Article(id=1282037439925567951, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.1477, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752681600000, receivedDateStr=2025-07-17, revisedDate=1758729600000, revisedDateStr=2025-09-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591144782, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591144782, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591144782, creator=13701087609, updateTime=1783591144782, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=196, endPage=203, ext={EN=ArticleExt(id=1282037440135283152, articleId=1282037439925567951, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Crowd safety risk assessment based on Seq2Seq-attention network, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

In order to explore the safety of interactions between pedestrian groups moving in different directions, a risk assessment method for pedestrian safety was proposed based on a Seq2Seq network integrated with an AM. By analyzing the interactions among pedestrians, local density was introduced as an additional input feature to better characterize dynamic interpersonal behaviors. The observed sequential features of pedestrians within a continuous time window were fed into an LSTM-based encoder-decoder architecture, and AM was employed to capture multidimensional critical information during motion, enabling the reconstruction of complex crowd movement patterns across various scenarios. Furthermore, pedestrian safety risks were quantified by introducing a crowd pressure metric, and the pressure value ranges corresponding to three typical movement states were calculated to enable risk stratification. The results show that the average displacement error (ADE) and final displacement error (FDE) in trajectory prediction are less than 0.3 m under both unidirectional and bidirectional flow conditions at different densities, indicating the model's high accuracy in trajectory prediction. Based on the risk variation observed in bidirectional flow scenarios, it is found that the probability of accidents increases significantly when pedestrians' velocities change abruptly under high-density conditions. Therefore, timely warnings of crowd states and proactive intervention measures are required to mitigate potential safety risks.

, authors=Shuchao CAO¹, Weibin GE¹, Conghui LI¹, Jun ZHANG², authorsList=Shuchao CAO, Weibin GE, Conghui LI, Jun 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=1282037440621822419, articleId=1282037439925567951, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于注意力Seq2Seq网络的人群安全风险评估, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为探究不同方向人群交互的安全性，提出一种基于序列到序列（Seq2Seq）网络结合注意力机制（AM）的行人安全风险评估方法。通过分析行人之间的相互作用，引入局部密度补充模型特征输入，从而准确刻画行人间的动态交互。将观测行人连续时间窗口序列的特征输入到基于长短期记忆网络（LSTM）的编-解码器，并利用AM捕捉行人运动的多维度关键信息，最终重现不同场景中人群复杂的运动过程。进一步通过引入人群压力指标量化行人安全风险，计算得到3种典型行人运动状态下的压力值范围，并对人群安全风险进行分级。结果表明：在距离误差评估中，单向流和相向流不同密度下的平均位移误差（ADE）和最终位移误差（FDE）均低于0.3 m，验证了该方法在行人轨迹预测中的准确性。基于相向流场景中的风险变化结果，发现在高密度情形下，当行人速度发生剧烈变化时，发生事故的概率显著增加，此时需要预警人群状态并采取措施以降低安全风险。

, authors=曹淑超副教授¹, 戈伟斌¹, 李聪慧¹, 张俊教授², authorsList=曹淑超副教授, 戈伟斌, 李聪慧, 张俊教授, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=bojk4iWCKZH4WwRmxiDRUA==, magXml=isD5tlitv4lsuHitRA4AKw==, pdfUrl=null, pdf=FgyZ1kr7qAP0EkwGlssDiQ==, pdfFileSize=7753026, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=WQnfJR9H1mJtTT4yTPwPQg==, mapNumber=null, fund=null)}, authors=[Author(id=1282037441045447131, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037439925567951, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=sccao@ujs.edu, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282037441129333213, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037439925567951, authorId=1282037441045447131, language=EN, stringName=Shuchao CAO, firstName=Shuchao, middleName=null, lastName=CAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282037441192247774, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037439925567951, authorId=1282037441045447131, language=CN, stringName=曹淑超副教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹江苏大学 汽车与交通工程学院，江苏 镇江 212013, bio={"content":"

曹淑超（1989—），男，山东莱芜人，博士，副教授，主要从事人群应急疏散与行人交通流等方面的研究。E-mail: sccao@ujs.edu。

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曹淑超（1989—），男，山东莱芜人，博士，副教授，主要从事人群应急疏散与行人交通流等方面的研究。E-mail: sccao@ujs.edu。

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