RBD: A Reference Railway Big Data System Model

  • Weilan Lin
  • Fanhua Xu
  • Meng MaEmail author
  • Ping WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)


The subway line is complex and involves many departments, resulting in unstandardized storage of relevant data in the Metro department. Data systems between different departments cannot cooperate. In this paper, we propose Railway Large Data Platform (RBD) to standardize the large data of rail transit. A large data platform system is designed to store the complex data of rail transit, which can cope with complex scenes. Taking the construction of rail transit platform in Chongqing as an example, we have made a systematic example.


Micro-service architecture Root cause Anomaly detection Impact graph Frequent subgraph mining 



This work is supported in part by National Key R&D Program of China No. 2017YFB1200700 and National Natural Science Foundation of China No. 61701007.


  1. 1.
    Yu-Ping, X.U., Qin, G., Zhang, Z.: Study on utilization of big data of urban rail transit investigation. Railw. Transp. Econ. 4, 024 (2015)Google Scholar
  2. 2.
    Zhang, X.: Historic data saving and application in urban rail transit building automation system. Urban Mass Transit 13(11), 21–25 (2010). Scholar
  3. 3.
    Cong, H., University, N.N.: Study on big data analysis based running route tracking of urban rail train. Mod. Electron. Tech. 41(5), 110–115 (2018)Google Scholar
  4. 4.
    Kim, W., Yong, H.K., Park, H.S., et al.: Analysis of traffic card big data by hadoop and sequential mining technique. J. Inf. Technol. Appl. Manag. 24, 187–196 (2017)Google Scholar
  5. 5.
    Oneto, L.: Delay prediction system for large-scale railway networks based on big data analytics. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds.) INNS 2016. AISC, vol. 529, pp. 139–150. Springer, Cham (2017). Scholar
  6. 6.
    Fumeo, E., Oneto, L., Anguita, D.: Condition based maintenance in railway transportation systems based on big data streaming analysis. Procedia Comput. Sci. 53(1), 437–446 (2015)CrossRefGoogle Scholar
  7. 7.
    Thaduri, A., Galar, D., Kumar, U.: Railway assets: a potential domain for big data analytics. Procedia Comput. Sci. 53(1), 457–467 (2015)CrossRefGoogle Scholar
  8. 8.
    Tianyun, S., Jun, L., Ping, L.I., et al.: Overall scheme and key technologies of big data platform for China Railway. Railw. Comput. Appl. 25(9), 1–6 (2016)Google Scholar
  9. 9.
    Xiaoning, M.A., Ping, L.I., Tianyun, S.: System framework of railway big data application. Railw. Comput. Appl. 25(9), 7–13 (2016)Google Scholar
  10. 10.
    Durazo-Cardenas, I., Starr, A., Tsourdos, A., et al.: Precise vehicle location as a fundamental parameter for intelligent self-aware rail-track maintenance systems. Procedia CIRP 22(1), 219–224 (2014)CrossRefGoogle Scholar
  11. 11.
    Jamshidi, A., Faghihroohi, S., Hajizadeh, S., et al.: A big data analysis approach for rail failure risk assessment. Risk Anal. Off. Publ. Soc 37, 1495–1507 (2017)CrossRefGoogle Scholar
  12. 12.
    Liu, J., Wang, X., Khattak, A.J., et al.: How big data serves for freight safety management at highway-rail grade crossings? A spatial approach fused with path analysis. Neurocomputing 181(C), 38–52 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhu, L., Yu, F.R., Wang, Y., et al.: Big data analytics in intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 22(99), 1–16 (2018)CrossRefGoogle Scholar
  14. 14.
    Kim, K.W., Kim, D.W., Noh, K.S., et al.: An exploratory study on improvement method of the subway congestion based big data convergence. J. Korea Inst. Inf. Commun. Eng. 13(2), 35–42 (2015)CrossRefGoogle Scholar
  15. 15.
    Cheng, W., Tianyun, S.: Railway automatic ticketing and gate monitoring system based on big data analysis. Railw. Comput. Appl. 2015(11), 42–45 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.National Engineering Research Center for Software EngineeringPeking UniversityBeijingChina
  3. 3.Key Laboratory of High Confidence Software Technologies (PKU), Ministry of EducationBeijingChina

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