Show simple item record

dc.contributor.authorYu, Jiangbo
dc.contributor.authorLow, Kian Hsiang
dc.contributor.authorOran, Ali
dc.contributor.authorJaillet, Patrick
dc.date.accessioned2014-05-09T13:52:23Z
dc.date.available2014-05-09T13:52:23Z
dc.date.issued2012-12
dc.identifier.isbn978-1-4673-6057-9
dc.identifier.isbn978-0-7695-4880-7
dc.identifier.urihttp://hdl.handle.net/1721.1/86892
dc.description.abstractRoute prediction is important to analyzing and understanding the route patterns and behavior of traffic crowds. Its objective is to predict the most likely or "popular" route of road segments from a given point in a road network. This paper presents a hierarchical Bayesian non-parametric approach to efficient and scalable route prediction that can harness the wisdom of crowds of route planning agents by aggregating their sequential routes of possibly varying lengths and origin-destination pairs. In particular, our approach has the advantages of (a) not requiring a Markov assumption to be imposed and (b) generalizing well with sparse data, thus resulting in significantly improved prediction accuracy, as demonstrated empirically using real-world taxi route data. We also show two practical applications of our route prediction algorithm: predictive taxi ranking and route recommendation.en_US
dc.description.sponsorshipSingapore-MIT Alliance for Research and Technology (Subaward Agreement 14 R-252-000-466-592)en_US
dc.description.sponsorshipSingapore-MIT Alliance for Research and Technology (Subaward Agreement 28 R-252-000-502-592)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/WI-IAT.2012.216en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleHierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agentsen_US
dc.typeArticleen_US
dc.identifier.citationYu, Jiangbo, Kian Hsiang Low, Ali Oran, and Patrick Jaillet. “Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents.” 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (n.d.).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorJaillet, Patricken_US
dc.relation.journalProceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technologyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsYu, Jiangbo; Low, Kian Hsiang; Oran, Ali; Jaillet, Patricken_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8585-6566
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record