Airspace Users Research Articles

Drones in the Airspace of the Republic of Poland - steps to Safe Flights of UAS Over Poland

The emergence of a new user of the airspace of the Republic of Poland, Unmanned Aerial Vehicles - drones, as part of regular flights, implies the need not only to provide an appropriate legal framework, but also to rebuild the entire air traffic management ecosystem for the integration of manned and unmanned aviation. The first attempts to include UAS in the integrated air traffic system in the Warsaw FIR were based on the electronic/digital planning, coordination and management of UAS flights called Pansa UTM (Polish Air Navigation Services Agency Unmanned Traffic Management). This system is an innovative solution used by the Polish Air Navigation Services Agency (PANSA) and potential drone users. The operation and development of the Pansa UTM system generates the need for air traffic management entities to take specific actions by planning and establishing regulations, rules, safety criteria and flight conditions for the new airspace user. This requires involvement in projects, pilotages and technology demonstrators aimed at creating adequate tools for the safe implementation of UAS flights in Poland. These activities, both already implemented and at the planning stage, aim to achieve a state in which it will be possible to talk about integrated and safe controlled air traffic over Poland. Due to the dynamic nature of the subject matter, this study is only an attempt to present a number of projects undertaken by a wide range of aviation-related entities that may allow achieving an acceptable level of air traffic management over the territory of the Republic of Poland.

Predicted Trajectory Accuracy Requirements to Reduce Aviation Impact of Space Launch Operations

Thousands of aircraft flight plans are affected by space launch and reentry operations each year, increasing the distances flown, causing flight delays, and increasing air traffic controllers workload. Due to regulations and procedures, predefined Aircraft Hazard Areas (AHAs) are used to protect aircraft from the risks of space launch and reentry debris, thus constraining the available airspace for other airspace users. While disruptive, there is no less impactful approach at present that adequately protects the flying public. In this paper, we explore the application of trajectory-based operations to evaluate the impact of an AHA on commercial aircraft, with the aim of reducing the number of flights that must be rerouted or rescheduled. This approach relies on precise trajectory predictions to the AHA boundary to determine which flights are expected to clear the AHA before its activation or remain clear of the AHA until after its deactivation. This paper derives the required predicted trajectory accuracy for air traffic automation systems to effectively predict flights impacted by an AHA. The required accuracy is derived based on a model for managing flights relative to the AHA using speed changes alone (as opposed to reroutes or holding) in the context of operational uncertainties like departure time delays and flight characteristics. Additionally, we derived a model to relate scheduling buffers to the AHA activation time, delivery accuracy at the AHA boundary, and AHA violation probability.

Prediction of ATFM impact for individual flights: A machine learning approach

Air Traffic Flow Management regulations consist of issuing pre-departure ground delays to deal with demand-capacity imbalances in the airspace, smoothing out the demand at congested infrastructures. This results in discrepancies between the initially planned and executed (regulated) flights, negatively impacting operational efficiency and economic performance. To mitigate downstream effects, airspace users need to assess the severity and impact of these regulations when defining their operational plan. It is crucial to anticipate these potential network issues and plan for mitigation measures during the pre-tactical phase (the day before operations (D−1)). This article presents four independent machine learning models to estimate the impact of Air Traffic Flow Management regulations for individual flights during this pre-tactical phase. An integrated view is proposed to combine the results of the different models, displaying the appropriate level of information. Two models are designed to provide information about the probability of a given flight being affected by Air Traffic Flow Management regulations and the reason for this regulation (airport or airspace congestion). These models have reported accuracy levels between 82% and 87%. The remaining models estimate the impact of the delay (amount of Air Traffic Flow Management delay issued to the flight if regulated), with a Mean Absolute Error of 9.5 min when predicting this amount of delay. The SHapely Additive exPlanations analysis is used to identify the most important factors for detecting Air Traffic Flow Management regulations for individual flights during the pre-tactical phase from a data-driven perspective. An integrated view is proposed to create a system which combines the results of the four different models, displaying the appropriate level of information and avoiding an overload of information. Finally, the results are compared against models considering only static or post-operational data.

