To explain day-to-day (DTD) route-choice behaviors and traffic dynamics observed in a series of lab experiments, Part I of this research proposed a discrete choice-based analytical dynamic model (Qi et al., 2023). Although the deterministic model could well reproduce the experimental observations, it converges to a stable equilibrium of route flow while the observed DTD evolution is apparently with random oscillations. To overcome the limitation, the paper proposes a route-dependent attraction-based stochastic process (RDAB-SP) model based on the same behavioral assumptions in Part I of this research. Through careful comparison between the model-based estimation and experimental observations, it is demonstrated that the proposed RDAB-SP model can accurately reproduce the random oscillations both in terms of flow switching and route flow evolution. To the best of our knowledge, this is the first attempt to explain and model experimental observations by using stochastic process DTD models, and it is interesting to find that the seemingly unanticipated phenomena (i.e., random route switching behavior) is actually dominated by simple rules, i.e., independent and probability-based route-choice behavior. Finally, an approximated model is developed to help simulate the stochastic process and evaluate the equilibrium distribution in a simple and efficient manner, making the proposed model a useful and practical tool in transportation policy design.

翻译：为解释一系列实验室实验中观察到的日常（DTD）路径选择行为及交通流动态特征，本研究第一部分提出了基于离散选择的分析型动态模型（Qi等，2023）。尽管该确定性模型能较好复现实验观测结果，但其收敛于路径流的稳定均衡状态，而实际观测的每日演化过程呈现明显随机波动。为克服这一局限，本文基于第一部分相同的行为假设，提出了一种路径依赖吸引力的随机过程（RDAB-SP）模型。通过模型估计值与实验观测值的系统对比表明，所提RDAB-SP模型能精确再现路径转换与路径流演化过程中的随机波动。据我们所知，这是首次尝试采用随机过程DTD模型解释并模拟实验观测数据，且有趣地发现看似难以预测的随机路径切换行为实际上受简单规则支配，即基于独立概率的路径选择行为。最后，本研究开发了近似模型以简单高效方式辅助模拟随机过程并评估均衡分布，使所提模型成为交通政策设计中的实用工具。