Relationship of Sector 

Activity and Sector 

Complexity to Air Traffic 

Controller Taskload

Carol A. Manning 

Elaine M. Pfleiderer

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

December 2006

Final Report

DOT/FAA/AM-06/29

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine technical reports are 

available in full-text from the Civil Aerospace Medical Institute’s publications 

Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-06/29 

4. Title and Subtitle 5. Report Date 

Relationship of Sector Activity and Sector Complexity to Air Traffic December 2006 

Controller Taskload 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Manning CA, Pfleiderer EM 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-HRR-524. 

16. Abstract 

This study compared the relative effectiveness of two constructs, sector activity and sector complexity, in predicting air 

traffic controller taskload. Sector activity was defined as the activity associated with aircraft moving through the sector and

was measured by counting the number of aircraft under the control of the sector during a traffic sample. Sector complexity 

describes a set of factors presumed to affect the difficulty experienced by a controller when controlling traffic. Sector 

complexity was measured in two ways. The first measure of complexity was a subjective rating made by supervisors and 

controllers to describe the complexity associated with specific traffic samples. The second was a composite variable that 

included measures reflecting several of the complexity variables found in the literature. Taskload was defined as controller 

activity and was measured by counting the number of data entries made by a controller during a traffic sample. The results 

appear to suggest that our hypothesis, that sector activity predicted controller taskload better than sector complexity, was 

incorrect. However, interpretation of these results depended on consideration of what each of the variables measured. The 

Complexity Rating predicted controller activity better than the number of aircraft alone, but the Complexity Value (based 

on a set of variables identified through previous research) did not contribute at all to that prediction. Additional analyses 

suggested that the Complexity Rating measured something very different than the Complexity Value. We believe that the 

Complexity Ratings estimated the workload that observers believed the controller at the sector experienced instead of the 

complexity of the situation. On the other hand, the complexity measures used here did not appear to be not good measures 

of the construct. This may have occurred because the measures used in this study had limited variability or because they 

were not very good measures of the construct even though they were derived from factors identified in the literature as 

contributing to sector complexity. While we expected that the number of aircraft alone might be sufficient to predict 

controller activity/ taskload, the results suggested that measuring both controller activity and extracting measures from other

routinely recorded data might be necessary to develop more objective staffing standards used to determine how many 

controllers are needed to provide ATC services to individual facilities. 

 

17. Key Words 18. Distribution Statement 

 

 

Air Traffic Control, Sector Activity, Sector Complexity, 

ATC Taskload 

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 12 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

1

Relationship of Sector Activity and Sector Complexity

To Air Traffic Controller Taskload

Introduction

The purpose of this paper is to compare the relative 

effectiveness of measures of two constructs, sector activity 

and sector complexity, in predicting air traffic controller 

taskload. In spite of research that suggests that the number 

of aircraft alone is insufficient to describe a controller’s 

workload, we argue that it is a good predictor of controller taskload. In fact, we believe that the number of 

aircraft is as good, if not better, a predictor of controller 

taskload than are a set of measures of sector complexity 

developed to predict when sectors will become too difficult for one controller to work. It is useful to be able 

to predict controller taskload because certain measures, 

such as FAA’s staffing standards (used to determine the 

number of controllers allowed to be on staff at a facility), are based on observations of the number and time a 

controller spends performing ATC-related tasks. If those 

observations can be replaced by more efficient or objective 

measurements of controller taskload, then the staffing 

standards could be updated more frequently.

Before addressing this issue, it is first necessary to define terminology. In this study, the criterion is air traffic 

controller taskload, defined here as controller activity, 

and measured by counting the number of data entries 

made during a traffic sample. The predictors in this study 

are sector activity, defined as the activity associated with 

aircraft moving through the sector, and measured by 

counting the number of aircraft under the control of the 

sector during a traffic sample. Sector complexity is a more 

difficult construct to measure than is sector activity. Sector 

complexity (also known as “air traffic complexity,” “cognitive complexity,” and sometimes “dynamic density”) is a 

term used to describe a set of factors presumed to affect 

the difficulty experienced by a controller when controlling traffic. The sector complexity construct is proposed 

to describe the important factors associated with a sector 

(besides simply the presence of aircraft) that can make 

the job of controlling aircraft more difficult. Complexity factors, as defined by different authors, often include 

such variables as the presence of climbing or descending 

aircraft, aircraft mix (different types of aircraft having 

different performance characteristics), special use airspace 

activity, and presence of severe weather. 

