A two-layer assessment model for resilience of civil airport transport services in severe weather

doi:10.16265/j.cnki.issn1003-3033.2025.09.1617

Abstract

Abstract:

In order to enhance the ability of civil airports to cope with adverse weather conditions, a two-layer assessment model for airport transportation service resilience was proposed, addressing both the overall level and specific elements. The first-layer model—a cloud-based assessment of airport overall resilience—was based on a clear definition and understanding of airport transportation service resilience. Starting from the four core resilience capabilities of absorption, adaptation, recovery, and optimization, a resilience indicator system comprising 28 evaluation elements was established. Based on the cloud model, evaluation standard clouds were determined, and the numerical characteristics of each layer of indicator clouds were calculated layer by layer to assess the overall resilience level of the airport. The second-layer model-identification of key resilience elements of civil airports was based on "importance + obstacle degree" of airport elements considering the contribution and obstruction degree to the overall resilience of the airport, proposed methods to characterize the importance and obstacle degree of airport elements, and comprehensively identified the key elements of airport resilience. Subsequently, a case study was conducted using Airport A as an example. The results indicate that the indicator system and evaluation model constructed in this paper can accurately assess the resilience level of airport transportation services and identify key resilience factors. Airport A's overall transportation service resilience score was 0.284 7, which is at a high resilience level and consistent with the actual situation. Among these, employee emergency rescue capabilities, employee psychological qualities, and employee years of service are key factors affecting airport resilience levels and require special attention and improvement.

Key words: severe weather, civil airport, transportation service resilience, two-layer assessment model, cloud model, key resilience factors

CLC Number:

X949

LI Hang, LIU Xinyue, HU Xiaobing. A two-layer assessment model for resilience of civil airport transport services in severe weather[J]. China Safety Science Journal, 2025, 35(9): 78-86.

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评价等级	评价区间	云数字特征值
低韧性	(0,20]	(0,10.300 0,0.130 9)
较低韧性	(20,40]	(30.900 0,6.366 7,0.080 9)
中韧性	(40,60]	(50,3.934 6,0.050 0)
较高韧性	(60,80]	(69.100 0,6.366 7,0.080 9)
高韧性	(80,100]	(100,10.300 0,0.130 9)

一级指标	一级指标权重	二级指标	二级指标权重	三级评估要素	三级评估要素权重
A	0.240 1	A₁	0.06 16	A₁₁	0.023 6
		A₁	0.06 16	A₁₂	0.038 0
		A₂	0.103 0	A₂₁	0.025 9
				A₂₂	0.029 8
				A₂₃	0.047 3
		A₃	0.075 6	A₃₁	0.035 5
				A₃₂	0.020 2
				A₃₃	0.019 9
B	0.316 6	B₁	0.115 3	B₁₁	0.069 7
		B₁	0.115 3	B₁₂	0.045 6
		B₂	0.087 8	B₂₁	0.056 8
		B₂	0.087 8	B₂₂	0.031 0
		B₃	0.113 5	B₃₁	0.032 6
				B₃₂	0.028 5
				B₃₃	0.029 1
				B₃₄	0.023 3
C	0.242 4	C₁	0.076 6	C₁₁	0.041 8
		C₁	0.076 6	C₁₂	0.034 8
		C₂	0.099 0	C₂₁	0.060 4
		C₂	0.099 0	C₂₂	0.038 6
		C₃	0.067 0	C₃₁	0.045 5
		C₃	0.067 0	C₃₂	0.021 5
D	0.200 8	D₁	0.051 0	D₁₁	0.051 0
		D₂	0.072 6	D₂₁	0.029 5
		D₂	0.072 6	D₂₂	0.043 1
		D3	0.077 3	D₃₁	0.024 7
				D₃₂	0.027 0
				D₃₃	0.025 7

一级指标	一级指标云数字特征
吸收能力A	(82.372 8,18.472 2,7.809 6)
适应能力B	(84.419 3,16.955 2,6.008 5)
恢复能力C	(84.079 6,16.178 1,4.537 9)
优化能力D	(83.600 4,18.913 1,4.205 3)

指标排序	指标	重要度	指标	障碍度
1	员工应急救援水平B₁₁	3.114 0	员工应急救援水平B₁₁	7.471 7
2	受损设备的可恢复性C₂₁	2.792 0	受损设备的可恢复性C₂₁	6.474 8
3	智能化应急系统的建设B₂₁	2.483 3	专业员工的气象分析与防控能力A₁₂	6.110 3
4	员工培训制度与实施D₁₁	2.338 2	员工心理素质水平B₁₂	6.110 3
5	员工心理素质水平B₁₂	2.338 1	智能化应急系统的建设B₂₁	6.088 9
6	应急广播系统的建设水平A₂₃	2.277 0	员工培训制度与实施D₁₁	5.922 7
7	员工开展恢复工作的经验C₁₁	2.237 6	应急抢修方案的制定C₃₁	4.877 5
8	应急抢修方案的制定C₃₁	2.158 5	员工开展恢复工作的经验C₁₁	4.854 3
9	设备设施升级计划与实施D₂₂	1.976 7	应急预案的演练B₃₂	4.073 5
10	沟通渠道的建设水平A₃₁	1.968 4	专业员工的工作年限A₁₁	4.005 6