Air Traffic Operations Research Articles

An important step forward to address fatigue risk in Federal Aviation Administration air traffic operations.

An important step forward to address fatigue risk in Federal Aviation Administration air traffic operations.

Development of a mathematical model for managing schedule delays in air traffic operations

The object of this research is delay in air traffic operations. The problem in this research that must be solved is how to reduce the impact of frequent delays which cause time efficiency but cause increased operational costs and make customers dissatisfied with air traffic services and then there is time complexity which is difficult to overcome. The interpretation of this research is to analyze existing problems and then apply mathematical methods so that it is possible to develop a model that is able to dynamically optimize flight rescheduling which can be beneficial for customers in reducing waiting times. This model will consider many important variables in managing delay schedules including real-time weather conditions, aircraft availability, airport capacity so that the results of this model show the ability to reduce the frequency and duration of delays which can increase customer satisfaction. This application shows that the model developed has main characteristics such as flexibility in adjusting schedules in terms of delays and accuracy in predicting potential delays so that the problems analyzed and researched can be resolved effectively and efficiently. This model can predict schedule delays with an accuracy level of 90 % according to predetermined input variables. Then there are quantitative benefits in the form of reducing operational costs for delays, increasing prediction accuracy and optimizing flight schedules. Qualitatively there are benefits in customer satisfaction and faster and more effective decision making. The scope of this research includes managing flight schedules at airports and international hubs. Implementation of this model is important to ensure high operational efficiency and minimize the impact of delays in various operational conditions

Fatigue in Air Traffic Operations—Opportunities to Enhance Safety

This Viewpoint reports the findings of a 2024 independent scientific evaluation of fatigue risk in air traffic operations commissioned by the US Federal Aviation Administration.

GNSS interference detection: Methodology utilising ADS-B NACp indicator and GPS almanac data

Abstract The threat of GNSS interference poses a great danger to many critical infrastructure systems including air navigation. With a focus on mitigating this threat, this paper proposes a methodology for detecting GNSS interference. The methodology utilises the quality indicator NACp transmitted in ADS-B messages and GPS almanac data for interference detection. The NACp indicator enables estimation of the position error derived from GPS, which is compared with the HDOP value of the GPS satellite constellation. Based on this comparison, the developed detection algorithm determines whether the aircraft is affected by jamming. The detection methodology is evaluated on datasets obtained during deliberate experiments with GPS jamming. The proposed methodology provides a way to detect GNSS interference, facilitating mitigation of its impact on air traffic operation.

Flight Arrival Scheduling via Large Language Model

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

Qualifying and quantifying the different ways in which people are annoyed by aircraft noise

Aircraft noise remains a major problem for many people living near airports, despite the efforts of the various air traffic operators and aircraft manufacturers to reduce the nuisance. Noise abatement usually relies on quantitative approaches based on noise measurements at source or in residential areas. However, field studies have shown that the level of annoyance experienced within a given noise exposure zone varies widely, demonstrating that noise levels alone do not explain the experience of local residents. The present work brought together a wide range of disciplines (aeronautics, acoustics, linguistics, psychoacoustics, social and environmental psychology) to define a common framework for reconciling field and laboratory studies on aircraft noise annoyance. A survey of 1250 people living near airports enabled us to precisely qualify the annoyance experienced, and to identify 6 highly differentiated annoyance profiles based on non-acoustic factors. Focus groups and laboratory experiments with people living near airports revealed an impact of annoyance profiles on both qualitative and quantitative evaluation of noise annoyance, which confirms the diversity of their experiences in the same listening situation.

Modeling sector route segment capacity under convective weather based on machine learning

Abstract Estimating airspace capacity under convective weather conditions is crucial for ensuring the efficiency and safety of air traffic operations. Sector route segments, which are essential components of flight routes, require timely capacity predictions during operationally critical periods. In this paper, initially, an enhanced Recursive Feature Elimination algorithm is used to select meteorological data and develop predictive features. Subsequently, the CWSRC model is established using the RF supervised learning algorithm. Finally, the paper takes ENH-YIH segment as an example to predict the capacity. Compared with other machine learning algorithms, the residual percentages for KNN, MLP and RF are 86.03%, 77.37% and 93.40%, respectively, within the range of [−0.2, 0.2]. In three separate day cases, results show that the CWSRC model’s MAE, MSE, RMSE and R2 significantly outperform traditional methods like Maxflow/Mincut and scanning line. The results confirm the CWSRC model’s superior predictive capabilities.

