This paper proposes an analyzable agent-based route choice modeling framework with good theoretical properties. This modeling framework allows heterogeneous individual learning rules and learning rates. As long as travelers' route choice behaviors conform to the framework, even though their learning rules and learning rates are heterogeneous, the network flows can be proven to be with asymptotically stable fixed points. An approximation for network flow distribution is proposed from the perspective of the stochastic process. Some phenomena observed in laboratory experiments are well captured by the agent-based framework. Many existing network-level day-to-day dynamic models can be regarded as special cases of the framework by setting the concrete learning rules and learning rates of the agents. Numerical simulations are used to show model properties. This study can deepen our understanding of the behavioral mechanism of individual-level day-to-day route choice and network-level day-to-day traffic flow dynamics.

本文提出了一种具有良好理论特性的可分析的基于代理的路径选择建模框架。该建模框架允许异构的个体学习规则和学习率。只要旅行者的路线选择行为符合框架，即使他们的学习规则和学习率是异构的，也可以证明网络流具有渐近稳定的不动点。从随机过程的角度提出了一种网络流量分布的近似。基于代理的框架很好地捕捉到了实验室实验中观察到的一些现象。许多现有的网络级日常动态模型可以通过设置代理的具体学习规则和学习率来视为框架的特例。数值模拟用于显示模型属性。本研究可以加深我们对个体层面日常路径选择和网络层面日常交通流动态的行为机制的理解。