URAN: A Unified Data Structure for Rendering and Navigation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10181)


Current route planning services like Google Maps exhibit a clear-cut separation between the map rendering component and the route planning engine. While both rely on respective road network data, the route planning task is typically performed using state-of-the art data structures for speeding-up shortest/quickest path queries like Hub Labels, Arc Flags, or Transit Nodes, whereas the map rendering task usually involves a rendering framework like Mapnik or Kartograph. In this paper we show how to augment Contraction Hierarchies – another popular data structure for speeding-up shortest path queries – to also cater for the map rendering task. As a result we get a unified data structure (URAN) which lays the algorithmic foundation for novel map rendering and navigation systems. It also allows for customization of the map rendering, e.g. to accommodate different display devices (with varying resolution and hardware capabilities) or routing scenarios. At the heart of our approach lies a generalized graph simplification scheme derived from Contraction Hierarchies with a very lightweight augmentation for extracting (simplified) subgraphs. In a client-server scenario it additionally has the potential to shift the actual route computation to the client side, both relieving the server infrastructure as well as providing some degree of privacy when planning a route.



This work was partially supported by the Deutsche For-schungsgemeinschaft (DFG) under grant FU 700/4-1 as part of the priority program 1894: Volunteered Geographic Information: Interpretation, Visualization and Social Computing.


  1. 1.
    The OpenStreetMap Project (2014).
  2. 2.
    Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, R.: Route planning in transportation networks. Technical report MSR-TR-2014-4, Microsoft Research, January 2014Google Scholar
  3. 3.
    de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer, Heidelberg (2008)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chimani, M., van Dijk, T.C., Haunert, J.-H.: How to eat a graph: computing selection sequences for the continuous generalization of road networks. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, vol. 28, pp. 243–252 (2014)Google Scholar
  5. 5.
    de Berg, M., van Kreveld, M., Schirra, S.: Topologically correct subdivision simplification using the bandwidth criterion. Cartogr. Geogr. Inf. Syst. 25(4), 243–257 (1998)CrossRefGoogle Scholar
  6. 6.
    Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartogr. 10(2), 112–122 (1973)CrossRefGoogle Scholar
  7. 7.
    Estkowski, R., Mitchell, J.S.B.: Simplifying a polygonal subdivision while keeping it simple. In: Proceedings of the 17th Annual Symposium on Computational Geometry, SCG 2001, pp. 40–49. ACM, New York (2001)Google Scholar
  8. 8.
    Geisberger, R., Sanders, P., Schultes, D., Vetter, C.: Exact routing in large road networks using contraction hierarchies. Transp. Sci. 46(3), 388–404 (2012)CrossRefGoogle Scholar
  9. 9.
    Schnelle, N., Funke, S., Storandt, S.: DORC: distributed online route computation - higher throughput, more privacy. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 344–347 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of StuttgartStuttgartGermany
  2. 2.JMU WürzburgWürzburgGermany

Personalised recommendations