Estimated Impacts Estimated Impacts

of September 11 of September 11

th

on US Travel on US Travel

U.S. Department of Transportation

Research and Innovative Technology Administration

Bureau of Transportation Statistics

Estimated Impacts

of September 11th

on US Travel

U.S. Department of Transportation

Research and Innovative Technology Administration

Bureau of Transportation Statistics
Research and Innovative Technology Administration

Bureau of Transportation Statistics

To obtain Estimated Impacts of September 11th on US Travel and other 

BTS publications

Phone: 202-366-DATA

Fax: 202-366-3197

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Recommended citation

U.S. Department of Transportation

Research and Innovative Technology Administration

Bureau of Transportation Statistics

Estimated Impacts of September 11th on US Travel

Washington, DC: 2006
U.S. Department of

Transportation

Maria Cino

Deputy Secretary

Research and Innovative

Technology Administration

John Bobo

Acting Administrator

Bureau of Transportation

Statistics

Terry T. Shelton

Acting Director

Produced under the direction of:

William Bannister

Assistant Director for

Advanced Studies

Project Manager

Ivy Harrison

Editor

William Moore

Major Contributors

David Chien

Jeffery Memmott

Peg Young

Cover Design & Report Layout

Alpha Glass Wingfi eld

Acknowledgments
Table of Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

CHAPTER 1

Results of National Household Travel Survey . . . . . . . . . . . . . . . 5

CHAPTER 2

Time Series Analysis of Passenger Data; Pre- and PostSeptember 11, 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

CHAPTER 3

Pre- and Post- 9/11 Econometric Analysis of Travel by Mode . . . . . . 23

APPENDIX A

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

APPENDIX B

NHTS Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

APPENDIX C

The Time Series Data . . . . . . . . . . . . . . . . . . . . . . . . . . 37

APPENDIX D

The Structural Time Series Model . . . . . . . . . . . . . . . . . . . . 43

APPENDIX E

Summary of Monthly Forecasts . . . . . . . . . . . . . . . . . . . . . 47

REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
1

EXECUTIVE SUMMARY

The terrorist attacks of September 11, 2001, had an immediate and visible 

impact on U.S. transportation. While the obvious impacts were temporary, 

there may have been less obvious yet longer lasting changes in U.S. travel patterns. The Research and Innovative Technology Administration’s Bureau of 

Transportation Statistics analyzed the impacts in three different ways. All three 

analyses found these post-9/11 travel trends:

Immediate and continuing impact in air travel,

Immediate but temporary decline in highway travel,

No impact on rail travel, and

Travelers switched from air to highway.

RESULTS

1. NHTS Data: A comparison of 2001-2002 National Household Travel Survey (NHTS) long-distance travel data for pre-9/11 and post-9/11 yielded the 

following initial fi ndings:

Reduction in the amount of long-distance travel,

Decrease in the rate of international trip taking,

Reduction in the rate of personal business travel, and

Changes in mode of travel depending on distance traveled.

2. Time Series Analysis: Forecasts using travel data from 1990 to 2001 were 

compared to what actually took place after 9/11. The comparisons found:

Actual Airline Revenue Passenger-Miles began in December 2004 to approach the forecasted values. Otherwise, up to then, Airline Revenue Passenger-Miles were signifi cantly lower than forecast.

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•

•

•

•

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•

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2 Estimated Impacts of September 11th on U.S. Travel

Rail Passenger-Miles showed no evidence of impact from 9/11.

Vehicle-Miles Traveled dropped for one 

month – September 2001 – compared to 

the expected level. In addition, the actual 

VMT level for September 2002 – one 

year later – was signifi cantly lower than 

expected, while the 11 months between 

September 2001 and September 2002 

did not show any unexpected deviations.

3. Econometric Analysis: A statistical 

analysis of economic data produced the following conclusions: 

There was a strong statistical relationship 

between the events of 9/11 and aviation 

and highway travel, but not rail travel.

Air travel dropped quickly after 9/11 

and then continued to drop for the following six months.

Highway travel also dropped quickly immediately after 9/11 but then leveled off 

in the following four months.

People switched from air travel to highway travel over the six-month period 

after 9/1l.

EXPECTED CHANGES THAT 

DIDN’T HAPPEN

The comparison of pre-9/11 and post 9/11 

NHTS data did not fi nd the following expected results: 

No signifi cant decline in the percent of 

business travel,

No signifi cant decline in overall trips by 

older Americans,

•

•

•

•

•

•

•

•

No change in the percent of air trips 

from individuals living in urban areas.

THE METHODOLOGY

1. NHTS Data: To assess the near-term 

impact on travel, we split the 2001-2002 

National Household Travel Survey (NHTS) 

long-distance travel data collection into 

pre-9/11 and post-9/11 datasets. Each was 

then weighted to produce an annual estimate of long-distance travel—one based on 

survey responses before 9/11 and the other 

based on responses after 9/11. The two new 

datasets have some limitations that impact 

our ability to draw comparisons: seasonality effects that are unknown and we do not 

know how economic changes affected travel 

behavior after 9/11. Therefore, we conducted additional time-series and econometric 

data analysis to help assess the before and 

after 9/11 travel picture.

Based on the entire data collection, there 

were an estimated 2.6 billion long-distance 

trips taken in 2001. Privately owned vehicles 

(POV) accounted for the largest portion of 

trips, 90 percent, followed by air at 7 percent. Bus was used for only 2 percent of 

the trips and “other,” which includes trains, 

ferries, and other transportation means, collectively accounted for only 1 percent.

The NHTS analysis focuses on long-distance trips because this type of travel was 

most impacted by 9/11 and is where measurable effects would most likely be found. 

The NHTS is a national survey with data 

on 45,000 long-distance trips of 50 miles or 

more from home.

•
Estimated Impacts of September 11th on U.S. Travel 3

Because the time period of the NHTS overlapped September 11, 2001, the data collected were divided into two fi les: a pre-9/11 fi le 

and a post-9/11 fi le, with both fi les weighted 

to produce estimates of a year’s worth of 

travel.