ATM strategies for, and impacts of, space launches

There is a rapid growth of national space launch ambitions and capabilities, e.g. delivering satellites into low-earth and sun-synchronous orbits. With vertical and horizontal delivery methods, and numerous locations under consideration in several continents, the industry has faced early challenges, such as failed launches and licencing timescales. This paper explores the increasing intersection between aviation and air traffic management (ATM) with higher airspace operations (HAOs). It introduces the background and principles of space launches, before addressing the particular impacts on aviation and ATM. The strategic challenges of planning launch windows to align both with orbiting asset congestion and ATM demands, plus promulgating such information to airspace users, is discussed. In the tactical phase, the consequences of impacts on airspace users (such as the re-routing of flights) and on air navigation service providers (such as the demands of coordinating airspace closures in the context of considerable re-entry/splashdown uncertainty) are discussed. A key contribution we make in this paper is the first aircraft-specific, fuel and operating cost analysis of HAO impacts, and the first such European cost assessment, with basic impact geometries. We also propose improved aircraft-specific impact models, which include passenger-centric costs.

Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools.

The exploration and practice of low-altitude airspace flight service and traffic management in China

Due to the inherent nature of being highly digitalized, networked and intelligent, Unmanned Aerial System (UAS) operations pose a huge challenge to traditional aviation regulation and technical systems. How to keep safe, efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world. This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China. New operational characteristics and associated challenges due to diverse low-altitude users are analyzed, including operation concepts, UAS traffic management, technological test and verification, and standards. Drawing light on the practices in Europe and the United States, this paper summarizes China's practices and progress in low-altitude operations management, and analyzes future technological development needs and trends, as well as feasible implementation pathways and measures based on actual needs.

АНАЛИЗ ПРИНЦИПОВ ОБРАБОТКИ НАВИГАЦИОННОЙ ИНФОРМАЦИИ И ПОСТРОЕНИЯ РАБОЧЕЙ ЗОНЫ МНОГОПОЗИЦИОННОЙ СИСТЕМЫ НАБЛЮДЕНИЯ

The use of modern surveillance equipment in accordance with ICAO recommendations is aimed at improving the efficiency of air transportation, increasing the capacity of airspace and airfields, and improving the safety of flights and ground operations. The introduction in the Russian Federation of a multi-position surveillance system (MPSS) based on automatic dependent surveillance of the broadcast type (ADS-B) is intended to facilitate the implementation of state and regional aviation development programs in terms of creating conditions for improving flight safety, availability and quality of air navigation services for airspace users. Therefore, the analysis of the principles of constructing a working area and processing navigation information in a multi-position surveillance system for improving the accuracy of aircraft position-fixing is an urgent research task. An approach to improving the efficiency of the MPSS operation when processing information in conditions of noise and interference has been considered. Analysis of the results of modeling the proposed algorithm based on the discrete Kalman filter shows high accuracy of estimating the planned coordinates of an aircraft. Specialized software has been developed to automate the process of calculating and constructing working areas of a multi-position surveillance system.

Untangling Complexity in ASEAN Air Traffic Management through Time-Varying Queuing Models

Free route airspace allows airspace users to freely plan a route in en-route airspaces within certain restrictions. It is anticipated to offer the benefit of fuel saving and operational flexibility. Regarding its efficient implementation into the ASEAN airspace, the key challenge would be reducing hotspots with clusters of potential conflicts. This paper designed a time-varying queuing network model, which contributed to untangle trajectory complexity in the most congested hotspot area. A series of fast-time simulation experiments were conducted to identify hotspots in en-route airspace in Singapore FIR. The application of departure time control using time-varying queuing networks successfully reduced up to 45% of potential conflicts. This was achieved within an average delay of 30 min by controlling time of less than 60% of candidate flights. The original contribution of this paper is to develop a novel modeling and simulation framework for composing ideal air traffic patterns. Lastly, we discussed the extension of this study toward a generalized application of the proposed approach in future air traffic management.

