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Conference Paper: Mining frequent trajectory patterns from GPS tracks
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TitleMining frequent trajectory patterns from GPS tracks
 
AuthorsChen, G1
Chen, B3
Yu, Y2
 
KeywordsSptiotemporal data
Clustering
Frequent trajectory patterns
Graph-based searching
Trajectory database
 
Issue Date2010
 
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003013
 
CitationThe 2010 International Conference on Computational Intelligence and Software Engineering (CiSE 2010), Wuhan, China, 10-12 December 2010. In Proceedings of CiSE, 2010, p. 1-6 [How to Cite?]
DOI: http://dx.doi.org/10.1109/CISE.2010.5677000
 
AbstractAs recent advances and wide usage of mobile devices with positioning capabilities, trajectory database that captures the historical movements of populations of moving objects becomes important. Given such a database that contains many taxi trajectories, we study a new problem of discovering frequent sequential patterns. The proposed method comprises two phases. First, we cluster the stay points of taxis to get collocation patterns for passengers. Then, for each pattern instance, we use an efficient graph-based searching algorithm to mine the frequent trajectory patterns, which utilizes the adjacency property to reduce the search space. The performance evaluation demonstrates that our method outperforms the Apriori-based and PrefixSpan-based methods. ©2010 IEEE.
 
ISBN978-1-4244-5392-4
 
DOIhttp://dx.doi.org/10.1109/CISE.2010.5677000
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorChen, G
 
dc.contributor.authorChen, B
 
dc.contributor.authorYu, Y
 
dc.date.accessioned2011-09-23T06:04:34Z
 
dc.date.available2011-09-23T06:04:34Z
 
dc.date.issued2010
 
dc.description.abstractAs recent advances and wide usage of mobile devices with positioning capabilities, trajectory database that captures the historical movements of populations of moving objects becomes important. Given such a database that contains many taxi trajectories, we study a new problem of discovering frequent sequential patterns. The proposed method comprises two phases. First, we cluster the stay points of taxis to get collocation patterns for passengers. Then, for each pattern instance, we use an efficient graph-based searching algorithm to mine the frequent trajectory patterns, which utilizes the adjacency property to reduce the search space. The performance evaluation demonstrates that our method outperforms the Apriori-based and PrefixSpan-based methods. ©2010 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.description.otherThe 2010 International Conference on Computational Intelligence and Software Engineering (CiSE 2010), Wuhan, China, 10-12 December 2010. In Proceedings of CiSE, 2010, p. 1-6
 
dc.identifier.citationThe 2010 International Conference on Computational Intelligence and Software Engineering (CiSE 2010), Wuhan, China, 10-12 December 2010. In Proceedings of CiSE, 2010, p. 1-6 [How to Cite?]
DOI: http://dx.doi.org/10.1109/CISE.2010.5677000
 
dc.identifier.doihttp://dx.doi.org/10.1109/CISE.2010.5677000
 
dc.identifier.epage6
 
dc.identifier.hkuros194324
 
dc.identifier.isbn978-1-4244-5392-4
 
dc.identifier.scopuseid_2-s2.0-79951622608
 
dc.identifier.spage1
 
dc.identifier.urihttp://hdl.handle.net/10722/140002
 
dc.languageeng
 
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003013
 
dc.publisher.placeUnited States
 
dc.relation.ispartofInternational Conference on Computational Intelligence and Software Engineering Proceedings
 
dc.relation.referencesReferences in Scopus
 
dc.rightsInternational Conference on Computational Intelligence and Software Engineering Proceedings. Copyright © IEEE.
 
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectSptiotemporal data
 
dc.subjectClustering
 
dc.subjectFrequent trajectory patterns
 
dc.subjectGraph-based searching
 
dc.subjectTrajectory database
 
dc.titleMining frequent trajectory patterns from GPS tracks
 
dc.typeConference_Paper
 
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<subject>Sptiotemporal data</subject>
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Author Affiliations
  1. Zhejiang University
  2. University of Illinois at Urbana-Champaign
  3. Chinese Academy of Sciences