Air Traffic Management Domain Research Articles

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).

Interoperable data exchange for safe and efficient launch and re-entry operations in an international environment

The frequency of commercial space launch and re-entry operations is increasing worldwide. Current regulations and procedures ensure safe operations by temporarily closing large volumes of airspace where risks to aircraft would exist in case of non-nominal events during launch or re-entry operations. To maintain the safety of air traffic as the number of space operations rises, effectively providing the right information to the right stakeholders at the right time is key. Through a cooperative agreement, the FAA and DLR are sharing their unique capabilities using the Commercial Space Integration Lab and Air Traffic Validation Center, located in the USA and Germany respectively, to improve situational awareness through real-time data exchange. The project seeks to answer whether U.S. and European ANSPs can respond adequately to a non-nominal event during a launch or re-entry operation that presents a hazard to the airspace system. It leverages existing international data standards and infrastructures by using a data exchange approach based on System Wide Information Management (SWIM). Within the project an initial assessment of the processes, roles and responsibilities for implementing launch and re-entry events in the air traffic systems on both sides of the Atlantic, as well as the requirements for basic functions and performance parameters of a SWIM-based integration, have been completed. The project developed a demonstration model across all system levels as far down as to the ANSPs, namely to the air traffic controllers. Through a series of demonstrations, covering launch scenarios from the U.S. with possible effects on European airspace and vice versa, the project evaluated the technical and operational feasibility of the concept. The key data parameters identified during the analyses shall enable information sharing among various users within the U.S. and European global airspace system. It has been shown, that the systems on both sides of the Atlantic could be connected via standardized protocols und used successfully for exercises in different scenarios. Further research on the best integration of the processes in international Air Traffic Management (ATM) networks and domains will be the follow-on.

Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts

With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model how conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations.

Effects of Language Ontology on Transatlantic Automatic Speech Understanding Research Collaboration in the Air Traffic Management Domain

As researchers around the globe develop applications for the use of Automatic Speech Recognition and Understanding (ASRU) in the Air Traffic Management (ATM) domain, Air Traffic Control (ATC) language ontologies will play a critical role in enabling research collaboration. The MITRE Corporation (MITRE) and the German Aerospace Center (DLR), having independently developed ATC language ontologies for specific applications, recently compared these ontologies to identify opportunities for improvement and harmonization. This paper extends the topic in two ways. First, this paper describes the specific ways in which ontologies facilitate the sharing of and collaboration on data, models, algorithms, metrics, and applications in the ATM domain. Second, this paper provides comparative analysis of word frequencies in ATC speech in the United States and Europe to illustrate that, whereas methods and tools for evaluating ASRU applications can be shared across researchers, the specific models would not work well between regions due to differences in the underlying corpus data.

Evaluating the Performance-Shaping Factors of Air Traffic Controllers Using Fuzzy DEMATEL and Fuzzy BWM Approach

In the air traffic management domain, a set of performance-shaping factors are defined to characterize how such factors influence the overall performance of air traffic controllers. While it is worth understanding the nature of these factors, including their priority for policymaking and strategy implementation, no research in the extant literature has conducted an in-depth investigation of this matter. Therefore, this paper aims to explore the performance-shaping factors of air traffic controllers by using fuzzy DEMATEL and fuzzy BWM—such methodologies in the classification of factors as well as their corresponding priority vector. As an illustration, a case study in the Mactan Control Tower of the Civil Aviation Authority of the Philippines (CAAP) is conducted. The key results of the hybrid methodology showed that causal factors are ‘situation awareness,’ ‘communication,’ ‘teamwork,’ ‘vigilance,’ ‘and ‘attention.’ Effect factors are ‘workload,’ ‘trust,’ ‘fatigue,’ and ‘stress.’ Furthermore, ‘communication’ is found to be of the highest priority among other factors. The results can provide relevant insights to the decision-makers of air traffic management in formulating programs and strategies related to the improvement of air traffic controllers’ performance. Note, however, that the study is limited to identifying the inherent characteristics of the factors and their priority ranking; specific plans of action for improving the performance of air traffic controllers are not provided. As a future research direction, the inputs obtained in this paper can pave the way to a more in-depth analysis of improving the performance of air traffic controllers.

Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts.

With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, this article proposes deep learning (DL) techniques that model Air Traffic Controllers’ reactions in resolving conflicts violating aircraft trajectories separation minimum constraints: This implies learning when the Air Traffic Controller reacts towards resolving a conflict, and how he/she reacts. Timely reactions, to which this article aims, focus on when do reactions happen, aiming to predict the trajectory points, as the aircraft state evolves, that the Air Traffic Controller (ATCO) issues a conflict resolution action. Towards this goal, the article formulates the Air Traffic Controllers’ reaction prediction problem for CD&R, presents DL methods that can model Air Traffic Controllers’ timely reactions, and evaluates these methods in real-world data sets, showing their efficacy in solving the problem with very high accuracy.

