论文标题
关于出发时间选择行为的研究，以随机瓶颈容量通勤问题：实验和建模
Study on departure time choice behavior in commute problem with stochastic bottleneck capacity: Experiments and modeling
论文作者
论文摘要
由于需求和供应的随机变化，运输系统不可避免地不确定性。它是影响旅客选择行为的最重要因素之一。基于Vickrey瓶颈模型的框架，我们设计并进行了实验室实验，以研究随机瓶颈能力对通勤出发时间选择行为的影响。研究了两种不同的情况，并研究了不同的信息反馈。实验结果表明，平均成本（e（c））与成本（σ）的标准偏差之间的关系均可用正斜率σ= e（c）/λ^*-M（λ^*> 0）拟合。这表明在不确定的环境下，旅行者可能会最大程度地减少其旅行成本预算，该预算定义为e（c）-λ^*σ，而λ^*> 0表示旅行者的行为偏爱风险。实验还发现，向通勤者提供所有出发时间的成本信息可以降低通勤者的风险偏好系数（即λ^*减少）。我们提出了一个增强学习模型，该模型可很好地重现主要的实验发现。
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 (σ) can all be fitted approximately linearly with a positive slope σ=E(C)/λ^*-m (λ^*>0). This suggests that under the uncertain environment, travelers are likely to minimize their travel cost budget, defined as E(C)-λ^* σ, and λ^*>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., λ^* decreases). We propose a reinforcement learning model, which is shown to reproduce the main experimental findings well.
