Static Sector 

Characteristics and 

Operational Errors

Scott Goldman

Carol Manning

Elaine Pfleiderer

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

March 2006

Final Report

DOT/FAA/AM-06/4

Office of Aerospace Medicine

Washington, DC 20591
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i

Technical Report Documentation Page 

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

DOT/FAA/AM-06/4 

4. Title and Subtitle 5. Report Date 

Static Sector Characteristics and Operational Errors March 2006 

6. Performing Organization Code 

 

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

Goldman S, Manning C, Pfleiderer E 

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- B-05-HRR-524; PD No. 081500001, AT/TO HF Task 3. 

16. Abstract 

A study was conducted to determine if static sector characteristics are related to the occurrence of operational 

errors (OEs) at the Indianapolis Air Route Traffic Control Center (ZID). The data consisted of a three-year 

sample of OEs that had occurred in ZID airspace. Sectors were treated as the unit of analysis (n=40). The static 

characteristics included: number of major airports, cubic volume in nautical miles, sector strata, number of 

shelves, number of VORTACs, number of satellite airports, and number of intersections. Pearson correlations 

revealed that cubic volume in nm (r = -.31, p = .049) and sector strata (r = .31, p = .049) were significantly 

correlated with the number of OEs. The static sector characteristics were entered into a regression procedure as 

predictors with the number of OEs as the criterion. The regression analysis produced a model containing cubic 

volume in nautical miles, number of major airports, and sector strata as significant predictors. This model 

accounted for 43% of the variance in OEs (R = .65). No other static sector characteristics were significant 

predictors of OE incidence in this sample. The correlation between cubic volume in nautical miles and number 

of OEs indicated that, as sector size decreased, the number of OEs increased. However, the predictive utility of 

cubic volume in nm may be due to underlying dynamic traffic characteristics inherent in different-sized sectors, 

rather than a direct relationship between sector size and incidence of OEs. This relationship needs to be explored 

in future research. The regression analysis suggests that static sector characteristics can account for some of the 

variance in OE occurrence in ZID airspace and, thus, can increase our understanding of the factors that lead to 

an OE.

 

17. Key Words 18. Distribution Statement 

 

 

Air Traffic Control, Airspace Complexity, Operational 

Errors, Sector Characteristics 

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 15 

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

iii

Executive Summary

Failure to reduce en route operational errors (OEs) in recent years has resulted in FAA efforts to develop programs 

aimed at OE reduction. Several studies identified airspace complexity as a major contributor to en route OEs. In FY04, 

the office of AT Investigations initiated a project to examine the relationship between en route sector characteristics 

and the occurrence of OEs. The purpose of the study was to determine whether factors that predicted OEs at the 

Atlanta Air Route Traffic Control Center (ZTL) in a study conducted by Rodgers et al. (1998) would also predict 

OE occurrence at other facilities. We expected to find that the same variables identified in the Rodgers et al. (1998) 

study would also predict OE occurrence in this study.

This report describes preliminary analyses conducted using sector characteristics and operational error data from 

the Indianapolis Center (ZID). The analyses were based on a three-year sample of ZID final OE reports and a set of 

sector characteristics derived from the ZID Adaptation Control Environmental System (ACES) files (containing map 

data). The analyses were preliminary because the only sector characteristics available for this analysis were “static,” 

or those that do not change according to the traffic situation. Static sector characteristics include sector size, shape, 

number of miles of jetways/airways, number of major and minor airports, etc. Additional information was available 

from the final OE report, such as time of day and number of aircraft in the sector when the OE occurred. 

Of the static sector characteristics available for this study, sector altitude strata, sector size, and number of major 

airports produced a regression model that accounted for 43% of the variance in sector OE incidence. Sector altitude 

strata and sector size had a similar level of influence in the model, while the number of major airports was the least 

influential predictor. However, all three variables were significant predictors. Higher altitude sectors had more errors 

than lower altitude sectors (though super-high altitude sectors had fewer). Smaller sectors had more errors than larger 

sectors. Sectors with more major airports had more errors than those with fewer major airports. 

