A HIERARCHICAL PROBABILISTIC APPROACH FOR RISK ASSESSMENTS OF AN AVIATION SAFETY PRODUCT PORTFOLIO

Previous research conducted by the authors included a meta-analysis of a probabilistic approach to aviation safety based on risk assessment. In analyzing the initial attempt at determining a risk assessment model for aviation safety referred to as the Aviation System Risk Model (ASRM), along with the current research based in case studies of specific aircraft accidents, researchers thought that conducting an overview of these analyses using a meta-analysis would quantify the increasing risk in a more productive fashion. To implement such an analysis, researchers used a Hierarchical Bayesian Network (HBN), which uses a normative expert system known as the Hugin Expert. Using a HBN, researchers could combine the disparate case studies on maintenance and risk prevention procedures into a single probability value. A HBN-based approach also yields an over-arching graphical representation of condensed information, which can be broken down into less compacted representations. Although Bayesian Networks carry with them certain restrictions, researchers felt that distributions can be made adequate for continuous variables through intervals in CPT. The article closes with an assertion that problems with Air Traffic Control (ATC) can be monitored in a similar way, and could then be mapped as an interdependent entity for future risk models.

  • Availability:
  • Corporate Authors:

    Air Traffic Control Association Institute, Incorporated

    2300 Clarendon Boulevard, Suite 711
    Arlington, VA  United States  22201
  • Authors:
    • Kardes, E
    • Luxhoj, J T
  • Publication Date: 2005

Language

  • English

Media Info

  • Features: Figures; References; Tables;
  • Pagination: pp 279-308
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01006916
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: BTRIS, TRIS
  • Created Date: Nov 8 2005 7:32AM