A spatiotemporal adaptive local search method for tracking congestion propagation in dynamic networks

Traffic congestion propagation poses significant challenges to urban sustainability, disrupting spatial accessibility. The cascading effects of traffic congestion propagation can cause large-scale network disruptions. 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 incorporates 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 captures the spatiotemporal relationship from congestion graphs by fusing four node features. The other one is the local search module, which employs local dominance to identify multi-scale congestion bottlenecks and trace 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 27.66 s, 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 the 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.