These results are similar to those found by Rodgers et al. (1998) for their analysis of ZTL data. Although some 

of the relationships between sector characteristics and OEs at both ZID and ZTL were very similar, there were also 

some differences. While this study found that sector altitude strata, sector size, and number of major airports were 

significant predictors of the number of OEs per sector at ZID, Rodgers et al. (1998) instead found that frequency 

congestion and the influence of special use airspace (variables not available for this study) predicted the number of OEs 

per sector at ZTL. Additionally, some variables that were significantly correlated with OEs at ZTL were not correlated 

with OEs in the ZID data-- such as number of VORTACS and traffic volume. These differences support the idea 

that some sector characteristics relevant to OE incidence at one en route center may not be relevant at another.

Additional research should include information about dynamic sector characteristics comparable to those used 

in the Rodgers et al. (1998) study. Dynamic sector characteristics cannot be obtained from ACES data or the final 

OE reports but must be obtained from information provided by controllers who are familiar with sector operations 

(either operational controllers, staff controllers in the Airspace and Procedures Office, or supervisors in the appropriate areas of specialization) or derived from operational data such as System Analysis Recording (SAR) files produced 

by the HOST computer or SATORI re-creations of OEs. 

Additional information about dynamic sector characteristics is essential before we can develop a more complete 

understanding of how sector characteristics influence operational errors or make recommendations about how the 

facility might use the information to affect their operations. Moreover, additional OEs need to be included in the 

analyses to provide more confidence in the accuracy of the findings. 

1

Static Sector Characteristics and Operational Errors

The rate of operational errors (OEs) in en route airspace 

has risen since 1997. Error rates per 1,000,000 operations 

increased from .098 in 1997 to .117 in 1998, .138 in 1999, 

and .157 in 2000 (FAA, 2001). Since 2000, error rates have 

remained essentially flat with .159 per million operations 

in 2001, .155 in 2002, and .157 in 2003 (FAA, 2004a). 

Although error rates did not increase between 2001 and 

2003, they remain higher than in 1997. Currently the 

Federal Aviation Administration (FAA) is implementing 

strategic safety initiatives aimed at reducing OE rates over 

the next four years (FAA, 2004b). The FAA’s previous 

efforts to reduce the number of OEs focused largely on 

the behavioral and organizational aspects of OE occurrence. While understanding the human component of 

OEs is extremely important, there is still a need to further 

investigate the role of environmental and contextual factors that may increase the likelihood of OEs. 

The environmental and contextual factors affecting 

controller workload— often referred to as “sector complexity”— encompass the physical characteristics of the 

airspace and their effects on aircraft movements (e.g., 

sector size, number of airports, restricted areas, weather, 

traffic). Kirwan et al. (2001) recently found that a sample 

of UK controllers rated airspace design as the second-most 

important factor in determining traffic complexity. Grossberg (1989) observed a correlation of .44 between an index 

of sector complexity based on the top four complexity 

factors and the number of operational errors in Chicago 

Center sectors. Rodgers et al. (1998) related Mogford et 

al.’s (1994) factors to the incidence of operational errors 

in Atlanta Center (ZTL) sectors. They discovered that 

whether sectors had a low, medium, or high incidence of 

OEs was predictive of several sector complexity factors. 

The study also revealed that several complexity factors 

were significantly correlated with the number of OEs that 

occurred in a sector. Pounds and Ferrante (2003) found 

that most of the errors identified in their validation study 

for the JANUS method of investigating OEs were related 

to sector or traffic characteristics. 

Sector complexity factors are typically described in 

two different ways: static and dynamic (Mogford et al., 

1995). Static sector characteristics are those that do not 

change at all or only change infrequently and are generally 

related to the airspace design. Static characteristics can 

include factors such as the size of the sector, the number of intersections, and the number of major airports. 

Kirwan et al. (2001) observed that static characteristics 

might be taken for granted by controllers because such 

sector characteristics are always there and are “just part 

of the job on that sector.” Thus, errors resulting from 

static sector characteristics may be under-reported because controllers and investigators tend to overlook them. 

Dynamic sector characteristics are those that may change 

over time and include weather, traffic volume, and the 

mix of aircraft with different performance characteristics. Dynamic characteristics are often the focus of OE 

reports where investigators examine aircraft activities, 

communications, etc. 