Several researchers (Mogford, Murphy, & Gutman, 

1994; Wyndemere, 1996; Laudeman, Shelden, Branstrom, & Brasil, 1998; Sridhar, Sheth, & Grabbe, 1998; 

Christien & Benkouar, 2003; Histon, Aigoin, Delahaye, Hansman, & Puechmorel, 2001; and Majumdar 

& Ochieng, 2000) have proposed different ways to 

measure sector complexity. Hilburn (2004) listed a set 

of 108 complexity factors comprising categories that 

encompassed most of the complexity factors identified 

in the literature. Most of these measures describe factors such as the variability among the aircraft in a sector 

(e.g., number of aircraft, percent of climbing/descending aircraft, aircraft mix, presence of military aircraft) 

variability among the physical characteristics of a sector 

(e.g., size; shape; number, direction, and intersection of 

flight paths and levels; number of shelves; and presence 

and activity of special use airspace), types of procedures 

used (e.g., required procedures, amount of coordination 

required, number of facilities with which interactions 

occur, complex routings, sequencing, and spacing), specific traffic circumstances (e.g., number and distance of 

conflicts, distance between aircraft) the extent to which 

operations are affected by winds and severe weather, and 

adequacy of radio or radar coverage.

For this study, sector complexity was defined two 

ways. The first measure of complexity was a subjective 

rating made by supervisors and controllers to describe 

the complexity associated with specific traffic samples 

(derived from data collected by Kopardekar & Magyrits, 

2003). The second was a composite variable that included 

measures reflecting several of the complexity variables 

found in the literature. The measures that were combined 

to form the composite complexity variable were: visual 

clustering (2-dimensional distances between aircraft), 

special use airspace (SUA) activity, presence of severe 

weather, frequency congestion, amount of coordination 

required, percentage of climbing/descending aircraft,

and aircraft mix.

The purpose of this study was to compare the effectiveness of the sector activity and sector complexity 

constructs to see which was a better predictor of controller taskload. An argument is often made that controller 

taskload is influenced by variables other than the number 

of aircraft in the sector. For example, Laudeman et al. 
2

(1998) found that, while the number of aircraft alone 

accounted for 33% of the variance in controller activity 

(r= .57), the prediction (r2) was increased to 55% (r=.74) 

when using their Dynamic Density factors. However, 

other research has found a strong relationship between 

the number of aircraft and taskload measures. Manning, Mills, Fox, Pfleiderer, and Mogilka (2001) and

Manning, Mills, Fox, Pfleiderer, and Mogilka, (2002)

found that the number of aircraft in a sector during a 

traffic sample loaded on the same principal component 

as several measures of controller activity, while variables 

related to sector complexity were more closely related to 

a different orthogonal component. These results suggest 

that the number of aircraft may be a better predictor of 

taskload than complexity, at least when the variables are 

measured using routinely recorded ATC data. 

We anticipated that sector activity would be a better 

predictor of controller taskload than sector complexity and that adding sector complexity to the regression 

equation would not contribute anything unique to the 

prediction of taskload over that contributed by sector 

activity alone. 

Method

Data 

The traffic samples came from a study conducted by 

Kopardekar and Magyrits (2003) to compare alternative 

dynamic density metrics. The data were extracted from 

ninety 6-minute traffic samples of routinely recorded 

System Analysis Recording (SAR) data collected from 

the Atlanta and Ft. Worth Air Route Traffic Control 

Centers, totaling 180 samples. Besides the SAR data, 

pilot-controller communications recorded during the 

same traffic samples were also analyzed. Sixteen traffic 

samples with missing communication files were excluded 

from the analysis, leaving 164 samples. 

In addition, real-time controller and supervisor ratings 

of complexity were made for the same traffic samples at 

2-minute intervals. The rating data were first averaged 

across raters, then the composite ratings for three 2-minute 

intervals were averaged together to provide one rating for 

each 6-minute interval. 

