Study on departure time choice behavior in commute problem with stochastic bottleneck capacity: Experiments and modeling

TL;DR

This study investigates how stochastic bottleneck capacity influences commuter departure time choices through experiments and models, revealing risk preferences and the impact of information feedback on decision-making.

Contribution

It introduces experimental evidence and a reinforcement learning model to analyze departure time choices under uncertainty, highlighting risk preferences and effects of information feedback.

Findings

Travelers tend to minimize a combined cost function E(C)-λ*σ under uncertainty.

Providing full cost information reduces travelers' risk preference coefficient.

The reinforcement learning model successfully replicates experimental results.

Abstract

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.