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, 1361–1367, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1361-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1361–1367, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1361-2019

  05 Jun 2019

05 Jun 2019

LOCAL MAXIMUM DENSITY APPROACH FOR SMALL-SCALE CLUSTERING OF URBAN TAXI STOPS

H. Wang1, X.-J. Chen2, Y. Wang1, and J. Shan3 H. Wang et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 3Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA

Keywords: Small-scale Hotspots, Local Maximum Density, Spatial-temporal Distribution, Taxi Stops

Abstract. Taxi trajectory data contains the detailed spatial and temporal traveling information of urban residents. By using a clustering algorithm, the hotspots’ distributions of pick-up and drop-off points can be extracted to explore the patterns of taxi traveling behaviors and its relationship with urban environment. Comparing with traditional methods that determine hotspots at a relatively large scale, we propose an approach to detect small-scale hotspots, so called docking points, to represent the local clusters in both sparse and dense stops areas. In this method, we divide the research area into grids and extract the docking points by finding local maximums of a certain range. The extracted docking points are classified into five levels for the subsequent analysis. Finally, to uncover detail characteristics of taxi mobility patterns, we analyze the distributions of docking points from three aspects – the overall, by day of the week, and by time of the day.