LAUSR

Abstract Two of the most popular pavement performance indicators are the International Roughness Index (IRI) and the Pavement Condition Index (PCI). The Long-Term Pavement Performance (LTPP) database does not include the latter. Therefore, limited research is available on the relationship between the PCI and IRI based on the LTPP roads. This study aims to cast light on the relationship between these two performance indicators using LTPP data. To this end, 3,954 records of IRI and PCI were collated to determine the correlation. The aggregate goodness of fit was not satisfactory (R2 = 0.31) as the data was collected over 61 different states and provinces and in a 28-years timeline. So, in the next step the data was clustered into more meaningful groups based on location (province/state) and functional class in the hope of improving the goodness of fit. It was observed that the R2 within each group was substantially higher than the aggregate data, with some reaching above 0.70. Preparing an unprecedentedly large dataset gave us the freedom of segmenting the data into smaller and less noisy subsets, which can result in more robust models with higher coefficients of determination. Moreover, another dataset collected by Ontario Ministry of Transportation (MTO) was studied and the results were contrasted against each other. It was observed that the MTO data is more cohesive, and the correlation between the IRI and the PCI was stronger in that dataset. Finally, this study investigated the variations not explained by regression models, i.e. reasons that road sections can have an excellent PCI and poor IRI and vice versa. The findings show that the relationship between the PCI and IRI can vary significantly based on factors such as location, functional class and slope.