The integration of artificial intelligence and automation into safety-critical domains, such as air traffic management (ATM), raises new challenges in managing operators' trust and fatigue under high-workload conditions. Although transparency regulation has been identified as a key factor shaping human-automation interaction, prior work has largely focused on driving or monitoring tasks. Little attention has been paid to managing complex work, such as air traffic control. Moreover, trust and fatigue are typically examined in isolation, with limited understanding of their dynamic interplay in human-machine collaboration. This article introduces a transparency-regulated ATM simulation platform that allows three fixed transparency levels (low, mid, and high) and a user-switchable (mix) mode, enabling controlled investigation of effects on operators' trust and fatigue. Multimodal data were collected from eye tracking, electroencephalography, and system status logs under varying transparency and workload conditions. By applying machine learning and deep learning approaches, we compare unimodal and multimodal prediction of trust and fatigue. The results show that increasing transparency enhances operators' understanding, trust, and willingness to rely on the system, while multimodal fusion achieves superior predictive accuracy compared with single-modality inputs. The findings reveal a positive but nonlinear coupling between trust and fatigue, suggesting that adaptive transparency can balance operators' reliance and cognitive effort. In particular, temporal deep models exhibit strong sensitivity to eye tracking features. Overall, this article contributes a unified framework linking transparency regulation, multimodal state estimation, and adaptive interface design, offering theoretical and practical insights for building resilient ATM automation systems that maintain appropriate trust while mitigating fatigue risks.

The objective of airport slot allocation is to distribute slots to airlines following specific procedures and regulations. With the rapid growth of air traffic and increasing congestion at major airports, optimizing slot allocation has become a critical issue to enhance operational efficiency and mitigate delays. While extensive research has been conducted on slot allocation at individual airports, less attention has been given to the complexities of slot allocation in a Multiple Airport System (MAS), where airlines must compete for both airport and airspace resources. In a single airport slot allocation, the only resource that airlines compete for is airport capacity; in an MAS, there are several resources that must be taken into account, such as airport capacity and fix (i.e., route point) capacity. In this article, a model is presented that considers the capacity of the airport and airspace, as well as the efficiency and fairness among airlines operating in the MAS. The goal of the model is to minimize total slot displacements of the MAS, while satisfying airport capacity and fix capacity constraints, operational constraints, and fairness constraints. To measure fairness, comprehensive fairness metrics for airlines are developed. The trade-off between efficiency and equity in an MAS slot allocation problem is investigated, with the Price of Fairness(POF) taken into account. The proposed model is tested using MAS slot request data from an MAS in China. The results demonstrate that the model can achieve a better Gini-based fairness by sacrificing a certain amount of slot displacements. These findings have significant implications for slot regulators in improving the efficiency of managing airport slot resources within an MAS. © 2025 Elsevier Ltd

Air traffic flow management has been a major means for balancing air traffic demand and airport or airspace capacity to reduce congestion and flight delays. However, unpredictable factors, such as weather and equipment malfunctions, can cause dynamic changes in airport and sector capacity, resulting in significant alterations to optimized flight schedules and the calculated pre-departure slots. Therefore, taking into account capacity uncertainties is essential to create a more resilient flight schedule. This paper addresses the flight pre-departure sequencing issue and introduces a capacity uncertainty model for optimizing flight schedule at the airport network level. The goal of the model is to reduce the total cost of flight delays while increasing the robustness of the optimized schedule. A chance-constrained model is developed to address the capacity uncertainty of airports and sectors, and the significance of airports and sectors in the airport network is considered when setting the violation probability. The performance of the model is evaluated using real flight data by comparing them with the results of the deterministic model. The development of the model based on the characteristics of this special optimization mechanism can significantly enhance its performance in addressing the pre-departure flight scheduling problem at the airport network level. © 2024

Predicting airport delays is of great importance for aviation operations, from the development of effective air traffic management strategies to the reallocation of airline resources. In this paper, the long-term prediction of the next 24 h of network-wide delays is investigated. The sensitivity to error propagation over long time periods as well as dynamic spatial correlations and non-linear temporal correlations of the aviation network are considered. An external impact modeling module is introduced to account for the influence of weather on flight delay patterns. A Spatial-Temporal Gated Multi-Attention Graph Network (STGMAGNet) considering external impact to predict airport delays is then developed. We validate our model on a flight delays dataset collected from the Bureau of Transportation Statistics of US, covering January 1, 2019, to December 31, 2019. In long-term (input-24-predict-24 setting) forecasting, STGMAGNet provides state-of-the-art accuracy, with a MAE reduction of at least 21% averaged in arrival delay prediction, 18% averaged MAE reduction in departure delay prediction compared to MLP, LSTM, Seq2Seq and Transformer. Our model can enable aviation management to shift from a reactive to proactive approach, thus enhancing its operational efficiency and overall performance. © 2023 Elsevier Ltd

Air traffic controllers play a crucial role in overseeing and managing dynamic flight operations. The issue of their fatigue is intrinsically linked to aviation safety, thus detecting fatigue states in controllers is vital for operational control and training. This paper provides a comprehensive review of the methods and research on detecting fatigue in air traffic controllers (ATCOs). It initially reviews the historical advancements and pertinent literature concerning the management and prevention of controller fatigue. Moreover, it examines the behavioral traits, physiological signals, and current single-source data methods utilized for detecting fatigue in ATCOs. The paper also underscores the fusion detection methods, models, and multi-source data-based research, serving as references for exhaustive and real-time analysis of controller fatigue states. Finally, it suggests future research directions for fatigue risk prevention in ATCOs, based on the existing studies.

