In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

Key air route segments refer to route segments that play a hub-supporting role in the air transportation network of a specific airspace, and whose traffic anomalies (such as saturation or delay) are likely to trigger chain reactions through segment correlations. Identifying these segments and implementing resource allocation bias and targeted optimization for them can significantly improve network operational efficiency and alleviate delays. Current key segment identification methods only consider the physical connection status between individual air route segments, ignoring the spatiotemporal correlation of traffic changes among segments. To address this issue, this paper proposes a key segment identification method based on a segment-related network. Firstly, redundant search paths are reduced through the pruning of low-impact indicators, and an improved FP-Growth algorithm is proposed to analyze segment correlations. On this basis, a segment-related network is constructed, where segments serve as nodes and correlations between segments serve as edges. This network integrates the changing relationship between segment flows and the physical connections between segments, thereby improving the accuracy of identification. Finally, in the segment-related network of the Guangdong-Hong Kong-Macao Multi-Airport Terminal Area, key segments such as Pingzhou-Conghua, Pingzhou-Gaoyao, and Zhuhai-Nanlang are identified using degree centrality, PageRank, eigenvector centrality, and global centrality analysis methods.

Addressing the challenge of identifying pivotal segments within the aviation network, exacer-bated by the escalating air traffic and airspace congestion, this study employs the FP-Growth algorithm to enhance the correlation analysis of the segment network's edges. This enhancement facilitates the construction and detailed examination of the segment network's correlation model. The methodology commences with the development of a segment correlation network model, leveraging relative hourly flight traffic data. Subsequently, a thorough analysis of the model network's topological characteristics is conducted. The findings reveal that the refined FP-Growth algorithm outperforms both the standard FP-Growth and Apriori algorithms in terms of efficiency and accuracy. Moreover, the application of degree centrality, pagerank, and eigenvector centrality successfully identifies influential segments, such as ENH-P373 and YIH-ENH, which play a crucial role in the operational efficiency and overall safety of the aviation network. This research provides a solid theoretical foundation for the precise identification and management of critical network segments.