Improving Pavement Management and Assessment through Connected Vehicle Technology and Highway Safety Analysis

Zeng, Huanghui, Civil Engineering - School of Engineering and Applied Science, University of Virginia
Smith, Brian, Department of Civil Engineering, University of Virginia

Transportation agencies devote significant resources towards the collection of highly detailed and accurate pavement condition data using instrumented vans to support pavement maintenance decisions. However, they often cannot afford to measure pavement condition annually for the whole roadway network. In addition, pavement maintenance is traditionally based only on asset management condition targets but do not explicitly account for the role of pavement condition in roadway safety. This dissertation introduced a connected vehicle-enabled approach to improve the pavement assessment method in terms of data collection cost and frequency, and applied highway safety analysis to demonstrate a new way to use pavement condition data beyond current practice.
Three related studies were conducted. The first study developed an improved acceleration-based metric, an index normalized by vehicle operating speed, for a connected vehicle-enabled pavement network screening application. The application can be used on a regular basis to “prescreen” pavement segments that are likely to deficient, and then a profile van can be sent to measure the accurate roughness condition. It was found that the proposed acceleration-based metric is able to correctly identify between 80 and 93 percent of all deficient pavement sections on three different functional classes of highways.
Considering that connected vehicle data will come from a good variety of vehicle dynamic systems, a follow-up study investigated the impact of vehicle dynamic systems on the acceleration-based roughness metric. Sensitivity analysis based on the quarter-car model found that vehicle vibration response is most sensitive to the spring stiffness of the sprung mass and least sensitive to the loading of the vehicle. Furthermore, the relationship analysis shows that the resulting acceleration-based metrics are linearly correlated between different vehicle systems. Assuming that transportation agencies will use agency-owned vehicles to build a pavement condition network screening system, a vehicle calibration procedure was developed to help them calibrate vehicles in the fleet.
The third study focused on filling the gap between traditional pavement management and highway safety management. It quantitatively evaluated the safety effectiveness of good pavement conditions versus deficient pavement conditions on rural two-lane undivided highways in Virginia. Using the Empirical Bayes method, it was found that good pavements are able to reduce fatal and injury (FI) crashes by 26 percent over deficient pavements, but do not have a statistically significant impact on the overall crash frequency. As a result, improving pavement condition from deficient to good can offer a significant safety benefit in terms of reducing crash severity.
In conclusion, the results from the first two studies point to the feasibility of using a cost-effective acceleration-based application for the purpose of network screening. The network screening process will reduce the total mileage of pavement sections that need to be measured by the instrumented van and meanwhile still identify locations where maintenance work is necessary. The third study enhances transportation agencies’ ability to account for safety in their pavement maintenance decision making process, which helps to better set priorities for maintenance.

PHD (Doctor of Philosophy)
pavement management and assessment, connected vehicle, highway safety, empirical bayes method
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