on Seblemasedace cairlineATC)

and controllers activity mediated by factors such as the controllers abilities, age, fatigue, and level of experience (Loftet al., 2007).

The present paper examines the relationship between task demands as dened by a set of ATC complexity factors,controllers actions and subjective workload in the frame of ATC real-time simulations. Thus, the present study attemptsto predict subjective workload as criteria on a moment-to-moment basis using a linear combination of ATC complexity

0968-090X/$ - see front matter 2010 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +36 1 297 2241; fax: +36 1 297 2228.E-mail addresses: jelena.djokic@eurocontrol.int (J. Djokic), bernd.lorenz@eurocontrol.int (B. Lorenz), fricke@i.tu-dresden.de (H. Fricke).

Transportation Research Part C 18 (2010) 930936

Contents lists available at ScienceDirect

Transportation Research Part Cdoi:10.1016/j.trc.2010.03.005of the air trafc controller. Therefore proposed solutions for increasing airspace capacity aim at reducing controller work-load - which includes: the delegation of separation tasks from ground to the aircraft (e.g. Airborne Separation AssuranceSystem (ASAS-TN2 Consortium, 2008)), a re-sectorisation of the airspace, and the introduction of new controller supporttools in order to reduce the amount of work, or at least the difculty of the controller tasks. As the work of air trafc con-trollers is predominantly cognitive in nature a considerable amount of research has been undertaken to understand thecomplex task demands that drive the workload of a controller (see Hilburn (2004) for a recent review). The term work-load denotes a subjective quality reecting the individual controllers perception of the task demand imposed on him/herby the current air trafc situation. Thus, many studies implicitly assume that controller workload varies as a function ofboth directly measurable air trafc factors (number of aircraft in the sector, speed variability, proximity of aircraft, etc.)1. Introduction

Over the past decade Air Navigatitium, 2006a). The most pressing prosufcient capacity to meet this incremaintained or even improved. Airspin turn means an economic loss to a

In the current air trafc control (rvice Providers (ANSPs) have coped with signicant trafc growth (SESAR Consor-facing the European Air Trafc Management (ATM), therefore, will be to provideair trafc demand, while at the same time the safety level of air travel has to bepacity that lags behind air trafc demand inevitably leads to ight delays, whichs (SESAR Consortium, 2006b).environment the key limiting factor to increasing sector capacity is the workloadAir trafc control complexity as workload driver

Jelena Djokic a,*, Bernd Lorenz a, Hartmut Fricke b

a EUROCONTROL CEATS Research, Development and Simulation Centre, Ferihegy 1, A Porta, H-1185 Budapest, Hungaryb Institute of Logistics and Aviation, Technische Universitt Dresden, Dresden, Germany

a r t i c l e i n f o

Article history:Received 15 August 2008Received in revised form 5 March 2010Accepted 10 March 2010

Keywords:ATC complexityTask demandControllers activityWorkload

a b s t r a c t

This paper describes an investigation into ATC complexity as a contributory factor inchanges of controllers workload. ATC complexity, together with equipment interfaceand procedural demands comprise the task demands on the controller; subsequent con-troller activities are mediated by performance shaping factors to create workload. In orderto establish a link between ATC complexity and a controllers subjective workload, com-plexity factors are identied and subsequently related to workload indicators. The studieddata comes from a real-time simulation using Controller-Pilot Data-Link Communication(CPDLC) technology, recently completed at EUROCONTROL CRDS in Budapest.

2010 Elsevier Ltd. All rights reserved.

journal homepage: www.elsevier .com/locate / t rc

will be discussed and conclusions drawn.

24 factors in detail. For a more thorough review of the listed factors readers are referred to the indicated source literature.

J. Djokic et al. / Transportation Research Part C 18 (2010) 930936 9313. Controller activity link between task demands and controllers workload

Even though task demand factors can capture one aspect of the ATC situation, it should be kept in mind that ATC is a dy-namic environment and that controllers actively interact with the trafc, and therefore have an important inuence on ATCcomplexity and hence the level of safety.

