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THE COMBINATION OF FLIGHT COUNT AND CONTROL TIME
AS A New METRIC OF AIR TRAFFIC CONTROL ACTIVITY

INTRODUCTION

An important aspect of the evaluation of new air
traffic control (ATC) systems is the comparison of
current systems and procedures with proposed ones. A
relevant factor in making these comparisons is the
level of various activities occurring in a specified unit
of airspace during a defined period of time. This
process has been conceptualized in different ways,
with the use of such terms as controller workload,
airspace complexity, and dynamic density. In general,
however, the studies conducted in this area have
included measures of controller actions, aircraft dy-
namics, and sector characteristics.

The exploration of measures of airspace activity is
useful in a number of significant ways. These include
the establishment of baseline ATC measures, as well as
the development of tools and procedures for airspace
management (See Galushka, Frederick, Mogford, &
Krois, 1995, and Pawlak, Bowles, Goel & Brinton,
1997, as examples). The establishment of baseline
measures is necessary for evaluating any changes re-
sulting from the introduction of new ATC systems or
procedures. Significant modifications have been pro-
posed for the United States’ National Airspace System
(NAS; FAA, 1997). The NAS Architecture is the
Federal Aviation Administration’s (FAA’s) compre-
hensive plan for modernizing the infrastructure of the
NAS. Version 2.5 of this plan calls for significant
changes in the interactions between air traffic control-
lers and pilots, incorporating the use of updated
navigation, surveillance, and communication equip-
ment and corresponding procedures. The impact of
these modifications on the NAS will need to be
assessed by researchers. Toward this goal, the estab-
lishment of baseline measures will allow airspace ac-
tivity, measured under the current system, to be
compared with activity assessed after new systems and
procedures have been introduced. These comparisons
will quantify any changes resulting from the imple-
mentation of new systems. As a simplified example, if
a new set of procedures designed to reduce handoff
activity were introduced to an ATC facility, the

effectiveness of the procedures could be assessed by
comparing measures of handoff activity collected be-
fore and those collected after the implementation of
the changes. The amount of reduction, if any, of
handoff activity would indicate the effectiveness of
the new set of procedures.

Another important use of airspace activity mea-
sures is for the development of tools for more effective
airspace management. Currently the FAA performs an
annual review of all en route sectors by using a
complexity workload formula (FAA, 1984). This for-
mula is designed to be applied to data from peak
traffic time periods and to be used to establish or
adjust sector configuration. However, the need for
dynamic activity assessment, i.e., real-time measure-
ment, is emerging with the aviation community’s
move toward the operational concepts collectively
known as “Free Flight.”

Free Flight refers to the proposed revision of the
NAS from a centralized system, in which air traffic
controllers assign aircraft to routes, to a distributed
system that allows pilots more freedom to select their
own routes and altitudes. Under Free Flight, pilots
presumably will assume more responsibility for main-
taining separation from other aircraft than they have
under the current system. The changes included in
Free Flight are considered to be necessary because of
the limitations of the current system, the significant
increases projected for air traffic, and desired im-
provements in efficiency for airlines. RTCA, Inc., an
organization that addresses requirements and techni-
cal concepts for aviation and functions as a Federal
Advisory Committee, has performed much of the
planning for Free Flight. The Final Report of RTCA
Task Force 3: Free Flight Implementation (RTCA,
1995), emphasizes the need for the FAA to develop a
method of assessing dynamic density, or “the projected
workload and safety of future traffic in an operational
environment.” Under Free Flight, this assessment
process could drive the dynamic reconfiguration of
sectors, thus increasing the capacity and operational


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Table 1. Comparison of ATC Measures

ATC Measure

Advantages

Disadvantages

Flight Count /
Total Time

(F/T)

Aggregate Control Time /
Total Time
(C/T)

Aircraft Activity Index
(F/T x C/T)

Sensitive to number of flights.

Sensitive to length of flights.

Not sensitive to length of
flights. Short flights (less
control activity) are not
distinguished from long flights
(more control activity).

Not sensitive to number of
flights. Cannot distinguish
between several short flights
(more control activity) and
fewer long flights (less control
activity).

Sensitive to both number of
flights and length of flights.

The AAI combines information from flight count,
flight length, and length of the time period being
analyzed, and therefore reflects changes in all three
metrics.

Figure 1 graphically demonstrates the different
sensitivities of the measures when applied theoreti-
cally to different flight count-flight length combina-
tions. Flight count cannot distinguish between two
short flights and two long flights, even though the
aggregate control time for the short flights is only half
as long as that for the long flights. Flight count can,
however, distinguish between two short flights and
one long flight, even when their aggregate control
times are equal. Aggregate control time can distin-
guish between two short flights and two long flights
butis not sensitive to the difference between two short
flights and one long flight. Unlike the simpler mea-
sures used alone, the AAI can distinguish between two
short flights and two long flights and between two
short flights and one long flight.

