Comparing across complexity

Consideration of the intersection of the three graphs
revealed a number of transitions that were fundamental
to the way in which controllers operated. Figure 3 shows
the arcs common to the three networks. Nine arcs are
shared by all three, thus 56% of the arcs in the low-
complexity and medium-complexity networks are fun-
damental, and 60% of the arcs in the high-complexity
network are fundamental.

There were two loops fundamental to the scenazios: a
CCOM loop and a MANIP !cop, with the former being
much more substantial than the Jatter in all three sce-
narios. The loops indicate thar the controllers often
followed one controller command with another and one
manipulation with another. Controller commands fol-
lowed controller commands abou‘ as often in the low-
complexity scenario as in the high. For manipulations,
on the other hand, not only did they occur ata higher rate
overall in the more complex scenarios (see Figure 1), they
tended to follow other manipulations slightly more often
in the high-complexity scenarios. {n addition to agree-
ment on the two loops, the three networks agreed on bi-
directional transitions between CCOM and WRITE,
and between SECTOR and WRITE. Each of the net-
worksalso had transitions between WRITE and MANIP,
between WRITE and LOOK, and berween PREQ and
CCOM. Thus, across complexity several important tran-
sitions appear to be fundamental to the control of air

traffic.

The MCNs of the high and low complexity also
differed in a number of interesting ways. These differ-
ences can supply insight into how controllers interact
with strips differently depending on the complexity of
the air traffic situation. We turn now to these differences.

One of the most frequent interactions between a
communication event and FPS activities is captured in
the arcs between CCOM and WRITE. This likely
reflects in part the role of the FPS as 2 legal record. The
fact that complexity affects this interaction is evident: As
complexity increased, the F -quency of the transition
from CCOM to WRITE decreased. It is important to
note that controllers do not write less in the complex

10

scenarios. On che contrary, the sizes of the nodes for
WRITE are virtually identical across the three scenarios
(see also Figure 1). What does change as a function of
complexity, however, is when writing takes place. As
complexity increases, the controllers no longer use a
command as a departure point for updating the strips.
An implication of this phenomenon is that in a high-
complexity traffic situation, the controller will begin to
fall behind in his or her updating of strip information. In
fact, whereas the CCOM to WRITE arc becomes less
prominent in the high-complexity scenario, a CCOM tv
LOOK arc emerges, suggesting that controllers must
now settle for 2 look toward the bay. Being unable to
record information on the strips as it is acquired may
have other consequences. For example, st appears that
controllers now engage in a series of controller queries
(the CQUERY loop) in the high-complexity scenario.
There may be too many aircraft for the controller to
remember all the altitudes, speeds, and headings, and
because the current information has not been w-itten on
the strips, the controller may need to ask the pilot to
provide that information, which adds further to the
workload.

Although each network showed a WRITE te MANIP
arc, it is apparent that the frequency of this transition
increased with complexity, mirroring the decrease with
complexity shown by the CCOM to WRITE arc. Not
only does the transition from WRITE to MANIP be-
come more prominent with increasing complexity, an
arc from MANIP to WRITE emerges in the high-
complexity scenario, presumably replacing the arc from
MANIP to CCOM found in the low. Apparently, in
high-complexity situations, the controller engages in
periods of uninterrupted interaction with the FPSs,
whereas in low-complexity situations, the manzgement
of the strips is more integrated into the control of traffic.
This tendency to engage in uninterrupted board man-
agement as complexity increases also helps explain why
controllers were much less likely to write on a strip
immediately after issuing a command in the high-com-
plexity scenario than they were in the low.

It is intriguing that in the low-complexity scenario,
the LOOK node is structurally similar to the MANIP
node from the high-complexity scenario; that is, in the


EN ROUTE AIR TRAFFIC CONTROLLERS’

USE OF FLIGHT PROGRESS STRIPS:
A GRAPH-THEORETIC ANALYSIS

BACKGROUND

In the United States, en route air traffic control
(ATC) of high-speed and high-altitude aircraft traveling
between airports, is currently accomplished with three
tools: the radar, communication devices (radio and
telephone), and a representation of flight information.
Flight information is displayed on a flight progress strip
(FPS), a rectangular piece of paper divided into 31
iogical fields, each of which displays particular informa-
tion available from the flight plan. When a flight enters
the volume of airspace (sector) for which a controller is
responsible, the flight is considered active, and the
controller moves the corresponding strip from 4 sus-
pense bay where the immineni entries to the sector are
held to the active bay; thereafter the controller interacts
with the corresponding FPSs by moving the strips within
the active bay, by writing on the scrip itself, and by
looking at the strip to acquire or confirm flight informa-
tion. The organization and upkeep of the suspense bay
and the active bay is referred to as board management.

