John Uhlarik

Doreen A. Comerford

Department of Psychology

Kansas State University

Manhattan, KS 66506

March 2003

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

 

Office of Aerospace Medicine

Washington, DC 20591

INFORMATION REQUIREMENTS

FOR TRAFFIC AWARENESS IN A

FREE-FLIGHT ENVIRONMENT: AN

APPLICATION OF THE FAIT ANALYSIS

DOT/FAA/AM-03/5
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

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ACKNOWLEDGMENTS

The authors thank Dr. Thomas E. Nesthus and Dr. Kurt M. Joseph, who provided invaluable 

assistance and many insightful comments while serving as Contract Office Technical Representatives 

for contract #98F80691.

iii

v

TABLE OF CONTENTS

1.0 INTRODUCTION ----------------------------------------------------------------------------------------------- 1

2.0 A REVIEW OF RELATED RESEARCH -------------------------------------------------------------------------- 1

2.1 Summary of Related Research----------------------------------------------------------------------------- 2

3.0 THE CURRENT RESEARCH: BACKGROUND INFORMATION------------------------------------------------- 3

3.1 The Goal of the Current Research------------------------------------------------------------------------ 3

3.2 A Definition of the Human-Machine System----------------------------------------------------------- 3

3.3 NASA-AMES CDTI --------------------------------------------------------------------------------------- 4

4.0 A STEP-BY-STEP DESCRIPTION OF THE FAIT ANALYSIS -------------------------------------------------- 5

4.1 Step 1: Develop a Model of Information Flow --------------------------------------------------------- 5

4.2 Step 2: Decompose the Model Into Characteristics---------------------------------------------------- 5

4.3 Step 3: Create a Matrix of the Characteristics and Their Interactions------------------------------- 6

4.4 Step 4: Obtain Rough Estimates of the Relative Importance of Characteristics ------------------- 6

4.5 Step 5: Identify Tradeoffs in the System ----------------------------------------------------------------10

4.6 Step 6: Identify Information Requirements ------------------------------------------------------------12

4.7 Concluding Remarks Regarding the Analysis ----------------------------------------------------------13

5.0 DISCUSSION AND IMPLICATIONS OF THE FINDINGS-------------------------------------------------------15

5.1 Summary of Findings--------------------------------------------------------------------------------------15

5.2 The Utility of the FAIT Analysis-------------------------------------------------------------------------16

6.0 FUTURE RESEARCH ------------------------------------------------------------------------------------------17

7.0 REFERENCES --------------------------------------------------------------------------------------------------17

8.0 APPENDIX A: FUNCTIONAL DESCRIPTION OF NASA-AMES CDTI---------------------------------- A1

8.1 The Route Assessment Tool (RAT)--------------------------------------------------------------------- A4

9.0 APPENDIX B. NODE DEFINITIONS -------------------------------------------------------------------------B1

9.1 The World---------------------------------------------------------------------------------------------------B1

9.2 The Human Side-------------------------------------------------------------------------------------------B1

9.2.1 Human Input-----------------------------------------------------------------------------------------------B1

9.2.2 Human Output---------------------------------------------------------------------------------------------B4

9.3 The Machine Side------------------------------------------------------------------------------------------B5

9.3.1 Machine Input----------------------------------------------------------------------------------------------B5

9.3.2 Machine Output -------------------------------------------------------------------------------------------B7

10.0 APPENDIX C. FAIT ANALYSIS MATRIX ------------------------------------------------------------------- C1

11.0 APPENDIX D. RELATIVE INFLUENCE OF CHARACTERISTICS ---------------------------------------------D1

1

INFORMATION REQUIREMENTS FOR TRAFFIC AWARENESS IN A FREE-FLIGHT

ENVIRONMENT: AN APPLICATION OF THE FAIT ANALYSIS

INTRODUCTION

The present research was undertaken to examine human 

factors issues associated with National Airspace System 

(NAS) modernization. The first phase of this research 

reviews the concept of situation awareness (SA) as it 

relates to pilot surveillance activities and is presented in 

a separate document (cf., Uhlarik & Comerford, 2002). 

The second phase of this research, presented in the current 

paper, identifies and classifies information requirements 

for pilot surveillance functions in the air carrier. Such 

informa?tion will be helpful in the process of designing 

new technologies for pilots in the future NAS.

1.0 A REVIEW OF RELATED RESEARCH

Williams and Joseph (1998) conducted a study examining the manner in which pilots mentally organize 

flight-related information. Their goal was to provide a 

foundation for the design of future data-link interfaces 

for general aviation (GA) pilots. They began by examining the functional architecture of the Operational and 

Supportability Implementation System (OASIS). This 

system will modernize existing Automated Flight Service 

Station (AFSS) equipment and provide GA pilots with 

important weather and flight planning information. Examination of the system’s architecture identified 48 pieces 

of data that a GA aircraft could receive from and send to 

AFSS via data link. Williams and Joseph presented GA 

pilots with the list of 48 items and asked them to rate 

each in terms of its importance in performing surveillance, communication, and navigation functions. For the 

purposes of the present paper, Williams and Joseph’s most 

relevant findings are represented in Figure 1.

Figure 1 illustrates the 26 pieces of information that 

GA pilots identified as being most important in performing surveillance activities, and it also represents pilots’ 

perceived relations among these items. Specifically, the 

number of lines that connect two boxes is an indication of the perceived relation between the information 

within those boxes. For example, the perceived relation 

between “Rerouting” and “Traffic conflicts” is relatively 

Figure 1. The typical conceptual network of data that is important in performing surveillance 

functions (from Williams and Joseph, 1998).
2 

strong, because the nodes are connected directly by only 

one line. The relation between “Rerouting” and “Cruising 

altitude” is perceived as relatively weaker, as these nodes are 

connected only indirectly through four other nodes. 

Although Williams and Joseph’s study was limited to 

GA, the items identified as important to surveillance 

functions appear general enough that they may also be 

relevant to the design of technologies for transport air 

carriers. Note however, that the goal of the present study 

is not limited to information that can be obtained via data 

link. Neverthless, the findings of Williams and Joseph 

(1998) can be combined with the findings presented in 

the following pages.

Endsley, Farley, Jones, Midkiff, and Hansman (1998) 

conducted a study that also is relevant to the present 

research. They sought to identify the information requirements for commercial airline piloting. To identify these 

requirements, Endsley et al. examined job and task analyses 

that were performed in the past and conducted interviews 

with active airline pilots. There were three outcomes of 

their study: a goal hierarchy, a goal-directed task analysis, 

and a list of information requirements. The goal hierarchy

identifies a pilot’s “basic goal” as “Get aircraft from origin 

to destination safely, legally, with satisfactory levels of 

comfort and service to passengers, on schedule, and in 

an efficient manner.” Four sub-goals were identified as 

being important in achieving the basic goal: “Select best 

path to destination,” “Execute desired flight path safely, 

efficiently, and ride with comfort,” “Manage resources 

effectively,” and “Satisfy the customer.” For each of the 

four goals, the necessary general tasks were identified, 

and for each general task, the necessary sub-tasks were 

identified. For example, the general task, “Assess flight 

plan” appears under the goal “Select best path to destination,” and the sub-task “Push back from gate” appears 

under the general task “Assess flight plan.” To perform 

the goal-directed task analysis, Endsley et al. used the goal 

hierarchy as a template and inserted the pilot information requirements under the appropriate headings. To 

produce the final list of information requirements, the 

goal-directed task analysis was used to extract information requirements that were common across goals, and 

this list was divided into several categories (e.g., aircraft 

state, airports, traffic).

The aim of the present research may appear redundant with the work of Endsley et al. (1998), in the sense 

that the questions posed in the present research can be 

answered by simply examining the relevant categories 

of information requirements identified by Endsley et al. 

In fact, several of the categories are directly relevant to 

the present research. For example, the list of information requirements contains categories such as “Traffic,” 

“Terrain/obstacles,” and “Weather.” However, there are at 

least two limitations of Endsley’s work that do not apply 

to the present research. First, their analysis performed by 

Endsley et al. was intended to be “as technology-free as 

possible” (p. 4). While a technology-free analysis is desirable for purposes of generalization, it may be unrealistic 

in the context of a technology-laden environment like 

the cockpit (and the free-flight environment, for that 

matter). Second, Endsley et al. purposefully ignored 

information that is static. For example, they did not 

include information related to procedures or rules. The 

present research does not attempt to make a distinction 

between static and dynamic information, insofar as this 

distinction can become quite unclear. 

The problem with the static/dynamic distinction is 

exemplified by the rationale that Endsley et al. marshaled 

in their argument against including static information. 

Specifically, Endsley et al. implied that a rule, by its very 

nature, is static. However, rules often have dynamic information embedded within them (e.g., “If I’m flying above 

X feet, I may fly at my cruise speed of Y knots. However, 

if I’m flying below X feet, I must fly at Z knots”). In 

other words, it can be argued that rules are dynamic in 

the sense that the appropriate rule changes, depending 

on the surrounding (dynamic) circumstances. 

An even more important reason for ignoring the distinction between static and dynamic information is related 

to the two primary goals for identifying information 

requirements: (1) the requirements might be used as a 

tool in developing a training program, and/or (2) they 

may be used as a tool in the development/enhancement 

of a user interface. For both of these applications, static 

information is just as important as dynamic information. One certainly would not want to develop a training 

program that ignores static information (e.g., rules), nor 

would one want engineers developing a piece of technology that fails to present the pilot with static information 

(e.g., a navigation tool that fails to present information 

regarding the location of unchanging fixes).

2.1 Summary of Related Research

The goal of the present paper is to identify and classify 

information requirements for pilot surveillance activities 

in the air carrier. The previously summarized research 

projects do not fully satisfy this goal. Although the 

research performed by Williams and Joseph is relevant, 

they examined only information that might be obtained 

via data link and concentrated on GA. Further, the research performed by Endsley et al. (1998) was limited in 

the sense that technology was not considered, and only 

dynamic aspects of the task were considered. 
3

3.0 THE CURRENT RESEARCH: 

BACKGROUND INFORMATION

3.1 The Goal of the Current Research

The research performed by Endsley et al. (1998) demonstrates how complex and detailed a task analysis of all 

surveillance activities would be. The research described 

below is restricted to an analysis of the pilot’s task of 

monitoring traffic during the cruise segment of flight. 

It is further limited to a free-flight environment, which 

has been proposed as the operational concept for the 

future NAS (RTCA, 1995). Therefore, the goal of the 

present research is to identify the information required 

for the air carrier pilot to achieve “traffic awareness” in 

the free-flight environment. 

Traffic awareness is one component of the more general 

construct of situation awareness. As discussed in a separate 

document (Uhlarik & Comerford, 2002), there are many 

definitions of situation awareness. According to Wickens 

(1995), situation awareness is the “continuous extraction 

of environmental information about a system or environment, the integration of this information with previous 

knowledge to form a coherent mental picture, and the use 

of that picture in directing further perception, anticipating, and responding to future events” (p. 1). Although 

research has been conducted on several components of 

SA that are related to traffic awareness (e.g., environment, 

spatial, temporal, and navigation awareness), research on 

traffic awareness has been sparse. As a result, no formal 

definition of traffic awareness exists. The following definition of traffic awareness is used for the present research: 

Traffic awareness is having knowledge of the information 

necessary to obtain and maintain self-separation in the 

free-flight environment, where successful self-separation is 

defined as keeping ownship separated from other aircraft 

by 5 miles horizontally or 1,000 feet vertically.

While the pilot in the free-flight environment will 

obtain traffic information from several sources (e.g., outthe-window information, radio communications), the 

primary source of information presumably will be the 

Cockpit Display of Traffic Information (CDTI). Generally speaking, a CDTI is a cockpit display that provides 

the pilot with traffic information (e.g., ownship position, the position of other aircraft).1 Because the CDTI 

will be the pilot’s primary source of traffic information, 

identifying the information required for traffic awareness 

will be helpful in the design and certification of a CDTI 

for the free-flight environment. 

The Function Allocation Issues and Tradeoffs (FAIT) 

analysis (cf., Riley, 1993) was chosen to identify the 

relevant information requirements. In general terms, 

FAIT analysis is a systematic procedure for identifying 

human factors issues in human-machine systems. The 

FAIT analysis was chosen because it appears to have great 

potential, and it provides substantially more information than a traditional task analysis. Among other things, 

the FAIT analysis (1) assists in identifying information 

requirements, (2) yields numerical values that represent 

the relative influence and the relative sensitivity of important aspects within the system, and (3) allows for the 

examination of tradeoffs in a human-machine system 

(e.g., when aspect X of the system reaches a desirable 

state, aspect Y consequently may reach an undesirable 

state). In addition, the FAIT analysis places equal emphasis on the human and machine components of the 

system, thereby, allowing the analyst to include purely 

psychological constructs (e.g., mental workload) of the 

human system component.

Despite its potential utility, the FAIT analysis has been 

used infrequently, and most published studies that utilize 

it have been conducted by its developer (i.e., Riley, 1989; 

Riley, 1992; Riley, Lyall, & Wiener, 1993). Therefore, in 

addition to identifying information requirements, the 

present research also provides a vehicle for evaluating 

the utility of the FAIT analysis. Specifically, to assess 

the utility of the FAIT analysis, the information requirements obtained via the FAIT analysis are compared with 

the information requirements identified by Endsley et 

al. (1998).

3.2 A Definition of the Human-Machine System

The FAIT analysis was developed with the assumption that it would be used for the analysis of complex 

human-machine systems (Riley, 1993). Toward this end, 

a necessary first step is to identify components of the human-machine system before the FAIT analysis is applied. 

For example, because complex human-machine systems 

generally include at least some level of automation, the 

analysis requires the researcher to determine the amount 

of control the machine possesses, and the following components must be identified: the human, the environment, 

and the machine. Obviously, the human component of 

the system to be analyzed in the current research is the 

pilot (although co-pilots, pilots of other aircraft, and 

air traffic controllers are ignored at this stage, they are 

taken into account later in the analysis). The environment for the human-machine system is identified as a 

modernized NAS, and therefore, the current research 

assumes that aircraft are equipped with Automatic Dependent Surveillance-Broadcast (ADS-B), which will 

allow aircraft to transmit and receive three-dimensional 

position information via data link. With such informa1 (A more detailed description of a particular example of a CDTI is provided later in this document.)
4 

tion, the pilot may be responsible for self-separation in 

most instances, but the air traffic manager (ATM) will 

monitor traffic and control by exception. In other words, 

the pilot will perform tasks necessary for self-separation, 

and the ATM will intervene only in cases where alert 

zones overlap. For the current purposes, an alert zone 

is defined as a spherical area that surrounds an aircraft 

by five nautical miles on the horizontal dimension and 

1,000 feet on the vertical dimension. However, if technology permits, these alert zones may be smaller. A final 

assumption was that the pilot would access the ADS-B 

information using a CDTI, which would be integrated 

into the current aircraft avionics suite. The CDTI was 

identified as the machine component of the system for 

the application of the FAIT analysis.

