Risk Factor Analysis and Path Identification Method for Runway Excursion During Landing Based on Bayesian Network

To address the complex relationships among risk factors of runway overrun and veer-off during landing, the difficulty of directly reflecting flight-physical relationships through statistical associations, and the lack of quantitative diagnosis of key risk paths, a runway excursion risk analysis and path identification method integrating association rule mining and Bayesian networks (BN) was investigated. Based on 224 valid landing records obtained from the quick access recorders (QAR) of a B737-800 fleet from an airline, 15 key parameters in three categories, including aircraft states, pilot operations, and environmental wind conditions, were selected. The Apriori algorithm was used to mine strong associations among the parameters and generate candidate links for the BN. The directions of these links were then verified according to the energy-dissipation mechanism of runway overrun and the lateral-dynamics mechanism of runway veer-off, and statistical associations lacking a physical basis were removed, thereby establishing a network structure jointly constrained by data associations and flight-physical mechanisms. The expectation-maximization (EM) algorithm was employed for network parameter learning, and mutual information (MI) was used to identify key risk factors. Bayesian backward inference combined with the ratio of variation (ROV) was further applied to quantitatively diagnose upstream association paths under high-risk target states. The results show that when ground speed was in a high-risk state, the ROV values of the paths associated with pitch angle and brake pressure were 0.348 and 0.306, respectively; when vertical load was in a high-risk state, the ROV value of the path associated with descent rate was 3.911; for runway veer-off, when bank angle was in a high-risk state, the ROV value of the path associated with rudder position was 0.222. The quantitative results distinguish key upstream association paths under different high-risk target states, extending runway excursion risk analysis from key risk factor identification to quantitative diagnosis of key risk paths.