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
Geometric containers for efficient shortest-path computation
Journal of Experimental Algorithmics (JEA)
Nondecreasing paths in a weighted graph or: how to optimally read a train schedule
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
SHARC: Fast and robust unidirectional routing
Journal of Experimental Algorithmics (JEA)
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
Nondecreasing paths in a weighted graph or: How to optimally read a train schedule
ACM Transactions on Algorithms (TALG)
Fast routing in very large public transportation networks using transfer patterns
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
Personalized tourist route generation
ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
Timetable information: models and algorithms
ATMOS'04 Proceedings of the 4th international Dagstuhl, ATMOS conference on Algorithmic approaches for transportation modeling, optimization, and systems
Efficient computation of time-dependent centralities in air transportation networks
WALCOM'11 Proceedings of the 5th international conference on WALCOM: algorithms and computation
A memetic algorithm for routing in urban public transportation networks
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Contraction of timetable networks with realistic transfers
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Hybrid approach for the public transportation time dependent orienteering problem with time windows
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Algorithm engineering for route planning: an update
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Parallel computation of best connections in public transportation networks
Journal of Experimental Algorithmics (JEA)
A label correcting algorithm for the shortest path problem on a multi-modal route network
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Integrating public transportation in personalised electronic tourist guides
Computers and Operations Research
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We consider two approaches that model timetable information in public transportation systems as shortest-path problems in weighted graphs. In the time-expanded approach, every event at a station, e.g., the departure of a train, is modeled as a node in the graph, while in the time-dependent approach the graph contains only one node per station. Both approaches have been recently considered for (a simplified version of) the earliest arrival problem, but little is known about their relative performance. Thus far, there are only theoretical arguments in favor of the time-dependent approach. In this paper, we provide the first extensive experimental comparison of the two approaches. Using several real-world data sets, we evaluate the performance of the basic models and of several new extensions towards realistic modeling. Furthermore, new insights on solving bicriteria optimization problems in both models are presented. The time-expanded approach turns out to be more robust for modeling more complex scenarios, whereas the time-dependent approach shows a clearly better performance.