<div class="csl-bib-body">
<div class="csl-entry">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.), <i>ASIM SST 2022 Proceedings Kurzbeiträge</i> (pp. 33–36). ARGESIM Publisher. http://hdl.handle.net/20.500.12708/139965</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/139965
-
dc.description.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.
en
dc.language.iso
en
-
dc.subject
Bayesian networks
en
dc.subject
rail networks
en
dc.subject
delay
en
dc.title
Analysis of Train Delays using Bayesian Networks
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
dwh GmbH, Neustiftgasse 57-59, Wien
-
dc.contributor.affiliation
dwh GmbH
-
dc.contributor.editoraffiliation
Wismar University of Applied Sciences, Germany
-
dc.relation.isbn
9783901608964
-
dc.relation.doi
10.11128/arep.19
-
dc.description.startpage
33
-
dc.description.endpage
36
-
dc.type.category
Full-Paper Contribution
-
tuw.booktitle
ASIM SST 2022 Proceedings Kurzbeiträge
-
tuw.container.volume
19
-
tuw.relation.publisher
ARGESIM Publisher
-
tuw.researchTopic.id
C6
-
tuw.researchTopic.name
Modeling and Simulation
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E101 - Institut für Analysis und Scientific Computing
