# Visual Search in Complex Displays: Effects of Traffic Load, Regularity, and Conflict Geometry on the Detection of Conflicts by Air Traffic Controllers 

# 

Roger W. Remington^\*^

#### James C. Johnston^\*^

Eric Ruthruff^\*^

Miri Gold^±^

Maria Romera^±^

### ^\*^NASA Ames Research Center

*^±^San Jose State University Foundation*

## Running Head: Searching for Conflicts in ATC Displays

Address correspondence to:

Roger W. Remington

NASA Ames Research Center

MS 262-4

Moffett Field, CA 94035

Phone: (650) 604-6243

Fax: (650) 604-3323

### Email: <rremington@mail.arc.nasa.gov> 

# Abstract

Recent "free flight" proposals to relax airspace constraints and give
greater autonomy to aircraft have raised concerns about their impact on
controller performance. Relaxing route and altitude restrictions would
reduce the regularity of traffic through individual sectors, possibly
impairing controller situation awareness. We examined the impact of this
reduced regularity in four visual search experiments that tested
controllers' detection of traffic conflicts in the four conditions
created by the factorial manipulation of fixed routes (present vs.
absent) and altitude restrictions (present vs. absent). These four
conditions were tested under varying levels of traffic load and conflict
geometry (conflict time and conflict angle). Traffic load and conflict
geometry showed strong and consistent effects in all experiments.
Color-coding altitude also substantially improved detection times. In
contrast, removing altitude restrictions had only a small negative
impact, while removing route restrictions had virtually no negative
impact. In some cases, conflict detection was actually better without
fixed routes. The implications and limitations of these results for the
feasibility of free flight are discussed.

# Searching for Conflicts in ATC Displays

The movement of aircraft through the National Airspace System (NAS) is
governed by strict procedures that specify permissible altitudes and
routes, as well as other constraints (e.g. the required separation
between aircraft under different conditions). The goal of these
procedures is to promote safe operations by providing a well-structured
airspace in which all participants (e.g. pilots, air traffic
controllers) understand their roles and responsibilities. There is
general consensus that the orderly nature of the NAS contributes
significantly to its exemplary safety record.

Currently, ground-based air traffic controllers route flights along
fixed airways, restricting altitude depending on the east/west direction
of flight. While providing for an orderly airspace, current routing
procedures do not correspond to any natural optimization of flight
parameters, such as fuel consumption or flight time. It would in general
be more fuel and time efficient to fly along a great circle route, the
shortest distance between two points on the surface of a sphere, with
modest adjustments for wind. The current system of airways was created
in response to limited navigational technology (see Cotton, 1995).
Improved aircraft technology, however, has now made it possible to fly
with precision any desired path from one point to another in the
continental United States (e.g. a great circle path), and to optimize
for whatever criteria is desired -- distance, time, fuel consumption,
etc. Greater optimization translates into decreased operating costs and
improved schedule adherence, increasingly important in the competitive
air carrier market (see Planzer & Jenny, 1995). As a result, the
operational feasibility of relaxing constraints on routes and altitudes
is being given serious consideration (see, for example, RTCA, 1994;
Wickens, Mavor, Parasuraman, & McGee, 1998, Chapter 9).

Research on the feasibility of increased aircraft autonomy has a
surprisingly long history, with at least one early laboratory study
having found improvements in some measures of safety and efficiency
during a simulated approach in which aircrews shared responsibility for
maintaining separation (Kidd & Kinkade, 1958). Currently, limited
operational tests of more flexible procedures are being conducted by the
Federal Aviation Administration (FAA), and point-to-point navigation is
currently permitted under specific conditions. In addition, NASA, in
conjunction with the FAA, has established the Advanced Aviation
Transportation Technologies (AATT) program to investigate a variety of
"free flight" concepts that embody various degrees of aircraft autonomy.
These concepts depend critically on advanced automation techniques to
more accurately schedule arrivals (Davis, Erzberger, & Bergeron, 1989;
Erzberger, 1995; Denery, Erzberger, Davis, Green, McNally, 1997),
departures (Idris, Delcaire, Anagnostakis, Hall, Hansman, Feron & Odoni,
1998; Idris, Delcaire, Anagnostakis, Hansman, Feron and Odoni, 1998),
and to automatically detect conflicts (Paielli & Erzberger, 1997;
Krozel, Mueller, & Hunter, 1996; Krozel & Peters, 1997). Automation
alone is not sufficient. Good human factors design will be needed to
ensure that controllers and pilots can continue to function at their
current high levels of performance (see Ball, DeArmon, & Pyburn, 1995;
Wickens, et al., 1997, 1998).

One of the principal challenges for human factors is to determine if
relaxing flight constraints would impair the ability of controllers to
manage traffic within a sector. Once aircraft are free to fly
individually optimized paths, the fixed routes with which controllers
have become familiar would be replaced by a much larger number of
opportunistic flight paths. Indeed, increased variability has been
confirmed in computer simulations of traffic flow with free flight
(Ball, et al., 1995; RTCA, 1994). When tracks of all the aircraft in the
NAS for a 24 hours period were perturbed to reflect traffic flow under
route and wind optimization, the number of individual paths increased,
and aircraft were distributed more evenly across the airspace. Traffic
patterns through sectors were also found to be less uniform and less
predictable.

This raises the concern that free flight would reduce the regularity and
predictability of within-sector traffic, making it more difficult to
acquire and maintain the mental representations needed to safely manage
sector traffic (Wyndemere, 1996; Wickens et al., 1998, pg. 235). A
reduction in traffic regularity would be realized as a decrease in the
organization of information on the controller's visual display of
traffic. Strong display organization has been shown to enhance the
efficiency of allocating attention (Treisman, 1986; Parasuraman, 1986),
to facilitate visual search (e.g., Schneiderman, 1987; Smith & Mosier,
1986, Section 2.5), and to provide conceptual support for user mental
models of the underlying system (Norman, 1988). Thus, there is
reasonable concern that reductions in traffic regularity associated with
free flight will increase the time needed to acquire information from
the visual display. Also, with highly variable traffic patterns,
displays might no longer be as effective in providing conceptual support
for controllers' mental representations. If so, situation awareness
might decline.

# The Present Experiments

A critical controller task that could be adversely affected by a
reduction in display organization is the detection of traffic conflicts.
Conflict detection makes intense use of the visual display of traffic.
Trajectories for pairs of aircraft must be estimated using aircraft
symbols, past histories, and other graphic representations of flight
path, together with the flight information provided in the data blocks
(e.g. altitude, airspeed). Although the implementation of free flight is
likely to include automated conflict detection probes (Paielli, &
Erzberger, 1997; Krozel, Mueller, & Hunter, 1996; Krozel, & Peters,
1997), controllers would remain responsible for the safety of aircraft
in their sectors, and conflict detection would remain a high priority.
More generally, the time to detect a conflict can serve as a proxy for
situation awareness --- that is, the quality of the controller's mental
representation of traffic patterns. If structure in the display
facilitates comprehensive mental representations of traffic through the
sector (situation awareness), then that structure should have measurable
consequences on the time to detect a conflict between two aircraft.
Thus, any compromise in conflict detection ability resulting from
relaxed constraints on traffic patterns would be a serious challenge to
the operational feasibility of free flight.

