COMETA: An Air Traffic Controller’s Mental Workload Model for Calculating and Predicting Demand and Capacity Balancing

Frutos, Patricia López de; Rodríguez, Rubén Rodríguez; Zhang, Danlin Zheng; Zheng, Shutao; Cañas, José J.; Muñoz-de-Escalona, Enrique

doi:10.1007/978-3-030-32423-0_6

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…As has been shown in previous studies, asking controllers to assess their workload at these intervals does not interfere with the main task performed under normal conditions (López de Frutos et al , 2019).…”

Section: Assessment Of Workloadsupporting

confidence: 60%

How much workload is workload? A human neurophysiological and affective-cognitive performance measurement methodology for ATCOs

Suárez

Valdés

Moreno

et al. 2022

AEAT

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Purpose Air traffic controllers (ATCOs) play a fundamental role in the safe, orderly and efficient management of air traffic. In the interests of improving safety, it would be beneficial to know what the workload thresholds are that permit ATCOs to carry out their functions safely and efficiently. The purpose of this paper is to present the development of a simulation platform to be able to validate an affective-cognitive performance methodology based on neurophysiological factors applied to ATCOs, to define the said thresholds. Design/methodology/approach The process followed in setting up the simulation platform is explained, with particular emphasis on the design of the program of exercises. The tools designed to obtain additional information on the actions of ATCOs and how their workload will be evaluated are also explained. Findings To establish the desired methodology, a series of exercises has been designed to be simulated. This paper describes the project development framework and validates it, taking preliminary results as a reference. The validation of the framework justifies further study to extend the preliminary results. Research limitations/implications This paper describes the first part of the project only, i.e. the definition of the problem and a proposed methodology to arrive at a workable solution. Further work will concentrate on carrying out a program of simulations and subsequent detailed analysis of the data obtained, based on the conclusions drawn from the preliminary results presented. Originality/value The methodology will be an important tool from the point of view of safety and the work carried out by ATCOs. This first phase is crucial as it provides a solid foundation for later stages.

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Section: Assessment Of Workloadsupporting

confidence: 60%

How much workload is workload? A human neurophysiological and affective-cognitive performance measurement methodology for ATCOs

Suárez

Valdés

Moreno

et al. 2022

AEAT

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show abstract

“…Therefore, assessing MW goes far beyond determining task complexity levels, and implies shifting the focus towards a human centred modelling of MW. We could say that ATCo's MW is likely to remain the single greatest functional limitation on the capacity of the ATM System (Majumdar & Polak, 2001;de Frutos et al, 2019). MW in the ATM domain has been attempted to be estimated and monitored using subjective, physiological and behavioural measures.…”

Section: Introductionmentioning

confidence: 99%

Developing a First-Approach Model of Air Traffic Controllers' Mental Workload based on Behavioural Measures: A Theory for Modelling Air Traffic Controllers' Mental Workload

Muñoz-de-Escalona,

Leva,

Frutos

2023

Proceeding of the 33rd European Safety and Reliability Conference

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Air Traffic Controller (ATCo) Mental Workload (MW) is likely to remain the single greatest functional limitation on the capacity of the ATM (Air Traffic Management) System. There is a need to develop computational models for monitoring real-time MW to facilitate the development of different approaches for task support. MW in the ATM domain has been attempted to be estimated and monitored using subjective, physiological and behavioural measures. However, the literature highlighted disadvantages with current subjective and physiological methodologies used to assess MW related to their impracticality in real work-environments. Therefore, what is needed is an unobtrusive MW calculation model based on ATCos' recordable behaviours that can be deployed unobtrusively in an ecologically valid environment. This research aims to offer a first-approach model of ATCos' MW based on data that can be collected from the log of the technological systems used for ATM: 1) their communication patterns and 2) their actions with the ATM technical systems. In a next stage, this model will be further validated during simulation sessions with real ATCos, using physiological measures (eye-tracking), alongside subjective measures of MW. The main outcome of this research project will be a real-time MW nonintrusive and automatic monitoring tool that would allow ATCOs and ATM systems to get adapted to task complexity variations throughout time, mitigating the disastrous effect of drops in human performance.

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“…We thankfully acknowledge the effort of EUROCONTROL for developing the NEST software and the access of using the huge amount of historical data. thus increase the airspace capacity [2] [3]. Conventionally, the DCB problem has been mathematically formulated with optimisation models, such as 0-1 Integer Programming [1], Eulerian-Lagrangian model [4], with ground holding, airborne holding and re-routing strategies involved.…”

Section: Introductionmentioning

confidence: 99%

Integrated Frameworks of Unsupervised, Supervised and Reinforcement Learning for Solving Air Traffic Flow Management Problem

Huang

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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This paper studies the demand-capacity balancing (DCB) problem in air traffic flow management (ATFM) with collaborative multi-agent reinforcement learning (MARL). To attempt the proper ground delay for resolving airspace hotspots, a multi-agent asynchronous advantage actor-critic (MAA3C) framework is firstly constructed with the long short-term memory network (LSTM) for the observations, in which the number of agents varies across training steps. The unsupervised learning and supervised learning are then introduced for better collaboration and learning among the agents. Experimental results demonstrate the scalability and generalization of the proposed frameworks, by means of applying the trained models to resolve different simulated and real-world DCB scenarios, with various flights number, sectors number and capacity settings.

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COMETA: An Air Traffic Controller’s Mental Workload Model for Calculating and Predicting Demand and Capacity Balancing

Cited by 7 publications

References 14 publications

How much workload is workload? A human neurophysiological and affective-cognitive performance measurement methodology for ATCOs

How much workload is workload? A human neurophysiological and affective-cognitive performance measurement methodology for ATCOs

Developing a First-Approach Model of Air Traffic Controllers' Mental Workload based on Behavioural Measures: A Theory for Modelling Air Traffic Controllers' Mental Workload

Integrated Frameworks of Unsupervised, Supervised and Reinforcement Learning for Solving Air Traffic Flow Management Problem

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