3.3 NASA-AMES CDTI

Because the FAIT analysis is intended for use in the 

early stages of the design process (Riley, 1993), a search 

was conducted for a candidate CDTI sill in the early stages 

of design. In fact, researchers at National Aeronautics and 

Space Association are currently examining design concepts

rather than a specific display format or configuration of 

their prototype CDTI. The configuration and capabilities of the NASA-AMES CDTI varies considerably (e.g., 

Cashion,Mackintosh, McGann, & Lozito 1997; Johnson 

et al., 1997; Mackintosh et al., 1998), and the display used 

in the current analysis consists of a somewhat arbitrary 

collection of NASA display concepts. A brief description 

of this display appears below, but Appendix A provides 

a more thorough description of the CDTI.

Figure 2 presents a schematic of the NASA-AMES 

CDTI, which allows the pilot to view ownship position 

(depicted as a white chevron in the lower center portion of 

Figure 2. The version of NASA’s CDTI that was used in the current analysis.
5

Figure 2) in relation to other aircraft. In addition, altitude 

of other aircraft can be assessed in relation to ownship 

(i.e., other aircraft are color-coded according to altitude 

and their datatags display altitude numerically).

Using the toolbar at the bottom of the display, the pilot 

can configure the CDTI with there viewing options: (1) 

altitude can be displayed in absolute or relative terms, (2) 

datatags can be viewed or hidden, and (3) the predicted 

routes of aircraft can be viewed or hidden. In addition, 

when the appropriate option is chosen, the pilot can: (1) 

request that datatags be rearranged for optimum viewing, (2) change the amount of time the predictor lines 

represent, and (3) view the predictor lines in one of two 

formats. Finally, the Route Assessment Tool (RAT) allows 

the pilot to assess and accept (if so desired) changes in 

altitude, vertical speed, and heading. Therefore, a pilot 

who is considering changes (e.g., in altitude) is allowed 

to determine if an alert zone contact is likely before the 

changes are initiated.

4.0 A STEP-BY-STEP DESCRIPTION OF 

THE FAIT ANALYSIS

This summrizes of the current findings and, in 

the context of the current findings, presents a brief 

overview of the FAIT analysis procedures. A complete 

description of the analysis appears in the Function 

Allocation Issues and Tradeoffs: User’s Manual (Riley, 

1993). The FAIT analysis is divided into six major 

steps for ease of understanding.

4.1 Step 1: Develop a Model of Information Flow

The goal in the first step is to determine the model 

of information flow within the system. There are three 

tasks to be completed in determining the appropriate 

model. The first task is to identify the machine’s level of 

autonomy. The FAIT user’s manual provides a description of 12 levels of autonomy (cf., Riley, 1993, p. 6-8). 

The highest level represents a machine with complete 

autonomy (i.e., the machine performs control actions 

without informing or interacting with the operator), and 

the lowest level represents a machine with no autonomy. 

Utilizing complex algorithms (cf., Johnson et al., 1997), 

the NASA-AMES CDTI attempts to identify other 

aircraft that are likely to have alert zone contact with 

ownship, and therefore, the current analysis placed the 

NASA CDTI at the “Simple aid” level of autonomy. This 

classification fits nicely with Riley’s (1993) example of a 

target recognizer that simply attempts to categorize radar 

returns as belonging to targets, and as such was classified 

as a “Simple aid.”

The second task in Step 1 is to identify the machine’s 

level of intelligence. Like the first task, the researcher is 

to choose the machine’s level of intelligence from a list 

of seven levels (cf., Riley, 1993, p. 8-9). The highest level 

represents a machine with a high degree of “intelligence” 

(i.e., the machine has information regarding the operator’s 

physical state and intent and therefore can predict the 

operator’s behavior), and the lowest level represents a 

machine with virtually no intelligence (i.e., it presents 

only raw data).

Within this category, a machine viewed as “Personalized” is one that can be configured according to the 

operator’s preferences. Therefore, because some aspects 

of the NASA CDTI can be manipulated in accordance 

with the pilot’s preferences (e.g., datatags may be shown 

or hidden, predictors may be shown or hidden, the length 

of the predictors may be varied according to taste), the 

current analysis placed the NASA CDTI in the “Personalized” category.

After the level of autonomy and level of intelligence 

have been identified, the third and final task in this step 

is aimed at creating an information flow model for the 

current system. The general model of information flow 

(Figure 3), serves as the foundation for all resultant models of information flow when using the FAIT analysis. 

The FAIT user’s manual provides the researcher with 

12 templates that correspond to the levels of autonomy 

and seven templates that correspond to the levels of 

intelligence. Generally speaking, the level of autonomy 

determines which nodes are removed from the “machine 

output” quadrant of the general model, because they are 

not relevant. Similarly, the level of intelligence determines 

which nodes are removed from the “machine input” quadrant, because they are not relevant. The two templates 

that result from the current level of autonomy (simple 

aid) and the current level of intelligence (personalized) 

are fused to produce a more specific model of information flow for the current analysis. This model is shown 

in Figure 4. 

4.2 Step 2: Decompose the Model Into 

Characteristics

The model of information flow that is created in the 

first step is used as a tool in the second step. Specifically, 

for each node in the model, important characteristics are 

listed. Although Riley (1993) does not provide a formal 

definition of a characteristic, here it is considered to be 

an important aspect of the system that can vary from a 

desirable to an undesirable state. Characteristics in desirable states are important to a system’s proper functioning. 

Characteristics in undesirable states are equally important 

because they may lead to malfunctions in the system. For 

example, Riley suggests that, in a typical system, characteristics of the “Plan own action” node might be “Level 

of mental workload” and “Crew coordination.”
6 

Although Riley (1993) includes numerous examples, 

the analyst is expected to determine the relevant characteristics for the nodes in the system of interest. In 

determining these characteristics, Riley states that you 

should consider the characteristics of the system under 

investigation, as well as all characteristics of the operational 

environment and all related systems that may influence 

or be influenced by how the particular system works. 

Therefore, the current analysis includes more than the 

pilot, the traffic environment, and the CDTI that were 

identified at the onset of the analysis. At this point, the 

analysis also includes such things as the flight management system, air traffic controllers, fellow crew members, 

pilots of other aircraft, etc.

All nodes from the general model of information flow 

and the outcome for Step Two of the current analysis are 

included in Appendix B. In addition, Riley’s definition 

(1993) of each node is provided. Nodes not included in 

the current model appear in gray, and justifications are 

made for their exclusion. Appendix B also presents a 

question for each node. These questions were formulated 

for the current analysis to assist in the identification of 

relevant characteristics. Finally, Appendix B presents the 

important characteristics that were identified for each 

node in the system.

A preliminary analysis yielded 116 characteristics. 

However, numerous characteristics yielded redundant 

information in later steps of the analysis. For example, 

the final analysis uses one characteristic, “Ownship state,” 

to represent six characteristics that yielded redundant 

information in the preliminary analysis (i.e., “Ownship 

position,” “Ownship speed,” “Ownship heading,” “Ownship vertical speed,” “Ownship altitude,” and “Ownship 

attitude”). The final analysis yielded a total of 68 unique 

characteristics. A characteristic was only counted once, 

even if it was associated with more than one node.

4.3 Step 3: Create a Matrix of the Characteristics 

and Their Interactions

The third step requires placement of characteristics in 

a matrix (illustrated in Table 1) that is created by placing 

a list of all characteristics along the left margin and the 

same list along the top of the matrix. Although the lists 

are identical, the characteristics along the left margin are 

referred to as the “drivers,” whereas the characteristics 

along the top are referred to as the “receivers.” These 

labels represent the manner in which questions are posed 

regarding the relation among characteristics.

The matrix is used to determine the relations among 

characteristics. Beginning with the first driver, the researcher asks two questions for each receiver: (1) “Is there 

any situation in which the driver characteristic influences 

the receiver characteristic?” and (2) “Is there any situation in which some limitation of the driver characteristic 

places a requirement on the receiver characteristic?” If 

the answer is “yes” to either of these questions, an entry 

is placed in the matrix cell where the driver and receiver 

intersect. Once every receiver has been considered in 

relation to the first driver, this process is repeated for the 

remaining drivers in the list. 

Appendix C contains the matrix produced for the 

current analysis. Matrix cells that are shaded dark gray 

represent the negative diagonal of the matrix. These cells 

represent points where characteristics intersect with themselves. The lighter shade of gray and bold numbering used 

in other matrix cells will be explained in Step 5.

4.4 Step 4: Obtain Rough Estimates of the 

Relative Importance of Characteristics

The fourth step in the FAIT analysis identifies the 

relative importance of the characteristics by summing the 

rows and columns of the matrix. Row totals and ranks 

represent “influence,” and a characteristic that is highly 

influential is one that affects many other characteristics of 

the system. Column totals and ranks represent “sensitivity,” and a characteristic that is highly sensitive is one that 

is vulnerable to the effects of many other characteristics 

of the system.

Appendix D provides a convenient format for examining the influence and sensitivity scores for the current 

system. Table D1 presents the 68 characteristics, their 

respective influence scores, and their relative rank. The 

characteristics are presented in an order consistent with 

their relative rank on the influence dimension. Similarly, 

Table D2 presents the 68 characteristics, their respective 

sensitivity scores, and their relative rank. They also are 

presented in an order consistent with their relative rank 

on the sensitivity dimension.

Although there are no formal criteria for identifying 

the most important characteristics, Riley (personal communication, June 1, 1999) suggests concentrating on 

characteristics that score the highest on influence and 

sensitivity. Hence, the relative frequency associated with 

the 90th percentile point was identified for the distributions 

of influence and sensitivity scores. This arbitrary cutoff 

created a more manageable list of characteristics. Seven 

scores fell above the 90th percentile for both distributions, 

and they are denoted by a gray star in Appendix D. 

The following characteristics were identified as having the most influence in the system and are presented 

in rank order:

1) Weather

2) General piloting skills

3) Time of day
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Figure 4. The model of information flow in the current system (simple aid/personalized) system.

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9

4) Terrain

5) Ownship state

6) Level of pilot mental workload

7) Perceived time pressure

The following characteristics were identified as being 

the most sensitive (or vulnerable) and are also presented 

in rank order:

1) Type of action chosen by the pilot

2) Level of pilot mental workload

3) Appropriateness of planned action

4) Ownship state

5) Air traffic manger’s amount of mental workload

6) Accuracy of current machine model

7) Level of confidence in planned action

Two characteristics (“Ownship state” and “Level of pilot 

mental workload”) were identified as both very influential 

and highly sensitive. Not surprisingly, the state of the 

aircraft affects many aspects of the system, and at the 

same time, is affected by many aspects of the system. The 

same may be said of pilot mental workload. Given they 

fall above the 90th percentile point on both dimensions, 

these two characteristics are probably important.

Because traffic awareness is so important in the freeflight environment, two specific characteristics (viz., “Accuracy of the current machine model” and “Accuracy of 

pilot’s current world model”) were combined. These two 

characteristics are denoted by black stars in Appendix D. 

These two characteristics, taken together, represent the 

more global characteristic of “Traffic awareness.” The 

analysis suggests that traffic awareness is no more influential than the higher ranking of the two characteristics 

of which it is composed (i.e., “Accuracy of pilot’s current 

world model” and “Accuracy of current machine model”). 

However, traffic awareness is a more sensitive characteristic 

than when its components are examined separately. When 

“Accuracy of the current machine model” and “Accuracy 

of pilot’s current world model” are removed from the 

analysis and are replaced with the more global characteristic of “Traffic awareness,” the list of most sensitive 

characteristics appears as follows:

1) Type of action chosen by the pilot

2) Level of pilot mental workload

3) Traffic awareness

4) Appropriateness of planned action

5) Ownship state

6) Air traffic manger’s amount of mental workload

7) Level of confidence in planned action

Despite having relatively little influence, the sensitivity of traffic awareness suggests that it is a vulnerable 

characteristic.

In summary, Step 4 was used to identify the most 

important characteristics in the system. The seven most 

influential characteristics and seven most sensitive 

characteristics were extracted from the list of 68 characteristics. Two characteristics (ownship state and level 

of pilot mental workload) were identified as being both 

highly influential and highly sensitive. Therefore, these 

characteristics have been identified as especially important 

characteristics. Finally, two components, “Accuracy of the 

current machine model” and “Accuracy of pilot’s current 

world model,” were combined to create the characteristic 

called “Traffic awareness.” Traffic awareness was no more 

influential than the higher ranking of its two components. 

However, traffic awareness was one of the most sensitive 

characteristics of the system.

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10 

4.1 Step 5: Identify Tradeoffs in the System

The fifth step in the FAIT analysis identifies potential 

tradeoffs, which are found by identifying symmetrical 

relations within the matrix. Symmetrical relations are 

identified by folding the matrix along the negative diagonal and identifying entries that are superimposed on 

each other. These entries represent potential tradeoffs 

between characteristics. For example, there is one symmetrical relation in Table 2, and it is identified by the 

two lightly shaded cells with the bold number “1” in 

them. The potential for a tradeoff exists in this case, 

because characteristic 1 influences characteristic 2 and 

characteristic 2 influences characteristic 1.

The coding scheme used in Table 2 also is used in Appendix C, where lightly shaded cells that contain bolded 

number “1”s identify symmetries. Because symmetries 

in Appendix C may be difficult to visualize, Table D3 of 

Appendix D presents a list of the 30 characteristics that 

have been identified as having the potential for a tradeoff 

with another characteristic. Note that Table D3 consists 

of 124 lines, but this number is misleading. For example, 

“Level of pilot mental workload” is listed under “Ownship 

state,” and “Ownship state” is listed under “Level of pilot 

mental workload.” However, these two lines represent 

only one potential tradeoff. Therefore, the current analysis 

identified a total of 62 potential tradeoffs.

Because there are 62 potential tradeoffs, the characteristics that were identified in Step Four were used to 

shorten this list (Riley, personal communication, June 

1, 1999), and the resulting, abbreviated matrix appears 

in Table D4 of Appendix D. This abbreviated matrix 

includes only the characteristics that are highly sensitive, 

highly influential, and are components of traffic awareness. 

Therefore, the matrix includes 13 characteristics. To make 

the symmetrical relations clear, the tradeoffs are coded 

with numbers in Table D4. For example, “Accuracy of 

pilot’s current world model” influences “Ownship state” 

and vice versa. Therefore, there are two boxes in Table 

D4 that are assigned the number “1.” The number assigned to a pair is used only to distinguish one pair from 

another. 