Flow-Based Assessment of the Impact of an All-Electric Aircraft on European Air Traffic

The consequences of new airspace entrants, such as novel aircraft concepts with innovative propulsion systems, on air traffic management operations need to be carefully identified. This paper aims to assess the impact of future aircraft with different performance envelopes on the European air traffic network from a flow-based perspective. The underlying approach assumes that all certification-related questions concerning airworthiness have been resolved and do not take into account any economic factors related to airline operations. For example, for an innovative propulsion system, a short range all-electric aircraft is considered in this study. Aircraft trajectory calculations are based on the dataset of base of aircraft data (BADA), which are developed and maintained by EUROCONTROL. The new design concept is integrated into BADA as well, resulting in a new set of coefficients for the all-electric aircraft. In addition to the adjusted parameters which affect airborne performances, ground-related aspects are also taken into account. This includes assumptions on operational procedures, charging capacities and adaptions in infrastructure. Investigations are carried out at the trajectory level as well as at the airport and the entire network.

Air Traffic Controllers' Perspectives on Unmanned Aerial Vehicles Integration into Non-Segregated Airspace

The integration of Unmanned Aerial Vehicles (UAVs) into non-segregated airspace presents both opportunities and challenges for air traffic control (ATC). The aim of the study is to explore the perspectives of air traffic controllers on the current and anticipated challenges, workload, stress factors, performance errors, and mitigation strategies related to UAV integration. The sample consisted of 213 air traffic controllers in Türkiye. UAV operations have been available in Türkiye not only for military purposes but also for purposes such as forest fires, earthquakes, security, and others for a long time, and these UAV operations are provided with air traffic services (ATS) by air traffic controllers. The results show that air traffic controllers are concerned about mid-air collisions due to UAV technology limits and regulatory gaps, along with managing risks and unique flight characteristics. Addressing technology limitations, regulatory ambiguity, and other factors necessitates a comprehensive strategy. Solutions must prioritize collision avoidance systems, clear communication guidelines, and defined no-fly zones. It is recommended that future studies focus on the comprehensive impact of UAVs on air traffic operations and the development of regulations.

Virtual/augmented reality-based human–machine interface and interaction modes in airport control towers

The concept of an innovative human–machine interface and interaction modes based on virtual and augmented reality technologies for airport control towers has been developed with the aim of increasing the human performances and situational awareness of air traffic control operators. By presenting digital information through see-through head-mounted displays superimposed over the out-of-the-tower view, the proposed interface should stimulate controllers to operate in a head-up position and, therefore, reduce the number of switches between a head-up and a head-down position even in low visibility conditions. This paper introduces the developed interface and describes the exercises conducted to validate the technical solutions developed, focusing on the simulation platform and exploited technologies, to demonstrate how virtual and augmented reality, along with additional features such as adaptive human–machine interface, multimodal interaction and attention guidance, enable a more natural and effective interaction in the control tower. The results of the human-in-the-loop real-time validation exercises show that the prototype concept is feasible from both an operational and technical perspective, the solution proves to support the air traffic controllers in working in a head-up position more than head-down even with low-visibility operational scenarios, and to lower the time to react in critical or alerting situations with a positive impact on the human performances of the user. While showcasing promising results, this study also identifies certain limitations and opportunities for refinement, aimed at further optimising the efficacy and usability of the proposed interface.

Agri-voltaics near airport facilities. Reconciling the risk of solar glare with power generation

The installation of photovoltaic systems on cultivated land enables both agricultural and green energy production simultaneously. The result is an efficient and inclusive use of land that promotes the rural economy, enables the recovery and maintenance of land and prevents its abandonment when it is considered not profitable. This intent is facilitated in semi-rural areas where there is already the presence of infrastructure connected to the national power grid, but at the same time is limited by other human activities. Runway approach and air traffic control operations impose very stringent constraints on the possibility that photovoltaic panels, reflecting solar radiation, may glare airport operators.This article provides a methodology for verifying such phenomena (control function) and mitigating them by maximizing solar power generation (objective function). Through a case study located in central Italy, positioned near an airport, it was possible to investigate the design variables to protect the landing air routes and the airport control tower.In this case study, with an azimuth angle of 47° and tilt angle of 25°, and thanks to a 5 m high tree screen placed on the edge of the land on which the agri-voltaic installation stands, it is possible to secure airport operations by eliminating all forms of solar glare. At the same time, 21.9 GWh of photovoltaic energy is produced annually, while avoiding the release of 8,773 tons of CO2 into the atmosphere. Compared with the optimal, unconstrained case, photovoltaic panels arranged in this way decrease their annual energy production by only 6.8 %.

