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ISSN 2753-3239
CCC: 1
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 23.8

Efficient timetable stability analysis using a graph contraction procedure

S. Butikofer, D. Fontana, A. Steiner and R. Wust

Zurich University of Applied Sciences, School of Engineering, Institute of Data Analysis and Process Design, Winterthur, Switzerland

Full Bibliographic Reference for this paper
S. Butikofer, D. Fontana, A. Steiner, R. Wust, "Efficient timetable stability analysis using a graph contraction procedure", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 23.8, 2022, doi:10.4203/ccc.1.23.8
Keywords: timetable stability, max-plus algebra, event activity network, graph contraction.

Abstract
Train density on the Swiss rail network has increased significantly in recent years. This demands much more from the stability of the system, especially in combination with single-track corridors. For railroad companies, it is therefore becoming increasingly important to optimize the transport network for stability in order to be able to offer the demanded service as reliably as possible on the existing infrastructure. An approach to numerical stability evaluation of timed discrete event systems has been developed to support planners in testing the timetable for operational stability. In this approach, the traffic system under consideration is modeled as a network with all relevant timetable events and links. The system modelled in this way can be examined for its behavior in the event of a possible disruption using methods from max-plus algebra. The typical computation time of an evaluation procedure takes more than 65 minutes for a signifcant partition of the line network. This is far too high for integration into a practical optimization procedure. In this paper we present a contraction procedure added to the existing evaluation framework, and thus reduce the computation time by more than 90% without compromising the result quality.

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