IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v110y2023ics0969699723000650.html
   My bibliography  Save this article

Risk prediction and early warning of pilots’ unsafe behaviors using association rule mining and system dynamics

Author

Listed:
  • Xiao, Qin
  • Luo, Fan
  • Li, Yapeng
  • Pan, Dan

Abstract

Risk prediction of pilots' unsafe behaviors is of great significance for preventing unsafe aviation incidents. However, there is a lack of effective and precise approach of dealing with this problem. This study proposes a hybrid approach of combining association rule mining (ARM) method and system dynamics (SD) model to predict and warn pilots' unsafe behaviors. Firstly, the risk factors are identified and classified according to the historical incident data by the human factor analysis and classification system. Then, the association rules between risk factors and unsafe behaviors are obtained by ARM. Finally, the SD is adopted to construct the risk prediction and early warning model for pilots' unsafe behaviors, and the applicability and effectiveness of the model are verified by the actual data from 2016 to 2020. The results of ARM show that there are 48 risk factors affecting pilots' unsafe behaviors, and 142 key association rules are formed between these risk factors and unsafe behaviors; environmental influences, pilots' adverse states, and organizational influences are all strongly related to pilots' unsafe behaviors, and the impact of environmental influences on unsafe behaviors mainly rely on the interaction with other factors. The results of SD demonstrate that although both of the cognitive error risk and the decision error risk show an increasing trend, the former increases slowly and keeps at the level of no alarm while the latter is growing faster and its risk may increase from no alarm to critical alarm; the operation error risk and the violation risk both show a downward trend, and finally remain at the no alarm level; the risk of pilots' unsafe behaviors has a stable fluctuation trend, and the risk values of most time are in the threshold ranges of major warning and critical warning. This work provides theoretical guidance for the decision-makers to develop measures to reduce incidents caused by pilots’ unsafe behaviors.

Suggested Citation

  • Xiao, Qin & Luo, Fan & Li, Yapeng & Pan, Dan, 2023. "Risk prediction and early warning of pilots’ unsafe behaviors using association rule mining and system dynamics," Journal of Air Transport Management, Elsevier, vol. 110(C).
  • Handle: RePEc:eee:jaitra:v:110:y:2023:i:c:s0969699723000650
    DOI: 10.1016/j.jairtraman.2023.102422
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699723000650
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jairtraman.2023.102422?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:jaitra:v:110:y:2023:i:c:s0969699723000650. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    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.