Multi-Agent Based Transfer Learning for Data-Driven Air Traffic
  Applications
Chuhao Deng Hong-Cheol Choi Hyunsang Park Inseok Hwang
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.