The present research employed a part-task methodology with conflict
detection as the primary dependent measure. Controllers viewed discrete
scenarios, each of which lasted less than 90 seconds and required
detection of a single traffic conflict. This methodology allowed us to
economically collect a large amount of data across a wide variety of
factors, including airspace constraints, traffic density, and conflict
geometry.

Controllers served in four conditions created by the factorial
combination of the presence and absence of route restrictions and
altitude restrictions. The resulting conditions included approximations
of the current procedures (both route and altitude restrictions),
complete free flight (no route or altitude restrictions), and two
intermediate conditions in which only one restriction or the other was
relaxed. A careful examination of the effects of relaxing route and
altitude restrictions must consider the possible interaction with range
of difficulty conditions typical of air traffic control operations.
Hence, we systematically varied traffic load and conflict geometry.

As traffic density (number of aircraft) increases, more aircraft pairs
may need to be searched, increasing detection times. (Note that number
of aircraft variable is analogous to the set size variable used in
classic visual search experiments.) Conflict geometry refers to a range
of trajectory conditions for the conflicting aircraft pairs that
includes the angle of convergence for conflict pairs (conflict angle)
and the length of time before a conflict would occur (conflict time).
Increases in conflict angle, the interior angle formed at the
intersection of the path crossings, have been shown to increase the time
required for trajectory extrapolation judgments (Ellis, 1982; Smith,
Ellis, & Lee, 1984). Since conflict detection requires the controller to
extrapolate the trajectories of pairs of aircraft, conflict angle would
be expected to have similar effects on conflict detection.
Time-to-conflict (conflict time) has not been systematically
investigated, but we anticipate that a longer time-to-conflict will
generally produce a longer detection time. Aircraft pairs whose conflict
point is further in the future will tend to be further apart spatially.
The greater distance increases the difficulty of trajectory
extrapolation and increases the likelihood that other aircraft will be
interspersed between conflict pairs. Hence, pairs with long times to
conflict should take longer to find, especially with high aircraft
density.

The magnitude of the effects of traffic load and conflict geometry will
provide a way to calibrate the magnitude of any effects of removing
airspace constraints (route and altitude restrictions). It should be
possible, for example, to express any increase in detection time for
removing airspace constraints in terms of the number of aircraft that
would need to be removed to produce an equivalent decline in
performance.

# Experiment 1

## Method

*Subjects*. Four retired air-traffic controllers were paid for their
participation. Three had extensive experience at enroute center control;
one had extensive experience as a TRACON controller.

*Stimulus Displays*. Air-traffic control (ATC) displays were simulated
on NEC Multisynch monitors connected to IBM-PC compatible computers.
Sample ATC displays are shown in Figures 1 and 2.

![](media/image1.png){width="5.998611111111111in"
height="4.8909722222222225in"}

*Figure 1.*

![](media/image2.png){width="5.998611111111111in"
height="4.8805555555555555in"}*Figure 2.*

The air space was represented by a large circle whose diameter subtended
approximately 20° visual angle from a viewing distance of 60 cm.
Conflicts occurred within a smaller "controlled region" contained within
a circular area of 15° diameter (not indicated on the display screen).
In the corners of the display, outside the airspace, several pieces of
additional information were displayed in light brown. The upper
right-hand corner contained the trial number along with the flight-path
restrictions in effect for that session (see below). The lower
right-hand corner showed the altitude restrictions, if any. The lower
left-hand corner contained a distance scale.

Aircraft were represented by purple, wedge-shaped icons as shown in
Figure 2. The long axis of the wedge (approximately 0.6°) was aligned
with the flight path. The short axis of the wedge subtended
approximately 0.3° at the rear of the icon. A thin, dark-gray line
connected the center of each wedge to its corresponding data block. A
data block consisted of three lines of textual data, modeled after those
of the NASA Center TRACON System (CTAS). In order, the lines contained
the following information: 1) aircraft identifier; 2) altitude (in
flight level, defined as altitude in feet divided by 100; e.g., 31,000
ft shown as FL 310) plus an equipage code letter; 3) aircraft type and
the ground speed. At the beginning of a trial, each data block was
positioned 1.3° visual angle from the center of the wedge at an angle of
0°, 90°, 180°, or 270° relative to its aircraft's direction of travel
(track). For each aircraft, the angle was chosen separately to minimize
overall display crowding. Once the trial was underway, controllers could
reposition the data block to any point equidistant from the aircraft.
All display information was updated every four seconds.

*Flight Path Restrictions*. Four cells were created by the presence
versus absence of altitude restrictions and route restrictions. In the
Fixed-Routes condition, aircraft flew along designated routes (see
Figure 2). The routes were unmarked, straight-line paths, chosen to
create a set of conflict angles that included both small (acute) and
large (obtuse) angles. In the No-Routes condition, aircraft could assume
any straight-line flight path through the airspace (with a few
restrictions noted below).

In all conditions, aircraft were restricted in altitude to four flight
levels (FL 310, FL 330, FL 350, and FL 370). In the Restricted Altitude
condition, westbound aircraft flew only at FL 310 or FL 350; eastbound
aircraft flew only at FL 330 or FL 370. In the Unrestricted Altitude
condition, aircraft were assigned randomly to the four flight levels
without regard for the direction of travel. Aircraft were randomly
assigned speeds ranging from 400 to 500 knots (kts), in increments of 5
kts. Aircraft did not change altitude or speed.

*Conflict pairs*. On every trial, the airspace contained 12, 16, or 20
aircraft (two of which were the conflict aircraft and the rest foils).
In the Fixed-Routes condition, conflicts occurred at each of the five
route crossings with the restriction that half of the trials in each
block produce a large angle of incidence and half of the trials produce
a small angle of incidence (relative to the median angle). In the
No-Routes condition, conflict pairs obeyed the following restrictions:
(a) the angle of incidence was at least 20 degrees, (b) the aircraft
obeyed appropriate altitude restrictions, and (c) the angle of incidence
was greater than the population median on half of the trials and less
than the median on half of the trials. Having chosen trajectories for
the conflict aircraft, we then achieved a desired time-to-conflict
(conflict time) by selecting appropriate initial positions. All conflict
pairs were visible on-screen at trial onset. Conflict time was randomly
selected from a uniform distribution of times between 4 and 6 minutes
for the short conflict-time condition, and between 6 and 8 minutes for
the long conflict-time condition.

