ABSTRACT
The current methods for estimating the risk of sociotechnical systems in air traffic management (ATM) mostly rely on accident and incident reports, expert judgment, or model-based approaches. The predictive risk estimation of novel systems, in particular, is traditionally performed by the subjective adaptation of the expert’s operational experiences to the expected operation after the hypothetical start-up of the target system. In this regard, the term risk complies with the definition: “Risk is defined as the probability that an accident occurs during a stated period of time” (Blom et al. 2003).
