Aviation transportation systems have developed rapidly in recent years and have become a focus for research on the modeling of epidemics. However, despite the number of studies on aggregated topological structures and their effects on the spread of disease, the temporal sequence of flights that connect different airports have not been examined. In this study, to analyze the temporal pattern of the Chinese Aviation Network (CAN), we obtain a time series of topological statistics through sliding the temporal CAN with an hourly time window. In addition, we build two types of Susceptible-Infectious (SI) spreading models to study the effects of linking sequence and temporal duration on the spread of diseases. The results reveal that the absence of links formed by flights without alternatives at dawn and night causes a significant decrease in the centralization of the network. The temporal sparsity of linking sequence slows down the spread of disease on CAN, and the duration of flights intensifies the sensitiveness of CAN to targeted infection. The results are of great significance for further understanding of the aviation network and the dynamic process, such as the propagation of delay.

We define metrics to quantify the level of overall delay and propose an agent-based data-driven model with four factors, including aircraft rotation, flight connectivity, scheduling process, and disturbance, to build a simulator for reproducing the delay propagation in aviation networks. We then measure the impact on the propagation by the delay at each airport and analyze the relevance to its temporal characteristics. When delay occurs, airline schedule planning may become infeasible, and rescheduling of flights is usually required to maintain the function of the system, so we then develop an improved genetic algorithm (GA) to reschedule flights and to relax the root delay. Results indicate that priority-based strategy rather than First-Come-First-Serve can achieve minimum overall delay when congestion occurs, and aircraft rotation is the most important internal factor contributing to delay propagation. Furthermore, the reschedule generated by the improved GA can decrease delay propagation more significantly compared to the agent-based model.

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