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Compositional modeling of railway Virtual Coupling with Stochastic Activity Networks
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Mälardalen University, Sweden. (ERES;DISA-SIG)ORCID iD: 0000-0002-2833-7196
Univ Campania Luigi Vanvitelli, Italy.
Univ Naples Parthenope, Italy.
Univ Naples Federico II, Italy.
2021 (English)In: Formal Aspects of Computing, ISSN 0934-5043, E-ISSN 1433-299X, Vol. 33, p. 989-1007Article in journal (Refereed) Published
Abstract [en]

The current travel demand in railways requires the adoption of novel approaches and technologies in order to increase network capacity. Virtual Coupling is considered one of the most innovative solutions to increase railway capacity by drastically reducing train headway. The aim of this paper is to provide an approach to investigate the potential of Virtual Coupling in railways by composing stochastic activity networks model templates. The paper starts describing the Virtual Coupling paradigm with a focus on standard European railway traffic controllers. Based on stochastic activity network model templates, we provide an approach to perform quantitative evaluation of capacity increase in reference Virtual Coupling scenarios. The approach can be used to estimate system capacity over a modelled track portion, accounting for the scheduled service as well as possible failures. Due to its modularity, the approach can be extended towards the inclusion of safety model components. The contribution of this paper is a preliminary result of the PERFORMINGRAIL (PERformance-based Formal modelling and Optimal tRaffic Management for movING-block RAILway signalling) project funded by the European Shift2Rail Joint Undertaking.

Place, publisher, year, edition, pages
Springer, 2021. Vol. 33, p. 989-1007
Keywords [en]
Railway, Performance Modelling, Stochastic Activity Networks, Moving block, Virtual Coupling
National Category
Transport Systems and Logistics Embedded Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-107471DOI: 10.1007/s00165-021-00560-5ISI: 000698581300001Scopus ID: 2-s2.0-85115655510Local ID: 2021OAI: oai:DiVA.org:lnu-107471DiVA, id: diva2:1603043
Available from: 2021-10-14 Created: 2021-10-14 Last updated: 2022-04-12Bibliographically approved

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Flammini, Francesco

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