Abstract
Given a person’s current and historical traces, a useful yet challenging task is to predict the future locations at high spatial-temporal resolution. In this study, we propose a Brownian Bridge model to predict a person’s future location by using the individual’s historical traces that exhibit similarity with the current trace. The similarity of the traces with the current trace is evaluated based on the notion of edit distance. The predicted location at the future point in time is a weighted result obtained from a modified Brownian Bridge model that incorporates linear extrapolation. Both Brownian Bridge and linear extrapolation aim to capture aspects of the individual’s mobility behaviors. Compared to using either historical records or linear extrapolation method alone, the proposed location prediction method shows lower mean prediction error in predicting locations at different time horizons.
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References
Gruteser, M., Grunwald, D.: Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. In: MobiSys, pp. 31–42. ACM, New York (2003)
Lin, M., Hsu, W.-J., Lee, Z.Q.: Predictability of Individuals’ Mobility with High-Resolution Positioning Data. In: UbiComp, pp. 381–390. ACM, New York (2012)
Song, C., Qu, Z., Blumm, N., Barabái, A.L.: Limits of Predictability in Human Mobility. Science 327, 1018–1021 (2010)
Kim, S.-W., Park, S., Chu, W.W.: An index-based approach for similarity search supporting time warping in large sequence databases. In: ICDE, pp. 607–614. IEEE Computer Society, Washington (2001)
Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement across Multiple Users. Personal Ubiquitous Comput. 7, 275–286 (2003)
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)
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: KDD, pp. 330–339. ACM, New York (2007)
Ester, M., Kriegel, H., Jörg, S., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231. AAAI (1996)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Chen, L., Lv, M., Chen, G.: A system for destination and future route prediction based on trajectory mining. Pervasive Mobile Computing 6, 657–676 (2010)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: A location predictor on trajectory pattern mining. In: KDD, pp. 637–646. ACM, New York (2009)
Shreve, S. E.: Stochastic Calculus for Finance II - Contunuous-Time Models. Springer (2004)
The Inference of the Parameter of the Brownian Bridge Model, https://dl.dropboxusercontent.com/u/6694774/BB_varianceInference.pdf
Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp. 791–800. ACM, New York (2009)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Ubicomp, pp. 312–321. ACM, New York (2008)
Zaidi, Z.R., Mark, B.L.: Mobility Tracking Based on Autoregressive Models. IEEE Transactions on Mobile Computing 10, 32–43 (2011)
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Lin, M., Hsu, WJ. (2014). Brownian Bridge Model for High Resolution Location Predictions. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_18
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DOI: https://doi.org/10.1007/978-3-319-06605-9_18
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