Overview of a Fuzzy Bayesian Network Approach for Dynamic Safety Analysis of Runway Incursion Occurrences

Abstract

The risk frequency of hazards related to airport runway incursions is of great uncertainty due to the variation of operational conditions associated with both internal and external factors, which leads to problematic risk analysis. In this paper, a dynamic risk analysis approach for runway incursion safety cases (RISC) based on Fuzzy Bayesian Network (FBN) is proposed. The bow-tie model (BT) is used to categorize hazards and display a RISC scenario progress, then the structure of the Bayesian network (BN) is mapped consequently. In case of limited data of occurrences, a probabilistic assessment algorithm correlating expert judgment and fuzzy set theory is applied to estimate the prior probabilities and the conditional probabilities of the BN nodes. As an application to the proposed methodology, a case study was carried out to show the scenario dynamic evolution at different moments. Finally, critical contributing factors leading to a RISC were identified. Results demonstrate that this method can dynamically characterize the changes in both the probabilities and the consequences of a RISC. Based on data from ANSPs from traffic forecast, meteorological conditions and active airport’s local parameters, the proposed method can provide an estimation of a dynamic risk for runway incursions.