Safety and Resilience Bayesian Belief Network (SR-BBN) for ATM

Abstract

This study delves into the realm of Air Traffic Management (ATM) and its criticality in ensuring the safety and resilience of aviation systems. Traditionally, safety has been approached reactively (Safety I), but with the complexities of socio-technical systems like ATM, a shift towards proactive measures is essential. This research explores Resilience Engineering (RE) and Safety II, emphasizing learning from a system's adaptability in everyday situations. ATM, a multifaceted system, relies on technology, organization, and human interactions, striving to maintain equilibrium among these pillars for safe and efficient operations. Any changes to these elements can disrupt this balance, necessitating a systemic perspective. Safety in ATM depends on resource availability, timeliness, and coordination among organizations and humans, while resilient performance extends safety beyond the expected operating conditions. To unify safety and resilience, this study introduces the Safety Resilience-Bayesian Network (SR-BBN) model. This model integrates data-driven and knowledge-based approaches, categorizing variables into separations, external factors, nominal conditions, and Air Traffic Controller (ATCO) strategies. The SR-BBN model aids in predicting safety outcomes and identifies influential variables.