dc.contributor.authorMitrovic, Nikola
dc.contributor.authorAsif, Muhammad Tayyab
dc.contributor.authorDauwels, Justin
dc.contributor.authorJaillet, Patrick
dc.date.accessioned2016-05-27T07:17:37Z
dc.date.available2016-05-27T07:17:37Z
dc.date.issued2015
dc.identifier.citationMitrovic, N., Asif, M. T., Dauwels, J., & Jaillet, P. (2015). Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2949-2954.en_US
dc.identifier.issn1524-9050en_US
dc.identifier.urihttp://hdl.handle.net/10220/40575
dc.description.abstractAdvanced sensing and surveillance technologies often collect traffic information with high temporal and spatial resolutions. The volume of the collected data severely limits the scalability of online traffic operations. To overcome this issue, we propose a low-dimensional network representation where only a subset of road segments is explicitly monitored. Traffic information for the subset of roads is then used to estimate and predict conditions of the entire network. Numerical results show that such approach provides 10 times faster prediction at a loss of performance of 3% and 1% for 5 and 30 minutes prediction horizons, respectively.en_US
dc.format.extent6 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Transactions on Intelligent Transportation Systemsen_US
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TITS.2015.2411675].en_US
dc.subjectLow-dimensional modelsen_US
dc.subjecttraffic predictionen_US
dc.titleLow-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Dataen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TITS.2015.2411675
dc.description.versionAccepted versionen_US


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