IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/3692151.html
   My bibliography  Save this article

Risk Chain and Key Hazard Management for Urban Rail Transit System Operation Based on Big Data Mining

Author

Listed:
  • Yongsheng Tang
  • Gengxin Sun

Abstract

With the promotion of the national transportation power strategy, super large operation networks have become an inevitable trend, and operational safety and risk management and control have become unavoidable problems. Existing safety management methods lack support from actual operational and production data, resulting in a lack of guidance of fault cause modes and risk chains. Large space is available to improve the breadth, depth, and accuracy of hazard source control. By mining multisource heterogeneous operation big data generated from subway operation, this study researches operation risk chain and refined management and control of key hidden dangers. First, it builds a data pool based on the operation status of several cities and then links them into a data lake to form an integrated data warehouse to find coupled and interactive rail transit operation risk chains. Second, it reveals and analyzes the risk correlation mechanisms behind the data and refines the key hazards in the risk chain. Finally, under the guidance of the risk chain, it deeply studies the technologies for refined control and governance of key hidden dangers. The results can truly transform rail transit operation safety from passive response to active defense, improving the special emergency rail transit operation plans, improving the current situation of low utilization of rail transit operation data, but high operation failure rate, and providing a basis for evidence-based formulation and revision of relevant industry standards and specifications.

Suggested Citation

  • Yongsheng Tang & Gengxin Sun, 2021. "Risk Chain and Key Hazard Management for Urban Rail Transit System Operation Based on Big Data Mining," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-10, December.
  • Handle: RePEc:hin:jnddns:3692151
    DOI: 10.1155/2021/3692151
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2021/3692151.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/ddns/2021/3692151.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/3692151?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnddns:3692151. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.