Article(id=1282032388213551718, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.09.1617, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744473600000, receivedDateStr=2025-04-13, revisedDate=1752595200000, revisedDateStr=2025-07-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589940359, onlineDateStr=2026-07-09, pubDate=1758988800000, pubDateStr=2025-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589940359, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589940359, creator=13701087609, updateTime=1783589940359, updator=13701087609, issue=Issue{id=1282032292440805784, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='9', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1758988800000', pubDateStr='2025-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589917526, creator='13701087609', updateTime=1783593837820, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048735404344166, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048735404344167, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=78, endPage=86, ext={EN=ArticleExt(id=1282032391665463911, articleId=1282032388213551718, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=A two-layer assessment model for resilience of civil airport transport services in severe weather, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Hang LI, Xinyue LIU, Xiaobing HU, authorsList=Hang LI, Xinyue LIU, Xiaobing HU, authorCompany=null, correspAuthors=null, 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=1282032396094648937, articleId=1282032388213551718, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=恶劣天气下民用机场运输服务韧性双层评估模型, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为提高民用机场应对恶劣天气的能力，从整体和要素2个层面提出机场运输服务韧性双层评估模型，第1层模型：基于云模型的机场整体韧性评估，在明确机场运输服务韧性定义和内涵基础上，从机场的吸收-适应-恢复-优化4个核心韧性能力出发，建立包含28个评价要素的韧性指标体系，并基于云模型确定评价标准云，逐层计算各层指标云数字特征值，评估机场整体韧性水平；第2层模型：基于“重要度+障碍度”的民用机场关键韧性要素识别，考虑对机场整体韧性的贡献和阻碍程度，提出机场要素的重要度和障碍度表征方法，进而综合识别机场韧性的关键要素，并以A机场为例开展验证分析。结果表明：文中构建的指标体系和评估模型能够准确评估机场运输服务韧性水平、识别关键韧性要素；A机场的运输服务韧性总体得分为0.284 7，处于高韧性水平，与实际情况相符，证明文中所提模型具有一定的科学性和有效性；员工应急救援水平、员工心理素质水平、员工的工作年限等为影响机场韧性水平的关键要素，需重点关注和提升。

, authors=李航讲师, 刘新月, 胡小兵教授, authorsList=李航讲师, 刘新月, 胡小兵教授, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=y9ddr3f+DxwB3WzDmxZM9A==, magXml=LZMhwbetmKWmIe8syKX2TA==, pdfUrl=null, pdf=XKPzR4wbLz3ZFhzmk3GtKg==, pdfFileSize=9200089, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=NfQKxr9427R1u1oT72UUdA==, mapNumber=null, fund=null)}, authors=[Author(id=1282032399978574446, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032388213551718, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=lih@cauc.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282032400398004848, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032388213551718, authorId=1282032399978574446, language=EN, stringName=Hang LI, firstName=Hang, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282032401173951089, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032388213551718, authorId=1282032399978574446, language=CN, stringName=李航讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国民航大学 安全科学与工程学院，天津 300300, bio={"content":"

李航（1989—），男，陕西岐山人，博士，讲师，主要从事复杂系统安全韧性、民航应急管理等方面的研究。E-mail:lih@cauc.edu.cn。

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李航（1989—），男，陕西岐山人，博士，讲师，主要从事复杂系统安全韧性、民航应急管理等方面的研究。E-mail:lih@cauc.edu.cn。

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