Volume 43 Issue 6
Dec.  2025
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(6): 33-41
LU Fei, ZHANG Xinyu, WANG Tian, ZHANG Zhaoning. A Study on Aircraft Safety Target Levels Based on Lasso-Random Forest Model[J]. Journal of Transport Information and Safety, 2025, 43(6): 33-41. doi: 10.3963/j.jssn.1674-4861.2025.06.004
Citation: LU Fei, ZHANG Xinyu, WANG Tian, ZHANG Zhaoning. A Study on Aircraft Safety Target Levels Based on Lasso-Random Forest Model[J]. Journal of Transport Information and Safety, 2025, 43(6): 33-41. doi: 10.3963/j.jssn.1674-4861.2025.06.004
shu

A Study on Aircraft Safety Target Levels Based on Lasso-Random Forest Model

doi: 10.3963/j.jssn.1674-4861.2025.06.004

  • College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

  • Received Date: 2025-03-25
    Available Online: 2026-03-13
    Abstract
  • As aviation safety continuously improves, transportation accidents exhibit small-sample and low-probability 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 insufficient 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-probability 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 conditions. 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 applicability. Civil aviation operational data of China from 2003 to 2022 are used for validation. Results indicate that the Lasso-random forest model achieves the lowest SRMSE value of 45.2 and the highest R2 value of 0.834. The model significantly 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 standards. The proposed method provides a robust and operational framework for low-probability aviation risk assessment and safety target level formulation.

     

    • FullText(HTML)
  • loading
    • References(22)
  • [1]
    DAVIES L, SHARPE A. Review of target level of safety for north Atlantic MNPS airspace[J]. Air Traffic Control Quarterly, 1994, 2(4): 249-272. doi: 10.2514/atcq.2.4.249
    [2]
    张健, 赵嶷飞, 卢飞, 等. 城市物流无人机平行航线横向间隔研究[J]. 交通信息与安全, 2024, 42(4): 118-124. doi: 10.3963/j.jssn.1674-4861.2024.04.013

    ZHANG J, ZHAO Y F, LU F, et al. Lateral separation of parallel routes for urban logistics unmanned aerial vehicles[J]. Journal of Transport Information and Safety, 2024, 42(4): 118-124. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2024.04.013
    [3]
    CAMPOS L, MARQUES J. On a dimensionless alternative to the ICAO target level of safety[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2016, 230(9): 1548-1557. doi: 10.1177/0954410015621336
    [4]
    ICAO. Safety management manual(SMM): Doc 9859[S]. Montreal: ICAO, 2013.
    [5]
    王剑辉, 李翰之. 基于改进灰色模型预测民航事故征候数[J]. 微型电脑应用, 2021, 37(12): 134-136, 140.

    WANG J H, LI H Z. Prediction of civil aviation accident incident numbers based on an improved grey model[J]. Microcomputer Applications, 2021, 37(12): 134-136, 140. (in Chinese)
    [6]
    熊明兰, 王华伟, 徐怡, 等. 基于鸟击事故征候预测的通用航空安全研究[J]. 系统工程与电子技术, 2020, 42(9): 2033-2040.

    XIONG M L, WANG H W, XU Y, et al. General aviation safety analysis based on bird strike incident prediction[J]. Journal of Systems Engineering and Electronics, 2020, 42 (9): 2033-2040. (in Chinese)
    [7]
    吴雨桐, 方学东. 基于组合模型的民航事故征候预测研究[J]. 综合运输, 2022, 44(12): 112-116.

    WU Y T, FANG X D. Prediction of civil aviation accident incidents based on a combination model[J]. China Transportation Review, 2022, 44(12): 112-116. (in Chinese)
    [8]
    BLEU LAINE M H, PURANIK T G, MAVRIS D N, et al. Multiclass multiple-instance learning for predicting precursors to aviation safety events[J]. Journal of Aerospace Information Systems, 2022, 19(1): 22-36. doi: 10.2514/1.I010971
    [9]
    TAKALE D G, GUNJAL S D, KHAN V N, et al. Road accident prediction model using data mining techniques[J]. NeuroQuantology, 2022, 20(16): 2094-2101.
    [10]
    文军, 陈建勤, 罗心雨, 等. 基于IOWGA的运输航空飞行事故征候组合预测[J]. 安全与环境学报, 2022, 22(1): 256-262.

