Quantifying the benefits of vehicle pooling with shareability networks
Identifieur interne : 000269 ( Main/Exploration ); précédent : 000268; suivant : 000270Quantifying the benefits of vehicle pooling with shareability networks
Auteurs : Paolo Santi [Italie] ; Giovanni Resta [Italie] ; Michael Szell ; Stanislav Sobolevsky ; Steven H. Strogatz ; Carlo RattiSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2014.
Abstract
Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time.
Url:
DOI: 10.1073/pnas.1403657111
PubMed: 25197046
PubMed Central: 4169909
Affiliations:
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<front><div type="abstract" xml:lang="en"><title>Significance</title>
<p>Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time.</p>
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