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Aerospace
Special Issues
Emerging Trends in Air Traffic Flow and Airport Operations Control
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Emerging Trends in Air Traffic Flow and Airport Operations Control

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2042

Special Issue Editors

Dr. Lei Yang

E-Mail Website
Guest Editor
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: air traffic management; air-ground integrated operations; multi-airport system traffic optimization; UAS traffic management
Prof. Dr. Yan Xu

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Interests: air traffic management; UAS traffic management; urban air mobility
Dr. Yutong Chen

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore
Interests: demand and capacity balancing; air traffic control; trajectory planning; human-AI teaming (HAT); UAS traffic management

Special Issue Information

Dear Colleagues, 

Global air transportation is undergoing a transformative shift, driven by the need for more integrated and adaptive approaches to optimize airspace and airport traffic operations in complex environments. Recent advances in digitalization, automation, artificial intelligence, and data analytics are transforming traditional approaches to air traffic flow and airport operations, enabling more adaptive, resilient, and environmentally conscious systems. 

This Special Issue aims to highlight the latest research, technological breakthroughs, and practical applications in the field of air traffic flow and airport operations control. We invite contributions that address emerging trends such as flow-centric dynamic airspace configuration, hierarchical collaboration of air traffic flow management and air traffic control, integrated departure, arrival and surface operation (IDASO) for airport or multi-airport systems, trajectory-based operations, and integration of unmanned aerial systems (UAS) into conventional air traffic flow. We also seek contributions addressing the operational and technological challenges of synchronizing airspace and airport resources and operations at network level, including the use of advanced artificial intelligence for predictive and adaptive control. We encourage submissions that employ theoretical, numerical, or experimental approaches, as well as interdisciplinary studies that bridge the gap between research and real-world implementation.

