MACROSCOPIC STATE-LEVEL ANALYSIS OF PAVEMENT ROUGHNESS USING TIME-SPACE STATISTICAL MODELING METHODS

Abstract

This thesis uses pavement condition data collected from FHWA between 2001-2006 aggregated by US state, to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement condition and preservation expenditure in time, time autocorrelation parameters are introduced in a spatial modeling scheme that accounts for spatial dependence, autocorrelation, and heterogeneity. Because pavement roughness across different roadway classes are anticipated to be affected by different explanatory parameters, separate time-space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications reveal that different time-space models are appropriate for pavement performance modeling across the roadway classes. Factors that are found to affect state-level pavement roughness in time and space include preservation expenditure, predominant soil type, and predominant climate conditions. The results of this thesis are anticipated to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.