The SAR files were first processed using the FAA’s Data 

Analysis and Reduction Tool (DART; Federal Aviation 

Administration, 1993), which produced output files that 

were then processed using the NAS Data Management 

System (NDMS) and POWER (Mills, Pfleiderer, & 

Manning, 2002). The result was a set of more than 20 

variables describing controller and aircraft activity. The 

voice tapes containing pilot/controller communications 

for the same traffic samples were transcribed, numbers 

and timing of transmissions were recorded, and content 

of the transmissions was coded. 

Computation of variables 

Taskload. Taskload was estimated by measuring observable controller activity. Controller activity is defined here 

as the number of data entries (number of commands, 

not individual keystrokes entered) made by a controller 

during the course of a traffic sample. Data entries are 

recorded at both the Radar (R) and Radar Associate (RA) 

positions at a sector, regardless of whether one or two 

controllers are physically present (because some entries 

can only be made at each position). The variable used 

in this study to describe controller activity was the sum 

of the R and RA data entries made at the sector during 

the traffic sample. 

Sector activity. Sector activity is related to the number 

of aircraft controlled by the R controller during the traffic 

sample. The number of aircraft might be characterized 

as either the total number of aircraft controlled or the 

maximum number of aircraft controlled at any one time 

during the traffic sample. We initially considered both 

the total number of aircraft and the maximum number of 

aircraft to represent sector activity. Analyses reported below 

suggested it was not necessary to include both variables in 

subsequent analyses, so we retained only the total number 

of aircraft controlled during a traffic sample. 

Sector complexity. Several studies have identified measures contributing to sector complexity. Most of these 

include characteristics associated with aircraft movement, 

such as the amount of climbing or descending aircraft 

and aircraft mix, or circumstances associated with specific 

traffic situations including military/special use airspace 

activity, presence of severe weather, amount of coordination required, or frequency congestion. 

In what way might these variables affect sector complexity? In general, an increased amount of these factors 

would be expected to increase the difficulty of dealing 

with specific traffic situations, create distractions, or reduce a controller’s ability to be flexible when controlling 

aircraft. More specifically, when aircraft climb or descend, 

it may be difficult for controllers to estimate how long it 

will take them to reach or leave an altitude. It may also 

be difficult to project whether two aircraft will conflict 

laterally if one is simultaneously moving both laterally 

and vertically. 

Aircraft mix may contribute to complexity because 

controllers must take differences in aircraft performance 

characteristics into account when determining how to 

sequence a line of aircraft into an airport and when 

deciding which aircraft to climb or descend to ensure 

separation is maintained. SUA activity limits the airspace 
3

available, thus limiting the possible choices of solutions to 

sequence aircraft or avoid conflicts. Similarly, the presence 

of severe weather can reduce available airspace, thereby 

constraining control options. Severe weather may also 

create new sector “choke points,” requiring controllers 

to watch for conflicts in unusual locations and use nontypical routings for aircraft. 

The amount of coordination required can increase 

sector complexity because coordination requires controllers to focus on communication rather than scanning the 

aircraft in their sector or formulating plans to sequence 

aircraft or maintain separation. Frequency congestion is 

similarly distracting but is also a problem because it can 

reduce the ability of a controller to contact a pilot to issue 

a clearance required to avoid a conflict. 

Table 1 lists the complexity variables described above 

and provides a brief definition for each. Complexity 

Ratings were subjective judgments made by controllers 

and supervisors who watched the traffic samples using 

the SATORI re-creation tool (Rodgers & Duke, 1993). 

The ratings were made on a 7-point scale, were obtained 

every 2 minutes, and were averaged over time and across 

controllers. SUA Activity and Weather were based on 

observations made by controller and supervisor subject 

matter experts (SMEs) about whether special use airspace 

was active or severe weather was present during each 

traffic sample. Climbing and Descending Aircraft was the 

percentage of aircraft in the traffic sample found to be 

climbing or descending, rather than in level flight, as 

determined from their recorded flight profiles. 

Three variables were identified that could potentially represent Coordination Required. These were 

the number of pointouts made by either the R or 

RA controller, the number of off-frequency messages 

made in each traffic sample, and the average amount 

of time required for off-frequency messages. Because 

the correlation between the number of off-frequency 

messages and the time required to make them was .95, 

only the number of pointouts and the time required for 

off-frequency messages were retained for analysis. 

