Architecture Design and Reliability Evaluation of a Novel Software-Defined Train Control System

Ming Chang , Nan Nan , Dongxiu Ou , Lei Zhang

Urban Rail Transit ›› 2022, Vol. 8 ›› Issue (1) : 45 -55.

PDF
Urban Rail Transit ›› 2022, Vol. 8 ›› Issue (1) : 45 -55. DOI: 10.1007/s40864-022-00165-y
Original Research Papers

Architecture Design and Reliability Evaluation of a Novel Software-Defined Train Control System

Author information +
History +
PDF

Abstract

Communication-based train control (CBTC) has been the prevailing technology of the urban transit signaling system. However, CBTC also faces a few issues to extend and maintain because of its complicated structure. This paper presents a novel urban transit signaling system architecture, software-defined train control (SDTC), which is based on cloud and high-speed wireless communication technology. The core functions of the proposed SDTC, including the onboard controller, are implemented in the cloud platform, with only sensors and input–output (IO) units remaining on the trackside and the train. Because of the scalable framework, the system function can be expanded according to the user’s demand, making signaling as a service possible. With warm standby server redundancy, SDTC has better reliability. Compared with the traditional CBTC architecture, the mean time between failures is improved by 39% by calculating typical project parameters by the Markov model based on some assumptions.

Keywords

Communication-based train control (CBTC) / Software-defined / Train control / Cloud computing / System architecture / Reliability

Cite this article

Download citation ▾
Ming Chang, Nan Nan, Dongxiu Ou, Lei Zhang. Architecture Design and Reliability Evaluation of a Novel Software-Defined Train Control System. Urban Rail Transit, 2022, 8(1): 45-55 DOI:10.1007/s40864-022-00165-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

IEEE 1474.1TM (2004). IEEE standard for communications-based train control (CBTC) performance and functional requirements. rail transit vehicle interface standards committee of the IEEE vehicular technology society.

[2]

Diemunsch K, and Rabindran N (2020) Origins and current status of the different communications-based train control products. JRC2020. doi: https://doi.org/10.1115/JRC2020-8020

[3]

Briginshaw D (2013) Alstrom’s simplified CBTC technology to debut in Lille. International Railway Journal, 53(6). https://trid.trb.org/view/1253251

[4]

Chen T, Wang H, Ning B, Zhang Y, Tang T, Li K. Architecture design of a novel train-centric CBTC system. Int Conf Intell Rail Transp (ICIRT), 2018, 2018: 1-5.

[5]

Liu J, Zhang Y, Han J, He J, Sun J, Zhou T. Intelligent hazard-risk prediction model for train control systems. IEEE Trans Intell Transp Syst, 2019

[6]

Song H, Schnieder E. Development and evaluation procedure of the train-centric communication-based system. IEEE Trans Veh Technol, 2018

[7]

Song H, Schnieder E. Availability and performance analysis of train-to-train data communication system. IEEE Trans Intell Transp Syst, 2019, 20(7): 2786-2795

[8]

Song H, Wu W, Dong H, Schnieder E. Propagation and safety analysis of the train-to-train communication system. IET Microw Antennas Propag, 2018

[9]

Wang X, Liu L, Tang T, Zhu L. Next generation train-centric communication-based train control system with train-to-train (T2T) communications. Int Conf Intell Rail Transp (ICIRT), 2018, 2018: 1-5.

[10]

Wang X, Liu L, Zhu L, Tang T. Joint security and QoS provisioning in train-centric CBTC systems under sybil attacks. IEEE Access, 2019

[11]

Wang X, Liu L, Zhu L, Tang T. Train-centric CBTC meets age of information in train-to-train communications. IEEE Trans Intell Transp Syst, 2019

[12]

SIEMENS. (2020). First hardware independent cloud-enabled interlocking in operation. 2020-11-26. https://press.siemens.com/global/en/pressrelease/first-signalling-cloud-operation.

[13]

ESG. (2018) On Design, Introduction and Operation Of Safety-critical Applications in a Data Center In the Railway System of Schweizerische Bundesbahnen SBB. 2018–06–14. https://www.smartrail40.ch/download/downloads/Safety-critical%20Applications%20in%20Data%20Center%20in%20the%20Railway%20System%20SBB.pdf

[14]

3GPP. TR 22.289 (2019). Technical specification group services and system aspects: mobile communication system for railways(Release 16).

[15]

Ashraf SA, Blasco R, Do H, Fodor G, Zhang C, Sun W. Supporting vehicle-to-everything services by 5G New radio release-16 systems. IEEE Commun Stand Magazine, 2020, 4(1): 26-32

[16]

Ge X. Ultra-reliable low-latency communications in autonomous vehicular networks. IEEE Trans Veh Technol, 2019, 68(5): 5005-5016

[17]

Sadio O, Ngom I, Lishou C. Design and prototyping of a software defined vehicular networking. IEEE Trans Veh Technol, 2020, 69(1): 842-850

[18]

Su Y, Lu X, Huang L, Du X, Guizani M. A novel DCT-based compression scheme for 5g vehicular networks. IEEE Trans Veh Technol, 2019, 68(11): 10872-10881

[19]

Jararweh Y, Al-Ayyoub M, Darabseh A, Benkhelifa E, Vouk M, Rindos A. Software defined cloud: Survey, system and evaluation. Futur Gener Comput Syst, 2016, 58: 56-74

[20]

Cao X, Huang H, Wang X Software defined grid: concept, architecture and samples. Autom Electric Power Syst, 2016, 40(6): 1-9.

[21]

Han S, Cao D, Li L, Li L, Li SE, Zheng N-N, Wang F-Y. From software-defined vehicles to self-driving vehicles: a report on CPSS-based parallel driving. IEEE Intell Transp Syst Mag, 2019, 11(1): 6-14

[22]

Mavromatis A, Colman-Meixner C, Silva AP, Vasilakos X, Nejabati R, Simeonidou D. A Software-defined IoT device management framework for edge and cloud computing. IEEE Internet Things J, 2020, 7(3): 1718-1735

[23]

Pérez Tijero H, Aldea Rivas M, Medina Ortega D. Multiprocessor platform for partitioned real time systems. Softw Pract Exper, 2017, 47(1): 61-78

[24]

Ghadhab M, Kaienburg J, Süßkraut M, Fetzer C. Is Software coded processing an answer to the execution integrity challenge of current and future automotive software-intensive applications? advanced microsystems for automotive applications, 2016, Cham: Springer

[25]

Srinivasa Rao TSS, Gupta UC. Performance modelling of the M/G/1 machine repairman problem with cold-, warm- and hot-standbys. Comput Ind Eng, 2000, 38(2): 251-267

[26]

Sousa E, Lins F, Tavares E, Maciel P. Cloud infrastructure planning considering different redundancy mechanisms. Computing, 2017, 99(9): 841-864

[27]

Bukowski JV, Goble WM. Using Markov models for safety analysis of programmable electronic systems. ISA Trans, 1995, 34(2): 193-198

[28]

Matos R, Dantas J, Araujo J, Trivedi KS, Maciel P. Redundant eucalyptus private clouds: availability modeling and sensitivity analysis. J Grid Comput, 2017, 15(1): 1-22

[29]

LENOVO. (2019). 150,000 hours MTBF certification. https://club.lenovo.com.cn/thread-5421014-1-1.html. 20 January 2019

Funding

National Key Research and Development Program of China(2018YFB1201403)

Research Program of Shanghai Science and Technology Committee(18DZ2202600)

AI Summary AI Mindmap
PDF

229

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/