As seen in Table D4, 11 potential tradeoffs require 

examination. Riley (1993) suggests that, in general, the 

most useful product of the FAIT is the documentation 

of possible scenarios. Potential tradeoffs are examined by 

creating a tradeoff scenario for each pair of characteristics 

that produce symmetry in the matrix. The following list 

provides scenarios that would exemplify the tradeoff associated with each pair of characteristics. Each scenario 

describes a set of possible circumstances that illustrates 

the potential tradeoff. If scenarios cannot be provided, 

then the tradeoff is probably nonexistent. However, if a 

reasonable scenario can be generated, then the tradeoff 

should be taken seriously. In other words, this task determines which of the potential tradeoffs (identified in 

the matrix) are true tradeoffs. Tradeoffs are examined by 

considering both characteristics in a pair (e.g., “Ownship 

state”/ “Accuracy of pilot’s current world model”) as a 

driver and a receiver. Each characteristic of the system 

can presumably be in various states ranging from optimal, to sub-optimal, to its poorest state. In other words, 

the state of each characteristic can range in terms of its 

desirability. For example, the accuracy of a pilot’s world 

model can range from desirable (i.e., it might be perfect) 

to undesirable (i.e., the pilot currently may not have a 

mental model of the world situation). To represent a true 

tradeoff, the characteristics in a pair must be inversely 

related in terms of desirability; that is, an increase in the 

desirability of Characteristic A must lead to a decrease in 

the desirability of Characteristic B and vice versa. Some 

pairs may yield direct relations (i.e., an increase in the 

desirability of Characteristic A may lead to an increase 

in the desirability of Characteristic B and vice versa); 

although these pairs may be important, they do not 

represent true tradeoffs.

The following scenarios were used in Step Five to 

explore the potential for tradeoffs in the system under 

study.

1) Ownship State/Accuracy of Pilot’s Current World 

Model. Tradeoff scenarios, per se, are difficult to imagine 

for these two characteristics. Specifically, one would not 

necessarily expect these two characteristics to be inversely 

related. Instead, a direct relation would be expected. For 

example, an undesirable aircraft attitude might affect the 

pilot’s ability to obtain information from the world (and 

therefore limit the accuracy of the pilot’s current world 

model). On the other hand, an inaccurate pilot world 

model might erroneously alter the control actions of the 

pilot (and create an undesirable state of ownship). While 

this relation appears to be a direct relation, the pairing 

of these two characteristics draws attention to what is 

probably an important synergistic relation.

2) Ownship State/Accuracy of Current Machine Model.

Like the previous pairing, an inverse relation between 

these two characteristics is difficult to imagine. Once 

again, a direct relation would be expected. For example, 

an undesirable aircraft state (e.g., it is too close to terrain) would result in the pilot attending to information 

other than that presented by the CDTI. Therefore, an 

undesirable aircraft state would diminish the quality of 

the pilot’s machine model, because the pilot presumably 

would have less knowledge of the machine state than would 

otherwise be the case. On the other hand, an inaccurate 

machine model might affect ownship state, in that the 

pilot’s actions would not be based on all relevant information. Although the pair does not represent a tradeoff, the 
11

pairing of these two characteristics again draws attention 

to what is probably an important synergistic relation.

3) Ownship State/Perceived Time Pressure. Tradeoff 

scenarios are easily imagined for this pairing. For example, 

when ownship is at an undesirable altitude, speed, or attitude, the pilot would feel at least some time pressure (i.e., 

as the acceptability of ownship state decreases, perceived 

time pressure increases). If pressured by time, the pilot may 

make errors that worsen ownship state (i.e., as perceived 

time pressure increases, the acceptability of ownship state 

may decrease). This pairing of characteristics represents 

a true tradeoff.

4) Ownship State/Level of Pilot Mental Workload. Mental workload is different than the characteristics discussed 

thus far; high mental workload is an undesirable state. 

Given this distinction, this pairing does not appear to 

represent a tradeoff. For example, if aircraft altitude were 

unacceptable (i.e., the acceptability of ownship state were 

low), then pilot mental workload would increase in the 

attempt to diagnose the problem or simply attain the 

correct altitude (i.e., pilot mental workload also would 

be in an undesirable state). On the other hand, as pilot 

mental workload increases (i.e. reaches an undesirable 

state), the pilot is more likely to ignore some responsibilities. Therefore, the acceptability of ownship state could 

become undesirable as mental workload increases. In 

most cases, this pair probably has a direct relation (does 

not represent a tradeoff). However, the relation can be 

unstable due to other factors. For example, a complacent pilot would not necessarily have increases in mental 

workload when ownship state is undesirable.

5) Ownship State/Type of Action Chosen by the Pilot. 

The pairing does not appear to represent a tradeoff. Instead, the pair has a direct relation. That is, as the suitability of the chosen action decreases, the acceptability 

of ownship state presumably would decrease. Further, as 

the acceptability of ownship state decreases, the action 

chosen by the pilot may be less desirable (e.g., in extreme 

cases, the pilot may panic and use poor judgment). Again, 

lack of a tradeoff does not imply anything regarding the 

importance of this pairing.

6) Level of Air traffic Manger’s Mental Workload/Level of 

Pilot Mental Workload. This pairing is interesting because 

scenarios can be imagined for both an inverse and a direct 

relation between the characteristics. If the pilot performs 

most of the duties associated with self-separation, the 

pilot’s mental workload would be high and the ATM’s 

mental workload would be relatively low. If a special situation arose (e.g., the ADS-B system were malfunctioning), 

the ATM’s mental workload would increase (as the ATM 

would have relatively more responsibilities). At the same 

time, the pilot’s mental workload would decrease (as the 

pilot would have relatively less responsibility). A direct 

relation also can be conceived easily. The obvious case arises 

when traffic is heavy. A pilot may request assistance from 

an ATM. However, because the ATM has high mental 

workload, the ATM may be unable to assist the pilot immediately. As a result, the pilot’s mental workload would 

be relatively high. Similarly, a pilot under conditions of 

high mental workload may not respond immediately to 

an ATM. As a result, the ATM’s mental workload may 

be increased by the pilot’s high mental workload. In sum, 

these characteristics may sometimes represent a tradeoff, 

and at other times, may have a direct relation.

7) Level of Air Traffic Managers’ Mental Workload/Type 

of Action Chosen by Pilot: This pairing of characteristics 

does not represent a tradeoff. When the action chosen by 

the pilot is less than desirable (e.g., the pilot unknowingly 

changes heading and creates the potential for an alert zone 

violation), the ATM’s mental workload might increase 

(i.e., reach an undesirable state), because it is the ATM’s 

duty to supervise the actions chosen by pilots. On the 

other hand, if an ATM has high mental workload, the 

ATM might not be as effective at noticing a potential 

conflict. Therefore, the pilot may choose an action with 

less information than might have otherwise been available. Therefore, when the ATM’s mental workload is 

high (i.e., in an undesirable state), there is a better chance 

that the action chosen by the pilot also will be less than 

optimal. 

8) Level of Confidence in Planned Action/Level of Pilot 

Workload. This pairing does not represent a tradeoff. As 

the pilot’s level of confidence in the planned action goes 

down (i.e., reaches a relatively less desirable state), the 

pilot will search the environment in an attempt to confirm or disconfirm the adequacy of the planned action. 

Therefore, the pilot’s mental workload will increase (will 

reach a relatively less desirable state). On the contrary, high 

pilot workload (an undesirable state) would decrease the 

pilot’s confidence in the planned action (an undesirable 

state). Under high workload conditions, time may not 

allow the pilot to assess all relevant information and leave 

the pilot feeling less confident.

9) Level of Confidence in Planned Action/Type of Action 

Chosen by Pilot. This pair of characteristics has a direct 

relation. Typically, a high level of confidence yields better 

performance. Further, when the most appropriate action 

is chosen, a high level of confidence results. The only 

exception to this rule is in the case of overconfidence. 

Specifically, an inverse relation occurs when the confidence 

level is so high that the pilot makes uninformed decisions 

(e.g., ignoring crew members), and these decisions can 

result in a less than desirable action.

10) Perceived Time Pressure/Type of Action Chosen by 

Pilot Tradeoff scenarios are easily imagined for this pairing. 

If pressured by time, the pilot may make decisions too 
12 

quickly. As a result, the chosen action may be less than 

satisfactory. Furthermore, if the action is inadequate (e.g., 

a change in heading that results in an alert zone conflict 

warning), the resulting situation may need to be rectified 

immediately.

11) Level of Pilot Mental Workload/Type of Action 

Chosen by Pilot. This pairing does not appear to represent a tradeoff. As pilot mental workload increases (i.e., 

reaches a less desirable state), the pilot is more likely to 

overlook relevant information. Therefore, as workload 

becomes less desirable, the quality of the chosen action 

may decrease. Furthermore, if the type of action chosen 

is unacceptable (e.g., aircraft is headed into terrain), then 

pilot mental workload naturally would increase (become 

less desirable) in an attempt to correct the mistake. 

In sum, the goal of Step 5 was to identify tradeoffs 

that occur among system characteristics. In all, 62 cases 

were identified in which there is the potential for a tradeoff. Due to the large number of cases, the analysis was 

limited to include only the tradeoffs associated with the 

13 most important characteristics that were identified in 

Step Four. These 13 characteristics yielded 11 cases of 

potential tradeoffs. Plausible scenarios were generated 

to determine which of these potential tradeoffs are truly 

tradeoffs. Two of the 11 pairs appear to represent true 

tradeoffs in the system (i.e., they are inversely related), 

while six pairs appear to be directly related. Three pairs 

appear to have unstable relations, suggesting that the 

circumstances are important in determining whether 

they represent a tradeoff or a direct relation. Although 

there were only two tradeoffs, an important relation was 

shown for every pair of characteristics.

4.6 Step 6: Identify Information Requirements

The final step in the current analysis is an identification of information requirements. First, the original 

matrix is used to identify errors in the system that lead 

to failures, incidents, or accidents (Riley, personal communication, June 1, 1999). For each potential error, an 

imagined scenario is documented. Once these scenarios 

are written, the analyst asks, “What information would 

assist the operator in preventing, detecting, or correcting 

the possible error?” Responses to this question result in 

a list of information requirements.

The creation of a scenario for each of the 767 pairings 

in the current matrix is beyond the scope of this document 

(i.e., there are 767 cell entries in Appendix C). Therefore, 

only the important characteristics, identified in Step Four, 

are utilized in this final step. Table D5 of Appendix D 

presents a matrix representing only the interactions 

between the important characteristics. Note that this 

matrix does not contain a negative diagonal because the 

characteristics on the left and top of the matrix are not 

identical. This summary matrix, which is derived from 

the matrix in Appendix C, yielded 51 pairs of characteristics that interact. Table D6 of Appendix D contains the 

51 possible scenarios that were written for the pairings 

identified in Table D5. Of course, these scenarios reflect 

only possible interactions that could occur in a free-flight 

traffic environment (i.e., the environment of interest).

Although Riley (1993) suggests asking only a single 

question to determine information requirements, the 

current analysis added more structure to the task by 

identifying the possible error(s) that might arise in each 

scenario and utilizing three questions for each scenario. 

The first question was “Whose knowledge of the situation 

would assist in the prevention, detection, or mitigation 

of the error?” In this step, the human component of the 

system was emphasized. The second question was “What

knowledge would assist in the prevention, detection, or 

mitigation of the error?” The scenarios are more helpful in 

identifying the required (i.e., necessary) knowledge rather 

than the required information. However, identifying the 

required knowledge is important. Available information 

is a necessary, but not sufficient, condition for knowledge, 

and the ultimate goal is to assist the pilot in obtaining 

knowledge. Once the necessary knowledge was identified, 

the third question was posed: “What information is necessary for the appropriate person to obtain the necessary 

knowledge?” The resulting information requirements for 

the current system are categorized according to the three 

aforementioned questions, and they appear in Table D7 

of Appendix D. 

The current analysis yielded a total of 100 information 

requirements. Because the information requirements for 

the human in the system (i.e., the pilot of ownship) and 

the requirements for the pilots in the world were identical, these information requirements were combined into 

one category (i.e., “Pilot”). Of the 100, 67 information 

requirements were for the air carrier pilot in the freeflight environment, and 33 information requirements 

were identified for the air traffic manager in the free-flight 

environment. However, one must be cognizant that, in 

this application of the FAIT analysis, the ATM is merely 

part of the world in this system (i.e., the ATM is not part 

of the human-machine system that is of primary interest). There probably would be many more information 

requirements identified for the ATM if, at the initial step 

of the FAIT analysis, the manager was identified as the 

human of primary interest.

The results obtained via the FAIT analysis were 

compared with those obtained from traditional task 

analyses. As discussed in the introduction, Endsley et al. 

(1998) performed a task analysis to identify information 

requirements for the commercial airline pilot. Endsley 
13

and Rodgers (1994) also performed a similar task analysis 

for an en route air traffic controller. As discussed earlier, 

one of the present goals was to compare the results of 

these two studies with the current findings.

While the precise names given to the information 

requirements varied slightly, 28 of the 100 information 

requirements identified by the FAIT are identical to those 

found by either Endsley et al. (1998) or Endsley and 

Rodgers (1994). These 28 information requirements are 

shaded gray in Table D7. The combined total of information requirements in the Endsely et al. (1998) and Endsley 

and Rodgers (1994) references exceeds 100. This amount 

is not surprising, given that they did not limit themselves 

to the task of traffic avoidance. For example, the list of information requirements in the present analysis recognizes 

the importance of terrain. However, because the CDTI 

is the only machine in the system under consideration, 

the only pertinent information is an alert that refers the 

pilot to another system. If the current analysis included 

all information required for piloting (as did Endsley et 

al.), the overall list of information requirements would 

be more extensive.

Given that only 28 of the information requirements 

identified in the FAIT analysis were redundant with that 

of Endsley et al. (1998) and Endsley and Rodgers (1994), 

the remaining 72 information requirements are unique 

to the analysis. The identification of unique information 

requirements is the result of at least four factors. First, 

unlike the research performed by Endsley, the current 

analysis was not “technology-free.” In fact, the FAIT 

analysis is necessarily technology-inclusive. Consequently, 

some information requirements are very specific to the 

CDTI. For example, Endsley et al. (1998) list two general information requirements for the commercial airline 

pilot: “time available to perform tasks” and “projected 

time until maneuver required.” An analogous, yet qualitatively different, information requirement identified in the 

present analysis is “time until the next alert zone contact 

watch/warning.” The latter piece of information would 

allow the pilot to assess how much time can be spent 

on tasks other than traffic avoidance and the amount of 

time before a traffic avoidance might become an issue 

(i.e., the time until there is a watch or warning). In other 

words, displaying “time until the next alert zone contact 

watch/warning” would allow the pilot to determine the 

amount of time the CDTI can be ignored.

