BUREAU OF TRANSPORTATION STATISTICS UNITED STATES DEPARTMENT OF TRANSPORTATION

Volume 2 Number 1

May 1999

ISSN 1094-8848

JOURNAL OF TRANSPORTATION

AND STATISTICS
ii

The Journal of Transportation and Statistics is

published by the

Bureau of Transportation Statistics

US Department of Transportation

Room 3430

400 7th Street SW

Washington DC 20590

USA

journal@bts.gov

US DEPARTMENT OF

TRANSPORTATION

RODNEY E. SLATER

Secretary

MORTIMER L. DOWNEY

Deputy Secretary

BUREAU OF TRANSPORTATION

STATISTICS

ASHISH K. SEN

Director

ROBERT A. KNISELY

Deputy Director

ROLF R. SCHMITT

Associate Director for Transportation

Studies

SUSAN J. LAPHAM

Acting Associate Director for Statistical

Programs and Services

About the Bureau of Transportation Statistics

The Bureau of Transportation Statistics (BTS),

an operating administration of the US Department

of Transportation (DOT), is a national statistical

agency, DOT's representative on the Federal

Geographic Data Committee, and the home of

the National Transportation Library. BTS compiles,

analyzes, and makes accessible information

about the nation's transportation systems; collects

information on various aspects of transportation;

and enhances the quality and effectiveness

of the Department's statistical programs through

research, the development of guidelines, and

the promotion of improvements in data acquisition

and use.

Subscription information

To receive a complimentary subscription for Volume 2:

mail Customer Service

Bureau of Transportation Statistics

US Department of Transportation

Room 3430

400 7th Street SW

Washington DC 20590

USA

phone 202.366.DATA

fax 202.366.3640

email orders@bts.gov

internet www.bts.gov/

Cover and text design Susan JZ Hoffmeyer

Layout Gardner Smith of OmniDigital

Cover photo Images® copyright 1999 PhotoDisc, Inc.

The Secretary of Transportation has determined that the

publication of this periodical is necessary in the transaction of

the public business required by law of this Department.
JOURNAL OF TRANSPORTATION AND STATISTICS

Volume 2 Number 1

May 1999

Contents

Letter from the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Papers in This Issue

Growth in Motor Vehicle Ownership and Use:

Evidence from the Nationwide Personal Transportation Survey

Don Pickrell and Paul Schimek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Dimensions of Motor Vehicle Crash Risk 

Jing-Shiarn Wang, Ronald R. Knipling, and Lawrence J. Blincoe . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Do Environmental Regulations Increase Construction Costs for Federal-Aid Highways?

A Statistical Experiment

V. Kerry Smith, Roger H. Von Haefen, and Wei Zhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Rural Transportation Voucher Program for People with Disabilities:

Three Case Studies

Brad Bernier and Tom Seekins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

The Usefulness of Current International Air Transport Statistics

Kenneth Button . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Office Development, Parking Management, and Travel Behavior:

The Case of Midtown Atlanta

Erik Ferguson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Guidelines for Manuscript Submission, Instructions to Authors . . . . . . . . . . . . . . . . . . . . . . . 109

BTS Product Order Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

iii
v

Letter from the Editor

Dear Readers,

This issue of the Journal of Transportation and Statistics (JTS) ushers in our

second year of publication, along with a few changes. First, we welcome the

new Director of the Bureau of Transportation Statistics, Ashish K. Sen.

As only the second director of this agency, we look to him to nurture it

toward maturity and to chart a course for its future. Before coming to the

BTS, Sen served as the Director of the Urban Transportation Center and

the Statistics and Evaluation Laboratory at the University of Illinois in

Chicago. His extensive background in transportation and statistics will

bring a valuable new perspective to BTS.

This year we will be adding new editorial board members from the statistical community. We will also accept articles in LaTeX, a language widely used by statisticians, as part of an effort to better serve the statistical

community. With these changes, we hope to encourage greater participation by statisticians. 

JTS’s fundamental objectives, however, remain unchanged. Articles will

still cover the following areas:

m measurement of transportation activity and the performance of transportation systems,

m measurement and analysis of the importance of transportation and its

consequences,

m measurement and analysis of transportation trends,

m advancement of the science of acquiring, validating, managing, interpreting, and disseminating transportation information.

As a publication of a federal statistical agency, the JTS will continue to focus

on data, description, and analysis and will explicitly avoid policy studies. 

After careful consideration, we have decided to delay quarterly publication until 2000. In the last issue of the JTS, I wrote about adding a comprehensive statistical series of transportation performance indicators

starting later this year. This will not be done until BTS completes a thorough review of available measures. We will keep you updated on our publication plans.

As always, your interests, ideas, and recommendations for improving

the JTS are important to us. We invite you to communicate with us via

email at journal@bts.gov, by visiting our website at www.bts.gov/, or by

writing to me at the address on page ii of the journal. Thank you for your

continued interest and support.

DAVID L. GREENE

Editor-in-Chief
1

ABSTRACT

The size, composition, and use of the nation’s

household vehicle fleet are subjects of major interest to analysts and policymakers concerned with

the economic motivations and enviromental consequences of travel. The 1995 Nationwide Personal

Transportation Survey (NPTS), together with similar surveys conducted in 1969, 1977, 1983, and

1990, reveals important insights into the changing

patterns of household motor vehicle ownership

and use, as well as the underlying behavior that

produces them. This paper uses information from

the NPTS to address three related subjects: 1)

growth in personal motor vehicle travel and its

sources; 2) changes in the number, types, and age

distribution of household motor vehicles; and 3)

the determinants of households’ vehicle utilization

patterns and demands for private motor vehicle

travel. The results presented here can be useful to

transportation professionals seeking to understand

the patterns and determinants of motor vehicle

travel, as well as to planners and policymakers in

their efforts to design and implement strategies that

reduce the environmental consequences of growing

motor vehicle use. 

Growth in Motor Vehicle Ownership and Use:

Evidence from the Nationwide Personal Transportation Survey

DON PICKRELL

PAUL SCHIMEK

Volpe National Transportation Systems Center

U.S. Department of Transportatation

Don Pickrell, Volpe Center, U.S. Department of

Transportation, Cambridge, MA 02142. Email: pickrell

@volpe.dot.gov.
NATIONWIDE PERSONAL

TRANSPORTATION SURVEY

The Nationwide Personal Transportation Survey

(NPTS) is a periodic survey of demographic characteristics, motor vehicle ownership, and daily

tripmaking by U.S. households. All trips by household members are surveyed for a single “travel

day,” with interviews conducted so that each of the

seven days of the week, including holidays, is represented as a travel day for some households. Trip

data collected include the time the trip began and

ended, its length, the size and composition of the

traveling party, the mode of transportation used,

the purpose of the trip, and the specific vehicle that

was used (if the trip was made in a householdowned vehicle). In addition, data on all members’

trips to destinations more than 75 miles from

home during the two-week period ending on the

interview day is obtained from a subset of households. Responses to the 1995 survey were supplemented by summary data from the 1990 U.S.

Census. This data described the characteristics of

the geographic area of each sample household and

where each member worked. 

The first three surveys (1969, 1977, and 1983)

were conducted by the Census Bureau using faceto-face home interviews of a sample of households

selected randomly from address files, while the

1990 and 1995 surveys were conducted by the

Research Triangle Institute using random digit telephone dialing. The sample sizes of the five surveys

varied widely: approximately 15,000 households

were interviewed for the 1969 NPTS; 18,000 for

1977; 6,500 during 1983; 22,000 during 1990;

and nearly 40,000 in the most recent survey.1 The

1995 survey was conducted from May 1995 to

July 1996.

HOUSEHOLD MOTOR VEHICLE TRAVEL

The 1995 NPTS contains three different types of

information that can be used to produce estimates

of total vehicle-miles traveled (VMT) in personal

motor vehicles: 1) estimates of the number and

usage of household motor vehicles, 2) data on the

number of drivers and their estimates of how much

they drive, and 3) estimates of the number and

length of trips by household members using personal motor vehicles. This section explains how

each of these types of information can be used to

estimate total VMT and compares the results;

table 1 reports the various estimates and the data

sources used to construct them. 

Vehicle-Based Estimates

One estimate of total VMT can be derived from

survey respondents’ estimates of the total number

of miles each household vehicle was driven by all

drivers during the previous 12 months.2 Multiplying their average estimate of vehicle use (12,226

miles per year) by the 1995 NPTS estimate of the

total number of household motor vehicles (176.1

million) produces a figure of 2.153 trillion annual

VMT (table 1, line 1). The 1995 survey also

obtained odometer readings for about 44% of all

household vehicles at the beginning and end of a

period of several weeks, and these can be extrapolated to their annual equivalents.3 The estimate of

annual vehicle use constructed from these odometer data is 11,801 miles per year, or about 4% less

than the self-reported estimate; the total household

VMT estimate based on this figure is 2.078 trillion

annually (table 1, line 2). Both of these estimates

presumably include commercial driving in household vehicles but not in vehicles garaged outside

the home. 

2 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 Approximately 24,000 households were included in the

national sample drawn for the survey, while the remaining

16,000 were included in order to enrich the sample in a

few specific states and urban areas.

2 The survey asked respondents to estimate use of each

vehicle “available to” the household, so presumably some

company-provided cars were included in addition to those

owned or leased by household members. The specific

question it asked was, “About how many miles was this

vehicle driven [in the last 12 months] since the date

(month/year) it was bought or received? Include mileage

driven by all drivers.” Mileage estimates for vehicles

owned less than 12 months were annualized during postprocessing of the data.

3 Any seasonal variation in vehicle use that might make

the annualized estimates of individual vehicles’ usage

unreliable should not significantly affect the estimate of

average annual vehicle mileage because the survey was

administered over approximately a year-long period and

thus included roughly equal numbers of mileage measurements recorded during each season of the year. 
Driver-Based Estimates

A second source of VMT estimates from the NPTS

can be derived from surveyed drivers’ estimates of

the total number of miles they each drove during

the previous 12-month period.4 Because respondents were specifically instructed to include commercial driving (“miles driven as a part of work”)

in their responses, the total VMT estimate from

this source should be higher than the vehicle-based

estimates, which included only driving in vehicles

owned by household by members. The resulting

average of 13,478 annual miles per driver multiplied by the NPTS estimate of 176.8 million drivers

produces an estimate of 2.383 trillion annual VMT

(table 1, line 3), which is indeed considerably larger than the two estimates that include only household vehicle use.

Trip-Based Estimates

A third source of VMT estimates can be constructed using the trip-level data recorded in NPTS

household travel diaries, which asked respondents

to itemize their trips ending on the previous day,

the “travel day,” and also all trips of 75 miles or

more ending during the previous two weeks, the

“travel period.” By counting only those trips where

the respondent was a driver of a personal motor

vehicle, we estimated their average daily miles of

travel.5 The resulting annual VMT estimates from

combining the travel day and travel period data are

2.181 trillion miles for personal travel only (table

1, line 4), slightly above the higher of the two 

vehicle-based estimates, and 2.279 trillion VMT

including commercial driving (table 1, line 5),

somewhat lower than the driver-based estimate. 

Comparing the Estimates

Although they are derived from completely separate

sections of the survey, the three VMT estimates that

include only driving in household-owned vehicles

are reasonably consistent with one another; the

range from lowest to highest is only about 5%.

Similarly, the difference between the two NPTS estimates that include commercial driving in nonhousehold vehicles is about 5%. For comparison,

the Federal Highway Administration (FHWA)

reports in its annual Highway Statistics publication

that 2.228 trillion miles were driven in passenger

cars and light trucks during 1995 (USDOT 1995)

(table 1, line 6), including both their personal and

commercial use; adding heavy-duty vehicle use

PICKRELL & SCHIMEK 3

TABLE 1 Estimates of Total Vehicle-Miles Traveled (VMT) During 1995

Estimate

Line Source Basis of estimate Type of data (trillion VMT)

1 NPTS Household vehicle use Owner estimates 2.153

2 NPTS Household vehicle use Odometer readings 2.078

3 NPTS Driving for all purposes Driver estimates 2.383

4 NPTS Driving for personal travel Trip diaries 2.181

5 NPTS Driving for all purposes Trip diaries 2.279

6 FHWA Light-duty vehicle travel State traffic counts 2.228

7 FHWA All motor vehicle travel State traffic counts 2.423

Sources: 1995 NPTS; and the U.S. Department of Transportation, Federal Highway Administration (FHWA), Highway Statistics 1995

(Washington, DC: 1995), table VM-1.

4 The question asked was, “About how many miles did

you personally drive during the past 12 months in all

licensed motorized vehicles? Include miles driven as a part

of work.”

5 The survey asked respondents who made more than 10

daily trips as a part of work (e.g., as a truck or taxi driver) to give a separate estimate of their total daily commercial driving. The trips made by commercial drivers

who made 10 or fewer trips on the travel day were included as part of the travel day diary. A comprehensive estimate of total annual VMT includes the sum of all three of

these components: travel day VMT, travel period VMT,

and daily commercial VMT. (The 2,900 travel day trips in

the sample, which were recorded in both the travel day

and travel period data, were eliminated from the travel

day VMT estimate to avoid double counting.)
brings the FHWA estimate of total VMT to 2.423

trillion (table 1, line 7)6 (USDOT 1995). As would

be expected, both of the NPTS-derived VMT estimates that include driving in nonhousehold vehicles (table 1, lines 3 and 5) fall between the two

FHWA figures.

Recent Growth in VMT

While several changes between the 1990 and 1995

NPTSs complicate the task of comparing VMT

estimates for these two years, the basic survey

method (household telephone survey) and the selfreported annual driving and vehicle-use questions

remained unchanged between the two surveys,

making VMT estimates using these two sources

directly comparable for 1990 and 1995. The total

VMT estimates derived from responses to these

questions show very different changes over the

five-year period between the two surveys: the total,

based on survey respondents’ estimates of household vehicle use, rose 4.4% (about 0.9% annually), while that based on their estimates of annual

driving increased 11.4% (2.2% annually).

Unfortunately, it is not possible to derive an estimate of VMT growth from the odometer-based

VMT estimate, because this method was introduced into the NPTS for the first time in 1995. 

Comparing the trip-based VMT estimates from

the 1990 and 1995 surveys is complicated by a

major change in methodology: while the 1990 survey asked respondents to recall their trips from the

previous day, the 1995 survey asked respondents

to record all of their trips on a designated travel

day in travel diaries, which were subsequently read

to interviewers. Not surprisingly, the 1995 method

recorded many more trips than the 1990 procedure

and earlier surveys; specifically, many short trips

that had apparently been overlooked using the

recall method were recorded by the diary method.

Although the change in survey method is likely to

have greatly improved the accuracy and completeness of trip recording, the 1990 NPTS trip-based

estimate of total VMT in retrospect seems likely to

have been an underestimate. Comparing the estimate of total household motor vehicle travel

reported in the 1990 NPTS almost certainly leads

to a substantial overestimate of the 1990 to 1995

growth in VMT.

For the 1994 pretest of the 1995 NPTS, some

surveys were completed with the new method

(diary) and some with the old (respondent recall),

so that the effects of the change in methodology

can be compared directly. (A full discussion of this

issue is presented in the appendix). Adjustment factors for trips and miles traveled were calculated

based on the pretest data (shown in appendix table

A-1) and applied to the 1990 trip-based data to

produce a VMT estimate more closely comparable

to the 1995 figure. The change between the resulting adjusted 1990 trip-based VMT estimate and

the 1995 figure, 18.1% over the five-year period or

3.4% per year, is much greater than the corresponding changes in the driver- and vehicle-based

VMT estimates reported previously. It is important

to emphasize, however, that even the adjusted

1990 trip-based VMT estimate is not completely

comparable to the 1995 figure, because these

adjustments do not account for other changes in the

survey, such as the inclusion of commercial driving

during 1995. In contrast, the questions and methods used in the driver and vehicle estimates of VMT

did not change between the 1990 and 1995 administrations of the survey, so the estimates of VMT

growth they produce should be more reliable. 

For comparison, the annual growth rates

implied by the FHWA Highway Statistics data for

1990 to 1995 are 2.3% annually for light-duty

vehicles only and 2.5% per year including heavyduty vehicles. These fall approximately midway

between the estimates based on NPTS respondents’

reports of household vehicle use (0.9% annually)

and the number and lengths of their trips (3.4%

per year), but they conform quite closely with the

growth rate (2.2% annually) derived from respondents’ estimates of their driving activity. On the

basis of its close agreement with the growth rates

implied by the FHWA data, it appears that the

NPTS driving-based estimate of total VMT may

provide the most reliable indicator of the pace of

recent growth in household travel.

4 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

6 The FHWA definition of light trucks includes all twoaxle, four-tire trucks.
Longer Term Growth in VMT

Fortunately, there is somewhat closer agreement

about the longer term pattern and average rate of

growth in motor vehicle travel, both between the

two NPTS methods and between their results and

other sources. Figure 1 reports annualized growth

rates for the NPTS driver- and vehicle owner-based

estimates of total travel in household-owned motor

vehicles from 1977 to 1995, as well as for all lightduty vehicles as reported in FHWA’s Highway

Statistics. As the figure indicates, the three sources

yield estimates of annual VMT growth over this

extended period ranging from 2.8% to 3.6%, not

an unreasonably wide interval considering the differences in methods and data used to produce them. 

All three sources also show the rate of VMT

growth accelerating sharply between 1983 and

1990, from its moderate 1977 to 1983 pace, and

then slowing from 1990 to 1995. The most significant disagreement seems to be over how much the

rate of growth slowed during this latter period. But

as figure 1 shows, the FHWA Highway Statistics

data indicate both a lower average growth rate over

the entire 1977 to 1995 period and more stability

among the three separate intervals comprising it.

The consistency of the procedures used to develop

the FHWA estimates throughout most of this period

and the continuous annual basis of that data series

probably mean that it provides a more reliable picture of both longer term average growth in travel

and shorter term variations in the pattern of its

growth than can be obtained from an occasionally

administered survey such as the NPTS. The primary

advantage of the NPTS is the insight it provides into

the changing patterns of household vehicle ownership and use and their underlying causes. 

SOURCES OF GROWTH IN

HOUSEHOLD TRAVEL

As an illustration, total VMT can be divided into

several individually meaningful components in

order to gain more insight into the forces producing growth in motor vehicle travel. Figure 2

employs the calculation previously used to derive

the NPTS driver-based estimate of total annual

VMT—average annual miles driven per licensed

driver multiplied by the estimated number of

licensed drivers—to show how the 1990 to 1995

change in total VMT can be broken down into

changes in annual miles per driver and in the number of licensed drivers.7 The NPTS shows that

PICKRELL & SCHIMEK 5

Highway Statistics light-duty vehicles

NPTS vehicle owner estimate

NPTS driver estimate

0.0 0.5 1.0 1.5 2.0 2.5

Trillion VMT per year

1.26

1.51

2.14

2.38

1.24

1.48

2.06

2.15

1.37

1.53

1.99

2.23

FIGURE 1 Comparison of Total VMT Estimates

1977

1983

1990

1995

Annual change

Each period

3.0%

5.1% 3.6%

2.2%

3.0%

4.8% 3.1%

0.9%

2.0%

3.8% 2.8%

2.3%

1977–95

7 One potential problem in interpreting the vehicle- and

driver-based VMT estimates in this way is that the number of household vehicles and the number of licensed drivers vary throughout the year, and some arbitrary date

must be chosen to count them. In effect, the NPTS sets this

date individually for each surveyed household, but this is

likely to be a very minor problem.
annual miles driven per licensed driver increased

2.7% over this period, while the number of

licensed drivers increased 8.5%; these changes

combine to produce the previously reported increase of 11.4% in the driver-based estimate of

total travel.8

As figure 2 also shows, the change in the number of licensed drivers can be further broken down

into changes in the total population, the share of

the population of driving age (16 years and above

in most states, but higher in a few states), and the

fraction of the driving-age population actually

licensed to drive. The number of licensed drivers

increased between 1990 and 1995 because all three

of its components grew, although most of the

growth in licensed drivers was accounted for by the

increased share of the population of driving age,

which rose 6.3%. In principle, it should also be

possible to break down the growth in annual miles

per driver into changes in annual miles driven per

vehicle and in the number of vehicles per driver,

but the instruction to survey respondents to

include driving as part of work in vehicles garaged

outside the household in their estimates of annual

driving (see footnote 2) makes this measure inconsistent with the NPTS estimate of the vehicle population, which is limited to household vehicles.9

More detailed analysis reveals that the increase

in annual VMT per licensed driver occurred primarily among women and older men. Table 2

reports that VMT for the youngest drivers (16 to

19 years old) declined significantly among both

men and women, although the percentage decline

among males was twice as large as that among

females. In contrast, driving increased significantly

and fairly uniformly among women aged 20 to 64

years; only among women 65 and older did it fail

to change significantly. Table 2 also shows that

changes in annual driving among the most active

drivers—men aged 20 to 64 years—were mixed,

the 20 to 34 age group showed a slight decline, the

men 35 to 54 years showed almost no change, and

6 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

FIGURE 2 Components of Change in Total VMT: 1990–95

Annual

VMT per

vehicle

Annual VMT

per driver

+2.7%

Licensed

drivers

+8.5%

Total

VMT

+11.4%

Vehicles

per driver

Total

population

+1.0%

Percentage of

population

aged 16 and over

+6.3%

Percentage of

16 and over

licensed to drive

+1.1%

Note: Percentages equal product, rather than sum, of the change in individual components.

8 The percentage change in total travel is equal to the

product, rather than the sum, of the change in the individual components.

9 Ignoring this inconsistency, which was also present in the

1990 survey, results in calculated changes of 4.5% in

annual vehicle use and –1.7% in the number of (household) vehicles per driver from 1990 to 1995, which

together produce the 2.7% increase in annual VMT per

driver reported in figure 2. The calculated increase in

annual vehicle use (4.5%) contrasts with the change in

respondents’ estimates of annual household vehicle use

reported in the 1990 and 1995 surveys, which is –2.0%.

This suggests that much of the growth in total VMT may

have been in the use of commercial and other vehicles

garaged outside the household. Unfortunately, no estimate

of the vehicle population that includes household vehicles

plus those others that NPTS respondents are likely to have

reported driving is readily available. 
those aged 55 to 64 showed a modest increase.

Taken as a single group, however, there was little

increase in driving, perhaps suggesting some tendency for motor vehicle use to stabilize among

those who are already the most active drivers.

Older males showed the largest increase in average

annual driving between 1990 and 1995, although

by 1995 they still drove considerably less than their

younger counterparts. 

Travel Mode and Vehicle Occupancy

Because the demand for vehicle travel ultimately

derives from the demand for person travel,10

growth in VMT can also be related to the underlying demand for person-miles of travel (PMT).

Specifically, total PMT can be translated into VMT

using the share of trips that are made using motor

vehicles and the average occupancy of motor vehicles used for each trip. The 1995 NPTS reveals

continuing, though modest, growth in the share of

trips made using household-owned motor vehicles:

from about 84% in 1977 to slightly above 87%

during 1990, and up to more than 89% by 1995.

The increase in motor vehicle use has come at the

expense of walking, public transit, and school bus

travel. While the share of bicycle trips increased

between 1977 and 1995, it remains under 1%.

At the same time, the survey shows that vehicle

occupancy continued to decline: the fraction of

vehicle trips made by a single occupant rose from

about 60% during 1977 to 67% by 1990 and to

68% by 1995. A more precise indicator of vehicle

occupancy is the average number of person-miles

per vehicle-mile, which implicitly weights vehicle

occupancy for each trip by its distance; this measure declined from 1.89 persons in 1977 to 1.64

during 1990, and further to 1.59 persons by 1995.

The combination of a rising share of trips in personal vehicles and declining occupancy of those

vehicles means that an increasing number of vehicle-miles are required to meet the same underlying

demand for person-miles, so that even the modest

recent growth in total person trips and miles of

travel has been reflected in the significant increases

in vehicle trips and vehicle-miles noted previously.

HOUSEHOLD VEHICLE OWNERSHIP

The NPTS also provides detailed information

about continuing changes in the number, types,

and use of motor vehicles owned by U.S. households. The two major developments revealed by

the succession of surveys are the trend toward

nearly ubiquitous ownership of at least one vehicle

among U.S. households, and the rapidly increasing

number of households owning multiple vehicles.

More recently, the 1990 and 1995 NPTSs highlight

the increasing substitution of vehicles classified as

light-duty trucks—pickup trucks, passenger and

cargo vans, and sport utility vehicles (SUVs)—for

automobiles in providing household transportation. While widespread use of pickup trucks as

household vehicles significantly predates that of

other light trucks (and displays a markedly different geographic pattern), recent purchases of vans

PICKRELL & SCHIMEK 7

TABLE 2 Changes in Annual VMT per Driver by Age and Sex: 1990–95

Male Female

Age 1990 1995 % change 1990 1995 % change

16–19 9,543 8,203 –14.0% 7,387 6,870 –7.0%

20–34 18,310 17,980 –1.8% 11,174 12,001 7.4%

35–54 18,871 18,859 –0.1% 10,539 11,463 8.8%

55–64 15,224 15,844 4.1% 7,211 7,795 8.1%

65+ 9,162 10,320 12.6% 4,750 4,788 0.8%

All ages 16,536 16,553 0.1% 9,528 10,143 6.5%

Source: Tabulated from 1990 and 1995 NPTS household files.

10 Person travel refers to all trips outside the home by

household members made by any means, including on

foot and by motorized or nonmotorized vehicles of all

types. As employed in the NPTS, vehicle travel includes

only household members’ trips that are made using personal motorized vehicles owned by that or another household.
and SUVs have substantially increased the share of

household vehicles and mileage accounted for by

light trucks. 

Increasing Vehicle Ownership 

Table 3 summarizes changes in the distribution of

vehicle ownership among U.S. households from

1977 through 1995. It also shows accompanying

changes in the average number of vehicles owned

and in the number of household members of driving age.11 Although the percentage of households

without vehicles was not large even at the outset of

this period, it declined sharply, while the proportions of households owning two and three or more

vehicles rose significantly. Thus, during 1977 the

number of carless households almost exactly

equaled the number owning three or more vehicles,

yet by 1995 the number of households with three

or more vehicles was more than twice as large as

the number without vehicles. 

Interestingly, however, these seemingly large

changes in the distribution of households among

vehicle ownership categories were translated into

comparatively modest growth in average vehicle

ownership. As table 3 shows, the average number

of vehicles per household rose from 1.59 during

1977 to 1.78 in 1995, an increase of only about

12% over a period spanning nearly two decades.

But the average number of household members of

license-eligible age fell slightly, as the effect of continuing declines in household size offset that of the

aging of the “baby boom” generation. Thus, as

table 3 shows, the number of vehicles per household member of driving age increased from 0.76 in

1977 to 0.89 (17%) in 1990, but remained unchanged in the 1995 survey.

The Increasing Importance of Light Trucks

As indicated previously, a major change in the

composition of the household vehicle fleet (i.e., the

increasing substitution of light-duty trucks for

automobiles) has taken place during the period

spanned by the NPTS. Table 4 reports the distribution of household vehicles during 1990 and 1995

by type; it indicates that passenger automobiles

represented only about 65% of household vehicles

in 1995, a significantly lower share than the more

than 71% they accounted for only five years

earlier.12 In contrast, SUVs accounted for 7% of

household vehicles by 1995, exactly double their

representation in 1990, while the share of passenger vans also increased rapidly, from 5.4% to

7.9% of household vehicles. Table 4 shows that the

representation of pickup trucks, the earliest light

truck models to be purchased on a widespread

basis for passenger transportation, rose only slightly between 1990 and 1995, although the share of

pickups, nearly 18%, still exceeds that of all other

light trucks. 

Because the nation’s household vehicle fleet

grew rapidly during the period covered by table 4,

what may seem like relatively modest changes in

8 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 3 Changes in Household Motor Vehicle

Ownership: 1977–95

Variable 1977 1983 1990 1995

% of households owning:

0 vehicles 15% 14% 9% 8%

1 vehicle 35% 34% 33% 32%

2 vehicles 34% 34% 38% 40%

3 or more vehicles 16% 19% 20% 19%

Vehicles/household 1.59 1.68 1.77 1.78

Household members 

16 or older 2.10 2.06 1.98 2.01

Vehicles/household 

member 16 or older 0.76 0.82 0.89 0.89

Source: 1990 NPTS, Summary of Travel Trends, tables 1, 2,

and 4; and 1995 NPTS.

11 The number of license-eligible household members is

used in this analysis because the number of licensed drivers per household is so closely related to the average

number of household vehicles. This suggests that the decision by a household member to obtain a driver’s license is

not separable from the household’s decision to acquire an

additional vehicle.

12 Unlike the 1995 NPTS, the 1990 survey did not include

a category for sport utility vehicles in its vehicle-type classification. The SUV category was recreated for this paper

by using the SUV vehicle make and model codes from the

1995 survey to identify SUVs in the 1990 sample.

Unfortunately, we were unable to tabulate the distribution

of vehicles among the same type classes shown in table 4

for previous survey years, although the passenger van and

SUV categories were probably quite small before 1990.
the proportions of vans, SUVs, and pickup trucks

obscures significant increases in their numbers.

The number of vans owned by households increased by nearly 5 million between 1990 and

1995, while the number of SUVs grew by more

than 6 million and the number of pickups by nearly 3 million. Thus, in total, the number of light

trucks owned by households grew by almost onethird from 1990 to 1995; as a result, they accounted for more than one-third of the household

vehicle fleet for the first time in 1995. In contrast,

table 4 shows that the number of passenger cars

actually declined during this period, suggesting

that households were replacing older automobiles

with new SUVs and vans. Recent sales figures suggest that the effect of this shift from conventional

automobiles to trucks on the composition of the

household vehicle fleet has not yet peaked, since

truck models currently represent nearly half of all

light-duty vehicles sold, with SUVs continuing to

exhibit the strongest sales growth of any passenger

vehicle category (Another Month 1999). 

AGING OF THE FLEET

The 1995 NPTS reveals continued aging of the

household vehicle fleet.13 As figure 3 shows, the

average age of household automobiles increased

sharply from 5.5 to 7.2 years between the 1977

and 1983 surveys and then more slowly, reaching

8.3 years through 1995. The average age of household-owned light-duty trucks followed a slightly

PICKRELL & SCHIMEK 9

TABLE 4 Household Vehicles by Type: 1990 and 1995

1990 1995

Number Percentage Number Percentage Percentage change

Vehicle type (millions) of total (millions) of total 1990–95

Passenger car 117.5 71.2% 113.3 65.2% –3.6%

Light truck:

Sport utility1 5.9 3.5% 12.2 7.0% 107.6%

Van 9.0 5.4% 13.8 7.9% 53.8%

Pickup 28.4 17.2% 31.1 17.9% 9.6%

Subtotal, light truck 43.2 26.2% 57.1 32.8% 32.1%

Other truck 1.0 0.6% 0.7 0.4% –27.9%

Recreational vehicle 0.9 0.5% 0.9 0.5% 6.0%

Motorcycle 2.2 1.3% 1.7 1.0% –24.2%

Other 0.4 0.2% 0.1 0.1% –57.6%

Subtotal, type known 165.1 100.0% 173.8 100.0% 5.3%

Unknown 0.1 2.3 2

TOTAL 165.2 176.1 6.6%

1 1990 data retabulated using 1995 definition of sport utility vehicles.

2 Computed percentage change is extremely large.

Source: Tabulated from 1990 and 1995 NPTS vehicle files.

13 There is no unambiguously “correct” way to translate

the distribution of vehicle model years recorded by the

NPTS into a fleet-average vehicle age, partly because the

NPTS surveys households over a period that typically

includes more than one calendar year. In addition, the difference between the calendar year and most vehicles’

model years means that it is not obvious how to code vehicle ages. This paper uses the average ages for 1977, 1983,

and 1990 reported as part of the 1990 NPTS (USDOT

1993, 3–40). The 1995 figures were then calculated in a

manner consistent with the 1990 data: the most recent

model year vehicles (1996 and a very few 1997 vehicles)

were assigned an age of one, as were one-year-old vehicles

(those with a model year of 1995); model year 1994 vehicles were assigned an age of two, model year 1993 vehicles were given an age of three, and so forth.
different pattern, as the figure shows: like that of

automobiles, it rose sharply between the 1977 and

1983 surveys, but then declined significantly

between 1983 and 1990 before increasing again by

1995. The gradual convergence of the average ages

of autos and light trucks shown in figure 3 again

probably reflects the increasing substitution of

light trucks, particularly the more recently marketed models such as minivans and SUVs, for household automobiles.

Because the timing of new vehicle purchases

(both to replace aging ones and to expand vehicle

availability) is sensitive to macroeconomic conditions, the patterns of vehicle aging shown in figure

3, particularly the sharp increase in average ages of

both cars and light trucks between 1977 and 1983,

may be partly attributable to the severe recession

of the early 1980s, the rapid recovery that followed, and the subsequent slowdown during the

early 1990s. Superimposed on this pattern, however, appears to be a gradual longer term increase in

the average age of household-owned vehicles that

must be explained by other factors. The accompanying changes in the age distribution of household

vehicles provides some useful suggestions about

the underlying causes of fleet aging. 

Changes in the Age Distribution of

Household Vehicles

Figure 4 displays the age distribution of the

nation’s household vehicle fleet for each of the four

NPTS years. As it shows, the number of U.S.

household-owned new vehicles that were up to

two years old in 1995 was only slightly greater

than the comparable figures for 1983 and 1990

and was well below the 1977 number, despite

rapid expansion of the total household vehicle fleet

throughout this period. After rising steadily from

1977 through 1990, the number of three- to-fiveyear-old vehicles also declined significantly in the

1995 survey. At the other end of the age distribution, however, the number of six- to nine-year-old

vehicles, which had risen only modestly between

10 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

5.0

6.0

7.0

8.0

9.0

Average age (years)

Automobiles

Light trucks

1977 1983

NPTS date

1990 1995

FIGURE 3 Trends in Vehicle Age by Type: 1977–95

0–2 3–5

Age category (years)

6–9 10+

0

10

20

30

40

50

60

Millions of vehicles

1977 1983 1990 1995

FIGURE 4 Distribution of Household Vehicles by Age: 1977–95
1983 and 1990, increased significantly by 1995,

while the size of the oldest vehicle age cohort, those

10 or more years old, continued the rapid growth

revealed by previous surveys. Thus, by 1995 vehicles that were 10 or more years old accounted for

more than one-third of all household vehicles,

almost exactly double the 17% share they represented in 1977. 

PATTERNS OF VEHICLE USE

The implications of the continued aging of household vehicles for transportation safety, urban air

pollution, and energy consumption depend not

only on their age distribution but also on utilization of vehicles of different ages. Specifically, if utilization declines rapidly with vehicle age, then the

effects of progressively tighter safety, emissions,

and fuel efficiency standards for new vehicles will

be quickly noticeable. However, if older vehicles

are used nearly as intensively as newer ones, the

effects of these measures will require many years

after they are adopted to be widely felt. The gradual retirement of vehicles of different “vintages” as

they age and the changing rates at which vehicles

accumulate mileage with increasing age interact to

determine the distribution of total household VMT

across vehicles of different ages, and the NPTS

reveals important information about each of these

effects. 

A Note on the NPTS Measures of Vehicle Use

As the earlier discussion of total VMT estimates

indicated, the NPTS includes two measures of vehicle usage. Households responding to the survey

were first asked to estimate the number of miles

each of the vehicles available to them were driven

during the previous 12 months; respondents provided usable estimates of annual mileage for more

than 80% of the 75,000 household vehicles identified in the survey. In addition, odometer readings

were obtained for each household’s vehicles at the

time of the interview, and surveyors subsequently

attempted to obtain odometer readings for each of

these same vehicles several weeks after the initial

interview. The difference in odometer readings

between these two dates was then adjusted for normal seasonal variation in household driving activity (the survey period spanned more than a year)

and extrapolated to an estimate of each vehicle’s

annual usage. The paired odometer readings necessary to construct these estimates of annual usage

were obtained for about 44% of all household

vehicles identified in the 1995 NPTS. 

While it might appear that the odometer-based

estimates provide a more reliable measure of vehicle use, two considerations complicate the choice

between the odometer-based and respondentreported estimates. First, while there are almost

certainly errors in household members’ estimates

of how much each vehicle was used, there may also

be important, if less obvious, sources of measurement error in the odometer-based estimates. Errors

could have arisen in reporting a vehicle’s odometer

readings either during the initial telephone interview or at the time of the followup call. Errors also

could have been introduced during the complex

process used to convert differences in odometer

readings to estimates of annual driving.14

Second, the estimates of average vehicle use, and

the relationships of vehicle use to other variables

that can be inferred from the subsample of vehicles

for which the two usage measures are available, are

both subject to potential bias. In part because the

owner-reported estimates of vehicle use were obtained for such a large fraction of all household

vehicles identified by the survey, the typical characteristics of both the vehicles for which these estimates were available and the households who

owned them closely mirror those characteristics of

all households and vehicles included in the NPTS.

In contrast, the vehicles for which odometer-based

estimates of annual usage were obtained are significantly newer on average, tend to include a higher

percentage of automobiles, and are more commonly owned by households with only one or two vehicles than is the case for all household vehicles

identified in the survey. 

Thus, it is difficult to choose between the

respondent-reported and odometer-based usage

estimates, and the two measures do not agree

closely. Their simple correlation among the more

PICKRELL & SCHIMEK 11

14 See USDOT 1997, appendix K, for a detailed discussion

of the procedure for annualizing the odometer reading

data.
than 25,000 vehicles for which both were obtained

is only 0.64. While the odometer-based estimates

seem likely to be inherently more reliable, they may

still contain significant measurement errors

because of the characteristics of this subsample.

Estimates of average usage and of the relationships

of use to other variables might be sources of bias

for the subsample. Because of the difficulty of

choosing between them, this section employs both

the owner-reported and odometer-based estimates

of vehicle use in each of the analyses it reports. 

Annual Utilization by Vehicle Age

Figure 5 shows the 1995 patterns of estimated

annual usage of different aged household vehicles

calculated from the two vehicle-use measures. Both

measures indicate that the five newest model years

in the household vehicle fleet are driven quite

intensively, averaging nearly 14,000 miles annually, according to the owner-reported use estimates,

and about 13,000 miles annually, according to the

odometer data. Surprisingly, vehicles between 6

and 10 years old seem to be driven nearly as much,

averaging 11,000 to 12,000 miles annually, depending on the measure used. Figure 5 shows that

according to both measures, it is not until approximately age 12 (model year 1983 in the 1995

NPTS) that annual utilization drops consistently

below the 10,000-mile threshold.15 While the small

samples of vehicles older than 15 years from which

odometer readings were obtained in both surveys

produces considerable variation in the average utilization of individual age cohorts, it appears that

usage reaches a “floor” of approximately 6,000

miles annually, even among the oldest vehicles

remaining in the household fleet. 

The Distribution of Household VMT

by Vehicle Age

Figure 6 shows the pattern of usage of household

vehicles by age during 1995 derived from the

12 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 2 3 4 5 6 7 8 9 10 12 14 16 18 20

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

Average miles driven per year

Owner-reported

Odometer estimate

Age of vehicle

FIGURE 5 Vehicle Age and Annual Miles

 Driven: 1995 NPTS

1 2 3 4 5 6 7 8 9 10 12 14 16 18 20

Percentage of VMT

Owner-reported

Odometer estimate

Age of vehicle

FIGURE 6 Vehicle Age and Percentage of Total

 VMT: 1995 

0%

2%

4%

6%

8%

10%

12%

15 The customary “model” of individual vehicles’ gradually

declining utilization with increasing age that is suggested by

cross-sectional analysis of the vehicle age distribution and

mileage accumulation may be misleading, or at least incomplete. Lave (1994) argues that an entirely different process

may be at work, wherein households with high travel

demands purchase new vehicles frequently and “wear them

out” quickly, while households with low travel demands

purchase new vehicles infrequently and retain them for

longer periods. Assuming some distribution of household

travel demands, this process would produce exactly the

same fleet age and mileage accumulation patterns revealed

by the 1995 NPTS and its predecessors. In fact, both of these

models are probably simultaneously at work within the

household vehicle fleet, although their relative contributions

to the patterns revealed in the data are difficult to assess. In

any case, they have similar implications for the effects of

fleet turnover on the age distribution of VMT and on problems such as safety, air pollution, and energy consumption.
NPTS to produce the distribution of total household VMT. As it indicates, the effect of declining

usage with increasing vehicle age offsets the larger

number of older vehicles, so that a higher fraction

of total VMT is accounted for by relatively new

vehicles than their representation in the fleet would

suggest. Thus, according to the NPTS, nearly 50%

of all household VMT in 1995 was driven by vehicles that were five years of age or newer, with most

of the remainder distributed among vehicles of

ages 6 through 15 years. 

