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.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.