Inferring Road Type in Crowdsourced Map Services

TLDR: A model-based approach to infer road types from taxis trajectories is proposed, taking into account both the topology of the road network and the historical trajectories, and can generate quality recommendations of road types for users to choose from.

Abstract: In crowdsourced map services, digital maps are created and updated manually by volunteered users. Existing service providers usually provide users with a feature-rich map editor to add, drop, and modify roads. To make the map data more useful for widely-used applications such as navigation systems and travel planning services, it is important to provide not only the topology of the road network and the shapes of the roads, but also the types of each road segment (e.g., highway, regular road, secondary way, etc.). To reduce the cost of manual map editing, it is desirable to generate proper recommendations for users to choose from or conduct further modifications. There are several recent works aimed at generating road shapes from large number of historical trajectories; while to the best of our knowledge, none of the existing works have addressed the problem of inferring road types from historical trajectories. In this paper, we propose a model-based approach to infer road types from taxis trajectories. We use a combined inference method based on stacked generalization, taking into account both the topology of the road network and the historical trajectories. The experiment results show that our approach can generate quality recommendations of road types for users to choose from.