Car or Public Transport—Two Worlds

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5760)


There are two kinds of people: those who travel by car, and those who use public transport. The topic of this article is to show that the algorithmic problem of computing the fastest way to get from A to B is also surprisingly different on road networks than on public transportation networks.

On road networks, even very large ones like that of the whole of Western Europe, the shortest path from a given source to a given target can be computed in just a few microseconds. Lots of interesting speed-up techniques have been developed to this end, and we will give an overview over the most important ones.

Public transportation networks can be modeled as graphs just like road networks, and most algorithms designed for road networks can be applied for public transportation networks as well. They just happen to perform not nearly as well, and to date we do not know how to route similarly fast on large public transportation networks as we can on large road networks.

The reasons for this are interesting and non-obvious, and it took us a long time to fully comprehend them. Once understood, they are relatively easy to explain, however, and that is what we want to do in this article. Oh, and by the way, happy birthday, Kurt!


Short Path Local Search Road Network Public Transport Road Segment 
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. 1.
    Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Delling, D.: Time-dependent SHARC-routing. In: Halperin, D., Mehlhorn, K. (eds.) ESA 2008. LNCS, vol. 5193, pp. 332–343. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Batz, G.V., Delling, D., Sanders, P., Vetter, C.: Time-dependent contraction hierarchies. In: 11th Workshop on Algorithm Engineering and Experiments (ALENEX 2009), pp. 97–105 (2009)Google Scholar
  4. 4.
    Pyrga, E., Schulz, F., Wagner, D., Zaroliagis, C.D.: Efficient models for timetable information in public transportation systems. ACM Journal of Experimental Algorithmics 12 (2007)Google Scholar
  5. 5.
    Müller-Hannemann, M., Schnee, M.: Finding all attractive train connections by multi-criteria pareto search. In: 4th Workshop on Algorithmic Methods for Railway Optimization (ATMOS 2004), pp. 246–263 (2004)Google Scholar
  6. 6.
    Schultes, D., Sanders, P.: Dynamic highway-node routing. In: Demetrescu, C. (ed.) WEA 2007. LNCS, vol. 4525, pp. 66–79. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Müller-Hannemann, M., Schulz, F., Wagner, D., Zaroliagis, C.D.: Timetable information: Models and algorithms. In: 4th Workshop on Algorithmic Methods for Railway Optimization (ATMOS 2004), pp. 67–90 (2004)Google Scholar
  8. 8.
    Sanders, P., Schultes, D.: Highway hierarchies hasten exact shortest path queries. In: Brodal, G.S., Leonardi, S. (eds.) ESA 2005. LNCS, vol. 3669, pp. 568–579. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Sanders, P., Schultes, D.: Engineering highway hierarchies. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 804–816. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Bauer, R., Delling, D., Wagner, D.: Experimental study on speed-up techniques for timetable information systems. In: 7th Workshop on Algorithmic Methods for Railway Optimization (ATMOS 2007) (2007)Google Scholar
  11. 11.
    Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: Faster and simpler hierarchical routing in road networks. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 319–333. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Bauer, R., Delling, D.: SHARC: Fast and robust unidirectional routing. In: 10th Workshop on Algorithm Engineering and Experiments (ALENEX 2008), pp. 13–26 (2008)Google Scholar
  13. 13.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  14. 14.
    Goldberg, A., Harrelson, C.: Computing the shortest path: A* search meets graph theory. In: 16th Symposium on Discrete Algorithms (SODA 2005), pp. 156–165 (2005)Google Scholar
  15. 15.
    Lauther, U.: An extremely fast, exact algorithm for finding shortest paths in static networks with geographical background. Münster GI-Tage (2004)Google Scholar
  16. 16.
    Köhler, E., Möhring, R.H., Schilling, H.: Acceleration of shortest path and constrained shortest path computation. In: Nikoletseas, S.E. (ed.) WEA 2005. LNCS, vol. 3503, pp. 126–138. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Bast, H., Funke, S., Matijevic, D.: Ultrafast shortest-path queries via transit nodes. In: DIMACS Implementation Challenge Shortest Paths (2006); An updated version of the paper appears in the upcoming bookGoogle Scholar
  18. 18.
    Bast, H., Funke, S., Matijevic, D., Sanders, P., Schultes, D.: In transit to constant time shortest-path queries in road networks. In: 9th Workshop on Algorithm Engineering and Experiments (ALENEX 2007) (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Max-Planck-Institute for InformaticsSaarbrückenGermany

Personalised recommendations