# Semantic Traffic Sensor Data:
## 1 Introduction  
Public administrations handle large amounts of data concerning their internal processes as well as to the services that they offer to citizens. Following the \"open by default\" worldwide recognized principle [1], a lot of public-sector information is increasingly published as open data in standard formats, to enhance interoperability and efficiency in data reuse. Among all public data, open transport data is one of the most frequently re-used data domains in the European Data Portal (EDP) [2] and has been identified as highly impactful. Indeed, 7600 datasets related to transport are published on the EDP, which provide information about bike-sharing and bicycle hiring systems, seasonal traffic conditions, and road construction. These datasets are accessible via the EDP, which is harvesting metadata from national open data portals. In turn, the national open data portals publish the data or harvest themselves the data from different institutions within their countries, such as federal, regional, and local portals, national, regional and local government bodies, and research institutions. Sharing traffic data in an anonymized form can lead to innovative products, such as services that enableusers to find available parking slots and provide the best route to reach those locations, apps able to make predictions about traffic conditions based on past data, as well as assistance agents that provide real-time traffic information for users and relevant government units.  
On the other hand, the analysis of traffic data is crucial not only for the development of smart traffic management systems (e.g., see [3]) but also for making the cities more safe, healthy, and sustainable [4]. Several European countries have adopted the Vision Zero and Safe System approach [5], to eliminate deaths and grave injuries on European roads. Furthermore, global efforts focus on achieving the transition to sustainable mobility of freight and people and meeting the sustainable development goals of the 2030 Agenda for Sustainable Development [6]. Moreover, since traffic is considered to be responsible for a large portion of urban pollutants released into the atmosphere (e.g., see [7; 8; 9]), understanding traffic flows can help in mitigating urban pollution, that is a major source of health problems (e.g., see [10; 11]).  
Motivated by the importance of sharing these data, this paper tackles the modeling of traffic-related data, and particularly the conversion of the data about traffic sensors' locations and measurements into Linked Data and their publication as Open Data. This is a small part of a bigger project called \"TRAFAIR--Understanding Traffic Flows to Improve Air Quality\". The TRAFAIR project is concerned about the study of how traffic impacts on the urban atmospheric pollution. Given that the traffic generated by motor vehicles is a principal source of air pollution, information related to traffic is highly valuable.  
This paper focuses on traffic data managed within TRAFAIR. Specifically, our experience with the modeling and semantic annotation of traffic data, as well as regarding the analysis and application of different tools and techniques used for those tasks, is explained. The structure of the rest of this paper is as follows. Firstly, some related work is discussed in Section 2. Secondly, in Section 3, the background of this work is presented, including a brief description of the goals and motivations of the TRAFAIR project and the modeling choices adopted for handling traffic data. Thirdly, in Section 4, our approach for annotating and publishing traffic data is described. Fourthly, in Section 5, the evaluation and validation of our proposal is tackled. Finally, in Section 6, conclusions and future work are sketched.