METHOD

Application of the AAI

Airspace activity should vary as a function of the
type of aircraft controlled because different aircraft
types have different characteristics that may influence
their interaction with ATC. Aircraft performance
characteristics are an important factor that must be
considered by the controller when assessing potential
conflicts or sequencing aircraft. One method of clas-
sifying aircraft according to performance characteris-
tics is to distinguish between three types of engines:
jet, turboprop, and piston propeller. Different types
of aircraft also may require various levels of interac-
tion with ATC, depending on their flight purpose and
the types of navigation and communication equip-
ment installed. These flights can be distinguished by
classifying them into three categories: commercial,
general aviation (GA), and military. For purposes of
demonstrating the AAI, flights included in this study


efficiency of the NAS. The RTCA report contends
that this type of capacity management is essential to
the Free Flight concept.

Several studies (cited below) have explored airspace
activity in various ways. The methodology most often
used assumes that many variables affecting activity in
an airspace also influence the perceived workload and
the objective task performance of the controller. For
example, as the flight count in an airspace increases
from an average number to a high number, one might
expect the controller to perceive a higher workload
level and to perform a higher number of objectively
measurable activities to maintain effective control of
the airspace. The variables used in such studies have
been described with the terms workload, complexity,
and dynamic density. Therefore, measures of subjec-
tive workload and of individual performance may be
related to airspace activity, including workload, com-
plexity, and dynamic density.

Buckley, DeBaryshe, Hitchner, and Kohn (1983)
conducted a study consisting of a series of experi-
ments in an ATC simulation environment and, as a
result, identified a set of four general factors (Con-
flict, Occupancy, Communications, and Delay) and
two auxiliary measures (Number of Aircraft Handled
and Fuel Consumption) that appeared to adequately
represent all other ATC measures. The authors rec-
ommended the use of these measures for subsequent
air traffic simulation studies. Stein (1985) exposed
controllers to different levels of airspace activity in
another simulation experiment and concluded that
three variables, Aircraft Count, Clustering, and Re-
stricted Airspace, significantly influenced workload.

More recently, Mogford, Murphy, & Guttman
(1994) used verbal reports from air traffic control
specialists and multidimensional scaling to identify a
list of 16 factors that contribute to airspace complex-
ity. Pawlak, Brinton, Crouch, and Lancaster (1996)
focused on controllers’ strategies and decision-making
activities; proposing a list of 15 factors that may
influence perceived air traffic complexity.

Although the studies cited above used different
conceptualizations and methods, most included meth-
ods of counting flights and/or the time aircraft were
under control, While these measures are useful for
assessing controller activity, it may be more informa-
tive to employ a measure that combines information
about both the number of flights and the duration of
control. In addition, it may be more useful to com-
pute the measure separately for different types of

aircraft in order to obtain a measure of airspace
activity that is sensitive to differences in specific
circumstances. Although the measure proposed here
is only one of many that influence airspace activity,
refinements such as this can add to our overall ability
to quantify operational aspects of the NAS.

An ATC Aircraft Activity Index

This paper introduces a new metric of ATC activity
that combines two existing measures (flight count and
the time aircraft are under control) to produce a more
informative metric than either measure provides when
used alone. Given an epoch of airspace activity (e.g.,
all air traffic controlled by a certain ATC position
during a specific period of time), two simple mea-
sures, (1) flight count, and (2) aggregate control time,
can be combined to produce a measure of activity, the
Aircraft Activity Index (AAI). By combining the two
measures, the AAI accounts for some of the limita-
tions of using each measure independently. Specifi-
cally, the product of the ratio of number of flights to
total epoch time and the ratio of aggregate control
time to total epoch time is a measure that is sensitive
to both number of flights and length of flights within
the epoch (See Equation 1). Ratios are used in the
equation so that the measure is sensitive to the length
of the epoch being evaluated.

Equation 1. Aircraft Activity Index

Flight Count
Epoch Time

Control Time

Aircraft Activity Index =
Epoch Time

Table 1 summarizes the relative advantages and
disadvantages of the different measurement techniques.
Flight count is sensitive to the number of flights
controlled during a certain time period; however, it is
not sensitive to the length of flights. Therefore, a
simple flight count cannot distinguish between peri-
ods when most flights are relatively short and periods
when they are relatively long. Although the aggregate
control time measure is sensitive to the length of
flights, it is not sensitive to the number of flights.
Consequently, it can only distinguish between periods
of short flight lengths and long flight lengths when the
flight counts of both periods are similar. It cannot
distinguish between a relatively large number of short
flights and a relatively small number of long flights.


