Big data analytics for smart mobility: a case study (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Big data analytics for smart mobility: a case study (Contributo in atti di convegno) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Alternative label

• Trasarti R., Furletti B., Gabrielli L., Nanni M., Pedreschi D.[*] (2014)
  Big data analytics for smart mobility: a case study
  in MUD 2014 - EDBT/ICDT 2014 Joint Conference, Athens, Greece, 3 March 2014
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Trasarti R., Furletti B., Gabrielli L., Nanni M., Pedreschi D.[*] (literal)

Pagina inizio

• 363 (literal)

Pagina fine

• 364 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://ceur-ws.org/Vol-1133/paper-57.pdf (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• 1133 (literal)

Note

• PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• CNR-ISTI, Pisa, Italy; [*] University of Pisa, Italy (literal)

Titolo

• Big data analytics for smart mobility: a case study (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

• K. Selçuk Candan, Sihem Amer-Yahia, Nicole Schweikardt, Vassilis Christophides, Vincent Leroy (literal)

Abstract

• This paper presents a real case study were several mobility data sources are collected in a urban context, integrated and analyzed in order to answer a set of key questions about mobility. The study of the human mobility is a very sensitive topic for both public transport (PT) companies and local administrations. This work is a contribution in the understanding of some aspects of the mobility in Cosenza, a town in the South of Italy, and the realization of corresponding services in order to aswer to the following questions identi- fied in collaboration with the PT experts. Question 1: How is PT able to substitute private mobility? The objective is to compare public and private mobility to verify the capability of PT to satisfy the user mobility needs. Question 2: How di!erent zones of the city are reachable using PT? This question focuses on understanding how much di!erent zones of the city are served by PT considering di!erent times of the day. Question 3: Are there usual time deviations between real travel times and o\"cial time tables? We want to verify if usual time deviations between real travel times and o\"cial time tables exist highlighting chronic delays in the service. Question 4: Can we spot visitors and commuters by their behavior? We aim at identifying important categories of people estimating their segmentation in order to evaluate the corresponding demand of services. (literal)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• Lorenzo Gabrielli (Unità di personale esterno)
• MIRCO NANNI (Unità di personale interno)
• DINO PEDRESCHI (Persona)
• BARBARA FURLETTI (Unità di personale interno)
• ROBERTO TRASARTI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Big data analytics for smart mobility: a case study" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• MIRCO NANNI (Unità di personale interno)
• DINO PEDRESCHI (Persona)
• Lorenzo Gabrielli (Unità di personale esterno)
• ROBERTO TRASARTI (Unità di personale interno)
• BARBARA FURLETTI (Unità di personale interno)

Insieme di parole chiave di

• Keywords of "Big data analytics for smart mobility: a case study" (Insieme di parole chiave)