The second, related reason the current analysis yields 

unique results is that it assumes some level of automation. For example, Endsely et al. (1998) listed “projected 

separation between aircraft” as an information requirement, whereas the FAIT analysis identified “alert zone 

contact watch/warning” as an information requirement. 

In other words, Endsley et al. examined the current flight 

environment, in which the pilot is responsible for taking 

raw data (i.e., separation between aircraft) and formulating predictions regarding future separation. In contrast, 

the current analysis places the responsibility of prediction 

upon the CDTI. Therefore, in a free-flight environment 

with a CDTI, the pilot merely needs to know if there is 

a watch (i.e., a moderate possibility of a future alert zone 

conflict) or a warning (i.e., high probability of a future 

alert zone contact). Rather than making predictions, the 

pilot needs to respond appropriately when the CDTI 

makes predictions of impending contact.

The third, and related, reason the current analysis yields 

unique results is that it examines a free-flight environment. 

Unlike the previous studies, the current analysis places 

more traffic avoidance responsibility on the pilot (and 

the CDTI) than on an air traffic manager. For example, 

Endsley and Rodgers (1994) dedicate several information 

requirements to the ATM acquiring knowledge regarding 

the impact of proposed changes (e.g., What would happen 

if Aircraft X increases altitude by 1,000 feet?). However, 

the current research categorizes somewhat analogous 

information as being requirements for the pilot (i.e., 

“hypothetical results of planned changes”). 

The fourth reason the current analysis yields unique 

results is that unlike most traditional task analyses, the 

FAIT analysis encourages the analyst to simultaneously 

consider specific components of the systems (e.g., characteristics of the CDTI) and the manner in which these 

components interact (e.g., characteristics of the CDTI

that might affect the pilot, characteristics of the pilot

that might affect the CDTI, characteristics of the world

that might affect the pilot, etc.). In addition, identifying the information flow in the system encourages the 

consideration of the real-time operation of the system. 

The creation of possible scenarios further enhances such 

consideration, in that the scenarios require deliberation 

regarding the effects of particular circumstances on the 

system when it is operating. 

4.7 Concluding Remarks Regarding the Analysis

The current research used the FAIT analysis to (1) 

estimate the relative importance of characteristics in a 

system, (2) identify tradeoffs in a system, and (3) identify 

information requirements. However, the FAIT User’s 

Manual (Riley, 1993) actually presents five different 

functional applications. The two options not applied 

here are related to the development of issues documents 

and requirements documents. 

Several factors influenced the decision to exclude 

the issues and requirements documents in favor of the 

other options. The primary reason is that the issues and 

requirements are determined from the interactions in the 

matrix. Specifically, creation of an issues or a requirement 
14 

document requires the creation of a scenario for every 

entry found in the matrix. The number of interactions 

identified in the current research (767 pairings) made 

these options impractical. Another option, in the current analysis, would be to examine only the issues and 

requirements associated with the important characteristics 

identified in Step 4. Many scenarios were written during 

the process of identifying tradeoffs in the system (Step 

5) and identifying information requirements (Step 6). 

These scenarios are adequate in identifying the important 

issues and requirements.

The current research utilizes another option that Riley 

(1993) implies. Specifically, Table 3 is a reproduction 

of a figure presented in the FAIT User’s Manual that 

allows for an identification of the types of issues that 

arise in the current system and the relative occurrence 

of each type.

Given the classification in Table 3, totals were calculated for each part of the matrix and are presented in 

Table 4. Table 4 suggests that the operator (the pilot) 

has the most influential characteristics in the system. 

Although not quite as great, the characteristics of the 

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15

environment have substantial influence in the system, 

and the machine’s (CDTI’s) characteristics has the least 

amount of influence in the system. The operator, by 

far, has the most sensitive characteristics in the system. 

The characteristics of the environment are substantially 

less sensitive than the operator’s characteristics, and the 

machine’s characteristics are the least sensitive of the 

three. Therefore, the characteristics of the pilot have 

the most influence on the system, and at the same time, 

the pilot’s characteristics are more vulnerable than any 

other component of the system.

The information from Table 3 and Table 4 are combined in Table 5, which identifies the types of issues that 

arise in the current system and the relative occurrence 

of each type. Few operator-driven system designs were 

identified. However, this finding may not be surprising, 

given that the NASA-AMES CDTI has only moderate 

“autonomy” and “intelligence” (i.e., it is a “personalized, 

simple aid”). Therefore, one would not expect operator 

characteristics to have a great amount of influence on 

the characteristics of the CDTI, nor would one expect 

the characteristics of the machine to be very vulnerable 

to the characteristics of the operator. Few automation 

issues were identified, and again this finding is probably 

because the NASA-AMES CDTI is only moderately 

“autonomous” and “intelligent.” The results shown in 

Table 5 suggest that training issues are quite important 

in the system. As already stated, it is clear that operator 

characteristics are the most vulnerable characteristics in 

the system. While operator characteristics interact with 

characteristics from all three components of the system 

(i.e., the environment, the operator, and the machine), 

the majority of interactions are environment/operator 

and operator/operator interactions. Referring back to 

Table 4, note that over half of the entries fall in the cells 

where “Environment” is the driver and “Operator” is the 

receiver (23%), and where “Operator” is the driver and 

“Operator” is the receiver (34%). This finding suggests 

that training would be most cost-effective if the majority of training for free flight were spent concentrating 

on characteristics of the environment and the operator. 

While free flight introduces a new piece of technology (i.e., 

the CDTI), this analysis suggests that it is not necessarily the equipment that is cause for concern. Instead, the 

human factors issues (especially as they relate to traffic 

awareness) lie in the novel procedures, types of human 

interactions, and environment. This suggestion is further 

supported by the findings presented earlier. Almost every 

important characteristic that was identified in Step 4 is a 

characteristic of the environment or the operator; these 

findings will now be reviewed in terms of their practical 

implications.

5.0 DISCUSSION AND IMPLICATIONS 

OF THE FINDINGS

5.1 Summary of Findings

The system of interest is one that includes a pilot, a 

free-flight traffic environment, and a CDTI. The FAIT 

analysis yielded seven characteristics that are highly influential in the system: weather, general piloting skills, time of 

day, terrain, ownship state (altitude, attitude, speed, etc.), 

level of pilot mental workload, and perceived time pressure. 

Because these characteristics are highly influential, they 

have a great impact on the functioning of the system as 

a whole. Specifically, when any of these seven characteristics are in an undesirable state, they have the ability to 

negatively affect many other characteristics in the system. 

Therefore, in the process of designing technology (e.g., a 

CDTI) and training programs for a free-flight environment, these seven characteristics should be emphasized. 

Specifically, efforts should be aimed at increasing the 

chances that these characteristics will achieve and remain 

at a desirable state. Of course, some of these characteristics (i.e., weather, time of day, and terrain) cannot be 

controlled. In such cases, the design of technology and 

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16 

training programs should assist in ameliorating the undesirable effects of these characteristics.

Seven characteristics were identified as being highly 

sensitive in the free-flight traffic environment: type of action 

chosen by the pilot, level of pilot mental workload, appropriateness of planned action, ownship state, level of air traffic 

managers’ mental workload, accuracy of current machine 

model, and level of confidence in planned action. These 

characteristics are more vulnerable than other characteristics of the system. Therefore, in the process of designing 

technology (e.g., a CDTI) and training programs for a 

free-flight environment, these seven characteristics also 

should be emphasized to decrease their vulnerability when 

related characteristics are in undesirable states. 

A separate analysis on traffic awareness showed that it 

was not a very influential characteristic. However, it was 

one of the most sensitive characteristics in the system, and 

therefore, technology and training should be designed to 

aid in decreasing the vulnerability of traffic awareness.

In general, special attention should be given to cases 

in which the highly influential characteristics interact 

with the highly sensitive characteristics. The present 

analysis suggested that, in a free-flight environment with 

a CDTI, there are 51 cases where highly influential and 

highly sensitive characteristics interact. Furthermore, two 

characteristics of the system proved to be both highly 

influential and highly sensitive (ownship state and level 

of pilot mental workload). Therefore, ensuring that these 

two characteristics remain in a desirable state would be 

of utmost importance when considering the surveillance 

activities associated with self-separation.

Eleven potential tradeoffs were identified for the 

system. Only two of these cases resulted in a pair of 

characteristics being inversely related. However, pairs of 

characteristics that are directly related also are important 

because each of these pairs represents a case in which the 

first characteristic influences the second and vice versa 

(be it inversely or directly). Therefore, the interaction of 

all 11 pairs of characteristics should be considered in the 

design of tools or procedures to be used in the free-flight 

environment.

One of the goals of the present research was to identify 

and classify information requirements for pilot surveillance functions in the air carrier. One hundred information

requirements were identified, and 67 of these information 

requirements were for pilots in the system. The remaining 33 information requirements were for ATMs. These 

information requirements might be helpful in the process of designing new technologies for pilots and ATMs. 

Specifically, these 100 information requirements may be 

utilized in the early phases of the design process. The list 

could be used to ensure that the necessary information 

is available to the appropriate party. Without such information, the pilot (or ATM) will be unable to obtain 

the knowledge necessary for traffic awareness. 

Finally, when compared with “operator-driven system 

design issues” and “automation issues,” the current analysis 

indicates that training may be the most important issue 

to address in the free-flight traffic environment. 

5.2 The Utility of the FAIT Analysis

The second goal of the current analysis was to examine the utility of the FAIT analysis. Like traditional task 

analyses, the FAIT analysis allows researchers to identify 

information requirements. However, the FAIT analysis 

has value beyond that of traditional task analyses because 

it yields additional information. Specifically, the FAIT 

analysis allows the analyst to examine the relative influence 

and the relative sensitivity of important characteristics 

within the system, as well as identify potential system 

tradeoffs. The FAIT analysis also encourages the researcher 

to emphasize both the various system components (i.e., 

the environment, the human, and the machine) and the 

system as a whole. Third, the FAIT analysis also recognizes 

the importance of considering real-time system operation 

(via scenarios), and it allows the researcher to include 

even the psychological aspects of the human in the system (e.g., mental workload). Finally, the FAIT analysis 

is a useful tool for examining the effects of a particular 

technology in that it yields results that are specific to a 

task environment.

The current research included several novel procedures 

that proved quite useful in adding structure to the FAIT 

analysis. First, a formal definition of the term “characteristic” was composed, and second, a formal and unique 

question was used to identify characteristics for each node 

(cf., Appendix B). Third, a more structured analysis was 

used to extract information requirements from scenarios. 

Specifically, rather than asking one question about the 

scenario, a series of steps was performed. Fourth, a method 

by which the analyst can limit the analysis was introduced. 

Specifically, the 90th percentile was used in several steps 

to identify the important characteristics from a large 

pool of characteristics. Riley (personal communication, 

May, 1999) arbitrarily recommends limiting the overall 

number of characteristics to 50. However, because the 

FAIT analysis is meant for complex systems, it is quite 

likely that the number of characteristics often will exceed 

50. In future research, analysts may opt to utilize the 90th

percentile as a cutoff when the system is too complex to 

limit the characteristics to 50. A fifth and related point 

pertains to the identification of information requirements. 

The current research utilized only the top 10% of characteristics in the analysis, and while the scenarios were 
17

surprisingly helpful, most information requirements were 

identified after addressing the top 20 scenarios. Therefore, 

in the analysis of complex systems, utilizing only the top 

5% of characteristics in the identification of information 

requirements may be adequate.

6.0 FUTURE RESEARCH

Future research is needed in three areas. First, the 

current analysis requires validation from domain experts. 

Input from domain experts would be invaluable. Their 

input could be obtained readily using the steps of the current analysis, that have been documented here. Domain 

experts would be invaluable in the validation process, as 

they could offer information that might further enhance 

the current application of the FAIT analysis. Specifically, 

domain experts might be queried regarding the importance of characteristics. They might also be asked about 

the frequency with which characteristics influence one 

another. Such information could be easily obtained 

through some sort of ranking procedure, or by using 

a Likert scale. Thereafter, the row and column totals 

found in the matrix could be weighted according to their 

importance and frequency of influence. In its current 

form, the FAIT analysis does not take the importance or 

frequency of occurrence into account. Second, the FAIT 

analysis should be applied to other tasks associated with 

surveillance functions (e.g., weather avoidance). Such an 

analysis would ensure that all information requirements 

have been identified and would provide further insight 

into the most important characteristics of the free-flight 

system as a whole. Finally, the FAIT analysis should be 

extended to include other piloting tasks. With applications 

of the FAIT analysis to multiple tasks, a fair comparison 

can be made between the FAIT analysis and traditional 

task analyses (e.g., Endsley et al., 1998).

7.0 REFERENCES

Cashion, P., Mackintosh, M., McGann, A., & Lozito, S. 

(1997). A study of commercial flight crew separation. In Proceedings of the 16th AIAA/IEEE Digital 

Avionics Systems Conference (pp. 6.3-19 – 6.3-25). 

New Jersey: Institute of Electrical and Electronic 

Engineers.

Endsley, M.R., Farley, T.C., Jones, W.M., Midkiff, A.H., 

& Hansman, R.J. (1998). Situation awareness information requirements analysis for commercial airline 

pilots. (NAG 2-716). National Aeronautics and 

Space Administration, Ames Research Center.

Endsley, M.R., & Rodgers, M.D. (1994). Situation 

awareness information requirements analysis for en 

route air traffic control. (DOT/FAA/AM-94/27). 

Washington, DC: Federal Aviation Administration, Office of Aerospace Medicine2

.

Horvath, S. (1998). ADS-B CDTI User Interface Specification (On-line). Available: www.iimorrow.com.

Johnson, W., Battiste, V. Deizell, S., Holland, S., Belcher, 

S., & Jordan, K. (1997). Development and Demonstration of a Prototype Free Flight Cockpit Display 

of Traffic Information. (SAE Technical Paper No. 

975554). Warrendale, PA: Society of Automotive 

Engineers.

Mackintosh, M., Dunbar, M., Lozito, S., Cashion, P., 

McGann, A., Dulchinos, V., Ruigrok, R., Hoekstra, 

J., Van Gent, R., & Valenti Clari, M.(1998). Selfseparation from the air and ground perspective. Air 

Traffic Management R&D Seminar. Orlando, FL.

Riley, V. (1989). A general model of mixed initiative 

human-machine systems. In Proceedings of the 

Human Factors Society 33rd Annual Meeting (pp. 

