Article(id=1308454754133238037, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1781280000000, receivedDateStr=2026-06-13, revisedDate=1782403200000, revisedDateStr=2026-06-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889523299, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889523299, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889523299, creator=13701087609, updateTime=1789889523299, 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=20, endPage=33, ext={EN=ArticleExt(id=1308454754393284886, articleId=1308454754133238037, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Artificial Intelligence-Based Short-Term Passenger Flow Prediction in Urban Rail Transit Systems: A Review, columnId=1308454713645618089, journalTitle=Urban Rapid Rail Transit, columnName=Special Feature: 200th Issue, runingTitle=null, highlight=null, articleAbstract=

With the continued expansion of urban rail transit networks and the increasing complexity of operating scenarios, passenger demand exhibits more complex spatiotemporal patterns, posing higher requirements for train operation organization and passenger flow control. Short-term passenger flow prediction is an important basis for urban rail transit operation management and provides key support for operational decision-making. In recent years, artificial intelligence-based methods have become an important technical approach for improving short-term passenger flow prediction performance, owing to their strong capability to extract complex spatiotemporal features. This review focuses on artificial intelligence-based methods for short-term passenger flow prediction in urban rail transit systems. It first constructs a task analysis framework from five dimensions: prediction scenarios, prediction targets, data sources, prediction methods, and evaluation metrics. Second, the development trajectory of prediction models is systematically reviewed. Furthermore, recent studies and major challenges are summarized for different prediction tasks, including station inflow and outflow passenger flow prediction, origin-destination passenger flow prediction, section passenger flow prediction, multi-type passenger flow collaborative prediction, and multimodal traffic prediction with rail transit as the backbone. Finally, future research directions are discussed from the perspectives of large language models, trustworthy and interpretable prediction, an end-to-end predict-and-optimize learning framework, and world models. This review shows that short-term passenger flow prediction in urban rail transit is evolving from single-flow-type, single-metric, and single-task prediction toward integrated development featuring multi-source perception, multi-type passenger flow collaboration, and multi-metric evaluation. Future research should further emphasize general adaptability, uncertainty awareness, trustworthy risk assessment, decision-oriented prediction, and world-model-driven dynamic deduction, with the aim of building an integrated intelligent prediction and simulation framework for complex operational scenarios and supporting refined operational decision-making in urban rail transit.

, authors=Feng Chen¹, Jinlei Zhang², authorsList=Feng Chen, Jinlei Zhang, 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=1308454754632360215, articleId=1308454754133238037, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=人工智能驱动的城轨短时客流预测方法综述, columnId=1308454713796613035, journalTitle=都市快轨交通, columnName=200期专辑, runingTitle=null, highlight=null, articleAbstract=

随着城市轨道交通线网规模持续扩大和运营场景日益多元，客流需求呈现出更复杂的时空特征，对列车运行组织和客流管控等提出更高要求。短时客流预测是城市轨道交通运营管理的重要基础，人工智能方法凭借较强的时空特征提取能力，已成为提升预测性能的重要手段。本文围绕人工智能驱动的城市轨道交通短时客流预测方法展开综述。首先，从预测场景、预测对象、数据基础、预测方法和评价指标5个维度构建任务分析框架；其次，梳理预测模型发展脉络；再次，面向进出站客流、起讫点（origin-destination, OD）客流、断面客流、多类型客流协同预测以及以轨道交通为骨干的多模式交通预测等任务，总结相关研究进展和主要挑战；最后，从大语言模型、可信可解释预测、端到端预测优化一体化框架以及世界模型等方面展望其未来发展方向。研究结果表明：城市轨道交通短时客流预测研究正由单一客流类型、单一精度评价和单一预测任务，逐步转向多源感知、多类型协同与多指标评估融合发展；未来研究将进一步聚焦通用适应性、不确定性感知、可信风险评估、决策导向预测以及世界模型驱动的动态推演，构建面向复杂运营场景的一体化智能预测推演体系，以支撑城市轨道交通精细化运营决策。

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陈峰，男，博士，教授，从事轨道交通运营管理、人工智能与大数据、客流预测等相关研究，fengchen@bjtu.edu.cn

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陈峰，男，博士，教授，从事轨道交通运营管理、人工智能与大数据、客流预测等相关研究，fengchen@bjtu.edu.cn

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