Utilizing Clustering Techniques in Estimating Traffic Data Input for Pavement Design

Abstract

This paper presents an objective approach for establishing similarities in vehicle classification and axle load distributions between traffic data collection sites. It is based on clustering techniques that identify in succession groups of sites of decreasing similarity on the basis of the attributes specified (e.g., either the percentage of vehicles by class or the percentage of axles by load interval, respectively). This method is implemented in identifying clusters of sites with similar vehicle class distribution and axle load distributions, respectively. Extended coverage weigh-in-motion data (i.e., more than