Fast Nearest Neighbor Search on Road Networks

  • Haibo Hu
  • Dik Lun Lee
  • Jianliang Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstra-like network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (VN 3) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected tree-based structures called SPIE’s. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions.


Road Network Near Neighbor Node Access Short Path Tree Query Node 
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.
    Berchtold, S., Keim, D.A., Kriegel, H.-P., Seidl, T.: Indexing the solution space: A new technique for nearest neighbor search in high-dimensional space. TKDE 12(1), 45–57 (2000)Google Scholar
  2. 2.
    Cho, H.-J., Chung, C.-W.: An efficient and scalable approach to cnn queries in a road network. In: VLDB (2005)Google Scholar
  3. 3.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. McGraw Hill, New York (2001)zbMATHGoogle Scholar
  4. 4.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numeriche Mathematik 1, 269–271 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Hanson, E., Ioannidis, Y., Sellis, T., Shapiro, L., Stonebraker, M.: Heuristic search in data base systems. Expert Database Systems (1986)Google Scholar
  6. 6.
    Jensen, C.S., Kolarvr, J., Pedersen, T.B., Timko, I.: Nearest neighbor queries in road networks. In: 11th ACM International Symposium on Advances in Geographic Information Systems (GIS 2003), pp. 1–8 (2003)Google Scholar
  7. 7.
    Kolahdouzan, M., Shahabi, C.: Continuous k-nearest neighbor queries in spatial network databases. In: STDBM (2004)Google Scholar
  8. 8.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB Conference, pp. 840–851 (2004)Google Scholar
  9. 9.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: VLDB Conference, pp. 802–813 (2003)Google Scholar
  10. 10.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD Conference, San Jose, California, pp. 71–79 (1995)Google Scholar
  11. 11.
    Shahabi, C.K., Kolahdouzan, M.R., Sharifzadeh, M.: A road network embedding technique for knearest neighbor search in moving object databases. In: 10th ACM International Symposium on Advances in Geographic Information Systems, GIS 2002 (2002)Google Scholar
  12. 12.
    Shekhar, S., Liu, D.R.: Ccam: A connectivity-clustered access method for networks and network computations. IEEE Transactions on Knowledge and Data Engineering 1(9), 102–119 (1997)CrossRefGoogle Scholar
  13. 13.
    Weber, R., Schek, H.-J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of the 24rd International Conference on Very Large Data Bases, pp. 194–205 (1998)Google Scholar
  14. 14.
    Xu, J., Tang, X., Lee, D.L.: Performance analysis of location-dependent cache invalidation schemes for mobile environments. IEEE Transactions on Knowledge and Data Engineering 15(2), 474–488 (2003)CrossRefGoogle Scholar
  15. 15.
    Yu, C., Ooi, B.C., Tan, K.-L., Jagadish, H.V.: Indexing the distance: An efficient method to knn processing. In: VLDB Conference, Roma, pp. 421–430 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Haibo Hu
    • 1
  • Dik Lun Lee
    • 1
  • Jianliang Xu
    • 2
  1. 1.Hong Kong Univ. of Science & Technology 
  2. 2.Hong Kong Baptist University 

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