Data-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systems

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dc.contributor.authorLaña, Ibai
dc.contributor.authorOlabarrieta, Ignacio Iñaki
dc.contributor.authorSer, Javier Del
dc.contributor.authorRodriguez, Luis
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:57:37Z
dc.date.available2024-07-24T11:57:37Z
dc.date.issued2020-09-20
dc.descriptionPublisher Copyright: © 2020 IEEE.
dc.description.abstractTunnel ventilation systems are strictly controlled by safety regulations. Such regulations define not only their operating conditions during fire situations, but also the way in which they should be activated when the accumulation of pollutant gases reaches certain thresholds that are considered unsafe. In addition to these exceptional circumstances, evacuation of tunnel gases is produced naturally on a regular basis, due to causes like air currents originated in pressure differences among the tunnel portals, or the well known piston effect, as a result of vehicles pushing the air when they pass. This work elaborates on the prediction of air-flow inside the tunnels boosted by traffic flow prediction, in order to assist the system activation, be it automated or manual. After experiments made over real tunnel data with a benchmark of machine learning predictive algorithms, results suggest that traffic flow inside the studied tunnels can be effectively predicted and used to enhance air flow predictions, specially in those cases where an air flow predictor alone is not enough to obtain an actionable forecast. The relevance of these results comes from their direct applicability wherein improving the ventilation activation cycles, by adjusting their automation or by informing operators of future air flow levels.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).
dc.description.statusPeer reviewed
dc.identifier.citationLaña , I , Olabarrieta , I I , Ser , J D & Rodriguez , L 2020 , Data-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systems . in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 . , 9294565 , 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/ITSC45102.2020.9294565
dc.identifier.citationconference
dc.identifier.doi10.1109/ITSC45102.2020.9294565
dc.identifier.isbn9781728141497
dc.identifier.urihttps://hdl.handle.net/11556/2752
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85099665249&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
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsDecision Sciences (miscellaneous)
dc.subject.keywordsInformation Systems and Management
dc.subject.keywordsModeling and Simulation
dc.subject.keywordsEducation
dc.subject.keywordsSDG 11 - Sustainable Cities and Communities
dc.titleData-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systemsen
dc.typeconference output
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