Air traffic management system domain and control strategy analysis

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

This paper/chapter deals with the key drivers for adopting and developing an Occupational Health and Safety System (OHS) with a special focus on air traffic management and traffic controller’s workplace. A such system includes regulation and legal compliance procedures, actions and monitoring for ensuring workplace safety, incentives and motivation for the air traffic controller and associate personnel health and wellbeing. By a systemic approach, the key characteristics of OHS towards air traffic management are presented, highlighting the key aspects for implementing a quality management system in air traffic control, which is the cornerstone of airport operation efficiency and productivity on one hand; and the nature of job and the intensive working environment is well recognised. Based on air traffic providers functional analysis the key occupational aspects for air traffic control are taken into consideration, providing the benefits for implementing quality management systems (QMS) and OHS is real business. Conventional wisdom is to highlight the importance for establishing and incorporating a modern custom-made OHS system in accordance with the requirements addressed by OHSAS 18001 to develop and implement a QMS for air traffic services. Contribution of this paper is to highlight the key priorities for managers and decision makers in field of air traffic services providers, depicting ways and recommendation for adopting an efficient path for implementing OHS in a QMS environment.

Decision support system in tactical air traffic flow management for air traffic flow controllers

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

This study focuses on improving the air traffic control and management of local airspace in China. We develop five modules to establish a decision-support system, which constitutes the analysis and strategy layer of a local airspace digital twin. This integrated solution utilizes multi-source multimodal data, ranging from flight status, weather, to real-time air traffic control strategies. Specifically, the first module reviews the operational performance of the airspace and assesses the maximum capacity of an airspace under particular weather scenarios. The second module forecasts the departure times of all incoming flights to inform potential congestion. The third module is the core module simulating the local airspace operation under given weather conditions and control strategies. In the fourth module, we compare the simulation results with the assessed capacities, and suggest adjustments to control strategies proposed by air traffic controllers. The fifth module applies mixed integer programming to obtain the optimal air traffic control strategies.

Facing flight centred air traffic control (ATC) and more monitoring tasks at future controller working positions (CWP), it becomes even more important that air traffic controllers (ATCo) always focus their attention at the relevant spots on their human machine interface (HMI). This paper outlines relevant literature about attention and attention guidance (AG) in different domains, explains the concept for an AG prototype for sectorless air traffic management (ATM) and the plan for its validation in the course of Single European Sky ATM Research (PJ.16-04-03, SESAR2020). The AG prototype considers three aspects. First, the desired area of attention: An assistance system calculates where the ATCo should look at depending on input data like radar and flight plan data. Second, an external system relying on eye tracking and user inputs determines where the current ATCo focus is. Third, if the desired and the actual area of attention are not the same, mechanisms to guide the ATCo’s attention will be triggered taking a strategy of escalating visual cues into account. The latter comprises an intelligent display of action indicators with respect to time, position and appearance as well as pre-tactical inattention indicators. The AG prototype aims to increase controller productivity, improve situation awareness as well as reduce workload and stress level in a future flight centred ATC environment.

Based on a long-term prediction by the International Civil Aviation Organization indicating steady increases in air traffic demand throughout the world, the workloads of air traffic controllers are expected to continuously increase. Air traffic control and management (ATC/M) includes the processing of various unstructured composite data along with the real-time visualization of aircraft data. To prepare for future air traffic, research and development intended to effectively present various complex navigation data to air traffic controllers is necessary. This paper presents a mixed reality-based air traffic control system for the improvement of and support for air traffic controllers’ workflow using mixed reality technology that is effective for the delivery of information such as complex navigation data. The existing control systems involve difficulties in information access and interpretation. Therefore, taking notice of the necessity for the integration of air traffic control systems, this study presents the mixed reality (MR) system, which is a new approach, that enables the control of air traffic in interactive environments. This system is provided in a form usable in actual operational environments with a head-mounted see-through display installed with a controller to enable more structured work support. In addition, since this system can be controlled first-hand by air traffic controllers, it provides a new experience through improved work efficiency and productivity.

Chapter 17 - An introduction to air traffic control and the application of human factors

A number of Federal Aviation Administration (FAA) automation systems and developmental programs interoperate to support the day-to-day operations of the National Airspace System (NAS). The integration of New Entrants (NEs), such as Remotely Piloted Aircraft (RPA) 1 and Space Vehicle (SV) operations, into the NAS will require modification to the current state of automation. Examining the relevant operational, technical, and procedural implications of integrated NE operations, in addition to exploring the potential impacts of accelerated RPA usage on NAS dynamics, are critically important efforts necessary to understanding operational impacts to NAS systems and users. Our paper presents the potential impacts of RPA integration on two of these systems: En Route Automation Modernization (ERAM) and Traffic Flow Management System (TFMS). A significant component of the impact on NAS systems comes from flight plan and related data processing since all SVs and many RPAs will fly non-standard flight patterns (e.g., RPA loitering and grid search). The processes and details of the non-traditional flight routes, as well different RPA performance specifications, need to be considered as missions are overlaid on the national jet route structure. Of particular concern and interest are the changes in air traffic control (ATC) and air traffic management (ATM) systems and sub-systems needed to address air traffic controller workload and workload complexity concerns associated with NE activities. One potential accommodation approach, presented in this paper, is the use of Dynamic Airspace Configuration both for RPA routine operations and contingency events. Additionally, we present an RPA 1 In this paper, UAS and RPA are used interchangeably without intending any difference per se. It is noted that the U.S. DoD and ICAO use RPA while NASA and the FAA use UAS. demand forecast tool developed under a National Aeronautics and Space Administration (NASA) project. This tool will assist the FAA in meeting its objectives for accommodation of RPA by providing accurate demand forecast information.

