| Summary: | Modern cities face pressing problems with transportation systems including, but not<br />limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations<br />have implemented roadside infrastructures such as cameras and sensors to collect data about<br />environmental and traffic conditions. In the case of traffic sensor data not only the real-time data<br />are essential, but also historical values need to be preserved and published. When real-time and<br />historical data of smart cities become available, everyone can join an evidence-based debate on the<br />city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project<br />seeks to understand how traffic affects urban air quality. The project develops a platform to provide<br />real-time and predicted values on air quality in several cities in Europe, encompassing tasks such<br />as the deployment of low-cost air quality sensors, data collection and integration, modeling and<br />prediction, the publication of open data, and the development of applications for end-users and<br />public administrations. This paper explicitly focuses on the modeling and semantic annotation of<br />traffic data. We present the tools and techniques used in the project and validate our strategies for<br />data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain).<br />An experimental evaluation shows that our approach to publish Linked Data is effective.data management; semantics; sensor data; data integration; data annotation; traffic in<br />smart cities
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