Article(id=1281685588172575192, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.06.0726, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736438400000, receivedDateStr=2025-01-10, revisedDate=1742400000000, revisedDateStr=2025-03-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783507256787, onlineDateStr=2026-07-08, pubDate=1751040000000, pubDateStr=2025-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783507256787, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783507256787, creator=13701087609, updateTime=1783507256787, updator=13701087609, issue=Issue{id=1281685530169545672, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='6', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1751040000000', pubDateStr='2025-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783507242958, creator='13701087609', updateTime=1783589309504, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029742274622413, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029742274622414, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=142, endPage=147, ext={EN=ArticleExt(id=1281685588382290393, articleId=1281685588172575192, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Research on influencing factors of rainstorm waterlogging resilience at rail transit stations based on complex network, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Liqiang MA, Ang ZHAO, Binbin ZHENG**, authorsList=Liqiang MA, Ang ZHAO, Binbin ZHENG, authorCompany=null, correspAuthors=Binbin ZHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281685588659114458, articleId=1281685588172575192, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=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, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

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。

, authors=马立强副教授, 赵昂, 郑彬彬副教授**, authorsList=马立强副教授, 赵昂, 郑彬彬副教授, authorCompany=null, correspAuthors=郑彬彬副教授, authorNote=

马立强(1974—),男,山东德州人,博士,副教授,主要从事安全与应急管理、消防安全管理等方面的研究。E-mail:

, correspAuthorsNote=
** 郑彬彬(1988—),男,山东东营人,博士,副教授,主要从事安全与应急管理等方面的研究。E-mail:
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马立强(1974—),男,山东德州人,博士,副教授,主要从事安全与应急管理、消防安全管理等方面的研究。E-mail:

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马立强(1974—),男,山东德州人,博士,副教授,主要从事安全与应急管理、消防安全管理等方面的研究。E-mail:

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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
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马立强副教授 , 赵昂 , 郑彬彬副教授 **
中国安全科学学报 | 公共安全 2025,35(6): 142-147
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中国安全科学学报 |公共安全 2025 , 35 (6) : 142 -147
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
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马立强副教授 , 赵昂, 郑彬彬副教授**
作者信息
  • 山东工商学院 管理科学与工程学院,山东 烟台 264005
通讯作者:
** 郑彬彬(1988—),男,山东东营人,博士,副教授,主要从事安全与应急管理等方面的研究。E-mail:
作者简介:

马立强(1974—),男,山东德州人,博士,副教授,主要从事安全与应急管理、消防安全管理等方面的研究。E-mail:

Research on influencing factors of rainstorm waterlogging resilience at rail transit stations based on complex network
Liqiang MA , Ang ZHAO, Binbin ZHENG**
Affiliations
  • School of Management Science and Engineering, Shandong Technology and Business University, Yantai Shandong 264005, China
出版时间: 2025-06-28 doi: 10.16265/j.cnki.issn1003-3033.2025.06.0726
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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。

复杂网络  /  轨道交通站点  /  暴雨内涝  /  韧性  /  影响因素

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.

complex network  /  rail transit stations  /  rainstorm waterlogging  /  resilience  /  influencing factors
马立强副教授, 赵昂, 郑彬彬副教授. 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. 中国安全科学学报, 2025 , 35 (6) : 142 -147 . DOI: 10.16265/j.cnki.issn1003-3033.2025.06.0726
Liqiang MA, Ang ZHAO, Binbin ZHENG. 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 . DOI: 10.16265/j.cnki.issn1003-3033.2025.06.0726
  • 国家自然科学基金资助(51804178)
  • 山东省社科规划研究项目(19CGLJ18)
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doi: 10.16265/j.cnki.issn1003-3033.2025.06.0726
  • 接收时间:2025-01-10
  • 首发时间:2026-07-08
  • 出版时间:2025-06-28
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  • 收稿日期:2025-01-10
  • 修回日期:2025-03-20
基金
国家自然科学基金资助(51804178)
山东省社科规划研究项目(19CGLJ18)
作者信息
    山东工商学院 管理科学与工程学院,山东 烟台 264005

通讯作者:

** 郑彬彬(1988—),男,山东东营人,博士,副教授,主要从事安全与应急管理等方面的研究。E-mail:
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2种不同金属材料的力学参数

Family
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种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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