*Foil aircraft*. Trajectories for foil aircraft satisfied several
constraints: 1) foil aircraft never came within 10 nautical miles of the
conflict aircraft or any other foil aircraft; 2) the location and
direction of foil aircraft were distributed randomly; and 3) all foil
aircraft were on-screen at the beginning of the trial. Applying these
constraints, we ran simulations in each flight path restriction
condition to determine how many times aircraft would come within 12
nautical miles in X-Y space, ignoring altitude. Because the orientation
of these XY-foils is close to that of a conflict pair, these aircraft
pairs should be more difficult to discriminate from conflict pairs. In
generating experimental trials, we ensured that half the trials within a
route-altitude condition contained less than the median number of
XY-foils and the other half contained more than the median number of
foils.

Altitude and speed were selected randomly for each foil aircraft, with
the restriction that there be at least two aircraft at each of the four
altitudes. In the Fixed-Routes condition, an aircraft was randomly
assigned to a route with the restriction that there be at least one
aircraft on each route. In the No-Routes condition, an aircraft was
assigned a random path that passed through the controlled region. The
direction of travel along that path was consistent with any altitude
restrictions in effect. We determined the starting position of each
aircraft at trial onset by choosing a location within the controlled
region and a time at which an aircraft would be at that location (drawn
randomly from a uniform distribution ranging from --20 to 100 sec after
trial onset). This ensured that some aircraft would be exiting the
controlled region at trial onset. Figure 2 shows sample traffic displays
from the four conditions formed by the crossing of route and altitude
restriction variables.

*The Task.* Controllers were instructed to find the conflict pair as
quickly as possible. When they believed they had found it, they pressed
the space bar, terminating display updates. They then clicked
consecutively on the two conflict aircraft using the mouse button,
causing the aircraft to turn light blue. They could undo a selection by
clicking on an aircraft a second time (like a toggle switch). Once
controllers felt they had selected the proper conflict pair, they
pressed the spacebar once again to confirm their selection. Feedback for
correct responses was a series of three high-pitched tones. Feedback for
incorrect responses was a single low-pitched tone; in addition, the
actual conflict aircraft turned white. Response time was measured from
trial onset to the first space bar press. If subjects failed to respond
within 90 seconds of trial onset, the trial was terminated and they were
given feedback indicating they had taken too long. The next trial began
after a 3 sec inter-trial interval.

*Procedure*. The experiment consisted of one practice day followed by
four days of data collection. On the practice day, controllers were
given a detailed introduction to the experiment, informed of all the
air-space constraints that would be in effect, and instructed in the
method of selecting conflict pairs. They also completed two practice
sessions in the Fixed-Routes condition (one with Restricted-Altitudes
and one with Unrestricted Altitudes), followed by two practice sessions
in the No-Routes condition (one with Restricted-Altitudes and one with
Unrestricted-Altitudes). The four practice sessions completed on day 1
consisted of two blocks of 24 trials each, and lasted about 50 minutes.

The experiment proper consisted of eight 90-minute sessions, each
containing five warm-up trials followed by three blocks of 24 trials.
Each day controllers performed either two Fixed-Routes sessions or two
No-Routes sessions; they performed one session with Restricted altitudes
and one without. Thus, Altitude Restrictions varied between sessions
within a day, whereas Route Restrictions varied only between days. Every
controller performed in the same sessions, viewing identical scenarios;
however, the order of the sessions was counterbalanced across subjects.
The eight sessions consisted of two cycles through the four
route-altitude conditions, where the order of sessions in the second
cycle was the reverse of the order in the first cycle. This served to
minimize any confounding of condition with level of practice within
subjects.

After the experiment was over, we asked the controllers a series of
questions concerning which experimental conditions they perceived to be
the most difficult. They rank ordered the conditions and were encouraged
to provide explanations, observations, and to suggest ways to improve
the simulation (some of which were adopted in Experiments 3 and 4).

*Design*. There were three between-session factors: Route Restrictions
(Fixed-Routes vs. No-Routes), Altitude Restrictions (restricted-altitude
vs. unrestricted altitude), and Practice (the first four sessions vs.
the last four sessions). There were four within-session factors: number
of aircraft (12, 16, 20), conflict angle (small vs. large), conflict
time (short vs. long), and number of XY-foils (many vs. few). However,
the number of XY-foils consistently had no effect and therefore was
eliminated from all analyses.

*Analysis*. Response time data showed a strong positive skew. To reduce
the impact of a very few high response times, we analyzed the mean
across controllers of the median response time in each cell of the
design. Omission errors, trials on which controllers failed to respond
within 90 seconds, were assigned a response time of 90 seconds and
included in the analyses. Commission errors, trials on which an
incorrect pair was selected, were excluded from the response-time
analysis. This is standard practice in response-time studies.

## Results

*Response Time Analysis.* The means of the median response times from
correct trials for each subject in each condition were entered into a
repeated measures analysis of variance with number of aircraft, conflict
time, conflict angle, altitude restrictions, route restrictions, and
practice as factors. Practice refers to the first vs. the second session
of the same route-altitude condition.

There were significant main effects of **number of aircraft** ({12, 16,
20} = {15.6, 21.5, 31.5 sec}, F\[2,6\]=54.83, p \< .001), **conflict
angle** ({small, large} = {17.8, 27.9 sec}, F\[1,3\] = 49.54, p \< .01),
and **conflict time** ({short, long} = {20.4, 25.3 sec}, F\[1,3\] =
17.88, p \< .05).

The main effect of **altitude restriction** was marginal ({restricted,
unrestricted} = {21.9, 23.8 sec}, F\[1,3\] = 6.51, p \< .10). There was
no effect of **route restriction** ({routes, no-routes} = {23.5, 22.2
sec}, F\[1,3\] = 4.90, p \> .10).

There were several significant interactions involving traffic load and
conflict geometry factors. Figure 3 plots the significant 3-way
interaction of **conflict time** **by** **conflict angle** **by**
**number of aircraft** (F\[2,6\] = 6.32, p \< .05). The 2-way
interaction of **number of aircraft by conflict angle** was also
significant (F\[1,3\] = 14.40, p \< 01), as was **conflict angle by
conflict time** (F\[1,3\] = 21.13, p \< .02). The interaction of
**practice by number of aircraft** was significant (F\[2,6\] = 10.06, p
\< .02).

![](media/image3.wmf){width="5.9944444444444445in"
height="4.820833333333334in"}

There were few significant interactions involving airspace constraint
factors. The **route restriction by altitude restriction** interaction,
shown in the left panel of Table 1, was not significant (F \< 1).
However, the 3-way interaction of **number of aircraft by conflict angle
by route restriction** was significant (F\[2,6\] = 5.65, p \< .05), as
was the 2-way **conflict angle by route restriction** interaction
(F\[1,3\] = 36.47, p \< .01).

