交通信息与安全2025，Vol.43Issue(6)：33-41,9.DOI:10.3963/j.jssn.1674-4861.2025.06.004
基于Lasso-随机森林模型的航空器安全目标水平预测方法
A Study on Aircraft Safety Target Levels Based on Lasso-Random Forest Model
摘要
Abstract
As aviation safety continuously improves,transportation accidents exhibit small-sample and low-proba-bility characteristics.Traditional prediction methods based on historical data struggle to characterize the evolution of aviation operational risks and refined safety management demands.To address prediction instability caused by in-sufficient accident samples,a method for calculating the target level of safety using a Lasso-random forest model is proposed.The method integrates Lasso regression and a random forest model to improve robustness under low-prob-ability conditions.An influencing factor set for transportation incident precursors is constructed by considering transport scale,operational efficiency,resource input,and operational intensity.Lasso regression combined with time-series cross-validation is applied for feature selection to alleviate multicollinearity under small-sample condi-tions.This procedure improves the stability and rationality of selected features.A random forest model is employed to predict transportation incident precursors.Feature importance analysis is applied to improve prediction accuracy.An error-driven model simplification strategy is used to reduce model complexity and enhance practical applicabili-ty.Civil aviation operational data of China from 2003 to 2022 are used for validation.Results indicate that the Las-so-random forest model achieves the lowest SRMSE value of 45.2 and the highest R2 value of 0.834.The model sig-nificantly outperforms linear regression and support vector regression models.After simplification,the SRMSE is further reduced by 6.14%.Based on the simplified model,flight hours and incident precursor occurrences for 2023 are predicted.The resulting en-route aircraft collision safety target level is,which satisfies applicable safety stan-dards.The proposed method provides a robust and operational framework for low-probability aviation risk assess-ment and safety target level formulation.关键词
航空安全/运输事故征候/Lasso回归/随机森林/安全目标水平Key words
aviation safety/transportation incident/Lasso regression/random forest/target level of safety分类
航空航天引用本文复制引用
卢飞,张欣宇,王田,张兆宁..基于Lasso-随机森林模型的航空器安全目标水平预测方法[J].交通信息与安全,2025,43(6):33-41,9.基金项目
国家自然科学基金项目(52272356)、中央高校基本业务费自然科学重点项目(3122022101)资助 （52272356）
