This paper presents a new simulation-based approach to address the stochastic Dynamic Traffic Assignment (DTA) problem, focusing on large congested networks and dynamic settings. The proposed methodology incorporates a random walk model inspired by the theoretical concept of the \textit{equivalent impedance} method, specifically designed to overcome the limitations of traditional Multinomial Logit (MNL) models in handling overlapping routes and scaling issues. By iteratively contracting non-overlapping subnetworks into virtual links and computing equivalent virtual travel costs, the route choice decision-making process is shifted to intersections, enabling a more accurate representation of travelers' choices as traffic conditions evolve and allowing more accurate performance under fine-grained temporal segmentation. The approach leverages Directed Acyclic Graphs (DAGs) structure to efficiently find all routes between two nodes, thus obviating the need for route enumeration, which is intractable in general networks. While with the calculation approach of downstream node choice probabilities, all available routes in the network can be selected with non-zero probability. To evaluate the effectiveness of the proposed method, experiments are conducted on two synthetic networks under congested demand scenarios using Simulation of Urban MObility (SUMO), an open-source microscopic traffic simulation software. The results demonstrate the method's robustness, faster convergence, and realistic trip distribution compared to traditional route assignment methods, making it an ideal proposal for real-time or resource-intensive applications such as microscopic demand calibration.

翻译：本文提出了一种新的基于仿真的方法来解决随机动态交通分配问题，重点关注大型拥堵网络和动态环境。该方法的理论基础源于等效阻抗方法中的随机游走模型，专门设计用于克服传统多项式Logit模型在处理重叠路线和规模问题时的局限性。通过迭代地将非重叠子网络收缩为虚拟链路并计算等效虚拟旅行成本，路径选择决策过程被转移到交叉口，从而能够更准确地反映交通条件演变中出行者的选择，并在精细时间分段下实现更精准的性能。该方法利用有向无环图结构高效地查找任意两个节点之间的所有路径，从而避免了路径枚举（这在一般网络中难以实现）。借助下游节点选择概率的计算方法，网络中的所有可用路径都能以非零概率被选中。为评估所提方法的有效性，在两种合成网络上使用开源微观交通仿真软件SUMO进行了拥堵需求场景实验。结果表明，相比传统路径分配方法，该方法具有更强的鲁棒性、更快的收敛速度以及更真实的出行分布，使其成为实时或资源密集型应用（如微观需求标定）的理想方案。