ABSTRACT
The last century has witnessed European commercial aviation flourishing at the cost of environmental degradation by boosting greenhouse gas and CO2 emissions in the atmosphere. However, the outcry for net-zero emissions compels the sector's supply chain to a minimum 55% reduction of greenhouse gas emissions below the 1990 level by 2030 and zero CO2 emissions by 2050. This study examines a European environmental sustainability path toward a green commercial aviation supply chain. Driven by literature and a review of related documents, two propositions were advanced to orient perspectives on the relationship between pollution and the commercial aviation supply chain and actions being taken toward environmental sustainability. In semi-structured interviews, seventeen aerospace associates endorsed pollution sources in the commercial aviation supply chain during the four stages of the aircraft life cycle, including extracting the raw materials, manufacturing, ground and flight operations, and end-of-service. They recommended transitioning into green commercial aviation through the widespread deployment of innovative technologies, from modifying airframes to changing aviation fuel, utilizing alternative propulsion systems, adopting circular manufacturing, and improving air traffic management.
ABSTRACT
More than 60 years have passed since the introduction of jet aircraft to civil aviation, and technological innovations have made aircraft much quieter. Nevertheless, people still complain that they experience severe suffering from aircraft noise. The changes in lifestyles, values concerning the sound environment, and aircraft operating conditions including the air traffic control system, over time, may have influenced the differences in annoyance responses. This paper overviews and considers the changes over time in the aircraft sound exposure level around the airport and the community annoyance caused by aircraft noise. Then it discusses the issue of recent noise complaints associated with the introduction of new air traffic management systems and flight routes as well as views the impact of coronavirus pandemic over the last two years or longer. Finally, it gives a minor consideration to how we should deal with these changes in the annoyance response. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.
ABSTRACT
The Electronic Navigation Research Institute and Korea Aerospace University have proposed an initial Free Route Airspace (FRA) concept for the Fukuoka and Incheon Flight Information Regions (FIR) to improve air traffic flows and air traffic management in northeast Asia. We are now working to elaborate the concept, quantify benefits, and identify implementation issues. This paper examines two air traffic flows in Fukuoka FIR: (1) Japanese domestic flights between the highest traffic city pairs, and (2) overflight traffic between Korea and North America across radar-controlled airspace. From an analysis of operations based on flight plan and radar data for 2019, prior to the COVID-19 pandemic, FRA design and implementation issues are considered. Our analysis and findings are expected to contribute to the planning of FRA implementation in Northeast Asia.
ABSTRACT
Air traffic, despite the recent dip due to Covid, is expected to grow 30-40% year on year. With the potential inclusion of UAVs (Unmanned Aerial Vehicles) into controlled airspace over the next decade, it is anticipated that the congestion levels in airspace will increase 10 fold. This paper presents an AI-based approach to air traffic control, with the aim of alleviating the load and improving the efficiency of human agents (air traffic controllers). One of the primary goals of air traffic control is to safely navigate an aircraft through controlled airspace using real-time control actions - such as changes to speed, heading (direction of travel) and altitude of an aircraft. The safety critical nature of this environment calls for precise explanations (why take an action) and counterfactual (why not take an action) explanations, real-time responsiveness, the ability to present succinct actions to a human agent, while simultaneously optimizing for air traffic delays, fuel burn rates, and weather conditions. This paper presents algorithms and a system architecture for anticipating separation losses (conflicts in airspace) and a lattice-based search space exploration AI planner to recommend actions to avoid such conflicts. The key contributions of the paper include: (i) fast detection (prediction) of conflicts in a controlled airspace, and (ii) fast lattice space exploration based AI solver to produce a set of feasible resolutions for the detected conflicts. Additionally, this paper discusses how to weight the different resolutions and how future work on optimisation techniques could improve the efficiency of the algorithm and address various known limitations of the current approach from both technical and human-agent perspective. The evaluations are conducted against an air traffic simulator, Narsim, showing the ability to avoid separation losses, while minimizing the number of actions even at 3 x normal capacity. © ATM 2021. All rights reserved.
