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Quantitative safety analysis of train control system based on statistical model checking

Lin Junting, Min Xiaoqin

Archives of Transport

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2022

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Vol. 61, iss. 1

7--19

EN

With the rapid development of communication technology, the Train-centric Communication-based Train Control (TcCBTC) system adopting the train-train communication mode to reduce the transmission link of control information, will become the direction of urban rail transit field development. At present, TcCBTC system is in the stage of key technology research and prototype development. Uncertain behavior in the process of system operation may lead to operation accidents. Therefore, before the system is put into use, it must undergo strict testing and security verification to ensure the safe and efficient operation of the system. In the paper, the formal modeling and quantitative analysis of train tracking operation under moving block are carried out. Firstly, the structure of TcCBTC system and the train tracking interval control strategy under moving block conditions are analyzed. The subsystem involved in train tracking and the uncertain factors in system operation are determined. Then, based on the Stochastic Hybrid Automata (SHA), a network of SHA model of train dynamics model, communication components and on-board controller in the process of train tracking is established, which can formally describe the uncertain environment in the process of system operation. UPPAAL-SMC is used to simulate the change curve of train position and speed during tracking, it is verified that the model meets the safety requirements in static environment. Finally, taking Statistical Model Checking (SMC) as the basis of safety analysis, the probability of train collision in uncertain environment is calculated. The results show that after accurately modeling the train tracking operation control mechanism through network of SHA, the SMC method can accurately calculate the probability of train rearend collision, which proves that the method has strong feasibility and effectiveness. Formal modeling and analysis of safety-critical system is very important, which enables designers to grasp the hidden dangers of the system in the design stage and safety evaluation stage of train control system, and further provides theoretical reference for the subsequent TcCBTC system design and development, practical application and related specification improvement.

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Extrapolation of an Optimal Policy using Statistical Probabilistic Model Checking

Rataj A., Woźna-Szcześniak B.

Fundamenta Informaticae

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2018

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Vol. 157, nr 4

443--461

EN

We present different ways of an approximate extrapolation of an optimal policy of a small model to that of a large equivalent of the model, which itself is too large to find its exact policy directly using probabilistic model checking (PMC). In particular, we obtain a global optimal resolution of non-determinism in several small Markov Decision Processes (MDP) or its extensions like Stochastic Multi-player Games (SMG) using PMC. We then use that resolution to form a hypothesis about an analytic decision boundary representing a respective policy in an equivalent large MDP/SMG. The resulting hypothetical decision boundary is then statistically approximately verified, if it is locally optimal and if it indeed represents a “good enough” policy. The verification either weakens or strengthens the hypothesis. The criterion of the optimality of the policy can be expressed in any modal logic that includes a version of the probabilistic operator P~p[·], and for which a PMC method exists.

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A GPGPU–based simulator for prism: statistical verification of results of PMC

Copik M., Rataj A., Woźna-Szcześniak B.

Scientific Issues of Jan Długosz University in Częstochowa. Mathematics

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2017

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Vol. 22

85--97

EN

We describe a GPGPU–based Monte Carlo simulator integrated with Prism. It supports Markov chains with discrete or continuous time and a subset of properties expressible in PCTL, CSL and their variants extended with rewards. The simulator allows an automated statistical verification of results obtained using Prism’s formal methods.