Performance Evaluation of Metro Regulations Using Probabilistic Model-checking - Archive ouverte HAL Access content directly
Conference Papers Year :

Performance Evaluation of Metro Regulations Using Probabilistic Model-checking

(1) , (2) , (1) , (2) , (2) , (1)
1
2

Abstract

Metros are subject to unexpected delays due to weather conditions , incidents, passenger misconduct, etc. To recover from delays and avoid their propagation to the whole network, metro operators use regulation algorithms that adapt speeds and departure dates of trains. Regulation algorithms are ad-hoc tools that are tuned to cope with characteristics of tracks, rolling stock, and passengers habits. However, there is no universal optimal regulation adapted in any environment. So, performance of a regulation technique must be evaluated before its integration in the network. In this work, we use probabilistic model-checking to evaluate the performance of regulation algorithms in simple metro lines. We model the moves of trains and random delays with Markov decision processes, and regulation as a controller that forces a decision depending on its partial knowledge of the state of the system. We then use the probabilistic model checker PRISM to evaluate performance of regulation: We compute the probability to reach a stable situation from an unstable one in less than d time units, letting d vary in a large enough time interval. This approach is applied on a case study, the metro network of Glasgow.
Fichier principal
Vignette du fichier
rssr19-Long.pdf (681.9 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02065365 , version 1 (12-03-2019)

Licence

Copyright

Identifiers

Cite

Nathalie Bertrand, Benjamin Bordais, Loïc Hélouët, Thomas Mari, Julie Parreaux, et al.. Performance Evaluation of Metro Regulations Using Probabilistic Model-checking. RSSRail 2019 - International conference on reliability, safety and security of railway systems: modelling, analysis, verification and certification, Jun 2019, Lille, France. pp.59-76, ⟨10.1007/978-3-030-18744-6_4⟩. ⟨hal-02065365⟩
137 View
249 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More