Visual Clustering described the number of aircraft that 

came within x lateral miles of another aircraft, regardless of the amount of altitude separation. This measure 

was based on Stein’s (1985) measure of “local density.” 

The variable was termed Visual Clustering because high 

values of this variable would be associated with aircraft 

that appeared to cluster together when looking at the 

2-dimensional radar display even though they might be 

separated by altitude. In this study, the lateral separation 

value used was 10 nautical miles. 

Frequency Congestion was measured as the amount 

of time either the R controller or a pilot spoke on the 

radio and was obtained from the voice communication 

recordings. Communication time may also be considered 

a measure of taskload, but because Mogford et al. (1994) 

identified frequency congestion as a complexity factor, 

we used it in that manner for this study.

Aircraft Mix was a number indicating the difference 

in engine types between all aircraft in a sector (Pfleiderer, 

2003). The aircraft mix index was computed by first, assigning aircraft type codes with values ranging from one 

(for Piston-driven aircraft) to four (for high-performance 

jets) to all aircraft. Second, a half-matrix of aircraft type 

differences (the absolute value of the difference between 

the aircraft type codes for each aircraft pair) was computed. Third, all values of the aircraft type differences 

in the half matrix were summed. The aircraft mix index 

was computed for all aircraft present during each radar 

update (approximately 12-second intervals) and averaged 

over each 6-minute traffic sample.

Table 1. Sector complexity measures. 

Name Description 

Complexity Ratings Subjective complexity ratings made by controllers and 

supervisors

SUA Controller assessment - was special use airspace 

active? 

Weather Controller assessment - was severe weather present? 

Climbing & Descending Aircraft % climbing, descending aircraft 

Coordination Required # pointouts made, time required for off-frequency 

messages 

Visual Clustering # aircraft within 10 nm 

Frequency Congestion Amount of transmission time 

Aircraft Mix Measure reflecting difference in engine types 
4

Results

Descriptive statistics

Taskload. The number of R and RA controller data 

entries was the only criterion variable. An average of 34.2 

R and RA controller entries (SD = 11.7) were made per 

traffic sample (N=164). 

Sector activity. Table 2 shows descriptive statistics and 

correlations for the two variables proposed to represent 

sector activity, the total number of aircraft and the 

maximum number of aircraft controlled simultaneously. 

Both variables were computed for each of the 6-minute 

traffic samples analyzed. The variables had a statistically 

significant correlation of .89 and were, thus, considered 

nearly equivalent. For this reason, only the total number 

of aircraft controlled was retained for analysis.

Sector complexity. Table 3 shows descriptive statistics 

and correlations for the sector complexity variables. SUA 

activity and weather were both bivariate distributions (with 

values of 0=not present and 1=present.) For about 75% 

of the traffic samples, SUA activity was equal to 0. For 

about 60% of the traffic samples, presence of significant 

weather was equal to 0. Distributions of the number 

of pointouts, number of off-frequency transmissions, 

off-frequency transmission time, and aircraft mix were 

positively skewed. For about half the traffic samples, each 

of these variables had values equal to 0. 

Complexity ratings were significantly correlated with 

five of the eight complexity variables. Visual clustering 

was correlated with five of the other sector complexity 

variables. SUA activity and the proportion of climbing 

and descending aircraft were significantly correlated with 

four other variables. 

Regression analysis

To reduce the number of variables used for analysis, 

we often compute a Principal Components Analysis 

to identify a set of components that account for the 

Table 2. Descriptive statistics and correlations between sector activity variables (N=164). 

Name Average Std. Dev Correlations 

Total # aircraft 15.0 4.0 

Maximimum # aircraft controlled 

simultaneously 11.3 3.2 .89**

**Significant at p < .01.

Table 3. Descriptive statistics and correlations between sepctor complexity variables (N=164). 

