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Title:
EMPIRICAL APPROACHES TO OUTLIER DETECTION IN INTELLIGENT TRANSPORTATION SYSTEMS DATA
Accession Number:
00966622
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 T-squared-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.
Supplemental Notes:
This paper appears in Transportation Research Record No. 1840, Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation.
Corporate Authors:
Transportation Research Board
500 Fifth Street, NW
Washington, DC 20001 United States
Authors:
Park, E S
Turner, S
Spiegelman, C H
Features:
Figures
(3)
; References
(4)
; Tables
(5)
Subject Areas:
Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning
Created Date:
Dec 16 2003 12:00AM
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