Annual Utilization by Vehicle Type

Table 5 compares average annual miles driven in

different types of household vehicles, again computed from both the owner-reported and odometer-based estimates of vehicle use. The two

measures disagree about exactly how much automobiles are typically driven. The odometer figure

is about 6% less than owners’ estimates, but both

indicate that automobiles tend to be less intensively used than other types of household vehicles. Vans are the most heavily used household

vehicles, while other light trucks—sport utility

vehicles and pickups—fall between automobiles

and vans. Since older vehicles of all types tend to

be used less than newer models, however, some of

the less intensive use of pickups may simply be

associated with their much higher average age.

The annual use estimates for all household vehicles, which are about 11,800 miles from the

odometer-based data and 12,200 miles from

owners’ estimates, are generally consistent with

those reported by other sources.16

A MODEL OF VEHICLE USE

Table 6 shows the results of an analysis designed to

clarify the independent effects of age and vehicle

type, as well as to explore the influence of household demographic and economic characteristics on

vehicle use. The table reports least-squares regression estimates of the parameters of a model relating annual utilization of vehicles to characteristics

of the households that own them, their locations,

and the vehicles themselves. Changes in vehicle

usage can be thought of as a short-run adjustment

to changes in factors influencing households’

demands for private vehicle travel, such as their

demographic composition or the price of fuel,

which allows households to respond to such

changes without altering their vehicle ownership

levels. Households are likely to adjust to permanent changes in these factors by varying their levels

of automobile ownership, thereby producing a tendency for average vehicle use to return to some

“target” or equilibrium level. Over the longer run,

however, this target level may itself rise or fall in

response to factors such as additional household

members reaching driving age, changing costs of

vehicle ownership and use, or improvements in

vehicle performance and durability. 

PICKRELL & SCHIMEK 13

TABLE 5 Mean Age and Annual Usage, 

by Vehicle Type

Mean age

Vehicle type (years) Reported1 Odometer2

Automobile 8.2 11,988 11,318

Van 6.7 14,256 14,389

Sport utility 6.6 13,853 13,436

Pickup 9.6 12,064 11,826

All household vehicles3 8.3 12,226 11,801

1Average of vehicle owners' estimates of annual usage.

2Average of estimated annual usage for vehicles from which

odometer readings were obtained.

3Includes other trucks, motorcycles,and recreational vehicles. 

Source: Tabulated from 1995 NPTS vehicle file. 

Average annual 

miles driven

16 For example, the Federal Highway Administration

(USDOT 1995, table VM-1, p. V-92) reports average

annual mileage of 11,489 for automobiles plus two-axle,

four-tire trucks, which corresponds roughly to the definition of household vehicles employed in the NPTS. This

number is somewhat below the odometer-based estimate

of average household vehicle use reported in table 5.

However, the FHWA figure is derived by dividing its estimate of total VMT driven in those vehicles by the number

of them registered during the year. The latter measure

overstates the actual number of those vehicles in use,

because the state registration data used by FHWA to compile it double count vehicles that are sold or moved

between states and thus registered twice during the same

year. Compared with survey data on the number of vehicles in use reported by R.L. Polk (AAMA 1996), the

FHWA vehicle stock estimate appears to be approximately 10% too large. Adjusting to compensate for the double

counting of vehicles in state registration data produces an

estimate of 12,638 miles per vehicle in 1995, somewhat

above the figure derived from NPTS respondents’ estimates of vehicles use that is reported in table 5.
Because the coefficient estimates reported in

table 6 are derived from a large cross-section of

households and individual vehicles, they theoretically represent the effects of the variables on households’ long-run target or desired levels of vehicle

use. The coefficient estimates for the categorical or

“count” variables included in the model (e.g.,

numadult or number of adults in the household)

indicate the proportional or percentage increase in

the annual number of miles driven in each of the

household’s vehicles that is associated with an

increase of one in the value of that variable (e.g.,

the presence of another adult in the household).

Because the continuous explanatory variables

included in the model are in logarithmic form (e.g.,

linc or the natural logarithm of the household’s

annual income), their estimated coefficients indicate the percentage change in annual vehicle use

associated with a 1% change in the value of each

variable. 

Demographic and Economic Effects

on Vehicle Use

The coefficient estimates reported in table 6 suggest

that the presence of another adult member of the

household increases vehicle use by 10% to 18%,

depending on whether the owner-reported or

odometer-based estimates of vehicle use are used.

Interestingly, the owner-reported use data suggest

that this effect is more than twice as large as that of

an additional child in the household, while the

odometer-based data indicate that the effect of an

additional child is nearly as large as than of another adult. Increasing vehicle ownership, as measured

by the variable hhvehcnt, reduces the utilization of

each vehicle, although there is some disagreement

about the size of this effect: the owner-reported data

suggest that average use declines 15% to 16% with

the presence of each additional vehicle, while the

odometer data suggest a 9% to 11% decline. The

larger effect implied by the owner-reported use data

appears to be more consistent with previous estimates, which indicate that a household’s ownership

14 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 6 Vehicle Usage Model: Regression Results

Estimated coefficients and t-statistics1 Estimated coefficients and t-statistics2

Independent Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

variable ß t ß t ß t ß t ß t ß t

constant 9.27 27.91 8.45 25.19 8.44 25.17 10.01 21.72 8.84 18.94 8.77 18.81

numadlt 0.10 12.66 0.11 14.00 0.11 13.97 0.17 14.11 0.18 15.12 0.18 15.13

numchild 0.04 8.95 0.04 8.18 0.04 8.36 0.14 21.31 0.14 20.87 0.14 21.03

age –0.06 –72.51 –0.06 –72.88 –0.06 –59.98 –0.08 –58.16 –0.08 –58.14 –0.07 –45.41

hhvehcnt –0.15 –24.83 –0.16 –27.44 –0.16 –27.56 –0.09 –9.48 –0.11 –11.78 –0.11 –11.85

linc 0.15 19.27 0.17 20.95 0.17 21.25 0.08 7.12 0.10 8.60 0.10 8.85

lpgas –0.29 –4.18 –0.08 –1.20 –0.08 –1.21 –0.34 –3.59 –0.05 –0.45 –0.04 –0.41

lbgden –0.04 –15.44 –0.04 –15.43 –0.06 –13.96 –0.06 –14.16

van 0.14 7.20 0.13 6.87 0.02 0.59 0.13 5.28 0.13 5.10 0.03 0.83

suv 0.11 5.58 0.10 4.90 0.00 –0.06 0.12 4.43 0.10 3.85 0.16 3.78

pickup 0.09 6.73 0.06 4.08 0.07 3.09 0.04 2.34 0.00 0.11 0.12 3.94

truck 0.16 1.87 0.12 1.37 0.80 4.71 0.06 0.39 0.03 0.21 0.10 0.32

rv –0.56 –7.98 –0.56 –8.06 –1.09 –7.29 –1.15 –9.41 –1.13 –9.27 –2.24 –8.13

age*van 0.016 4.34 0.016 3.12

age*suv 0.014 4.12 –0.009 –1.68

age*pickup –0.001 –0.63 –0.015 –4.86

age*truck –0.043 –4.57 –0.006 –0.31

age*rv 0.041 4.04 0.083 4.43

Adj. R sq. 0.145 0.149 0.150 0.181 0.187 0.189

1 Dependent variable: Natural logarithm of annualized miles driven, derived from vehicle owner estimates (mean = 8.92).

2 Dependent variable: Natural logarithm of annualized miles driven, derived from vehicle odometer readings (mean = 8.92).
of additional vehicles substantially reduces their

average use.17 One possibility is that the explicit

controls for the ages and types of household vehicles included in this model capture some effects of

increasing vehicle specialization and the matching

of vehicles to specific household trip demands that

have previously been attributed to a simple count of

the number of vehicles a household owns. 

The estimated values of the coefficient on the

household income variable shown in table 6 indicate that increases in a household’s income have

only a slight effect on utilization of the vehicles

owned. Since there is considerable evidence that

rising household income significantly increases

members’ motor vehicle travel, this result suggests

that most of that increase occurs through the

mechanism of higher vehicle ownership.18 In contrast, the estimated coefficient on gasoline prices

(lpgas) provides some suggestion that the per mile

cost of driving may influence households’ target

levels of vehicle use, although collinearity between

this variable and the residential density of the

neighborhoods where surveyed households reside

(lbgden) makes it difficult to tell which of these

two variables independently affects vehicle use.19 

Effects of Vehicle Type and Age

The regression results shown in table 6 also confirm that vans are particularly intensively utilized,

but they suggest that much of this greater utilization is explained by a less rapid decline in van

usage with age than occurs with other vehicle

types. This is evidenced by the fact that when the

age*van variable is added to the regression, its positive coefficient reduces the negative effect on usage

of the age variable itself, and the magnitude of the

coefficient on the van variable declines sharply.

The intensive van usage result may be partly an

artifact of the different transportation functions

served by older passenger and cargo vans (i.e.,

commercial purposes as well as household travel),

and the more recently marketed mini-vans, which

more clearly substitute for automobiles and thus

tend to serve more limited travel purposes.

However, both data sources suggest a tendency for

vans of both types to be used more intensively than

other types of household vehicles throughout their

lifetimes. 

The higher average utilization of both SUVs and

pickups, as shown by the mostly positive coefficients on the suv and pickup variables in table 6,

may also reflect frequent use of these vehicles for

recreational travel, joint household and business

use, or other nonpassenger transportation uses.

Interestingly, the odometer-based data suggest that

the decline in usage of pickups with increasing age

is slightly more pronounced than for conventional

automobiles; since the average age of pickups is

significantly higher than other vehicle types, their

more intensive utilization seems difficult to explain

in light of this result. While it may simply mean

that pickups are more readily adaptable to various

commercial and nonpassenger household transportation functions than are other vehicles, the

effect of introducing the neighborhood density

PICKRELL & SCHIMEK 15

17 Using 1990 NPTS data, Walls et al. (1993, 22) estimate

that a household’s acquisition of each additional vehicle

reduces its average annual driving per vehicle by as much

as 40%. 

18 For example, Schimek (1997, 88) reports that estimates

of the income elasticity of total motor vehicle travel range

from 1.2 to 1.4, but that income elasticities of average

vehicle use are typically in the range of 0.2 to 0.3.

19 This collinearity may arise from the procedure used to

develop the gasoline price variable, which is the average of

monthly retail prices including all taxes over the 15-

month survey period, May 1995 through July 1996, in the

state where the household or vehicle is located. Most of

the variation in this measure among locations is due to

differences in state fuel taxes rather than to geographic

variation in the pretax price of gasoline. More urbanized

states appear to have higher fuel tax rates. As a consequence, households facing higher fuel prices are apparently more likely to reside in higher density neighborhoods,

thus making it difficult to disentangle the separate effects

of fuel prices and residential density on vehicle use. While

it might seem desirable to use gasoline price data for the

exact month in which the household was surveyed, the

effect of seasonal fluctuations in gasoline demand is significant variation in its price. As a result, using monthly

gasoline prices does not allow the specific response we are

attempting to measure—movement along the demand

curve in response to gasoline price changes—to be separated from the effects of seasonal shifts in the gasoline

demand curve itself. In the absence of a structural model

of gasoline supply estimated simultaneously with the

models of vehicle usage and household travel demand, the

resulting “identification problem” can be minimized by

using average gasoline prices over the entire survey period, since these can more properly be considered exogenous from the standpoint of households’ travel demands

and vehicle utilization decisions.
measure on the coefficient of the pickup variable

suggests that their more intensive use stems partly

from their more frequent ownership by households

outside urban areas, where longer trip distances

increase vehicle use significantly. 

CONCLUSIONS

Analysis of the 1995 Nationwide Personal Transportation Survey and its predecessors reveals continuing growth in household vehicle ownership

and use, although at somewhat slower rates than

prevailed during the 1980s. The 1995 survey

reveals only modest growth in annual VMT per

licensed driver since 1990, indicating that the

growth rate in total driving may ultimately decline

toward the rate of increase in the number of

licensed drivers. However, rapid increase in the driving-age population provided a significant boost to

VMT growth during this period and may continue

to do so for some time. In addition, the modest

overall increase in VMT per driver obscured rapid

growth in driving among women across a broad

age spectrum (20 to 64 years) and among older

men, developments that may continue to offset the

apparent stabilization of driving among young

adult and middle-aged men. 

The most recent NPTS also shows that vehicle

ownership, both per household and per person of

driving age, remained virtually unchanged between

1990 and 1995, as did the fraction of households

owning multiple vehicles. These developments provide some suggestion that vehicle ownership may

be stabilizing, although at levels only slightly

below one vehicle per household member of driving age level. Further, the historical decline in

average vehicle occupancy slowed markedly during

this same period, suggesting that it may also be

approaching some floor as vehicle availability

becomes virtually ubiquitous among the population of driving age. Combined with a continuing

increase in the fraction of household members’

trips made by private motor vehicles, the decline in

vehicle occupancy meant that even modest growth

in person travel continued to generate significantly

increased vehicle travel. 

In contrast to the apparent stabilization of these

variables, the aging of household motor vehicles

accelerated sharply from its historic pace in recent

years, primarily as the result of declining retirement rates for vehicles over 10 years of age. The

1995 NPTS also shows that the usage of older

vehicles is considerably greater than is generally

assumed in the modeling of fleetwide air pollutant

emissions and energy consumption levels, raising

the possibility that the contribution of older vehicles to these undesirable byproducts of automobile

use may be significantly understated. The survey

also clearly documents the increasing substitution

of light trucks, particularly vans and sport utility

vehicles, for passenger automobiles and shows that

household-owned light trucks tend to be more

intensively utilized than automobiles. While light

trucks grew from about one-quarter to nearly onethird of all household vehicles between 1990 and

1995 alone, they continue to represent a still larger fraction of new vehicle sales, so this figure will

undoubtedly continue to rise over the foreseeable

future. 

REFERENCES

American Automobile Manufacturers Association (AAMA).

1996. Motor Vehicle Facts and Figures, 1996. Washington, DC.

Another Month, Another Peak: Big Truck Sales Rose 12.3%

in December. 1999. Ward’s Automotive Reports 74, no.

3:2. 18 January.

Lave, C. 1994. What Really Matters is the Growth Rate of

Vehicle Miles Traveled. Presented at the 72nd Annual

Meeting of the Transportation Research Board, Washington, DC.

Schimek, P. 1997. Gasoline and Travel Demand Models

Using Time Series and Cross-Section Data from the United

States. Transportation Research Record 1558:83–89.

U.S. Department of Transportation (USDOT), Federal Highway Administration. 1993. 1990 Nationwide Personal

Transportation Survey Databook, Volume 1. Washington,

DC.

____. 1995. Highway Statistics. Washington, DC.

____. 1997. 1995 Nationwide Personal Transportation Survey Users Guide. Washington, DC.

Walls, M., A. Krupnick, and H.C. Hood. 1993. Estimating

the Demand for Vehicle-Miles Traveled Using Household

Survey Data: Results from the 1990 Nationwide Personal

Transportation Survey, discussion paper ENR93-25.

Resources for the Future, Washington, DC.

16 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
APPENDIX 

Comparing Survey Methods Using

the 1994 NPTS Pretest

The 1995 NPTS uses a different survey method

from earlier editions of the survey, making comparisons with earlier NPTS statistics difficult. In

order to anticipate the various consequences of this

change in survey methods, a 1994 pretest of the

1995 NPTS employed both the 1990 survey

method, retrospective recall, and the 1995 survey

method, a diary mailed in advance of the travel

day; households were randomly assigned to be surveyed using the two different methods.20 The difference in average measures from each of the two

survey methods in the pretest can be used to

approximate the difference due to the change in

sampling technique alone.

Table A-1 shows average trip length, the number of trips, and their product, total travel, estimated using the two survey methods. These three

statistics are shown for all person trips (excluding

airplane trips), personal motor vehicle trips (driver

and passenger), and motor vehicle trips (using the

trip data for drivers only). This third statistic produces an estimate of VMT. The diary method

recorded more short motor vehicle trips: the number of vehicle trips was nearly 10% higher using

this method, although their average trip length was

nearly 8% shorter. The net result is that the diary

method revealed only 1.1% more VMT than the

retrospective method.

Many more person trips were also recorded

when the diary method was used, but these trips

were longer on average than those already counted

using the retrospective method. About 14% more

trips in total were counted under the diary method,

and these trips were 1.5% longer on average; thus

the retrospective method appears to understate the

number of PMT by nearly 16%. These differences

due to survey method for PMT, VMT, and their

components were used in this paper to adjust the

1990 NPTS data to make them more comparable

with the 1995 data. Other inconsistencies between

the two survey methods were not accounted for,

however, such as the treatment of commercial driving. Therefore even the adjusted 1990 data are

not completely comparable to the corresponding

measures obtained from the 1995 survey. 

PICKRELL & SCHIMEK 17

TABLE A-1 Estimates of Person Trips, Average Trip Length, and Total Person Travel

Using Different Survey Methods: 1995 NPTS Pretest

Retrospective method Trip diary method % difference between methods

Average Number Person- Average Number Person- Average Number Personlength of trips miles length of trips miles length of trips miles

Statistic (miles) (billions) (billions) (miles) (billions) (billions) (miles) (billions) (billions)

All person trips1 7.77 80.4 625 7.89 91.7 723 1.5% 14.1% 15.7%

Person trips by 8.51 69.9 595 8.75 79.7 698 2.8% 14.0% 17.3%

motor vehicle 

Motor vehicle trips2 8.65 48.6 420 7.97 53.3 425 –7.9% 9.7% 1.2%

1 Excluding airplane trips.

2 Trips by drivers only.

20 The 1994 pretest also used a third technique, a memory jogger, which is essentially a simpler form of the travel

diary. Since this method was not chosen for subsequent

use in the 1995 survey, it is not discussed here.
19

ABSTRACT

A valid assessment of motor vehicle crash risks and

the potential impact of safety interventions

requires a precise understanding of the types of

crashes involved, the types of vehicles likely to be

equipped or otherwise affected, the most relevant

referent to the intervention (e.g., national annual

crash total, vehicle mileage, and vehicle life), and

the scope of monetary crash costs to be considered.

This paper analyzes the U.S. police-reported,

motor vehicle crash problem in four dimensions:

crash involvement type/role (e.g., single-vehicle

roadway-departure, left-turn-across-path); subject

vehicle body type (i.e., passenger cars, light

trucks/vans, heavy combination-unit trucks, medium/heavy single-unit trucks, and motorcycles);

type of metric (i.e., crashes, involved vehicles, persons killed/injured, and monetary cost); and problem size referent (i.e., U.S. annual, per crash, per

vehicle, per driver, and per mile traveled). 

The Dimensions of Motor Vehicle Crash Risk

JING-SHIARN WANG

National Highway Traffic Safety Administration

U.S. Department of Transportation

RONALD R. KNIPLING

Federal Highway Administration

U.S. Department of Transportation

LAWRENCE J. BLINCOE

National Highway Traffic Safety Administration

U.S. Department of Transportation

Jing-Shiarn Wang, Office of Regulatory Analysis,

National Highway Traffic Safety Administration, USDOT

NHTSA, Room 5208, 400 Seventh Street SW, Washington, DC 20590. Email: jwang@nhtsa.dot.gov.
INTRODUCTION

Safety interventions to prevent motor vehicle

crashes are often characterized by specificity; that

is, they are designed to prevent a specific type of

crash involvement. In the area of Intelligent

Transportation Systems (ITS), for example, vehicle-based sensors are being developed to prevent

specific crash types, such as the headway/forward

obstacle detection system designed to prevent rearend crashes (Knipling et al. 1993a; Najm and

Burgett 1997). Other safety interventions may target a vehicle type; for example, Federal Motor

Carrier Safety Regulations focus primarily on large

trucks, particularly combination-unit trucks

(CUTs), which include tractor- and semitractortrailers. These vehicles constitute the majority of

interstate commercial vehicles. Thus, there is a

need to dissaggregate crash statistics along multiple dimensions, including crash and vehicle type.

Further, the analysis of crash consequences may

focus on different types of measures (most obviously, measures of injuries and lives lost versus

measures of monetary loss) and different frames of

reference, such as annually for the nation or per

miles of vehicle exposure.

In the area of advanced vehicle-based technology, the National Highway Traffic Safety

Administration (NHTSA) (Najm et al. 1995) and

others (e.g., Fancher et al. 1994) have conducted

research to identify and define promising opportunities for crash prevention. One major NHTSA

effort was a multidisciplinary project (Najm et al.

1995) to define the principal ITS crash scenarios

and identify effective countermeasures. Associated

crash data analyses (Knipling et al. 1993a; Wang

and Knipling 1994a, b, c, d) have quantified crashes by type, using metrics such as annual numbers

of crashes and injuries and rates of occurrence. In

addition, the studies documented significant differences in crash involvement patterns among various

vehicle body types—in particular, passenger vehicles (i.e., cars and light trucks), CUTs, and medium/heavy single-unit trucks (SUTs)—also known

as “straight trucks” (see box 1).

NHTSA has also assessed the overall economic

costs of motor vehicle crashes (Blincoe 1996; Blincoe and Faigin 1992). These studies focused on

direct economic losses, and provided estimates of

the monetary value society places on the human

consequences of crashes, including functional

impairment due to injury, “pain and suffering,”

and even loss of life. According to Blincoe, in 1994,

the average economic cost of a police-reported

(PR) crash was approximately $12,360, and the

total economic cost of U.S. motor vehicle crashes

(PR plus nonpolice-reported (NPR)) was $150.5

billion. On a comprehensive scale incorporating

derived valuations for life and pain and suffering

(Blincoe 1996), in addition to direct economic loss,

the estimates were $34,490 per PR crash and

$379.5 billion (PR and NPR) for the national total. 

These monetary studies provided analytical

breakdowns of various categories of economic

20 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

BOX 1 Abbreviations and Acronyms

C comprehensive cost

CUT combination-unit truck

E economic cost

FARS Fatality Analysis Reporting System

GES General Estimates System

ITS intelligent transportation systems

LC/M lane change/merge

LTAP left-turn-across-path

LT/V light trucks/vans

LVM lead vehicle moving

LVS lead vehicle stopped

MAIS Maximum Abbreviated Injury Scale

NASS National Automotive Sampling System

NHTSA National Highway Traffic Safety

Administration

NPR nonpolice-reported

OD opposite direction

PR police-reported

PAR Police Accident Report

RE rear-end

RE-LVM rear-end, lead vehicle moving

RE-LVS rear-end, lead vehicle stopped

SI/PCP signalized intersection/perpendicular

crossing path

SUT single-unit truck

SV subject vehicle

SVRD single-vehicle roadway-departure

UI/PCP unsignalized intersection/perpendicular

crossing path

VMT vehicle-miles traveled
loss, including property damage, economic losses

due to lost production, and medical expenses. They

also demonstrated the huge proportion of crash

costs associated with alcohol—approximately

30% of all crash costs. Recently, Miller et al.

(1997) developed estimates of the economic costs

and harm associated with crashes of specific

geometries. This analysis was based primarily on

Crashworthiness Data System statistics, which include only passenger vehicles.

To date, only limited analyses have been performed of the economic costs of various crash scenarios involving specific vehicle body types, which

characterize these costs from the standpoint of the

expected “per transportation unit” crash experience

of vehicles or drivers. Such per unit statistics are likely to be more meaningful than national statistics to

regulators, system developers, vendors, and buyers,

because they provide a basis for assessing the potential cost-benefits of new safety interventions applied

to some part of the vehicle/crash universe. For

example, disaggregation by vehicle type is important

because marketing strategies for many vehicle-based

devices involve initial deployment in a specific vehicle-type fleet (most frequently CUTs) followed by

deployment to other vehicle types. 

Accordingly, this paper assesses the U.S. motor

vehicle crash problem focusing on a number of

major crash involvement types/roles and vehicle

body types. Both nonmonetary (e.g., crashes, persons killed or injured) and monetary metrics are

employed. In addition, this paper analyzes motor

vehicle crashes from the perspective of different

problem-size “referents”; that is, the U.S. annual

national total as well as various “per unit” referents, including per crash, per vehicle, per mile, and

even per driver. All four of these analytical dimensions—crash type, vehicle type, problem-size metric, and problem-size referent—are fundamental to

a valid assessment of the potential crash amelioration benefits, and thus market opportunities, of

motor vehicle safety interventions.

METHOD

Unless otherwise noted, all crash data were

retrieved or derived from the General Estimates

System (GES) for the five-year period 1989–93 and

are intended to be representative of the population

of U.S. PR crashes. Fatalities were adjusted to the

1989–93 levels reported in the Fatality Analysis

Reporting System (FARS). Four analytical dimensions—crash involvement types/roles, subject vehicle body type, type of metric, and problem-size

referent—are discussed below (see table 1).

WANG, KNIPLING & BLINCOE 21

TABLE 1 Crash Analysis Dimensions 

and Categories

Crash involvement types/roles:

n All PR crashes

n Single-vehicle roadway-departure (SVRD)

n Pedestrian

n Rear-end, lead vehicle stopped (RE- LVS), striking

vehicle

n Rear-end, lead vehicle moving (RE- LVM), striking vehicle

n Lane change/merge (LC/M)

n Backing

n Opposite direction, encroaching vehicle

n Left-turn-across-path (LTAP)

Subject vehicle (SV) body type:

n All vehicles

n Combination-unit trucks (CUTs)

n Single-unit trucks (SUTs)

n Passenger cars

n Light trucks/vans (LT/Vs)

n Motorcycles

Metrics:

n Crashes (equal to SVs in crashes)

n Crash-involved vehicles

n Crash-involved persons

– Total

– Not injured

– MAIS 1–2

– MAIS 3–Fatal

n Monetary cost

– Economic (E)

– Comprehensive (C)

n Fatal equivalents

Referents:

n U.S. annual total

n Per police-reported target crash

n Per mile traveled (or per 100 million VMT)

n Per vehicle annually (or per 1,000 vehicles

annually)

n Per vehicle operational life

n Per driver career
Crash Involvement Types/Roles

Crash involvement1 types and roles are primarily

those that have been analyzed and defined in

NHTSA-sponsored studies of crash causation and

countermeasure applicability (Najm et al. 1995).

Note that, with the exception of “all crashes,” each

category includes an explicit or implicit definition

of the crash subject vehicle (SV). The SV is the vehicle regarded as having the critical precipitating role

in the crash, such as the striking vehicle in rear-end

crashes. While the SV is generally the vehicle whose

driver is “at fault” in the crash, there are many

exceptions to this general rule. For example, some

single-vehicle roadway-departures (SVRDs) are

precipitated by an evasive maneuver to avoid an

encroaching vehicle, and some left-turn-across-path

(LTAP) crashes are associated with a traffic signal

violation by the vehicle going straight. Crash

involvement type/role categories examined were:

1. All crashes (the universe).

2. SVRD crashes, including struck parked-vehicle

crashes.

3. Pedestrian (first harmful event only—not pedestrian impacts occurring as a result of a prior collision).

4. Rear-end, lead vehicle stopped (RE-LVS) crashes (SV is the striking vehicle).

5. Rear-end, lead vehicle moving (RE-LVM) crashes (SV is the striking vehicle). This category

includes crashes where the lead vehicle was

coded as traveling more slowly than the following vehicle or coded as decelerating at the time

of impact. The differentiation of RE-LVS versus

RE-LVM crashes is based on police accident

report (PAR) information only. RE crashes not

identified as either LVS or LVM were distributed

proportionately across the two subtypes.

6. Lane change/merge (LC/M) crashes, not including any rear-end crashes (SV is the vehicle making lane change/merge maneuver).

7. Backing crashes, including both “encroachment” and “crossing path” subtypes (Wang and

Knipling 1994a) but not including pedestrian

impacts (SV is the vehicle making the backing

maneuver).

8. Opposite-direction (OD) crashes, including

head-on collisions and opposite-direction sideswipes (SV is the encroaching vehicle). For the

small number of OD crashes in which the SV

was not identifiable, the SV designation was distributed among vehicle types in proportion to

their known roles in other OD crashes.

9. LTAP at intersection crashes (SV is the left-turning vehicle).

With the exception of the “all crashes” category,

the above crash types were defined in a manner

that ensured mutual exclusivity. For example, the

lane change/merge category excluded rear-end

crashes resulting from such maneuvers. Similarly,

the backing crash category excluded backing-intopedestrian crashes.

Obviously, not all crash types are addressed

here. For example, rear-end crashes could be analyzed from the perspective of the struck vehicle to

provide insights into the potential benefits of safety enhancements to rear brake light or other rear

signaling systems (Knipling et al. 1993b). Two key

intersection crash types, signalized and unsignalized perpendicular crossing path crashes (Wang

and Knipling 1994c), have not been subjected to

detailed analysis because of the difficulty of identifying the SV (i.e., the vehicle violating the right-ofway) based on GES-coded variables alone for the

five years under study. Limited, nonvehicle-typespecific statistics for these two crash subtypes are

provided, however.

Subject Vehicle Body Type

Six vehicle types were addressed: all motor vehicles, passenger cars, light trucks/vans (LT/Vs),

CUTs, SUTs, and motorcycles. These vehicle types

were defined as in previous reports (Wang and

Knipling 1994a, b, c, d) and as suggested by the

taxonomy of the GES body type variable.

“Passenger cars” here include standard automobiles and derivatives. LT/Vs include van-based light

trucks, pickups, utility vehicles, and other light

trucks of less than 4,500 kg gross vehicle weight

rating (GVWR). The CUT category includes bobtails. For all crash types/roles, the vehicle type was

the SV in the crash; for example, the left-turning

vehicle in an LTAP crash. 

22 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 Crash involvement in this context refers to a specific role

in a specific type of crash, e.g., the left-turning vehicle in

a left-turn-across-path crash.
Type of Metric

“Type of metric” refers to what is actually counted

in the statistic. The current analysis counted crashes, SVs, involved vehicles, involved persons (classified by injury severity), monetary cost, and fatal

equivalents. The term “crashes” is self-explanatory, although it is worth noting that the number of

crashes also equals the number of SVs involved in

crashes (except for “all crashes,” in which no SV is

defined). Two levels of “involved vehicles” are

quantified: 1) all of the vehicles of a particular

body type involved in a crash (e.g., all the light

trucks/vans involved in LTAP crashes, regardless of

crash role) and 2) all of the vehicles involved in a

crash regardless of body type.

Involved persons were classified by injury severity level, and include all persons regardless of vehicle role or type (i.e., not just those in the SV). The

KABCO injury severity scale values—K, killed; A,

disabling injury; B, evident injury; C, possible

injury; and O, no apparent injury (National Safety

Council 1990)—were converted to Maximum

Abbreviated Injury Scale (MAIS) values from 0 to

6 (AAAM 1985) using matrices generated for

injuries occurring in crashes involving the different

vehicle types based on 1982–86 National Automotive Sampling System data. In addition to a

count of all persons involved in crashes we

assessed, the following three categories are presented: not injured (MAIS 0), minor-to-moderate

injury (MAIS 1–2), and serious-to-fatal injury

(MAIS 3–fatal). Fatality counts are not presented

separately because unacceptably large sampling

errors would be associated with the small fatality

estimates for specific crash/vehicle types (USDOT

NHTSA 1992) and because GES generally undercounts fatalities. These sources of error are reduced

by aggregating injury data across multiple severity

levels (e.g., MAIS 3, 4, 5, and Fatal).

The GES data on which this study was based

provide estimates of the relative frequency of different crash types. Sampling errors associated with

GES crash, vehicle, and person estimates are not

provided. For some small estimates, these may be

significant (USDOT NHTSA 1992), although the

use of five-year averages rather than single-year

estimates reduces sampling errors.

This paper also contains a number of monetary

metrics of the U.S. crash problem size. Unlike the

nonmonetary crash statistics based principally on

the GES, most monetary metrics used in this paper

were adjusted to account for undercounting of PR

and NPR crashes. These adjustments were derived

from Blincoe and Faigin (1992). 

Monetary assessments of crash problem size

may be based on narrow economic loss criteria or

comprehensive societal value criteria (Blincoe

1996). This paper provides both economic (E) and

comprehensive (C) monetary crash problem-size

metrics. Unit costs from Blincoe were adjusted to

1997 price levels using Consumer Price Index statistics. E costs represent the value of goods and services that must be purchased as a result of motor

vehicle crashes; they include medical care, legal services, emergency services, vehicle repair services,

and insurance administration costs. In addition,

economic costs include the value of both workplace and household productivity lost due to death

or injury, the value of travel delay to noninvolved

motorists, and costs incurred due to workplace disruption when an employee is killed, injured, or

delayed.

By contrast, C costs incorporate not only economic losses, but a valuation for less tangible consequences such as “pain and suffering” and loss of

life. These values have been derived from “willingness-to-pay” studies that examine marketplace

behavior to determine the value that people place

on reducing risk. There is far more uncertainty

involved with these estimates than those based on

direct economic costs. These less tangible impacts,

however, are often the most devastating aspects of

serious motor vehicle injuries, and they should be

incorporated whenever a direct comparison is

made of costs and benefits or of the potential benefits of competing safety measures. Failure to consider these aspects could result in a serious

underestimation of the true harm caused by motor

vehicle crashes or the societal benefits associated

with proposed safety measures.

In this paper, both E and C costs are expressed

in 1997 dollars using a 4% annual discount rate to

reflect the decreased value of future economic losses (e.g., lost wages). A 4% annual discount rate

was also applied to calculations of expected moneWANG, KNIPLING & BLINCOE 23
tary cost “per vehicle over operational life” and

“per driver over driver career.” These metrics are

defined and discussed later in this section.

One way to simplify the metrics of motor vehicle crash consequences is to express them as “fatal

equivalents.”2 This is achieved by dividing the

annual monetary cost of any given crash by the

cost of a fatality. For example, the annual C cost of

all crashes in 1997 dollars is $431.9 billion and the

C cost of a fatality is $3,091,420. The total annual fatal equivalents associated with all crashes

equals $431.9 billion/$3,091,420 = 139,699.3

NHTSA uses C costs and this method to derive a

cost per-equivalent-fatality for their analyses of

proposed safety regulations. For this study, fatal

equivalents provide a convenient single-number

basis for comparing crash consequences across

crash types and vehicle types. 

Problem-Size Referent

Crash problem sizes must be expressed in relation

to a referent; for example, most traffic crash statistics refer to a particular time (e.g., a year) and place

(the United States). Six different referents are used

in this paper:

1. U.S. annual total (average of 1989–93).

2. Per PR target crash by type.

3. Per mile traveled. To avoid the use of very high

or low numbers, crash involvement rates are

expressed as the number of crash involvements

per 100 million vehicle-miles traveled (VMT);

whereas crash monetary costs are expressed in

cents per mile. Passenger car and LT/V VMT

were obtained from Walsh (1995). All other

VMT statistics were obtained from Highway

Statistics (USDOT Annual releases 1990–94).

4. Per registered vehicle annually (for numbers of

crashes, expressed as the number per 1,000 registered vehicles annually). Passenger car and

LT/V registrations were based on Shelton

(1995). All other vehicle registration statistics

were obtained from Highway Statistics.

5. Per manufactured vehicle over its expected operational life.

6. Per driver over his/her expected driving career.

The first four of the above referents are selfexplanatory and commonly used in traffic safety

research. The fifth (per manufactured vehicle over

its expected operational life) is relevant to quantifying a crash problem in relation to the average or

expected experience of individual vehicles that

may, for example, be equipped at the factory or

dealership with a particular safety device lasting

the life of the vehicle. The expected number of

crash involvements over a vehicle’s life is derived

by the formula:

Expected number = Average annual number of

involvements 3 Average vehicle life 4 Average

annual number of registered vehicles

The following values were used for average vehicle

life by vehicle type: all vehicles, 13.1 years; passenger cars, 11.8 years; LT/Vs, 16.0 years; CUTs and

SUTs, 14.7 years; and motorcycles, 7.5 years

(Miaou 1990; Wang and Knipling 1994a).

The referent “per driver over his/her expected

driving career” attempts to capture the expected

lifetime driving experience of the average driver. It

is derived by the formula:

Expected number = Average annual number of

involvements 3 Average driving career (years)

4 Average annual number of registered drivers

The current average life expectancy of a beginning

driver (e.g., 17-year-old) is approximately 76 years

(USDHHS 1991). Such a person might drive for a

total of 55 to 60 years. For example, a person who

starts driving at age 17 and stops at age 75 would

have driven for 58 years. This “years of driving”

value—58 years—is used here although it is an

approximation. The extrapolation of five years of

crash data (1989–93) across 58 years of driving is

also acknowledged to be inexact, since many

crash-relevant factors (e.g., driver behavior, road

safety, vehicle safety, emergency medicine) may

change over such a long time period.

“Per driver over his/her expected driving career”

statistics are derived for “all vehicle types” only.

Disaggregation by vehicle type would be very diffi24 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

2 Fatal equivalents are the number of fatalities that would

be equivalent in cost to all costs associated with crashes

including costs for nonfatal injuries and property damage.

3 The figure 139,699 was calculated before rounding.
cult, because many drivers operate several different

vehicle types during their careers and may drive

certain vehicle types (e.g., large trucks, motorcycles) for only a few years.

Statistics: Metric/Referent Combinations

Each metric above could be applied to each referent to constitute a specific crash statistic; for example, crashes (a metric) per year in the United States

(a referent). The current analysis includes the statistics listed below. All vehicle, injury, and monetary measures of the crash problem size include all

individuals and vehicles involved in the crash, not

just those in the SV. All statistics on crashes—

involved vehicles and persons, and all “per crash”

statistics (monetary value, fatal equivalents)—are

based on PR crashes as retrieved from the GES.

Monetary and fatal equivalent statistics for the

United States, per mile traveled, per registered vehicle, per vehicle over its operational life, and per

driver over his/her driving career include both PR

and NPR crashes. 

1. Annual U.S. number of PR crashes (also equals

the number of SVs involved in these crashes)

2. Annual number of vehicles involved (of each

body type) in PR crashes

3. Annual number of vehicles involved (regardless

of body type) in PR crashes

4. Annual number of persons involved in PR

crashes

m Total

m Not injured (MAIS 0)

m Minor to moderate (MAIS 1–2)

m Serious to fatal (MAIS 3–fatal)

5. Vehicle involvement rate in PR crashes

m Per 100 million VMT

m Per 1,000 registered vehicles

6. Expected involvements in PR crashes

m Per vehicle over its operational life

m Per driver over his/her driving career (“all

vehicles” only)

7. Annual U.S. monetary cost (includes PR + NPR

crashes)

m Economic cost (E)

m Comprehensive cost (C)

8. Average monetary cost (E and C)

m Per PR crash

m Per vehicle-mile (in cents)

m Per registered vehicle annually (PR + NPR

crashes)

9. Expected monetary cost (E and C)

m Average per vehicle over its operational life

(PR + NPR crashes)

m Average per driver over his/her driving career

(“all vehicles” only; PR + NPR crashes)

10. Fatal equivalents

m Annual national total (PR + NPR crashes)

m Average per PR crash.

