Knowledge extraction from crowdsourced data for the enrichment of road networks

In current navigation systems quantitative metrics such as distance, time and energy are used to determine optimal paths. Yet, a "best path", as judged by users, might take qualitative features into account, for instance the scenery or the touristic attractiveness of a path. Machines are unable to quantify such "soft" properties. Crowdsourced data provides us with a means to record user choices and opinions. In this work, we survey heterogeneous sources of spatial, spatio-temporal and textual crowdsourced data as a proxy for qualitative information of users in movement. We (i) explore the process of extracting qualitative information from uncertain crowdsourced data sets employing different techniques, (ii) investigate the enrichment of road networks with the extracted information by adjusting its properties and by building a meta-network, and (iii) show how to use the enriched networks for routing purposes. An extensive experimental evaluation of our proposed methods on real-world data sets shows that qualitative properties as captured by crowdsourced data can indeed be used to improve the quality of routing suggestions while not sacrificing their quantitative aspects.