Article(id=1281608101883252903, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.0517, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760371200000, receivedDateStr=2025-10-14, revisedDate=1766160000000, revisedDateStr=2025-12-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488782616, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488782616, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488782616, creator=13701087609, updateTime=1783488782616, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=244, endPage=251, ext={EN=ArticleExt(id=1281608103506448552, articleId=1281608101883252903, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Deep learning prediction for subway section passenger flow integrating physical information and snow geese optimization, columnId=1277328338121261909, journalTitle=China Safety Science Journal, columnName=Public Safety and Emergency Management, runingTitle=null, highlight=null, articleAbstract=

The rapid growth of passenger volume of urban rail transit and the urgent need for intelligent operation have made accurate section passenger flow prediction a key technical challenge to improve the level of dynamic scheduling and safety control. To this end, this paper innovatively integrated the physical information constraint mechanism, data-driven method and SGA, and proposed a new deep learning framework. Firstly, a physical residual term was designed and embedded into memory cells as a regulation signal, forcing the model to learn the physical laws of passenger flow while retaining the temporal characteristics of passenger flow. Secondly, a dual-objective fitness function based on physical loss and data loss was innovatively proposed to achieve further optimization of model performance while establishing a constraint mechanism. Finally, SGA was used to balance the differentiated and synergistic effects of hyperparameters in the model. Experimental results show that the constructed model exhibits good predictive performance on both the training set and the validation set. The improved fitness function can narrow the error range of the model prediction results. In the two-stage ablation experiment, the mean square error range of the proposed deep learning framework is reduced by 71.03% compared with the long short-term memory model, which verifies the synergistic enhancement of the model prediction ability by the simultaneous introduction of physical constraint mechanism and intelligent optimization algorithm.

, authors=Jiahui Wan¹, Xiaoxia Yang^{**, 1}, Yuanlei Kang², Chuang Shao³, authorsList=Jiahui Wan, Xiaoxia Yang, Yuanlei Kang, Chuang Shao, 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=1281608104076873899, articleId=1281608101883252903, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=融合物理信息与雪雁优化的地铁断面客流深度学习预测, columnId=1277328338280645463, journalTitle=中国安全科学学报, columnName=公共安全与应急管理, runingTitle=null, highlight=null, articleAbstract=

为提升城市轨道交通客运量的动态调度与安全管控水平，融合物理信息约束机制、数据驱动与雪雁优化算法（SGA），提出一种新型深度学习框架。首先，设计一种物理残差项，并作为调节信号嵌入记忆细胞，强制模型在保留时序特征的同时学习客流物理特征；然后，提出一种基于物理损失与数据损失的双目标适应度函数，在建立约束机制的同时实现对模型性能的再度优化；最后，采用SGA平衡模型中超参数的差异化作用与协同影响。结果表明：所构建的模型在训练集与验证集上均表现出良好的预测性能，改进后的新适应度函数能够使模型预测结果的误差范围更小。在两阶段消融试验中，所提出的深度学习框架较长短期记忆模型的均方误差范围缩小71.03%，验证了物理约束机制与智能优化算法的同步引入对模型预测能力的协同增强作用。

, authors=万佳慧¹, 杨晓霞教授^{**, 1}, 康元磊高级工程师², 邵闯³, authorsList=万佳慧, 杨晓霞教授, 康元磊高级工程师, 邵闯, authorCompany=null, correspAuthors=杨晓霞教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=1+iM4C4hU/+5XZOPKy+Ftg==, magXml=t7cz/KLTDFEFKm9U1NrZsQ==, pdfUrl=null, pdf=oJP0Pcwy+G95k3h4/Z+iDg==, pdfFileSize=3800719, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=tyw6oMgIbQ7LCou8bU4p+g==, mapNumber=null, fund=null)}, authors=[Author(id=1281608104655687862, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608101883252903, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wanjiahmm@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608104731185336, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608101883252903, authorId=1281608104655687862, language=EN, stringName=Jiahui Wan, firstName=Jiahui, middleName=null, lastName=Wan, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Information and Control Engineering, Qingdao University of Technology, Qingdao Shandong 266520, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608104794099897, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608101883252903, authorId=1281608104655687862, language=CN, stringName=万佳慧, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹青岛理工大学 信息与控制工程学院，山东 青岛 266520, bio={"content":"

万佳慧（2001—），女，山东青岛人，硕士研究生，研究方向为智能交通与协同控制。E-mail: wanjiahmm@163.com。

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万佳慧（2001—），女，山东青岛人，硕士研究生，研究方向为智能交通与协同控制。E-mail: wanjiahmm@163.com。

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