This paper examines existing day-to-day models based on a virtual day-to-day route choice experiment using the latest mobile Internet technologies. 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 that take the Wardrop’s user equilibrium as (part of) their stationary states. The nonlinear effects of path flows and path time differences on path switching 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. The 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。利用实验中已实现的日常路径流量和路径行进时间，我们校准了几种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。然后研究了路径流动和路径时间差对路径切换的非线性影响。还检查了参与者的路径偏好，时变敏感性以及在日常过程中的学习行为。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”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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