Time-Series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

Environmental Impact by Air Traffic: Assessing aircraft noise nearby Hong Kong International Airport

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by aircraft operations around a major airport, namely the Hong Kong International Airport (HKIA). To assess a pre-existing aircraft noise prediction platform vis-à-vis the specificities of the local aviation scene, the ground noise impacts of 70+ aircraft flying in and out of HKIA are live-measured in two locations of Hong Kong city. This is achieved through in-situ audio recordings of aircraft during take-off/landing flight phases, along with a real-time tracking of their characteristics (aircraft and engine types, flightpaths, atmospheric conditions, etc.). Once suitably post-processed, the measurements are analyzed to characterize better the noise impacts by these various flights in regard to their specificities (e.g., aircraft types, flight operations). In a second stage, these noise impacts are compared against their digital twins, which are simulated via the noise prediction platform, using the actual flight characteristics (aircraft types, flightpaths, thrust, airspeed, etc.). This comparative assessment is achieved for a total of 60 flights consisting of 37 departures (resp. 23 arrivals) and involving 6 (resp. 5) aircraft types. Overall, the comparison between the field tests and their digital twins proves to be reasonably good, with both measured and predicted noise levels falling within a range of a few decibels across all flights, on average. Finally, specific analyses are conducted to explore the uncertainties weighing on the predictions, possibly explaining some of the mismatches observed between the experimental and computational results. Overall, the study further demonstrates that aircraft noise prediction methods such as the present one constitute a valuable means to assess the environmental impacts of air traffic operations, including when the latter are highly specific — as in Hong Kong city, owing to its complex airspace and meteorological conditions.

Tangible digital twin with shared visualization for collaborative air traffic management operations

In recent years, digital twin technology has become increasingly popular and been widely applied across different industries including air traffic management (ATM). While previous studies on digital twin in ATM focused on factors such as system architecture, synthetic data generation and transfer, and real-time simulation, there is currently a lack of research on visualization and interaction design of digital twin from human factors’ perspective. Moreover, recent advances in virtual and mixed reality (VR/MR) technology give potential for designing interactive digital twin in 3D space that can assist human operators in performing their duties. Therefore, this research aims to explore visualization and interaction design of an MR-based digital twin prototype that can assist air traffic controllers (ATCOs) in carrying out ground control tasks. Specifically, MR headsets (Microsoft HoloLens 2) are leveraged to project out-of-tower view of airport traffic onto a 3D printed airport model at a 1:1 scale. The spatially aligned tangible system enables ATCOs to perform typical ATM operations by directly touching the 3D printed airport model. In addition, collaborative features are implemented into the system, which enable multiple ATCOs to have a shared view of the airport traffic and collaboratively perform ATM operations when wearing the MR headsets. Finally, a user study was conducted with the recruitment of ten licensed ATCOs to investigate usability of the system and ATCOs’ workload, situational awareness and trust when using the system. The feedback from the ATCOs in the user study suggested that: (1) The visual and interactive design were acceptable; (2) The ATCOs indicated varied workload under different scenarios; (3) The ATCOs developed a high level of shared situational awareness with shared view of the system; and (4) The ATCOs were open and confident to use the system in its’ mature form. The findings show that the MR-based tangible airport digital tower system has high potential in collaborative ATM operations by improving user performance and user experience.

Influence of a large commercial airport on the ultrafine particle number concentration in a distant residential area under different wind conditions and the impact of the COVID-19 pandemic

Exposure to ultrafine particles has a significant influence on human health. In regions with large commercial airports, air traffic and ground operations can represent a potential particle source. The particle number concentration was measured in a low-traffic residential area about 7 km from Frankfurt Airport with a Condensation Particle Counter in a long-term study. In addition, the particle number size distribution was determined using a Fast Mobility Particle Sizer.The particle number concentrations showed high variations over the entire measuring period and even within a single day. A maximum 24 h-mean of 24,120 cm−3 was detected. Very high particle number concentrations were in particular measured when the wind came from the direction of the airport. In this case, the particle number size distribution showed a maximum in the particle size range between 5 and 15 nm. Particles produced by combustion in jet engines typically have this size range and a high potential to be deposited in the alveoli. During a period with high air traffic volume, significantly higher particle number concentrations could be measured than during a period with low air traffic volume, as in the COVID-19 pandemic.A large commercial airport thus has the potential to lead to a high particle number concentration even in a distant residential area. Due to the high particle number concentrations, the critical particle size, and strong concentration fluctuations, long-term measurements are essential for a realistic exposure analysis.

