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Including computational human performance models in current simulations and using human-in-the-loop simulations is critical. Blom, H. Bakker, P. Blanker, J. Daams, M. Everdij, and M. Accident risk assessment for advanced air traffic management. Also in G.

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Donohue and A. Zellweger, eds. Air Transportation Systems Engineering. Progress in Astronautics and Aeronautics Series. Reston, Va. Checkland, P. Systems Thinking, Systems Practice. Gore, B. The study of distributed cognition in free flight: A human performance modelling tool structural comparison.

Air Traffic Management | ScienceDirect

Leveson, N. Daouk, N. Dulac, and K. This paper is organized as follows. Section 2 presents a literature review on the leading ATM problems and the approaches used for their resolutions. In Section 3 , several air traffic management concepts related to this research are presented.

Balancing performance and cost

Section 4 presents the methodology used to solve the AHP. Section 5 presents the proposed solution called the AHPM. Section 6 presents the case studies and their results, and Section 7 contains the final considerations. In some situations, problems may even be seriously aggravated, whereby the attempt to solve the original problem generates other, perhaps greater, problems.

According to Crespo et al. The scale of the impact that failure to take into consideration all necessary factors could bring about is thus obvious. Air traffic management is a complex activity. It is still possible to further divide it into subproblems: distance between airplanes en route, ground delay and route redefinition [ 9 ].

The concept used by Agogino and Tumer 9 in addressing the solution of problems related to air holding, through the application of restrictive measures by varying the distance between airplanes en route, can be used in some simple scenarios. However, in more critical situations involving congested or saturated sectors, the results have not shown to be satisfactory 6.

What is UTM? The Future of Digital Air Traffic Management

Because each sector is assigned one air traffic controller, its capacity is determined by the number of aircraft where a controller can handle safely and effectively. In Brazil, a sector is considered congested when it contains more than 11 aircraft and is considered saturated with more than 13 aircraft. Sectors are defined, in more detail, in Section 3. Applying these measures will only delay congestion in the sectors or only reduce it in that particular moment, because the impact of the measure in a particular moment is not taken into account.

When applied, it is possible to organize the line of aircraft en route in every sector, but this does not guarantee, for example, that increasing the distance between the aircraft will reduce congestion in a certain sector without impacting other sectors, thus acting without any control. This impact can be controlled when actions are taken to reduce saturated sectors or congested ones that are likely to become saturated.

The automation processes must be carefully chosen, such as activity control, fault control and analysis, research and planning. DSS allows us to use data and models related to a domain of interest to resolve semistructured and unstructured problems 10 , 11 , 21 - According to Agogino and Tumer 9 , it is essential that air transportation support systems be designed to offer means for automated and flexible management to meet the inherent needs of this kind of management. The reasons for some suggestions are not sufficiently clear to be accepted by the air traffic controller, as a result of the high complexity involved in the analysis of scenarios and the evaluation of these possible suggestions.

Among the concepts presented in the literature 5 , 6 , 11 , 12 , 17 , 19 , 20 , DSSs can be classified into four groups by their way of operation: No autonomy : The system presents information and the human specialist must decide what is useful for every situation; Total autonomy : The system, which holds previous knowledge, analyzes every situation and makes the decisions; Semiautomatic more automatic : The system holds intelligence to evaluate several situations, makes the decision in most situations; however, in some situations, it will require the human specialist to take the decision; Semiautomatic more human : The system holds enough intelligence to analyze situations and present solutions to the specialist, who will ultimately decide what needs to be done.

In this research, the fourth concept—semiautomatic more human —is used. Multiagent systems consist of agents who interact in an organized fashion to obtain a certain objective. This kind of system is used with highly complex problems so that autonomous agents can find a solution by dividing the original problem amongst several interacting agents who have to meet their objectives. These distributed objectives are part of the main objective for success of the system. There may be one or more agents interacting in a given environment.

In some cases, agents can act on their own while in others, they can communicate with others. In more sophisticated systems, it may be necessary to add an agent whose sole purpose is to manage the other agents in the environment.