*Analysis of Commission Errors.* The overall rate of commission errors
was 7.9%. Error data showed several significant effects, none of which
indicated a speed-accuracy trade-off, or other possible problems for the
interpretation of response times. Analysis of variance on error data
found main effects of **number of aircraft** ({12, 16, 20} = {3.8, 7.9,
11.9}, F\[2,6\] = 12.98, p \< .01), **conflict angle** ({small, large} =
{6.3, 9.5}, F\[1,3\] = 16.96, p \< .05), and **practice** ({first,
second} = {9.3, 6.4}, F\[1,3\] = 19.12, p \< .05). In these analyses,
changes in error rates are in the same direction as changes in response
time. Significant 3-way interactions were found for **number of aircraft
by conflict time by altitude restriction** (F\[2,6\] = 5.58, p \< .05),
**conflict time by altitude restriction by route restriction** (F\[1,3\]
= 19.13, p \< .05), **conflict angle by conflict time by practice**
(F\[1,3\] = 11.39, p \< .05), and **conflict angle by altitude
restriction by practice** (F\[1,3\] = 122.10, p \< .01). We will not
attempt to interpret these interactions except to note that there is no
evidence of a speed-accuracy trade-off.

## Discussion

*Traffic Load & Conflict Geometry Factors*. Our attempts to manipulate
conflict detection difficulty by varying traffic load and conflict
geometry were highly successful. Mean response times ranged from 15 to
31 sec as a function of increasing number of aircraft, from 18 to 28 sec
for conflict angle, and from 20 to 25 sec for conflict time. In all
cases, the direction of the response time change was consistent with
expectations. For the easiest combination of conditions (12 aircraft,
short conflict time, small conflict angle) mean response time was 10.7
sec, versus 48 sec for the hardest combination of conditions (20
aircraft, long conflict time, large conflict angle). Thus, in
combination, our difficulty factors produced over a four-fold increase
in response time.

The pattern of interaction between traffic load and conflict geometry
factors (conflict angle & conflict time) provides clues as to how
controllers searched for conflicts. Note that high traffic density had a
significantly greater effect when combined with the most difficult
levels of other factors. This over-additive pattern is consistent with
the hypothesis that the screen distance between conflict aircraft was a
mediating variable. Aircraft symbols and data tags for conflict pairs
with [short]{.underline} conflict times closing at [small]{.underline}
conflict angles will be [close]{.underline} to each other on the
display. Symbols for conflict pairs with [long]{.underline} conflict
times closing at [large]{.underline} conflict angles will tend to be
[far]{.underline} from each other on the display. Increasing the number
of aircraft increases the likelihood that symbols for non-conflict
aircraft will lie between symbols for the conflict pair. If controllers
tended to first check adjacent or nearby aircraft symbols and data tags,
they would have searched on average more non-conflict pairs the greater
the distance between the two conflicting aircraft, leading to an
overadditive interaction.

The over-additive interaction of these factors may also be explained by
applying the general hypothesis that over-additive interactions are
associated with factors that affect the same stage of mental processing
(Sternberg, 1969). While this explanation has traditionally been applied
to very short response times, it also makes sense applied to stages on a
longer time scale. Search lends itself to overadditive interactions
because it requires an iterative application of elementary mental
processes that first find candidate targets and then evaluate their
\"goodness\". Factors that increase the difficulty of an underlying
operation will interact over-additively with factors that increase the
number of pairs examined.

*Airspace Constraint Factors.* The principal factors of interest were
the presence and absence of altitude and route restrictions, which
represent a comparison of present day practices with recent proposals to
relax airspace constraints. Surprisingly, airspace restrictions failed
to substantially increase response times or error rates. The objection
might be raised that our methods are just very insensitive; perhaps we
can't tell whether or not controllers are having a problem. However, the
rest of our results refute this objection. Our methods were sensitive
enough to pick up a wide variety of influences on controller performance
including traffic load, conflict time, and conflict angle. Clearly our
experimental methods [are]{.underline} sensitive to the difficulty of
conflict detection. Hence, we conclude that our results mean what they
appear to mean: removal of airspace restrictions had very little effect
on conflict detection difficulty.

Removal of altitude restrictions did produce a modest deterioration in
performance. The obvious explanation for this effect is that altitude
restrictions considerably reduced the number of conflict pairs with
large intersection angles, which are the most difficult to find. Note
that this reduction in large conflict angles is an inevitable
consequence of east-west altitude restrictions, both in the real world
and in our simulations. Also note that the reduction in large conflict
angles necessarily causes a reduction in the average initial separation
of conflict aircraft. If controllers are sensitive to this change, then
they might bias their search strategies toward looking for nearby
conflict pairs in the Restricted Altitude condition. This strategy shift
should both reduce the time to detect short-time conflicts (which have
small initial separations) and also increase the time to detect
long-time conflicts (which have large initial separations). Such a
strategy shift can therefore explain why the presence of altitude
restrictions decreased response time by 4.8 sec in the short
conflict-time condition, but actually [increased]{.underline} response
time by 1.1 sec in the long conflict-time condition.

Removal of route restrictions failed to produce any decline in measured
performance at all. In fact, without routes conflict detection times
were actually slightly [shorter]{.underline} (although the trend was not
statistically significant). This finding is surprising given that the
No-Routes displays appeared chaotic whereas the Route displays appeared
well-structured and orderly. Why did the loss of display structure not
impair performance? We believe that there are two reasons. First,
controllers are not as helpless in the No-Routes condition as one might
have feared. They have available strategies, such as the clockwise
search path used in training, that are usable even without routes.
Second, there are display advantages to the No Route condition that
offset the loss in structure. The Fixed-Routes condition concentrates
traffic into a small portion of the airspace, while the No-Routes
condition distributes traffic more uniformly over the airspace. The
result is that aircraft symbols and data blocks are more closely packed
in the Fixed-Routes condition, with a greater likelihood of overwriting.
Note that differences in aircraft distribution are inherent in the two
schemes and will be reflected in actual operations in much the same way
as in our experiment.

Because traffic distributions are different for the Fixed-Routes and
No-Routes conditions, these conditions may respond differently to
factors that change the display of flight and route information. The
subsequent experiments investigated this hypothesis.

# Experiment 2

To determine if two aircraft will conflict, controllers must assess the
direction of travel and altitude for each. In Experiment 1, as in
current air traffic control operations, altitude was acquired by reading
alphanumeric text within a data block. This required an eye fixation
along with an extended allocation of spatial attention to one specific
display location. Could controllers detect conflicts faster if they were
able to assess altitude using an easily perceptible visual cue?

To answer this question, Experiment 2 tested controller performance with
and without redundant coding of altitude by color. According to theory,
focused spatial attention is not required to determine if two objects
are the same color, so long as the colors are sufficiently
discriminable. If so, then color should be effective in segmenting
visual displays, thus restricting the number of elements that must be
searched (Egeth, Varsi, & Garbart, 1984). In keeping with this
conjecture, Johnston, Edmiston, & Horlitz (1993) have shown that
color-coding altitude reduces the time to detect conflicts in static air
traffic control displays (with both altitude and route restrictions in
place). We expect that controller performance under free flight
conditions will also benefit from color coding of altitude. Such a
demonstration would provide an important link between basic research on
visual search and information acquisition from air traffic control
displays that should be useful in exploring ways to reduce controller
workload.

Since this experiment was not intended to assess directly the potential
drawbacks of free flight, controllers were tested only in the condition
with no routes and no altitude restrictions. Controllers participated in
a single day of testing consisting of one session with color coding plus
one session without. Note that the monochrome session replicates the no
routes, no altitude restriction condition of Experiment 1.