The relationship between sector characteristics and 

OEs may differ from one sector to another. Mogford et 

al. (1994) observed, after comparing their results with 

those of Grossberg (1989), that “salient complexity factors [may] vary from one en route center to another.” 

They further hypothesized that certain factors that occur 

frequently in a facility’s airspace, such as Jacksonville 

Center’s large amount of military airspace and frequent 

thunderstorms, may make those factors particularly relevant to that facility, whereas they may not be as relevant 

to other facilities. Thus, it is premature to conclude that 

sector characteristics that predict OE incidence at one 

center will generalize to others. 

To increase our understanding of the relationship 

between sector characteristics and OEs, it was necessary 

to determine whether factors found to predict ZTL OEs 

in the Rodgers et al. (1998) study would also predict the 

occurrence of OEs at other facilities.

Method

Operational Error Data

A three-year sample of final OE reports was obtained 

from the Indianapolis Air Route Traffic Control Center 

(ZID). This sample contained information describing 134 

OEs that occurred in ZID airspace between December 

2000 and May 2003. The information obtained from 

the final OE reports included vertical and horizontal 

separation between the aircraft involved in the OE, the 

altitude at which the OE occurred, the number of aircraft 

in the sector at the time of the OE, the severity of the 

OE, whether training was in progress, and the time of 

day when the OE occurred. 

Sector Characteristics Data

Sector characteristics data were extracted from ZID’s 

Adaptation Control Environmental Systems (ACES) 

files using the OpenCreate software package. The static 
2

sector characteristics available for analysis were number 

of major airports, number of satellite airports, sector size 

(in cubic nautical miles), number of shelves (use of several 

minimum or maximum altitude levels within one sector), 

number of VORTACs (navigational aids), number of 

miles of jetways and Victor airways, number of intersections (points at which two airways or an airway and an 

arrival or departure route cross), and sector altitude strata 

(whether the sector was considered to be super high-, 

high-, or low-altitude airspace). Sectors were treated as 

the unit of analysis, and the final sector characteristics 

dataset contained data for 40 sectors. 

A variable was created to classify sectors into either 

high or low error frequency groups based on the mean 

number of errors. Sectors with greater than 3 errors (n 

= 14) across the 30-month time period were classified 

as high-error, and sectors with 3 or fewer errors (n = 26) 

were classified as low-error. 

Results

Operational Error Data

The distribution of errors by time of day (reported 

in Figure 1) indicates that the majority of errors (80%) 

occurred between 0800 and 2000 hours. This may reflect 

the typical traffic activity at ZID, but no normative traffic data were available to verify this hypothesis. Figure 2 

presents the distribution of errors across the number of 

aircraft under control at the time the OE occurred. The 

number of aircraft under positive control at the time of 

the OE was approximately normally distributed, with a 

mean of 7.86 aircraft and a standard deviation of 2.73. 

More than 46% of the OEs occurred when there were 

between 6 and 9 aircraft under positive control in the 

sector. There were 17 cases with missing data. 

Figure 3 presents the distribution of OEs by the flight 

level at which the error occurred. The majority of OEs 

(55%) occurred at flight levels between 25,000 and 

35,000 feet. There was a significant correlation between 

the number of aircraft in the sector at the time of the 

OE and flight level (r = .30, p < .01), demonstrating that 

more aircraft were usually flying at higher altitudes when 

these OEs occurred.

Figure 4 presents the distribution of OEs by severity. 

Examination of OE severity indicated that over 70% 

of the OEs in this sample were classified as moderately 

severe. Only 5 (4.3%) of the errors were classified as high 

severity. Thirty OEs (26%) were classified as low severity. 

There were 17 cases with missing data on this variable. A 

chi-square statistic was computed to test the independence 

of OE severity and sector altitude strata. This test was 

non-significant, χ2(4) = 5.60, p = .23, suggesting that 

high-altitude sectors were no more likely than low-altitude 

sectors to be associated with more severe OEs. 

