IA0-A125 357 SOCICECONONIC SCENARIOS VOLUMN I JU) ACVUMEN ICS RESEARCH 1/2

0 P~~ND TECHNOLOGY INC 8ETHESDa M FEBUCSIFE SR-RP-11-VL2DTF7M i-3 . /1721N

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A e~ ~5iTechnical Report Documentation Pageo1. Report No. 2. Government Accession No. 3. Recipient' Cptalo, No. -

FAA-,APO-81-ll '3 of 6) A [S 3-574. Title and Subtitle 5. Report Date

February 20, 1981Socioeconomic Scenarios, Volume 11 6. Performing Organization Code0

_____________________________________________________________ 8. Performing Organization Report No.7. Author's)

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 7

Acumenics Research and Technology, Inc.04340 East-West Highway 11. Contract or Grant No.

Suite 808 DOTFA78WAI-932Bethesda, Maryland 20814 13. Type of Report and Period Covered

12. Sponsoring Agency Name and Address

Department of TransportationFederal Aviation AdministrationOffice of Aviation Policy and Plans 14. Sponsoring Agency Code

IWashington, D. C. 20591 APO-21015. Supplementary Notes

t4"C 16. Abstract

DTICEECTE

- 83037 1983D

_ _ _ _ _ _ _ _ _ 04 084117. Key Words 18. Distribution Statement

Document is available to the publicthrough the National TechnicalInformation Service, Sprinpfield,

________________ Virginiaol16 0~~I___19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price

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

w~~~~~ W w '

.

The contents of this report reflect neither

a position nor an official policy of the

Department of Transportation. This document is

disseminated in the interest of information exchange.

The United States Government assumes no liability

for the contents of this document or use thereof.

Accossion for

- INTIS GPAk&IDTTC TAR flL.-innnounced El

Justificatio

Distribution/SAvailabi11ty Codes

Avail Rnd/orDint Spec I Pl

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[ .TABLE OF CONTENTS

[mr Page

Scenario Themes and Overviews. . . . . . . . . . . . . . . . . 3

r Overview of Balanced Growth Scenario . . . . . . . . . . . 5Overview of Rapid Growth .... ..... ... . .. . . 5

_ Overview of Stagflation . .. . ........... 6

I Balanced Growth Scenario . . . . . . ............... . 7

Balanced Growth Economy . . . . . . . . . . . . . . . . . 7Aviation Industry . . . . . . . . . . . . . . . . . . . . 10Demand in Communication Industry......... . . . . 19

[ Rapid Growth Scenario. .. .. ... .. . . . .. .. .. . . 19

Rapid Growth Economy. . . . . . . . . . . . . . . . . . . 19Aviation Industry . . . . . . . . . . . . . .. ...... 20

Demand in Communications Industry . . . .......... 27

Stagflation Scenario . .................... . 27

Stagflation Economy . ......... . . . . . . . . . 27Aviation Industry . . . . . . . ..... . . . . . . . . 28Demand in Communications Industry . . . . . . . . . . . . 34

Pilot Composition ...... . . ........... . . . . 36

StagflationScenario . . . . .:.. .. . 36"Balanced Growth Scenario .. .. .. .. .. .. .. . .. 38

Rapid Growth Scenario . . . . . . . . . . . . . . . . . . 40

Air Carrier Fleet . . . . . . ................... . 42

Balanced Growth Scenario . . . . . . . . . . . . . . . . . 42Rapid Growth Scenario . . . . . . ........... . 42Stagflation Scenario . . . . . . . . . . . . . . . . . . . 42

Summary . . . . . . . . . . . . . . . . . . . . . . . . . .. . 45

Appendix: Forecast Method . . . . ........ . . . . . . A-1

. I iACUMENICS

.- . * , - ..

LIST OF TABLES

Page

1 Forecasted Balanced Growth Economic Variables . . . . . 9

2 Statistical Analysis System . . . . . ......... 12

3 Statistical Analysis System . . . . . ......... 15

4 Statistical Analysis System . . . . . . . . . . . . . . 17

5 Forecasted Rapid Growth Economic Variables . . . . . . . 21

* 6 Forecasted Stagflation Economic Variables . . . . . . . 29

17 Pilot Composition - Stagflation . . . . . . . . . . . . 37

8 Pilot Composition - Balanced Growth . . . . . . . . . . 39

9 Pilot Composition - Rapid Growth. . . . . . . . . . . . 41

10 Aircarrier Fleet - All Scenarios . . . . . . . . . . . . 44

11 Forecasted GNP - Trillion $/1972 . . . . . . . . . . . . 49

12 Forecasted DPI - Trillion $/1972 . . . . . . . .. . . . 50

13 Forecasted Employment - Millions...... . . . . . . 51

A-i Linear Approximations of Official Baseline Forecast, A-71970-1972 . . . . . . . . . . . . . . . . . . . . ..

A-2 List of Forecast Equations...... . . . . . . . . . A-9

A-3 Total Operations . . . . . . . . . . . . . . ... . . . . A-11A-4 Local Operations . . . . . . . . . . . . . . . . . . . . A-12

A-5 Itinerant Operations . . . . .. . . . . . . . . . . . . A-13

A-6 Total Active GA Aircraft . . . . . . . . . . . . . . . . A-14

A-7 Single Engine GA Aircraft . . . . . . . . . . . . . . . A-15A-8 Multiple Engine GA Aircraft . . . . . . . . . . . . . . A-16

g A-9 En Route Total Aircraft Handles . . . . . . . . . . . . A-17

A-10 IFR Departures . . . . . . . . . . . . . ... .. A-18A- f1 Total Overs . . . . . . . . . . . . . . . . . . . . . A19

I A-12 Flight Services . . . . . . . . . . . . . . . . . . . . A-20

A-13 Total Flight Plans Filed .................... A-21

A-14 Pilot Briefing . . . . . . . . . . . . . . . . . . . . . A-22

A-15 Total Pilots . . . . . . . . . . . . . . . . . . . . . . A-23[ A-16 Student Pilots..... . . . . . . . . . . . . . .A-24

ACUMENICS

-I LIST OF TABLES (Continued)

j Page

A-17 Transport Pilots . . . .* .. ... . . . . . . . . A-25

A-18 Private Pilots . . . . . o . . . . . . . . . . . . . . A-26

A-19 Controller Staffing Equations . . . . . . . . . . . . . A-27

A-20 Definition of Terms . . . . . . . . . . . . . . . . . . A-28

ACMNC

ir LIST OF FIGURES

SPage1 1 G.A. Aircraft Balanced Growth . ............ 132 Balanced Growth Operations . ............. . 16

r- 3 En Route Workload Measures Balanced Growth . ...... 184 G.A. Aircraft Rapid Growth ............... 23

5 Rapid Growth Operations ............... . 25

6 En Route Workload Measures Rapid Growth . . . . . . . . 267 G.A. Aircraft Stagflation ............... 31

, 8 Stagflation Operations . ............... . 339 En Route Workload Measures Stagflation . . . . . . . . . 35

10 Forecasted GNP All Scenarios . ......... . 46[ 1 Forecasted DPI All Scenarios .............. 47

12 Employment Forecast All Scenarios . . . . ....... 4813 Total GA Aircraft By Scenario . . . .. .. . . . . . . 5214 Multi-Engine A.C. By Scenario . ............ 53

[ 15 Single Engine A.C. By Scenario .. . . . . . . . . . . . 54

16 Total Operations By Scenario . . . . . . . . .. .. . . 56

17 Itinerant Operations By Scenario . . .......... 57

18 Local operationsBy Scenario ...... . . . . 5819 Total Aircraft Handled By Scenario .. .. .. .. . . . 5920 Total IFR Departures By Scenario ...... . . . . .6021 Total Overs By Scenario . . . . . ..... . 61

A-1 Illustration of Linear and Logistic Forms ....... A-6

Iii!

iv ACUMENICS

r Introduction

Technology does not evolve in a sterile environment. The nature

and rate of technological change are inexorably bound with socio-

[ economic phenomena. Technology, when introduced, may alter

historic social and economic relationships. At present, the

[ precise factors which induce technological innovation are not

rknown. Such change is indicated by factors which measure economic

* . and social growth. The purpose of technology assessment is to

ridentify and measure the social effects of innovation. As of yet,

the identification has been more successful than measurement of

L effects. It is difficult to measure that which is little under-

stood. The measurement problem exists because effects are often

multifarious. For example, it is clear that the telephone has

L yielded major social change. It has altered the conduct of personal

and business communications. It has brought places and persons

of vast geographical distances together. Yet one cannot fully

Lgrasp or measure the effects of the telephone.

