Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloMARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings
Anno di pubblicazione2020
Formato
  • Elettronico
  • Cartaceo
Autore/iMay Petry L.; Leite Da Silva C.; Esuli A.; Renso C.; Bogorny V.
Affiliazioni autoriUniversidade Federal de Santa Catarina (UFSC), Florianópolis, Brazil; Universidade Federal de Santa Catarina (UFSC), Florianópolis, Brazil; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Universidade Federal de Santa Catarina (UFSC), Florianópolis, Brazil
Autori CNR e affiliazioni
  • CHIARA RENSO
  • ANDREA ESULI
Lingua/e
  • inglese
AbstractThe increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1428
Pagine a1450
Pagine totali-
RivistaInternational journal of geographical information science (Print)
Attiva dal 1997
Editore: Taylor & Francis, - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1365-8816
Titolo chiave: International journal of geographical information science (Print)
Titolo proprio: International journal of geographical information science. (Print)
Titolo abbreviato: Int. j. geogr. inf. sci. (Print)
Numero volume della rivista34
Fascicolo della rivista7
DOI10.1080/13658816.2019.1707835
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85077905632)
  • ISI Web of Science (WOS) (Codice:000505651700001)
Parole chiaveTrajectory classification, Multiple-aspect trajectory, Semantic trajectory classification, Geohash embedding, Recurrent neural network
Link (URL, URI)https://www.tandfonline.com/doi/full/10.1080/13658816.2019.1707835?needAccess=true
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei
Allegati
MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings (documento privato )
Descrizione: Published version
Tipo documento: application/pdf
postprint
Descrizione: postprint version
Tipo documento: application/pdf