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Volume 44 Issue 1
Jan.  2022
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Article Contents
WANG Yan-tao, YANG Zhi-yuan, LIU Kun, XIE Chun-sheng. Flight operation risk propagation and control based on a directional-weighted complex network[J]. Chinese Journal of Engineering, 2022, 44(1): 114-121. doi: 10.13374/j.issn2095-9389.2020.06.15.002
Citation: WANG Yan-tao, YANG Zhi-yuan, LIU Kun, XIE Chun-sheng. Flight operation risk propagation and control based on a directional-weighted complex network[J]. Chinese Journal of Engineering, 2022, 44(1): 114-121. doi: 10.13374/j.issn2095-9389.2020.06.15.002

Flight operation risk propagation and control based on a directional-weighted complex network

doi: 10.13374/j.issn2095-9389.2020.06.15.002
More Information
  • Corresponding author: E-mail: caucwyt@126.com
  • Received Date: 2020-06-15
    Available Online: 2020-11-14
  • Publish Date: 2022-01-01
  • The flight operation risk is equal to the occurrence probability multiplied by the severity of the consequences. Flight operation risks include many types, forms, and numbers, and they frequently change with conditions. In the face of this complex system, through principle analysis, the risk formation mechanism research, and the spreading process, a scientific risk management and control method can be constructed. Based on the risk management technology, an informative and automated management control system can be developed and applied. The overall safety level of flight operations will be effectively improved. To analyze and study the flight operations risk propagation and then effectively control flight safety based on the complex network theory, 29 terminal factors were selected as network nodes according to the Civil Aviation Administration’s advisory notice, initially including the flight cabin crew, civil aviation aircraft, and operating environment. Civil aviation safety monitoring records from 2009 to 2014 were counted, and an undirected network was constructed based on node relationships. The relationships and occurrence probability between the nodes were counted, and a directed and weighted network was constructed. The concepts of improved infection rate and improved recovery rate were introduced, and an improved susceptible-infected-recovered (SIR) model suitable for flight operation risks was proposed. Finally, the initial infection range was clearly defined, and a multi-parameter control method was adopted. For directed networks, large-scale propagation and control simulations were calculated. The results indicate that the average shortest path of the directed network was 1.788, which belonged to the small-world network. The directed network infection node decreased to 37.4% with conventional control measures. After controlling top three or four nodes of the entry degree value sequence, the infected nodes peak drop rate was the biggest, as high as 50.6%/58.1%, the risk spread in the network was significantly suppressed. The results confirm that controlling nodes based on the entry degree value is the most effective method to suppress risk propagation in the directed and weighted network.

     

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  • [1]
    Coleman M E, Marks H M. Qualitative and quantitative risk assessment. Food Control, 1999, 10(4-5): 289 doi: 10.1016/S0956-7135(99)00052-3
    [2]
    Janic M. An assessment of risk and safety in civil aviation. J Air Transp Manage, 2000, 6(1): 43 doi: 10.1016/S0969-6997(99)00021-6
    [3]
    Bolczak C N, Fong V, Jehlen R. NextGen flight security risk assessment information concept//2009 IEEE/AIAA 28th Digital Avionics Systems Conference. Orlando, 2009: 6
    [4]
    孫瑞山, 劉漢輝. 航空公司安全評估理論與實踐. 中國安全科學學報, 1999, 9(3):69 doi: 10.3969/j.issn.1003-3033.1999.03.014

    Sun R S, Liu H H. Assessment theory and practice for airline safety. China Saf Sci J, 1999, 9(3): 69 doi: 10.3969/j.issn.1003-3033.1999.03.014
    [5]
    孫瑞山, 唐品. 航班飛行安全風險快速評估工具研究. 交通信息與安全, 2013, 31(2):88 doi: 10.3963/j.issn.1674-4861.2013.02.020

    Sun R S, Tang P. Rapid assessment tool for flight safety risk. J Transp Inf Saf, 2013, 31(2): 88 doi: 10.3963/j.issn.1674-4861.2013.02.020
    [6]
    王巖韜, 李蕊, 盧飛, 等. 基于多因素分析的航班運行風險評估體系. 天津工業大學學報, 2014, 33(3):84 doi: 10.3969/j.issn.1671-024X.2014.03.017

    Wang Y T, Li R, Lu F, et al. Risk assessment system of flight operation based on multiple factor analysis. J Tianjin Polytech Univ, 2014, 33(3): 84 doi: 10.3969/j.issn.1671-024X.2014.03.017
    [7]
    王巖韜, 李蕊, 盧飛, 等. 基于運行數據的航班運行關鍵風險因素推斷. 交通運輸系統工程與信息, 2016, 16(1):182 doi: 10.3969/j.issn.1009-6744.2016.01.029

    Wang Y T, Li R, Lu F, et al. Flight operation key risk factors inference based on operation data. J Transp Syst Eng Inf Technol, 2016, 16(1): 182 doi: 10.3969/j.issn.1009-6744.2016.01.029
    [8]
    王巖韜, 趙嶷飛. 基于多算法協作的航班運行風險辨識研究. 中國安全科學學報, 2018, 28(6):166

