Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/107223
DC FieldValueLanguage
dc.contributor.authorLaña, Ibaien_US
dc.contributor.authorSánchez Medina, Javier Jesúsen_US
dc.contributor.authorVlahogianni, Eleni I.en_US
dc.contributor.authorDel Ser, Javieren_US
dc.date.accessioned2021-05-14T19:33:33Z-
dc.date.available2021-05-14T19:33:33Z-
dc.date.issued2021en_US
dc.identifier.issn1424-8220en_US
dc.identifier.urihttp://hdl.handle.net/10553/107223-
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_US
dc.languageengen_US
dc.relation.ispartofSensors (Switzerland)en_US
dc.sourceSensors (Switzerland) [ISSN 1424-8220], v. 21(4), 1121, (Febrero 2021)en_US
dc.subject3327 Tecnología de los sistemas de transporteen_US
dc.subject120903 Análisis de datosen_US
dc.subject.otherIntelligent transportation systemsen_US
dc.subject.otherFunctional requirementsen_US
dc.subject.otherMachine learningen_US
dc.subject.otherModel actionabilityen_US
dc.subject.otherModel evaluationen_US
dc.titleFrom data to actions in intelligent transportation systems: a prescription of functional requirements for model actionabilityen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typearticleen_US
dc.identifier.doi10.3390/s21041121en_US
dc.description.lastpage34en_US
dc.identifier.issue4-
dc.description.firstpage1en_US
dc.relation.volume21en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.description.observacionesThis 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_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,653
dc.description.jcr3,275
dc.description.sjrqQ1
dc.description.jcrqQ1
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptIUCTC: Centro de Innovación para la Sociedad de la Información-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptInformática y Sistemas-
crisitem.author.orcid0000-0003-2530-3182-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.fullNameSánchez Medina, Javier Jesús-
Appears in Collections:Artículos
Thumbnail
Adobe PDF (458,93 kB)
Show simple item record

Page view(s)

74
checked on Jul 31, 2021

Download(s)

9
checked on Jul 31, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.