Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment

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dc.contributor.authorDel Ser, Javier
dc.contributor.authorLaña, Ibai
dc.contributor.authorManibardo, Eric L.
dc.contributor.authorOregi, Izaskun
dc.contributor.authorOsaba, Eneko
dc.contributor.authorLobo, Jesus L.
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorVlahogianni, Eleni I.
dc.contributor.institutionIA
dc.contributor.institutionQuantum
dc.date.issued2020-09-20
dc.descriptionPublisher Copyright: © 2020 IEEE.
dc.description.abstractIn short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activity reported in this long-standing research field has been lately dominated by different Deep Learning approaches, yielding overly complex forecasting models that in general achieve accuracy gains of questionable practical utility. In this work we elaborate on the performance of Deep Echo State Networks for this particular task. The efficient learning algorithm and simpler parametric configuration of these alternative modeling approaches make them emerge as a competitive traffic forecasting method for real ITS applications deployed in devices and systems with stringently limited computational resources. An extensive comparison benchmark is designed with real traffic data captured over the city of Madrid (Spain), amounting to more than 130 Automatic Traffic Readers (ATRs) and several shallow learning, ensembles and Deep Learning models. Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.en
dc.description.sponsorshipACKNOWLEDGMENTS The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELKARTEK programs, as well as the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project). Eric L. Manibardo receives funding support from the Basque Government through its BIKAINTEK PhD support program (grant no. 48AFW22019-00002).
dc.description.statusPeer reviewed
dc.identifier.citationDel Ser , J , Laña , I , Manibardo , E L , Oregi , I , Osaba , E , Lobo , J L , Bilbao , M N & Vlahogianni , E I 2020 , Deep Echo State Networks for Short-Term Traffic Forecasting : Performance Comparison and Statistical Assessment . in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 . , 9294200 , 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 , Institute of Electrical and Electronics Engineers Inc. , 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 , Rhodes , Greece , 20/09/20 . https://doi.org/10.1109/ITSC45102.2020.9294200
dc.identifier.citationconference
dc.identifier.doi10.1109/ITSC45102.2020.9294200
dc.identifier.isbn9781728141497
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85099643137&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
dc.relation.ispartofseries2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
dc.relation.projectIDCentro para el Desarrollo Tecnológico Industrial, CDTI
dc.relation.projectIDEusko Jaurlaritza, IT1294-19
dc.relation.projectIDMinisterio de Ciencia e Innovación, MICINN, 48AFW22019-00002
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsDecision Sciences (miscellaneous)
dc.subject.keywordsInformation Systems and Management
dc.subject.keywordsModeling and Simulation
dc.subject.keywordsEducation
dc.titleDeep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessmenten
dc.typeconference output
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