Weigh-in-Motion (WIM) technology is one of the primary tools used for pavement management. It can provide essential and accurate truck traffic information, including vehicle class and speed, vehicle count, gross vehicle weight (GVW), single axle (SA) and tandem axle (TA) weights, axle spacing, and the date and time of the event. The State Departments of Transportation (DOTs) gather WIM data for various applications, including highway planning, pavement and bridge design, commercial vehicle weight enforcement, asset management, and freight planning and logistics. Overloaded trucks pose severe challenges to road transport operations. Overloaded trucks can cause more damage to the pavement systems than trucks loaded within legal weight limits. Truck overloading can also lead to severe consequences if involved in a traffic accident. Law enforcement agencies divert potentially overloaded trucks to static scales and issue tickets based on the information collected at a WIM station. Because of the wide range of applications, the data obtained at WIM stations must be accurate, consistent, and reflect actual field conditions.This study addressed four critical concerns related to WIM equipment performance, calibration needs, traffic loading data quality, and applications. Precisely, the current research advanced the state of the practice knowledge about (a) potential factors impacting WIM system accuracy, (b) accuracy and consistency of traffic loading data and calibration needs of WIM stations, (c) revised/modified guidelines for WIM equipment calibration, and (d) estimation of commercial freight tonnage from Gross Vehicle Weight (GVW) data. The research objectives were accomplished by synthesizing and analyzing the WIM performance and traffic loading data available in the Long Term Pavement Performance (LTPP) traffic database and data available through other state DOTs. The WIM sites analyzed in this study are from 30 states within the United States and 3 Canadian provinces. Several factors can affect the WIM system accuracy (i.e., measurement error). The potential site-related factors include road geometry, pavement stiffness, surface distresses, roughness, and climate. Decision tree models were developed in this study to illustrate a potential for estimating the expected WIM measurement error range using information about the WIM site and sensor-related factors. The results show that the sensor array and sensor types are the most important predictors, followed by WIM controller functionality (speed points). The data analysis and results also show that the climate can be important for some sensor types. One can integrate this information with equipment installation and life cycle costs to determine the most reliable and economical WIM equipment while also considering accuracy requirements by WIM data users.One way to evaluate WIM measurement errors is by using the data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. Consequently, an alternative approach was needed to characterize temporal variations in WIM data consistency. This study presents a method to estimate WIM system accuracy based on axle load spectra attributes [Normalized Axle Load Spectra (NALS) shape factors]. This analysis's main objective is to determine WIM system errors based on axle loading without physically performing equipment calibration. Using NALS to estimate WIM system accuracy can save a significant amount of time and resources, usually spent on equipment calibrations yearly.Successful WIM equipment calibration can eliminate systematic weight, speed, and axle spacing errors. The suggested changes in current WIM calibration procedures related to truck type (loaded truck), number of truck runs, and truck speed (multiple speed points) can significantly reduce the time and resources needed for successful equipment calibration. Accurate freight tonnage estimates and trends are essential due to their implications on economic, infrastructure development, and transportation policy decision-making. This study presents a practical application of WIM data to estimate freight tonnage and classify commodity types. The payloads computed for Class 9 trucks from GVW data strongly correlated with the average freight tonnage obtained from a commercial data source, i.e., Transearch from the IHS market. The user can independently verify the freight estimates from surveys at locations close to WIM sites.
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