Airport risk propagation network oriented to aviation network

被引:0
|
作者
Guan X. [1 ,2 ]
Zhao S. [3 ]
机构
[1] CAAC Key Laboratory of General Aviation Operation, Civil Aviation Management Institute of China, Beijing
[2] Zhejiang Key Laboratory of General Aviation Operation Technology, General Aviation Research Institute of Zhejiang Jiande, Hangzhou
[3] School of Electronic and Information Engineering, Beihang University, Beijing
基金
中国国家自然科学基金;
关键词
aviation network; complex network; Granger causality test; risk coupling; risk propagation;
D O I
10.13700/j.bh.1001-5965.2021.0469
中图分类号
学科分类号
摘要
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. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
收藏
页码:1342 / 1351
页数:9
相关论文
共 27 条
  • [1] CAI K Q, ZHANG J, DU W B, Et al., Analysis of the Chinese air route network as a complex network, Chinese Physics B, 21, 2, (2012)
  • [2] ZHANG J, CAO X B, DU W B, Et al., Evolution of Chinese airport network, Physica A:Statistical Mechanics and Its Applications, 389, 18, pp. 3922-3931, (2010)
  • [3] KAFLE N., Modeling flight delay propagation: A new analytical-econometric approach, Transportation Research Part B:Methodological, 93, pp. 520-542, (2016)
  • [4] NAYAK N, ZHANG Y., Estimation and comparison of impact of single airport delay on national airspace system with multivariate simultaneous models, Transportation Research Record:Journal of the Transportation Research Board, 2206, 1, pp. 52-60, (2011)
  • [5] PYRGIOTIS N, MALONE K M, ODONI A., Modelling delay propagation within an airport network, Transportation Research Part C:Emerging Technologies, 27, pp. 60-75, (2013)
  • [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, IFAC-PapersOnLine, 49, 3, pp. 359-364, (2016)
  • [7] ABDELGHANY A, EKOLLU G, NARASIMHAN R, Et al., A proactive crew recovery decision support tool for commercial airlines during irregular operations, Annals of Operations Research, 127, 1, pp. 309-331, (2004)
  • [8] ABDELGHANY A, ABDELGHANY K, EKOLLU G., A genetic algorithm approach for ground delay program management: The airlines’ side of the problem, Air Traffic Control Quarterly, 12, 1, pp. 53-74, (2004)
  • [9] WU C L, LAW K., Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model, Transportation Research Part E:Logistics and Transportation Review, 122, pp. 62-77, (2019)
  • [10] LI J S, DING J L., Analysis of flight delay propagation using Bayesian networks, Acta Aeronautica et Astronautica Sinica, 29, 6, pp. 1598-1604, (2008)