Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. In this paper, we describe an experiment that held within the museum of natural history of La Rochelle with a class of high school volunteers. We will explain our systems that has been built to work in this specific case, and among other things formalize our algorithm for indoor localization that has not had an equivalent in the state of the art. The minimal zone searching algorithm (MZS) can compute in real time the position of a visitor, shaped as a zone with an average surface of 3 m${}^2$ when resources are limited and when the placement of nodes must respect the constraints imposed by the room’s layout. This method offered good results with data collected during the experimentation, such as a meaningful representation of the position of a visitor and most importantly a stable execution during the whole experience even when the subject was in tight spaces.