Estimation of Truck Traffic Inputs for Mechanistic–Empirical Pavement Design in California

Abstract

This study developed default truck traffic inputs for mechanistic–empirical pavement design procedures for the California highway system based on California weigh-in-motion (WIM) data. Both cluster analysis and regression analysis were applied to develop the default axle load spectra. Regression analysis produced unsatisfactory results so it was not used. On the basis of cluster analysis of axle load spectra, the WIM sites were divided into several groups, and default truck traffic inputs were estimated for each group. A decision tree was developed to help designers select the appropriate default factors based on easily available information: geographic location and traffic volume and composition. These data can be obtained from the California Department of Transportation annual report of annual average daily truck traffic. Traffic inputs were developed for both the Caltrans Mechanistic–Empirical Pavement Design and NCHRP Mechanistic–Empirical Pavement Design Guide software.

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