Distributional multi-agent reinforcement learning for air traffic flow and capacity management in a multiple-airport system

The Key Role of National Environment Coordinators in Improving Air Traffic Flow and Capacity Management

Dynamic air traffic flow management (DATFM) aims at flexibly balancing air traffic demand with limited airspace by scheduling aircraft, particularly during unforeseen events, to maintain efficiency in aviation operations. Genetic programming (GP) has shown success in evolving effective heuristics across various domains. However, directly adopting GP to DATFM may be less effective due to the computationally intense simulations required for large-scale aircraft decision-making over broad airspace. To address the issue, we develop a novel multifidelity surrogate-assisted GP. The core idea is that if a computationally efficient low-fidelity surrogate provides enough information to guide the population toward promising areas effectively, then employing more accurate but resource-intensive evaluations would only increase computational effort without enhancing the direction of evolution. A key innovation in our method is a surrogate management strategy that automatically determines when and which surrogate model to use, based on collective information from the evolving population. This approach allows for more effective management of computational resources during the evolutionary process, enabling exploration of a broader range of the heuristic space and increasing the likelihood of identifying promising solutions. The proposed method has been tested on various benchmark instances derived from actual air traffic data. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms current state-of-the-art methods in both effectiveness and efficiency.

Flight disruptions due to destination airport unavailability present significant challenges for air traffic management and airline operations. These situations may lead to cascading delays, increased fuel consumption, and reduced passenger satisfaction. A key response strategy is the timely identification of alternative air traffic services (ATS) routes to suitable diversion airports while ensuring flight safety and operational continuity. However, existing diversion approaches often rely on static contingency plans or real-time decisions by air traffic controllers, which may not perform well under dynamic conditions. To address this, a robust multi-objective optimization model based on the A-star algorithm is proposed to dynamically identify optimal alternative air traffic services routes when the planned destination becomes inaccessible. The model accounts for multiple objectives, including route efficiency, safety, and operational feasibility, across pre-tactical and tactical phases of air traffic flow management. By integrating airspace constraints and traffic flow considerations, the model supports adaptive, data-informed decision-making. Simulation results demonstrate the model’s ability to reduce network disruptions and support safe, efficient diversions under various traffic scenarios. This study contributes to enhancing the resilience of the air transportation system and provides a foundation for future integration into intelligent air traffic management tools and decision support systems.

This study aims to improve the efficiency of Air Traffic Flow Management (ATFM) at Cheongju International Airport, a small airport operated under a military-civilian integrated system. With the simultaneous increase in both military and civilian aircraft operations, the airport is facing complex constraints including slot allocation, flight punctuality, and increased controller workload. In particular, delays in decision-making and inefficient information exchange during the coordination process between military and civilian flights result in delays for civil aviation, which leads to passenger dissatisfaction, higher fuel consumption, and reduced operational efficiency. This study analyzes the airport’s operational and airspace structure, the characteristics of military and civilian flights, and the control environment, to propose improvements to the ATFM strategy, ultimately enhancing the efficiency and safety of integrated military-civilian air traffic operations.

Civil aviation activities are open to various ambiguities regarding air traffic flow. Small changes in political, economic and technological bodies of civil aviation can change the direction of air traffic flow and air navigation. Likewise, the civil aviation industry and its dependent branch air logistics lived through hard times during the COVID-19 period. The airlines, airports, and service providers suffered from a lot of negativities. It was an expected result for Air Traffic Flow Management to come near a financial crisis with capacity deficiencies. This paper aims to investigate this period of 2017-2021 one more time, but with a slightly different simulation methodology which assumes the period lasted for 300 years and with specific variables of Return on Investment (ROI), Return on Assets (ROA), Return on Equity (ROE), Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) in Air Navigation Service Provider Industry of Europe. According to findings, geographical location (for Central Europe, Turkey, the Mediterranean region and the United Kingdom) and the situation of states regarding development, routes and the situation of airports are the main variables of profitability and investment structures of Air Navigation Service Providers. It also concluded that the financial and economic situation of Air Navigation Service Providers in Europe cannot be changed considering these variables if this period continues for 300 years because of air the main air traffic routes and airflow order of Europe.

