Understanding the propagation interactions among intersections in city road networks and uncovering their traceability patterns is vital for proactive traffic management and control. However, measuring the propagation strength between intersections is difficult due to the dynamic nature of traffic flow and the interference at the network level caused by interactions among many nearby intersections. Additionally, mining traceability patterns requires a comprehensive representation of complex propagation influences among intersections and the ability to detect subtle changes in network structure. This study introduces a detailed framework for extracting traceability patterns in urban road networks. It identifies high-impact intersections using the mean excess function, constructs an interaction graph with these critical nodes, applies graph structural entropy to describe the global topological features of the interaction graph, and uses k-means clustering to classify different traceability patterns. The proposed method was validated using real-world traffic data, showing superior performance in estimating propagation strength compared to benchmark models. Kolmogorov–Smirnov tests confirmed the statistical reliability of high-impact and high-impact intersection identification results. Furthermore, the study identified four core interaction structures—chain, collider, fork, and circle—and four representative traceability patterns formed by these structures.
Zhang et al. (Tue,) studied this question.