.4L It seems reasonable that the retrospective identification and

measurement of social effects due to technology innovation could

[ be accomplished, as the events that induced such change and the

attendant results are locked in history. Yet, the thought of

measuring the effects of the telephone are bewildering.

I.ACUMEN ICS

im-

Since the problems of measuring the effects of accepted technology

[ are difficult, the problems of estimating impacts of future

technology on distant socio-economic settings require additional

considerations. As the future is not locked in history, variations

of future social settings will change the magnitude and timing of

effects. Since the future is not known, it is reasonable to

posit likely scenarios within which to consider the impact of

technology. A brief review of history indicates that society

does not follow a predictable pattern. However, one may posit

I future scenarios as bounds for which there seems to be a

Ireasonable probability of occurance. The scenarios presented

in this section are such bounds.

The purpose of this section is to present three reasonable future

I societal options within which one may consider the impact of

aviation communications technology. The information contained

in succeeding sections provides only the glimpse of future bounds

I[ necessary for the conduct of the study. Speculation for specu-

lation sake has been eliminated. That is, the scenarios contain

I[ only those factors found necessary to examine the primary impacts

of technology. - ..

I

AC2MENICS2

I

Scenario Themes and Overviews

The following three themes were used to characterize the alternative

future scenarios: "Balanced Growth", "Rapid Growth", and

[! "Stagflation". The official FAA aviation forecasts extrapolate

the present to the year 1992 on the basis of four scenarios:

"Baseline", "High Prosperity", "Energy Conservation", and "Slow

Growth"(1). The socio-economic assumptions underlying the FAA's

scenarios were used as the basis for the construction of the

*1 scenarios used in this study. The alternative scenarios correspond

to the FAA scenarios in the following manner:

TECHNOLOGY FORECAST SCENARIOS FAA SCENARIOS

Balanced Growth ----------------- Baseline

:1 Rapid Growth --------------------- High Prosperity

Stagflatioa ----------------------- Slow Growth

The time period considered in the present study is 1985 to 2020.

Il The historic and forecast data developed in the FAA document have

been taken as fact for the purposes of the present study. Although

a plethora of variables could be used in the construction of the

scenarios, the research in this report demonstrates that only a

select few variables, also used in the FAA forecast need be

considered. These measures included the following three economic

I variables, gross national product (GNP), disposable personal income

I ACUMEN ICS

3

(DPI) and civilian employment. Measures of aviation activity used

in this studV include:

* aircraft operations;

size and composition of the active general aviation fleet;

e size of the air carrier fleet;

" size and composition of the cadre of pilots; and,

a measures of FAA air traffic control measures; e.g. aircrafthandled, IFR departures, pilot briefings, etc.

The measures estimated for the three scenarios included in this

report are consistent with those included in the recent FAA fore-

cast.

Examination of the FAA aviation forecasts indicates that the

,* "Baseline" scenario forecast shows moderate economic growth rates

with a decline in unemployment and inflation. The "High Prosperity"

and "Slow Growth" scenarios werebased on modifications of the

"Baselino" forecast. The "High Prosperity" scenario assumes growth

rates and related economic conditions similar to those experienced

in the U.S. between 1961 and 1966. On the other hand, the "Slow

* Growth" scenario assumes growth rates and related conditions similar

to those that prevailed in the U.S. between 1971 and 1975. The

socio-economic phenomena associated with the FAA scenarios are

expanded in time to develop the alternative scenarios detailed in

.. this report'(2 ).I

ACUMENICS----------

Overview of Balanced Growth Scenario

The Balanced Growth Scenario depicts a society that has deliberately

constrained economic and technological growth in order to allay

- negative social and economic impacts derived from uncontrolled

growth. Contrary to the past, a mutually cooperative relation-

-- ship exists between government and private industry allowing for

the attainment of specific social goals.-- Government intervention

in the affairs of private industry is of a twofold nature. Govern-

ment regulations serve to establish and maintain an increasingly

competitive market structure in all industries. Simultaneously,

the government examines industries' compliance with regulations

*designed to promote social welfare. The latter govermental task

* is executed in a manner that places minimal restrictions on economic

• "-'nd technological development and growth for all industries.

* Population growth continues with a fertility rate equal to the

replacement level. The result is that the U.S. slowly approaches

"zero population growth". The population distribution patterns

for this scenario exhibit an increasing populace in small cities,

towns and rehabilitated inner cities.

Overview of Rapid Growth

The Rapid Growth scenario portrays a society where technological

4 change is the driving force in societal transition. National

policy reflects the attitudes of society, as the government takes

minimal steps to influence the rate and direction of technological

ACUMENICS

5

-4

and economic change. Government intervention in private industry

* is limited to enforcing regulations that allow for maximum compe-

* tition and efficiency within market structures. Therefore, the

development of new technology is limited only by market forces.

The nation's population increases as the fertility rate approaches

- . post World War II levels and the replacement rate remains constant.

Population distribution patterns reveal continual suburban develop-

ment in the Southwest, Eastern and Great Lake Regions.

Overview of Stagflation

In the Stagflation Scenario social goals or objectives are in

conflict with the nature and direction of technological change.

As such, technological innovation is inhibited, since social

objectives include the minimization of the untoward effects of

technology without consideration of the benefits.

I" Attempts to formulate and implement a national policy for control-

led economic and technological growth meet with uniform opposition

from industrial interest. As such, the relationship between

Iindustry and government deteriorates. Government programs result

in gerrymander regulations and laws that retard economic and

I technological growth. Industrial productivity decreases,

unemployment increases, and inflation continues to diminish the

currency. The lack of social progress increases social pressures

to effect changes through increased government activity. One

result of increased government activity is to reverse the deregu-

ACUMENICS1 6

hA

lation trend initiated in the 1970's. Previously regulated

communications industries are subject to new regulation in the

public interest.

Balanced Growth Scenario

In the Balanced Growth Scenario economic and technological growth

is assumed to occur at a slow but constant rate. Government inter-

vention into private industry affairs serves to promote highly

*competitive market structures while simultaneously fostering

social welfare. Inflation and unemployment are reduced as the

general level of affluence rises gradually but steadily.

