In traffic characterization, axle load spectra (ALS) are one of the most critical inputs in the new Mechanistic-Empirical Pavement Design Guide (M-E PDG). Axle load spectra have a significant impact on the predicted pavement performance. At the design stage, it is typically assumed that axle load spectra as measured by the weigh-in-motion (WIM) systems have adequate data quality and accuracy. In fact, the quality of WIM-based data has inherent uncertainties due to inaccuracy and systematic bias. While WIM data accuracy depends on the sensor technology, calibration inadequacies may introduce a systematic bias. Several studies in the past have investigated the impact of traffic data collection technologies, data coverage, accuracy,and calibration errors on pavement loading and performance prediction. However, these studies were limited to a few distress measures and did not address the design reliability aspects as considered in the M-E PDG. This study investigates the impact of probable WIM errors on the ALS and quantifies the effects of these errors on the performance of both flexible and rigid pavements. Furthermore, the impact of uncertainties in ALS on design reliabilities was investigated and documented in this paper. Although most of the findings further reinforce existing concepts, the investigation provides a systematic overview of WIM data accuracy and calibration requirements and the impact of associated uncertainties in the pavement design process. The results show that cracking in both flexible and rigid pavements is the most impacted distress while rutting in flexible pavements is moderately influenced by ALS variations.
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