关键词: 复杂网络, 因果推断, 冲偏出跑道事故(REA), 风险演化, 关联规则挖掘(ARM)

Abstract:

Runway excursion accidents (REAs) were represented as one of the most frequent threats to aviation safety. To enhance aviation safety management and optimize preventive strategies, it was essential to systematically understand the risk evolution mechanisms underlying REAs. This study develops a three-stage analytical framework-ARM, Weighted Complex Network Analysis, and causal inference based on Linear Non-Gaussian Acyclic Model with Independent Component Analysis (ICA-LiNGAM)-drawing upon 88 representative global REA accident reports (2008-2020) and Quick Access Recorder (QAR) data from 1 345 Boeing 737-700 flights at Dali Fengyi Airport. Firstly, ARM was utilized to identify frequent factor combinations and critical risk elements. Subsequently, a weighted complex network was established to quantify the coupling strength and network properties of these risk factors. Finally, the causal relationships among risk factors and the pathways of risk evolution were analyzed based on the ICA-LiNGAM causal inference method. The results indicate that go-around decision errors and excessive landing speed function as critical hub nodes dominating risk propagation. Environmental factors (e.g., crosswinds) and operational parameters (e.g., approach speed) exhibit bidirectional causal interactions, as evidenced by crosswinds significantly increasing landing speed (Effect value=0.201, p<0.001). This "human-machine-environment" cross-domain evolutiong triggers risk cascades—illustrated by high descent rates inducing runway directional instability (Effect value=19.713,p<0.05)-necessitating cross-domain collaborative interventions targeting critical nodes to disrupt propagation pathways.

Key words: causal inference, complex network, runway excursion accident (REA), risk evolution, association rule mining (ARM)