Concerned citizens across the United States are increasingly asking officials about the effects of proposed new highways and their alternatives, such as transit and road pricing, on how their communities will grow, the air their children will breathe, and the amount of time they will have to spend in traffic commuting to work. It is widely acknowledged, however, that the models used to assess these effects have limited accuracy and sensitivity to alternatives to highway expansion. This study attempts to move beyond the issues of uncertainty in models used to forecast the travel, land use, and air quality effects of transportation projects and policies by (1) reviewing the literature on error and uncertainty in travel and land use models to understand key sources, likely confidence bounds, and potential biases; (2) conducting interviews with modeling experts to gain insight into how uncertain models may be improved and better applied in transportation studies; and (3) presenting a series of cases studies that illustrate innovative and, possibly, more credible approaches to modeling given different study objectives, model capability, and knowledge of model uncertainty.