Several researchers agree that workload is a result of such a complex interaction between the task demand and theway the controller actively manages the situation (e.g. Hilburn, 2004; Loft et al., 2007; Majumdar et al., 2004; Pawlaket al., 1996). Moreover controllers, by performing certain activities, regulate the evolution of the task demands withthe aim of keeping workload at an acceptable level. Nevertheless, not all controller tasks are observable. As dened by(Histon and Hansman, 2002), there are four controller tasks while managing the ATC situation: monitoring, evaluating,planning and implementing the formulated plan. Furthermore, out of these four tasks only one is observable, and thatis the implementation process. It means that by taking only objectively measurable (sub)tasks into consideration, it is pos-sible to capture only one aspect of comprehensive controller activity involved. However, as this aspect of the controllersactivity is directly connected with changes made by the controller on the ATC situation, we considered it sufcient for ourstudy.

Thus, in the current study, controllers input (data entries) and radio communication were used as the representatives ofperformed controllers activities (the study is based only on the executive controller data entries, and not planning control-ler, and therefore no phone communication is not considered here).2. ATC complexity

A straightforward determinant of controller workload is simply the number of aircraft for which the controller is respon-sible in a specied time and sector. This measure is referred to as the sector load. Predicting sector load and avoiding sectoroverload is the basic tool upon which current trafc ow management is built. However, the level of difculty experiencedby the controllers depends on additional factors beyond the number of aircraft present in a sector (Sridhar et al., 1998). To beable to capture more accurately ATC complexity, it is necessary to take into consideration also ight characteristics of eachindividual aircraft as well as interactions between pairs of aircraft. Important ight characteristics of aircraft relate to instan-taneous changes of the state of the aircraft, e.g. changes in altitude, heading or speed. Interactions between aircraft are con-sidered not only in terms of potential conicts but also include the pattern of how aircraft converge and the degree of what in(Delahaye and Puechmorel, 2000) has been referred to as the disorder among aircraft, i.e. the variability in headings andspeeds of aircraft. Despite the fact that ATC complexity has been the subject of a signicant number of studies (see Hilburn(2004) for a recent review), and many complexity factors have been proposed, up to now a comprehensive and generallyaccepted set of measures has not been dened.

For the purpose of the present study, a list of complexity factors was selected that has been consistently found to beimportant and for which detailed calculation formula have been reported. The factors were partially elicited from work de-scribed by Delahaye and Puechmorel (2000), Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazza andGuittet (2006), Laudeman et al. (1998) and Chatton (2001).

The selected overall set of 24 complexity factors is presented in Table 1. It is out of the scope of this paper to describe allfactors and controllers activity measures as predictors. Apart from enriching the research base on ATC complexity as anunderlying driver of controller workload the knowledge of this relationship can be of quite a practical use for the planningand the conduct of ATC real-time simulations. In real-time simulations quite often the number of aircraft to be handled bythe controller is systematically increased to either investigate capacity margins or to demonstrate that a proposed new oper-ational concept has the potential to increase capacity, i.e. to improve the accommodation of increased trafc by the new con-cept. However, increasing the number of trafc may simultaneously induce changes in other trafc factors that likewise havean impact on the controllers workload and performance. Therefore, it is necessary to identify all potential complexity vari-ables and determine their interrelationships in order to better predict how a chosen trafc sample would increase the work-load. This appears to be even more important when the simulation team wants to employ a series of trafc samples withidentical difculty, i.e. identical complexity, when e.g. familiarisation effects of using the same trafc samples across re-peated exercises are to be minimized. Accordingly, it was one of the more practical aims of the present study to derive aset of suitable complexity factors with which a pre-analysis of trafc samples in the context of real-time simulations canbe performed.

The paper is organized as follows: In the following section we will give a brief overview of research on ATC complexityand the derivation of task demand metrics from which a selection for the purpose of the present study was made. A moredetailed description of controllers activity and workload measures used in the study follows. Next is described the real-timesimulation experiment which provided the data base for the calculation/collection of the predictor and criterion metrics.What follows is a description of the approach for statistical analysis and the presentation of the results. Finally, these results

and Guittet (2006)

932 J. Djokic et al. / Transportation Research Part C 18 (2010) 9309363 Number of descending aircraft Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazzaand Guittet (2006)

4 Number of aircraft with heading change greater than15

Kopardekar and Magyarits (2003), Laudeman et al. (1998)

5 Number of aircraft with the speed change greater than10 knots

Kopardekar and Magyarits (2003), Laudeman et al. (1998)

6 Number of aircraft with lateral distance between 025 nm and vertical separation less than 2000 ft above29,000 ft

Kopardekar and Magyarits (2003), Laudeman et al. (1998)

7 Horizontal proximity measure 1 (C5) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazzaand Guittet (2006)

8 Vertical proximity measure 1 (C6) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazzaand Guittet (2006)

9 Horizontal proximity measure 2 (C7) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003)10 Vertical proximity measure 2 (C8) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazza

and Guittet (2006)11 Horizontal proximity measure 3 (C9) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003)Table 1Complexity factors.