Purpose. The research purpose is to improve the data flow management of the air traffic control system of the Danylo Halytskyi International Airport “Lviv”. Design / Method / Approach. The following methods and approaches were consistently used in the research: system approach; modeling; content analysis; statistical analysis; project approach; economic analysis. Findings. Recommendations have been developed for optimizing data flow processing, including improving technological solutions, increasing the level of automation, and implementing strategies to reduce stress on controllers. The features and effectiveness of air traffic management and the data flow network of the automated control system of the Danylo Halytskyi International Airport “Lviv” have been studied. The program has been developed to improve the effectiveness of air traffic management and the data flow network of the automated control system of the Danylo Halytskyi International Airport “Lviv”. The effectiveness of the implementation of the program to improve air traffic management and the data flow network of the automated control system of the Danylo Halytskyi International Airport “Lviv” has been substantiated. Theoretical Implications. Methodological aspects of data flow management of the automated air traffic control system network have been investigated. Practical Implications. Directions for increasing the efficiency of air traffic control and data flow management of the automated control system network of the Danylo Halytskyi International Airport “Lviv” have been developed and substantiated. Originality / Value. The implementation of the modern technical and methodological solutions proposed in the article for the automation of air traffic control and data processing will contribute to reducing risks, increasing the speed of decision-making and ensuring the stable operation of all aviation processes. Research Limitations / Future Research. Future research into mechanisms for improving air traffic management and data flows at airports is an important task from both a scientific and a practical point of view. Article Type. Applied Research. PURL: https://purl.org/cims/4.281

Airport capacity prediction and optimal allocation for strategic air traffic flow management at Sao Paulo/Guarulhos International Airport

Urban air mobility (UAM) is an emerging transport mode, offering on-demand and automated air transit of passengers or cargo within urban ecosystems. Unrestricted by terrestrial road networks, UAM provides a timely and efficient way of transportation. Due to its profound significance, industries, regulatory authorities, and academic researchers have made extensive efforts toward its development and improvement. This article contributes to these ongoing endeavors by providing a comprehensive and systematic review of existing literature on UAM from the perspective of airspace and traffic management. Specifically, this article chronologically encapsulates the development history of UAM, aiming to foster a holistic understanding of the contributions made by industries, academics, and regulatory authorities. It also highlights specific advances, such as the integration of AI in traffic flow algorithms and the development of collision avoidance systems, addressing current technological and regulatory hurdles. Furthermore, this article provides an in-depth review of the studies on the topic of urban air transportation network design, urban air traffic flow management, and urban flight safety management. The diverse scenarios and methodologies employed across those studies are discussed and compared, identifying gaps in current research and potential for future exploration. Finally, this article highlights opportunities and prospective directions for future research in relation to traffic management strategies, thus propelling UAM toward greater efficiency.

In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. To mitigate this shortage, we propose a hybrid neural network method, called Dynamic Multi-graph Convolutional Spatial-Temporal Network (DMCSTN), to predict network-level airport arrival flow considering the multiple operation constraints and flight interactions among airport nodes. Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. In the temporal dimension, an enhanced self-attention mechanism is utilized to mine the arrival flow evolution patterns. Experiments on a real-world dataset from an ATFM system validate the effectiveness of DMCSTN for arrival flow forecasting tasks.

This paper introduces an innovative method for air traffic flow management in advanced air mobility. Our approach integrates a dynamic speed model with network augmentation to optimize air traffic routing, ensuring efficient and collision-free arrival schedules. The proposed model allows for dynamic speed adjustments to minimize travel time and conflicts in air traffic, reducing traffic network complexity and computational overhead. Simulation results demonstrated significant reductions in travel distances, processing times, and energy consumption compared to traditional methods. Real-world case studies further demonstrated the effectiveness of the approach, highlighting its scalability and applicability in diverse air traffic scenarios. This work offers a promising framework for integrating advanced air mobility into existing airspace, contributing to a more sustainable and efficient transportation system.

In the face of escalating climate change, airports worldwide are finding themselves at the mercy of extreme weather events. This research paper presents a comprehensive system that models key indicators, aiding airport management during such challenging weather conditions. The system adopts an integrated approach, combining various machine learning models to provide a detailed projection of an airport's future state, drawing from past occurrences. The heart of the system is a model that focuses on the airport's peak service rate. This model meticulously correlates weather conditions and runway configurations with the 99th percentile of observed throughput from the training dataset. As such, the peak service rate model provides an estimate of the airport's capacity, which is essential for effective planning and resource allocation. Moreover, the system includes a predictive model that assesses the likelihood of air traffic flow management regulations based on weather data and calendar information. The robustness of this model against noise and uncertainty in the training dataset is fortified by the application of confident learning techniques and the inclusion of monotonic constraints. The system further enhances its capabilities by forecasting the potential entry rate of regulations, expressed in hourly arrivals, providing valuable insights that can guide proactive decision-making. By seamlessly integrating these three models, the system serves as an effective tool for airport operators and airlines. It enables operational optimisation and the development of strategic plans to mitigate the effects of increasing weather-related disruptions.

