Flight situational awareness in civil aviation relies on the semantic understanding of both the key details and the full picture from the Air Traffic Controller (ATCo) and pilot communication. This paper proposes a novel end-to-end Multi-Task Hierarchical Network (MTHN) for automatically understanding ATCo-pilot communication, handling slot filling, role detection, and intent recognition at different levels while adaptively integrating them. Specifically, we introduce a word-based knowledge-masked slot distillation module that constructs an ATC knowledge base to dynamically mask keywords during teacher-student distillation. Considering the distinct intent differences between ATCos and pilots, we design a sentence-based role-aware intent attention module that extracts role label space vectors as context to enrich intent representations. To exploit the complementarity across different semantic levels in ATCo-pilot communication, we explicitly develop an adaptive bi-interaction flow module that dynamically explores semantic dependencies among tasks. Extensive experiments on real-world datasets collected in China show the superior performance of MTHN, compared to state-of-the-art baselines in both general natural language understanding and ATC-specific text processing. Our results highlight that MTHN achieves 99.26%, 97.25%, and 96.22% accuracy across key slots, as well as 96.59% accuracy in speaker role classification. Moreover, it can perceive multi-label deep intents behind sentences. These analytical findings demonstrate the potential to reduce human errors in high-concurrency ATCo-pilot interactions under dense operational conditions.

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.