Article(id=1308454731576275391, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.019, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753718400000, receivedDateStr=2025-07-29, revisedDate=1782748800000, revisedDateStr=2026-06-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889517921, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889517921, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889517921, creator=13701087609, updateTime=1789889517921, 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=161, endPage=169, ext={EN=ArticleExt(id=1308454731794379200, articleId=1308454731576275391, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Short-Term Metro Inbound Passenger Flow Prediction Using Multi-Granularity Temporal Features and Graph Attention, columnId=1152669335454658940, journalTitle=Urban Rapid Rail Transit, columnName=Academic Discussion, runingTitle=null, highlight=null, articleAbstract=

To address the insufficient fusion of multi-granularity temporal features and inadequate modeling of spatial heterogeneity among stations in short-term metro inbound passenger flow forecasting, this paper proposes the Multi-Granularity and Attention-enhanced Passenger Flow Prediction model (MGAPF). In the temporal dimension, four one-dimensional convolutional branches with different receptive fields are constructed, and a granularity attention mechanism is used to adaptively fuse multi-scale temporal features. In the spatial dimension, a graph attention mechanism is introduced to dynamically learn the correlation strengths among adjacent stations. External temporal contextual features, including holidays, weekends, and weather conditions, are also incorporated. A gated fusion structure, residual connections, and a Transformer encoder are further combined to perform spatiotemporal feature interaction and generate prediction outputs. Experimental results based on real operational data from Hangzhou Metro show that MGAPF achieves MAPE values of 16.93% and 9.52% in the 10-minute and 30-minute forecasting tasks, respectively, representing error reductions of approximately 4.5% and 7.8% compared with the second-best baseline model. MGAPF also performs better in terms of MAE, RMSE, and prediction stability, indicating that the fusion of multi-granularity temporal features, spatial correlations among stations, and external factors can effectively improve the accuracy and stability of short-term metro inbound passenger flow prediction.

, authors=Pengju Shen^{1, 2}, Liying Song^{1, 2}, authorsList=Pengju Shen, Liying Song, authorCompany=null, correspAuthors=Liying Song, 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=1308454732121534913, articleId=1308454731576275391, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=融合多时间粒度与图注意力的地铁短时进站客流预测方法, columnId=1152669335668568445, journalTitle=都市快轨交通, columnName=学术探讨, runingTitle=null, highlight=null, articleAbstract=

为解决地铁短时进站客流预测中多粒度时间特征融合不足、站点空间关联异质性刻画不充分等问题，提出一种融合多粒度感知与图注意力机制的地铁短时客流预测模型（multi-granularity and attention-enhanced passenger flow, MGAPF）。模型在时间维度构建4种不同感受野的一维卷积分支，并通过粒度注意力机制自适应融合多尺度时间特征；在空间维度引入图注意力机制，动态学习邻接站点间的关联强度；同时融合节假日、周末及天气等外部特征，并结合门控融合结构、残差连接和Transformer编码器完成时空特征交互与预测输出。基于杭州市地铁真实运营数据的实验结果表明，在10 min与30 min客流预测任务中，MGAPF的MAPE（平均绝对百分比误差）分别达到16.93%与9.52%，相较次优模型分别下降约4.5%与7.8%，且在MAE（平均绝对误差）、RMSE（均方根误差）及预测稳定性方面表现更优，表明融合多时间粒度特征、站点空间关联及外部影响因素能够有效提升地铁短时进站客流预测的精度与稳定性。

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沈鹏举，男，博士研究生，研究方向为交通大数据挖掘，21114022@bjtu.edu.cn

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沈鹏举，男，博士研究生，研究方向为交通大数据挖掘，21114022@bjtu.edu.cn

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