Evaluating node importance in metro-bus networks using network attack experiments

To evaluate the importance of stations in the urban subway-bus dual-layer transportation network, this paper takes the subway-bus network in Shanghai's central urban area as the research object and proposes a station importance evaluation method based on cyber attack experiments. In terms of node importance identification, traditional single-indicator methods based on degree, betweenness, and k-core are designed, as well as three multi-indicator fusion methods incorporating entropy weight, coefficient of variation, and CRITIC Criteria Importance Through Intercriteria Correlation. Based on these node rankings, simulated network construction experiments are conducted to evaluate network response characteristics from both structural and functional perspectives. The selected indicators include network efficiency, maximum connectivity ratio, network accessibility, and travel detour coefficient. The experimental results indicate that degree-based ranking demonstrates the highest accuracy in identifying critical nodes among traditional methods, while CRITIC-based ranking performs best among fusion methods. Further comparison reveals consistent analytical conclusions at both structural and functional levels, validating the feasibility of the node importance evaluation method based on cyber attack experiments. The research findings provide new insights for identifying key stations in complex transportation networks.