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
Links to Exploration step
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<front><div type="abstract" xml: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.</div>
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<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>
<biblScope><date>2011</date>
</biblScope>
<idno type="pISSN">0302-9743</idno>
<idno type="eISSN">1611-3349</idno>
<idno type="series-Id">558</idno>
</series>
<series><title level="s">Lecture Notes in Artificial Intelligence</title>
<editor><persName><forename type="first">David</forename>
<surname>Hutchison</surname>
</persName>
<affiliation>Lancaster University, Lancaster, UK</affiliation>
</editor>
<editor><persName><forename type="first">Takeo</forename>
<surname>Kanade</surname>
</persName>
<affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Josef</forename>
<surname>Kittler</surname>
</persName>
<affiliation>University of Surrey, Guildford, UK</affiliation>
</editor>
<editor><persName><forename type="first">Jon</forename>
<forename type="first">M.</forename>
<surname>Kleinberg</surname>
</persName>
<affiliation>Cornell University, Ithaca, NY, USA</affiliation>
</editor>
<editor><persName><forename type="first">Friedemann</forename>
<surname>Mattern</surname>
</persName>
<affiliation>ETH Zurich, Zurich, 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 Gakuen, 3-1-10, Naka, Kunitachi, 184-0004, Tokyo, Japan</affiliation>
</editor>
<editor><persName><forename type="first">Jianping</forename>
<surname>Yin</surname>
</persName>
<email>jpyin@nudt.edu.cn</email>
<affiliation>School of Computer, National University of Defense Technology, 410073, Changsha, China</affiliation>
</editor>
<editor><persName><forename type="first">Jun</forename>
<surname>Long</surname>
</persName>
<email>junlong@nudt.edu.cn</email>
<affiliation>Department of Network Engineering, National University of Defense Technology, Yanwachi Street 137, 410073, Changsha, Hunan, China</affiliation>
</editor>
<idno type="pISSN">0302-9743</idno>
<idno type="eISSN">1611-3349</idno>
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<idno type="DOI">10.1007/978-3-642-22589-5_5</idno>
<idno type="ChapterID">5</idno>
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<profileDesc><creation><date>2011</date>
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<langUsage><language ident="en">en</language>
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<abstract xml:lang="en"><p>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.</p>
</abstract>
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<item><term>Artificial Intelligence (incl. Robotics)</term>
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<item><term>Information Systems Applications (incl.Internet)</term>
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<item><term>Data Mining and Knowledge Discovery</term>
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<change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-09-22">References added</change>
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<Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName>
<OrgAddress><City>Rehovot</City>
<Country>Israel</Country>
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</Affiliation>
<Affiliation ID="Aff8"><OrgName>University of Bern</OrgName>
<OrgAddress><City>Bern</City>
<Country>Switzerland</Country>
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<OrgAddress><City>Madras</City>
<Country>India</Country>
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<OrgAddress><City>Edmonton</City>
<Country>Canada</Country>
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<Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName>
<OrgAddress><City>Saarbrücken</City>
<Country>Germany</Country>
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</Affiliation>
<Affiliation ID="Aff18"><OrgName>DFKI and University of Saarland</OrgName>
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<Contact><Email>vtorra@iiia.csic.es</Email>
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<Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Yasuo</GivenName>
<FamilyName>Narakawa</FamilyName>
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<Contact><Email>narukawa@d4.dion.ne.jp</Email>
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<Editor AffiliationIDS="Aff21"><EditorName DisplayOrder="Western"><GivenName>Jianping</GivenName>
<FamilyName>Yin</FamilyName>
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<Contact><Email>jpyin@nudt.edu.cn</Email>
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<Editor AffiliationIDS="Aff22"><EditorName DisplayOrder="Western"><GivenName>Jun</GivenName>
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<Contact><Email>junlong@nudt.edu.cn</Email>
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<Affiliation ID="Aff19"><OrgDivision>Artificial Intelligence Research Institute (IIIA) Spanish National Research Council (CSIC)</OrgDivision>
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<OrgAddress><Street>Campus Universitat Autonoma de Barcelona</Street>
<Postcode>08193</Postcode>
<City>Bellaterra</City>
<State>Catalonia</State>
<Country>Spain</Country>
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</Affiliation>
<Affiliation ID="Aff20"><OrgName>Toho Gakuen</OrgName>
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<OrgAddress><Postcode>410073</Postcode>
<City>Changsha</City>
<Country>China</Country>
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<Affiliation ID="Aff22"><OrgDivision>Department of Network Engineering</OrgDivision>
<OrgName>National University of Defense Technology</OrgName>
<OrgAddress><Street>Yanwachi Street 137</Street>
<Postcode>410073</Postcode>
<City>Changsha</City>
<State>Hunan</State>
<Country>China</Country>
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<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>
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<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>
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<KeywordGroup Language="En"><Heading>Keywords</Heading>
<Keyword>Information Fusion</Keyword>
<Keyword>Intelligent Transportation Systems</Keyword>
<Keyword>Cyber-infrastructure</Keyword>
<Keyword>Parallelization</Keyword>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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