Weigh-in-motion (WIM) systems are a vital means for collecting traffic data—critical input for pavement and bridge designs—used for making transportation and freight planning decisions and in highway safety investigations. There are, however, many potential sources of error in WIM measurements which make it difficult for data collectors to evaluate data accuracy and consistency.
For over a decade, the Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) program collected a massive amount of WIM data, along with information about the performance of WIM equipment. This includes the WIM validation and calibration data from 24 LTPP Specific Pavement Studies (SPS) test sites across North America. This and other data sets provide an opportunity to develop more advanced WIM tools to help state highway practitioners perform WIM site selection, sensor selection, maintenance, development of calibration procedures including frequency, and data quality acceptance. These tools could help improve WIM data accuracy and consistency by considering factors such as temperature and seasonal effects, vehicle speed, pavement condition, changes in truck population and configurations, data sampling frequencies, system age, and other factors.
The objective of this research is to develop the next generation of tools and procedures to improve accuracy and increase reliability of WIM data through (1) more appropriate site selections; (2) WIM system selection, installation, calibration, and maintenance; (3) data analysis methods; and (4) quality control/quality assurance (QC/QA) procedures.