Forecasting traffic and toll revenues for new highway projects involves great uncertainty because of the inherent uncertainty in the models used to make forecasts. As private investment becomes more common in project financing, quantifying the levels of risk and uncertainty associated with such projects becomes critical. This paper represents a review of many key studies and reports dealing with uncertainty in traffic and revenue forecasts for highway projects. These studies found that tolled projects tend to suffer from substantial optimism bias in forecasts, with predicted traffic volumes exceeding actual volumes by 30% or more about half the time. Moreover, projects with greater uncertainty tend to overestimate Year 1 traffic volumes more and stabilize at lower final traffic volumes. But after one controls for added optimism bias in traffic forecasts (compared with nontolled projects), there is little difference in uncertainty levels between tolled and nontolled forecasts. A typical way to address uncertainty in traffic forecasts is through sensitivity testing via variations in key inputs and parameters. A more extensive and less arbitrary version of this, Monte Carlo simulation, can provide probability distributions of future traffic and revenue, although it tends to require many simulations, demanding greater computational effort and time, unless networks are streamlined. Nonetheless, if reasonable assumptions for model input and parameter distributions can be made, Monte Carlo simulation generates a variety of useful information and establishes the actual likelihood of loss (rather than more basic win–lose indicators from a limited set of stress tests).