No abstract

The condition of the pavement surface can have a significant effect on highway safety. For example, skidding crashes are often related to pavement rutting, polishing, bleeding, and dirt. When transportation agencies develop paving schedules for their roadways, the agencies often base decisions on asset management condition targets but do not explicitly account for the role of pavement condition in roadway safety. The Virginia Department of Transportation began automated data collection of pavement condition with digital images and an automated crack detection methodology in 2007. Automated collection allows the department to track historical information on pavement condition; this tracking facilitates research into the effect of pavement condition on safety. Information on how pavement condition influences safety can inform paving decisions and the setting of priorities for maintenance. This study quantitatively evaluated the safety effectiveness of good pavement conditions versus deficient pavement conditions on rural, two-lane undivided highways in Virginia. The empirical Bayes method was used to find that good pavements could reduce fatal and injury crashes by 26% compared with deficient pavements, but good pavements did not have a statistically significant impact on overall crash frequency. Further analysis indicated that there was no statistically significant change in the safety benefit of improvements in pavement condition for fatal and injury crashes as the lane or shoulder width increased. The improvement of pavement condition from deficient to good offers a significant safety benefit in reducing crash severity.

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Transportation agencies devote significant resources toward the collection of highly detailed and accurate pavement roughness data by using profiler vans to support pavement maintenance decisions. However, these agencies often cannot afford to measure roughness annually for the whole pavement network. This study introduced an improved acceleration-based metric, an index normalized by vehicle operating speed, to be used on a regular basis to screen pavement segments that are likely to be deficient; then a profile van can be sent to accurately measure the roughness condition. A profile van collected pavement profile data on 50 mi (80 km) of roadway; these data were then used to calculate the international roughness index (IRI). Meanwhile, two tablet computers were placed on the vehicle floor to collect data, including three-way accelerations, GPS coordinates, and vehicle speeds. A normalized acceleration-based index was created by incorporating a speed factor. Furthermore, logistic regression models were created to evaluate the effectiveness of the proposed index in identifying deficient pavement sections [IRI ≥ 140 in./mi (2.21 m/km)]. The proposed acceleration-based metric was able to identify between 80% and 93% of all deficient pavement sections. This research points to the feasibility of using a cost-effective acceleration-based application for network screening, a process that will reduce the total mileage of pavement sections needing to be measured and meanwhile still identify locations for which maintenance work is necessary.