Prior research on slot allocation has focused on a single airport, with little attention paid to the multiple-airport systems (MAS) that consist of at least two major airports. Scheduled flights at different airports may have conflicts regarding shared fixes (i.e., route points) or routes, thus causing airspace congestion and flight delays. Traffic demand at a critical fix depends on both the departure/arrival time of the flights and the flying times between the airport and the fix, whereas flying times exhibit a stochastic nature due to various factors such as air traffic control strategies, aircraft performance, and weather. In this paper, we develop a chance-constrained slot allocation model for an MAS that optimizes slot allocation for multiple airports while considering fix capacity constraints. To capture the uncertainty of flying times, stochastic chance constraints are formulated and a scenario generation method is proposed to solve the model. We apply our model to allocate slots in the MAS of Guangdong–Hong Kong–Macao Greater Bay area. The results show that the schedules generated by the proposed model outperform those from the certainty model and the original schedules. Traffic flow at critical fixes is more robust to various operating scenarios with the cost of a small number of increased slot displacements. Our findings highlight the importance of flying time uncertainty in allocating slot and airspace capacity, and the proposed model provides a useful tool for slot coordinators seeking to effectively manage airport slots in an MAS. © 2023 Elsevier Ltd

Most Level 3 airports around the world suffer severe congestion and flight delays. Airlines have to obtain airport slots in order to schedule flights at such airports. The main way for airlines to acquire slots is primary slot allocation, in which a slot coordinator distributes slots to airlines according to certain rules and regulations. Due to excessive demand for slots and restrictions on allocation rules, it is difficult for some airlines to obtain the desired slots in this manner. Another way for airlines to obtain slots is through secondary slot trading, in which slots can be redistributed among airlines without being returned to slot pool. The secondary trading of airport slots has played a positive role in promoting the efficient utilization of slot resources, but systematic studies are insufficient. This paper discusses the reasons for the existence of slot secondary trading, sorts out the main policies and rules governing the mechanism, investigates its impacts on the industry and society, and points out the major problems and challenges. The paper provides a reference for subsequent research and practical application of airport slot secondary trading in the future. © 2023 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.

As demand for air transport continues to grow, the complexity of the operating environment of air traffic has intensified. Air Traffic Controllers (ATCos) are faced with the critical task of making timely decisions in response to rapidly changing air traffic. In this context, maintaining Situation Awareness (SA) becomes critical, directly influencing ATCos’ decision making and preventing potential traffic accidents or incidents. This paper presents a systematic review of the theoretical development of SA measurement techniques for ATCos. Firstly, the measurement techniques developed to assess individual and team SA are discussed and summarized. Additionally, some recently developed novel measurement methods are introduced. Four specific techniques applied to evaluate ATCos’ SA are highlighted. Second, the article analyzes the comprehensive utilization of neurophysiological measurement techniques, with a particular focus on eye movement and EEG methodologies. These techniques exhibit promising potential in measuring ATCos’ SA and are explored in-depth. Furthermore, four distinct types of sensitivity factors that primarily affect ATCos’ SA are summarized. This paper provides some insight into the current state of SA measurement techniques for ATCos. It concludes with recommendations for future research, specifically addressing the evaluation of ATCos SA in high-automation environments. ©2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong-Hong Kong-Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS.

Flight delays persist and spread in airport networks due to high interconnectivity in the air transportation infrastructure. How quickly delay propagates between two airports is determined by factors such as the number of flights between airports, the duration of the flight, presence of disruptions, and schedule buffers. Accurate estimation of the time for delay propagation can improve system predictability and reliability. However, noisy airport delay data, along with a lack of visibility into airline scheduling and disruption management strategies, result in a challenging estimation problem for such propagation timescales. We present an algorithm to estimate statistically significant time lags between airport delays from noisy, aggregate operational data. The algorithm uses sliding correlation windows to extract the airport pairs with stable delay lags. We apply our method to identify different timescales of interactions for US airport delays in 2017. Our analysis yields two main results: (1) The most stable lags between airport delays involve the Northeast airports; (2) The stable lags between two airports are negatively correlated to the scheduled flight times between the same two airports. These results regarding delay propagation speeds have potential implications for delay prediction models and airline schedule design. © 2022 Elsevier Ltd