Exploring the delay causality between airports and comparing the delay propagation patterns across different airport networks is critical to better understand delay propagation mechanisms and provide effective delay mitigation strategies. A novel attention-based recurrent graph convolutional neural network is proposed to identify the hidden delay causality relationship among airports in three different airport networks of China. The selected three airport networks show great heterogeneities in topological characteristics, such as average intensity, modularity and eigenvector centrality. The modeling results indicate that the identified delay causality networks of three airport networks are greatly varied in terms of complexity, delay propagation distance and efficiency. Moreover, the delay state of each airport is categorized into three levels, and the delay state transition of the three networks is explored. The results indicate that delay state transition in the North China Control Area exhibits an obvious bidirectional transition form that mainly propagates between the large-degree airports and small-degree airports, while severe delays of some hub airports account for a relatively large proportion in the other two networks. The results of this study could better reveal the delay propagation mechanism among airports and help airport operators develop effective strategies to alleviate flight delays and improve airport operation efficiency.

The emergence of surface trajectory-based operation (STBO) has promoted the development of taxi automation systems to plan conflict-free aircraft trajectories for efficient airport operations. Unlike traditional airport ground movement, aircraft guided by the taxi automation system possess a significant degree of freedom during taxiing, requiring the system to coordinate aircraft movement on the surface with timely responses to uncertainty. This study proposes a dynamic routing and scheduling approach with an adaptive surface situation characterization mechanism. A link-level unimpeded taxi time estimation method is designed to fine-grain characterize airport operation. A comprehensive evaluation system is then developed to identify similar situation patterns. Finally, a dynamic routing and scheduling approach is proposed to optimize airport ground movement with an adaptive and refined surface situation mechanism. The case study of actual operational data collected from a hub airport reveals that the proposed approach achieves efficient movement plans and collaborative control for all aircraft, as well as a more balanced utilization of the runway. The embedded adaptive situation mechanism also makes a better tradeoff between computational efficiency and optimal solutions. The study provides a promising approach to generating conflict-free four-dimensional trajectories for airport operations in future STBO scenarios.