In this study, we develop a stochastic dynamic network loading (DNL) model for the mixed traffic with autonomous vehicles (AVs) and human-driven vehicles (HVs). The source of stochasticity is the uncertainty inherent in the arrival process of the two classes of vehicular flow. The developed model captures both within-link and between-link traffic flow dependencies and evaluates the network state distribution in an analytical manner. The model has two main components, a probabilistic link model and a probabilistic node model. The link model is a stochastic formulation of the link transmission model (LTM), which captures the boundary conditions of a link and approximates the evolution of link state distribution. The node model, on the other hand, characterizes the flow transmissions across a network node. It reflects the between-link dependency by evaluating the expected transmission flow through an iterative algorithm, with an explicit consideration of the interactions between supply and demand constraints associated with a node. The developed model is validated versus replicated running of the deterministic LTM as well as microscopic traffic simulations, and the results reveal that it yields relatively accurate estimations. We also present two applications of the proposed model, including a traffic signal control problem and a class-based ramp metering problem, to demonstrate its practical value.

在这项研究中，我们开发了一种随机动态网络负载（DNL）模型，用于自动驾驶车辆（AV）和人类驾驶车辆（HV）的混合交通。随机性的根源在于两类车流到达过程中固有的不确定性。开发的模型捕获链路内和链路间的流量依赖性，并以分析方式评估网络状态分布。该模型有两个主要组成部分：概率链接模型和概率节点模型。链路模型是链路传输模型 (LTM) 的随机公式，它捕获链路的边界条件并近似链路状态分布的演化。另一方面，节点模型描述了跨网络节点的流传输。它通过迭代算法评估预期传输流来反映链路之间的依赖性，并明确考虑与节点相关的供应和需求约束之间的相互作用。所开发的模型通过确定性 LTM 的重复运行以及微观交通模拟进行了验证，结果表明它产生了相对准确的估计。我们还提出了所提出模型的两个应用，包括交通信号控制问题和基于类别的匝道计量问题，以证明其实用价值。