Role Of Air Traffic Controllers Research Articles

Air Traffic Controller Fatigue Detection by Applying a Dual-Stream Convolutional Neural Network to the Fusion of Radiotelephony and Facial Data

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic controllers can impair their work ability and even threaten flight safety, which makes it necessary to carry out research into how to optimally detect fatigue in controllers. Compared with single-modality fatigue detection methods, multi-modal detection methods can fully utilize the complementarity between diverse types of information. Considering the negative impacts of contact-based fatigue detection methods on the work performed by air traffic controllers, this paper proposes a novel AF dual-stream convolutional neural network (CNN) architecture that simultaneously extracts controller radio telephony fatigue features and facial fatigue features and performs two-class feature-fusion discrimination. This study designed two independent convolutional processes for facial images and radio telephony data and performed feature-level fusion of the extracted radio telephony and facial image features in the fully connected layer, with the fused features transmitted to the classifier for fatigue state discrimination. The experimental results show that the detection accuracy of radio telephony features under a single modality was 62.88%, the detection accuracy of facial images was 96.0%, and the detection accuracy of the proposed AF dual-stream CNN network architecture reached 98.03% and also converged faster. In summary, a dual-stream network architecture based on facial data and radio telephony data is proposed for fatigue detection that is faster and more accurate than the other methods assessed in this study.

Job Analysis for Air Traffic Control

Abstract. This article presents a review and categorization of job analyses on the role of air traffic controllers (ATCO). There are three parts – how the role has been conceptualized, why it was conceptualized in this manner, and what we can conclude from developments in ATCO job analysis. The article includes a history of job analysis in air traffic control and two tables summarizing task and worker analyses. A large amount of information is available on tasks and attributes and we conclude that ATCO job analyses have been carried out in a varied and disunited manner. While there is no universally accepted analysis for the role of ATCO, previous analyses could nonetheless be used as a foundation for future analytic work.

Conceptual development of an advanced air traffic controller workstation based on objective workload monitoring and augmented reality

NextGen, SESAR, and other international projects intend to develop the coming air traffic management (ATM) environment to cope with the present problems, including e.g. capacity, efficiency, safety, and environmental impact. These projects also introduce considerable changes in the role of air traffic controllers (ATCOs), and shift some of the present tasks from ground to on-board (pilots) and diversify automation. Due to the consequential effects of these developments, (i) the models describing the present operational circumstances, (ii) the working conditions, and (iii) the working environment (e.g. human–machine interactions, task-work and mental loads, cognitive aspects, situation awareness, decision support) must be reconsidered and adapted to the future role of operators. This paper aims to describe a new approach to model the ATCOs’ working processes, and also introduces a new concept for the ATCOs’ future working environment. The developed model covers the situation awareness and decision making processes, including the subjective aspects of the ATCOs decision making based on their workload, mental condition, knowledge, or practice. The proposed new working environment contains special information displays, the decision support system and the ATCOs workload and mental condition measurement / monitoring system, based on objective, on-line, continuous, and nonintrusive measurement techniques. Some of the core systems developed in collaboration with HungaroControl were exhibited at the World ATM congress in 2014 and 2015.

심리학으로 바라본 항공교통관제사 인지능력

Aviation safety issues have drawn much attention from the international community due to the growing demand for air travel. Although it is a widely accepted fact that human-related factors are closely linked to aviation safety, there is lack of understanding what roles those factors play in aviation, in the field of air traffic control in particular. It has been reported that the role of air traffic controllers significantly affects air safety. This review will discuss cognitive aspects of expertise in air traffic control including time perception, working memory, reasoning, perception, attention, scanning/vigilance, decision making, and planning with examples of how each aspect can be dealt with in performing air traffic control duties. The relevant studies in psychology have also been briefly reviewed in the interest of enhancing understanding of characteristics of air traffic control tasks and the related human factors. This review concludes with a call for more in-depth research into cognitive factors in air traffic control.

Air Traffic Controllers' Situation Awareness and Workload under Dynamic Air Traffic Situations

Abstract As air-traffic control (ATC) services are provided for the purpose of achieving flight safety and efficient aircraft operations, the role of air-traffic controllers in the aviation system is vital. Since the accident rates caused by human error have been increasing, major aviation institutes worldwide are actively conducting research on the human factors affecting controllers. Among human factors in aviation, situation awareness (SA) and workload have been at the center of many research activities for years. Also, many people have expressed an interest in how these two constructs relate to each other. In this article, the relations between controllers' SA and workload were explored under different taskload conditions. An experiment that included ATC simulation to measure the controller's SA and workload at various air-traffic situations was conducted. Based on the results of this study, the measures to keep the controller's SA and workload at a manageable level and the effective training programs can be explored.

A study on how to improve communication effectiveness of air traffic controllers

As the objective of air traffic control (ATC) services is to achieve flight safety and efficient aircraft operations, the role of air traffic controllers is vital in the aviation system as a result. In this paper, the controller’s interactions were explored in terms of communications. The study was conducted through survey on the Republic of Korea Air Force (ROKAF) ATC controllers to identify any discrepancies in their viewpoints on the role of communication in their profession. In order to diminish differing points of view and ensure effective communication, the experienced controllers should commit to creating the desired work environment and every controller must keep in mind that mutual trust is a vital part of a successful organization. Instructions in accordance with the regulations, improvements in job satisfaction and self esteem, communication training, and individual endeavors fostering effective communication are all measures which will go a long way toward improving effective communications between the ATC specialists.

LEARNING INDIRECT ACTIONS IN COMPLEX DOMAINS: ACTION SUGGESTIONS FOR AIR TRAFFIC CONTROL

Providing intelligent algorithms to manage the ever-increasing flow of air traffic is critical to the efficiency and economic viability of air transportation systems. Yet, current automated solutions leave existing human controllers "out of the loop" rendering the potential solutions both technically dangerous (e.g. inability to react to suddenly developing conditions) and politically charged (e.g. role of air traffic controllers in a fully automated system). Instead, this paper outlines a distributed agent-based solution where agents provide suggestions to human controllers. Though conceptually pleasing, this approach introduces two critical research issues. First, the agent actions are now filtered through interactions with other agents, human controllers and the environment before leading to a system state. This indirect action-to-effect process creates a complex learning problem. Second, even in the best case, not all air traffic controllers will be willing or able to follow the agents' suggestions. This partial participation effect will require the system to be robust to the number of controllers that follow the agent suggestions. In this paper, we present an agent reward structure that allows agents to learn good actions in this indirect environment, and explore the ability of those suggestion agents to achieve good system level performance. We present a series of experiments based on real historical air traffic data combined with simulation of air traffic flow around the New York city area. Results show that the agents can improve system-wide performance by up to 20% over that of human controllers alone, and that these results degrade gracefully when the number of human controllers that follow the agents' suggestions declines.