English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/194874
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers

AuthorsBistaffa, Filippo ; Blum, Christian ; Cerquides, Jesús ; Farinelli, Alessandro; Rodríguez-Aguilar, Juan Antonio
Issue DateNov-2019
CitationIEEE Transactions on Intelligent Transportation Systems
AbstractPeer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-topeer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO2 emissions and up to 80.08% in traffic congestion.
Publisher version (URL)https://ieeexplore.ieee.org/document/8917688
URIhttp://hdl.handle.net/10261/194874
DOI10.1109/TITS.2019.2954982
Appears in Collections:(IIIA) Artículos
Files in This Item:
File Description SizeFormat 
BistaffaEtAl2019T-ITS.pdfAccepted Version5,02 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.