Skip to main content
Springer Nature Link
Log in

Intelligent quantitative safety monitoring approach for ATP system by neural computing and probabilistic model checking

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Online quantitative safety monitoring is the key technology for ensuring the operational safety of the automatic train protection (ATP) system for the future advanced train control system. However, the traditional formal verification method can only deal with the system of small scale since these verification algorithms exhaustively search all executable paths of formal models. Especially for the formal models with uncertain parameters, the problem of memory overflow will occur due to the state space explosion, which makes verification algorithms fail to converge. To solve this problem, an intelligent quantitative safety monitoring approach is proposed by integrating the probabilistic model checking method (PMCM) with neural network in this paper. To begin with, the instantiated continuous-time Markov Chains model is verified by PMCM offline. Then, the neural network is constructed and optimized based on the offline verification results. The quantitative safety boundaries (QSBs) for all quantitative safety levels are also computed by the designed algorithm. Furthermore, hierarchical iterative evaluation method is applied to efficiently evaluate the reliability performance of the ATP online. Finally, the quantitative safety level of ATP or its subsystem will be determined by both reliability performance and the previous QSBs or the neural network online.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The datasets used in this study are available from the corresponding author on reasonable request.

References

  1. Ning B, Tang T, Dong H et al (2011) An introduction to parallel control and management for high-speed railway systems. IEEE Trans Intell Transp Syst 12(4):1473–1483

    Article  Google Scholar 

  2. Chen D, Yin J, Chen L, Xu H (2017) Parallel control and management for high-speed maglev systems. IEEE Trans Intell Transp Syst 18(2):431–440

    Article  Google Scholar 

  3. Cheng R, Chen D, Gai W, Zheng S (2019) Intelligent driving methods based on sparse LSSVM and ensemble CART algorithms for high-speed trains. Comput Ind Eng 127:1203–1213

    Article  Google Scholar 

  4. Zhang Y, Wang H, Yuan T et al (2019) Hybrid online safety observer for CTCS-3 train control system on-board equipment. IEEE Trans Intell Transp Syst 20(3):925–934

    Article  Google Scholar 

  5. Cheng R, Cheng Y, Chen D, Song H (2021) Online quantitative safety monitoring approach for unattended train operation system considering stochastic factors. Reliab Eng Syst Saf 216:107933

    Article  Google Scholar 

  6. Chai M, Zhang X, Schlingloff B et al (2024) Online hazard prediction of train operations with parametric hybrid automata based runtime verification. Reliab Eng Syst Saf 241:109621

    Article  Google Scholar 

  7. Zhang Y, Wang H, James P et al (2022) A train protection logic based on topological manifolds for virtual coupling. IEEE Trans Intell Transp Syst 23(8):11930–11945

    Article  Google Scholar 

  8. Su S, Wang X, Cao Y, Yin J (2020) An energy-efficient train operation approach by integrating the metro timetabling and eco-driving. IEEE Trans Intell Transp Syst 21(10):4252–4268

    Article  Google Scholar 

  9. Hou Z, Dong H, Gao S et al (2019) Energy-saving metro train timetable rescheduling model considering ATO profiles and dynamic passenger flow. IEEE Trans Intell Transp Syst 20(7):2774–2785

    Article  Google Scholar 

  10. Song Y, Song Q, Cai W (2014) Fault-tolerant adaptive control of high-speed trains under traction/braking failures: a virtual parameter-based approach. IEEE Trans Intell Transp Syst 15:737–748

    Article  Google Scholar 

  11. Gao S, Li M, Zheng Y et al (2022) Fuzzy adaptive protective control for high-speed trains: an outstretched error feedback approach. IEEE Trans Intell Transp Syst 23(10):17966–17975

    Article  Google Scholar 

  12. Li K, Yao X, Chen D et al (2015) HAZOP study on the CTCS-3 onboard system. IEEE Trans Intell Transp Syst 16(1):162–171

    Article  Google Scholar 

  13. Song H, Liu H, Schnieder E (2019) A train-centric communication-based new movement authority proposal for ETCS-2. IEEE Trans Intell Transp Syst 20(6):2328–2338

    Article  Google Scholar 

  14. Cheng R, Zhou J, Chen D, Song Y (2016) Model-based verification method for solving the parameter uncertainty in the train control system. Reliab Eng Syst Saf 145:169–182

    Article  Google Scholar 

  15. Cheng R, Yu W, Song Y, Chen D, Ma X, Cheng Y (2019) Intelligent safe driving methods based on hybrid automata and ensemble CART algorithms for multi-high speed trains. IEEE Trans Cybernet 49(10):3816–3826

    Article  Google Scholar 

  16. Desgeorges L, Piriou P, Lemattre T, Chraibi H (2021) Formalism and semantics of PyCATSHOO: a simulator of distributed stochastic hybrid automata. Reliab Eng Syst Saf 208(4):107384

