Quantifying the benefits of vehicle pooling with shareability networks.
Identifieur interne : 000165 ( PubMed/Curation ); précédent : 000164; suivant : 000166Quantifying the benefits of vehicle pooling with shareability networks.
Auteurs : Paolo Santi ; Giovanni Resta ; Michael Szell [États-Unis] ; Stanislav Sobolevsky [États-Unis] ; Steven H. Strogatz [États-Unis] ; Carlo Ratti [États-Unis]Source :
- Proceedings of the National Academy of Sciences of the United States of America [ 1091-6490 ] ; 2014.
Abstract
Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.
DOI: 10.1073/pnas.1403657111
PubMed: 25197046
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Paolo Santi<affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; Istituto di Informatica e Telematica del Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy; and.</nlm:affiliation><wicri:noCountry code="subField">Italy; and</wicri:noCountry></affiliation><affiliation><nlm:affiliation>Istituto di Informatica e Telematica del Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy; and.</nlm:affiliation><wicri:noCountry code="subField">Italy; and</wicri:noCountry></affiliation>Le document en format XML
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Strogatz</name><affiliation wicri:level="2"><nlm:affiliation>Department of Mathematics, Cornell University, Ithaca, NY 14853.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">État de New York</region></placeName><wicri:cityArea>Department of Mathematics, Cornell University, Ithaca</wicri:cityArea></affiliation></author><author><name sortKey="Ratti, Carlo" sort="Ratti, Carlo" uniqKey="Ratti C" first="Carlo" last="Ratti">Carlo Ratti</name><affiliation wicri:level="2"><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139;</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge</wicri:cityArea></affiliation></author></analytic><series><title level="j">Proceedings of the National Academy of Sciences of the United States of America</title><idno type="eISSN">1091-6490</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="PubMed-not-MEDLINE"><PMID Version="1">25197046</PMID><DateCreated><Year>2014</Year><Month>09</Month><Day>17</Day></DateCreated><DateCompleted><Year>2015</Year><Month>03</Month><Day>24</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1091-6490</ISSN><JournalIssue CitedMedium="Internet"><Volume>111</Volume><Issue>37</Issue><PubDate><Year>2014</Year><Month>Sep</Month><Day>16</Day></PubDate></JournalIssue><Title>Proceedings of the National Academy of Sciences of the United States of America</Title><ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation></Journal><ArticleTitle>Quantifying the benefits of vehicle pooling with shareability networks.</ArticleTitle><Pagination><MedlinePgn>13290-4</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1073/pnas.1403657111</ELocationID><Abstract><AbstractText>Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Santi</LastName><ForeName>Paolo</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; Istituto di Informatica e Telematica del Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy; and.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Resta</LastName><ForeName>Giovanni</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Istituto di Informatica e Telematica del Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy; and.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Szell</LastName><ForeName>Michael</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; mszell@mit.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sobolevsky</LastName><ForeName>Stanislav</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139;</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Strogatz</LastName><ForeName>Steven H</ForeName><Initials>SH</Initials><AffiliationInfo><Affiliation>Department of Mathematics, Cornell University, Ithaca, NY 14853.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ratti</LastName><ForeName>Carlo</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139;</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>09</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Proc Natl Acad Sci U S A</MedlineTA><NlmUniqueID>7505876</NlmUniqueID><ISSNLinking>0027-8424</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Science. 2006 Jan 6;311(5757):88-90</RefSource><PMID Version="1">16400149</PMID></CommentsCorrections><CommentsCorrections RefType="CommentIn"><RefSource>Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):E5489</RefSource><PMID Version="1">25503368</PMID></CommentsCorrections><CommentsCorrections RefType="CommentIn"><RefSource>Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):E5488</RefSource><PMID Version="1">25503369</PMID></CommentsCorrections></CommentsCorrectionsList><OtherID Source="NLM">PMC4169909</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">carpooling</Keyword><Keyword MajorTopicYN="N">human mobility</Keyword><Keyword MajorTopicYN="N">maximum matching</Keyword><Keyword MajorTopicYN="N">urban computing</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2014</Year><Month>9</Month><Day>2</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>9</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>9</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>9</Month><Day>10</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25197046</ArticleId><ArticleId IdType="pii">1403657111</ArticleId><ArticleId IdType="doi">10.1073/pnas.1403657111</ArticleId><ArticleId IdType="pmc">PMC4169909</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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