  In aviation safety, runway overruns are of great importance because they are
the most frequent type of landing accidents. Identification of factors which
contribute to the occurrence of runway overruns can help mitigate the risk and
prevent such accidents. Methods such as physics-based and statistical-based
models were proposed in the past to estimate runway overrun probabilities.
However, they are either costly or require experts' knowledge. We propose a
statistical approach to quantify the risk probability of an aircraft to exceed
a threshold at the speed of 80 knots given a set of influencing factors. This
copula based D-vine regression approach is used because it allows for complex
tail dependence and is computationally tractable. Data obtained from the Quick
Access Recorder (QAR) for 711 flights are analyzed. We identify 41 flights with
an estimated risk probability > 0.001 for a chosen threshold and rank the
effects of each influencing factor for these flights. Also, the complex
dependency patterns between some influencing factors for the 41 flights are
shown to be non symmetric. The D-vine regression approach, compared to
physics-based and statistical-based approaches, has an analytical solution, is
not simulation based and can be used to estimate very small or large
probabilities efficiently.
