Panel Data Models for Pavement Friction of Major Preventive Maintenance Treatments

Abstract

Although accurate evaluation of pavement friction promises significant safety benefits to highway agencies, the development of such models has proven to be challenging due to the lack of complete and quality pavement surface data for friction studies. In this study, the state-of-the-art three-dimensional (3D) laser imaging technology and the Grip Tester, which is a type of continuous friction measurement equipment (CFME), are used to collect 1-mm 3D pavement surface data and friction data, respectively, at highway speed in the field; whereas the Aggregate Imaging System (AIMS) is used in the laboratory to analyze the surface characteristics of aggregates. Forty-five pavement sites, including six major preventive maintenance (PM) treatments and seven typical types of aggregates in Oklahoma, are identified as the testing beds. Multiple field data collection events have been performed from 2015 to 2017. Panel data analysis (PDA), which is able to investigate the differences of cross-sectional information (the time series changes over time), is conducted to identify the most significant influencing factors for pavement friction prediction model development. Statistical analyses indicate that the random-effects panel model outperforms the fixed-effects model and the traditional ordinary least-squares regression model. The panel model developed in this study could assist decision makers in the selection of PM treatments and aggregates for optimized skid resistance performance.