Expected monetary costs over a vehicle’s life

were calculated using the same vehicle usage-byvehicle-age projections employed by NHTSA to

analyze its safety regulations and, as noted, using a

4% annual discount rate. Motorcycle usage-by-age

projections were based on the passenger car pattern, but they were accelerated to reflect the shorter operational life of motorcycles. The “all

vehicles” projection was a weighted average of the

individual vehicle types. Driver discounting was

based on the 1989–93 distribution of crash

involvements by driver age. The cumulative discounting for the different vehicle types and for drivers (reflecting their different operational lives)

was as follows: all vehicles, 17.45%; passenger

cars, 16.73%; LT/Vs, 19.82%; CUTs and SUTs,

18.48%; motorcycles, 11.69%; drivers, 44.56%.

For example, the discounted “all vehicles” expected monetary costs over a vehicle’s life was derived

by first obtaining a gross cost estimate (calculated

using the formula shown earlier) and then reducing

this gross value by 17.45%.

The “all vehicles” value provided is not simply

the sum or weighted average of the five specific

vehicle types. First, “all vehicles” includes a relatively small number of other vehicle types such as

buses. More importantly, for most statistics the

crashes, vehicles, injuries, or dollars may be counted under more than one specific vehicle type column. For example, for all crashes (see table 2),

those involving both a passenger car and an LT/V

are counted in both columns. Since many of the

statistics provided include all involved vehicles and

persons (e.g., injuries to persons in non-SVs), the

columns are not additive; such additions would

constitute double counting. For all crashes, the per

VMT, per registered vehicle annually, and per vehicle over its operational life values for “all vehicles”

WANG, KNIPLING & BLINCOE 25
are less than any of the individual vehicle types,

because the aggregation of all vehicles eliminates

the possibility of counting “other” involved vehicles. These all vehicle/all crash statistics (see

boxed values within table 2), unlike those in

other columns and tables, do not reflect consequences to other involved vehicles and their occupants. Similarly, the table 2 values for all crashes

“per driver over driving career” do not incorporate consequences to other involved vehicles and

their occupants. 

The following rounding rules were applied to all

the statistics presented in this paper. For crash and

injury statistics, values were rounded to the nearest

1,000 if they are 2,000 or greater, or to the nearest

100 if they are less than 2,000. Monetary costs,

except for costs per VMT, were rounded to the

nearest $10. Costs per VMT were rounded to the

26 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 2 Statistics for All Crashes

Crashes involving

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 6,261,000 5,307,000 2,209,000 214,000 154,000 89,000

Annual number of this vehicle type 

involved in PR crashes* 10,964,000 7,929,000 2,485,000 221,000 157,000 90,000

Annual number of all vehicles 

involved in PR crashes* 10,964,000 9,688,000 4,141,000 392,000 287,000 145,000

Annual U.S. number of persons 

involved in PR crashes* 15,905,000 14,101,000 5,932,000 494,000 376,000 183,000

Not injured (0)* 12,278,000 10,936,000 4,684,000 399,000 307,000 90,000

Minor to moderate (MAIS 1–2)* 3,433,000 3,020,000 1,183,000 85,000 65,000 78,000

Serious to fatal (MAIS 3–fatal)* 194,000 146,000 65,000 9,000 5,000 15,000

Vehicle involvement rate in PR crashes

Per 100 million VMT 500.41 556.15 415.59 225.52 289.33 927.65

Per 1,000 registered vehicles annually 59.33 64.91 47.87 135.14 36.60 21.54

Expected involvements in PR crashes

Over vehicle operational life 0.7789 0.7640 0.7684 1.9866 0.5380 0.1615

Per driver over driving career 3.7383

Annual U.S. monetary cost* (E) $164.4B $146.8B $57.7B $9.5B $5.4B $6.5B

(C) $431.9B $353.7B $147.9B $22.1B $11.6B $22.6B

Average monetary cost

Per PR crash* (E) $17,950 $18,650 $17,580 $39,540 $31,870 $57,190

(C) $52,610 $50,190 $50,750 $89,400 $66,370 $206,460

Per VMT* (E) 7.50¢ 10.29¢ 9.65¢ 9.68¢ 9.99¢ 66.52¢

(C) 19.71¢ 24.81¢ 24.73¢ 22.57¢ 21.50¢ 233.05¢

Per registered vehicle annually* (E) $890 $1,200 $1,110 $5,800 $1,260 $1,540

(C) $2,340 $2,900 $2,850 $13,520 $2,720 $5,410

Expected monetary cost

Per vehicle over operational life* (E) d $9,640 $11,780 $14,310 $69,540 $15,140 $10,230

(C) d $25,330 $28,380 $36,660 $162,040 $32,580 $35,830

Per driver over driving career (E) d $31,070

(C) d $81,630

Total annual U.S. fatal equivalents* 139,699 114,423 47,829 7,160 3,763 7,320

Average fatal equivalents per PR crash* 0.01702 0.01623 0.01642 0.02855 0.02120 0.06678

* Inclusive; i.e., includes all crash-involved vehicles and persons, except for the boxed area in “all vehicles” column. For these

statistics, a crash or injury may be counted in two different columns (e.g., a crash involving a passenger car and a combinationunit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; PR = police-reported; VMT = vehicle-miles

traveled.
nearest 0.01¢. Other statistics, including rates and

expected involvements, have been rounded in a

manner to ensure that the smallest value in each

row contains at least two, and usually three, significant digits. The total annual national fatal

equivalent was rounded to the nearest 1. As a

result of rounding, some table entries may not sum

to the totals. In addition, percentage estimates and

the derived statistics in the tables were calculated

before numbers were rounded.

RESULTS AND DISCUSSION

Results are shown in table 2 for all crashes and

appendix tables A through H (at the end of this

paper) for specific crash types. The statistics provided are too numerous to describe completely.

This discussion will focus on major findings,

caveats, and clarifications of appropriate interpretations.

“All Crashes” Statistics

At the highest level of analysis are the statistics for

all crashes and all vehicles types. Between

1989–93, there were an average of 6,261,000 PR

crashes annually involving 10,964,000 vehicles

and 15,905,000 persons. There were 500 vehicle

involvements in PR crashes per 100 million VMT

and 59.3 involvements per 1,000 registered vehicles. Each vehicle can be expected to be involved in

0.78 PR crashes during its operational life and each

driver can be expected to be involved in 3.74 PR

crashes during his or her driving career.

The average annual total economic cost of

motor vehicle crashes (PR + NPR) was $164 billion. Average annual national comprehensive costs

were $432 billion. The average PR crash resulted

in direct economic losses of $17,950 and had a

comprehensive cost of $52,610. Not shown in

table 2 are the economic costs of NPR crashes;

Blincoe and Faigin (1992) estimated that 22% of

all injuries, mostly minor, are not accounted for in

PARs. In addition, 48% of all property-damageonly crashes are unreported.

Each mile traveled by a vehicle is associated

with crash costs (PR + NPR) of 7.5¢ (E) or 19.7¢

(C). On average, each registered vehicle annually

experiences crash consequences with a value of

$890 (E) or $2,340 (C). Over the total operational

life of the vehicle, these values are extrapolated to

discounted values of $9,640 (E) or $25,330 (C).

Extrapolation of the 1989–93 statistics across a 58-

year driving career (discounted to current value)

indicates that each driver would be expected to be

involved in crashes with a value of $31,070 (E) or

$81,630 (C). As noted earlier, the per VMT, per registered vehicle annually, and per vehicle over its

operational life values for the specific vehicle types

are all higher than the “all vehicles” value because

they incorporate the consequences to other vehicles

involved in the crashes (e.g., LT/Vs involved in passenger car crashes and vice versa).

The statistics indicate an annual national average of 139,699 fatal equivalents associated with

motor vehicle crashes. Each PR-crash results in an

average of 0.0170 fatal equivalents (the total of all

involved persons).

The above statistics can be used to assess potential benefits from the application of safety interventions, whether real or hypothetical, to specific

vehicle types. For example, a vehicle-based device

lasting the life of a passenger car and capable of

reducing all its crash involvements by 5% would

have a societal economic value of $11,780 3 0.05

= $590 (E) or $28,380 3 0.05 = $1,420 (C) for

each equipped passenger car. These represent timeof-purchase monetary values because the cost projections were discounted. This monetary benefit

would be shared by the occupants of equipped passenger cars and those in other vehicles who would

have crashed with the equipped car had the device

not been installed. 

A new driver education program or similar

intervention capable of reducing a driver’s lifetime

crash involvements by 10% would have a start-ofdriving societal economic value of $31,070 3 0.10

= $3,110 (E) or $81,630 3 0.10 = $8,160 (C) for

each young driver exposed. This benefit would be

shared by the driver, his or her passengers, and any

nonmotorists (e.g., pedestrians) who would have

been affected by these crashes. In addition to this

benefit, there would be benefits to other vehicles

and their occupants whose crashes with the subject

driver were also prevented.

As noted previously, the “per vehicle over operational life” and “per driver over driving career”

monetary cost estimates are discounted to reflect

WANG, KNIPLING & BLINCOE 27
the current economic value of future costs. For drivers, this discounting is substantial (44.56%). The

nondiscounted value of all expected crash involvements during a typical 58-year driving career is

$56,050 (E) or $147,250 (C). Applying the cumulative discount of 44.56% yields the discounted

value presented in table 2.

Another way of expressing the nondiscounted

driver values presented in the paragraph above is

to consider per-driver-per-year crash costs. On an

annual basis, the driver on average can be expected to be involved in crashes with a total value (not

including consequences to other vehicles and their

occupants) of $970 (E) or $2,540 (C). The potential cost-benefits of ongoing, continuously applied

safety programs, such as public service announcements, might be assessed using these values.

Crash-Type Comparisons

Table 3 provides some comparative all-vehicle-type

statistics on the eight specific crash types addressed

in appendix tables A through H, plus two additional major crash types, signalized and unsignalized intersection perpendicular crossing path

(SI/PCP and UI/PCP) crashes (Wang and Knipling

1994c). For each crash scenario, three summarizing statistics are provided: annual U.S. number of

PR crashes, average monetary cost (E) per PR

crash, and annual U.S. monetary cost. The three

statistics represent comparative measures of PR

crash frequency, average PR crash severity, and

total societal problem size (PR + NPR). Other statistics could have been chosen from the tables to

provide essentially the same comparisons.

Table 3 shows that the most numerous crash

categories are rear-end crashes (1.45 million annual PR crashes for RE-LVS + RE-LVM), SVRD (1.31

million annual PR crashes), and intersection crossing path crashes (1.30 million annual PR crashes

for LTAP + SI/PCP + UI/PCP). The most severe

crash types are OD ($50,770 per PR crash) and

pedestrian crashes ($42,340 per PR crash). The

highest annual U.S. total monetary costs are associated with intersection crossing path crashes

($39.3 billion for the three subtypes combined),

RE crashes ($33.8 billion for the two subtypes

combined), and SVRD crashes ($33.2 billion).

A specific caveat relating to the backing crash

problem size is that of the crashes shown in tables

3 and appendix table F the majority are crossing

path backing crashes, where a vehicle backs into

traffic and is struck by a another vehicle (Wang

and Knipling 1994a). Crossing path backing crashes are probably less amenable to technological

solution (i.e., rear object detection) than are

encroachment backing crashes, in which a vehicle

backs into a stationary object.

Vehicle-Type Comparisons

For all crashes and for each of the individual crash

types, passenger cars and light trucks/vans dominate the statistics for total number of crashes, associated injuries, and monetary costs. For example, a

comparison of the annual national total economic

28 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 3 Crash Type Comparisons

(All vehicle types combined, 1989–93 average)

Crash type Annual U.S. PR crashes Average cost (E) per PR crash Annual U.S. monetary cost

All crashes 6,261,000 $17,950 $164.4B

SVRD crashes 1,310,000 $19,060 $33.2B

Pedestrian crashes 176,000 $42,340 $9.7B

RE-LVS crashes 974,000 $14,170 $22.3B

RE-LVM crashes 480,000 $15,120 $11.5B

LC/M crashes 234,000 $10,080 $4.1B

Backing crashes 171,000 $7,390 $2.4B

OD crashes 190,000 $50,770 $12.7B

LTAP crashes 396,000 $20,500 $11.9B

SI/PCP crashes 266,000 $21,690 $8.4B

UI/PCP crashes 633,000 $20,490 $19.0B

Key: B = billion; E = economic; PR = police-reported.
cost (E) row in table 2 indicates that total costs for

all vehicle types combined were $164 billion. The

costs of crashes involving the individual vehicle

types were: passenger cars, $147 billion; LT/Vs,

$58 billion; CUTs, $10 billion; SUTs, $5 billion;

and motorcycles, $7 billion. (The individual vehicle types add up to greater than $164 billion,

because each vehicle-type statistic includes all vehicles (and people) involved in the crashes.) Thus,

from a national perspective, safety interventions

are not likely to have dramatic effects unless they

address the huge passenger car and LT/V crash situations.

Passenger cars represented more than three

times as many vehicle crash involvements than

LT/Vs between 1989–93, but otherwise these two

large vehicle populations were similar in their

crash profiles. Compared to passenger cars, however, LT/Vs have somewhat lower involvement

rates, monetary costs per VMT, and average annual monetary costs per registered vehicle.

CUTs are associated with a very different

crash-size profile, however. Although they have

very low crash rates, their high mileage exposures,

long operational lives, and the severity of crashes

(Miaou 1990; Clarke et al. 1991) combine to give

them very high per vehicle crash costs. Indeed, for

“all crashes” and each of the eight specific crash

types/roles, CUTs stand out as having the highest

per vehicle crash costs and thus the highest potential crash-reduction benefits on a per vehicle basis.

For example, in table 2 it is shown that the per

vehicle life monetary costs of all CUT crashes is

$69,540 (E) or $162,040 (C). These costs are more

than four times as great as those for any other

vehicle type. From a percentage cost-benefit standpoint, this means that crash avoidance systems can

generally afford to be considerably more expensive

and/or less effective for CUTs and still be more

attractive than the same device installed on other

vehicle types. Still, the national impact of such

deployments will be limited; only 3.4% of all

crashes and 5.8% of associated monetary costs are

associated with CUT crashes.

SUTs have a less dramatic crash picture than do

CUTs. SUTs represent 1.4% of all vehicle crash

involvements and, compared with other vehicles,

they have low involvement rates, both on a per

mile traveled and per registered vehicle basis annually. Their crashes are more severe, measured by

average monetary cost per PR crash, than those of

passenger cars or LT/Vs, but they are less severe

than those of CUTs or motorcycles. The per vehicle operational life costs of SUT crashes are about

20% of those of CUTs and are only slightly higher

than LT/Vs.

Safety interventions for CUTs applied on an

annual basis as well as those lasting the life of a

vehicle can be very effective. Annually, on average,

each CUT was involved in crashes with a monetary

value of $5,800 (E) or $13,520 (C). This is four to

five times as great as the values for crashes of other

vehicle types. Thus, an annual safety intervention

(e.g., vehicle safety inspections) would have four to

five times the payoff for CUTs as for other vehicle

types, assuming equivalent intervention costs and

effectiveness.

An important caveat, which bears repeating, is

that the current CUT and SUT monetary cost statistics are based on an assumption of zero unreported crash costs. Since in reality there are some

such crashes, the current monetary cost statistics

understate CUT and SUT crash costs somewhat.

This underestimation, however, is not likely to be

more than a few percentage points.

The motorcycle crash picture presents another

sharp contrast to that of other vehicle types.

Motorcycles represent a relatively small percentage of overall national crashes, but their per crash

costs are high; for example, $57,190 per PR crash

(E) versus $17,950 for all vehicle types combined.

Of course, this reflects the relatively high vulnerability of motorcycle riders to crash injuries. The

average PR motorcycle crash is associated with

0.067 fatal equivalents—nearly four times the

value of all vehicle types combined. In addition,

motorcycles have a rate of involvement in crashes

per VMT that is nearly twice that of all vehicle

types combined. These two factors have a multiplicative effect in making motorcycle travel 6 to

10 times more costly per mile traveled than other

vehicle types.

The irony of the motorcycle crash picture—

exactly opposite of that of CUTs—is that they have

low mileage exposure and relatively short operational lives. These factors make the vehicle operaWANG, KNIPLING & BLINCOE 29
tional life crash costs of motorcycles among the

lowest of the vehicle types. From a strict per vehicle produced monetary cost-benefit perspective,

this makes motorcycles a relatively unattractive

platform for safety devices lasting the life of the

vehicle, assuming equivalent costs and effectiveness

levels. On the other hand, this type of vehicle is an

extremely attractive platform for safety devices

having a limited mileage life. For example, assuming comparable effectiveness, a general safety

device (i.e., targeting all crashes) installed for 1,000

miles on an motorcycle would produce more than

six times the expected benefit as the same device

installed for 1,000 miles on a passenger car.

Figure 1 provides four sets of comparative histograms for four monetary metrics: a) per PR

crash, b) per mile traveled, c) per registered vehicle

annually, and d) per vehicle life cycle. For each, the

horizontal line shows the weighted average of the

value of the vehicle types, while the vertical bars

represent the five specific vehicle types. These relative values are based on the economic (E) monetary

values in table 2. Generally, the passenger car and

LT/V values are similar to each other and the

weighted average across all four comparisons.

Since these two vehicle types together represent

about 95% of vehicles involved in crashes, they are

the principal determinants of the weighted averages for each set. Motorcycles sustain the highest

costs per crash, followed by those of CUTs and

SUTs, which are also significantly greater than average. In costs per mile of travel, motorcycles are

about six times the weighted average; surprisingly,

perhaps, both CUTs are SUTs are slightly lower

than average. In the per vehicle annually set, CUTs

are far above the weighted average, while all the

other vehicle types are near the average. The per

vehicle life cycle set is similar, but not identical since

different vehicle types have different average operational lives. In this set, CUTs are again far above

average, but motorcycles are now below average

reflecting, in part, their short operational lives.

For the individual vehicle types, the current statistics do not provide information to disaggregate

“inside” versus “outside” damage, injuries, and

associated costs. It is well known, however, that

there are major differences across vehicle types in

this disaggregation, with CUTs and motorcycles

30 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

a) Cost per PR crash

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

PC LT/V CUT SUT MC

b) Cost per mile traveled

PC LT/V CUT SUT MC

c) Cost per vehicle annually

PC LT/V CUT SUT MC

d) Cost per vehicle life cycle

PC LT/V CUT SUT MC

FIGURE 1 Vehicle-Type Comparisons of Four 

 Monetary (E) Crash Statistics

Weighted costs indicated by reference line

Vehicle type

Vehicle type

Vehicle type

Vehicle type

0¢

10¢

20¢

30¢

40¢

50¢

60¢

70¢

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

Key: E = economic cost; PC= passsenger car; 

MC = motorcycle.
representing the extremes. A supplemental analysis

(not shown in the tables) indicates that approximately 67.2% of the monetary costs of CUT crashes are associated with damage and injuries outside

the truck; for example, occupants of other involved

vehicles. In contrast, only 12.5% of the monetary

costs of motorcycle crashes are “outside” the vehicle. (The small number of crashes involving multiple CUTs or multiple motorcycles were excluded

from this analysis.)

Crash- and Vehicle-Type Interactions

The crash- and vehicle-type statistics provided in

appendix tables A through H are too numerous to

discuss in detail. For each vehicle type, SVRD and

RE crashes (when the RE-LVS and RE-LVM categories are combined) are the most numerous of

those shown here. Intersection crossing path crashes are also numerous (Wang and Knipling 1994c),

although statistics for only one subcategory of

these crashes (i.e., LTAP crashes) are presented

here for individual vehicle types.

Comparison of crash statistics across various

crash and vehicle types reveals several notable

examples of overrepresentation or underrepresentation of particular vehicle types in particular crash

types/roles. For example, LT/Vs represent 22.7%

of all vehicles involved in crashes but 36.2% of

SVs in backing crashes.

The largest relative overinvolvement of CUTs is

in LC/M crashes. CUTs comprise only 2.0% of

vehicles involved in crashes, but represent 8.5% of

vehicles involved in LC/M crashes as the SV. On a

per vehicle life cycle basis, CUT involvements in

LC/M crashes are about 12 times as costly as those

of SUTs and 14 times as costly as those of all vehicle types combined. CUTs are also relatively underrepresented in certain crash types; for example,

they represent only 1.1% of the SVs in RE-LVS

crashes and 0.5% of those in LTAP crashes.

Nevertheless, for every crash type, CUTs have the

highest crash costs per vehicle over the operational

life of the vehicle.

The only major overrepresentation of SUTs is in

backing crashes; they represent 1.4% of vehicles

involved in all crashes, but account for 5.3% of

SVs in backing crashes. CUTs and SUTs show a

different pattern of SV involvements in RE-LVS

versus RE-LVM crashes; CUTs have more RELVM crashes, whereas SUTs have more RE-LVS

involvements. This likely reflects the different

exposure patterns of these two large truck types;

CUTs accumulate most of their mileage on highways whereas SUTs accumulate relatively more

mileage on secondary/local roads.

Motorcycles are relatively overrepresented in

SVRD crashes. They represent 0.8% of all vehicles

involved in crashes but 1.2% of SVRD crash involvements. Furthermore, motorcycle SVRD crashes are approximately four times as severe as those

of any other vehicle type. On a per mile traveled

basis, motorcycle SVRD crashes are an order of

magnitude more costly than those of all vehicle

types combined. The per vehicle life cycle monetary

costs of motorcycle SVRD crashes are actually

slightly higher than the “all vehicles” average, an

exception to the general rule that motorcycle life

cycle crash costs are generally low compared with

other vehicle types.

Figure 2 is based on the monetary costs (E) for

the different crash type roles and vehicle types.

They are shown in terms of relative percentage of

that vehicle type’s crash costs in comparison to a

weighted average of all vehicle types combined (the

latter statistics are not shown). The horizontal line

in each histogram set represents the weighted average across vehicle types for each crash type. The

passenger car set (2a) deviates very little from the

weighted average of all five vehicle types, since passenger cars dominate these weighted average statistics. LT/Vs (2b) show a relative overinvolvement

in backing crashes. For CUTs (2c), there are large

relative overinvolvements in LC/M and backing

crashes and notable relative underinvolvements in

SVRD, RE-LVS, and LTAP crashes. SUTs (2d)

show a marked relative overinvolvement in backing crashes. Both CUTs and SUTs show overinvolvements in “other” types of crash roles, since

they are very often the non-SV (i.e., nonculpable)

vehicle in two-vehicle crashes. For motorcycles

(2e), the greatest relative overinvolvements are in

SVRD crashes with marked relative underinvolvements in RE-LVS, backing, and LTAP crashes.

Recall that the LTAP statistics reflect only SV (left

turning) roles in crashes; motorcycles rarely play

WANG, KNIPLING & BLINCOE 31
32 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

FIGURE 2 Relative Distribution of Crash Costs (E) by Crash Type/Role for Five Vehicle-Type Categories

a) Passenger cars

SVRD Ped/Cyc RE-LVS RE-LVM LC/M

Crash type/role

Backing OD LTAP Other

SVRD Ped/Cyc RE-LVS RE-LVM LC/M

Crash type/role

Backing OD LTAP Other

0

0.5

1.0

1.0

1.5

2.0

2.0

2.5

3.0

1.05 1.02 1.10 1.04 0.98 0.78

1.15 1.20 1.08

c) Combination-unit trucks 

b) Light trucks and vans

SVRD Ped/Cyc RE-LVS RE-LVM LC/M

Crash type/role

Backing OD LTAP Other

0

0.5

1.0

1.5

2.0

2.5

3.0

3.0

d) Single-unit trucks 

SVRD Ped/Cyc RE-LVS RE-LVM LC/M

Crash type/role

Backing OD LTAP Other

0

0.5

1.0

1.5

2.0

2.5

3.0

 e) Motorcycles 

SVRD Ped/Cyc RE-LVS RE-LVM LC/M

Crash type/role

Backing OD LTAP Other

0

0.5

1.0

1.5

2.0

2.5

3.0

0.43

1.00

0.38

1.05

2.48

2.06

0.58

0.34

1.29

0

0.5

1.5

2.5

0.96 0.96 0.92 0.97 0.86

1.27

0.75 0.75 1.08

0.48 0.70

0.83

0.75

0.97

2.46

0.53

0.26

1.31

1.51

0.78

0.24

0.62

0.31 0.18

1.04

0.2

1.15
this role in LTAP crashes, but are frequently in the

non-SV (going straight), since they are often not

seen by other drivers. 

“Most-Relevant Referents” for

Countermeasure Benefit Assessments

This paper presents crash problem-size statistics that

are more specific and heuristic than traditional

national annual totals. To perform a meaningful and

heuristic benefits assessment, we selected appropriate

statistics based on four dimensions of motor vehicle

crash risk: crash types/roles, vehicle types, metrics,

and referrants. Perhaps the most subtle of these

dimensions is problem-size referent. Safety initiatives

may vary dramatically in their patterns or “spans” of

application and, therefore, in the most appropriate

perspective from which to assess their potential benefits. Below are examples of countermeasures with

qualitatively different patterns of application and

their corresponding most-relevant referents.

For national public information campaigns or

other initiatives applied diffusely to the driver/vehicle population, the most-relevant referent would be

U.S. annual. Annual program expenditures would

be compared with the national annual problem

size as measured by various metrics, whether monetary or nonmonetary.

Some safety interventions are applied proportionally to miles traveled; for such interventions,

rate-per-mile statistics are most applicable. One

example would be improved brake pads with a

limited mileage life; benefits would be best assessed

by comparing per mile crash rates or monetary

cost rates for applicable crash and vehicle types.

Another example of a mileage-based safety intervention is roadside inspections for commercial

vehicles (primarily CUTs and SUTs). In general, the

number of roadside inspections large trucks receive

is proportional to their mileage exposure. Recall

that CUTs and SUTs have almost identical crash

costs per mile of travel (9.7¢ versus 10.0¢ (E),

respectively; see table 2 and figure 1b). Assuming

that per inspection costs and the crash-reduction

effectiveness of inspections are similar for these

two vehicle types, the cost-benefits of roadside

inspections would also be similar, in spite of the

vastly greater per vehicle mileage exposure of

CUTs compared with SUTs.

Annual vehicle inspections, in contrast, have a

time-based span of application; that is, one year.

The most relevant referent would be per vehicle

year. From the table 2 per-registered-vehicle-annually monetary cost values and figure 1c, we see that

benefits are potentially far greater for CUTs than

for other vehicle types. The importance of choosing the most relevant referent is illustrated by the

comparison of CUTs with SUTs; unlike the situation above for roadside inspections where the benefits pictures were similar, for annual inspections

the per vehicle benefits would be far greater for

CUTs than for SUTs.

Most vehicle-based safety devices, whether they

are crash avoidance- or crashworthiness-related,

are installed at the factory or dealership, or are

purchased in the aftermarket for use over the entire

life of the vehicle. For example, an ITS crash avoidance device such as a headway/forward obstacle

detector would target RE crashes (both LVS and

LVM) and would operate over the entire vehicle

life cycle. Where the span of application is vehicle

operational life, per-vehicle-life-cycle statistics are

most relevant to a determination of cost-benefits.

Extreme differences among vehicle types are evident in these statistics; for example, assuming

equal device cost and effectiveness, a RE-LVM

countermeasure installed on a CUT would have 6

times the potential monetary benefits of the same

device installed on a passenger car and 12 times

that of a motorcycle. As noted earlier, for all crash

types studied, the per-vehicle-life-cycle costs for

CUTs are higher than for any other vehicle type,

even though CUTs generally have low crash rates

per mile and are markedly underinvolved in several crash types in relation to their own overall crash

picture. The high per-vehicle-life-cycle costs for

CUTs reflect their high per vehicle mileage exposure and the great severity of their crashes when

they occur.

There do not seem to be any safety interventions

with a pure “per crash” span of application. Air

bags deploy only during crashes (of certain threshold severities), but they are purchased and applied

over the vehicle life cycle (albeit they must be

replaced after a crash deployment). Costs to government for emergency medical services responding to crashes are not necessarily proportional to

WANG, KNIPLING & BLINCOE 33
the number of crashes to which they respond. Per

crash statistics, however, are obviously relevant to

any projections of the absolute number of crashes

that may be prevented by a safety intervention even

if the intervention does not have a per crash span

of application.

Finally, there are new driver training/education

programs that are conceptualized to affect drivers’

entire lifetime driving experience. Here, the intended span of application is the individual driving

career. Because drivers are on average involved in

multiple crashes over their lifetimes, relatively

small reductions in crash risk can be highly costbeneficial.

CONCLUSION

This paper attempted to dissect the U.S. crash picture in ways that make crash and vehicle type differences more salient and that support realistic

assessments of potential countermeasure benefits.

Of course, any benefits assessment must include an

estimation of the actual crash reduction effectiveness of interventions, which we did not address in

this paper, but the above discussion and examples

show that identification of the most relevant

dimensions of motor vehicle crash risk is even

more fundamental to developing a framework for

enlightened safety benefits assessment and decisionmaking.

ACKNOWLEDGMENTS

The principal analysis supporting this work was

performed by the first two authors while working

at the Office of Crash Avoidance Research of the

National Highway Traffic Safety Administration

of the U.S. Department of Transportation. An earlier version of this paper was presented under the

title “Motor Vehicle Crash Involvements: A MultiDimensional Problem Size Assessment” at the

Sixth Annual Meeting of the Intelligent Transportation Society of America in Houston, Texas, in

April 1996. Special thanks to Ted Miller and

Rebecca Spicer of National Public Services Research Institute for providing per unit monetary

data.

REFERENCES

Association for the Advancement of Automotive Medicine

(AAAM). 1985. The Abbreviated Injury Scale 1985. Des

Plaines, IL.

Blincoe, L.J. 1996. The Economic Cost of Motor Vehicle

Crashes, 1994, NHTSA Technical Report, Publication

Number DOT HS 808 425. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety

Administration. July.

Blincoe, L.J. and B.M. Faigin. 1992. The Economic Cost of

Motor Vehicle Crashes, 1990, NHTSA Technical Report

DOT HS 807 876. Washington, DC: U.S. Department of

Transportation, National Highway Traffic Safety Administration. September.

Clarke, R.M., R.W. Radlinski, and R.R. Knipling. 1991.

Improved Brake Systems for Commercial Motor Vehicles,

NHTSA Technical Report, DOT HS 807 706. Washington, DC: U.S. Department of Transportation, National

Highway Traffic Safety Administration. April.

Fancher, P., L. Kostyniuk, D. Massie, R. Ervin, K. Gilbert, M.

Reiley, C. Mink, S. Bogard, and P. Zoratti. 1994. Potential

Safety Applications of Advanced Technology, Technical

Report Number FHWA-RD-93-080. Washington, DC:

U.S. Department of Transportation, Federal Highway

Administration. January.

Knipling, R.R., M. Mironer, D.L. Hendricks, L. Tijerina, J.

Everson, J.C. Allen, and C. Wilson. 1993a. Assessment of

IVHS Countermeasures for Collision Avoidance: RearEnd Crashes, NHTSA Technical Report, Publication

Number DOT HS 807 995. Washington, DC: U.S. Department of Transportation, National Highway Traffic

Safety Administration. May.

Knipling, R.R., J.S. Wang, and H.M. Yin. 1993b. Rear-End

Crashes: Problem Size Assessment and Statistical Description, NHTSA Technical Report, Publication Number

DOT HS 807 994. Washington, DC: U.S. Department of

Transportation, National Highway Traffic Safety

Administration. May.

Miaou, S.P. 1990. Study of Vehicle Scrappage Rates. Oak

Ridge, TN: Oak Ridge National Laboratory. August.

Miller, T., D. Lestina, M. Galbraith, T. Schlax, P. Mabery, and

R. Deering. 1997. United States Passenger Vehicle Crashes

by Crash Geometry. Accident Analysis and Prevention 29,

no. 3:343–352.

Najm, W.G. and A. Burgett. 1997. Benefits Estimation for

Selected Collision Avoidance Systems, 4th World Congress on Intelligent Transportation Systems (ITS), Berlin,

Germany. October.

Najm, W.G., M. Mironer, J.S. Koziol, Jr., J.S. Wang, and R.R.

Knipling. 1995. Examination of Target Vehicular Crashes

and Potential ITS Countermeasures, Report for Volpe

National Transportation Systems Center, DOT HS 808

263, DOT-VNTSC-NHTSA-95-4. Washington, DC: U.S.

34 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
Department of Transportation, National Highway Traffic

Safety Administration. June.

National Safety Council. 1990. Manual on Classification of

Motor Vehicle Traffic Accidents, 5th edition, ANSI D16.1-1989. Itasca, IL.

Shelton, T.S.T. 1995. Registered Passenger Cars and Light

Trucks, DOT HS 808 235. Washington, DC: U.S.

Department of Transportation. February.

U.S. Department of Health and Human Services (USDHHS),

National Center for Health Statistics, Public Health

Service. 1991. Vital Statistics of the United States for

1988. Washington, DC.

U.S. Department of Transportation (USDOT), Federal

Highway Administration. 1989–94. Highway Statistics.

Washington, DC: Annual releases.

U.S. Department of Transportation (USDOT), National

Highway Traffic Safety Administration (NHTSA). 1992.

Technical Note for 1988, 1989, 1990 National

Automotive Sampling System General Estimates System,

Report No. DOT HS 807 796. Washington, DC.

February.

Walsh, W.H. 1995. Review of Adjustments to Vehicle Miles

Traveled Data (memo from the NHTSA National Center

for Statistics and Analysis). June.

Wang, J.S. and R.R. Knipling. 1994a. Backing Crashes:

Problem Size Assessment and Statistical Description,

NHTSA Technical Report, Publication Number DOT HS

808 074. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration.

January.

____. 1994b. Lane Change/Merge Crashes: Problem Size

Assessment and Statistical Description, NHTSA Technical

Report, Publication Number DOT HS 808 075. Washington, DC: U.S. Department of Transportation, National

Highway Traffic Safety Administration. January.

____. 1994c. Intersection Crossing Path Crashes: Problem Size

Assessment and Statistical Description, NHTSA Technical

Report, Publication Number DOT HS 808 190. Washington, DC: U.S. Department of Transportation, National

Highway Traffic Safety Administration. September.

____. 1994d. Single Vehicle Roadway Departure Crashes:

Problem Size Assessment and Statistical Description,

NHTSA Technical Report, Publication Number DOT HS

808 113. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration.

March.

