Abstract
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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Aoun J, Quaglietta E, Goverde RMP(2020) Exploring demand trends and operational scenarios for Virtual Coupling railway signalling technology. In: 2020 IEEE 23rd International conference on intelligent transportation systems, ITSC 2020
Courtney T, Gaonkar S, Keefe K, Rozier EWD, Sanders WH (2009) Möbius 2.3: an extensible tool for dependability, security, and performance evaluation of large and complex system models. In: 2009 IEEE/IFIP International conference on dependable systems and networks. IEEE, pp 353–358
Cao Y, Wen J, MaL (2021) Tracking and collision avoidance of Virtual Coupling train control system. Alex Eng J 60(2):2115– 2125
Chen M, Xun J, Liu Y (2020) A coordinated collision mitigation approach for Virtual Coupling trains by using model predictive control. In: 2020 IEEE 23rd International conference on intelligent transportation systems, ITSC 2020
Di Meo C, Di Vaio M, Flammini F, Nardone R, Santini S, Vittorini V (2020) ERTMS/ETCS Virtual Coupling: proof of concept and numerical analysis. IEEE Trans Intell Transport Syst 21(6):2545–2556
Fantechi A, Flammini F, Gnesi S (2014) Formal methods for railway control systems. Int J Softw Tools Technol Transf 16(6):643–646
Flammini F (2012) Railway safety, reliability, and security: technologies and systems engineering. IGI Global, Hershey
Fraga-Lamas P, Fernández-Caramés TM, Castedo L (2017) Towards the internet of smart trains: a review on industrial IoT-connected railways. Sensors (Switzerland) 17(6)
Flammini F, Marrone S, Nardone R, Petrillo A, Santini S, Vittorini V (2018) Towards railway Virtual Coupling. In: 2018 IEEE International conference on electrical systems for aircraft, railway, ship propulsion and road vehicles and international transportation electrification conference (ESARS-ITEC). IEEE, pp 1–6
Furness N, van Houten H, Arenas L, Maarten B (2017) ERTMS level 3: the game-changer
Gomez AA, Mozo E, Bernado L, Zelenbaba S, Zemen T, Parrilla F, Alberdi A (2018) Performance analysis of ITS-G5 for smart train composition coupling. In: Proceedings of 2018 16th international conference on intelligent transport system telecommunications, ITST 2018
Goikoetxea J (2016) Roadmap towards the wireless virtual coupling of trains. In: International workshop on communication technologies for vehicles. Springer, Berlin, pp 3–9
Le HQ, Lehner A, Sand S (2015) Performance analysis of ITS-G5 for dynamic train coupling application. In: Kassab M, Berbineau M, Vinel A, Jonsson M, Garcia F, Soler J (eds) Communication technologies for vehicles. Springer International Publishing, Cham, pp 129–140
Liu L, Wang P, Zhang B, Wei W (2019) Coordinated control method of virtually coupled train formation based on multi agent system. Smart Innov Syst Technol 129:225–233
Mingozzi E, Cau G, Cavaliere F (2002) The train communication network in the trains of FS fleet: optimisation, integration and interoperability of railway functionality. WIT Trans Built Environ 61
Mitchell I et al (2016) ERTMS level 4, train convoys or virtual coupling. IRSE International Technical Commitee
Marrone S, Flammini F, Mazzocca N, Nardone R, Vittorini V (2014) Towards model-driven V&V assessment of railway control systems. Int J Softw Tools Technol Transf 16(6):669–683
Meyer JF, Movaghar A, Sanders WH (1985) Stochastic activity networks: structure, behavior, and application. In: International workshop on timed Petri Nets. IEEE Computer Society, Washington, DC, pp 106–115
Quaglietta E, Wang M, Goverde RMP (2020) A multi-state train-following model for the analysis of Virtual Coupling railway operations. J Rail Transp Plan Manag 15
Ren W, Green D (1994) Continuous platooning: a new evolutionary operating concept for automated highway systems. In: Proceedings of 1994 American control conference-ACC’94, vol 1. IEEE, pp 21–25
Schumann T (2017) Increase of capacity on the Shinkansen high-speed line using Virtual Coupling. Int J Transp Dev Integr 1(4):666–676
Shift2Rail Joint Undertaking. Multi-annual action plan, Nov 2015
She J, Li K, Yuan L, Zhou Y, Su S (2020) Cruising control approach for virtually coupled train set based on model predictive control. In: 2020 IEEE 23rd International conference on intelligent transportation systems, ITSC 2020
Sanders WH, Meyer JF (2001) Stochastic Activity Networks: Formal definitions and concepts. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 2090, pp 315–343
Unterhuber P, Lehner A, de Ponte Müller F (2016) Measurement and analysis of ITS-G5 in railway environments. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9669, pp 62–73
UNISIG (2016) ERTMS/ETCS: System requirements specification—SUBSET-026, issue 3.6.0
Acknowledgements
This research has received funding from the Shift2Rail Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation programme under Grant Agreement N. 101015416 PERFORMINGRAIL. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.
Funding
Open access funding provided by Mälardalen University.
Author information
Authors and Affiliations
Corresponding author
Additional information
Alessandro Fantechi, Anne Haxthausen and Jim Woodcock.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Flammini, F., Marrone, S., Nardone, R. et al. Compositional modeling of railway Virtual Coupling with Stochastic Activity Networks. Form Asp Comp 33, 989–1007 (2021). https://doi.org/10.1007/s00165-021-00560-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00165-021-00560-5