This study develops day-to-day models to explore how unreliability of public transit service affects the day-to-day evolution of travel choices. We consider two dynamical processes that incorporate transit service unreliability, i.e., travelers' learning and perception updating process (LPUP) and proportional-switch adjustment process (PSAP). The conditions for existence, uniqueness, and stability of the fixed point of each model are analytically derived. These conditions are then examined using real-world public transit data from the Greater Sydney area. We find that with some aggregations, the system stability conditions are satisfied in both models. The observed weighted average flow change between two successive days is around 6.5% over the observation period, which may reflect the system stochasticity rather than instability. Among a series of empirical findings, it is noteworthy that in the Sydney case, the value of service schedule delay is around 3.27 times that of in-vehicle time.

这项研究开发了日常模型，以探索公共交通服务的不可靠性如何影响出行选择的日常演变。我们考虑了结合公交服务不可靠性的两个动态过程，即旅行者的学习和感知更新过程（LPUP）和比例转换调整过程（PSAP）。通过分析得出每个模型不动点的存在性，唯一性和稳定性的条件。然后使用来自大悉尼地区的真实世界公交数据来检查这些条件。我们发现，通过一些聚合，两个模型都满足了系统稳定性条件。在观察期内，连续两天观察到的加权平均流量变化约为6.5％，这可能反映了系统的随机性而不是不稳定。