WANG, KNIPLING & BLINCOE 35
36 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

Appendix Tables

TABLE A Statistics for Single-Vehicle Roadway-Departure (SVRD) Crashes

Crashes involving

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 1,310,000 907,000 323,000 31,000 23,000 16,000

Annual number of this vehicle type 

involved in PR crashes 1,310,000 907,000 323,000 31,000 23,000 16,000

Annual U.S. number of persons 

involved in PR crashes 1,791,000 1,277,000 435,000 34,000 27,000 19,000

Not injured (0) 1,190,000 849,000 290,000 27,000 22,000 3,000

Minor to moderate (MAIS 1–2) 553,000 397,000 133,000 7,000 4,000 12,000

Serious to fatal (MAIS 3–fatal) 48,000 32,000 12,000 800 300 3,000

Vehicle involvement rate in PR crashes

Per 100 million VMT 59.79 63.62 53.97 31.64 41.91 167.65

Per 1,000 registered vehicles annually 7.09 7.43 6.22 18.96 5.30 3.89

Expected involvements in PR crashes

Over vehicle operational life 0.0931 0.0874 0.0998 0.2787 0.0779 0.0292

Per driver over driving career 0.4466

Annual U.S. monetary cost (E) $33.2B $22.8B $8.1B

(C) $62,200 $60,870 $62,650 $40,060 $32,190 $263,040

Per VMT (E) 1.52¢ 1.60¢ 1.36¢ 0.62¢ 0.71¢ 14.82¢

(C) 4.67¢ 4.88¢ 4.24¢ 1.44¢ 1.54¢ 53.76¢

Per registered vehicle annually (E) $180 $190 $160 $370 $90 $340

(C) $550 $570 $490 $870 $200 $1,250

Expected monetary cost

Per vehicle over operational life (E)d $1,950 $1,830 $2,020 $4,450 $1,080 $2,280

(C)d $6,010 $5,580 $6,280 $10,370 $2,330 $8,270

Per driver over driving career (E)d $6,280

(C)d $19,360

Total annual U.S. fatal equivalents 33,125 22,507 8,190 458 269 1,689

Average fatal equivalents per PR crash 0.02012 0.01969 0.02027 0.01279 0.01028 0.08509

* Inclusive, i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two

different columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; E = economic cost; M = million; PR = police-reported; VMT = vehicle-miles traveled.
WANG, KNIPLING & BLINCOE 37

TABLE B Statistics for Pedestrian/Cyclist (Ped/Cyc) Crashes

Crashes involving

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 176,000 133,000 37,000 1,200 1,500 2,000

Annual number of this vehicle type 

involved in PR crashes* 178,000 134,000 37,000 1,200 1,500 2,000

Annual number of all vehicles 

involved in PR crashes* 178,000 134,000 37,000 1,300 1,500 2,000

Annual U.S. number of person 

involved in PR crashes* 415,000 315,000 85,000 3,000 3,000 5,000

Not injured (0)* 245,000 188,000 50,000 1,400 1,800 1,400

Minor to moderate (MAIS 1–2)* 155,000 117,000 32,000 900 1,300 3,000

Serious to fatal (MAIS 3–Fatal)* 15,000 10,000 3,000 400 200 500

Vehicle involvement rate in PR crashes

Per 100 million VMT 8.12 9.39 6.15 1.21 2.79 24.07

Per 1,000 registered vehicles annually 0.96 1.10 0.71 0.73 0.35 0.56

Expected involvements in PR crashes

Over vehicle operational life 0.0126 0.0129 0.0114 0.0107 0.0052 0.0042

Per driver over driving career 0.0607

Annual U.S. monetary cost* (E) $9.7B $6.5B $2.4B $410M $162M $218M

(C) $31.1B $20.3B $7.8B $1.1B $427M $779M

Average monetary cost

Per PR crash* (E) $42,340 $36,850 $51,020 $287,620 $92,180 $74,660

(C) $141,480 $121,410 $171,580 $788,970 $240,720 $271,760

Per VMT* (E) 0.45¢ 0.45¢ 0.40¢ 0.42¢ 0.30¢ 2.25¢

(C) 1.42¢ 1.42¢ 1.30¢ 1.15¢ 0.79¢ 8.03¢

Per registered vehicle annually* (E) $50 $50 $50 $250 $40 $50

(C) $170 $170 $150 $690 $100 $190

Expected monetary cost

Per vehicle over operational life*(E)d $570 $520 $600 $3,000 $450 $350

(C)d $1,830 $1,630 $1,930 $8,250 $1,200 $1,230

Per driver over driving career (E)d $1,840

(C)d $5,880

Total annual U.S. fatal equivalents* 10,065 6,554 2,523 360 136 252

Average fatal equivalents per PR crash* 0.04577 0.03927 0.05550 0.25198 0.07688 0.08791

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; VMT = vehicle-miles

traveled.
38 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE C Statistics for Rear-End, Lead Vehicle Stopped (RE-LVS) Crashes

Crashes involving vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 974,000 696,000 229,000 11,000 12,000 3,000

Annual number of this vehicle type 

involved in PR crashes* 2,144,000 1,331,000 319,000 13,000 14,000 4,000

Annual number of all vehicles 

involved in PR crashes* 2,144,000 1,532,000 504,000 24,000 27,000 7,000

Annual U.S. number of persons 

involved in PR crashes* 3,107,000 2,020,000 652,000 27,000 34,000 9,000

Not injured (0)* 2,469,000 1,608,000 523,000 21,000 26,000 6,000

Minor to moderate (MAIS 1–2)* 618,000 401,000 125,000 6,000 7,000 3,000

Serious to fatal (MAIS 3–fatal)* 20,000 11,000 4,000 300 300 300

Vehicle involvement rate as SV 

in PR crashes

Per 100 million VMT 44.46 48.85 38.30 10.98 22.98 33.51

Per 1,000 registered vehicles annually 5.27 5.70 4.41 6.58 2.91 0.78

Expected involvements as SV in 

PR crashes

Over vehicle operational life 0.0692 0.0671 0.0708 0.0967 0.0427 0.0058

Per driver over driving career 0.7308

Annual U.S. monetary cost* (E) $22.3B $14.6B $4.8B $325M $400M $140M

(C) $48.0B $30.0B $9.9B $613M $765M $443M

Average monetary cost

Per PR crash* (E) $14,170 $12,680 $12,620 $28,480 $29,940 $31,400

(C) $35,190 $30,260 $30,640 $51,820 $55,410 $107,430

Per VMT* (E) 1.02¢ 1.02¢ 0.80¢ 0.33¢ 0.74¢ 1.44¢

(C) 2.19¢ 2.10¢ 1.66¢ 0.63¢ 1.41¢ 4.57¢

Per registered vehicle annually* (E) $120 $120 $90 $200 $90 $30

(C) $260 $250 $190 $370 $180 $110

Expected monetary cost

Per vehicle over operational life*(E)d $1,310 $1,170 $1,190 $2,380 $1,120 $220

(C)d $2,810 $2,410 $2,460 $4,490 $2,140 $700

Per driver over driving career (E)d $4,220

(C)d $9,070

Total annual national fatal equivalents* 15,522 9,705 3,215 198 247 143

Average fatal equivalents per PR crash* 0.01138 0.00979 0.00991 0.01655 0.01770 0.03475

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; E = economic cost; C = comprehensive cost; d = discounted; M = million; PR = police-reported; SV = subject vehicle

(striking vehicle); VMT = vehicle-miles traveled.
WANG, KNIPLING & BLINCOE 39

TABLE D Statistics for Rear-End, Lead Vehicle Moving (RE-LVM) Crashes

Crashes involving vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 480,000 329,000 118,000 10,000 7,000 3,000

Annual number of this vehicle type 

involved in PR crashes* 1,057,000 624,000 165,000 13,000 8,000 4,000

Annual number of all vehicles 

involved in PR crashes* 1,057,000 724,000 260,000 22,000 15,000 7,000

Annual U.S. number of persons 

involved in PR crashes* 1,522,000 966,000 341,000 28,000 17,000 8,000

Not injured (0)* 1,212,000 772,000 273,000 21,000 14,000 5,000

Minor to moderate (MAIS 1–2)* 299,000 188,000 66,000 6,000 3,000 3,000

Serious to fatal (MAIS 3–fatal)* 11,000 6,000 2,000 500 200 500

Vehicle involvement rate as SV in 

PR crashes

Per 100 million VMT 21.92 23.07 19.76 10.41 12.53 34.14

Per 1,000 registered vehicles annually 2.60 2.69 2.28 6.24 1.59 0.79

Expected involvements as SV in 

PR crashes

Over vehicle operational life 0.0341 0.0317 0.0365 0.0917 0.0233 0.0060

Per driver over driving career 0.3603

Annual U.S. monetary cost* (E) $11.5B $7.2B $2.6B $473M $192M $187M

(C) $25.8B $15.2B $5.8B $1.1B $361M $637M

Average monetary cost

Per PR crash* (E) $15,120 $13,390 $13,880 $41,830 $26,800 $43,000

(C) $38,960 $32,950 $35,230 $91,530 $48,670 $154,350

Per VMT* (E) 0.53¢ 0.50¢ 0.44¢ 0.48¢ 0.36¢ 1.93¢

(C) 1.18¢ 1.07¢ 0.97¢ 1.09¢ 0.67¢ 6.56¢

Per registered vehicle annually* (E) $60 $60 $50 $290 $50 $50

(C) $140 $130 $110 $650 $80 $150

Expected monetary cost

Per vehicle over operational life* (E)d $680 $580 $660 $3,460 $540 $300

(C)d $1,510 $1,220 $1,430 $7,810 $1,010 $1,010

Per driver over driving career (E)d $2,180

(C)d $4,880

Total annual U.S. fatal equivalents* 8,347 4,926 1,870 341 115 206

Average fatal equivalents per PR crash*0.01260 0.01066 0.01140 0.02923 0.01554 0.04993

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; SV = subject vehicle

(striking vehicle); VMT = vehicle-miles traveled.
40 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE E Statistics for Lane Change/Merge (LC/M) Crashes

Crashes involving lane changing/merging vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 234,000 155,000 55,000 20,000 5,000 1,000

Annual number of this vehicle type 

involved in PR crashes* 476,000 267,000 65,000 20,000 5,000 1,000

Annual number of all vehicles 

involved in PR crashes* 476,000 315,000 111,000 40,000 11,000 2,000

Annual U.S. number of persons 

involved in PR crashes* 689,000 464,000 160,000 53,000 13,000 3,000

Not injured (0)* 595,000 400,000 140,000 46,000 12,000 2,000

Minor to moderate (MAIS 1–2)* 91,000 62,000 19,000 7,000 1,500 800

Serious to fatal (MAIS 3–fatal)* 3,000 2,000 600 400 100 100

Vehicle involvement rate as SV in 

PR crashes

Per 100 million VMT 10.68 10.85 9.13 20.04 9.93 10.29

Per 1,000 registered vehicles annually 1.27 1.27 1.05 12.01 1.26 0.24

Expected involvements as SV 

in PR crashes

Over vehicle operational life 0.0166 0.0149 0.0169 0.1766 0.0185 0.0018

Per driver over driving career 0.1622

Annual U.S. monetary cost* (E) $4.1B $2.7B $947M $443M $100M $38M

(C) $7.9B $4.9B $1.8B $757M $148M $119M

Average monetary cost

Per PR crash* (E) $10,080 $9,680 $9,940 $21,760 $18,300 $27,380

(C) $22,880 $20,990 $22,580 $35,840 $26,170 $93,220

Per VMT* (E) 0.19¢ 0.19¢ 0.16¢ 0.45¢ 0.19¢ 0.39¢

(C) 0.36¢ 0.34¢ 0.30¢ 0.77¢ 0.27¢ 1.22¢

Per registered vehicle annually* (E) $20 $20 $20 $270 $20 $10

(C) $40 $40 $40 $460 $40 $30

Expected monetary cost

Per vehicle over operational life*(E)d $240 $210 $240 $3,240 $280 $60

(C)d $460 $390 $450 $5,540 $420 $190

Per driver over driving career (E)d $780

(C)d $1,490

Total annual U.S. fatal equivalents* 2,542 1,570 585 245 48 38

Average fatal equivalents per PR crash*0.00740 0.00679 0.00730 0.01145 0.00836 0.03015

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; SV = subject vehicle

(lane changing/merging vehicle); VMT = vehicle-miles traveled.
WANG, KNIPLING & BLINCOE 41

TABLE F Statistics for Backing Crashes

Crashes involving backing vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 171,000 88,000 62,000 10,000 9,000 300

Annual number of this vehicle type 

involved in PR crashes* 332,000 150,000 73,000 9,000 9,000 300

Annual number of all vehicles 

involved in PR crashes* 332,000 170,000 122,000 17,000 17,000 500

Annual U.S. number of persons 

involved in PR crashes* 456,000 235,000 167,000 21,000 22,000 600

Not injured (0)* 406,000 207,000 151,000 19,000 20,000 400

Minor to moderate (MAIS 1–2)* 49,000 27,000 16,000 2,000 2,000 200

Serious to fatal (MAIS 3–fatal)* 1,000 500 200 100 0 0

Vehicle involvement rate as SV 

in PR crashes

Per 100 million VMT 7.81 6.17 10.33 9.39 16.35 2.74

Per 1,000 registered vehicles annually 0.93 0.72 1.19 5.63 2.07 0.06

Expected involvements as SV 

in PR crashes

Over vehicle operational life 0.0122 0.0085 0.0191 0.0828 0.0304 0.0005

Per driver over driving career 0.1133

Annual U.S. monetary cost* (E) $2.4B $1.3B $800M $208M $140M $12M

(C) $3.9B $2.1B $1.1B $365M $177M $41M

Average monetary cost

Per PR crash* (E) $7,390 $7,660 $6,280 $21,460 $15,910 $34,300

(C) $14,180 $15,000 $10,240 $35,780 $19,630 $125,190

Per VMT* (E) 0.11¢ 0.09¢ 0.13¢ 0.21¢ 0.26¢ 0.13¢

(C) 0.18¢ 0.15¢ 0.19¢ 0.37¢ 0.33¢ 0.43¢

Per registered vehicle annually* (E) $10 $10 $20 $130 $30 $0**

(C) $20 $20 $20 $220 $40 $10 

Expected monetary cost

Per vehicle over operational life* (E)d $140 $100 $200 $1,520 $390 $20

(C)d $230 $170 $280 $2,670 $500 $70

Per driver over driving career (E)d $460

(C)d $740

Total annual U.S. fatal equivalents* 1,262 676 361 117 57 13

Average fatal equivalents per PR crash*0.00459 0.00485 0.00331 0.01143 0.00627 0.04049

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

** Less than 10 dollars.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; SV = subject vehicle

(backing vehicle); VMT = vehicle-miles traveled.
42 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE G Statistics for Opposite Direction (OD) Crashes

Crashes involving encroaching vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 190,000 137,000 44,000 4,000 2,000 1,800

Annual number of this vehicle type 

involved in PR crashes* 378,000 230,000 58,000 4,000 3,000 1,800

Annual number of all vehicles 

involved in PR crashes* 378,000 274,000 87,000 7,000 5,000 4,000

Annual U.S. number of persons 

involved in PR crashes* 557,000 408,000 127,000 10,000 6,000 5,000

Not injured (0)* 386,000 274,000 91,000 7,000 5,000 3,000

Minor to moderate (MAIS 1–2)* 154,000 121,000 33,000 2,000 1,300 1,600

Serious to fatal (MAIS 3–fatal)* 17,000 13,000 3,000 300 200 700

Vehicle involvement rate as SV 

in PR crashes

Per 100 million VMT 8.68 9.63 7.30 3.77 4.47 18.21

Per 1,000 registered vehicles annually 1.03 1.12 0.84 2.26 0.56 0.42

Expected involvements as SV 

in PR crashes

Over vehicle operational life 0.0135 0.0132 0.0135 0.0332 0.0083 0.0032

Per driver over driving career 0.1289

Annual U.S. monetary cost* (E) $12.7B $9.7B $2.5B $318M $165M $388M

(C) $39.9B $30.6B $7.6B $795M $411M $1.4B

Average monetary cost

Per PR crash* (E) $50,770 $54,130 $42,490 $74,210 $59,910 $177,420

(C) $168,190 $178,200 $139,120 $182,040 $146,120 $630,630

Per VMT* (E) 0.58¢ 0.68¢ 0.41¢ 0.32¢ 0.31¢ 3.99¢

(C) 1.82¢ 2.14¢ 1.27¢ 0.81¢ 0.76¢ 14.02¢

Per registered vehicle annually* (E) $70 $80 $50 $190 $40 $90

(C) $220 $250 $150 $490 $100 $330

Expected monetary cost

Per vehicle over operational life*(E)d $740 $780 $610 $2,330 $460 $610

(C)d $2,340 $2,450 $1,890 $5,820 $1,150 $2,160

Per driver over driving career (E)d $2,390

(C)d $7,550

Total annual U.S. fatal equivalents* 12,918 9,886 2,461 254 131 440

Average fatal equivalents per PR crash* 0.05441 0.05764 0.04500 0.05814 0.04667 0.20399

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics, a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; SV = subject vehicle

(encroaching vehicle); VMT = vehicle-miles traveled.
WANG, KNIPLING & BLINCOE 43

TABLE H Statistics for Left-Turn-Across-Path (LTAP) Crashes

Crashes involving left-turning vehicle as

Light Combin- SingleAll Passenger trucks/ ation- unit MotorType of statistics vehicles cars vans unit trucks trucks cycles

Annual number of PR crashes 396,000 318,000 71,000 2,000 2,000 900

Annual number of this type of vehicle 

involved in PR crashes* 792,000 571,000 87,000 3,000 2,000 1,000

Annual number of all vehicles 

involved in PR crashes* 792,000 637,000 141,000 5,000 4,000 1,900

Annual U.S. number of persons 

involved in PR crashes* 1,178,000 948,000 209,000 6,000 6,000 3,000

Not injured (0)* 865,000 696,000 155,000 5,000 4,000 1,600

Minor to moderate (MAIS 1–2)* 297,000 241,000 51,000 1,400 1,600 800

Serious to fatal (MAIS 3–fatal)* 16,000 11,000 3,000 200 100 100

Vehicle involvement rate as SV 

in PR crashes

Per 100 million VMT 18.07 22.34 11.82 2.44 4.09 9.65

Per 1,000 registered vehicles 

annually 2.14 2.61 1.36 1.46 0.52 0.22

Expected involvements as SV 

in PR crashes

Over vehicle operational life 0.0281 0.0307 0.0219 0.0215 0.0076 0.0017

Per driver over driving career 0.2700

Annual U.S. monetary cost* (E) $11.9B $9.1B $2.2B $163M $74M $65M

(C) $31.2B $23.2B $6.0B $413M $149M $218M

Average monetary cost

Per PR crash* (E) $20,500 $19,290 $21,720 $59,880 $30,940 $53,780

(C) $59,910 $54,970 $64,720 $148,190 $60,740 $186,810

Per VMT* (E) 0.54¢ 0.64¢ 0.37¢ 0.17¢ 0.14¢ 0.67¢

(C) 1.42¢ 1.63¢ 1.00¢ 0.42¢ 0.28¢ 2.24¢

Per registered vehicle annually* (E) $60 $70 $40 $100 $20 $20

(C) $170 $190 $120 $250 $40 $50

Expected monetary cost

Per vehicle over operational life* (E)d $700 $730 $550 $1,200 $210 $100

(C)d $1,830 $1,860 $1,480 $3,020 $420 $350

Per driver over driving career (E)d $2,250

(C)d $5,890

Total annual national fatal equivalents* 10,077 7,496 1,927 134 48 70

Average fatal equivalents per PR crash*0.01938 0.01778 0.02094 0.04733 0.01940 0.06043

* Inclusive; i.e., includes all crash-involved vehicles and persons. For these statistics a crash or injury may be counted in two different

columns (e.g., a crash involving a passenger car and a combination-unit truck). Thus, the columns are not additive.

Key: B = billion; C = comprehensive cost; d = discounted; E = economic cost; M = million; PR = police-reported; SV = subject vehicle

(left turning vehicle); VMT = vehicle-miles traveled.
45

ABSTRACT

This paper uses Federal-Aid Highway Program

information for 1990 to 1994 to define a natural

experiment that evaluates whether compliance

with federal environmental regulations increases

construction costs. This is accomplished by considering whether indirect measures of the environmental resources in each state affect construction

expenditures for federal-aid highways. The test

assumes that both positive and negative measures

of environmental resources and amenities, such as

counts of endangered species and historic sites, and

the number of locations with Superfund sites, will

serve as indirect indicators of the likelihood that

environmental regulations could impact federally

supported highway construction. Statistical analyses suggest that the expenditures for federal-aid

highway construction and repair were influenced

by these factors and by the regulatory activities

likely to be associated with environmental mandates. Similar models applied to construction

Do Environmental Regulations Increase Construction Costs 

for Federal-Aid Highways? A Statistical Experiment

V. KERRY SMITH

Nicholas School of the Environment

Duke University

and Resources for the Future

ROGER H. VON HAEFEN 

Center for Environment and Resource Economics

Duke University

WEI ZHU

Department of Economics 

Duke University

V. Kerry Smith, Arts and Sciences Professor of

Environmental Economics, Duke University, Box 90328,

Durham, NC 27708-0328. Email: vks@duke.edu.
expenditures for state roads, which are not subject

to the full set of federal regulations, did not find

the proxy measures for the potential impact of

these environmental regulations as positive influences on construction costs.

INTRODUCTION

Over 20 federal statutes impose a variety of environmental mandates on the construction, repair,

and maintenance activities undertaken within the

federal highway system.1 Little is known about the

added costs of these requirements.2 One of the few

sources of information, a retrospective set of cost

allocations conducted by the Wisconsin Department of Transportation (DOT), suggests the added

costs due to environmental regulations may well be

8% to 10% of construction expenditures for federal-aid highway projects (Novick 1995).

Unfortunately, these estimates rely on practitioner

judgments and not a specific record of the added

costs for compliance. 

A survey of all the designated environmental

officials at state DOTs indicated only a few agencies kept records that would allow the costs arising

from compliance with environmental regulations

to be distinguished from other costs (see Smith et

al. 1997). This response is surprising given the

widespread belief that environmental regulations

impose large costs and long delays. Indeed, the

Wisconsin estimates would imply over $2 billion of

the six-year appropriation for the National

Highway System under the reauthorization of federal highway support (the Transportation Equity

Act for the 21st Century—TEA-21) would be

absorbed by compliance with environmental regulations.3 Environmental regulations also involve a

different type of compliance process, relying on

negotiation among public agencies to meet the conflicting mandates of different federal statutes. 

This study evaluates whether the Federal

Highway Administration’s (FHWA) statistics on

construction expenditures for federal-aid highways

provide evidence that environmental regulations

have increased construction costs. We use the

FHWA definition of construction costs for federalaid and state-funded highways as reported in

Highway Statistics. Federal-aid highway projects

are subject to all federal environmental regulations. A comparison of the federal-aid construction

costs with the construction costs for roads financed

exclusively with state funds offers an approximate

“natural” experiment to gauge the impacts of different regulations on each type of highway project.4 To our knowledge, this type of statistical

analysis has not been considered before. 

Our findings suggest, after controlling for some

of the primary characteristics of federal-aid and

state roads, proxy measures likely to be related to

increased regulatory stringency or the effects of

environmental regulations had significant positive

influences on construction costs for federal-aid

projects. Most of these same variables did not

affect the costs for roads completely financed with

state funds. The measures of environmental

resources included counts of endangered species,

the number of historic sites, and the number of

National Priority List (NPL) hazardous waste sites,

as well as the size of coastal areas.

Our test uses a panel composed of states’ construction expenditures (in constant dollars) from

1990 through 1994. It was assembled using

46 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 See Smith and von Haefen (1996) and Tarrer (1993) for

a summary of the relevant statutes. We have adopted a

broad interpretation of environmental impacts that

include historic and archeological effects as well as

descriptions of more conventional impacts to environmental resources, because this is the framework most

often used in the transportation literature.

2 Carlin et al. (1996) present a summary of the most

recent Environmental Protection Agency evaluation of the

costs of environmental regulations. The economic literature has focused on three issues: welfare consistent measures of costs (Hazilla and Kopp 1990) and general

equilibrium analysis (Jorgensen and Wilcoxen 1990); productivity impacts (Gray 1987; Gray and Shadbegian

1994); and most recently plant-level evaluations of the

“net” costs of regulations (Morgenstern et al. 1998). 

3 This estimate is based on the Department of Transportation’s summary of TEA-21 at website http://

www.istea.ORG/DOCS/tea21/suminves.htm/.

4 The term “natural experiment” is used in social science

research to describe a set of circumstances representing

constraints or existing conditions similar to a policy being

evaluated. In this type of analysis, selected cases mimic the

hypothesis to be tested (a “natural” variation) and it is

argued that the results of the analysis will shed light on the

effects of the policy. See Moffitt (1991) for discussion of

the term in a different policy context.
FHWA statistics on construction expenditures for

federal-aid highways and for state roads. The

Federal-Aid Highway Program is a grant-in-aid

program supported by the federal Highway Trust

Fund. It allocates funds to states based on formulas that take account of population, area, mileage,

relative costs, and percentage of prior apportioned

funds. This fund derives revenues from motor fuel

taxes and federal excise taxes on highway users.

Federal-aid support to state and local projects generally involves an 80/20% federal/state (or local)

share of costs in response to specific apportionment rules.5

The next section presents a brief overview of the

environmental regulations that can affect highways. We describe the data and results in the following section, and conclude with a summary of

the qualifications to and implications of our findings for reforms in the process of implementing

environmental mandates.

BACKGROUND

Two statutes are especially important in order to

understand the effects of environmental regulations on federal-aid highways: Section 4f of the

1966 Department of Transportation Act, and the

1970 National Environmental Policy Act (NEPA).

Section 4f prohibits the use of publicly owned

parks, recreation areas, wildlife areas, and historic

sites of national, state, or local importance from

being used in transportation projects unless the

Secretary of Transportation determines there are

“no feasible and prudent alternatives.” A Supreme

Court ruling in 1971, Citizens To Preserve

Overton Park v. Volpe, made Section 4f and subsequent environmental laws serious concerns for federal-aid transportation projects. Indeed, DOT’s

Deputy Chief Council noted that in the initial period after this decision senior federal DOT officials

felt compelled to review Section 4f provisions personally (Kussy 1996).

The second key statute, NEPA, was intended to

enumerate the potential environmental impacts of

and mitigation for any federally funded projects

before the resources for them were committed. It

does not have a direct regulatory role. The FederalAid Highway Program has been responsible for

about 10% of the approximately 6,000 NEPA

cases (Kussy 1996, 12). Three types of actions document the effects of a proposed project: environmental impact statements (EIS); findings of no

significant environmental impact (FONSI); and

environmental assessments.6 An EIS is the most

extensive documentation NEPA requires. 

The set of regulatory mandates for federal-aid

projects is complex and overlapping. Table 1 summarizes, by type of resource, a selection of the primary statutes and Executive Orders along with the

oversight agency and the regulatory mechanism(s)

used in implementation (see Freeman (1978) and

Jafee et al. (1995) for a related discussion of environmental regulations). This summary uses a fairly broad definition of what comprises an

environmental impact. To some degree, the NEPA

requirement for preparing an EIS as well as the

documentation required by Section 4f serve to

identify the problem areas caused by regulations

related to environmental resources. The timing of

this coordination, however, has not always assured

that the design and planning process will avoid

SMITH, VON HAEFEN & ZHU 47

6 The final product of the NEPA review process is a summary report detailing all the environmental concerns. This

can be an EIS, an Environmental Assessment, or a

Categorical Exclusion. The latter is associated with a

FONSI. For large projects, state DOTs must prepare the

EIS and may jointly file it with an interested federal

agency. The standard format for an EIS includes the following components: a) purpose and need for the project;

b) alternatives considered; (c) description of the effect of

environmental resources of the project; (d) nature of the

environmental consequences; and e) identification of irreversible commitments of resources.

After the draft EIS is circulated, a public hearing identifying concerns is held and a final EIS is distributed. A

Record of Decision issued within 30 days of the final EIS’s

release signifies project approval.

A final EIS is a record of the final selection and a subset of the alternatives considered along with discussion

justifying the decision. Comparison of the final alternative

with others reported does not reveal incremental mitigation costs to meet regulations because the EIS records the

consensus that was reached, not all the alternatives avoided through the negotiation process. See Smith and von

Haefen (1996) for further discussion.

5 Table FA-4A of FHWA’s 1994 Highway Statistics provides an example of these rules. It outlines the apportionment formulas for the Federal-Aid Highway Program for

fiscal year 1994. States are keenly aware of their payments

into the trust fund in relation to their receipts.
48 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 1 Selected Environmental Statutes Impacting Highways by Resourcea

Resource Statute/Executive Order Agency Regulatory mechanism

All resources NEPA (1970) Council on Environmental EIS

Quality

Land Section 4f, DOT (1966) FHWA Documentation and 

Public parks permits

Wildlands Wilderness Act (1988) U.S. Forest Service Land-use restrictions

Historic sites National Historic State Historic Preservation Cultural Resource

Coastal areas Preservation Act (1966) Office Assessment

National Wildlife Refuge Department of the Interior Land-use restrictions

Administration Act

(1988)

National Forest Department of Agriculture Land-use restrictions

Management Act (1988,

1993)

Endangered Species Act Departments of the Conservation Plan listing

(1973) — Habitat Interior and Commerce

Coastal Zone Management Department of Commerce Coastal Zone Management

Act (1988, 1991) Plan Certification; funding

Coastal Barrier Act (1982) Department of Commerce restrictions

Wetlands Clean Water Act Section Corps of Engineers 404 permits

404 (1972) EPA

Executive Order (1977) FHWA EIS

DOT Order 5660.1A FHWA

(1978) Public review

Navigable waterways Rivers and Harbors Act Corps of Engineers Section 10 permits

(1899)

Fish and wildlife, Fish and Wildlife Fish and Wildlife Service NEPA provisions

including en- Coordination Act 

dangered species (1988)

Migratory Bird Treaty Act Fish and Wildlife Service Permits

(1918) 

Endangered Species Act Departments of the Biological Assessment

(1973) Interior and Commerce Conservation Plan

National Wildlife Refuge Department of the Interior Land-use restrictions

Admin. Act (1988)

Rivers Wild and Scenic Rivers Department of the Interior Prohibits development

Act (1988, 1993)

Water Clean Water Act (1972); EPA NPDES permit for point 

Clean Water Act Sections State water quality source management plans,

208 and 319 (1978) agencies Memoranda of

Safe Drinking Water Act Understanding

(1988)

Air Clean Air Act (1970, EPA National Ambient Air

1977, 1990) Quality Standards for criteria air pollutants; State 

Implementation Plans; restrictions imposed on activities in nonattainment areas

Noise Noise Control Act (1972) EPA Standards on construction 

a

This table is based on a detailed summary in Smith and von Haefen (1996) and Tarrer (1993). It does not include

Executive Orders and statutes governing farmland, floodplains, and Superfund and other hazardous waste sites.
delays and adjustments for environmental regulations. The table also omits additional mandates

that could be considered a part of this process.

These requirements are related to preservation of

private farmlands, liability related to hazardous

waste sites, or requirements imposed on projects

undertaken in floodplain areas that can be important to the design of highway projects. 

The impact of this process on wetlands best

illustrates its complexity. While the Army Corps of

Engineers has primary responsibility for the

Section 404 permitting process, the set of agencies

with concerns about a wetlands decision varies

with each proposed action and by state. Wetlandrelated legislation gives six federal agencies responsibilities in this area: the U.S. Army Corps of

Engineers and the Environmental Protection

Agency (EPA), along with the Natural Resources

Conservation Service (previously the Soil Conservation Service) and the Agricultural Stabilization

and Conservation Service (both Department of

Agriculture), the Fish and Wildlife Service (Department of the Interior), and the National Marine

Fisheries Service (Department of Commerce).

Thus, substantial coordination with multiple federal and state agencies can be required. With such a

diverse group, it is not surprising that one of the

key difficulties that has arisen in this process stems

from the differences across agencies in the definition of a wetland.7

This brief overview suggests that two types of

balancing are inherent in the ways environmental

regulations impact federal-aid highways. The first

is illustrated by the provisions summarized in table

1 and involves compromises across different types

of environmental resources, because highways can

impact several different resources simultaneously.

Here the decisions convey judgments about the relative importance of impacts across different environmental resources. These tradeoffs implicitly

assign values to the resources involved, such as

wetlands versus historic sites or air quality. The

second type of balancing is among the priorities as

defined by the statutory mandates of the different

agencies for the same class of resources. While

these can be linked to the resource balancing

process, they need not be.

As part of a larger review of the impact of environmental regulations, we looked at several final

Environmental Impact Statements. These EISs

confirmed our observations about the process.

The alternatives described in each final EIS include

a range of different types of resource effects. One

example can be found in a 1991 EIS for a project

involving a six-mile roadway through downtown

Wilmington, North Carolina. The project had

wetlands impacts (about 23.1 acres), encountered

two landfills with hazardous substances, and had

a potential water supply impact due to the possibility of releasing hazardous substances into a

nearby aquifer from proposed bridge pilings

required to avoid one of the waste sites.8 Air quality was also likely to be an issue for the Wilmington project, but was not discussed in the

supplementary EIS. Finally, the project was in the

100-year floodplain for the Northeast Cape Fear

River and three large creeks. This feature alone

required elevating the roadway above the 100-

year flood level. It also impacted four areas of

environmental concern identified in North Carolina’s Coastal Management Plan (a requirement of

the Coastal Zone Management Act). Adjustments

to the final route and the specific design criteria

responded to some of these concerns. These adjustments are due to both the project and presumably different agencies’ requirements to protect

“their” resources.

Cross-agency negotiation can be expected to differ with each project considered. Our statistical

analysis is based on annual costs, so it reflects the

outcomes of final projects and the negotiation to

balance ongoing projects.9 These aggregates reflect

SMITH, VON HAEFEN & ZHU 49

7 After a period of considerable controversy about a proposed reconciliation of definitions, practice has reverted

to the Army Corps’ 1987 definition for most activities

that would affect highways. For a summary of this controversy and of the permitting process see Kusler (1992)

and the National Research Council report on wetlands

(NRC 1995).

8 See Smith and von Haefen (1996) for a more detailed

summary and USDOT (1991) for the original source.

9 FHWA is not specific about how the states’ construction

expenditures for work in progress is reported. Based on

what is reported, it would appear to include expenditures

related to payments made to states for work in progress.
50 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

different mixes of complexities arising from environmental regulations and other mandates related

to the projects included in each year’s construction

expenditures. As a result, the expenditure data do

not allow us to isolate the effects of the individual

environmental regulations that may have been

associated with specific highway projects in a particular state. Instead, the best our approach can

recover is whether differences in construction

costs, after controlling for the federal-aid system’s

general characteristics in each state, can be attributed to measures of the environmental resources

that are likely to be associated with the regulations

relevant to projects in that state.

Clearly, statistical “control” using these types of

proxy variables is not as desirable as more detailed

information with direct measures of compliance

costs. Unfortunately, as we noted earlier, these data

do not exist. Thus, this more approximate scheme

may result in underestimation of the effects of the

regulations. For example, the actual costs of meeting environmental mandates may be temporally

shifted or “averaged in” with costs from projects

with few impacts. As a result the environmental

requirements may appear to have little to do with

the temporal and cross-sectional variations in construction costs. 

DATA AND RESULTS

Highway construction costs have two primary

components. The first arises from the expenditures

to support the staff and equipment of state (and

local) transportation departments. The second

involves public expenditures for the private contractors involved in highway projects. Environmental regulations affect both sets of activities. Our cost

measure will not fully reflect both of these effects.

As we noted, however, without a special purpose

cost study it would be difficult to include a more

complete record of the costs. Few states track the

environmental compliance costs for construction

and repair projects or for their ongoing maintenance programs for highways. Indeed, the

General Accounting Office’s 1994 review of

agencies’ practices preparing environmental reviews

noted that:

Although the agencies have developed the integrated processes to expedite NEPA and Section

404 reviews, they have not developed a system

to evaluate their success. Specifically, the agencies have not developed baseline data on the

time required to complete reviews under the

traditional processes, nor have they developed

plans to track projects’ time frames under the

integrated processes. (USGAO 1994, 7)

In describing state’s activities the same report

observed that: 

FHWA and the American Association of State

Highway and Transportation officials (AASHTO) do not collect or track data on all environmental costs associated with highway

projects. FHWA has collected information on

the costs related to noise barriers, and AASHTO has collected data on the costs of mitigating impacts on wetlands.... In addition, none

of 11 states we contacted routinely tracks data

on all environmental costs. (p. 10)

Our analysis exploits the ability to construct a

panel data set using FHWA’s Highway Statistics

from 1990 to 1994, along with variables designed

to represent changes in key environmental

resources over this time in each state. The latter

data were assembled from a diverse array of

sources.10 Table 2 defines some of the primary variables used (or evaluated for use) in the analysis and

documents their sources. Our focus is on the annual federal-aid construction expenditures in each

state as the source of our “experiment;” we developed our models using this variable. Models for

construction expenditures on state roads are treated as providing a crude “control” relationship.

The statistical model used to evaluate our panel

of states’ reported experience over the period

1990 to 1994 assumes there are two errors. We

follow the simplest form of the random effects

framework (see Baltagi (1995) for more details) as

in equation (1):

in equation (1):

where a is the intercept, b is a K × 1 parameter vector for the determinants of the dependent variable

yjt (in our case an expenditure measure) that is

yjt = a + bT Zjt + uj + ejt (1)

10 A more detailed data appendix is available on request

from the first author.
SMITH, VON HAEFEN & ZHU 51

TABLE 2 Federal-Aid and State-Funded Highway Expenditure Analysis: Data Description and DocumentationVariable Description Source Notesconstruct Total annual capital outlays for highway From Table SF-12 series, Highway Statistics, Costs associ

construction published annually by the Federal Highway provements, 

Administration (FHWA), U.S. Department of and other rigTransportation, adjusted to 1994 dollars by ing, constructhe Bureau of Labor Statistics’ Producer resurfacing, Price Index for intermediate materials, costs of roadsupplies, and component materials. installation o

lanemiles Total estimated lane mileage Calculated from Table HM-60, Number of l

Highway Statistics. lane mileage 

miles Total public road and street mileage Tables HM-10, HM-14, HM-15, Center-lane 

Highway Statistics. existing road

bridges Total count of bridges Table HM-41, Highway Statistics. A continuou

vehicle bridgto 20 feet.

eis Counts of all EISs (draft, final, These counts were constructed from a 1995 values 

supplemental, etc.) issued to all computer printout of all EISs issued in the 1996 valuesfederal agencies for each year, 1990–95 period January 1, 1990 to December 31, inserted for 

1995. This printout was generated bythe EPA Office of Federal Activities’Environmental Review Tracking System.

fhw_eis Counts of all EISs (drafts, final, See documentation for eis. 1995 values 

supplemental, etc.) issued to FHWA 1996 value, for each year, 1990–95 inserted for 

spec Count of federal endangered/threatened A February 29, 1996, snapshot of the counts

species protected by the Endangered of endangered species for each of the 50Species Act of 1973 states was obtained from the Endangered Species 

Program’s Web page, http://www.fws.gov/~r9endspp/listmap.html. This information was combined with a chronological listing of species obtained from the ESP to construct the ES List for the years 1990–95. 
52 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999 TABLE 2 Federal-Aid and State-Funded Highway Expenditure Analysis: Data Description and Documentation (continued) Variable Description Source Notes npl Count of Proposed and Final National These data are compiled annually in the 1995 counts 

Priority List Sites for the years 1989–95 Statistical Abstract of the United States, missing valu

Bureau of the Census, from EPA press releases and proposed rules. Also, The World Almanac & Book of Facts, World Almanac Books, compiles the same information from similar EPA documents. hist Count of National Register Sites, Obtained from John Byrne, Database These data a

Objects, Structures, and Districts in Manager at the National Register of each of the yeach state for the years 1989 to present Historic Places, Department of the Interior, a data set consisting of all National Register Sites as of July 1996, which allowed us to construct these yearly counts. coastmi Miles of coast (counting barrier islands) Obtained from NOAA’s Coastal Zone These estima

for each state Management Plan Web page, across the pa

http://wave.nos.noaa.gov/ocrm/czm/ welcome.html.

fed/spec Estimated acres of all federally owned For years 1989–91, the annually published A compariso

lands for 1989–93, divided by the count of Public Land Statistics, a U.S. Department of the two data 

federal endangered/threatened species the Interior, Bureau of Land Management estimates we(i.e., the variable spec defined above) document. For 1992–93, the publication, so 1993 valu

Summary Report of Real Property Owned the missing dby the United States Throughout the World, U.S. General Services Administration, was used.

farm Estimated acres of farmland where a farm These data are published annually in Farm No estimate

is defined as any establishment from which Numbers and Land in Farms, U.S. Depart- 1995–96, so 

$1,000 or more of agricultural products ment of Agriculture, National Agricultural for these yeaare sold or would normally be sold during Statistics Service.the year
SMITH, VON HAEFEN & ZHU 53

assumed constant across the j and t dimensions and

with the levels of the K × 1 vector, Zjt of independent variables. uj is constant over the t dimension

and varies with j. In the analysis of federal-aid

expenditures, j will be states. ejt varies with both j

and t. The time subscript, t, in this case will be

years. Both uj and ejt are assumed to be classically

well behaved. The composite error yields a nonspherical covariance matrix, because the covariance

for different time periods in the same state is not

zero, E((ujt + ejt )(uj

+ ejs)) ≠ 0. One common measure

of the importance of uj is defined by equation (2):

where se = standard deviation for ejt 

su = standard deviation for uj

T = the number of time periods 

observed for each cross-sectional unit.

The measure of importance of states’ effects in

equation (2) assumes balanced samples. When they

are not, the available time periods will vary with

the sample of time periods for each state, so T

would be replaced by Tj in (2) (see Baltagi and Li

1990). The random effects estimator uses the structure assumed for uj and ejt to construct feasible

generalized least squares (FGLS). Estimates using

FGLS are reported for most of the random effects

models. The Hausman (1978) specification test

compares the ordinary least squares (OLS) fixed

effects format with the generalized least squares

(GLS) estimator associated with the random effects

error structure.11 This test gauges orthogonality of

the random effects with the independent variables.

Testing this hypothesis is one way to evaluate

whether a random effects specification is superior

to a fixed effects approach for taking account of

differences in states.

Federal-aid construction costs are deflated to

1994 dollars using the Producers Price Index

(adccf) and the dependent variable is expressed in

logarithmic form.12 A semi-log model was adopted

after plotting the deflated federal-aid construction

costs by year. These plots are reported as figures 1a

and b and suggest that the log transformation

appears appropriate for these data, especially since

our statistical tests rely on the assumption of normally distributed errors. The panel is unbalanced

because of missing data for some states.13

The second column in table 3 reports a model

that evaluates whether compliance costs due to

environmental regulations can account for the

variation in federal-aid construction costs after

accounting for the highway system variables center-lane road mileage, lane-miles, and a count of

bridges as control factors. These variables’ estimated effects are given in the rows labeled 1 through 3

of the table. The specification of our model avoids

two other sources of problems with a test of the

effects of environmental regulations. One of these

arises from the regulations and a second from the

implications of the federal-aid system’s funding

formula. As we noted, the ultimate form of environmental regulations is the outcome of a negotiated process. Thus, measurement of their impacts

would be problematic even if we had access to project-level information. The stringency and form of

the regulations at the project level would be

endogenous outcomes of the process. At the state

level, we do not have records of these resolutions.

Instead, our proxy measures indicate the extent of

environmental resources (or problems) that would

likely be associated with the need for such negotiations on projects. Thus, while these indirect variables make our test more difficult they avoid the

u = 1 –

Ts2

u + s2 = e

se (2)

11 Both are consistent estimates under the null hypothesis

and OLS is inefficient. Under the alternative, OLS is consistent and GLS is not. Thus, a failure to reject the null

hypothesis provides support for the random effects formulation of the model.

12 With a neoclassical cost function including factor prices,

there would be no need to deflate. Because such cost functions are homogeneous of degree one in factor prices,

adjustment for inflation that affects all factor prices equally is unnecessary. One can interpret our deflator as an

attempt to use the price index as a control for factor prices

over time. This follows because our deflated cost is

expressed in logarithmic terms (i.e., ln(adccf) = ln(ccf/PI) =

ln(ccf) – ln(PI)). Ideally, one would like to account for differences in factor costs by state, but the required factor

price indexes were not available.

13 The data reporting system is voluntary so that in some

years states failed to report some key variables for the

model. Rather than impute the missing values for construction cost or the mileage variables, we deleted the

observations from the panel. 
potential endogeneity of variables based on the

outcome-specific measures of the actual requirements imposed on individual projects. 

The second issue arises with the formula funding

of the federal-aid system. At one simple level, the

cost sharing provides a bound on the added costs of

federal environmental regulations. These statutes

must add less to highway construction costs than

the federal cost-sharing rates, otherwise the states

would not participate. This insight is not especially

informative as an upper bound estimate with a federal cost share of 80% of the construction costs.

Nonetheless, it highlights a somewhat different

issue. The features of specific projects will be adapted in other ways to maximize the federal cost share.