The responsibility for board management depends on
the number of controllers assigned to control a sector.
When a sector is handled by a team of two, the “R”
(radar) controller is primarily responsible for observing
the radar screen and for talking to the pilot, whereas the
“D” (data) controller (sometimes called the radar associ-
ate), seated next to the R-side controller and in front of
the strip bays, is usually responsible for the FPSs. On
several occasions, traffic loads permitting, both func-
tions are frequently assigned to one controller, as they
were in the study described here. This requires the
controller to integrate board manzgement with aircraft
separation responsibilities.

This research focused on the FPS for two major
reasons, First, although controllers must use FPSs to
maintain a legal record of control actions as dictated by
the ATC Handbook 7110.65, it is otherwise unclear
why or when the controller uses the FPSs. Anecdotes
from controllers often support the notion that the FPS is

viewed as unimportant. This perceived lack of impor-
tance was reinforced by the results of a structured inter-
view conducted by Human Technology Inc. (1990).
Controllers were asked about their priorities of activities
unde+ normal workleads. On a scale of 1 to 9 (9 being
lowe:t priority), experts gave Reviewing FPSs a 6.0 and
Writing on FPSs an 8.8.

On the other hand, observers of the air traffic control
situation have advanced compeiling arguments that the
FPS, even ifused only because of legal requirements, may
provide coincidental, but substantial, benefits to the
centrollers (e.g., Hopkin, 1988; Means, Mumaw, Roth,
et al., 1988; see Vortac & Gettys, 1990, for a review).
Hogkin believes that the relatively effortless incidental
encoding of flight data which results from following the
legal requirements helps to build understanding and
memory. For example, when an aircraft is given a new
speed, the old speed is crossed out and the new speed
written on the strip (Weston, 1983), making the strip
more distinctive and presumably easier to locate and
remember. Finally, it is clear that not all the interaction
with the strips is legally required. For example, the
controilers may offset a strip from the bay as a reminder
to take a future action.

A second reason to focus on the FPS is that the
nation’s air traffic control « ystem will undergo a period
of radical and unprecedented change in the next decade.
The Advanced Automation System (AAS), to be phased
in during the 1990s, will have substantial ramifications
for all aspects of air traffic control (e.g., Ammerman &
Jones, 1988). ‘ine first stage of AAS in the en route
environment involves introduction of the Initial Sector
Suite System (ISSS). The area of greatest change due to
ISSS involves the way in which flight information is
displayed and manipulated. The paper FPS will be
replaced by electronic Flight Data Entries. It is the
automation of the FPS that has attracted the attention of
aviation psychologists. If the introspections of control-
lers are correct, then the automation of the strips will

make little or no difference. If these introspections are


the only other event to vary significantly with complexity
was MANIP, A(2,16)=15.51, MSe=.03, p < .0002, which
increased until in the high-complexity scenario, ma-
nipulations occurred once per minute.

At the other extreme, PREQ or CQUERY were
relatively rare, never reaching over 4% of the recorded
events, and not revealing any significant changes with
complexity.

Transition analyses

A7x7 matrix of transitions from one event to another
was computed for each subject, normalized by dividing
by the total number of evenis. and weighted by the
temporal separation between events. The temporal
weighting was accomplished in the following way. When
compiling the frequency of transitions, the increment to
the frequency count was a negative exponential function
of the elapsed time. Specifically, the time-weighting
function was:

I-07 a)

where srefers to the number of seconds between the two
events and lis a decay parameter, with larger values of 1
corresponding to more rapid decay, or increasingly less
impact from more time-distant events. The increment [
was used as the increment to the transition count. All
analyses used a value of 1of 9.1, which yielded a time-
weighting function of the desired characteristics. Thus,
two events that occurred simultaneously (s =0) would
produce an increment of unity ( J-1), two that were
separated by 1 swould be given an increment of 90, and
so on. Sequential events separated by 20s (or more)
contribute little to the transition count between those
two events (/=.13). All analyses to be reported were
conducted on these normalized and time-weighted tran-
sition matrices. Inter-observes reliability for these tean-
sition matrices ranged between .75 and .90.

Individual differences

Before creating a description of the “averaze” air
traffic controller, the presence of individual differences
between controllers was addressed (see discussion by
Smith, 1991, with regard to training issues). In the worst

case, if variability between controllers exceeds variability
between different levels of the situational variables, litle
meaningful generalization of results is possible. Transi-
tion matrices were averaged across subjects for each level
of scenario complexity, and individual matrices were
then correlated with the average matrices. All the corre-
jations berween the individual matrices and the average
matrices were moderate to strong (range .62 to .97). The
number of correct classifications appeats in Table 2. For
high- and low-complexity scenarios, classification was
quite good, with no individual being classified at the
other extreme. On the other hand, the individual me-
dium-complexity scenarios were nat classified well sug-

Group
Individual Low Med High
Low 7 2 G
Med 2 4 3
High ie) 2 7

Table 2. The number of individual transition
matrices most highly correlated with each
group transition matrix.

gesting that the medium-complexity scenarios shared
properties of both the low- and high-complexity scenar-
ios. This lack of uniqueness for the medium-complexity
level scenaric manifested itself in other analyses.

