Quantitative Dependability Evaluation of Train Control Systems in Presence of Uncertainty: A Systematic Literature Review
Technological advances in modern Train Control Systems (TCSs) promise to improve dependability of railway transportation in terms of safety, availability, and capacity, notably by employing novel distancing policies such as Moving Block (MB) signaling and Virtual Coupling (VC), fueled by advanced train localization methods such as satellite positioning. At the same time, these technological advances raise notable concerns about the effects that uncertainty in critical TCS parameters (such as train position and speed) may have on dependability-related attributes. Recently, various approaches have been proposed to characterize such effects through quantitative measures, leveraging formal stochastic modeling and evaluation of the TCS behavior. In this paper, we illustrate the results of a systematic review of the literature on quantitative evaluation of dependability-related attributes of TCSs under uncertainty on vital parameters. Specifically, we have finally selected 42 relevant papers, published between 2011 and 2023, that succeed in giving, through an empirical perspective and classification, a comprehensive view of current research and practice in quantitative dependability assessment of TCSs.
Authors
- Stefania Gnesi (ORCID: https://orcid.org/0000-0002-0139-0421)
- Laura Carnevali (ORCID: https://orcid.org/0000-0002-5896-4860)
- Gloria Gori (ORCID: https://orcid.org/0000-0002-8482-2612)
- Alessandro Fantechi (ORCID: https://orcid.org/0000-0002-4648-4667)
- Felicita Di Giandomenico (ORCID: https://orcid.org/0000-0002-8760-7299)
Institutions
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- National Research Council (IT)
- University of Florence (IT)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1109/tits.2025.3530112
- Citations
- 5
- Primary Topic
- Reliability and Maintenance Optimization
- Type
- article
- Field-Weighted Citation Impact
- 6.52