2. Time Series Analysis of Long-Distance 

Passenger Data: Differences due to seasonality could be confounding the measurement 

of the 9/11 impact in the NHTS data. To 

understand the seasonality characteristics of 

the passenger data, we analyzed three different sets of monthly data: air revenue passenger miles (RPM), rail passenger miles (PM), 

and highway vehicle miles traveled (VMT). 

To acquire a base from which to measure 

the 9/11 effect, we forecast these three series beyond September 2001 based on data 

from January 1990 through August 2001.

3. Econometric Analysis of Travel by 

Mode: Monthly seasonally adjusted modal 

data from January 2000 through June 2003 

were used to estimate travel equations with 

a 9/11 dummy variable in order to measure the effects of 9/11 on travel by mode. 

Air, highway, and rail travel equations were 

econometrically estimated. 

There was a statistically signifi cant substitution relationship over the estimation period 

between air travel and highway travel, although the cross elasticity is small at -0.041. 

This cross elasticity was derived from the air 

travel coeffi cient and indicates that for any 

given 10 percent drop in air travel, highway 

travel will increase at approximately 0.41 

percent.

5

CHAPTER 1 

Results of National Household 

Travel Survey

INTRODUCTION

The terrorist attacks in the United States in September 2001 had an immediate and visible impact on transportation. While the obvious impacts 

were temporary, there may have been less obvious yet longer lasting changes 

in U.S. travel patterns. The purpose of this study is to provide a greater understanding of the passenger travel behavior patterns of persons making longdistance trips before and after 9/11. 

The 2001 National Household Travel Survey (NHTS) provides an opportunity 

to explore some of those changes in travel patterns. The survey was conducted between March 2001 and May 2002. The tragic events of 9/11 happened 

to occur during the data collection period. As such, even though the survey 

was not designed to capture 9/11 impacts, it can be used to look at some of 

the changes that occurred with travel patterns during that period. To examine 

the changes in travel patterns, the dataset was divided up into a pre-9/11 dataset and a post-9/11 dataset. These datasets were each reweighted by the U.S. 

population to produce nationally representative annual datasets. These datasets 

were then analyzed and the results compared to determine what, if any, interesting changes in travel patterns could be identifi ed. 

However, there are two major diffi culties in using these datasets to estimate 

the impacts of 9/11 on travel patterns. First, the datasets are not seasonally 

adjusted. Because the two datasets cover trips for only a portion of the year, 

seasonal travel patterns can affect the number of trips and trip characteristics 

within each dataset. Because NHTS data are not collected on an annual basis, 

there is no way to use previous survey data to adjust for those seasonal patterns. The second diffi culty is the changing economic environment during 

the time the survey was conducted. These changing economic conditions also 
6 Estimated Impacts of September 11th on U.S. Travel

likely affected the number of trips and trip 

characteristics when comparing the pre-9/11 

and post-9/11 datasets. As a result of these 

limitations in the NHTS data, this report 

also contains two additional 9/11 analyses, a 

time series analysis using seasonally adjusted 

historical and forecast data by mode, and an 

econometric analysis looking at the impact 

by mode of 9/11 and several economic 

variables.

PRE- AND POST- 9/11 

ENVIRONMENT

As discussed above, economic changes can 

have positive or negative effects on travel, 

making it hard to isolate the impacts of 

9/11. And different economic factors can simultaneously infl uence changes in opposite 

directions for different types of travel, as 

can be surmised from the following discussion.

Prior to September 2001, the economy was 

experiencing fl at or slightly declining growth 

as real Gross Domestic Product (GDP) 

remained around the $9.87 trillion to $9.90 

trillion range (in chained real 2000 dollars) 

for over fi ve quarters, from the second 

quarter of 2000 through the third quarter 

of 2001. An offi cial recession started from 

the economic peak in March 2001 until the 

economic trough in November 2001, according to the National Bureau of Economic Research (NBER). 1 After 9/11, from the 

fourth quarter of 2001 to the second quarter of 2003, GDP showed a steady growth 

1

 National Bureau of Economic Research, Department of Commerce, The NBER Business-Cycle 

Dating Procedure, October 21, 2003, available at 

http://www.nber.org/cycles.

(fi gure 1). From these data, one might 

predict an increase in travel after 9/11, commensurate with the recovering economy. 

Travel did start to recover after 9/11, but it 

did not quickly return to previous levels. For 

example, the number of airline passengers 

did not surpass its 2001 peak until July 2004.

Besides the economic downturn identifi ed 

by the NBER, there were other signs that 

the economy had started to weaken prior to 

9/11. Industrial production is a good indicator of business activity and refl ects the level 

of business travel and overall production 

of the economy. The economic downturn 

is refl ected in the rapid decline of the Industrial Production Index (fi gure 2), which 

had fallen from a high of 115 in July 2000 

to 110.6 by September 2001 as business 

inventories began to accumulate. Therefore, 

declining industrial production prior to 9/11 

may have contributed to an overall drop 

in travel. However, after January 2002, the 

index began to rise and, although it did not 

reach the levels seen in 2000, it did indicate 

an economic upturn, which might have 

contributed to a predicted increase in travel 

after 9/11. 

Beginning in January 2000, the unemployment rate steadily increased from just below 

4 percent to almost 5 percent by September 

2001, and by May of 2003 had risen above 6 

percent (fi gure 3). It might reasonably be expected that rising unemployment would lead 

to reductions in personal travel throughout 

the period covered by the 2001 NHTS. 

Even though the air travel price index 

(ATPI) (fi gure 4) indicates that real ticket 

prices were already declining from the fi rst 

quarter of 2001 through the third quarter 
Estimated Impacts of September 11th on U.S. Travel 7

Figure 1

Q1 - 2000

Q2 - 2000Q3 - 2000

Q4 - 2000

Q1 - 2001Q2 - 2001

Q3 - 2001

Q4 - 2001

Q1 - 2002Q2 - 2002Q3 - 2002Q4 - 2002Q1 - 2003Q2 - 2003

$9,500

$9,600

$9,700

$9,800

$9,900

$10,000

$10,100

$10,200

$10,300

Real GDP (2000 chained billions)

SOURCE: U.S. Department of Commerce, Bureau of 51. Business Economic Analysis, Current-Dollar 

and “Real” Gross Domestic Product, seasonally adjusted, www.bea.gov.

