An Intelligent Interactive Conflict Solver Incorporating Air Traffic Controllers' Preferences Using Reinforcement Learning

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

The increasing volume of traffic in the air transportation is leading to excessive workload on air traffic controllers. Developing automated air traffic management (ATM) tools is a critical technology in reducing the workload of air traffic controllers and hence increasing the airspace capacity. The existing approaches to automated ATM either use overly-simplified air traffic and aircraft dynamics models to reduce computational complexity or end up being computationally intractable for large-scale ATM scenarios. This paper presents a new hybrid system description of modeling the decision process of the air traffic controllers in en-route and approach operations. The model is based on the domain expertise provided by the State Airport Authority and Air Navigation Service Provider (ANSP) of Turkey. The emulation of air traffic controller decision process in the hybrid model provides realistic conflict resolution maneuvers and separation assurance in 3D, while being computationally tractable. The algorithm has polynomial iteration complexity in the number of waypoints of the aircraft, which makes it scalable to large-scale ATM scenarios with more than 100 aircraft. The algorithm is validated on the real air traffic data over the Istanbul region extracted from the ALLFT+ dataset provided by EUROCONTROL, which includes over 7000 flights in a 96-hour period. The developed algorithm is also integrated into a Boeing 737-800 flight deck simulator with a custom radar display to demonstrate the applicability to existing avionics systems.

Task difficulty and physiological measures of mental workload in air traffic control: a scoping review

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

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

The existing Air Traffic Management (ATM) system can evolve into a Space and Air Traffic Management System (SATMS), as space operations become a more common event. An assessment tool has been developed as a catalyst for this evolution; the Dynamic Flight Path Tool was developed to utilize real time situational information to minimize the impact of space flight to operations while at the same time ensuring a level of safety acceptable to the public. This tool capitalizes on methods that rapidly compute the debris clouds within a four-dimensional space (3D space plus time). The tool has two modes of operation: 1) Pre- mission Planning and 2) Real-time Mission Mode. The Dynamic Flight Path Tool has been applied to a range of sample scenarios that demonstrate how the impact of space launch operations on air traffic can be significantly reduced. 1 . An assessment tool has been developed as a catalyst for this evolution; the Dynamic Flight Path Tool, a Research and Development prototype, was developed to utilize real time situational information to minimize the impact of space flight to operations while at the same time ensuring a level of safety acceptable to the public. This tool capitalizes on methods that rapidly compute the debris clouds within a four-dimensional space (3D space plus time). The tool has two modes of operation: 1. Pre-mission Planning. The Tool enables the planner to identify scheduled air traffic affected by a planned operation and potentially schedule a launch operation during the time of lowest air traffic density and thus, lowest overall risk and disruption. 2. Real-time Mission Mode. The Tool performs trajectory prediction to identify what air traffic will enter the operational area of interest. The Tool provides the Flight Safety Officer (FSO) and Air Traffic Control (ATC) with situational awareness information on the space and air traffic. This allows the FSO and ATC to dynamically manage the airspace as the launch operation progresses, as opposed to issuing a blanket NOTAM (Notice to Airmen) restricting a large area of airspace. The Dynamic Flight Path Tool has been applied to a range of sample scenarios that demonstrate how the impact of space launch operations on air traffic can be significantly reduced. While destruct events are very rare and the vast majority of launch operations are nominal, the dynamic understanding of the space and air traffic picture between the FSO and ATC facilitated by the Dynamic Flight Path Tool gives all users of the airspace considerably more flexibility in managing the needs of their respective airspace users while guaranteeing an acceptable level of safety. Presently the Federal Aviation Administration (FAA) considers the risk to flying and not how to respond to the real time situation that exists during a space launch. The FAA follows its strategic planning and does not formulate contingency plans - rather it relies on tactically moving aircraft - to account for the actual location of

