Comparison between Alternative Methods for Estimating Vehicle Class Distribution Input to Pavement Design

Abstract

The recently developed mechanistic-empirical pavement design guide (M-E PDG) represents a significant improvement over the AASHTO design guide. One of the major improvements to the M-E PDG is traffic characterization. The M-E PDG does not use the equivalent single axle load (ESAL) approach that is employed in the AASHTO design guide. Instead, it follows a more rational approach that is based on describing traffic in terms of number of axles by axle type and axle load distributions. In recent years, several research studies have been conducted to evaluate the sensitivity of the M-E PDG to the required traffic inputs. It was determined that vehicle class distribution (VCD) has a significant influence on the design of pavement structures. This study investigated various methods for estimating the VCD factors based on functional classification (FC), truck traffic classification (TTC), cluster analysis, and short-term counts. An extensive traffic monitoring data set that included information from 143 permanent traffic monitoring sites, composed of 93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites distributed throughout the state of Ohio, was used to evaluate these methods. A sensitivity analysis was also conducted to determine the effect of these methods on the predicted pavement service life. It was found that the functional classification, M-E PDG TTC grouping system and cluster analysis methods may significantly underestimate or overestimate the predicted pavement service life, whereas the short-term count method produces small variations in the predicted pavement service life. This suggests that the short-term count method is the most accurate method for estimating the VCD factors to be used in pavement design.