Mining discriminative patterns for classifying trajectories on road networks

Jae Gil Lee, Jiawei Han, Xiaolei Li, Hong Cheng

Research output: Contribution to journalArticlepeer-review


Classification has been used for modeling many kinds of data sets, including sets of items, text documents, graphs, and networks. However, there is a lack of study on a new kind of data, trajectories on road networks. Modeling such data is useful with the emerging GPS and RFID technologies and is important for effective transportation and traffic planning. In this work, we study methods for classifying trajectories on road networks. By analyzing the behavior of trajectories on road networks, we observe that, in addition to the locations where vehicles have visited, the order of these visited locations is crucial for improving classification accuracy. Based on our analysis, we contend that (frequent) sequential patterns are good feature candidates since they preserve this order information. Furthermore, when mining sequential patterns, we propose to confine the length of sequential patterns to ensure high efficiency. Compared with closed sequential patterns, these partial (i.e., length-confined) sequential patterns allow us to significantly improve efficiency almost without losing accuracy. In this paper, we present a framework for frequent pattern-based classification for trajectories on road networks. Our comparative study over a broad range of classification approaches demonstrates that our method significantly improves accuracy over other methods in some synthetic and real trajectory data.

Original languageEnglish (US)
Article number5560657
Pages (from-to)713-726
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number5
StatePublished - 2011


  • Trajectory classification
  • frequent pattern-based classification
  • road network analysis
  • sequential patterns

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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