95976 20210902121716.0 doi 10.3390/app10175882 sideral 119539 ART-2020-119539 eng Desimoni, F. Semantic traffic sensor data: The TRAFAIR experience 2020 Access copy available to the general public Unrestricted Modern 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. info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR info:eu-repo/grantAgreement/ES/DGA/T64-20R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.679 2020 PHYSICS, APPLIED 73 / 160 = 0.456 2020 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 38 / 91 = 0.418 2020 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 101 / 178 = 0.567 2020 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 201 / 333 = 0.604 2020 Q3 T2 0.435 2020 Computer Science Applications 2020 Q2 Engineering (miscellaneous) 2020 Q2 Process Chemistry and Technology 2020 Q2 Instrumentation 2020 Q2 Materials Science (miscellaneous) 2020 Q2 Fluid Flow and Transfer Processes 2020 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion (orcid)0000-0002-7073-219X Ilarri, S. Universidad de Zaragoza Po, L. Rollo, F. (orcid)0000-0001-6008-1138 Trillo-Lado, R. Universidad de Zaragoza 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 10, 17 (2020), 5882 [30 pp] Appl. sci. APPLIED SCIENCES-BASEL 2076-3417 699885 http://zaguan.unizar.es/record/95976/files/texto_completo.pdf Versión publicada 482396 http://zaguan.unizar.es/record/95976/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:95976 articulos driver 2021-09-02-09:25:19 ARTICLE