WIM data quality and its influence on predicted pavement performance

Abstract

In this study, quality of weigh-in-motion (WIM) traffic data is checked based on a set of algorithms implemented in a program developed in visual basic application (VBA). As a first step, several algorithms are written to check whether the data are within the acceptable range prescribed by the traffic monitoring guide (TMG). Algorithms are also written to checks whether the number of axles is consistent with the vehicle class and the spacing of axles. Next, the VBA program determines the frequencies of vehicle class distribution, directional distribution, lane distribution, monthly distribution, hourly distribution, gross vehicle weight distribution and steering axle weight distribution. To test the quality of data, gross weight and steering axle weight distributions are tested by a set of rules recommended by TMG. It is evident from this study that bending plates provide high quality WIM data compared to piezoelectric WIM systems. It is possible that piezoelectric sensors are affected by temperature and surface condition. To this end, the effect of WIM data quality is evaluated by determining the influence of axle load spectra on the pavement performance predicted by the mechanistic empirical pavement design guide (MEPDG). Using the data collected by three bending plate WIM sites, a subroutine is written to produce a positive and negative calibration bias in the WIM data. Next, single, tandem, tridem and quad axle load spectra are developed for 0, 10, 20 and 30% positive and negative weight measurement bias by using an algorithm based on the axle spacing. From the results, it is observed that the influence of weight measurement bias on predicted longitudinal and alligator cracking is critical and can lead to overestimation or underestimation of the pavement thickness. The effect of weight data bias on predicted rutting is moderate. However, weight measurement error does not affect predicted transverse cracking and international roughness index (IRI).

Keywords:

• Weigh-in-motion
• MEPDG
• traffic
• Axle load spectra
• Pavement
• Vehicle class distribution
• Directional distribution
• Pavement performance
• Bias

The research team thanks the New Mexico Department of Transportation (NMDOT) for funding and making possible this research. Special thanks to Keli Daniell, Virgil Valdez, Jeff Mann, and Josh McClenahan for their assistance.