Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1173-1180, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1173-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1173-1180, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1173-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

Y. Z. Gu1, K. Qin1,2, Y. X. Chen3, M. X. Yue1, and T. Guo1 Y. Z. Gu et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, China
  • 2Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China
  • 3Nanjing University of Posts and Telecommunications, China

Keywords: Spectral Clustering, Trajectory data, Spatiotemporal Clustering, DTW, Multi-Thread, Urban Computing

Abstract. Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.