Data analytics and optimization for assessing a ride sharing system

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dc.contributor.authorArmant, Vincent
dc.contributor.authorHoran, John
dc.contributor.authorMahbub, Nahid
dc.contributor.authorBrown, Kenneth N.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2016-04-21T11:16:11Z
dc.date.available2016-04-21T11:16:11Z
dc.date.issued2015-10
dc.date.updated2016-01-11T14:13:39Z
dc.description.abstractRide-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.sponsorshipScience Foundation Ireland (Grant No.12/RC/2289)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationArmant, V., Horan, J., Mabub, N. and Brown, K.N. (2015) ‘Data analytics and optimisation for assessing a ride sharing system’, in E. Fromont, T. De Bie, and M. Van Leeuwen (eds) Advances in Intelligent Data Analysis XIV. Cham: Springer International Publishing, pp. 1–12. Available at: https://doi.org/10.1007/978-3-319-24465-5_1en
dc.identifier.doi10.1007/978-3-319-24465-5_1
dc.identifier.endpage12en
dc.identifier.isbn978-3-319-24465-5
dc.identifier.issn0302-9743
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/2475
dc.identifier.volume9385en
dc.language.isoenen
dc.publisherSpringer International Publishing AGen
dc.relation.ispartofAdvances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015.
dc.relation.ispartofseriesLecture Notes in Computer Science;9385
dc.rights© Springer International Publishing AG. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-24465-5_1en
dc.subjectData analyticsen
dc.subjectOptimizationen
dc.subjectRide sharingen
dc.titleData analytics and optimization for assessing a ride sharing systemen
dc.typeConference itemen
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