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Mobility Prediction Based on POI-Clustered Data

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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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References

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

    Google Scholar 

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

    Article  Google Scholar 

  3. 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)

    Google Scholar 

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

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  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_10

    Chapter  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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_9

    Chapter  Google Scholar 

  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)

    Google Scholar 

  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_5

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  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)

    Google Scholar 

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Acknowledgements

The work is supported in part by the NSFC (No. 61501527), Science, Technology and Innovation Commission of Shenzhen Municipality (No. JCYJ20170816151823313), Guangdong Innovative and Entrepreneurial Research Team Program (No. 2013D014), State’s Key Project of Research and Development Plan (No. 2016YFE0122900-3), the Fundamental Research Funds for the Central Universities, Guangdong Science and Technology Project (No. 2016B010126003), and 2016 Major Project of Collaborative Innovation in Guang-zhou (No. 201604046008).

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Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 511400, China

    Haoyuan Chen, Yali Fan, Jing Jiang & Xiang Chen

  2. Key Lab of EDA, Research Institute of Tsinghua University in Shenzhen (RITS), Shenzhen, 518075, China

    Haoyuan Chen, Yali Fan, Jing Jiang & Xiang Chen

  3. Starway Communications Inc., Guangzhou, 510663, China

    Xiang Chen

Authors
  1. Haoyuan Chen
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  2. Yali Fan
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  3. Jing Jiang
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  4. Xiang Chen
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Corresponding author

Correspondence to Xiang Chen .

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Editors and Affiliations

  1. Zhejiang University of Technology, Hangzhou Shi, China

    Limin Meng

  2. Department of Networks, Simula Research Laboratory, Oslo, Norway

    Yan Zhang

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chen, H., Fan, Y., Jiang, J., Chen, X. (2018). Mobility Prediction Based on POI-Clustered Data. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-00557-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

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