Mining large-scale gps streams for connectivity refinement of road maps

As people increasingly rely on road maps in the digital age, manually maintained maps cannot keep up with the demand for accuracy and freshness, evidenced by the recent iOS map incident and the bidding war for Waze. There are many research works on automatic map inference using GPS data, and some have suggested that Google and Waze automate their map update processes to some degree with user data. However, existing published work focuses on refining road geometry. In reality, connectivity issues at intersections, including missing connections and unmarked turn restrictions, are much more prevalent and also more difficult to infer. In this paper, we report on our study on the connectivity issues in the OSM Shanghai map using 21 months of GPS data from over 10, 000 taxis. We first adapt a robust map matching algorithm to detect missing intersections, and then train a time-series detection model for every turn possibility of every intersection using supervised learning.