Traffic Characteristics and their Impact on Pavement Performance for theImplementation of the Mechanistic-Empirical Pavement Design Guide inIdaho

摘要

Traffic data is one of the most important inputs for any pavement design procedure. However,traffic data is often associated with the highest level of uncertainty. This study addresses thedevelopment of traffic characteristics to facilitate the implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG) in Idaho. Classification and weight data collectedat 25 weigh-in-motion (WIM) sites were procured from Idaho Transportation Department (ITD).Among these 25 sites, only 12 sites were found to have complete and accurate data. Site-specificaxle load spectra (ALS), monthly adjustment factors (MAF), vehicle class distribution factors(VCD), and number of axles per truck type were developed. Predicted distresses andInternational Roughness Index (IRI) based on a typical pavement section and traffic dataobtained at the investigated WIM sites (level 1) were compared to predicted distresses and IRIusing statewideational (level 3) default traffic inputs. This comparison revealed that ALS,VCD, and MAF input level have significant impact on longitudinal cracking. Statewide ALSyielded high errors in alligator cracking predictions while MAF and VCD yielded only moderateerrors compared to site-specific ALS. Very low prediction errors occurred in rutting whenstatewideational default ALS, MAF, and VCD (Level 3 input) were used as opposed to sitespecificdata. The level of input of the investigated traffic parameters did not affect IRI. Finally,it was found that statewideational number of axles per truck can be used instead of site-specificvalues without sacrificing accuracy of pavement performance predictions.