Scaling law of urban ride sharing
- Remi Tachet,
- Oleguer Sagarra,
- Paolo Santi,
- Giovanni Resta,
- ,
- Steven H Strogatz
- Massachusetts Institute of Technology,
- The Institute of Informatics and Telematics,
- Cornell University
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewPublication Information
Output type
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOriginal language
Undefined/UnknownPages from-to (Number of pages)
Page 42868 (1 page)Journal (Volume, Issue Number)
Scientific Reports (Volume 7)Publication milestones
- Published - 2017
Publication status
Published - 2017
ISSN
2045-2322Publication IDs
- Scopus: 85014772512
Abstract
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.
Publication metrics
PlumX, opens in new tab
Captures
269
Citations
147
Mentions
6