ABSTRACT
BEL and AAI collaborate on Air Traffic Management Systems In a major boost to its own diversification drive into non-defence and the Government's 'Make in India' programme, Bharat Electronics Limited (BEL) and Airports Authority of India (AAI), under it's R&D initiative, at Wings India 2022, entered into an agreement for the joint, indigenous development of systems for air traffic management and surface movement of aircraft at airports in the country which were hitherto being imported. Under this Agreement, BEL and AAI will jointly develop Civil Air Traffic Management System (ATMS) with Advanced-Surface Movement Guidance and Control System (ASMGCS), a complex ground surveillance system that manages air traffic at airports and in Indian Civil Airspace for safe operation of flights from take-off to landing. The aim of ATMS with ASMGCS is to provide the air traffic controller with the complete air traffic picture of the coverage area while interacting with Primary/ Secondary Radar, Automatic Dependent Surveillance-Broadcast (ADS-B), Multi-lateration System (MLATs), and navigational equipment such as GPS, Instrument Landing System (ILS) and Doppler Very High Frequency Omni Range (DVOR). Boeing: India to lead South Asia air traffic growth Boeing shared projections for South Asia's commercial aviation sector over the next 20 years, with the region leading the world in yearly passenger traffic growth.
ABSTRACT
The increasing attention of opinion towards climate change has prompted public authorities to provide plans for the containment of emissions to reduce the environmental impact of human activities. The transport sector is one of the main ones responsible for greenhouse emissions and is under investigation to counter its burdens. Therefore, it is essential to identify a strategy that allows for reducing the environmental impact produced by aircraft on the landing and take-off cycle and its operating costs. In this study, four different taxiing strategies are implemented in an existing Italian airport. The results show advantageous scenarios through single-engine taxiing, reduced taxi time through improved surface traffic management, and onboard systems. On the other hand, operating towing solutions with internal combustion cause excessive production of pollutants, especially HC, CO, NOX, and particulate matter. Finally, towing with an electrically powered external vehicle provides good results for pollutants and the maximum reduction in fuel consumption, but it implies externalities on taxiing time. Compared to the current conditions, the best solutions ensure significant reductions in pollutants throughout the landing and take-off cycle (−3.2% for NOx and −44.2% for HC) and economic savings (−13.4% of fuel consumption).
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The articles in this special section focus on current applications and innovations of artificial intelligence and machine learning in aerospace. Artificial intelligence (AI) and machine learning (ML) play an increasingly important role in aerospace applications and serve various military, commercial aviation, and space exploration sectors to ensure safety, dependability, and customer loyalty. AI/ML contributes to provide various automated systems used in aviation, such as fuel efficiency, smart maintenance, smart air traffic management, pilot training, passenger identification, threat identification, remote sensing, and fully autonomous aerial vehicles among other systems. AI/ML is concerned with algorithms and techniques that allow systems to “learn” and “reason” based on algorithms and techniques employing computational and statistical methods. It can significantly enhance speed, efficiency, workload, and safety to enable the integrating of more complex technologies, such as autonomous visionbased navigation and data ecosystems. Recently advanced data analytics provided the aviation industry a way to respond to COVID and advise airlines on when to swap aircraft for bigger or smaller planes and how the global health restrictions may change flight schedules. While there are many other innovative use cases of AI/ML in aviation and aerospace, the overarching conclusion is that the implementation must be driven by safety.
ABSTRACT
The Single European Sky Air Traffic Management Research (SESAR) program aims at modernizing and harmonizing the European airspace, which currently has a strongly fragmented character. Besides turbulence and convection, in-flight icing is part of SESAR and can be seen as one of the most important meteorological phenomena, which may lead to hazardous flight conditions for aircraft. In this study, several methods with varying complexities are analyzed for combining three individual in-flight icing forecasts based on numerical weather prediction models from Deutscher Wetterdienst, Météo-France, and Met Office. The optimal method will then be used to operate one single harmonized in-flight icing forecast over Europe. As verification data, pilot reports (PIREPs) are used, which provide information about hazardous weather and are currently the only direct regular measure of in-flight icing events available. In order to assess the individual icing forecasts and the resulting combinations, the probability of detection skill score is calculated based on multicategory contingency tables for the forecast icing intensities. The scores are merged into a single skill score to give an overview of the quality of the icing forecast and enable comparison of the different model combination approaches. The concluding results show that the most complex combination approach, which uses iteratively optimized weighting factors for each model, provides the best forecast quality according to the PIREPs. The combination of the three icing forecasts results in a harmonized icing forecast that exceeds the skill of each individual icing forecast, thus providing an improvement to in-flight icing forecasts over Europe.