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DOT/EAA/AM-98/15 The Combination of Flight Count
_ . and Control Time as a New Metric
Office of Aviation Medicine

Washington, D.C. 20591 of Air Traffic Control Activity

Scott H. Mills

Civil Aeromedical Institute
Federal Aviation Administration
Oklahoma City, OK 73125

May 1998

Final Report

This document is available to the public
through the National Technical Information
Service, Springfield, Virginia 22161.

2

U.S. Department
of Transportation

Federal Aviation
Administration


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Figure 1. Sensitivity of ATC Measures

Flight Control Aircraft
Count/ Time/ Activity
Time (F/T) Time Index'
(CM)
Flight_| Flight 0.5 0.5 0.25
Time Units: 1 2 3 4 it | |
Cannot Can Can
Distinguish Distinguish Distinguish
T T T
[ Flight | Flight | 0.5 1.0 0.5
1 2 3 4 | ! !
Can Cannot Can
Distinguish Distinguish Distinguish
T T T
[ Flight l 0.25 1.0 0.25
1 2 3 4

" Aircraft Activity Index = (Flight Count / Epoch Time) X (Aggregate Control Time / Epoch Time)

were analyzed by aircraft type, with type being distin-
guished as one of the nine possible combinations of
the above classifications. This resulted in the follow-
ing nine types: Commercial-Jet, Commercial-Turbo-
prop, Commercial-Piston, GA-Jet, GA-Turboprop,
GA-Piston, Military-Jet, Military-Turboprop, and
Military-Piston.

The AAI was applied to data from 10 days of
System Analysis Recordings (SAR) obtained from the
Seattle Air Route Traffic Control Center (ZSE). Se-
lected data were extracted from the SAR recordings by
the NAS Data Analysis and Reduction Tool (DART).
A computer program, the NAS Data Management
System, was developed to encode the text-based DART
output reports into relational databases. An addi-
tional computer program analyzed the databases and
identified and categorized aircraft types associated
with each controlled flight.

Flight count was calculated for each defined epoch
by counting all flights controlled by Seattle Center for
at least 6 seconds (i.e., one update of the Track file
produced by DART) during that epoch. Aggregate
control time was obtained by adding the elapsed time
of all such updates for every aircraft controlled by the
Center during the epoch.

RESULTS

The Aircraft Activity Index (AAI) was calculated
for each aircraft type for each of the 11 most active
hours of the day (0600-1600 hours, local time). For
the data from all 10 days, mean flight lengths are
shown in Figure 2. Mean flight counts (per day) are
shown in Figure 3. More detailed data from a repre-
sentative day are displayed for illustration purposes in
Figures 4 - 6. Flight count, aggregate control time,
and the AAI are displayed for Friday, October 22,
1993. The AAI for all aircraft types combined is
displayed in Figure 7.

The advantages of the AAI become apparent when
different aircraft types consistently have different mean
flight lengths, as was the case with the Seattle data (see
Figure 2). An example is the comparison between two
aircraft types, Commercial-Turboprops and GA-Pis-
ton, which showed similar hour-long flight counts
between 1000 and 1200 hours throughout the ten
days of data (see Figure 4). If flight count were
considered alone as a measure of activity, the two
aircraft types could be considered to require similar
amounts of a controller’s attention during that time
period. However, the types had different mean flight




lengths with GA-Piston flights (M=44.83, SE=0.59)
being about 7 minutes longer than Commercial-Tur-
boprop flights (M=37.58, SE=0.25), and possibly
requiring more of the controller’s attention. A more
dramatic example is the comparison between Com-
mercial-Piston flights and Military-Turboprop flights.
These groups had similar hour-long flight counts
throughout the data, but the average duration of the
military flights (M=52.28, SE=2.46) was approxi-
mately 20 minutes longer than the commercial flights
(M=31.52, SE=0.77).

DISCUSSION

Some of the disadvantages of using flight count and
control time by themselves as activity measures are
apparent in the Seattle data. Because Commercial-
Piston flights were usually the shortest, the amount of
attention required to control them may be over-
represented in the flight count measure, as this mea-
sure is insensitive to flight lengths. Conversely, the
controller attention associated with Commercial-Pis-
ton flights may be under-represented in the aggregate
control time measure—more flights were controlled
per unit of control time. Similarly, the controller
attention required by Commercial-Jets, which usually
had longer flight lengths, may be under-represented
in the flight count measure and over-represented in
the aggregate control time measure. The AAI is amore
informative measure than either of the simpler mea-
sures alone because it is sensitive to both the number
and length of flights.

