Towards Knowledge-Enriched Path Computation

TL;DR

This paper introduces a method to compute paths that consider both distance and popularity by integrating crowdsourced spatial relations into road network routing, resulting in more user-friendly routes.

Contribution

It presents a novel approach that combines probabilistic modeling of spatial relations from travel blogs with Bayesian inference to enrich road networks for improved routing.

Findings

Paths are more popular and user-friendly.

Enriched routes are comparable in length to shortest paths.

Crowdsourced data effectively enhances routing quality.

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 travel blogs, that define closeness between pairs of points of interest (PoIs) and quantify each of these relations using a probabilistic model. Subsequently, we create a relationship graph where each node corresponds to a PoI and each edge describes the spatial connection between the respective PoIs. Using Bayesian inference we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we obtain an altered cost function which does not exclusively rely on distance, and enriches an actual road networks taking crowdsourced spatial relations into account. Finally, 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 paths computed w.r.t.\ our alternative cost function yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.