Identifying the High‐Risk Trigger Events in Aviation Activities: A Directed Weighted CN‐SIR Methodology

High‐risk trigger events are incredibly threatening to aviation activities, which often lead to aviation accidents, resulting in casualties and property losses. Targeted risk control strategies are essential to ensure the organized operation of aviation activities. We propose an integrated methodology to identify the high‐risk trigger events and explore the risk transmission characteristics for risk control, which consists of event tree analysis, complex network (CN), and susceptible‐infected‐recovered (SIR) model. We focus on the construction of the aviation accident causation network (AACN) using event tree analysis based on the features extracted from 324 actual aviation accident reports. To accurately evaluate the network characteristics based on the structure, a novel topological index, that is, risk strength (RS) is introduced. Moreover, in contrast with the conventional one, an improved SIR model is constructed, which is verified to be more effective in exploring risk transmission characteristics. The results indicate that trigger events with high out‐degree and risk out‐strength exhibit greater risk transmission capacity. Additionally, trigger events related to management and human factors demonstrate a stronger ability for risk transmission. Furthermore, applying the improved CN‐SIR methodology to identify high‐risk trigger events of Haneda Airport collision accident, facilitating targeted control measures, and validating the effectiveness of the proposed methodology.