Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach

Abstract

To better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport delay. Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. An integrated airport-based Susceptible-Infected-Recovered-Susceptible (ASIRS) epidemic model for air traffic delay propagation is developed from the network-level perspective, to create a simulator for reproducing the delay propagation in airport networks. The evolution of airport delay propagation is obtained by analyzing the phase trajectory of the model. The simulator is run using the empirical data of China. The simulation results show that the model can reproduce the evolution of the delay propagation in the long term and its accuracy for predicting the number of delayed airports in the short term is much higher than the probabilistic prediction method. The model can thus help managers as a tool to effectively predict the temporal and spatial evolution of air traffic delay.