EMPIRICAL APPROACHES TO OUTLIER DETECTION IN ITS DATA
This paper presents novel methods for implementing detector-level multivariate-screening methods. The methods use current day data and classify data as outliers based on comparisons to empirical cutoff points derived from extensive archived data rather than from standard statistical tables. In addition, while many of the ideas of classical Hotelling's T2 statistic are used, modern statistical trend removal and blocking are incorporated. The methods are applied to ITS data from San Antonio and Austin, Texas. These examples show how the suggested new methods perform on high quality traffic data and apparently lesser quality traffic data. All our algorithms were implemented using SAS
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Supplemental Notes:
- Publication Date: 2003. Transportation Research Board, Washington DC. Remarks: Paper prepared for presentation at the 82nd annual meeting of the Transportation Research Board, Washington, D.C., January 2003. Format: CD ROM
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Corporate Authors:
University of California, Berkeley
California PATH Program, Institute of Transportation Studies
Richmond Field Station, 1357 South 46th Street
Richmond, CA United States 94804-4648California Department of Transportation
1120 N Street
Sacramento, CA United States 95814University of California, Berkeley
Department of Electrical Engineering and Computer Sciences
Berkeley, CA United States 94720 -
Authors:
- Park, Eun Sug
- Turner, Shawn
- Spiegelman, Clifford H
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Conference:
- Transportation Research Board 82nd Annual Meeting
- Location: Washington DC, United States
- Date: 2003-1-12 to 2003-1-16
- Date: 2003
Language
- English
Media Info
- Pagination: 23 p.
Subject/Index Terms
- TRT Terms: Computer algorithms; Databases; Loop detectors; Reliability; Traffic flow
- Subject Areas: Operations and Traffic Management;
Filing Info
- Accession Number: 00941670
- Record Type: Publication
- Source Agency: UC Berkeley Transportation Library
- Files: PATH, STATEDOT
- Created Date: May 1 2003 12:00AM