Correlations

Name Avg SD CR SUA Wx %CD PO OFT VC FC

Complexity 

ratings

3.4 1.1

SUA 0.2 0.4 -.06

Weather 0.4 0.5 .24* -.21**

% of climbing 

& descending 

aircraft

0.5 0.2 -.26* -.28** -.04

Coordination 

required (POs)

1.4 1.9 .09 -.19* .23** -.01

Off freq message 

time

15.7 20.8 -.39** .02 .08 .07 .02

Visual clustering 6.2 3.0 .45 .17 -.05 -.06 -.03 -.24

Frequency 

congestion

216.1 55.7 .57** .08 .03 .41** .03 -.09 .41**

Aircraft mix 11.3 15.3 -.13 -.05 -.12 .23 -.05 .01 .23 .06

* p < .05. ** p < .01.
5

variability in a set of variables and use the component 

scores to predict the criterion measure. However, in this 

analysis, the sector complexity variables were assumed to 

be of equal weight, and a higher value of each variable 

was presumed to be associated with a higher amount of 

complexity. Consequently, z-scores were computed for 

all sector complexity variables (except the Complexity 

Rating) and were summed to compute a composite 

complexity measure for each traffic sample. We then used 

this composite complexity variable, called “Complexity 

Value,” in subsequent analyses. The Complexity Rating was not standardized, but was instead retained as a 

separate variable. 

Table 4 shows the correlations between the variables 

entered into the regression analysis, number of R and 

RA data entries, total number of aircraft, the Complexity 

Rating, and the Complexity Value. All zero-order correlations were significant except the correlation between the 

Complexity Value and the total number of aircraft and 

the correlation between the Complexity Value and the 

number of R and RA data entries. The correlation between 

the Complexity Value and the Complexity Rating, while 

statistically significant, was lower than the correlations 

between the other variables. Notably, the zero-order correlation between the number of aircraft and the average 

complexity ratings was very high (r = .66). 

A set of analyses was performed to assess the effectiveness of alternative multiple regression models in predicting 

controller activity. We used a method that allowed us to 

compare specific regression models instead of an analysis 

such as stepwise linear regression because we wanted to 

assess the relative contribution of specific variables to 

the model rather than simply those variables that made 

statistically significant contributions, such as would result 

when conducting a multiple regression analysis. 

Table 5 shows the results of these analyses. Row 1 shows 

the multiple correlation of the full model containing all 

three predictor variables (Number of Aircraft, Complexity 

Rating, and Complexity Value) with the criterion variable 

(number of R and RA controller data entries). The multiple 

correlation of the full model with the number of R and 

RA controller data entries was R = .62, accounting for 

about 39% of the variance in controller activity. Succeeding lines show multiple correlations between alternative 

(reduced) regression models containing fewer than the 

total number of predictors. 

The column containing F for the test of R2 change 

compares the relative effectiveness of a reduced model 

with the effectiveness of the full model in predicting 

the number of R and RA data entries. If the probability 

is greater than .05 that the change in R2 between the 

two models is significantly different from zero, then the 

reduced model is considered to be as effective as (i.e., 

no different than) the full model in predicting controller activity. On the other hand, if the probability is less 

than or equal to .05 that the change in R2 between the 

two models is significantly different from zero, then 

the reduced model is not considered to be as effective 

as the full model in predicting controller activity. The 

goal of the analysis is to identify a reduced model that 

contains as few predictors as possible but accounts for a 

high enough percentage of the variance in the dependent 

variable to be considered equivalent to the full model. 

In this way, we will assess the relationship between 

each specific predictor and the criterion measure of 

controller taskload.

Table 4. Descriptive statistics and correlations between variables included in multiple regression model 

(N=164). 

Name Avg SD Correlations 

 # R&RA 

data entries N aircraft Complexity 

Rating

Complexity 

Value

R & RA data entries 41.1 13.7 1.0 

N aircraft 15.0 4.0 .53** 1.0 

Complexity Rating 3.45 1.11 .58** .66** 1.0 

Complexity Value 0.0 3.0 .01 .11 .17* 1.0

* p < .05. ** p < .01. 
6

The analysis of six reduced models is shown in Table 

5 (see rows 2-7). The first group of analyses (rows 2-4) 

compared three reduced models consisting of individual 

predictor variables with the full model. The second group 

of analyses (rows 5-7) compared three different reduced 

models consisting of pairs of the different predictor variables with the full model. 

As an example, Row 2 compared a reduced model containing only the number of aircraft as a predictor with the 

full model. The model containing the number of aircraft 

had an R2 of .29, compared with the full model’s R2 of 

.39. The F computed to assess the R2 change of .10 had 

a value of 13.46. The probability was less than .001 that 

the change in R2 was greater than zero. The reduced model 

containing only the number of aircraft was significantly 

different than the full model in predicting controller activity and, thus, was not as effective as the full model. 