Balanced Growth Economy

The balance-between economic and technological growth and social

needs results in modest economic growth. Industrial production

increases at a slow but constant rate, consistent with national

policy. Throughout the forecast period, GNP rises at an average

annual rate of 2.8%. GNP expressed in 1972 dollars doubles in a

*twenty-four year period from $1740 trillion in 1985 to $3480

trillion by the year 2009. By the year 2020, GNP will increase

170% over the 1985 level to $4700 trillion. Disposable personal

income increases at an average annual rate of 3.01% throughout

the forecast period. Expressed in 1972 dollars, disposable

Ipersonal income doubles in the first twenty-four year period of the

forecast to $2500 trillion. By the year 2020, disposable personal

income will increase 191% from $1250 trillion in 1985 to $3640

I 1 ~ACUMEN ICS

[-.-

trillion. Throughout the forecast period, civilian employment

experiencesl steady increase at an average annual rate of,1.16%.

From 1985 to the year 2020, civilian employment increases 51%

from 104.3 million workers to 158 million workers. Data for the

period 1985 - 2020 for each economic variable are presented in

Table B-i.

The expected values for the economic variables will have a signi-

ficant impact upon the aviation and communication industries.

GINP, a measure of industrial production, constantly increases

during the forecast period. The rise in industrial activity

reveals potential for an increase in the utilization of general

aviation transportation by private industry. Factors increasing

the utilization of general aviation transportation by private

industry include the following:

*Cost saving advantage for some travel distances andgeographical areas;

*Business dispersions and centralized management;

* e Changing air route structures; and,4

* Rapid escalation of fuel prices.

*GNP, a measure of the level of investment, indicates that capital

41 will be available during the forecast period for the development

of sophisticated communciation technology required to control

1. the increasing levels of air traffic.'.4I As indicated previously, disposable personal income, (a measure

of consumer purchasing power) and civilian employment increase

8 ACUMENICS

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steadily throughout the forecast period. The increased values

* , of these economic variables implies that more individuals will

become involved with aviation transportation. This phenomena

will occur because of the increasing number of individuals who

will be able to afford air transporation services.

Aviation Industry

* Throughout the forecast period, the demand for air transportation

*is evidenced in the size and composition of the general aviation

fleet mix. The total number of aircraft in the general aviation

fleet increases at an average annual rate of 1.3% throughout

the forecast period. Between 1985 and the year 2020, the general

aviation fleet increases by 62% from 254,000 to 413,.000 aircraft.

Private industry's increased demand for heavier more sophisticated

aircraft accounts for the slow but increasing growth of multi-

v' engine aircraft active in the general aviation fleet. Throughout

the forecast period the number of multiengine aircraft in the

1 general aviation fleet increase at an average annual rate of 1.6%.

* Between 1985 and the year 2020, the number of active multiengine

1. aircraft in the general aviation fleet increases by 82% from28,709 to 52,174 multiengine aircraft. The increase in the number

of single-engine aircraft active in the general aviation fleet

Lis a result of a combination of industrial and public demand forair transportation. The number of single-engine aircraft, used

I primarily for private and instructional flying, increases at an

Iaverage annual rate of 1.2% throughout the forecast period.

10 ACUMENICS

p . ..

Between 1985 and 2020, the number of active single-engine aircraft

in the general aviation fleet rises 56% from 202,481 to 315,264

single-engine aircraft. The numbers and types of aircraft pro-

jected to be active in the general aviation fleet mix is shown

* in Table B-2 for the three alternative scenarios. The general

aviation fleet mix for the Balanced Growth Scenario is presented

in Figure B-i.

The effects of the projected demand levels on air transportation

is reflected also in FAA workload measures. FAA provides the

aviation community with several operational services including the

following two activities: air traffic control at FAA towered

*airports and traffic surveillance and separation at Air Route

Traffic Control Centers. The need for FAA operational services

will increase as a result of growth in aviation activity. Total

aircraft operations at FAA towered airports are projected to

increase at an average annual rate of 1.9% throughout the forecast

period. Between 1985 and 2020, total aircraft operations at

FAA towered airports increase 101% from 83 million to 166 million

aircraft operations. Itinerant aircraft operations rise at an

average annual rate of 1.71 throughout the forecast period.

Between 1985 and the year 2020, itinerant aircraft operations at

FAA towered airports increases 86% from 55.9 million operations to

104.3 million oper; ins. The increase in itinerant operations

reflects both the increases in utilization of general aviation by

business and the increase in demand for air carrier services by

ACUMENICS

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the general public. Local aircraft operations increases at an

average annual rate of 2.3% throughout the forecast period. Between

1985 and the year 2020, local aircraft operations performed at FAA

towered airports increase 131% from 27 million to 62 million. The

* increase in local aircraft operations derives from an increase in

* pilot instruction which will in turn create more air traffic after

students become licensed pilots. The complete tabulations for FAA,

towered airport workload measures are given in Table B-3. The

measures displayed graphically in Figure B-2 are the FAA towered

airport workload measures for the Balanced Growth Scenario.

The growth in the number of aircraft handled by the Air Route

Traffic Control Centers increases at an average annual rate of

1.7% throughout the forecast period. From 1985 to 2020, total

IFR aircraft handled by Air Route Traffic Centers increases 88%

from 36 million to 68 million aircraft. The increases in

operations at Air Route Traffic Control Centers will be a result

of increase in general aviation traffic associated with increases

in pilot capabilities and an increased use of larger more

sophisticated aircraft. The complete tabulations of enroute1 center work measures are given in Table B-4. The enroute work

4 measures for the Balanced. Growth Scenario are displayed graphically

in Figure B-3.

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at fn N N N N in N w) Nr N- Nr on y N- N. N N N N . N N ND N N on N in N N an N Na a,I Z in inP %D PW VN r &nwnnN I 9p N04

N~- V Nn %00 W N w t% m N w0 -04 N m~ " & &A N1a0% N n m m. %- N4 0I a. a. N N

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F%4 0 ) %N- = 0 On F%- 6 0 NSr I0N r- N0 4 N N% 0 0 n OWd- -6 =~ 0 -V 0 10 10 W CD Nz u wms O 1m %o 4 %Dcy r cy o " m b0 o s yr -o, i- o th 4c ty 0iaoqN ca w

02 . . . . . .. . .. . .. .. .. . .. 4 6 1 v e .- 41. . an. .

0 60o0AOla N CD 10 N so 106 w N co9 0WN 10 Nso 1 N 0 ON 4.t 10 s "on wl 10

c oo N a4s n o so n "s NO in . in c Nc v, 60opi N o i cy a, IA 10 N , w- N N .w a. -606 NY

f ~ " a- 40 N 106 N pn- N D N N0 N% C1 W- a.0 NP. W wI a.-N 1 W% m 0I WI N 60. .A .n .I . . .. .I .I 60 . .mm 066 V .~ .N .mgn 0 W W; an ;~. .60 CN&I mnW 0 Im m NO&%Dc & 0 rN6NW a, -A0 NIr %& 0 m nC100AI 60.NWIIW

P. Z -6an60a-40m-6 460 an N-'1 an -6an.0a160N a- an P6 an -4 IV-

so %l .6g0C Nan. ,'Y " w WN 10 an C WCDI N m4%& Nl an N so a.- C N 10a W; 0;64 an C1 9;

9% 10 00Na C0"6&0W.-4 P. so km-4 eyW)Iina 0 oNC a. " WITIA N goN a, m W

al10 N an an an aYn a n 101000 N - N Nl N1 Nl 0P 06000. aI"CFnW&. N. o00=0.at 0 00 00 , 0.0 -a ,a ma 4- "0P... yC

O4'aa0 -W aamM M a D*w~ w =~----I0Wa1-------- WI000~aO~a-~m0NO

N0 o~en wU m0 0,m anN M, -A. 0 ~aa- D n F04 a N .m inZ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ o N .0NIIC .1N0I Fn0. 6 01 N10. an O0C N in-

4 II60NC06a.-4WImImmACIN40NCI6m-60I0C170W

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a1 lMA0 :

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=& a -A U.