124-8). Santa Monica, CA: The Human Factors 

and Ergonomics Society.

Riley, V. (1992). Human Factors Issues of Data Link: 

Application of a Systems Analysis. (SAE Technical 

Paper No. 922021). Warrendale, PA: Society of 

Automotive Engineers.

Riley, V. (1993). Function Allocation Issues and Tradeoffs: 

User’s Manual. (198002014-1981). Columbus, 

Ohio: Honeywell Technology Center.

Riley, V. (1996). Analyzing the dynamics of a next generation air transportation management system: Possible 

test scenarios for investigating human error in free 

flight. (NAS2-14288). Columbus, Ohio: Honeywell Technology Center

Riley, V., Lyall, E., & Wiener, E. (1993). Analytic Methods 

for Flight-Deck Automation Design and Evaluation, 

Phase Two Report: Pilot Use of Automation. (DTFA01-

91-C-00039). Washington, DC: Federal Aviation 

Administration.

RTCA (1995). Report on the RTCA board of directors’ select 

committee on free flight. Washington, DC:RTCA, 

Incorporated.

2

All Office of Aerospace Medicine technical reports are available in full-text from the Civil Aerospace Medical Institute’s publication Web site: 

http://www.cami.jccbi.gov/aam-400A/Abstracts/Tech_Rep.htm.
18

Uhlarik, J., & Comerford, D. (2002). A Review of 

Situation Awareness Literature Relevant to Pilot 

Surveillance Functions. (DOT/FAA/AM-02/3). 

Washington, DC: Federal Aviation Administration, Office of Aviation Medicine2

.

Wickens, C.D. (1995). Situation awareness: Impact of 

automation and display technology. In Proceedings 

of the AGARD AMP Symposium on “Situation Awareness: Limitations and Enhancement in the Aviation 

Environment,” CP575. Brussels, Belgium: NATO 

AGARD.

Williams, K.W., & Joseph, K.M. (1998). Developing data 

link user-interface designs using pilot conceptual 

networks. In Proceedings of the Human Factors and 

Ergonomics Society 42nd Annual Meeting (pp. 77-

81). Santa Monica, CA: The Human Factors and 

Ergonomics Society.

2

All Office of Aerospace Medicine technical reports are available in full-text from the Civil Aerospace Medical Institute’s publication Web site: 

http://www.cami.jccbi.gov/aam-400A/Abstracts/Tech_Rep.htm.
A1

8.0 APPENDIX A

FUNCTIONAL DESCRIPTION OF NASA-AMES CDTI

Figure A1. The version of NASA’s CDTI that was used in the current analysis.

Figure A1 presents a snapshot of the CDTI simulation that was used for the current analysis. This figure 

represents the candidate CDTI in its most basic form. 

Regardless of operator inputs, there are several aspects of 

the display that remain constant in this simulation:

1) The pilot’s ownship is at the bottom, center of the 

screen. (Here, it is represented by a filled, white 

chevron.) 

2) The upper portion of the display presents a compass 

rose.

3) The upper, center of the display presents the pilot’s 

current magnetic heading (e.g., “336”). 

4) A dotted white line extends from ownship and 

aligns with the current magnetic heading on the 

compass. 

5) A shaded solid line (which is colored magenta in 

the actual display) also extends from ownship and 

represents the current route of ownship.

6) Shaded, star-like shapes (colored magenta in the actual display) represent way points and have shaded 

labels attached to them (e.g., “OAL –099”).

(a) The filled, star-like shape represents the active 

waypoint.

 (b) Unfilled, star-like shapes represent waypoints 

to be reached in the future.
A2 

7) The upper right-hand corner of the display presents 

the:

 (a) active waypoint (e.g., “OAL –099”), 

 (b) time to the active waypoint (Note: here it reads 

“0000.0z,” because the simulation requires adjunct 

software to obtain this information. Normally, the 

time to reach OAL –099 would be inserted in this 

field), and

 (c) nautical miles to the active waypoint (Again, 

here it reads “0.0 NM,” because additional software 

is needed to obtain this value).

8) The upper left-hand corner of the screen displays 

the pilot’s: 

 (a) current ground speed (e.g., “GS 491”), 

 (b) true air speed (e.g., “TAS 491”), and 

 (c) altitude (or flight level) in hundreds of ft. (e.g., 

“FL400” represents an altitude of 40,000 ft.). 

9) The display represents 160 miles of airspace, and 80 

miles of airspace is denoted by the center tick mark 

labeled “80.”

10) The very bottom of the display contains a toolbar 

that allows the pilot to manipulate the CDTI. 

Outside of any manipulations the pilot can perform, 

there are certain aspects of the display that change 

with circumstances:

1) Under normal circumstances, aircraft are coded according to their altitude.

 (a) Ownship is coded white.

 (b) Aircraft at ownship’s altitude are coded white.

 (c) Aircraft below ownship are coded brown.

 (d) Aircraft above ownship are coded blue.

2) Aircraft within a +/- 6,000 ft range of ownship automatically have 

 (a) their altitudes displayed as a tail tag.

 (b) a one-minute predictor line extending from 

the aircraft symbol. (This is the case for ownship as 

well)

3) An arrow (áor â) appears:

 (a) on the upper left-hand corner of the display 

(i.e., aside the altitude reading) when ownship is 

ascending or descending.

 (b) next to any other aircraft that is ascending or 

descending 

4) When a potential for alert zone contact exists, the 

display changes in several ways. These changes are 

listed below and can also be seen in Figure A2.

 (a) an alert message appears on the bottom, lefthand corner of the screen. This message presents the 

estimated time to closest approach (e.g., “ALERT 

CTA: 2:17”).

 (b) the datatag associated with the intruder aircraft 

is automatically displayed.

 (c) both the ownship and the intruder aircraft are 

coded differently than normal.

 (1) The chevron representing the aircraft is 

coded (in yellow on the actual display).

 (2) On the actual display, a (yellow) line extends 

from the aircraft symbol with a 2.5 nm circle at the 

end of it. (Overlap of two 2.5 nm circles results in the 

aircraft’s 5 nm alert zone being contacted.) However, 

the portion of the one-minute predictor line is coded 

white. 

NASA’s CDTI provides the pilot with many options. 

In this version of the simulation, all changes to the display 

are performed with a mouse. All options available on the 

current simulation are listed below. However, they are in 

an order that is not consistent with the toolbar. Instead, 

they are in an order that is conducive to discussion.

1) Altitude may be displayed according to preference. 

The pilot’s current selection (e.g., Abs) is indicated 

on the toolbar via the fourth button from the left. 

The pilot is able to toggle between the following two 

options by simply pressing the button.

 (a) Absolute altitude (e.g., “420” represents 42, 000 

ft.) is displayed when the button reads “Abs.”

 (b) Relative altitude (e.g., “+20” represents 2, 000 

ft. above ownship) is displayed when the button reads 

“Rel.”

2) An aircraft’s complete datatag (containing an aircraft’s 

identification number, flight level, and speed) is displayed when:

 (a) an aircraft is selected. The aircraft symbol turns 

green upon selection. When the mouse is moved away 

from the symbol, the aircraft returns to its previous 

color and the datatag disappears.

 (b) the IDs button is selected. In this case, all aircraft 

datatags are displayed until the button is selected 

again.

3) Datatags may be repositioned by:

 (a) selecting a particular datatag and moving it with 

the mouse.

 (b) selecting the S Tags button. In this case, datatags 

will be moved to reduce overlap and clutter. (The 

S Tags button will not affect any datatags that have 

been moved manually by the pilot.)

4) Predictor lines can be displayed for all aircraft by 

selecting the Pred button.
A3

5) When predictor lines are displayed, two further options are available.

 (a) Selecting the third button from the right changes 

the time represented by the predictor lines. The time 

represented can range from 2 to 20 minutes (in 2-

minute intervals).

 (b) The format of the predictor lines may be varied. 

The current selection is indicated on the toolbar via 

the first button on the left.

 (1) The predictor lines are displayed in standard 

format when the button reads “Static.” When in this 

mode, selecting this button causes the lines to be 

displayed in pulsed format.

 (2) The predictor lines are displayed in a pulsed 

format when the button reads “Pulsed.” When in this 

Figure A2. An example of the symbols used to display an alert zone contact on NASA’s 

CDTI.

mode, the aircraft appears to “shoot” these pulses 

down the predictor line. This option is helpful if 

two aircraft have predictor lines that overlap. When 

in this mode, selection of the button causes the lines 

to be displayed in the static format.

6) The Rtes and Cpt buttons do not have functions in 

the current simulation.

7) The RAT button offers several options, but these 

options are more complex than the ones previously 

described. Therefore, this button is addressed below 

in paragraph form. The Undo and Enter buttons also 

are discussed in the context of the RAT button.
A4 

8.1 The Route Assessment Tool (RAT)

The RAT tool essentially serves four functions. First, 

it assists the pilot in assessing the effects of changes in 

altitude (i.e., it allows the pilot ask “what if?”). Second, 

after assessing these changes, it permits the pilot to accept 

or reject changes in altitude. The third function of the 

RAT tool allows the pilot to assess the effects of changes 

in course (again, it allows the pilot to ask “what if?). 

Finally, the fourth function permits the pilot to accept 

or reject the proposed changes in course. The following 

paragraphs describe the manner in which these four 

functions are used.

Regardless of the pilot’s intentions (i.e., changing altitude or changing course), the RAT tool is always activated 

by simply selecting the RAT button. After selecting the 

button, a minimized window appears on the bottom, 

right-hand corner of the screen (see Figure A3).

The arrow on the right of this window (s) is always 

available when the RAT is activated. Whenever this arrow is selected, it returns the window to this minimized 

state and moves the window to an even lower part of the 

screen. Therefore, the purpose of this arrow is to declutter 

the screen when necessary. 

To use the RAT, the pilot must select the arrow pointing right (>). When this arrow is selected, the menu 

appears as in the bottom, right-hand corner of Figure 

A4. Initially, the “null point” is selected. The null point 

represents a point in time/space at and before which the 

pilot is not allowed to make changes. When the RAT 

is activated, this point always appears on the route line 

as a green dot, and the route line changes to the color 

green after this point in time/space. In other words, the 

color-coding suggests that the pilot may make changes 

at any point beyond the green dot. If a waypoint appears below this null point, it is represented in pink to 

suggest that changes cannot be made at that waypoint. 

If a waypoint appears above the null point, it is outlined 

in green, and its label is displayed in green. Because the 

field (box) adjacent to the label “Name” is empty and 

there is an orange circle around the null point, the pilot 

would know the null point is currently selected. Therefore, 

the window in Figure A4 represents a case in which the 

aircraft’s current heading is 328 degrees and the heading 

at the null point will change to 336 degrees. 

Figure A3. The minimized window that appears 

immediately after the RAT button is selected.

To make an altitude change (at a waypoint beyond 

the null point), the pilot again would select the arrow 

pointing right (>). This arrow allows the pilot to select 

any of the waypoints that are currently visible on the 

display but are beyond the null point. For example, if the 

CDTI appeared as in Figure A4, selecting the right arrow 

once would result in the window appearing as it does in 

Figure A5. If the pilot selected waypoint OAL –099 (as 

in Figure A5), a corresponding, orange circle would appear around the star representing waypoint OAL –099. 

The window in Figure A5 would inform the pilot that 

the current plan of action is to head into OAL –099 at 

336 degrees and change heading to 335 degrees at that 

waypoint. In addition, the pilot would know that the 

planned altitude at that waypoint is 40,000 feet.

The pilot has the option of selecting any point along 

the route line, even if it is not pre-defined as a waypoint. 

To do so, the pilot places the mouse (which is the instrument for interface in this simulation) over the route line. 

A large arrow appears, to acknowledge that a point may 

be selected. Thereafter, the pilot clicks the display at the 

desired point. Consequently, the new waypoint appears 

on the display and is labeled. This new waypoint becomes 

an option when toggling through the name field with the 

right (>) and left (<) arrows.

To change altitude at any pre-defined or customized 

waypoint, the pilot first must select the waypoint (via the 

menu). Once the waypoint is selected, the pilot must click 

the field labeled “Alt.” As shown in Figure A6, the fields 

corresponding to altitude are highlighted in the original 

window, and a scroll bar appears. The pilot is allowed 

to assess changes in altitude by manipulating either of 

the two windows. Both the arrows (sand r) on the 

original window to the right and the white arrows on the 

scroll bar window (<< and >>) to the left allow the pilot 

to assess changes in altitude in intervals of 1,000 feet. 

The remaining white arrows on the scroll bar window 

(< and >) to the left allow the pilot to assess changes in 

intervals of 100 feet. The pilot also is able to select the 

pointer and slide it along the scroll bar to obtain any 

desired value.

When a new altitude is proposed, the CDTI changes 

appearance in several ways. Figure A7 provides an example 

of these changes. In this situation, the aircraft currently 

is at 40,000 feet, but an altitude of 43,000 feet is being 

considered. The scroll bar represents these changes by 

presenting the proposed altitude in both absolute and 

relative terms (e.g., 43,000 (+3,000) ). Similarly, the “Alt 

s/c” field represents the altitude at which the aircraft will 

start its climb (i.e., 40,000 feet), and the “Alt End” field 
A5

Figure A5. An example of the window when a 

waypoint is selected.

Figure A4. NASA’s CDTI with the RAT tool and its respective window activated point” is 

selected.

Figure A6. An example of the windows that are manipulated to assess changes in altitude.
A6 

indicates the altitude at which the aircraft will end (i.e., 

fly level at 43,000 feet).

The upper portion of the display also changes accordingly. A green diamond appears on the route line, and 

it is labeled “S/C.” This diamond represents the point 

at which the aircraft must begin its ascent. The point at 

which the desired altitude will be reached (in this case 

OAL -099) is labeled with the desired altitude (i.e., FL 

430). The portion of the route over which the aircraft 

will climb is colored in pink. 

The “V-Spd” field presents the optimal vertical speed 

for the proposed climb (e.g., 1150 ft/min). Like proposed 

altitudes, variations in vertical speed can be examined. 

When V-Spd is selected, the arrows on the menu can be 

used to toggle through potential vertical speeds at onehundred-foot intervals or the scroll bar window may be 

used to examine 100-foot intervals, 10-foot intervals, or 

any value so desired (via the scroll arrow). However, one 

Figure A7. The CDTI as it appears when a new value for altitude is proposed.

current drawback of the simulation is that it does not put 

any limits on vertical speed (S. Holland, personal communication, May, 1999). That is, the CDTI simulation 

has not been programmed to take the limitations of an 

aircraft into account. 

