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New vehicle technology is leading to efficient methods for assessing the condition of the National Highway System. The use of simple sensors such as accelerometers, installed in vehicles, could provide a cost-effective way to assess ride quality for pavement management. A pilot study compared data gathered from accelerometers with the current state-of-the-art practices for measuring ride quality. After a review of relevant previous studies involving probe vehicles, this study assessed the use of probe vehicles’ acceleration measurements to evaluate the pavement profile. The repeatability of acceleration measurements with cross-correlation and standard deviation was obtained. With visual methods and the coherence function, acceleration measurements were compared with profile measurements obtained from inertial profilers. The literature review reinforced the view that using probe vehicles for pavement condition data collection would be promising and that measuring pavement condition with typical onboard sensors could provide a cost-effective way to collect data for pavement management. Probe vehicles are most practically used in pavement management applications to describe ride quality by using vehicle accelerometers and the Global Positioning System. The pilot study confirmed that the acceleration runs were repeatable. Visual inspection of the acceleration and profile plots suggested that the acceleration profiles and smoothness measurements were similar. Analysis with the coherence function also confirmed this strong relationship. The tested methodology provides a practical way to evaluate smoothness while providing a wider base of coverage compared with that of inertial profilers.

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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.

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