Efficient Route Compression for Hybrid Route Planning
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Abstract
We describe an algorithmic framework for lossless compression of route descriptions. This is useful for hybrid route planning where routes are computed by a server and then transmitted to a client device in a car using some mobile radio communication where bandwidth may be low. Compressed routes are represented by only a few via nodes which are the connection points when the route is decomposed into unique optimal segments. To reconstruct the route efficiently a client device needs basic but fast route planning capability. Contraction hierarchies make this approach fast enough for practice: Compressing takes only a few milliseconds. And previous experiments suggest that a client can decompress each route segment virtually instantaneously. So, as the segments can be decompressed successively while driving, it is not likely that the driver experiences any delay except for the time needed by the mobile communication.
Keywords
Short Path Road Network Optimal Route Minimal Representation Route PlanningPreview
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References
- 1.Delling, D., Wagner, D.: Time-Dependent Route Planning. In: Ahuja, R.K., Möhring, R.H., Zaroliagis, C.D. (eds.) Robust and Online Large-Scale Optimization. LNCS, vol. 5868, pp. 207–230. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 2.Batz, G.V., Delling, D., Sanders, P., Vetter, C.: Time-Dependent Contraction Hierarchies. In: Proceedings of the 11th Workshop on Algorithm Engineering and Experiments (ALENEX 2009), pp. 97–105. SIAM (April 2009)Google Scholar
- 3.Batz, G.V., Geisberger, R., Neubauer, S., Sanders, P.: Time-Dependent Contraction Hierarchies and Approximation. In: [21], pp. 166–177Google Scholar
- 4.Kieritz, T., Luxen, D., Sanders, P., Vetter, C.: Distributed Time-Dependent Contraction Hierarchies. In: [21] pp. 83–93Google Scholar
- 5.Batz, G.V., Sanders, P.: Time-Dependent Route Planning with Generalized Objective Functions. In: Epstein, L., Ferragina, P. (eds.) ESA 2012. LNCS, vol. 7501, pp. 169–180. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 6.Delling, D.: Time-Dependent SHARC-Routing. Algorithmica 60(1), 60–94 (2011), Special Issue: European Symposium on Algorithms (2008)Google Scholar
- 7.Brunel, E., Delling, D., Gemsa, A., Wagner, D.: Space-Efficient SHARC-Routing. In: [21], pp. 47–58Google Scholar
- 8.Geisberger, R., Kobitzsch, M., Sanders, P.: Route Planning with Flexible Objective Functions. In: Proceedings of the 12th Workshop on Algorithm Engineering and Experiments (ALENEX 2010), pp. 124–137. SIAM (2010)Google Scholar
- 9.Delling, D., Wagner, D.: Pareto Paths with SHARC. In: Vahrenhold, J. (ed.) SEA 2009. LNCS, vol. 5526, pp. 125–136. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 10.Delling, D., Goldberg, A.V., Pajor, T., Werneck, R.F.: Customizable Route Planning. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 376–387. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 11.Abraham, I., Delling, D., Goldberg, A.V., Werneck, R.F.: Alternative Routes in Road Networks. In: [21], pp. 23–34Google Scholar
- 12.Luxen, D., Schieferdecker, D.: Candidate Sets for Alternative Routes in Road Networks. In: Klasing, R. (ed.) SEA 2012. LNCS, vol. 7276, pp. 260–270. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 13.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
- 14.Geisberger, R., Sanders, P., Schultes, D., Vetter, C.: Exact Routing in Large Road Networks Using Contraction Hierarchies. Transportation Science (accepted for publication, 2012)Google Scholar
- 15.Sanders, P., Schultes, D., Vetter, C.: Mobile Route Planning. In: Halperin, D., Mehlhorn, K. (eds.) ESA 2008. LNCS, vol. 5193, pp. 732–743. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 16.Batz, G.V., Geisberger, R., Luxen, D., Sanders, P.: Compressed Transmission of Route Descriptions. Technical report, Karlsruhe Institute of Technology (KIT) (2010), arXiv:1011.4465v1Google Scholar
- 17.Tao, Y., Sheng, C., Pei, J.: On k-skip shortest paths. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, pp. 421–432. ACM, New York (2011)Google Scholar
- 18.Mehlhorn, K., Sanders, P.: Algorithms and Data Structures: The Basic Toolbox. Springer (2008)Google Scholar
- 19.Möhring, R.H., Schilling, H., Schütz, B., Wagner, D., Willhalm, T.: Partitioning Graphs to Speed Up Dijkstra’s Algorithm. In: Nikoletseas, S.E. (ed.) WEA 2005. LNCS, vol. 3503, pp. 189–202. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 20.Goldberg, A.V., Harrelson, C.: Computing the Shortest Path: A* Search Meets Graph Theory. In: Proceedings of the 16th Annual ACM–SIAM Symposium on Discrete Algorithms (SODA 2005), pp. 156–165. SIAM (2005)Google Scholar
- 21.Festa, P. (ed.): SEA 2010. LNCS, vol. 6049. Springer, Heidelberg (2010)zbMATHGoogle Scholar