Travel time reliability plays an important role in travelers’ route choice behaviors. Based on a previously developed generalized Bayesian traffic model, we propose different types of perceived knowledge (i.e., mean-variance-based type, relative gap-based type, and penalty-based type) to model travelers’ daily route choice behavior concerning travel time reliability. We theoretically demonstrate the flexibility of the generalized Bayesian model in capturing various existing UE-based travel behaviors and other non-UE-based travel behaviors (e.g., penalty-based) in stochastic transportation systems. Three major conclusions are obtained. First, the route choice dynamics induced by the Bayesian model with an infinitely long memory and mean-variance-based perceived knowledge will converge to the mean-variance UE condition. Second, the convergence of route choice dynamics to a UE condition is not affected by adding a bounded weight on the daily perceived knowledge. Thirdly, non-UE-based formulations of perceived knowledge also lead to fixed points for the mean route choice proportion. The convergences of the models with different types of perceived knowledge are verified based on numerical studies and the underlying day-to-day route choice dynamics with both recurrent and non-recurrent unreliability are examined.

出行时间可靠性在旅行者的路线选择行为中起着重要作用。基于先前开发的广义贝叶斯交通模型，我们提出了不同类型的感知知识（即，基于均值方差的类型，基于相对差距的类型和基于罚分的类型）来建模与旅行时间相关的旅行者日常路线选择行为可靠性。我们从理论上证明了广义贝叶斯模型在捕获随机交通运输系统中各种现有的基于UE的旅行行为和其他非基于UE的旅行行为（例如，基于罚金）时的灵活性。得到三个主要结论。首先，由贝叶斯模型诱导的具有无限长记忆和基于均值方差的感知知识的路由选择动力学将收敛至均值方差UE条件。第二，路由选择动力学到UE条件的收敛不受在日常感知知识上增加有界权重的影响。第三，非基于UE的感知知识表述也会导致平均路线选择比例的固定点。基于数值研究，验证了具有不同类型感知知识的模型的收敛性，并检验了具有重复和非重复不可靠性的基本日常路线选择动力学。