An evaluation of practitioners’ perceptions of a security risk assessment methodology in air traffic management projects

Cyber security is a key enabler for safe Air Traffic Management (ATM). This paper presents results from an empirical study, in which we have investigated and evaluated the use of the Security Risk Assessment Methodology for SESAR (SecRAM) in European ATM research and development projects. The study was performed with the intention to find and document common issues and aspects that could be improved in the methodology. The results from the study reveal that while most of the practitioners had a positive perception of the methodology itself, they were less satisfied with the process of applying it in their projects. Based on the results, we provide a number of recommendations, which aim to improve the security risk assessment process in the ATM domain.

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

Physics Informed Deep Reinforcement Learning for Aircraft Conflict Resolution

A novel method for aircraft conflict resolution in air traffic management (ATM) using physics informed deep reinforcement learning (RL) is proposed. The motivation is to integrate prior physics understanding and model in the learning algorithm to facilitate the optimal policy searching and to present human-explainable results for display and decision-making. First, the information of intruders’ quantity, speeds, heading angles, and positions are integrated into an image using the solution space diagram (SSD), which is used in the ATM for conflict detection and mitigation. The SSD serves as the prior physics knowledge from the ATM domain which is the input features for learning. A convolution neural network is used with the SSD images for the deep reinforcement learning. Next, an actor-critic network is constructed to learn conflict resolution policy. Several numerical examples are used to illustrate the proposed methodology. Both discrete and continuous RL are explored using the proposed concept of physics informed learning. A detailed comparison and discussion of the proposed algorithm and classical RL-based conflict resolution is given. The proposed approach is able to handle arbitrary number of intruders and also shows faster convergence behavior due to the encoded prior physics understanding. In addition, the learned optimal policy is also beneficial for proper display to support decision-making. Several major conclusions and future work are presented based on the current investigation.

European Airspace (De)Fragmentation Assessment Model

Airspace fragmentation represents an issue that began to be more frequently mentioned within the Air Traffic Management (ATM) domain in the last two decades. Primarily, it is frequently listed as one of the main causes contributing to inefficiency of the ATM system in Europe. However, even though the issue of the European airspace fragmentation has been recognized back in the 1990s, over the past decades it has neither been frequently studied nor comprehensively addressed. Accordingly, minor progress has been made to describe this issue in more depth. Therefore, this research paper deals with the research of performance-based airspace fragmentation (one of several European airspace fragmentation types). It presents the conceptual and methodological framework of a novel model that can be used to obtain answers to hypothetical questions of where, when, how, and whether it is possible to achieve performance-based airspace defragmentation. Accordingly, it is expected that further studies of the developed model will deliver relevant information that may contribute to a more inclusive, smart, and spatially oriented development of the ATM system in Europe.

Hierarchical multiagent reinforcement learning schemes for air traffic management

In this work we investigate the use of hierarchical multiagent reinforcement learning methods for the computation of policies to resolve congestion problems in the air traffic management domain. To address cases where the demand of airspace use exceeds capacity, we consider agents representing flights, who need to decide on ground delays at the pre-tactical stage of operations, towards executing their trajectories while adhering to airspace capacity constraints. Hierarchical reinforcement learning manages to handle real-world problems with high complexity, by partitioning the task into hierarchies of states and/or actions. This provides an efficient way of exploring the state–action space and constructing an advantageous decision-making mechanism. We first establish a general framework of hierarchical multiagent reinforcement learning, and then, we further formulate four alternative schemes of abstractions, on states, actions, or both. To quantitatively assess the quality of solutions of the proposed approaches and show the potential of the hierarchical methods in resolving the demand–capacity balance problem, we provide experimental results on real-world evaluation cases, where we measure the average delay per flight and the number of flights with delays.

Assessment of the GIS-Aided Precise Approach Using the GNSS-GBAS Landing Systems

The radio navigational Instrument Landing Systems (ILSs) are currently intended to guide the aircrafts in lateral and vertical dimensions to the runway surface safely and precisely. Therefore, they are strongly related to the geographic location of an airport and its runway(s). The ILS systems use the aids of the radio frequency radiation to achieve this purpose, depending on the ground emitting stations, and providing the guidance to the runway centreline location along with the glide slope guidance during the Final Approach Segment (FAS). Furthermore, the new ILS systems are fully aided by the coordinates of the Global Positioning System (GPS) instead of the ground radiations, they use the waypoint fixes during the landing phase of flight by means of transmitting their corrections to the on-board receivers. Those new invented Ground Based Augmentation Systems (GBAS) are more precise and trustable, they also increase the capacity of the huge air traffic demands nowadays by multiple and non-straight approaches. As a result, the Geographic Information System (GIS) of any airport supported by the GBAS system is intended to be fully used and implemented in both instrumental and procedural aids. Many previous studies had indicated that the old procedural approaches should be changed to the new GIS aided ones, but without pointing out when and how to implement such important transfer. The purpose of this study is to assess the performance of the GIS aided precision procedures using the GBAS stations, and to identify to what extent they can enhance the navigational aviation in the air traffic management domain. A special focus will be put on the Hungarian Budapest international airport in terms of both capability motivating factors and the current GIS infrastructure aiding. Results showed a promising chance for more investment in installing the GBAS stations in the airport. That will enable more capacity and easier approaches in all weather conditions.

