DOT/FAA/AM-02/22

Peter M. Moertl

University of Oklahoma

John M. Canning

White Oak Technologies

Scott D. Gronlund

University of Oklahoma

Michael R.P. Dougherty

University of Maryland

Joakim Johansson

Ericsson, Inc.

Scott H. Mills

SBC Technology Resources, Inc.

December 2002

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, VA 22161.

Aiding Planning in Air Traffic

Control: An Experimental

Investigation of the Effects of

Perceptual Information Integration

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship of

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

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page

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

DOT/FAA/AM-02/22

4. Title and Subtitle 5. Report Date

Aiding Planning in Air Traffic Control: An Experimental Investigation of

the Effects of Perceptual Information Integration

December 2002

6. Performing Organization Code

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

Moertl PM1, Canning JM2, Gronlund SD1, Dougherty MRP3, Johansson J4, and

Mills SM5

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

1 University of

Oklahoma

Norman, OK 73109

2 White Oak Technologies,

Inc.

Silver Springs, MD 20910

3 University of Maryland

College Park, MD 20742

4 Ericsson, Inc.

Stockholm, Sweden

5 SBC Technology

Resources,Inc.

Austin, TX 78759

11. Contract or Grant No.

Grant #97-G-037

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

Office of Aerospace Medicine

Federal Aviation Administration

800 Independence Ave., S.W.

Washington, DC 20591 14. Sponsoring Agency Code

15. Supplemental Notes

Work was accomplished under approved subtask AM-A-00-HRR-522. Carol Manning (AAM-500) served as the

COTR.

16. Abstract

Prior research examined how controllers plan in their traditional environment and identified various information

uncertainty by perceptually representing important constraints. This included integrating spatial information on

the radar screen with discrete information (planned sequences of air traffic). Canning et al. (1999) and Moertl et

al. (2000) reported improved planning performance and decreased workload in the planning aid condition. The

purpose of this paper was to determine the source of these performance improvements. Analysis of computer

interactions using loglinear modeling showed that the planning interface led to less repetitive, but more

integrated, information retrieval gave rise to the performance improvements. Potential applications of this

research include the design and evaluation of interface automation that keeps users in active control by

modification of perceptual task characteristics.

17. Key Words 18. Distribution Statement

Information Integration, Air Traffic Control, Planning

Aids, Strip Marking

Document is available to the public through

the National Technical Information Service,

Springfield, VA 22161

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

Unclassified Unclassified 12

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

iii

ACKNOWLEDGMENTS

The support of the Federal Aviation Administration under agreement 97-G-037 is gratefully

acknowledged. We thank Frank Durso (Texas Tech University) and Robert Terry (University of

Oklahoma) for their methodological expertise, and Carol Manning and Scott Shappell (FAA-Civil

Aerospace Medical Institute) for their comments on this manuscript. We also extend our thanks

to Ron Andrei and Paul DeBenedittis (of the FAA Academy) and Duane Duke and Rob Enos ( FAA

Office of Information Services) for their expertise.

1

AIDING PLANNING IN AIR TRAFFIC CONTROL: AN EXPERIMENTAL

INVESTIGATION OF THE EFFECTS OF PERCEPTUAL INFORMATION INTEGRATION

Air traffic control equipment has changed in recent

years as the Federal Aviation Administration (FAA)

adapts its procedures to the growing volume of air

traffic across the country. However, two major components of control equipment have stayed constant

over the years. Specifically, generations of air traffic

controllers have utilized a radar screen and flight

progress strips as separate representations of aircraft

entering their controlled sector and cognitively integrated those representations. This equipment has

proven to be highly beneficial and, therefore, forms

the foundation from which any innovation to the air

traffic control system should begin.

If controllers are to manage the increasing volumes

of air traffic, planning will be of increasing importance. This is evidenced by recent efforts to provide

controllers with plan-aiding technology (e.g., URET,

User Request Evaluation Tool, Arthur & McLaughlin,

1998; CTAS, Center-Tracon Automation System,

Denery, & Erzberger, 1995; ERATO, En Route Air

Traffic Organizer, Bressolle, Benhacene, Boudes, &

Parise, 2000). Such interfaces offer additional functions to the controller like conflict detection algorithms (URET), automated traffic advisory functions

for descending, sequencing, and spacing aircraft

(CTAS), and decision aid tools like filtering options

and problem reminders (ERATO). The approach we

took in this study can enhance plan-aiding technology

by identifying essential informational elements that

support air traffic planning and determining the extent to which air traffic planning could be improved

by optimizing the representation of that information.