SINGLE CROSS-BORDER FREE ROUTE AIRSPACE CONCEPT IN THE ASEAN REGION

Single Cross-Border Free Route Airspace (FRA) represents the ultimate long-term idealistic routing rules allowing airspace users freely plan a route in en-route airspace, which is not immediately possible from the administrative and technological point of view. In Single Cross-Border FRA, the whole ASEAN airspace is treated as a single-large FRA where all 12 Flight Information Regions (FIRs) implement FRA, with direct routing between the entry (horizontal entry into FIR or end of Standard Instrument Departure Route, SID) and the exit (horizontal exit from FIR or beginning of Standard Arrival Route, STAR). Single Cross-Border FRA is the most fuel-efficient reference that we could ever reach in the future.All otherhypothetical routing rules fall between the Single Cross-Border FRA and those hypo-thetical routing rules in terms of fuel efficiency. In this paper, we described the potential in fuel efficiency (or benefit achievement scaling) with refer-ence to this ultimate basic fuel requirement and scaled the excess fuel with different routings. We evaluated the fuel burn with a fast-time simulation (FTS) in 12 different scenarios (3 traffic volume levels × 4 routing scenarios). As for the traffic volume scenarios, we simulated 1-day of commercial ASEAN flight schedule data of 3 traffic volume levels (100%, 150%, and 200% of pre-COVID19 traffic volume). As for four routing scenarios with increasing levels of directness: conventional Air Traffic Service route network (ATS) (R1), hypothetical Several-Small FRA route network (R2), Single Cross-Border FRA route network with Danger Areas (R3) and Single Cross-Border FRA network without Danger Areas (R4). Results showed that flight time, fuel burn, and distance travelled were less in R2, R3, and R4 than in R1. There was an average difference of 2.2% in flight time, 1.9% in fuel burn, and 2.2% in distance travelled from R1 to R4.

The effectiveness of supervised clustering for characterising flight diversions due to weather

Flight diversions due to adverse weather have a significant negative impact on airspace users, airports, and the environment. Previous work proposed a tree-based model that learned which flights are more likely to be diverted due to adverse weather conditions at the destination airport from historical traffic and weather data. This model was trained using confident learning to exclude from training unpredictable flight diversions caused by reasons other than weather, such as medical emergencies or technical problems. Aggregated performance metrics computed on the test set demonstrated high precision and moderate recall, and inspection of the Shapley values unravelled the most important features. Initial human observations made during the live trial, however, have raised a potential connection between the quality of predictions and the specific weather conditions that lead to flight diversions. This study aims to provide empirical evidence to support this hypothesis. To accomplish this, a data-driven methodology should be used to characterise the main conditions that cause flight diversions. Concurrently, the effectiveness of the model in each of these conditions should be evaluated. The dataset under consideration, however, presents a challenge because it contains a plethora of high-cardinality categorical features (e.g., destination airport and aircraft operator) as well as skewed numerical features with a wide range of scales (e.g., visibility and wind gust). This scenario significantly increases the difficulty of characterising the main conditions that cause flight diversions and conducting subsequent analyses even with established methods like factor analysis of mixed data. To address this challenge, this paper proposes to utilise supervised clustering, a method that combines state-of-the-art feature attribution, dimensionality reduction, and clustering algorithms to identify the most representative decisions of a model. Following that, the distinctive characteristics of the various decisions (e.g., extreme obscuration at the airport), each associated with a cluster, are determined by examining them. Finally, conventional classification and uncertainty metrics are computed for each cluster to assess the performance of the model. This paper shows that supervised clustering is effective in characterising flight diversions due to weather, and provides valuable insights into the performance of the model in different conditions, highlighting situations where predictions require careful consideration.

Opening Up for Autonomy

Digital flight offers a way for different types of airspace users to self-separate within a single integrated airspace

Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts

Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings.

