  Uncertainty is inevitable in transportation system due to the stochastic
change of demand and supply. It is one of the most important factors affecting
travelers' choice behavior. Based on the framework of Vickrey's bottleneck
model, we designed and conducted laboratory experiment to investigate the
effects of stochastic bottleneck capacity on commuter departure time choice
behavior. Two different scenarios with different information feedback are
investigated. The experimental results show that the relationship between the
mean cost (E(C)) and the standard deviation of cost (\sigma) can all be fitted
approximately linearly with a positive slope \sigma=E(C)/\lambda^*-m
(\lambda^*>0). This suggests that under the uncertain environment, travelers
are likely to minimize their travel cost budget, defined as E(C)-\lambda^*
\sigma, and \lambda^*>0 indicates that the travelers behave risk preferring.
The experiments also found that providing the cost information of all departure
times to the commuters lowered the commuters' risk preference coefficient
(i.e., \lambda^* decreases). We propose a reinforcement learning model, which
is shown to reproduce the main experimental findings well.
