Landmark-Based Routing in Dynamic Graphs

  • Daniel Delling
  • Dorothea Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4525)


Many speed-up techniques for route planning in static graphs exist, only few of them are proven to work in a dynamic scenario. Most of them use preprocessed information, which has to be updated whenever the graph is changed. However, goal directed search based on landmarks (ALT) still performs correct queries as long as an edge weight does not drop below its initial value. In this work, we evaluate the robustness of ALT with respect to traffic jams. It turns out that—by increasing the efficiency of ALT—we are able to perform fast (down to 20 ms on the Western European network) random queries in a dynamic scenario without updating the preprocessing as long as the changes in the network are moderate. Furthermore, we present how to update the preprocessed data without any additional space consumption and how to adapt the ALT algorithm to a time-dependent scenario. A time-dependent scenario models predictable changes in the network, e.g. traffic jams due to rush hour.


Short Path Edge Weight Priority Queue Query Time Dynamic Graph 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  2. Wagner, D., Willhalm, T.: Speed-Up Techniques for Shortest-Path Computations. In: Thomas, W., Weil, P. (eds.) STACS 2007. LNCS, vol. 4393, pp. 23–36. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. Delling, D., Holzer, M., Müller, K., Schulz, F., Wagner, D.: High-Performance Multi-Level Graphs. In: 9th DIMACS Challenge on Shortest Paths (2006)Google Scholar
  4. Bast, H., Funke, S., Matijevic, D., Sanders, P., Schultes, D.: InTransit to Constant Time Shortest-Path Queries in Road Networks. In: Algorithm Engineering and Experiments (ALENEX) pp. 46–59 (2007)Google Scholar
  5. Goldberg, A.V., Harrelson, C.: Computing the shortest path: A * meets graph theory. In: 16th ACM-SIAM Symposium on Discrete Algorithms, pp. 156–165 (2005)Google Scholar
  6. Cooke, K., Halsey, E.: The shortest route through a network with time-dependent intemodal transit times. Journal of Mathematical Analysis and Applications 14, 493–498 (1966)zbMATHCrossRefMathSciNetGoogle Scholar
  7. Ikeda, T., Hsu, M., Imai, H., Nishimura, S., Shimoura, H., Hashimoto, T., Tenmoku, K., Mitoh, K.: A fast algorithm for finding better routes by AI search techniques. In: Vehicle Navigation and Information Systems Conference (1994)Google Scholar
  8. Flinsenberg, I.C.M.: Route planning algorithms for car navigation. PhD thesis, Technische Universiteit Eindhoven (2004)Google Scholar
  9. Wagner, D., Willhalm, T., Zaroliagis, C.: Geometric containers for efficient shortest-path computation. ACM Journal of Experimental Algorithmics 10, 1–30 (2005)MathSciNetGoogle Scholar
  10. Sanders, P., Schultes, D.: Dynamic Highway-Node Routing. In: 6th Workshop on Experimental Algorithms (WEA) (to appear 2007)Google Scholar
  11. Delling, D., Sanders, P., Schultes, D., Wagner, D.: Highway Hierarchies Star. In: 9th DIMACS Challenge on Shortest Paths (2006)Google Scholar
  12. Narvaez, P., Siu, K.Y., Tzeng, H.Y.: New dynamic algorithms for shortest path tree computation. IEEE/ACM Trans. Netw. 8, 734–746 (2000)CrossRefGoogle Scholar
  13. Demetrescu, C., Italiano, G.F.: A new approach to dynamic all pairs shortest paths. J. ACM 51, 968–992 (2004)CrossRefMathSciNetGoogle Scholar
  14. Goldberg, A.V., Harrelson, C.: Computing the shortest path: A * meets graph theory. Technical Report MSR-TR-2004-24, Microsoft Research (2004)Google Scholar
  15. Goldberg, A.V., Werneck, R.F.: An efficient external memory shortest path algorithm. In: Algorithm Engineering and Experimentation (ALENEX), pp. 26–40 (2005)Google Scholar
  16. Sedgewick, R., Vitter, J.S.: Shortest paths in Euclidean space. Algorithmica 1, 31–48 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  17. Goldberg, A., Kaplan, H., Werneck, R.: Reach for A*: Efficient Point-to-Point Shortest Path Algorithms. In: Algorithm Engineering and Experiments (ALENEX), pp. 129–143 (2006)Google Scholar
  18. Dashtinezhad, S., Nadeem, T., Dorohonceanu, B., Borcea, C., Kang, P., Iftode, L.: TrafficView: a driver assistant device for traffic monitoring based on car-to-car communication. In: Vehicular Technology Conference, pp. 2946–2950. IEEE, New York (2004)Google Scholar
  19. Kaufman, D.E., Smith, R.L.: Fastest paths in time-dependent networks for intelligent-vehicle-highway systems application. IVHS Journal 1, 1–11 (1993)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Delling
    • 1
  • Dorothea Wagner
    • 1
  1. 1.Universität Karlsruhe (TH), 76128 KarlsruheGermany

Personalised recommendations