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Searching time-table networks

Published online by Cambridge University Press:  27 February 2009

Eduard Tulp  and
Laurent Siklóssy
Eduard Tulp
Affiliation:
Centrum Voor Informatieverwerking, Croeselaan 22 NL-3521 CB Utrecht, The Netherlands
Laurent Siklóssy
Affiliation:
Université de Savoie, LIA, BF1104, 73011, Chambéry Cedex, France
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Abstract

In this paper we present an application of AI search techniques to a class of problems that arise in transportation systems analysis. Rather than adapting the time-space network formulation typically used in Operations Research, we propose a discrete dynamic network to represent a scheduled service network. In a discrete dynamic network, there are finite, discrete, predetermined possibilities for moving from one vertex to another. Visiting a vertex has a cost (possibly zero), which may depend both on how the vertex was reached and how it will be left.

We describe the DYNET search algorithm for finding optimal paths in discrete dynamic networks. DYNET has been implemented in a working system (TRAINS) which searches the entire Dutch railway services network. An optimal path in a discrete dynamic network makes us arrive at our destination as early as possible (given our planned earliest departure time), and given this earliest arrival time (eat), will allow us to leave as late as possible, thereby guaranteeing a shortest path relative to the eat. DYNET first conducts a forward search to find the earliest possible arrival time, then a backward search which uses results of the forward search, to find the latest departure to arrive at that eat. Various AI techniques (symmetries, abstraction spaces, distance estimates, etc.) improve the performance of DYNET.

Type
Research Article
Information
AI EDAM , Volume 5 , Issue 3 , August 1991 , pp. 189 - 198
Copyright
Copyright © Cambridge University Press 1991

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References

Clercq, F. Le 1972. A public transportation assignment method. Traffic Engineering and Control, June.Google Scholar
Cooke, K. L. and Halsey, E. 1966. The shortest route through a network with time-dependent internodal transit times. Journal of Mathematical Analysis and Applications 14, 493–498.CrossRefGoogle Scholar
Denardo, E. V. and Fox, B. L. 1979. Shortest-route methods: 1. Reaching, pruning, and buckets. Operations Research 27, 161–186.CrossRefGoogle Scholar
Deo, N. and Pang, C. 1984. Shortest-path algorithms: taxonomy and annotation. Networks 14, 257–323.CrossRefGoogle Scholar
Dijkstra, E. W. 1959. A note on two problems in connection with graphs. Numerische Mathematiscle 1, 269–271.CrossRefGoogle Scholar
Dial, R. B. 1969. Algorithm 360: shortest path forest with topological ordering. Communications of the ACM 12, 632–633.CrossRefGoogle Scholar
Dial, R. B. et al. 1979. A computational analysis of alternative algorithms and labelling techniques for finding shortest path trees. Networks 9, 215–248.CrossRefGoogle Scholar
Dreyfus, S. E. 1964. An appraisal of some shortest-path algorithms. Operations Research 17, 1979, 395–412.CrossRefGoogle Scholar
Even, S. 1979. Graph Algorithms. Potomac, MD: Computer Science Press.Google Scholar
Gallo, G. and Pallottino, S. 1984. Shortest path methods in transport networks in Transportation Planning Models, pp. 227–26. Amsterdam: Elsevier Science Publishers.Google Scholar
Gilsinn, J. and Witzgall, C. 1977. A performance comparison of labelling algorithms for calculating shortest path trees. U.S. Department of Commerce.Google Scholar
Glover, F. et al. 1984. New polynomially bounded shortest path algorithms and their computational attributes, University of Texas, Austin, Center for Business Decision Analysis.Google Scholar
Golden, B. 1976. Shortest-path algorithms: A comparison. Operations Research, 24, 1164–1168.CrossRefGoogle Scholar
Hillier, F. S. and Lieberman, G. J. 1986. Introduction to Operations Research, 4th edn. Oakland, Ca: Holden-Day Inc, pp. 337 and 501–502.Google Scholar
Nilsson, N. J. 1971. Problem-solving Methods in Artificial Intelligence, New York: McGraw HillGoogle Scholar
Pape, U. 1974. Implemention and efficiency of Moore algorithms for the shortest route problem. Mathematical Programming 7, 212–222.CrossRefGoogle Scholar
Pallottino, S. 1984. Shortest path methods: complexity, interrelations and new propositions. Networks 14, 257–268.CrossRefGoogle Scholar
Pollack, M. and Wiebenson, W. 1960. Solution of the shortest route problem; a review. Operations Research 8, 224–230.CrossRefGoogle Scholar
Sacerdoti, E. D.Planning in a hierarchy of abstraction spaces. Artificial Intelligence 4, 145–180.Google Scholar
Siklóssy, L. 1978. Impertinent question-answering systems: justification and theory. Proceedings of the ACM Annual Conference, pp. 39–44.Google Scholar
Siklóssy, L. and Tulp, E. 1991. The space reduction method. Information Processing Letters 38, 87–192.CrossRefGoogle Scholar
Tulp, E. and Siklóssy, L. 1988. TRAINS, an active time-table searcher. Proceedings of the 8th European Conference on Artificial Intelligence, Munich, pp. 170–175. London: Pitman.Google Scholar
Tulp, E. and Siklóssy, L. 1989. TRAINS, a case study of active behaviour. Proceedings of the International Workshop on Industrial Applications of Machine Intelligence and Vision (MIV-89), Tokyo, pp. 259–263.Google Scholar
Tulp, E., Verhoef, M. L. and Tulp, W. L. 1990. TRAINS, further implementation of active behaviour Proceedings of COCNITIVA 90, Madrid, pp. 711–714.Google Scholar
van Vliet, D. 1978. Improved shortest path algorithms for transport networks. Transportation Research 12, 7–20.CrossRefGoogle Scholar
van Vuren, T. and Jansen, G. R. M. 1988. Recent developments in path finding algorithms: a review. Transportation Planning and Technology 12, 57–71.CrossRefGoogle Scholar
Winston, P. H. 1984. Artificial Intelligence. Reading, MA: Addison Wesley.Google Scholar