Airspace Operations Research Articles

Analysing Sector Safety Performance Based on Potential Conflicts: Complex Network Approach

This paper presents an in-depth analysis of Sector Safety Performance (SeSPe), focusing on potential conflicts within the Spanish Air Traffic Network. SeSPe serves as a comprehensive analysis for evaluating the safety levels of individual airspace sectors, taking into account both the frequency and severity of potential conflicts, along with their geospatial characteristics. Using Complex Network Theory, this study applies a novel methodology to four Madrid ACC sectors, analysing one month of flight track data. Multiple weighted spatial-temporal networks are built using 60 min intervals, allowing for the identification of critical nodes and the examination of interconnections and structural dynamics within the air traffic network. By incorporating temporal variations, the analysis uncovers evolving patterns. The research introduces an innovative approach for calculating potential conflicts by integrating radar data with airspace structure, thus offering a deeper understanding of sector risk profiles. The development of the SeSPe indicator, which combines historical safety data with topological features, enables a more informed and strategic evaluation of Air Traffic Management (ATM) system performance, ultimately contributing to safer and more efficient airspace operations.

Building interfaces between unmanned aircraft systems (UAS), air traffic controllers (ATCo), and the national airspace system (NAS): A software training platform

Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

AbstractThe field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air‐taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT‐4, to enhance the UAM Requirement discovery process.This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user's question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human‐readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

Four-Dimensional Aircraft Trajectory Prediction Based on Generative Deep Learning

Aircraft four-dimensional (4D) trajectory prediction (TP) is among the critical techniques of current existing automation systems in air traffic management (ATM), which is also the basis of future airspace operation models. This paper proposes a 4D TP method based on a conditional tabular generative adversarial network (CTGAN). The model is trained and tested using historical 4D trajectory data from the Hong Kong region. It is then compared with the most extensively researched conditional generative adversarial network (CGAN) and LSTM methods in the field of TP. The results show that Jensen–Shannon (JS) divergence, maximum mean discrepancy (MMD), and mean Euclidean distance (MED) of the model evaluation metrics for the selected route are 0.0539, 0.0150, and 0.0085, respectively. This confirms that the model for predicting 4D trajectory can accurately learn complex airspace’s trajectory distribution characteristics. Compared with the CTGAN algorithms shown in previous studies, the enhanced CTGAN approach developed in our research can reduce around 10% running time and 20% MED. Compared to CGAN, it results in an 81.55% reduction in MED. Compared to LSTM, when predicting with a 10-minute time span, the average mean absolute error for parameters latitude (Lat), longitude (Lon), geometric height (GH), and ground speed (GS) decreases by 7.76, 84.48, 81.98, and 36.51%, respectively.

Commercial spaceflight: Regulatory framework assessment and safety perspectives

Recent years are witnessing the rapid technological development in airspace domain, actually paving the way to the development of a commercial space market. Until the recent past, space operations have been essentially performed by research centers or military agencies, in usually segregated areas to ensure third parties’ safety, governed by launch base regulations and organized in an unscheduled manner. However, with the entry of private companies into the space domain, a new market niche is being created: that of commercial space transportation. For example, a promising area is the one related to operations performed by commercial suborbital flights, whether aimed at space tourism or simply transporting things and/or passengers from one point to another on the Earth's surface. The traffic volumes are supposed to increase in next years and segregating airspace does not represent a sustainable solution for the future.The present paper will first assess the state of the art of the regulatory framework currently applicable to operators in order to obtain authorization to perform space missions for commercial use, then propose a comparison between the United State of America and European regulatory frameworks. Main challenges related to regulatory aspects will be identified and perspectives on possible higher airspace operations integration in the medium-long term will be derived. Finally, safety considerations deriving from a seamless accommodation of higher airspace operations in current Air Traffic Management will be derived for the medium-long term. In conclusion, this work reveals that neither the United States nor Europe has formally approved a legal framework establishing certification procedures for sub-orbital space transport systems. Currently, the US legislation is the most applicable as it has comprehensive rules to allow operators to obtain flight authorization ensuring compliance with requirements through a compliance matrix periodically updated.

