000095976 001__ 95976 000095976 005__ 20210902121716.0 000095976 0247_ $$2doi$$a10.3390/app10175882 000095976 0248_ $$2sideral$$a119539 000095976 037__ $$aART-2020-119539 000095976 041__ $$aeng 000095976 100__ $$aDesimoni, F. 000095976 245__ $$aSemantic traffic sensor data: The TRAFAIR experience 000095976 260__ $$c2020 000095976 5060_ $$aAccess copy available to the general public$$fUnrestricted 000095976 5203_ $$aModern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city''s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective. 000095976 536__ $$9info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R$$9info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR$$9info:eu-repo/grantAgreement/ES/DGA/T64-20R 000095976 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000095976 590__ $$a2.679$$b2020 000095976 591__ $$aPHYSICS, APPLIED$$b73 / 160 = 0.456$$c2020$$dQ2$$eT2 000095976 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b38 / 91 = 0.418$$c2020$$dQ2$$eT2 000095976 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b101 / 178 = 0.567$$c2020$$dQ3$$eT2 000095976 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b201 / 333 = 0.604$$c2020$$dQ3$$eT2 000095976 592__ $$a0.435$$b2020 000095976 593__ $$aComputer Science Applications$$c2020$$dQ2 000095976 593__ $$aEngineering (miscellaneous)$$c2020$$dQ2 000095976 593__ $$aProcess Chemistry and Technology$$c2020$$dQ2 000095976 593__ $$aInstrumentation$$c2020$$dQ2 000095976 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ2 000095976 593__ $$aFluid Flow and Transfer Processes$$c2020$$dQ2 000095976 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000095976 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri, S.$$uUniversidad de Zaragoza 000095976 700__ $$aPo, L. 000095976 700__ $$aRollo, F. 000095976 700__ $$0(orcid)0000-0001-6008-1138$$aTrillo-Lado, R.$$uUniversidad de Zaragoza 000095976 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000095976 773__ $$g10, 17 (2020), 5882 [30 pp]$$pAppl. sci.$$tAPPLIED SCIENCES-BASEL$$x2076-3417 000095976 8564_ $$s699885$$uhttps://zaguan.unizar.es/record/95976/files/texto_completo.pdf$$yVersión publicada 000095976 8564_ $$s482396$$uhttps://zaguan.unizar.es/record/95976/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000095976 909CO $$ooai:zaguan.unizar.es:95976$$particulos$$pdriver 000095976 951__ $$a2021-09-02-09:25:19 000095976 980__ $$aARTICLE