New federal-aid project characteristics could then

be expected to be related to the federal-aid construction costs. We avoided this potential endogeneity by including only the attributes of the each

state’s existing system. Moreover, FHWA statistical

reports do not include specific information on the

characteristics of new construction in each year. 

54 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

Year = = 90 Year = = 91

0.7

0.4

0.1

Year = = 93

0.7

0.4

0.1

15 100 300 15 100 300

Year = = 94

0.7

0.4

0.1

Total

0.7

0.4

0.1

Year = = 92

0.7

0.4

0.1

0.7

0.4

0.1

15 100 300

FIGURE 1a Real Construction Costs for Federal-Aid Projects: Log Normal Scale Fraction

FIGURE 1b Real Construction Costs for Federal-Aid Projects: Normal Scale

Year = = 90

0.7

0.4

0.1

Fraction

Year = = 91

0.7

0.4

0.1

Year = = 92

0.7

0.4

0.1

Year = = 93

0.7

0.4

0.1

15 1000 3000

Year = = 94

0.7

0.4

0.1

Total 

0.7

0.4

0.1

15 1000 3000

15 1000 3000
We conducted a brief telephone survey of each

state DOT. Twenty-five states provided some information for some of the years in our sample. The

average new miles of federal-aid highway added in

each state ranged from 88.1 miles (in 1990) to 61.2

miles (in 1994). It is not clear whether these new

miles are reflected in each year’s reports, because they

relate to miles added during the year. Furthermore,

they account for only about 0.5% of the existing system in the average state and thus are unlikely to be

an important influence on treating existing mileage

as an exogeneous control variable in our tests.

SMITH, VON HAEFEN & ZHU 55

TABLE 3 The Determinants of Federal-Aid Versus State-Funded Expenditures on Highway Construction Costs 

(1994 dollars): Estimated Parameters and t-ratios from Random Effects Modelsa 

Federal-aid State-funded

Row Dependent-variable: construct construction cost construction cost 

1 lanemiles .017*** -.027

(2.86) (-1.32)

2 miles -.026** .045

(-2.45) (1.26)

3 bridges (scaled by 1,000) .026 -.010

(1.34) (-0.58)

4 eis -.004* -.003

(-1.90) (-0.43)

5 fhw_eis .010 .011

(1.17) (0.44)

6 spec .004* .008

(1.72) (1.22)

7 npl .009*** .002

(3.05) (0.18)

8 hist (scaled by 10) .003*** .008**

(3.18) (2.01)

9 coastmi .046** .057

(2.01) (0.63)

10 fed/spec -.039* -.047

(-1.81) (-0.59)

11 farm -.002 .015

(-0.43) (0.72)

12 intercept 11.845*** (8.95)***

(90.59) (14.55)

u .726 .824

R2 within .054 .027

Between .778 .091

Overall .727 .090

Number of observations 238 235

Hausman test 11.85 7.69

(p-value) (0.295) (0.659)

Breusch-Pagan test 209.38 186.84

(p-value) (0.000) (0.000)

a The numbers in parentheses below the estimated parameters (rows 1–12) are the t-ratios for the null hypothesis

of no association. Table 2 describes the sources of definitions of each variable in detail.

Key: 

*** p-value of 0.01

** p-value of 0.05

* p-value of 0.10
If environmental regulations impact highway

construction costs, then we should expect, after

controlling for the characteristics of each state’s

existing federal-aid roadway (and bridges), that the

factors likely to increase the stringency of environmental regulations would raise construction costs.

By contrast, measures hypothesized to be related to

experience and to the resources available for facilitating inter-agency negotiations about the form of

environmental regulations would reduce costs. The

latter effect occurs because the costs due to delay

are reduced. We tested this hypothesis by including

measures that (in most cases) varied by state and

year for the environmental resources likely to influence the stringency of regulations. These factors

are hypothesized to be exogenous influences on the

negotiated form of the regulations. We also included measures of the physical characteristics of

resources in the state likely to be related to Coastal

Zone Management Plans, private farmland, and

measures of the level of activity (and experience) in

preparing environmental impact statements under

NEPA. The estimated parameters for these variables along with their t-statistics are given in row 4

through 11 of table 3.

The estimated coefficients measure the proportionate change in federal-aid construction costs

with a change in the respective independent variable. Thus, increases in the count measures for federal endangered and threatened species with

habitat in a state or of National Registry sites, all

else being equal, increase federal-aid construction

costs. Both are statistically significant with at least

a 10% p-value. The greater the number of species

and historic sites, all else being equal, the more

likely they are to be affected by new highway projects in a state. Likewise, a state with a long coastline is likely to encounter issues with the provisions

of the relevant Coastal Zone Management Plan

important to highways. A large number of NPL

hazardous waste sites could mean a greater chance

of having to deal with one in a highway project.

These factors are general indicators of the effects of

increased environmental stringency on highway

construction. The EIS for the Wilmington project

referred to above illustrated how both of these

types of effects can influence the strategy adopted

in a project.

Federal land relative to the count of the endangered species in a state, and a large number of

EIS’s prepared, may be factors that reduce costs.

In the first case, federal land may serve as both a

restriction on new highways and/or a potential

source of habitat for the species affected by a project. In the case of the effects of the total number

of EIS’s prepared, the reduced costs may result

from experience or the cost spreading that can

arise when permanent staff are added to state

agencies to meet similar mandates in other contexts. The farmland measure has a negative and

insignificant effect on costs.

The column in table 3 reporting state-funded

construction costs shows estimates of comparable

roadway mileage and bridge measures corresponding to features of the state system. With the exception of the count of National Registry sites, none of

these factors would be close to being statistically

significant determinants of construction costs for

roads funded completely by the state (these are not

likely to be impacted as much by federal environmental regulations). 

Overall, these statistical results are striking.

Given the aggregate nature of the analysis as well

as the proxy variables used to represent the potential for environmental factors differentially affecting federally funded projects in specific states,

regulations could have had a marked influence at

the project level and yet we might not have found

significant factors influencing construction costs at

the aggregate level. In situations where we must

rely on judgment and proxy variables, however,

there is an inevitable tendency to question statistical approximations. Three issues seem especially

worthy of further consideration. First, it is widely

recognized that states differ in their propensities to

protect the environment. Would state-specific

effects have changed the model? Second, wetlands’

effects are the most commonly cited sources of

increased environmental compliance cost (see

Scodari 1997). We were unable to develop a reliable measure of wetland acreage by state over time.

Most of the measures we considered either had

incorrect signs, imposed significant restrictions on

the available sample, or were highly correlated

with other independent variables used to proxy the

other resources. None were statistically significant

56 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
determinants of federal-aid construction costs. In

the presence of these data limitations, it is reasonable to ask whether there is any evidence supporting a wetlands effect? Finally, the catchall “omitted

variable” concern plagues any effort to detect the

effects of unobservable rules hypothesized to influence behavior.

The first and the last questions are partially

answered with the Hausman and Breusch-Pagan

(1980) test results. As noted earlier, the Hausman

test indirectly compares a fixed-effects model (i.e.,

state-level dummy variables) with a random effects

model. By testing the orthogonality of random

effects to the independent variables we indirectly

gauge whether omitted separate state-specific

effects were important enough to cause a rejection

in the null hypothesis. Neither the federal-aid nor

the state-financed models reject the orthogonality

hypothesis. Of course, it could well be the case that

state effects were themselves not distinctive. The

Breusch-Pagan test rejects this hypothesis. We can

conclude based on this test that the variance in the

state effect error is not zero for either model.

Overall then, while we acknowledge that our

model is incomplete, the results of these tests suggest that what may have been “left out” does not

appear important enough to influence tests for

specification errors and maintained assumptions

that could change conclusions on the effects of federal environmental regulations.

The last issue—wetlands effects—is more complex. The only indirect measure we could construct

to reflect wetland-related delay costs was the time

to process Section 404 permits. The Wilmington,

North Carolina, office of the Army Corps of

Engineers provided records for over 1,300 permit

applications for projects in North Carolina with

decisions between 1994 and 1995. This included

general and individual permits (see U.S. Army

Corps of Engineers (1994); Kusler (1992); and

Smith et al. (1998) for descriptions of the various

types of permits). The Corps staff identified

whether each permit was requested as part of a

DOT project (i.e., DOT = 1, otherwise = 0). Our

hypothesis is direct. Applications for Section 404

permits for transportation projects are, as a rule,

more likely to involve other environmental impacts. Part of the reason for increased construction

costs stems from the delay due to multiple, conflicting mandates of the environmental regulations

facing highways. The states are required to develop a negotiated “balance” among environmental

claims. Using this logic, we would expect DOT

applications to take longer after controlling for

other influences.

Equation (3) reports our estimated model for

delay (days to process and obtain a final decision

for a Section 404 permit using the North Carolina

sample). The model is estimated as a random effects

specification comparable to the description in equation (1). Here each county is treated as the source

of the random effect error. Equation (3) reports the

estimates. The database does not allow other features of the application to be identified. The numbers in parentheses are the t-ratios for the model

and test the null hypothesis of the association.

Processing time = 119.08 + 215.06 DOT (3)

(17.14) (5.84)

R2

within .136

between .259

overall .160

Hausman x2(1) = 1.84

p-value = 0.175

Breusch-Pagan x2(1) = 7.79

p-value = 0.005

The estimates are consistent with our expectations.

DOT projects on average take 215 days longer to

obtain a permit. The Hausman and Breusch-Pagan

tests are also consistent with using this simple

approach to control for other effects. Thus, to the

extent delay adds to compliance costs for environmental regulations and North Carolina’s experience is representative, the available data confirm

the informal record suggesting that the permitting

process associated with wetlands takes more time

for transportation projects. This would be consistent with the interactions we outlined, and imply

wetlands regulations are part of the environmental

compliance cost picture.

SMITH, VON HAEFEN & ZHU 57
CONCLUSIONS AND IMPLICATIONS

This paper statistically compares construction

expenditures for highways that address federal

environmental regulations to approximate a natural experiment and, in the process, to evaluate

whether those regulations impose significant compliance costs on highway construction and repair.

The federal-aid system is subject to over 20 different statutes. In addition, states using Federal-Aid

Highway Program funding may encounter subsets

of the nearly 30 different federal agencies with

some oversight responsibilities for the environmental resources covered by these statutes. We have

argued the resulting system is one that has public

agencies negotiating with other public agencies

about the exact nature of compliance on multiple

environmental mandates.14 Our statistical analyses

suggest that environmental regulations appear to

increase construction expenditures for federal-aid

highways. Only the wetlands’ effects could not be

specifically linked to our cost measures. Permitlevel data for one state appears to confirm that

when highway projects require Section 404 permits, this factor alone seems to increase permitting

delays. Of course, it is also important to note that

we do not know much more about these applications and as a result have treated other sources of

heterogeneity as random.

There are a number of serious qualifications to

these findings. Two are especially important. The

federal-aid highway system and the state road systems are quite different. The state road system is

likely to be more heterogeneous, with some roadways providing transportation comparable to that

of the federal-aid system and others that do not.

Thus, one should expect that the construction

requirements will be different irrespective of the

effects of federal environmental regulations. This

implies that direct comparisons of the estimated

parameters from the models using the two construction expenditure measures would be inappropriate.

As a result, our test considered only the statistical

significance and sign of the estimated effects for the

proxy variables reflecting the regulations. 

Second, and equally important, we do not have

direct measures of the stringency of the environmental regulations. Instead, we have variables that

reflect the amounts of resources in each state that

may be associated with increases in the likelihood

that some environmental regulations would apply

to projects in that state. This is not the same as

knowing that specific projects were affected by the

regulations due to specific federal statutes. In short,

we are testing our hypothesis using “weak signals”

of the influence of the requirements imposed by

environmental mandates with “noisy” records of

their outcomes for costs. 

An optimist reading our results will find the significant effects under these circumstances a clear

reflection of the impacts of the regulatory process.

This reasoning would suggest that the odds are

“stacked against” finding anything. Moreover, our

“experiment” implies we should observe influences

on federal-aid construction costs and not find the

associations for state-financed construction costs.

The twofold requirement would seem to reduce the

chances of nonsense correlations “explaining” the

58 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

14 New legislation authorizing federal funding for highways recognizes these complications and their effect on

compliance costs in its provisions calling for “environmental streamlining” (Section 1309). The legislation

encourages development of an integrated decisionmaking

process to coordinate permitting and to encourage early

consideration of environmental impacts. It encourages the

Secretary of DOT to enter into Memoranda of Agreement

(MOAs) with the agencies responsible for receiving the

environmental documents under NEPA or for conducting

other environmental reviews, analyses, opinions, or issuing licenses, permits, or approvals related to highway projects. The expectation is that MOAs will lead to

cooperatively determined time periods for reviews and

integrated reviews. The Secretary is also given authority

to close the record. But this authority to issue a record of

decision, closing the record when another agency fails to

meet an agreed upon deadline, is limited to matters pending before the Secretary. If projects require a Section 404

permit the Secretary may not restrict the Corps’ review

with respect to that permit. 

The legislation also includes provisions that allow the

additional costs associated with this streamlined process

to be considered eligible project expenses under the

Federal-Aid Highway Program. They are, however, only

for federal agencies meeting the deadlines for environmental reviews when these new deadlines are less than the

customary time allowed for the reviews. 

These details clearly suggest Congress received the

informal messages about time delays and compliance

costs arising from lack of coordination in the environmental reviews. Nonetheless, to the extent the delays are

inevitable, given the conflicting “absolute” mandates, the

reauthorized legislation provides no guidance on how a

hierarchy of the mandates is to be established.
constant findings for both cases. The pessimist

will, by contrast, conclude the experiment is not

ideal and our results could equally well reflect a

number of omitted variables. State roads are subject to state-level environmental regulations and

can, under some conditions, also encounter the

federal mandates. Because each construction measure aggregates over several projects, this line of

argument would suggest both measures could be

influenced by the federal environmental statutes.

We side with the optimistic perspective, but do

so cautiously. The primary reason for our acceptance of this perspective is that other independent

evidence (e.g., General Accounting Office reports

and the ratings of DOT environmental officials

(see Smith et al. 1997) confirm the specific sources

isolated in the statistical analysis as the most

important effects of environmental regulations for

highways.

Further progress in evaluating how the environmental mandates impose federal-aid highway

impact costs will require a detailed study of individual projects—either reconstructing ex post the

adjustments made to accommodate the relevant

environmental requirements, or estimating ex ante

what appears to be their likely consequences for a

specific set of projects. This would be a very significant research effort.

Before undertaking such an effort, it is important to consider why environmental regulations

have received so much attention. The regulations

seem to be increasing delays and costs, but we do

not know whether the modifications to what was

best practice in the planning, design, and construction (or repair) of highways are worth at least the

added costs. To address this question requires consideration of the net benefits of environmental

modifications. While including benefits involves

another significant set of complexities, it is important to recognize that the present process is not

“neutral” on what these benefits might be.

Decisions to set priorities among the absolute mandates associated with each environmental regulation implicitly assign values to the environmental

changes that are avoided (or not avoided) by the

modifications made to the construction and repair

practices for roadways. There is currently no effort

to include economic measures of what people

would want from these decisions. Thus, even if the

benefit estimates are approximate they are likely to

help the negotiations of the alternatives. TEA-21

recognizes the need for early coordination and

integrated reviews. It does not, however, provide

guidance on how to prioritize conflicting environmental mandates. Benefit analysis would offer one

way to help set these priorities. 

ACKNOWLEDGMENTS

This research was supported under grant no. 46-

0003-01 from the Center for Transportation and

the Environment, North Carolina State University.

A number of individuals contributed to this

research by providing us information, data, comments and reactions to the preliminary findings.

Thanks are due to John Fisher, Winston

Harrington, David Harrison, Wayne Kobler,

Thomas D. Larson, Barney O’Quinn, Bill Phillips,

Phyllis Ramel, Charles Thompson, and Wayne

Wright. Thanks are also due to participants in seminars at UCLA, the NBER Public Economics 1997

Summer Workshop, FHWA and three anonymous

referees for comments, and especially to research

assistants who helped in assembling literature and

developing or coding the survey and other data

used for this research, including Rachel Cleetus,

Chris Dockins, Nancy Graham, Martin Heintzelman, Brent McLamb, and Donald Rayno. Alice

Bowser, Kris McGee, and Kelly DeMarchis prepared and edited numerous drafts of this manuscript. The views presented are the authors’ and

not those of these individuals or the sponsoring

organization.

SMITH, VON HAEFEN & ZHU 59
REFERENCES

Baltagi, B.H. 1995. Econometric Analysis of Panel Data. New

York, NY: John Wiley & Sons.

Baltagi, B.H. and Q. Li. 1990. A Lagrange Multiplier Test for

the Error Components Model with Incomplete Panels.

Econometric Reviews 9, no. 1:103–107.

Breusch, T. and A. Pagan. 1980. The LM Test and Its

Application to Model Specification in Econometrics.

Review of Economic Studies 47:239–254. January.

Carlin, A., F. Scodari, and D.H. Garner. 1992. Environmental Investments: The Costs of Cleaning Up. Environment

34:12–20, 38–44. March.

Freeman, A.M. III. 1978. Air and Water Pollution Policy.

Current Issues in U.S. Environmental Policy. Edited by

Paul R. Portney. Baltimore, MD: Johns Hopkins

University Press.

Gray, W.B. 1987. The Cost of Regulation: OSHA, EPA and

the Productivity Slowdown. American Economic Review

77, no. 5:998–1006.

Gray, W.B. and R.J. Shadbegian. 1994. Pollution Abatement

Costs, Regulation and Plant-Level Productivity, discussion

paper. U.S. Department of Commerce, Center for

Economic Studies.

Hausman, J.A. 1978. Specification Tests in Econometrics.

Econometrica 46:1251–1271. November.

Hazilla, M. and R.J. Kopp. 1990. Social Cost of Environmental Quality Regulation: A General Equilibrium Analysis.

Journal of Political Economy 98, no. 4:853–873.

Jaffee, A.B., S.R. Peterson, P.R. Portney, and R.N. Stavins.

1995. Environmental Regulations and the Competitiveness of U.S. Manufacturing: What Does the

Evidence Tell Us? Journal of Economic Literature 33:

132–163.

Jorgenson, D.W. and P.J. Wilcoxen. 1990. Environmental

Regulation and U.S. Economic Growth. Rand Journal of

Economics 21, no. 2:314–340.

Kusler, J. 1992. Wetlands Delineation: An Issue of Science or

Politics? Environment 34:7–11, 29–37.

Kussy, E.V.A. 1996. Surface Transportation and Administrative Law: Growing Up Together in the 20th Century.

Transportation Research Record, No. 1527 Planning and

Administration; Transportation Law. Washington, DC:

National Academy Press.

Moffitt, R. 1991. Program Evaluation with Nonexperimental

Data. Evaluation Review 15:291–314. June.

Morgenstern, R.D., W.A. Pizer, and J.S. Shih. 1998. The Costs

of Environmental Protection, discussion paper 98-36,

Quality of the Environment. Resources for the Future. May.

National Research Council (NRC), Committee on Characterization of Wetlands. 1995. Wetlands: Characteristics

and Boundaries. Washington, DC: National Academy

Press.

Novick, J.B. 1995. Summary of Wisconsin Department of

Transportation analysis of environmental compliance

costs, private correspondence. 16 May.

Scodari, P.F. 1997. Measuring the Benefits of Federal Wetland

Program. Washington, DC: Environmental Law Institute.

Smith, V.K. and R. von Haefen. 1996. Legislative Sources of

Environmental Regulations and Past Efforts To Measure

Their Costs, unpublished working paper. Center for

Environmental and Resource Economics, Duke University. September.

Smith, V. K., R. von Haefen, and M. Heintzelman. 1997.

Environmental Compliance Costs for Highways, Center

for Environmental and Resource Economics Research

Note, No. 1–97. Duke University. August. 

Smith, V.K. and R. von Haefen and W. Zhu. 1998. Environmental Compliance Costs Where Rubber Meets the Road,

unpublished revised working paper. Center for Environmental and Resource Economics, Duke University. June.

Tarrer, A.R. 1993. Impacts of Environmental, Health and

Safety Regulations on Highway Maintenance, final report

prepared for National Cooperative Highway Research

Program, Transportation Research Board. Department of

Chemical Engineering, Auburn University. May.

U.S. Army Corps of Engineers, New England Division. 1994.

Guide to Permit Applications.

U.S. Department of Transportation (USDOT), Federal Highway Administration. 1991. Smith Creek Parkway and

Downtown Spur Wilmington, North Hanover County,

North Carolina, State Project No. 8.22 50101, U-92

Federal Project No. M-5851(2), Administrative Action,

Final Supplemental Environmental Impact State. 15 July.

____. 1994. Mon/Fayette Transportation Project From I-68

in Monogalia County, West Virginia to SR43 (formerly

the Chadville Demonstration Project) in Fayette County,

Pennsylvania, Final Environmental Impact Settlement/

Section 4(f) Evaluation, Section 404 Permit Application.

July 14.

U.S. General Accounting Office (USGAO). 1994. Highway

Planning: Agencies are Attempting To Expedite

Environmental Reviews, But Barriers Remain, report to

the Chairman, Subcommittee on Transportation, Committee on Appropriations, U.S. House of Representatives,

GAO/RCED-94-211. Washington, DC. August.

60 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
61

ABSTRACT

The lack of transportation is one of the most frequently cited problems facing people with disabilities living in rural areas. This research explores a

voucher program for rural transportation. Three

case studies of programs implementing a voucher

framework, supported in part by volunteers, are

presented. These demonstrations were conducted

in seven rural, “frontier,” counties in two states.

The population density of the seven counties averaged less than six people per square mile. One program was administered by an independent living

center; two, by developmental disabilities case

management service programs. The vouchers

themselves provided a measurement method for

evaluating the scope and use of transportation.

Our analysis shows that 35,000 miles of rides were

provided for employment, daily living, evening and

weekend social purposes, and non-emergency medical treatment at a relatively low cost. 

INTRODUCTION

There are approximately 13.2 million people with

disabilities living in rural areas of the United States

(Seekins 1995). The lack of transportation is one of

the most frequently reported problems facing this

population and the rehabilitation providers who

Rural Transportation Voucher Program for People 

with Disabilities: Three Case Studies

BRAD BERNIER

Access Alaska

TOM SEEKINS

Research and Training Center on Rural Rehabilitation

The University of Montana

Tom Seekins, Ph.D., RTC: Rural, 52 Corbin Hall, The University of Montana, Missoula, MT 59812-0001. Email:

ruraldoc@selway.umt.edu.
serve them (Kidder 1989; Page 1989; TonsingGonzales 1989; Nosek et al. 1992). Despite the significance of this problem, few models for

delivering transportation to people with disabilities

in rural areas have been reported. 

Over 20 years ago, Kidder (1989) demonstrated a rural transportation model for people with

disabilities that involved forging transportation

cooperatives among agencies who had purchased

vehicles with federal funds. For example, a senior

citizens’ program, a mental health center, and a

developmental disability service provider could

form a cooperative by combining their vehicles

and transportation budgets to create a community transportation cooperative serving people with

disabilities and not just each agency’s clients. Her

research demonstrated that such an approach was

financially feasible and effective. Unfortunately,

her approach has not been widely adopted

because, in part, it requires agreement and cooperation between multiple agencies, which often

fear loss of control and income. Further, it

requires a community of sufficient size to have at

least two agencies with access to vans and are

willing to cooperate. This means that smaller,

more remote, rural areas are often without a

transportation option.

In response, a handful of voluntary transportation programs have emerged to address the transportation needs of individuals with disabilities

living in sparsely populated rural areas (e.g.,

Mathews 1992). Forms of this model have

emerged in several rural service programs, ranging

from agencies that coordinate volunteer rides to

those that administer funds to subsidize individuals

who seek their own transportation. These programs include organizations operated by statewide

support groups for adults with visual impairments

(e.g., Visions Northwest, in Oregon) and independent living centers (e.g., SUMMIT, Inc. in Montana). The systems vary in their structure and

operation but share the characteristic of transportation provided by volunteers using their personal vehicles, and by public and private providers.

An additional feature of such systems is that

they are compatible with an independent living

philosophy that calls for maximizing individual

control and community integration of people

with disabilities (DeJong 1983; National Council

on Disability 1997, 19). These programs offer a

relatively unexplored alternative to rural transportation for people with disabilities, using community volunteers or local transportation

providers to get resources directly to consumers.

These models can be referred to in general as

Supported Voluntary Rural Transportation

Systems (SVRTS). In essence, these projects point

to the development of a rural transportation

voucher system. However, such projects have

received little attention.

Voucher, or user-side subsidy systems, are permissible under the Non-Urbanized Area Formula

(Section 5311) of the Urban Mass Transportation

Act (USDOT 1992). A variety of individuals and

organizations are eligible to apply for these funds,

including nonprofit organizations and local transportation districts. Interestingly, voucher systems

are also permissible under the Elderly and Persons

with Disabilities Program (Section 5310), which

is typically used for purchasing vehicles for private nonprofits. These funds may be used for

operating and capital expenses for periods of

longer than one year. 

A review of the use of vouchers systems, however, indicates that fewer than 25 communities

around the nation have employed this approach

(USDOT 1994). These programs included 6 area

agencies on aging, 5 private taxi companies, and

14 fixed-route bus services. Of these, 18 (72%)

specifically addressed issues of transportation for

people with disabilities.

The goal of this research was to develop and

evaluate the practicality of a voucher model for

increasing access to transportation for people with

disabilities living in rural areas. A secondary goal

was to develop measures for monitoring such programs. Specifically, we examined the legal and

operational issues of expanding community-based

services using trip vouchers supported by existing

providers and by volunteer drivers.1

62 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 The Community Transportation Association of America

uses two general administrative categories when assessing

the effectiveness of volunteer and other transportation services. Agency-based services are operated by nonprofit

groups usually with paid staff. Community-based services

are operated by volunteer boards of directors set up

specifically for transportation, including services using

volunteer staff or drivers (Studebaker 1990).
METHODS

A total of 232 individuals with disabilities were

identified as eligible to participate in three projects.

Of these, 90 used vouchers for trips during the

three studies. The target areas for this research

were 7 counties in Montana and South Dakota,

comprising some 15,376 square miles with a population of 81,214, which is 5.28 people per square

mile. Table 1 lists the three agencies, the counties

they serve, and the census counts of the number of

people with disabilities in each.

In each of the three major settings, the voucher

system was implemented by a private nonprofit

agency serving the area. In northeastern Montana,

a case management agency, Aware, Inc., took primary responsibility for operating the program in

the five counties it served. This program serves

adults and children with developmental disabilities. In Ravalli County in southwestern Montana,

a local residential and work program for adults

with developmental disabilities, Ravalli Services

Corporation (RSC), operated the model. In

Yankton, South Dakota, an independent living

center serving primarily adults with physical disabilities, Prairie Freedom Center (PFC), operated

the voucher program.

Each of the transportation systems reported

here had both shared and unique features. Unique

features of each are described in detail below.

Programs shared the following features: 1) each

was administered by an established agency that

provided liability coverage through volunteer

clauses in the agency’s insurance,2 2) each used

vouchers that were given to consumers directly, 3)

each agency was a 501 C3 community-based program providing services to people with disabilities,

4) the vouchers were used to purchase rides from

independent providers, and 5) the vouchers themselves provided a means for evaluating the system.

Each transportation coordinator received a copy

of operating instructions for an SVRTS Program.3

The manual provides background and guidelines

for starting a local voucher program. It includes

descriptions of existing voucher programs, examples of vouchers, how to calculate reimbursement

rates, how to look for additional funding, and, if

necessary, how to apply for vehicles. The observations from each site are reported below.

BERNIER & SEEKINS 63

2 There are several legal issues involved in providing transportation services. In a system that uses volunteers, one of

the major issues is liability insurance. In general, small

transportation programs using volunteers can be covered

through an agency’s liability and excess non-owned auto

insurance. For a copy of a legal brief on these liability

issues, contact the first author.

3 This manual is available by contacting the RTC: Rural,

52 Corbin Hall, University of Montana, Missoula,

Montana 59812, by calling 1-800-732-0323, or by email

at http://ruralinstitute.umt.edu/rtcrural.

TABLE 1 Demographics

Program Total 1993 People with People with mobility

and state County population work disabilities and self-care limitations Square miles

Aware, Inc.,

Montana Daniels 2,266 64 11 1,426

Richland 10,716 562 148 2,084

Roosevelt 10,999 541 142 2,356

Valley 8,239 453 60 4,921

Sheridan 4,732 216 56 1,677

Ravalli Services,

Montana Ravalli 25,010 1,733 332 2,394

Prairie Freedom Center,

South Dakota Yankton 19,252 1,013 212 518

Total 7 81,214 4,582 961 15,376
DATA COLLECTION AND ANALYSIS

A four-part carbon-copy voucher form was used to

facilitate tracking across multiple agencies. Each

voucher presents places for the user to record the

date of the trip, its purpose, the provider, and (in

studies 2 and 3), a mileage estimate. A participant

filled out the voucher form and gave it to the driver. The driver kept the original and submitted the

rest to the sponsoring agency for reimbursement. A

copy went to the case manager for records. The

final two copies went to the agency bookkeeper for

reimbursement and then on to the two funding

sources for evaluation. 

Three case studies (Cook and Campbell 1979,

207; Kazdin 1992; Yin 1993) of the use of the

voucher system are presented. Data are summarized and plotted as cumulative rates (Skinner

1969, 81) to provide for visual inspection of the

time-series data on system performance (Furlong

and Wampold 1982).

Study #1: Yankton, South Dakota

Two staff members, the area director and the

bookkeeper, of the PFC allocated a small portion

of their time to this project and administered the

Yankton program. The PFC is an independent living center (ILC)4 satellite office in Yankton County,

South Dakota. First, the PFC staff and a state

Department of Transportation consultant conducted an assessment of local transportation needs of

their consumers and of several other agencies

(Schauer 1994). This assessment of the entire county showed an estimated 16,000 unmet trips per

year for 982 potential riders, including people with

disabilities, the elderly, and other transportationdisadvantaged groups. Second, the PFC staff took

the lead role among agencies for printing and distributing vouchers. Third, the PFC established

cooperative agreements with the local taxi service

and the local community transportation provider.

These providers agreed to honor the vouchers and

provide rides when requested at the prevailing rate

or fare structure. Fourth, procedures for defining

eligibility and for allocating vouchers were developed, based on disability and income. Twenty-six

specific disability conditions or characteristics

(e.g., SSDI recipient, observable physical impairment, medical report) were used as guidelines.

Table 2 shows the sliding scale used to determine

voucher allocation. 

The Yankton site chose to limit its service area

to Yankton County and to use two established

transportation providers. In order to avoid issues

of organizational liability, the PFC also chose not

to use volunteer drivers.

The program began in June 1995. Thirty-five

individuals applied for the program, and 32 met

eligibility criteria and were accepted. Additional

applicants applied over the next several weeks. A

total of 59 individuals applied for participation in

the voucher program and 55 (93%) met the criteria, were accepted, and received 1,632 vouchers. 

After distribution, participants were free to use

the vouchers as they needed. Consumers were

responsible for arranging their own rides, and

providers were responsible for submitting vouchers for payment. Other than processing new applicants and payment, the ILC staff had no other

responsibilities.

Of the 1,632 vouchers distributed, 891 (55%)

were reimbursed by the ILC. Use paralleled distribution but was consistently lower. This means that

either consumers still held a considerable number

of vouchers at the end of the project, providers did

64 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

4 Independent living centers are typically private nonprofit organizations that provide peer counseling or support,

advocacy, and other disability-related services to individuals with disabilities. The majority of the boards of directors of these organizations are required to be individuals

with disabilities.

TABLE 2 Eligibility Determination Guidelines

Declaration Subsidy

of income per trip

Single individuals

$0–$500 per month $1.25

$501–$750 per month $1.00

$751–$900 per month $ .75

Married or single applicants with dependents

$0–$700 per month $1.25

$701–$900 per month $1.00

$901–$1,200 per month $ .75
not submit some vouchers for reimbursement, or a

combination of both.

Figure 1 presents the cumulative number of

rides provided over the 581 days of the project.

The rate of utilization remained low for several

months but began increasing after about 200 days.

A total of 1,143 rides were provided over the 581

days of service for an average of approximately 2

rides per day. 

Figure 2 presents the types of rides taken over

the 581 days. Of those rides for which a purpose

was recorded, 88 (14%) were reported as being for

non-emergency medical purposes; 301 (50%) were

for daily activities or social purposes; and 215

(36%) were for education or employment. 

The total reimbursed cost of rides was

$1,278.25 for an average of $1.12 per ride. In this

case study, data were not available to calculate the

distance of trips or the match of personal resources

and vouchers.

Study #2: Northeastern Montana

The northeastern Montana program was administered by Aware, Inc., a case management provider

for people with developmental disabilities that

serves five counties. One case manager distributed

vouchers and monitored their use. The organization’s bookkeeper allocated a small portion of her

time to making payments to drivers for vouchers.

The Montana Developmental Disabilities Planning and Advisory Council (DDPAC) identified

rural transportation as one of the more pressing

issues to address. In response, the researchers, in

collaboration with Aware, Inc., developed and

evaluated a voucher system for addressing the

transportation needs of adults and children with

developmental disabilities. DDPAC and the

University of Montana provided the funds to operate the system. The university-based researchers

evaluated the program. Aware, Inc. served as the

lead agency for the system’s operation. This included providing liability coverage for volunteer drivers through its corporate insurance.

Unlike the Yankton site, this program used volunteer drivers almost exclusively. Aware, Inc. sent

a letter to consumers, family members, and other

service providers describing the program and soliciting the participation of volunteer drivers. Drivers

were required to have valid licenses, the state’s

minimum liability insurance for their vehicles, and

good driving records. These requirements were

verified by the case manager. In addition, drivers

provided this information on the voucher form. A

total of 28 drivers participated over 12 months. 

To determine the value for the vouchers and the

number each participant could receive, researchers

examined average one-way trips based on Section

5311 and 5310 data reported to the Montana

Passenger Bureau (Montana Department of

Commerce 1990) by the only taxi located in one

community of the service area and by bus services

in the five-county area. These data suggest that an

average trip within limited service areas and times

would cost approximately $3. Volunteer drivers

were reimbursed at 29¢ per mile with a potentialBERNIER & SEEKINS 65

1,200

Cumulative number of trips

FIGURE 1 Cumulative Number of Trips Taken in 

 Yankton, South Dakota

1,000

800

600

400

200

0

50 100 150 200 250 300

Days

350 400 450 500 550

Cumulative number of trips

FIGURE 2 Types of Trips Taken in Yankton,

 South Dakota

300

Social

Employment

Medical

250

200

150

100

50

0

50 100 150 200 250 300

Days

350 400 450 500 550
ly unlimited service area or schedule. This reimbursement rate for volunteers was set to avoid triggering personal tax consequences.

Aware, Inc. developed criteria appropriate for

the region to be served. One criterion was that the

voucher recipient be eligible for developmental disability case management services. Additional criteria included people who were receiving no services

from existing social or vocational providers, were

on waiting lists, were receiving limited services,

could not access existing transportation resources

from their current provider (e.g., vocational program van services that did not serve the area or did

not operate during times when rides were needed),

or could not afford to pay for transportation. The

transportation coordinator/case manager identified

143 consumers who met these criteria and distributed vouchers to them. 

Figure 3 presents the cumulative number of trips

taken over the one year of operation. Twenty-nine

individuals (20.3%) used the vouchers for a ride at

least once. During this time, consumers took

approximately 176 trips, totaling 30,957 miles.

Employment trips made up 2% of the trips taken,

36% were for non-emergency medical trips, and

63% were used for shopping or visits to family. 

Figure 4 presents the cumulative number of

miles of travel provided over the year. The distance

of trips averaged approximately 176 miles but

ranged from 4 to 1,037 miles. Figure 5 presents the

cumulative costs for providing rides.

Consumers frequently used vouchers to visit

families who lived at great distance. A substantial

number of trips were also made to larger towns for

shopping and recreational activities not available

locally. Most of these trips were taken on weekends and holidays when transportation is often not

available from the agencies serving consumers. The

total cost of reimbursed rides was $8,978.

Individuals decided how to use their vouchers,

where they wanted to go, and when. The case manager, family members, or agency staff provided

assistance in arranging rides. After providing rides,

drivers submitted the vouchers to Aware, Inc., for

payment. 

Examining these three figures reveals a general

parallel in both trips and miles. These do not necessarily correspond, however, since trips with

shorter distances during a given period allowed

more total trips during that period.

Figures 3, 4, and 5 show a sporadic start for the

program, perhaps because people were unfamiliar

66 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

200

150

100

50

50 100 150 200 250

Days

300 350 400 450 0

Cumulative number of trips

FIGURE 3 Cumulative Number of Trips Taken in 

 Northeastern Montana

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Days

Cumulative miles

FIGURE 4 Cumulative Miles Traveled by 

 Participants in Northeastern Montana

50 100 150 200 250 300 350 400 450

$10,000

$8,000

$6,000

$4,000

$2,000

$0

Days

Cumulative cost of trips

FIGURE 5 Cumulative Costs of Trips Taken

 in Northeastern Montana

50 100 150 200 250 300 350 400 450
with it. Figure 3 also shows an extended period of

no transportation between days 133 and 170 when

the demonstration was temporarily suspended.

Over time, the rate of trips slowly increased and

became more regular. At the same time, the miles

of trips began to decline. These patterns provide an

interesting contrast with those of the following

program.

Study #3: Ravalli, Montana

Ravalli Services Corporation (RSC) expressed interest in starting an SVRTS program to address

transportation problems in its rural service area of

southwestern Montana. RSC, a developmental disabilities case management and vocational services

provider, served as the lead agency. This included

providing liability coverage for volunteer drivers

through the volunteer clause of its corporate insurance. The RSC Supported Employment (SE) Coordinator distributed vouchers, monitored their

use, and managed payments. 

Like the northeastern Montana site, this program used volunteer drivers almost exclusively.

This program, however, was used primarily for

employment-related transportation. Participants

were defined as those who were served by RSC but

who were unable to access employment because of

transportation problems. The SE Coordinator,

who coordinated the voucher program, identified

34 consumers who met this criterion. He recruited

potential drivers from the agency and the community. Drivers were required to have valid licenses,

the state’s minimum liability insurance for their

vehicles, and good driving records. These requirements were verified by the Coordinator. In addition, drivers had to provide this information on the

voucher form. A total of 17 drivers participated

over 3 months.

The SE Coordinator was aware of consumers’

work schedules, so he scheduled the trips. The drivers then contacted the consumers to make

arrangements for the ride (i.e., pickup place, destinations, etc.). The value of rides was set at 29¢ per

mile, based on IRS reimbursement rates for volunteers.

Figure 6 presents the cumulative number of trips

taken in the program. To date, of the 34 eligible

individuals, six clients (17.6%) have used the

vouchers for trips to work during the program’s

operation. Some individuals used more than one

driver, and some only used their vouchers for one

trip. These six consumers took 74 trips over 87

days of operation. Of these, 72 trips (97%) were

trips to and from work. Two were trips for medical

reasons. 

Figure 7 presents the cumulative miles of trips

taken over the 87 days of operation. Trips averaged 31 miles but ranged from 14.1 to 283 miles.

Figures 6 and 7 are presented on the same scale as

figures 3 and 4 to permit ease of comparison.

Examination of these graphs shows that the

Ravalli program, focusing on employment-related

rides, had a faster and more stable rate of trips

taken. The slope of the cumulative number of miles

traveled shows a stable but slower rate of growth.

