Mobility data mining: discovering movement patterns from trajectory data (Contributo in atti di convegno)

Type
Label
  • Mobility data mining: discovering movement patterns from trajectory data (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Alternative label
  • Giannotti F.; Nanni M.; Pedreschi D.; Pinelli F.; Renso C.; Rinzivillo S.; Trasarti R. (2010)
    Mobility data mining: discovering movement patterns from trajectory data
    in International Workshop on Computational Transportation Science, San Jose, CA, USA
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giannotti F.; Nanni M.; Pedreschi D.; Pinelli F.; Renso C.; Rinzivillo S.; Trasarti R. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: IWCTS'10 - International Workshop on Computational Transportation Science (San Jose, CA, USA, 3-5 November 2010). Proceedings, pp. 7 - 10. ACM, 2010. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: The analysis of movement data has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location-aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks [2]. These have made available massive repositories of spatio-temporal data recording human mobile activities, such as location data from mobile phones, GPS tracks from mobile devices, etc.: is it possible to discover from these data use- ful and timely knowledge about human mobility? The GeoPKDD project [1], since 2005, investigated this direction of research; the lesson learned is that there is a long way to go from raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation managers. Such analysts reason about semantically rich concepts, such as systematic vs. occasional movement behavior and home- work commutin (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Department of Computer Science, University of Pisa (literal)
Titolo
  • Mobility data mining: discovering movement patterns from trajectory data (literal)
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