Air traffic controllers manage a complex flow of

aircraft through their airspace. They maintain strict

rules of separation between the aircraft while allowing

all aircraft to reach their destinations as safely and

expeditiously as possible. In planning the routes for

the aircraft, two forms of planning can be distinguished. Controllers make tactical plans when they

make decisions that relate to the current moment and

involve the separation of (usually) pairs of aircraft that

could soon violate the separation rules and hence,

need immediate action. They make strategic plans

when their plans span longer periods of time (about

10 minutes or longer) and typically involve multiple

aircraft. An examination of strategic planning in air

traffic control is timely, given future concepts being

proposed. For instance, there have been discussions

regarding the creation of a strategic controller position (N. Lawson & K. Thompson, personal communication, Dec. 15, 1997; see also Vivona, Ballin,

Green, Bach, & McNally, 1996). The proposal provides for one person who would be responsible for a

multiple-sector airspace, making decisions about traffic in that airspace, and delegating responsibility for

tactical decisions to sector-level controllers. A goal of

our project was to develop interface tools for a strategic controller position.

Dougherty, Gronlund, Durso, Canning, and Mills

(1999) studied how air traffic controllers make strategic plans for en route traffic (high altitude, high speed

traffic between destinations) using the radar screen

and paper flight progress strips. They identified aircraft sequencing for approach to a common destination as a strategic planning task by analyzing controller

verbalizations and use of flight progress strips. The

specific sequence of a group of aircraft is determined

by many factors– aircraft speed, altitude, destination,

and airspace restrictions. Therefore, sequencing aircraft was a complex cognitive task that involved the

consideration of many aircraft over an extended period of time. Dougherty et al. (1999) argued that

controllers could benefit from an interface that supported planning the sequences of aircraft.

We begin by outlining the relevant aspects of the

traditional air traffic control environment that guided

our interface design. Following that, we describe the

electronic planning aid and outline its design principles. Finally, we report the evaluation of the planning aid by comparing participants’ planning

performance using the interface to their performance

in the traditional air traffic control environment.

Traditional En Route Air Traffic Control

Environment

Air traffic controllers primarily use information

from two different sources, the radar screen and the

flight progress strips. The radar screen shows the

spatial position and progress of aircraft, together with

some characteristics of the controlled sector (e.g.,

boundaries and airways). The radar screen displays

the spatial location of aircraft as well as the most vital

flight information (identifiers, altitude, speed, and

sometimes, flight destination). Discrete information
2

about an aircraft (e.g., its destination, flight origin,

planned altitude) can be read from the strips (see

Federal Aviation Administration, 1995; this information also is available from the Computer Readout

Display). Flight progress strips are small paper strips

that are computer-printed and posted next to the

controller about 20 minutes prior to arrival of that

flight in the sector. Strips also are used to indicate the

progress of traffic, as controllers manually update the

strips when information about an aircraft changes. In

addition, strips carry identifiers that link them to their

representations on the radar screen. However, the

linkage is not direct because controllers have to search

for the aircraft on the radar screen to coordinate the

representations. The radar is of primary importance

because it tells the controllers where the aircraft currently are. The strips can be useful for determining

where the aircraft are going to be.

Dougherty et al. (1999) described how controllers

use strips for strategic planning purposes. Controllers

often classified aircraft into logical groups. These

groupings were based on flight plan characteristics

(e.g., destination, route, or altitude), and/or flow

restrictions. Then strips were ordered to show the

planned sequence for these aircraft. In other words,

controllers used strips as planning aids with which

they externally represented their planned sequences.

For example, if a controller wanted to plan a sequence

of aircraft A, B, and C, the controller would arrange

the three strips in that order. In this way, the strips

physically hold the planned sequence of aircraft. In

contrast, the radar screen represents the actual air

traffic situation. It is important to note that the

planned and current sequences of aircraft are not

linked to one another, and changes on the radar screen

are not reflected by changes to the strips.