Complexity factors Used in

1 Number of aircraft Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazzaand Guittet (2006)

2 Number of climbing aircraft Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazza4. The real-time simulation experiment

4.1. Simulation

In order to obtain relevant values, data were recorded during the 2 week LINK2000+ Small Scale Real-Time Simulation 2experiment (LINK 2000+ SSRTS2). The aim of this simulation was to develop and validate new principles of task delegationbetween the planning and executive controller with the aim to best accommodate the Controller-Pilot Data-Link Communi-cation (CPDLC) capability in an en-route environment (EUROCONTROL, 2007; Schuen-Medwed et al., 2007). The simulationinvolved three different sectors of the Central European Air Trafc Services (CEATS) airspace. The simulated measured areaconsists of parts of the Austrian and Hungarian airspace from FL 285 to FL 460 without vertical split.

The data used for the present study are data obtained for the two busiest sectors simulated. Namely, within thosetwosectors the peak number of controlled aircraft on frequency goes beyond 13, while in the third sector that number isseven.

4.2. ATC complexity measures

The recorded own trajectories were used as input data for the complexity factors calculations listed in Table 1. More-over, values used are those related to instantaneous position of the aircraft (latitude, longitude, altitude), as well as datareecting movements of the aircraft within the sector (climb or descent rate, heading and speed). Customized softwarewas developed to calculate these values for each 2 min time steps.

12 Vertical proximity measure 3 (C10) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003)13 Time-to-go to conict measure (C13) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003)14 Variance of ground speed (C14) Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazza

and Guittet (2006)15 Ratio of standard deviation of speed to average speed

(C15)Chatterji and Sridhar (2001), Kopardekar and Magyarits (2003), Gianazzaand Guittet (2006)

16 Density indicator (mean) Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

17 Variability in headings (track_disorder) (mean) Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

18 Variability in speed (speed_disorder) (mean) Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

19 Divergence between pairs of aircraft (mean) Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

20 Convergence between pairs of aircraft (mean) Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

2122 Sensitivity indicator (a/c converging-mean; a/cdiverging-mean)

Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

2324 Insensitivity indicator (a/c converging-mean; a/cdiverging-mean)

Delahaye and Puechmorel (2000), Chatton (2001), Gianazza and Guittet(2006)

J. Djokic et al. / Transportation Research Part C 18 (2010) 930936 9334.3. Controller activity measures

All inputs made by the executive controller recorded during the simulation were extracted. These inputs refer to assign-ments of vertical rate, exit ight levels/planned entry levels, cleared ight levels, headings, speed instructions, and directclearances. These were summed across each 2-min-time step and across input, resulting in only one measure namedActions_SUM. Furthermore, cumulative duration of radio calls (=frequency occupancy time per 2-min-time step) wascalculated as well as the average duration of single calls. Altogether, we used three measures of the controllers activity Actions_SUM, frequency occupancy time and average radio duration obtained for every 2-min-time steps.

4.4. Workload measures

For the same time steps, controllers were providing workload ratings. To collect workload measures during the simula-tion the Instantaneous Self Assessment (ISA) technique as operator-subjective metric was applied, where the air trafc con-troller gives subjective ratings of workload. This tool was developed by the UK NATS and offers ve points rating scale. Onevery time step controller can opt a level of workload ranging from very low to very high.

Descriptive statistics of the extracted measures are given in Table 2.

Table 3Results of the Principal Component Analysis.

Components Eigenvalue % of variance Cum.% of variance

Comp. 1 4.935 20.563 20.563Comp. 2 3.442 14.343 34.906Comp. 3 1.775 7.395 42.301Comp. 4 1.450 6.042 48.343Comp. 5 1.344 5.602 53.944Comp. 6 1.129 4.706 58.650Comp. 7 1.036 4.316 62.966Comp. 8 1.030 4.291 67.257Table 2Descriptive statistics of dependent measures.

