Key Nodes Identification in Urban Traffic Networks via a Domirank Centrality Approach
Mengting Zhou, Zhao Zhou, Haili Liang, Hualing Liu, Yubo Zhang · 2025
Rapid urbanization has exacerbated traffic congestion, presenting significant challenges to the resilience of urban traffic networks. According to complex network theory, network resilience is intrinsically linked to the strength and distribution of connections between nodes. In this context, accurately assessing node importance is essential for understanding and enhancing network resilience. This study evaluates the centrality of intersections in urban road networks using the Domirank centrality metric. The urban traffic network is modeled as a weighted topological graph, with weights assigned based on road segment length, traffic flow, and average speed to reflect each segment's capacity to accommodate demand. Domirank centrality is then applied to identify critical intersections and assess their contribution to overall network resilience. Results show that this method effectively highlights key nodes that significantly impact traffic network performance.