Pathfinder

The normalized and time-weighted transition matri-
ces were submitted to the Pathfinder algorithm. The
algorithm produces a network where the communica-
tion events and the FPS events are represented by nodes
and the transitions are represented by arcs between the
nodes. The weight of the arc reflects the frequency with
which that transition occurred.

The particular application of Pathfinder that we used
was one guaranteed to produce the simplest network, the
minimal cost network (MCN). The mean 7 x7 transition
matrices were submitted to the Pathfinder algorithm for
each of the three levels of complexity. The resultant
MCNs appear in Figure 2. The arcs were supplied by


In an effort to reveal the underlying structure in our
transition matrices, the Pathfind -r scaling algorithm was
selected (Schvaneveldt, 1990; Schvaneveldt & Durso,
1981; Schvaneveldt, Durso, & Dearholt, 1989). The
Pathfinder analysis represents the relationships among
events graphically so that the underlying structure in the
transition matrix can be more readily interpreted. The
algorithm reduces a matrix of proximity data (eg.,
transitions) by eliminating those connections that de not
satisfy the metric properties of a network. Thus, the
connections remaining are those that are ordinally nec-
essary (Hutchinson, 1989).

The algorithm has been successfully employed in a
variety of domains within cognitive psychology, engi-
neering, and artificial intelligence (see Schvaneveldt,
1990). For example, Pathfinder has been used to articu-
late the structure of natural categories (Durso & Coggins,
1990), to distinguish between expert and novice fighter
pilots (Schvaneveldt, Durso, Goldsmith, etal., 1985), to
predict free recall (Cooke, Durso, Schvaneveldt, 1986),
to develop menus for automated cockpits (Roske-
Hofstrand & Paap, 1986), and to establish connections
in hypertext (McDonald, Paap, & McDonald, 1990).
The mathematical foundations can be found in
Schvaneveldt, et al. (1985). They will not be reviewed
here so the focus can remain on the interpretation of the
data.

METHOD
Subjects
Nine full-performance-level (FPL) controllers par-

ticipated. They had served as en route FPLs from 3.5 to
9.3 years (M = 5.7) and had last been in the field 2 to 16

months prior to their participation (M = 7.4). The study
was conducted at the Radar Training Facility at the
FAA’s Mike Monroney Aeronautical Center in Okla-
homa City, which can provide high fidelity en route
traffic simulations using the fictitious AERO Center
airspace used in training. Because subjects had to be
familiar with AERO Center, bu naive to the particular
selection of scenarios, FAA Academy instructors in the
nonradar screen program were used.

Scenarios

All subjects wers observed under low, medium, and
high levels of complexity, according to a randomized
counterbalancing schedule. Across subjects, numerous
different scenarios were used. Therefore, conclusions
drawn for a level of complexity are unlikely to be due to
a particular scenario. We used existing scenarios as
outlined in the FAA Academy Scenario Guide (FAA
Publication #EIL-10a-2). The scenarios varied in com-
plexity from 25% to 95%. Complexity was measured
using the complexity worksheet found in the Instruc-
tional Program Guide, (Appendix B, section 3, Phase 8A
for nonradar). Complexity is computed in the following
way: departures received $ points; arrivals, en route
aircraft needing a control action, emergencies, and radio
failures each received 4 points; special flights received 3
points; en route flights not needing a control action
received 2 points; and each additional coordination
action (e.g., a point-out) received 1 point.

All but the most coinplex scenarios represented a
level of traffic density that, in the ‘ield, could be handled
byasingle FPL controller. The high-complexity scenario
was comparable to a situation in the field where a
supervisor would provide o- a controller might request a

| Complexity ATC Guide —_— Departures
| Complexity

| Low 50% 2.4

' Medium 75% 3

High 95% 42

L

Arrivals Overflighis Length |
(minutes) |

3.8 4.0 30 '
5.6 5.2 30
8.6 18.6 60

Table 1. Sumnary of Scenarios for Different Complexities



incomplete or incorrect, and the observations offered by
human factors experts turn out to be true, automation
could have a substantial impact.

Unfortunately, litcle empirical evidence is available
that details when FPSs are typically used. Work by
Standard Technology Inc. (1990) suggests that FPS
usage does not bear a simple relationship to air traffic
complexity. Although strip activity was weakly predicted
by the time aircraft spent in a sector for the two-
controller situation, no scenario characteristic was pre-
dictive of strip activity for the one-controller situations.
Nevertheless, other actions taken by the air traffic con-
troller (for example, the commands he or she issues) and
other types of information received by the air traffic
controller (for example, communications from aircraft
and other ATC facilities) may illuminate ways in which
the FPSs are used.