This is consistent with the substantially shorter

BERNIER & SEEKINS 67

200

150

100

50

0

Days

Cumulative number of trips

FIGURE 6 Cumulative Number of Trips Taken in

 Ravalli County, Montana

50 100 150 200 250 300 350 400 450

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Cumulative number of miles

FIGURE 7 Cumulative Miles Traveled by 

 Participants in Ravalli County, Montana

Days

50 100 150 200 250 300 350 400 450

 
average trip distance in the Ravalli case. The contrast makes sense in light of the primary use of

vouchers in these two areas. Use of vouchers in the

northeast program was mainly for weekend, holiday, and shopping trips, which require longer but

intermittent trips. Vouchers in the Ravalli program

were used for daily trips to work. 

COST COMPARISONS

An important consideration in assessing any transportation program is the efficiency with which it

provides services. Ten regular transportation programs in Montana serving rural elderly and disabled people reported the average annual cost per

person was $1,054 and ranged from $500 to

$1,900. Their cost per mile ranged from 64¢ to

$3.20 per mile (e.g., Montana Department of

Commerce 1990). The three SVRTS programs had

slightly different purposes, consumer bases, service

areas, and structures; they averaged less than 29¢

per mile and had an annual cost per person that

ranged from $28 to $566. While comparing programs in this manner should be done cautiously,

these data suggest that SVRTS programs can be a

cost-effective option in rural areas. Also, it is important to note that the Yankton SVRTS program represents a sliding scale subsidy while the other

SVRTS programs offered fixed-rate reimbursement.

These variations make comparisons difficult. 

DISCUSSION

This report summarizes the efforts of three rural

communities to expand transportation options

available to people with disabilities through the use

of a voucher system. The vouchers themselves provided the primary measures for evaluating the program. Over 35,000 miles of transportation were

provided to 90 people with a broad range of disabilities living in rural areas. The trips were for

employment, medical, daily living, and social purposes at a relatively low cost. Agency staff easily

organized and administered the program.

Consumers used the vouchers to secure rides to

meet their needs. As such, these data suggest that

voucher systems can be a viable means of providing transportation to people with disabilities living

in rural areas.

The case study approach used here has several

advantages. It is relatively easy to organize within

one community at a time. Developing interventions

in one community at a time permits flexibility in

adapting to local circumstances. The small-scale,

incremental steps also allow for refinement of procedures and measures. This research method is also

relatively inexpensive. It does not provide for

direct comparison of models or applications using

statistical controls, however. Rather, it requires the

accumulation of examples to build understanding

and confidence. It can also serve as a preliminary

step toward a large-scale study.

An important limitation to the study is that no

data were collected about other transportation

methods used by participants. Further, no baseline

data of transportation used were collected. As

such, these data do not allow us to determine

whether vouchers increased the amount of transportation, supplanted, or simply supplemented

current access. Further research is needed to collect

data on the use of various transportation modes

and needs for an extended baseline period. Such

data would permit an examination of the relative

use of modes of transportation by individuals.

Two surprising observations were that fewer

people became involved in the SVRTS program

than were eligible and, of those involved, fewer

people used the vouchers or used fewer vouchers

than anticipated. There are many potential explanations for lower participation in the system than

expected. Local rehabilitation providers may have

overestimated needs. Agencies may not have advertised or recruited eligible individuals outside of

their immediate service networks. In those programs where consumers arranged their own rides,

some may have been reluctant to participate

because of the greater responsibility placed on

them. Given the structure of everyday life experienced by many actual and potential riders with disabilities, vouchers may have required more

individual responsibility and effort than some were

willing to take. Anecdotally, some potential participants also declined to participate when informed

of the program and, in fact, objected to it because

it was sponsored by a government agency.

For those who did participate, lower use may

have been associated with participating in a new

68 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
program. During site visits some consumers, case

managers, and drivers expressed awkwardness in

using vouchers or receiving reimbursement. Use

clearly increased over time. The fact that each of

the programs was in operation for approximately

one year suggests that a long-term commitment to

making vouchers available may be needed in order

to see more extensive day-to-day use. 

Another explanation for the lower than expected rates of use may be that some participants were

saving vouchers for longer trips or emergencies.

Anecdotally, many participants in the northeast

Montana demonstration saved their vouchers and

used them for trips to cities or for visits to distant

family, rather than for local travel.

On the other hand, the lower than expected use

may reflect less unmet demand than was assumed

by advocates and providers. Service providers consistently report transportation as one of the most

pressing problems for people in rural areas. To

date, however, little effort has gone into using comprehensive data from multiple sources to document

how transportation can be used to meet estimated

demand. 

Although this review does not offer a detailed

cost analysis of an SVRTS program, adding a

voucher component to an existing system may not

increase the administrative and maintenance costs

significantly, since many community agencies provide transportation that people with disabilities

might easily purchase with vouchers. Expanding

available transportation using vouchers and volunteers may be less expensive than hiring additional

drivers or purchasing, maintaining, and insuring a

vehicle.

A perennial question when organizing transportation services that involve volunteers is liability and excess non-owned auto insurance. Aware.,

Inc. and Ravalli Services Corporation each had

policies that provided coverage for volunteers who

used their own vehicles, which presented no additional costs.

Voucher systems can offer many advantages

over traditional systems. First, more hours of service can be available to riders because rides are not

necessarily restricted to the time and days of operation of scheduled services. Second, there may be

less direct cost to service agencies. Third, vouchers

can increase public/private cooperation and business for local bus services or taxis. Fourth, voucher systems can be started incrementally with

minimal investment or risk. Finally, the use of

vouchers can be monitored with a high degree of

detail and accuracy because trips are documented

and paid for as they occur, similar to a fee-for-service arrangement. 

There may also be disadvantages to voucher systems, including the potential for unexpected

increases in trip demand that surpass capacity, unexpected surpluses in available vouchers, limits in

the number of subsidized trips available to riders,

and the potential for misuse without an adequate

monitoring program. Some service agencies may

also be reluctant to shift to a voucher system

because they fear losing a visible identity in their

community (e.g., a van with their name on it). 

Transportation remains a significant problem

for people with disabilities living in rural areas and

for those who serve them. Research into innovative

models using small-scale, case-study methods may

be a particularly useful approach to exploring

alternatives. It also provides a flexible strategy that

allows for creative experimentation and the tailoring of projects to community customs and needs.

ACKNOWLEDGMENTS

This research was supported primarily by a grant

from the National Institute of Disability and

Rehabilitation Research (NIDRR #H133B20002).

Additional support was provided by the Montana

Development Disabilities Planning and Advisory

Council and the University of Montana. Opinions

expressed are those of the authors and do not necessarily reflect those of the funding agencies. We

are particularly indebted to the staff of Prairie

Freedom Center and Aware, Inc. We also wish to

thanks Charles Sperry for his review of this manuscript and his helpful comments.

BERNIER & SEEKINS 69
REFERENCES

Cook, T.D. and D.T. Campbell. 1979. Quasi-Experimentation:

Design and Analysis Issues for Field Settings. Chicago, IL:

Rand McNally College Publishing Co.

DeJong, G. 1983. Defining and Implementing the Independent Living Concept. Independent Living for Physically Disabled People. Edited by N.M. Crewe and I.K.

Zola. San Francisco, CA: Jossey-Bass Publishers.

Furlong, M.J. and B.E. Wampold. 1982. Intervention Effects

and Relative Variation as Dimensions in Experts’ Use of

Visual Inference. Journal of Applied Behavior 15, no.

3:415–423.

Kazdin, A.E. 1992. Drawing Valid Inferences from Case

Studies. Methodological Issues and Strategies in Clinical

Research. Washington, DC: American Psychological

Association.

Kidder, A. 1989. Passenger Transportation Problems in Rural

Areas. Profitability and Mobility in Rural America. Edited

by W.R. Gillis. University Park, PA: The Pennsylvania

State University Press.

Mathews, R.M. 1992. Innovations in Rural Independent

Living. American Rehabilitation 18, no. 1:11–13.

Montana Department of Commerce. 1990. Transportation in

Montana. Helena, MT: Montana Department of

Commerce.

National Council on Disability. 1997. Achieving Independence: The Challenge for the 21st Century. Washington, DC. 

Nosek, M., Y. Zhu, and C. Howland. 1992. The Evolution of

Independent Living Programs. Rehabilitation Counseling

Bulletin 35, no. 3:174–189.

Page, C.M. 1989. Rural Rehabilitation: Its Time Is Now.

Meeting the Rehabilitation Needs of Rural Americans.

Edited by G. Foss. Missoula, MT: Rural Institute on

Disabilities.

Schauer, P. 1994. Report of Technical Assistance Provided to

the Yankton Community Transportation Task Force.

Boonville, MO: Peter Schauer Associates.

Seekins, T. 1995. Rural Rehabilitation. Encyclopedia of

Disability and Rehabilitation. Edited by A.E. Dell Orto

and R.P. Marinelli. New York, NY: Simon and Schuster.

Skinner, B.F. 1969. Contingencies of Reinforcement: A

Theoretical Analysis. Englewood Cliffs, NJ: Prentice-Hall,

Inc.

Studebaker. 1990. Using Volunteers in Transportation.

Washington, DC: Community Transportation Association

of America.

Tonsing-Gonzales, L. 1989. Rural Independent Living:

Conquering the Final Frontier. Meeting the Rehabilitation

Needs of Rural Americans. Edited by G. Foss. Missoula,

MT: Rural Institute on Disabilities.

U.S. Department of Transportation (USDOT), Federal Transit

Administration. 1992. FTA Circular 9070.1C. Washington, DC.

____. 1994. Rural Transit Assistance Program. Washington,

DC.

Yin, R.K. 1993. Applications of Case Study Research.

Newbury Park, CA: Sage Publications.

70 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
71

ABSTRACT

International air transport is the fastest growing

segment of transport. It performs a major function

in the globalization process and is a significant feature of the late 20th century. Public policy regarding international air transport has undergone a sea

change in recent years as markets liberalized and

airlines privatized. New management techniques,

partly stimulated by enhanced information technology systems, have resulted in significant

changes on the air services supply side. Aircraft and

air traffic control systems have improved. These

developments pose new challenges for those responsible for overall policy and for those supplying

air transport services. Successfully confronting

these issues requires a solid and relevant statistical

database. This paper sets out to explore the data

that are available, highlights their strengths and

limitations, and indicates areas where improved

statistics may be beneficial. 

The Usefulness of Current International 

Air Transport Statistics

KENNETH BUTTON

The Institute of Public Policy

George Mason University

Kenneth Button, Professor of Public Policy, The Institute

of Public Policy, 3C6, George Mason University, Fairfax,

VA 22030-4444. Email: kbutton@gmu.edu
INTRODUCTION

Trade in international air transport services is extensive and growing.1 This trade has an importance in its own right, in terms of balance of

payments considerations for example, but air

transport also acts as a lubricant for trade in a wide

range of other goods and services. It has been

instrumental in stimulating many of the globalization trends that have been seen over the past 30

years. It is also a sector that has been, and continues to be, the subject of considerable governmental

interventions and one that requires sophisticated

management to perform efficiently. Performance

indicators are thus of major importance both for

public policy formulation and for monitoring internal efficiency.

The late 20th century has also seen a general

appreciation of the need for changing how international aviation is controlled.2 The institutional

structure developed in the mid-1940s allowed considerable progress to be realized in international air

transport markets during the four decades following World War II, but since then technology has

progressed, the demands of air transport users

have both changed and expanded, and new markets have emerged (OECD 1997).

Quantitative information is important to this

sector. Where tight economic regulation continues,

information is needed in the international horsetrading that is an integral part of the bilateral system of air traffic agreements pertaining to such

regulations. Where there are efforts to reform, and

especially to liberalize the regulatory structure, statistics are needed for both private and government

sector use. 

Airlines, airport authorities, and suppliers of aircraft and other material inputs require quantitative

information to make effective management decisions and to permit longer term planning. Labor

unions require data to effectively negotiate with

employers. Governments need to monitor the state

of the industry to enforce general regulations over

such things as anti-trust behavior and to be in a

position to conduct international negotiations.

Increasingly more data is being demanded on the

wider, social implications of air transport as matters of safety and environmental damage attract

public attention. Finally, researchers require reliable and consistent data on international air transportation to perform basic analytical as well as

policy-relevant research. 

This paper focuses on the current availability of

international air transport statistics. It looks at the

uses made of the available sources and highlights

the strengths and weaknesses of current data.3 To

this end, the paper initially provides context by setting out some of the important recent trends in

international air transport. It then moves on to

look at the main uses made of statistics, both by

those concerned with international air transport

policy and by those working in the sector or making use of its services.4 An appendix to the paper

critiques the available statistical sources, given the

types of current and likely future demands placed

on them.

TRENDS IN INTERNATIONAL AIR TRANSPORT

Two forces have helped shape the air transport

industry: developments in markets for air transport

services and institutional reform. Globalization

and internationalization have been major industrial trends of the late 20th century. Part of this pattern is reflected in the significant trade growth that

has taken place. Real export growth in the industrialized countries that make up the Organization

for Economic Cooperation and Development

(OECD) grew at over 7% per year from 1964 to

1992. Comparatively, first world production was

72 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

1 Air transport is a network industry, and, from a technical perspective, it is somewhat artificial to separate international air transport from domestic air services; not only

do they often use the same infrastructure and equipment,

but the route networks are themselves interlinked. From

many policy points of view and considering institutional

structures, domestic services are normally treated differently from international ones.

2 For instance, on the European side of the Atlantic there

was the Comité de Sages for Air Transport (1994); on the

U.S. side, the U.S. National Commission To Ensure a

Strong Competitive Airline Industry (1993).

3 The paper is not concerned with the more general attributes of good statistical series, data collection, and presentation,

but focuses entirely on the issues peculiar to international air

transport.

4 To provide realistic boundaries, the focus of the paper is

almost exclusively on the airline sector, and little attention

is paid to the aircraft manufacturers’ needs.
up by 9%, exports by 12%, and cross-border lending by 23%. Equally, there has been a significant

rise in foreign ownership of assets that are now

estimated to total about $1.7 trillion (U.N. Conference on Trade and Development 1994). 

Whether these trends are passing fads or genuine long-term adjustments to the way production

and trade is conducted is premature to judge.

Preliminary indications are, however, that despite

short-term problems with some Asian and South

American economies, the observed trends are more

than transient.

All this has taken place while the institutional

structure of air transport services has seen significant developments (Kasper 1988). U.S. economic

deregulation of its domestic air freight markets in

1977 and its passenger markets in 1978, combined

with subsequent commitments to an “Open Skies”

approach to international aviation in 1979,

changed the way U.S. policy in this area is conducted. It has also, through both demonstration

and direct knock-on effects, affected the ways in

which other air transport markets are now regulated (Button 1990; OECD 1993, 1997). 

The intra-European market is moving rapidly

toward a situation found within the United States.

Many European countries unilaterally liberalized

their domestic markets, while the European Union

(EU) since 1988 has moved, through a succession

of “Packages,” to a position that has left air transport largely free from economic regulation since

mid-1997.5 There are also indications that the

European Union is slowly developing a single

external approach to international aviation with

the European Commission acquiring rights, albeit

limited, to conduct negotiations on some “doingbusiness” aspects of international agreements. 

Outside of Europe and North America, the

majority of national markets in South America

have been liberalized with a variety of privatization

programs. In the Pacific region, the Australian and

New Zealand domestic markets have also been

deregulated. Additionally, the creation of the

World Trade Organization (WTO) brought into

play, albeit in an extremely small role,6 a new and

geographically wider policymaking institution to

supplement those already in existence, such as the

International Civil Aviation Organization (ICAO)

and the International Air Transport Association

(IATA) (Katz 1995). Aviation issues are also on the

agenda of new regional groupings such as the

Asian-Pacific Economic Council.

This combination of market trends and institutional reforms, combined with rising incomes and

increased leisure time, have contributed to the

steady growth of demand in aviation markets.

Additionally, technological advances have allowed

aircraft efficiency to rise and air traffic control systems to handle greater volumes of traffic, thus

exerting positive effects on the cost side of the

international air transport equation. The airlines

themselves, partly as a response to these developments, have adopted more aggressive marketing

strategies with the introduction of frequent flier

programs, yield management, and code-sharing

alliances (Gellman Research Associates 1994;

USGAO 1995c).

As a result, air passenger traffic since 1960 has

grown worldwide at an average yearly rate of 9%

with freight and mail traffic growing by some 11%

and 7%, respectively. In 1995, for example, some

1.3 billion passengers were carried by the world’s

airlines (Boeing Commercial Airline Group Annual

a). Civil aviation has become a major service industry, contributing to both domestic and international transport systems. It facilitates wider business

communications and is a key component in the

growth of tourism, which is now one of the world’s

major employment sectors (World Travel and

Tourism Council 1993). 

In addition to passenger transport, aviation is an

important form of freight transport, with some

estimates suggesting it carries over 30% of world

trade by value and is forecasted to rise, with some

short-term volatility, 400% by 2015 (Boeing

Commercial Airplane Group Annual a). 

BUTTON 73

5 For details of the various “Packages” of reforms, see

Button et al. (1998); Vincent and Stasinopoulos (1990);

Stasinopoulos (1992, 1993).

6 A number of countries, especially the United States, have

tended to oppose the multilateralism implied in air transport agreements like the General Agreement on Tariffs

and Trade (GATT) and have preferred to use their bargaining power within the bilateral structure.
The growth trend has not been even but has

exhibited geographical variation. For the period

1982 to 1992, ICAO data show that traffic grew by

11.4% on European-Asian/Pacific routes, but only

by 5.0% in mid-Atlantic markets. The EuropeanAfrican traffic hardly grew at all. Airlines have seen

a steady growth in their traffic within Europe since

1992. Overall traffic increased by 8.1% in 1994,

the biggest annual rise in 15 years, except for a

9.1% increase in 1992 following the drop in traffic

recorded in 1991. This trend continued in 1995,

with growth reaching 6.1%. This fairly sustained

growth in traffic, coupled with a more moderate

increase in capacity, is reflected in an improved load

factor for all the national carriers which, like productivity improvements, brought many of the airlines back into profitability.

Cargo traffic also exhibits important geographical variations, with the greatest growth in the mid1990s coming in the Intra-Asian market (20.4% in

1994 and 15% in 1995) and a much more steady

expansion in more mature markets such as the

European-North American market (5.3% in 1995).

After running a deficit for several years, many airlines managed to get back into the black in 1995. Net

profits for the 12 main EU airlines were in the region

of US$800 million, against a net overall loss on the

same scale in 1994. However, only British Airways,

Finnair, and KLM achieved universally favorable

results over the entire period from 1990 to 1994.

Globally, British Airways, Singapore International

Airlines, and Northwest Airlines were the most profitable in 1995, with net incomes of $740 million,

$622 million (up to $624 million in 1996), and $506

million ($536 million in 1996), respectively. Some

carriers such as Air France (–$581 million) and

Canadian Airlines International (–$143 million) continued to report losses in 1995. All major Asian carriers achieved a positive net income in 1995.7 The

financial situation of carriers has tightened more

recently, with profit levels declining as Asian markets

have been in recession and competition heightened in

markets like the North Atlantic.

In line with other sectors, aviation has experienced a significant move toward globalization and

internationalization. Indeed, it is the stated objective of British Airways that it intends to become a

“global carrier.” In pursuit of wider market coverage and in an effort to enhance their own internal

efficiency, other airlines have followed a similar

course. The most recent development, and perhaps

the most controversial, is the formation of various

airline alliances.8

WHAT DO WE NEED FROM INTERNATIONAL

AIR TRANSPORT STATISTICS?

There is no single user of air transport data.

Rather, international air transport statistics are collected and analyzed for a variety of purposes. Such

statistics are important to various public and private sector groups in their decisionmaking processes. The needs of each group, however, often differ.

The use made of data can also vary within a group,

according to the issues at hand. Over time these

issues can also take on new dimensions. In consequence, compromise is inevitable in the way data is

collected and summarized.

The aim here is to look at the current and potential future needs for these statistics. The needs are,

therefore, the context for overall medium-term

developments in air transport markets, public policy priorities, and commercial requirements. The

needs of the public and private sectors inevitably

overlap in many instances, requiring similar data.9 

Policy Analysis

In recent years, there have been significant changes

in the institutions governing international air trans74 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

7 How much of the net revenue is attributable to international operations is difficult to assess in most cases

because of joint operations with domestic services. For

example, in 1995, 98.1% of British Airways revenue tonkilometers was international as was 91.1% of Air France’s

and 100% of Singapore International Airlines, but only

30.7% of Delta Air Lines’, 20% of Continental’s, and

7.4% of U.S. Air’s were international.

8 Some indication of the growth in airline alliances can be

found in the annual surveys conducted by Airline

Business. The most recent survey indicates some 502

alliances globally, up 38% from 1997. The data, while

indicative use a fairly broad definition of alliances. One

thing that they highlight is the volatility of the arrangements that exist. 

9 One area of considerable public concern that is not

touched on here is security. Almost by definition, there is

limited knowledge of exactly what data are available to

the security agencies and to what extent they are

exchanged at an international level.
port. International air transport has traditionally

been heavily regulated in terms of the fares and

cargo rates that could be levied, the level of service

that could be offered, and the airlines that could

operate. Further, many carriers outside of the

United States are state-owned. The situation that

emerged from the Chicago Convention of 1944

was that each country retains rights over its own

air space, and countries have tended to use this to

negotiate bilateral air service agreements with

other states. These agreements have varied over

time in their detail but generally cover the capacity

supplied, specify the permitted carriers, control

fares and entry points, and pool revenues. Within

this framework, IATA acted as a clearinghouse for

information, with fares, capacity, and other features of the market very closely monitored. Data

were relatively simple to collect because many key

parameters were effectively determined by fiat

within the framework of an international cartel. 

More recently, many of the bilateral air service

agreements have been liberalized. Since 1979, the

U.S. government has pursued an Open Skies policy

with respect to many of its bilateral negotiations in

an effort to remove the more binding restrictions.10

Within Europe, the Third Package of aviation

reforms has led to a multilateral structure in the

European Union (Stasinopoulos 1993).

Much of the debate over international liberalization has been conducted in the abstract, with

logical argument being deployed in support of regulatory reform.11 Statistical information has supplemented these theoretical and political arguments, with evidence drawn from a range of studies showing the benefits of freer aviation markets.

The post-1978 developments in the U.S. domestic

market have formed the bedrock for much of this

work, but early lessons were also learned from

analyzing some of the liberal bilateral arrangements. Examples include Barrett (1990) and the

U.K. Civil Aviation Authority’s (1993) work on the

European markets. This latter work has provided

retrospective assessments of the implications of

change and, in doing so, had important demonstration effects for further reform. In general, however, the number of studies of this kind looking

strictly at international air transport have been relatively small in number.

Recently, the increased internationalization of

airline services has created the need for more current analysis and a longer term need for different

types of statistics. As a prerequisite, there are

important initial issues of definition. To take two

examples of the latter, there is considerable commercial interest, together with accompanying public and legal debate, about the desirability of

certain types of airline alliances.12 Debates of this

nature require good data if ultimately they are to

prove constructive, but a major problem is that

there is no accepted definition of what exactly constitutes an alliance. This has led to the emergence

of different sets of statistics.13 Somewhat linked

with this, reforms are resulting in the restructuring

of airline service networks, but this leads to major

problems in defining what constitutes an air service; simple point-to-point data, for example, provide only a partial picture because most trips

involve transit through a hub airport.14 This definition problem also brings to the fore the important

BUTTON 75

10 This was a logical extension of the deregulation of

domestic U.S. cargo markets in 1977 and U.S. domestic

passenger markets in 1978.

11 The new attitude toward economic regulation is reflected in the criteria by which regulated markets are now

assessed. Regulation, aside from purely protective measures, has traditionally been viewed as containing monopoly power while permitting economies of scale to be

enjoyed. The new emphasis focuses on minimizing X-inefficiency and maximizing dynamic efficiency. The result is

that the institutional structure shifts to a greater focus on

cost minimization and innovation. Inevitably this requires

different models and data. Some examples follow: allocative efficiency requires first- or second-best pricing of the

final product; scale efficiency requires possible limitation

on sub-optimal entry to industry; technical efficiency or

X-efficiency require cost minimization by the incumbent

firms; and product choice and dynamic efficiency require

innovation by incumbents.

12 The majority of airline alliances that have attracted the

most public interest have involved one or more European

carriers joining with a large U.S. airline. Alliances are certainly not unique to air transport, nor to transport in general, but are one of the most rapidly growing forms of this

business practice.

13 As an illustration, one can compare the number of

alliances recorded in recent reports in the Economist,

Airline Business, and Avmark Aviation Economist. 14 This definitional question is important in assessing the

degree of competition between carriers when there is a

need to delineate the markets being served, e.g., anti-trust

immunity matters surrounding alliance approvals.
interface between domestic and international air

transport. From a transport perspective, travelers

and shippers are concerned about origin to destination characteristics, but the data generally available to policymakers are divided between the local

and the trunk elements of a passenger or cargo

movement.

The traditional data on physical features of the

international airline activities, such as route-miles

served, are important for some aspects of the new

policy environment; however, they are generally

inadequate for the negotiations that take place

leading to reform. Negotiations usually focus on

the comparative advantage of each nation’s airlines

rather than with physical parameters. Yet, it is this

type of commercial data that is generally lacking,

and the adoption of yield management practices

makes it difficult to collect. 

Freer market conditions, once they are attained,

inevitably bring with them concerns about the performance of markets and the conduct of the airlines within them. In the air transport context these

take two forms. At one extreme, there is concern

that network industries are inherently unstable

and, in economic terms, lack a “core.”15 Undersupply is the result. At the other extreme is the fear

that given the economies of scale, scope, density,

and market presence that are seen as features of the

sector, there will be a long-term oligopolization of

the sector; again undersupply results. Linked to the

latter is a concern that opportunities for new market entry will be further restricted by incumbent

airlines pursuing predatory practices.16

While tackling these problems may involve individual case studies, background statistics are

important as benchmarks against which to assess

behavior. This raises questions regarding such

things as industrial rates of return in different market types, degrees of rate and service variability,

differences in service quality, and comparative cost

structures. 

The need for more and better data is also likely

to be stimulated further in the future by recent

broader trends in trade policy. The gradual development of international liberalization of trade in

services under the administration of the WTO has

to date had minimal effects on the air transport

sector, but as the role of WTO expands, the likelihood is that better quantitative information will be

sought by those developing policy.17 It will also

become increasingly germane as multilateral policing and monitoring of the regime demand standardization of information. 

Forecasting Demand

Often linked to public policy analysis and formation but also extending beyond it is the role of statistical information in predicting demand for

international air transport. As a sector, international aviation will continue expanding into the

foreseeable future, although at different rates in

various geographic submarkets. A number of international agencies, aircraft manufacturers, and airlines regularly produce forecasts of aviation traffic,

mainly related to scheduled services (e.g., Airbus

Industry Annual; Boeing Commercial Airplane

Group Annual b, ICAO Annual a; Daimler-Benz

Aerospace 1995; Douglas Aircraft Co. Annual).

While forecasting remains more of an art than a

science, it seems likely, taking an overview of these

forecasts, that passenger traffic will grow at a rate

between 5% and 7% into the foreseeable future,

much of it in the Asian-Pacific region (up to 9% a

year). Forecasts also show slower growth in the more

mature U.S. and European air transport markets.

The nature of the long-term growth trend in air

transport is less important than the details about

individual markets and fluctuations in mediumterm traffic levels. The evidence to date is that

many forecasts are not particularly accurate in this

76 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

15 The lack of a core means that because of the features of

the market (e.g., decreasing costs) there is no stable equilibrium. If all potential suppliers are aware of this, then

none will enter the market because they appreciate that

their position is unsustainable. For a technical discussion

of these issues see Button and Nijkamp (1998). There may

be links between the creation of dominant market actors

and concerns over an empty core, because one mechanism

for private companies, such as airlines, to internalize the

instability problem is to form a cartel or to seek monopoly power.

16 This has parallels with traditional trade issues like

dumping. 

17 The annex to the General Agreement on Trade in

Services (GATS) dealing with aviation covers no firm rights

and is limited to three doing business issues: aircraft repair

and maintenance services, the selling and marketing of air

transport services, and computer reservation systems.
latter respect18 (see table 1). These medium-term

forecasts are, however, particularly important in

many managerial decisions, such as the ordering of

new aircraft. 

A problem with demand forecasting is that it traditionally has relied on extrapolations of patterns

discerned in time series econometric analysis of past

events and relationships. Establishing the nature of

past and existing relationships between a range of

variables and air travel demand is far from easy even

with reliable data, but forecasting the future path of

independent variables is generally more problematic. Assuming current and past relations will remain

constant over the forecast period is also difficult.19

More recently airlines such as British Airways

and manufacturers such as Boeing have been using

various forms of scenario analysis in an attempt to

develop strategies to confront uncertain futures

(British Airway 1995). These involve less traditional statistical inputs but more qualitative material,

and also require a range of forecasts of possible

trends in those factors that influence the demand

for air services and the costs of providing them.

Cost and Productivity Analysis

Costs of airline services are relevant for policymakers

and airlines. The airlines have a commercial interest

in having a finger on the pulse of their cost flows,

especially as operations are increasingly market driven. Governments require cost data to assess not

only legal matters regarding general industrial policy

but also for such things as the way taxation policy is

applied to the sector. There have been considerable

advances in recent years in the ways in which airline

costs and their efficiency can be measured.

The conventional accounting procedures and

basic econometric models (e.g., using Leontief- or

Cobb-Douglas-based functions) deployed until the

late 1980s have been supplemented by more rigorous

techniques as the understanding of the subtleties of

cost functions has advanced, as methods of estimation have developed, and as relevant computer software has become available (e.g., Caves et al. 1987).

On the theoretical side, traditional neoclassical

ideas of economies of scale have been supplemented by notions of economies of scope, density, networks, and experience.20 On the demand side,

ideas of economies of market presence have come

to the fore in terms of influencing the optimal scale

BUTTON 77

TABLE 1 ICAO Seven-Year Forecast and Actual Flights on the North Atlantic (thousands)

Base-year forecasts

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1980 127.6 130.2 132.7

1981 125.7 128.0 130.4 133.0

1982 125.8 128.1 131.4 131.6 132.0

1983 127.8 129.1 131.7 131.7 133.6 135.3

1984 132.7 138.3 139.8 142.8 147.9 152.0 153.6

1985 140.8 143.5 144.2 148.5 151.5 156.0 159.5

1986 141.4 149.8 151.9 154.8 159.0 164.0 168.9

1987 158.5 177.2 187.3 195.3 203.4 209.8 215.4

1988 175.7 187.6 197.3 204.5 212.9 221.6 229.1

1989 192.0 205.4 216.4 225.6 235.6 244.4 253.6

1990 206.1 205.0 213.9 224.4 236.7 249.3 263.6

1991 213.0 220.3 233.5 247.0 258.1 268.1 277.6

1992 228.2 238.0 252.2 263.3 278.9 294.3 312.5

1993 242.8 252.8 264.5 277.2 291.2 305.2 320.1

Note: Actual figures in italics.

18 European Civil Aviation Administration (1994) offers a

general discussion of air transport forecasting issues.

19 Efforts at supplementing these procedures with Delphi

and similar techniques do not resolve the problems and

can, in some instances, worsen them.

20 Economies of scale reflect declining costs of production

as an airline’s output increases; economies of scope are present when one airline can produce two or more services

more cheaply than if these services were produced by separate airlines; economies of traffic density occur when the

average unit cost of production declines as the amount of

traffic increases between any given set of points served;

economies of network size exist when the average cost of

production declines as the number of city points served by

an airline’s network increases; economies of experience

reflect falling costs with total sales in a market over time.
of activities and largely underlie the development

of frequent flier programs and code-sharing

alliances. Econometric models accommodate these

features and allow parameter estimation through

the use of flexible form models. The transcendental-logarithmic (trans-log) function is, for instance,

now widely deployed because of its flexibility,

although assigning input prices on items such as

capital still poses serious problems.21

The recognition that inappropriately regulated

market structures can lead to intervention failures,

with associated high levels of X-inefficiency because

carriers are not forced to produce on their lowest

cost curves, has led to the adoption both of refined

econometric procedures (e.g., the stochastic frontier

approach) and programming techniques (particularly, data envelopment analysis) in empirical analysis. While technical disputes continue concerning

the validity and usefulness of the alternatives, with

institutional change, many of the assumptions of

the more traditional ways of treating carrier cost

functions are often no longer relevant.

Overall, these developments have implications

for the type of data needed to carry out detailed

cost and productivity analysis and pose particular

problems in policy assessment because much of the

data requirement, for example, that regarding

yields, has commercial value associated with it.

While this is an issue for analysis of any domestic

system, it is even more of a problem when international comparisons are being attempted.

Infrastructure

Modern international air transport relies on an

extensive infrastructure. This includes not only airports and the navigation and air traffic control systems but extends to the infrastructure required to

get passengers and cargoes to and from air terminals. There is also the communications infrastructure necessary to coordinate the activities of

customers of airline services and suppliers of services to airlines.22 Much of this infrastructure is

used by both domestic and international airlines,

with airports serving as interchange points in the

larger air transport network.

The infrastructure data requirements relate to

ongoing activities for immediate management and

operational planning (e.g., slot, gate, and parking

space allocations) and longer term concerns about

capacity planning and investments. The latter,

given the time it takes to gain acceptance for capacity expansion, can require 30- to 40-year forecasts.23 In many instances at major international

hub airports, the banking pattern of flight arrivals

and departures (i.e., flights converging and then

departing within narrow timeframes), coupled

with the need to integrate domestic and international connections, involves considerable interchange of information between airlines and airport

authorities on the local scheduling committee. 

In many markets with a large international presence, the available infrastructure is increasingly

reaching its technical capacity.24 While some of the

problems lie in the poor management of much of

the infrastructure (e.g., X-inefficiencies associated

with public ownership and noneconomic pricing),

there remain very real capacity issues. Expansion

of capacity is, however, difficult because of concern

over adverse environmental impacts.25 In most

industrialized countries, investment in more physical capacity requires extensive public inquiry pro78 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

21 The standard approach is to estimate the -log cost (or

production) function in combination with share equations

using Zellner’s seemingly unrelated least squares procedures. This increases degrees of freedom and means the

estimates are invariate to the share equation omitted and

converge to yield maximum likelihood estimates. Oum

and Yu (1995) offer an example of this approach.

22 The research on the role of computer reservation systems

and passenger air transport is fairly extensive, but that on

the cargo side is more limited (Button and Owens 1999).

23 These need to be relatively accurate for engineering

design purposes, but since costs and benefits are discounted in cost-benefit assessments, they can be less accurate

over the longer period. Good short-term data is also

required for slot allocation purposes. At present, allocations are usually done administratively (e.g., Castles 1997).

24 A report published by IATA (1990), for instance, concluded that without further enhancements, capacity of 16

European airports would be severely limited by the turn of

the century, with Madrid, Frankfurt, Heathrow, Gatwick,

Barcelona and Milan (Linate) the most severely affected.

Even if potential measures help to increase capacity, without new runways put into place, 13 airports would still

remain constrained by 2010.

25 It is no accident that perhaps one of the most comprehensive and expensive cost-benefit studies conducted was

on the location of a new “Third London Airport.” The

recommendations of the inquiry team were rejected

(Commission on the Third London Airport 1971).
cedures to be conducted. These inquiries are costly

and take considerable time to complete.26

Safety

Air travel is, by most objective criteria, the safest

way to travel. Nevertheless, there are accidents.

Between 1987 and 1996, there were 205 commercial jet aircraft losses in the world, 41 by U.S. operators (Boeing Commercial Airline Group Annual

a). Over 65% of these were during takeoff or landing. Over the same period, there were 142 fatal

accidents involving jet aircraft, of which 108

involved passenger airlines and killed 6,156 people. Globally, data on smaller commercial aircraft

accidents are less complete. Aircraft accidents often

attract considerable media attention and public

concern because of the large numbers of individuals that can be involved in any incident.27

The current rate of accidents involving all U.S.

carriers has remained fairly constant since the mid1970s at about 0.05 fatalities per 100,000 departures or 0.0008 per million aircraft-miles. NonU.S. carriers as a group have slightly worse statistics. The actual number of incidents varies quite

considerably year by year because of the unevenness of the fatality incident rate. The problem is

that while on a purely mileage-related basis, air

transport seems safe, the predicted growth in the

sector means the absolute number of incidents will

rise unless safety per se is improved. Improving

safety, or indeed even convincing the public of the

generally good safety record of international air

transport, requires a solid statistical foundation.

In practice, aviation safety data are often incomplete, inconsistent, or have serious gaps. This is in

part because countries collect data and define

terms differently. The U.S. National Transportation Safety Board, for instance, uses a very broad

definition of an accident: any incident that involves, for example, a broken bone is classified as

an accident. Other countries use tighter definitions.

Equally, there are differences in the way air misses

are defined and reported.

Efforts have begun to improve the ways in

which these safety data are collected across countries. The 33 European Civil Aviation Conference

states have followed the U.S. Federal Aviation

Administration (FAA) in establishing a system

called “Safety of Foreign Aircraft” to collect data

on incidents with foreign aircraft. It is intended

that the incident database will provide enough evidence to approach the responsible state authority

on the operational and technical qualities of its carriers’ operations. The practical problem is how to

collect these data.28

Safety policy involves making tradeoffs. Accidents have a variety of immediate financial costs to

aircraft loss and damage, and there are also quantifiable financial costs of preventive policies,

though statistics on these are less easily obtained.

The costs of lost life and injuries pose more of a

challenge. There are techniques available to put

monetary values on these derived from both

revealed preference and stated preference methodologies that are used in benefit-cost analysis, but

they are not free of criticism. 

Environmental Issues

Aviation activities impinge on the environment.

There is a longstanding concern about aircraft

noise nuisance29 and the local implications of

accessing airports by land. Recently, however, the

focus has changed to atmospheric pollution

around airports and from flights themselves. The

change in focus is linked to increased concerns over

the emissions of greenhouse gases and the potential

damage to the ozone layer, but also reflects the

increased marginal utility that societies enjoying

BUTTON 79

26 The process is not speeded up by the need in virtually

all cases for the decision to be based on entirely new sets

of studies. The evidence to date is that there seems to be

little capacity to deploy value transfer procedures in this

area (Johnson and Button 1997).

27 Technically, this would seem to imply the need for

Bayesian statistical analysis, but Guassian modeling still

demonstrates the technical literature. There is some evidence, though, that safety considerations can affect the

viability of air carriers (Button 1997).

28 At present, the United States does not make extensive use

of the standard recording devices on aircraft except when a

major incident occurs, in contrast to many other countries

(e.g., the United Kingdom). In 1998, FAA began seeking

ways to make better use of this data to gain insights on situations where incidents have been averted and on the general way crews perform their duties so that a more proactive

approach to safety may be developed. The main issues concern an unwillingness of labor to have incidents highlighted. 

29 Nelson (1979) offers some early analysis of these issues.
rising material living standards place on environmental conservation and the well-being of future

generations.

Exact calculations are difficult, both because

there are gaps in the scientific understanding that

we have of the damage done by aircraft emissions

and because data on the physical emissions themselves are limited. At the global level, British

Airways (1994) estimated that commercial aviation produces some 500 metric tons of carbon

dioxide emissions per year and thus contributes

1.25% to 1.5% of the greenhouse gas emissions.30

The ozone layer may also be affected by nitrogen

oxide emissions in the middle atmosphere, and at

lower levels it may contribute at the margin to acid

rain. Concern has also been expressed about the

implications of ice crystals from engine exhausts in

the stratosphere. Good data are required to monitor the use of aviation fuel by various types of aircraft in differing operating conditions to provide a

more exact estimate of the environmental implications of air transport. 

Large aircraft are noisy, and noise complaints

are common around international airports. There

are various methods of presenting the noise problem in a quantitative form using indices,31 but in

and of themselves are seldom useful for policy

debates other than to show changes in the scale of

the problem. For policy debates, efforts have been

made to provide monetary valuations of the noise

measure, based on either hedonic price methods or

contingent valuation techniques (Feitelson et al.