=I @,

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a a0.1m

op .0 =1 -4 180

-- - - - - - -

Demand on Communicatio: IndustryinGPtruhtte

The slow but steady increases observed in GNP throughout the

Jforecast period, indicate that capital will be available fortechnological development in the communications industry. The

* net effect of increases in the demand for air transportation has

special significance for the communications industry. Throughout* I

the forecast period, aircraft operations and fleet size rise

steadily which also increases the amount of air traffic. In order

' to efficiently handle the increased air traffic, new communications

technology will have to be developed to ensure the safety of the

air routes.

Rapid Growth Scenario

In the Rapid Growth Scenario it is assumed that society makes no

attempt to constrain or channel economic and technological growth.

National policy encourages economic and technological growth as

the government acts to remove or reduce all barriers to such

development.

Rapid Growth Economy

The result of rapid economic and technological growth is reflected

in the GNP statistics for this scenario. Throughout the forecast

1 period, GNP will rise at an average annual rate of 4.2,. Between

1985 and the year 2020, GNP expressed in 1972 dollars increases

3421 from $1900 trillion to $8400 trillion. The average annual rate

I 19 ACUMENICS

Y of increase for Disposable Personal Income is approximately the

same as the rate of growth in the GNP for the same forecast period.

[ However, Disposable Personal Income (also expressed in 1972 dollars)

increases 345% from 1310 trillion in 1985 to $5830 trillion in the

-F year 2020. Civilian Employment rises steadily at an average annual

rate of 10 throughout the forecast period. Between 1985 and the

year 2020, the civilian workforce is projected to increase 55%

from 105.7 million workers to 163.9 million workers. The values

of the economic variables for each year in the 1985-2020 period

[ are included in Table R-1.

The GNP statistics indicate that industrial production increases

I constantly throughout the forecast period. An increase in

industrial activity indicates that there will be a great demand

for general aviation transportation. The GNP measures in this

forecast also indicate that substantial levels of capital will

I be available to develop technology in the aviation and communica-

tions industry. The projected annual rate of increase for

disposable personal income and civilian employment suggest that

an increasing amount of individuals will have available the

resources necessary to utilize air transportation.

Aviation Industry

*The size and composition of the general aviation fleet reflects

society's demand for air transportation. The total general

aviation fleet rises at an average annual rate of 2.41 through-

I out the forecast period. By the year 2020, the total general

1 20 ACUMENICS

-4

.1L4j

an

an

in %r .M CD riD .60 a C, N4 .CYnI wl . so V.~ ' In -A S cC do C2 Cy r c c* ~ A N cy Vc@ r NC'. ' I- N Sri .W Ny Cit 0' Fn N W) Kl W) %tI qr 'ri Ir yi l n ti ViN in wl

* 4

Z 1 CF0. W)2-&M , cy '0 N o nr c " 1 O C CPC-IinCMM Pn'AIN &M 0W ' - .- CY C CY .~ j N .r. N )W O ) onn iAn r)% %r~~A 0'~i in na ao- IAIrf r. 46-do

=rCC-- - N N N N N cm N N m m mn Fn %,P -- wi -r %r %It .4 r LetVM In Si I.0 .01- 4 - -- 4 4 - 444 -4-1 4 4~ -e----1 -1.4 ---. LA:of

49 &MA 400 C -NM~rkf~ol . CN'0C.~ N..?U1'0n %a0'9x 40 00 0 ,0 r '0. a-CP a CCCCC C )C E-4.-

4NN.NNN NlIcCMN NeiNY N NwN N

LU C4P- P-41 P". v4 N N N NN NC~ C\N NV en on W on

21

aviation fleet rises 144% from 264,975 aircraft to 645,874

aircraft. T1roughout the forecast period, the number of multi-

engine aircraft active in the general aviation fleet increases

at an average annual rate of 2.9%. Between 1985 and the year

2020, the number of multiengine aircraft active in the general

aviation fleet increases 185% from 298,456 aircraft to 849,775

aircraft. The increase in multiengine aircraft activity is

attributed to the increasing utilization of general aviation by

private industry. Single-engine airzraft active in the general

aviation fleet rises at an average annual rate of 2.4%. By the

year 2020, active single-engine aircraft in the general aviation

* fleet increase 137% from 209,826 to 497,668 single-engine aircraft.

The increase in the utilization of single-engine aircraft reflects

an increase in instructional and recreational flying. The general

aviation fleet mix for the Rapid Growth Scenario is shown graphi-

cally in Figure R-1. (The complete tabulation for the general

aviation fleet is shown in Table B-2).

As noted previously in the Balanced Growth Scenario presentation,

an increase in demand for air transportation results in an increase

of FAA operational services. In the Rapid Growth Scenario, the

increasing demand for air transportation further intensifies

the utilization of FAA operational services. Total aircraft

operations at FAA towered airports is projected to increase at an

I1 average annual rate of 2.30 throughout the forecast period.

22 ACUMENICS

%Da

02 1 CD E

KU I

EE

S+

Nc I

n E

0 +0

I 0Y

0 4n " toI an

0 cocaI N.

LU M LULLI4ftINan Kn 4nV)

" ====ii

IL fn +0M

De 000

n >-)I ad nK *

eC*LL w.'L n E +

IL CL 4t SE I

- in ~o - in K

+30

4000I.-I

000 23 inI

Between 1985 and the year 2020, total aircraft operations at

airports with FAA towers are forecast to increase by 135% from

- 96 million to 226.3 million operations. Itinerant aircraft

operations at FAA towered airports rises at an average annual

rate of 2.5%. Between 1935 and the year 2020, itinerant aircraft

operations at FAA towered airports increase 149% from 96.249

-* million operations to 226.299 million aircraft operations.

The increasing utilization of general aviation services by industry

and the increasing demand for air carrier services by the general

public will account for the increasing itinerant aircraft operations

at FAA towered airports. Throughout the forecast period, local

aircraft operations at FAA towered airports, increases at an

average annual rate of 1.9%. By the year 2020, local aircraft

operations will increase 103% beyond the 1985 figure of 28.9

million to 58.7 million local aircraft operations. The steady

F increase in local aircraft operations at FAA towerd airports,

implies that the number of pilots undergoing flight training

will increase throughout the forecast period. The complete

tabulations for FAA towered workload measures are given in

Table B-3. Displayed graphically in Figure R-2, are the FAA

I towered airport workload measures for the Rapid Growth Scenario.

The growth in the number of aircraft handled by Air Route Traffic

Control Centers reflects increases in general aviation traffic

associated with increases in pilots capable of IFR flights. The

21 24ACUMENICS

I. III- iI ,-

I

- INoo ,I a-

- -I , o,,aa

I,- ,I...

*,,, I

OO04

Mi4

In

4D

I II

xr

IL IK

9 IxNoILj LL L IN

coo I-

I In

- + 41coon~I

AL CL9

P" Io Ilk

I i

0 InC, n4 v n DII-. t 41 F. LeCD 6

w ty 11 2

1 ai

5 0 4.-

o n.4nmnIm

me IN

46 4ft U) G

IKI +

'n cooI eyw = r- x II

I- I

L e =96 S

1. 49.- .aj

MPI I

0 Ix aKa

ofw coo In

A L Li II-

dc az 9 9It

a = a a6

I* number of aircraft handled by Air Route Traffic Control Centers

increases at an average annual rate of 2.6%. Between 1985 and

the year 2020, IFR aircraft handled by Air Route Traffic Control

Centers increases 156% from 43 million to 110.3 million aircraft.

