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dc.contributor.authorLaña, Ibai
dc.contributor.authorSanchez-Medina, Javier J.
dc.contributor.authorVlahogianni, Eleni I.
dc.contributor.authorDel Ser, Javier
dc.date.accessioned2021-02-17T09:19:23Z
dc.date.available2021-02-17T09:19:23Z
dc.date.issued2021-02-05
dc.identifier.citationLaña, Ibai, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, and Javier Del Ser. “From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability.” Sensors 21, no. 4 (February 5, 2021): 1121. doi:10.3390/s21041121.en
dc.identifier.urihttp://hdl.handle.net/11556/1080
dc.description.abstractAdvances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.en
dc.description.sponsorshipThis work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleFrom Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionabilityen
dc.typearticleen
dc.identifier.doi10.3390/s21041121en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsIntelligent transportation systemsen
dc.subject.keywordsFunctional requirementsen
dc.subject.keywordsMachine learningen
dc.subject.keywordsModel actionabilityen
dc.subject.keywordsModel evaluationen
dc.identifier.essn1424-8220en
dc.issue.number4en
dc.journal.titleSensorsen
dc.page.initial1121en
dc.volume.number21en


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