## Method

Except where noted, the method was identical to that of Experiment 1.

*Subjects*. The same four retired air-traffic controllers participated
in Experiment 2.

*Stimuli*. Aircraft were drawn in a color corresponding to their
altitude. Aircraft at FL 310 were red, aircraft at FL 330 were yellow,
aircraft at FL 350 were blue, and aircraft at FL 370 were green. Since
aircraft did not change altitude, only these four colors were needed.

*Procedure*. Controllers performed only the Free-Flight,
Unrestricted-Altitude condition. Two of the controllers performed the
color-coding session followed by the no-color-coding session. The other
two performed these conditions in the opposite order.

## Results

*Response Time Analysis.* Repeated measures analysis of variance of the
means of the median response times for each subject in each condition
revealed significant main effects of **number of aircraft** ({12, 16,
20} = {13.4, 17.9, 24.5}, F\[2,6\] = 13.65, p \< .01), **conflict
angle** ({small, large} = {15.1, 22.1}, F\[1,3\] = 43.44, p \< .01), and
**color-coding** of altitude ({color, no-color} = {15.1, 22.1}, F\[1,3\]
= 12.66, p \< .05). The main effect of **conflict time** was not
significant ({short, long} = {17.1, 20.1}, F\[1,3\] = 5.20, p \> .10),
though the trend was in the direction seen in Experiment 1. The
**color-coding by conflict angle** interaction was significant (F\[2,6\]
= 263.27, p \< .001), as well as the **color-coding by conflict time**
interaction (F\[1,3\] = 18.96, p \< .05). The **color-coding by number
of aircraft** interaction was not significant (F \< 1).

*Commission Errors.* Overall error rate was 7.3%. Analysis of variance
on error data showed no significant main effects or interactions.

## Discussion

The present practice of providing flight information, including
altitude, as text within the data block forces controllers to fixate and
attend the fine detail of the data block to determine whether two
potential conflict aircraft are at the same altitude. Experiment 2
showed that the performance of controllers can be improved substantially
by providing them with perceptual cues, such as color, that allow them
to assess altitude without focally attending data blocks. Overall,
color-coding altitude reduced conflict detection times by 7 sec, a
reduction of about 30%.

While color substantially facilitated overall performance, the pattern
of interaction of the color factor with other factors did not
consistently follow theoretical expectations. Had controllers used color
optimally to reduce the size of the search set, then color should have
provided a larger benefit with higher traffic density (cf. Treisman,
1986; Egeth, et al., 1984; Kahneman, & Henik, 1981). Surprisingly, this
trend was not found (see Figure 4). It is interesting that the Johnston
et al. (1993) study, using a sample of non-controller subjects, did find
this trend as did an informal replication of the present Experiment 2
using non-controller subjects.

> ![](media/image4.png){width="4.44375in"
> height="6.610416666666667in"}*Figure 4*

The use of color to guide visual attention should also have been
especially helpful to controllers in finding conflict pairs separated by
intervening aircraft. While the presence of intervening aircraft was not
manipulated directly, it should be highly correlated with the conflict
time variable and conflict angle variable. Long-time conflicts are more
likely to have intervening icons between the two target icons, and hence
should benefit more from color coding, but no such benefit was observed.
Likewise, large-angle conflicts are more likely to involve intervening
aircraft and hence should benefit more from color. This prediction was,
in fact, confirmed: Color coding shortened mean response time by 8.9 sec
for large-angle conflicts compared to 5 sec for small-angle conflicts.
This mixed set of results leaves us unable to draw any definitive
conclusions about how controllers used color--coding. It is clear that
controllers benefited substantially from using color, but it also seems
clear that they did not consistently use the simple strategies that
would appear to make optimal use of color coding. Perhaps it should not
be too surprising, given that controllers have had many years of
practice using strategies optimized for monochrome displays that they
did not quickly adopt new strategies optimized for color displays. We
are planning studies with eye-movement monitoring to more directly
observe controller search strategies.

# Experiment 3

In Experiment 1, the removal of route and altitude restrictions had
little effect on performance. Before concluding that conflict detection
is unaffected by the removal of these restrictions, we wanted to
replicate our findings, taking further steps to ensure that controllers
were able to make full use of route information in detecting conflicts.
In Experiment 3, visible route markers (dotted lines) were added to the
traffic displays, increasing the salience of routes and removing the
need for controllers to retrieve them from memory. Experiment 3 used the
same routes and the same controllers who had participated in Experiment
1, so it also allowed us to sample performance with higher levels of
practice on the identical routes.

Several other changes were made in response to controller comments on
Experiment 1. Controllers expressed a desire for "leader" lines that
show the extrapolated future position of all aircraft. Such indicators
are in common use on current air traffic consoles. In Experiment 3, we
added a conflict detection tool that allowed controllers to examine the
closest point of approach for any one pair of aircraft. Conflicts were
indicated by an intersection of the future path lines for each aircraft.
We also revised the algorithm that determined the initial placement of
data blocks in the Fixed-Routes condition to reduce overwriting on
initial presentation. A further change allowed controllers to lengthen
the line connecting data block with aircraft symbol, giving them more
flexibility in placing data blocks.

All conditions benefited from the conflict tool and increased
flexibility of data block placement. It is important to emphasize,
however, that the other changes benefited only the Fixed-Routes
condition. Improvements in the initial placement of data blocks were
confined to the Fixed-Routes condition; initial data block placements in
the No-Routes condition were unchanged. The adoption of visible route
markers also affected only the Fixed-Routes condition. Therefore, the
net effect of our methodological changes should have been to produce a
relative benefit for conflict detection in the Fixed-Routes condition
over the No-Routes condition.

## Method

Except where noted, the method was identical to that of Experiment 1.

*Subjects*. The same four retired air-traffic controllers participated
in Experiment 3.

*Stimuli & Displays.* Each route in the Fixed-Routes condition was
represented by a light-brown dotted line. The algorithm determining
initial placement of the data blocks in the Fixed-Routes condition
located the tag 90° clockwise relative to the aircraft's direction of
travel. This eliminated much of the overwriting, and helped the
controllers to determine at a glance the direction of travel of each
aircraft. Initial data block placement in the no-routes condition
remained the same. The data block placement algorithm was modified to
allow changes in the distance of the data block from the aircraft symbol
in addition to its radial placement.

*Procedure*. The conflict detection tool allowed the controllers to
query the computer for the closest approach point of any particular pair
of aircraft. To use the tool, they first depressed the space bar and
selected a pair of aircraft as if they were selecting the conflict pair
in Experiment 1. Once having selected an aircraft pair they pressed the
'a' key to examine the closest point of approach. Light blue lines
indicated the future path of the two aircraft, and a small bulb at the
end of the line indicated the location of that aircraft at its closest
approach to the other aircraft. If the trajectories showed a conflict,
controllers then pressed the spacebar to confirm selection of the pair.
If the tool indicated no conflict, they pressed the 'd' key to cancel
the selection, and continued their search. The tool informed the
controllers when they had selected two aircraft at different altitudes.
Controllers could use this tool as much or as little as they liked (the
tool was actually used on more than 95% of trials). The display was
updated every 4 seconds until subjects confirmed selection of a conflict
pair or until 2 minutes had elapsed since the beginning of the trial. In
one further change, we raised the maximum permitted search time per
display from 90 secs in the previous experiments to 120 secs, although
the extra time was rarely used.

