The modelling of accident frequency using risk exposure data for the assessment of airport safety areas

This thesis makes significant contributions to improving the use of Airport Safety Areas (ASAs) as aviation accident risk mitigation measures by developing improved accident frequency models and risk assessment methodologies. In recent years, the adequacy of ASAs such as the Runway End Safety Area and Runway Safety Area has come under increasing scrutiny. The current research found flaws in the existing ASA regulations and airport risk assessment techniques that lead to the provision of inconsistent safety margins at airports and runways. The research was based on a comprehensive database of ASA-related accidents, which was matched by a representative sample of normal operations data, such that the exposure to a range of operational and meteorological risk factors between accident and normal flights could be compared. On this basis, the criticality of individual risk factors was quantified and accident frequency models were developed using logistic regression. These models have considerably better predictive power compared to models used by previous airport risk assessments. An improved risk assessment technique was developed coupling the accident frequency models with accident location data, yielding distributions that describe the frequency of accidents that reach specific distances beyond the runway end or centreline given the risk exposure profile of the particular runway. The application of the proposed methodology was demonstrated in two case studies. Specific recommendations on ASA dimensions were made for achieving consistent levels of safety on each side of the runway. Advances made in this study have implications on the overall assessment and management of risks at airports.