Vehicle Detector Evaluation Based on Concepts of Traceability and Confidence Interval
Vehicle detectors are essential devices for advanced traffic management and information systems. Hence, the accuracy of traffic data from detectors is fundamentally important. When evaluating detectors, one uses a baseline data source that generates reference data. However, reference data also contain errors and uncertainties, albeit small ones. Nevertheless, little research has been performed to address the errors and uncertainties of reference data. This study proposed a new detector evaluation scheme to resolve that problem. The scheme takes errors and uncertainties of reference data into account for reliable detector evaluation. Moreover, the scheme applies the interval estimation technique, instead of the traditional point estimation, to present detector evaluation results. The suggested scheme was thoroughly verified by using the South Korea detector test bed, a verification that the scheme produced reliable detector evaluation results.
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- Summary URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309295314
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Authors:
- Jang, Jinhwan
- Nakatsuji, Takashi
- Publication Date: 2014
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 12–20
- Monograph Title: Urban and Traffic Data Systems 2014, Volume 2
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Issue Number: 2443
- Publisher: Transportation Research Board
- ISSN: 0361-1981
Subject/Index Terms
- TRT Terms: Acceptance tests; Accuracy; Confidence intervals; Data quality; Traffic data; Traffic surveillance; Vehicle detectors
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01506672
- Record Type: Publication
- ISBN: 9780309295314
- Report/Paper Numbers: 14-0189
- Files: TRIS, TRB, ATRI
- Created Date: Feb 7 2014 10:13AM