Assessment of Factors Affecting Measurement Accuracy for High-Quality Weigh-in-Motion Sites in the Long-Term Pavement Performance Database

• Published in: Transportation research record Vol. 2674; no. 10; pp. 269 - 284
• Main Authors: Haider, Syed Waqar, Masud, Muhamad Munum, Selezneva, Olga, Wolf, Dean J.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-10-2020
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/0361198120937977

Weigh-in-motion (WIM) is a primary technology used for monitoring and collecting vehicle weights and axle loads on roadways. Highway agencies collect WIM data for many reasons, including highway planning, pavement and bridge design, freight movement studies, motor vehicle enforcement, and regulatory studies. Therefore, the data collected at WIM systems must be accurate and represent actual field loadings. Several factors or field conditions can affect the WIM system accuracy (i.e., measurement error). The potential site-related factors include road geometry, pavement stiffness, pavement surface distresses, road roughness, and climate. The WIM calibration and equipment-related factors may include sensor type and array, calibration speed and speed points, and sensors’ age. The WIM data for Long-Term Pavement Performance (LTPP) research-quality sites were considered to estimate benchmark accuracies for different sensors and evaluate the effects of different factors on WIM measurement errors. These are the 35 sites with WIM calibration data that meet the ASTM E1318-09 error tolerances for Type I WIM systems and are consistently calibrated using the LTPP protocol with a complete set of supporting data about WIM site performance and WIM site conditions. The data for the LTPP research-quality sites showed that for the sensor arrays utilized, the best achievable total errors based on GVW are ±5% for load cell (LC), ±9% for bending plate (BP), and ±9.8% for the quartz piezo (QP) sensors. These accuracy levels for different sensor types provide highway agencies with the benchmark values demonstrating the practically achievable accuracy of WIM measurements after calibration for different WIM sensor types. Based on available data, WIM sensor accuracy can be significantly affected by climate, especially for QP and polymer piezo sensors. Also, the longitudinal roadway slope at a WIM site, sensor array, and speed points may significantly affect the WIM system accuracy.