The discrepancy between the planned sequence

represented by the strips and the positions of the

aircraft on the radar screen is essential information for

a controller. It makes apparent that something must

be done to update the intended path of the aircraft. In

order to assess the discrepancy between the planned

and current sequence, controllers have to locate the

strip and the corresponding aircraft representation on

the radar screen and mentally link those representations to one another. In this way, they determine the

mismatch between the planned sequence and the

current air traffic situation. In other words, the controller is responsible for linking spatial information

(location of the aircraft in the sector, distance to other

aircraft, distance to specific points in the sector) to

discrete information (flight identifier, optimal sequence of aircraft, flight plan, and aircraft type).

In order to help link the spatial and discrete aircraft

representations, controllers have to “declutter” the radar

screen. A block of information (the datablock) is connected to each aircraft on the radar containing important

flight information about that aircraft. As more and more

aircraft come into a sector, datablocks overlap and become illegible. Therefore, controllers often adjust

datablocks into one of eight different positions around

the aircraft target and also adjust the distance between

the datablock and its aircraft. However, some information on the datablock is redundant; controllers can get

the same information from strips by matching a unique

identifier on the strip to the radar. Therefore, decluttering

the radar screen to make aircraft distinguishable could be

viewed as a hindrance to more important tasks like

strategic planning. Though controllers can utilize radar

decluttering to create memory cues, a perceptuallyimproved interface might supplant the memory demands. The necessity for decluttering seems to be caused

by the separation of the different information sources.

We addressed this problem by designing a new

interface that took over the task of linking spatial and

discrete information. The interface should allow controllers to perceptually assess the difference between

the current air traffic sequence and the planned sequence

without having to frequently declutter the radar screen.

We discuss this electronic planning aid next.

Electronic Planning Aid

The electronic planning aid changed the task of

planning air traffic sequences by perceptually representing a major constraint in the controllers’ environment that was not visible in the traditional environment

(see http://www.ou.edu/cas/psychology/HTIC/

FOPA/ for screenshots of the interface). It linked

discrete information to its spatial representation on

the radar screen. This dynamic linkage between the

planning aid and the radar screen was implemented by

presenting the participants with two computer screens:

Figure 1 shows a snapshot of the two screens.

The planning aid was linked to the radar screen and

the controller could move the cursor between the two

screens. Aircraft in a sector were represented on the

planning aid by rectangular tokens as shown in Figure

1. The tokens contained the aircraft’s identification

number, destination airport, equipment type, and

altitude. Aircraft tokens were marked and unmarked

by clicking on them, in which case a check mark

symbol appeared or disappeared on the token. All

aircraft were represented on both screens and could be

perceptually located on one screen by moving the

cursor over the token on the planning aid or by

clicking on its target on the radar screen.
3

The planning aid allowed participants to categorize

aircraft and updated that representation on the radar

screen in the color corresponding to their category.

Figure 1 shows two aircraft (UAL 755 and DAL80)

that a controller placed into different categories. Because each column of aircraft was in different colors,

participants do not have to encode and remember the

categories of each aircraft after they were categorized

because they were represented perceptually on the

radar screen.

The planning aid also allowed the controller to

automatically sort categorized air traffic according to

time or distance to specific points (fixes) along the

route. This information was displayed adjacent to

each aircraft token. Note that this automatic sorting

did not include higher-level conflict information; it

simply was based on distance/time measures and therefore provided only an initial approximation of aircraft

order. These initial sequences needed manual updating and checking. Participants also could get distance

information between points on the radar screen by

using a distance-measuring tool. This tool was similar

to how controllers measure distance on traditional

radar screens.

One essential consequence of this design was that

a planned sequence could be perceptually compared

with the current sequence by sliding the cursor across

the sequence on the planning screen. This allowed the

controller to observe how the sequenced position of

each aircraft corresponded to its current position on

the radar screen. Any discrepancy between the planned

sequence and the current sequence was therefore made

perceptually salient to the controller. If the planned

sequence differed from the current sequence, this

discrepancy signaled the need for modifications to

either the planned sequence or to an aircraft’s path.

The discrepancy represented an important constraint

as it guided the controller toward the aspects of the

situation where control interventions were needed.

Figure 1. The dynamic linkage between the planner screen and the radar screen. On the radar, every

diamond-shaped aircraft representation is linked to a datablock of flight information (in order from top, left

to right: aircraft identification, altitude in 100 feet, computer identification number, and speed). The two

aircraft on the radar screen (UAL755 and DAL80) are grouped into different categories of traffic (i.e., final

destination Dallas and Unclassified). Different categories of traffic are represented in different colors.

