Multi-Criteria Optimization for Regional TimetableSynchronization in Public Transport

Ingmar Schüle (), Anca Diana Dragan (), Alexander Radev (), Michael Schröder () and Karl-Heinz Küfer ()
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Ingmar Schüle: Fraunhofer Institut für Techno- und Wirtschaftsmathematik (ITWM)
Anca Diana Dragan: Fraunhofer Institut für Techno- und Wirtschaftsmathematik (ITWM)
Alexander Radev: Fraunhofer Institut für Techno- und Wirtschaftsmathematik (ITWM)
Michael Schröder: Fraunhofer Institut für Techno- und Wirtschaftsmathematik (ITWM)
Karl-Heinz Küfer: Fraunhofer Institut für Techno- und Wirtschaftsmathematik (ITWM)

Chapter 51 in Operations Research Proceedings 2008, 2009, pp 313-318 from Springer

Abstract: Summary The need for synchronizing timetables occurs when several public transportation companies interact in the same area, e.g. a city with busses, trains and trams. Existing approaches that optimize a global, waiting time oriented objective function reach their limit quite fast concerning real-life applications. While optimizing public transportation timetables, \winners" and \losers" will inevitably come about and the traffic planner has to keep control of this process. Thus, we propose an optimization approach that enhances the overall situation without losing sight of the defienciencies arising at particular stations. In our concept for optimizing public transportation timetables, the goal is to synchronize a set of lines (e.g. busses, trains, ...) by changing their starting times such that passengers can transfer more conveniently. For this, we do not take the often used approach of simply minimizing waiting times, but we use a concept of trying to achieve transfers that can be called convenient. For these, the time should not be too long, but also not too short, in order to reduce the risk of missing a transfer if the arriving vehicle is delayed. For optimization purposes, we assign to all possible waiting times at a transfer a corresponding penalty-value. One of our goals is to minimize the sum of these penalties over all transfers. For a detailed introduction, see [4].

Keywords: Simulated Annealing; Public Transport; Pareto Front; Quadratic Assignment Problem; Multiobjective Evolutionary Algorithm (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-3-642-00142-0_51

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