    Article  Google Scholar 

  17. Babykina G, Brînzei N, Aubry JF et al (2016) Modeling and simulation of a controlled steam generator in the context of dynamic reliability using stochastic hybrid automaton. Reliab Eng Syst Saf 152:115–136

    Article  Google Scholar 

  18. Lages D, Borba E, Tavares E et al (2023) A CPN-based model for assessing energy consumption of IoT networks. J Supercomput 79:12978–13000

    Article  Google Scholar 

  19. Hafaiedh IB, Slimane MB (2022) A distributed formal-based model for self-healing behaviors in autonomous systems: from failure detection to self-recovery. J Supercomput 78(17):18725–18753

    Article  Google Scholar 

  20. Agos Jawaddi SN, Ismail A, Mohammad Hatta MNH, Kamarulzaman AF (2024) Insights into cloud autoscaling: a unique perspective through MDP and DTMC formal models. J Supercomput 80(4):5073–5107

    Article  Google Scholar 

  21. Weik N, Volk M, Katoen JP et al (2022) DFT modeling approach for operational risk assessment of railway infrastructure. Int J Softw Tools Technol Transfer 24(3):331–350

    Article  Google Scholar 

  22. Yan F, Zhang S, Majumdar A et al (2020) A failure mapping and genealogical research on metro operational incidents. IEEE Trans Intell Transp Syst 21(8):3551–3560

    Article  Google Scholar 

  23. Song H, Schnieder E (2019) Availability and Performance Analysis of Train-to-Train Data Communication System. IEEE Trans Intell Transp Syst 20(7):2786–2795

    Article  Google Scholar 

  24. Zhang W, Zhang Y, Su H et al (2014) Reliability analysis on ATP system of CTCS-3 based on dynamic fault tree. Chinese J Eng Des 21(1):18–26

    MathSciNet  Google Scholar 

  25. Kang R, Wang J, Chen J et al (2022) A method of online anomaly perception and failure prediction for high-speed automatic train protection system. Reliab Eng Syst Saf 226:108699

    Article  Google Scholar 

  26. Zang Y, Shangguan W, Cai B et al (2019) System-level fault prognosis for high-speed railway on-board systems. Transp Res Rec 2673(22):584–595

    Article  Google Scholar 

  27. Lin S, Jia L, Zhang H et al (2022) Reliability of high-speed electric multiple units in terms of the expanded multi-state flow network. Reliab Eng Syst Saf 225:108608

    Article  Google Scholar 

  28. Kwiatkowska M, Norman G, Parker D (2004) Probabilistic symbolic model checking with PRISM: a hybrid approach. Int J Softw Tools Technol Transfer 6(2):128–142

    Article  Google Scholar 

  29. Baier C, Cloth L, Haverkort BR et al (2010) Performability assessment by model checking of Markov reward models. Formal Methods in System Design 36:1–36

    Article  Google Scholar 

  30. Hahn EM, Hermanns H, Wachter B, Zhang L (2010). PARAM: a model checker for parametric Markov models. In: Computer Aided Verification: 22nd International Conference, CAV 2010, Edinburgh, UK, July 15-19, 2010. Proceedings 22. Springer, Berlin, Heidelberg, pp 660-664

  31. Hilt A, Járó G, Bakos I (2016) Availability prediction of telecommunication application servers deployed on cloud. Period Polytech Electr Eng Comput Sci 60:72–81

    Article  Google Scholar 

  32. Cheng R, Chen D, Ma X, Cheng Y, Chen H (2023) Intelligent quantitative safety monitoring approach for ATP using LSSVM and probabilistic model checking considering imperfect fault coverage. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2023.3332348

    Article  Google Scholar 

  33. Wang J, Li Y, Zhang Y (2016) Research on parallel control mechanism and its implementation in ATP. IEEE Trans Intell Transp Syst 17(6):1652–1662

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the China Academy of Railway Sciences Corporation Limited.

Funding

This work has been partially funded by the Research and Development Plan of China Academy of Railway Sciences Co. LTD under Grant No.2023YJ026, Research Project Supported by Shanxi Scholarship Council of China under Grant 2022-142.

Author information

Authors and Affiliations

  1. Postgraduate Department, China Academy of Railway Sciences, Beijing, 100081, China

    Yu Cheng

  2. Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, 100081, China

    Yu Cheng, Jinzhao Liu, Xinliang Jiang & Xinyu Du

  3. School of Electrical and Control Engineering, North University of China, Taiyuan, 030051, China

    Ruijun Cheng

  4. Shanxi Provincial Laboratory of Ultra-High Speed and Low Vacuum Pipeline Maglev Transportation, North University of China, Taiyuan, 030051, China

    Ruijun Cheng

Authors
  1. Yu Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinzhao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinliang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyu Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruijun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This paper is equally contributed by each author.

Corresponding author

Correspondence to Jinzhao Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, Y., Liu, J., Jiang, X. et al. Intelligent quantitative safety monitoring approach for ATP system by neural computing and probabilistic model checking. J Supercomput 80, 19696–19718 (2024). https://doi.org/10.1007/s11227-024-06110-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06110-z

Keywords