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dc.contributor.author
Trivella, Alessio
dc.contributor.author
Corman, Francesco
dc.date.accessioned
2023-07-03T07:16:07Z
dc.date.available
2023-07-03T05:54:33Z
dc.date.available
2023-07-03T07:16:07Z
dc.date.issued
2023-11-15
dc.identifier.issn
0957-4174
dc.identifier.other
10.1016/j.eswa.2023.120650
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/619571
dc.identifier.doi
10.3929/ethz-b-000619571
dc.description.abstract
Rail operators around the globe are striving to improve the efficiency, automation, safety, and sustainability of railway systems. Despite significant advances in technologies such as artificial intelligence and automated train operations (ATO), achieving these goals is challenging for complex rail networks when accounting for unpredictable factors that alter real-time operations. In this paper, we model railway traffic in a corridor as a string of interacting cruising trains, each subject to random speed variations that are described by a stochastic process. We simulate this dynamic system under assumptions that model human drivers and ATO systems, and compute performance measures focusing on energy consumption and the power peaks arising when multiple trains accelerate simultaneously. Different strategies to smooth these peaks are investigated, including the use of regenerative braking energy, potentially coupled with an electric energy storage, and a rule that uses fixed waiting times before re-acceleration. Our findings shed light on when and why these strategies can be effective at reducing energy consumption and/or shaving the peaks. They also show that employing a well-calibrated ATO controller in which vehicles exchange information about their location improves energy performance compared to a model of a human driven. Finally, a trade-off between energy performance and traffic regularity is exposed, i.e., strategies to reduce power peaks may slow rail traffic down, reducing capacity utilization.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Intelligent transport systems
en_US
dc.subject
Railway traffic dynamics
en_US
dc.subject
Automated train operations
en_US
dc.subject
Stochastic processes
en_US
dc.subject
Energy efficiency
en_US
dc.subject
Power peaks
en_US
dc.title
Modeling system dynamics of interacting cruising trains to reduce the impact of power peaks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2023-06-02
ethz.journal.title
Expert Systems with Applications
ethz.journal.volume
230
en_US
ethz.journal.abbreviated
Expert syst. appl.
ethz.pages.start
120650
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2023-07-03T05:54:34Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-07-03T07:16:09Z
ethz.rosetta.lastUpdated
2024-02-03T00:58:17Z
ethz.rosetta.versionExported
true
ethz.COinS
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