Development Of Traffic Inputs For The Mechanistic Empirical Pavement Design Guide In New York State

Proper characterization of traffic data is a prerequisite for the determination of appropriate traffic inputs to Mechanistic-Empirical Pavement Design Guide (MEPDG). The development of proper traffic inputs helps reflect the traffic conditions over the life of pavement which would decrease the maintenance, repair and traffic disruptions and improve the traffic conditions of a road network. The objective of the study was to characterize the traffic data and suggest the sitespecific, regional or state wide average values for traffic inputs to MEPDG for New York State. Vehicle class distribution (VCD), monthly distribution factors (MDF), hourly distribution factors (HDF), average number of axle groups per vehicle (AGPV) and axle load spectra were obtained from vehicle classification and WIM sites in New York State for the years of 2007-2011. These traffic data was processed with TrafLoad software. Cluster analysis was performed on the processed VCD, MDF and HDF data collected during the time period. This statistical analysis could not be done for AGPV values and axle load spectra due to the unavailability of sufficient number of WIM sites. However, MEPDG runs were carried out to investigate the effect of the variability of traffic inputs on the pavement performance of typical new flexible and rigid pavement structures. The statistical analysis showed consistent results for VCD and HDF over the years. However, the results of statistical analysis on MDF were not consistent over the time period. Site specific values for VCD, MDF, AGPV and axle load spectra showed little variation with statewide average values after the cluster analysis and MEPDG runs for the vehicle classification and WIM data of the year of 2010. This was observed for both flexible and rigid pavements. However, HDF did not show any effect on the design of pavement with MEPDG. These findings were also verified from the analysis of vehicle classification and WIM data of the other years.