Abstract
Using the A*-algorithm to solve point-to-point-shortest path problems, the number of iterations depends on the quality of the estimator for the remaining distance to the target. In digital maps of real road networks, iterations can be saved by using a better estimator than the Euclidian estimator. An approach is to integrate Segmentation Lines (SegLine) into the map modelling large obstacles. An auxiliary graph is constructed using the Seg-Lines wherein a shortest path is calculated yielding a better estimate. Some computational results are presented for a dynamic version of this approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dubois N., Semet F. (1995) Estimation and determination of shortest path length in a road network with obstacles. EJOR 83:105–116.
Ertl, G. (1998) Shortest path calculations in large road networks, OR Spektrum 20:15–20.
Gelperin, D. (1977) On the Optimality of A*, AI 8:69–76.
Goldberg, A.V. (2006) Point-to-Point Shortest Path Algorithms with Preprocessing, Technical Report, Microsoft Research.
Goldberg, A.V., Harrelson, C. (2004) Computing the Shortest Path: A* Search Meets Graph Theory, MSR-TR-2004-24, MS Research.
Gutman, R. (2004) Reach-based Routing: A New Approach to Shortest Path Algorithms Optimized for Road Networks, Proc. 6th International Workshop on Algorithm Engineering and Experiments, 100–111.
Hahne, F. (2000) Kürzeste und schnellste Wege in digitalen Straßenkarten, Dissertation, Universität Hildesheim.
Hahne, F. (2005) Analyse der Beschleunigung des A*-Verfahrens durch verbesserte Schätzer für die Restdistanz, OR Proceedings 2005.
Hart, P.E., Nilson, N.J., Raphael, B. (1968) A formal basis for the heuristic determination of minimal cost paths. IEEE Tr. on SSC 4:100–107.
Hart, P.E., Nilson, N.J., Raphael, B. (1972) Correction to: ‘A formal basis for the heuristic determination of min. cost paths’. Sigart NL 37:28–29.
Hasselberg, S. (2000) Some results on heuristical algorithms for shortest path problems in large road networks, Dissertation, Universität Köln.
Ikeda T., Hsu M., Imai H. (1994) A fast algorithm for finding better routes by a.i. search techniques, VNIS Conference Proceedings 1994.
Klunder, G.A., Post, H.N. (2006) The shortest path problem on large scale real road networks, Networks 48:184–192.
Lauther, U. (2004) An Extremely Fast, Exact Algorithm for Finding Shortest Paths in Static Networks with Geographical Background, IfGIprints 22, Institut für Geoinformatik, Universität Münster 219–230.
Nilsson, N. (1971) Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York.
Pijls, W. (2007) Heuristic estimates in shortest path algorithms, Statistica Neerlandica 61:64–74.
Sanders, P. Schultes, D. (2005) Highway hierarchies hasten exact shortest path queries, in: Brodal, G.S., Leonardi, S. (eds), Proc. 17th ESA, Lecture Notes in CS 3669, Springer, 568–579.
Stausberg, V. (1995) Ein Dekompositionsverfahren zur Berechnung kürzester Wege auf fast-planaren Graphen, Diploma Thesis, Univ. Köln.
Wagner, D., Willhalm, T. (2003) Geometric Speed-Up Techniques for Finding Shortest Paths in Large Sparse Graphs, Proc. 11th ESA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hahne, F., Nowak, C., Ambrosi, K. (2008). Acceleration of the A*-Algorithm for the Shortest Path Problem in Digital Road Maps. In: Kalcsics, J., Nickel, S. (eds) Operations Research Proceedings 2007. Operations Research Proceedings, vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77903-2_70
Download citation
DOI: https://doi.org/10.1007/978-3-540-77903-2_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77902-5
Online ISBN: 978-3-540-77903-2
eBook Packages: Business and EconomicsBusiness and Management (R0)