Abstract
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.
Highlights
Public administrations handle large amounts of data concerning their internal processes as well as to the services that they offer to citizens
A mapping schema is proposed to map the sensor data to semantic data, as in [30], in such a way that the sensor data can be automatically represented as instances of the SSN ontology; the property observed is Car_flow property
Public administrations and citizens lack a complete set of tools to allow the estimation of the level of pollution at an urban scale, which depends on the variable traffic conditions, which would lead to an optimization of the control strategies and an increase of the air quality awareness
Summary
Public administrations handle large amounts of data concerning their internal processes as well as to the services that they offer to citizens. 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. Motivated by the importance of sharing these data, this paper tackles the modeling of traffic-related data, and 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”.
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