    WEN J, CHEN J Q, LUO X Y, et al. Combined prediction of transport aviation accident incidents based on IOWGA[J]. Journal of Safety and Environment, 2022, 22(1): 256-262. (in Chinese)
    [11]
    梁文娟, 李雪艳. 基于ARIMA、LS-SVM和BP神经网络组合模型的航空运输飞行事故征候预测[J]. 安全与环境工程, 2018, 25(1): 130-136.

    LIANG W J, LI X Y. Flight incidents prediction of air transportation based on the combined model of ARIMA, LS-SVM and BP neural network[J]. Safety and Environmental Engineering, 2018, 25(1): 130-136. (in Chinese)
    [12]
    陈芳, 张迪, 卫微, 等. 基于ARIMA和BP神经网络的民航事故征候率预测[J]. 安全与环境学报, 2019, 19(6): 2040-2047.

    CHEN F, ZHANG D, WEI W, et al. Integrated forecasting model for civil aviation incident rate based on the ARIMA and BP neural network[J]. Journal of Safety and Environment, 2019, 19(6): 2040-2047. (in Chinese)
    [13]
    叶雪飞. 基于数据挖掘的民航旅客运输事故征候研究[D]. 广汉: 中国民用航空飞行学院, 2023.

    YE X F. Study on civil aviation passenger transport incident based on data mining[D]. Guanghan: Civil Aviation Flight University of China, 2023. (in Chinese)
    [14]
    王永刚, 吕学梅. 民航事故征候的灰色马尔可夫预测[J]. 安全与环境学报, 2008, 8(1): 163-165.

    WANG Y G, LYU X M. Grey Markov model for forecasting civil aviation incidents[J]. Journal of Safety and Environment, 2008, 8(1): 163-165. (in Chinese)
    [15]
    刘智玉, 陈南梁, 汪军. 基于随机森林算法的小样本纱线质量预测[J]. 东华大学学报(自然科学版), 2023, 49(6): 80-86.

    LIU Z Y, CHEN N L, WANG J. Yarn quality prediction for small samples based on random forest algorithm[J]. Journal of Donghua University(Natural Science), 2023, 49(6): 80-86. (in Chinese)
    [16]
    曹冲, 程林松, 张向阳, 等. 基于多变量小样本的渗流代理模型及产量预测方法[J]. 力学学报, 2021, 53(8): 2345-2354.

    CAO C, CHENG L S, ZHANG X Y, et al. Seepage proxy model and production forecast method based on multivariate small samples[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 2345-2354. (in Chinese)
    [17]
    MIOLANE L, MONTANARI A. The distribution of the Lasso[J]. The Annals of Statistics, 2021, 49(4): 2313-2335.
    [18]
    随泽远, 王军, 彭宏, 等. 基于门控脉冲神经P系统模型的概率负荷预测[J/OL]. 中国电力: 1-10(2025-08-28)[2025-12-14]. https://link.cnki.net/urlid/11.3265.tm.20250828.0927.002.

    SUI Z Y, WANG J, PENG H, et al. Probabilistic load forecasting based on gated spiking neural P system model[J/OL]. Electric Power: 1-10(2025-08-28)[2025-12-14]. https://link.cnki.net/urlid/11.3265.tm.20250828.0927.002. (in Chinese)
    [19]
    BREIMAN L. Random forests[J]. Machine Learning, 2001, 45 (1): 5-32. doi: 10.1023/A:1010933404324
    [20]
    卢飞, 宋佳佳. 随机森林算法在擦机尾事件预测中的应用研究[J]. 安全与环境学报, 2024, 24(8): 3028-3035.

    LU F, SONG J J. Application of the random forest algorithm in predicting tail strike events[J]. Journal of Safety and Environment, 2024, 24(8): 3028-3035. (in Chinese)
    [21]
    吴潇雨, 和敬涵, 张沛, 等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测[J]. 电力系统自动化, 2015, 39 (12): 50-55.

    WU X Y, HE J H, ZHANG P, et al. Short-term load forecasting of power systems based on an improved random forest algorithm using gray projection[J]. Automation of Electric Power Systems, 2015, 39(12): 50-55. (in Chinese)
    [22]
    PETER S, PHILIP S G. Safety minima study: review of existing standards and practices[R]. Brussels: EUROCONTROL, 2000.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (11) PDF downloads(2) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return