Pavement Surface Characteristics Evaluation Using Vehicle-Based Data Collection

Abstract

Various methods are used to collect pavement surface conditions, varying from manual checks by employees to specialized equipment. However, traditional methods usually require expensive specialized equipment and are time-consuming and costly. This thesis examines the use of connected vehicles (CV) to collect pavement surface data estimated based on sensors mounted in the cars. This concept was first analyzed through a literature review, where CV technology was examined to measure roughness, friction, and pothole data. Data collection was performed by a specialized company, utilizing sensor data from standard manufactured cars. A sample of data from the Richmond district of Virginia, was used to compare the estimated values to the standard International Roughness Index (IRI) values used by VDOT. Data collected using both methods were matched using Matlab code to have a common linear referencing system. Subjective visual comparison showed that both data sets had similar trends, highlighting roads with rough sections. A quantitative analysis performed to compare the average results of the two methods on a sample of uniform sections, showed a high correlation. A technology assessment was also conducted to evaluate the maturity level of the CV, which was found to be at least a TRL 7. This suggests that CV technology can be a valuable addition to the traditional methods for collecting pavement surface data.