For example, Buckley, DeBaryshe, Hitchner, and
Kohn (1983) measured some 28 potential indices of
ATC system performance, including a variety of behav-
ioral measures as well as various radar-related (Plan View
Display) indices (e.g., aircraft separation, time under
control for each aircraft, etc.). A factor analysis revealed
that a set of four factors could account for most of the
variance between scenarios of different complexity. Three
of these factors (iabeled confliction, occupancy, and delay)
summarized the PVD-related technical measures and are
difficult to observe, whereas the fourth factor (communi-
cation) subsumed behaviors of the controller. Thus,
looking at the relationship between communication
events and FPS activities seems an appropriate first step.

The goal of this study was to understand better when
controllers currently use FPSs to control traffic in an en
route environment. Potential implications of automa-
tion (Wise, Hopkin, & Smith, 1991) can only be as-
sessed in comparison toa well-understood existing system.
The present study is an observational study. The research
focuses on data obtained from a sample of controllers
who each worked individually, fulfilling both R and D
functions. The data involve the on-line classification of
communication events (i.e., controller commands, con-
troller queries, pilot requests, and sector transitions) and
FPS activities (i.e., looking at FPSs, writing on FPSs, and
manipulating FPSs). These data were used to construct

transition matrices which summarized, for example,
how often a look at the FPSs was followed by a controller
command.

However, attempts to determine the behavioral struc-
ture that underlies air traffic control from the raw
transition matrices is unsatisfactory for at least two
reasons. First, the transition matrices are quite complex.
Second, and more important, the matrices do not distin-
guish between those transitions present in the data that
reflect the latent structure of controlling air traffic and
those present in the data because of random noise. What
is needed is a way to reduce the complexity by eliminat-
ing those transitions that merely reflect noise in the data,
so that the remaining transitions provide structural
insights. By assuming that the underlying structure is a
gtaph (in the mathematical sense), we can distinguish
between those transitions that are necessary to the struc-
ture and those that are net.

A graph is a formalism in which the concepts (e.g.,
controller command) are represented by a node and the
transitions (e.g., controller command to write on FPS) are
represented by arcs connecting nodes. The graphs for
these data should be directed (i.c., event A leads to event
B) because the transition matrix is asymmetric. Thus, the
proportion of transitions from write on FPS to look at
FPSneed not be the sameas the proportion of transitions
from look at FPS to write on FPS. lf the arcs are weighted
so that some arcs are traversed more frequently than
others, the resultant formalism is a network.

One node can be connected to another in a network
either directly, by an arc from that node to the other, or
indirectly, by a path through other nodes. The simplest
such network consists of only those paths between two
nodes that are the most efficient (shortest) (see
Schvaneveldt, Dearholt, & Duzso, 1985). The represen-
tation of all such paths yields a network that represents
the shortest distance between all pairs of nodes. This is
important because this network will satisfy the triangle
inequality: The distance from A to B plus the distance
from B to C cannot be less than the distance from A to
C. In most modern scaling procedures, violations of the
triangle inequality are assumed to be due to random
noise and distortions and not valid indicators of the
underlying structure.


and returned to look at the FPSs. Thus, a long, single
look at the FPSs, or a search through the strips, was coded
as a single LOOK.

Procedure

Subjects first completed a brief background sketch.
They were chen given the opportunity to organize the
strip bay in preparation for the scenario. Subjects were
provided with all the strips for the problem at this time.
In our situation, the strip bay was located to the right of
the radar screen. The two observers sat behind and to the
right and left of the controller, with notebook computers
on their laps. The computers were used for on-line data
collection. Both computers were sy.achronized with the
clock on the radar screen, and oosesvers coded behaviors
by pressing different keystroke combinations as they
occurred, yielding a time-indexed behavioral record of
each scenario. Simultaneous events were codable, bur
appeared as sequences in the data trace. Coding errors
could be corrected on-line by escaping from thesubmenus.

In addition to the observers, two ghost pilots were
required to contral the planes, and another assumed the
communication functions of adjacent centers and other
ATC facilities. Radio and audio communications were
recorded using a multi-track cassette recorder. One
input channel included communications of the control-

ler, the pilots, and the center. Each observer wore a lapel
microphone and «was recorded on their own input chan-
nel, which allowed them to annotate orally their event-
recording.

Subjects were not informed about the emphasis placed
on FPSs in this study, but were told :o control traffic
normally. Each experimental session lasted approxi-
mately 3 houss {2 x 30-minute scen ssios plus | hour for
the most complex scenario). Break periods between
scenarios were approximately 20 rrinutes.