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The amount of air traffic is rapidly recovering from the COVID-19 pandemic and beginning to rise above previous levels. As a result, the VHF band is expected to become saturated in the near future, harmfully affecting air traffic management. As one solution for the increased need for aeronautical connectivity, the terrestrial LDACS data link has been designed and is in the process of ICAO standardization. However, LDACS development has been primarily focused on data communication and digital voice protocols have not been fully defined yet. This paper presents the proposed LDACS digital voice architecture developed in the framework of the Single European Sky ATM Research (SESAR) program. The LDACS digital voice air-ground communication protocol is validated and evaluated in computer simulations. It is shown that the SESAR-specified functional and performance requirements are satisfied. © 2022 IEEE.
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Purpose>The purpose of this paper is to create and analyze the effectiveness of a new runway system, which is totally created for the future free route operations.Design/methodology/approach>This paper researches and analyses the new generated runway concept with the fast time simulation method. Fuel consumption and environmental effect of the new runway system are calculated based on simulation results.Findings>According to different traffic density analyses the Omnidirectional Runway with Infinite Heading (ORIH) reduced fuel consumption and CO2 emissions up to 46.97%. Also the total emissions of the ORIH concept, for the hydro carbon (HC), carbon monoxide (CO) and nitrogen oxides (NOx) pollutants were lower than the total emissions with the conventional runway up to 83.13, 74.36 and 51.49%, respectively.Practical implications>Free route airspaces bring many advantages to air traffic management and airline operations. Direct routes become available from airport to airport thanks to free route airspace concept. However, conventional single runway structure does not allow aircraft operations for every direction. The landing and take-off operations of a conventional airport with a single runway must be executed with only two heading direction. This limitation brings a bottleneck direct approach and departure route usage as convenient with free route airspace concept. This paper suggests and analyzes the omnidirectional runway with infinite heading (ORIH) as a solution for free route airspace.Originality/value>This paper suggests a new and futuristic runway design and operation for the free route operations. This paper has its originality from the suggested and newly created runway system.
ABSTRACT
This paper proposes a scientific and systematic method for designing future air traffic management systems by integrating data science, theoretical modeling, and simulation evaluation. Also, it presents a part of a case study focusing on the data-driven and theoretical modelings of arriving traffic flow in airports. A stochastic data analysis was conducted using actual radar tracks and flight plans before the impacts of COVID-19, where the queuing model parameters were estimated based on the conducted analysis. The proposed data-driven modeling approaches contribute to the analysis of the bottlenecks in air traffic and to their solutions. Overall, we believe that the outcomes of this study provide insights on future operational strategies and system designs, which can realize more efficient air traffic management systems. © 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.
ABSTRACT
As air traffic increases, certain airspaces are becoming more crowded than the controllers can handle. Despite the current plunge due to Covid-19, demand for air travel is expected to recover and increase in the future, so efficient traffic handling will be essential. Ground delay programs (GDPs) have been introduced to balance demand and capacity, and excessive demand not managed by GDP is handled by speed control and vectoring in the terminal area of the arrival airport. In order to decrease air traffic controller (ATC) workload and provide more orderly flow, the point merge system (PMS) was introduced at Haneda Airport in 2019. Compared to conventional vectoring, PMS can absorb more delay in a more predictive manner because the path stretching is done following pre-defined arc-shaped paths. When the delay to be absorbed cannot be managed within the PMS area only, additional flow control is necessary in the airspace prior to PMS. This research evaluates the potential effects of time management for cruising aircraft and PMS on the airspace prior to PMS area. The metrics chosen for analysis are 1) distance flown, 2) number of headings (to describe vectoring) and 3) fuel consumption. Results show that the introduction of time management for cruising aircraft and PMS leads to a decrease in distance flown, number of headings and fuel consumption in the airspace prior to PMS, which leads to efficient flights and less ATC workload. © 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.
