Abstract
For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.
Highlights
To accommodate the growing demand of air traffic, the modern Air Traffic Management (ATM) system becomes one of the most complex and vast systems
To make up for it, we propose a flight trajectory prediction framework by combining the following two approaches: The collective behavior of a set of similar flight trajectories is represented as a machine learning model trained from historical data; and for the aircraft’s observed states until the current timestep, the machine learning model is used to generate the expected states in the future timesteps, which are fed into an estimation-based method that combines the expected states from the data with the propagated states from the current state into the future timesteps based on the aircraft dynamics
The dataset for both Gaussian Mixture Model (GMM) and Long Short-Term Memory (LSTM) is divided into a training dataset and a test dataset with the ratio 8:2, and 20% of the training dataset for LSTM is used as a validation dataset
Summary
To accommodate the growing demand of air traffic, the modern Air Traffic Management (ATM) system becomes one of the most complex and vast systems. In the estimation-based methods, the expected future behaviors of an aircraft rely on the assumption that the aircraft follows its flight plan or an a priori known trajectory pattern In this paper, such information is extracted from historical data by using machine learning techniques that can represent the collective behavior (or pattern) of the aircraft which operated in terminal airspace. To make up for it, we propose a flight trajectory prediction framework by combining the following two approaches: The collective behavior of a set of similar flight trajectories is represented as a machine learning model trained from historical data; and for the aircraft’s observed states until the current timestep, the machine learning model is used to generate the expected states in the future timesteps, which are fed into an estimation-based method that combines the expected states from the data with the propagated states from the current state into the future timesteps based on the aircraft dynamics.
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