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LP-HMM: Location Preference-Based Hidden Markov Model

  • Jianhua HuangEmail author
  • Feixia Wu
  • Weiqiang Meng
  • Jian Yao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

Lots of mobility data have been generated with the emergence of smart devices and location-based services. The prediction of user mobility has become a key factor driving the rapid development of many location applications. Location prediction has attracted more and more attention in various fields, and many location prediction algorithms have been proposed. The data currently used for researches has many problems such as data noise and redundancy. Many researches directly used raw data and did not consider spatiotemporal characteristics of historical data enough, which leads to low prediction accuracy. This paper proposes a point-of-interest discovering algorithm, which fully considers spatiotemporal characteristics of data. By combining the location preference of users for location with the Hidden Markov Model (HMM), we propose LP-HMM (Location Preference-based Hidden Markov Model), a location prediction model based on location preference and HMM. The proposed model is compared with other location prediction models driven by the massive and real mobile dataset Geolife. The experiment results show that the prediction accuracy of the proposed model can achieve 6.4% and 7% higher than Gaussian Mixture Model (GMM) and traditional HMM respectively.

Keywords

Hidden Markov Model Location preference Location prediction 

References

  1. 1.
    Sungjun, L., Junseok, L., Jonghun, P.: Next place prediction based on spatiotemporal pattern mining of mobile device logs. Sensors 16(2), 145–163 (2016)CrossRefGoogle Scholar
  2. 2.
    Lin, Y., Huang, P.: Prefetching for mobile web album. Wirel. Commun. Mob. Comput. 16(1), 18–28 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Yang, J., Qiao, Y., Zhang, X.: Characterizing user behavior in mobile internet. IEEE Trans. Emerg. Top. Comput. 3(1), 95–106 (2015)CrossRefGoogle Scholar
  4. 4.
    Qiao, Y., Cheng, Y., Yang, J., et al.: A mobility analytical framework for big mobile data in densely populated area. IEEE Trans. Veh. Technol. 66(2), 1443–1455 (2017)CrossRefGoogle Scholar
  5. 5.
    Wu, R., Luo, G., Yang, Q., Shao, J.: Individual moving preference and social interaction for location prediction. IEEE Access 6(1), 10675–10687 (2018)CrossRefGoogle Scholar
  6. 6.
    Lee, W.C., Ye, M.: Location-based social networks. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Yi, X.: Research of Tourist Traffic Flow Characteristic Based on Phone Signaling Data. Southeast University, Nanjing (2017)Google Scholar
  8. 8.
    Jiang, S., Ferreira, J., Gonzalez, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3(2), 208–2193 (2017)CrossRefGoogle Scholar
  9. 9.
    Hu, Y., Zhu, X., Ma, G.: Location prediction model based on k-means algorithm. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds.) Advances in Computer and Computational Sciences, vol. 554, no. 1, pp. 681–687 (2018)Google Scholar
  10. 10.
    Kavak, H., Vernon-Bido, D., Padilla, J.J.: Fine-scale prediction of people’s home location using social media footprints. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 183–189. Springer, Heidelberg (2018)CrossRefGoogle Scholar
  11. 11.
    Ikanovic, E.L., Mollgaard, A.: An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6(1), 6–12 (2017)CrossRefGoogle Scholar
  12. 12.
    Wei, Z.: Research on Key Technologies of Moving Object Location Prediction. Nanjing University of Aeronautics and Astronautics (2009)Google Scholar
  13. 13.
    Chen, N.C., Xie, W., Welsch, R.E., et al.: Comprehensive predictions of tourists’ next visit location based on call detail records using machine learning and deep learning methods. In: IEEE International Congress on Big Data. IEEE, Piscataway (2017)Google Scholar
  14. 14.
    Wu, F., Fu, K., Wang, Y., et al.: A spatial-temporal-semantic neural network algorithm for position prediction on moving objects. Algorithms 10(2), 99–110 (2017)CrossRefGoogle Scholar
  15. 15.
    Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Next place prediction using mobility markov chains. In: Proceedings of the First Workshop on Measurement, Privacy and Mobility. ACM (2012)Google Scholar
  16. 16.
    Qiao, W., Si, Z., Zhang, Y., et al.: A hybrid Markov-based model for human mobility prediction. Neurocomputing 7(1), 278–290 (2017)Google Scholar
  17. 17.
    Lv, Q., Qiao, Y., Ansari, N., et al.: Big data driven hidden Markov model based individual mobility prediction at points of interest. IEEE Trans. Veh. Technol. 66(6), 5204–5216 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jianhua Huang
    • 1
    Email author
  • Feixia Wu
    • 1
  • Weiqiang Meng
    • 1
  • Jian Yao
    • 2
  1. 1.East China University of Science and TechnologyShanghaiChina
  2. 2.China United Network Communications Limited, Shanghai BranchShanghaiChina

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