| Summary: | Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road links, and ensuring the smooth operation of urban transportation systems. However, the existing road link importance evaluation metrics mostly rely on complex network metrics and traffic metrics, which may lead to biased results. In this paper, we propose a critical road link identification framework based on the fusion of dynamic and static features. First, we propose a directed dual topological traffic network model that considers the subjectivity of road links, traffic circulation characteristics, and time-varying characteristics, which addresses the limitations of existing traffic network topology construction. Subsequently, we employ a novel graph representation learning network to learn the road link node low-dimensional embeddings. Finally, we utilize clustering algorithms to cluster each road link node and evaluate critical road links using the average importance evaluation indicator of different categories. The results of comparison experiments using real-world data demonstrate the clear superiority and effectiveness of our proposed method. Specifically, our method is able to achieve a reduction in traffic network efficiency of 70–75% when less than 25% of the road links are removed. In contrast, the other baseline methods only achieve a reduction of 50–70% when removing the same proportion of road links. These findings highlight the significant advantages of our approach in identifying the critical links.
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