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Scaling Law of Urban Ride Sharing.
Tachet, R; Sagarra, O; Santi, P; Resta, G; Szell, M; Strogatz, S H; Ratti, C.
Afiliação
  • Tachet R; Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Sagarra O; Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Santi P; Complexity Lab Barcelona, Universitat de Barcelona, 08028 Barcelona, SPAIN.
  • Resta G; DRIBIA Data Research, 08012 Barcelona, SPAIN.
  • Szell M; Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Strogatz SH; Istituto di Informatica e Telematica del CNR, 56124 Pisa, ITALY.
  • Ratti C; Istituto di Informatica e Telematica del CNR, 56124 Pisa, ITALY.
Sci Rep ; 7: 42868, 2017 03 06.
Article em En | MEDLINE | ID: mdl-28262743
Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2017 Tipo de documento: Article