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dc.contributor.author
Luan, Xiaojie
dc.contributor.author
De Schutter, Bart
dc.contributor.author
Meng, Lingyun
dc.contributor.author
Corman, Francesco
dc.date.accessioned
2020-09-24T07:30:36Z
dc.date.available
2020-09-24T06:20:47Z
dc.date.available
2020-09-24T07:30:36Z
dc.date.issued
2020-11
dc.identifier.issn
0191-2615
dc.identifier.issn
1879-2367
dc.identifier.other
10.1016/j.trb.2020.09.004
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/442206
dc.description.abstract
This paper introduces decomposition and distributed optimization approaches for the real-time railway traffic management problem considering microscopic infrastructure characteristics, aiming at an improved computational efficiency when tackling large-scale railway networks. Based on the nature of the railway traffic management problem, we consider three decomposition methods, namely a geography-based (GEO) decomposition, a train-based (TRA) decomposition, and a time-interval-based (TIN) decomposition, in order to partition the large railway traffic management optimization problem into several subproblems. In particular, an integer linear programming (ILP) model is developed to generate the optimal GEO solution, with the objectives of minimizing the number of interconnections among regions and of balancing the size of regions. The decomposition creates couplings among the subproblems, in terms of either capacity usage or transit time consistency; therefore the whole problem gets a non-separable structure. To handle the couplings, we introduce three distributed optimization approaches, namely an Alternating Direction Method of Multipliers (ADMM) algorithm, a priority-rule-based (PR) algorithm, and a Cooperative Distributed Robust Safe But Knowledgeable (CDRSBK) algorithm, which operate iteratively. We test all combinations of the three decomposition methods and the three distributed optimization algorithms on a large-scale railway network in the South-East of the Netherlands, in terms of feasibility, computational efficiency, and optimality. Overall the CDRSBK algorithm with the TRA decomposition performs best, where high-quality (optimal or near-optimal) solutions can be found within 10 s of computation time. © 2020 Elsevier Ltd
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Real-time traffic management
en_US
dc.subject
Decomposition
en_US
dc.subject
Distributed optimization
en_US
dc.subject
Large-scale railway network
en_US
dc.title
Decomposition and distributed optimization of real-time traffic management for large-scale railway networks
en_US
dc.type
Journal Article
dc.date.published
2020-09-17
ethz.journal.title
Transportation Research Part B: Methodological
ethz.journal.volume
141
en_US
ethz.journal.abbreviated
Transp. res., Part B: methodol.
ethz.pages.start
72
en_US
ethz.pages.end
97
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.relation.isSupplementedBy
10.3929/ethz-b-000426430
ethz.date.deposited
2020-09-24T06:20:57Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-09-24T07:31:06Z
ethz.rosetta.lastUpdated
2020-09-24T07:31:06Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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