The authors identified three items to be studied and obtain the necessary improvements: The impact of actions of two new agents: the delay of aircraft on the ground and the redefinition of routes and distance between aircraft; The impact of coupling between the agents' actions; The benefits of providing rewards for the agents using previously calculated evaluation functions. It is possible to verify how a multiagent model can solve some specific problems of air traffic flow. In this model, there are six agents that communicate among them to avoid one sector with possible congestion.

Thus, when the aircraft are input in the model, it analyzed possible risk situations and issues that need to be solved. In this case, two agents will suggest the reroute for some aircraft and reduce the possible congestion. Various interacting agents can be identified: ATFM; airport terminal management and the integrated management of the FIRs and others Thus, the notion of identifying and organizing agents in this structure and making them interact to achieve the best results has become the main focus of research in the field.

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Reinforcement learning RL is the process in which an agent, without previous knowledge and in a predetermined number of interactions, interacts with the environment to achieve its objectives and, in doing so, is rewarded for its actions and objectives achieved. All the decisions taken by the agent take into account the history of actions previously executed to achieve an optimal result. One of the fundamentals of RL is that the reward for every action is such that the agent is stimulated if great results are achieved and discouraged if the results would be worse than previously obtained.

In this way, the agent can always look for the best action to be taken based on past actions, while trying to improve its results. According to Souza et al. A relevant contribution of the studies that adopts this concept in the recommendations based on analysis of previous decisions taken, making it possible the system to learn with air traffic controllers on a daily basis 6 , 11 , Air traffic management ATM focuses on providing means to manage air traffic, taking to account factors such as safety, fairness, weather and financial factors ATM is necessary to monitor airspace, controlling the flow of aircraft in predefined airways, and it can be managed in an integrated way, thus obtaining greater effectiveness with made decisions.

ATM can be divided in three groups: airspace management, whose goal is to improve airspace to meet the demand without physical expansion of the system; air traffic control, which focuses on controlling the aircraft traffic, providing the mandatory information to maintain flight safety; and ATFM, which focuses on providing information for the environment, so aircraft flow can be safely maintained and the impact of unforeseen situations at other levels be minimized.

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ATFM at the tactical level is the main focus of this study. The reason could be the closing of an airport—for example, in the case of adverse weather conditions or terrorist actions—or because of excessive traffic in a certain airspace sector. Some studies that seek to resolve this problem focus only on the moment of arrival of the aircraft and the necessary actions to reduce the impact, while others focus on the interaction of intelligent agents acting in certain areas with the human being 11 , 15 , The Brazilian airspace is comprised of the airspace above its territory, in addition to an area over the Atlantic Ocean.

The FIRs are divided into control sectors for better flow management and greater control. The sectors are under supervision of the air traffic controllers stationed at the area control center.

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  • Each FIR is under the responsibility of an area control center, which is responsible for delivering area control services, providing information and supervising aircraft both en route and in the act of landing or takeoff. This organizational structure makes it possible to maintain minimum separation between aircraft, as well as keep the air traffic flow in an organized way in its respective airspace.

    New concepts and methods in air traffic management

    The number of aircraft in a sector has a direct influence on the management complexity, that is, the more aircraft in a certain sector, the greater the safety risks in the ATM 6. The focus of the study is on the concept of critical scenarios, that is, when more aircraft are flying and more sectors are saturated or congested. These scenarios are currently of special interest to Brazil because the country is preparing to host two large international sports events during which air traffic volumes are forecast to increase dramatically.

    For this reason, dates, times and airports with high air traffic volumes were chosen for this study. In the proposed model, the concept of analysis of adjacent sectors is used, even if these sectors are in a FIR outside of the experiment. This modeling strategy is of great importance because it prevents actions taken to resolve problems in a certain sector from resulting in severe traffic issues in an adjacent sector. For this reason, in each case study, all sectors from FIRs that were not part of the study but were adjacent to a FIR that was part of the study were also included.

    Shumsky, M. Hansen, A. Odoni, G. Gosling, "Safe At Home? March-April , Number: 2. Pgs: Barnhart, C. Belobaba, and A. Kniker, and M. Farahat, and M.