, Volume 42, Issue 4, pp 561–579 | Cite as

Mining activity pattern trajectories and allocating activities in the network

  • Mahdieh Allahviranloo
  • Will Recker


GPS enabled devices, generating high-resolution spatial–temporal data, are opening new lines of possibilities for transportation applications in both planning and research. Mining these rich and large datasets to infer people’s travel behavior, the activity patterns resulting from their behavior, and allocating activities in the network is the focus of this paper. Here we introduce a methodology that relies only on geocoded location data and socioeconomic characteristics to infer types of activities in which individuals engage at different locations in the network. Depending on the duration of the stop, arrival time and geographic distance to home location and previous activities, the type of activity is inferred at the census tract level using adaptive boosting algorithm. Then, using a model based on Markov chains with conditional random field to capture dependency between activity sequencing and individuals’ socioeconomic attributes, the spatial–temporal trajectory of activity/travel engagement is generated. The model is trained on data obtained from the California Household Travel Survey data 2000–2001 and subsequently applied to an out-of sample test set to validate the accuracy and performance.


Activity pattern trajectory Spatial–temporal analysis Data mining Pattern inference 



Anonymous reviewers of the paper are gratefully acknowledged for their valuable comments. This research was supported, in part, by a grant from the University of California Transportation Center.


  1. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, IEEE Computer Society Press, pp 3–14 (1995). doi: 10.1109/ICDE.1995.380415
  2. Allahviranloo, M., Jeliazkov, I.: Bayesian analysis of personal daily activity patterns. In: 93rd Annual Meetings of Transportation Research Board of the National Academies, (2013)Google Scholar
  3. Allahviranloo, M., Recker, W.: Daily activity pattern recognition by using support vector machines with multiple classes. Transp. Res. B Elsevier Ltd 58(December), 16–43 (2013). doi: 10.1016/j.trb.2013.09.008 CrossRefGoogle Scholar
  4. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)Google Scholar
  5. Bohte, W., Maat, K.: Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp. Res. Part C 17(3), 285–297 (2009). doi: 10.1016/j.trc.2008.11.004
  6. Caltrans.: California Statewide Household Travel Survey. California Statewide Household Travel Survey Final Report. vol. 78746. California Department of Transportation (2002)Google Scholar
  7. Cao, X., Cong, G., Jensen, C.S.: Mining significant semantic locations from GPS data. Proc. VLDB Endow. 3(1–2), 1009–1020 (2010). doi: 10.14778/1920841.1920968 CrossRefGoogle Scholar
  8. Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: 2011. Exploring millions of footprints in location sharing services. In: Fifth International AAAI Conference on Weblogs and Social Media, 2010, pp 81–88Google Scholar
  9. Forrest, T.L., Pearson, D.F.: Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transp. Res. Rec. J. Transp. Res. Board 1917, 63–71 (2002)Google Scholar
  10. Giannotti, F., Nanni, M., Pedreschi, D., Renso, C., Trasarti, R.: Mining mobility behavior from trajectory data. In: 2009 International Conference on Computational Science and Engineering, IEEE, pp. 948–951 (2009). doi: 10.1109/CSE.2009.542
  11. Giannotti, F.: GeoPKDD geographic privacy-aware knowledge discovery and delivery (2009)Google Scholar
  12. Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., Trasarti, R.: Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J. 20(5), 695–719 (2011). doi: 10.1007/s00778-011-0244-8 CrossRefGoogle Scholar
  13. Gidófalvi, G., Pedersen, T.B.: Spatio-temporal rule mining. In: Proceedings of Data Warehousing and Knowledge Discovery Conference, 2005, pp. 275–284. Springer, Berlin (2005)Google Scholar
  14. Hato, E.: Development of behavioral context addressable loggers in the shell for travel-activity analysis. Transp. Res. C 18(1), 55–67 (2010). doi: 10.1016/j.trc.2009.04.013 CrossRefGoogle Scholar
  15. Keuleers, B., Wets, G., Timmermans, H., Arentze, T., Vanhoof, K.: Stationary and time-varying patterns in activity diary panel data explorative analysis with association rules. Transp. Res. Rec. 1752, 9–15 (1998)Google Scholar
  16. Kusakabe, T., Asakura, Y.: Behavioural data mining of transit smart card data: a data fusion approach. Transp. Res. Part C 46, 179–191 (2014). doi: 10.1016/j.trc.2014.05.012
  17. Laube, P., Purves, R.S.: An approach to evaluating motion pattern detection techniques in spatio-temporal data. Comput. Environ. Urban Syst. 30(3), 347–374 (2006). doi: 10.1016/j.compenvurbsys.2005.09.001 CrossRefGoogle Scholar
  18. Liao, L.: Location-Based Activity Recognition. University of Washington, Seattle (2006)Google Scholar
  19. Liao, L., Fox, D., Kautz, H.: Extracting places and activities from GPS traces using hierarchical conditional random fields. Int. J. Robot. Res. 26(1), 119–134 (2007). doi: 10.1177/0278364907073775 CrossRefGoogle Scholar
  20. Oliver, M., Badland, H., Mavoa, S., Duncan, M.J., Duncan, S.: Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors. J. Phys. Act. Health 7(1), 102–108. (2010)
  21. Quercia, D., Hare, N.O., Cramer, H.: Aesthetic capital: what makes London look beautiful, quiet, and happy? In: Computer-Supported Cooperative Work, Maryland, USA, 2014Google Scholar
  22. Schuessler, N., Axhausen, K.W.: Processing raw data from global positioning systems without additional information. Transp. Res. Rec. J. Transp. Res. Board 2105(1), 28–36 (2009). doi: 10.3141/2105-04 CrossRefGoogle Scholar
  23. Shafique, M.A., Hato, E.: Use of acceleration data for transportation mode prediction. Transportation (2014). doi: 10.1007/s11116-014-9541-6 Google Scholar
  24. Shen, L., Stopher, P.R.: A process for trip purpose imputation from global positioning system data. Transp. Res. Part C 36, 261–267 (2013). doi: 10.1016/j.trc.2013.09.004
  25. Wagner, D.P.: Lexington Area Travel Data Collection Test. Federal Highway Administration, U.S. Department of Transportation, Columbus (1997)Google Scholar
  26. Ye, Y., Zheng, Y., Chen, Y., Feng, J., Xie, X.: Mining individual life pattern based on location history. In: 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, 2009, pp. 1–10. IEEE. doi: 10.1109/MDM.2009.11
  27. Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web—WWW’09, New York, p. 791. New York: ACM Press, 2009. doi: 10.1145/1526709.1526816
  28. Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif. Intell. 184–185, 17–37 (2012). doi: 10.1016/j.artint.2012.02.002 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Civil EngineeringThe City College of New York-CUNYNew YorkUSA
  2. 2.Department of Civil and Environmental Engineering, Institute of Transportation StudiesUniversity of California IrvineIrvineUSA

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