Title: A Random Walk Approach for Simulation-Based Continuous Dynamic Traffic Assignment Show Chinese title

Title: 基于仿真的一类连续动态交通分配的随机游走方法

Abstract: 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. Show Chinese abstract

Abstract: 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（DTA）问题，重点关注大型拥堵网络和动态环境。所提出的方法结合了一个受\textit{等效阻抗}方法理论概念启发的随机游走模型，专门设计用于克服传统多项逻辑（MNL）模型在处理重叠路径和扩展性问题上的局限性。通过迭代地将非重叠子网络收缩为虚拟链接并计算等效虚拟旅行成本，路线选择决策过程被转移到交叉口，从而更准确地表示随着交通状况演变的出行者选择，并在细粒度时间分段下实现更准确的性能。该方法利用有向无环图（DAG）结构来高效地找到两个节点之间的所有路径，从而避免了路径枚举的需要，这在一般网络中是难以处理的。虽然通过下游节点选择概率的计算方法，网络中的所有可用路径都可以以非零概率被选择。为了评估所提出方法的有效性，在拥挤需求场景下使用仿真城市移动性（SUMO）——一种开源微观交通仿真软件——对两个合成网络进行了实验。结果表明，与传统路径分配方法相比，该方法具有更强的鲁棒性、更快的收敛速度和更真实的行程分布，使其成为实时或资源密集型应用（如微观需求校准）的理想方案。