Article(id=1308453883651576732, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.03.001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756569600000, receivedDateStr=2025-08-31, revisedDate=1766246400000, revisedDateStr=2025-12-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889315759, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889315759, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889315759, creator=13701087609, updateTime=1789889315759, updator=13701087609, issue=Issue{id=1308453882472984927, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='3', pageStart='1', pageEnd='150', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889315479, creator='13701087609', updateTime=1789889814158, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455974147224139, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455974151418444, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=10, ext={EN=ArticleExt(id=1308453884490437533, articleId=1308453883651576732, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Research on an Intelligent Dispatching Decision Generation Model for Urban Rail Transit Based on an Event Evolution Graph, columnId=1308453883261514080, journalTitle=Urban Rapid Rail Transit, columnName=Artifical Intelligence, runingTitle=null, highlight=null, articleAbstract=

To address the issues of high reliance on manual experience, poor dynamic adaptability of emergency decision-making, and low efficiency of decision generation in urban rail transit emergency response, a decision-making model for emergency response is proposed, which integrates a "predicate-centered event representation framework" and a "causal graph-retrieval augmented generation (RAG) integrated architecture". Firstly, a non-static trigger word is adopted to construct a process-oriented knowledge structure for emergency response that is superior to the static knowledge graph. Then, by combining information grouping, situation assessment, and an "event-situation" two-stage query rewriting strategy, the proposed model enhances traditional RAG with a stronger ability to understand the logic of event chains, thereby generating emergency response decisions that are more actionable and better adapted to specific scenarios. Finally, evaluation indicators such as the decision compliance rate, decision generation time, and decision efficiency improvement are defined and applied to comparative experiments to evaluate the model's performance. The research results show that compared with the traditional RAG method, the proposed model improves key-node accuracy by 29.84% in the decision compliance evaluation and achieves a logical-relation correctness rate of 84.74%. Meanwhile, compared with traditional manual decision-making, the proposed model improves generation efficiency by 57.14% indicating that the model can shorten the response time while meeting compliance requirements, providing a reference for constructing an intelligent dispatch decision generation system for urban rail transit.

, authors=Huixi ZONG¹, Li BAI¹, Kaiming YAN², Ming ZHANG¹, Jianfeng ZHONG², authorsList=Huixi ZONG, Li BAI, Kaiming YAN, Ming ZHANG, Jianfeng ZHONG, 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=1308453884876313502, articleId=1308453883651576732, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于事理图谱的城轨智能调度决策生成模型研究, columnId=1308453883420897634, journalTitle=都市快轨交通, columnName=人工智能, runingTitle=null, highlight=null, articleAbstract=

针对城市轨道交通应急处置面临的人工经验依赖度高、应急决策动态适应性差、决策生成效率不高等问题，提出一种“以谓词为核心的事件表示框架”与“事理图谱-检索增强生成（retrieval-augmented generation, RAG）一体化架构”的突发事件应急处置决策生成模型。首先，采用非静态触发词构建优于静态知识图谱的应急处置流程性知识结构；然后，结合信息组团、态势研判、“事件-态势”两阶段查询重写等策略，赋予传统RAG更优的事件链逻辑理解能力，从而生成可操作性更强、更适配场景的应急处置决策；最后，构建决策合规率、决策生成耗时和决策效率提升等评价指标，应用于对比实验，从而对模型的效果进行评价。研究结果表明：相较于传统RAG方法，模型生成决策的合规率评价中，关键节点准确率提升了29.84%、逻辑关系正确率达84.74%；相较于传统人工决策，模型生成效率提升57.14%，说明模型能在满足合规要求的同时缩短响应时间，为构建城轨智能调度决策生成体系提供参考。

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宗慧曦，女，硕士，助理研究员，主要从事城市轨道交通研究，youxianghaoji2025@163.com

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