Approximation of Pareto optima in multiple-objective, shortest-path problems
Operations Research
Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length
Journal of the ACM (JACM)
Robust and efficient methods for solving path problems
Robust and efficient methods for solving path problems
Dijkstra's algorithm on-line: an empirical case study from public railroad transport
Journal of Experimental Algorithmics (JEA)
Using Multi-level Graphs for Timetable Information in Railway Systems
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
Pareto Shortest Paths is Often Feasible in Practice
WAE '01 Proceedings of the 5th International Workshop on Algorithm Engineering
Resource Constrained Shortest Paths
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Engineering Route Planning Algorithms
Algorithmics of Large and Complex Networks
Multi-criteria shortest paths in time-dependent train networks
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
Multi-objective and multi-constrained non-additive shortest path problems
Computers and Operations Research
Timetable information: models and algorithms
ATMOS'04 Proceedings of the 4th international Dagstuhl, ATMOS conference on Algorithmic approaches for transportation modeling, optimization, and systems
Acceleration of shortest path and constrained shortest path computation
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Algorithm engineering for route planning: an update
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Algorithms for time-dependent bicriteria shortest path problems
Discrete Optimization
Parallel computation of best connections in public transportation networks
Journal of Experimental Algorithmics (JEA)
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We consider efficient algorithms for timetable information in public transportation systems undermultiple objectives like, for example, travel time, ticket costs, and number of interchanges between different means of transport. In this paper we focus on a fully realistic scenario in public railroad transport as it appears in practice while most previous work studied only simplified models. Algorithmically this leads to multi-criteria shortest path problems in very large graphs. With several objectives the challenge is to find all connections which are potentially attractive for customers. To meet this informal goal we introduce the notion of relaxed Pareto dominance. Another difficulty arises from the fact that due to the complicated fare regulations even the single-criteria optimization problem of finding cheapest connections is intractable. Therefore, we have to work with fare estimations during the search for good connections. In a cooperation with Deutsche Bahn Systems we realized this scenario in a prototypal implementation called PARETO based on a timeexpanded graph model. Computational experiments with our PARETO server demonstrate that the current central server of Deutsche Bahn AG often fails to give optimal recommendations for different user groups. In contrast, an important feature of the PARETO server is its ability to provide many attractive alternatives.