This paper proposes a new sequencing and scheduling algorithm for the point merge system based on mixed integer linear programming considering the uncertain flight time of aircraft. In the first stage of the proposed algorithm, for a static environment, deterministic robust solutions are determined. To consider the uncertainty, an extra buffer is introduced in the sequencing and scheduling algorithm, and the buffer size is analytically derived by generating a deterministic robust counterpart problem. In the second stage, to compensate for unforeseen situations under a dynamic environment, the static solution determined in the first stage is adjusted by using the proposed heuristic algorithm, with a sliding time window to reduce the computational load. The performances of the proposed algorithm are verified via numerical simulations based on historical data analysis.

Aircraft trajectory prediction plays a crucial role in air traffic management and significantly enhances operational safety and efficiency. In this work, we propose an aircraft trajectory prediction model that utilizes an Inverted Transformer, which is a state-of-the-art model for time-series prediction. In a vanilla Transformer, temporal tokens containing the multivariate features are embedded for each time step, but in the Inverted Transformer, each series is embedded independently into variate tokens. The Inverted Transformer captures multivariate correlations in a trajectory sequence, resulting in improved prediction accuracy. The proposed model was validated with actual air traffic surveillance data from the terminal airspace of Incheon International Airport, South Korea. The experimental results demonstrate that the prediction accuracy from the proposed model is superior to those of other deep learning-based prediction models.

The effective sequencing of arriving flights is the primary goal of air traffic management. Although various automated tools have been developed to support air traffic controllers, these tools do not accommodate the cognitive processes of the human controllers, which are necessary for application to actual operations. This paper proposes a new framework for predicting arrival sequences based on a preference learning approach that emulates the sequencing strategies of human controllers by learning from historical data. The proposed algorithm works in two stages: it first learns the pairwise preference functions between arrivals using a binomial logistic regression, and then it induces the total sequence for a new set of arrivals by comparing the score of each aircraft, which sums the pairwise preference probabilities. The proposed model is validated using historical traffic data at Incheon International Airport, and its performance is evaluated using Spearman's rank correlation and a dynamic simulation analysis.

Accurate prediction of future air traffic situations is an essential task in many applications in air traffic management. This paper presents a new framework for predicting air traffic situations as a sequence of images from a deep learning perspective. An autoencoder with convolutional long short-term memory (ConvLSTM) is used, and a mixed loss function technique is proposed to generate better air traffic images than those obtained by using conventional L₁ or L₂ loss function. The feasibility of the proposed approach is demonstrated with real air traffic data.

한국항공운항학회지(Journal of the Korean Society for Aviation and Aeronautics)