Classification of Pavement Texture Properties Using LTPP Data Sets

Pavement surface characteristics, such as smoothness, skid resistance, rolling resistance, drainage, spray, splash, and noise, play a pivotal role in enhancing driving comfort and road safety. Nevertheless, the acquisition of field data for these attributes presents practical challenges, including the need for sophisticated testing equipment for each attribute, limitations in reliable data collection technology, associated costs, and quality constraints on the collected data. Concurrently, it is widely known that the surface texture of pavements has proven correlations with these diverse characteristics. Surface texture is conventionally assessed using non-contact laser-based techniques at highway speeds, which offer advantages over traditional data collection methods. The primary objective of this research is therefore to investigate the array of surface texture properties on flexible pavements and organize them into well-defined clusters. Surface characteristics data relating to pavement texture and other pertinent parameters were extracted from the extensive Long-Term Pavement Performance (LTPP) databases, encompassing 679 highway sections with a diverse range of pavement surface types across 46 U.S. states. The study involved the analysis of more than 20 texture indicators and their associated statistics. Subsequently, categorical logit models were developed to categorize the texture groups based on readily obtainable factors, such as mixture volumetric parameters, treatment ages, traffic patterns, and prevailing climate conditions. This classification of texture groups could hold the potential to estimate the performance of other surface characteristics. The outcomes of this study can contribute to advancing the understanding and practical application of texture parameters in pavement condition assessment.