Aircraft are selected by clicking on them (UAL755). Moving the cursor over any token on the planning

screen or a target on the radar screen puts a rectangle around the two representations (DAL80).
4

Do participants rely to the same extent on the radar

screen when using the planning aid as when using the

strips? The answer to this question should provide

evidence of the extent to which the participants in our

study made differential usage of the same radar functions in each condition. For example, they should not

have to declutter the radar as much. From observed

differences, we can infer how participants used and

accessed information differently in the two conditions.

METHOD

Twelve en route air traffic controllers participated

in the experiment. All participants were full-performance-level controllers who served as instructors at

the FAA Academy. Full-performance-level controllers

are qualified to control traffic in a sector by themselves. The experiment was conducted at the FAA

Civil Aerospace Medical Institute in Oklahoma City.

All participants were familiar with the airspace (i.e.,

sector) used in the experiment.

Each of the 12 participants was given the task of

planning and maintaining aircraft sequences for a

generic en route sector and to communicate the sequence orally to a tactical controller (tactician) who

implemented it. The tactician, another en route air

traffic controller, was the subject-matter expert. All

participants performed the same task under two conditions: 1) using the electronic planning aid without

strips (planner condition) and 2) using paper flight

strips without the electronic planning aid (strips condition). Participants used the same radar screen in

both conditions, although some of the radar functionality was absent without the planning aid (i.e., the

color coding of aircraft). The two scenarios were busy;

up to 44 aircraft were in the sector during 20 minutes

and included arrivals, departures, and overflights.

The order of the scenarios and the order of the two

conditions (planner or strips) were counterbalanced

across participants.

Each participant completed a 45-minute training

session on the use of the planning aid and radar screen.

One or two days later, each participant completed a 1-

hour practice session. No training was given on the

use of the flight progress strips, as all participants had

extensive experience with them in the field. The

experiment started a day or two following the practice

session. Immediately prior to testing, participants

practiced with the planning aid for a few minutes to

re-familiarize themselves with its operation.

For each scenario, the participant was told about

any special airspace restrictions in effect and was given

sufficient time to set up either the planning aid or

paper strips while the aircraft in the scenario remained

paused. During this time, they verbally conveyed their

plan to the tactician. The simulation was started after

the plan was conveyed. The simulation ran for 20

minutes, with a brief pause after 10 minutes.

RESULTS AND DISCUSSION

We have previously described the details of the

participants’ superior performance and reduced

workload when using the planning aid (see Canning,

Johansson, Gronlund, Dougherty, & Mills, 1999;

Moertl et al., 2000). The advantage for the planning

aid condition was especially noteworthy because participants had, on average, about 10 years experience

with paper strips and less than 2 hours practice with

the planning aid. In the present study, we were interested in the differential usage of the radar screen

between the conditions and what that can tell us about

why the planning aid led to superior planning performance and reduced workload.

Loglinear Modeling of User Interactions

Olson, Herbsler and Rueter (1994) presented

loglinear modeling as a technique for analyzing human-computer interactions. Loglinear models allow

the test of main effects and interactions of models that

fit empirical human-computer interaction frequencies and thereby allow one to determine sequential

structure and structure changes between conditions.

Olson et al. (1994) used loglinear modeling on a set of

interaction data that were semantically categorized by

human judges. Such a procedure can prove highly

useful but has the disadvantage of costing considerable time resources for the manual categorizations of

actions. In contrast, we used this approach to analyze

raw data-files that were not categorized by human

judges. Therefore, we sidestepped the objection that

the analysis of categorized data might reflect the

judgments of human judges, in addition to actual

empirical patterns in the computer interactions. In

contrast to Olson et al. (1994), our design included

repeated measurements for which we accounted in

our models.

Although participants interacted with the radar

screen more frequently in the strip condition (significant main effect of experimental condition, Wald
5

χ2(1) = 17.07, p < 0.01), of primary interest was

determining the source of these differences. We compared the observed frequencies with the expected

frequencies and determined how participants interacted with the radar screen differently in the two

conditions.

Participants performed nine different types of actions on the radar screen (listed in Table 1). We

compared observed frequencies for each action with

expected frequencies assuming no differences between

conditions (i.e., standardized residuals for each action

and condition). The standardized residuals were calculated as the difference between the predicted and

observed frequency divided by the square root of the

predicted frequency. Table 1 displays the results of

this analysis; the last column shows the standardized

residuals. The model predicted the observed frequencies satisfactorily (within a 95% confidence interval)

for all but three of the actions. This meant that these

three actions occurred with differing frequencies in

the two conditions. We discuss these three user actions in turn.

