Inaccurate weigh-in-motion (WIM) data may result in significant over-or under-estimation of the pavement performance period, leading to over-or under-design pavements. Therefore, the data collected at WIM systems must be accurate and consistent. The paper presents an approach to estimate WIM system accuracy based on axle load spectra attributes [normalized axle load spectra (NALS) shape factors]. This alternative approach to assess WIM system accuracy is needed to characterize temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to NALS shape factors for Class 9 vehicles. This analysis's main objective is to determine WIM system errors based on axle loading without physically performing equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the tandem axle (TA) can be estimated with TA NALS shape factors with an acceptable degree of error for bending plate (BP) and quartz piezo (QP) sensors. Further, the results obtained using different statistical methods for model development and validation show reasonable goodness of fit. The use of NALS to estimate the TA WIM accuracy can save a significant amount of time and resources, which are usually spent on equipment calibrations every year.
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