Synthetic Road Networks

  • Reinhard Bauer
  • Marcus Krug
  • Sascha Meinert
  • Dorothea Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6124)


The availability of large graphs that represent huge road networks has led to a vast amount of experimental research that has been custom-tailored for road networks. There are two primary reasons to investigate graph-generators that construct synthetic graphs similar to real-world road-networks: The wish to theoretically explain noticeable experimental results on these networks and to overcome the commercial nature of most datasets that limits scientific use. This is the first work that experimentally evaluates the practical applicability of such generators. To this end we propose a new generator and review the only existing one (which until now has not been tested experimentally). Both generators are examined concerning structural properties and algorithmic behavior. Although both generators prove to be reasonably good models, our new generator outperforms the existing one with respect to both structural properties and algorithmic behavior.


Road Network Voronoi Diagram Speedup Technique Voronoi Region Algorithmic Behavior 
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.
    Cgal, Computational Geometry Algorithms Library,
  2. 2.
    Abraham, I., Fiat, A., Goldberg, A.V., Werneck, R.F.: Highway Dimension, Shortest Paths, and Provably Efficient Algorithms. In: Proceedings of the 21st Annual ACM–SIAM Symposium on Discrete Algorithms, SODA 2010 (2010)Google Scholar
  3. 3.
    Bauer, R., Delling, D., Wagner, D.: Experimental Study on Speed-Up Techniques for Timetable Information Systems. In: Liebchen, C., Ahuja, R.K., Mesa, J.A. (eds.) Proceedings of the 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS 2007), Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, pp. 209–225 (2007)Google Scholar
  4. 4.
    Bose, P., Carmi, P., Farshi, M., Maheshwari, A., Smid, M.: Computing the Greedy Spanner in Near-Quadratic Time. In: Gudmundsson, J. (ed.) SWAT 2008. LNCS, vol. 5124, pp. 390–401. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Delling, D., Sanders, P., Schultes, D., Wagner, D.: Engineering Route Planning Algorithms. In: Lerner, J., Wagner, D., Zweig, K.A. (eds.) Algorithmics of Large and Complex Networks. LNCS, vol. 5515, pp. 117–139. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Demetrescu, C., Goldberg, A.V., Johnson, D.S. (eds.): The Shortest Path Problem: Ninth DIMACS Implementation Challenge. DIMACS Book, vol. 74. American Mathematical Society, Providence (2009)zbMATHGoogle Scholar
  7. 7.
    Eppstein, D., Goodrich, M.T.: Studying (non-planar) road networks through an algorithmic lens. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM Press, New York (2008)Google Scholar
  8. 8.
    Peleg, D., Schäffer, A.A.: Graph spanners. Journal of Graph Theory 13(1), 99–116 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Preparata, F.P., Shamos, M.I.: Computational geometry: an introduction. Springer, New York (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Reinhard Bauer
    • 1
  • Marcus Krug
    • 1
  • Sascha Meinert
    • 1
  • Dorothea Wagner
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)Germany

Personalised recommendations