CLUSTERING ANALYSIS OF PERMANENT COUNT SITES TO ENHANCE DATA ACCURACY THROUGH IMPROVED QUALITY CONTROL RULES
PI: Jidong J Yang
Co-PI(s): Stephan Durham, Sonny Kim, and Mi Geum Chorzepa
Institution(s): University of Georgia
Abstract
Data quality assurance is a critical component of statewide traffic monitoring programs. The current state of the practice uses quality control rules and examine data at each count station individually, which may not be robust since it disregards inherent dependency of stations in close vicinity. This research study aims to identify groups or clusters of continuous count stations (CCS) that are spatiotemporally correlated and leverage such correlations to improve the quality control of traffic data in support of various planning and engineering practices and decision-making at GDOT.
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