Airspace Configuration Research Articles

Adaptive Multi-Agent Reinforcement Learning Solver for Tactical Conflict Resolution in Diverse Urban Airspace Configurations

Adaptive Multi-Agent Reinforcement Learning Solver for Tactical Conflict Resolution in Diverse Urban Airspace Configurations

Entry Count vs Occupancy Count to assess sector capacity with Fast Time Simulation

An air traffic controller should manage aircraft movements per hour to ensure the safety and efficiency of the traffic system. This study proposed an innovative methodology to estimate the air traffic controllers’ workload through sector occupancy. Starting with a model for the workload of sector controllers designed by Eurocontrol for tower controllers, the authors defined a taskload model from real audio that occurred in an examined Terminal Control Area (TMA). Moreover, trombones have been implemented to sequence arrival flows in the simulated TMA. Therefore, an airside model has been reproduced using Fast Time Simulation software, and its capacity has been calculated in terms of occupancy due to approaching movements. The results permit to evaluate the impact of infrastructural and operative modifications on the sector capacity in terms of hourly entry count and occupancy. Total and maximum occupancy values have been deepened to identify the best predictive factor of controllers’ workload and to manage the airspace configuration.

Conceptual Framework for Dynamic Optimal Airspace Configuration for Urban Air Mobility

In this work, a framework for optimizing the configuration of service areas in airspace into disparate partitions is demonstrated in the context of urban air mobility (UAM) operations. This framework is applied to a conceptual UAM airspace configuration, where a free-flight-based routing service and a corridor-based routing service are dynamically allocated to control different portions of the airspace over time, based on traffic demand. This allocation seeks to determine the least amount of structured coordination (in terms of active flight corridors) needed to safely meet traffic demand. This framework integrates several modeling components, including a novel spatiotemporal graph theoretic UAM traffic model capable of optimizing vehicle trajectories while maintaining multiple flight constraints. Airspace complexity and trajectory efficiency metrics are both implemented to quantify the overall safety and cumulative cost of routing a set of missions according to a given airspace configuration. Finally, spatial airspace partitions are managed using a support vector machine-based algorithm. Metrics are then applied to optimize the airspace configurations, according to desired objectives. Simulated results show that this framework can produce airspace configurations that ensure safety, while providing trajectory efficiency more effectively than purely uniform free-flight or corridor-based flight. This is demonstrated for both low- and high-density traffic scenarios.

An Integrated Approach for On-Demand Dynamic Capacity Management Service in U-Space

This article presents an integrated approach for on-demand dynamic capacity management (DCM) service to be offered in U-space. The approach involves three main threads, including flight planning (demand), airspace configuration (capacity), and demand-capacity balancing (DCB). The flight planning thread produces unmanned aerial systems (UAS) trajectories for each flight that together reflect the estimated traffic demand. The airspace configuration thread defines the fundamental airspace structure and proposes dynamic adjustment schemes that determine the capacity distribution. It also enables the flight planning to reschedule alternative trajectory options to route away from possible congested areas. The last DCB thread takes the previous inputs and then computes for the optimal slot allocation and trajectory selection, as well as the optimal airspace configuration. Simulation case studies have been performed through mimicking an envisioned U-space operating scenario. Results suggest that the integrated approach can achieve the best outcome in almost all the key performance areas than any other cases, where only partial functions are realized.

A Trajectory Evaluation Platform for Urban Air Mobility (UAM)

Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictions.

Flexibility in strategic flight planning

A deterministic model that indicates flexibility of flights at the strategic level (up to 6 months ahead) taking into account changing airspace configurations and capacity is formulated. Flexibility is quantified by means of time windows (TWs). Flights complying with TWs guarantee that they will not impact negatively any other flight. Three variants of the model and three types of TWs are tested on a large-size data instance (the European network for an entire day of traffic). The model output specifies the constrained flights (i.e., with TWs shorter than the maximum size allowed for their definition), the constraining sector-hours and provides a list of saturated sector-hours. The meaning of each of the results is explored, across the three TW model variants, as well as the capability of the model variants to assure that capacity limits will not be exceeded. The criticality index, a measure of the sector-hour saturation, is introduced. This index can be used to identify areas for potential improvements. Sharing the information obtained from the TW model results at a strategic level can help both airlines and air navigation service providers (ANSPs) to improve the network status: airlines can decide to re-route heavily constrained flights (e.g., with one minute wide TWs), whereas ANSPs could decide to re-organise the capacity provision of the saturated airspace portions. The TW model can be re-run with the proposed changes, with the goal to assess the impact on both the individual stakeholders and the network. Thus, the model offers the measure of flight flexibility, and can be used as a tool to assess the impact of changes, helping in decision-making processes of airlines and ANSPs.

