Nonmaterialized Motion Information in Transport Networks

  • Hu Cao
  • Ouri Wolfson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3363)


The traditional way of representing motion in 3D space-time uses a trajectory, i.e. a sequence of (x,y,t) points. Such a trajectory may be produced by periodic sampling of a Global Positioning System (GPS) receiver. The are two problems with this representation of motion. First, imprecision due to errors (e.g. GPS receivers often produce off-the-road locations), and second, space complexity due to a large number of samplings. We examine an alternative representation, called a nonmaterialized trajectory, which addresses both problems by taking advantage of the a priori knowledge that the motion occurs on a transport network.


Global Position System Road Network Range Query Transport Network Global Position System Receiver 
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.


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  1. 1.
    Special issue on data reduction techniques. IEEE Data Engineering 20(4) (1998)Google Scholar
  2. 2.
    Alt, H., Guibas, L.J.: Discrete geometric shapes: Matching, interpolation, and approximation A survey. Technical Report B 96-11, Institut für Informatik, Freie Universität Berlin (1996)Google Scholar
  3. 3.
    Cao, H., Wolfson, O., Trajcevski, G.: Spatiotemporal data reduction with deterministic error bounds. In: DIALM-POMC 2003, pp. 33–42 (2003)Google Scholar
  4. 4.
    Chakrabarti, K., Garofalakis, M., Rastogi, R., Shim, K.: Approximate query processing using wavelets. In: VLDB 2000 (Septermber 2000)Google Scholar
  5. 5.
    Chen, C., Thakkar, S., Knoblock, C., Shahabi, C.: Automatically annotating and integrating spatial datasets. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Chen, Z., Gehrke, J., Korn, F.: Query optimization in compressed database systems. In: ACM SIGMOD 2001, pp. 271–282. ACM Press, New York (2001)CrossRefGoogle Scholar
  7. 7.
    Florizzi, L., Guting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. Technical Report 260-10, Fern-Universität Hagen (1999)Google Scholar
  8. 8.
    Frentzos, E.: Indexing objects moving on fixed networks. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Ghosh, S.K.: Computing the visibility polygon from a convex set and related problem. Journal of Algorithms 12, 75–95 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Gibbons, P.B., Matias, Y., Poosala, V.: Fast incremental maintenance of approximate histograms. In: VLDB (1997)Google Scholar
  11. 11.
    Greenfeld, J.S.: Matching gps observations to locations on a digital map. In: The 81th Annual Meeting of the Transportation Research Board, Washington D.C. (2002)Google Scholar
  12. 12.
    Guibas, L.J., Hershberger, J.E., Mitchell, J.S.B., Snoeyink, J.S.: Approximating polygons and subdivisions with minimum link paths. In: ISAAC 1991 (1991)Google Scholar
  13. 13.
    Meratnia, N., de By, R.A.: Spatiotemporal compression techniques for moving point objects. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 765–782. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Pfoser, D., Jensen, C.S.: Indexing of network constrained moving objects. In: ACM GIS, pp. 25–32. ACM Press, New York (2003)Google Scholar
  15. 15.
    Thrun, S.: Robotic mapping: a survey. In: Exploring artificial intelligence in the new millennium, pp. 1–35. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  16. 16.
    White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transportation Research Part C 8, 91–108 (2000)CrossRefGoogle Scholar
  17. 17.
    Yin, H., Wolfson, O.: A weight-based map matching method in moving objects databases. In: SSTDM (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hu Cao
    • 1
  • Ouri Wolfson
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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