Research on assessment of air traffic control operation quality based on track data

AbstractEvaluating the quality of air traffic control operations is crucial for enhancing airspace management. Thus, this paper proposes a data mining approach for conducting a comprehensive assessment of control operation quality (COQ) in increasingly complex operation environments. First, the authors establish a COQ evaluation index system that combines both subjective and objective measures. Key index parameters are determined using wavelet filtering and interval estimation techniques on the basis of data mining results. Second, the authors apply an entropy‐weighted cloud model to label data samples and classify COQ into ‘excellent’, ‘good’, and ‘fair’ levels. Finally, the authors establish an support vector machine‐based COQ assessment model using XGBoost feature combinations to verify the practical feasibility and scientific validity of their approach.

UTJECAJ LJUDSKIH ČIMBENIKA NA SIGURNOST ZRAČNOG PROMETA

This paper explores the significant impact of human factors on aviation safety, with a particular emphasis on air traffic operations as a complex chain where human factors play a crucial role. With the rapid growth and technological advancement in aviation, operations are becoming increasingly complex, leading to increased workload on the crew and other personnel. This intricate chain encompasses various phases, including flight planning, execution, air traffic control, and ground operations, when human factors plac a pivotal role at every step. Air traffic operations are highly sensitive to human errors, especially under conditions of high stress, fatigue, or emotional tension. The rapid growth of air traffic and technological advancement often lead to the neglect of human factors. However, research indicates that these factors remain one of the primary causes of incidents and accidents in aviation. To ensure flight safety in this dynamic environment, it is crucial to understand and effectively manage human factors by providing support to personnel and ensuring compliance with regulations and best practices.

Demonstration of Fixed Flight-Path Angle Descent via Scheduled Commercial Flights

Toward implementation of fuel-saving descent operations, this study demonstrated a fixed flight-path angle descent using regularly scheduled commercial flights arriving at Kansai International Airport. This paper summarizes the authors' industry/academia collaboration research including 1) operational bottlenecks and weather analysis, 2) proposals of pilot and air traffic controllers’ operational procedures, 3) validation experiments using an A320 flight simulator, and 4) flight demonstrations conducted across 24 days using A320neo and A320ceo aircraft. The flight demonstration results were evaluated comparing flight recorder data of the new descent procedure relative to current descent operations. A total of 92% of the flights were approved to fly new approach procedure under actual air traffic operations, with an approximate average of 150 lb fuel reduction per flight (230 lb maximum).

Machine learning for predicting the impact of construction activities on air traffic operations during airport expansion projects

Construction activities during airport expansion projects disrupt air traffic operations and often need to be performed in phases to minimize their disruptive impacts. This paper presents a machine learning methodology for quantifying the impact of alternative construction phasing plans on air traffic operations. The methodology is implemented in four stages: data collection, data preprocessing, model training, and evaluation stages. A case study is analyzed to highlight the original contributions of the methodology that include (1) development of five machine learning models for accurately and efficiently quantifying the impact of construction-related airport closures on flights ground movement time, (2) comparison of the performance and prediction accuracy of the developed models, and (3) efficient assessment of the impact of alternative construction phasing plans on airport operations without the need for time-consuming simulations. This is expected to provide planners with much-needed support to identify construction phasing plans that minimize total flight delays.

Effect of deicing agents on the performance of airport asphalt pavement

Deicing agents are widely used to remove ice and snow on airport pavement, ensuring air traffic operation and safety. However, excessive spraying may affect the performance of airport pavement. The effects of different chloride deicing agents on the performance of airport asphalt and mixture have been assessed by previous studies. The three deicing agents (NaCl, KHF-1A, and NW056) were selected. Conducting various tests to examine their influence on the performance of asphalt and asphalt mixture. The tests include the DSR test, BBR test, indirect tensile test, bending test of small beam, and freeze-thaw splitting test. The results indicate that the asphalt anti-rutting factor G*/sinδ varies by 25%–45%, the asphalt creep stiffness increases by 15%–50%, and the anti-fatigue factor (G* sinδ) increases by 10%–40% under the effect of the deicing agents. The splitting strength of the mixture decreases by 10%–40%, the bending stiffness modulus increases by 6%–20%, and the freeze-thaw splitting strength ratio does not change significantly under the effect of the deicing agents. NW056 deicing agent has minimal effect on the performance of airport pavement and has optimal performance. The replacement of traditional chloride deicing agents helps to prolong the service life of airport asphalt pavement.