Data analytics and optimization for assessing a ride sharing system

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dc.contributor.author Armant, Vincent
dc.contributor.author Horan, John
dc.contributor.author Mahbub, Nahid
dc.contributor.author Brown, Kenneth N.
dc.date.accessioned 2016-04-21T11:16:11Z
dc.date.available 2016-04-21T11:16:11Z
dc.date.issued 2015-10
dc.identifier.citation ARMANT, V., HORAN, J., MAHBUB, N. & BROWN, K. N. 2015. Data Analytics and Optimisation for Assessing a Ride Sharing System. In: FROMONT, E., DE BIE, T. & VAN LEEUWEN, M. (eds.) Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Cham: Springer International Publishing. en
dc.identifier.volume 9385 en
dc.identifier.startpage 1 en
dc.identifier.endpage 12 en
dc.identifier.isbn 978-3-319-24465-5
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10468/2475
dc.identifier.doi 10.1007/978-3-319-24465-5_1
dc.description.abstract Ride-sharing schemes attempt to reduce road traffic by matching prospective passengers to drivers with spare seats in their cars. To be successful, such schemes require a critical mass of drivers and passengers. In current deployed implementations, the possible matches are based on heuristics, rather than real route times or distances. In some cases, the heuristics propose infeasible matches; in others, feasible matches are omitted. Poor ride matching is likely to deter participants from using the system. We develop a constraint-based model for acceptable ride matches which incorporates route plans and time windows. Through data analytics on a history of advertised schedules and agreed shared trips, we infer parameters for this model that account for 90% of agreed trips. By applying the inferred model to the advertised schedules, we demonstrate that there is an imbalance between riders and passengers. We assess the potential benefits of persuading existing drivers to switch to becoming passengers if appropriate matches can be found, by solving the inferred model with and without switching. We demonstrate that flexible participation has the potential to reduce the number of unmatched participants by up to 80%. en
dc.description.sponsorship Science Foundation Ireland (Grant No.12/RC/2289) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer International Publishing AG en
dc.relation.ispartof Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015.
dc.relation.ispartofseries Lecture Notes in Computer Science;9385
dc.rights © Springer International Publishing AG. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24465-5_1 en
dc.subject Data analytics en
dc.subject Optimization en
dc.subject Ride sharing en
dc.title Data analytics and optimization for assessing a ride sharing system en
dc.type Conference item en
dc.internal.authorcontactother Vincent Armant, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: vincent.armant@insight-centre.org en
dc.internal.availability Full text not available en
dc.date.updated 2016-01-11T14:13:39Z
dc.description.version Accepted Version en
dc.internal.rssid 332356489
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Lecture Notes in Computer Science en
dc.internal.copyrightchecked No. !!CORA!! AV permitted by publisher, as listed on Sherpa Romeo, and detailed at http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 and copyright form ftp://ftp.springer.de/pub/tex/latex/llncs/LNCS-Springer_Copyright_Form.pdf en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Saint Etienne, France en
dc.internal.IRISemailaddress vincent.armant@insight-centre.org en


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