In order to handle increasing demand in air transportation, high-level automation support seems inevitable. This article presents an optimization-based autonomous air traffic control (ATC) system and the determination of airspace capacity with respect to the proposed system. We model aircraft dynamics and guidance procedures for simulation of aircraft motion and trajectory prediction. The predicted trajectories are used during decision process and simulation of aircraft motion is the key factor to create a traffic environment for estimation of airspace capacity. We define the interventions of an air traffic controller (ATCo) as a set of maneuvers that is appropriate for real air traffic operations. The decision process of the designed ATC system is based on integer linear programming (ILP) constructed via a mapping process that contains discretization of the airspace with predicted trajectories to improve the time performance of conflict detection and resolution. We also present a procedure to estimate the airspace capacity with the proposed ATC system. This procedure consists of constructing a stochastic traffic simulation environment that includes the structure of the evaluated airspace. The approach is validated on real air traffic data for enroute airspace, and it is also shown that the designed ATC system can manage traffic much denser than current traffic.

In anticipation of a projected rise in demand for air transportation, NASA and the FAA are researching new air-traffic-management (ATM) concepts that fall under the paradigm known broadly as :free flight. This paper documents the software development and engineering efforts in progress by Seagull Technology, to develop a free-flight simulation (FFSIM) that is intended to help NASA researchers test mature-state concepts for free flight, otherwise referred to in this paper as distributed air / ground traffic management (DAG TM). Under development is a distributed, human-in-the-loop simulation tool that is comprehensive in its consideration of current and envisioned communication, navigation and surveillance (CNS) components, and will allow evaluation of critical air and ground traffic management technologies from an overall systems perspective. The FFSIM infrastructure is designed to incorporate all three major components of the ATM triad: aircraft flight decks, air traffic control (ATC), and (eventually) airline operational control (AOC) centers.

This paper describes a simulation tool developed to aid the analysis of air traffic management domains and potential air traffic control strategies. The simulation represents aircraft and automated air traffic control support tool functionality based on a computational model of aircraft flight management system trajectories. It simulates the effects of environmental disturbances and air traffic controller intervention on air traffic in fast time. The paper outlines a methodology that leverages these capabilities to help with tool design and evaluation, and discusses the results of preliminary simulations aimed toward using the proposed methodology for characterizing the effectiveness of particular strategy-tool combinations for supporting more efficient terminal-area air traffic management. This research is supported by NASA's Vehicle Systems Program Quiet Aircraft Technology project and Airspace Systems Program Advanced Air Transportation Technology project.

Objective To illustrate how fatigue affects the workload, situation awareness, and control strategy of air traffic controllers. Background As air traffic control becomes more complex and demanding due to the increasing number of aircraft movements in major airport hubs, the need to particularly look into the interplay of performance shaping factors has also intensified. Despite the previous efforts of scholars to understand the framework of air traffic control based on factors on workload and situation awareness (SA) and its impact to shift in control strategy, the role of fatigue in such factors is ignored notwithstanding its relative impact on air traffic controllers as tasks are carried out in varying workload and work shift. Methods A case study is conducted in an actual tower control center in the Philippines to demonstrate the hypothesized relations among fatigue, workload, SA, and control strategy. Questionnaires based on SPAM, SASHA, Samn-Perelli fatigue scale, and visual attention self-report are deployed to measure air traffic controllers’ fatigue levels, SA, and control strategy. Results Fatigue has an inverse relationship with workload and has a significant effect on SA. Furthermore, fatigue is also found to have a significant relationship with some areas of interest including visual display terminal. Conclusion Such results underpin the resource allocation decisions of stakeholders in the commercial aviation industry geared toward fostering the welfare of air traffic controllers.

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.

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.

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.

In recent years, flight safety is still one of the main issues for all airlines. En route civil airplanes may encounter adverse weather conditions. Some fatal airplane accidents happened because of the weather disturbance. Moreover, we should also design path to avoid the prohibited area. Therefore a good path planning algorithm plays an increasingly important role in air traffic management. An efficient path planning algorithm can help the plane to avoid severe weather conditions, restricted areas and moving obstacles to ensure the safety of the cabin crews and passengers. Here, we build our algorithm based on the A* search algorithm. Moreover, our algorithm can also find the path with least energy costs. As a result, our algorithm can improve the safety operation of the airplanes and reduce the workload of pilots and air traffic controllers.

Measuring the interactions between air traffic control and flow management using a simulation-based framework