* [ The complete tabulations of enroute work measures are given in

Table B-3. Displayed graphically in Figure R-3 are the enroute

measures for this scenario.

Demand in Communications Industry

As indicated by the GNP for this scenario, capital will be available

for technological development in the communications industry. All

I measures of aviation activity confirm increases in air traffic

L and in turn, the need for new communications technology to direct

air traffic.

Stagflation Scenario

I This Stagflation Scenario is built upon the theme of acute tension

I between society and technology in the U.S. Government policies

designed to retard technological development results in low

I. economic growth. Poor economic conditions, in turn, impedes

I further technological development.

Stagflation Economy

Throughout the forecast period, GNP and Disposable Personal Income

(both rise at an average annual rate of 1.51. Between 1985 and the

year 2020, GNP expressed in 1972 dollars increases 68% from

SI $1605.1 trillion to $2662.9 trillion. In the forecast period

27 ACUMENICS

• from 1985 to 2020, Disposable Personal Income also increases 68%

I from $1114.6 trillion to $1876.8 trillion. During the forecast

period, Civilian Employment experiences a low average annual

* growth rate of .48%. Between 1985 and the year 2020, Civilian

I Employment increases only by 19% from 103.7 million workers to

123 million workers. The tabulations of the economic variables

Lare given in Table S-1.

I The slu-ggish economic conditions portrayed by the variables

I depicted above, portend grave conseqences for the aviation and

communications industries. The slow increases in GNP imply

low business activity in the stagflation economy. Low business

activity, in turn, suggest a decrease in general aviation trans-

portation demanded by private industry. The minimal increases in

GNP also imply that capital will not be available to advance

technology in the aviation or communications industries. Due to

Isociety's negative attitude towards technological development,economic growth increases at a very low annual rate. The reduced

levels of employment coupled with minimal increases in Disposable

Personal Income suggest a low demand for air transportation by the

general public.

Aviation Industry

I The demand for general aviation transportation decreases as a

result of low business production. Evidence of this phenomena is

shown in the projected number of aircraft active in the general

I aviation fleet. The total number aircraft active in the general

1 28 ACUMENICS

I1

0I L

POI 4 w .4 N 0 r = %D No w~ m~ In b In C an sn CD m = in m m r- mC nC

ft.If ' &n 1 4 ~4 a: ; .4.4 .W 4 ; .4 W ----- 1: 4 1 0 0 C C --

kn o 4 N m sr 'aN- *f 4 O N on .12 In .0 P.e M 6 40 e N on %A@ e. ' D P~. e ago V. CD

o ft

I .49x"IYF %T &td~ln to cm I" C.4.4nr i Mn.0 o CP% -4N W)'T 9 'D . ,C) 44N Lei %

-. 0 in -------.---. 4 CC C C.4 -0 1" N""N NNN NN Ne e

I:29

f I aviation fleet increases at an average annual rate of .44%

throughout the forecast period. Between 1985 and 2020, the total

[ number of active aircraft in the general aviation fleet only

increases 18% from 248,542 to 292,195 aircraft. The demand for

multiengine aircraft by business entities accounts for most

Fof the growth of active aircraft in the general aviation fleet.

Throughout the forecast period, the number of multiengine air-

craft active in the general aviation fleet increases at an

.1 average annual rate of 1.7%. Between 1985 and the year 2020,

the number of multiengine aircraft, increases 86% from 254,538

I to 472,770 aircraft. The number of single-engine aircraft active

l in the general aviation fleet increases at an average annual

rate of .4%. By the year 2020, the number of single-engine air-

I craft active in the general aviation fleet increases 16% from

F197,622 aircraft to 229,459 aircraft. The decrease in the

utilization of single-engine aircraft is attributed to the effects

!i j of low Civilian Employment levels and low Disposable Personal

6 Income levels. As the consumer's purchasing power decreases, so

i ! will the demand for recreational and instructional flying performed

in single-engine aircraft. The general aviation fleet mix for

the Stagflation Scenario is shown graphically in Figure S-I.

[ (The complete tabulation for the general aviation fleet is shown

in Table B-2).. II

ACUMENICS30

a C,

-~ C, C *

P"

U I wU,

in + -44.I C.

CY

I aY

4n to IQ In

-C Malmo e E

-peLW n EQ

U. en EQ>->.1 en n

I I~tI.I- X- enK0%-ae 46n en

0000IV- Ij a 1 A-

IL IIAL4 II PM

+ do

- en go

t- an Ka- enn W1 N I 1P0 -

[ I The decrease in demand for air transportation is reflected in FAA

workload measures. Total aircraft operations at FAA towered air-

[- ports increase at an average annual rate of .89% throughout the

I forecast period. By the year 2020, total aircraft operations

at FAA towered airports increase 39% from 80 million aircraft

j operations in 1985 to 111 million operations.

Itinerant aircraft operations at FAA towered airports increase at

an average annual rate of .87% throughout the forecast period.

Between 1985 and the year 2020, itinerate aircraft operations

at FAA towered airports increase 38% from 53 million operations

to 74 million operations. The general public's inability to

afford aircarrier services because of the low level of Disposal

Personal Income accounts for the lcw annual increase in itinerant

I aircraft operations. Another factor that relates to the low

* levels of itinerant aircraft operations at FAA towered airports,

is the low level of business production associated with a stag-

flation economy. Throughout the forecast period, local aircraft

, operations performed at FAA towered airports increase at an average

I rate of .93%. By the year 2020, local aircraft operations at FAA

towered airports increase by 41% from 26 million operations in

1985 to 37 million. The low growth of local aircraft operations

I imply that fewer pilots will be trained during the forecast period.

This phenomena further suggest a decrease in air traffic due to

the decreases in the pilot population. The complete tabulations

I2 AU ACUMENICS

32

or~ s-IenII

I U

I'D-

41)

4y in

I-

I

* m4a 4N

at I In"i I

-a

00I > Ir.

to o M 14In IL InI4

+ -P

00

00

A6n

Ca~~L + aso a a a

I ~33 II

~4I

F:

for FAA towered airport workload measures are shown in Table B-3.

( I The measures displayed graphically in Figure S-2 are the FAA towered

[ airport workload measures for the Stagflation Scenario.

The growth rate in the number of aircraft handled by Air Route

Traffic Control Centers increases at an average annual rate of .91%.

I By the year 2020, the number of aircraft handled by the Air Route

q Traffic Control Centers increases 40% from 34.9 million air-

craft in 1985 to 48.8 million aircraft. The increasingly low levels

of operations at Air Route Traffic Control Centers results from

decreases in general aviation traffic and the pilot population

level. The complete tabulations of enroute center work measures

are given in Table B-3. The enroute measures for the Stagflation

Scenario are displayed graphically in Figure S-3.

Demand in Communications Industry

The reduced demand for air transportation by all segments of society

will effect the aviation industry's demand for communication

I technology. Throughout the forecast period, aircraft operations,

the pilot population and the general aviation fleet size experience

very minimal growth trends, thereby reducing the need for advanced

I communication technology for the aviation industry.

