Performance based planning requires a measurement of how well a regional plan meets stated goals. For many regions, traffic congestion is an existing and growing problem, and regional plan goals should be set to reduce congestion and its negative effects on the economy and quality of life. This paper describes the methodology used to forecast congestion level outcomes using the Capital Area Metropolitan Planning Organization (CAMPO) regional travel demand model for various potential congestion reduction scenarios for 2035 in the Austin, Texas region. The paper also describes how the technical process was communicated to policy makers in the Austin, Texas region, the business community, and the public, and actions taken by the community as a result to reduce congestion. This method uses the commonly tracked and trended Travel Time Index to measure congestion levels. Travel Time Index is the ratio of congested peak period travel times to off-peak, less congested travel times. The method presented used a travel demand model by calibrating speed models to locally-observed historical travel time indices. Traditional trip based or activity based models can be used to describe the potential of trip reduction strategies as well as supply-side strategies by defining goals for congestion levels and using scenario based techniques to describe the potential benefit of trip reduction strategies. Definition of scenarios included transportation system response to a No Build scenario, building the 2035 Metropolitan Transportation Plan (MTP) roadway and transit projects, increasing telecommuting 10%, shifting trips out of the congested peak period, increasing non-Single Occupant Vehicle (SOV) mode shares, and implementing high density mixed use growth centers. Congestion goals were set as a policy directive by the business community through the Austin Chamber of Commerce. Trips in the travel demand model were reduced in trip generation, after trip distribution, or by modifying trip length and internal zonal capture. The resulting forecasted travel time indices are portrayed using a wedge chart, a common method used to display alternative scenarios with varying impacts that conveyed a message of magnitude, timing, and need.