Modelling Aircraft Priority Assignment by Air Traffic Controllers During Taxiing Conflicts Using Machine Learning

Abstract

Conflicts between taxiing aircraft are resolved by making the aircraft with lower priority wait, slow down, or change their path. Prevalent priority assignment is based on rules such as First Come First Serve. However, this is not viable as priority assignment done by an air-traffic controller (ATC) based on multiple factors. Thus, a machine learning approach is proposed to mimic an ATC's priority assignment. Firstly, the potential conflict scenarios between two aircraft from historical data, which are resolved, are detected and extracted. Then a Random Forest model is developed to learn ATC's behaviors. The model mimics ATC's behavior with an accuracy of 89% and can thus be an effective approach for priority assignment in path-planning and conflict resolution. Further analysis indicates that features such as unimpeded time difference, distance to destination and start, and speed are major considerations that affect the ATC's decisions.

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

“…An effective approach involves real-world historical data to establish the conflict resolution model from a realistic perspective that is more acceptable to ATCOs [24]. Duggal proposed a random forest model to learn the controller's behavior to assign aircraft priority, and effectively resolve potential taxiing conflicts by arranging a lowpriority aircraft to decelerate [25]; Pham integrated deep reinforcement learning with prior knowledge, employing the Generative Adversarial Imitation Learning (GAIL) and PPO algorithms to learn the distribution of an aircraft's taxi speed from historical taxi trajectories and provide acceleration recommendations at each time interval [26]. These studies have focused on learning the assignment of aircraft priority and the distribution of taxiing speeds in real-world scenarios.…”