Article(id=1308454750563889741, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1780416000000, receivedDateStr=2026-06-03, revisedDate=1781539200000, revisedDateStr=2026-06-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889522448, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889522448, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889522448, creator=13701087609, updateTime=1789889522448, updator=13701087609, issue=Issue{id=1308454712513156008, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='4', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1789889513376, creator='13701087609', updateTime=1789889839088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308456079029986178, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308456079029986179, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=80, endPage=92, ext={EN=ArticleExt(id=1308454750979125838, articleId=1308454750563889741, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Review of Optimization and Rescheduling of Train Operation Plans under Virtual Coupling in Rail Transit, columnId=1308454713645618089, journalTitle=Urban Rapid Rail Transit, columnName=Special Feature: 200th Issue, runingTitle=null, highlight=null, articleAbstract=

To review the research progress of Virtual Coupling (VC) technology in rail transit transportation organization and identify its applications in train operation plan optimization and rescheduling, this paper presents a comprehensive literature review of recent studies in the field. From an operational and service-oriented perspective, 42 representative publications published between 2022 and 2025 were collected from major academic databases, including Web of Science, Scopus, IEEE Xplore, and CNKI. Research on train operation plan design, timetable optimization, integrated timetable and rolling stock circulation planning, as well as train delay rescheduling, disruption management during disruption and recovery phases, was reviewed and compared. The results indicate that VC technology can significantly enhance line capacity and operational flexibility through reduced train headways and dynamic coupling and decoupling operations. In terms of operation planning, existing studies have evolved from isolated timetable optimization to integrated optimization of timetables, stopping patterns, train routing, and rolling stock circulation. In terms of operation rescheduling, VC has demonstrated considerable potential in improving transportation organization resilience and passenger evacuation efficiency under disruption scenarios. However, current research still faces several limitations, including insufficient integration among planning elements, limited consideration of complex rail networks, inadequate investigation of multi-strategy collaborative rescheduling, and a lack of unified frameworks covering the entire disruption process. Future research should focus on integrated optimization of timetables, stopping patterns, train routing, and rolling stock circulation; integrated modeling of train operation organization, dispatching, and control; multi-strategy collaborative rescheduling; full-process disruption rescheduling; and the application of intelligent optimization approaches such as deep reinforcement learning in VC-enabled rail transit systems.

, authors=Qi Zhang, Xinyu Bao, Yinghao Xu, Zhuoyi Li, authorsList=Qi Zhang, Xinyu Bao, Yinghao Xu, Zhuoyi Li, 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=1308454751222395471, articleId=1308454750563889741, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=轨道交通虚拟编组列车运行计划优化与调整研究综述, columnId=1308454713796613035, journalTitle=都市快轨交通, columnName=200期专辑, runingTitle=null, highlight=null, articleAbstract=

为梳理虚拟编组技术在轨道交通运输组织领域的研究进展，明确其在列车运行计划优化与运行调整中的应用现状及未来发展方向，对近年来国内外相关文献进行综述。基于运营与服务视角，以2022—2025年发表于Web of Science、Scopus、IEEE Xplore及中国知网等数据库的42篇代表性文献为研究对象，对开行方案编制、时刻表优化、时刻表与车底周转一体化优化，以及列车延误调整、中断持续阶段调整和中断恢复阶段调整等研究进行归纳与比较分析。结果表明：虚拟编组通过缩小列车运行间隔和支持动态重联/解编，能够显著提升线路通过能力与运力配置灵活性；在运行计划优化方面，研究已由单一时刻表优化逐步发展为时刻表、停站方案、交路及车底周转的一体化优化；在运行调整方面，虚拟编组可有效提高中断场景下的运输组织韧性与客流疏解效率。当前研究仍存在一体化程度有限、复杂网络场景考虑有限、多策略协同机制缺乏以及全过程中断调整研究欠缺等问题。未来应重点关注时刻表—停站—交路—车底协同优化、行-调-控一体化建模、多策略协同调整、全过程中断调整以及深度强化学习等智能优化方法在虚拟编组场景下的应用，为虚拟编组技术在轨道交通运输组织中的深入应用与工程实践提供理论参考和方法指导。

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张琦，女，博士，副教授，从事轨道交通智能运输组织领域研究，qzhang6@bjtu.edu.cn

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张琦，女，博士，副教授，从事轨道交通智能运输组织领域研究，qzhang6@bjtu.edu.cn

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