The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1579–1585, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1579-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1579–1585, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1579-2019

  05 Jun 2019

05 Jun 2019

A METHOD OF URBAN ROAD NETWORK EXTRACTION BASED ON FLOATING CAR TRAJECTORY DATA

C. Mi1,2 and F. Lu1,2,3,4 C. Mi and F. Lu
  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
  • 4Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Keywords: floating car trajectory data, road network extraction, adaptive radius centroid drift clustering, WaveCluster, GPS data

Abstract. With the gradual opening of floating car trajectory data, it is possible to extract road network information from it. Currently, most road network extraction algorithms use unified thresholds to ignore the density difference of trajectory data, and only consider the trajectory shape without considering the direction of the trajectory, which seriously affects the geometric precision and topological accuracy of their results. Therefore, an adaptive radius centroid drift clustering method is proposed in this paper, which can automatically adjust clustering parameters according to the track density and the road width, using trajectory direction to complete the topological connection of roads. The algorithm is verified by the floating car trajectory data of a day in Futian District, Shenzhen. The experimental results are qualitatively and quantitatively analyzed with ones of the other two methods. It indicates that the road network data extracted by this algorithm has a significant improvement in geometric precision and topological accuracy, and which is suitable for big data processing.