Air Traffic Complexity Assessment Based on Ordered Deep Metric

Abstract

Since air traffic complexity determines the workload of controllers, it is a popular topic in the research field. Benefiting from deep learning, this paper proposes an air traffic complexity assessment method based on the deep metric of air traffic images. An Ordered Deep Metric (ODM) is proposed to measure the similarity of the ordered samples. For each sample, its interclass loss is calculated to keep it close to the mean of the same class and far from the difference. Then, consecutive samples of the same class are considered as a cluster, and the intracluster loss is calculated to make the samples close to the samples within the same cluster and far from the difference. Finally, we present the ODM-based air traffic complexity assessment method (ATCA-ODM), which uses the ODM results as the input of the classification algorithm to improve the assessment accuracy. We verify our ODM algorithm and ATCA-ODM method on the real traffic dataset of south-central airspace of China. The experimental results demonstrate that the assessment accuracy of the proposed ATCA-ODM method is significantly higher than that of the existing similar methods, which also proves that the proposed ODM algorithm can effectively extract high-dimensional features of the air traffic images.

A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas

“…Situation awareness has two main components, situation recognition and situation prediction [2]. In the field of air traffic, A number of researchers have attempted to study the traffic situation on routes and terminal areas using methods such as complex network theory [3][4] [5] [6]. These studies focus on the analysis and comprehension of traffic situations by analyzing the evolutionary patterns of air traffic situations combined with the corresponding theories.…”