I

1 34ACUMENICS

34J

IV

I a~4

* I CC* = a1

1 a,

IN

I C',C

= acc

(n 0fU) + CD

ofa I CF

IIN

+ a,

'4= aL LLLf coC ' ,

I

I-

-. 1~I -. a

+ 4

CD LM LMI nC.M,~~~ -1 C l

= a35

IThePilot Composition

I Stagflation Scenario

The decrease in aviation activity due to the levels of the

* j economy effects the slow increase in pilot populations. Between

1995 and 2020, the total pilot population is expected to increase

at an average annual rate of 1.3% and by the year 2020, the total

I pilot population will increase only 40% from 1.01 million pilots

to 1.5 million pilots. The low levels of business activity

f account for the low levels of increase in private pilots who

pilot general aviation aircraft. Between 1995 and 2020, the

* number of private pilots increases at an average annual rate of

1.2% and by the year 2020, the number of private pilots will

increase 37% from 437,348 pilots to 599,215 pilots. The number

of transport pilots increases at an average annual rate of 2.2%

and by the year 2020, the number of transport pilots will increase

770 from 110,880 pilots to 195,948 pilots. The student pilot

J population will increase at an average annual rate of .97% and

by the year 2020, the student pilot population is expected to

I increase only 29% from 242,912 to 312,202 student pilots. Table S-2

Idisplays the pilot compositions for the Stagflation Scenario.

I3

I

S36 ACUMEN ICS

4

ad

w 00 L"W, n-a%

m= 444 N N " ii4a n na n r an in n m an aun o% uwI.0

0 f~.0 .0 n OV-I 0- , c@ N"M -r WO , 0

o- . w uO4 i.-a n u --a r Zw %an N -.Ia%go "' ,Ww"'- ,

to a, ta I - 0 4 0 -4 .4 -a . - -400 W%4C4 - --- W 4 a

an1-0 InFUc. r% vW .a - o=C 1,a0 0 u .

P.0 o a owP oi n-, u"- Da oP o-

I-1; ;1 Nar4Nn .0; ; .0-a0ea9aN .0 e; 0:

a- al C, a- a, - C, 'M = C-a~n U. C , C, C, I* 10OC. 0 I..- 4 vI

CU o -lC Y MC C y

A.37

.I

Balanced Growth Scenario

The constant.increases in the size of the general aviation fleet

and the air carrier fleet connotes an increase in the pilot

population. Between 1995 and 2020, the total pilot population

[ and the number of private pilots is expected to increase at an

average annual rate of 1.5%. By the year 2020, the total pilot

population will increase 49% from 1.16 million pilots to 1.75

[ million pilots and the number of private pilots will increase

46% from 471,738 to 687,113 pilots. The increase in private

4 pilots is associated with the increase in the size of the general

I aviation fleet. The increase in the number of transportation

pilots reflects the increase in the utilization of air carrier

CI service. Between 1995 and 2020, the number of transport pilots

* increases at an average annual rate of 2.5% and by the year 2020,

the number of transport pilots will increase 88% from 128,899

I pilots to 246,512 pilots. Between 1995 and 2020, the number of

student pilots increases at an average annual rate of 1.2% and

I by the year 2020, the number of student pilots will increase 35%

from 256,187 pilots to 349,847 pilots. The pilot composition

for the Balanced Growth Scenario is shown in Table B-6.

I4,IEl

4 3 ACUMEN ICS

. .. .. ..38

In

Iwo

fU

to 6 a-1 C0 -4 N0 'n w 9 0 r ~0- ~ 0 0

a. Nl CD "00 P. = r M N % D .6 C2, 1? .0 N O NI U.0 4 9%a,

% 0. -rNV %0" 00.O0-NUI..0N%

r. a- 0 0 0 0 II in 00 od .N N, NI V. 0 0 0 In r. D U,'r-j o 0 -a.0 . ca .4 N.UOed 4w w r .I&

N N0NN-a .. t '06.- tv CY 0 N NY .~ . . . W) ", NON wl .

4n .4 4 0 CDoka..0 0 0 soo00 0N 0 F .r.P 0 0 AD %Do. - so n cy w. vi cy r- 4 sowi N0.0%aW m @Ne 4 i

. . . . . . . . . . . . .0.. 0 ~ 0 U 6 . . . . .

Q ag =Nw ?% a0O U ,0%T'00 4 wl ' m = N r ,0WI'o0*cDcinU60 ~ ~ Ww wNg N ~ ? ,,,, ,0000 N

A c in 1,0 0 a- . YFTL NU.NO %0 V 0 0on NI tU,'D P00S- O-

39

I Rapid Growth Scenario

The increase in aviational activity gives rise to the increase in

[" the pilot population. The total pilot population increases at

an average annual rate of 2.1% between 1995 and 2020 and by the

Syear 2020, the total pilot population will increase 72% from 1.48million to 2.55 million pilots. The number of private pilots

I • increases at an average annual rate of 2% and the number of private

pilots is expected to increase 67% from 586,877 pilots to 978,777

*) pilots. The continuing demand for air carrier service is reflected

i in the increase of transport pilots. The number of transport

pilots increases at an average annual rate of 2.9% and by the year

2020, the number of transport pilots will increase 109% from 189,194

to 594,739 pilots. The number of student pilots increases at an

K 1. average annual rate of 1.7% and by 2020, the number of studentpilots is expected to increase 55% from 309,098 pilots to 480,470

I student pilots. The pilot composition for the Rapid Growth Scenario

is presented in Table R-2.

4

I

ACUMENICSK ___40

Ir I T- I- -

4

. soI p Y0 D= )N ftc Df n4 I 4f-I"4;

w' -a i - Ip -iw% nP ,- r ,v

o- w p - )ri ,P oc.cD. 4N"q n% .r w ;.

nCC-',- CC!- .--!!!- '

"-4

No %ir u, ono "4 NV) m. 4p, 4w -v) -4 N w i tn I-o N IA ac. IA E-4 I

D I-. a N4 . n - wl .0 N a. to 0 w 4 %0 IO so Ir CD ND 04 0 N 4D " N)

CL W;00... 'O C; N; 0: N ; N; 604a606060600. a; U .0. U. UP- @.0Iir 4i % D- nc n40C DC l0 )s ~ aC 1o* '0600 IA IA N0. "4 N .0600 4 oII N cy 0 o '6 U)

I .U. N =I IA CNI CO 60. I"4Co UV, N '0Y 1* Col CC,60Y Nl CV, IA"4 060.0 "O N

0 ~4"4NNNNNNNNIAAIAAIAAIIAIIA41A

I

J Air Carrier Fleet

Balanced Growt. Scenario

An increase in disposable personal income and civilian employment

[ suggest that more of the public will become involved in travelling.

Between 1985 and the year 2020, the air carrier fleet is expected

L to increase concurrently with the steady growth in the general

economy. The air carrier fleet increases at an average annual

rate of 1.1% between 1995 and 2020 and by the year 2020 the air

* j carrier fleet will increase 34% from 3338 aircraft to 4488

aircraft. (See Table B-5 for complete tabulation of air carrier

I fleet).

I Rapid Growth Scenario

1 The steady increase in the level of the general economy places

further demands on air carrier service. The effects of this demand

is evidenced in the number of aircraft that comprise the air

carrier fleet. Between 1995 and the year 2020, the air carrier

fleet increases at an average annual rate of 1.8% and by the4.

year 2020 the air carrier fleet will increase 58% from 4288

aircraft to 6759 aircraft. (Refer to Table B-5 for tabulation

Iof air carrier fleet for this scenario).

Stagflation Scenario

Between 1995 and the year 2020, the air carrier fleet decreases

I at an average annual rate of .6% and by the year 2020 the air

carrier fleet will decrease 13% from 2381 aircraft to 2063 aircraft.

ACUMENICS42

I

! i Factors influencing the decrease in demand for air carrier service

I include the ;ow levels of disposable income and civilian employment.

i j associated with a Stagflation economy. The economic variables

indicate that the public's desire to travel will be curtailed

.' because of inflationary prices and the low levels of increase

in consumer purchasing power. (Refer to Table B-5 for tabulations

of air carrier fleet).

