Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

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.