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Research article
First published January 2003

Empirical Approaches to Outlier Detection in Intelligent Transportation Systems Data

Eun Sug Park, Shawn Turner, and Clifford H. SpiegelmanView all authors and affiliations
Volume 1840, Issue 1
https://doi.org/10.3141/1840-03

Abstract

Novel methods for implementation of detector-level multivariate screening methods are presented. The methods use present data and classify data as outliers on the basis of comparisons with empirical cutoff points derived from extensive archived data rather than from standard statistical tables. In addition, while many of the ideas of the classical Hotelling’s T2-statistic are used, modern statistical trend removal and blocking are incorporated. The methods are applied to intelligent transportation system data from San Antonio and Austin, Texas. These examples show how the suggested new methods perform with high-quality traffic data and apparently lower-quality traffic data. All algorithms were implemented by using the SAS programming language.

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References

1. Turner S. M., Albert L., Gajewski B., and Eisele W. Archived Intelligent Transportation System Data Quality: Preliminary Analyses of San Antonio Transguide Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1719, TRB, National Research Council, Washington, D.C., 2000, pp. 77–84.
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Published In

Volume 1840, Issue 1
Pages: 21 - 30
Article first published: January 2003
Issue published: January 2003

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© 2003 National Academy of Sciences.
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Authors

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Eun Sug Park
Texas Transportation Institute, Texas A&M University System, College Station, 405G CE/TTI Building, TX 77843-3135
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Shawn Turner
Texas Transportation Institute, Texas A&M University System, College Station, 405G CE/TTI Building, TX 77843-3135
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Clifford H. Spiegelman
Department of Statistics, Texas Transportation Institute, 404F Blocker Building, Texas A&M University System, College Station, TX 77843-3143
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405C CE/TTI Building, Texas Transportation Institute, Texas A&M University System, College Station, TX 77843-3135

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