Select token. Participants selected significantly more

aircraft on the radar screen in the strip condition than

in the planning aid condition. Selecting an aircraft

creates a border around its datablock that enhances its

visibility. Participants in the planning aid condition

did not need this perceptual aid as frequently, presumably because they could rely on the dynamic

linkage between the two screens to perceptually locate

aircraft on the radar screen.

Distance measurement. Participants measured the

spatial distance between points on the radar screen

more frequently in the strip condition. Distance information was crucial for planning, as it allowed

estimation of when aircraft would reach specific points

in the sector. Participants in the planning aid condition did not measure the distances on the radar screen

as frequently, presumably because they could rely on

the time/distance information that was presented to

them next to each aircraft on the planning aid.

Datablock adjustment. Datablock adjustments included changing and adjusting datablock position.

Participants adjusted datablocks more frequently in

the strip condition than in the planning aid condition. As mentioned above, controllers declutter their

radar screen to make datablock information visible.

These adjustments are an important index of the usage

of the radar screen. In the strip condition, participants

had to get their flight information from the radar

screen and had to declutter the radar screen to get to

this information. However, when using the planning

aid, participants adjusted datablock position less frequently and instead relied on the planning aid to

review flight information. This was consistent with

the greater ease of information access in the planning

aid condition and more time spent in the strips

condition on “housekeeping” functions.

Participants interacted with the radar screen less

when they worked with the planning aid. They manipulated aircraft less (visually highlighted or marked

aircraft less) and adjusted datablocks less often. They

Table 1.

Observed and Expected Frequencies for a Loglinear Model Assuming no Difference Between

Experimental Conditions

User action

Frequency

in strip

condition

Frequency in

planner

condition

Predicted

Frequency

Standardized

residual for strip

condition

Adjust Vector Length 3 6 4.5 -0.71

Invalid command (error) 72 83 77.5 -0.62

Zoom in/out 10 11 10.5 -0.15

Move information table 2 2 2 0

Altitude filter 12 10 11 0.30

Invalid track (error) 3 2 2.5 0.32

Select Token 482 408 445 1.75*

Distance measurement 108 56 82 2.87*

Datablock adjustment 661 520 590.5 2.90*

Note. * Observed frequency is outside a 95% confidence interval around the expected frequency.
6

also assessed spatial distance less often. The greater

ease of accessing information was the result of replacing radar functionality with the functionality of the

planning aid. The prior results examined interaction

frequencies. In what follows, sequences of user interactions were examined to provide additional insights

into how participants used the radar screen differently

between the two conditions.

User Interactions Sequence Analysis

Differences in sequences of interactions between

conditions were also testable using loglinear modeling. For each condition, the empirical probabilities of

an interaction occurring after another interaction

were compared with the estimated probabilities, assuming that there were no differences between conditions. In a loglinear model, the difference between

conditions was tested by a three-way interaction of the

main factors (antecedent action, consequent action,

and experimental condition, see Olson et al., 1994).

We found a significant three-way interaction (Wald

χ2

 (13) = 610.97, p < 0.01), which indicates different,

non-random sequences between the conditions. Accordingly, we found significant differences by comparing specific action sequences between conditions.

Again, we calculated the standardized residuals using

antecedent and consequent actions, and their interaction. Because there were 115 empirically occurring

sequences, only the four sequences that were not

predicted by the model are reported in Table 2. These

differences reinforced the usage differences between

the conditions noted above.

Participants had more frequent repetitions of distance measurement and datablock adjustment in the

strip condition. They also cycled between adjusting

datablocks and selecting aircraft, and vice versa, more

frequently in the strip condition. Information retrieval

in the strip condition appeared more repetitive, compared with the planning aid condition. Participants

needed to declutter the radar screen more because they

had to integrate the strips with the aircraft on the radar

screen. Furthermore, the integration effort seemed

not to occur as an isolated event but happened in

bursts of repeated decluttering activity. It is hard to

see how these bursts of decluttering activity could

entail any memory-enhancing function. This manual

matching was much less prevalent in the planning condition, where participants used fewer interactions in

absolute numbers and fewer self-repeating interactions

to retrieve the necessary information.

