In the area of urban transportation networks, a growing number of day-to-day (DTD) traffic dynamic theories have been proposed to describe the network flow evolution, and an increasing amount of laboratory experiments have been conducted to observe travelers' behavior regularities. However, the "communication" between theorists and experimentalists has not been made well. This paper devotes to 1) detecting unanticipated behavior regularities by conducting a series of laboratory experiments, and 2) improving existing DTD dynamics theories by embedding the observed behavior regularities into a route choice model. First, 312 subjects participated in one of the eight decision-making scenarios and make route choices repeatedly in congestible parallel-route networks. Second, three route-switching behavior patterns that cannot be fully explained by the classic route-choice models are observed. Third, to enrich the explanation power of a discrete route-choice model, behavioral assumptions of route-dependent attractions, i.e., route-dependent inertia and preference, are introduced. An analytical DTD dynamic model is accordingly proposed and proven to steadily converge to a unique equilibrium state. Finally, the proposed DTD model could satisfactorily reproduce the observations in various datasets. The research results can help transportation science theorists to make the best use of laboratory experimentation and to build network equilibrium or DTD dynamic models with both real behavioral basis and neat mathematical properties.

翻译：在城市交通网络领域，近年来涌现出大量逐日交通流动态理论用以描述网络流量演化过程，同时越来越多实验室实验被设计用于观测出行者的行为规律。然而，理论研究者与实验研究者之间尚未建立良好的"沟通机制"。本文致力于：1）通过开展系列实验室实验发现意料之外的行为规律；2）将观测到的行为规律嵌入路径选择模型以改进现有逐日动态理论。首先，312名受试者参与八种决策场景之一，在可拥堵的平行路径网络中反复进行路径选择。其次，发现三种经典路径选择模型无法完全解释的路径转换行为模式。第三，为增强离散路径选择模型的解释能力，引入路径依赖吸引力（即路径依赖惯性及偏好）的行为假设，据此提出解析型逐日动态模型，并证明该模型将稳定收敛至唯一均衡状态。最后，所提逐日模型能够良好复现多个数据集中的观测结果。研究结果可帮助交通科学理论研究者充分利用实验室实验方法，构建兼具真实行为基础与优美数学特性的网络均衡或逐日动态模型。