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

Lin Junting, Min Xiaoqin

Archives of Transport

2022

Vol. 61, iss. 1

7--19

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.

Functional safety verification of train control procedure in train-centric CBTC by colored Petri net

Lin Junting, Xu Qian

Archives of Transport

2020

Vol. 54, iss. 2

43--58

Communication-based Train Control (CBTC) system is a widely-used signaling system. There is an increasing demand for innovating the traditional ground-centric architecture. With the application of train-train communication, object control and other advanced techniques, Train-centric CBTC (TcCBTC) system is expected to be the most promising tendency of train control system. The safe tracking interval would be reduced as well as the life-cycle costs. Formal methods play an essential role in the development of safety-critical systems, which provides an early integration of the verifiable design process. In the paper, the architecture design of TcCBTC is first analyzed. The official system specification of TcCBTC has not issued, so it takes efforts to perform the systematic summarization of the functional requirements. Secondly, we propose an integrated framework that combines the Colored Petri Net (CPN) models with the functional safety verification of the underlying systems. Functional safety depends on the logic accuracy and is a part of overall safety. The framework also specifies what kinds of functions, behaviors or properties need to be verified. The train control procedure of TcCBTC is regarded as the link among new functional modules, thus it is chosen as the modelling content. Thirdly, the scenarios and the color sets are prepared. Models are established with the novel design thought from top to bottom. Simulation and testing are implemented during the model establishment to discover the apparent errors. Lastly, the model checking by state space is performed. All possible states are checked in detail. Standard behavioral properties and other user-defined properties are verified by state space report and ASK-CTL (Computation Tree Logic) queries, respectively. Verification results reveal that the models are reasonable to depict the dynamic behaviors of train control procedure. The functional safety properties are satisfied and prepared for further drafting the system functional specification.