Noisy Road Network Matching

  • Yago Diez
  • Mario A. Lopez
  • J. Antoni Sellarès
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5266)


Let \(\mathcal{N}\) and \(\mathcal{M}\) be two road networks represented in vector form and covering rectangular areas R and R′, respectively, not necessarily parallel to each other, but with R′ ⊂ R. We assume that \(\mathcal{N}\) and \(\mathcal{M}\) use different coordinate systems at (possibly) different, but known scales. Let \(\mathcal{B}\) and \(\mathcal{A}\) denote sets of ”prominent” road points (e.g., intersections) associated with \(\mathcal{N}\) and \(\mathcal{M}\), respectively. The positions of road points on both sets may contain a certain amount of ”noise” due to errors and the finite precision of measurements. We propose an algorithm for determining approximate matches, in terms of the bottleneck distance, between \(\mathcal{A}\) and a subset \(\mathcal{B}'\) of \(\mathcal{B}\). We consider the characteristics of the problem in order to achieve a high degree of efficiency. At the same time, so as not to compromise the usability of the algorithm, we keep the complexity required for the data as low as possible. As the algorithm that guarantees to find a possible match is expensive due to the inherent complexity of the problem, we propose a lossless filtering algorithm that yields a collection of candidate regions that contain a subset S of \(\mathcal{B}\) such that \(\mathcal{A}\) may match a subset \(\mathcal{B}'\) of S. Then we find possible approximate matchings between \(\mathcal{A}\) and subsets of S using the matching algorithm. We have implemented the proposed algorithm and report results that show the efficiency of our approach.


Road Network Critical Event Matching Algorithm Rigid Motion Road Intersection 
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.
    Alt, H., Mehlhorn, K., Wagener, H., Welzl, E.: Congruence, similarity and symmetries of geometric objects. Discrete & Computational Geometry 3, 237–256 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Brent, R.P.: Algorithms for Minimization Without Derivatives. Prentice-Hall, Englewood Cliffs (1973)zbMATHGoogle Scholar
  3. 3.
    Chen, C., Shahabi, C., Kolahdouzan, M., Knoblock, C.A.: Automatically and Efficiently Matching Road Networks with Spatial Attributes in Unknown Geometry Systems. In: 3rd Workshop on Spatio-Temporal Database Management (STDBM 2006) (September 2006)Google Scholar
  4. 4.
    Chen, C.: Automatically and Accurately Conflating Road Vector Data, Street Maps and Orthoimagery. PhD Thesis (2005)Google Scholar
  5. 5.
    Diez, Y., Sellarès, J.A.: Efficient Colored Point Set Matching Under Noise. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 26–40. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Efrat, A., Itai, A., Katz, M.J.: Geometry helps in Bottleneck Matching and Related Problems. Algorithmica 31, 1–28 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Eppstein, D., Goodrich, M.T., Sun, J.Z.: The skip quadtree: a simple dynamic data structure for multidimensional data. In: 21st ACM Symposium on Computational Geometry, pp. 296–305 (2005)Google Scholar
  8. 8.
    Hopcroft, J.E., Karp, R.M.: An n 5/2 Algorithm for Maximum Matchings in Bipartite Graphs. SIAM Journal on Computing 2(4), 225–231 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hunt, K.H.: Kinematic Geometry of Mechanisms, chs. 4,7. Oxford University Press, Oxford (1978)zbMATHGoogle Scholar
  10. 10.
    Johnson, D.S.: A Theoretician’s Guide to the Experimental Analysis of Algorithms. In: Goldwasser, M., Johnson, D.S., McGeoch, C.C. (eds.) Proceedings of the fifth and sixth DIMACS implementation challenges. American Mathematical Society, Providence (2002)Google Scholar
  11. 11.
    Saalfeld, A.: Conflation: Automated Map Compilation. International Journal on Geographical Information Systems 2(3), 217–228 (1988)CrossRefGoogle Scholar
  12. 12.
    Walter, V., Fritsch, D.: Matching Spatial Data Sets: a Statistical Approach. International Journal of Geographical Information Science 13(5), 445–473 (1999)CrossRefGoogle Scholar
  13. 13.
    Van Wamelen, P.B., Li, Z., Iyengar, S.S.: A fast expected time algorithm for the 2-D point pattern matching problem. Pattern Recognition 37(8), 1699–1711 (2004)CrossRefGoogle Scholar
  14. 14.
    Ware, J.M., Jones, C.B.: Matching and Aligning Features in Overlayed Coverages. In: 6th ACM International Symposium on Advances in Geographic Information Systems, pp. 28–33 (1998)Google Scholar
  15. 15.
    U.S. Census Bureau Topologically Integrated Geographic Encoding and Referencing system, TIGER®, TIGER/Line® and TIGER®-Related Products,
  16. 16.
    U.S. Census Bureau TIGER/Line® files, Technical documentation. 1edn. (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yago Diez
    • 1
  • Mario A. Lopez
    • 2
  • J. Antoni Sellarès
    • 1
  1. 1.Universitat de GironaSpain
  2. 2.University of DenverUSA

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