Article(id=1242136187780018462, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242136177642381640, articleNumber=null, orderNo=7, doi=10.3981/j.issn.1000-7857.2017.14.001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1494518400000, receivedDateStr=2017-05-12, revisedDate=1497369600000, revisedDateStr=2017-06-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1501296569233, onlineDateStr=2017-07-29, pubDate=1501171200000, pubDateStr=2017-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1501296569233, onlineIssueDateStr=2017-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774077944937, creator=sys-migrate, updateTime=1774077944937, updator=sys-migrate, issue=Issue{id=1242136177642381640, tenantId=1146029695717560320, journalId=1146031591421210625, year='2017', volume='35', issue='14', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='1501171200000', pubDateStr='2017-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774077942521, creator='sys-migrate', updateTime=1774077942521, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=15, endPage=22, ext={EN=ArticleExt(id=1242136191689109807, articleId=1242136187780018462, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Advances in modeling spatial interaction network, columnId=1242133161577095994, journalTitle=Science & Technology Review, columnName=Special Issues, runingTitle=null, highlight=null, articleAbstract=The spatial interaction network is the space-embedded directed network with flows of people, goods or information among places. Examples of spatial interaction networks include the international trade network, the migration network, the transportation network and the inter-city telecommunication network. Understanding and predicting spatial interaction patterns of these networks are of importance in various disciplines, including the regional science, the transportation science and the economic geography, with many practical applications in the urban or transportation system planning, the epidemiology of infectious diseases, and the location-based services. This paper first introduces briefly two classic spatial interaction models:the gravity model and the intervening opportunity model, and then reviews some recent advances in data-driven spatial interaction models, including the radiation model, the population-weighted opportunity model and the random walk models on spatial interaction networks. This paper also discusses some challenging problems in modeling the spatial interaction networks, such as the individual mobility behavior diversity, the group choice decision behavior experiment and the datadriven modeling approach for the human activity-travel behavior., authors=YAN Xiaoyong, authorsList=YAN Xiaoyong, authorCompany=Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=Lsfd9QWFad+7v/lMTB6VEA==, pdfFileSize=2308413, 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=1242136189461930328, articleId=1242136187780018462, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=空间交互网络研究进展, columnId=1242116902361830275, journalTitle=科技导报, columnName=专题论文, runingTitle=null, highlight=null, articleAbstract=空间交互网络是人、商品和信息等在地点之间流动而形成的嵌入在空间中的有向流网络。典型的空间交互网络包括国际贸易网络、人口迁移网络、人群出行网络及电话通信网络等。理解和预测空间交互网络中的流量分布模式不仅是区域科学、交通科学、经济地理学等很多领域长期以来的一个重要研究主题，在城市和交通规划、疾病传播防控、商业服务等领域也具有广泛应用价值。本文在简要介绍引力模型、介入机会模型等经典空间交互模型的基础上，着重对近年来复杂系统研究领域在空间交互网络建模方面的研究成果进行介绍，包括辐射模型、人口权重机会模型及空间交互网络上的随机游走模型等，并且对空间交互网络研究中存在的挑战性问题进行探讨，包括个体多样性行为建模、群体空间交互决策行为实验、数据驱动的活动-出行行为研究等。, authors=闫小勇, authorsList=闫小勇, authorCompany=北京交通大学交通系统科学与工程研究院, 北京 100044, correspAuthors=null, authorNote=闫小勇,副教授,研究方向为复杂网络和出行行为复杂性,电子信箱:yanxy@bjtu.edu.cn, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=2Xyy2bSMGQcWHsU48awdtg==, pdfFileSize=2308413, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, 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