Inferring destination from mobility data

Show simple item record Naeem, Ali A. Brown, Kenneth N. 2018-09-24T12:37:02Z 2018-09-24T12:37:02Z 2017
dc.identifier.citation Naeem, A. A. and Brown, K. N. (2017) 'Inferring destination from mobility data', Proceedings of the 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin Institute of Technology, 7 - 8 December, pp. 153-165 en
dc.identifier.issued 2086
dc.identifier.startpage 153
dc.identifier.endpage 165
dc.identifier.issn 1613-0073
dc.identifier.issn 1613-0073
dc.description.abstract Destination prediction in a moving vehicle has several applications such as alternative route recommendations even in cases where the driver has not entered their destination into the system. In this paper a hierarchical approach to destination prediction is presented. A Discrete Time Markov Chain model is used to make an initial prediction of a general region the vehicle might be travelling to. Following that a more complex Bayesian Inference Model is used to make a fine grained prediction within that destination region. The model is tested on a dataset of 442 taxis operating in Porto, Portugal. Experiments are run on two maps. One is a smaller map concentrating specificially on trips within the Porto city centre and surrounding areas. The second map covers a much larger area going as far as Lisbon. We achieve predictions for Porto with average distance error of less than 0.6 km from early on in the trip and less than 1.6 km dropping to less than 1 km for the wider area. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher CEUR Workshop Proceedings ( en
dc.rights © 2017, the Authors. Copying permitted for private and academic purposes.
dc.subject Markov chain model en
dc.subject Bayesian inference model en
dc.subject Destination prediction en
dc.subject Taxis en
dc.title Inferring destination from mobility data en
dc.type Conference item en
dc.internal.authorcontactother Kenneth Brown, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Science Foundation Ireland
dc.description.status Peer reviewed en
dc.identifier.journaltitle CEUR Workshop Proceedings en
dc.internal.IRISemailaddress en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/

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