4V I

w *I

6 t

:4

' I ACUME NICS

C.

C.4N C! C "!r !U

r ~~~~~~~~~~~ ~ ~ ~ ~ ~ ~ in- nr oc * % n&o- ,a 0% 1C 4C or

I-. o d 00 o 0 0000 0 Pr, Fr% , r 1.-.0 %D %DID.I %-AN , o't .

cyr o =Fni r a 4.r s D in fla n -a ocy w%

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v) - mF Ti ' oa m0 minrv ,o kc -4 oi wwes.Nv% aI.,

44

Summary

Each scenario in this report has been constructed in an internally

consistent manner allowing for comparative analysis. In each

I scenario, society's differing attitudes toward technological

development are reflected in the economic variables projected

I-- for each scenario. A graph for each economic variable is shown

* in Figures C-i, C-2, and C-3. The charts depict the growth trend

corresponding to each alternative scenario. The same informationis supplied in tabular form in Tables C-I, C-2, and C-3. The

future for a society which chooses not to constrain technological

development is illustrated in the growth trends represented by

the Rapid Growth Scenario. On the contrary, national policy

which dictates the rate and direction of economic and technological

development, results in an economy with a low or a moderate

growth rate. This phenomena is illustrated in the growth trends

represented by the Balanced Growth Scenario and Stagflation

Scenario.

The level of industrial activity in each scenario directly affects

the demand for general aviation transportation. Graphs of the

number of component aircraft types that comprise the general4

aviation fleet are shown in Figures C-4, C-5, and C-6. The

growth rates in the utilization of the aircraft for each scenario

are also depicted in the figures. The rise in the level ofI

industrial activity associated with the Rapid Growth Scenario

ACUMENICS

45

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accounts for the increased utilization of the general aviation

fleet for that scenario. As a result of the low levels of

industrial activity associated with the Stagflation Scenario, the

graphs exhibit a low growth rate for the utilization of the

[general aviation fleet in that scenario.It has been demonstrated that FAA workload measures reflect growth

in the aviation activity. A graph for each aircraft operation

performed by the FAA at the FAA towered airports is shown in

[ r Figures C-7, C-8, C-9. The charts depict growth trends corresponding

to each scenario. As indicated previously, the demand for air

:1 transportation by industry and the general public affect the level

of aircraft operations at FAA towered airports. Specifically,

local aircraft operations also indicate the number of pilots

I undergoing flight instruction. The effects of a rapidly expanding

economy versus a moderate and low growth economy is reflected in

the graph for each type of aircraft operations at FAA towered

I aircraft operation airports.

The level of operations at Air Route Traffic Control Centers is

a function of the level of general aviation traffic, the type of

I aircraft in the general aviation fleet, and the number of pilots

capable of conducting IFR flights. A graph for each enroute

center workload measure is shown in Figures C-1O, C-Il, and C-12.

The number of aircraft operations generated by each scenario are

also displayed graphically. An increase in business activity,

ACUMENICSf 55

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which in turn creates a demand for more general aviation trans-

portation, is associated with the Rapid Growth and Balanced

Growth Scenarios. The increased number of operations at Air

Route Traffic Control Centers reflects the phenomena associated

I with the scenarios discussed above. As illustrated in Figures

[C-1O, C-I, and C-12, the curves representing the Rapid and

q " Balanced Growth Scenario assume higher values in comparison to

I the Stagflation Scenario Curve.

I,I

II

I.

I

,IACUMENICS

62

4,'V

II APPENDIX A

FORECAST METHOD

ACMIIC

APPENDIX

m FORECAST METHOD

iThe purpose of this section is to describe the method employedto forecast levels of aviation activity. As noted in previous

sections, the purpose of this project is to estimate the nature

and, to the extent possible, the magnitude of primary effects

emanating from the introduction of new aviation communication

[ technology.

The adoption rate of new technology will be in part influenced

by the activity within an industry. In addition, the adoption of

new technology may influence the nature and magnitude of industrial

activity. As such, identification and estimation of future values

SI for typical industry activity measures are necessary to the assess-

* [ ment of technological effects.

[ The aviation industry measures included in the scenario fore-

cast derive from FAA Aviation Forecast Fiscal Years 1981-1992. 1

In particular, parameters that describe aggregate activity levels

for aircarriers and general aviation must be considered. In addi-

I tion, measures that reflect the service provided to the industry

by the FAA must also be included. As such, the measures of import

for this study included:

IlOffice of Aviatir-n Policy (AVA), "FAA Aviation Forecasts

Fiscal Years 1981-1992", U.S. Department of Transportation,Federal Aviation Administration, Washington, D.C. September 1980.

IACUMENICS

A-1

I.

a) total operations

b) lc7cal operations

C) itinerant operations

d) general aviation fleet size and composition

e) aircarrier fleet size and composition

f) FAA workload measures

1) aircraft handles

I 2) IFR departures

" 3) overs

4) flight plans filed

1 5) aircraft contacts.

In addition to the measures identified above other variables of

r import included;

a) controller, flight service, and maintenanceI personnel;

b) FAA investment in traffic control and communications[ investment.

The relationship of each of the preceding factors to the assess-

I ment effort will be described in succeeding sections of the text.

[ I The purpose of this section is to describe the general approach

used to estimate future values for each of the variables.

The present effort required examination of the technology

[induced impacts under three socio-economic scenarios: balanced

growth, rapid growth and stagflation. The analytic differences

among scenarios are measured in terms of economic variables. The

1IA-2 ACUMEN ICS

.1

r economic variables of import for this project are those employed

in the "FAA Aviation Forecast" i.e., Disposable Personal Income[(DPI), Gross National Product (GNP) and Employment (Employ).

2

[ A review of the FAA Forecast indicated the official estimates

represented the balanced growth scenario. The upper and lower

[bound FAA estimates were deemed equivalent to the rapid growth andstagflation scenarios. 3 The economic variables developed used in

the scenarios are shown in the scenario section of the report.

The general method used to forecast aviation activity and

[ FAA workload measures included:

1) examine the official FAA forecast

2) develop linear approximations of activity andworkload measures

3) develop time series estimates of activity andworkload measures

4) forecast aviation and workload measures basedIupon time series estimates.I The official forecast4 is based upon numerous analytic models.

The estimates provided by the analytic models are reviewed by

appropriate agency personnel. The analytic estimates are modified

based upon the expert judgement of the FAA personnel. As such,

~ 2 1bid, page 61.

SI 3 1bid, AVP, pages 28-35.

41bid, AVP.

1I A- ACUMEN ICS

orI

C it was decided that forecast for this project would derive from

analog models, The analog models employ the economic assumptions

I embodied in the FAA forecast as well as the official FAA estimates.

r The use of such analogs allow one to incorporate the expert judgement

of agency personnel embodied in the official estimates. See

V Table 1.

[ Histograms for several of the official estimates (e.g.

operations, pilots, aircraft) were developed by the project team.

A review of the histograms indicated that on surface the time

series relationships were linear. However, further examination

suggested that the historic data as well as the latter portion

j I of the official forecast were non-linear. The most likely analog

form for forecasting was the logistic curve.

An illustration of the difference between a linear and

[ logistic curve approximation is shown in Figure 1. The

general form of a linear approximation is:

Y = At + B

where Y = dependent forecast variable

t = independent time variable

4 I A = slope of the curve

B = intercept

The logistic curve is defined as

'y A

(1 + e(B + Ct))

A-4 ACUMENICS

I where Y = dependent forecast variable

t7= independent time variable, and

A, B, C are empirically determined coefficients.

