Risk identification and propagation in apron operations based on directed complex networks

Ensuring the identification of risks in apron operations is crucial for airport safety and efficiency. However, current research primarily focuses on optimizing apron process operations and identifying static hazards, neglecting the mechanisms of dynamic risk propagation. To address this gap, this paper proposes a comprehensive model based on Event Tree Analysis and complex networks. Event chains are extracted to construct an apron operation risk network encompassing four dimensions: personnel, equipment, environment, and management. Considering the delay effects in safety control, the SIRS model is used to simulate the processes of risk occurrence, spread, mitigation, and recurrence. Network-wide analysis results indicate that management factors have a significant influence on risk propagation. Interaction analysis within event chains further reveals how management factors amplify risk propagation through compounding effects. Sensitivity analysis identifies infection rate and recovery probability as the most critical parameters influencing risk dynamics, highlighting their pivotal roles in controlling risks. Experimental results validate that the model effectively reproduces the mechanisms and patterns of risk propagation in apron operations. Specifically, management factors such as ‘inadequate risk management’ and ‘non-standardized technical operation management’ are identified as key nodes with peak infection rates of 0.479 and 0.467, respectively. Targeted safety management for these key nodes can effectively suppress risk propagation, offering novel insights and methodologies for enhancing apron safety management.