Enhancing Reliability in Automated Pavement Condition Data with a Data Quality Check Approach for Highway Agencies

Abstract

Automated methods have been widely used by highway agencies to collect pavement condition data. However, there are still accuracy and precision issues associated with the reliability of the existing automated data collection methods. Therefore, this research aims to develop data quality check procedures to improve the reliability of automated pavement condition data for highway agencies. The study comprises three main components: identification of data quality check indexes; establishment of data quality thresholds; and implementation of data quality check procedures. Annual pavement rating data collected by a vendor using automated technologies and manual audit data from an independent third party were utilized to develop thresholds and test the procedures. Three districts, including two urban and one rural districts in Texas, were selected for the data quality check implementation. The results indicate that the proposed procedures, along with defined indexes and thresholds, efficiently identify pavements with data quality issues at both the section level and the county level. By pinpointing problematic areas, highway agencies can allocate resources for quick quality checks, enhancing the accuracy and precision of automated pavement condition data. The study can help highway agencies enhance the accuracy and precision of automated pavement condition data, ultimately leading to improvements in their pavement conditions.