Figure 2

Jan-2000

May-2000

Sep-2000

Jan-2001

May-2001

Sep-2001

Jan-2002

May-2002

Sep-2002

Jan-2003

May-2003

Sep-2003

Nov-2003

104

105

106

107

108

109

110

111

112

113

114

115

116

Industrial Production Index 

(Seasonally Adjusted 1997=100)

SOURCE: Economic Report of the President, TABLE B-Industrial production indexes, major industry 

divisions 1955-2002.
8 Estimated Impacts of September 11th on U.S. Travel

Figure 3

Jan-2000

Mar-2000

May-2000

Jul-2000

Sep-2000

Nov-2000

Jan-2001

Mar-2001

May-2001

Jul-2001

Sep-2001

Nov-2001

Jan-2002

Mar-2002

May-2002

Jul-2002

Sep-2002

Nov-2002

Jan-2003

Mar-2003

May-2003

0

1

2

3

4

5

6

7

Unemployment Rate

Percent

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Unemployment Rate, seasonally 

adjusted, LNS14000000, www.bls.gov.

Figure 4

Q1 - 2000Q2 - 2000

Q3 - 2000

Q4 - 2000

Q1 - 2001

Q2 - 2001Q3 - 2001Q4 - 2001

Q1 - 2002

Q2 - 2002

Q3 - 2002Q4 - 2002

Q1 - 2003Q2 - 2003

100

102

104

106

108

110

112

114

116

118

U.S. Origin National Air Travel Price Index 

(1995 Q1=100)

SOURCE: U.S. Department of Transportation, Bureau of Transportation 

Statistics, National-Level ATPI Series (1995 Q1 to 2005 Q4), seasonally 

adjusted, www.bts.gov.

Figure 5

Jan-2000

May-2000

Sep-2000

Jan-2001

May-2001

Sep-2001

Jan-2002

May-2002

Sep-2002

Jan-2003

May-2003

70

75

80

85

90

95

100

105

Transportation Service Passenger Index

(Seasonally Adjusted, 2000=100)

SOURCE: U.S. Department of Transportation, Bureau of Transportation 

Statistics, Transportation Services Index, Jan 1990 - Dec 2005, seasonally adjusted, www.bts.gov.
Estimated Impacts of September 11th on U.S. Travel 9

of 2001, travel had just begun to respond to 

lower ticket prices prior to 9/11. The transportation service passenger index (fi gure 5), 

a measure of transportation activity strongly 

infl uenced by airline travel, was rising in July 

and August of 2001, from 101.2 to 103.2. 

After 9/11 the index fell signifi cantly to 

79.3. After 9/11, ticket prices, as measured 

by the ATPI, continued to fall almost to 

1995 price levels in the fourth quarter of 

2001 and rose slightly thereafter with some 

volatility, but has not reached the levels of 

the fi rst two quarters of 2001. With these 

relatively lower ticket prices after 9/11, 

the transportation service passenger index 

began to increase slowly (with a small dip in 

March 2003 corresponding with the beginning of the Iraq war), but has not reached 

the levels prior to 9/11. Therefore, it appears that travel was infl uenced by factors 

other than just changes in airline ticket prices. Some of these factors will be discussed 

in the econometric analysis in chapter 3. 

NHTS BACKGROUND 

This section of the study examines results 

from the 2001 National Household Travel 

Survey (NHTS). NHTS 2001 is a national 

household survey of both daily and longdistance travel, providing the most recent 

comprehensive look at travel by Americans. 

The NHTS data collection occurred from 

March 2001 to May 2002. The long-distance 

components of the NHTS 2001 data were 

divided and reweighted into two nationally 

representative annual datasets to compare 

pre-and post-9/11 travel patterns because 

9/11 occurred during the NHTS data collection. 

Changes in trip volumes, mode choice, and 

the characteristics of individuals traveling in 

the United States before and after 9/11 are 

examined to see if any general patterns can 

be noted. Also, changes in the proportion 

of long-distance trips made domestically 

versus internationally are investigated. Some 

of the topics covered by mode choice for 

both the pre-and post-datasets include trip 

purpose, age, gender, income, trip distance, 

and trip location. 

Long-Distance Travel

Long-distance trips in the 2001 NHTS are 

defi ned as trips of 50 miles or more from 

home to the farthest destination traveled. 

A long-distance trip includes the outbound 

portion of the trip to reach the farthest 

destination as well as the return trip home 

and any stops made along the way to change 

transportation modes or for an overnight 

stay. Long-distance travel includes trips made 

by all modes, including privately owned 

vehicle (POV), airplane, bus, train, and ship; 

and for all purposes, such as commuting, 

business, pleasure, and personal and family 

business. Train and ship long-distance travel 

are combined into an “other” category due 

to the small number of observations.

NHTS PRE- AND POST-9/11 COMPARISONS 2

The annualized full NHTS 2001 data indicate that there were more than 2.6 billion 

long-distance trips taken in 2001. Approxi2

 Not all fi gures display the same modes. Only the 

modes that were found to have statistically signifi cant 

differences between pre- and post- 9/11 data for a 

particular variable are shown.
10 Estimated Impacts of September 11th on U.S. Travel

mately 90 percent were by privately owned 

vehicle (POV). Trips by airplane accounted 

for 7 percent of long-distance trips. Travel by 

bus accounted for 2 percent of these trips, and 

train trips represented less than 1 percent.

The total estimated number of yearly trips was 

lower using the post-9/11 sample as compared 

to the pre-9/11 sample – 2.7 billion trips from 

the pre-9/11 fi le and 2.4 billion trips from the 

post-9/11 fi le, an 11 percent decline. The POV 

trips were estimated to be 2.4 billion trips from 

the pre-9/11 fi le and 2.2 billion trips from the 

post-9/11 fi le, an 8 percent decline. Air trips 

were estimated to be 216 million from the 

pre-9/11 fi le and 169 million from the post9/11 fi le, the largest decline of all the modes at 

nearly 22 percent. Further analysis of the two 

datasets to identify changes in long-distance 

travel patterns before and after provides additional insight into the possible effects of 9/11.