[ As indicated in Figure 1 the linear and logistic forms may be

congruent for some portion of the forecast period. However, the

[logistic form yields more conservative estimates than the linearform for extended time periods. As such, the logistic function

[ tends to conform with the caution imported by expert judgement.

As noted above the development of forecast equations was

a three step process. The first step was the development of

linear approximations for aviation activity and agency workload

measures. In as much as the differences among scenarios are

measured in terms of economic variables, the initial linear

I approximations were of the form

aviation activity orworkload measure = function (economic variables)

Linear regression equations for the official baseline forecast

data were developed using a stepwise regression program. The

linear approximations were obtained for the appropriate historic

data period to fiscal 1992. The relationships developed using the

I stepwise procedure and the attendant statistics are shown in

Tables 3-19. Table 20 defines the terms used throughout this

section. Based upon the equations developed in this step and

the scenario economic variables, estimates were prepared of future

aviation activity and workload measure. That is, the values for

A' ] A-5ACUMEN ICS

xl Illustration of Linear and Logistic Forms

[ A

I( + e(B + CT)]

Time

Figure A-1

I A-6

Table A-i

r- LINEAR APPROXIMATIONS OF OFFICIAL BASELINE FORECAST 1970-1972r [ A. Number of Aircraft

TOTGA = -319.5067 + 5.5072 Employ

(t) (-17.94) (31.49) R2 = .984

U SEGA = -226.2078 + 4.1067 Employ[ (t) (-18.00) (33.27) R2 = .986

MEGA = -30.4632 + .5690 EmployI (t) (-10.32) (19.63) R2 = .960

B. Operations

TOPS = - 7o4334 + .0549 GNP(t) (-2.57) (28.79) R 2 = .975

LOPS = 5.4211 + .0133 GNP

(t) (5.96) (22.12) R2 = .959

I ITOPS = -12.8037 + .0416 GNP(t) (-6.06) (29.85) R2 = .977

C. FAA Air Route Traffic Control Centers

I TAIRHAND = -10.3391 + .0283 GNP

(t) (-8.12) (33.64) R2 = .982

TOVERS = - 3.8307 + .1072 Employ(-9.30) (25.43) R2 = .968

IFRDEP = - 5.0314 + .0171 DPI(-9.41) (33.56) R2 = .982

I*iA-

Table A-1- (Continued)

F [D. Flight Service Station (FSS)

r PLTBRF = -10.1438 + .0297 DPI(t) (-15.42) (47.56) R2 - .991

TOTFSS = -25.3077 + .0660 GNPF (-11.75) (46.41) R2 .990

TOTFLTPL = -17.0895 + .2879 Employ(-14.93) (24.60) R- = .966

E. Pilots

TOTPLT = 68.0713 + .7568 DPI(t) (3.24) (40.06) R2 = .990

I PRIVPLT = 64.3252 + .1951 GNP

(t) (6.80) (32.96) R2 = .985

TRANPLT = -85.2893 + .1024 GNP[ (t) (-16.05) (30.84) R2 = .983

STUPLT = 82.4102 + .1203 DPI(t) (10.80) (17.51) R2 = .950

II A-8

Table A- 2

.1 LIST OF FOPECASP EUATIONS

NAME BG RG SF

Total Operations Logistic LIN Logistic

Local Operations LIN LIN LIN

" Itinerant Operations Logistic LIN Logistic

Total Active GA Aircraft Logistic Logistic Logisticr Single Engine GA Aircraft Logistic Logistic Logistic

Mitiple Engine GA Aircraft Logistic Logistic LIN

[ Enroute Aircraft Handles Logistic Logistic LIN

IFR Departures Logistic LIN Logistic

Total Overs Logistic LIN Logistic

[ Flight Services LIN LIN LIN

Flight Plans Filed LIN LIN LIN

Pilot Briefing Logistic LIN LIN

Total Pilots LIN LIN LN

* Student Pilots LIN LIN LIN

Transport Pilot LIN LIN LIN

Private Pilot LIN LIN LIN

[ Linear Approximations 1970-1992

Number of Aircraft LIN LIN LIN

Operations LIN LIN LIN

j FAA Air Route Traffic Control Centers LIN LIN LIN

Flight Service Station LIN LIN LIN

[ Pilots LIN LIN LIN

Controller Staffing

Center Controllers LIN LIN LIN

Terminal LIN LIN LIN

Total Aircraft LIN LIN LIN

I Dployment Logistic

Note: BG = Balanced GrowthI1 RG = Rapid GrowthSF = Stagflation

A-9

C r the economic variables for the stagflation and rapid growth scenarios

were substituted in the equations to calculate appropriate values

I [ for activity and workload measures under each social scheme. The

r forecast for aviation activity and workload measures for each

scenario is presented in Volume 2.

In summary, the effort under this step provided estimates

r of aviation activity and workload fiscal measures for each scenario

for the period 1981 to 1992. The estimates of independent variables

I obtained in the preceding element were used to develop the long

range time series forecast equations for each scenario. It was

assumed initially that all long range forecast equations would be

I [ logistic in form. Time series logistic equations were developed

for aviation activity measures under each scenario using a non-

linear regression program.5 In the event a logistic form could

not be developed for a specific variable, linear time series were

constructed.

The variables forecast and attendant form of the equation

are shown in Table 2. The specific forecast equations for each

variable under each scenario, as well as the appropriate forms of

[ the equations, and the statistics are shown in Tables 3 to 18. The4

equations for controller staffing are shown in Table 19.

5 SAS/ETS User's Guide, Economitric and Time Series Library,1980 Edition. (SAS Institute, Inc. Box 8000, Cary, North

AI ACUJMEN ICS

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~cr Table A-l9

!CONTROLLER STAFFING EQUATIONS

S A. Center Controllers

(1) CENT = -5215.64 + 6.3305TAC(t) (-7.09) (24.43) R 2 = .973

(2) TERM = 5204.604 + 34.790 TOTGA

(t) (6.67) (8.19) R2 = .9437

B. Total Air Carrier Aircraft

1TAC = 1216.9761 + 1.4659 DPI(t) (14.05) (18.80) R2 = .957

C. Employment

I Employ = A R2 = .9944

[1 + e(B + Ct)]

A = .140.17 (t = 23.00)B -. 204435 (t =-2.32)

C = -. 0580251 (t =-9.78)

L

,I

I

! 1 A-27

AD-Ai25 357 SOCIOECONOMIC SCENARIOS VOLUME 11(U) ACUMENICS RESEARCH 2/2AND TECHNOLOGY INC BETHESDA MD 26 FEB SIFAA-PO-S-ii-YOL-2 DOT-FR78WRI-932

UNCLASSIFIED *F/G 17/2.1£ NI

*FlE D

ILO

* MICROCOPY RESOLUTION TEST CHART

NATIONAL BUREAU OF STANDARDS- 1963-A

ID

-- -. .- .... -. -- -...

I .Table A-20

DEFINITION OF TERMS

r TERM DEFINITION

I EMPLOY Employment

TOTGA Active total general aircraftSEGA Single engine general aircraftMEGA Multiple engine general aircraft

TOPS °Total operationsLOPS Local operationsITOPS Itinerant operations

GNP Gross National ProductDPI Disposable Personal Income

TAIRHAND Total enroute air handlesTOVERS Total oversIFRDEP IFR departures

PLTBRF Pilot briefingTOTFSS Flight servicesTOTFLTPL Flight plans filed

I TOTPLT Total pilotsPRIUPLT Private pilotsTRANPLT Transport pilotsSTUPLT Student pilotsCENT Center staffTAC Tactical Air CommandTERM Terminal staffFSS Flight service center

41!

01A-2P