Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina

Abstract

This paper presents attempts to generate regional average truck axle load distribution factors (ALFs), monthly adjustment factors (MAFs), hourly distribution factors (HDFs), and vehicle class distributions (VCDs) for North Carolina. The results support Mechanistic–Empirical Pavement Design Guide (MEPDG) procedures. Weigh-in-motion data support the analysis and generate seasonal factors. MEPDG damage-based sensitivity analysis shows that pavement performance is sensitive to North Carolina site-specific ALFs, MAFs, and VCDs. Similar results occur for national default values of ALF, MAF, and VCD. Hierarchical clustering analysis based on North Carolina ALFs and MAFs develops representative seasonal traffic patterns for different regions of the state. Findings show that seasonal truck traffic has distinct characteristics for the eastern coastal plain, the central Piedmont, and the western mountains. A simplified decision tree and a related table help the pavement designer select the proper representative patterns of ALF and MAF. To develop VCD factors, the approach uses 48–h classification counts and a seasonal factoring procedure to account for day-of-week and seasonal variations. The approach incorporates site-specific truck traffic to improve the accuracy of pavement design. On the basis of sensitivity analysis results, pavement performance is found to be insensitive to North Carolina site-specific and national default values of HDF; thus, the average statewide HDF values may be used as input to MEPDG. Specific contributions of this research are the relative insensitivity of pavement performance to HDF, the use of 48-h classification counts to estimate VCD inputs, and a decision tree and table to help pavement designers select the proper ALF and MAF inputs.