Ten Diverse Formal Models for a CBTC Automatic Train Supervision System

Communications-based Train Control (CBTC) systems are metro signalling platforms, which coordinate and protect the movements of trains within the tracks of a station, and between different stations. In CBTC platforms, a prominent role is played by the Automatic Train Supervision (ATS) system, which...

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Main Authors: Franco Mazzanti, Alessio Ferrari
Format: Article
Language:English
Published: Open Publishing Association 2018-03-01
Series:Electronic Proceedings in Theoretical Computer Science
Online Access:http://arxiv.org/pdf/1803.10324v1
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spelling doaj-239a7043df644bd0a5ebd3683d5f6f5e2020-11-25T01:48:42ZengOpen Publishing AssociationElectronic Proceedings in Theoretical Computer Science2075-21802018-03-01268Proc. MARS/VPT 201810414910.4204/EPTCS.268.4:5Ten Diverse Formal Models for a CBTC Automatic Train Supervision SystemFranco Mazzanti0Alessio Ferrari1 ISTI-CNR ISTI-CNR Communications-based Train Control (CBTC) systems are metro signalling platforms, which coordinate and protect the movements of trains within the tracks of a station, and between different stations. In CBTC platforms, a prominent role is played by the Automatic Train Supervision (ATS) system, which automatically dispatches and routes trains within the metro network. Among the various functions, an ATS needs to avoid deadlock situations, i.e., cases in which a group of trains block each other. In the context of a technology transfer study, we designed an algorithm for deadlock avoidance in train scheduling. In this paper, we present a case study in which the algorithm has been applied. The case study has been encoded using ten different formal verification environments, namely UMC, SPIN, NuSMV/nuXmv, mCRL2, CPN Tools, FDR4, CADP, TLA+, UPPAAL and ProB. Based on our experience, we observe commonalities and differences among the modelling languages considered, and we highlight the impact of the specific characteristics of each language on the presented models.http://arxiv.org/pdf/1803.10324v1
collection DOAJ
language English
format Article
sources DOAJ
author Franco Mazzanti
Alessio Ferrari
spellingShingle Franco Mazzanti
Alessio Ferrari
Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
Electronic Proceedings in Theoretical Computer Science
author_facet Franco Mazzanti
Alessio Ferrari
author_sort Franco Mazzanti
title Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
title_short Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
title_full Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
title_fullStr Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
title_full_unstemmed Ten Diverse Formal Models for a CBTC Automatic Train Supervision System
title_sort ten diverse formal models for a cbtc automatic train supervision system
publisher Open Publishing Association
series Electronic Proceedings in Theoretical Computer Science
issn 2075-2180
publishDate 2018-03-01
description Communications-based Train Control (CBTC) systems are metro signalling platforms, which coordinate and protect the movements of trains within the tracks of a station, and between different stations. In CBTC platforms, a prominent role is played by the Automatic Train Supervision (ATS) system, which automatically dispatches and routes trains within the metro network. Among the various functions, an ATS needs to avoid deadlock situations, i.e., cases in which a group of trains block each other. In the context of a technology transfer study, we designed an algorithm for deadlock avoidance in train scheduling. In this paper, we present a case study in which the algorithm has been applied. The case study has been encoded using ten different formal verification environments, namely UMC, SPIN, NuSMV/nuXmv, mCRL2, CPN Tools, FDR4, CADP, TLA+, UPPAAL and ProB. Based on our experience, we observe commonalities and differences among the modelling languages considered, and we highlight the impact of the specific characteristics of each language on the presented models.
url http://arxiv.org/pdf/1803.10324v1
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AT alessioferrari tendiverseformalmodelsforacbtcautomatictrainsupervisionsystem
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