Dr. Lei Yang
Prof. Dr. Yan Xu
Dr. Yutong Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • capacity and delay prediction
  • dynamic airspace configuration
  • air traffic flow and capacity management
  • departure, arrival and surface management
  • integrated airport and airside operations control
  • collaborative multi-airport system operations
  • 4D trajectory planning and management
  • human-automation interactions
  • integration of new entrants into controlled airspace
  • resilient air traffic operation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 1796 KB  
Article
Research on Time Constraint Strategy of Flight Ground Support Operations Based on Causal Inference
by Xiaoqing Xing, Wenjing Wang, Hongyun Fan, Lei Xu and Mian Zhong
Aerospace 2026, 13(3), 272; https://doi.org/10.3390/aerospace13030272 - 13 Mar 2026
Viewed by 344
Abstract
To improve the punctuality of flight schedules, causal inference methods are introduced to model the potential causal structure and intervention effects among ground support operations of flights. The effectiveness of these methods in improving flight punctuality is verified under experimental conditions. When the [...] Read more.
To improve the punctuality of flight schedules, causal inference methods are introduced to model the potential causal structure and intervention effects among ground support operations of flights. The effectiveness of these methods in improving flight punctuality is verified under experimental conditions. When the causal relationship of Flight Ground Support (FGS) is determined, the research initiates from the perspective of FGS. A time-constrained strategy based on the Q-learning causal optimal strategy algorithm is proposed to transform causal effects into causal strategies. Initially, the influencing factors of FGS operations are classified into intervention groups. The causal effects of these influencing factors on their target support operations are calculated, and the influence degrees of the causes on the results within the causal relationship are investigated. Subsequently, the time constraint of the FGS process is characterized as a Markov decision process. The experimental results indicate that, compared with the traditional probability strategy, the causal strategy that considers the causal relationship enables over 51% of the flight plans to depart on time, with an average increase of 2.79%. The proposed method is not restricted to a specific airport or a single ground handling process configuration. Under the condition that ground handling operations are observable and sufficient historical operational data are available, it provides an interpretable optimization framework for time-constraint decision-making in flight ground handling operations across airports of different scales. Full article
(This article belongs to the Special Issue Emerging Trends in Air Traffic Flow and Airport Operations Control)
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29 pages, 3715 KB  
Article
Bi-Level Scheduling for Beijing-Tianjin-Airport Cluster Departures
by Ying Peng, Zhaokun Wan, Bin Jiang and Longhui Ran
Aerospace 2026, 13(2), 190; https://doi.org/10.3390/aerospace13020190 - 16 Feb 2026
Viewed by 618
Abstract
The rapid growth of air traffic demand and limited airspace resources have made efficient coordination in multi-airport systems a critical challenge. This paper develops a bi-level air–ground collaborative scheduling model for the Beijing-Tianjin-Airport cluster, integrating terminal-area departure sequencing (upper level) with airport surface [...] Read more.
The rapid growth of air traffic demand and limited airspace resources have made efficient coordination in multi-airport systems a critical challenge. This paper develops a bi-level air–ground collaborative scheduling model for the Beijing-Tianjin-Airport cluster, integrating terminal-area departure sequencing (upper level) with airport surface taxi and pushback scheduling (lower level), where the upper-level model minimizes departure delays, maximizes airport satisfaction, and reduces fairness deviation, while the lower-level model optimizes taxi routing and pushback timing. To solve the model, NSGA-II is applied to the upper-level sequencing problem and a Genetic-Simulated Annealing algorithm is used for surface scheduling. Empirical evaluation using operational data from Beijing Capital, Beijing Daxing, and Tianjin Binhai airports shows that the proposed approach reduces total departure delay by 49.4%, lowers average taxi time by up to 40.4%, and improves overall airport satisfaction by 5.2%, while reducing fairness deviation by 52.6%. These results demonstrate that the framework effectively enhances efficiency and equity in multi-airport departure operations. Full article
(This article belongs to the Special Issue Emerging Trends in Air Traffic Flow and Airport Operations Control)
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45 pages, 1326 KB  
Article
Cross-Domain Deep Reinforcement Learning for Real-Time Resource Allocation in Transportation Hubs: From Airport Gates to Seaport Berths
by Zihao Zhang, Qingwei Zhong, Weijun Pan, Yi Ai and Qian Wang
Aerospace 2026, 13(1), 108; https://doi.org/10.3390/aerospace13010108 - 22 Jan 2026
Cited by 1 | Viewed by 480
Abstract
Efficient resource allocation is critical for transportation hub operations, yet current scheduling systems require substantial domain-specific customization when deployed across different facilities. This paper presents a domain-adaptive deep reinforcement learning (DADRL) framework that learns transferable optimization policies for dynamic resource allocation across structurally [...] Read more.
Efficient resource allocation is critical for transportation hub operations, yet current scheduling systems require substantial domain-specific customization when deployed across different facilities. This paper presents a domain-adaptive deep reinforcement learning (DADRL) framework that learns transferable optimization policies for dynamic resource allocation across structurally similar transportation scheduling problems. The framework integrates dual-level heterogeneous graph attention networks for separating constraint topology from domain-specific features, hypergraph-based constraint modeling for capturing high-order dependencies, and hierarchical policy decomposition that reduces computational complexity from O(mnT) to O(m+n+T). Evaluated on realistic simulators modeling airport gate assignment (Singapore Changi: 50 gates, 300–400 daily flights) and seaport berth allocation (Singapore Port: 40 berths, 80–120 daily vessels), DADRL achieves 87.3% resource utilization in airport operations and 86.3% in port operations, outperforming commercial solvers under strict real-time constraints (Gurobi-MIP with 300 s time limit: 85.1%) while operating 270 times faster (1.1 s versus 298 s per instance). Given unlimited time, Gurobi achieves provably optimal solutions, but DADRL reaches 98.7% of this optimum in 1.1 s, making it suitable for time-critical operational scenarios where exact solvers are computationally infeasible. Critically, policies trained exclusively on airport scenarios retain 92.4% performance when applied to ports without retraining, requiring only 800 adaptation steps compared to 13,200 for domain-specific training. The framework maintains 86.2% performance under operational disruptions and scales to problems three times larger than training instances with only 7% degradation. These results demonstrate that learned optimization principles can generalize across transportation scheduling problems sharing common constraint structures, enabling rapid deployment of AI-based scheduling systems across multi-modal transportation networks with minimal customization and reduced implementation costs. Full article
(This article belongs to the Special Issue Emerging Trends in Air Traffic Flow and Airport Operations Control)
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