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 Field
Value
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
Author Affiliations
  1. Zhejiang University
  2. University of Illinois at Urbana-Champaign
  3. Chinese Academy of Sciences