Encouraging departure time shifts could relieve peak-hour congestion. To understand departure time choice behavior is of great importance to evaluate the effectiveness of Travel Demand Management (TDM) instruments, such as pricing and incentive. Previous studies on the departure time choice behavior modeling can be generally divided into random-utility discrete choice models and machine learning models. In this work, we conduct a laboratory virtual experiment as a cheap approach to collect real person’s departure time choice data. An Inverse Reinforcement Learning (IRL) method is proposed to capture travelers’ preferences to times of departure. In IRL, the weights of the reward function are used to depict the departure time choice behavior based on observed data, so as to rationalize the strategy of real travelers. The results show that the IRL model can reproduce individual choice behavior. With the pricing and incentive schemes, travelers shift their departure times significantly. Based on the obtained reward function, the departure time choices after modifying the control measure could be predicted. The famous psychologic phenomenon of loss aversion is also observed in the IRL model.

鼓励发车时间轮班可以缓解高峰时段的拥堵。了解出发时间选择行为对于评估旅行需求管理 (TDM) 工具（如定价和激励）的有效性非常重要。以往对出发时间选择行为建模的研究一般可分为随机效用离散选择模型和机器学习模型。在这项工作中，我们进行了实验室虚拟实验，作为一种廉价的方法来收集真实人的出发时间选择数据。提出了一种逆强化学习 (IRL) 方法来捕捉旅行者对出发时间的偏好。在 IRL 中，奖励函数的权重用于描述基于观察数据的出发时间选择行为，以合理化真实旅行者的策略。结果表明，IRL模型可以再现个体的选择行为。通过定价和奖励计划，旅行者可以显着改变他们的出发时间。基于获得的奖励函数，可以预测修改控制措施后的出发时间选择。在 IRL 模型中也观察到了著名的损失厌恶心理现象。