Research in developing data-driven models for Air Traffic Management (ATM) has gained a tremendous interest in recent years. However, data-driven models are known to have long training time and require large datasets to achieve good performance. To address the two issues, this paper proposes a Multi-Agent Bidirectional Encoder Representations from Transformers (MA-BERT) model that fully considers the multi-agent characteristic of the ATM system and learns air traffic controllers' decisions, and a pre-training and fine-tuning transfer learning framework. By pre-training the MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and specific air traffic applications, a large amount of the total training time can be saved. In addition, for newly adopted procedures and constructed airports where no historical data is available, this paper shows that the pre-trained MA-BERT can achieve high performance by updating regularly with little data. The proposed transfer learning framework and MA-BERT are tested with the automatic dependent surveillance-broadcast data recorded in 3 airports in South Korea in 2019.

翻译：近年来，开发面向空中交通管理（ATM）的数据驱动模型研究引起了极大关注。然而，数据驱动模型存在训练时间长、需要大量数据集才能获得良好性能的缺陷。为解决这两个问题，本文提出了一种充分考虑ATM系统多智能体特性、能够学习空中交通管制员决策的多智能体双向编码器表示（MA-BERT）模型，以及一个预训练-微调迁移学习框架。通过在大型机场的大规模数据集上预训练MA-BERT，再将其微调至其他机场和特定空中交通应用场景，可以大幅节省总训练时间。此外，对于新启用程序和无历史数据的新建机场，本文证明预训练后的MA-BERT可通过少量数据定期更新实现高性能。所提出的迁移学习框架和MA-BERT模型已在2019年韩国三个机场记录的自动相关监视广播数据上进行了测试。