Skip to main content

Predictability Analysis of Aperiodic and Periodic Model for Long-Term Human Mobility Using Ambient Sensors

  • 814 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8940)


The predictive technique proposed in this project was initially designed for an indoor smart environment wherein intrusive tracking techniques, such as cameras, mobile phones, and GPS tracking systems, could not be appropriately utilized. Instead, we installed simple motion detection sensors in various areas of the experimental space and observed movements. However, the data collected cannot provide as much information about human mobility as data from a GPS or mobile phone. In this paper, we conducted an exhaustive analysis to determine the predictability of future mobility of people using only this limited dataset. Furthermore, we proposed an aperiodic and periodic predictive technique for long-term human mobility prediction that works well with our limited dataset. The evaluation of the dataset collected of the movement and daily activity in the smart space for three months shows that our model is able to predict future mobility and activities of participants in the smart environment setting with high accuracy – even for a month in advance.


  • Human mobility
  • Smart environment
  • Long-term prediction
  • Fano’s inequality
  • Predictability analysis

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-14723-9_8
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   39.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-14723-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   49.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.


  1. 1.

    Placement of each point of interest \(x_i\) can be found in Fig. 1a.


  1. Das, B., Chen, C., Dasgupta, N., Cook, D.J., Seelye, A.M.: Automated prompting in a smart home environment. In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops (ICDMW ’10), pp. 1045–1052 (2010)

    Google Scholar 

  2. Das, B., Cook, D.J., Schmitter-Edgecombe, M., Seelye, A.M.: PUCK: an automated prompting system for smart environments: toward achieving automAted Prompting-challenges Involved. Pers. Ubiquitous Comput. 16(7), 859–873 (2012)

    CrossRef  Google Scholar 

  3. Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’09), pp. 637–646 (2009)

    Google Scholar 

  4. Krumm, J., Horvitz, E.: Predestination: inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  5. Song, L., Deshpande, U., Kozat, U., Kotz, D., Jain, R.: Predictability of WLAN mobility and its effects on bandwidth provisioning. In: Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM ’06), pp. 1–13 (2006)

    Google Scholar 

  6. Haddadi, H., Hui, P., Brown, I.: MobiAd: private and scalable mobile advertising. In: Proceedings of the Fifth ACM International Workshop on Mobility in the Evolving Internet Architecture (MobiArch ’10), pp. 33–38 (2010)

    Google Scholar 

  7. Yu, S.I., Yang, Y., Hauptmann, A.: Harry Potter’s Marauder’s Map: localizing and tracking multiple persons-of-interest by nonnegative discretization. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’13), pp. 3714–3720 (2013)

    Google Scholar 

  8. Roth, S.: Discrete-continuous optimization for multi-target tracking. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’12), pp. 1926–1933 (2012)

    Google Scholar 

  9. Beleznai, C., Schreiber, D., Rauter, M.: Pedestrian detection using GPU-accelerated multiple cue computation. In: Computer Vision and Pattern Recognition Workshops (CVPRW ’11), pp. 58–65 (2011)

    Google Scholar 

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

    CrossRef  Google Scholar 

  11. Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)

    CrossRef  MATH  MathSciNet  Google Scholar 

  12. 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)

    CrossRef  Google Scholar 

  13. Baeg, M., Park, J.H., Koh, J., Park, K.W., Baeg, M.H.: Building a smart home environment for service robots based on RFID and sensor networks. In: International Conference on Control, Automation and Systems (ICCAS ’07), pp. 1078–1082 (2007)

    Google Scholar 

  14. Hussain, S., Schaffner, S., Moseychuck, D.: Applications of wireless sensor networks and RFID in a smart home environment. In: Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference (CNSR ’09), pp. 153–157 (2009)

    Google Scholar 

  15. Pei, J., Pinto, H., Chen, Q., Han, J., Mortazavi-Asl, B., Dayal, U., Hsu, M.C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering (ICDE ’01), pp. 215–224 (2001)

    Google Scholar 

  16. Fano, R.M.: Transmission of information: a statistical theory of communications. Am. J. Phys. 29, 793–794 (1961)

    CrossRef  Google Scholar 

  17. Navet, N., Chen, S.H.: On predictability and profitability: Would gp induced trading rules be sensitive to the observed entropy of time series? In: Brabazon, A., O’Neill, M. (eds.) Natural Computing in Computational Finance. SCI, vol. 100, pp. 197–210. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  18. Sadilek, A., Krumm, J.: Far out: predicting long-term human mobility. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI ’12) (2012)

    Google Scholar 

  19. Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)

    CrossRef  MathSciNet  Google Scholar 

  20. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  21. Ferris, B., Fox, D., Lawrence, N.D.: Wifi-slam using gaussian process latent variable models. In: IJCAI, vol. 7, pp. 2480–2485 (2007)

    Google Scholar 

  22. Ferris, B., Haehnel, D., Fox, D.: Gaussian processes for signal strength-based location estimation. In: Proceedings of Robotics Science and Systems, Citeseer (2006)

    Google Scholar 

  23. Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th annual international conference on Mobile computing & networking, pp. 27–38, ACM (2013)

    Google Scholar 

  24. Cielniak, G., Bennewitz, M., Burgard, W.: Where is...? learning and utilizing motion patterns of persons with mobile robots. In: IJCAI, pp. 909–914 (2003)

    Google Scholar 

  25. Nguyen, N., Venkatesh, S., Bui, H.: Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association. In: BMVC 2006: Proceedings of the 17th British Machine Vision Conference, British Machine Vision Association, pp. 1239–1248 (2006)

    Google Scholar 

Download references


This work was partly supported by JSPS Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation and JSPS Core-to-Core Program, A. Advanced Research Networks.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Danaipat Sodkomkham .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sodkomkham, D., Legaspi, R., Fukui, Ki., Moriyama, K., Kurihara, S., Numao, M. (2015). Predictability Analysis of Aperiodic and Periodic Model for Long-Term Human Mobility Using Ambient Sensors. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14722-2

  • Online ISBN: 978-3-319-14723-9

  • eBook Packages: Computer ScienceComputer Science (R0)