Everyday predictive systems typically present point predic-tions, making it hard for people to account for uncertainty when making decisions. Evaluations of uncertainty displays for transit prediction have assessed people's ability to extract probabilities, butnotthe qualityoftheir decisions.Ina con-trolled, incentivized experiment, we had subjects decide when to catchabus using displays withtextual uncertainty, uncer-tainty visualizations, or no-uncertainty (control). Frequency-based visualizations previously shown to allow people to bet-ter extract probabilities (quantile dotplots) yielded better deci-sions. Decisions with quantile dotplots with 50 outcomes were (1) better on average, having expected payoffs 97% of optimal (95% CI: [95%,98%]), 5 percentage points more than con-trol (95% CI: [2,8]); and (2) more consistent, having within-subject standard deviation of 3 percentage points (95% CI: [2,4]),4percentage points less than control (95% CI: [2,6]). Cumulative distribution function plots performed nearly as well, and both outperformed textual uncertainty, which was sensitive to the probability interval communicated. We dis-cuss implications for realtime transit predictions and possible generalization to other domains.