Simplified Truck Traffic Classification (TTC) Groupings for DARWin-ME

摘要: Traffic loads obtained using automated traffic collection techniques such as Weigh-In-Motion (WIM) are one of the key data elements required in Mechanistic-Empirical Pavement Design Guide (MEPDG) and subsequent DARWin-ME. Due to limited number WIM stations within a state agency, successful use new design guide is be able recognize loading clusters so estimate full axle load spectrum occurring at particular site. Even though various clustering approaches have been proposed, they either computationally extensive or requiring site-specific truck count data. In most cases for designing pavements, not available before pavement open traffic; therefore it desirable develop simplified grouping technique DARWin-ME when information missing. Following Truck Classification (TTC) concept DARWin-ME, K-Means cluster analysis algorithm applied historical from Arkansas Highway Transportation Department (AHTD) TTC developed. input generated based on approach compared analyses conducted. A case study provided demonstrate applicability generate Level 2 inputs design. The method developed paper only requires prior knowledge trucking patterns that occur specific roads will greatly alleviate preparation spectra procedure, especially less important projects low-volume secondary roads.