AI-Enhanced Techniques for Air Traffic Management

Topic Information

Dear Colleagues,

It is well known that air traffic management (ATM) is fundamental for the air transportation industry; any effort to improve air traffic safety and efficiency, from any aspect, deserves support. Fortunately, thanks to the large amount of available industrial data storage and widespread applications of information technology, it is possible to obtain extra real-time traffic information to make contributions to air traffic operation. Recently, AI-based techniques have attracted widespread attention in research all over the world, as evident from the high level of publications on this topic in the Nature journals[1,2]. The published works explore advanced research topics that seek to combine the AI and ATC fields. In addition, other techniques have also been considered to improve air traffic safety and efficiency, including air traffic controller training, automatic planning, etc. This Special Issue focuses on applying artificial intelligence approaches to research topics related to air traffic management, including but not limited to the following: 1) Traffic dynamic perception, such as the understanding of spoken instructions; 2) Understanding of traffic situations, such as conflict detection and trajectory processing; 3) Traffic planning, such as TBO; 4) Automatic decisions, such as reinforcement learning; 5) Air traffic safety enhancements: system, techniques, or case studies; 6) Other research topics related to air traffic and machine learning. We enthusiastically seek contributions from those with expertise in air traffic management and computer science to present their papers on this Topic and to share their knowledge and experiences with both academic and industry audiences.

[1] D. Guo, Z. Zhang, B. Yang, J. Zhang, H. Yang, Y. Lin, Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control. Nat. Commun. 15, 9662 (2024).
[2] Z. Zhang, D. Guo, S. Zhou, J. Zhang, Y. Lin, Flight trajectory prediction enabled by time-frequency wavelet transform. Nat. Commun. 14, 5258 (2023).

Dr. Yi Lin
Dr. Honggang Chen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	4.0	2014	21.3 Days	CHF 2400	Submit
AI ai	3.1	6.9	2020	18.9 Days	CHF 1600	Submit
Future Transportation futuretransp	-	3.8	2021	41.1 Days	CHF 1000	Submit
Applied Sciences applsci	2.5	5.5	2011	18.4 Days	CHF 2400	Submit

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Published Papers (2 papers)

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All Aerospace AI Future Transportation Applied Sciences

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20 pages, 7297 KiB  

Open AccessArticle

Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features

by Xiuyi Li, Yue Zhou, Weiwei Zhao, Chuanyun Fu, Zhuocheng Huang, Nianqian Li and Haibo Xu

Aerospace 2025, 12(6), 523; https://doi.org/10.3390/aerospace12060523 - 10 Jun 2025

Abstract

Eye movement features of pilots are critical for aircraft landing, especially in low-visibility and windy conditions. This study conducts simulated flight experiments concerning aircraft approach and landing under three low-visibility and windy conditions, including no-wind, crosswind, and tailwind. This research collects 30 participants’ [...] Read more.

Eye movement features of pilots are critical for aircraft landing, especially in low-visibility and windy conditions. This study conducts simulated flight experiments concerning aircraft approach and landing under three low-visibility and windy conditions, including no-wind, crosswind, and tailwind. This research collects 30 participants’ eye movement data after descending from the instrument approach to the visual approach and measures the landing position deviation. Then, a random forest method is used to rank eye movement features and sequentially construct feature sets by feature importance. Two machine learning models (SVR and RF) and four deep learning models (GRU, LSTM, CNN-GRU, and CNN-LSTM) are trained with these feature sets to predict the landing position deviation. The results show that the cumulative fixation duration on the heading indicator, altimeter, air-speed indicator, and external scenery is vital for landing position deviation under no-wind conditions. The attention allocation required by approaches under crosswind and tailwind conditions is more complex. According to the MAE metric, CNN-LSTM has the best prediction performance and stability under no-wind conditions, while CNN-GRU is better for crosswind and tailwind cases. RF also performs well as per the RMSE metric, as it is suitable for predicting landing position errors of outliers. Full article

(This article belongs to the Topic AI-Enhanced Techniques for Air Traffic Management)

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22 pages, 8909 KiB  

Open AccessArticle

Multi-Scale Localization Grouping Weighted Weakly Supervised Video Instance Segmentation and Air Cruiser Application

by Yunnan Deng, Yaomin Liu, Yinhui Zhang and Zifen He

Appl. Sci. 2025, 15(7), 4025; https://doi.org/10.3390/app15074025 - 5 Apr 2025

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Abstract

Implementing video instance segmentation (VIS) to detect, segment, and track targets based on vision system is important research for air cruiser. Large data with high sampling difficulty result in inefficient network training and limit the air cruisers in adapting to natural scenes during [...] Read more.

Implementing video instance segmentation (VIS) to detect, segment, and track targets based on vision system is important research for air cruiser. Large data with high sampling difficulty result in inefficient network training and limit the air cruisers in adapting to natural scenes during mission. A multi-scale localization grouping weighted weakly supervised VIS (MLGW-VIS) is proposed. Firstly, a spatial information refinement module is designed to supplement the multi-scale spatial location information of the high-level features of the feature pyramid. Secondly, feature interaction among the channels in each sub-space of mask features is strengthened by grouping weighting module. Thirdly, projection and color similarity loss are introduced to achieve weak supervised learning. The experimental results on the data from YouTube-VIS 2019 show that MLGW-VIS has increased the average segmentation accuracy by 5.7% and reached 37.9%, and has achieved positive effects on the perception and location accuracy of objects on the air cruiser platform. Full article

(This article belongs to the Topic AI-Enhanced Techniques for Air Traffic Management)

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