File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Mining frequent trajectory patterns from GPS tracks

TitleMining frequent trajectory patterns from GPS tracks
Authors
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
Citation
The 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?
Abstract
As 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.
Persistent Identifierhttp://hdl.handle.net/10722/140002
ISBN
References

 

Author Affiliations
  1. Zhejiang University
  2. University of Illinois at Urbana-Champaign
  3. Chinese Academy of Sciences
DC FieldValueLanguage
dc.contributor.authorChen, Gen_HK
dc.contributor.authorChen, Ben_HK
dc.contributor.authorYu, Yen_HK
dc.date.accessioned2011-09-23T06:04:34Z-
dc.date.available2011-09-23T06:04:34Z-
dc.date.issued2010en_HK
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. 1-6en_HK
dc.identifier.isbn978-1-4244-5392-4-
dc.identifier.urihttp://hdl.handle.net/10722/140002-
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.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003013-
dc.relation.ispartofInternational Conference on Computational Intelligence and Software Engineering Proceedingsen_HK
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 dataen_HK
dc.subjectClusteringen_HK
dc.subjectFrequent trajectory patternsen_HK
dc.subjectGraph-based searchingen_HK
dc.subjectTrajectory database-
dc.titleMining frequent trajectory patterns from GPS tracksen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChen, G: zjucg@zju.edu.cnen_HK
dc.identifier.emailChen, B: baoquan.chen@gmail.com-
dc.identifier.emailYu, Y: yzyu@cs.hku.hk-
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CISE.2010.5677000en_HK
dc.identifier.scopuseid_2-s2.0-79951622608en_HK
dc.identifier.hkuros194324en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951622608&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-
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.scopusauthoridYu, Y=8554163500en_HK
dc.identifier.scopusauthoridChen, B=36974946000en_HK
dc.identifier.scopusauthoridChen, G=35336243800en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats