Towards knowledge-enriched path computation

Abstract

Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.