A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data
Identifieur interne : 000144 ( Istex/Corpus ); précédent : 000143; suivant : 000145A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data
Auteurs : Yingjie Xia ; Chengkun Wu ; Qingjie Kong ; Zhenyu Shan ; Li KuangSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Information fusion technology has been introduced for data analysis in intelligent transportation systems (ITS) in order to generate a more accurate evaluation of the traffic state. The data collected from multiple heterogeneous traffic sensors are converted into common traffic state features, such as mean speed and volume. Afterwards, we design a hierarchical evidential fusion model (HEFM) based on D-S Evidence Theory to implement the feature-level fusion. When the data quantity reaches a large amount, HEFM can be parallelized in data-centric mode, which mainly consists of region-based data decomposition by quadtree and fusion task scheduling. The experiments are conducted to testify the scalability of this parallel fusion model on accuracy and efficiency as the numbers of decomposed sub-regions and cyberinfrastructure computing nodes increase. The results show that significant speedups can be achieved without loss in accuracy.
Url:
DOI: 10.1007/978-3-642-22589-5_5
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China</affiliation><affiliation>Department of Automation, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, P.R. China</affiliation></author><author xml:id="author-2"><persName><forename type="first">Chengkun</forename><surname>Wu</surname></persName><email>chengkun.wu@postgrad.manchester.ac.uk</email><affiliation>Manchester Interdisciplinary Biocentre, University of Manchester, M1 7DN, Manchester, United Kingdom</affiliation></author><author xml:id="author-3"><persName><forename type="first">Qingjie</forename><surname>Kong</surname></persName><email>qjkong@sjtu.edu.cn</email><affiliation>Department of Automation, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, P.R. China</affiliation></author><author xml:id="author-4"><persName><forename type="first">Zhenyu</forename><surname>Shan</surname></persName><email>shanzhenyu@zju.edu.cn</email><affiliation>Hangzhou Institute of Service Engineering, Hangzhou Normal University, 310012, Hangzhou, P.R. China</affiliation></author><author xml:id="author-5"><persName><forename type="first">Li</forename><surname>Kuang</surname></persName><email>kuangli@zju.edu.cn</email><affiliation>Hangzhou Institute of Service Engineering, Hangzhou Normal University, 310012, Hangzhou, P.R. 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Switzerland</affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation>Stanford University, Stanford, CA, USA</affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation>University of Bern, Bern, Switzerland</affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation>Indian Institute of Technology, Madras, India</affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Dortmund, Germany</affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>University of California, Los Angeles, CA, USA</affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Randy</forename><surname>Goebel</surname></persName><affiliation>University of Alberta, Edmonton, Canada</affiliation></editor><editor><persName><forename type="first">Jörg</forename><surname>Siekmann</surname></persName><affiliation>University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Wolfgang</forename><surname>Wahlster</surname></persName><affiliation>DFKI and University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Vicenç</forename><surname>Torra</surname></persName><email>vtorra@iiia.csic.es</email><affiliation>Artificial Intelligence Research Institute (IIIA) Spanish National Research Council (CSIC), IIIA-CSIC, Campus Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain</affiliation></editor><editor><persName><forename type="first">Yasuo</forename><surname>Narakawa</surname></persName><email>narukawa@d4.dion.ne.jp</email><affiliation>Toho 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The data collected from multiple heterogeneous traffic sensors are converted into common traffic state features, such as mean speed and volume. Afterwards, we design a hierarchical evidential fusion model (HEFM) based on D-S Evidence Theory to implement the feature-level fusion. When the data quantity reaches a large amount, HEFM can be parallelized in data-centric mode, which mainly consists of region-based data decomposition by quadtree and fusion task scheduling. The experiments are conducted to testify the scalability of this parallel fusion model on accuracy and efficiency as the numbers of decomposed sub-regions and cyberinfrastructure computing nodes increase. 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Artificial Intelligence</BookTitle></PartContext></PartInfo><SubPart ID="SubPart1"><SubPartInfo><SubPartID>1</SubPartID><SubPartSequenceNumber>1</SubPartSequenceNumber><SubPartTitle>Aggregation Operators and Decision Making</SubPartTitle><SubPartChapterCount>6</SubPartChapterCount></SubPartInfo><Chapter ID="Chap5" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>5</ChapterID><ChapterDOI>10.1007/978-3-642-22589-5_5</ChapterDOI><ChapterSequenceNumber>5</ChapterSequenceNumber><ChapterTitle Language="En">A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data</ChapterTitle><ChapterFirstPage>31</ChapterFirstPage><ChapterLastPage>42</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>2</PartID><BookID>978-3-642-22589-5</BookID><BookTitle>Modeling Decision for Artificial Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff23 Aff24"><AuthorName DisplayOrder="Western"><GivenName>Yingjie</GivenName><FamilyName>Xia</FamilyName></AuthorName><Contact><Email>Xiayingjie@zju.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff25"><AuthorName DisplayOrder="Western"><GivenName>Chengkun</GivenName><FamilyName>Wu</FamilyName></AuthorName><Contact><Email>chengkun.wu@postgrad.manchester.ac.uk</Email></Contact></Author><Author AffiliationIDS="Aff24"><AuthorName DisplayOrder="Western"><GivenName>Qingjie</GivenName><FamilyName>Kong</FamilyName></AuthorName><Contact><Email>qjkong@sjtu.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Zhenyu</GivenName><FamilyName>Shan</FamilyName></AuthorName><Contact><Email>shanzhenyu@zju.