Automated pavement data collection techniques are commonly used by highway agencies to collect pavement surface distress data at the network level. While an immense amount of data is collected at the network level, agencies realize that there is a lack of understanding in the quality of data that has been collected. Traditionally, either the overall pavement condition rating or individual distress ratings have been used to evaluate the quality of the condition data. However, each measure has its own pros and cons, rendering the use of a single measure inadequate. This paper presents a set of performance measures that can be used by highway agencies to quantify the quality of the collected and processed pavement condition data. The set of measures consists of (i) pavement condition rating and the hypothesis testing for differences; (ii) percentage cumulative difference in pavement condition rating over its entire range; and (iii) kappa statistics for individual distresses. This set of performance measures can be used to assess the effectiveness of an automated pavement condition data collection method and the effect of sampling on the pavement condition ratings obtained from automated techniques. Effectiveness of an automated technique is assessed against benchmark manual visual surveys. The performancemeasures offer a complete assessment of the effect of sampling on the overall pavement condition rating, its variation over the entire range, and the identification of individual surface distresses.
Samenvatting