This study investigates the relation of quality measures of asphalt mixture properties during production and construction to long-term in-service performance of flexible pavements. Data from flexible pavement sections taken from the Long-Term Pavement Performance (LTPP) program along with the pavement sections of 30 Wisconsin projects were compiled in a single relational database to establish a georeferenced architecture that embraces the natural variability within the database. An in-depth analysis was performed on seven selected projects using two modeling programs that are based on statistical regression and mechanistic-empirical methods. The three research approaches allowed for assessing the effects of the variation in quality measures during construction and production: air void (Va); voids in mineral aggregates (VMA); and in-place density. The analysis showed that the deviation from the standard target for in-place density influences fatigue, longitudinal, and transverse cracking, as well as the International Roughness Index (IRI). The study showed the shortcomings of over-averaging pavement characteristics in the modeling of predicted in-service performance. Last, this study showed that implementing the proposed georeferenced relational database architecture within LTPP would increase the impact of this program.