Statistical analysis and consequence prediction of aircraft runway excursion accidents

Abstract

Abstract:

In order to study the main characteristics of the occurrence and consequences of aircraft runway excursion accidents, 100 global aircraft runway excursion accidents from 2010 to 2019 were statistically analyzed in this paper,

and the regularity of aircraft runway excursion accident was deeply analyzed from the aspects of accident types,

accident causes, accident spatiotemporal distribution, aircraft types involved in accidents, and accident consequences. Based on back propagation (BP) neural network, a prediction model of the consequences of aircraft

runway excursion accidents was constructed to predict the severity of accident consequences under different

conditions. The results showed that veering off runway accidents were the main aspect of aircraft runway excursion accidents, wet, slippery and polluted pavement and crosswind were the main causes of accidents. Aircraft

runway excursion accident had strong spatiotemporal distribution regularity, and different influencing factors can

cause varying degrees of consequences and losses. The prediction model constructed in this article achieved a

prediction accuracy of 76.67% for the consequences of aircraft runway excursion accidents, and the accuracy of

the test set and training set reach the same level, indicating that the model has good predictive performance and

can provide early-warning basis for aircraft landing safety management.

Key words:

aircraft runway excursion accidents, statistical analysis, neural network, consequence prediction

CLC Number:

V328

CAI Jing, ZHOU Ning, WANG Yan, DAI Xuan, LI Yue.

Statistical analysis and consequence prediction of aircraft runway excursion accidents

[J]. Journal of Civil Aviation University of China, 2025, 43(5): 37-43.

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