Training was in progress in 10 (7.5%) of the OEs in 

this sample and 17 (12.7%) of the records had missing 

data. The 107 remaining cases reported that training was 

not in progress during the incident. Analysis of whether 

the controller was aware of an impending OE revealed 

that only 6 (4.5%) of the OEs in this sample occurred 

10

23

47

38

16

0

5

10

15

20

25

30

35

40

45

50

0400-0800 0801-1200 1201-1600 1601-2000 2001-2400

Time of Day

Number of OEs

Figure 1. Distribution of OEs by Time of Day. 
3

1

5

7

10

15

16 16

18

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8

5 5

2

0

2

4

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8

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14

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2 3 4 5 6 7 8 9 10 11 12 13 15

Aircraft Count

Number of OEs

Figure 2. Distribution of OEs by Number of Aircraft in Sector at Time of Error. 

2 2

16

10

21

26

48

8

1

0

10

20

30

40

50

60

 0-50 50-100 101-150 151-200 201-250 251-300 301-350 351-400 401-450

Altitude (ft x 100)

Number of OEs

Figure 3. Distribution of OEs by Flight Level. 
4

5

30

82

0

10

20

30

40

50

60

70

80

90

HIGH LOW MODERATE

OE Severity

Number of OEs

Figure 4. Distribution of OEs by Severity of OE. 

9

7

25

36

30

0

5

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15

20

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40

0 to 1 1.01 to 2 2.01 to 3 3.01 to 4 4.01 to 5

Horizontal Separation (in nautical miles)

Number of OEs

Figure 5. Distribution of OEs by Horizontal Separation of the Aircraft. 
5

when the controller was aware they were developing. The 

majority (82%) of OEs occurred when the controller was 

unaware of their development.

Figure 5 presents the distribution of OEs by horizontal 

separation in nautical miles. The distribution of horizontal 

separation was negatively skewed with a mean of 3.14 mi 

and a standard deviation of 1.13. Only 9 OEs occurred 

with 1 mi or less of horizontal separation between the 

aircraft. Eighty-five percent of the OEs reported horizontal 

separation of between 2 and 4.99 nautical miles. 

Figure 6 presents the distributions of OEs by the 

amount of vertical separation between aircraft at the time 

of the accident. The vertical separation data were split 

into 2 groups based on separation requirements relevant 

to different altitudes. Up to FL290, 1000 ft of vertical 

separation is required while above FL290 requires 2000 

ft of vertical separation. The mean amount of vertical 

separation for OEs occurring at or below FL290 was 

519 ft with a standard deviation of 274 ft. Of the OEs 

that occurred at or below FL290, 13 (23.2%) had 200 

ft or less of vertical separation. For OEs occurring above 

FL290, the mean amount of vertical separation was 

1022 ft with a standard deviation of 508 ft. Twenty-eight 

of the OEs (46%) above FL290 had 1000 ft or less of 

vertical separation. Six of those OEs had 200 ft or less 

of vertical separation. The remaining 33 OEs (54%) 

that occurred above FL290 maintained over 1000 ft of 

vertical separation. 

Relationship of Static Sector Characteristics to 

Operational Errors

Figure 7 presents the frequency distribution of OEs 

by sector. Only 2 sectors had no OEs during the 3 years 

included in this sample. Eleven sectors had one occurrence 

of an OE. Three sectors (all high altitude) accounted 

for 22% of the OEs. Table 1 presents the means and 

standard deviations for each static sector characteristic. 

The mean number of OEs per sector was 3.35, with a 

standard deviation of 2.8. 

Figure 8 presents the distribution of OEs by sector size. 

As indicated in Table 1, the mean sector size was 25,962 

cubic nautical miles with a standard deviation of 29,419 

cubic nautical miles. Only 3 OEs were reported in sectors 

with a size of less than 5000 cubic nautical miles. The 

majority of OEs (72, or 53%) occurred in sectors with 

a size between 5,000 and 10,000 cubic nautical miles. 

Twenty-one OEs (16%) occurred in sectors with a size 

of more than 25,000 cubic nautical miles. 

A t-test was performed to test the difference in size 

between high- and low-error sectors. Levene’s test for 

equality of variances was significant indicating heterogeneity of variances; thus, an adjusted t is reported. The 

results showed that high error sectors were significantly 

smaller in size than low-error sectors, t(31.73) = 3.101, 

p = .004. 

