Data veracity in intelligent transportation systems: the slippery road warning scenario

Intelligent transportation systems rely on the availability of high quality data in order to allow its multiple actors to make correct decisions in diverse traffic situations. Traditionally, high quality is associated with the correctness of the data, its timeliness or integrity. Going beyond data quality, this paper explores the notion of data veracity, which we approach from the perspective of the truthfulness of the data with respect to reality, or, in other words, its ability to be free from 'lies'. Starting from the concrete case of the slippery road warning scenario (which comes from an industrial player), we define an initial taxonomy of data veracity (which is derived from the study of the literature) and use such taxonomy as a means to analyze the threats to data veracity in the above mentioned scenario. Additionally, this paper has the ambition to draw the attention of researchers and practitioners on the emerging challenges in the fiels of data veracity and to define a research roadmap to tackle such challenges.