## Results

*Response Time Analysis.* Overall response time in Experiment 3 was 18.2
sec compared to 22.8 in Experiment 1. Analysis of variance on the means
of the median response times for each subject in each condition yielded
a pattern of results that largely replicated Experiment 1. There were
significant main effects of **number of aircraft** ({12, 16, 20} =
{11.2, 18.5, 25.0 sec.}, F\[2,6\] = 14.37, p \< .01), and **conflict
angle** ({small, large} = {16.1, 20.4 sec.}, F\[1,3\] = 14.55, p \<
.05). There was a significant main effect of **route restriction**
({Fixed-Routes, No-Routes} = {21.2, 15.3 sec.}, F\[1,3\] = 12.08, p \<
.05) which showed better performance without routes. The effect of
**conflict time** was in the same direction as Experiment 1, but was
only marginally significant ({short, long} = {16.3, 20.1 sec.}, F\[1,3\]
= 11.75, p \< .06). The small effect of **altitude restriction** was not
significant ({restricted, unrestricted} = {17.2, 19.2 sec.}, F\[1,3\] =
2.38, p \> .20). **Conflict time by altitude restriction** (F\[1,3\] =
11.75, p \< .05) was the only significant interaction.

*Analyses of Commission Errors.* Overall rate of commission errors for
Experiment 3 (now with the benefit of the conflict-probe tool) was 1%.
Errors were too rare to permit meaningful analysis.

## Discussion

*Traffic Load & Conflict Geometry Factors.* The procedural and display
changes appear to have succeeded in making the task easier. Overall
response time was reduced by more than 4 sec compared to Experiment 1.
Despite the improved performance, substantial effects of traffic load
and conflict geometry were again observed, as shown in the middle panel
of Figure 3. The order of difficulty of conditions was identical to that
in Experiment 1. Short conflict times coupled with small conflict angles
produced the fastest detection times. Long conflict times coupled with
large conflict angles produced the longest detection times. There was a
trend toward the overadditive interaction seen in Experiment 1, but it
did not reach significance here.

*Airspace Constraint Factors.* The right panel of Table 1 shows the mean
response times for the Fixed-Routes and No-Routes conditions with and
without altitude restrictions. The pattern of results is very similar to
that obtained in Experiment 1; the only apparent difference is that the
small advantage for the No-Routes condition seen in Experiment 1 is now
much larger, and is statistically significant. Overall, the results of
Experiment 3 confirm the robustness of the most important results of
Experiment 1. It is important that we still found no benefits for the
Fixed-Routes condition in spite of a number of differences that might
have benefited that condition---greater controller practice with the
same routes, increased visual support for the routes, and a reduction in
overwriting.

# Experiment 4

Thus far we have found no evidence that relaxing restrictions on routes
or altitudes would jeopardize conflict detection. Before reaching this
conclusion, however, it is important to explore potential interactions
of the aircraft symbols (icons) with route restrictions. Routes not only
help organize the traffic flow they also provide information about the
direction of travel. The wedges used in Experiments 1-3 also provide
strong information about the direction of travel. In fact, we chose the
wedges largely because we expected that conflict detection without
routes would be extremely difficult in the absence of strong orientation
cues. Given our results so far, which are encouraging for free flight,
it is natural to ask a further question, whether use of oriented
aircraft icons is [necessary]{.underline} for controllers to be able to
handle free flight. This is an important question, because current
practice does not use oriented aircraft symbols, although controllers
typically have the option of displaying history markers that show the
last three to five positions of all aircraft.

So, in Experiment 4, we investigated the impact of free flight on
conflict detection with aircraft icons that did not show direction of
travel. The main possibility of interest is that the weaker perceptual
support for aircraft track direction would much more drastically impair
performance in the No-Routes condition compared to the Fixed-Routes
condition. This might reduce the performance advantage for the No-Routes
condition or even produce an advantage for the Fixed-Routes condition.

## Method

Except where noted, the method was identical to Experiment 3.

*Subjects*. Two of the four air-traffic controllers from Experiments 1-3
participated in Experiment 4 along with two active air-traffic
controllers who had not participated in any of our previous experiments.

*Stimuli*. Aircraft position was indicated by a diamond centered on the
"true" position. The orientation of the diamond did not depend on the
aircraft's direction of travel. History markers consisted of an 'x'
placed in the position the aircraft occupied 12, 24, and 36 seconds
prior to the current update. Controllers could turn the history markers
on and off by pressing the 'h' key, though they tended to leave them on.

*Procedure*. Only the restricted-altitude condition was used in this
experiment. Controllers performed four abbreviated practice sessions on
the initial day, followed by four days of testing. Each test day
consisted of two sessions of either the Fixed-Routes or No-Routes
condition. Order of testing for two controllers on days one through four
consisted was as follows: Fixed-Routes, No-Routes, No-Routes,
Fixed-Routes. For the other two controllers the order was the opposite:
No-Routes, Fixed-Routes, Fixed-Routes, No-Routes.

## Results

*Response Time Analysis.* The results were in many respects similar to
those of the previous experiments. Analysis of variance on the means of
the median response times for each subject in each condition yielded
significant main effects of **number of aircraft** ({12, 16, 20} =
{11.1, 15.8, 20.5 sec.}, F\[2,6\] = 26.39, p \< .001), **conflict
angle** ({small, large} = {13.1, 18.5 sec.}, F\[1,3\] = 18.08, p \<
.05), and **conflict time** (short, long} = {12.9, 18.5 sec.}, F\[1,3\]
= 19.75, p \< .05). The main effect of **route restriction** was not
significant although the trend now favored fixed routes ({Fixed-Routes,
No-Routes} = {15.1, 16.4 sec.}, F\[1,3\] = 2.70, p \< .2). The **number
of aircraft by conflict angle by conflict time** interaction was
significant (F\[2,6\] = 47.95, p \< .001), as were the **conflict angle
by conflict time** interaction (F\[1,3\] = 32.86, p \< .02), and the
**number of aircraft by conflict time** interaction (F\[2,6\] = 7.40, p
\< .05).

*Analysis of Commission Errors.* The overall rate of commission errors
in Experiment 4 was 2.3%. Analysis of variance on the error data found
no significant main effects or interactions.

## Discussion

*Traffic Load & Conflict Geometry Factors.* The right panel of Figure 3
shows that these factors once again had robust effects in the same
direction as before.