Leveraging demand-capacity balancing to reduce air traffic emissions and improve overall network performance

Improvements in European air traffic management (ATM) constitute one of the key levers to reduce aviation emissions in the future to reach ambitious goals. In this paper, we evaluate the benefits of improved network-centric ATM with coordinated optimisation of capacity, delays, re-routings and emissions vs the present fragmented ATM which has generated high delays and extensive re-routing due to sub-optimal capacity provision. In addition, we analyse the impact on flight efficiency of a novel trajectory-independent airport-pair charging scheme, which makes shortest routes the cheapest for Airspace Users (AUs), which is not always the case with the current charging scheme in Europe. We test and evaluate proposed changes in ATM using a case study based on real data covering 3,000–4,000 flights in large parts of Western European airspace. We find that network-centric ATM can reduce variable costs borne by AUs by 21% and in particular re-routing cost (and associated emissions) by almost 64%. Furthermore, airport-pair charging can save almost 11% of costs for AUs and up to 320,000 tons of CO2 emissions — if accompanied by adequate capacity changes that reflect the shift in demand towards shorter trajectories. We conclude that various stakeholder initiatives to reduce the fragmentation in European ATM and to move towards a more network-centric approach have merits and should be pursued by the policy makers.

A column generation-based framework for ATFM incorporating a user-driven prioritization process

A column generation-based framework is proposed for air traffic flow management (ATFM) incorporating a user-driven prioritization process (UDPP). Airspace Users' (AUs') preferences and priorities can be explicitly reflected in the framework. AUs are allowed to propose their preferred routes as initial options and AUs' delay cost structure can be customized and not revealed to other AUs. Major UDPP features, including Fleet Delay Reordering, Selective Flight Protection, and Margins, are fully incorporated in the proposed framework. In the master problem, the model selects 4D-routes for flights subject to sector capacity constraints. In the subproblems, new 4D-routes are generated. Numerical experiments demonstrate that the proposed framework can efficiently solve the ATFM problem considering UDPP features with often zero or small optimality gaps. The effectiveness of UDPP is also verified using case studies. Parallelism of subproblems considerably further reduces the runtime and the large-scale scenario computational time can be reduced to around 30 min.

Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

Integration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. Simulated probable traffic scenarios are superimposed on a desired Uncrewed Aircraft operation and then sampled using Monte Carlo methods. The results are used to estimate the operation-specific collision probability with known uncertainty in the output. The methodology is demonstrated for an example medical logistics operation in the United Kingdom, and a Target Level of Safety is used as a benchmark to decide whether the operation should be permitted.

Evaluation of Extended Projected Profiles impact on vertical trajectory prediction

The future Air Traffic Management System (ATM) is based on the concept of Trajectory Based Operations (TBO), in which all stakeholders have access to a consistent and synchronised view of flight trajectories. In the TBO framework, the trajectory prediction plays a key role. Inaccuracy of current trajectory predictors caused by the lack of a common understanding of stakeholders’ intents can be reduced by the sharing of information by airspace users. A step towards a shared information network is the Extended Projected Profile (EPP), downlinked from airborne to ground via automatic dependent surveillance contract (ADS-C). In summary, it contains the flight intent from the Flight Management System (FMS) and other information. This paper explores the EPP messages and the possible performance enhancement they introduce in the predictions of the top of descent (TOD) and the descent vertical profile. 1