Measures Min. Max. Mean Std. deviation

Actions_SUM (count) 0.000 13.000 3.320 2.372Frequency occupancy time (s) 0.000 50.400 23.991 8.779Average radio duration (s) 0.000 7.200 3.245 0.674ISA (rating 15) 1.000 5.000 2.870 0.7364.5. Participants and data extraction procedure

The LINK2000+ SSRTS2 experiment involved a total of 18 controllers out of which six controllers worked on the two sec-tors considered here. The data used for the statistical analysis were derived from these six participants only. Each controllercompleted an overall of eight exercises of 1 h and 20 min, from which 1-h-recordings were extracted for analysis. Scoreswere derived for every 2 min, resulting in 30 measurements per exercise. These data were obtained for each indicator(ATC complexity measures, workload measures and safety measures). The overall dataset comprised 6 (controllers) 8(exercises) 30 (time segments) = 1440 measurements for each indicator. Prior inspection of the data set revealed that dur-ing the whole simulation (all eight exercises) one of the six controllers always rated workload as fair, hence there were novariations in workload measure. The data of this participant was discarded from the analysis, with 1200 measurementsremaining. In 58 time segments (4.8%) data was missing. Therefore, subsequently reported results are based on measure-ments obtained in 1142 time segments.

5. Statistical evaluation and results

5.1. Principal Component Analysis

In a rst analysis step, a Principal Component Analysis (PCA) on all 24 complexity metrics was computed in order to de-rive a reduced number of uncorrelated predictor variables for the subsequent computation of regression models.

Principal components having an eigenvalue >1 were extracted and subsequently rotated using the VARIMAXmethod. Thisanalysis resulted in the extraction of eight principal components that accounted for 67.26% of the total variance in the met-

934 J. Djokic et al. / Transportation Research Part C 18 (2010) 930936rics. Table 3 displays these components sorted by the sizes of their eigenvalues and along with the percentage of variancethey account for.

Table 4 shows the rotated component matrix which contains the loadings of the 24 complexity factors on the eight ex-tracted principal components. Loading below 0.4 are suppressed to better visualize the pattern. By inspection of Table 4, thefollowing component meanings could be derived. Note, that the loading of a given metric on a given component is equivalent

Table 4Rotated component matrix.

Complexity factors Component

1 2 3 4 5 6 7 8

Variance of ground speed (C14) .884Ratio of standard deviation of speed to average speed (C15) .845Divergence between pairs of aircraft (mean) .787Convergence between pairs of aircraft (mean) .785Variability in speed (speed disorder) (mean) .703 .452Number of aircraft .816Horizontal proximity measure 2 (C7) .672 .431Variability in headings (track disorder) (mean) .657Number of climbing aircraft .643Number of aircraft with heading change greater than 15 .442Horizontal proximity measure 1 (C5) .894Density indicator (mean) .815Number of descending aircraft .785Number of aircraft with the speed change greater than 10 knots .732Vertical proximity measure 1 (C6) .569Number of aircraft with lateral distance between 025 nm and vertical separation less

than 2000 ft above 29,000 ft.559

Sensitivity indicator (a/c diverging-mean) .772Sensitivity indicator (a/c converging-mean) .751Time-to-go to conict measure (C13) .623Insensitivity indicator (a/c converging-mean) .723Insensitivity indicator (a/c diverging-mean) .686Vertical proximity measure 3 (C10) .849Vertical proximity measure 2 (C8) .540Horizontal proximity measure 3 (C9) .908to the correlation between that metric and that component. Therefore, the metric with the highest loading by and largeguides the interpretation of the component.