CONCLUSIONS AND IMPLICATIONS

Complex cognitive tasks such as planning the sequencing of air traffic can be supported by integrating

different information sources. The planning aid resulted in less repetitive, but more integrated, information retrieval compared with the traditional planning

environment. Less repetitive interactions were possible because the interface itself provided the physical

integration of the information. This provided one

reason why Moertl et al. (2000) found that planner

workload was reduced. The integration of the discrete

flight information with the radar information allowed

participants to develop their plan 6.3 minutes faster in

the planning aid condition than in the strip condition.

Not only did the planning aid make planning

easier, it also improved the quality of those plans.

Moertl et al. (2000) found no difference in plan

quality during the first half of the scenario (although

remember, those plans were developed much faster).

However, in the second half of the scenario, when

participants were changing and updating their plans,

planning performance was superior in the planning

Table 2.

Sequences of User Actions. The Positive Standardized Residuals Indicate That User Action

Sequences Occurred in the Strip Condition More Frequently Than Expected by the Model

Sequence of user actions

Frequency

in strip

condition

Frequency in

planner

condition

Frequency

predicted

Standardized

residual for

strip condition

Repeated distance measurement 60 14 37 3.78*

Repeated datablock adjustment 202 129 165.5 2.83*

Aircraft selection – Datablock 

adjustment

406 331 368.5 1.95*

Datablock adjustment – Aircraft 

selection

430 361 395.5 1.73*

Note. * Observed frequency is outside a 95% confidence interval around the expected frequency.
7

aid condition. The planning aid especially was beneficial in this situation because adapting plans to the

current traffic situations depended strongly on the

integration of planned sequence information with the

current air traffic situation. The planning aid was

designed to do exactly that. Participants could see on

the radar the sequence of aircraft that they had proposed on the planning aid. By sliding the cursor over

the corresponding aircraft on the planner, they could

see how the plan was progressing. This visual display

of a planned sequence on the radar gave the controller

an important indication of where changes were required. Aircraft that were out of sequence and did not

“light-up” where they should have would focus the

participant on relevant decision points.

The current interface has many characteristics of an

ecological interface (e.g., Effken, Kim, & Shaw, 1997;

Lintern, 2000; Pawlak & Vicente, 1996; Rasmussen

& Vicente, 1990; Vicente & Rasmussen, 1990). Ecological interface design argues for a perceptual formulation of user goals within the interface. The interface

then facilitates actions as the user perceives his or her

goals mirrored in the affordances of the interface. In

this way, the interface guides users’ interactions without major intrusions or the need for automation. It

replaces effortful cognitive processes with parallel,

perceptual processes.

Perceptual information integration proved a successful design principle when we examined the cognitive task of planning air traffic in isolation. Future

experiments should be directed at integrating strategic planning with other controlling tasks (e.g., tactical

planning). Only then can it be determined if the

planning aid can replace strips, or if other controller

tasks still require the availability of paper flight progress

strips. Also, it is important to keep in mind our focus

on strategic planning and the accompanying decision

to isolate the strategic planning tasks from the tactical

planning tasks by assigning these responsibilities to

two different individuals. It is possible that a single

controller responsible for both tactical and strategic

planning would not find the planning aid useful.

However, that was not the goal of our project; the goal

was to develop an interface for a possible future

strategic controller position. A different interface may

have resulted if our goal had been to develop an

interface to enhance the strategic planning capabilities of a controller working a sector alone. An important next step will be to compare the planning aid with

conflict probe software to determine what aspects of

the air traffic control task can best be accomplished

through information re-organization and what can be

best handled by automation.

Recent research suggests the advantage of active

control over passive monitoring in air traffic management (e.g., Metzger & Parasuraman, 2002). Our

findings can be applied to the design and evaluation of

interfaces that keep the controller in-the-loop. Specifically, reliance on perceptual processes could serve

as an alternative to outsourcing plan development to

a piece of software. This allows the controller to do

what humans are good at (parallel perceptual processing) while allowing the computer to do what it is good

at (organization and linking information). Simple

modifications to the perceptual properties of an interface will decrease task difficulty and increase human

performance without impinging on higher-level cognition. Furthermore, the perceptual optimization of

interfaces should be accompanied by an empirical

analysis of behavioral differences between the new

and old interface. As a result, tasks that are best

accomplished by a human operator could be delineated from those more appropriately left to a computer. The design and evaluation of interfaces would

benefit from this process.

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