基于复杂网络的轨道交通站点暴雨内涝韧性影响因素研究

doi:10.16265/j.cnki.issn1003-3033.2025.06.0726

摘要/Abstract

摘要：

为提升暴雨内涝下城市轨道交通站点运营的稳定性,提出一种基于复杂网络的韧性影响因素重要性评估方法。基于韧性的定义和内涵,从稳定性、抵抗性、恢复性和适应性4个方面,提取23个暴雨内涝韧性影响因素,并根据其相互影响关系构建韧性影响因素复杂网络,建立包含点度中心性(DC)、接近中心性(CC)、介数中心性(BC)、聚类系数的影响因素重要性评价指标体系;采用序关系分析法(G1)-变异系数法(CV)组合赋权法赋权指标,运用多准则妥协解排序法(VIKOR)识别关键影响因素。结果表明:风险监测预警系统、移动通信设备、站点挡水能力、防汛设备维护与保养频率、应急供电设备、防汛工作人员专业水平、站点排水能力、防汛培训演练频率是城市轨道交通站点韧性的关键影响因素。

关键词: 复杂网络, 轨道交通站点, 暴雨内涝, 韧性, 影响因素

Abstract:

To enhance the operational stability of urban rail transit stations during rainstorms and waterlogging for safe resident travel, a method for evaluating the importance of resilience influencing factors was proposed based on complex networks. Based on the definition and connotation of resilience, 23 influencing factors of rainstorm waterlogging resilience were extracted from four aspects of stability, robustness, resilience and adaptability. The complex network of influencing factors of resilience was constructed according to the influence relationship between influencing factors. The evaluation index system for the importance of these factors was established, including degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), and clustering coefficient. The order relation analysis method(G1)-Coefficient of Variation (CV) combination weighting method was used to weight the index, and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)method was used to identify the key influencing factors. The results show that risk monitoring and early warning system, mobile communication equipment, water retaining capacity of stations, maintenance and repair frequency of flood control equipment, emergency power supply equipment, professional level of flood control staff, drainage capacity of stations, and frequency of flood control training and drilling are the key factors influencing the resilience of urban rail transit stations. The research results can provide new ideas and targeted suggestions for the emergency waterlogging prevention strategies of urban rail transit stations in the rainstorm waterlogging scenario.

Key words: complex network, rail transit stations, rainstorm waterlogging, resilience, influencing factors

中图分类号:

X951交通运输安全

马立强, 赵昂, 郑彬彬. 基于复杂网络的轨道交通站点暴雨内涝韧性影响因素研究[J]. 中国安全科学学报, 2025, 35(6): 142-147.

MA Liqiang, ZHAO Ang, ZHENG Binbin. Research on influencing factors of rainstorm waterlogging resilience at rail transit stations based on complex network[J]. China Safety Science Journal, 2025, 35(6): 142-147.

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因素	F₁	F₂	F₃	F₄	F₅	F₆	F₇	F₈	…	F₂₂	F₂₃
F₁	0	0	1	0	1	1	0	0	…	0	0
F₂	0	0	1	1	0	0	0	0	…	0	0
F₃	1	1	0	1	0	0	0	0	…	0	0
F₄	1	1	0	0	0	1	0	0	…	0	0
F₅	1	1	0	1	0	0	0	0	…	0	0
F₆	0	0	1	1	0	0	0	0	…	0	0
F₇	0	0	0	0	0	0	0	1	…	0	0
F₈	0	0	0	0	0	0	0	0	…	0	0
︙	︙	︙	︙	︙	︙	︙	︙	︙		︙	︙
F₂₂	0	0	0	0	0	0	0	0	…	0	0
F₂₃	0	1	1	1	1	1	0	0	…	1	0

属性	因素	DC_j	CC_j	BC_j	E_j
稳定性	F₁	0.273	0.413	14.83	0.417
	︙	︙	︙	︙	︙
	F₅	0.273	0.412	1.500	0.400
	F₆	0.318	0.451	3.670	0.367
抵抗性	F₇	0.227	0.39	1.500	0.450
	︙	︙	︙	︙	︙
	F₁₂	0.182	0.330	0	0.500
恢复性	F₁₃	0.682	0.400	43.330	0.214
	︙	︙	︙	︙	︙
	F₁₈	0.636	0.783	0	0.220
适应性	F₁₉	0.182	0.366	0	0.583
	︙	︙	︙	︙	︙
	F₂₃	0.636	0.791	0	0.220

权重	DC	CC	BC	聚类系数
ω_ai	0.268	0.223	0.159	0.350
ω_bi	0.224	0.238	0.368	0.170
ω_i	0.255	0.240	0.252	0.253

因素	S_j	R_j	Q_j
F₁	0.73	0.21	0.59
F₂	0.57	0.18	0.26
F₃	0.59	0.20	0.42
F₄	0.47	0.16	0.05
F₅	0.79	0.25	0.83
F₆	0.76	0.24	0.77
F₇	0.79	0.25	0.84
︙	︙	︙	︙
F₂₂	0.91	0.26	1
F₂₃	0.59	0.25	0.66