Comp. 1 ground speed variance and divergence/convergence: strongly related to the variance of the ground speed (0.884)and the ratio of the standard deviation to the mean ground speed (0.845). Also, the strong correlation with divergence andconvergence factors (0.787 and 0.785 respectively) was recognised, which is in compliance with speed signicance, asdivergence/convergence factors actually measure how fast aircraft are moving toward/from each other.Comp. 2 aircraft count: this component has the strongest correlation with the number of the aircraft in the sector(0.816).Comp. 3 horizontal proximity: this component can be considered as addition to the previous one, as it shows high cor-relation with the horizontal distance between aircraft taking into consideration the aircraft count horizontal proximitymeasure (C5): 0.894 and density_mean: 0.815. Together these two components (Comp. 2 and Comp. 3) can be represen-tatives of so-called sector density.Comp. 4 aircraft vertical transitioning: highly correlated to the number of descending aircraft (0.785) as well as speedchange related to this vertical evolution (0.732).Comp. 5 conict sensitivity: this component is loaded highly by both sensitivity indicators (Sd+(i): 0.772 and Sd(i):0.751). Sensitivity is related to the gradient of the relative distance between aircraft. This indicator measures the changein terms of relative distance in response to changes in speed and heading of the involved aircraft. If sensitivity is high onlysmall changes in heading and speed imply a high impact on relative distance. This is the case, e.g. when two aircraft areheading towards each other. The sensitivity indicators are designed to set a weight on potential conicts that are difcultto solve (see Gianazza and Guittet, 2006). Note that a situation with high sensitivity is easier to resolve for the controllerthan one with a low sensitivity (Delahaye and Puechmorel, 2000).Comp. 6 insensitivity: This component is strongly related to the insensitivity indicators both for convergence and diver-gence of the aircraft (insen_c: 0.723 and insen_d: 0.686). It is not simply an analogue with the opposite direction to theprevious component. High insensitivity is given for a pair of aircraft when the degree of convergence is high while sen-sitivity for convergence is low.Comp. 7 vertical separation: high correlation with the measure of the vertical separation of aircraft in close horizontalproximity (C10) denes this component (0.849).

J. Djokic et al. / Transportation Research Part C 18 (2010) 930936 935Comp. 8 horizontal separation: analogously to the previous component, this component is dened based on the corre-lation with the measure of horizontal separation of the aircraft in close vertical proximity C9 (0.908).

The PCA yielded eight component scores for each 2-min-interval which were used as predictors in the subsequent multi-ple regression analyses.

5.2. Multiple regression analyses

Two sets of multiple regression models were computed. The rst set was performed to assess the effectiveness in predict-ing ISA workload ratings on the basis of ATC complexity and controller activity metrics.

5.2.1. ISA regression modelsInstead of using the stepwise linear regression involving all predictors, we rst compared two alternative multiple

regression equations to t the data. For the rst multiple regression equation all eight component scores were forced intothe model regardless of their single signicance. In the second equation the three activity metrics entered the equation. Thiswas done in order to assess the contribution of ATC complexity components in relation to the controller activity metrics.Table 5 contains the global statistics of these two equations. It can be seen that the rst equation containing only complexitycomponents yielded a multiple R of 0.36 corresponding to R2 of 0.13 or in other words corresponding to 13% of variance ofthe ISA workload ratings. Adding the controller activity measures in the second equation contributed to a signicant increasein the multiple R although it increased the percentage of variance explained by only 3% to a total of 16%. Therefore, it can be

Table 5Comparison of alternative multiple regression models for prediction of ISA.

Regression equation containing Mult. R R2 R2 change F change df Sig. F change

Complexity components 0.36 0.13 0.13 21.20 8, 1131 p < 0.001Complexity components and controllers activity measures 0.40 0.16 0.03 13.77 3, 1128 p < 0.001

Table 6Parameter statistics of the optimised model for the prediction of ISA workload ratings.

B Std. error Beta t Sig.

Comp. 1 0.052 0.020 0.071 2.596 p < 0.05Comp. 2 0.102 0.021 0.139 4.921 p < 0.001Comp. 3 0.101 0.020 0.137 5.009 p < 0.001Comp. 4 0.092 0.020 0.124 4.561 p < 0.001Comp. 5 0.147 0.021 0.200 7.166 p < 0.001Comp. 6 0.074 0.020 0.100 3.676 p < 0.001Frequency occupancy time 0.012 0.003 0.142 4.627 p < 0.001Average radio duration 0.182 0.033 0.164 5.488 p < 0.001concluded that both sources of information, ATC complexity and controller activity, have a unique contribution to the pre-diction of ISA workload ratings.

A nal multiple regression model was computed using traditional stepwise linear regression approach in order to identifythose predictors that are responsible for the signicant contribution to workload prediction. This model we refer to as theoptimised model as all insignicant variables have been removed. The parameter statistics of this model is given in Table 6.The model consists of eight parameters (Comp. 1Comp. 6, frequency occupancy time and average radio duration).

