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2018 | OriginalPaper | Buchkapitel

Mobility Prediction Based on POI-Clustered Data

verfasst von : Haoyuan Chen, Yali Fan, Jing Jiang, Xiang Chen

Erschienen in: Machine Learning and Intelligent Communications

Verlag: Springer International Publishing

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Abstract

Predicting users’ mobility trajectories is significant for service providers, such as recommendation systems for tourist routing, emergency warning, etc. However, the former researchers predict the next location merely by observing the past individual trajectories, which usually performs poor in the accuracy of trace prediction. In this paper, POIs (Points of Interest) information is used to adjust the weight parameters of the predicted results, and the rationality and precision would be improved. The cellular towers are firstly classified into seven types of functional area through POIs. Then the target user’s next possible functional area could be speculated, which acts as a supervision of the ultimate prediction outcome. We use the DP (Dirichlet Process) mixture model to identify similarity between different users and predict users’ locations by leveraging these similar users. As is shown in the results, the methods proposed above are highly adaptive and precise when being utilized to predict users’ mobility trajectories.

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Perangkaan, M.J.: 1980 population and housing census of Malaysia. General report of the population census, vol. 1, Kuala Lumpur, Malaysia (2005)

Deville, P., et al.: Dynamic population mapping using mobile phone data. Proc. Nat. Acad. Sci. U.S.A. 111(45), 15888–15893 (2014)CrossRef

Xu, F., Zhang, P., Li, Y.: Context-aware real-time population estimation for metropolis. In: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, pp. 1064–1075. ACM (2016)

Bagrow, J., Wang, D., Barabasi, A.L.: Collective response of human populations to large-scale emergencies. PLoS ONE 6(3), e17680 (2011)CrossRef

Bengtsson, L., Xin, L., Thorson, A., Garfield, R., Schreeb, J.V.: Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med. 8(8), e1001083 (2011)CrossRef

Fan, Z., Song, X., Shibasaki, R., Adachi, R.: CityMomentum: an online approach for crowd behavior prediction at a citywide level. In: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, pp. 559–569. ACM (2015)

Shimosaka, M., Maeda, K., Tsukiji, T., Tsubouchi, K.: Forecasting urban dynamics with mobility logs by bilinear poisson regression. In: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, pp. 535–546. ACM (2015)

Chon, Y., Shin, H., Talipov, E., Cha, H.: Evaluating mobility models for temporal prediction with high-granularity mobility data. In: 2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, vol. 25, pp. 206–212. IEEE (2012)

Song, L., Kotz, D., Jain, R., He, X.: Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Trans. Mob. Comput. 5(12), 1633–1649 (2006)CrossRef

10.

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). https://doi.org/10.1007/978-3-642-21726-5_10CrossRef

11.

Jacquet, P., Szpankowski, W., Apostol, I.: An universal predictor based on pattern matching: preliminary results. IEEE Trans. Inf. Theory 48(6), 1462–1472 (2002)CrossRefMATH

12.

Mcinerney, J., Zheng, J., Rogers, A., Jennings, N.R.: Modelling heterogeneous location habits in human populations for location prediction under data sparsity. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, Zurich, pp. 469–478. ACM (2013)

13.

Jeong, J., Leconte, M., Proutiere, A.: Cluster-aided mobility predictions. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, San Francisco, vol. 18, pp. 1–9. IEEE (2016)

14.

Yao, Z., Fu, Y., Liu, B., Liu, Y., Xiong, H.: POI recommendation: a temporal matching between POI popularity and user regularity. In: 2016 IEEE 16th International Conference on Data Mining, Barcelona, pp. 549–558. IEEE (2017)

15.

Zhang, Z., Liu, Y., Chen, H., Liu, Q.: POI recommendation with geographical and multitag influences. In: International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), Durham, pp. 1–6. IEEE (2017)

16.

Carmo, M.B., Afonso, A.P., Ferreira, A., Claudio, A.P., Silva, G.: PoI awareness, relevance and aggregation for augmented reality. In: 2016 IEEE 20th International Conference on Information Visualisation, Lisbon, pp. 300–305. IEEE (2016)

17.

Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and POIs. In: 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, pp. 186–194. ACM (2012)

18.

Mimno, D., Mccallum, A.: Topic models conditioned on arbitrary features with dirichlet-multinomial regression. In: 2008 24th Conference on Uncertainty in Artificial Intelligence, arXiv. 1206.3278v1, pp. 411–418 (2012)

19.

Ferguson, T.S.: A Bayesian analysis of some nonparametric problems. Ann. Stat. 1(2), 209–230 (1973)MathSciNetCrossRefMATH

20.

Gershman, S.J., Blei, D.M.: A tutorial on Bayesian nonparametric models. J. Math. Psychol. 56(1), 1–12 (2012)MathSciNetCrossRefMATH

21.

Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRef

22.

Fang, Z., Ma, C., Wang, X., Qu, J.: Mining popular mobility patterns from user GPS trajectories. In: 2016 9th International Conference on Service Science, Chongqing, pp. 180–181. IEEE (2016)

23.

Wirz, M., Franke, T., Roggen, D., Mitleton-Kelly, E., Lukowicz, P., Troster, G.: Inferring crowd conditions from pedestrians’ location traces for real-time crowd monitoring during city-scale mass gatherings. In: 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, Hammamet, pp. 367–372. IEEE (2012)

24.

Noulas, A., Mascolo, C., Frias-Martinez, E.: Exploiting foursquare and cellular data to infer user activity in urban environments. In: 2013 IEEE 14th International Conference on Mobile Data Management, Milan, vol. 1, pp. 167–176. IEEE (2013)

25.

Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011)CrossRef

26.

Isaacman, S., et al.: Identifying important places in people’s lives from cellular network data. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 133–151. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21726-5_9CrossRef

27.

Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: 18th International Conference on World Wide Web, Madrid, pp. 791–800. ACM (2009)

28.

Yoon, H., Zheng, Y., Xie, X., Woo, W.: Smart itinerary recommendation based on user-generated GPS trajectories. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 19–34. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16355-5_5CrossRef

29.

Zonoozi, M.M., Dassanayake, P.: User mobility modeling and characterization of mobility patterns. IEEE J. Sel. Areas Commun. 15(7), 1239–1252 (1997)CrossRef

30.

Calabrese, F., Lorenzo, G.D., Ratti, C.: Human mobility prediction based on individual and collective geographical preferences. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, pp. 312–317. IEEE (2010)

Titel: Mobility Prediction Based on POI-Clustered Data
verfasst von: Haoyuan Chen
Yali Fan
Jing Jiang
Xiang Chen
Verlag: Springer International Publishing
Buch: Machine Learning and Intelligent Communications
Print ISBN: 978-3-030-00556-6

Electronic ISBN: 978-3-030-00557-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00557-3_7

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