Using the RAT to assess altitude changes is helpful 

in couple of ways. First, it allows the pilot to visualize the 

space over which the proposed change will take place. For 

example, the diamond in Figure A7 (which is colored 

green in the actual display) would move farther down the 

screen (i.e., closer to ownship) if the pilot was interested 

in reaching 45,000 feet (vs. 43,000) at OAL –099. On the 

other hand, the diamond would move farther up the screen 

(i.e., closer to OAL –099) if the pilot was interested in 

reaching 41,000 feet at OAL –099. Furthermore, decreasing the vertical speed from 1150 to 1250 ft/min would 

cause the diamond to move farther down the route line 

(i.e., closer to ownship), whereas increasing vertical speed 
A7

from 1150 to 1350 ft/min would cause the diamond to 

move farther up the route line (i.e., closer to OAL –099). 

A second, and probably more important, benefit gained 

from the RAT is in terms of conflict alerting. If there is 

a possibility that the proposed change will result in an 

alert zone contact, ownship and the appropriate intruder 

aircraft are coded yellow in the actual display.

If an altitude and/or vertical speed change is assessed 

and the pilot finds it unacceptable, three courses of action 

are possible. The pilot can select the Undo button, and 

the values are set to their original states. On the other 

hand, the pilot can simply edit the proposed values. For 

example, the pilot may want to evaluate the effects of a 

descent after finding the effects of ascent unacceptable. 

(Proposing a descent is much the same as proposing an 

ascent. The only difference is that the label “S/C” is 

replaced with “T/D” to signify top of descent.) Finally, 

the pilot can select the RAT button. In which case, the 

values are set to their original state, and the RAT tool is 

deactivated. 

If the pilot assesses the effects of an altitude change 

and would like those changes to occur, the Enter button 

is selected. When this button is selected, a small menu 

appears. Thereafter, the pilot has another chance to negate 

the changes by selecting “Reject.” If the pilot would like 

the proposed changes to occur, the “Accept” option is 

selected. Once the proposed changes are accepted by the 

pilot, the display is altered. An example of the alteration 

can be seen in Figure A8. As can be seen in Figure A8, 

a dotted line (which is multicolored in pink and blue 

in the actual display) is used to represent the area over 

which the climb will occur. Blue color-coding is used to 

represent the area over which the aircraft will fly at the 

newly accepted flight level. (In cases of descent, blue is 

replaced by brown color-coding.)

Figure A8. The CDTI as it appears when a new value for altitude is accepted.
A8 

When assessing and accepting changes in heading, the 

CDTI functions in much the same way as it does with 

altitude changes. The RAT tool first must be activated. 

Thereafter, a pre-existing waypoint or a customized waypoint must be selected with the mouse. For example, the 

pilot might select waypoint OAL –198, and the data 

associated with that point might appear (Figure A9). 

To make changes in heading, the selected waypoint is 

“dragged” to another location on the screen. Figure A10 

presents a case where a selected waypoint, OAL –0198, 

has been moved to create a new waypoint, OAL –214. 

Notice the original waypoint, OAL 198, remains intact. 

However, a new waypoint appears on the display. The 

data for this new waypoint appear in the window, and it 

reflects the proposed route. In this example, the aircraft 

will approach waypoint OAL –214 at a heading of 25o 

Figure A9. Data for a sample waypoint to be used 

in making changes to planned heading.

Figure A10. The CDTI as it appears when changes to planned heading are assessed.

and change to 311o when OAL –214 is reached. If the 

original route is used (Figure A9), the aircraft will head 

to waypoint OAL 198 at 335o and change to 334o when 

that waypoint is reached. 

Therefore, the pilot is able to assess both the proposed 

route and its data before accepting the change. As with 

assessing altitude changes, the pilot also is allowed to assess 
A9

whether a separation violation will occur as a result of 

the proposed changes. To accept or reject the proposed 

heading changes, the pilot follows the same procedures 

as those described above. If the pilot were to accept the 

changes proposed in Figure A10, the CDTI would appear as in Figure A11.

There are two important aspects of the RAT tool that 

require further comments. First, as a precautionary measure, the RAT tool automatically deactivates if a threat 

is detected in real-time. Second, one apparent drawback 

of the RAT is that it does not allow the pilot to assess 

changes in ground speed or true airspeed.

Figure A11. The CDTI as it appears when changes to heading have been accepted.

Although the important features of the simulation 

were summarized above, the preceding summary is not 

complete. In fact, pilots complete five to six hours of 

training before participating in research at NASA (S. 

Holland, personal communication, May, 1999). The 

interested reader should consult supplemental sources 

for a complete understanding of the work at NASA (e.g., 

Cashion et al., 1997; Johnson et al., 1997; Mackintosh 

et al., 1998).

B1

9.0 APPENDIX B

Appendix B contains Riley’s definition (1993) of all nodes in the general model of information flow. If a node 

appears in gray, it was not included in the current model and a justification is provided for its exclusion. Each 

node included in the current model is accompanied by the question used to identify characteristics as well as a list 

of the identified characteristics.

9.1 The World

1)World: “Because the World node represents everything outside your particular system, parameters of the larger 

system that your system fits into should be included in this node” (Riley, 1993, p. 28).

a) Our question: What affects the traffic situation in a free-flight environment (i.e., outside of the pilot and the 

NASA CDTI)?

 b) Characteristics:

 i) Ownship

 (1) State (i.e., position, speed, heading, vertical speed, altitude, attitude)

 (2)Destination (immediate)

 (3)Limitations/capabilities of 

 (a) State of other systems (i.e., systems other than the one being examined here)

 ii) Other Aircraft

 (1)Amount of (i.e., density of traffic)

 (2) State (i.e., position, speed, heading, vertical speed, altitude, attitude)

 (3)Destination (immediate)

 (4)Limitations/capabilities of

 (a) State of other systems (i.e., systems other than the one being examined here)

 (5)Available Technology (i.e., whether they also are equipped with a CDTI)

 iii) Other Operators (1)Other Pilots

 (a) Levels of SA

 (b) Amount of mental workload

 (2)Air Traffic Managers

 (a) Levels of SA

 (b) Level of mental workload

 iv)Environmental Conditions

 (1)Weather

 (2)Wind

 (3)Turbulence

 (4)Time of day

 (5)Terrain

 (6)Restrictions due to traffic and facilities (e.g., airport closings or overuse)

9.2 THE HUMAN SIDE

9.2.1 Human Input

1)Perceive World: “This node represents the operator’s access to information about the operational environment 

through all methods other than through the system being modeled” (Riley, 1993, p. 34).

a) Our question: What affects the pilot’s ability to perceive characteristics of the traffic situation (i.e., information other than that presented by the CDTI)?

b) Characteristics:

 i) Out-the-window

 (1)Time spent viewing out-the-window information

 (2)Amount of information available to the pilot

 ii) Other instruments

 (1)Time spent reading other instruments

 (2)Readability of displays outside of the system (e.g., glare, color of symbols/text, size of symbols/text)
B2 

 iii) Accuracy of pilot’s previous world model (i.e., previous SA regarding the world)

 iv) Level of pilot mental workload

 v) Physical state of pilot (e.g., vision)

 vi) Amount of noise in cockpit environment (e.g., may affect ability to hear radio exchanges)

 vii) General piloting skills (e.g., experience, ability, training)

 viii) Adequacy of physical feedback from the aircraft (e.g., can pilot perceive changes in aircraft altitude, 

 etc.?)

2)Perceive Machine Behavior: “This node refers to the operators ability to sense what action the system is currently 

performing” (Riley, 1993, p. 34).

NOT INCLUDED- The CDTI does not perform any control actions.

3)Perceive Displays: “This node refers to the operators act of reading the displays” (Riley, 1993, p. 34).

a) Our question: What affects the pilot’s ability to perceive information from the CDTI?

b) Characteristics:

 i) Pilot skill with the CDTI (e.g., experience, ability, training)

 ii) Time spent reading displays in the system

 iii) Time spent reading other instruments

 iv) Readability of displays in the system (i.e., the CDTI and the FMS)

 v) Amount of noise in cockpit environment (e.g., may affect ability to hear auditory warnings from the 

 CDTI)

 vi) Accuracy of current machine model (i.e., current SA regarding the CDTI)

4)Infer World State: “This node refers to the operator’s process of making sense out of the situation and gaining or 

maintaining situation awareness” (Riley, 1993, p. 34).

a) Our question: What affects the processes involved in the pilot’s gaining/maintaining awareness of the traffic 

 situation (outside of perceptual issues)?

b) Characteristics:

 i) Level of pilot mental workload

 ii) General piloting skills (e.g., experience, ability, training)

 iii) Accuracy of pilot’s previous world model (i.e., previous SA regarding the world)

 iv) Number of errors in perceiving the world

 v) Level of confidence in perception of the world

 vi) Accuracy of current machine model (i.e., current SA regarding the CDTI)

5)Infer Machine State: “This node refers to the operator’s process of understanding what the machine is doing” 

(Riley, 1993, p. 34).

a) Our question: What affects the pilot’s process of understanding of what the CDTI is doing?

b) Characteristics:

 i) Level of pilot mental workload

 ii) Pilot skill with the CDTI (e.g., experience, ability, training)

 iii) Number of errors in perceiving the CDTI

 iv) Level of confidence in perception of the CDTI

 v) Level of confidence in the CDTI (i.e., can I trust the machine?)

 vi) Accuracy of pilot’s current world model (i.e., current SA regarding the world)

 vii) Accuracy of pilot’s previous machine model (i.e., previous SA regarding the CDTI)

6)World Model: “This represents the operators level of understanding about the operational environment” (Riley, 

1993, p. 35).

 a) Our question: What affects the pilot’s current mental model of the traffic situation or what affects the opera

 tor’s awareness of the traffic situation? 

b) Characteristics
B3

 i) Accuracy of pilot’s current world model (i.e., current SA regarding the world) (This characteristic is merely a 

 place holder, because most characteristics were identified under “Infer World State.”)

7)Machine Model: “This represents the operator’s level of understanding about the system, such as its current level 

of reliability” (Riley, 1993, p. 35). 

a) Our question: What affects the pilot’s current mental model of the CDTI? 

b) Characteristics

 i) Accuracy of current machine model (i.e., current SA regarding the CDTI) (This characteristic is merely 

 a place holder, because most characteristics were identified under “Infer Machine State.”)

8)Machine’s Goals: “This represents the operators understanding of the machine’s current goals and targets” (Riley, 

1993, p. 35).

a) Our question: What affects the pilot’s ability to understand the current “goals” of the CDTI?

b) Characteristics

 i) Pilot skill with the CDTI (e.g., experience, ability, training)

 ii) Level of confidence in the CDTI (i.e., can I trust the machine?)

9)Predict Machine Behavior: “This node refers to the operator’s anticipation of the next actions to be taken by the 

machine” (Riley, 1993, p. 35).

a) Our question: What affects the pilot’s ability to anticipate the warnings given by the CDTI?

b) Characteristics

 i) Accuracy of pilot’s current world model (i.e., current SA regarding the world)

 ii) Accuracy of current machine model (i.e., current SA regarding the CDTI)

9.2.2 Human Output

1)Plan Own Action: “This refers to the operator’s process of deciding what to do next” (Riley, 1993, p. 35).

a) Our question: What affects the appropriateness of the action chosen by the pilot?

b) Characteristics:

 i) Accuracy of pilot’s current world model (i.e., current SA regarding the world)

 ii) Level of confidence in pilot’s current world model (i.e., in current SA regarding the world)

 iii) Accuracy of current machine model (i.e., current SA regarding the CDTI)

 iv) Level of confidence in current machine model (i.e., in current SA regarding the CDTI)

 v) Accuracy of self model

 vi) Accuracy in predicting the machine’s behavior

 vii) Perceived time pressure

 viii) Current team SA

 ix) Appropriateness of operator’s goals

 x) Level of confidence in the CDTI (i.e., can I trust the machine?)

 xi) Level of confidence in position sensors

2)Operator’s Goals: “This node represents the operator’s actual intentions, and can contain characteristics that 

are the operator’s counterparts of the Operator’s Goals and Machine’s Goals nodes on the Machine side of the 

model” (Riley, 1993, p. 35).

a) Our question: What affects the intentions of the pilot?

b) Characteristics:

 i) Accuracy of pilot’s previous world model (i.e., previous SA regarding the world)

 ii) Accuracy of pre-flight planning

3)Self Model: “This refers to the operator’s assessment of his or her own current abilities or state” (Riley, 1993, p. 

35).

a) Our question: What affects the pilot’s ability to assess his/her abilities to deal with the traffic situation (phys

 ical and/or mental)?

b) Characteristics:

 i) Level of trust in automated systems performing control actions
B4 

 ii) Level of trust in the CDTI

 iii) General piloting skills (e.g., experience, ability, training)

4)Take No Action or Monitor: “This is basically a place holder for one of the possible actions of the operator” (Riley, 1993, p. 36).

a) Our question: What affects the pilot in accurately monitoring the CDTI?

b) Characteristics:

 i) Appropriateness of planned action

5)Provide Information: “This node represents the process by which the operator enters data into the system or provides other types of information” (Riley, 1993, p. 36).

a) Our question: What affects the pilot in accurately providing data to the CDTI or the FMS?

b) Characteristics:

 i) Appropriateness of planned action

 ii) Accuracy of the pilot in providing information to the CDTI or FMS (e.g., via keystroke errors or the 

 pilot possesses inaccurate information)

6)Request Information: “This node represents the process by which the operator enters requests for information, 

such as calling up new display pages” (Riley, 1993, p. 36).

a) Our question: What affects the pilot in requesting information from the CDTI (e.g., a prediction regarding 

the flight path of another aircraft)?

b) Characteristics:

 i) Appropriateness of planned action

 ii) Accuracy of the pilot in providing information to the CDTI (e.g., via keystroke errors or the pilot possesses inaccurate information)

 iii) Level of confidence in planned action (i.e., does the pilot need more information?)

7)Command: “This node represents inputs made by the operators to change the state of the system through a 

command to automation” (Riley, 1993, p. 36).

a) Our question: What affects the pilot in commanding the CDTI to change its state (e.g., changing the dis

 play options like viewing of the datatags)?

b) Characteristics:

 i) Appropriateness of planned action

 ii) Accuracy of the pilot in providing information to the CDTI (e.g., via keystroke errors or the pilot pos

 sesses inaccurate information)

 iii) Level of confidence in planned action (i.e., does the pilot need more information?)

8)Control: “This refers to inputs made by the operators through manual control” (Riley, 1993, p. 36).

a) Our question: What affects the pilot in controlling the aircraft’s position?

b) Characteristics:

 i) Appropriateness of planned action

 ii) Accuracy of physical control

 (1) Adequacy of physical feedback from the aircraft (e.g., can pilot perceive changes in aircraft altitude, 

 etc.?) 