RESULTS

Any coding errors committed by the observers that
were noted on the audio-tape were corrected prior to
analysis.

Event rates

Events per minute were computed for each of the
seven classes of events (See Figure 1). An ANOVA was
conducted for each event class, with a test-wise @ of .007
to yield an experiment-wise @ of .05. As would be
expected, the rate of sector transitions increased with
complexity, F(2,16)=23.88, MSe=.02, p< .0001. The
most frequent events, CCOM and WRITE, tended to
occur at the same rate regardless of complexity. In fact,

S Low
@ Medium
@ High

Events per 1-2
minute 14

CCOM CQUERY

PREQ

‘|

SECTOR

LOOK

WRITE MANIP

Figure 1. Events (per minute) as a function of scenario complexity for ail classes of events.



AD-A259 062 -

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DOT/FAA/AM-92/31

Office of Aviation Medicine
Washington, D.C. 20591

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DTIC

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DEC 2 91992

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{ ict public releese and sale; ite
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En Route Air Traffic
Controllers' Use of
Flight Progress Strips: A
Graph-Theoretic Analysis
O. U. Vortac
Mark B. Edwards
Judi P. Jones
University of Oklahoma
Norman, Oklahoma 73019
Carol A. Manning
Civil Aeromedical Institute
Federal Aviation Administration

Oklahoma City, Oklahoma 73125

A.J. Rotter

FAA Academy

Federal Aviation Administration
Oklahoma City, Oklahoma 73125

November 1992
Final Report

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

@

U.S. Department
of Transportation
Federal Aviation


Pathfinder and the width of the arcs reflects the weight
of the transition, with thick arcs having occurred propor-
tionately more often than thin arcs. The figures are
further augmented by the proportional frequencies of
occurrence of the events as represented by the size of the
nodes. Events that occurred proportionately more often
are represented by larger node:. Note that in contrast to
multi-dimensional scaling solutions, in Pathfinder net-
works the physical distances between nodes in the depic-
tion is meaningless. All that matters is whether the nodes
are linked or not, the directicn of the arc, and how
“strong” that arc is.

Visual inspection of the MCNs reveals that all of the
networks are greatly simplified compared with the origi-
nal input matrices, which contained 49 transitions. The
graphs aiso look remarkably similar, both quatitatively

and quantitatively. Qualitatively, 2 number of arcs ap-
pear in all hree networks. Quantitatively, cften-traveled
arcs (thick lines) in one network tend to appear in the
others. In fact, the medium-complexiry MCN contained
only one arc (z pilot request loop) not present in either
the high or low-complexity MCN. As in the individual-
to-group correlations, we see that the graph represent2-
tions of the medium.-level scenarios were composites of
both the high and low-complexity scenarios.

Computation of various graph-theoretic measures
(see Table 3) confirms that, at least ata macroscopic level
of analysis. the networks are quite similar. The aerworks
have a comparable number of arcs, and the most distant
path (the diameter; of each network is similar with
perhaps the low-complexity network being slightly less
compact.

Complexity
Low |) Medium | High
Number of links 16 16 15
Diameter (3.30) (2.88) (2.90)
—+—
in-degree CCOM CCOM &|CCOM &
{number of arcs) | (5) WRITE WRITE
(4) (4)
Prestige in-center WRITE WRITE WRITE
(distance} (1.36) (1.88) _ G91)
in-median WRITE WRITE WRITE
(distance) (4.33) _ (1.08) (4.35)
out-degree WRITE WRITE WRITE
{number of arcs) | (4) (6) (5)
Influence out-center WRITE WRITE CCOM
(distance) (1.93) (1.84) _ (1.91)
out-median WRITE WRITE WRITE
(distance) (1.36) (1.34) (1.22)

Table 3. Globat MCN Characteristics



Figure 2. Minimai cost networks (MCNs) for low (top), medium (middie), and high (bottom) com-
plexity. Size of the nodes reflects the proportion of times that cvent occurred. Transition is repre-
sented by an arrow (arc) from one node to another, Thickness of the arcs reflect the proportion of
times that transition occurred.

~~


“D” side controller to assist. Table 1 summarizes the
scenarios used in this study.

Behavioral Categories

The onset of four types of communication events and
three types of FPS activities were coded. Communica-
tion Events were categorized into Con:roller Commands,
Controller Queries, Pilot Requests, and Sector Transitions.
FPS activities were categorized into Looking at FPSs,
Writing on an FPS, and Manipulating FPSs.

Communication Events
Controller Command (CCOM)

A controller command involved one or several of the
following six sub-corponents: 1) change route, 2) change
speed, 3) change altitude, 4) dispense information, 5)
issue clearance, or 6) other.