*Route Constraint Factor.* Performance with fixed routes did not differ
statistically from performance without routes, consistent with the
earlier experiments. However, for the first time the trend favored fixed
routes by 1.3 sec. This difference was small and fell far short of
statistical significance; it would therefore be inappropriate to claim a
true advantage for fixed routes. Further, even if real, the performance
decrement from removal of route constraints was quite modest. Compare,
for example, the 1.3 sec advantage for fixed routes with 5.4 sec effect
of conflict angle, or the 5.6 sec effect of conflict time. Further, the
slope of the line relating detection time to number of aircraft was
approximately 1.2 sec per aircraft. Thus, the penalty of removing the
routes was equivalent to the penalty of adding a single aircraft to the
display. Still, the direction of the trend is consistent with our
argument that the importance of routes to conflict detection should
increase as other cues to direction of travel are reduced. Clearly the
current results provide a caution flag: any operational plan to
eliminate routes should be combined with efforts to improve displays so
that controllers have the greatest degree of perceptual support possible
for situation awareness.

*Symbol Orientation Effects.* Table 2 compares response times for the
diamond symbols in Experiment 4 to the wedge symbols in the restricted
altitude condition of Experiment 3 as a function of number of aircraft.
The diamond symbols coupled with history plots produced slightly better
performance---conflicts were detected just over one sec faster. This
comparison must be interpreted cautiously, however. Two of the retired
controllers carried forward from earlier experiments were now more
highly practiced, while the two new controllers added were younger,
active controllers. Hence, the comparison unfairly favors Experiment 4
(diamonds) over Experiment 3 (wedges).

# General Discussion

## Relaxing Airspace Constraints.

The experiments reported here examined the effects of relaxing airspace
constraints (route and altitude restrictions) on the detection of
traffic conflicts by experienced controllers. Airspace constraints were
varied in conjunction with manipulations of traffic load (number of
aircraft) and conflict geometry (conflict angle, conflict time).
Strikingly large effects were observed for traffic load and conflict
geometry factors, indicating that we were successful in manipulating
conflict detection difficulty. In Experiment 4, for example, response
time in the fastest condition (12 aircraft, short conflict time, small
conflict angle) was 8.3 sec compared to 32.7 sec in the slowest
condition (20 aircraft, long conflict time, large conflict angle), a
24.3 sec difference. In contrast, airspace constraint factors (route and
altitude restrictions) had relatively small effects, and in no case did
removal of restrictions produce any substantial decrement in
performance.

The effects of removing altitude restrictions were modestly but
consistently negative (1.9 sec in Experiment 1 and 2.0 sec in Experiment
3). We believe that these small effects are real and deserve to be taken
seriously. It is inherent in the nature of the east-west altitude
restrictions that they eliminate high angle conflicts which our research
shows take the longest to find. Furthermore, altitude restrictions have
practical benefits that go beyond our research. The very conflicts
eliminated by altitude restrictions, namely head-on and near head-on
collisions, also involve the highest risk should they be missed by
controllers. Since they involve high closing speeds, there is less
opportunity for pilots to recognize and avoid them. Thus east-west
altitude restrictions serve to buffer the system against a dangerous
class of "misses'; under high load or abnormal traffic this buffering
could be critical for maintaining safety. An additional consideration is
that outside of our experiments (which were designed to have exactly one
conflict per trial) under east-west altitude restrictions fewer
potential conflicts will be generated. In summary, keeping altitude
restrictions is favored both by our empirical data and by operational
safety considerations.

The effects of removing route restrictions depended on the symbology
used. With oriented wedge aircraft icons, removing routes actually
[improved]{.underline} performance (by a modest 1.3 sec in Experiment 1,
and a more substantial 5.9 sec in Experiment 3); with the
orientation-neutral diamond icons, removing routes hurt performance
slightly (1.3 sec in Experiment 4). The latter result provides a
cautionary note: if there are no routes to implicitly cue aircraft
track, it is critical that aircraft icons convey this information
pictorially (e.g. by our wedge symbology). Since Experiment 3
(wedge-shaped icons) actually found that removing routes produced a
statistically significant improvement in performance, it is worth
considering why this might have happened. We think that the most likely
reason is that traffic in the Fixed-Routes condition is more
concentrated in space. Congestion has at least two negative
consequences. It increases the over-printing of icons and data-blocks,
obscuring important display information, and makes it more difficult to
ascertain the spatial relationships within sets of aircraft sharing the
same altitude.

We would like to close this section with two different additional
remarks, which cut in opposite directions. On the one-hand, we believe
that our experiments do not suffer from any sensitivity problem. The
numerous large effects of traffic density and aircraft geometry make it
clear that we are able to measure differences in controller situation
with appropriate sensitivity. We are confident that if loss of routes
impaired controller situation awareness to any important degree in our
paradigm, such an impairment would have shown up in the data An
important and much more difficult issue, however, is whether free flight
might cause problems outside of our part-task paradigm, in the more
complex environment of actual operations. The next section takes up this
question.

*Converging Methods in the Evaluation of Free Flight.* It might appear
that our results provide a "clean bill of health" for the relaxation of
route restrictions. We wish to emphasize, however, that our experiments
were designed to test for only one subset of possible problems with free
flight---situation awareness problems due to the loss of organization of
traffic displays. Furthermore, we found it necessary to make a number of
methodological simplifications that might have hindered our ability to
find problems that would actually occur in the full real-world
implementation of free flight. Conflict detection is only one of many
controller responsibilities. Furthermore, a sequence of independent
trials of short duration is uncharacteristic of the real-world task for
controllers. In addition, the scenarios included a variety of
simplifications of traffic patterns. For instance, in our experiments,
unlike the real world, all aircraft flew straight-line paths at a
constant altitude. These simplifications allowed us to efficiently
examine conflict detection performance across a range of difficulty
factors. Our purpose was not to provide a full evaluation of free flight
but to rapidly explore a wide range of conditions that would inform such
a validation. Experiments such as ours are inherently asymmetrical---if
we find problems, then those problems need to be taken seriously, but if
we fail to find problems that does not mean that the concept being
tested has been validated. Validation of a concept as complex as free
flight can only emerge as the result of numerous studies, under a much
wider variety of conditions.

It will be necessary to conduct some of the validation studies in highly
realistic large-scale human-in-the-loop simulations to ensure that
details of procedures can be checked under operating conditions. As
noted earlier, we were able to identify one early simulation (Kidd &
Kincade, 1958) which noted improvements in certain measures related to
safety. However, such simulations have limitations of their own. Because
of the detailed scenario development required, it is not feasible to
vary airspace parameters widely to explore a large space of possible
conditions. Also, because the need for realism keeps the demand on
controllers similar to that in actual operations, only a miniscule
portion of the data will be collected while controllers are operating
near the peak of their mental capacities. This is unfortunate, because
often important clues to behavior emerge only near performance limits.
Large amounts of data relevant to peak performance can be collected more
efficiently in simplified settings such as ours. In summary, there is a
clear need for convergent methods in validating free flight. It is
premature to reach firm conclusions about its operational feasibility at
this early point in time.

