Mobility Mining Using Nonnegative Tensor Factorization

  • Hamid Eslami Nosratabadi
  • Hadi Fanaee-T
  • Joao Gama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

Mobility mining has lots of applications in urban planning and transportation systems. In particular, extracting mobility patterns enables service providers to have a global insight about the mobility behaviors which consequently leads to providing better services to the citizens. In the recent years several data mining techniques have been presented to tackle this problem. These methods usually are either spatial extension of temporal methods or temporal extension of spatial methods. However, still a framework that can keep the natural structure of mobility data has not been considered. Non-negative tensor factorizations (NNTF) have shown great applications in topic modelling and pattern recognition. However, unfortunately their usefulness in mobility mining is less explored. In this paper we propose a new mobility pattern mining framework based on a recent non-negative tensor model called BetaNTF. We also present a new approach based on interpretability concept for determination of number of components in the tensor rank selection process. We later demonstrate some meaningful mobility patterns extracted with the proposed method from bike sharing network mobility data in Boston, USA.

Keywords

Mobility mining Nonnegative tensor factorization BetaNTF 

References

  1. 1.
    Asif, M., et al.: Data compression techniques for urban traffic data. In: 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) (2013)Google Scholar
  2. 2.
    Abadi, A., Tooraj, R., Petros, A.: Ioannou.: traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)Google Scholar
  3. 3.
    Bader, B.W., Berry, M.W., Broene, M.: Discussion tracking in ernron email using PARAFAC. In: Berry, M.W., Castellanos, M. (eds.) Survey of Text Mining II, pp. 147–163. Springer, London (2008)CrossRefGoogle Scholar
  4. 4.
    Cichocki, A., et al.: Non-negative tensor factorization using alpha and beta divergences. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP 2007, vol. 3. IEEE (2007)Google Scholar
  5. 5.
    Chen, M., Xiaohui, Y., Yang, L.: Mining moving patterns for predicting next location. Inf. Syst. 54, 156–168 (2015)CrossRefGoogle Scholar
  6. 6.
    Han, Y., Fabien, M.: Analysis of large-scale traffic dynamics in an urban transportation network using non-negative tensor factorization. Int. J. Intell. Transp. Syst. Res. 14(1), 36–49 (2016)Google Scholar
  7. 7.
  8. 8.
    Castellanos, J.L., Gomez, S., Guerra, V.: The triangle method for finding the corner of the L-curve. Appl. Numer. Math. 43(4), 359–373 (2002)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika 35, 283–319 (1970)CrossRefMATHGoogle Scholar
  10. 10.
    Ran, B., et al.: Traffic speed data imputation method based on tensor completion. Comput. Intell. Neurosci. 2015, 22 (2015)CrossRefGoogle Scholar
  11. 11.
    Ran, B., et al.: Estimating missing traffic volume using low multilinear rank tensor completion. J. Intell. Transp. Syst. 20(2), 152–161 (2016)CrossRefGoogle Scholar
  12. 12.
    Sorber, L., van Marc, B., de Lieven, L.: L.: Optimization-based algorithms for tensor decompositions: canonical polyadic decomposition, decomposition in rank-(L_r, L_r,1) terms, and a new generalization. SIAM J. Optim. 23(2), 695–720 (2013)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Sorber, L., van Marc, B., de Lieven, L.: Unconstrained optimization of real functions in complex variables. SIAM J. Optim. 22(3), 879–898 (2012)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Tan, H., et al.: A tensor-based method for missing traffic data completion. Transp. Res. Part C: Emerg. Technol. 28, 15–27 (2013)CrossRefGoogle Scholar
  15. 15.
    Tan, H., et al.: Low multilinear rank approximation of tensors and application in missing traffic data. Adv. Mech. Eng. 6, 157597 (2014)CrossRefGoogle Scholar
  16. 16.
    Tan, H., et al.: Traffic volume data outlier recovery via tensor model. Math. Prob. Eng. 2013, 164810 (2013)MathSciNetGoogle Scholar
  17. 17.
    Tan, H.: Traffic missing data completion with spatial-temporal correlations. Department of Civil and Environmental Engineering, University of Wisconsin-Madison (2014)Google Scholar
  18. 18.
    Tan, H., et al.: A new traffic prediction method based on dynamic tensor completion. Procedia-Soc. Behav. Sci. 96, 2431–2442 (2013)CrossRefGoogle Scholar
  19. 19.
    Tan, H., et al.: Correlation analysis for tensor-based traffic data imputation method. Procedia-Soc. Behav. Sci. 96, 2611–2620 (2013)CrossRefGoogle Scholar
  20. 20.
    Versichele, M., et al.: Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: a case study of Ghent. Belg. Tour. Manag. 44, 67–81 (2014)CrossRefGoogle Scholar
  21. 21.
    Zheng, W., Xiaoting, H., Yuan, L.: Understanding the tourist mobility using GPS: where is the next place? Tour. Manag. 59, 267–280 (2017)CrossRefGoogle Scholar
  22. 22.
    Itakura, F., Saito, S.: Analysis synthesis telephony based on the maximum likelihood method. In: Proceedings of 6th of the International Congress on Acoustics, pp. C–17–C–20, Los Alamitos. IEEE (1968)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hamid Eslami Nosratabadi
    • 1
  • Hadi Fanaee-T
    • 1
  • Joao Gama
    • 1
  1. 1.LIAAD-INESC TECPortoPortugal

Personalised recommendations