License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ATMOS.2020.16
URN: urn:nbn:de:0030-drops-131527
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Haehn, Rebecca ; Ábrahám, Erika ; Nießen, Nils

Probabilistic Simulation of a Railway Timetable

OASIcs-ATMOS-2020-16.pdf (0.6 MB)


Railway systems are often highly utilized, which makes them vulnerable to delay propagation. In order to minimize delays timetables are desired to be robust, a property that is often estimated by simulating the respective timetable for different deterministic delay values. To achieve an accurate estimation under consideration of uncertain delays many simulation runs need to be executed. Most established simulation systems additionally use microscopic models of the railway systems, which further increases the simulations running times and makes them applicable rather for small areas of interest for complexity reasons.
In this paper, we present a probabilistic, symbolic simulation algorithm for given timetables, this means we do not simulate individual executions, but all possible executions at once. We use a macroscopic model of the railway infrastructure as input. This way we consider the railway systems in less detail but are able to examine certain performance indicators for larger areas. For a given input model this simulation computes exact results. We implement the algorithm, examine its results, and discuss possible improvements of this approach.

BibTeX - Entry

  author =	{Rebecca Haehn and Erika Ábrah{\'a}m and Nils Nie{\ss}en},
  title =	{{Probabilistic Simulation of a Railway Timetable}},
  booktitle =	{20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020)},
  pages =	{16:1--16:14},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-170-2},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{85},
  editor =	{Dennis Huisman and Christos D. Zaroliagis},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-131527},
  doi =		{10.4230/OASIcs.ATMOS.2020.16},
  annote =	{Keywords: Railway, Modeling, Scheduling, Probabilistic systems, Optimization}

Keywords: Railway, Modeling, Scheduling, Probabilistic systems, Optimization
Collection: 20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020)
Issue Date: 2020
Date of publication: 10.11.2020

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