Demographic Factors for Prioritization of Airport Safety Audits
Johns, Alexander, Systems Engineering - School of Engineering and Applied Science, University of Virginia
Lambert, James, Department of Systems and Information Engineering, University of Virginia
With the large-scale complex and emergent nature of airport systems, comprehensive and adaptive risk management is necessary to address safety in a planning horizon of multiple years. Factor based prioritization has been used to identify and rank facilities with higher potential for incidents, allowing stakeholders and officials to effectively allocate resources for maximum effectiveness and learning. Past analytical approaches have focused the physical characteristics of facilities, as well as the rare-event frequencies of historical incident types and their precursors. The US Federal Aviation Administration cites human error as a significant future threat to aviation safety. Nevertheless, little work has addressed the identification and measurement of human and cultural safety factors at the airport scale. This thesis explores how to include demographic factors of the vicinities of the facilities in order to begin to account for potential human, cultural, and organizational issues in the prioritization of regulatory safety audits. The approach will define several new factors, assess the factors from available databases, test their uniqueness and relationships to other factors, and integrate them to existing frameworks of prioritizing safety audits. The methods to be adapted and integrated for this purpose are exploratory data analysis, multivariate statistical inference, expert elicitation and model building, hierarchical data models, multicriteria analysis, and uncertainty analysis. The results will be useful to regulators and airport managers in their oversight of a range of current and future technologies, diverse geographic locations, organizations, and time and spatial scales. In addition, the results will be useful in other technology domains seeking to use available and indirect evidence of human, organizational, and cultural issues in systemic risk management of large-scale complex systems.
MS (Master of Science)
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