13

5

15

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6 6

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< 201 201-400 401-600 601-800 801-1000 1001-

1200

1201-

1400

1401-

1600

1601-

1800

> 1800

Vertical Separation (in feet)

Number of OEs

Below FL290

Above FL290

Figure 6. Distribution of OEs by Vertical Separation of the Aircraft.
6

2

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2

3 3

1 1 1

0

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0 1 2 3 4 5 6 7 8 10 12

Number of OEs

Number of Sectors

Figure 7. Distribution of Sectors by Number of OEs. 

3

72

35

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40

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80

<5000 5,000 - 10,000 10,000 - 15,000 15,000 - 25,000 >25,000

Sector Size (cubic nm)

Number of OEs

Figure 8. Distribution of OEs by Sector Size. 
7

Table 1. Descriptive Statistics for Sector Variables. 

Sector Variables Mean Std. Dev.

Number of OEs 3.4 2.8Number of Shelves 2.1 1.6Sector Size (in cubic nm) 25,962.0 29,419.0Number of Major Airports 11.7 5.6Number of Satellite Airports 6.7 3.8Number of VORTACs 3.3 1.7Number of Intersections 3.1 3.3Miles of victor routes 216.6 326.7Miles of jetways 473.8 270.8Number of aircraft 7.54 2.06

Table 2. Pearson Correlations Between Sector Variables. 

Pearson Correlations 

Sector Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

1. Number of OEs 1 2. Number of shelves -.18 13. Sector size (in cubic nm) * -.31 * -.36 14. Number of major airports .24 .00 -.05 15. Number of satellite airports -.12 .15 -.07 .20 16. Number of VORTACs -.03 -.17 .05 .10 * .37 1 7. Number of intersections -.07 * .39 -.16 .27 * .35 -.13 1 8. Sector stratum *.31 * -.71 * .44 -.14 * -.44 -.17 * -.41 1 9. Miles of victor routes -.25 * .43 -.29 .15 * .56 * .52 .26 * -.69 110. Miles of jetways .03 * -.36 * .32 .01 .01 * .49 -.14 * .35 -.06 111. Average number of aircaft .17 * -.42 .17 .00 -.08 .26 .00 .25 -.24 * .49

* p <= .05
8

Figure 9 reports the distribution of OEs by sector 

altitude strata. Seventeen of the sectors were low altitude 

sectors, 13 were high altitude, and 10 were super-highaltitude (for purpose of this analysis, intermediate high 

sectors were classified as high altitude). However, the 

largest number of OEs (n = 74, 53%) occurred in high 

altitude sectors (FL240 to FL340). Low-altitude sectors 

(FL230 and below) accounted for 40 (31%) of the OEs 

in this sample. The remaining 20 OEs (16%) occurred 

in super high altitude sectors (FL350 and above). A chisquare test was performed to examine the relationship 

between error frequency and sector altitude strata. This 

resulted in a significant chi-square, χ2(2) = 10.43, p = 

.005, suggesting that sector altitude strata was related to 

the incidence of OEs. The proportion of errors in high 

altitude sectors was greater than expected given their 

number, while both super high- and low-altitude sectors 

had a lower proportion of OEs than expected. 

Traffic volume at the time of an OE was examined to 

detect any differences between high and low-error sectors. 

The mean number of aircraft in low-error sectors (7.3) was 

slightly lower than the mean number in high error sectors 

(7.7). However, a t-test revealed that the difference was 

not statistically significant, t(35) = -.549, p = .58. Thus, 

there does not appear to be a linear relationship between 

traffic volume and OE frequency in this sample. 

Table 2 presents correlations between all sector variables. The correlation between each sector characteristic 

and the number of OEs in a sector was used to screen 

predictors for a multiple regression analysis. Pearson 

correlations revealed that only sector size (r = -.31) and 

sector altitude strata (r = .31) were significantly correlated with the number of OEs that occurred in a sector. 

However, because the correlation between the number of 

major airports in a sector and OE occurrence approached 

significance (r = .24, p = .14) and because Rodgers et al. 

(1998) had reported that it was significantly correlated 

with OE occurrence at Atlanta center, the variable was 

included in the regression analysis. A standard multiple 

regression analysis used to predict number of errors indicated that sector altitude strata, sector size, and number 

of major airports produced a model with a multiple correlation (R = .66) significantly different from zero, F (3, 

39) = 9.00, p < .005. Table 3 presents the output of the 

regression analysis. The regression model accounted for 

43% of the variance in sector OE incidence (R2 = .43). 

