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Di Martino, Sergio

University of Naples Federico II

in Cooperation with on an Cooperation-Score of 37%

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Chart of shared publication
Matteo Principe, Sinogrante - 1 publications, 1 shared
Landolfi, Enrico - 2 publications, 1 shared
Starace, Luigi Libero Lucio - 2 publications, 2 shared
Asprone, Dario - 3 publications, 3 shared
Festa, Paola - 5 publications, 3 shared
Bock, Fabian - 10 publications, 6 shared
Origlia, Antonio - 1 publications, 1 shared
Norman Vitale, Vincenzo - 1 publications, 1 shared
Sester, Monika - 21 publications, 2 shared
Rossi, Silvia - 1 publications, 1 shared
Kwoczek, Simon - 4 publications, 1 shared
Robino, Camilla - 2 publications, 2 shared
Di Rocco, Laura - 2 publications, 2 shared
Guerrini, Gian Luca - 3 publications, 2 shared
Bertolotto, Michela - 3 publications, 2 shared
Rocco, Laura Di - 1 publications, 1 shared
Attanasio, Yuri - 1 publications, 1 shared
Ferrucci, F. - 3 publications, 3 shared
Giorio, C. - 1 publications, 1 shared
Galiero, R. - 1 publications, 1 shared
Sarro, F. - 1 publications, 1 shared
Costagliola, Gennaro - 2 publications, 2 shared
Montemurro, U. - 1 publications, 1 shared
Paliotti, A. - 1 publications, 1 shared
Oliviero, G. - 1 publications, 1 shared
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chapter

Multigranular Spatio-Temporal Exploration: An Application to On-Street Parking Data

  • Robino, Camilla
  • Di Rocco, Laura
  • Di Martino, Sergio
  • Guerrini, Gian Luca
  • Bertolotto, Michela
  • Rocco, Laura Di
Abstract

Traffic congestions cost billions of dollars to the society every year and are often aggravated by road users looking for parking. One way of alleviating the parking problem is providing decision makers of smart cities with powerful exploratory tools to analyse the data and find more effective solutions. This paper proposes a novel visual analytics tool for decision makers that allows multigranular spatio-temporal on-street parking data exploration. Even if the tool has been designed to deal with on-street parking data, it relies on a generic logic that makes it adaptable to more general spatio-temporal datasets.

Topics
  • traffic congestion
  • data
  • street
  • data file
  • on street parking
  • traveler
  • information system