Article(id=1281608145067815150, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.03.0113, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760371200000, receivedDateStr=2025-10-14, revisedDate=1766160000000, revisedDateStr=2025-12-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488792912, onlineDateStr=2026-07-08, pubDate=1774627200000, pubDateStr=2026-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488792912, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488792912, creator=13701087609, updateTime=1783488792912, updator=13701087609, issue=Issue{id=1281608117335068953, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='3', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1774627200000', pubDateStr='2026-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488786300, creator='13701087609', updateTime=1783588670896, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027063758533259, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027063758533260, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=186, endPage=193, ext={EN=ArticleExt(id=1281608145235587311, articleId=1281608145067815150, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Emergency evacuation bus scheduling for toxic gas leakage scenarios, columnId=1277328338121261909, journalTitle=China Safety Science Journal, columnName=Public Safety and Emergency Management, runingTitle=null, highlight=null, articleAbstract=

To improve the efficiency of large-scale personnel evacuation and reduce casualties after toxic gas leakage accidents, a CFD model and wind direction statistical probability were adopted to simulate the spatiotemporal distribution of toxic gas concentrations. Combined with the facilities and personnel conditions of evacuation sites, a dynamic comprehensive risk measurement method for evacuation sites was proposed. First, based on the differences in dynamic comprehensive risks of evacuation sites, a multi-trip emergency evacuation bus scheduling model with splitable evacuation demand was constructed, with the objectives of minimizing evacuation time and total risk expectation. Then, the augmented weighted Chebyshev method was used to convert the multi-objective model into a single-objective model, and a genetic algorithm integrated with adaptive large neighborhood search was designed for solution, in which destruction and repair operators were adopted to improve the search capability of the algorithm. Finally, the rationality of the model and the effectiveness of the algorithm were verified through numerical example analysis.The results show that the emergency evacuation bus scheduling model with the objectives of minimizing evacuation time and total evacuation risk expectation can generate scheduling schemes that prioritize the evacuation of personnel from high-risk evacuation sites, thereby improving evacuation efficiency and reducing the total risk expectation. Compared with the expected total evacuation risk, evacuation time is more sensitive to changes in the latest time window of evacuation sites, and the number of required buses decreases with the extension of the evacuation time window. In comparison with the traditional genetic algorithm, the genetic algorithm integrated with adaptive large neighborhood search can reduce the objective functions by 4.46% and 2.44%, respectively.

, authors=Yuanyuan LIU, Zefeng TAN, Shuang HAN^**, Mengting XU, authorsList=Yuanyuan LIU, Zefeng TAN, Shuang HAN, Mengting XU, authorCompany=null, correspAuthors=Shuang HAN, 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=1281608145726320882, articleId=1281608145067815150, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=有毒气体泄漏场景下的应急疏散公交调度, columnId=1277328338280645463, journalTitle=中国安全科学学报, columnName=公共安全与应急管理, runingTitle=null, highlight=null, articleAbstract=

为提高毒气泄漏事故后大规模人员疏散效率，降低人员伤亡，采用计算流体力学（CFD）模型和风向统计概率模拟毒气质量浓度时空分布，结合疏散点设施及人员情况，提出疏散点动态综合风险度量方法。首先，基于疏散点动态综合风险差异，以疏散时间和总风险期望值最小为目标，构建疏散需求可拆分的多行程应急疏散公交调度模型；然后，采用增广加权切比雪夫方法将多目标模型转化为单目标模型，设计融合自适应大邻域搜索的遗传算法求解，采用破坏和修复算子提高算法搜索能力；最后，通过算例分析验证模型的合理性和算法的有效性。结果表明：以疏散时间和疏散总风险期望值最小为目标的应急疏散公交调度模型能够得到优先疏散高风险疏散点人员的调度方案，提高疏散时效并降低总风险期望值；相比于疏散总风险期望值，疏散时间对疏散点最迟时间窗的变化更敏感，所需公交车数量随疏散时间窗的延长而减少；与传统遗传算法相比，融合自适应大邻域搜索的遗传算法可使目标函数分别下降4.46%和2.44%。

, authors=刘圆圆副教授, 谭泽峰, 韩霜讲师^**, 徐梦婷, authorsList=刘圆圆副教授, 谭泽峰, 韩霜讲师, 徐梦婷, authorCompany=null, correspAuthors=韩霜讲师, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vKrm69xxCgXYloa69KNAEg==, magXml=KnM1PHpEDcUwQS9+mKjdKQ==, pdfUrl=null, pdf=ifK8okAMqfm5NcxaekP2NA==, pdfFileSize=3906242, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=3iPq1GHVzhkGBG2w17k3WQ==, mapNumber=null, fund=null)}, authors=[Author(id=1281608146011533559, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608145067815150, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=liuyy@gdut.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608146078642425, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608145067815150, authorId=1281608146011533559, language=EN, stringName=Yuanyuan LIU, firstName=Yuanyuan, middleName=null, lastName=LIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou Guangdong 510006, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608146187694330, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608145067815150, authorId=1281608146011533559, language=CN, stringName=刘圆圆副教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=广东工业大学 土木与交通工程学院，广东 广州 510006, bio={"content":"

刘圆圆（1990—），女，山东潍坊人，博士，副教授，主要从事低碳交通、交通全寿命周期评估理论、智慧与可持续交通等方面的研究。E-mail: liuyy@gdut.edu.cn。

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刘圆圆（1990—），女，山东潍坊人，博士，副教授，主要从事低碳交通、交通全寿命周期评估理论、智慧与可持续交通等方面的研究。E-mail: liuyy@gdut.edu.cn。

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