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Using interactions and dynamics for mining groups of moving objects from trajectory data / Corrado Loglisci in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 7-8 (July - August 2018) . - pp 1436 - 1468 Titre : Using interactions and dynamics for mining groups of moving objects from trajectory data Type de document : Article/Communication Auteurs : Corrado Loglisci, Auteur Année de publication : 2018 Article en page(s) : pp 1436 - 1468 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] données spatiotemporelles [Termes IGN] objet mobile [Termes IGN] similitude [Termes IGN] trajectoire (véhicule non spatial) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Advances in tracking technology enable the gathering of spatio-temporal data in the form of trajectories, which when analysed can convey useful knowledge. In particular, discovering groups of moving objects is a valuable means for a wide class of problems related to mobility. The task of group mining has been investigated by considering mostly the spatial closeness and similarity of the trajectories, while little attention has been paid to the relationships between the trajectories and time-changing nature of the trajectories. The relationships may provide evidence of interactions between the moving objects. The time-changing nature may provide evidence of dynamics of the movements. Therefore, interactions and dynamics can be sources of information to be considered in order to discover new forms of groups. Motivated by this, we introduce the concept of crews and propose a method to discover crews. A crew gathers moving objects with similar interactions and similar dynamics. The proposed method relies on i) new movement parameters, which explicitly consider interactions and dynamics, and ii) a distance-free clustering algorithm, which groups objects based on the similarity of the movement parameters. We conduct extensive experiments, which include a quantitative evaluation of the quality of the crews and comparison with alternative solutions. Numéro de notice : A2018-280 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2017.1416473 Date de publication en ligne : 21/12/2017 En ligne : https://doi.org/10.1080/13658816.2017.1416473 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90362 [article]

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