Controller Queries (CQUERY)

A controller query was a controller-initiated request
for information from the pilot. A controller query oc-
curred whenever the controller asked a pilot to report: 1)
aircraft speed, 2) altitude, 3) route, or 4) other. The
“other” subcategory tended to be requests for a verifica-
tion of aircraft call sign or characteristics (e.g., “heavy”).

Pilot Request (PREQ)

A pilot request was a pilot-initiated request of the
controller. Six possible pilot requests were coded: 1) a
chan je in route, 2) speed, 3) altitude, 4) information, 5)
clearance (direct clearance to destination), and 6) other.
Some of the “other” requests concerned air-refueling,
and so on.

Sector Transitions (SECTOR)

Another event of interest involved interactions be-
tween the controllerand adjacent centersand other ATC
facilities, in particular when aircraft are entering or
exiting a sector. Three components were coded: 1)
departures, 2) turnovers, and 3) initial contact. A depar-

ture was a communication initiated by another ATC
facility, a turnover was initiated by the controller, and
initial contact was initiated by the pilot. The distinction
between PREQ category above and the initial contact
subcategory of SECTOR was entailed in the concent of

the pilot’s communication.

In order to examine the relations between the events
above and controller activities relating directly to the
EPS, the following FPS activities were coded.

FPS Activities
Write (WRITE

A WRITE was coded whenever the controller verified
or changed an entry onan FPS. Verification involved one
or more of the fullowing four symbols: 1) “D” for
departure, 2) “R” for radar contact, 3) 2 check mark
when a new altitude was achieved, and 4) “C” for hand-
off to the next sector. Change involved any other mark-
ing on the FPS, such as a revision of altitude or change
in route.

Manipulate (MANIP)

The manipulation of an FPS was any physical contact
with the strip that did not involve writing. Five catego-
ries of FPS manipulation were coded. These were: 1)
moving a strip from the suspense to the active bay when
a flight entered the airspace, 2) sequencing the strips
within the active bay, 3) offsetting or “cocking” a strip as
a reminder, 4) flattening or removing the offset, and 5)
tearing down or removing the strip from the active bay
when a flight leaves the airspace.

Look (LOOK)

A LOOK was coded whenever the controller looked
at the suspense or the active bay. Because a look obvi-
ously precedes other FPS activities, this category only
included those looks at the FPSs that were not immedi-
ately followed by writing or manipulating. Those events
were instead coded as 2 WRITE or a MANIP. In
addition, multiple LOOKS at the FI'Ss imply that the
controller looked away (presumably to the radar display)


ACKNOWLEDGMENTS

Q. U. Vortac represents the collaborative research efforts of Francis T. Durso, Scott D. Gronlund,
and Stephan Lewandowsky of the Department of Psychology at the University of Oklahoma,
Norman, OK 73019-0535, USA. We wish to thank Gwen Sawyer, Manny Torres, Claude J. Schuldr,
and Tom Lynch, all of the FAA Academy in Oklahoma City, for their help, and Cornelia Rea of the
University of Oklahoma for her assistance.

Wealso thank the following people for their insightful comments: Bob Blanchard, William Collins,
Pam Della Rocco, Ron Lofaro, Richard Mogford, Betty Muxphy, Dave Schroeder, Michael Wayda,
and Hilda Wing.

O.U. Vortac may be contacted by phone (405) 325-4511, FAX (405) 325-4737, or electronic mail:
ouvortac@oucog!.psy.uoknor.edu,

DTIC QUALIT: IN:

Accesion For
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low scenario, LOOK has a loop and connects to the
network only through WRITE. Ir is tempting to specu-
late that looking serves the Board Management function
in less dense traffic situations, whereas looking is re-
placed, or at least augmented, by manipulating (e.g.,
moving, offsetting) in the more dense traffic situations.

if controllers are more sikely to engage in uninter-
rupted board management as the Pathfinder analysis
suggests, then we should find, in the original data, more
frequent sequences of board management activity in the
high-complexity scenarios than in the low-complexity
scenarios. An analysis of higher-order clusters was per-
formed, which tabulated the frequency of occurrence of
allxriples and quadruples terminating in an FPS activity
across the data for all subjects. An n-tuple was defined as
any sequence of N events that occurred together within
a 10-second window and that ended in an FPS activity.
Ninety-three per cent of these clusters involved writing,
supporting our assertion that WRITE is the most pres-
tigious and influential of the nodes in the Pathfinder
solutions. The clusters support the assertion that -on-
trollers adapt to high-complexity situations by partition-
ing the board management duties into an activity separate
from the remaining responsibilities. None of the low-
complexity nor the medium-complexity triples exclu-
sively involved writing and manipulating, but 19% of
the high-complexity uiples did. With regard to four-
tuples of activities, the five most frequent four-tuples in
the high-complexity sccnarios did zot involve any
CCOM, suggesting again that controllers devote more
uninterrupted clusters of time to FPS board manage-
ment in the high-complexity scenario, and relatively less
uninterrupted time to communication activity.

