From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Detecting road intersections from GPS traces
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Mining large-scale, sparse GPS traces for map inference: comparison of approaches
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Map inference in the face of noise and disparity
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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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.