This paper investigates the implementation of a dynamic reference point scheme to capture traveler’s mental characteristics, with their day-to-day route choice behavior and heterogeneity. The traveler’s heterogeneity focuses on their different risk attitudes. On each day, travelers choose the routes based on their estimated travel costs, which can be affected by the reference point structure and its update. Most existing studies on the day-to-day traffic assignment models are proposed to capture day-to-day flow fluctuations through a learning model based on traveler’s past experience and information, but did not work on the consideration of gain and loss, which is described by the reference point scheme, comparing with traveler’s previous experience. This study aims to develop a day-to-day dynamic evolution model, in which travelers take on a tendency to refer to their previous travel experience as a reference when coping with different travel scenarios. First, the multi-class dynamic system is proposed to model traveler’s route choice behavior in a transportation network with two traveler classes. Then, the equilibrium state and stability of the evolution model is examined. We further investigate the class-specified update structure of the reference point. Finally, numerical experiments are presented to illustrate the application of our method.

本文研究了动态参考点方案的实现，以捕获旅行者的心理特征及其日常路线选择行为和异质性。旅行者的异质性集中在他们不同的风险态度上。每天，旅行者都会根据估计的旅行费用选择路线，这可能会受到参考点结构及其更新的影响。提出了大多数有关日常交通分配模型的研究，以通过基于旅行者过去的经验和信息的学习模型来捕获日常流量波动，但并未考虑收益和损失，这是通过参考点方案进行描述，并与旅行者以前的经历进行比较。这项研究旨在开发日常动态演化模型，在应对不同的旅行场景时，旅行者倾向于将以前的旅行经历作为参考。首先，提出了多类动态系统，以在具有两个旅行者类别的运输网络中对旅行者的路线选择行为进行建模。然后，研究了演化模型的平衡状态和稳定性。我们进一步研究参考点的类指定更新结构。最后，通过数值实验说明了该方法的应用。研究了演化模型的平衡状态和稳定性。我们进一步研究参考点的类指定更新结构。最后，通过数值实验说明了该方法的应用。研究了演化模型的平衡状态和稳定性。我们进一步研究参考点的类指定更新结构。最后，通过数值实验说明了该方法的应用。