Optimized ST-Moran’s I Model for Characterizing the Dynamic Evolution of Terminal Airspace Congestion

This study aims to unveil the spatiotemporal evolution of congestion within terminal airspace, offering an in-depth analysis of congestion concerns to effectively utilize airspace resources and devise targeted control strategies, thereby enhancing airspace operation safety and efficiency. Initially, converting segment flow rates into equivalent speeds serves as a quantitative benchmark for operational status. Subsequently, an enhanced version of the ST-Moran’s I index model, specifically tailored to terminal airspace, is developed by incorporating improvements across spatial weight matrices, standard state parameters, and temporal dimensions. Validating this model with actual operational data from Chengdu’s terminal airspace, the research demonstrates significant advancements. Compared to conventional models, the proposed model enhances recognition rates for congestion in spatial and temporal dimensions by 62.5% and 43.61%, respectively. Congestion within terminal airspace predominantly occurs at the intersection of departure-climb and approach-departure segments, exhibiting evident spatiotemporal migration behavior. The proposed model accurately delineates the spatiotemporal characteristics of segment congestion, offering support for tailored congestion management strategies.

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.

Evaluation of Airspace Operation Safety Level based on PMS

The point merge (PM) flight procedure is a new type of flight procedure of performance -based navigation (PBN). The capacity of the point merge procedure is evaluated through the parameters of the point merge flight procedure. Through the parameters of the point merge flight procedure and the parameters of the aircraft flying in the point merge flight procedure, the collision risk between two aircraft in the merge area and the overall collision risk of the aircraft in the point merge procedure are calculated. The evaluation methods and calculation formulas of these three parameters are summarized, and a reasonable method of airspace operation safety level evaluation based on point merge system (PMS) is sought. The inferred method of airspace operation safety level evaluation based on PMS is to compare the operation capacity with the calculated theoretical capacity, and compare the collision risk between two aircraft in the fusion area calculated according to track data and radar data and the overall collision risk of aircraft in PMS with the target safety level specified by China’s air transportation industry.

Delay in the Air or Detour on the Ground?—A Case Study in Guangzhou Baiyun International Airport

Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).

A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics

The study of airspace sector demarcation can help controllers to deal with complex air situations, provide reference for air traffic control services, reduce the workload of controllers, and ensure safe and efficient airspace operation. Based on complex network theory, this paper proposes a sector division method based on a mean shift clustering algorithm and a Voronoi diagram. Firstly, a flight conflict network is constructed based on the structural characteristics of the airborne flight situation, combined with the three-dimensional velocity barrier method and the aircraft protected area model. Secondly, six network topology indexes, such as the total node degree, average point strength and network density, are selected to construct the flight-situation-assessment index system, and the busy and idle time segments of the airspace are divided according to the comprehensive network indexes; finally, based on the historical flight data, the airborne aircraft in the busy and idle time segments are clustered using the mean shift clustering algorithm and the Voronoi diagram method, respectively, so as to obtain a sector division scheme. The simulation results show that this method can equalize the network topology indexes and provide a reference for the scientific allocation of controllers’ energy.

Temporal Precursor Discovery Using Long Short-Term Memory with Feature Attention

The continuous growth of demand on commercial airlines has made it crucial to guarantee the safety of airspace operations. Although adverse events are rare, once they happen, they can cause unpredictable risky factors and degrade airspace efficiency. Thus, studying historical air traffic data to discover precursors, features, or events that contribute to the occurrence of the adverse event in the future is important and has gained interest in recent years. In this paper, a novel and real-time applicable temporal precursor discovery (TPD) framework based on the long short-term memory neural network and the feature attention mechanism is proposed. The feature attention mechanism enables the framework to pay attention to certain features at a certain time, and the attention score is defined as the temporal precursor. The temporal precursor reflects the rationale behind the neural network’s prediction at each time step, providing a data-driven explanation of how the adverse event occurs. The proposed TPD framework was tested with real air traffic data and weather data recorded at Incheon International Airport in South Korea in 2019.

