A Review of Research on Flight Delay Propagation: Current Situation and Prospect

The correlation between flight routes makes the initial delays easy to cause chain delays of downstream airports, forming a large area of delays. Studying delay propagation can block its propagation path and alleviate large‐scale delay problems. This paper first systematically collects relevant academic literature from the past decade and constructs a co‐occurrence keyword network to analyze and reveal the research hotspots in flight delay propagation. Then, based on the two major categories of delay propagation within airlines and between airports, this paper provides a detailed review and summary of their research methods. For the research of delay propagation within airlines, scholars mainly use econometric models, Bayesian networks, function models, and propagation trees to analyze the influencing factors and propagation characteristics of delay propagation. Among the methods for predicting delay propagation, models based on machine learning algorithms account for a large proportion and have shown good prediction performance. For the more complex delay propagation problem between airports, researchers mainly use the time interval Petri net, queuing network model, Cox proportional hazards model, and complex network theory to analyze the delay propagation mechanism in aviation networks. In addition, deep learning models and spatiotemporal network models have improved the accuracy of interairport flight delay prediction due to their ability to process large datasets and high‐dimensional feature data. Finally, this paper summarizes the progress and shortcomings in flight delay propagation. The results show that there are significant differences in the delay propagation mechanism between airlines and airports, which requires full consideration of their applicability when selecting predictive models. Traditional machine learning methods perform well in the internal delay prediction of airlines, but there are some limitations in dealing with the complex and changeable propagation environment between airports. On the contrary, deep learning models and spatiotemporal models have opened up a new path for improving prediction accuracy by their powerful data processing and analysis capabilities. At the same time, researchers also need to constantly explore and optimize algorithms to overcome their current limitations and further improve the reliability of predictions.

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