 (2) Adequacy of physical feedback from the control device (e.g., force)

 iii) Level of confidence in planned action (i.e., does the pilot need more information?)
B5

9.3 THE MACHINE SIDE

9.3.1 Machine Input

1)World Sensors: “This node is intended to contain all the potential sources of information coming into the system” (Riley, 1993, p. 29).

a) Our question: What affects the information received by the world sensors, or what affects the quality/accuracy 

of this information?

b) Characteristics:

 i) Resolution and update rate of position sensors

 ii) State of position sensors hardware (i.e., functioning or malfunctioning)

 iii) Weather

2)Control Sensors: “This node is meant to represent all the ways the operators can put information into the system, 

typically through controls” (Riley, 1993, p. 29).

a) Our question: What affects the information received by the control sensors, or what affects the quality/ 

 accuracy of this information?

b) Characteristics:

 i) Type of action chosen by pilot

 ii) Resolution and update rate of control sensors

 iii) State of control sensors hardware (i.e., functioning or malfunctioning) for all forms of input (control 

 devices and keypad entries)

3)Operator Sensors: “This node is relevant for systems that include sensors to detect the operator’s cognitive or 

physiological state” (Riley, 1993, p. 29).

NOT INCLUDED- The CDTI does not monitor the state of the pilot.

4)Machine’s Goals: “This node represents the machine’s current targets, operational parameters, or understanding 

of the current mission goals” (Riley, 1993, p. 29).

a) Our question: What affects the “ability” of the CDTI to “understand” its role?

b) Characteristics:

 i) Accuracy of machine’s goals (e.g., did the programmer’s model match the users model?)

5)Infer Operator State: “This node represents functions that can use the information generated by the Operator 

Sensors node and infer what the operator’s current cognitive or physiological state is” (Riley, 1993, p. 30).

NOT INCLUDED- The CDTI does not monitor the state of the pilot.

6)Operator’s Goals: “This node represents the machine’s understanding of the operator’s current goals. It is used in 

models of systems that can infer the operator’s intentions based partly on this understanding” (Riley, 1993, p. 

29).

a) Our question: What affects the “ability” of the CDTI to “understand” the operator’s current and specific 

goals (e.g., what affects the ability of the CDTI to understand current heading, intended destination, etc.)?

b) Characteristics:

 i) Accuracy of the pilot in providing information to the CDTI (e.g., via keystroke errors or the pilot pos

 sesses inaccurate information)

 ii) State (i.e., functioning or malfunctioning) of the system’s hardware/software (i.e., the CDTI and FMS 

 hardware/software and the software that allows them to “communicate)

7)Infer Operator Intent: “This node represents the machine’s process of inferring what the operator’s intentions are. 

This is relevant for the Operator Intent Responsive level of intelligence and up... ” (Riley, 1993, p. 30).

NOT INCLUDED- The CDTI does not infer intent and respond.
B6 

8)Infer World State: “This node represents the machine’s process of understanding what the current state of the operational environment is, based on the information provided to it from the World Sensors” (Riley, 1993, p. 29).

a) Our question: What affects the processes involved in the CDTI gaining/maintaining an “understanding” of 

 the traffic situation?

b) Characteristics:

 i) Accuracy of information the CDTI has regarding operator’s goals

 ii) Accuracy of machine’s goals

 iii) State of position sensors hardware (i.e., functioning or malfunctioning)

 iv) State (i.e., functioning or malfunctioning) of the system’s hardware/software (i.e., the CDTI and FMS 

 hardware/software and the software that allows them to “communicate)

 v) State of position sensors hardware (i.e., functioning or malfunctioning)

9)Operator Models: “This node represents internal representations of the human operators used by the machine to 

draw inferences about the operator’s intention and possible future actions. Again, no such capabilities are currently provided in commercial transport equipment ... This node would be relevant to a system that can merely 

be personalized for a particular operator” (Riley, 1993, p. 29).

a) Our question: What affects the ability of the CDTI to “understand” what the operator wants personalized?

b) Characteristics:

 i) Accuracy of the pilot in providing information to the CDTI (e.g., via keystroke errors or the pilot pos

 sesses inaccurate information)

 ii) State of control sensors hardware (i.e., functioning or malfunctioning) for all forms of input (control 

 devices and keypad entries)

 iii) State (i.e., functioning or malfunctioning) of the system’s hardware/software (i.e., the CDTI and FMS 

 hardware/software and the software that allows them to “communicate)

10) Infer Operator’s Knowledge: “This node is intended to represent the machine’s process of inferring the operator’s knowledge about the situation” (Riley, 1993, p. 31).

NOT INCLUDED- The CDTI does not infer what the pilot does/does not know.

11) World Model: “This node represents the machine’s internal representation of the operational environment” 

 (Riley, 1993, p. 29).

 a) Our question: What affects the CDTI’s current model of the traffic situation? 

b) Characteristics:

 i) Accuracy of the machine’s world model (This characteristic is merely a place holder, because most char

 acteristics were identified under “Infer World State.”)

12) Predict Operator’s Behavior: “This node is relevant for very complex systems that can not only infer the 

operator’s intentions and cognitive and physiological state but can also predict potential errors the operator may 

make, or other actions so it can assist with those actions” (Riley, 1993, p. 31).

NOT INCLUDED- Although the RAT tool assists the operator in assessing what should be done in the future, 

the CDTI does not autonomously predict an operator’s behavior.

9.3.2 Machine Output

1)Determine Operator’s Need for Information: “This node contains different items depending on the overall levels 

of capability of the system...” (Riley, 1993, p. 31).

NOT INCLUDED- The CDTI does not decide (for the operator) what information should be displayed.

2)Plan Own Action: “This node represents the machine’s process of planning its own future actions” (Riley, 1993, 

p. 32).

NOT INCLUDED- The CDTI does not plan its own actions, because it does not make any control actions.

3)Check Permission: “This node refers to the machine’s checking whether or not the operator has granted it permission to take over operator tasks when it determines that the operator needs help” (Riley, 1993, p. 32).

NOT INCLUDED- The CDTI does not make control actions.
B7

4)Request Permission: “This node refers to the machine’s process of requesting permission from the operator to take 

over a task which might benefit from automation but for which the operator has not granted standing permission so the automation can take over” (Riley, 1993, p. 32).

NOT INCLUDED- The CDTI does not make control actions.

5)Request Information: “This node refers to the machine’s process of requesting information from the operator that 

the machine would not otherwise have available” (Riley, 1993, p. 32).

NOT INCLUDED- The CDTI does not request information.

6)Provide Decision: “This represents the decisions made by the machine to the human operator” (Riley, 1993, p. 

32).

a) Our question: What affects the CDTI’s current model of the traffic situation? 

b) Characteristics: What affects the CDTI’s ability to identify other aircraft that are likely to have an alert zone 

 contact with ownship?

 i) Accuracy of machine’s goals (e.g., the adequacy of the alerting logic used in programming)

7)Machine’s Goals: “This node is the same as the previous Machine’s Goals nodes. Its characteristics need not be 

repeated because they have already been entered into the list from the previous one” (Riley, 1993, p. 32).

8)Self Model: “This node represents the machine’s knowledge of its own operational state” (Riley, 1993, p. 33).

NOT INCLUDED- To our knowledge, the CDTI does not have its own built-in test.

9)Prioritize Information: “This node represents the process of prioritizing all the information awaiting display to 

the operator and putting it into a queue for assignment to display devices” (Riley, 1993, p. 33).

NOT INCLUDED- The CDTI presents all information it has available.

10) Construct Displays: “This node refers to the process of taking the information to be sent to the crew and generating the displays required to do so...it merely represents the process of converting the information available 

to the system into display formats for the crew” (Riley, 1993, p. 33).

a) Our question: What affects the “ability” of the CDTI to process the information?

b) Characteristics:

 i) Accuracy of the machine’s “operator’s model”

 ii) Accuracy of the machine’s world model

 iii) CDTI’s processing time

11) Cache: “This represents a memory store where information that cannot be displayed to the crew due to dis

 play limitations waits to be displayed” (Riley, 1993, p. 33).

 NOT INCLUDED- The CDTI presents all information it has available.

12) Displays: “This node refers to the physical display devices” (Riley, 1993, p. 33).

a) Our question: What affects the “ability” of the CDTI to present the information?

b) Characteristics:

 i) Accuracy in constructing the display

 ii) Refresh rate of the CDTI

13) Action: “This node refers to the process of actually performing some function that changes the state of the 

 operational environment” (Riley, 1993, p. 33).

 NOT INCLUDED- The CDTI does not make any control actions.

C1

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D6 

Driver/Receiver

1. Ownship state/Level of air traffic managers’ mental workload: For obvious reasons, the state of ownship affects the 

air traffic manager’s amount of mental workload. For example, if the position of ownship is within 5 nm of 

another aircraft, the free-flight environment requires the air traffic manager to assume responsibilities that are 

otherwise the pilot’s responsibilities. Therefore, the imagined scenario is one in which the air traffic manager’s 

amount of mental workload is high because ownship’s alert zone contact has been intruded.

2. Ownship state/Accuracy of pilot’s current world model: This pairing was already addressed when tradeoffs were examined, and it was identified as having a direct relation. However, what is currently of interest is the influence 

of ownship state on the accuracy of the pilot’s current world model. As stated when addressing tradeoffs, an 

undesirable aircraft attitude might affect the pilot’s ability to obtain information from the world (and therefore 

negatively affect the accuracy of the world model). Therefore, the imagined scenario is one in which the world 

model is inaccurate because ownship state does not allow the pilot to obtain the relevant information from the 

world.

3. Ownship state/Accuracy of current machine model: These two characteristics were already identified as having a tradeoff relation. However, what is currently of interest is only the influence of ownship state on the accuracy of 

the pilot’s current machine model. There are many cases in which ownship state would have a negative effect 

on the current machine model. Specifically, when ownship state is undesirable in any manner that is unrelated 

to traffic (e.g., altitude is too low given the weather situation), the pilot presumably would spend more time attending to other displays, out-the-window-information, and control devices. Therefore, the imagined scenario 

is one in which the machine model is inaccurate because the pilot is attending to sources other than the CDTI.

4. Ownship state/Appropriateness of planned action: For obvious reasons, the state of ownship has an effect on 

whether the planned action is appropriate. For example, if ownship is suddenly in an undesirable state (e.g., 

on a course or at a speed that will lead to an alert zone contact), the action that is planned may be inappropriate (e.g., the planned action may be to take no action). Similarly, if ownship is in a desirable state, the planned 

action may be inappropriate (e.g., changes in heading create an alert zone contact). Therefore, the imagined 

scenario is one in which the state of the ownship causes the planned action to be one that is inappropriate.

5. Ownship state/Level of confidence in planned action: A scenario is easily imagined in which ownship state influences 

the pilot’s level of confidence in the planned action. Specifically, if the position of ownship is such that an 

alert zone conflict is occurring, the pilot is required to make a decision quickly. Therefore, it might be difficult 

for the pilot to have complete confidence in such a decision.

6. Ownship state/Level of pilot mental workload: These two characteristics were already identified as having a tradeoff 

relation. While the inverse relation is discussed below, what is currently of interest is only the influence of 

ownship state on the level of pilot mental workload. As discussed in terms of tradeoffs, an example of this 

pairing would occur if aircraft altitude were unacceptable (i.e., the current altitude will create an alert zone 

contact). Pilot mental workload problem would increase in the attempt to rectify the problem (e.g., attain the 

correct altitude or contact the appropriate pilot).

7. Ownship state/Type of action chosen by the pilot: These characteristics were already identified as having a direct 

relation in the section that addresses tradeoffs. As indicated in that section, the acceptability of ownship state 

affects the action chosen by the pilot, in that as ownship state becomes less desirable the action chosen by the 

pilot may be less desirable (e.g., in extreme cases, the pilot may panic and use poor judgment).

Table D6. Scenarios Representing a Combination of the Most Influential and Most Sensitive Characteristics.
D7

8. Weather/Ownship state: For obvious reasons (e.g., lightning, wind), the weather can affect the state of ownship 

and the pilot’s ability to control ownship.

9. Weather/ Level of air traffic managers’ mental workload: Weather affects the air traffic manager’s amount of workload. Under normal conditions, the air traffic manager is responsible only for monitoring the ability of the 

pilots to maintain self-separation. With the advent of a storm, the air traffic manager must also monitor the 

ability of the pilots to avoid weather formations. Under severe weather conditions, the air traffic manager presumably will receive more verbal reports regarding the presence and severity of the current weather situation.

10. Weather/Accuracy of pilot’s current world model: The weather may affect the pilot’s current world model in cases 

where it limits visibility. Therefore, the pilot may lose visual contact with nearby aircraft. Because the pilot 

may have to concentrate on controlling the aircraft, it indirectly may affect the model by diverting attention 

from all other relevant information in the world (traffic in this case).

11. Weather/Accuracy of current machine model: Similar to the previous effect, weather may affect the machine model 

by diverting attention from the CDTI.

12. Weather/Appropriateness of planned action: For obvious reasons, the surrounding weather situation has an effect 

on whether the planned action is appropriate. For example, under normal circumstances, no action by the 

pilot may be appropriate. However, planning the same lack of action may be inappropriate if the aircraft is 

headed for a weather formation.

13. Weather/Level of confidence in planned action: Poor weather may affect the level of confidence in planned action 

because of its mere existence (i.e., the pilot may wonder whether the aircraft will avoid the weather given the 

current plans). Here, what is more important is the effect of the weather on the traffic situation. Imagine 

ownship is in congested airspace and the weather is poor. Due to low visibility, the pilot may have relatively 

little confidence that the planned action will result in the successful avoidance of all aircraft alert zones.

14. Weather/Level of pilot mental workload: Because of the mere existence of poor weather, the pilot’s mental 

workload increases. Specifically, the pilot’s attention may be focused upon the avoidance of the weather and 

controlling the aircraft within these conditions. Therefore, in poor weather conditions, the pilot’s ability to 

monitor the traffic situation (whether through the world or the CDTI) may be hindered by the limits of 

working memory.

15. Weather/Type of action chosen by the pilot: This relation is similar to the one between Weather and Appropriateness of Planned Action. For obvious reasons, the surrounding weather situation has an effect on whether the 

control action is appropriate. For example, under normal circumstances, no action by the pilot may be appropriate. However, under poor weather conditions, the same lack of action may place the aircraft in a weather 

formation.

16. Time of day/Ownship state: The pilot’s previous actions may have been related to whether the aircraft was flying 

under IFR or VFR rules. Therefore, time of day can have an indirect effect on the acceptability of the current 

state of ownship.