1996). However, the consensus has not yet

emerged as to a universally applicable figure.

WHAT DO WE KNOW?

There are a variety of statistics available on different aspects of international air transport. Many are

simply repetitive and others derivative, but even

allowing for this, there are a number of primary

and secondary sources that can be used. The goal

of this section is not to give a detailed breakdown

and commentary on all sources of data relating to

international air transport; even a summary description of U.S. air travel would be excessively

lengthy. Rather, this highlights the main types of

data sources.

Some of the more easily accessible published

data series are detailed in the appendix at the end

of this paper. In addition to published statistics,

there are numerous academic and consultant studies that have collected primary data that may be

useful for meta-analysis.32 The competitive nature

of the international air passenger and air cargo sectors, however, means that much of the consultants’

material is often confined to the “grey literature.”

For a large number of the national agencies, global international air transport statistics are a secondary concern, and most agencies tend to focus

their attention on the markets served by their own

carriers. International data sets from international

organizations are selective and limited. They tend to

reflect those areas of activity over which the agency

has responsibility. Mainly because of resource constraints and a lack of legal authority, organizations

such as ICAO and IATA usually and inevitably rely

on airlines and national governments to feed data to

them. These data come in at varying speeds, often

making full sets of statistics dated by the time of

publication. In addition, the data are reduced to the

lowest common denominator for comparative

analysis. Methods of collection can vary between

countries, and data reliability is sometimes suspect. 

Those directly responsible for providing air

transport offer a range of data sources. The aircraft

manufacturers provide technical details of fleets

and, in some instances, develop particular data sets

to address issues of interest to them. This is particularly true regarding the age of aircraft fleets, their

geographical dispersion, the technical reliability of

individual types of aircraft, and their safety records. 

Airlines provide standardized financial accounts

at a national level to meet auditing requirements,

but since such requirements differ between countries, comparability is often lacking.33 In many

80 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

30 Over time, aircraft are becoming more fuel efficient, but

this is more than offset by the increased amount of air traffic.

31 Quinet (1990) offers some examples.

32 Button et al. (1999) provides a general discussion of the usefulness of meta-analysis in examining microeconomic issues.

33 There are differences in the type of ownership of carriers, and in the types of information they need to make

available for auditing purposes. A state-owned carrier is

generally subject to different legal accounting rules from

an airline that is a publicly traded or a private company,

even within the same country.
countries, in part because of domestic regulations,

data relating to service characteristics such as delays

are published. Further, the global agencies, such as

ICAO, and regional bodies, such as the Association

of European Airlines and the Cargo Airlines

Association, provide readily accessible compilations

of secondary data and also periodic specialized

studies using data gathered from members.

Providers of air transport infrastructure are useful sources of network-related information. The air

traffic control and navigation systems provide aircraft flow data,34 while the airports collect and

publish data on international passenger and cargo

movements through them. The need for customs

and immigration clearance at many airports provides supplementary sources of data. Periodically,

social and economic impact studies are conducted

at individual airports, often as part of a statutory

assessment procedure related to expansion programs of one kind or another. Such case studies

generate insights into a variety of key parameters

that become the bases for subsequent value transfer exercises.

The academic literature provides a range of data

sources, parameter estimates, and information useful in value transfer and comparative analysis exercise. While the more general transport journals

such as the various Transportation Research series,

Transportation Research Record, Journal of

Transport Economics and Policy, International

Journal of Transport Economics, Transportation,

Transport Policy, and Transportation Reviews

often carry international air transport-related

material, other specialized academic journals such

as the Avmark Aviation Economist and the Journal

of Air Transport Management, supplemented by

trade-based publications such as Airline Business

and Aviation Daily, are more regular suppliers of

data and statistical analysis of international air

transport issues.35

WHERE ARE THE GAPS AND WEAKNESSES?

No single set of statistics will ever meet everyone’s

needs. There will always be gaps in data and the

collection of redundant series. Statistics collection

involves inevitable compromise and prioritization.

International air transport is a complicated sector

that can make the collection and subsequent presentation of statistics difficult. While offering a

summary of the strengths and weaknesses of what

is available is inevitably going to be subjective, and

not in small part influenced by personal interests

and knowledge, table 2 provides a simplified, normative assessment of the situation. It offers an indication of where data are available to meet the main

needs of users, and where there would seem to be

important gaps or weaknesses. The aspects of data

considered are what might be thought of as basic—

physical data, economic data, and social data—but

added to this is a “modeling” criterion reflecting

the types of technical instruments that are available

for each need.36

Policy Assessment

In addition to the international sources of data that

exist, many countries collect and publish additional statistics for their own, essentially policymaking,

BUTTON 81

34 In some cases, this is collected mainly for long-term

planning purposes, but some agencies, such as Eurocontrol, have a major revenue-collecting function, and

data is collected for accounting purposes.

35 One might also add to this list of academic journals the

disciplined-based publications in areas such as economics,

engineering, and physics that carry pertinent material for

many forms of air transport study.

TABLE 2 A General Indication of the Adequacy of

International Air Transport Data

Type of data

Need Physical Economic Social Modeling

Policy

assessment ++ + + +

Forecasting

demand ++ +* – +*

Infrastructure + + + +

Costing + + – ++

Safety + – – –

Environment – ... ... –

*The weaknesses here concern data availability and modeling

procedures suitable for medium-term forecasting.

Scale: The most adequacy is indicated by ++, while the least

adequate data are shown with ...

36 There is no intent to imply that each type of data is of

equal importance for all needs. 
purposes. This has traditionally been true at the

more macro level for negotiating bilateral air service agreement adjustments, and more recently

when considering such matters as responding to

the emergence of a new strategic airline alliance

(Dresner and Windle 1996). 

In some instances, however, there have been criticisms concerning the nature of this information

and its usefulness. For example, the U.S. General

Accounting Office (1995c) has in the past been

critical of the amount of suitable economic data

available for conducting international bilateral

negotiations.37

The initial thrust of the Open Skies policy in

1979 and prior de facto initiatives in stimulating

the introduction of more liberal bilateral agreements by the United States between 1976 and 1981

were estimated by Dresner and Tretheway (1992)

to have generated as much as $325 million in savings in North Atlantic fares alone in 1981. More

global analysis was limited by the lack of complete

data from less developed countries. Even the routes

that were included could only be assessed as either

the full economy fare or the lowest minimum fare.

The problems are not that data do not exist but

rather that what exists is of limited use in addressing the questions under review. Physical data on

passenger flows and flights are available, but these

are often only of partial use in addressing what are

fundamentally economic questions. While intellectually, for example, there may exist an a priori case

for freer trade in international air transport services, in practice, bilateral negotiations are concerned with the costs and benefits of adjusting

existing regulatory structures and, as we see below,

data here are often lacking.38

Much of the data is also too aggregate to

address key issues. This point was recently made in

a study of alliances: “Probably because of the difficulty in obtaining consistent route specific data

and the difficulty of separating effects of alliances

from other changes, we are aware of only four

empirical studies which attempted to measure the

effects of airline alliances on carriers and consumers” (Oum and Park 1997).

Forecasting

Forecasting of both supply and demand is important for the effective longer term management of

the international air transport system, but the time

horizons differ for the various actors. Most major

airlines have their own divisions responsible for

making use of published statistics and for collecting and analyzing additional commercial material.

The advent of computer reservation systems (CSRs)

following the creation of Sabre by American

Airlines provides for rapid feedback and response

on an individual, specific services level. This information is the key to the successful dynamic price

discrimination that now characterizes much of the

sector. For short-term forecasts, because of this

built-in, interactive data-collection/application element, CRSs tend to be efficient.39

Longer term planning by airlines and others

requires more aggregate levels of information. One

source is their own internal market surveys and

another the publicly available forecasts of aggregate trends and predictions of change by market

segments. Bringing these and other data together at

the airline level is, however, not easy, as exemplified by the volatile cycles the sector experiences

and the often overreaction of the airlines.

The evidence from the past is that airlines are

often rather poor at forecasting the longer term

demands that they are likely to encounter. The dramatic swings in profitability of the sector is a man82 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

37 The USGAO (1995b) has also argued that the U.S. policy response to code-sharing has been hindered by “... such

[things] as a lack of detailed data on foreign carriers’ codeshare traffic traveling to and from the United States” and

again, “Data problems handicap DOT’s efforts to place a

value upon the access rights to the U.S. market that it relinquishes to foreign governments in exchange for improved

access or code-sharing. (See also USGAO 1995a.)

38 In very strict terms, trade is about comparative advantage rather than competitive advantage in a single sector

and in this sense the types of negotiations that treat trade

in air transport separately from trade in all goods and services is somewhat illogical. 

39 The issues here are rather ones of how much information the owner of a CRS should be allowed to keep confidential and the extent to which airlines should be

permitted to use their own systems in a competitive air

transport market. Good information on revenues and

prices is required for public policy and infrastructure policy formulation, but also important is that for competition

to be effective, air carriers require that they be allowed to

exploit any comparative advantage that they may enjoy.
ifestation of airlines’ responses to uncertainty.

Little work has been completed looking at just why

airlines have been so poor at predicting relatively

short-term shifts in their markets and in making

decisions about aircraft purchases. 

The long-term aggregate market forecasts that

the airlines and infrastructure providers use are relatively good at pinpointing broad trends, but much

less reliable at foreseeing the turning points in

medium-term cycles. The evidence available indicates that one of the difficulties is less the inadequacy of the statistics available within aviation and

more the difficulties of predicting determining variables, such as income levels. In the U.S. domestic

market, for instance, Morrison and Winston

(1995) found that the high levels of excess capacity that existed in the market in 1990 to 1993 were

largely due to airlines finding it difficult to predict

the future levels of key economic indicators, such

as income.40

Costing and Productivity

Cost analysis can involve looking at the cost function of either air services suppliers or users. The

technical analysis of costing and productivity

analysis of supply has advanced a long way in

recent years. There is now a plethora of models

that can be applied to the costing problem and a

range of econometric and programming software

available to conduct the empirical estimation.

Often the issue is less one of finding a suitable

modeling framework than that of selecting the

most appropriate from those available. 

Problems still remain, however, regarding the

data to feed into the models. Some of these were

touched on earlier in the discussion of policy. In

particular, the greater the commercialization of

international air transport as economic regulations

are lessened and airlines are increasingly privatized

means that cost data are less readily available.41

Where these data are released it is often done so in

very broad categories. 

More specific weaknesses have emerged in some

recent studies and take a variety of forms. First,

outside of the United States few countries have the

information required for detailed cost analyses.

For example, in his work on international airline

productivity using a total factor productivity

model, Windle (1991), while having data on U.S.

carriers’ fuel inputs, had to impute fuel use for

non-U.S. carriers.

In other cases, even when some data are available, there is the lack of consistent time series statistics. One example of the problem of point data

is seen in the simulation study of European air

transport networks conducted by Berechman and

de Wit (1996). With only 1992 data on the distribution of business and nonbusiness class passengers, a time series for 1986 to 1992 had to be

imputed. The aggregate nature of information on

yield and the fact that it was available only for U.S.

carriers on North Atlantic routes caused Maillebiau

and Hanson (1995) to rely on a partial database in

their log-linear estimation that they speculated

would produce an error-in-variables bias leading to

underestimation of fare elasticities.

The theoretical debates concerning the desirability of competitive international markets are founded on a set of assumptions that imply, for instance,

that costs are divisible, demand is relatively elastic,

and that suppliers do not all have identical Ushaped cost curves. Violation of such conditions

can lead to an empty core and inadequate supply

(instability conditions). Button (1996) was forced

to rely on variety of proxy variables when seeking

to establish whether market stability conditions

existed for international air transport within the

European Union.

The issue of capital, while conceptually quite

straightforward, is always difficult to deal with in

applied analysis. Setting aside physical problems

such as capacity measurements, there are inevitable

difficulties in putting a monetary value on a capital

stock and the opportunity costs of using it in its

BUTTON 83

40 This is certainly not a problem peculiar to international air transport. In the 1960s, the U.K. statistical authorities attempted to use a logistic curve extrapolation of car

ownership to predict future national income trends on the

pragmatic basis that, whatever the intellectual problems

involved, this could produce more accurate forecasts. This

was also prior to Friedman’s famous judgments about the

criteria upon which to evaluate economic models.

41 It was not unknown for “creative accounting” to be

practiced when government intervention was more widespread.
current activity. Historic costing has now largely

been abandoned in analytical work, though official

data often include it. Replacement costing, while

having an appeal in many sectors, poses particular

problems in air transport where technical change is

rapid. These general issues are compounded at the

international level when different countries pursue

different accounting conventions regarding such

things as depreciation.42

From the perspective of costs to air transport

users, there are considerable gaps in our knowledge. Yield management often makes it difficult to

conduct analysis beyond that of simply trying to

explain average yield and its effects on demand.

Even if this were not so, a full analysis of user costs

would embrace assessment of the “generalized

costs” of using air transport. Such a cost function

would entail, in particular, the monetary equivalent of the overall time costs involved. While travel time valuations are available (e.g., they are used

by FAA), they seldom reflect such important features as the unreliability of time taken for a trip or

differentials for various components of a trip (e.g.,

travel to the airport, waiting time at the airport,

changing planes, and flight time). The evidence

available is that different types of air traveler’s seek

different time attributes from air services,43 and

this needs to be reflected in an appropriate abstract

mode model.44 Related to this is the need to look

at trips using a definition that includes travel time

to and from airports as part of the overall cost of

trip making. There are gaps in the way that user

costs are modeled and in the data that are available

for estimation. 

Infrastructure

Airports are diverse, engaging in multifaceted operations, and obtaining comparable data is not easy

(Doganis 1992). At the very least, it is difficult to separate the implications of international air transport

movements from those of domestic traffic. ICAO

(Annual b) is the only agency that collects comparable financial data on airports, but separation of data

by various traffic types is not comprehensive. The

number of countries participating is also small, and

the data provided are limited.45 Even at the national

level, because airports are generally under the

authority of local governments or state agencies,46

data on key economic indicators are often lacking.

One of the practical problems with financial statistics is that airports provide a range of different

services, but many of these (e.g., handling of baggage and freight, and aircraft maintenance) are

treated as commercial activities separate from the

airport itself. The difficulty is compounded because

the degree to which these types of service are handled by individual airports differs widely. Airports

themselves vary considerably in terms of government involvement and control, and with this come

differences in the nature of data disclosed. Differing

national accounting practices add to the problem of

comparability. The latter is also a major problem

when examining air traffic control systems.

There is also the practical problem of deciding

what the financial data actually implies; the

increasing trend toward privatization and corporatization in many countries means that profit trends

may well reflect the degree of monopoly power

rather than efficiency. Total factor productivity

models (e.g., involving such techniques as nonparametric index number) are now available, offering more useful guidance to efficiency (Hooper and

Hensher 1997). 

84 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

42 The concept of depreciation in an expanding network

industry is a complex one in itself. While there is a case in

engineering terms and with respect to maintenance costs

in assuming that links in a network depreciate with time

or use, if the network is expanding, the external benefits

from being a part of that larger network mean that a link

may gain in value. Its economic value is appreciating, and

it becomes a negative cost item. 

43 Leisure travelers put a premium on low fares, but business travelers put service quality (including frequency,

duration of overall trip, etc.) as their prime concern. For

details of the underlying theory of the money value of

time, see Sharp (1981).

44 For an account of the theory of abstract modes, see

Quandt and Baumol (1983).

45 Within Europe, as early as 1984 the European Commission attempted to establish indicators that would provide

a basis for inter-airport comparisons as well as international comparisons. 

46 A very small number of major international airports

such as Heathrow in the United Kingdom are privately

owned, and here conflicts arise regarding availability of

data. Commercial considerations point toward a degree of

confidentiality, but at the same time the monopoly power

of airports such as Heathrow has led to regulation and

with it the requirement of public accountability.
Furthermore, there are issues concerning air traffic control and navigation systems. Many systems

throughout the world are outdated and considered

technologically obsolete. Countries such as the

United States are attempting to update their systems

and to incorporate new ideas such “Free Flight”

into the way air traffic is managed. Elsewhere, most

notably the European Union, the effort is on standardizing what are presently a diverse collection of

national systems. Overlapping this are new initiatives for stimulating greater efficiency in the management of systems, for example, the Canadian

move to corporatizing their system and the United

Kingdom’s introduction of private financing.

Efficient use of resources in this rapidly changing

environment requires not only good physical data

but also reliable models and carefully constructed

financial data. The financial data are particularly

important if economically based charging is to be

more widely adopted, both from a strict accounting

perspective and as insurance at the international

level that there is no exploitation of systems.

Safety

Safety statistics at the aggregate level are extensive

and available in long time series. There is a need for

greater international consistency, especially regarding the developing nations and the former communist states of Europe, but even here the international

agencies are making progress. Safety data are sparse;

incidents are infrequent and, increasingly because of

improved technology and regulatory controls, often

unique. As a result, the emphasis tends to be increasingly on looking at conditions where a potential for

a serious incident existed but was avoided. Data on

air misses has a long pedigree, but at the international level there are problems in that definitions of

air misses vary between countries, and reporting of

incidents is not always consistent.47 As noted above,

there are also major national differences in the way

that cockpit recorded data are collected and used, in

part because of labor relations problems and

because insensitive collection could lead to adverse

feedback on cabin crew behavior. These microdata

are important, however, since the cause of many

accidents is not known (e.g., flying into terrain) or

results of a series of actions may not be immediately clear from the currently available data.

Presentation and explanations of safety data

pose serious problems. It is generally agreed by

economists that from a cost-benefit point of view,

air transport may be too safe, that is, resources

devoted to airline safety would yield a higher social

return in some other use. Media coverage of incidents is part of the explanation (accidents involve

a spatial and temporal concentration of deaths and

injuries that make for spectacular journalism),48 as

is a general lack of education on the nature of

probabilities. How this problem could be resolved

is uncertain, but public knowledge of transportation safety is, in general, inadequate.

Environment

Concern about the environmental damage associated with air transport, beyond issues of noise nuisance, is comparatively recent and growing

(USGAO 1992). It is not surprising that there is

still considerable uncertainty about the physical

links involved, let alone the economic and social

implications. The gaps in this area are, thus, considerable. Part of the problem lies in the need for

more pure scientific analysis to ascertain how various pollutants associated with air transport

adversely affect the environment. Before this issue

is fully clarified, it is impossible to place viable

monetary values on environmental effects.49

In many ways, broad criticisms of environmental data transcend any discussion of the air transport sector. Many of the issues are new, the impacts

often long term, and the underlying relationships

BUTTON 85

47 Domestically in the United States, there have been periodic

changes in rules of reporting with, for example, immunity

from liability being given when incidents were reported

between 1968 and 1971 but removed after 1971. Rates of

reported incidents were found to go down significantly after

1972 (McKenzie and Shughart 1988). There was a dramatic

drop in the number of air misses recorded in the period immediately following President Reagan’s dismissal of striking air

traffic controllers in the early 1980s, possibly because the

there were not enough controllers available to take reports. 

48 As a personal aside, I have often wondered what the situation would be if newspapers were legally forced to give

the same number of column inches to a transport death

irrespective of the mode involved.

49 There are additional problems in that aviation fuel can

be bought at various points on an aircraft’s flight itinerary,

and hence, the amount of fuel burned on any particular

flight is difficult to ascertain. 
not fully understood (Button 1993; Levinson et al.

1998). The issues often have more to do with a

need for basic scientific research than with largescale data collection. What is missing, however, is

a systematic effort to bring together existing

knowledge and to ensure that ongoing and future

analyses present findings and data in ways that

allow for viable synthesis. There are many ways in

which quantitative information and empirical findings may be brought together, but for full efficiency common reporting procedures are generally

required.50

CONCLUSIONS

Reliable statistics are important both for public

policymaking in international air transport and for

the commercial vitality of operators. The world in

which air transport operates is, however, a rapidly

changing one. There are major technological

advances not only in aircraft but also in the information and control systems that control their use.

Institutional adjustments mean that the role of

government is now different from what it was 20

years ago, with market forces, privatization, and

commercialism playing a much stronger part in the

way the sector functions. This produces new challenges in terms of information requirements.

It is not only the airlines’ component of the sector that is undergoing transformation. Airports are

being privatized or are being required to operate in

a more commercially oriented manner, and air traffic control is in some instances being put on a more

accountable basis. Successful change requires reliable and germane data if ex ante policy decisions

are to meet specified criteria and ex post operations

are to be efficient. At the international level, greater

transparency will inevitably be needed if liberalization is to continue and not be thwarted by concerns

of market manipulations at the infrastructure level.

What is encouraging is that international air

transport statistics are in many ways improving

and becoming more consistent. The coming together of national groupings, such as the European

Union, to develop consistent “internal” air transport policies has necessitated this in some cases.

The major international agencies in air transport,

such as IATA and ICAO, have new roles to play in

this respect, as the traditional structure of the

industry changes and as new markets, particularly

in the Pacific rim area and in Eastern and Central

Europe, grow, albeit at a rather stuttering pace.

The emergence of more competitive markets and

greater commercialism in the sector as a whole

poses additional challenges but equally stimulate

actors to participate more fully in internalizing

their data needs.

ACKNOWLEDGMENTS

The author would like to acknowledge the considerable help rendered by two referees and by the

editor in improving earlier drafts of this paper.

REFERENCES

Airbus Industry. Annual. Market Perspectives for Civil Jet

Aircraft. Paris, France.

Barrett, S.D. 1990. Deregulating European Aviation: A Case

Study. Transportation 16:311–27.

Berechman, J. and J. de Wit. 1996. An Analysis of the Effects

of European Aviation Deregulation on an Airline’s

Network Structure and Choice of a Primary West European Hub Airport. Journal of Transport Economics and

Policy 30:25–75.

Boeing Commercial Airplane Group. Annual a. Current

Market Outlook. Seattle, WA.

____. Annual b. World Air Cargo Forecast. Seattle, WA.

British Airways. 1994. Annual Environmental Report.

London, England.

____. 1995. Global Scenarios 1995–2005. London, England.

Button, K.J., ed. 1990. Airline Deregulation: An International

Perspective. London, England: David Fulton.

____. 1993. Transport, the Environment and Economic Policy.

Aldershot, England: Edward Elgar.

____. 1996. Liberalizing European Aviation: Is There an

Empty Core Problem? Journal of Transport Economics

and Policy 30:275–291.

____. 1997. Interaction of Global Competition, Airline

Strategic Alliances and Air Traffic Safety. Aviation Safety.

Edited by H.M. Soekkha. Utrecht, The Netherlands: VSP.

86 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

50 Button (1998) provides a critique of techniques for synthesis of data sets and empirical findings, and offers comment on

the respective merits of traditional “literary reviews,” metaanalysis, and subjective quantitative assessment methods.
____. 1998. The Three Faces of Synthesis: Bringing Together

Quantitative Findings in the Field of Transport and

Environmental Policy. Environment and Planning C 16:

516–528.

Button, K.J., K. Haynes, and R. Stough. 1998. Flying into the

Future: Air Transport Policy in the European Union.

Cheltenham, England: Edward Elgar.

Button, K.J., S. Jongma, and J. Kerr. 1999. Meta-Analysis

Approaches and Applied Microeconomics. International

Journal of Development Planning Literature 3.

Button, K.J. and P. Nijkamp. 1998. Economic Stability in

Network Industries. Transportation Research Series E 34:

13–24.

Button, K.J. and C. Owens. 1999. Transport and Information

Systems: A Case Study of EDI Development by the Air

Cargo Industry. International Journal of Transport Economics 26.

Castles, C. 1997. Development of Airport Slot Allocation in

the European Community, presented at the Privatization

and Deregulation in Transport Seminar, Oxford

University.

Caves, R., L. Christensen, M. Tretheway, and R.J. Windle.

1987. An Assessment of the Efficiency of U.S. Airline

Deregulation via an International Comparison. Public

Regulation: New Perspectives on Institutions and Policies.

Edited by E. Bailey. Cambridge, MA: MIT Press.

Comité de Sages for Air Transport. 1994. Expanding

Horizons—Civil Aviation in Europe, An Action Program

for the Future. Brussels, Belgium: European Commission. 

Commission on the Third London Airport. 1971. Report.

London, England: HMSO.

Daimler-Benz Aerospace. 1995. World Market Forecast 1995–

2014 for Civil Air Transport. Munich, Germany.

Doganis, R. 1992. The Airport Business. London, England:

Routledge.

Douglas Aircraft Co. Annual. Outlook for Commercial Aircraft. Long Beach, CA.

Dresner, M. and M.W. Tretheway. 1992. Modeling and

Testing the Effect of Market Structure on Price: The Case

of International Air Transport. Journal of Transport Economics and Policy 25:171–183.

Dresner, M. and R. Windle. 1996. Alliances and CodeSharing in the International Airline Industry. Built Environment 22:201–211.

European Civil Aviation Administration. 1994. Report of

ECAC/APATSI Seminar on Forecasting Demand.

London, England.

Feitelson, E.I., R.E. Hurd, and R.R. Mudge. 1996. The

Impact of Airport Noise on Willingness To Pay for

Evidence. Transportation Research D: Transport and Environment 1:1–14.

Gellman Research Associates. 1994. A Study of International

Airline Code Sharing, Office of Aviation and International Economics. Washington, DC: U.S. Department

of Transportation, Office of the Secretary.

Hooper, P.G. and D.A. Hensher. 1997. Measuring Total

Factor Productivity of Airports—An Index Number

Approach. Transportation Research E—Logistics and

Transportation Review 33:249–260.

International Air Transport Association (IATA). 1990.

International Traffic Forecasts 1993–1997: Scheduled

Passengers. Geneva, Switzerland.

International Civil Aviation Organization (ICAO). Annual a.

Outlook for Air Transport to the Year 2006. Montreal,

Canada.

____. Annual b. Airport and Route Facilities. Montreal,

Canada.

Johnson, K. and K.J. Button. 1997. Benefit Transfers: Are

They a Satisfactory Input to Benefit Cost Analysis? An

Airport Noise Nuisance Case Study. Transportation Research Series D, Transport and Environment 2:223–231.

Kasper, D. 1988. Deregulation and Globalization: Liberalizing International Trade in Air Services. Washington,

DC: American Enterprise Institute.

Katz, R. 1995. The Great GATS. Airline Business: 81–82.

September.

Levinson, D.M., D. Gillen, and A. Kanafani. 1998. The

Social Costs of Intercity Transportation: A Review and

Comparison of Air and Highway. Transport Reviews

18:215–240.

Maillebiau, E. and M. Hansen. 1995. Demand and Consumer Welfare Impacts of International Airline Liberalization. Journal of Transport Economics and Policy 29:

115–136.

McKenzie, R.B. and W.F. Shughart. 1988. Deregulation’s

Impact on Air Safety: Separating Fact from Fiction.

Regulation 11:42–51.

Morrison, S. and C. Winston. 1995. The Evolution of the

Airline Industry. Washington, DC: Brookings Institution.

Nelson, J. 1979. Airport Noise, Location Rent, and the

Market for Residential Amenities. Journal of Environmental Economics and Management 6:320–331.

Organization for Economic Cooperation and Development.

1993. International Air Transport: The Challenges Ahead.

Paris, France.

____. 1997. The Future of International Air Transport Policy:

Responding to Global Change. Paris, France.

Oum, T.H. and J-H Park. 1997. Airline Alliances: Current

Status, Policy Issues, and Future Directions. Journal of Air

Transport Management 3:133–144.

BUTTON 87
Oum T.H. and C. Yu. 1995. A Comparative Study of the

Cost Competitiveness of the World’s Airlines. Airline

Economics and Policy. Edited by T.H. Oum. Seoul, Korea:

Korean Reserach Foundation for the 21st Century.

Quandt, R.E. and W.J. Baumol. 1983. The Demand for

Abstract Transport Modes: Theory and Measurement.

Journal of Regional Science 6:13–26. 

Quinet, E. 1990. The Social Costs of Land Transport, OECD

Environment Monograph No. 32. Paris, France: Organization for Economic Cooperation and Development. 

Sharp, C.H. 1981. The Economics of Time. Oxford, England:

Martin Robertson.

Stasinopoulos, D. 1992. The Second Aviation Package of the

European Community. Journal of Transport Economics

and Policy 26:83–87.

____. 1993. The Third Phase of Liberalization in Community

Aviation and the Need for Supplementary Measures.

Journal of Transport Economics and Policy 27:323–328.

U.K. Civil Aviation Authority. 1993. Airline Competition in

the Single European Market, CAP 623. London, England.

U.N. Conference on Trade and Development. 1994. World

Investment Report 1994—Transnational Corporations,

Employment and Workplace. Geneva, Switzerland.

U.S. General Accounting Office (USGAO). 1992. Air Pollution: Global Pollution from Jet Aircraft Could Increase in

the Future, GAO/RCED-92-72. Washington, DC.

____. 1995a. Better Data on Code-Sharing Needed by DOT

for Monitoring and Decision Making, GAO/T-RCED-95-

170. Washington, DC.

____. 1995b. DOT Needs Better Data for Monitoring and

Decision-Making, GAO/T-RCED-95-240. Washington, DC.

____. 1995c. International Aviation: Airline Alliances

Produce Benefits but Effect on Competition Is Uncertain,

GAO/RCED-95-99. Washington, DC.

U.S. National Commission To Ensure a Strong Competitive

Airline Industry. 1993. Change, Challenge and Competition. Washington, DC: U.S. Government Printing Office.

Vincent, D. and D. Stasinopoulos. 1990. The Aviation Policy

of the European Community. Journal of Transport Economics and Policy 24:95–100.

Windle. R.J. 1991. The World’s Airlines: A Cost and Productivity Comparison. Journal of Transport Economics

and Policy 25:31–49.

World Travel and Tourism Council. 1993. Travel and Tourism Forecasts. Brussels, Belgium.

88 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
APPENDIX

International air transport data are available from

a number of national and international organizations and commercial undertakings. While not an

exhaustive inventory of data sources, this listing

represents most of the major sources of data and

what they attempt to show.51 They are divided by

the national or international agency source. Much

of the data summaries from U.S. sources are adapted from the Directory of Transportation Data

Sources, 1996, produced by the Bureau of Transportation Statistics, U.S. Department of Transportation.

U.S. Department of Transportation, Bureau of

Transportation Statistics

Name: North American Transportation Statistics on Canadian, Mexican, and

United States Transportation

Frequency: Biennial

Summary: This source provides data on the size,

scope, and use of the various transportation modes in the three nations.

Most of the data pertain to land

modes (rail, highway, etc.), but there

are some aviation data.

Name: Combined T-9 and Service Segment

(Data Bank 27T)

Frequency: Monthly

Summary: This databank shows point-to-point

international traffic data between

the United States and the originating/terminating nation. It does not

show final passenger destinations;

such data would be covered by

domestic sources. This data set was

replaced by Form 41 Schedule T-100

in 1990.

Name: Schedule P-12(a) Fuel Consumption

by Type of Service and Entity

Frequency: Monthly

Summary: This source reports fuel consumption by aircraft operations by geographic area and service type

(scheduled vs. nonscheduled). 

Name: T-100 International Segments (Data

Bank 28IM and 28IS)

Frequency: Monthly

Summary: All relevant data are collected relating to particular nonstop international flights for U.S. air carriers.

Data are embargoed for three years

before release. 

Name: Origin and Destination Survey

(Data Bank 1A)

Frequency: Quarterly

Summary: This database is derived from a

10% survey of air carriers. It provides a full itinerary of the air travel

for all large U.S. certificated air carriers. The data sample all travel, so

not all are international in nature.

Name: American Travel Survey

Frequency: 5 years

Summary: This survey provides limited longdistance international travel data.

U.S. Customs Service

Name: U.S. Exports of Domestic and

Foreign Merchandise (EM-545)

Frequency: Monthly

Summary: This database shows the weight,

quantity, nation of destination, and

assessed valuation of merchandise

leaving the United States.

Name: U.S. General Imports and Imports

for Consumption (IM-145)

Frequency: Monthly

Summary: This source contains data on the net

quantity, valuation, and nation of

origin for imports.

BUTTON 89

51 The author is indebted to Kirk Johnson for his assistance in compiling the list of data sources. What this list

makes no attempt to cover are series that normally appear

in a more general data series (e.g., in National Income

Accounts, Labor Statistics, etc.). Data from these sources

are often used in meso-level comparative analyis.
U.S. Department of Commerce,

International Trade Administration

Name: Canadian Travel to the

United States

Frequency: Annual

Summary: This sample details tourism from

Canada to the United States (by

individual state). Data include visitor nights, mode of transport, purpose, spending patterns, lodging,

and seasonality.

Name: Historical Arrivals Database

Frequency: Annual

Summary: The data show annual arrivals from

over 100 nations and regions.

Name: In-Flight Survey of International

Air Travelers; Overseas Visitors to

the U.S.; U.S. Travelers to Overseas

Countries 

Frequency: Annual

Summary: These data sets provide information

on travel and spending patterns

both to and from the United States.

The data include places visited,

demographics, means of booking,

and duration stayed. The data summarize other sources.

Name: International Air Passenger

Database 

Frequency: Monthly (with both quarterly and

annual summaries)

Summary: This data set shows international

flight characteristics such as arrival/

departure, class of aircraft, date of

flight, flight number, and total number of U.S. nationals and foreigners.

Name: Outlook for International Travel to

and from the United States

Frequency: Annual

Summary: This data set forecasts international

travel to and from the United States,

taking into account economic and

political factors (e.g., recession, war,

fluctuating currencies).

United Nations, International Civil Aviation

Organization (ICAO)

Name: Civil Aviation Statistics of the World 

Frequency: Annual

Summary: Provides summary data for a variety

of air transportation sources, such

as arrival/departure information,

scheduled services, and the like.

Name: The World of Civil Aviation

Frequency: Annual

Summary: This publication provides an

accounting of the major developments and trends in the international air transport arena over the past

couple years and forecasts some of

the potential trends in the shortterm future on both a global and

regional basis. It also discusses economic influences, policy issues, and

the role of air carriers and airports

in current and future industry

trends.

Name: Accident/Incident Reporting

(ADREP)

Frequency: Annual

Summary: This source reports annual statistics

of accidents and incidents occurring

within the ICAO reporting area.

Name: Surveys of International Air

Transport Fares and Rates

Frequency: Annual

Summary: This source discusses the differences

in international airfares. A separate

publication exists to discuss regional

differences in rates and costs.

Name: Outlook for Air Transport to the

Year 2005

Frequency: Annual

Summary: This report forecasts broad trends in

air transportation on a variety of

topics into the middle of the next

decade.

90 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
Name: Digests of Statistics

Frequency: Annual

Summary: This is a set of reports that discusses

air travel demands, financial data

for the industry, origin and destination statistics, and the like.

International Air Transportation Association

(IATA)

Name: North Atlantic Report 

Frequency: Annual

Summary: The database shows information on

changes in traffic for individual airlines and the total route market. 

Name: Monthly International Statistics

Frequency: Monthly

Summary: This shows traffic and capacity

results from 85 major, scheduled airlines in the survey. The reporting

delay is only about four weeks for

these data.

Name: World Air Transport Statistics 

Frequency: Annual

Summary: This annual report shows 10-year

trends in traffic, capacity, finance,

and fleet for IATA’s member airlines,

of which there are currently 85.

Name: ASIA/PACIFIC Air Transport

Forecast (1980–2010)

Frequency: Annual

Summary: This publication forecasts travel

demand between the Asian-Pacific

and the rest of the world, assessing

various traffic areas. It analyzes passenger traffic volumes and past

growth, and discusses current hub

activities and congestion.

Name: European Air Transport Forecast

(1980–2010)

Frequency: Annual

Summary: This report forecasts air travel

demand for European markets,

much like the previous Asia-Pacific

report, and includes statistics, albeit

limited, on air travel in the former

Soviet Union states.

Name: North America Air Transport

Forecast (1980–2010)

Frequency: Annual

Summary: This publication forecasts air travel

demand for the United States,

Canada, and Mexico.

Name: Passenger Forecast (1996–2000)

Frequency: Annual

Summary: This report gives an aggregated view

of the world’s major airlines to forecast passenger demand for 66 countries. The report also disaggregates

trends to 17 different world regions.

Although not comprehensive, the

statistics cover approximately 86%

of the world’s total international air

traffic demand. 

Australian Department of Transport

Name: AVSTATS—International Scheduled

Air Transport

Frequency: Monthly and annually

Summary: This data set includes figures on revenue passenger, freight, and mail

data, airline market shares, seat utilization, and the like for operations

to and from Australia. The publication contains trend analysis from the

previous year for comparison.

Airports Council International

Name: Worldwide Airport Traffic Report

Frequency: Annual

Summary: The data show passenger, freight,

mail, and aircraft takeoffs/landings

for major world airports.

BUTTON 91
European Union

Name: New Cronos

Frequency: Annual

Summary: Contains data on international passenger characteristics, origin/destination by country (especially within

the E.U. member states) with aggregation for world regions (e.g., 

South America and the Indian

Subcontinent). Also, arrival/departure data are available from major

airports within E.U. member states.

Air Transport Association

Name: ATA/Gallup Air Travel Survey 

Frequency: Annual

Summary: These data summarize a Gallup poll

on American air travel for those 18

and older. It shows percentages of

both domestic and international

flights.

Name: Passenger and Cargo Traffic History

Frequency: Annual

Summary: This report shows aggregated figures

on revenue passenger-miles and

cargo capacity for U.S. carriers flying internationally.

Name: Passenger Load Factor History

Frequency: Annual

Summary: This source shows load percentages

for U.S. carriers flying internationally.

Name: Monthly Passenger Traffic Statistics

Frequency: Monthly

Summary: These data include revenue passengers, load factors, enplanements, etc.

for the international operations of

U.S. carriers.

Name: Monthly Cargo Traffic Statistics

Frequency: Monthly

Summary: These data include international

freight and mail delivery for U.S.

carriers.

Reed Travel Group

Name: Official Airlines Guide (OAG)

Frequency: Monthly

Summary: The OAG is a set of publications

that describe the flights available for

all major airlines on both a domestic

and international basis, including

information on fares. 

92 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
93

ABSTRACT

The effects of special parking provisions in zoning

ordinances are assessed based on a case study of

Midtown Atlanta. The study results indicate that it

is somewhat easier to promote increased office

development around rail transit stations than it is

to reduce parking construction associated with

such office development. It also appears that spillover parking is a much more likely commuter

response to parking pricing than is alternative

mode use, especially where the private automobile

is the dominant mode of commuter transportation

and reasonably priced alternative parking lots are

conveniently located.

INTRODUCTION 

Zoning ordinances often require more parking

than is required to serve the access needs of new

development (Shoup and Pickrell 1978). The result

in most suburban office settings is ample free parking or, from a different perspective, the absence of

parking pricing needed to discipline travel markets

(Shoup 1982). Even in central business districts

Office Development, Parking Management, and 

Travel Behavior: The Case of Midtown Atlanta

ERIK FERGUSON

ETF Associates

Erik Ferguson, ETF Associates, P.O. Box 888729,

Dunwoody, GA 30356. Email: etferg@mindspring.com.
(CBDs), where parking pricing is the rule rather

than the exception, as much as half or more of all

employees may receive free parking, either directly

provided or reimbursed by their employers (Roche

and Willson 1986).

Transportation influences land use by allowing

higher density development as greater accessibility

is provided (Giuliano 1989). Beltways have transformed many urban areas. Suburban highway

junctures can develop into employment centers

rivaling the CBD in terms of both size and influence (Payne-Maxie and Blayney-Dyett 1980).

Rapid rail transit is posited to have similar effects,

though perhaps on a somewhat smaller scale

(Cervero and Landis 1993).