ABSTRACT
After COVID-19, a full recovery compared to the 2019 situation with a subsequent growth of global air traffic is expected for the next three to six years [1]. Regarding carbon dioxide emissions, Coronavirus lockdown helped the environment to bounce back, but this will be a temporary situation. It is important to continue investigating additional mitigation measurements to achieve long-term environmental benefits, especially after the recovery. At that point, the question of how to reduce aviation's impact on the climate change will certainly arise again, and will re-gain its importance for the world-wide community. Since no fundamental breakthroughs in CO reduction in aviation are expected in the near future, research should focus on several measures to sustainably reduce the environmental impact of aviation. The air traffic management can contribute to an overall reduction of emissions of greenhouse gases by optimizing traffic flows not only towards maximum airspace capacity and maximum efficiency, but also increasingly towards minimum environmental impact. A set of concept elements that were investigated in the frame of the European-Chinese project Greener Air Traffic Operations (GreAT) can already constitute simple and suitable means towards a greener air traffic management. One of these concept elements is the 'Lowest Impact of Deviation' principle: Whenever two flights need to deviate from their most fuel-efficient route, speed or altitude due to de-conflicting, this deviation should be done by the flight with the lowest fuel consumption, and consequently, with the lowest amount of emissions produced with this maneuver. This principle is currently neither reflected in air traffic control regulations, nor in common practices. In the frame of the work presented in this paper, this principle has been further investigated and analyzed with a fast-time simulation, which models a free route airspace environment under ideal conditions. The flights are generated according to a configurable traffic density. De-conflicting is done automatically either by following the standard right of way rules, which also often serve as a guiding principle for air traffic controllers;or by following the 'Lowest Impact of Deviation' principle. Based on EUROCONTROL's Base of Aircraft Data (BADA), the simulation estimates the fuel consumption for each flight as well as for the whole simulation, and consequently also the CO emissions, as a function of traffic density.This paper gives basic information about the principle itself, which is then further tailored down and applied to a free route airspace environment for en-route traffic. It briefly describes the used fast time simulation and illustrates the obtained results. This paper quantifies the theoretical benefit that can be achieved by applying the mentioned principle in the described way. When knowing the traffic density of real air traffic control sectors, the results can easily and directly be transferred to them. © 2021 IEEE.
ABSTRACT
In recent years, prior to COVID-19, capacity shortfalls in airspace and airports inevitably caused an increase in aircraft delays. Therefore, when it returns to normal conditions, the airspace will exhibit the same capacity limits, even under normal weather conditions. To ensure that air traffic remains safe, reliable, and efficient in adverse weather conditions, planning and coordination activities through a Collaborative Decision Making process are required to deliver the most effective Air Traffic Flow and Capacity Management services to Air Traffic Control and Aircraft Operators. Nowadays, this task is based on air traffic controllers' experience and historical data. That means that the Flow Manager Positions and the Network Manager operators have to process a huge amount of information, and the detection of future overloads is based on past experiences. Moreover, due to the inherent uncertainty of weather information, a reliable decision support framework is required to handle these situations as efficiently as possible. We propose a Deep Learning model able to extract the relationship between both the historical data and the implemented actions, accurately identifying the intervals of time that must be regulated. The proposed model achieves an accuracy between 80% and 90% across six traffic volumes belonging to both the MUAC and REIMS regions, a recall higher than 85%, and an F1-score higher than 0.8 in all the cases. Furthermore, the confidence-level analysis shows a really high activation when making a prediction. Finally, the SHapley Additive exPlanations method is applied to identify the most relevant input features. © 2021 IEEE.