Quantifying Air Traffic Controller Mental Workload Nicolas Suarez - - PowerPoint PPT Presentation

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Quantifying Air Traffic Controller Mental Workload Nicolas Suarez - - PowerPoint PPT Presentation

Mar 21, 2024 388 likes •697 views

Quantifying Air Traffic Controller Mental Workload Nicolas Suarez nstetzlaff@e-crida.enaire.es Patricia Lpez pmldefrutos@e-crida.enaire.es Eva Puntero epuntero@e-crida.enaire.es Sara Rodriguez srodriguezg@e-crida.enaire.es Fecha 1

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Quantifying Air Traffic Controller Mental Workload

Nicolas Suarez nstetzlaff@e-crida.enaire.es Patricia López pmldefrutos@e-crida.enaire.es Eva Puntero epuntero@e-crida.enaire.es Sara Rodriguez srodriguezg@e-crida.enaire.es

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SLIDE 2

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Index

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  • 2. Modelling ATCo workload
  • 1. Introduction
  • 5. Conclusions
  • 3. Workload Analysis Component (WAC)
  • 4. Validation
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Air Traffic Complexity Mental Workload

INTRODUCTION

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Basic Concepts

  • Difficulty perceived by

ATCo

Air Traffic Complexity

  • Ability to perceive and

respond to variables

  • Prior experience
  • Prior developed

personal constructs

ATCo Mental Complexity

  • Mental structures used

to interpret & respond

  • How each one relates,
  • verlaps & influences

the others determines cognitive complexity

Personal constructs

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SLIDE 6

How do we define complexity

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Currently:

  • “…measure of the difficulty that a

particular traffic situation will present to an air traffic controller…”

SESAR

  • “number of simultaneous or near-

simultaneous interactions of trajectories in a given volume of airspace”

Factors impacting complexity:

  • Geometrical nature of the air traffic
  • Operational procedures and practices

used to handle the traffic

  • Characteristics and behaviour of

individual controller

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SLIDE 7

Two basic hypotheses

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Complexity inside a sector is a function of the ATCo mental workload Assessing the ATCo mental workload inside a sector will provide an estimation of the associated complexity

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What is ATCo Mental Workload? The ATCo Mental Workload Framework Multiple Resource Workload Model (MWM) Experimental system

MODELLING ATCO WORKLOAD

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Primitive Operator Tasks

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Visual & Auditory Processing Visual Auditory Fixate Object Track Object Search with pattern Scan with pattern Listen Monitor Audio Signal

Perception (Visual & Auditory) Compre- hension Strategic Thinking Decision Making Responding (Manual & Verbal)

Human Behavior can be represented/segmented in Primitive Operator Tasks

  • C. D. Wickens, “Multiple Resources and Mental Workload,” Hum. Factors J. Hum. Factors Ergon. Soc., vol. 50, no. 3, pp. 449–455, Jun. 2008.
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Primitive Operator Tasks

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Central Processing

Comprehension Strategic Thinking Decision Making

Recall Recognise Select Compare Compute Decide

Perception (Visual & Auditory) Compre- hension Strategic Thinking Decision Making Responding (Manual & Verbal)

Human Behavior can be represented/segmented in Primitive Operator Tasks

  • C. D. Wickens, “Multiple Resources and Mental Workload,” Hum. Factors J. Hum. Factors Ergon. Soc., vol. 50, no. 3, pp. 449–455, Jun. 2008.
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SLIDE 11

Primitive Operator Tasks

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Perception (Visual & Auditory) Compre- hension Strategic Thinking Decision Making Responding (Manual & Verbal)

Responding Processing Manual Verbal Reach Object Press with foot Move with pattern Grasp Touch Push & hold Write Type …… Say a message

Human Behavior can be represented/segmented in Primitive Operator Tasks

  • C. D. Wickens, “Multiple Resources and Mental Workload,” Hum. Factors J. Hum. Factors Ergon. Soc., vol. 50, no. 3, pp. 449–455, Jun. 2008.
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What is workload?

