-
摘要:
随着航空网络运输量与日俱增,机场间运行呈现出较强耦合关联。机场风险传播特性严重制约航空网络安全高效运行。目前机场运行风险量化计算及在航空网络的传播机理尚缺乏深入研究。综合考虑安全、效率等风险要素基于聚类算法提出机场运行风险耦合量化方法;构造机场运行风险时间序列,应用因果检验方法并基于复杂网络构建机场运行风险传播网络;通过对比不同类型网络,分析机场运行风险传播网络特征,挖掘机场运行风险传播规律。结果表明:风险传播网络度分布满足双区对数分布特点,呈现小世界特征,不仅具有较短的网络直径和较高的社区性,而且可被分为若干连接密集区域,且风险传播网络效率较低,表明在全局传播的难度较高。
Abstract:Aviation network traffic has increased day by day, and operations between airports are closely coupled. Airport risk diffusion has grown to be a major issue that affects their ability to operate safely and effectively. However, the spread mechanism of airport risk at the network level has not yet been fully understood. This paper first proposes an airport risk coupling quantification method based on clustering algorithm and taking into account multiple risk factors, then constructs risk time series and applies causality testing methods to construct airport risk propagation network in order to better study the mechanism of airport risk propagation at the network level. By comparing the performance of different types of networks, analyzing the characteristics of risk propagation networks, and studying the overall characteristics and laws of risk propagation. The findings demonstrate that the degree distribution of the risk propagation network meets the requirements for dual-zone logarithmic distribution, exhibits small-world properties, has a small network width and high community, and can be separated into a number of densely connected areas. The low network efficiency of the risk propagation network indicates that it is more difficult for the risk to spread globally.
-
Key words:
- aviation network /
- risk coupling /
- Granger causality test /
- risk propagation /
- complex network
-
表 1 风险传播网络的基本属性
Table 1. Basic attributes of risk propagation network
网络 节点数 边数 平均出入度 网络密度 平均聚类系数 最大连通子图 互异性 网络直径 社区性 网络效率 风险传播网络 223 2772 12.43 0.06 0.11 160 0.04 3 0.23 0.31 随机网络 223 2772 12.43 0.06 0.06 223 0.02 3 0.17 0.55 航线网络 223 1656 14.85 0.07 0.51 179 1 4 0.14 0.33 -
[1] CAI K Q, ZHANG J, DU W B, et al. Analysis of the Chinese air route network as a complex network[J]. Chinese Physics B, 2012, 21(2): 028903. doi: 10.1088/1674-1056/21/2/028903 [2] ZHANG J, CAO X B, DU W B, et al. Evolution of Chinese airport network[J]. Physica A:Statistical Mechanics and Its Applications, 2010, 389(18): 3922-3931. doi: 10.1016/j.physa.2010.05.042 [3] KAFLE N. Modeling flight delay propagation: A new analytical-econometric approach[J]. Transportation Research Part B:Methodological, 2016, 93: 520-542. doi: 10.1016/j.trb.2016.08.012 [4] NAYAK N, ZHANG Y. Estimation and comparison of impact of single airport delay on national airspace system with multivariate simultaneous models[J]. Transportation Research Record:Journal of the Transportation Research Board, 2011, 2206(1): 52-60. doi: 10.3141/2206-07 [5] PYRGIOTIS N, MALONE K M, ODONI A. Modelling delay propagation within an airport network[J]. Transportation Research Part C:Emerging Technologies, 2013, 27: 60-75. doi: 10.1016/j.trc.2011.05.017 [6] BASPINAR B, URE N K, KOYUNCU E, et al. Analysis of delay characteristics of European air traffic through a data-driven airport-centric queuing network model[J]. IFAC-PapersOnLine, 2016, 49(3): 359-364. doi: 10.1016/j.ifacol.2016.07.060 [7] ABDELGHANY A, EKOLLU G, NARASIMHAN R, et al. A proactive crew recovery decision support tool for commercial airlines during irregular operations[J]. Annals of Operations Research, 2004, 127(1): 309-331. [8] ABDELGHANY A, ABDELGHANY K, EKOLLU G. A genetic algorithm approach for ground delay program management: The airlines’ side of the problem[J]. Air Traffic Control Quarterly, 2004, 12(1): 53-74. doi: 10.2514/atcq.12.1.53 [9] WU C L, LAW K. Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model[J]. Transportation Research Part E:Logistics and Transportation Review, 2019, 122: 62-77. doi: 10.1016/j.tre.2018.11.004 [10] 李俊生, 丁建立. 基于贝叶斯网络的航班延误传播分析[J]. 航空学报, 2008, 29(6): 1598-1604.LI J S, DING J L. Analysis of flight delay propagation using Bayesian networks[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(6): 1598-1604(in Chinese). [11] 王珊珊, 王建东, 丁建立. 航班延误波及链的有色出现网模型[J]. 计算机科学, 2009, 36(2): 241-244.WANG S S, WANG J D, DING J L. Colored occurrence net model of flight delay propagation chain[J]. Computer Science, 2009, 36(2): 241-244(in Chinese). [12] 李善梅, 徐肖豪, 孟令航. 基于聚类神经网络的机场拥挤等级预测[J]. 计算机工程与应用, 2013, 49(17): 254-257.LI S M, XU X H, MENG L H. Forecasting of airport congestion level based on cluster and neural network algorithms[J]. Computer Engineering and Applications, 2013, 49(17): 254-257(in Chinese). [13] DU W B, ZHANG M Y, ZHANG Y, et al. Delay causality network in air transport systems[J]. Transportation Research Part E:Logistics and Transportation Review, 2018, 118: 466-476. doi: 10.1016/j.tre.2018.08.014 [14] ZANIN M, BELKOURA S. Network analysis of Chinese air transport delay propagation[J]. Chinese Journal of Aeronautics, 2017, 30(2): 491-499. doi: 10.1016/j.cja.2017.01.012 [15] XIONG J, HANSEN M. Value of flight cancellation and cancellation decision modeling[J]. Transportation Research Record:Journal of the Transportation Research Board, 2009, 2106(1): 83-89. doi: 10.3141/2106-10 [16] 中国民用航空局空管行业管理办公室. 机场时刻容量评估技术规范: AP-93-TM-2017-01[S]. 北京: 中国民用航空局, 2017: 3-4.Air Traffic Management Industry Management Office of Civil Aviation Administration of China. Technical specification for airport time capacity evaluation: AP-93-TM-2017-01[S]. Beijing: Civil Aviation Administration of China, 2017: 3-4 (in Chinese). [17] 李善梅. 空中交通拥挤的识别与预测方法研究[D]. 天津: 天津大学, 2014.LI S M. Research on identification and prediction methods of air traffic congestion[D]. Tianjin: Tianjin University, 2014 (in Chinese). [18] JAIN A K. Data clustering: 50 years beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8): 651-666. doi: 10.1016/j.patrec.2009.09.011 [19] CHEUNG Y W, LAI K S. Lag order and critical values of the augmented dickey–Fuller test[J]. Journal of Business & Economic Statistics, 1995, 13(3): 277-280. [20] 沃尔夫冈·哈德勒, 利奥波德·西马. 应用多元统计分析[M]. 陈诗一, 译. 北京: 北京大学出版社, 2011: 157-183.WOLFGANG H, LEOPOLD S. Applied multivariate statistical analysis[M]. CHEN S Y, translated. Beijing: Peking University Press, 2011: 157-183 (in Chinese). [21] DE LAAT M, LALLY V, LIPPONEN L, et al. Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis[J]. International Journal of Computer-Supported Collaborative Learning, 2007, 2(1): 87-103. doi: 10.1007/s11412-007-9006-4 [22] FAGIOLO G. Clustering in complex directed networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2007, 76(2 Pt 2): 026107. [23] BRODER A, KUMAR R, MAGHOUL F, et al. Graph structure in the web[J]. Computer Networks, 2000, 33(1-6): 309-320. doi: 10.1016/S1389-1286(00)00083-9 [24] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684): 440-442. doi: 10.1038/30918 [25] ARENAS A, DUCH J, FERNÁNDEZ A, et al. Size reduction of complex networks preserving modularity[J]. New Journal of Physics, 2007, 9(6): 176. doi: 10.1088/1367-2630/9/6/176 [26] NEWMAN M E J. Modularity and community structure in networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577-8582. doi: 10.1073/pnas.0601602103 [27] LATORA V, MARCHIORI M. Efficient behavior of small-world networks[J]. Physical Review Letters, 2001, 87(19): 198701. doi: 10.1103/PhysRevLett.87.198701 -