The only model that predicted controller activity as 

well as the full model is shown on Row 5. The reduced 

model, containing both the number of aircraft and the 

Complexity Rating, had an R2 of .62, the same as the 

full model. The F computed to compare the R2 change 

of .01 (which was greater than 0 due to rounding) had a 

value of 1.95, and the probability was less than .17 that 

the change in R2 was greater than 0. Thus, the reduced 

model containing both the number of aircraft and the 

Complexity Rating predicted the number of R and RA 

data entries as well as the full model.

The model containing only the Complexity Value 

contributed virtually nothing to the prediction of R and 

RA controller data entries. Furthermore, when the variable was entered into a model with either of the other 

predictors, it also added nothing to the prediction of the 

criterion measure. Note that while both the number of 

aircraft and the Complexity Rating had significant zeroorder correlations with the criterion measure, neither, in 

isolation, was sufficient to predict the criterion as well 

as the full model. 

Principal Components Analysis

A Principal Components Analysis was conducted using 

the four variables to assess their interrelationships. Based 

on previous research (Manning et al., 2002; Pfleiderer, 

2005), we expected that the total number of aircraft and 

the number of data entries would load on a single Activity

component. We also expected that, because they purportedly measured the same construct, the two complexity 

variables would load on a second Complexity component, 

which should be different from Activity. 

Two components were extracted. Table 6 shows the 

factor loadings, rotated with the Varimax rotation method. 

The first component extracted had an eigenvalue of 2.21 

(accounting for 55 % of the variance in the data), while 

the second had an eigenvalue of 1.00 (accounting for 

25% of the variance in the data). 

Table 5. Results of analyses comparing alternative multiple regression models prediction of controller 

activity. 

Regression model R R2

 R2

change

F for test 

of R2

change

df p 

1. Full model containing number of 

aircraft, Complexity Rating, and 

Complexity Value 

0.62 0.39 N/A N/A 

Individual predictors 

2. Model containing only number of 

aircraft

0.53 029 0.10 13.46 2, 160 .000 

3. Model containing only the 

Complexity Rating 

0.58 0.34 0.05 6.31 2, 160 .002 

4. Model containing only Complexity 

Value

0.01 0.00 0.39 50.72 2, 160 .000 

Predictor pairs 

5. Model containing n aircraft and 

Complexity Rating 

0.62 0.38 0.01* 1.95 1, 160 .165** 

6. Model containing N aircraft and 

Complexity Value 

0.54 0.29 0.10 26.37 1,160 .000 

7. Model containing Complexity 

Rating and Complexity Value 

0.59 0.35 0.04 10.78 1,160 .001 

* Difference in R2 was greater than 0 due to rounding. 

** Indicates reduced models that predicted controller data entries as well as the full model. 
7

The results were only partly consistent with our 

expectations. As expected, Component 1 was clearly 

related to controller and sector activity. However, three 

variables instead of two--the Complexity Rating, R and 

RA data entries, and the Number of Aircraft--had high and 

equivalent correlations with this component. The second 

component was defined exclusively by the Complexity 

Value variable. The Complexity Value did not correlate 

significantly with the Activity component, nor did any of 

the other three variables correlate significantly with the 

Complexity component. 

Conclusions

The results appear to suggest that our hypothesis that 

sector activity would predict controller taskload better than 

sector complexity was incorrect. The regression analyses 

showed that only a model containing both the number 

of aircraft and the Complexity Rating was equivalent to a 

full model containing all three variables when predicting 

controller taskload.

However, when interpreting these results, the reader 

must consider what each of the variables measured. We 

found that the Complexity Rating (based on SMEs’ 

subjective judgments about the complexity of the traffic 

samples) predicted controller activity better than the 

number of aircraft alone, but the Complexity Value 

(based on a set of variables identified through previous 

research as contributing to sector complexity) did not 

contribute at all to that prediction. A subsequent Principal Components Analysis revealed that the number 

of R and RA data entries, the number of aircraft, and 

Complexity Ratings loaded equally well on a component 

best described as Activity while the Complexity Value was 

the only variable that loaded on a separate component 

that we called Complexity. This result suggests that the 

Complexity Rating variable measured something very 

different than the Complexity Value. 