Table 4 reports the beta weights and significance tests 

for each predictor in the model. Sector altitude strata 

was the most influential variable in the model with a 

standardized beta weight of .597. Sector size (β = -.56) 

was almost as influential as altitude strata. However, the 

negative beta weight indicated that the influence of the 

variable was negative; that is, smaller sectors had more 

OEs. The number of major airports was the least influential predictor (β = .29); however, all 3 variables were 

significant predictors in the model.

40

74

20

0

10

20

30

40

50

60

70

80

Low Hi Super Hi

Sector Stratum

Number of Errors

Figure 9. Distribution of OEs by Sector Stratum. 
9

Discussion

The environmental and contextual factors present 

during OE incidence at ZID were very similar to the 

conditions at ZTL reported by Rodgers et al. (1998). 

The positively skewed distribution of OEs at both ZID 

and ZTL were very similar, with the exception that proportionally fewer ZID than ZTL sectors had no errors. 

Most of the errors in both ZID and ZTL airspace occurred 

between 0800 and 2000 hours local time. Distributions 

of vertical and horizontal separation of the aircraft at the 

time of the OEs were very similar to those reported for 

ZTL. The disproportionately high number of OEs in 

high-altitude sectors and low number of errors in super 

high-altitude sectors at ZID mirrored the results reported 

at ZTL. However, the proportion of OEs in low-altitude 

airspace was much smaller for ZID than for ZTL. Also, 

the proportion of OEs in low-altitude airspace at ZID 

was actually smaller than statistically expected. Another 

interesting point of convergence between the ZID and 

ZTL data is that higher error sectors at both facilities 

were smaller in size. This relationship was statistically 

significant in both studies. 

The similarities between static sector characteristics 

and OE conditions at both ZID and ZTL suggest that 

some factors related to OE occurrence may be common 

across multiple centers. However, it is not clear how this 

information can be used to affect operations. Sectors are 

designed to be small or large for certain reasons. It may 

not be possible to change their size without affecting 

other aspects of operations. For example, workload might 

be increased if 2 smaller sectors were combined, perhaps 

requiring additional staffing. Changing sector boundaries 

might reduce the number of options controllers have to 

maneuver aircraft in their airspace. However, knowledge 

that some smaller sectors have more errors than larger 

sectors might be taken into consideration when a new 

sector is constructed by minimizing other factors found to 

be related to OE occurrence. Supervisors might monitor 

controllers more closely when working in sectors known 

to have characteristics related to a higher incidence of 

errors than when working in other sectors. Additionally, 

sectors known to have characteristics related to a higher 

incidence of OEs should be considered first when assessing the need for additional staffing or perhaps increasing 

targeted training. 

Although some of the sector characteristics and OE 

conditions at both ZID and ZTL centers were similar, 

there were also some striking differences. The current 

study found that sector altitude strata, sector size, and 

number of major airports significantly predicted the 

number of OEs by sector at ZID. Rodgers et al. (1998) 

instead found that frequency congestion and the influence of special-use airspace (variables not available for 

this study) were predictive of the number of OEs per 

sector at ZTL. Additionally, some variables that were 

significantly correlated with the number of OEs per sector at ZTL were not correlated with OE incidence in the 

ZID data— such as number of VORTACS and traffic 

volume. These differences support the idea that not all 

sector characteristics relevant to OE incidence at one en 

route center may be of equal importance at another. 

Table 3. Regression Statistics. 

Model Sum of 

Squares df MeanSquare F Sig. R R2

 Adj. R2 Std. Error 

Regression 130.77 3 43.59 9.00 0.00 0.655 0.429 0.381 2.200

Residual 174.33 36 4.84 

Total 305.10 39 

Table 4. Beta Weights for Sector Variables. 

Sector Variables ȕ t Sig. 

Constant -.219 -1.482 .147 

Sector Stratum .597 4.220 .000 

Sector Size -.562 -4.004 .000 

No. of Major Airports .286 2.247 .031 
10

Further research is needed to validate the findings 

from this and the Rodgers study at ZTL and ZID, and 

see if they apply to other centers. While sector altitude 

strata, sector size, and number of major airports were 

significant predictors of OEs in this study, the regression 

model accounted for less than half of the variance in OE 

incidence per sector. Only a limited amount of static 

sector characteristic information (9 variables) for ZID, 

along with information about the OE extracted from the 

final operational error reports, was available for analysis. 

