International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Download
Citation
Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 255–260, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-255-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 255–260, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-255-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

UNCOVERING THE AGGREGATION PATTERN OF GPS TRAJECTORY BASED ON SPATIOTEMPORAL CLUSTERING AND 3D VISUALIZATION

C. K. Liu1,2,3, K. Qin4, K. Chen4, and R. Ma1,2,3 C. K. Liu et al.
  • 1Changjiang Survey, Planning, Design and Research Co., Ltd, Wuhan, China
  • 2Changjiang Spatial Information Technology Engineering Co., Ltd, Wuhan, China
  • 3Water Resources Information Perception and Big Data Engineering Research Center of Hubei Province, Wuhan, China
  • 4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Keywords: GPS trajectory, Spatiotemporal clustering, 3D visualization, Aggregation pattern, Similarity measurement

Abstract. Constrained by road network structure, travel choice and city function zoning, GPS trajectory data exhibits significant spatiotemporal correlation. Unveiling the clustering and distribution patterns of GPS trajectory can help to better understand the travel behaviour as well as the corresponding spatial and temporal characteristics. This paper proposes an approach to identify and visualize the aggregation pattern from GPS trajectory data. Firstly, slow feature trajectory sequences are extracted from raw taxi trajectory data. Together with taxi states information, these sequences are processed as shorter length tracks for faster discovery of cluster similarity. Thereafter, the temporal and spatial similarity and dissimilarity metrics between the trajectories are established, and the temporal and spatial distances between the trajectories are defined to form a space-time cylinder model. Next, based on the idea of density clustering, the DBSCAN spatiotemporal expansion of trajectory data is proposed. Feature trajectory sequences are then clustered into groups with high similarity. Finally, for a more intuitive understanding of the trajectory aggregate distribution, time dimension info of each point in the sequences is used as Z axis, thus the sequences are stretched on the map in different colour for 3D visualization. The proposed method is validated by a case study of taxi trajectory data analysis in Wuhan City, China.