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dc.contributor.authorLaña, Ibai
dc.contributor.authorOlabarrieta, Ignacio (Iñaki)
dc.contributor.authorVélez, Manuel
dc.contributor.authorDel Ser, Javier
dc.date.accessioned2018-04-05T10:27:25Z
dc.date.available2018-04-05T10:27:25Z
dc.date.issued2018-05
dc.identifier.citationLaña, Ibai, Ignacio (Iñaki) Olabarrieta, Manuel Vélez, and Javier Del Ser. “On the Imputation of Missing Data for Road Traffic Forecasting: New Insights and Novel Techniques.” Transportation Research Part C: Emerging Technologies 90 (May 2018): 18–33. doi:10.1016/j.trc.2018.02.021.en
dc.identifier.issn0968-090Xen
dc.identifier.urihttp://hdl.handle.net/11556/523
dc.description.abstractVehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.en
dc.description.sponsorshipThis work has been supported by the Basque Government through the ELKARTEK program (Ref. KK-2015/0000080 and the BID3ABI project), as well as by the H2020 programme of the European Commission (Grant No. 691735).en
dc.language.isoengen
dc.publisherElsevier Ltden
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleOn the imputation of missing data for road traffic forecasting: New insights and novel techniquesen
dc.typearticleen
dc.identifier.doi10.1016/j.trc.2018.02.021en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/691735/EU/REnaissance of Places with Innovative Citizenship and TEchnolgy/REPLICATEen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsTraffic forecastingen
dc.subject.keywordsMissing dataen
dc.subject.keywordsCluster analysisen
dc.subject.keywordsData imputationen
dc.journal.titleTransportation Research Part C: Emerging Technologiesen
dc.page.final33en
dc.page.initial18en
dc.volume.number90en


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