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Review and classification of trajectory summarisation algorithms: From compression to segmentation

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Grupo de Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorAmigo Herrero, Daniel
dc.contributor.authorSánchez Pedroche, David
dc.contributor.authorGarcía Herrero, Jesús
dc.contributor.authorMolina López, José Manuel
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-06-29T08:11:07Z
dc.date.available2023-06-29T08:11:07Z
dc.date.issued2021-10-30
dc.description.abstractWith the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.en
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-Ren
dc.identifier.bibliographicCitationAmigo D, Sánchez Pedroche D, García J, Molina JM. Review and classification of trajectory summarisation algorithms: From compression to segmentation. International Journal of Distributed Sensor Networks. 2021;17(10). doi:10.1177/15501477211050729en
dc.identifier.doihttps://doi.org/10.1177/15501477211050729
dc.identifier.issn1550-1329
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue10
dc.identifier.publicationlastpage27
dc.identifier.publicationtitleInternational Journal of Distributed Sensor Networksen
dc.identifier.publicationvolume17
dc.identifier.urihttps://hdl.handle.net/10016/37673
dc.identifier.uxxiAR/0000032762
dc.language.isoeng
dc.publisherSage Journalsen
dc.relation.projectIDGobierno de España. TEC2017-88048-C2-2-Res
dc.rights© The Author(s) 2021en
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.othertrajectory summarisationen
dc.subject.othertrajectory segmentationen
dc.subject.othertrajectory compressionen
dc.subject.otherdata compressionen
dc.subject.otherDouglas-Peuckeren
dc.subject.otherspatial data analysisen
dc.subject.othertrajectory partitioningen
dc.titleReview and classification of trajectory summarisation algorithms: From compression to segmentationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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