Impact of Higher Airspace Operations on Air Traffic in Europe

Historically, higher airspace has been used for military exercises and as transit for space vehicles. Riding on commercial space operations’ coattails, more and more vehicles are under development that will make use of higher airspace resources. This will lead to increasing interactions with conventional air traffic since these new vehicles will have to transit through lower airspaces. The management of these operations is necessary to ensure the safe and practicable shared usage of these airspaces. This paper outlines an assessment of the impact of higher airspace operations on conventional air traffic in Europe. Initially, a synthesis of possible use cases was performed, and demand scenarios were developed that served as input to a fast-time simulation. The impact on air traffic was measured by means of flight efficiency parameters. The simulation results showed that the impact is dependent on the type of operation. High-altitude platform system flights and orbital launches cause the largest deviations in flight distance, flight duration and fuel consumption. Higher airspace operation parameters, including location, time, and duration, strongly affect the impact on the conventional air traffic.

Optimal Service Migration for Data and Reasoning Fabric

In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.

Discretization Method to Improve the Efficiency of Complex Airspace Operation

With the increase in airspace flow, the complexity of the airspace operation environment has also increased. Against this backdrop, improving the operational efficiency of airspace is crucial to ensure its efficient operation. The discrete division of controlled airspace represents a novel methodology for achieving this end. This approach involves visualizing the use of the airspace, quantifying and evaluating the operational efficiencies of airspace environments, and assessing specific metrics during an allocated time period. In this study, a discrete unit model was constructed to hierarchically subdivide complex airspace into static obstacles and aircraft-occupied space units, which facilitated the optimization of decision-making operations for multiple aircraft in airspace using the discrete method. Furthermore, busy airspace units could be effectively avoided. Finally, by using the extended analytic hierarchy process, we evaluated the threshold value of airspace operational efficiency improvement when operation efficiency metrics were enhanced via discrete approaches. The results indicated that the threshold value was 0.02168, classified as “good”, which represented an improvement in comparison with the original value of airspace operational efficiency (0.03173). These findings demonstrated that the application of the discrete division methodology significantly improved the overall operational efficiency of the airspace.

Airspace Designs and Operations for UAS Traffic Management at Low Altitude

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, in building a safe path, the UTM needs to determine the time and space allocated to each flight. Ideas for discretizing and structuring airspace in various forms have been proposed to enhance the efficiency of system operation and improve traffic congestion through effectual airspace allocation. Additionally, various methods of allocating UAVs to structured unit spaces have been studied in the literature. In this paper, the methods and structural designs for allocating airspace that have appeared in related studies are classified into several types, and their strengths and weaknesses are analyzed. The structured airspace designs are categorized into three models: Air-Matrix, Air-Network, and Air-Tube, and analyzed according to their sub-structures and temporal allocation methods. In addition, a quantitative analysis is conducted by re-categorizing the structured airspace and operation methods and building their combinations.

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

With the rapid increase in unmanned aerial vehicles (UAVs), ensuring the safety of airspace operations and promoting sustainable development of airspace systems have become paramount concerns. However, research dedicated to investigating the population exposure risks of UAV operations in urban areas and their spatial pattern is still missing. To address this gap, this study evenly divides the urban space into uniform grids and calculates critical areas for two UAV types within each grid. By integrating geospatial data, including buildings, land use, and population, data-driven risk maps are constructed to assess the spatial distribution patterns and potential population exposure risks of two UAV types and compare them with commonly used census units. The results indicate that the mean time between failures (MTBF) for the selected generic and rotary-type UAVs can be up to 9.04 × 108 h and 1.22 × 108 h, respectively, at acceptable risk levels, considering uncertainties. The spatial pattern of population exposure risk exhibits spatial heterogeneity and multi-scale effects in urban areas, aligning with population distribution. High-risk areas concentrate in regions characterized by high population mobility, such as transport hubs, commercial service areas, residential zones, and business districts. Additionally, the comparation emphasizes the potential bias introduced by using census units in risk assessment, especially in regions with significant urban build-up. This framework enables the evaluation of safety and acceptability across diverse urban land use areas and offers guidance for airspace management in megacities, ensuring the safe integration of UAVs in urban environments.

