We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until the destination (a.k.a. value function that is a solution to a dynamic programming problem). Existing recursive route choice models and the corresponding solution approaches are based on the assumption that network attributes are deterministic. Hence, they cannot be applied to stochastic networks which are the focus of this paper.

We propose an efficient algorithm for solving the value function and its gradient, critical for parameter estimation. It is based on partitioning the state space and decomposing costly matrix operations into a series of simpler ones. We present numerical results using a synthetic network and a network in Stockholm, Sweden. The estimation running time has a 20-30 times speed-up due to matrix decomposition. The estimated model parameters have realistic interpretations. Specifically, travelers are more likely to be adaptive to realized travel times during a longer trip, and more sensitive to travel time when travel time variability is higher. The STD-RL model performs better in predicting route choices than the RL model in a corresponding static and deterministic network.

我们提出了一种递归 Logit (STD-RL) 模型，用于随机时间相关 (STD) 网络中的路由策略选择，其中路由策略是从状态到下一步在哪个链路上采取的动作的映射，状态定义为节点、时间和信息。路由策略封装了旅行者在进行路线选择时对显示的交通状况的适应。STD-RL 模型绕过了选择集生成，这是一个与估计和预测相关的已知问题的过程。在给定的状态下，旅行者做出他们的链接选择，最大化输出链接的效用和直到目的地的预期最大效用的总和（又名价值函数，是动态规划问题的解决方案）。现有的递归路由选择模型和相应的解决方法是基于网络属性是确定性的假设。因此，它们不能应用于作为本文重点的随机网络。

我们提出了一种有效的算法来求解值函数及其梯度，这对参数估计至关重要。它基于划分状态空间并将代价高昂的矩阵运算分解为一系列更简单的运算。我们使用合成网络和瑞典斯德哥尔摩的网络呈现数值结果。由于矩阵分解，估计运行时间有 20-30 倍的加速。估计的模型参数具有现实的解释。具体而言，旅行者在较长的旅行中更有可能适应实际的旅行时间，并且在旅行时间可变性较高时对旅行时间更敏感。在相应的静态和确定性网络中，STD-RL 模型在预测路线选择方面比 RL 模型表现更好。