## Display Organization

Our failure to find performance impairments when route restrictions were
eliminated raises questions about how to apply general principles of
display organization. Guidelines on visual information presentation
usually cite proper organization of information on visual displays as
fundamental to good display design (see e.g., Smith & Mosier, 1986).
Because fixed routes provide a regular organizing principle for air
traffic control displays, it was surprising to find that their removal
produced no performance decrements. Indeed, air-traffic control displays
clearly appear to be more orderly with routes (see Figure 1) than
without (see Figure 2). Our controllers spontaneously commented on this
as well.

Why did the loss of subjective display organization fail to produce a
net observed performance decrement? As we have already noted, it may be
that certain helpful aspects of the No-Routes displays (e.g. less
overprinting and increased ability to selectively attend to individual
conflict pairs) may have outweighed the disadvantages of poor subjective
organization. But it is also possible that the task we used is not one
for which subjective organization is especially helpful. Benefits of
display organization are typically found when users access information
from one category out of many displayed categories. In cockpits, for
example, information about heading, airspeed, attitude, and other flight
parameters is displayed in distinct locations. Similarly, in organizing
information for multifunction displays, in which a single computer
display is capable of displaying data for several systems, guidelines
recommend grouping information so that a single page relates to a
specific category of function, such as navigation (see Smith & Mosier,
1986, Section 2.5). In these examples the underlying principle is to
align the organization with the information retrieval demands.

Applying this principle to conflict detection suggests that the
organization created by routes may not be aligned well to the
information retrieval demands. In our paradigm, the vast majority of
conflicts were between aircraft travelling along different routes. As a
consequence, there was no advantage to organizing search around routes.
In fact, controllers in our experiments reported that they did not
organize their search around the routes, but instead performed
systematic clockwise searches of displays. Adoption of this strategy
could have eliminated much of the potential advantage of the display
organization afforded by the routes. It is worth noting in passing that
the display organization provided by the color-coding of altitude would
appear to be very well suited to the demands of our paradigm.
Color-coding did, in fact, produce a substantial improvement in
performance in Experiment 2.

The present analysis suggests that in some specific air traffic control
operations routes might play a more significant role in conflict
detection. For example, as aircraft are put in fixed landing sequences
they are assigned routes that are guaranteed to be conflict free with
other routes. There, conflicts result primarily from speed differences
(overtaking) within a given route, or deviations from assigned altitudes
and headings. Within route variations play the dominant role. It would
not be surprising to find that the organization provided by routes have
a greater effect there than observed in the present experimental
conditions, which simulated enroute sectors.

This example highlights the situation-specific nature of strategic
behavior in complex, real world tasks. Behavior is often contingent on
subject biases, task-specific details, instructions, and particulars of
the display. The contribution of situation-specific factors makes it
difficult to generalize across even small changes in task demands, and
has contributed to the difficulty in writing useful human factors
guidelines of wide applicability. As a result, caution is required
before generalizing the present results too widely. However, it is
important to note that we were, in fact, able to draw on basic research
in visual search to predict the main effects of color and the direction
of the effects of traffic load and conflict geometry. If contextual
factors dominated completely, little leverage would have been gained by
drawing on basic research results or even results of other applied
studies of related disciplines. This suggests that it may be possible to
generalize if one can carefully abstract out the core task demands and
identify situation-specific features likely to modify the predictions.

# Summary

## Empirical Findings

\(a\) Traffic load and conflict geometry had large effects on conflict
detection times; there was a 400% increase from the easiest to most
difficult condition (8 sec to 33 sec).

\(b\) Removal of route restrictions had very little detrimental effect
(\< 2 sec); in some cases the net effect was actually positive.

\(c\) The presence of altitude restrictions led to a small reduction in
detection time, especially when the time to conflict was short.

\(d\) Color-coding of altitude significantly reduced conflict detection
times (\~30%).

\(e\) Performance with history markers was roughly equivalent to
performance with aircraft symbols oriented to aircraft track (i.e.
wedges).

## Conclusions 

\(1\) Our experiments provided no evidence that relaxing route
restrictions will impair conflict detection. We are careful to restrict
our conclusion to the detection of horizontal traffic conflicts, and do
not extend it to other controller tasks. We also acknowledge that there
may be special cases where the use of routes would produce benefits.

\(2\) Removing altitude restrictions had only a small effect on conflict
detection time. However, altitude restrictions provide insurance against
conflicts with high closing rates that are difficult to detect. In a
safety-conscious environment there are strong reasons to keep them.

\(3\) Use of non-oriented aircraft symbols together with track history
to depict direction of travel (current practice) did not produce a large
overall drop in performance, but the use of oriented aircraft icons
would appear to have special advantages for free flight. Color-coding
altitude, or providing an equivalent perceptual cue, might provide even
more substantial benefits under free flight.

# Acknowledgements

The work was supported with funding from the Advanced Air Transportation
Technologies program at NASA Ames Research Center. Portions of the work
were completed while Eric Ruthruff was a National Research Council
Post-Doctoral Fellow at NASA Ames. We thank Irene Laudeman, R. Key
Dismukes, Michael Shafto, Sandy Lozito, and two anonymous reviewers for
helpful comments on earlier drafts.

# List of Figures

Figure 1: Sample traffic display with route and altitude restrictions.

Figure 2: Sample traffic display without route and altitude
restrictions.

Figure 3: Mean response time as a function of Conflict Time, Conflict
Angle, and Number of Aircraft in Experiments 1, 3, & 4.

Figure 4: Mean response time as a function of Color-coding and Number of
Aircraft in Experiment 2

.

# List of Tables

Table 1: Mean response times for each route and altitude condition in
Experiments 1 & 3.

Table 2: Mean response time as a function of number of aircraft for
Experiment 3 (wedge-shaped aircraft icons) and Experiment 4
(diamond-shaped aircraft icons).

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# Biographies

Roger W. Remington, Ph.D. Human Experimental Psychology, 1978,
University of Oregon. Dr. Remington has been at NASA Ames since 1980. He
is currently the Group Lead for the Cognition Group at NASA Ames
Research Center. His research has focused on issues of human attention,
visual search, and computational modeling of human behavior.

James C. Johnston, Ph.D. Human Experimental Psychology, 1974, University
of Pennsylvania. Dr. Johnston has been at NASA Ames since 1984. He has
conducted extensive research on human attention focused on understanding
the nature and role of resource limitations in human information
processing.

Eric Ruthruff, Ph.D. Cognitive Psychology, 1995, University of
California at San Diego. Dr. Ruthruff has been at NASA Ames since 1995.
His research is concerned with the nature of resource limitation in
dual-task and task switching.

Miri Gold, M.A. Developmental Psychology, 1984, University of
Connecticut. Ms. Gold was a research associate at NASA Ames from
1992-1997. She is currently in Israel working on her doctorate degree.

Maria Romera, San Jose State University. Ms. Romera is a research
assistant in the Cognition Laboratory and is currently a graduate
student at San Jose State University working toward an M.S. degree in
human factors.