    Wang Y T, Zhao Y F. Research on flight operations risk identification based on multi-algorithm collaboration. China Saf Sci J, 2018, 28(6): 166
    [9]
    王巖韜, 劉宏, 唐建勛, 等. 動態預測技術在航班運行風險中的應用. 控制與決策, 2019, 34(9):1946

    Wang Y T, Liu H, Tang J X, et al. Dynamic prediction technology in the application of flight operation risk. Control Decis, 2019, 34(9): 1946
    [10]
    王巖韜, 李景良, 谷潤平. 基于多變量混沌時間序列的航班運行風險預測模型. 工程科學學報, 2020, 42(12):1664

    Wang Y T, Li J L, Gu R P. Flight operation risk prediction model based on the multivariate chaotic time series. Chin J Eng, 2020, 42(12): 1664
    [11]
    Lalis A, Socha V, Kraus J, et al. Conditional and unconditional safety performance forecasts for aviation predictive risk management//2018 IEEE Aerospace Conference. Big Sky, 2018: 1
    [12]
    Zhang X G, Mahadevan S. Ensemble machine learning models for aviation incident risk prediction. Decis Support Syst, 2019, 116: 48 doi: 10.1016/j.dss.2018.10.009
    [13]
    張繼凱. 深圳航空公司航班運行風險管理研究[學位論文]. 蘭州: 蘭州大學, 2019

    Zhang J K. Study of Flight Operations Risk Management on Shenzhen Airlines [Dissertation]. Lanzhou: Lanzhou University, 2019
    [14]
    Belkoura S, Cook A, Maria Pena J, et al. On the multi-dimensionality and sampling of air transport networks. Transp Res Part E, 2016, 94: 95 doi: 10.1016/j.tre.2016.07.013
    [15]
    Voltes-Dorta A, Rodriguez-Deniz H, Suau-Sanchez P. Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports. Transp Res Part A, 2017, 96: 119
    [16]
    武喜萍, 楊紅雨, 韓松臣. 基于復雜網絡的空中交通特征與延誤傳播分析. 航空學報, 2017, 38(增刊1): 108

    Wu X P, Yang H Y, Han S C. Analysis of properties and delay propagation of air traffic based on complex network. Acta Aeron Astron Sin, 2017, 38(Suppl1): 108
    [17]
    王興隆, 潘維煌, 趙末. 空中交通相依網絡的脆弱性研究. 航空學報, 2018, 39(12):268

    Wang X L, Pan W H, Zhao M. Vulnerability of air traffic interdependent network. Acta Aeron Astron Sin, 2018, 39(12): 268
    [18]
    齊雁楠, 高經東. 空域扇區網絡級聯失效抗毀性及優化策略. 航空學報, 2018, 39(12):349

    Qi Y N, Gao J D. Cascading failure invulnerability and optimization strategy of airspace sector network. Acta Aeron Astron Sin, 2018, 39(12): 349
    [19]
    吳明功, 葉澤龍, 溫祥西, 等. 基于復雜網絡的空中交通復雜性識別方法. 北京航空航天大學學報, 2020, 46(5):839

    Wu M G, Ye Z L, Wen X X, et al. Air traffic complexity recognition method based on complex networks. J Beijing Univ Aeron Astron, 2020, 46(5): 839
    [20]
    王巖韜, 劉毓. 基于復雜網絡的航班運行風險傳播分析. 交通運輸系統工程與信息, 2020, 20(1):198

    Wang Y T, Liu Y. Flight operation risk propagation based on complex network. J Transp Syst Eng Inf Technol, 2020, 20(1): 198
    [21]
    中國民用航空總局飛行標準司. 航空承運人運行控制風險管控系統實施指南, 2015-09-28

    Flight Standard Administration of CAAC. Air Carrier Operation Control Risk Control System Implementation Guielines. 2015-09-28
    [22]
    趙怡晴, 覃璇, 李仲學, 等. 尾礦庫隱患及風險演化系統動力學模擬與仿真. 北京科技大學學報, 2014, 36(9):1158

    Zhao Y Q, Qin X, Li Z X, et al. Dynamic modeling and simulation of hazard and risk evolution for mine tailing ponds. J Univ Sci Technol Beijing, 2014, 36(9): 1158
    [23]
    Barabási A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509 doi: 10.1126/science.286.5439.509
    [24]
    倪順江. 基于復雜網絡理論的傳染病動力學建模與研究[學位論文]. 北京: 清華大學, 2009

    Ni S J. Research on Modeling of Infectious Disease Spreading Based on Complex Network Theory [Dissertation]. Beijing: Tsinghua University, 2009
    [25]
    Reluga T C, Medlock J. Resistance mechanisms matter in SIR models. Math Biosci Eng, 2007, 4(3): 553 doi: 10.3934/mbe.2007.4.553
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