STALS Data and Code

Traffic congestion propagation poses significant challenges to urban sustainability, disrupting spatial accessibility. The cascading effect of traffic congestion propagation can cause large-scale disruptions to networks. Existing studies have laid a solid foundation for characterizing the cascading effects. However, they typically rely on predefined graph structures and lack adaptability to diverse data granularities. To address these limitations, we propose a spatiotemporal adaptive local search (STALS) method, which feeds the dynamically adaptive adjacency matrices into the local search algorithm to learn propagation rules. Specifically, the STALS is composed of two data-driven modules. One is a dynamic adjacency matrix learning module, which learns the spatiotemporal relationship from congestion graphs by fusing four node features. The other one is the local search module, which introduces local dominance to identify multi-scale congestion bottlenecks and search their propagation pathways. We test our method on the four benchmark networks with an average of 15,000 nodes. The STALS remains a Normalized Mutual Information (NMI) score at 0.97 and an average execution time of 27.66s, outperforming six state-of-the-art methods in robustness and efficiency. We also apply the STALS to three large-scale traffic networks in New York City, the United States, Shanghai, China, and Urumqi, China. The ablation study reveals an average modularity of 0.78 across three cities, demonstrating the spatiotemporal-scale invariance of frequency-transformed features and the spatial heterogeneity of geometric topological features. By integrating dynamic graph learning with Geo-driven spatial analytics, STALS provides a scalable tool for congestion mitigation.交通拥堵传播对城市可持续性构成严峻挑战，破坏空间可达性。交通拥堵传播的级联效应可引发网络的大规模中断。现有研究已为刻画该级联效应奠定了坚实基础，但此类研究通常依赖预定义图结构，且难以适配多样化的数据粒度。为解决上述局限，本文提出一种时空自适应局部搜索（spatiotemporal adaptive local search, STALS）方法，将动态自适应邻接矩阵输入局部搜索算法以学习传播规则。具体而言，STALS包含两个数据驱动模块：其一为动态邻接矩阵学习模块，该模块通过融合四类节点特征，从拥堵图中学习时空关联关系；其二为局部搜索模块，该模块引入局部支配性以识别多尺度拥堵瓶颈，并搜索其传播路径。本文在平均包含15000个节点的4个基准网络上对所提方法进行测试，STALS的归一化互信息（Normalized Mutual Information, NMI）得分稳定在0.97，平均执行时间为27.66秒，在鲁棒性与效率上均优于6种当前最优方法。此外，本文将STALS应用于美国纽约市、中国上海市与中国乌鲁木齐市的3个大规模交通网络。消融实验结果显示，3个城市的平均模块度为0.78，证实了频域变换特征的时空尺度不变性与几何拓扑特征的空间异质性。通过将动态图学习与地理驱动的空间分析相结合，STALS为拥堵缓解提供了一款可扩展的工具。