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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    Title :

    Empirical Approaches to Outlier Detection in Intelligent Transportation Systems Data


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2003-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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