This paper examines existing day-to-day models based on a virtual day-to-day route choice experiment using the latest mobile internet techniques. With the realized day-to-day path flows and path travel times in the experiment, we calibrate several well-designed path-based day-to-day models who take the Wardrop’s user equilibrium as (part of) their stationary states. The nonlinear effects of path flows and path time differences on the path swapping are then investigated. Participants’ path preferences, time-varying sensitivity and learning behavior in the day-to-day process are also examined. The prediction power of various models with various settings (nonlinear effects, time-varying sensitivity, and learning) is compared. Assumption of rational behavior adjustment process in Yang and Zhang (2009) is further verified. Finally, evolutions of different Lyapunov functions used in the literature are plotted and no obvious diversity is observed.
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机译：本文使用最新的移动互联网技术，基于虚拟的日常路线选择实验，研究了现有的日常模型。利用实验中实现的日常路径流量和路径行进时间，我们校准了一些设计良好的基于路径的日常模型，这些模型将Wardrop的用户平衡作为其稳态的一部分。然后研究了路径流量和路径时间差对路径交换的非线性影响。还检查了参与者在日常过程中的路径偏好，时变敏感性和学习行为。比较了具有各种设置（非线性效应，时变灵敏度和学习）的各种模型的预测能力。 Yang and Zhang（2009）的理性行为调整过程的假设得到了进一步验证。最后，绘制了文献中使用的不同Lyapunov函数的演化图，没有观察到明显的多样性。
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