The stepwise regression analysis revealed that the rst six out of eight complexity components remained in the predic-tion model. The components that showed the strongest correlation with ISA ratings are Comp. 3 and Comp. 5 which considerhorizontal proximity and the conict sensitivity. The higher horizontal proximity, i.e. the closer the aircraft in the horizontalplane, the higher was controller workload. When sensitivity of the conict increased, the workload ratings of the controllerdecreased, which is consistent with (Delahaye and Puechmorel, 2000).

Frequency occupancy time and average radio duration representing the communication load also remained in the model.When frequency occupancy time, i.e. overall frequency occupancy time is increasing, the workload rating is also higher. Onthe other hand, the average duration decreased as controllers perceived workload increased.

6. Discussion and conclusions

The focus of the paper was the investigation of the relationship between ATC complexity, controllers activity measuresand subjective workload. Based on the previous work in the eld an initial set of 24 complexity factors was dened. In orderto reduce this set, a Principal Component Analysis (PCA) was performed, which resulted in eight components. (Gianazza and

936 J. Djokic et al. / Transportation Research Part C 18 (2010) 930936Guittet, 2006) also performed a PCA using a set of 27 complexity indicators as input variables. There is a big overlap betweentheir and our set of input variables (see complexity factors that are used also in (Gianazza and Guittet, 2006) in Table 1).However, their data was extracted in 1-min-time steps from real-trafc recorded in a total of 103 sectors across one dayof trafc. Their PCA revealed six components (using the same extraction criterion of eigenvalue >1 as in the present paper)that accounted for 76% of the total variance. Aircraft count had the highest loading on the rst component accounting for46.7% of the variance in their PCA which corresponds to the second component in the PCA of the present study. Comparingthe loadings of the complexity indicators on the remaining ve components suggests that the rst component of our PCAcorresponds to a mix of their second, fourth and fth component. Finally, their sixth component is more or less equivalentto our seventh component. Their third component in (Gianazza and Guittet, 2006) was correlated with a metric representingthe degree of incoming sector ows, which was not considered here. Therefore, a good agreement between the PCA resultsobtained in our simulation study and their real-trafc study can be concluded.

The scores for the eight components were calculated and further entered in different multiple regression models in orderto reveal their correlation with ISA workload measures, controller activity indicators.

First of all, it was found that subjective controller workload as measured by the ISA ratings depends on additional factorsrather than only on aircraft count. This is in agreement with a couple of other studies (e.g. (Delahaye and Puechmorel, 2000),(Laudeman et al., 1998), (Gianazza and Guittet, 2006)). With regard to the aim mentioned above of using complexity factorsin the design of trafc samples in ATC real-time simulations, there are two ways how the present ndings can be employed.First, PCA component scores can be derived for the generated trafc samples and used for comparing and adjusting them.Alternatively, the marker metrics of these components, i.e. those complexity factors with the highest loadings on thePCA components, can be calculated and used for trafc sample comparison and adjustment. The latter option would simplifythe necessary computations.

The results suggested that subjective workload hinges on other aspects not only of ATC complexity but also on commu-nication load of the controller. Both the total frequency occupancy time and the average radio duration signicantly correlatewith ISA workload ratings. These task demand factors are more closely linked to how the controllers interact with the trafcdemand. The nding that the average time for an individual communication is negatively related to workload (see also Man-ning et al., 2001) reects already some kind of an active adaptation on behalf of the controller to cope with increased taskload in that the controller reduces the amount of time that she/he spends on a single communication as the situation getsbusier.

Acknowledgments

This work is funded by the EUROCONTROL CEATS Research, Development and Simulation Centre. The authors would alsolike to thank Mr. Michael Schultz and Mr. Mustafa Keskin for their valuable support throughout the project.

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Air traffic control complexity as workload driverIntroductionATC complexityController activity link between task demands and controllers workloadThe real-time simulation experimentSimulationATC complexity measuresController activity measuresWorkload measuresParticipants and data extraction procedure

Statistical evaluation and resultsPrincipal Component AnalysisMultiple regression analysesISA regression models

Discussion and conclusionsAcknowledgmentsReferences