Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide

US Transportation Collectionhttps://doi.org/10.5703/12882843172272020PDFTech ReportBao, JieyiHu, XiaoqiangPeng, ChengJiang, YiLi, ShuoNantung, TommyPurdue University. Joint Transportation Research ProgramIndiana. Dept. of TransportationUnited States. Department of Transportation. Federal Highway AdministrationPurdue University. Joint Transportation Research ProgramIndianaUnited StatesAxle loadsBus trafficInterstate highwaysMechanistic-empirical pavement designTraffic dataTraffic platooningTruck trafficTwo lane highwaysVisualizationPavement designWeigh in motionVehicle classificationAxle load spectraVehicle platoonEquivalent Single Axle Load (ESAL)Automatic traffic recorderAverage Annual Daily Truck Traffic (AADTT)Truck volume distributionFHWA/IN/JTRP-2020/21SPR-4231Final ReportThe Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra playa critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data.120