A trajectory clustering approach based on decision graph and data field for detecting hotspots

Pengxiang Zhao, Kun Qin, Xinyue Ye, Yulong Wang, Yixiang Chen

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Spatial clustering can be used to discover hotspots in trajectory data. A trajectory clustering approach based on decision graph and data field is proposed as an effective method to select parameters for clustering, to determine the number of clusters, and to identify cluster centers. Synthetic data and real-world taxi trajectory data are utilized to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can automatically determine the parameters for clustering as well as perform efficiently in trajectory clustering. Hotspots are identified and visualized during different times of a single day and at the same times on different days. The dynamic patterns of hotspots can be used to identify crowded areas and events, which are crucial for urban transportation planning and management.

Original languageEnglish (US)
Pages (from-to)1101-1127
Number of pages27
JournalInternational Journal of Geographical Information Science
Volume31
Issue number6
DOIs
StatePublished - Jun 3 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Keywords

  • Trajectory clustering
  • data field
  • decision graph
  • dynamic pattern
  • hotspots

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