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.

最受欢迎的两种路面性能指标是国际粗糙度指数（IRI）和路面状况指数（PCI）。长期路面性能（LTPP）数据库不包括后者。因此，关于基于LTPP道路的PCI和IRI之间关系的研究有限。这项研究旨在利用LTPP数据阐明这两个绩效指标之间的关系。为此，整理了3954条IRI和PCI记录以确定相关性。总的拟合优度不令人满意（R ² = 0.31），因为数据是在28年的时间范围内从61个不同的州和省收集的。因此，在下一步中，基于位置（省/州）和功能类别将数据分为更有意义的组，以期提高拟合优度。观察到R ²每组中的数据均显着高于汇总数据，其中一些数据高于0.70。准备一个前所未有的大数据集使我们可以自由地将数据分割为更小和更少噪音的子集，这可以导致具有更高确定系数的更健壮的模型。此外，还研究了安大略省交通部（MTO）收集的另一个数据集，并将结果进行了对比。观察到，MTO数据更具凝聚力，并且在该数据集中，IRI和PCI之间的相关性更强。最后，本研究调查了回归模型无法解释的变化，即道路路段具有优良的PCI和较差的IRI的原因，反之亦然。