关键词: 航空运输, 航班运行风险, 自适应套索, 随机森林, 主成分分析, 径向基函数, 支持向量机

Abstract:

In order to solve the problem of high computation complexity of flight operations high-dimensional arrays, and to avoid models over-fitting and the prediction precision influence, the 450 real flights data of certain airlines and the risk assessment system issued by the Civil Aviation Administration of China are used. Firstly, Adaptive Lasso is used to reduce dimensions, the fifteen independent variables are selected from 63 risk factors. Then, 12 items with the highest importance ranking and lowest error are retained according to the random forest over-fitting test results. Finally, the two methods are combined as a two-step hybrid algorithm; the principal component analysis, RBF neural network and support vector machine are selected for comparison and analysis; 10-fold cross validation is used to confirm outcome. The results show that the results accuracy does not change significantly after 48 indicators deleted by Adaptive Lasso method，and the assessment accuracy after RF processing is better than before RF. The prediction accuracy and stability of the Adaptive Lasso-RF hybrid model are better than that of principal component analysis, RBF neural network and support vector machine. All above conclusions indicate the Adaptive Lasso-RF prediction method is feasible and effective to solve the flight risk prediction problem, which can reduce dimensions, avoid models over-fitting and improve accuracy significantly.

Key words: air transportation, flight operation risk, Adaptive Lasso, RF, PCA, RBF, SVM