摘要: |
Runway assignments at major airports are made by air traffic controllers subject to various
constraints, and to achieve various objectives. In this research, we describe our efforts training
machine learning (ML) models to predict both departure and arrival runway assignments
using an entirely data-driven approach. This approach is compared to existing rule-based
approaches developed in previous research using input from Subject Matter Experts. The
models have features derived from various FAA data feeds, and leverage multiple machine
learning algorithms. Results for models trained for nine major U.S. airports are described
and compared to one another across various important dimensions. Particular attention was
paid to developing a repeatable framework for training these models so the approach could be
scaled to other airports, and to developing models that are useful in a real-time environment.
In addition, the models were designed to be functional in a real-time environment to support
NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the
performance of the fielded system. |