The value of calculating separate index values for
different aircraft types can be illustrated by comparing
Figures 6 and 7. For example, the activity associated
with hours 1200 and 1500 appears to be the same
when aircraft types are combined (Figure 7). How-
ever, when aircraft types are considered separately as
in Figure 6, the activity appears to be noticeably
different, with more Commercial-Jet activity at 1200
than at 1500.

In the future, this index of activity can be used in
conjunction with other ATC measures to establish
baseline data for specific sectors or groups of sectors.
This use will facilitate evaluation of new systems and
procedures. As new technologies are applied to both
ATC systems and to aircraft, resulting changes in

11

airspace activity, including controller taskload, must
be evaluated. Objective measures such as the AAI will
contribute to such evaluations. Other measures that
may reflect controller taskload are also being investi-
gated, including aircraft activity (numbers and
amounts of changes in heading, speed, and altitude),
controller activities (numbers and types of keyboard
entries), and sector characteristics. The refinement
and development of these measures will enhance capa-
bilities for evaluating ATC systems and effectively
managing airspace.

REFERENCES

Buckley, E. P., DeBaryshe, B. D., Hitchner, N., &
Kohn, P. (1983). Methods and measurements in
real-time air traffic control system simulation. (Re-
port No. DOT/FAA/CT-83/26). Atlantic City,
NJ: Federal Aviation Administration Technical
Center.

Federal Aviation Administration. (1984). Establishment
and validation of en route sectors. (FAA Order No.
7210.46). Washington, DC: Author.

Federal Aviation Administration. (1997). National Air-
space System (NAS) Architecture, Version 2.5. Of-
fice of System Architecture and Program Evalua-
tion (ASD-1), Washington, DC: Author.

Galushka, J., Frederick, J., Mogford, R., & Krois, P.
(1995). Plan view display baseline research report.
(Report No. DOT/FAA/CT-TN95/45). Atlantic
City, NJ: Federal Administration Technical Center.

Mogford, R. H., Murphy, E. D., & Guttman, J. A.
(1994). Using knowledge exploration tools to study
airspace complexity in air traffic control. The
International Journal of Aviation Psychology,
4(1), 29-45.

Pawlak, W. S., Bowles, A., Goel, V., & Brinton, C. R.
(1997). Initial evaluation of the Dynamic
Resectorization and Route Coordination (DI-
RECT) System. NASA Final Contract Report No.
NAS2-97057.

Pawlak, W. S., Brinton, C. R., Crouch, K., & Lancaster,
M. (1996). A framework for the evaluation of air
traffic control complexity. Proceedings of the ALAA
Guidance Navigation and Control Conference,
San Diego, CA.


Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No.
DOT/FAA/AM-98/15

4. Title and Subtitle 5. Report Date

The Combination of Flight Count and Control Time as a New Metric of | May 1998

Air Traffic Control Activity

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.
Mills, S.H.
9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

FAA Civil Aeromedical Institute
P.O. Box 25082
Oklahoma City, OK 73125

11. Contract or Grant No.

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

Federal Aviation Administration 14. Sponsoring Agency Code

800 Independence Ave., S.W.

Washington, DC 20591

15. Supplemental Notes.

16. Abstract

The exploration of measures of airspace activity is useful in a number of significant ways, including the establishment of
baseline air traffic control (ATC) measures and the development of tools and procedures for airspace management. This
report introduces a new metric of ATC activity that combines two existing measures (flight count and the time aircraft
are under control). The Aircraft Activity Index (AAI) is sensitive to changes in both flight count and flight length, and
therefore is a superior measure for comparing aircraft activity between two epochs of time. The AAI was applied to
data from 10 days of System Analysis Recordings obtained from the Seattle Air Route Control Center. The advantages
of the AAI were most apparent when different aircraft types consistently had different mean flight lengths. Possible
uses of the AAI and other ATC measures for the evaluation of new systems and procedures are discussed.

17. Key Words 18. Distribution Statement
Air Traffic Control, Complexity, Workload, Aircraft Document is available to the public through the
Activity Index, Flight Count National Technical Information Service,
Springfield, Virginia 22161
19, Security Classi, (of this report) 20. Security Classif. (ofthis page) 21, No. of Pages 22. Price
Unclassified Unclassified 15

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



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 or use thereof.


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Robertson, A., Grossberg, M., & Richards, J. (1979). Stein, Earl S. (1985). Air traffic controller workload: An
Validation of air Traffic controller workload models.

examination of workload probe. (Report No. DOT/
(Report No. FAA-RD-79-83). Cambridge, MA: FAA/CT-TN84/24). Atlantic City, NJ: Federal
U.S. Department of Transportation Research and Aviation Administration Technical Center.
Special Programs Administration - Volpe National

Transportation System Center (VNTSC).

RTCA. (1995). Final Report of RTCA Task Force 3:

Free Flight Implementation, Washington, DC:
RTCA Incorporated.

12