Article(id=1242137749944344917, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242137733611725071, articleNumber=null, orderNo=11, doi=10.3981/j.issn.1000-7857.2019.16.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1550678400000, receivedDateStr=2019-02-21, revisedDate=1557590400000, revisedDateStr=2019-05-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1567065196373, onlineDateStr=2019-08-29, pubDate=1566921600000, pubDateStr=2019-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1567065196373, onlineIssueDateStr=2019-08-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078317386, creator=sys-migrate, updateTime=1774078317386, updator=sys-migrate, issue=Issue{id=1242137733611725071, tenantId=1146029695717560320, journalId=1146031591421210625, year='2019', volume='37', issue='16', pageStart='1', pageEnd='116', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078313493, creator=sys-migrate, updateTime=1774078313493, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=74, endPage=82, ext={EN=ArticleExt(id=1242137753907962213, articleId=1242137749944344917, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Forecast models for commercial street pedestrian traffic flow data based on intelligent video analysis, columnId=1242137740062564645, journalTitle=Science & Technology Review, columnName=Exclusive: Modern Emergency Management, runingTitle=null, highlight=null, articleAbstract=The sampling, the preprocessing, and the modeling for the commercial street pedestrian traffic flow data based on the intelligent video analysis are presented in this paper. The Xidan mall is taken as an example, the by-date grouping-style vertical time series are established, consisting of multi surveillance points and different points-in-time. The modeling and forecast results show that all vertical time series are stationary and of non-white noise with similar ARMA expression formulas, which can be well applied to the forecast of pedestrian traffic flow data., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=t7MYuw3b8/KXx7RVeNiIqg==, pdfFileSize=3006236, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. Research Laboratory of Safety and Emergency Management, Beijing Municipal Institute of Labour Protection, Beijing 100054, China;
2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China, fund=null, authors=NI Huihui¹, WU Bohong^2,3, authorsList=NI Huihui, WU Bohong), CN=ArticleExt(id=1242137752758722911, articleId=1242137749944344917, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=面向视频智能分析的商业街行人交通流预测建模, columnId=1242137739290812697, journalTitle=科技导报, columnName=专题：现代应急管理, runingTitle=null, highlight=null, articleAbstract=阐述了利用面向视频智能分析技术，对商业区行人交通流数据进行样本提取、预处理和建模分析的方法全过程。以北京西单商业区为例，构建了包含不同类型监测点、不同时间点的日期分组式纵向时间序列，并完成了预测建模和效果对比。研究表明，所有序列均为平稳非白噪声序列，具有相似的自回归移动平均（ARMA）模型形式，能较好地实现对行人流量的预测。, correspAuthors=null, authorNote=倪慧荟,助理研究员,研究方向为安全与应急管理,电子信箱:nihuihui@kingehs.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=XjU0+wTUN4/e3bn4W80GLw==, pdfFileSize=3006236, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 北京市劳动保护科学研究所安全与应急管理研究室, 北京 100054;
2. 中国科学院科技战略咨询研究院, 北京 100190;
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