A novel method for road network mining from floating car data

Abstract

ABSTRACT Vehicles have been increasingly equipped with GPS receivers to record their trajectories, which we call floating car data. Compared with other data sources, these data are characterized by low cost, wide coverage, and rapid updating. The data have become an important source for road network extraction. In this paper, we propose a novel approach for mining road networks from floating car data. First, a Gaussian model is used to transform the data into bitmap, and the Otsu algorithm is utilized to detect road intersections. Then, a clothoid-based method is used to resample the GPS points to improve the clustering accuracy, and the data are clustered based on a distance-direction algorithm. Last, road centerlines are extracted with a weighted least squares algorithm. We report on experiments that were conducted on floating car data from Wuhan, China. To conclude, existing methods are compared with our method to prove that the proposed method is practical and effective.