International Travel Down 

Prior to 9/11, international travel (originating 

from the United States) represented 3 percent of all long-distance trips, but after 9/11 

that travel dropped to 2 percent of all trips, a 

statistically signifi cant decline. The mode with 

the largest decrease in international travel was 

air, which fell from 16 to 12 percent of all air 

trips after 9/11. Air travel accounts for approximately one-half of all international trips. 

POV long-distance trips for international travel 

experienced a small decline after 9/11 (fi gure 

6). POV travel accounts for slightly more than 

40 percent of international trips.

Personal Business Travel Decreased

Long-distance trips for personal business also 

experienced a statistically signifi cant decline 

after 9/11, from nearly 14 percent of all trips 

to 12 percent. Personal business trips include 

medical visits, shopping trips, and trips to 

attend weddings and funerals. POV accounts 

for approximately 90 percent of all longdistance personal business trips. Air travel, 

which accounts for only about 5 percent of 

all personal business trips, experienced a 3 

percent decrease from 9 to 6 percent of air 

trips (fi gure 7). 

More Shorter Distance Trips Taken 

The only trip distance that showed a signifi -

cant increase after 9/11 was in the shortest 

distance category of between 50 to 99 miles. 

All modes combined in this distance category 

showed an increase from 45 to 50 percent of 

total trips after 9/11 (fi gure 8). POV trips, 

which account for more than 95 percent of 

all trips between 50 to 99 miles, increased 

from 49 to 54 percent after 9/11, bus trips 

increased from 23 to 36 percent, and the 

“other” category increased from 44 to 65 percent. Air was the only mode not to register a 

signifi cant increase, but air travel accounts for 

a negligible portion (nearly zero percent) of 

the trips in the 50 to 99 mile range. Modes 

that were not statistically signifi cant were not 

included in the fi gures. 

Appendix A describes the methodology of 

the NHTS and how the pre-9/11 and post9/11 data fi les were created. Appendix B 

contains tables with the data estimates used in 

this report and their standard errors. Standard 

errors are in the same metric as the estimates. 

All comparisons in the text and graphics are 

statistically signifi cant at a 0.05 level unless 

otherwise noted.
Estimated Impacts of September 11th on U.S. Travel 11

Figure 6

0

2

4

6

8

10

12

14

16

18

Percent

Pre 9/11 Post 9/11

International Trips (originating from U.S.)

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics, NHTS 

2001 Long Distance Pre-9/11 and Post-9/11 Files.

Figure 7

0

2

4

6

8

10

12

14

16

Percent

Pre 9/11 Post 9/11

Long-Distance Trips for Personal Business

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics, NHTS 

2001 Long Distance Pre-9/11 and Post-9/11 Files.

Figure 8

0

10

20

30

40

50

60

70

80

Percent

Pre 9/11 Post 9/11

Trips Between 50 and 99 Miles

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics, NHTS 

2001 Long Distance Pre-9/11 and Post-9/11 Files. 
12 Estimated Impacts of September 11th on U.S. Travel

Expected Results Not Observed

Immediately following the 9/11 terrorist 

attack, experts were trying to determine how 

these attacks would impact our economy, 

business and leisure travel, and the attitudes 

of Americans. The terrorist attack was expected to have a different degree of impact 

on the various travel markets. Here are some 

of the expected repercussions that were not 

borne out by the NHTS data: 

No signifi cant decline in the percent of 

business air travel. This can be seen in 

table B1, appendix B, as the difference 

in pre-9/11 and post-9/11 business air 

travel was not statistically signifi cant at the 

0.05 level. There was no signifi cant decline in the percent of business travel in 

general. The Pre- and Post-9/11 Econometric Analysis section discusses economic factors consistent with this fi nding.

No signifi cant decline in overall trips by 

older Americans. Older Americans are 

more risk averse and might be expected 

to travel proportionately less than other 

age groups when faced with the risk of 

terrorist attacks. However, those 65 and 

older did not show any statistically signifi cant difference between pre-9/11 and 

post-9/11 when all modes were combined (table B2, appendix B).

Little reduction in female long-distance 

•

•

•

travel patterns. It was hypothesized that 

females tend to be more risk averse than 

males and thus might show a decline in 

their proportion of long-distance travel 

after 9/11. However, female travel as a 

portion of all long-distance travel, with 

the exception of bus trips, showed no signifi cant reduction (table B3, appendix B).

No change in the percent of air trips 

from individuals living in urban areas, 

potentially the most affected by the 9/11 

attacks, as compared to the percent of air 

trips from individuals living in rural areas. 

See table B7, appendix B, which shows 

urban travel by mode for pre-and post9/11 travel. No signifi cant change in 

percent of air trips from individuals living 

in urban areas was noted.

CONCLUSIONS

Without controlling for seasonality and 

economic effects, the NHTS data indicate 

that there was a decline in long-distance 

travel after 9/11. There was a decrease in 

the percentage of travelers traveling for 

international travel and personal business 

after 9/11. The decrease in travel for international and personal business occurred 

for both air and all modes combined. In 

addition, the proportion of trips under 100 

miles increased after 9/11. 

•
13

CHAPTER 2

Time Series Analysis of Passenger 

Data: Pre- and PostSeptember 11, 2001

INTRODUCTION

I

n the previous chapter, we noted that some of the differences between the 

pre- and post-September 2001 data could be attributed to the nature of time 

series data. Differences due to seasonality confound the measurement of the 

September 2001 impact. In order to understand the time series characteristics 

of the passenger data, we turn our attention to different sets of data that measure monthly passenger movement. The following sections will analyze three 

sets of monthly data: air revenue passenger miles, rail passenger miles, and 

vehicle miles traveled. The data do not separate local and long-distance travel.

To measure the effect of September 11, 2001, we will forecast these three series 

based on the data from January 1990 through August 2001. The forecasts are 

then compared to what actually occurred in the data. Air showed the greatest 

differences: the values of the aviation time series began to enter the prediction 

intervals in December 2003, indicating that the aviation miles only began to 

approach in 2004 the previously expected values. Rail miles did not appear to 

experience an immediate impact from September 11, 2001. Vehicle miles experienced a one-month drop for September 2001, and an additional one-month 

drop was also experienced for September 2002. 

