基于多场景实验室实验研究日常路线选择。第一部分：路径依赖吸引力及其建模
Investigating day-to-day route choices based on multi-scenario laboratory experiments. Part I: Route-dependent attraction and its modeling
摘要：
在城市交通网络领域，越来越多的日常交通动力学理论被提出来描述网络流的演化，越来越多的实验室实验被用来观察出行者的行为变化。然而，理论家和实验家之间的“沟通”并没有做好。本文致力于1）通过一系列的实验室实验来检测非预期的行为干扰，2）通过将观察到的行为干扰嵌入到路由选择模型中来改进现有的DTD动力学理论。首先，312名受试者参与了八个决策情景之一，并在可消化的平行路线网络中重复进行路线选择。第二，观察到三种经典路径选择模型不能完全解释的路径转换行为模式。第三，为了丰富离散路径选择模型的解释能力，路径依赖吸引物的行为假设，即，路径依赖惯性和偏好。在此基础上，提出了一个解析的DTD动力学模型，并证明了该模型稳定地收敛到一个唯一的平衡态。最后，所提出的DTD模型可以令人满意地再现各种数据集的观察。研究结果可以帮助交通科学理论家充分利用实验室实验，建立既有真实行为基础又有简洁数学性质的网络平衡或DTD动态模型。
英文摘要：
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.
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