Required psychological resources Available psychological Resources

Workload

Task load

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SLIDE 13

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Demanded Mental Resources (Task Load):

  • Physical and mental

activities demanded to carry out perceptual actions, cognitive actions and motor skills

  • Empirical research

and psychological theories of human cognitive processes Available Mental Resources:

  • Physical and mental

abilities (perceptual actions, cognitive actions and motor skills) that an ATCo has available to provide the control service

  • Psychological factors

(e.g. fatigue, stress) shape the available resources Threshold:

  • Value beyond which

Demanded Mental Resources (Task Load) exceeds the Available Mental Resources

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Underload How a Controller does it Operating modes What aircraft do What a Controller needs to do Demanded Mental Resources (Task Load) Available Mental Resources Flight Events Control Events

Situationa l Awarenes s Decision Making Response

Overload Operating Concept Threshold

Mental Workload Framework

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This is nice, but how do we use it?

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Identification of flight events Identification of control events

  • Required cognitive

channels

  • Interference matrix
  • Resolution of the

Matrix

Multiple Resources Workload Model algorithm (MWM)

Develop an experimental system that is able to estimate the workload

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Flight Event Layer:

  • Identification &

use of flight events

  • Traffic demand /

Traffic data or sector configuration data

  • Aircraft behaviour

within a specific airspace Controller Event Layer:

  • Actions expected

from an ATCo

  • e.g. solve

conflict Cognitive Process Layer:

  • Manner in which

an ATCo performs a specific action

  • Operating

concept

  • Mental processes

required to perform said actions

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What is WAC? Use of WAC

WORKLOAD ANALYSIS COMPONENT (WAC)

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Workload Analysis component

Stand alone workload estimation and measurement Calculates workload per sector / volume

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A sample application of WAC

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Use of mental workload to estimate sector capacity Assessment of the coherency of mental workload results through the comparison of Predicted and Perceived Workload Further work

VALIDATION

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Validating the model

Not easy Difficult to measure the actual workload Using indirect methods to perform the validation

  • Use of Mental Workload to Estimate Sector Capacity
  • Assessment of the coherency of mental workload results through

the comparison of Predicted and Perceived Workload

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Use of mental workload to estimate sector capacity

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  • Representative sample of

actual traffic

  • Use WAC component to

estimate capacity

Estimate actual capacity using workload Access data base to identify actual capacity

  • Similar or Equal 

workload assessment were accurate

  • Different  workload

assessment were accurate

Compare both values

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Assessment of the coherency of mental workload results through the comparison of Predicted and Perceived Workload

Record ISA (Instantaneous Self Assessment)

  • Normalized values

Calculate workload using WAC Issues

  • Low correlation (R = 0.75, p = 0.35)

20 40 60 Cognitive WL ISA WL

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20 40 60 Cognitive WL ISA WL

Causes:

  • Time shift
  • Need to improve calibration of non-nominal events

With correction

  • Strong correlation (R = 0.93, p = 0.14)
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Do we think that the validation work is completed?

SESAR EXE-04.07.01-VP-003 Resolving complexity by dynamic management of airspace

  • December 2014

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R 0,908 0,908

0,904

0,927 0,741 0,910

0,774

p 0,003 0,002

0,002

0,009 0,035 0,002

0,024

R 0,894

0,878

0,934 0,934 0,818

0,656

p 0,003

0,004

0,001 0,001 0,013

0,077

R

0,902

0,858 0,858 0,840

0,819

p

0,002

0,006 0,006 0,009

0,001 Ha, Kim and Seong (2006)

Physiological measures Subjective Measures

Correlation factors

EXPERIMENT RESULTS BETWEEN WORKLOAD MEASURES

Eye closure fraction Blink duration Number of fixation on Regions Fixation time on Regions

Fixation-related parameters Blink-related parameters

Information flow rate NASA-TLX MCH score NASA-TLX MCH score Blink frequency

NO

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Estimating complexity How to improve the model

CONCLUSIONS

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Estimating complexity

Key enabler for several SESAR concepts Using workload as an indicator of air traffic complexity Mental Workload framework:

  • Demanded resources
  • Available resources
  • Thresholds.

Workload estimation algorithm (MWM) Workload Analysis Component (WAC)

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Validation

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On-going Needs to be improved

  • More calibration experiments
  • Identifying more direct workload

methods

Results

  • Workload estimations are accurate
  • Useful for ATM
  • Suggest relationship between

perceived & predicted mental workload

But…

  • Improvements must be made on

both the framework and the algorithm

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Areas of improvement

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Introducing psychological factors (fatigue, stress & emotion) Introducing dynamic thresholds Enhancement of the

  • perating mode

definition Impact of system automation features Full development and integration of situational awareness and decision making processes

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Fecha Titulo 30

Centro de Referencia I+D+i ATM