Modeling Checkpoint-Based Movement with the Earth Mover’s Distance

  • Matt Duckham
  • Marc van Kreveld
  • Ross Purves
  • Bettina Speckmann
  • Yaguang TaoEmail author
  • Kevin Verbeek
  • Jo Wood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9927)


Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements.


Gravity Model Movement Constraint Metro Station Minimum Cost Flow True Flow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abdul-Rahman, A., Pilouk, M.: Spatial Data Modelling for 3D GIS. Springer, Heidelberg (2008)Google Scholar
  2. 2.
    Andrienko, N.V., Andrienko, G.L.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Vis. Comput. Graph. 17(2), 205–219 (2011)CrossRefGoogle Scholar
  3. 3.
    Ban, X., Herring, R., Margulici, J.D., Bayen, A.M.: Optimal sensor placement for freeway travel time estimation. In: Lam, W.H.K., Wong, S.C., Lo, H.K. (eds.) (ISTTT18), pp. 697–721. Springer, New York (2009)Google Scholar
  4. 4.
    Both, A., Duckham, M., Laube, P., Wark, T., Yeoman, J.: Decentralized monitoring of moving objects in a transportation network augmented with checkpoints. Comput. J. 56(12), 1432–1449 (2013)CrossRefGoogle Scholar
  5. 5.
    Buchin, K., Speckmann, B., Verbeek, K.: Angle-restricted steiner arborescences for flow map layout. Algorithmica 72(2), 656–685 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Giudice, N.A., Walton, L.A., Worboys, M.: The informatics of indoor, outdoor space: a research agenda. In: Proceedings of 2nd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pp. 47–53 (2010)Google Scholar
  7. 7.
    Goh, M.: Congestion management and electronic road pricing in Singapore. J. Transp. Geogr. 10, 29–38 (2002)CrossRefGoogle Scholar
  8. 8.
    Greene, R.P., Pick, J.B.: Exploring the Urban Community - A GIS Approach. Prentice Hall, Upper Saddle River (2006)Google Scholar
  9. 9.
    Gudmundsson, J., Laube, P., Wolle, T.: Movement patterns in spatio-temporal data. In: Shekhar, S., Xiong, H. (eds.) Encyclopedia of GIS, pp. 726–732. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Gunopulos, D., Trajcevski, G.: Similarity in (spatial, temporal and) spatio-temporal datasets. In: Proceedings of 15th International Conference on Extending Database Technology, EDBT, pp. 554–557 (2012)Google Scholar
  11. 11.
    Ho, H.W., Wong, S.C., Yang, H., Loo, B.P.Y.: Cordon-based congestion pricing in a continuum traffic equilibrium system. Transp. Res. Part A: Policy Pract. 39, 813–834 (2005)Google Scholar
  12. 12.
    Hoogendoorn, S.P., Bovy, P.H.L.: State-of-the-art of vehicular traffic flow modelling. Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng. 215(4), 283–303 (2001)CrossRefGoogle Scholar
  13. 13.
    Huff, D.: Defining, estimating a trade area. J. Market. 28, 34–38 (1964)CrossRefGoogle Scholar
  14. 14.
    Huff, D., Black, W.: The Huff model in retrospect. Appl. Geogr. Stud. 1, 83–93 (1997)CrossRefGoogle Scholar
  15. 15.
    Jeszenszky, P., Weibel, R.: Measuring boundaries in the dialect continuum. In: Proceedings of AGILE (2015)Google Scholar
  16. 16.
    Laube, P.: Computational Movement Analysis. Springer Briefs in Computer Science. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Mao, B., Harrie, L., Ban, Y.: Detection and typification of linear structures for dynamic visualization of 3D city models. Comput. Environ. Urban Struct. 36, 233–244 (2012)CrossRefGoogle Scholar
  18. 18.
    Nakaya, T.: Local spatial interaction modelling based on the geographically weighted regression approach. GeoJournal 53(4), 347–358 (2001)CrossRefGoogle Scholar
  19. 19.
    Ott, T., Swiaczny, F.: Time-Integrative Geographic Information Systems. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  20. 20.
    Reilly, W.J.: The Law of Retail Gravitation. Knickerbocker Press, New Rochelle (1934)Google Scholar
  21. 21.
    Rense, C., Spaccapietra, S., Zimányi, E. (eds.): Mobility Data - Modelling, Management, and Understanding. Cambridge University Press, Cambridge (2013)Google Scholar
  22. 22.
    Rodrigue, J.-P., Comtois, C., Slack, B.: The Geography of Transport Systems. Routledge, Abingdon (2006)Google Scholar
  23. 23.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  24. 24.
    Simini, F., Gonález, M.C., Maritan, A., Barabázi, A.-L.: A universal model for mobility and migration patterns. Nature 484, 96–100 (2012)CrossRefGoogle Scholar
  25. 25.
    Wang, J., Duckham, M., Worboys, M.: A framework for models of movement in geographic space. Int. J. Geogr. Inf. Sci. 30, 970–992 (2016)CrossRefGoogle Scholar
  26. 26.
    Wood, J.: Visualizing personal progress in participatory sports cycling events. IEEE Comput. Graph. Appl. 35(4), 73–81 (2015)CrossRefGoogle Scholar
  27. 27.
    Wood, J., Dykes, J., Slingsby, A.: Visualisation of origins, destinations and flows with OD maps. Cartographic J. 47(2), 117–129 (2010)CrossRefGoogle Scholar
  28. 28.
    Zhang, X., Yang, H.: The optimal cordon-based network congestion pricing problem. Transp. Res. Part B: Methodological 38, 517–537 (2004)CrossRefGoogle Scholar
  29. 29.
    Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Matt Duckham
    • 1
  • Marc van Kreveld
    • 2
  • Ross Purves
    • 3
  • Bettina Speckmann
    • 4
  • Yaguang Tao
    • 1
    Email author
  • Kevin Verbeek
    • 4
  • Jo Wood
    • 5
  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.Department of Computing and Information SciencesUtrecht UniversityUtrechtThe Netherlands
  3. 3.Department of GeographyUniversity of ZurichZürichSwitzerland
  4. 4.Department of Mathematics and Computer ScienceTU EindhovenEindhovenThe Netherlands
  5. 5.Department of Computer ScienceCity University LondonLondonUK

Personalised recommendations