Assessing the Effect of Considering Multiple Data Properties on Detecting Potential Errors in Pavement Condition Data

Abstract

The quality of pavement condition data can affect the assessment of current condition, predictions of future condition, and the reliability of maintenance and rehabilitation plans and funding need estimates at the network level. Thus, improving the quality of pavement condition data is an ongoing process for transportation agencies. Detecting potential errors in network-level pavement condition data is a primary step in assessing and enhancing the accuracy of this data. Current error detection techniques tend to focus on analyzing time series trends in pavement condition to identify unexpected changes that may denote data errors. However, there are additional properties of these data that can be used to identify potential errors, including variability within uniform performance families and consistency between multiple performance indicators. This paper assesses the effect of considering those data properties on detecting potential errors in pavement condition data. Three case analyses were defined such that each considered a different combination of these properties to identify likely errors. The analyses were performed on a pavement condition data set representing the Brownwood District roadway network of the Texas Department of Transportation. The results of this investigation indicate that considering such properties in a combined manner can reduce the numbers of false positive errors and false negative errors.