Flight flexibility in strategic traffic planning: visualisation and mitigation use case

The concept of strategic traffic planning that takes into account changing airspace configurations, their capacity, and allows the quantification of flight flexibility is presented in this paper: the visualization of the results and an example of possible use. The concept is implemented through two deterministic optimization models. Here, we focus on the output of the models, which identifies the departure times, trajectories, flight flexibility and the list of saturated sector-hours throughout the day, based on the configurations used during the day. In order to make the output understandable to various stakeholders, we use a visualization tool and a set of performance indicators. The information on the saturated sectors, and their impact on flexibility (criticality index) is taken as an input in the example of mitigation action application by Air Navigation Service Providers, aimed at improving the situation. A mitigation strategy of increasing capacity of saturated airspace is implemented, and results show that the improvements in flexibility can be achieved.

Towards Sustainability in Air Traffic Management

The International Civil Aviation Organization is estimated that the number of domestic and international passengers will be expected to reach six billion by 2030. This exponential growth in air transport has resulted in a wide range of adverse effects such as environmental impacts. The purpose of this research is to develop new air traffic management, and operator (pilots, air traffic controllers) load measuring systems in order to save fuel, and flight time, thereby reducing environmental impact, carbon emission, greenhouse gas generation, noise pollution, and operating cost. This paper deals with: (i) dynamic sectorization and airspace configuration (ii) introduction of the highly dynamic approach and landing procedures, (iii) dilemmas of human in sustainability (related to the individuals, the society, the non-governmental organizations, and the managers), and (iv) development of dedicated non-intrusive operator supporting systems based on eye-tracking, heart rate, and electrodermal activity. Due to the consequent effects of these developments, the dynamic sectorization and air space configuration may eliminate the task overload and reduce the actual operator load by 30–40%. With the developed concept of dynamic approach and landing procedures, aircraft will be able to follow better trajectories to avoid residential areas around airports to (i) reduce ground noise, and emission, (ii) avoid encounters severe weather and prevent incidents and accidents, and (iii) decrease landing distance up to 56% in compared to the “published transition route”.

A study of capacity regulations to define European air traffic management network states

ABSTRACT The European Air Traffic Management Network is unique in its complexity due to numerous stakeholders, airspace configurations and accommodated traffic volumes. Such complexity challenges both tactical visibility and network resiliency in general. Therefore, we review different perspectives of resiliency and robustness to explore advantages of performance variability in understanding network states. The methodology is based on capturing emergent disruptions as drivers of performance variability. We claim that capacity regulations reveal restorative mechanisms for tactical planning. Consequently, we conduct statistical evaluations to provide quantitative measures for network state definitions. The methodology is even able to distinguish non-nominal disruptions to either crisis or critical states. The proposed approach is then demonstrated by a data sample covering six months. Furthermore, to assess the severity of non-nominal states, we estimate probability distributions for different regulation types. The study is concluded by addressing long-term network resiliency based on estimated probability distributions.

Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic

Large-scale adoption of drone-based delivery in urban areas promise societal benefits with respect to emissions and on-ground traffic congestion, as well as potential cost savings for drone-based logistic companies. However, for this to materialise, the ability of accommodating high volumes of drone traffic in an urban airspace is one of the biggest challenges. For unconstrained airspace, it has been shown that traffic alignment and segmentation can be used to mitigate conflict probability. The current study investigates the application of these principles to a highly constrained airspace. We propose two urban airspace concepts, applying road-based analogies of two-way and one-way streets by imposing horizontal structure. Both of the airspace concepts employ heading-altitude rules to vertically segment cruising traffic according to their travel direction. These airspace configurations also feature transition altitudes to accommodate turning flights that need to decrease the flight speed in order to make safe turns at intersections. While using fast-time simulation experiments, the performance of these airspace concepts is compared and evaluated for multiple traffic demand densities in terms of safety, stability, and efficiency. The results reveal that an effective way to structure drone traffic in a constrained urban area is to have vertically segmented altitude layers with respect to travel direction as well as horizontal constraints imposed to the flow of traffic. The study also makes recommendations for areas of future research, which are aimed at supporting dynamic traffic demand patterns.

Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.

Synchronised demand-capacity balancing in collaborative air traffic flow management

This paper introduces a novel approach for synchronised demand-capacity balancing within a proposed Collaborative Air Traffic Flow Management framework. The approach is aimed to realise optimising traffic flow and scheduling airspace configuration in a more harmonised manner. Options such as delay assignment and alternative trajectories (generated and submitted by Airspace Users) are intended for regulating the traffic flow. Airspace reconfiguration involves, on the other side, adjusting the opening schemes of predefined configurations, or creating new ones (if needed) through dynamic sectorisation. Results suggest that, using the proposed approach, the required system delay can be reduced remarkably, whereas the number of opened sectors (and thus operating cost) and the total capacity provision (supplied by the Air Navigation Service Providers) decrease at the same time, due to the increased capacity utilisation per operating sector.

The developmental relationship between stomata and mesophyll airspace.

The quantitative and spatial coordination of stomatal pores in the epidermis and airspaces in the underlying mesophyll tissue is vital for efficient gas exchange in the leaf. The mechanisms that determine the distribution of stomata in the epidermis have been studied extensively, but how this relates to the regulation of mesophyll airspace configuration is poorly understood. Recent studies have investigated how development is coordinated between these tissue layers. The evidence suggests that multiple mechanisms are likely to work concurrently to coordinate stomatal and mesophyll development for optimal leaf gas exchange, and that both genetic and physiological factors contribute to this regulation. Such advances in our understanding of leaf development have important implications for potential improvement of crop water use efficiency.

Impact of Trajectories’ Uncertainty in Existing ATC Complexity Methodologies and Metrics for DAC and FCA SESAR Concepts

The most relevant SESAR 2020 solutions dealing with future Capacity Management processes are Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA). Both concepts, DAC and FCA, rely on traffic flow complexity assessment. For this reason, complexity assessments processes, methods and metrics, become one of the main constraints to deal with the growing demand and increasing airspace capacity. The aim of this work is to identify the influence of trajectories’ uncertainty in the quality of the predictions of complexity of traffic demand and the effectiveness of Demand Capacity Balance (DCB) airspace management processes, in order to overcome the limitations of existing complexity assessment approaches to support Capacity Management processes in a Trajectory-Based Operations (TBO) environment. This paper presents research conducted within COTTON project, sponsored by the SESAR Joint Undertaking and EU’s Horizon 2020 research and innovation program. The main objective is to deliver innovative solutions to maximize the performance of the Capacity Management procedures based on information in a TBO environment.

Dynamic reconfiguration of terminal airspace during convective weather: Robust optimization and conditional value-at-risk approaches

This paper presents robust optimization and Conditional Value-at-Risk (CVaR) approaches to mitigate the effect of weather forecast uncertainty by dynamically reconfiguring the terminal airspace. For busy airports, a terminal area is highly affected by a large volume of aircraft flying through narrow portion of space in a short period of time, and thus the terminal area is very sensitive to disruptions especially from convective weather. The inherent uncertainty of the weather forecast may create more delays and widespread disruptions in the capacity-limited airspace system. Accordingly, the objective of this research is to identify new and robust airspace structures that can cope with different levels of weather forecast uncertainty. In this research, robust optimization and CVaR approaches are proposed to find new terminal airspace routes that have a high average probability to be open and, at the same time, are robust to uncertainty during convective weather. A numerical study is conducted and the results show that the proposed approaches effectively identify robust airspace configurations, as the trade-off between optimality and conservatism is controllable.

Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions.

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

Regulação para o ADS-B1 no espaço aéreo brasileiro

The Automatic Dependent Broadcasting Surveillance (ADS-B) is a well-known technology, internationally standardized and recognized as an inexpensive way to carry out radar-like surveillance, even in places where conventional radar surveillance is not adequate. The implementation of this technology means the introduction of new monitored airspace configurations and new operational capabilities that should be understood by the aeronautical community and the regulation of the Department of Airspace Control (DECEA) and the National Civil Aviation Agency (ANAC) should provide adequate processes for civil aviation operations in order to face this evolutionary stage, improving concepts and adapting procedures. This study, whose objective is to contribute to the strengthening of the regulation that will support ADS-B in Brazil, was developed with a multi-methodological approach, including a literature review, expert consultation, international benchmark and case study, and resulted in the identification of 15 possible regulatory improvements with a positive impact on the implementation of ADS-B in national airspace.

A knowledge-transfer-based learning framework for airspace operation complexity evaluation

A sector is a component airspace whose operation is allocated to an air traffic controller. The operation complexity of a sector plays a critical role in the current Air Traffic Management system, e.g. it determines the workload volume of air traffic controllers and serves as a reliable index for airspace configuration and traffic flow management. Therefore, accurately evaluating the sector operation complexity is a problem of paramount importance in both practice and research. Due to numerous interacting factors, traditional methods based on only one single complexity indicator fail to accurately reflect the true complexity, especially when these factors are nonlinearly correlated. In light of these, the attempt to use machine learning models to mine the complex factor-complexity relationship has prevailed recently. The performance of these models however relies heavily on sufficient samples. The high cost of collecting ample data often results in a small training set, adversely impacting on the performance that these machine learning models can achieve. To overcome this problem, this paper for the first time proposes a new sector operation complexity evaluation framework based on knowledge transfer specifically for small-training-sample environment. The proposed framework is able to effectively mine knowledge hidden within the samples of the target sector, i.e. the sector undergoes evaluation, as well as other sectors, i.e. non-target sectors. Moreover, the framework can properly handle the integration between the knowledge derived from different sectors. Extensive experiments on real data of 6 sectors in China illustrate that our proposed framework can achieve promising performance on complexity evaluation when only a small training set of the target sector is available.

A sequence model for air traffic flow management rerouting problem

With the continuous growth of air transportation industry, the current Air Traffic Management systems can hardly meet the demand recently. In this work, an integer optimization model for Air Traffic Flow Management (ATFM) which integrates 4D trajectory-based operations is proposed to improve the situation. Besides addressing the issues of rerouting, ground-holding delay, fuel consumption and flight cancellation on a flight-by-flight basis, the model also employs sector-less airspace configurations. In order to solve the model efficiently, Dantzig-Wolfe decomposition and a heuristic approach via column generation are developed to generate 4D conflict-free trajectories for each flight. By applying commercial optimization software, solutions could be obtained in 20 min for the ATFM rerouting problems of the whole Southeast Asia region.