Region-Aware Route Planning

  • Sabine StorandtEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10819)


We consider route planning queries in road or path networks which involve a user preference expressed in relation to a spatial region, as e.g. ‘from Nanjing to Shanghai along Yangtze river’ or ‘from home to work through Central Park’. To answer such queries, we carefully define relevant subgraphs of the network for each region-of-interest and guide the route towards them. To extract these subgraphs, we need to solve several non-trivial geometric problems (as computing weak visibility regions), which require to interpret the embedded network both as a graph and as an arrangement of line segments. We describe a suitable preprocessing framework, taking the special structure of road networks into account to increase its performance. Our query answering algorithm then allows to trade detour length against time spent within or close to the desired region. Using acceleration techniques, region-aware routes can be planned efficiently even in networks with millions of edges, and also when considering large or complex regions.


  1. 1.
    Abraham, I., Delling, D., Goldberg, A.V., Werneck, R.F.: Alternative routes in road networks. J. Exp. Algorithm. 18, 1.3:1.1–1.3:1.17 (2013)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Eisner, J., Funke, S.: Sequenced route queries: getting things done on the way back home. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 502–505. ACM (2012)Google Scholar
  3. 3.
    Funke, S., Storandt, S.: Personalized route planning in road networks. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 45. ACM (2015)Google Scholar
  4. 4.
    Dibbelt, J., Strasser, B., Wagner, D.: Fast exact shortest path and distance queries on road networks with parametrized costs. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 66. ACM (2015)Google Scholar
  5. 5.
    Funke, S., Storandt, S.: Personal routes with high-dimensional costs and dynamic approximation guarantees. In: Proceedings of the 16th International Symposium on Experimental AlgorithmsGoogle Scholar
  6. 6.
    Gemsa, A., Pajor, T., Wagner, D., Zündorf, T.: Efficient computation of jogging routes. In: Bonifaci, V., Demetrescu, C., Marchetti-Spaccamela, A. (eds.) SEA 2013. LNCS, vol. 7933, pp. 272–283. Springer, Heidelberg (2013). Scholar
  7. 7.
    Bast, H., Sternisko, J., Storandt, S.: ForestMaps: a computational model and visualization for forest utilization. In: Pfoser, D., Li, K.-J. (eds.) W2GIS 2013. LNCS, vol. 8470, pp. 115–133. Springer, Heidelberg (2014). Scholar
  8. 8.
    Kobitzsch, M., Radermacher, M., Schieferdecker, D.: Evolution and evaluation of the penalty method for alternative graphs. In: ATMOS-13th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems-2013, vol. 33, pp. 94–107. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2013)Google Scholar
  9. 9.
    Luxen, D., Schieferdecker, D.: Candidate sets for alternative routes in road networks. J. Exp. Algorithm. (JEA) 19, 2–7 (2015)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Geisberger, R., Rice, M.N., Sanders, P., Tsotras, V.J.: Route planning with flexible edge restrictions. J. Exp. Algorithm. (JEA) 17, 1–2 (2012)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Gajentaan, A., Overmars, M.H.: On a class of \(O(n^2)\) problems in computational geometry. Comput. Geom. 5(3), 165–185 (1995)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Nouri, M., Zarei, A., Ghodsi, M.: Weak visibility of two objects in planar polygonal scenes. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007. LNCS, vol. 4705, pp. 68–81. Springer, Heidelberg (2007). Scholar
  13. 13.
    Ghosh, S.K., Mount, D.M.: An output-sensitive algorithm for computing visibility graphs. SIAM J. Comput. 20(5), 888–910 (1991)MathSciNetCrossRefGoogle Scholar
  14. 14.
    de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.C.: Computational geometry. In: Computational Geometry, pp. 1–17. Springer, Heidelberg (2000).
  15. 15.
    Overmars, M.H., Welzl, E.: New methods for computing visibility graphs. In: Proceedings of the Fourth Annual Symposium on Computational Geometry, pp. 164–171. ACM (1988)Google Scholar
  16. 16.
    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). Scholar
  17. 17.
    Delling, D., Goldberg, A.V., Werneck, R.F.: Faster batched shortest paths in road networks. In: OASIcs-OpenAccess Series in Informatics, vol. 20. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2011)Google Scholar
  18. 18.
    Delling, D., Goldberg, A.V., Nowatzyk, A., Werneck, R.F.: Phast: hardware-accelerated shortest path trees. J. Parallel Distrib. Comput. 73(7), 940–952 (2013)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Heidi, M.: Heuristics for the generation of random polygons*. In: Canadian Conference on Computational Geometry, vol. 5, p. 38. McGill-Queen’s Press-MQUP (1996)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WürzburgWürzburgGermany

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