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Article: PutMode: Prediction of uncertain trajectories in moving objects databases

TitlePutMode: Prediction of uncertain trajectories in moving objects databases
Authors
KeywordsCTBN
Moving objects databases
Trajectory clustering
Trajectory prediction
Issue Date2010
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669X
Citation
Applied Intelligence, 2010, v. 33 n. 3, p. 370-386 How to Cite?
AbstractObjective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy. © 2009 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/139820
ISSN
2015 Impact Factor: 1.215
2015 SCImago Journal Rankings: 0.777
ISI Accession Number ID
Funding AgencyGrant Number
National Natural Science Foundation of China60773169
Science and Technology Development of China2006BAI05A01
Youth Software Innovation Project of Sichuan Province2007AA0032
2007AA0028
Australian Research Council
Australian Research Council through the ICT Centre of Excellence
Funding Information:

This work is supported by the National Natural Science Foundation of China under Grant No. 60773169, the 11th Five Years Key Programs for Science and Technology Development of China under Grant No. 2006BAI05A01, the Youth Software Innovation Project of Sichuan Province under Grant No. 2007AA0032 and 2007AA0028, and NICTA which is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

References

 

DC FieldValueLanguage
dc.contributor.authorQiao, Sen_HK
dc.contributor.authorTang, Cen_HK
dc.contributor.authorJin, Hen_HK
dc.contributor.authorLong, Ten_HK
dc.contributor.authorDai, Sen_HK
dc.contributor.authorKu, Yen_HK
dc.contributor.authorChau, Men_HK
dc.date.accessioned2011-09-23T05:56:58Z-
dc.date.available2011-09-23T05:56:58Z-
dc.date.issued2010en_HK
dc.identifier.citationApplied Intelligence, 2010, v. 33 n. 3, p. 370-386en_HK
dc.identifier.issn0924-669Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/139820-
dc.description.abstractObjective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy. © 2009 Springer Science+Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0924-669Xen_HK
dc.relation.ispartofApplied Intelligenceen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectCTBNen_HK
dc.subjectMoving objects databasesen_HK
dc.subjectTrajectory clusteringen_HK
dc.subjectTrajectory predictionen_HK
dc.titlePutMode: Prediction of uncertain trajectories in moving objects databasesen_HK
dc.typeArticleen_HK
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_HK
dc.identifier.authorityChau, M=rp01051en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10489-009-0173-zen_HK
dc.identifier.scopuseid_2-s2.0-78149282726en_HK
dc.identifier.hkuros193034en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78149282726&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume33en_HK
dc.identifier.issue3en_HK
dc.identifier.spage370en_HK
dc.identifier.epage386en_HK
dc.identifier.isiWOS:000283087200011-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQiao, S=16205409900en_HK
dc.identifier.scopusauthoridTang, C=8689531100en_HK
dc.identifier.scopusauthoridJin, H=35758815800en_HK
dc.identifier.scopusauthoridLong, T=55245180000en_HK
dc.identifier.scopusauthoridDai, S=15123917000en_HK
dc.identifier.scopusauthoridKu, Y=23392932300en_HK
dc.identifier.scopusauthoridChau, M=7006073763en_HK
dc.identifier.citeulike4181202-

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