Risk Topics Discovery and Trend Analysis in Air Traffic Control Operations—Air Traffic Control Incident Reports from 2000 to 2022

The safety of air traffic control (ATC) operations is an important cornerstone for the sustainable development of the civil aviation industry. In order to clarify the risk factors in the control operation process and to achieve digital representation of the safety risks of civil aviation control operations, starting from the ATC incident reports, we fully mine the safety risk information and unspoken rules of ATC operations. A risk perception model for air traffic control operations safety based on the Latent Dirichlet Allocation (LDA) topic model and the Semantic Network Based on BERT (BSN) model is suggested. First, 17 risk topics and keywords were found in the incident reports collected using the LDA topic model. These topics included those pertaining to the stage of aircraft operation, human factors in control operation, and the sector or airspace operation status and structure. The findings indicate that while most risk subjects have not changed significantly, they do show an upward tendency. Human factors and operational rules and procedures account for the highest share of all key causes, and they also have a significant impact on how risk topics evolve over time. Finally, the BSN model in the air traffic control field was built based on the keywords of each risk issue in order to highlight any potential correlations between distinct risk topics. The results show that some risk topics have interrelated risk characteristics, and there are regularities of mutual evolution between these risk topics. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

Stochastic conformal anomaly detection and resolution for air traffic control

Safety is of the utmost importance in the air traffic system. In recent years, data-driven algorithms have emerged to identify anomalous and potentially unsafe operations based on machine learning techniques. Although many algorithms have shown notable progress in anomaly detection, they have hardly considered the fact that data can be corrupted by noise and uncertainty (e.g., navigation system error) can lead to frequent misdetection and false alarms, which could disturb air traffic controllers and result in system performance degradation. Therefore, an accurate and reliable assessment of emerging safety risks that accounts for and alleviates the effect of uncertainty in data is required for safe and efficient airspace operations. To achieve this goal, this paper proposes a conformal prediction-based framework that explicitly examines uncertainty for reliable anomaly detection and learns online from new streaming data. In addition to supporting the monitoring task of air traffic controllers, the proposed method takes one step forward and provides support for the control task, by offering a resolution strategy when anomaly probability violates the predefined threshold. The proposed method is demonstrated with real air traffic data, called automatic dependent surveillance-broadcast data.

The Roles of Autonomy and Assurance in the Future of Uncrewed Aircraft Systems in Low-Altitude Airspace Operations

The Roles of Autonomy and Assurance in the Future of Uncrewed Aircraft Systems in Low-Altitude Airspace Operations

Strategic Planning for Drone Company in Indonesia (Case: PT Terra Drone Indonesia)

Terra Drone Indonesia has the vision to be the No. 1 drone service company in Southeast Asia and contribute to the nation’s sustainable growth. The drone business condition in Indonesia is getting tight with competition and regulation. A new strategic plan should be proposed to answer the vision of Terra Drone Indonesia. Understand drone business opportunities to explore more for Indonesia’s drone industry business issues. The internal environment (SWOT Analysis) and the external environment (5 forces, PESTEL) will be a study for the proposed business strategy. The feasibility study and new business strategy comparison analysis turn into a strategy implementation for Terra Drone Indonesia. 2 new drone businesses might be applicable in Indonesia. Those are Drone spraying and Drone light show businesses. These 2 applications also the drone services that can comply with the waiver drone operation in Indonesia. Drone Spraying complies with a specific airspace operation and drone light show is more than one drone operation. New business strategic planning should be arranged by TDID as a more narrow Strategy. Based on the analysis, drone light show business fit more. Low competition and higher financial projections are the key points compared to the drone spraying business. The implementation plan should be monitored well to make sure the new strategy solves the problem.