The following sections provide the details behind these fi ndings. 

ABOUT THE DATA

The fi rst three fi gures (fi gures 9, 10, and 11) provide graphs of the passenger 

time series data: air revenue passenger miles (RPM), rail passenger miles (PM), 
14 Estimated Impacts of September 11th on U.S. Travel

Figure 9

Jan

1990

Jan

1992

Jan

1994

Jan

1996

Jan

1998

Jan

2000

Jan

2002

Jan

2004

0

10

20

30

40

50

60

70

80

Air Revenue Passenger Miles (billions)

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics, Offi ce of 

Airline Information, Air Carrier Traffi c Statistics (Monthly T-100).

Figure 10

Jan

1990

Jan

1992

Jan

1994

Jan

1996

Jan

1998

Jan

2000

Jan

2002

Jan

2004

0

100

200

300

400

500

600

700

Rail Passenger Miles (millions)

SOURCE: FRA, Offi ce of Safety website, Table 1.02 Operational Data 

Tables, available at http://safetydata.fra.dot.gov/Offi ceofSafety/

Figure 11

Jan

1992

Jan

1994

Jan

1996

Jan

1998

Jan

2000

Jan

2002

Jan

2004

Jan

1990

0

50

100

150

200

250

300

Vehicle Miles Traveled (billions)

SOURCE: USDOT / Federal Highway Administration, Traffi c Volume 

Trends report available at http://www.fhwa.dot.gov/policy/ohpi/travel/

index.htm
Estimated Impacts of September 11th on U.S. Travel 15

and vehicle miles traveled (VMT). The air 

RPM are found in the T-1 dataset compiled 

for the T-100 database, taken from the U.S. 

Department of Transportation, Research 

and Innovative Technology Administration, 

Bureau of Transportation Statistics (BTS), 

Offi ce of Airline Information (OAI). The 

data for rail PM are compiled from the U.S. 

Department of Transportation, Federal 

Railroad Administration (FRA), Offi ce of 

Safety. VMT data are taken from the Traffi c Volume Trends reports from the Federal 

Highway Administration, Offi ce of Highway Policy Information.3

 The datasets to be 

studied are all monthly, and each time series 

initiates at January 1990. The data values for 

the three series are through June 2004. Each 

of the three graphs contains a vertical line 

indicating September 2001. (Appendix C 

provides a table with all the data.)

SEASONAL ANALYSIS

A quick perusal of the three passenger datasets reveals that the data are strongly seasonal. But it is diffi cult to compare the degree 

of seasonality across the three time series. 

The next three graphs attempt to simplify 

the comparison. Figures 12, 13, and 14 provide histograms of the seasonality of each 

series, which required an additional assumption that the seasonality of each series does 

not change much over time to justify averaging the monthly components. The seasonali3

 Quality information on the air RPM and rail PM 

can be found at: http://www.bts.gov/programs/

economics_and_fi nance/transportation_services_index/html/source_and_documentation_and_

data_quality.html. Information on the quality of the 

VMT data can be found at: http://www.fhwa.dot.

gov/ohim/tvtw/tvtpage.htm.

ty is measured as the percent deviation from 

the underlying trend and consists of the 

average for the fi ve years of monthly data 

prior to September 2001, that is, September 

1996 through August 2001. The method for 

decomposing the seasonality and the trend 

will be dealt with in the next section.

Note that the vertical axis for each graph 

runs from +25% to -25% deviation. In this 

way, the degree of deviation is comparable 

across the fi gures. While the patterns of seasonality are comparable (less travel in winter, 

and more travel in summer), we note that 

rail PM tends to vary the most and VMT 

the least. Part of this may be accounted for 

by the fact that rail deals with fewer miles, 

while VMT incorporates more; extreme values have less of an impact when the number 

of observations is large. 

The next section describes how the seasonal 

and trend components were created.

STAMP MODELING

One approach to studying seasonality is to 

decompose a time series into three components: the trend, the seasonal factors, and 

the irregular components. STAMP, which 

stands for Structural Time Series Analyser, 

Modeller and Predictor,4

 allows us to take a 

set of time series data and break that dataset 

down (or “decompose” it) into components 

that cannot be observed directly, but have 

intuitive appeal. Most readers have an understanding that a trend component will represent the long-term direction of the data; a 

seasonal component will refl ect changes due 

4

 See Koopman et al. (2000) for more detail on 

STAMP.
16 Estimated Impacts of September 11th on U.S. Travel

Figure 12

Jan

Feb

Mar

Apr

May

Jun

Jul

AugSep

Oct

NovDec

–25

–20

–15

–10

–5

0

5

10

15

20

25

Percent deviation from trend

5 Year Average of Air Revenue 

Passenger Mile Seasonality

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

Figure 13

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

NovDec

–25

–20

–15

–10

–5

0

5

10

15

20

25

Percent deviation from trend

5 Year Average of Rail Passenger Mile Seasonality

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

Figure 14

–25

–20

–15

–10

–5

0

5

10

15

20

25

Jan

Feb

Mar

Apr

May

Jun

Jul

AugSep

Oct

NovDec

Percent deviation from trend

5 Year Average of Vehicle Miles Traveled Seasonality

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics.
Estimated Impacts of September 11th on U.S. Travel 17

to the time within the year; and the irregular 

component will illustrate what is “left-over” 

or not explained by the trend or seasonal 

behavior. (For an explanation of the theory, 

the reader can obtain the details in appendix 

D.) These three components can be stochastic, or changing over time (S); fi xed, or unchanging over time (F); or nonexistent (N). 

Through statistical testing, we ascertained 

the best fi tting model for each time series 

for the time period of January 1990 through 

August 2001 (see table 1). By fi tting the 

data prior to September 2001, we can then 

forecast that model out through the current 

time and compare the results of these preSeptember 2001 forecasts to the actual data. 

Table 1: STAMP Model Specifi cations for 

Passenger Time Series.

