Article(id=1282032579066970717, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.09.0600, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742745600000, receivedDateStr=2025-03-24, revisedDate=1749312000000, revisedDateStr=2025-06-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589985863, onlineDateStr=2026-07-09, pubDate=1758988800000, pubDateStr=2025-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589985863, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589985863, creator=13701087609, updateTime=1783589985863, updator=13701087609, issue=Issue{id=1282032292440805784, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='9', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1758988800000', pubDateStr='2025-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589917526, creator='13701087609', updateTime=1783593837820, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048735404344166, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048735404344167, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=228, endPage=235, ext={EN=ArticleExt(id=1282032581524832864, articleId=1282032579066970717, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=An early warning model for passenger flow status in urban rail transit stations based on YOLO11-DeepSort algorithm, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

In order to enhance safety management within urban rail transit stations, a passenger flow detection and early warning model based on the YOLO11-DeepSort algorithm was proposed. First, a pedestrian head dataset was established and put into YOLO11 to train model parameters for extracting passenger flow density. Then, DeepSort was used for dynamic tracking of passengers' walking paths to extract parameters of passenger flow and walking speed. According to the parameters of passenger flow status at station bottlenecks such as transfer passageways, stairs and platforms, FCM algorithm was used to divide the passenger flow status into 6 subcategories, and 4 early warning levels were divided corresponding to the passenger flow status. Finally, taking Guanyinqiao Station of Chongqing Rail Transit as an example, the first 4 days of real-scene station videos were used to identify passenger flow and extract parameters, and the FCM model was trained based on those parameters to get clustering centers and thresholds. By using the model after training, the 5^th day's real-scene video was classified, and the classification results of full-day, peak and off-peak periods were compared. The results show that the platforms in the station have the longest congestion time and the greatest risk, with the peak-hour congestion duration accounting for 70% and the Level 1 early warning duration accounting for 15%. Stairs in the station have the second longest congestion time and risk, with the peak-hour congestion duration accounting for 46% and the Level 1 early warning duration accounting for 10%. Passageways in the station have shorter congestion time and lower risk, with the peak-hour congestion duration accounting for 41% and the Level 1 early warning duration accounting for 5%.

, authors=Jiaxin KONG¹, Hongyan XIANG^{**, 1}, Zhe YANG¹, Wenbo FAN², authorsList=Jiaxin KONG, Hongyan XIANG, Zhe YANG, Wenbo FAN, authorCompany=null, correspAuthors=Hongyan XIANG, 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=1282032586180510308, articleId=1282032579066970717, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于YOLO11-DeepSort算法的城市轨道交通站内客流状态预警模型, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为提高城市轨道交通车站内的安全管理，提出一种基于YOLO11-DeepSort算法的城市轨道交通站内客流检测及预警模型。首先，建立行人头部数据集，并输入YOLO11训练模型参数，提取客流密度；其次，结合DeepSort动态跟踪乘客步行路径，提取客流量、步行速度；然后，针对换乘通道、楼梯、站台等局部瓶颈的客流状态参数提取结果，运用模糊C均值聚类（FCM）算法将客流状态划分为6小类，并根据客流状态对应的危险等级分为4个预警等级；最后，以重庆轨道交通观音桥站为例，用前4天真实场景视频进行参数识别分析，训练FCM算法后得到聚类中心与阈值。运用训练后的算法对第5天的视频进行分类，并比较全天、高峰、平峰时段的分类结果。结果表明：站台是全天客流拥挤时间最长、风险最大的区域，站台高峰时段拥挤时长占比70%，一级预警时长占比15%；其次是楼梯，高峰拥挤时长占比46%，一级预警时长占比10%；最后是通道，高峰时段拥挤时长占比41%，一级预警时长占比5%。

, authors=孔佳鑫¹, 向红艳教授^{**, 1}, 杨哲¹, 范文博副教授², authorsList=孔佳鑫, 向红艳教授, 杨哲, 范文博副教授, authorCompany=null, correspAuthors=向红艳教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=sKTrs9DjVoipfJ2QdXrNoQ==, magXml=t/JR28FiK2zesoQJi2Bz7A==, pdfUrl=null, pdf=9+6HUGGszq0BfrUrS65zNA==, pdfFileSize=9014141, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=0CbqL/BqWl13KcFk6wVh+A==, mapNumber=null, fund=null)}, authors=[Author(id=1282032590618083948, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032579066970717, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=98140317@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282032591914123886, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032579066970717, authorId=1282032590618083948, language=EN, stringName=Jiaxin KONG, firstName=Jiaxin, middleName=null, lastName=KONG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282032593155637871, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032579066970717, authorId=1282032590618083948, language=CN, stringName=孔佳鑫, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹重庆交通大学 交通运输学院，重庆 400074, bio={"content":"

孔佳鑫（1999—），男，河南焦作人，硕士研究生，主要研究方向为交通运输规划与管理。E-mail:98140317@qq.com。

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孔佳鑫（1999—），男，河南焦作人，硕士研究生，主要研究方向为交通运输规划与管理。E-mail:98140317@qq.com。

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