Hierarchical methodology to evaluate the quality of disparate axle load data sources for pavement design

Abstract

Axle load data are an essential input for pavement design, yet for most North American agencies, there is uncertainty about the quality of axle load data obtained from weigh-in-motion (WIM) systems, the applicability of these data for pavement design, and potential opportunities to integrate axle load data from disparate sources. This article presents a novel and practical methodology to evaluate the quality of axle load data from WIM systems and roadside weigh scales through a series of hierarchical analyses designed to test data validity. When applied using data from Manitoba, Canada, the methodology quantified the uncertainty of axle loads measured at the weigh scales and piezo-quartz WIM, concluding that both could be used for pavement design applications. Data collected at piezo-polymer WIM sites exhibited poorer data validity; however, application of site-specific temperature correction factors significantly improved data validity at these sites. The article describes how other data quality dimensions, including spatial coverage, temporal coverage, and long-term data availability, could be considered when determining the suitability of disparate axle load data sources for pavement design. Application of the methodology enables a pragmatic evaluation of the quality and limitations of commonly-available axle load data, revealing uncertainties and data needs relevant for pavement design practice. • Hierarchical methodology was used to evaluate disparate axle load data sources. • Truck-pairing revealed reasonableness of piezo-quartz weigh-in-motion (WIM) data. • Temperature corrections improved data quality at piezo-polymer WIM sites. • Static weigh scales produce quality data but labor-intensive sampling is required. • Beyond validity, data suitability depends on temporal and spatial coverage.