The datAcron Ontology for the Specification of Semantic Trajectories

As the number of moving objects increases, the challenges for achieving operational goals w.r.t. the mobility in many domains that are critical to economy and safety emerge dramatically. In domains such as air traffic management, this dictates a shift of operations’ paradigm from location based, as it is today, to trajectory based, where trajectories are turned into “first-class citizens”. Additionally, the increasing amount of data from heterogenous and disparate data sources implies the need for advanced analysis methods that require exploiting spatio-temporal mobility data in appropriate forms and at varying levels of abstraction. All these call for an in-principle way for organising integrated views of mobility data, with trajectories playing the main role. In this paper, we propose an ontology for modelling semantic trajectories, integrating spatio-temporal information regarding mobility of objects, at multiple, interlinked levels of abstraction. Our work builds upon a comprehensive framework that identifies fundamental spatio-temporal data types and specific conversions among these types. We validate the ontological specifications towards satisfying the needs of visual analysis tasks in the complex air traffic management domain, using real-world data.

Rotorcraft in the Performance Based Navigation International Civil Aviation Organization Implementation

Abstract European Commision adopted in July new regulations about laying down airspace usage requirements and operating procedures concerning performance based navigation. It is next step in realization of the the global program PBN ICAO. At the 36th General Assembly of ICAO held in 2007, the Republic of Poland agreed to ICAO resolution A36-23 which urges all States to implement PBN. In future aviation concepts the use of Performance Based Navigation (PBN) is considered to be a major Air Traffic Management (ATM) concept element. ICAO has drafted standards and implementation guidance for PBN in the ICAO Doc 9613 “PBN Manual”. The Based Performance Navigation Concept represents and shift from sensor-based to performance based navigation connected with criteria for navigation: accuracy, integrity, availability, continuity and functionality depending on the phase of the flight. Through PBN and changes in the communication, surveillance and ATM domain, many advanced navigation applications are possible to improve airspace efficiency, improve airport sustainability, reduce the environmental impact of air transport in terms of noise and emission, increase safety and improve flight efficiency.

Demonstration of the “Work Models that Compute” Simulation Framework for Objective Function Allocation

This document describes a demonstration of the computational simulation framework Work Models that Compute (WMC). WMC is a framework for the objective evaluation of function allocation between humans and robots. The WMC framework has been used to study the impact of function allocation on both the air traffic management and spacecraft operations work domains. Recent advances include modeling of human-robot control modes, locomotion, failures and physical resources. We propose a demonstration of WMC showcasing how WMC can provide useful, objective and quantitative insight in the trade-offs asso-ciated with function allocation. In the demonstration, we will analyze function allocation for an on-orbit maintenance scenario. We will first show how minor changes to a function allocations can have major ef-fects on the emergent work patterns that result from the simulation framework. Second, we will demon-strate an analysis of 10-15 possible function allocations (defined in advance) and cross-compare their char-acteristics based on measures such as the idle time, taskload for each agent, information transfer require-ments, physical resources exchanges and others. This will show the framework’s capability to rapidly ex-plore the function allocation trade-space and allow the designer to make more informed trade-offs.

Online four dimensional trajectory prediction method based on aircraft intent updating

To facilitate decision support in the air traffic management domain, an online four dimensional trajectory prediction (4D-TP) method was proposed in this paper. First, this study outlined the processes of online 4D-TP. Second, four major components of offline 4D-TP were discussed and presented, such as computation model, aircraft intent, environmental conditions and performance parameters. Third, this paper came up with an approach of current trajectory updating by using ADS-B Receiver and the corresponding data processing algorithm. Furthermore, the strategies of aircraft horizontal and vertical intent updating were also put forward for online 4D-TP. And the aircraft intent should be updated while the deviation between the current and predicted trajectory exceeding the pre-defined threshold. Finally, two types of case studies were carried out to demonstrate the performance and effectiveness of the proposed online 4D-TP method. The results indicated that the proposed online 4D-TP method is able to increase the prediction accuracy by triggering 4D-TP while the position or speed deviation is beyond the pre-defined threshold.

