The main notations used in this paper.

Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. In this paper, we present an feedback fluid queueing network model to address CPDSE, integrating random traffic demand, time-varying transition probabilities, and state-dependent stochastic service capabilities. A recursive algorithm is developed to analyze the feedback queueing network model. Simulation experiments reveal that the proposed model and algorithm perform effectively, irrespective of variations in traffic intensity. Compared to the mean results of 200 simulations, the average absolute error is 0.5152 vehicles, and the average relative error is 6.43% across three demand scenarios. Based on the proposed feedback queueing network model, two optimization frameworks are established for traffic signal control, aimed at minimizing either the average vehicle delay time or total costs, including fuel consumption. We propose a rolling optimization strategy that incorporates the mesh adaptive direct search algorithm to achieve real-time traffic signal control. Numerical experiments using actual survey data from Kunshan City yield several noteworthy findings: (1) An optimal moderate-sized time step exists for rolling optimization to minimize either the average delay time or total costs; specifically, an excessively small time step may increase vehicle average delay time or total costs; (2) The percentage of delay reduction achieved by our method, compared to Synchro software, reaches a maximum of approximately 70% when traffic demand is moderate and the initial state is low; and (3) The percentage reduction in average delay or total costs compared to Synchro initially increases and then decreases with rising traffic intensity.在动态随机交通环境下捕捉交叉口不同设施间的拥堵传播机制面临诸多严峻挑战，尤其在过饱和交通状态下难度更甚。本文提出一种反馈流体排队网络模型（feedback fluid queueing network model）以解决该拥堵传播捕捉问题，该模型整合了随机交通需求、时变转移概率以及状态依赖的随机服务能力。针对该模型，本文研发了一种递归分析算法。仿真实验结果表明，所提模型与算法在交通强度变化的各类场景下均能实现有效性能。相较于200次仿真的平均结果，在三种需求场景下，模型的平均绝对误差为0.5152辆，平均相对误差为6.43%。基于所提出的反馈流体排队网络模型，本文构建了两类交通信号控制优化框架，分别以最小化车辆平均延误时间或最小化包含燃油消耗在内的总成本为目标。本文提出了一种融合网格自适应直接搜索算法（mesh adaptive direct search algorithm）的滚动优化策略，以实现实时交通信号控制。基于昆山市实际调研数据开展的数值实验得到了多项值得关注的结论：(1) 用于最小化平均延误或总成本的滚动优化存在最优的适中步长；具体而言，过小的步长可能会提升车辆平均延误或总成本；(2) 相较于Synchro软件，本文所提方法的延误降低率在交通需求适中且初始状态较低时可达约70%的峰值；(3) 相较于Synchro软件，平均延误或总成本的降低率随交通强度提升呈现先上升后下降的趋势。