Recognizing network trip patterns using a Spatio-Temporal vehicle trajectory clustering algorithm

Zihan Hong, Ying Chen, Hani S. Mahmassani*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper presents a spatio-temporal trajectory clustering method for vehicle trajectories in transportation networks to identify heterogeneous trip patterns and explore underlying network assignment mechanisms. The proposed algorithm ST-TOPOSCAN is designed to consider both temporal and spatial information in trajectories. We adopt the time-dependent shortest-path distance measurement and take advantage of topological relations of a predefined network to discover the shared sub-paths among trajectories and construct the clusters. The proposed algorithm is implemented with a trajectory dataset obtained in the Chicago area. The results confirm the method's ability to extract and generate spatio-temporal (sub-)trajectory clusters and identify trip patterns. Extensive numerical experiments verify the method's performance and computational efficiency. Through spatio-temporal data mining, this paper contributes to exploring traffic system dynamics and advancing state-of-the-art spatio-temporal clustering for vehicle trajectories.

Original languageEnglish (US)
Article number8082114
Pages (from-to)2548-2557
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
StatePublished - Aug 2018


  • Data mining
  • dynamic network trip patterns
  • shortest path distance
  • spatio-temporal data clustering
  • vehicle trajectories

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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