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Li</GivenName><FamilyName>Kuang</FamilyName></AuthorName><Contact><Email>kuangli@zju.edu.cn</Email></Contact></Author><Affiliation ID="Aff23"><OrgDivision>Hangzhou Institute of Service Engineering</OrgDivision><OrgName>Hangzhou Normal University</OrgName><OrgAddress><Postcode>310012</Postcode><City>Hangzhou</City><Country>P.R. China</Country></OrgAddress></Affiliation><Affiliation ID="Aff24"><OrgDivision>Department of Automation, School of Electronic, Information, and Electrical Engineering</OrgDivision><OrgName>Shanghai Jiao Tong University</OrgName><OrgAddress><Postcode>200240</Postcode><City>Shanghai</City><Country>P.R. China</Country></OrgAddress></Affiliation><Affiliation ID="Aff25"><OrgDivision>Manchester Interdisciplinary Biocentre</OrgDivision><OrgName>University of Manchester</OrgName><OrgAddress><Postcode>M1 7DN</Postcode><City>Manchester</City><Country>United Kingdom</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Information fusion technology has been introduced for data analysis in intelligent transportation systems (ITS) in order to generate a more accurate evaluation of the traffic state. The data collected from multiple heterogeneous traffic sensors are converted into common traffic state features, such as mean speed and volume. Afterwards, we design a hierarchical evidential fusion model (HEFM) based on D-S Evidence Theory to implement the feature-level fusion. When the data quantity reaches a large amount, HEFM can be parallelized in data-centric mode, which mainly consists of region-based data decomposition by quadtree and fusion task scheduling. The experiments are conducted to testify the scalability of this parallel fusion model on accuracy and efficiency as the numbers of decomposed sub-regions and cyberinfrastructure computing nodes increase. The results show that significant speedups can be achieved without loss in accuracy.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Information Fusion</Keyword><Keyword>Intelligent Transportation Systems</Keyword><Keyword>Cyber-infrastructure</Keyword><Keyword>Parallelization</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></SubPart></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data</title></titleInfo><name type="personal"><namePart type="given">Yingjie</namePart><namePart type="family">Xia</namePart><affiliation>Hangzhou Institute of Service Engineering, Hangzhou Normal University, 310012, Hangzhou, P.R. China</affiliation><affiliation>Department of Automation, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, P.R. China</affiliation><affiliation>E-mail: Xiayingjie@zju.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Chengkun</namePart><namePart type="family">Wu</namePart><affiliation>Manchester Interdisciplinary Biocentre, University of Manchester, M1 7DN, Manchester, United Kingdom</affiliation><affiliation>E-mail: chengkun.wu@postgrad.manchester.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Qingjie</namePart><namePart type="family">Kong</namePart><affiliation>Department of Automation, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, P.R. China</affiliation><affiliation>E-mail: qjkong@sjtu.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Zhenyu</namePart><namePart type="family">Shan</namePart><affiliation>Hangzhou Institute of Service Engineering, Hangzhou Normal University, 310012, Hangzhou, P.R. China</affiliation><affiliation>E-mail: shanzhenyu@zju.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Li</namePart><namePart type="family">Kuang</namePart><affiliation>Hangzhou Institute of Service Engineering, Hangzhou Normal University, 310012, Hangzhou, P.R. China</affiliation><affiliation>E-mail: kuangli@zju.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="proceedings"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Information fusion technology has been introduced for data analysis in intelligent transportation systems (ITS) in order to generate a more accurate evaluation of the traffic state. The data collected from multiple heterogeneous traffic sensors are converted into common traffic state features, such as mean speed and volume. Afterwards, we design a hierarchical evidential fusion model (HEFM) based on D-S Evidence Theory to implement the feature-level fusion. When the data quantity reaches a large amount, HEFM can be parallelized in data-centric mode, which mainly consists of region-based data decomposition by quadtree and fusion task scheduling. The experiments are conducted to testify the scalability of this parallel fusion model on accuracy and efficiency as the numbers of decomposed sub-regions and cyberinfrastructure computing nodes increase. The results show that significant speedups can be achieved without loss in accuracy.</abstract><relatedItem type="host"><titleInfo><title>Modeling Decision for Artificial Intelligence</title><subTitle>8th International Conference, MDAI 2011, Changsha, Hunan, China, July 28-30, 2011, Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Vicenç</namePart><namePart type="family">Torra</namePart><affiliation>Artificial Intelligence Research Institute (IIIA) Spanish National Research Council (CSIC), IIIA-CSIC, Campus Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain</affiliation><affiliation>E-mail: vtorra@iiia.csic.es</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Yasuo</namePart><namePart type="family">Narakawa</namePart><affiliation>Toho Gakuen, 3-1-10, Naka, Kunitachi, 184-0004, Tokyo, Japan</affiliation><affiliation>E-mail: narukawa@d4.dion.ne.jp</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jianping</namePart><namePart type="family">Yin</namePart><affiliation>School of Computer, National University of Defense Technology, 410073, Changsha, China</affiliation><affiliation>E-mail: jpyin@nudt.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jun</namePart><namePart type="family">Long</namePart><affiliation>Department of Network Engineering, National University of Defense Technology, Yanwachi Street 137, 410073, Changsha, Hunan, China</affiliation><affiliation>E-mail: junlong@nudt.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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|area= CyberinfraV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:E31045D3C1FACAD68ED41A5EE1612C6A5C7EBFD6
|texte= A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data
}}
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