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A state-based approach to modeling general aviation accidents

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  • Rao, Arjun H.
  • Marais, Karen

Abstract

This paper develops a state-based accident model that we apply to General Aviation (GA) accidents recorded in the National Transportation Safety Board (NTSB) accident database. We demonstrate our approach on 6180 helicopter accidents that occurred in the United States between 1982 and 2015, with emphasis on inflight loss of control (LOC-I) accidents. Our model helps remove the redundancies in the NTSB coding system by logically grouping various NTSB accident codes that convey the same meaning. Further, this model checks for logical gaps or omissions in NTSB accident records, and potentially fills the omissions in. This approach uses NTSB coding data to define a set of states (safe or hazardous) for a system and triggers that move the system into (or out of) these states. We identify the most frequent triggers for LOC-I and compare the results from the state-based approach with those obtained from a conventional analysis of NTSB accident codes.

Suggested Citation

  • Rao, Arjun H. & Marais, Karen, 2020. "A state-based approach to modeling general aviation accidents," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832019303424
    DOI: 10.1016/j.ress.2019.106670
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    1. Rao, Arjun H. & Marais, Karen, 2018. "High risk occurrence chains in helicopter accidents," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 83-98.
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    Cited by:

    1. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

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