Inferring Road Maps from Sparsely-Sampled GPS Traces

In this paper, we proposed a new segmentation-and-grouping framework for road map inference from sparsely-sampled GPS traces. First, we extended DBSCAN with the orientation constraint to partition the whole point set of traces to clusters representing road segments. Second, we proposed an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly straight line segments. Third, the line segments are grouped according to the ‘Good Continuity’ principle of Gestalt Law to form a ‘Stroke’ for recovering the road map. Experiment results show that our algorithm is robust to noise and sampling rate. In comparison with previous work, our method has advantages to infer the road maps from sparsely-sampled GPS traces.