基于元胞传输模型的机场跑滑系统网络韧性评估

taptap下载安装安卓学报 ›› 2025, Vol. 43 ›› Issue (3): 53-61.

基于元胞传输模型的机场跑滑系统网络韧性评估

taptap下载安装安卓交通科学与工程学院，天津 300300

收稿日期:2023-04-14 修回日期:2023-06-20 出版日期:2025-07-12 发布日期:2025-07-12
作者简介:张宇辉（1984— ），男，山西吕梁人，副教授，博士，研究方向为机场工程
基金资助:
国家重点研发计划（2021YFB2600500）；天津市教委自然科学基金项目（2018KJ245）

Network resilience assessment of airport running and sliding system based

on cell transmission model

College of Transportation Science and Engineering, CAUC, Tianjin 300300, China

Received:2023-04-14 Revised:2023-06-20 Online:2025-07-12 Published:2025-07-12

摘要/Abstract

摘要：

跑道容量是影响机场飞行区正常运行的关键要素之一，外部扰动事件会造成机场动态容量下降。本文根据

机场航空器滑行规则，基于元胞传输模型（CTM，cell transmission model）提出一种适用于扰动事件发生后评

估机场飞行区运行网络韧性的模型。首先，基于 CTM 获得网络性能参数；其次，以网络等待飞机数量与网络

运行效率为基础性能指标，建立受干扰网络韧性动态变化的韧性指标；最后，搭建了基于 CTM 的机场跑滑

系统网络韧性评估框架，并收集、处理和分析了 2019 年天津滨海国际机场运行数据。研究结果表明，仿真

的机场离场运行数据与实际离场运行数据的趋势具有较高一致性，仿真准确率在 90%以上，能够准确描述

机场跑滑系统网络交通流量分布。本文提出的两项机场韧性评估指标可以全面反映扰动事件发生前后系统

网络性能从退化到恢复的完整动态变化过程，为机场跑滑系统网络管理提供新支持。

关键词:

机场跑滑系统, 韧性评估, 元胞传输模型, 网络运行效率

Abstract:

Runway capacity is one of the key elements that affect the normal operation of airport flight areas, and external disturbance events can cause a decrease in the dynamic capacity of the airport. According to airport aircraft taxiing

rules, this article proposes a model to evaluate the resilience of airport flight area operation network after disturbance events, based on cell transmission model (CTM). Firstly, network performance parameters are obtained

based on the CTM. Then, taking the number of aircraft waiting in the network and the efficiency of network operation as basic performance indicators, a resilience index for the dynamic changes of the resilience of the disturbed

network is established. Finally, the CTM based network resilience assessment framework for airport running and

sliding system is established, and operational data from Tianjin Binhai International Airport in 2019 are collected,

processed, and analyzed. The research results indicate that the trend of simulated airport departure operation data

is highly consistent with the actual departure operation data, with a simulation accuracy of over 90%, and can accurately describe the network traffic flow distribution of the airport running and sliding system. The two proposed resilience assessment indicators for airports can comprehensively reflect the complete dynamic change process of

system network performance from degradation to recovery before and after disturbance events occur, providing new

support for network management of airport running and sliding system.

Key words:

airport running and sliding system, resilience assessment, cell transmission model, network operational efficiency

中图分类号:

V351.11

张宇辉, 文雯.

基于元胞传输模型的机场跑滑系统网络韧性评估

[J]. taptap下载安装安卓学报, 2025, 43(3): 53-61.

ZHANG Yuhui, WEN Wen.

Network resilience assessment of airport running and sliding system based

on cell transmission model

[J]. Journal of Civil Aviation University of China, 2025, 43(3): 53-61.

参考文献 14

	[1]
	MURRAY-TUITE P M． A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions
[C]	//Proceedings of the 2006 Winter Simulation Conference, December
	3-6, 2006, Monterey, CA, USA． IEEE, 2006: 1398-1405．
	[2]
	LI B J, DU W B, LIU C, et al． Topologic and dynamic resilience model
	of Chinese airport network[C]//11th IEEE International Conference on
	Control & Automation (ICCA), June 18-20, 2014, Taichung, Taiwan,
	China． IEEE, 2014: 1460-1465．
	[3]
	WANG X L, CHEN Z Y, LI K N． Quantifying the resilience performance
	of airport flight operation to severe weather[J]． Aerospace, 2022, 9(7): 344．
	[4]
	NAN C, SANSAVINI G． A quantitative method for assessing resilience
	of interdependent infrastructures[J]． Reliability Engineering & System
	Safety, 2017, 157: 35-53．
	[5]
	WANG Y J, ZHAN J M, XU X H, et al． Measuring the resilience of an
	airport network[J]． Chinese Journal of Aeronautics, 2019, 32(12): 2694-
	2705．
	[6]
	ZHOU L, CHEN Z H． Measuring the performance of airport resilience to
	severe weather events[J]． Transportation Research Part D: Transport and
	Environment, 2020, 83: 102362．
[7]	王兴隆, 赵俊妮, 王进. 恶劣天气下机场离场航班运行韧性评估
	及恢复[J]. 北京航空航天大学学报, 2024: 50(1): 110-121.
[8]	张静, 徐肖豪, 王飞. 天气季节性影响的机场到达容量概率分
布[J]	西南交通大学学报, 2011, 46(1): 154-161
[9]	杨磊, 胡明华, 尹苏皖, 等．大型繁忙机场场面离场交通流拥堵特
	征分析[J]．航空学报, 2016, 37(6): 1921-1930．
[10]	薛清文, 陆键, 姜雨．大型机场滑行道航空器交通流特性仿真[J]．
	北京航空航天大学学报, 2019, 45(3): 567-574．
[11]	DAGANZO C F． The cell transmission model: a dynamic representation
	of highway traffic consistent with the hydrodynamic theory[J]． Transportation Research Part B: Methodological, 1994, 28(4): 269-287．
[12]	SUN D F, BAYEN A M. Multicommodity Eulerian-Lagrangian largecapacity cell transmission model for en route traffic[J]. Journal of Guidance, Control, and Dynamics, 2008, 31(3): 616-628.
[13]	EZAKI T, NISHINARI K. Potential global jamming transition in aviation networks[J]. Physical Review E, 2014, 90(2): 022807.
[14]	吕彪, 谢智宇, 康宇翔, 等．基于动态分流元胞传输模型的城市道
	路网络韧性评估[J]．交通运输系统工程与信息, 2022, 22(6): 134-143．