Using Multi-level Graphs for Timetable Information in Railway Systems

In many fields of application, shortest path finding problems in very large graphs arise. Scenarios where large numbers of on-line queries for shortest paths have to be processed in real-time appear for example in traffic information systems. In such systems, the techniques considered to speed up the shortest path computation are usually based on precomputed information. One approach proposed often in this context is a space reduction, where precomputed shortest paths are replaced by single edges with weight equal to the length of the corresponding shortest path. In this paper, we give a first systematic experimental study of such a space reduction approach. We introduce the concept of multi-level graph decomposition. For one specific application scenario from the field of timetable information in public transport, we perform a detailed analysis and experimental evaluation of shortest path computations based on multi-level graph decomposition.

[1]  Karsten Weihe,et al.  Dijkstra's algorithm on-line: an empirical case study from public railroad transport , 2000, JEAL.

[2]  S. Azuma,et al.  Map navigation software of the electro-multivision of the '91 Toyoto Soarer , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[3]  D. ChaudhuriChristos,et al.  Shortest Paths in Digraphs of Small Treewidth . Part II : Optimal Parallel Algorithms , 1995 .

[4]  Mihalis Yannakakis,et al.  High-Probability Parallel Transitive-Closure Algorithms , 1991, SIAM J. Comput..

[5]  Laurent Siklóssy,et al.  The Space Reduction Method: A Method to Reduce the Size of Search Spaces , 1991, Inf. Process. Lett..

[6]  Shashi Shekhar,et al.  Materialization Trade-Offs in Hierarchical Shortest Path Algorithms , 1997, SSD.

[7]  Greg N. Fredericicson Planar Graph Decomposition and All Pairs Shortest Paths , 1991 .

[8]  Shashi Shekhar,et al.  Path computation algorithms for advanced traveller information system (ATIS) , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[9]  Ulrik Brandes,et al.  Travel Planning with Self-Made Maps , 2001, ALENEX.

[10]  Karsten Weihe,et al.  Pareto Shortest Paths is Often Feasible in Practice , 2001, WAE.

[11]  Paul G. Spirakis,et al.  Hammock-on-Ears Decomposition: A Technique for the Efficient Parallel Solution of Shortest Paths and Other Problems , 1994, Theoretical Computer Science.

[12]  Andrew U. Frank,et al.  Modelling a Hierarchy of Space Applied to Large Road Networks , 1994, IGIS.

[13]  Sakti Pramanik,et al.  HiTi graph model of topographical road maps in navigation systems , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[14]  Jacob Shapiro,et al.  Level graphs and approximate shortest path algorithms , 1992, Networks.

[15]  H. V. Jagadish,et al.  Algorithms for Searching Massive Graphs , 1994, IEEE Trans. Knowl. Data Eng..

[16]  Greg N. Frederickson Using Cellular Graph Embeddings in Solving All Pairs Shortest Paths Problems , 1995, J. Algorithms.

[17]  Christos D. Zaroliagis,et al.  Shortest Paths in Digraphs of Small Treewidth. Part I: Sequential Algorithms , 2000, Algorithmica.

[18]  Madhav V. Marathe,et al.  Formal Language Constrained Path Problems , 1998, SWAT.

[19]  Thomas Preuss,et al.  An Integrated Traffic Information System , 1997 .

[20]  Madhav V. Marathe,et al.  A computational study of routing algorithms for realistic transportation networks , 1999, JEAL.