The Complexity Rating was measured on a 7-point 

Likert-type scale where 1 represented low complexity 

and 7 represented high complexity. The study for which 

these data were collected did not define these terms and 

did not collect SME feedback about how they interpreted 

complexity. Thus, it is not clear what the controllers were 

estimating when they provided the Complexity Ratings. 

Perhaps the Complexity Ratings were instead an estimate 

of the workload that observers believed the controller at 

the sector experienced. This interpretation is supported by 

the strong relationship between the Complexity Rating, 

data entries, and number of aircraft, and the corresponding lack of a relationship between Complexity Rating and 

Complexity Value. Similar relationships were observed 

between data entries, number of aircraft, and a real-time 

subjective workload assessment (when measured using 

the Air Traffic Workload Input Technique during Manning et al., 2002) when all variables loaded on a similar 

Activity component.

These results suggest that activity and workload are 

very similar. First, the controllers did not appear to rate 

Complexity because the PCA showed that their ratings 

loaded on an Activity component rather than a Complexity

component. Second, the Complexity Value, as measured 

here, was not very closely related to either sector activity 

or controller taskload because it loaded on a completely 

different component than did those variables. 

Perhaps the complexity measures used here were not 

good measures of the construct. Even though the individual measures used in this study were derived from 

factors identified by numerous researchers as contributing to sector complexity, certain distributional problems 

may have limited their effectiveness. Distributions of the 

individual variables included in the composite did not 

have much variability, which may have indicated that the 

traffic samples used were not very complex. The mean 

of the complexity ratings was 3.4, less than the halfway 

point on the 7-point Likert scale. Only 29% of the 

individual complexity ratings exceeded the mid-point. 

We often find that samples of real traffic are of relatively 

low workload (as seen in Manning et al., 2002). Perhaps 

complexity metrics should be tested in simulations (like 

those conducted by Lee, 2005) that increase traffic and 

other variables beyond normal levels to produce higher 

Table 6. Rotated component matrix. 

Variable name Components 

Component 1: ‘Activity’ Component 2: ‘Complexity’ 

R & RA data entries .83 

N aircraft .85 

Complexity Rating .87 

Complexity Value .99 

Note: Component loadings < .3 were eliminated. 
8

values of complexity. Alternatively, these variables may be 

insufficient, and different variables should be developed 

to represent the complexity of traffic in the real world. 

The third part of this discussion deals with the relationship between taskload, workload, and aircraft activity. 

If we assume that the complexity ratings in this study 

actually measured workload, it suggests that 1) activity 

and workload are very similar and yet 2) activity and 

workload are better predictors of taskload when paired 

together, even though both are fairly good predictors 

in isolation. 

Perhaps we should be asking: What are we trying to 

predict, and how should we measure it? One set of studies attempts to predict workload with the goal of determining whether new systems or procedures will unduly 

increase it. Another set of studies develops complexity 

measures for the purpose of predicting sector activity 

at some time in the near future (such as 20 minutes in 

advance), although the present study suggests that there 

is not much of a relationship between sector complexity 

variables and current sector activity. 

Studies that attempt to predict taskload do not occur 

often in the literature. However, taskload prediction could 

be useful for setting staffing standards that are used to 

determine how many controllers are needed to provide 

ATC services to individual facilities. Previous efforts to 

set staffing standards were based on time-and-motion 

studies, in which observers recorded how long it takes 

controllers to perform observable activities. Software such 

as POWER (Mills et al., 2002) would facilitate obtaining 

such measures through analysis of routinely recorded data, 

not requiring special observational studies (that might 

influence controller behavior) to be conducted. 

We proposed in this paper that the number of aircraft 

alone might be sufficient to predict controller activity/

taskload, but the results suggest that this is not accurate. 

However, perhaps measuring controller activity (e.g., 

counting data entries) and extracting measures from 

other routinely recorded data could provide sufficient 

information to develop more sophisticated, or at least 

more objective, staffing standards. 

It should be noted that workload was not explicitly 

measured in this study. While it appears that the SMEs 

did not rate sector complexity, we cannot be certain 

whether they rated workload instead. Consequently, it is 

not appropriate for us to make specific statements about 

how these results about the prediction of taskload may 

relate to the prediction of workload. Research on the 

relationship between those variables is still needed.

