New Approach for Managing Pavement Friction and Reducing Road Crashes

Abstract

Evaluation of crash count data as a function of roadway characteristics allows departments of transportation (DOTs) to predict expected average crash risks to assist in identifying segments that could benefit from various treatments. Crash risk is modeled using negative binomial regression, as a function of annual average daily traffic (AADT) and other variables. For this paper, a crash study was carried out for the Interstate, primary, and secondary routes in the Salem District of Virginia. The data used in the study included the following information obtained from Virginia DOT records: 2010 to 2012 crash data, 2010 to 2012 AADT, and horizontal radius of curvature. In addition, tire–pavement friction, or skid resistance, was measured with a continuous friction measurement, fixed-slip device called a GripTester. Negative binomial regression was used to relate the crash data to the AADT, skid resistance, and horizontal radius of curvature. To determine which of the variables to include in the final models, researchers performed the Akaike information criterion test. By mathematically combining the information acquired from the negative binomial regression models and the information contained in the crash counts, researchers empirically estimated the parameters of each network’s true average crash risks with the empirical Bayes approach. The new estimated average crash risks were then used to rank segments according to their empirically estimated crash risk and to prioritize segments according to their expected crash reduction if a friction treatment were applied.