Modeling and validation of typical scenarios of high-speed railway automatic train operation based on Alvis

Yi Zhang; Kaicheng Li; Guodong Wei; Lei Yuan; Fei Wang

doi:10.1117/12.2652694

21 November 2022 Modeling and validation of typical scenarios of high-speed railway automatic train operation based on Alvis

Yi Zhang, Kaicheng Li, Guodong Wei, Lei Yuan, Fei Wang

Author Affiliations +

Proceedings Volume 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022); 123400H (2022) https://doi.org/10.1117/12.2652694
Event: International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 2022, Lanzhou, China

Abstract

Formal methods are used to improve the credibility and correctness of scenarios in Chinese Train Control Systems (CTCS). At present, there are few formal studies on the high-speed Automatic Train Operation system that developed from CTCS-3 level high-speed railway technology. In this paper, we adopt a new formal method, Alvis, which combines the advantages of high-level programming language and graphical modeling and select the interzone TSRS handover scene in high-speed railway ATO system as the research object. The messaging behaviors of the scene are analyzed using UML sequence diagrams, and an Alvis model of the system is built, which is converted into an smv model supporting the formal validation of the model checking tool nuXmv. The real-time performance of the extracted messaging was verified on demand, inconsistency between technical specifications and actual operational scenarios was identified and solution was provided. The results of this method can provide some reference for the technical promotion of ATO train control system in China.

Citation Download Citation

Yi Zhang, Kaicheng Li, Guodong Wei, Lei Yuan, and Fei Wang "Modeling and validation of typical scenarios of high-speed railway automatic train operation based on Alvis", Proc. SPIE 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 123400H (21 November 2022); https://doi.org/10.1117/12.2652694

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