References

Christien, R. & Benkouar, A. (2003, June). Air traffic 

complexity indicators and ATC sectors classification. In Proceedings of 5th Eurocontrol/FAA Air Traffic 

Management R&D Seminar, Budapest, Hungary.

Federal Aviation Administration. (1993). Multiple Virtual Storage (MVS); User’s manual; Data Analysis 

and Reduction Tool (DART). (NASP-9247-PO2). 

Washington, DC: Author.

Hilburn, B. (2004, March). Cognitive complexity in air 

traffic control: A literature review. (EEC Note No. 

04/04). Brétigny-sur-Orge: France: Eurocontrol 

Experimental Centre.

Histon, J.M., Aigoin, G., Delahaye, D., Hansman, R.J., 

& Puechmorel, S. (2001, December). Introducing 

structural considerations into complexity metrics. 

In Proceedings of 4th Eurocontrol/FAA Air Traffic 

Management R&D Seminar, Santa Fe, NM.

Kopardekar, P. & Magyrits, S. (2003, June). Measurement 

and prediction of dynamic density. In Proceedings of 

5th Eurocontrol/FAA Air Traffic Management R&D 

Seminar, Budapest, Hungary.

Laudeman, I., Shelden, S., Branstrom, R., & Brasil, C. 

(1998). Dynamic density: An air traffic management 

metric. Report NASA/TM-1998-112226. Ames Research Center, Moffett Field, California: NASA. 

Lee, P. (2005, October). A nonlinear relationship between 

controller workload and traffic count. In Proceedings of Human Factors and Ergonomics Society 49th

Annual Meeting, Orlando, FL. 

Majumdar, A. & Ochieng, W.Y. (2000). The factors affecting air traffic controller workload: A multivariate 

analysis based upon simulation modelling of controller 

workload. Centre for Transport Studies Department 

of Civil and Environmental Engineering Imperial 

College of Science, Technology and Medicine. 

London, United Kingdom.

Manning, C., Mills, S., Fox, C., Pfleiderer, E., & Mogilka, 

H. (2001, July). Investigating the validity of Performance and Objective Workload Evaluation Research 

(POWER). (DOT/FAA/AM-01/10). Washington, 

DC: FAA Office of Aerospace Medicine.
9

Manning, C., Mills, S., Fox, C., Pfleiderer, E., & Mogilka, 

H. (2002). The relationship between air traffic 

control communication events and measures of 

controller taskload and workload. Air Traffic Control 

Quarterly, 10(2), 69-83.

Mills, S.H., Pfleiderer, E.M., & Manning, C.A. (2002, 

February). POWER: Objective activity and taskload 

assessment in en route air traffic control. (DOT/

FAA/AM-02/2). Washington, DC: FAA Office of 

Aerospace Medicine.

Mogford, R., Murphy, E., & Gutman, J. (1994). Using knowledge exploration tools to study airspace 

complexity in air traffic control. The International 

Journal of Aviation Psychology, 4(1), 29-45. 

Pfleiderer, E.M. (2003, May). Development of an 

empirically­-based index of aircraft mix. (DOT/

FAA/AM-03/8). Washington, DC: FAA Office of 

Aerospace Medicine.

Pfleiderer, E.M. (2005, August). Relationship of the 

aircraft mix index with Performance and Objective 

Workload Evaluation Research (POWER) measures 

and controllers’ subjective complexity ratings. (DOT/

FAA/AM-05/16). Washington, DC: FAA Office of 

Aerospace Medicine.

Rodgers, M.D., & Duke, D.A. (1993). SATORI: Situation Assessment Through Re-creation of Incidents. 

The Journal of Air Traffic Control, 35(4), 10-14.

Sridhar, B., Sheth, K.S., & Grabbe, S. (1998, December). Airspace complexity and its application in air traffic management. In Proceedings 

of 2nd Eurocontrol/FAA Air Traffic Management 

R&D Seminar, Orlando, FL.

Stein, E.S. (1985). Air traffic controller workload: An 

examination of workload probe. (DOT/FAA/CTTN84/24). Atlantic City, NJ: Federal Aviation 

Administration Technical Center. 

Wyndemere (1996). An evaluation of air traffic control 

complexity (Contract number NAS2-14284). 

Boulder, CO: Author.