With the reliability of travel time high on the political agenda, tools are needed to predict these indicators for the reliability of travel time in ex ante evaluations. In this paper such a framework is developed for assessing traffic measures and policies under a range of scenarios, with a particular focus on the resulting travel time distribution. The framework uses Monte Carlo sampling to generate stochastic realizations of demand and supply characteristics. The former characteristics relate to variations in travel patterns, the latter to variations in drive behavior (capacities, speeds). An extensive effort is made to account for variability in demand and supply resulting from weather, road work, special events, and so forth. The overall conclusion is that incorporating the variability of demand and supply characteristics has a large effect on the results of evaluation studies. This effect is demonstrated in an example case on a real Dutch traffic network, in which a typical dynamic measure, the opening of a peak hour lane, is evaluated with the framework. In this case, the average demand and supply conditions used for the one-shot evaluation lead to an underestimation of spillback effects, which are captured when evaluating over a wide range of demand and supply characteristics. As a result, the gains in average travel time are significantly underestimated in the one-shot procedure (6% instead of 26%). Further research should focus on improving speed and validity over a wider range of circumstances of the developed framework.