Cross-Border Capacity Planning in Air Traffic Management Under Uncertainty

In European air traffic management (ATM), it is an important decision how much capacity to provide for each airspace, and it has to be made weeks or even months in advance of the day of operation. Given the uncertainty in demand that may materialize until then along with variability in capacity provision (e.g., due to weather), Airspace Users could face high costs of displacements (i.e., delays and reroutings) if capacity is not provided where and when needed. We propose a new capacity sharing scheme in which some proportion of overall capacities can be flexibly deployed in any of the airspaces of the same alliance (at an increased unit cost). This allows us to hedge against the risk of capacity underprovision. Given this scheme, we seek to determine the optimum budget for capacities provided both locally and in cross-border sharing that results in the lowest expected network costs (i.e., capacity and displacement costs). To determine optimum capacity levels, we need to solve a two-stage newsvendor problem: We first decide on capacities to be provided for each airspace, and after uncertain demand and capacity provision disruptions have materialized, we need to decide on the routings of flights (including delays) as well as the sector opening scheme of each airspace to minimize costs. We propose a simulation optimization approach for searching the most cost-efficient capacity levels (in the first stage), and a heuristic to solve the routing and sector opening problem (in the second stage), which is [Formula: see text]-hard. We test our approach in a large-sized simulation study based on real data covering around 3,000 flights across Western European airspace. We find that our stochastic approach significantly reduces network costs against a deterministic benchmark while using less computational resources. Experiments on different setups for capacity sharing show that total variable costs can be reduced by more than 8% if capacity is shared across borders: even though we require that no airspace can operate lower capacities under capacity sharing than without (this is to avoid substitution of expensive air traffic controllers with those in countries with a lower wage level). We also find that the use of different technology providers is a major obstacle to reap the benefits from capacity sharing and that sharing capacities across airspaces of the same country may instead be preferred. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Horizon 2020 Framework Programme [893380]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1210 .

A corpus-based study on aviation English from the perspective of systemic functional linguistics

Aviation English (AE) is a distinct register of English used by pilots and air traffic controllers. As it is one of the contributing factors to aviation safety, ICAO and its Member States’ aviation authorities require the airspace users to have the proficiency in using AE effectively. In recent years, the training and testing have gained more attention, but little work has been done to describe its linguistic features. The study set out to describe AE from the perspective of systemic functional linguistics with an aim to illustrate its linguistic features as compared to conversational English (CE). To achieve this goal, the corpora of CE and AE communications between native English speakers from the United States were respectively constructed and then scrutinized to demonstrate that AE has a significant difference from CE in functional-semantic aspects. The findings of this study reveal how distinct AE with CE in terms of speech functions. Some pedagogical implications were then proposed for enhancing AE training to cultivate the students’ competence in semantics and interaction.

Intelligent Spectrum Management for Future Aeronautical Communications

The emergence of new aerial vehicles into the airspace and the continued growth of aviation operations will result in an increased demand for voice and data communications throughout the National Airspace System (NAS). However, recent studies have shown that the continued use of existing aviation spectrum allocations will not meet the anticipated needs of future aerospace applications; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating the use of modern advancements in wireless communications, artificial intelligence (AI), machine learning (ML), and other advanced concepts in order to implement a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace. This technical investigation considers various airspace communications configurations, including air-ground and air-air communications, and can be applied to the existing air traffic control (ATC) system, as well as to future applications, such as the emerging Advanced Air Mobility (AAM) initiative. To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development, which will continue to evolve in support of new and advanced airspace applications. It is anticipated that this proposed spectrum management concept will allow for increased spectrum utilization efficiency and enhanced airspace capacity, which will better serve the needs of future NAS applications.

Live Trials of Dynamic Geo-Fencing for the Tactical Avoidance of Hazard Areas

The project City-ATM, launched by the German Aerospace Center DLR in 2018, aims to integrate new airspace users like unmanned aerial vehicles into uncontrolled airspace. An air traffic management and traffic flow control concept were developed. The phase 1 demonstration in 2019 showed a bridge inspection featuring, amongst other elements, the flight of several drones in a limited area, beyond visual line of sight operation, strategic flight planning considering strategic geo-fences and tactical conflict detection. To enrich the base concept from phase 1 with more U-Space services, the concept has been extended for phase 2 by dynamic geo-fencing for the tactical avoidance of danger spots. This paper describes how dynamic geo-fences can be modeled around a hazard area, how the dynamic opening and closure of these fences can be distributed to relevant systems, and how the fences are considered by on-ground and already in air vehicles. The complete concept has been implemented and tested at the National Experimental Test Center in Cochstedt. Finally, the execution of successful flight trials is described.