17. Time of day/ Level of air traffic managers’ mental workload: Although it depends on the sector, the volume of traffic varies with the time of day. Therefore, air traffic manager’s workload varies with the time of day.

18. Time of day/Accuracy of pilot’s current world model: Too much sunlight and lack of sunlight can affect the pilot’s 

ability to view out-the-window information. Therefore, the accuracy of the pilot’s current world model can 

vary with the time of day.
D8 

19. Time of day/Accuracy of current machine model: Glare and direct sun light can affect the pilot’s ability to view the 

CDTI. Therefore, the accuracy of the current machine model can vary with the time of day.

20. Time of day/Level of confidence in planned action: Volume of traffic and visibility both vary with time of day. Daytime traffic volumes and nighttime low visibility conditions will both decrease the pilot’s confidence that alert 

zone conflicts will be avoided successfully.

21. Time of day/Level of pilot mental workload-Similar to the previous description, daytime traffic volumes and nighttime low visibility conditions will both increase the pilot’s level of mental workload. As a result, the pilot’s 

attention may not be directed at the most relevant information/

22. Time of day/Type of action chosen by the pilot: The action chosen by the pilot may vary with time of day. Nighttime conditions call for IRF rules, and the pilot is working under conditions of little or no visibility. Daytime 

conditions, on the other hand, may yield high traffic volumes and strong sunlight. Given these hindrances, the 

action chosen by the pilot may sometimes be inappropriate.

23. Terrain/Ownship state: Assuming the pilot was accurate, the flight plans take the terrain into account. Therefore, the terrain presumably affects ownship state. However, if the pilot fails to remember the geography, a 

situation could arise where an alert zone contact is unavoidable (i.e., two planes are trapped by geographical 

boundaries).

24. Terrain/ Level of air traffic managers’ mental workload: Presumably, the effects of terrain would vary between air 

traffic managers (rather than within an air traffic manager). Specifically, a sector with dangerous terrain would 

be more difficult to monitor than a sector with flat terrain. Therefore, an air traffic manager assigned to a sector with dangerous terrain might spend relatively more time assisting aircraft in avoiding terrain. As a result, 

such an air traffic manager might be less effective at monitoring the self-separation of aircraft.

25. Terrain/Accuracy of pilot’s current world model: The accuracy of the pilot’s current world model might be sacrificed if terrain occluded out-the-window information. In addition, given knowledge of perceptual illusions, 

the pilot may misjudge distance to a particular geographical formation (e.g., a mountain). Misjudging distance 

to the terrain may also lead to misjudgments regarding the position of visible aircraft.

26. Terrain/Appropriateness of planned action: For obvious reasons, the surrounding terrain has an effect on whether 

the planned action is appropriate. For example, if the pilot plans an action that deviates from the original 

flight plan, the newly planned action may be appropriate. However, the same change of plans may result in a 

collision with terrain or a situation where the terrain makes an alert zone contact unavoidable (i.e., two aircraft 

are trapped).

27. Terrain/Level of confidence in planned action: Because terrain can occlude out-the-window information, the pilot 

may have relatively little confidence that the planned action will result in the successful avoidance of alert 

zones.

28. Terrain/Level of pilot mental workload: When an aircraft nears potentially dangerous terrain, the pilot is more 

likely to utilize attentional resources on the avoidance of the terrain. Therefore, the pilot is more likely to 

ignore traffic information from both the CDTI and the forward field of view. 

29. Terrain/Type of action chosen by the pilot: The pilot may suddenly change the state of the aircraft if the aircraft 

nears terrain. As a result, the pilot unknowingly may place the aircraft in an alert zone conflict or an alert zone 

conflict watch/warning.
D9

30. Perceived time pressure/Ownship state: A pilot under time pressures is more likely to ignore certain aspects of the 

environment. Therefore, the acceptability of ownship state may suffer (e.g., ownship may be on a collision 

course).

31. Perceived time pressure/Accuracy of pilot’s current world model: If a crisis arises and it is not related to self-separation, the pilot obviously will spend relatively less time attending to the traffic-related information. As a result, 

the out-the-window information regarding traffic or radio communications regarding traffic may be ignored. 

Therefore, the risk of a separation violation increases.

32. Perceived time pressure/Accuracy of current machine model: If a crisis arises and it is not related to self-separation, 

the pilot obviously will spend relatively less time attending to the CDTI. As a result, the pilot’s machine model 

will be less accurate, and the risk of a separation violation will be more likely.

33. Perceived time pressure/Appropriateness of planned action: If the pilot feels pressured by time, the pilot may make 

decisions too quickly. As a result, the planned action is less likely to be appropriate.

34. Perceived time pressure/Level of confidence in planned action: If the pilot feels pressured by time, the pilot may make 

decisions too quickly. As a result, the pilot may feel less than confident in the action that has been planned.

35. Perceived time pressure/Level of pilot mental workload: When a pilot feels the pressures of time, working memory is 

taxed. As a result, mental workload necessarily increases. If mental workload is too high, the pilot is not able 

to attend to all relevant stimuli in the environment. Therefore, if the situation that requires attention is not 

related to self-separation, information regarding the traffic situation may be what is ignored.

36. Perceived time pressure/Type of action chosen by the pilot: Although this pair has already been identified as representing a tradeoff, what is of interest here is the influence that perceived time pressure has on the action chosen 

by the pilot. If the pilot feels pressured by time, the pilot may make decisions too quickly. As a result, the chosen action may be less than satisfactory (i.e., the aircraft may come into an alert zone contact).

37. Level of pilot mental workload/Ownship state: These two characteristics were already identified as having a tradeoff relation. What is important here is the effect mental workload has on ownship state. As pilot mental 

workload increases, the pilot is more likely to ignore some responsibilities. Therefore, as workload increases, it 

is possible for the acceptability of ownship state to decrease.

38. Level of pilot mental workload/ Level of air traffic managers’ mental workload: These characteristics have already 

been identified as having an inverse relation. Specifically, if the pilot performed most of the duties associated 

with self-separation, the pilot’s mental workload would increase in high-density traffic situations. The fewer 

self-separation responsibilities the pilot performed, the higher the ATM’s mental workload would be. These 

characteristics were also shown to have a direct relation. For example, a pilot under conditions of high mental 

workload may not respond immediately to an ATM. As a result, the ATM’s mental workload may be increased 

by the pilot’s high mental workload.

39. Level of pilot mental workload/Accuracy of pilot’s current world model: As pilot mental workload increases, the pilot 

is more likely to ignore some relevant information in the world (e.g., radio communications). Therefore, as 

workload increases, it is possible that the pilot will have a less accurate “picture” of traffic-related information.

40. Level of pilot mental workload/Accuracy of current machine model: As pilot mental workload increases, the pilot is 

more likely to ignore some relevant information. Therefore, as workload increases, it is possible that the pilot 

will attend less to the CDTI.
D10 

41. Level of pilot mental workload/Appropriateness of planned action: As pilot mental workload increases, the pilot is 

more likely to ignore some relevant information in the world. Therefore, as workload increases, it is possible 

that the pilot will make hastier decisions and possibly plan a dangerous course of action (e.g., one that leads 

to a penetration of the alert zone).

42. Level of pilot mental workload/Level of confidence in planned action: These characteristics have already been identified 

as representing a tradeoff relation. However, what is important here is the effect of workload on the pilot’s 

confidence in the planned action. High pilot workload might decrease the pilot’s confidence in the planned 

action. Under high workload conditions, time may not allow the pilot to assess all traffic-related information 

and leave the pilot feeling less than confident.

43. Level of pilot mental workload/Type of action chosen by the pilot: This pairing has already been identified as representing a tradeoff. However, what is of interest are the effects of workload on the type of action chosen. 

As pilot mental workload increases, the pilot is more likely to overlook relevant information. Therefore, as 

workload increases, it is possible for the quality of the chosen action to decrease.

44. General piloting skills/Ownship state: Because a pilot’s experience, ability, and training affect decision making, the 

state of the ownship (acceptable vs. unacceptable) will, on average, correlate with the pilot’s level of skill.

45. General piloting skills/ Level of air traffic managers’ mental workload: As a pilot’s experience, ability, and training 

increase, the amount of assistance needed by the ATM presumably decreases. Therefore, a pilot with a great 

amount of skill will lessen the mental workload of the ATM. 

46. General piloting skills/Accuracy of pilot’s current world model: A pilot with a great amount of skill is generally better 

at knowing when their “picture” of the situation is incomplete. Therefore, a pilot with a great amount of skill 

will know when to attend to information from the forward field of view or radio communications.

47. General piloting skills/Accuracy of current machine model: A pilot with a great amount of skill is generally better at 

knowing when their “picture” of the situation is incomplete. Therefore, a pilot with a great amount of skill 

will know when to attend to information on the CDTI.

48. General piloting skills/Appropriateness of planned action: A pilot with a great amount of skill is generally more able 

to predict what will happen in the future. Therefore, the skilled pilot, on the average, will generally plan an 

action that is more appropriate.

49. General piloting skills/Level of confidence in planned action: A pilot with more skill will be more confident in planning their actions. A pilot with less skills will feel less confident in their ability and experience, and as a result, 

confidence will be relatively lower. 

50. General piloting skills/Level of pilot mental workload: A pilot with less skill presumable is not able to “chunk” information as well as the more experienced pilot. Therefore, put in the same situation, pilots of different skill 

levels may experience different amounts of mental workload.

51. General piloting skills/Type of action chosen by the pilot: The pilot with greater skills will presumably choose actions 

that may not be the most desirable, but they achieve the objective and are efficient. The pilot with less skill 

may, on average, choose a less appropriate action than that of the pilot with great skills.
D11

Table D7. Information Requirements for Traffic Awareness in the Free-Flight Environment

Note: Highlighted information requirements are identical to those presented by either Endsely et al. (1998) or 

Endsley and Rodgers (1994).

• = knowledge Ø = information

Pilot:

• Ownship state

Ø Identification of ownship

Ø Horizontal position of ownship

Ø Heading of ownship

Ø Speed of ownship

Ø Vertical speed of ownship

Ø Altitude of ownship

Ø Attitude of ownship

Ø Immediate destination of ownship

Ø Route of ownship

• Future of ownship state

Ø Future horizontal position of ownship

Ø Future heading of ownship

Ø Future speed of ownship

Ø Future vertical speed of ownship

Ø Future altitude of ownship

Ø Future destination of ownship

Ø Future route of ownship

Ø Future violations of aircraft capabilities 

(e.g., speed restrictions)

• Potential for future alert zone contact

Ø Alert zone contact watch

Ø Alert zone contact warning

• Ownship planned changes

Ø Heading changes of ownship

Ø Speed changes of ownship

Ø Altitude changes of ownship

Ø Immediate destination changes of ownship

Ø Route changes of ownship

• Hypothetical results of planned changes

Ø Hypothetical horizontal position of ownship

Ø Hypothetical heading of ownship

Ø Hypothetical speed of ownship

Ø Hypothetical vertical speed of ownship

Ø Hypothetical altitude of ownship

Ø Hypothetical immediate destination of 

ownship

Ø Hypothetical route of ownship

Ø Hypothetical violations of aircraft capabilities (e.g., speed restrictions)

Ø Hypothetical alert zone watch

Ø Hypothetical alert zone warning

Ø Hypothetical alert zone contact

• Other aircraft states

Ø Id of other aircraft

Ø Horizontal position of other aircraft

Ø Heading of other aircraft

Ø Speed of other aircraft

Ø Vertical speed of other aircraft

Ø Altitude of other aircraft

Ø Immediate destination of other aircraft

Ø Route of other aircraft

• Future of other aircraft states

Ø Future horizontal position of other aircraft

Ø Future heading of other aircraft

Ø Future speed of other aircraft

Ø Future vertical speed of other aircraft

Ø Future altitude of other aircraft

Ø Future destination of other aircraft

Ø Future route of other aircraft

• Number of aircraft nearby

Ø Number of aircraft that are within a userspecified range of ownship (This range 

may vary depending on the day, the location, or the situation.)

• Occurrence of alert zone warning/watch

Ø Auditory signal of an alert zone warning/

watch

Ø Type of alert zone warning/watch (temporary or one that is likely to lead to alert 

zone contact)
D12

• Best plan of action to avoid a future or 

discontinue a current alert zone contact

Ø Optimal changes to horizontal position of 

ownship

Ø Optimal changes to heading of ownship

Ø Optimal changes to speed of ownship

Ø Optimal changes to vertical speed of 

ownship

Ø Optimal changes to altitude of ownship

Ø Optimal changes to destination of ownship

Ø Optimal changes to route of ownship

Ø Request other pilot to make changes

• Weather formation existence

Ø Ownship within weather formation 

(Inform pilot to consult ATM or display 

outside of system)

Ø Ownship headed for weather formation 

(Inform pilot to consult ATM or display 

outside of system)

• Existence of Potentially Dangerous Terrain

Ø Ownship headed for potentially dangerous terrain (Inform pilot to consult ATM 

or display outside of system)

• Time frame allowed for ignoring the CDTI

Ø Time until the next alert zone contact 

watch/warning

• Current CDTI settings

Ø Current brightness settings

Ø Current contrast settings

ATM:

• Aircraft states

Ø Id of aircraft

Ø Horizontal position of aircraft

Ø Heading of aircraft

Ø Speed of aircraft

Ø Vertical speed of aircraft

Ø Altitude of aircraft

Ø Immediate destination of aircraft

Ø Route of aircraft

• Future of aircraft state

Ø Future horizontal position of aircraft

Ø Future heading of aircraft

Ø Future speed of aircraft

Ø Future vertical speed of aircraft

Ø Future altitude of aircraft

Ø Future destination of aircraft

Ø Future route of aircraft

• Aircraft Changes

Ø Heading changes of aircraft

Ø Speed changes of aircraft

Ø Altitude changes of aircraft

Ø Immediate destination changes of aircraft

Ø Route changes of aircraft

• Weather formation avoidance

Ø Aircraft within weather formation

Ø Aircraft headed for weather formation

Ø Optimal changes to altitudes of aircraft

Ø Optimal changes to routes of aircraft 

• Terrain avoidance

Ø Aircraft headed for terrain

Ø Optimal changes to altitudes of aircraft

Ø Optimal changes to routes of aircraft 

• Time frame allowed for working with the 

present conflict

Ø Time until the next alert zone contact 

watch/warning

Ø Number of aircraft pairs that will have an 

alert zone contact watch/warning within 

a user-specified span of time (This range 

may vary depending on the day, the sector, or the situation.)

• Potential for future alert zone contact

Ø Alert zone contact watches

Ø Alert zone contact warnings

• Pilots in need of assistance

Ø Aircraft requesting immediate assistance

Ø Aircraft requesting long-term assistance