IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v232y2018i2p151-163.html
   My bibliography  Save this article

The case for IT transformation and big data for safety risk management on the GB railways

Author

Listed:
  • Coen van Gulijk
  • Peter Hughes
  • Miguel Figueres-Esteban
  • Rawia El-Rashidy
  • George Bearfield

Abstract

This article presents the case for IT transformation and big data for safety risk management on the GB railways. This article explains why the interest in data-driven safety solutions is very high in the railways by describing the drivers that shape risk management for the railways. A brief overview of research projects in the big data risk analysis programme supports the case and helps understand the research agenda for the transformation of safety and risk on the GB railways. The drivers and the projects provide insight in the current research needs for the transformation and explains why safety researchers have to broaden their skill set to include digital skills and potentially even programming. The case for IT transformation of risk management systems is compelling, and this article describes just the tip of the iceberg of opportunities opening up for safety analysis that, after all, depends on data.

Suggested Citation

  • Coen van Gulijk & Peter Hughes & Miguel Figueres-Esteban & Rawia El-Rashidy & George Bearfield, 2018. "The case for IT transformation and big data for safety risk management on the GB railways," Journal of Risk and Reliability, , vol. 232(2), pages 151-163, April.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:2:p:151-163
    DOI: 10.1177/1748006X17728210
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X17728210
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X17728210?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
    ---><---

    References listed on IDEAS

    as
    1. Garrett Grolemund & Hadley Wickham, 2014. "A Cognitive Interpretation of Data Analysis," International Statistical Review, International Statistical Institute, vol. 82(2), pages 184-204, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tan, Samson & Moinuddin, Khalid, 2019. "Systematic review of human and organizational risks for probabilistic risk analysis in high-rise buildings," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 233-250.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seokmin Kang & Sungyeun Kim, 2022. "Lessons Learned from Topic Modeling Analysis of COVID-19 News to Enrich Statistics Education in Korea," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    2. Schweinsberg, Martin & Feldman, Michael & Staub, Nicola & van den Akker, Olmo R. & van Aert, Robbie C.M. & van Assen, Marcel A.L.M. & Liu, Yang & Althoff, Tim & Heer, Jeffrey & Kale, Alex & Mohamed, Z, 2021. "Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 165(C), pages 228-249.
    3. Chris J. Wild, 2016. "Discussion: Locating Statistics in the World of Finding Out," International Statistical Review, International Statistical Institute, vol. 84(2), pages 194-202, August.
    4. Irena Ograjenšek & Iddo Gal, 2016. "Enhancing Statistics Education by Including Qualitative Research," International Statistical Review, International Statistical Institute, vol. 84(2), pages 165-178, August.
    5. Jim Ridgway, 2016. "Implications of the Data Revolution for Statistics Education," International Statistical Review, International Statistical Institute, vol. 84(3), pages 528-549, December.
    6. Nicholas Berente & Stefan Seidel & Hani Safadi, 2019. "Research Commentary—Data-Driven Computationally Intensive Theory Development," Service Science, INFORMS, vol. 30(1), pages 50-64, March.
    7. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    8. Marcos Gonçalves Perroni & Claudimar Pereira da Veiga & Zhaohui Su & Fernando Maciel Ramos & Wesley Vieira da Silva, 2023. "Dynamic Equilibrium of Sustainable Ecosystem Variables: An Experiment," Sustainability, MDPI, vol. 15(8), pages 1-21, April.

    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:sae:risrel:v:232:y:2018:i:2:p:151-163. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (email available below). General contact details of provider: .

    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.