Article(id=1308453946222207328, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.02.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749139200000, receivedDateStr=2025-06-06, revisedDate=1766592000000, revisedDateStr=2025-12-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889330677, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889330677, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889330677, creator=13701087609, updateTime=1789889330677, updator=13701087609, issue=Issue{id=1308453915104665783, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='2', pageStart='1', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889323259, creator='13701087609', updateTime=1789889766020, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455772237615687, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455772237615688, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=46, endPage=56, ext={EN=ArticleExt(id=1308453946406756705, articleId=1308453946222207328, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Subway Station Entry Passenger Flow Prediction Based on a Hybrid Deep Learning Method, columnId=1152669335454658940, journalTitle=Urban Rapid Rail Transit, columnName=Academic Discussion, runingTitle=null, highlight=null, articleAbstract=

To address the critical issue of predicting the time-series passenger flow at subway stations, a novel hybrid deep learning method, AFT-K-GRU⁺, is proposed to provide more accurate decision support for subway operation and management. Leveraging the multi-scale temporal dependencies inherent in subway passenger flow data, the model innovatively integrates three core components: the attention-free transformer (AFT), the gated recurrent unit (GRU), and the KAN network. Specifically, the AFT module captures long-term periodic patterns in passenger flow data, the GRU module focuses on extracting short-term fluctuation characteristics, and the KAN network enhances the model's expressive capacity. To enhance the robustness of the model's predictions, this study innovatively designs a knowledge-driven correction controller based on residual feedback, which enables online self-correction of the output. Experimental validation using real-world card swipe data from Beijing subway stations demonstrates that the AFT-K-GRU⁺ model effectively predicts station passenger flow, achieving mean error reductions of 25.3% and 18.7% compared to Transformer and LSTM methods, respectively. This model offers effective methodological support for subway operators, assisting management departments in passenger flow control and daily planning, thereby enhancing the operational efficiency and service quality of subway lines.

, authors=Changlong LI¹, Xiaoxia YANG¹, Guoqing ZHANG¹, Chunyue CUI², Jiahui WAN¹, authorsList=Changlong LI, Xiaoxia YANG, Guoqing ZHANG, Chunyue CUI, Jiahui WAN, authorCompany=null, correspAuthors=Xiaoxia 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=1308453946708746594, articleId=1308453946222207328, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于组合深度学习方法的地铁进站客流预测, columnId=1152669335668568445, journalTitle=都市快轨交通, columnName=学术探讨, runingTitle=null, highlight=null, articleAbstract=

针对地铁车站进站客流量时序预测低的问题，提出一种新型组合深度学习方法AFT-K-GRU⁺。基于地铁进站客流多尺度时间依赖性特征，构建融合无注意力变压器模型（attention-free transformer, AFT）、门控循环单元（gated recurrent unit, GRU）和科尔莫戈洛夫-阿诺德网络（Kdmogorov-Arndd networks, KAN）网络3种核心组件的模型。AFT模块专门用于捕捉客流数据的长期周期性规律，GRU重点提取客流短期波动特征，KAN网络用于增强模型表达能力。为增强模型预测结果的鲁棒性，设计基于残差反馈的知识驱动修正控制器，能够对输出结果进行在线自校正。通过北京地铁站实际刷卡数据验证，AFT-K-GRU⁺模型在进站客流预测方面表现较好，相较于Transformer与长短期记忆网络（long short-term memory, LSTM）方法，平均误差分别减少25.3%和18.7%。该模型可为地铁运营方提供有效的方法支撑，帮助管理部门进行客流管理与日常规划，提升地铁线路的运行效率与服务水平。

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李昌隆，男，硕士研究生，从事智能交通研究，lichanglong@stu.qut.edu.cn

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李昌隆，男，硕士研究生，从事智能交通研究，lichanglong@stu.qut.edu.cn

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