Record link: 
http://hdl.handle.net/20.500.12708/139965
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Title: 
Analysis of Train Delays using Bayesian Networks
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Citation: 
Barzal, V., Rössler, M., Wastian, M., Breitenecker, F., & Popper, N. (2022). Analysis of Train Delays using Bayesian Networks. In F. Breitenecker, C. Deatcu, U. Durak, A. Körner, & T. Pawletta (Eds.), ASIM SST 2022 Proceedings Kurzbeiträge (pp. 33–36). ARGESIM Publisher. http://hdl.handle.net/20.500.12708/139965
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Barzal, Valentin 
Rössler, Matthias 
Wastian, Matthias 
Breitenecker, Felix 
Popper, Nikolas 
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Organisational Unit: 
E101 - Institut für Analysis und Scientific Computing 
E105-06 - Forschungsbereich Computational Statistics 
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Published in: 
ASIM SST 2022 Proceedings Kurzbeiträge 
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ISBN: 
9783901608964
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Volume: 
19
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DOI of the book: 
10.11128/arep.19
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Date (published): 
20-Jul-2022
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Event name: 
26. Symposium Simulationstechnik - ASIM 2022 
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Event date: 
25-Jul-2022 - 27-Jul-2022
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Event place: 
Wien, Austria
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Number of Pages: 
4
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Publisher: 
ARGESIM Publisher
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Keywords: 
Bayesian networks; rail networks; delay
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Abstract: 
Bayesian networks can be used for analysis and representation of dependencies in large data sets. Due to their property of operating with graphs, they are suitable for analyzing delays in rail networks. After getting an overview of the theory of Bayesian networks, this article deals with recent literature about Bayesian networks and train delays. Furthermore, the presented methods will be applied to data from the Austrian railway network.
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Research Areas: 
Modeling and Simulation: 100%
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Science Branch: 
1010 - Mathematik: 100%
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Appears in Collections:
Conference Paper

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