The number of highway lane-miles in the United States increased by 7% from 1980 to 2007, while vehicle-miles of travel almost doubled. During the same period, the Federal Highway Trust Fund (the major source of funding for highways) grew by only 40% in constant 1980 dollars. With growth in trade and commerce, truck traffic levels are expected to increase significantly in the future. Highway agencies throughout the United States are facing complex decisions about maintaining, repairing, and renewing existing pavements in the most cost-effective ways. Decision makers need to learn: to what degrees different pavement preservation treatments will improve a pavement condition; how pavement conditions will change over time; when to apply which treatment to what section; and what budget level will be needed to maintain and improve pavement conditions. The objectives of this dissertation are to 1) estimate the effectiveness of appropriate different levels of pavement preservation treatments, 2) evaluate pre-treatment and posttreatment pavement performances, and 3) use the uniformed results (of the first two objectives) to develop a decision making tool for integrated pavement management systems. The dissertation will utilize data from the Long Term Pavement Performance (LTPP) program. LTPP data will be used to estimate statistical models of the benefit effectiveness of preservation-related treatments and pavement performance, including models of performance jump--i.e., the instantaneous improvement in the performance or condition of a pavement due to a maintenance treatment. The forecast values from the statistical models will be used as inputs to optimization models that will allow for the simultaneous solution of several objectives or constraints. The results will benefit pavement management systems and improve pavement preservation planning in the United States.