Local communities often attempt to steer development through zoning ordinances, providing tax

breaks, density bonuses, and other incentives to

attract certain types of development activity to specific locations within their jurisdiction (Forkenbrock 1990; Cervero 1994). Atlanta, Georgia, is

one such city. The city of Atlanta modified its zoning ordinance in 1981 to promote economic development by easing restrictions on building

construction near rapid rail stations. An example

of this is the elimination of all parking requirements in redevelopment zones called Special Public

Interest Districts (SPIDs). This paper will analyze

the local effects of SPIDs on land development and

travel behavior in Midtown Atlanta.

Data

The data used in this analysis were derived from a

case study of Midtown Atlanta, a major employment center in the city of Atlanta, located just four

miles north of the traditional CBD (Nelson et al.

1995). For the purposes of this study, a cordon line

was draw around three nearly contiguous SPIDs in

the Midtown area: the North Avenue, Midtown,

and Arts Center rail stations. Preliminary data on

availability, occupancy, and pricing were collected

for all high rise office buildings and commercial

parking facilities located in the study area through

a combination of telephone interviews and windshield surveys. Two sites were identified within the

study area’s boundaries for further analysis, based

on detailed employer and employee surveys. Each

site selected was composed of two adjacent buildings. The first site was located just inside, the second site just outside, Midtown’s SPID boundaries

(see figure 1).

Employers located at both sites were surveyed

regarding employee parking policies. Of the 74

tenants in the 4 buildings surveyed, 29 returned

surveys for an overall 39% response rate.

Employees of all building tenants were surveyed

simultaneously regarding mode of travel to work,

parking, and related issues. Of the 74 tenants resident in the 4 buildings, 36 (49%) returned 1 or

more employee surveys. Of the 674 employees of

these 36 responding firms, 350 (52%) returned

employee surveys. This response rate is adequate to

ensure a representative sample of employees and

employers within the sites selected.

Methodology

The methodology employed here is simple and

straightforward. We have a single study area composed of a unified land market in a fairly homogeneous neighborhood commonly recognized as such

and therefore called Midtown. Within the study

area there are three SPIDs. Buildings, building tenants, and their employees must be either inside or

outside an SPID.

94 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

Arts

Center

Midtown

North

Avenue

Map legend

Parking structures

Special park district

Study area

Interstates

Other rail

Roads

MARTA

stations

Commercial

parking

Buildings surveyed

Monthly parking

rate

$XX

Miles

0 0.2 0.4

N

$65

$40

$40

$40

$45

$85

$45

$80

$35

FIGURE 1 Midtown Study Area

✠

✠

✠

✪

✪ ✪

✪

✪✪

✪

✪
We will look at the dynamics of speculative high

rise office development inside and outside SPIDs

within the Midtown study area. In addition, we

will also analyze travel behavior and parking utilization among employees inside and outside SPIDs

within the Midtown study area.

The following hypotheses will be tested:

Group I

1. SPIDs increase development

2. SPIDs reduce parking supply

3. SPIDs increase parking pricing

Group II

4. SPIDs modify travel behavior

5. SPIDs encourage transit use

The first three hypotheses can be tested explicitly based on analysis of a full inventory or a census

of the local market for commercial office space

before and after SPID implementation. The last

two hypotheses require the use of statistical methods applied to a survey sample of employees inside

and outside SPIDs. The statistical methods used

here include categorical analysis based on crosstabulation and non-linear regression analysis using

the logit model.

The mode choice model used is a basic logit

model of the following form:

P = eU / (1 + eU)

where

P = probability of mode choice, and

U = utility of mode choice

for the binary mode choice case. Most of the choices modeled here are binary. Mode choice for work

trips is modeled as three mutually exclusive levels of

alternative mode use: a) never, b) occasional, and c)

regular. Mode choice for non-work trips is modeled as four non-exclusive binary choices: rail, bus,

walk, and bicycle. Parking location is modeled as a

single binary choice: a) onsite and b) offsite.

These dependent variable specifications are

quite simple and largely data driven. Further

breakdowns by specific alternative modes for work

trips or specific trip purposes for non-work trips

were hindered by the limited variability available

in this particular data set. The results presented

here are, for the most elaborate models, consistent

with the initial construction of implicit hypotheses

as embodied in the original survey instrument and

the limitations inherent in the actual data.

Parking location and mode choice can be modeled simultaneously (Westin and Gillen 1977). In

this analysis, these two elements of travel demand

are treated separately, mainly because of limited

variability in mode choice among the few regular

users of alternative modes found in Midtown

Atlanta. In order to model parking location and

mode choice together, it would be necessary at the

very least to separate travel modes into those that

require parking and those that do not. The overwhelming reliance of Midtown Atlanta commuters on the solo driven private automobile

creates a lot of statistical power sufficient to

model parking location.

THE BUILT ENVIRONMENT

The Atlanta metropolitan region, with a population of more than 31

/2 million in 1997, is one of

the 10 largest urban areas in the United States, as

well as one of the fastest growing. The city of

Atlanta, with a population barely exceeding

400,000, is one of the smallest central cities in the

United States and among the most stagnant in

terms of population growth. The city of Atlanta

actually declined in population in the 1970s, stabilizing somewhat only after 1980. Meanwhile,

the surrounding Atlanta suburbs have grown by

leaps and bounds. Annexation is not a serious

option for Atlanta, due to special provisions of the

state constitution that make both local annexation

efforts and the incorporation of entirely new cities

unusually difficult to accomplish.

The Midtown area was one of Atlanta’s first

true residential suburbs. It was laid out by upscale

developers in the early part of the 20th century,

just four miles north of the original CBD. The

Midtown area was annexed by the city of Atlanta

after World War I and grew to maturity as one of

its most prestigious residential neighborhoods.

The first office boom in Midtown occurred in the

late 1960s, with 7 buildings of 8 to 24 stories

FERGUSON 95
going up between 1964 and 1974. Midtown

developed a bad reputation in the late 1970’s as

the aging housing stock suffered a serious decline.

In the 1980s, Midtown residential property values

rebounded as Yuppies moved in, becoming urban

pioneers of sorts. Meanwhile, Midtown’s second

office boom ran from 1986 to 1994, with 10 new

office towers of 10 to 50 stories augmenting the

Midtown skyline.

The Midtown area has excellent access to both

highways and public transit (see figure 2). The

“Connector,” a short, merged link of I-75 and I-85,

runs directly adjacent to the area on the west side,

with two full-service Midtown exit ramps, including partial access roads both north and south. The

Metropolitan Atlanta Regional Transportation

Authority (MARTA) is a combined rail and bus

transit system. Over 90% of MARTA’s bus passengers are routed to the rail system by design, making MARTA one of the most rail-dominated transit

systems in the world. There are three MARTA rail

stations in the Midtown area, strung together like

beads on a string and closely spaced along

Peachtree Street, the central traffic artery running

from the CBD through Midtown and Buckhead all

the way out to the farthest northern suburbs.

MARTA was created by an act of the Georgia

State Legislature in 1965, with a mandate to

operate existing bus service while planning and

implementing a proposed rapid rail system in

and around Atlanta. The rail system plan was

completed in 1971. A referendum to support

construction of the rail system through the institution of a regional 1¢ sales tax was approved by

voters in Fulton and DeKalb counties but rejected by voters in Cobb and Gwinnett counties.

Portions of the east-west rail line opened for service to the general public in 1979. The northsouth rail line opened shortly thereafter, with

service to the three Midtown rail stations starting

in 1981 and 1982.

Special Public Interest Districts

In 1981, the city of Atlanta established SPIDs

around MARTA rail stations in three sections of

the city: Downtown, Midtown (the study area),

and Buckhead (see figure 2). The purpose of all

three of these SPIDs was to promote high-density

commercial office development in conjunction

with the location of MARTA rail stations:

1. Developments inside SPIDs were freed from any

parking requirements whatsoever, and were

allowed to build to any height desired under

permissible density restrictions.

2. Developments outside SPIDs were required to

construct a minimum of 2 parking spaces per

1,000 square feet of commercial office space,

and existing height restrictions effectively limited buildings to 30 stories or less.

SPID boundaries were drawn approximately

1,000 feet around each MARTA rail station, with

major street thoroughfares used as the actual

dividing lines. Walking distance to MARTA was

thus the default criterion used to determine

whether any particular proposed development

project fell inside or outside the domain of SPID

provisions (see figure 1).

Office Development

The Midtown office market was dormant both

during and after the deep economic recession that

struck the nation in the early 1980s. It was not

until 1986, five years after SPIDs were first put in

place, that the Midtown office market began to

revive. At the beginning of this new office development cycle, there were 4.8 million square feet of

96 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

Study

area

location

FIGURE 2 MARTA Service Area
commercial office space available in Midtown,

two-thirds of which was located outside SPID

boundaries (see figure 3). Parking ratios for preexisting office developments were 1.65 inside and

1.78 outside SPIDs.

During the occasionally frenzied land speculation and office development activity that occurred

in Midtown in the late 1980s, a net 3.5 million

square feet of office space was added to the preexisting stock. Just over 70% of this new office development occurred inside rather than outside SPID

boundaries. Given more than sufficient availability

of vacant and underutilized land in the Midtown

area, SPIDs, it seems, had been successful in promoting office development within comfortable

walking distance of MARTA rail stations.

There was one problem. New Midtown developments outside SPIDs built an average of 2.07

parking spaces per 1,000 square feet of commercial

office space, barely above the minimum requirements set by the city. Interestingly, however, new

Midtown developments inside SPIDs built an average of 2.03 parking spaces per 1,000 square feet of

commercial office space, barely distinguishable

from new developments outside SPIDs and certainly well above the city’s requirement of zero. Far

from building no new parking, Midtown developers inside SPIDs added parking at virtually the

same rate as did their local competitors outside

SPIDs. Based on this evidence, one cannot conclude that SPIDs resulted in any decrease in the

availability of Midtown parking at all.

Building Management

Building managers influence employee travel

behavior primarily through parking policies.

Building managers may choose to charge for parking, provide it free of charge, or include it in signed

lease agreements. They may restrict parking to tenants, employees, and their visitors or open it up to

anyone who happens to pass by. Building managers can reserve some, all, or no parking for tenants and their employees. Building managers who

charge for parking may do so on an hourly, daily,

weekly, or monthly basis.

FERGUSON 97

1987

0

1

2

3

4

5

6 2.6

2.4

2.2

2.0

1.8

1.6

1.4

Office space (sq. ft.) and rail ridership (millions)

Rail ridership–Midtown

Office space–inside SPID

Parking ratio–outside SPID

Parking ratio–inside SPID

Office space–outside SPID

Parking ratio (spaces/1,000 sq. ft.)

1988 1989 1990 1991 1992 1993 1994

FIGURE 3 Midtown Office Development
Shoup (1982) found that free employee parking

is almost always bundled with long-term office

leases, at least in Southern California. Building

managers in Midtown Atlanta generally provide

no free parking, either to tenants or their employees, with or without long-term office leases. This

practice seems to be a basic condition of the local

market, applicable to all Midtown office buildings,

not just those included in the present study.

Midtown tenants are given the option to reserve

parking for their employees, up to a limit of 2

parking spaces per 1,000 square feet of leased

office or retail space. Any such “reserved” spaces

must be paid for separately and in addition to the

office lease, either by the tenant or the employee.

Commercial parking rates in Midtown Atlanta

varied widely in 1995, from $1.25 to $8.50 on a

daily basis, and from $22.50 to $85 on a monthly

basis depending on location and site-specific

amenities. The four buildings included in this

analysis were toward the high end of this scale,

with maximum daily rates of $6 and $7.50 and

regular monthly rates of $65 and $85, inside and

outside SPIDs, respectively. Commercial parking

lots with daily rates of $2 to $4 and monthly rates

of $30 to $40 were concentrated mainly in the central Midtown area (see figure 1). Several inexpensive surface commercial parking lots were

immediately adjacent to Site 2, located just outside

the central Midtown SPID, while only one such lot,

located just inside the northern Midtown SPID,

was within convenient walking distance of Site 1.

EMPLOYEE DEMOGRAPHICS

The demographics of the two sites surveyed were

reassuringly similar (see table 1). Employees inside

SPIDs were significantly more likely than those

outside to hold professional job titles and to have

attended some graduate school. Employees inside

SPIDs were slightly older than those outside, with

higher incomes and more autos, and more likely to

live in the suburbs. None of these latter differences

were statistically significant, even at the 0.10 level,

though age and auto availability bordered on statistical significance.

Overall, it appears that the demographics of the

two sites were almost, but not quite, identical. It

appears that employees inside SPIDs were perhaps

slightly more “upscale” than those outside, but this

tendency, if one can even call it that, was mathematically weak, and therefore unlikely to skew the

numerical results or invalidate the experimental

design. Midtown employee demographics were

comparable to both the Atlanta region and the

United States as a whole. Major differences includ98 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 1 Demographics by Site

Percentage 

of employees

Inside Outside 

Demographics SPID SPID x2 (d.f.)†

Profession:

Manager/supervisor 20.3 29.6

Professional/technical 46.0 30.3 9.23 (2)***

Other 33.7 40.2

Age:

Under 30 32.6 33.3

30–39 28.8 37.3 3.88 (2)

40+ 38.6 29.4

Gender:

Male 34.2 38.7

Female 65.8 61.3 0.74 (1)

Race:

White 85.4 81.0

Non-white 14.6 19.0 3.10 (1)*

Education:

Some graduate school 45.7 31.2

Some college 47.3 60.4

No college 7.0 8.4 7.56 (2)**

Annual household 

income:

<$30,000 33.9 39.1

$30,000–74,999 27.1 23.9 0.98 (2)

>$75,000 39.0 37.0

Household auto

availability:

N cars < N adults 8.7 11.2

N cars = N adults 66.7 72.4 3.53 (2)

N cars > N adults 24.6 16.4

Residential location:

Fulton/DeKalb counties 59.8 62.5

Cobb/Gwinnett counties 25.7 24.3 0.27 (2)

Other 14.5 13.2

† level of significance

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
ed a higher proportion of women than the national average and a lower proportion of minorities

than the Atlanta average.

Working Conditions

Unlike demographics, which varied consistently if

slightly, working conditions varied either tremendously or not at all by site location (see table 2).

Length of employment and the average amount of

time spent at work in an average day were virtually identical inside and outside SPIDs. Workers

inside SPIDs had greater flexibility in choosing

when to start their work day and were more likely

ever to have worked at home. It appears that the

slightly more upscale workers inside SPIDs had

much greater autonomy in selecting key aspects of

their employment conditions.

The most significant difference in working conditions had to do with who pays for employee parking. Employees inside SPIDs were more than twice

as likely to receive free parking from their employers. Employees outside SPIDs were more than twice

as likely to have to bear the full burden of their own

parking costs. The slightly more upscale and much

more autonomous workers inside SPIDs were significantly more likely than those outside SPIDs to

receive parking subsidies from their employers.

This effect, although unanticipated from an experimental design perspective, seems reasonable on its

face, given the employee demographics and working conditions found in Midtown Atlanta. The

slightly higher parking rates outside SPIDs may also

have contributed to this phenomenon.

Travel to Work

Over 90% of Midtown employees drove alone to

work on a regular basis (see table 3). About a third

had tried carpooling, and a quarter had tried

MARTA rail at least once in their lives as an alternative mode of transportation for the journey to

work. Only 1 in 10 Midtown employees regularly

commuted using any mode of transportation other

than driving alone. Half of those who did use alternative modes sometimes also drove alone on a regular basis. Driving alone is thus not only the

dominant mode of transportation for work travel

in Midtown Atlanta, it is overwhelmingly dominant. There were no significant differences

between employees inside SPIDs and those outside,

in terms of mode choice for the journey to work on

either a regular or an occasional basis.

There may have been virtually no difference

between sites in terms of how Midtown employees

got to work. There was a major difference in where

they parked their cars once they got there, however. Midtown employees outside SPIDs were 10

times as likely as those inside to park their cars offsite rather than onsite, a very significant difference.

Employees outside SPIDs in this particular instance

faced higher parking fees onsite, were less likely to

receive parking subsidies from their employers,

and had more convenient access to competitively

priced commercial parking than did their confreres

inside SPIDs (see figure 1).

FERGUSON 99

TABLE 2 Working Conditions by Site

Percentage 

of employees

Inside Outside 

Working conditions SPID SPID x2 (d.f.)†

Length of

employment:

< 12 months 32.8 28.9

12–35 months 35.5 40.1 0.90 (2)

> 36 months 31.7 30.9

Average time spent 

at work:

< 9 hours 28.1 28.1

9–9.5 hours 42.7 41.1 0.12 (2)

> 9.5 hours 29.2 30.8

Flexibility in 

arrival time:

< 15 minutes 20.9 35.7

15–30 minutes 51.6 54.5 19.23 (2)***

> 30 minutes 27.5 9.8

Ever worked 

at home:

No 42.9 62.4

Yes 57.1 37.6 13.11 (1)***

Who pays for 

parking:

Employer pays 56.8 25.5

Shared cost 15.1 6.4 55.71 (2)***

Employee pays 28.1 68.2

† level of significance

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
Non-Work Travel 

Only one in five vehicle trips are made for commuting purposes these days (Pisarski 1992). Nonwork trips, when chained together with work trips,

may interfere with the ability of commuters to

adopt alternative modes of travel (Bhat 1996).

Midtown Atlanta employees were asked if they

ever used MARTA bus or rail, walked, or rode

bicycles for six varieties of non-work travel:

1. work-related business

2. personal business/errands

3. shopping/dining

4. recreation/entertainment

5. education/school

6. social/visit friends

There were few statistically significant differences between employees inside and outside SPIDs

in their use of alternative modes of transportation

for such non-work travel (see table 4). Overall,

employees inside SPIDs were slightly more likely to

use MARTA rail for non-work travel, a reasonable

finding given the relative proximity of these

employees to the Arts Center MARTA station.

Interestingly, most of the observed difference in

MARTA rail travel for non-work trips is accountable by one trip purpose, namely work-related

business, though personal business/errands also

contributes to the phenomenon.

PARKING PRICING 

The effects of SPIDs should not be felt directly by

commuters but rather indirectly. Thus, parking

construction requirements, limitations, or freedoms should translate directly into parking prices,

whether higher or lower. Higher parking prices

associated with reduced parking supply might then

result in measurable shifts in mode choice for the

work trip, an indirect effect. Atlanta’s SPIDs had

no effect on parking supply, at least not the expected negative effect, but even this should not diminish the independent contribution of parking pricing

to mode choice changes in any way. About half of

the Midtown employees surveyed had to pay some

or all of their parking costs (see table 2).

Oddly enough, however, paying for parking did

not seem to translate into mode choice changes of

any significance in Midtown Atlanta (see table 5).

This is naturally somewhat disappointing, given the

literature on the subject, which constantly reiterates

that parking price elasticities are indeed very significant (Shoup 1995), and that demand management

strategies based on parking pricing are the ones most

likely to achieve measurable changes in travel behavior under normal circumstances (Higgins 1990).

Midtown Atlanta employees who paid for parking were slightly less likely to use alternative modes

of transportation to get to work and slightly more

likely to use three out of four alternative modes for

non-work travel, but none of these measured differences were even close to being statistically significant. Furthermore, while employees who paid for

parking were significantly more likely to park offsite

100 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 3 Travel to Work by Site

Percentage 

of employees

Inside Outside 

Travel to work SPID SPID x2 (d.f.)†

Use of alternative 

modes:

Never use 36.5 34.4

Occasionally use 54.7 54.1 0.70 (2)

Regularly use 8.9 11.5

Regular modes 

of travel:

Drive alone 94.3 93.0 0.24 (1)

Carpool 5.2 5.7 0.05 (1)

MARTA bus 1.0 1.9 0.46 (1)

MARTA rail 2.6 3.2 0.10 (1)

Walk 1.0 3.2 2.02 (1)

Occasional modes 

of travel:

Drive alone 2.1 5.7 3.21 (1)*‡

Carpool 30.7 33.8 0.36 (1)

MARTA bus 9.4 8.3 0.13 (1)

MARTA rail 29.7 24.2 1.31 (1)

Walk 0.5 1.3 0.57 (1)

Bicycle 5.2 1.9 2.62 (1)

Taxi 1.0 3.2 2.02 (1)

Where park:

Onsite 96.4 63.7

Offsite 3.6 36.3 61.52 (1)***

† level of significance

‡ statistical artefact

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
than those who were entitled to receive free parking,

this finding is not surprising and contributes little to

science. If simple cross-tabulations cannot produce

the anticipated results, it is possible that a more

powerful statistical tool, such as multiple linear

regression, may succeed where others have failed.

LOGISTIC REGRESSION 

Preliminary results based on bivariate hypothesis

testing seem to suggest that the choice of parking

location varies with parking management strategies, but mode choice does not, at least not in

Midtown Atlanta. These results, although mathematically quite convincing, are at least partially

counterintuitive from a purely theoretical point of

view. In addition, we have not yet controlled for

either working conditions or demographic variables, both of which might conceivably alter some

or all of these findings.

Logistic regression was performed on all dependent variables (parking location and mode choice

for the work trip and for non-work travel) using a

partial stepwise technique (see table 6). Building

location (inside or outside SPIDs) and parking

price (or rather, the level of employer parking subFERGUSON 101

TABLE 4 Non-Work Travel by Site

Percentage 

of employees

Inside Outside 

Non-work travel SPID SPID x2 (d.f.)†

Any trip purpose:

MARTA bus 8.3 10.2 0.36 (1)

MARTA rail 64.6 55.4 3.04 (1)*

Walk 39.1 40.1 0.04 (1)

Bicycle 22.9 18.5 1.03 (1)

MARTA bus:

Work-related business 4.2 3.8 0.03 (1)

Personal business/ 3.1 3.8 0.13 (1)

errands

Shopping/dining 1.0 2.5 1.16 (1)

Recreation/ 5.2 6.4 0.22 (1)

entertainment

Education/school 0.5 0.6 0.02 (1)

Social/visit friends 1.6 3.2 1.01 (1)

MARTA rail:

Work-related business 40.6 17.2 22.54 (1)***

Personal business/ 26.0 15.9 5.24 (1)**

errands

Shopping/dining 9.9 6.4 1.41 (1)

Recreation/ 44.8 42.0 0.27 (1)

entertainment

Education/school 3.1 2.5 0.10 (1)

Social/visit friends 4.7 5.1 0.03 (1)

Walk:

Work-related business 8.3 8.3 0.00 (1)

Personal business/ 14.1 15.9 0.24 (1)

errands

Shopping/dining 13.5 18.5 1.58 (1)

Recreation/ 25.5 30.6 1.10 (1)

entertainment

Education/school 2.1 0.6 1.28 (1)

Social/visit friends 14.6 14.6 0.00 (1)

Bicycle:

Work-related business 1.6 0.0 2.47 (1)

Personal business/ 2.1 6.4 4.12 (1)** ‡

errands

Shopping/dining 2.6 2.5 0.00 (1)

Recreation/ 20.8 17.2 0.74 (1)

entertainment

Education/school 0.5 0.0 0.82 (1)

Social/visit friends 6.8 3.8 1.46 (1)

† level of significance

‡ statistical artefact

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level

TABLE 5 Employee Behavior by Employer 

Parking Payment

Employer pays 

for parking

Travel behavior No Yes x2 (d.f.)†

Work travel:

Alternative modes

Never use 37.0 34.6

Occasionally use 53.0 55.3 0.46 (2)

Regularly use 10.0 10.1

Non-work travel:

Alternative modes

Bus 10.1 10.0 0.25 (1)

Rail 62.5 57.7 0.82 (1)

Walk 41.5 36.9 0.75 (1)

Bicycle 21.0 20.8 0.00 (1)

Parking location:

Onsite 68.0 100.0

Offsite 32.0 0.0 58.39 (1)***

† level of significance

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
sidy, an inverse function of parking price) were

forced into each of the equations for hypothesis

testing purposes. All remaining variables (both

working conditions and demographics) were

forced into each equation at first and then

removed one at a time until only those variables

that were at least marginally statistically significant (t ≥ 1) remained. This procedure limited mul102 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999

TABLE 6 Employee Travel Behavior by Site, Working Conditions and Demographics

Park

Variable offsite

Use of alternative modes

Work trips Non-work trips

Regular Occasional Bus Rail Walk Bicycle

Intercept 2.75** 0.23 0.40 –0.41 2.56** 1.52 0.33

(1.30) (1.40) (0.90) (1.50) (1.03) (0.95) (0.94)

Outside SPID 2.35*** –0.46 0.34 0.25 –0.41 0.22 –0.40

(1 = yes, 0 = no) (0.48) (0.47) (0.46) (0.30) (0.27) (0.31)

ln (length of –0.38** (0.27)** 0.22*

employment) (0.17) (0.10) (0.12)

ln (arrival time 0.28**

flexibility) (0.11)

ln (employer –0.53*** –0.09 0.04 –0.13 –0.12* –0.01 –0.01

parking subsidy) (0.12) (0.12) (0.06) (0.11) (0.07) (0.06) (0.07)

Managerial –0.59 –0.64**

(1 = yes, 0 = no) (0.51) (0.32)

Professional 0.64 –0.81***

(1 = yes, 0 = no) (0.47) (0.31)

Male (1 = yes, 0 = no) –0.50 –0.51*

(0.44) (0.28)

Black (1 = yes, 0 = no) 1.71*** –0.49

(0.52) (0.40)

Graduate school –1.19** 0.42 0.80*** 0.49*

(1 = yes, 0 = no) (0.56) (0.27) (0.30) (0.27)

ln (income) –0.95*** –0.43 –0.28 –0.60* –0.74*** –0.38* –0.37*

(0.32) (0.35) (0.22) (0.35) (0.25) (0.22) (0.22)

Live outside core –1.84*** –0.32 –1.12** 0.60**

(1 = yes, 0 = no) (0.64) (0.25) (0.53) (0.27)

Log likelihood— –209 –209 –202 –208 –194 –216 –218

initial

Log likelihood—

at convergence –90 –77 –194 –81 –166 –202 –160

N observations 302 301 292 300 280 311 315

Percentage correctly 88 91 61 91 70 62 79

predicted

Note: Standard errors are in parentheses.

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
ticollinearity while providing maximum information on the overall statistical power of the model

and each of the independent explanatory variables

thus considered.

Site Effects

The regressions reaffirm the significant contribution made by building location to choice of parking

location. The building site surveyed outside SPIDs

was actually closer to the heart of Midtown, while

the building site surveyed inside SPIDs was perched

on the northern fringe of Midtown (see figure 1).

The building site surveyed outside SPIDs had a

somewhat higher monthly parking rate onsite ($85

vs. $65), far fewer employees who received free

parking from their employers (25% vs. 57%), and

much more convenient access to lower priced commercial parking (four lots within one block vs. one

lot within two blocks). The site variable captures

the effects of differential parking rates and availability simultaneously. Parking subsidies are treated

as a separate variable in this analysis.

There was no statistically significant relationship found between building location and mode

choice. There was not even a strongly identifiable

pattern to the marginally significant or clearly

insignificant signs in the equations. Employees outside SPIDs were less likely to commute regularly

but more likely to commute occasionally via alternative modes, less likely to use MARTA rail but

more likely to use MARTA bus, less likely to ride

bicycles but more likely to walk for non-work travel purposes. The results shown in table 4, then,

remain as a more useful guide than these insignificant regression findings.

Parking Subsidies

The elasticity of demand for offsite parking with

respect to employer parking subsidies in Midtown

Atlanta is both high (–0.50) and very significant 

(t > 4.00). The elasticity of demand for alternative

modes of transportation with respect to employer

parking subsidies is both low (circa –0.10) and

barely significant (t < 2.00) in just one out of six

cases. Removing the site variable from the equations has little effect on estimated mode choice

price elasticities but increases the offsite parking

price elasticity to an even higher –0.70. Thus, –0.50

is a conservative estimate of the price elasticity of

demand with respect to parking location, based on

these data.

Shoup (1995) identified seven case studies in

which the elasticity of demand for solo automobile

commuting with respect to parking price varied

from a low of –0.08 to a high of –0.23, with an

average of –0.15. An average of 67% of employees

receiving free parking drove alone to work, versus

42% of those who had to pay for parking across

Shoup’s seven cases. Four of Shoup’s cases are traditional CBDs, while the remaining three are large,

high-density urban and suburban employment

activity centers not unlike Midtown Atlanta, in

terms of urban design and the built environment.

The parking price elasticities measured here for

regular commuting and rail and bus transit use for

non-work travel are not statistically very powerful

but are clearly within the range of previous studies,

albeit at the low end of that range.

Feeney (1989) found that parking price elasticities can be even lower than those reported by

Shoup in suburban enclaves, with a range of –0.01

to –0.05 based on the limited European examples

he provides. The results found here suggest that

Midtown Atlanta, with highway and transit access

similar to many CBDs, in addition to a physical

location just four miles north of the original

Atlanta CBD, exhibits travel behavior characteristics that are perhaps a bit more like what one

would expect to find somewhere out on the exurban periphery of the metropolis. The overall transit mode share in Midtown Atlanta is estimated to

be about 7% (Nelson 1995). There was no

increase in transit ridership observed during the

second office building boom of 1986–94, however,

indicating that office workers may not be the best

market for transit ridership in mixed use Midtown

Atlanta (see figure 3).

The upshot of these findings is that Midtown

Atlanta employees are far more likely to park offsite than to stop driving alone to work in response

to parking pricing. The only requirement for offsite

parking is that reasonably priced, conveniently

located parking alternatives must exist. Such parking alternatives might include onstreet parking in

adjacent streets or offstreet parking in lots or

garages. Onstreet parking can be controlled using

FERGUSON 103
time limits, parking meters, or residential permits.

Offstreet parking can be controlled using pricing,

gates, or guards. Where parking is limited in supply or high in price, spillover parking may quickly

become a problem unless all parking in both the

public and private sectors is controlled in one way

or another to restrict access to those for whom that

parking was originally intended.

Other Working Conditions

Some working conditions fared better than employer parking subsidies in the equations; others, worse.

Neither average length of time spent at work (a

measure of job commitment, perhaps) nor ever having worked at home (another measure of job commitment as well as work autonomy) remained in

any of the equations once the stepwise regression

procedure was completed, showing just how

strongly insignificant these two variables were.

Length of employment was negatively associated with parking offsite, showing that walking in

the rain eventually becomes a nuisance. Length of

employment (i.e., seniority) surprisingly was not

associated with the level of employer parking subsidies provided. In fact, other than choice of parking location, employer parking subsidies were not

associated with any other variables in the analysis,

with the minor exceptions of gender and race, discussed below.

Length of employment was positively associated

with ever using a) any alternative to driving alone

to get to work, and b) MARTA rail for any nonwork travel. This presumably is a simple matter of

probabilistic chance associated with longevity, as

well as car reliability and job commitment.

Individuals with greater flexibility in arrival time at

work were more likely to have used MARTA rail

for non-work travel, particularly work-related

business and personal business and errands.

Demographic Effects

There were a variety of interesting demographic

effects on travel behavior to complement those previously reported. Income appeared with a negative

sign in all seven equations. Income was not significantly related to mode choice for the work trip,

however. Income was significantly related to a

lower probability of alternative mode use for nonwork travel.

Residential location operated very much like the

income variable in the equations, with all negative

signs. Residential location appeared in only four

equations, however; significantly, in only three.

Midtown workers who lived outside the urban

core and the MARTA service area defined by

Fulton and DeKalb counties were significantly less

likely to use alternative modes of transportation to

get to work on a regular basis and were also significantly less likely to use MARTA rail or bus for

non-work travel. The estimated effect on regular

commuting, however, was much larger than the

one for non-work travel using MARTA.

Age was significantly related to a few of the

dependent variables, but was highly correlated with

many of the other independent variables as well.

This led to multicollinearity, excessively large standard errors for age and other variables, and wide

fluctuations in the estimation of model parameters

with and without the age variable in the model. As

a result, age was eliminated from the model on an a

priori basis and does not appear in any of the final

equations. If age had been allowed to remain in the

model, it would have appeared as a significant variable in the same equations with the same signs but

larger coefficients than length of employment, a

variable that does appear in the model. Thus, length

of employment may be viewed as a proxy for age.

Education betrayed some interesting effects in

the model. Highly educated persons were significantly less likely to use alternative modes for commuting, but significantly more likely to use

MARTA rail and to walk for non-work trip purposes. Education has been shown to be negatively

associated with the use of alternative modes for

commuting (Ferguson 1997) and positively associated with the number of trips generated on a daily

basis and with average trip length (Lave 1998).

Occupation barely made it into any of the equations. The primary finding was that managers and

professionals alike were significantly less likely to

walk during non-work travel. Managers’ principal

trips involved socializing and visiting friends; professionals’ trips were for personal errands, shopping, and dining.

104 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
Within the equations, men acted much like managers and professionals, which is not surprising,

given that almost 90% of Midtown men held managerial or professional job titles, as compared with

50% of Midtown women. In addition, men were

significantly more likely to receive employer parking subsidies than were women in Midtown.

Blacks were significantly more likely than nonblacks to use alternative modes of travel to get to

work on a regular basis. Blacks were significantly

more likely to use MARTA rail to get to work on

an occasional basis but significantly less likely to

carpool to work on an occasional basis, producing

an overall effect on the occasional use of alternative modes for commuting that was not significant.

Blacks were significantly more likely to get

parking subsidies than were non-blacks in

Midtown Atlanta, but this was probably a statistical artefact. A large proportion of blacks employed

in Midtown Atlanta worked for building managers

rather than building tenants. Building managers

were among the few firms to offer free parking to

all employees, for the fairly obvious reason that

they had complete control of all onsite parking and

relatively few employees.

MODE OF TRAVEL AND PARKING LOCATION

There may be a deeper and more significant relationship between choice of parking location and

mode choice for the journey to work. If one is at

least a partial substitute for the other, does this

make these two apparently independent choices

related to each other as extended travel behavioral

alternatives? It appears that the answer to this

question is yes, at least in part. With no inference

regarding causality intended, it appears that alternative mode users are significantly more likely to

park offsite than are dedicated solo drivers, and

this propensity increases with the regularity of

alternative mode use (see table 7).

The observed relationship is fairly weak but

always consistent. It is statistically significant only

when controlling for parking pricing. These results

would be substantially improved if the sample of

regular alternative mode users was larger, but this

cannot be helped in the present instance. The percentages would not have to change, only the sample size on one end of the distribution, in order for

most of these conditional probabilities to be significantly different from each other.

A full model of parking location would explicitly treat tradeoffs between parking price differentials, walk access times, and other characteristics of

parking amenities (e.g., covered parking vs. parking exposed to the elements and parking attendant

always on duty vs. empty lot). For a relatively large

number of alternative parking facilities, such a

model has more in common with destination

choice than with mode choice models, and might

benefit from the formulation of a gravity-type

model interface for the more accurate estimation of

model parameters.

FERGUSON 105

TABLE 7 Parking Location by Work Travel, Parking Payment, and Site

Percentage of employees who park offsite

by use of alternative modes

Conditional statements Regular Occasional Never x2 (d.f.)†

All employees 22.9 20.0 14.5 2.04 (2)

n = 35 n = 190 n = 124

Employee pays full 50.0 38.4 23.7 5.32 (2)*

cost of parking n = 16 n = 86 n = 59

Employee pays full cost

of parking and building 72.7 51.7 38.9 4.11 (2)

is located outside SPID n = 11 n = 60 n = 36

† level of significance

* significant at 0.10 level

** significant at 0.05 level

*** significant at 0.01 level
Combining a gravity-type destination choice

model for parking location with a logit-type mode

choice model for work trips is certainly possible,

but would not be particularly easy to accomplish

and would require a much larger and more elaborate database than provided here. Consider that

parking location is relevant for solo commuters,

carpoolers, and bicyclists, but not for transit users

and walkers. Bicycle parking is an entirely separate

issue from automobile parking in most instances.

Developing such a model and finding the data

required to estimate its parameters might be a very

worthwhile future research undertaking.

CONCLUSIONS

It appears that SPIDs were more effective in promoting commercial office development around

MARTA rail stations than in promoting the use of

MARTA rail for commuting to and from Midtown

Atlanta. The parking provisions of SPIDs appear to

have been more successful in increasing parking

supply and thereby reducing spillover parking than

in increasing the price of parking or inducing mode

choice changes for the journey to work among

Midtown commuters.

Parking price elasticities with respect to driving

alone appear to be on the order of –0.10 in

Midtown Atlanta. This number is somewhat on

the low side perhaps but is clearly in line with previous research, at least with respect to suburban

operating environments. Parking price elasticities

with respect to parking location appear to be on

the order of –0.5, a very significant finding in and

of itself. It appears that concerns about spillover

parking are not unwarranted, given the much

greater elasticity of demand for parking somewhere else, as opposed to finding another way to

get to work in an automobile-dominated employment environment. Spillover parking is often illegal, and where it is not illegal, it is often considered

to be illicit, except where approved markets have

been established, as is the case of commercial parking lots in Midtown Atlanta. Because of the illicit

nature of many kinds of spillover parking, there

are few, if any, previous studies that estimate the

effect of parking pricing on the use of alternative

parking facilities, as has been done here.

There appears to be a weak substitution effect

between parking location and mode choice. That is

to say, those people who are more willing to park

offsite appear to be more willing to use alternative

modes of travel and vice versa. On the one hand,

the elimination of spillover parking might thereby

induce greater use of alternative modes. On the

other hand, attempts to forcibly encourage alternative mode use might easily result in spillover

parking instead.

ACKNOWLEDGMENTS

This research was funded by the Transit Cooperative Research Program of the Transportation

Research Board of the National Research Council

of the National Academies of Science and

Engineering. The author would like to thank Todd

Trego, two anonymous building managers, and

four anonymous reviewers for their assistance.

REFERENCES

Bhat, C.R. 1996. Work Travel Mode Choice and Number of

Non-Work Commute Stops. Transportation Research

31B:41–54.

Cervero, R. 1994. Rail Transit and Joint Development: Land

Market Impacts in Washington, DC and Atlanta. Journal

of the American Planning Association 6:83–94.

Cervero, R. and J. Landis. 1993. Assessing the Impacts of

Urban Rail Transit on Local Real Estate Markets Using

Quasi-Experimental Comparisons. Transportation Research 27A:13–22.

Feeney, B.P. 1989. A Review of the Impact of Parking Policy

Measures on Travel Demand. Transportation Planning

and Technology 13:229–244.

Ferguson, E. 1997. The Rise and Fall of the American

Carpool: 1970–90. Transportation 24:349–376.

Forkenbrock, D.J. 1990. Putting Transportation and

Economic Development into Perspective. Transportation

Research Record 1274:3–11.

Giuliano, G.M. 1989. New Directions for Understanding

Transportation and Land Use. Environment and Planning

21A:145–159.

Higgins, T.J. 1990. Demand Management in Suburban Settings: Effectiveness and Policy Considerations. Transportation 17:93–116.

Lave, C. 1998. Personal communication.

Nelson, A.C., E. Ferguson, M.D. Meyer, and C.L. Ross.

1995. Special Public Interest Districts in Atlanta. Portland, OR: Portland State University.

106 JOURNAL OF TRANSPORTATION AND STATISTICS MAY 1999
Payne-Maxie and Blayney-Dyett. 1980. The Land Use and

Urban Development Impacts of Beltways. Washington,

DC: U.S. Department of Transportation.

Pisarski, A.E. 1992. New Perspectives in Commuting.

Washington, DC: U.S. Department of Transportation.

Roche, P. and Willson, R.W. 1986. Ridesharing Requirements

in Downtown Los Angeles: Achieving Private Sector

Commitments. Transportation Research Record

1082:1–6.

Shoup, D.C. 1982. Cashing Out Free Parking. Transportation

Quarterly 36:351–364.

____. 1995. An Opportunity To Reduce Minimum Parking

Requirements. Journal of the American Planning

Association 61:14–28.

Shoup, D.C. and D.H. Pickrell. 1978. Problems with Parking

Requirements in Zoning Ordinances. Traffic Quarterly

32:253–262.

Westin, R. and D. Gillen. 1977. Parking Location and Transit

Demand. Journal of Econometrics 8:75–101.

FERGUSON 107