Passenger 

Mode Level S/F/N Slope S/F/N

Season 

S/F/N

Air RPM S F S

Rail PM S N S

VMT S F S

NOTE: S = stochastic, F =fi xed, N = nonexistent

While all three series have models with levels 

and seasonality changing stochastically over 

time, the air RPM and VMT exhibit fi xed underlying trends. The rail PM has no long-term 

trend for the period under study.

Using the above models, we forecast from 

September 2001 through December 2004 to 

help us understand the changes that occurred 

over that forecast period. We next provide the 

graphs of the three sets of data, with forecasts 

from September 2001 through December 

2004 (fi gures 15, 16, and 17). The prediction 

intervals for the forecasts are also provided.

The following three graphs (fi gures 18, 19, 

and 20) provide a comparison of the forecasts 

based on the pre-September 2001 data with 

the actual data from September 2001 forward. 

The large Is on the graphs indicate the values 

within the prediction interval.

As was expected, air RPM experienced the 

greatest impact. The forecasted RPMs and the 

actual RPMs have yet to match one another; 

only in 2004 did the actual air RPMs cross 

over into and remain in the 95 percent prediction interval, which indicates that aviation only 

began to return to what would have been the 

expected set of RPMs in 2004. Rail PM actuals tended to be close to the forecasted values. 

VMT seems to show little difference between 

the actuals and the forecasts, with the exception of the months of September 2001 and 

September 2002, thereby indicating little longterm impact on overall VMT levels. 

In order to more accurately defi ne the impact of September 2001 on the actual data, 

we attempted to model the impact of September 11, 2001, using the full set of data 

(January 1990 to June 2004), with the threestep intervention procedure developed by 

Ord and Young (2004). Three components 

of the intervention are tested: an additive 

outlier (AO) on September 2001, a temporary decay (TC) starting October 2001, and a 

level shift (LS) starting November 2001. 

For air RPM, the AO was signifi cant, as was 

expected; the TC of 8 percent 5

 also proved 

to be signifi cant. The LS term was not signifi cant – indicating that there may not be a 

permanent shift downward in the trend of 

the time series. 

5

 Four decay rates were tested: 6, 7, 8, and 9 percent. 

For air RPM, the best fi tting decay rate was 8 percent, which results in a half-life of 3 months beyond 

September, or December 2001.
18 Estimated Impacts of September 11th on U.S. Travel

Figure 15

Oct

1995

Oct

1996

Oct

1997

Oct

1998

Oct

1999

Oct

2000

Oct

2001

Oct

2002

Oct

2003

Oct

2004

40

50

60

70

80

Forecasts of Air Revenue Passenger Miles (billions)

Air RPM

Lower prediction interval

Upper prediction interval

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

Figure 16

Oct

1995

Oct

1996

Oct

1997

Oct

1998

Oct

1999

Oct

2000

Oct

2001

Oct

2002

Oct

2003

Oct

2004

300

350

400

450

500

550

600

650

700

Rail PM

Lower prediction interval

Upper prediction interval

Forecasts of Railroad Passenger Miles (millions)

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

Figure 17

150

170

190

210

230

250

270

290

Oct

1995

Oct

1996

Oct

1997

Oct

1998

Oct

1999

Oct

2000

Oct

2001

Oct

2002

Oct

2003

Oct

2004

Forecasts of Vehicle Miles Traveled (billions)

VMT

Lower prediction interval

Upper prediction interval

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.
Estimated Impacts of September 11th on U.S. Travel 19

Figure 18

Jan

2001

Jul

2001

Jan

2002

Jul

2002

Jan

2003

Jul

2003

Jan

2004

Jul

2004

30

40

50

60

70

80

Air Revenue Passenger Miles (billions)

Forecast of Air RPM before 9-11

Air RPM

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics.

Figure 19

Jan

2001

Jul

2001

Jan

2002

Jul

2002

Jan

2003

Jul

2003

Jan

2004

Jul

2004

300

400

500

600

700

Rail Passenger Miles (millions)

Forecasts of Rail PM

Rail PM

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics.

Figure 20

Jan

2001

Jul

2001

Jan

2002

Jul

2002

Jan

2003

Jul

2003

Jan

2004

Jul

2004

200

220

240

260

280

Vehicle Miles Traveled (billions)

Forecast of VMT

VMT

SOURCE: U.S. Department of Transportation, Research and Innovative 

Technology Administration, Bureau of Transportation Statistics.
20 Estimated Impacts of September 11th on U.S. Travel

For rail RPM, none of the three-step intervention terms were signifi cant, indicating that 

September 2001 did not have a signifi cant 

impact on the series. However, as can be seen 

in the graph, there was an unexpected shortterm drop in the rail PM the following year 

(September through November), which may 

indicate an avoidance of travel on the oneyear anniversary.

For VMT, the AO at September 2001 was 

signifi cant, but then the data returned to the 

expected pattern. So it may be that people 

avoided car travel in September 2001, but 

then returned to their usual driving behavior 

the following month. The summary of these 

intervention results are provided in table 2.

The next section conducts an analysis comparing the NHTS data with the time series 

analysis; the time series summaries used in 

the analysis are available in appendix E.

COMPARISON OF PRE- AND 

POST- 9/11 MONTHLY TRIPS

In order to simplify the comparison between 

the time series analysis and the NHTS data, 

only the forecast results from the time series 

analysis for the months of the NHTS data 

collection are considered. The three tables 

in appendix E show, according to the time 

series forecasting, both the actual values and 

the forecasts, along with the corresponding 

calculated forecast errors.

The travel estimates by mode presented in 

appendix E can be compared to the NHTS 

trip estimates to indicate what portion, if 

any, of the changes in the number of trips 

between the NHTS pre-and post-9/11 datasets can be attributed to the events of 9/11 

and the following months covered by the 

NHTS survey.

Both the pre-and post-9/11 NHTS datasets 

were reweighted to give annual trip estimates 

for the U.S. population. However, the survey periods covered by each dataset do not 

give equivalent time periods before and after 

September 11, 2001. The NHTS survey was 

conducted from March 2001 to May 2002. 

The pre-and post-9/11 datasets divide the 

persons surveyed during that period into 

two groups, March 2001 to September 11, 

2001 in the pre-9/11 dataset, and September 12, 2001 to May 2002 in the post-9/11 

dataset. The pre-9/11 dataset covers three 

full months and parts of four other months. 