The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.

This chapter reviews the International Civil Aviation Organization air traffic management (ATM) work programme along with ATM developments taking place around the world. It examines the various elements of the ATM system and suggests some of die research, trials and demonstrations. Airspace management is traditionally recognized mainly as involving a dynamic or tactical sharing of airspace by civil and military users. Air Traffic Services itself is comprised of several subelements, one of these being air traffic control. The increasing number of long-range interregional flights may require integration of regional Air Traffic Flow Management systems and procedures. Free Right is the guiding vision, mission and operational concept of the Federal Aviation Administration, based on Communications, Navigation, Surveillance/Air Traffic Management systems (CNS/ATM). Africa is composed of many delicate national economies separated by vast wilderness areas. The main scope of Airspace management will be toward a strategic planning function of airspace infrastructure, and flexibility of airspace use.

Air Traffic Management (ATM) is a complex decision-making process. Air traffic controllers' decision on aircraft trajectory control actions directly leads to the efficiency of traffic flow management. In the Automated Point Merge Trajectory Planning (APMTP) problem, it aims to realize an automated routine trajectory management in Terminal Manoeuvring Area (TMA) with an intelligent decision-making agent. An Artificial Intelligence-based approach, mainly Reinforcement Learning (RL) algorithm, is applied to adaptively and smartly integrate four types of de-conflict actions for solving conflicts with fewer delays on the environment. In this paper, we will mainly discuss the policy optimization in APMTP, focus on improving the agent's learning quality and exploration efficiency. Firstly, application of RL in adaptive trajectory planning is presented. APMTP problem is adaptively divided into several sub-problems. For each sub-problem, an online policy π is applied to guide the simulation and optimization modules to find out the conflict-free and less-delay solution. The online policy π is a scale of weight distribution for choosing desirable actions. It follows the rule of Roulette-wheel selection with weighted probability. The highest desirable decision variable has the largest share of the roulette wheel, while the lowest desirable decision variable has the smallest share of the roulette wheel. The RL direct policy optimization algorithm is designed to update the online policy π, Finally, experiments are built up for validation of the proposed policy optimization algorithm for the intelligent decision-making in APMTP. The results in the test environment show that learning agent with different exploration and exploitation ability will result in different system performance in conflict resolution and delay.

Air Traffic Management (ATM) is defined as a broad domain that consists of complex activities such as Air Traffic Flow Management (ATFM). Within this context this paper presents two prototypes of a module (MAAD) assembled in a decision support device that is based on Artificial Intelligence, which generates suggestions for air traffic flow control according to each traffic saturation scenario. The device known as Distributed Decision Support System for Air Traffic Flow Management (SISCONFLUX) was built with a computational agent based on Reinforcement Learning (Module MAAD), which uses Q-learning algorithm. Two prototypes were built and one of them incorporated the specialist's (human agent) experience. The performance of the prototypes was evaluated through a case study modeled from real traffic demands verified on certain dates. The results obtained in the case study are promising; the behavior of the prototypes demonstrated that the Q-learning algorithm converged in a satisfying way. The prototypes generated actions that contributed effectively to the reduction of saturation in the air traffic scenarios under test.

The paper deals with the diagnosis and development of core skills of air traffic (AT) controllers based on a professiogram analysis of their work. The aim was to assess how professional skills are related to experience and to explore the opportunities of purposeful training of these skills. The study was conducted with the participation of air traffic (AT) controllers who were graduates of an ATC school aged 22–25 years and had no experience and eleven first-class operators aged 31–37 years with eight to ten years of experience. The professiogram analysis of work was carried out using a specially crafted skill tests battery followed by training (ten sessions during ten school days) for inexperienced air traffic controllers, which was tied up to regular refresher training. The study pinpointed the core skills that were improved significantly by the special training and proved the possibility of their development using a training cycle during planned teaching.

The definition, identification, and use of “user intent” have become hot topics in the design and development of Decision Support Tools (DSTs) for air traffic control (ATC) and air traffic management (ATM) applications. Proper use of intent information is critical to the successful development of clearance and maneuver advisories, both on the ground and in the air. Complications arise during intent modeling due to proper selection of intent source and content. Due to the inevitability of missing or inexplicit intent, DSTs must be designed to identify when their internal model of intent is incomplete and adapt their behavior accordingly. Two NASA DSTs, the ground-based En Route Descent Advisor (EDA) and the cockpit-based Autonomous Operations Planner (AOP), have developed effective approaches to handling user intent. Comparisons of the similarities and differences between these two systems illustrate the challenges in correctly modeling user intent for different applications.