Temporal outlier detection in vehicle traffic data

Xiaolei Li, Zhenhui Li, Jiawei Han, Jae Gil Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

92 Scopus citations

Abstract

Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. In contrast to other applications, this particular domain includes a very dynamic dimension: time. Many existing algorithms have studied the problem of outlier detection at a single instant in time. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points. Outliers are calculated from drastic changes in the trends. Experiments with real world traffic data show that this approach is effective and efficient.

Original languageEnglish (US)
Title of host publicationProceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009
Pages1319-1322
Number of pages4
DOIs
StatePublished - Jul 8 2009
Event25th IEEE International Conference on Data Engineering, ICDE 2009 - Shanghai, China
Duration: Mar 29 2009Apr 2 2009

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other25th IEEE International Conference on Data Engineering, ICDE 2009
CountryChina
CityShanghai
Period3/29/094/2/09

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Li, X., Li, Z., Han, J., & Lee, J. G. (2009). Temporal outlier detection in vehicle traffic data. In Proceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009 (pp. 1319-1322). [4812530] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2009.230