The post-9/11 dataset covers 5 full months 

and parts of 4 other months. The repeated 

months between the two datasets include 

March 2001 and March 2002, April 2001 

and April 2002, May 2001 and May 2002, 

and the pre-and post-9/11 portions of 

September 2001. As a consequence of the 

unequal time periods covered in each dataset, the total number of estimated trips from 

each dataset cannot be compared to each 

other. However, it is possible to compare 

the trip estimates by calculating a monthly 

average number of trips from each dataset 

to adjust for the unequal time periods. Table 

3 gives the comparison of NHTS monthly 

trip estimates to the travel estimates by 

mode in tables E1 through E3. 

Unfortunately the comparisons are very 

rough. For highways, vehicle miles traveled 

includes all personal vehicle, public transit, 

and freight travel over all public highways 

in the United States. This is compared to 

NHTS trip estimates that include only long-
Estimated Impacts of September 11th on U.S. Travel 21

Table 2: Intervention Analysis in STAMP on Passenger Time Series.

Passenger mode

Additive Outlier (AO)

Sep-01

Temporary Change (TC)

Oct-01

Level Shift (LS)

Nov-01

Air RPM Signifi cant Signifi cant (decay rate = 8%) Not Signifi cant

Rail PM Not Signifi cant Not Signifi cant Not Signifi cant

VMT Signifi cant Not Signifi cant Not Signifi cant

SOURCE: STAMP analysis, U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation 

Statistics.

Table 3: Comparison of NHTS Trip Estimates to Other Travel Data

Monthly average

pre-9/11 period

(Mar. 2001-Sept. 11 2001)

Monthly average

post-9/11 period

(Sept. 12, 2001 - May 2002) Percent change

Vehicle Miles Traveled

 Actual 242.3 225.1 -7.1

 Forecast in absence of 9/11 242.3 225 -7.2

NHTS Personal Vehicle Trips (millions) 

 Estimated 201.8 181.2 -10.2

Air RPM (millions.)

 Actual 63.1 46.4 -26.4

 Forecast in absence of 9/11 63.1 58.1 -7.8

NHTS Air Trips (millions)

 Estimated 18.0 14.1 -21.7

Rail RPM (millions.) 

 Actual 508.3 439.1 -13.6

 Forecast in absence of 9/11 508.3 436.4 -14.1

NHTS Train Trips (millions.)

 Estimated 1.7 1.6 -5.5

NHTS Bus Trips (millions.)

 Estimated 5.1 4.5 -12.1

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

NOTE: Shaded difference not signifi cant at a 0.05 level. Pre- and Post-9/11 monthly estimates of VMT are prorated for the NHTS survey overlap 

months of March, April, and May 2001 and 2002, using NHTS pre-and post-9/11 datasets. The weights for the overlap months are calculated using 

the proportion of trips taken in those months from the pre-and post-9/11 datasets. September 2001 is prorated using 0.33 for the pre-9/11 weight, 

and 0.67 for the post-9/11 weight.
22 Estimated Impacts of September 11th on U.S. Travel

distance personal vehicle trips. For passenger rail, Amtrak passenger data is compared 

to the NHTS long-distance trips by rail, 

which likely includes some commuter rail 

trips. There are no long-distance bus data 

available for comparison to the NHTS bus 

trip estimates. 

Air trips provide the most interesting comparison. Average monthly air RPM experienced a drop of 26.4 percent between the 

pre-9/11 time period and the post-9/11 time 

period. When the post-9/11 time period was 

forecast using previous historical trends, the 

drop was only 7.8 percent. The difference 

between those two percentage decreases, 

about 18.6 percent, can be viewed as the additional decrease in air travel beyond normal 

historical seasonal trends. The estimated 

decline from NHTS data of about 22 percent is roughly comparable to the 26 percent 

decline in air RPM. While the 22 percent 

cannot be divided up into seasonal and nonseasonal components, using the proportional change in air RPM as a proxy, this gives 

roughly about a 6 to 7 percent decline for 

normal seasonal trends and the rest, about 

15 to 16 percent, can be attributed to other 

factors, including 9/11.
23

CHAPTER 3

Pre- and Post-9/11 Econometric 

Analysis of Travel by Mode

PREFACE

The following analysis supplements the previous chapters, which initially 

reviewed the NHTS datasets and then developed seasonally adjusted time 

series travel estimates. The econometric analysis to follow includes equation 

estimates for three modes of travel: air, highway, and rail passenger. Each 

equation will be reviewed for statistical qualities, followed by a discussion 

that will establish a relationship between travel by mode and the catastrophic 

events of 9/11. 

DATA

The seasonally adjusted monthly travel data used in the econometric analysis 

spans the period of January 2000 through June 2003, and is identical to the data 

used in the time series analysis. The exponential weights for the dummy variable used in the VMT equation for the 5-month period from September 2001 

to January 2002 are: 1.000, 0.6494, 0.4217, 0.2738, and 0.1778. These weights 

were approximated by applying a standard natural exponential curve to the 

data, which would result in a function ranging from 1.0 to 0. These 5 months 

were chosen based on their proximity to 9/11 and appeared to be the time 

frame when travel appeared to be most affected by 9/11. Other time periods 

were chosen and were not found to be statistically signifi cant.

The economic data comes from the Economic Report of the President, which 

was obtained from the web.6

 The fuel price data originates from the U.S. Department of Energy, Energy Information Administration. Revenue Passenger 

Miles per Departure data came from the Bureau of Transportation Statistics, 

6

 http://www.gpoaccess.gov/eop/
24 Estimated Impacts of September 11th on U.S. Travel

Offi ce of Airline Information. Although the 

equations listed in the tables below contain 

only those variables that were statistically 

signifi cant, several variables were tested, 

but did not meet the statistical signifi cance 

tests at either the 5 or 10 percent probability 

levels. The variables that were excluded in 

the equations consisted of industrial production and wholesale and retail sales. However, 

all variables were tried in logarithmic and 

lagged conversions, and equation specifi cations were estimated in linear and nonlinear 

formats.