All aviation stakeholders require accurate estimated times of arrival in order to run flight operations as efficiently as possible. The time of arrival, however, is difficult to predict because it is affected by the uncertainties of the previous flight phases, with take-off time variability being the most significant contributor. At present, estimated time of arrival predictions are computed by the Enhanced Traffic Flow Management System, which collects data from a variety of sources to provide the best estimate throughout the entire duration of the flight. This paper introduces a novel approach that leverages existing machine learning models to enhance the accuracy of estimated time of arrival predictions, also during the pre-departure phase. More specifically, the first model (Knock-on) anticipates rotational reactionary delays arising from unrealistic available turn-around times; the second model (FADE) forecasts the evolution of air traffic flow management delays for regulated flights; and the third model, AirborneTime, was trained to identify systematic discrepancies between reported and actual airborne times. Using a dataset comprised of historical traffic and meteorological data collected during one year, this paper presents a comprehensive evaluation of this ensemble of models, referred to as PETA, against the current predictions across various time horizons, ranging from 6 hours before departure to the moment of take-off. The results indicate that the proposed solution surpasses the existing system in approximately two-thirds of the predictions. When the proposed solution performs better, the average and median improvements are 14 minutes and 7 minutes, respectively. However, when it underperforms, the average and median deteriorations are 7 minutes and 4 minutes, respectively. The optimal time frame appears to be between 2 and 6 hours before the departure time. This quantitative data is supported by feedback from European airlines, air navigation service providers and airports who used PETA in a live trial.

Convective clouds classification based on weather forecast for air traffic flow management in Kualanamu Airport

Abstract Air Traffic Flow Management (ATFM) is the backbone of modern aviation and ensures that aircraft move safely and efficiently through increasingly congested skies. As global air travel grows, managing air traffic has become more pressing than ever. This review assesses ten years of the ATFM literature, the period between 2014 and 2024, and discusses 162 studies published in peer-reviewed journals. Employing VOSViewer and Biblioshiny, this review analyzes the history of ATFM research. It explores the trends and gaps in research, which suggest there is room for improvement for more sound approaches. While optimization techniques have significantly improved efficiency and eased bottlenecks, the future lies in real-time solutions that can handle unpredictable events, from weather disruptions to technical failures. The review identified key areas for optimizing ATFM, categorized by primary focus: delay minimization, airspace congestion, and scheduling. It suggests ways in which more dynamic ATFM systems are possible in the growing global aviation network. By synthesizing the current research landscape, this review addresses the progress made. It offers a roadmap for future innovations that will enhance the safety, efficiency, and sustainability of air traffic management.

Air traffic flow management (ATFM) is the key driver of efficient aviation. It aims at balancing traffic demand against airspace capacity by scheduling aircraft, which is critical for air navigation service providers in delivering secure and sustainable air transport. Nowadays, the scale of scheduled aircraft grows dramatically along with the sharp increase in air traffic demand, which brings heavy pressure to efficient scheduling. Regarding safety and efficiency as two fundamental objectives of air transport, this paper proposes a cooperative co-evolutionary algorithm to solve large-scale multi-objective ATFM problems. First, a new multi-objective co-evolution framework with an evolving external archive is devised, in which the subcomponents collaborate with each other via the knee solution of the archive. Second, a novel fuzzy decomposition method is specifically designed to split the large-scale ATFM problem into small-size subcomponents by utilizing the spatiotemporal correlations of aircraft. During optimization, the proposed algorithm can continuously receive feedback from the optimization process and make the decomposition more likely better suited to the problem. Third, a new contribution-based probabilistic resource allocation mechanism is developed to automatically assign the computing resources to the unbalanced subcomponents. Finally, a test suite with different scales extracted from real air traffic data is created. Extensive experimental results show that, given the same number of fitness evaluations, the proposed algorithm significantly outperforms the state-of-the-art baselines in terms of effectiveness on all the benchmark instances.

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations.

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving.

A User-Driven Prioritisation Process implementation and optimisation for ATFM hotspot resolution

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.