Additional dynamic complexity variables used in the 

Rodgers study were not available in this sample. Rodgers 

and associates obtained those data through questionnaires 

administered to Airspace and Procedures specialists within 

each area of specialization at ZTL. 

Additional research on ZID OEs should include information about dynamic sector characteristics similar 

to those used in the Rodgers study. Dynamic sector 

characteristics cannot be obtained from ACES data or 

final OE reports but must be either obtained from information provided by controllers who are familiar with 

sector operations (operational controllers, staff controllers 

in the Airspace and Procedures Office, or supervisors 

working in the area of specialization) or derived from 

other operational data such as System Analysis Recording (SAR) files produced by the HOST computer. SAR 

data are currently used to compute a suite of measures 

called Performance and Objective Workload Evaluation Research (POWER; Mills, Pfleiderer, & Manning, 

2002; Manning, Mills, Fox, Pfleiderer, & Mogilka, 2002; 

Pfleiderer, 2003). SATORI re-creation files would also be 

useful for determining the values of the dynamic sector 

characteristics at the time of the error. Additional data 

sources include Sign-In Sign-Out (SISO) logs and tapes 

of voice communications between pilots and controllers 

or controllers and other controllers.

Information about dynamic sector characteristics that 

can be derived from controller ratings includes sector 

geometry, number of intersecting flight paths and their 

angle of intersection, traffic flows, direction of flight, 

military operations, terrain, multiple functions required, 

required procedures, amount of coordination required, 

complex routings, longitudinal and lateral spacing and 

sequencing required, adequacy of radio and radar coverage, amount of frequency congestion, and teamwork. 

The type of information derived from SAR data that 

might be related to OE occurrence includes the average 

amount of traffic per sector in a given time period, maximum amount of aircraft in a sector at one time, average 

sector transit time, aircraft mix, amount of climbing or 

descending traffic, number of handoffs, and number of 

altitude and heading changes. SISO data could indicate 

typical staffing levels at each sector (by time of day and 

day of week), and voice tapes could provide an objective 

indicator of the amount of frequency congestion that 

occurs at each sector. 

POWER measures extracted from SATORI re-creations for OEs can be compared with POWER measures 

extracted from SAR data recorded during typical operations to help identify differences in events that occurred 

at times when OEs did and did not occur. These kinds 

of data, along with controller ratings of other dynamic 

complexity factors, would be useful to improve our 

understanding of how other sector characteristics affect 

OE occurrence at ZID and what role those characteristics 

may play, if any, at other en route facilities. 

The results of this study are very limited in their present 

form. While the study found that sector altitude strata, 

sector size, and number of major airports were significantly 

related to OE occurrence in the dataset we analyzed, it is 

possible that one or more of these variables could also be 

related to other measures that might be more meaningful. For example, a preliminary analysis investigated the 

relationship between some of the POWER measures and 

sector size. The analysis revealed that most of the variance 

in sector size could be explained by the combination of 

average traffic count and the number of heading changes. 

However, this analysis was based on insufficient SAR 

data; thus, it is not reasonable to try to draw conclusions 

without obtaining additional data. 

Without additional data about dynamic sector characteristics, it will be difficult to develop useful findings 

about how sector characteristics influence operational 

errors and make recommendations regarding how the 

information might be used to affect facility operations. 

Additionally, the only factors used for analysis in this 

project relate to static and dynamic sector characteristics. 

It would be erroneous to assume that sector characteristics are the only factors that explain OE occurrence. 

Individual and organizational factors are also likely to 

contribute to OEs. 

Though it will not provide all the answers about the 

causes of OEs, this line of research can provide information 

used to supplement and organize the JANUS OE investigation procedure with regard to airspace characteristics. 

OE investigators will be able to collect information to 

augment the factors they look for when identifying trends 

in OE occurrence. Increased understanding of the causes 

of OEs will contribute to the development of strategies 

that will reduce future OE occurrence.
11

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