Abstract
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This study demonstrated the use of multiple techniques for big data processing and subsequent exploratory analyses, and investigated the relationships between climatic factors and a typical asphalt pavement distress - roughness, through three types of analyses - correlation, regression and causality. Analyses were conducted separately for four different climate regions using the data of 1823 pavement sections collected from Long-term Pavement Performance (LTPP) InfoPave™. The analyses revealed that key climatic factors affecting asphalt pavement roughness differ in different climate regions. The climatic factors reflecting the regional climate characteristics were captured by Kendall’s correlation coefficient and grey relational analysis. Regional climate characteristics were further distinguished by the relative contribution from beta coefficients and average accuracy change of ridge regression models. Relative to the averaged climatic factors, the cumulative climatic factors showed higher levels of correlation with International Roughness Index (IRI) but lower transfer entropy to IRI.