, B., Porta, T.L.: Spatial and temporal considerations in next place predictions. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 390–395. IEEE (2015)Google Scholar
  21. 21.
    Prabhala, B., Wang, J., Deb, B., Porta, T.L., Han, J.: Leveraging periodicity in human mobility for next place prediction. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 2665–2670 (2014)Google Scholar
  22. 22.
    Rieck, K., Laskov, P.: Linear-time computation of similarity measures for sequential data. J. Mach. Learn. Res. 9, 23–48 (2008)zbMATHGoogle Scholar
  23. 23.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  24. 24.
    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). doi: 10.1007/978-3-642-21726-5_10 CrossRefGoogle Scholar
  25. 25.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  26. 26.
    Ying, J.J.C., Lee, W.C., Tseng, V.S.: Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Trans. Intell. Syst. Technol. (TIST) 5(1), 2:1–2:33 (2013)Google Scholar
  27. 27.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: Proceeding of the 19th International Conference on the WWW, pp. 1029–1038. ACM (2010)Google Scholar
  28. 28.
    Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceeding of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008)Google Scholar
  29. 29.
    Zheng, Y., Xie, X., Ma, W.Y.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 312–321 (2010)Google Scholar
  30. 30.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining correlation between locations using human location history. In: Proceeding of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 472–475. ACM (2009)Google Scholar
  31. 31.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceeding of the 18th International Conference on WWW, pp. 791–800. ACM (2009)Google Scholar
  32. 32.
    Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., Terveen, L.: Discovering personal gazetteers: an interactive clustering approach. In: Proceeding of the 12th Annual ACM International Workshop on Geographic Information Systems, pp. 266–273. ACM (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ben-Gurion University of the NegevBeer-ShevaIsrael

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