DISCUSSION

By observing controllers in situations of varying traf-
fic complexity, it was possible to discover the relation-
ships between communication events and strip activity.
This was done by applying the Pathfinder scaling algo-
rithm to matrices of the average transitions between
events. Several aspects of air traffic control were shown
zo generalize to all levels of complexity, including the
central role that writingon the strips plays in the current
syste.

ll

It was also possible to delineate those aspects of air
traffic control that tended to change as a function of the
traffic complexity. Changes due to complexity applied to
all controllers, indicating that individual differences
were minor relative to situational factors. Moderate
levels of complexity proved to be a composite of low-
complexity and high-complexity.

The clearest difference between low-complexity and
high-complexity air traffic situations was in the manipu-
jation of the strips. At higher complexity, manipulations
occurred at a higher rate, tended to occur with other
board management functions, and were less integrated
with communication events. The pattern of findings
implies that board management is affected by the com-
plexity of the scenario. In the more complex scenarios,
the controller is forced to find time to keep the board
configured and updated, and he or she does this in
concentrated time segments. During these times, con-
trollers may fee} that they have “been taken away” from
the PVD and the traffic situation. In this sense, the
individual controller temporarily divorces from the ra-
dar to perform board management duties, essentially
serving as his or her own D-side. Fortunately, with a
traffic situation as complex as that found in our complex
scenario, these D-side, board management duties would
normally be performed by a different individual, sparing
the controller from the schism of switching from R-side
to D-side. In fact, several of controllers indicated the
need for a D-side during the high-complexity scenario.

In contrast. while controlling simpler traffic situa-
tions the controller could manage the board as an inte-
gral pare of controlling air traffic. That is. he or she more
effectively time-shared between controlling traffic and
managing the board: Strips were updated immediately
after issuing a command and board management was
imegrated with other controller activities.

This result implies that automation may in fact facili-
tate control in higher-complexity situations if it allows
the computer to take responsibility for the, now appar-
ently segregated, board management duties. This is a
very viable outcome of automation. However, an un-
wanted by-product of automation may be an increased
workload in other areas (e.g., more keyboard-intensive)


Schvaneveldt, R. W., Dearholt, D. W., & Durso, F. T.
(1985). Graph theoretic foundations of Pathfinder
networks. Computers and mathematics with applica-
tions, 15, 337-345.

Schvaneveldt, R. W., & Durso, F. T. (1981). General
semantic networks. Paper presented at the annual
meetings of the Psychonomic Society, Philadelphia, PA.

Schvaneveldt, R. W, Durso, F. T., & Dearholt, D. W.
(1989). Network structures in proximity data. In G.
H. Bower (Ed.), The Psychology of Learxing and Moti-
vation: Advances in Research and Theory Vol 24), (pp
249-284), Academic Press: New York .

Schvaneveldt, R. W., Durso, F.T., Goldsmith, TE., Breen,
T. J. Cooke, N. M., Tucker, R. G., & DeMaio, J.C.
(2985) Measuring che secuctuce af expertise. (ucerna-
tional Journal of Man-Machine Studies, 23, 699-728.

Smith, M. L. (1991). Adaptive taining to accommodate
automation in the air traffic control system. In J. A.
Wise, V. D, Hopkin, & M. L. Smith (Eds.). Automa-
tion and systems issues in air traffic control. (pp. 481-
495). Springer-Verlag: Berlin.

Standard Technology, Inc. (1990). FAA staffing standards
for air route traffic control centers, Management Engi-
neering Branch: Office of Management Systems.

Vortac, 0. U. & Gemtys, C. F. (1990). Cognitive factorsin the
use of flight progress strips: Implications for automation.
Cognitive Processes Laboratory, University of Okla-
homa, Norman, OK.

Weston, R. C. W, (1983). Human factors in air traffic
control. Journal of Aviation Safety, 1, 94-104.

Wise, J. A., Hopkin, V. D., & Smith, M. L.(Eds.) . (1991).

Automation and systems issues in air traffic control.
Springer-Verlag: Berlin.

*U.S. GOVERNMENT PRINTING OFFICE: 1992-761-068/60038

13


Technical Report Documentation Page

J. Report No.

DOT/FAA/AM-92/31

2. Government Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle

En Route Air Traffic Controllers’ Use of Flight Progress

Strips:

A Graph-Theoretic Analysis

5. Report Date

November 1992

6. Performing Organizanen Cooe

7, Author's) Q , Vortac, Mark B. Edwards, Judi P. Jones,
Carol A. Manning, and A.J. Rotter

8. Performing Organization Report No.

9. Pettorming Organization Nama and Address
FAA Civil Aeromedical Institute
P.O. Box 25082

Oklahoma City, OK 73125

University of Oklahoma
Dept. of Psychology
755 W. Lindsey

Norman, OK 73019

10. Work Unit No. (TRAIS)

V1. Contract or Grant No.

