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Scaling Law of Urban Ride Sharing


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.
Nature Scientific Reports, 2017

Autori esterni: Remi Tachet (MIT), Oleguer Sagarra (Universitat Barcelona), Michael Szell (Northeastern University), Steven Strogatz (Cornell University), Carlo Ratti (MIT)
Autori IIT:

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: srep42868.pdf

Attività: Algoritmica per reti wireless