Modeling Delay Propagation in Airport Networks via Causal Biased Random Walk

Due to the significance of air traffic delay propagation in the operational robustness of air transport systems, it has recently received increased attention from both industry and academia. The purpose of delay propagation analysis is to capture the dynamic traveling trajectories of root delays in air traffic networks. Few studies have investigated delay propagation while taking consideration of temporal context information, leading to incomplete traveling trajectories of root delays. In this paper, a novel systematic framework based on temporal causal inference is proposed to model delay propagation in airport networks. More specifically, considering the lagged nature caused by the transition time in delay propagation, a fully-connected delay propagation network based on transfer entropy is developed. Additionally, a causal biased random walk is embedded to explore the temporal cascading effect of root delays in air transport systems and to generate delay propagation trees for each airport. Extensive experiments on a real-world dataset indicate that our framework is able to effectively model the dynamic evolution of air traffic delays in an airport network with acceptable performance. This paper also presents a real-world case study of the Chinese airport network, which reveals that the traveling trajectories of air traffic delays differ significantly at various daily delay levels and airport throughput.