Mining Large-Scale GPS Streams for Connectivity Refinement of Road Maps

Abstract

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 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. Then, we propose simple algorithms for detecting missing intersections, turn restrictions and road closures. Empirical results show that our algorithms of connectivity refinement for road maps are effective.

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