Contract DTFA-02-91-C~91089

12. Sponsoring Agency Nome and Address

FAA Office of Aviation Medicine
Federal Aviation Admin.

800 Independence Ave., S.W.

FAA Research and
Development Service
Federal Aviation Admin.
800 Independence Ave,SW

13. Type of Report and Period Covered

147 Sponsoring Agency Code

Washington, D.C. 20591 Washignton, D.C. 20591

13. Supplementary Notes

This work was performed undezx task AM--D-92-HRR-141 and Contract No. 91-C-91089.

16. Abstract

In the United States, flight data are represented on a paper Flight Progress Strip
(FPS). The role of the FPS has recently attracted attention because of plans to
automate this aspect of air traffic control. The communication activities and FPS
activities of air traffic controllers were categorized while they controlled air
traffic of varying complexity. Transition networks were derived from the empirical
transitions. These networks indicated that several aspects of air traffic control
generalize across complexity, including the centrality of writing-on-the-FPSs to
the control of traffic. Complexity was a factor when FPSs were used with high com—
plexity traffic situations, requiring the controller to direct uninterrupted periods
of time to the management of the FPSs rather than integrating these board management
responsibilities with the responsibilities of separating aircraft.

17. Key Words | 18. Distribution Statement

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through the National Technical Information
Service, Springfield, Virginia 22161.

Automation
Air Traffic Control

Zi. No. of Pages

17

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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.


Centrality

The nedes also seem to play similar graph-theoretic
roles in the three networks. For example. WRITE plays
acentral roje in all three networks. Table 3 also reports
three different measures of centrality for both the incom-
ing transitions (prestige) and the outgoing transitions
(influence).

Prestige. A node that receives a large number of arcs,
or that serves as a central location for incoming arcs, is
said to have prestige. Cue measure of prestige is the in-
degree, a simple count of the number vf arcs terminating
ona node. Another measure of this type is the in-center,
the node tha: minimizes the distance to the farthest
node. A final measure of prestige is the in-median. This
measure minimizes the distance to all the nodes. Interest-
ingly. che WRITE node appears to be the most presti
gious regardless of the complexity of the scenario. In part
this may be the result of the mandatory strip marking
done for legal purposes.

Influence. Influence measures reflect the centrality of
the node in terrns of the number of outgoing transitions.
Thus the oue-degree is a simple count of the number of
arcs leaving a node, the out-center is the node that

minimizes travel from that node to the most distant
node, and the out-median is the node that minimizes
travel to all the nodes. Again, WRITL plays a central role
in the three scenar. s.

Thecentrality of WRITE in all three networks strongly
supports the importance of studying strip activity. We
had expected, perhaps naively, chat CCOM would have
been at the center, at least in terms of prestige. The fact
that WRITE appears to be a hub of both incoming and
outgoing arcs suggests that much of air traffic control is
organized around this strip activity. We should note that
this centrality has two potential consequences for auto-
mation. One possibility is that air traffic control will be
greatly facilitated by aliowing the computer to control
more strip management. The other possibility is that
such automation will have negative side effects on con-
trolling traffic (e.g., Hopkin, 1991). Of course, this is the
auestion that originated this project. However, it is now
clear that the debate focuses on an issue of true impor-
tance. When the center of a transition network is modi-
fied, it will bave definite effects on the structural whole
of the network. Whether these effects are ultimately
positive or negative requires additional investigations,
but the effects will be there and they may be substantial.

SECTOR

S

MANIP

CQUERY

aT

Figure 3. Fundamental arcs. Subgraph of the arcs shared by all networks regard-

less of complexity.



which may offset any advantage gained from having the
computer take over board management ducies. Never-
theless, it is likely that any facilitating effects of automa-
tion will be more pronounced in more complex situations,
and that controllers in those situations would be betrcr
able to control traffic without the assistance of a D-side
if che automation truly allows the controller to integrate
board management duties with traffic separation.

FINDINGS

* Writing is central. Much of ATC ackivity is orga-
nized around this activity. Because automation
will affect this hub of ATC activity, it should
substantially impact controller performance.
Whether it has positive or negative impact awaits
the final design of the system and additional
research,

Board management is segregated from controlling
traffic as traffic complexity increases. Automation
may facilitate control in higher-complexity situa-
tions if the computer takes control of the segre-
gated board management duties. However, if
automation increases board management workload
(more keyboard intensive), it may offset any ad-
vantage gained by automation.

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