Short-time prediction of flight operation risk based on time series models

doi:10.16265/j.cnki.issn1003-3033.2020.05.006

Abstract

Abstract: In order to address the lack of flight operation risk prediction technology in China, ARMA method was used to build a univariate prediction model of flights' daily operation risk. Then, a multivariate prediction model was constructed by using VAR method. Finally, short-term prediction efficiency of two models was compared through stability test. The results show that the 3^rdday prediction accuracy of ARMA-based single variable prediction model can be 80.76%, and its available forecast period is 1-3 days while that of VAR-based model can be as high as 92% for the 1^st day and still keep at 80.64% for 7^thday with an applicable prediction period of 1-7 days. It is proved that ARMA and VAR-based time series models can predict flight operation risk in a short term, but the VAR-based multivariate prediction model has higher accuracy, which meets airlines' actual needs better.

Key words: flight operation risk, short-time prediction, time series models, vector auto-regression (VAR) model, auto-regressive moving average (ARMA) model

CLC Number:

X949

WANG Yantao, CHEN Guanming. Short-time prediction of flight operation risk based on time series models[J]. China Safety Science Journal, 2020, 30(5): 33-38.

References

[1] SEULKI K, DONGWOOK L, KYUNGHOON L. Reduced-order modeling applied to the aviation environmental design tool for rapid noise prediction[J]. Journal of Aerospace Engineering, 2018,31(5):04018056.
[2] 周志靖,陈金良,沈华,等. 空中交通航空飞行流量优化预测仿真[J]. 计算机仿真, 2016,33(8):54-57,71.
ZHOU Zhijing, CHEN Jinliang, SHEN Hua, et al. Simulation of air traffic flow optimization prediction[J]. Computer Simulation, 2016,33(8):54-57,71.
[3] 王平安.军航空域与民航航路侧向间隔碰撞风险预测[J].火力与指挥控制,2019,44(1): 87-92.
WANG Ping'an. Collision risk prediction about lateral interval between military training airspace and civil route[J]. Fire Control & Command Control, 2019,44(1): 87-92.
[4] LALIS A, SOCHA V, KRAUS J, et al.Aconditional and unconditional safety performance forecasts for aviation predictive risk management[C]. IEEE Aerospace Conference, 2018: 1-8.
[5] ZHANG Xiaoge, MAHADEVAN S. Ensemble machine learning models for aviation incident risk prediction[J]. Decision Support Systems, 2019,116: 48-63.
[6] 孙瑞山, 刘汉辉. 航空公司安全评估理论与实践[J]. 中国安全科学学报,1999,9(3): 69-74.
SUN Ruishan, LIU Hanhui. Theory and practice of airline safety assessment[J]. China Safety Science Journal, 1999,9(3): 69-74.
[7] 王岩韬,李蕊,卢飞,等.基于多因素分析的航班运行风险评估体系[J].天津工业大学学报,2014,33(3): 84-88.
WANG Yantao, LI Rui, LU Fei, et al. Risk assessment system of flight operation based on multiple factor analysis[J]. Journal of Tianjin Polytechnic University, 2014,33(3): 84-88.
[8] 王岩韬, 李蕊, 卢飞, 等. 基于运行数据的航班运行关键风险因素推断[J]. 交通运输系统工程与信息, 2016,16(1): 182-188.
WANG Yantao, LI Rui, LU Fei, et al. Flight operation key risk factors inference based on operation data[J]. Journal of Transportation Systems Engineering and Information Technology, 2016,16(1): 182-188.
[9] 王岩韬,赵嶷飞.基于多算法协作的航班运行风险辨识研究[J].中国安全科学学报,2018,28(6): 166-172.
WANG Yantao, ZHAO Yifei. Research on flight operations risk identification based on multi-algorithm collaboration[J]. China Safety Science Journal, 2018,28(6): 166-172.
[10] 陈晔, 陈萌, 徐海燕, 等. 基于FCM-DEMATEL航班风险评估与改进[J]. 工业工程, 2017, 20(3): 1-8,27.
CHEN Ye, CHEN Meng, XU Haiyan, et al. FCM-DEMATEL-based flight safety evaluation and improvement method[J]. Industrial Engineering Journal,2017,20(3):1-8,27.
[11] 谢华, 朱学华, 刘继新, 等. 基于中介真值程度度量的航班起飞风险评估方法[J]. 交通运输工程与信息学报, 2018, 16(1): 106-114.
XIE Hua, ZHU Xuehua, LIU Jixin, et al. Flight operation risk assessment based on measurement of medium truth degree [J].Journal of Transportation Engineering and Information, 2018,16(1):106-114.
[12] 王岩韬, 刘宏, 唐建勋, 等. 动态预测技术在航班运行风险中的应用[J]. 控制与决策, 2019,34(9): 1 946-1 954.
WANG Yantao, LIU Hong, TANG Jianxun, et al. Dynamic prediction technology in the application of flight operation risk[J]. Control and Decision, 2019,34(9): 1 946-1 954.
[13] CHRISTIANO L J. Christopher a sims and vector auto-regressions[J]. Scandinavian Journal of Economics, 2012,114(4):1 082-1 104.
[14] 黄轩,张青龙.基于ARMA-GARCH模型的沪深300指数波动率分析与预测[J].中国物价,2018(6): 44-46.
HUANG Xuan, ZHANG Qinglong. Analysis and forecasting the volatility based on ARMA-GARCH model of the CSI 300 index[J]. China Price,2018(6):44-46.
[15] 吕靖烨,杨华,郭泽. 人民币汇率对中国各区域碳价冲击效应的研究:基于VAR模型[J].价格月刊,2019(6): 77-84.
LYU Jinghua, YANG Hua, GUO Ze. Research on the impact of RMB exchange rate on China's regional carbon price:based on VAR model[J]. China Price,2019(6): 77-84.
[16] 刘广为.中国碳排放强度预测及其影响因素动态效应建模[D].天津:天津大学,2012.
LIU Guangwei. The carbon emission intensity forecast of China and its influence factors dynamic effect model construction[D]. Tianjin: University of Tianjin,2012.
[17] 白润才,柴森霖,刘光伟,等.基于Mallat算法与ARMA模型的露天矿卡车故障率预测[J].中国安全科学学报,2018,28(10): 31-37.
BAI Runcai, CHAI Senlin, LIU Guangwei, et al. Method for predicting truck's failure rate in open-pit mine based on Mallat algorithm and ARMA model[J]. China Safety Science Journal, 2018,28(10): 31-37.
[18] 吴继贵,叶阿忠.中国现代交通运输业效率波动性和影响因素研究:基于交叉效率DEA和VAR模型的分析[J].交通运输系统工程与信息,2014,14(6):8-14,29.
WU Jigui, YE Azhong. Efficiency volatility and influence factors of Chinese modern transportation industry based on the model of cross-efficiency DEA and VAR[J]. Transportation Systems Engineering and Information Technology, 2014,14(6):8-14,29.