Risk-based airport selection for runway safety assessments through the development and application of systems-driven prioritization methodologies

Abstract

A runway incursion is the erroneous presence of an aircraft or other object on the runway. Runway incursions are rare precursors to aviation accidents and result from a variety of complex factors. Current quantitative methods are inadequate for analysis, forecasting, and understanding the risk profiles of airports. The Federal Aviation Administration (FAA) biennially employs runway safety action teams (RSATs) to evaluate airports with the aim of reducing runway incursions. This manuscript describes the development of a decision-making tool that combines systems methodologies to help the FAA select airports at which employing RSATs would be the most beneficial for the reduction of collision risk. These methodologies include hierarchical Bayesian modeling (HBM) and analytic hierarchy process (AHP). HBM leverages the similarities between airports through conditioning incursion rate estimates on both historical data and the data sampled from hyper-distributions fitted using data from similar airports. AHP decomposes these RSAT placement decisions into a hierarchy of decisions to be examined independently; as the system evolves, the method allows the FAA to reflect changes in its knowledge and preferences in the weights of corresponding risk factors. The key deliverable of this project, a workbook decision tool, places HBM within the AHP hierarchy. The final methodology output presents an ordered list of airports according to potential risk-reduction from RSATs. This combination further incorporates risk-based and decision-based approaches in order to better allocate resources and reduce runway incursions. The tool meets project goals by utilizing the FAA's data to provide an analytically justifiable prioritized list, while building on current best practices for the RSAT selection process.