A methodology used for the development of an Air Traffic Management functional system architecture

The Air Traffic Management (ATM) system provides a safe, economical, efficient, dynamic and integrated management of air traffic and airspace through the collaborative integration of humans, infrastructure (technology and facilities) and organisations. At present, it is widely accepted that the ATM system is one of the leading complex socio-technical systems in terms safety performance. To maintain this reputation, safety management of the ATM system needs to be able to cope with not only rising travel demand, but also the increased automation, the tighter coupling between its component elements and greater complexity of the ATM system itself. As a way of ensuring this, in Europe the European Union Regulation 1035/2011 requires the Air Navigation Service Providers (ANSPs) responsible for the provision of ATM, to describe and model their systems by accounting for the functional interactions between the equipment, procedures and human resources of the ATM system. However, despite the number of available models of the ATM system, none of them meets this requirement. Typically the existing models focus on the technical functions and describe the system usage via operational scenarios. Therefore this paper proposes a novel methodology used for the development of a functional system architecture−Model of ATM Reality In Action (MARIA)−with the aim to provide a sound base for system analysis, including safety, namely by describing the whole system and the interdependencies between its functions. By overcoming the limitations of the existing models MARIA has the potential to improve understanding of the ATM services safety, system resilience and meet the requirements of the Regulation 1035/2011. Lastly, the methodology applied in the ATM domain presented in this paper is equally transferable to systemic modelling of other complex socio-technical systems.

A Method for SWIM-Compliant Human-in-the-Loop Simulation of Airport Air Traffic Management

System Wide Information Management(SWIM), as envisioned by theSingle European Sky Air Traffic Management Research(SESAR) program, is the application of service oriented architectures to the air traffic management domain. Service oriented architectures are widely deployed in business and finance but usually tied to one specific technological implementation. SWIM goes one step further by defining only the semantic layer of the application integration and leaving the implementation of the communication layer open to the implementer. The shift from legacy communication patterns to SWIM is fundamental for the expected evolution of air traffic management in the next decades. However, the air traffic management simulators currently in use do not reflect this yet. SWIM compliance is defined by semantic compatibility to theAir Traffic Management Information Reference Model(AIRM) and a SWIM service may implement one or more communication profiles, which specify a communication layer implementation. This work proposes a SWIM-compliant communication profile suitable to integrate SWIM-compliant tools into human-in-the-loop simulations for air traffic management research. We achieve this objective by implementing a SWIM communication profile using XML-based multicast messaging and extending the message format to support distributed human-in-the-loop simulations. We demonstrate our method by the evaluation of Hamburg Airport operations.

Threats Management Throughout the Software Service Life-Cycle

Software services are inevitably exposed to a fluctuating threat picture. Unfortunately, not all threats can be handled only with preventive measures during design and development, but also require adaptive mitigations at runtime. In this paper we describe an approach where we model composite services and threats together, which allows us to create preventive measures at design-time. At runtime, our specification also allows the service runtime environment (SRE) to receive alerts about active threats that we have not handled, and react to these automatically through adaptation of the composite service. A goal-oriented security requirements modelling tool is used to model business-level threats and analyse how they may impact goals. A process flow modelling tool, utilising Business Process Model and Notation (BPMN) and standard error boundary events, allows us to define how threats should be responded to during service execution on a technical level. Throughout the software life-cycle, we maintain threats in a centralised threat repository. Re-use of these threats extends further into monitoring alerts being distributed through a cloud-based messaging service. To demonstrate our approach in practice, we have developed a proof-of-concept service for the Air Traffic Management (ATM) domain. In addition to the design-time activities, we show how this composite service duly adapts itself when a service component is exposed to a threat at runtime.

Threat Analysis in Goal-Oriented Security Requirements Modelling

Goal and threat modelling are important activities of security requirements engineering: goals express why a system is needed, while threats motivate the need for security. Unfortunately, existing approaches mostly consider goals and threats separately, and thus neglect the mutual influence between them. In this paper, the authors address this deficiency by proposing an approach that extends goal modelling with threat modelling and analysis. The authors show that this effort is not trivial and a trade-off between visual expressiveness, usability and usefulness has to be considered. Specifically, the authors integrate threat modelling with the socio-technical security modelling language (STS-ml), introduce automated analysis techniques that propagate threats in the combined models, and present tool support that enables reuse of threats facilitated by a threat repository. The authors illustrate their approach on a case study from the Air Traffic Management (ATM) domain, from which they extract some practical challenges. The authors conclude that threats provide a useful foundation and justification for the security requirements that the authors derive from goal modelling, but this should not be considered as a replacement to risk assessment. The usage of goals and threats early in the development process allows raising awareness of high-level security issues that occur regardless of the chosen technology and organizational processes.