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In response to new challenges, the application characteristics of dynamic network algorithms were reviewed. These challenges were posed by complex and diversified urban emergency scenarios. Heterogeneous data fusion and algorithmic collaboration were involved. Additionally, approaches for multi-algorithm collaboration and integration were explored. Firstly, based on 128 publications from the past five years in the Scopus database, four major research themes: path planning, traffic regulation, risk prevention, and resilience analysis, which were identified using keyword frequency statistics and cluster analysis. Then, through content analysis, four core algorithm categories: planning, simulation, clustering, and deep learning were summarized. Their theoretical frameworks and task adaptation logic were examined in conjunction with empirical data, simulations, and hybrid data sources. Finally, a multidimensional evaluation system was constructed to comparatively assess the applicability, strengths, and limitations of different algorithms across various emergency scenarios. The results show that multiple algorithms are integrated in application, which include planning, simulation, clustering, and deep learning. This integrated approach enables a multidimensional response to urban emergency management tasks. Such tasks involve path planning, traffic regulation, risk prevention, and resilience analysis. The adaptability of emergency systems are enhanced. The robustness of these systems is also improved. Data-driven intelligent algorithms further improve the responsiveness of urban road networks, supporting real-time strategy adjustment and resource optimization. Future development should focus on the construction of intelligent collaborative architectures for algorithm integration and coupled analysis across multiple networks, to advance the efficient application of dynamic network algorithms in multi-scenario emergency management.

, authors=Ziyang ZHANG^{1, 2}, Saini YANG^{**, 1}, authorsList=Ziyang ZHANG, Saini YANG, authorCompany=null, correspAuthors=Saini YANG, 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=1282034786000679785, articleId=1282034785287648101, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=动态网络算法在城市路网应急场景中的应用综述, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为解决城市应急场景日益复杂化、多元化对动态网络算法应用带来的异构数据融合和算法协同应用新挑战，系统梳理动态网络算法的情景应用特征，探索多算法协同与集成的实现路径。首先，基于Scopus数据库近5年128篇文献，采用关键词词频统计与聚类分析方法，识别出路径规划、流量调控、风险防范及韧性分析4大研究主题；然后，通过文献内容分析归纳规划、模拟、聚类与深度学习4类核心算法，结合实证、仿真及混合数据源，解析其理论架构与任务适配逻辑；最后，建立多维度评估体系，综合对比算法在不同应急场景中的适用性、优势及短板。结果表明：通过规划、模拟、聚类和深度学习4类算法的多元集成，可实现城市应急管理中路径规划、流量调控、风险防范与韧性分析的多维响应，提升应急体系的适应性与稳健性；数据驱动下的智能算法将进一步提高城市路网响应效率，促进实时策略调整与资源优化；未来发展方向应聚焦于多网融合的耦合分析及算法集成的智能协同架构建设，助力动态网络算法在多场景应急管理中的高效应用。

, authors=张子扬^{1, 2}, 杨赛霓教授^{**, 1}, authorsList=张子扬, 杨赛霓教授, authorCompany=null, correspAuthors=杨赛霓教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vsfAjztaFzkQbr7NW//IDA==, magXml=MwqfJk/HDp0lbC2/RQxDFQ==, pdfUrl=null, pdf=U2QbmGd9TkNMSg1ohtuAgg==, pdfFileSize=6335560, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=DbxhxnMEaEr8t9yOQKHbwA==, mapNumber=null, fund=null)}, authors=[Author(id=1282034786432693105, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034785287648101, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=202231250021@mail.bnu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282034786520773492, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034785287648101, authorId=1282034786432693105, language=EN, stringName=Ziyang ZHANG, firstName=Ziyang, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
²School of Systems Science, Beijing Normal University, Beijing 100875, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282034786617242485, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034785287648101, authorId=1282034786432693105, language=CN, stringName=张子扬, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹北京师范大学 国家安全与应急管理学院，北京 100875
²北京师范大学 系统科学学院，北京 100875, bio={"content":"

张子扬（1995—），男，河南商丘人，博士研究生，主要研究方向关键基础设施与应急管理、气候变化风险。E-mail: 202231250021@mail.bnu.edu.cn。

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张子扬（1995—），男，河南商丘人，博士研究生，主要研究方向关键基础设施与应急管理、气候变化风险。E-mail: 202231250021@mail.bnu.edu.cn。

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