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2017 | OriginalPaper | Chapter

MIAC: A Mobility Intention Auto-Completion Model for Location Prediction

Authors : Feng Yi, Zhi Li, Hongtao Wang, Weimin Zheng, Limin Sun

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

Location prediction is essential to many proactive applications and many research works show that human mobility is highly predictable. However, existing works are reported with limited improvements in using generalized spatio-temporal features and unsatisfactory accuracy in complex human mobility. To address these challenges, a Mobility Intention and Auto-Completion (MIAC) model is proposed. We extract mobility patterns to capture common spatio-temporal features of all users, and use mobility intentions to characterize these mobility patterns. A new predicting algorithm based on auto-completion is then proposed. The experimental results on real-world datasets demonstrate that the proposed MIAC model can properly capture the regularity in human mobility by simultaneously considering spatial and temporal features. The comparison results also indicate that MIAC model significantly outperforms state-of-the-art location prediction methods, and can also predict long range locations.

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Literature
1.
2.
Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 791–800, New York, NY, USA. ACM (2009)
3.
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 21–24 August 2011, pp. 1082–1090 (2011)
4.
Itoh, M., Yokoyama, D., Toyoda, M., Tomita, Y., Kawamura, S., Kitsuregawa, M.: Visual fusion of mega-city big data: an application to traffic and tweets data analysis of metro passengers. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 431–440, October 2014
5.
Li, H., Sun, L., Zhu, H., Lu, X., Cheng, X.: Achieving privacy preservation in wifi fingerprint-based localization. In: 2014 Proceedings IEEE INFOCOM, pp. 2337–2345. IEEE (2014)
6.
Liu, W., Li, H., Chen, Y., Zhu, H., Sun, L.: Lares: latency-reduced neighbour discovery for contagious diseases prevention. Int. J. Ad Hoc Ubiquitous Comput. 16(1), 3–13 (2014)CrossRef
7.
Wang, Y., Yuan, N.J., Lian, D., Xu, L., Xie, X., Chen, E., Rui, Y.: Regularity and conformity: location prediction using heterogeneous mobility data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1275–1284. ACM (2015)
8.
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquitous Comput. 7(5), 275–286 (2003)CrossRef
9.
Bar-Yossef, Z., Kraus, N.: Context-sensitive query auto-completion. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, 28 March–1 April 2011, pp. 107–116 (2011)
10.
Yang, J., Xu, J., Xu, M., Zheng, N., Chen, Y.: Predicting next location using a variable order markov model. In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014, pp. 37–42. ACM (2014)
11.
Chen, M., Liu, Y., Yu, X.: Predicting next locations with object clustering and trajectory clustering. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 344–356. Springer, Cham (2015). doi:10.​1007/​978-3-319-18032-8_​27 CrossRef
12.
Gao, H., Tang, J., Liu, H.: Mobile location prediction in spatio-temporal context. In: Nokia Mobile Data Challenge Workshop, vol. 41, p. 44 (2012)
13.
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 CrossRef
14.
Lian, D., Xie, X., Zheng, V.W., Yuan, N.J., Zhang, F., Chen, E.: Cepr: a collaborative exploration and periodically returning model for location prediction. ACM Trans. Intell. Syst. Technol. 6(1), 8:1–8:27 (2015)CrossRef
15.
Likhyani, A., Padmanabhan, D., Bedathur, S.J., Mehta, S.: Inferring and exploiting categories for next location prediction. In: Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, Florence, Italy, 18–22 May 2015 - Companion Volume, pp. 65–66 (2015)
16.
Ying, J.J.-C., Lee, W.-C., Tseng, V.S.: Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM TIST 5(1), 2 (2013)
17.
Ye, J., Zhu, Z., Cheng, H.: What’s your next move: user activity prediction in location-based social networks. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 171–179 (2013)
18.
Yu, C., Liu, Y., Yao, D., Yang, L.T., Jin, H., Chen, H., Ding, Q.: Modeling user activity patterns for next-place prediction. Syst. J. IEEE, (99), 1–12 (2015)
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). doi:10.​1007/​11823285_​96 CrossRef
20.
Giannotti, F., Pappalardo, L., Pedreschi, D., Wang, D.: A complexity science perspective on human mobility. In: Mobility Data: Modeling, Management, and Understanding, pp. 297–314. Cambridge University Press (2013)
21.
22.
Fan, Z., Song, X., Shibasaki, R.: Cityspectrum: a non-negative tensor factorization approach. In: The 2014 ACM Conference on Ubiquitous Computing, UbiComp 2014, Seattle, WA, USA, 13–17 September 2014, pp. 213–223 (2014)
23.
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)MATH
24.
Schneider, C.M., Belik, V., Couronné, T., Smoreda, Z., González, M.C.: Unravelling daily human mobility motifs. J. Royal Soc. Interface 10(84) (2013)
Metadata
Title
MIAC: A Mobility Intention Auto-Completion Model for Location Prediction
Authors
Feng Yi
Zhi Li
Hongtao Wang
Weimin Zheng
Limin Sun
Copyright Year
2017
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63558-3_37

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