METHODOLOGY

Travel was econometrically estimated for 

aviation, highway, and rail passenger modes 

from monthly data. Both linear and log 

specifi cations were tested with combinations 

of the following economic variables: unemployment, wholesale and retail sales, industrial production, income per driver, fuel 

prices by mode and fuel type or fuel cost per 

mile. Two types of dummy variables were 

used, a typical 0,1 value for September 2001 

through February 2002, and another with an 

exponential decay curve. 

Corrective procedures 

were also implemented 

when needed, such as 

those associated with 

serial or time dependent 

correlation over several 

periods. Lagged variables through two time 

periods were also used, 

and in cases where serial 

correlation were present, 

AR(1) (auto-regressive 

terms of one period) 

were then included in 

the estimations. Other 

independent variables were also used, such 

as the airline revenue passenger miles per 

departure. In all, over 30 estimations per 

mode were attempted with varying combinations of variables and equation specifi cations. 

RESULTS AND STATISTICAL 

ANALYSIS

Aviation

The results of the aviation estimation (table 

4) indicate that there was a statistically signifi cant negative impact of 9/11 on air travel per person above age 16. Other relevant 

variables such as income per person above 

age 16, jet fuel price, and the unemployment 

rate were signifi cant as well, with all variables statistically signifi cant at the α = 0.01 

level. All variables have the correct a priori 

mathematical sign. For example, income has 

a positive effect on travel, while both unemployment and jet fuel price negatively impact 

travel. 

Overall, the statistical fi t accounts for 51 

percent of the variation from the dependent 

Table 4: Seasonally Adjusted Aviation Revenue Passenger-Miles per 

Driving Age 16+ Population Equation Estimation with Unemployment Variable

Dependent Variable: Aviation Revenue Passenger Miles (seasonally adjusted) per 

Population Driving Age 16+

Method: Least Squares

Sample: January 2000 to June 2003

Included observations: 42

Variable Coeffi cient Standard error t-statistic Probability 

Income per population age 16+ 0.014 0.002 9.171 0.000

Jet fuel price -0.195 0.064 -3.018 0.005

Dummy 9/11 -105.270 20.604 -5.109 0.000

Unemployment -26.308 8.148 -3.229 0.003

R-squared 0.512

Adjusted R-squared 0.474

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.
Estimated Impacts of September 11th on U.S. Travel 25

variable mean. The Durbin-Watson test is 

inconclusive for autocorrelation among error terms, so no adjustments were made to 

the original equation.

Where:

Income per population aged 16+ = income7

per population aged 16+8

 (in real 1996 dollars per population aged 16 and over)

Jet fuel price = kero-jet fuel price9

 (1/10th 

cent per bbl in nominal dollars)

Dummy 9/11 = dummy variable, where 

value equals 1 for September 2001 through 

February 2002

Unemployment = unemployment (percent)10

Highway

The highway vehicle-miles traveled (VMT) 

equation estimation (table 5) has an 

R-squared value of 

74.8 percent, which is 

much higher than the 

aviation or rail equations. The premise for 

using the revenue-passenger miles variable 

(RPM_DEPART) was 

to capture the effect 

of travel declining as a 

7

 U.S. Department of Commerce, Bureau of Economic 

Analysis, table 2.6, Personal Income and Its Disposition, 

Monthly, seasonally adjusted, www.bea.gov.

8

 Economic Report of the President, Table B–35, 

Civilian population and labor force, 1929–2001, 

monthly data seasonally adjusted, 2004.

9

 U.S. Department of Energy, Energy Information, 

State Energy Price and Expenditure Report, 2004. 

http://www.eia.doe.gov.

10 U.S. Department of Labor, Bureau of Labor 

Statistics, Unemployment Rate, seasonally adjusted, 

LNS14000000, www.bls.gov.

result of 9/11, especially very short air fl ights 

(100 miles or less), which may be leading to 

more highway travel. The RPM variable is 

signifi cant at α = 0.05 level, while all other 

variables are signifi cant at the α = 0.01 level. 

This indicates that there is a strong relationship between the events of 9/11 on highway 

travel. All of the variables also have the proper sign, such as when air travel declines, VMT 

increases, indicating that they are substitutes. 

Travel increases with rising income as well. 

The EXP_9/11 variable consists of a weighted dummy variable for 9/11, in which the 

weights decline exponentially over a 4-month 

period after 9/11. The exponential form of 

the dummy variable was found to be statistically signifi cant, but the (0,1) dummy was not 

statistically signifi cant. The original DurbinWatson statistic tested positive for positive 

serial correlation (α = 0.01), so an adjustment 

was made to include an autoregressive (AR) 

period 1 error term variable.

Where:

RPM per departure = Revenue passenger 

miles11 per departure

11 U.S. Department of Transportation, Research 

and Innovative Technology Administration (RITA), 

Bureau of Transportation Statistics, Transportation 

Services Index (TSI), http://www.bts.gov/xml/tsi/

Table 5: Highway Vehicle-Miles Traveled per Driving Age 16+ 

Population Equation Estimation

Dependent variable: vehicle miles traveled per driver

Method: least squares

Sample (adjusted): February 2000 to June 2003

Included observations: 41 after adjustments

Variable Coeffi cient Standard error t-statistic Probability 

RPM per departure -0.989 0.398 -2.483 0.018

Income per population 16+ 0.035 0.001 58.356 0.000

Exponential Dummy 9/11 -44.820 13.488 -3.323 0.002

AR(1) 0.797 0.107 7.415 0.000

R-squared 0.748 

Adjusted R-squared 0.727 

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau 

of Transportation Statistics.
26 Estimated Impacts of September 11th on U.S. Travel

Exponential Dummy 9/11 = exponentially 

weighted dummy variable, where value exponentially decays through 4 time periods.

AR(1) = Auto-regressive error terms of 

time period one12

Rail

Only the logarithmic equation specifi cation (table 6) was found to be statistically 

acceptable, in terms of fi t and statistical 

signifi cance. Notice that the equation has no 

9/11 variable, because it was found to not 

be statistically signifi cant in all combinations 

of log and linear specifi cations as well as 

combinations of all variables. Both the log 

of the time period lag of one for the dependent variable and the log of the income per 

driver were found to be statistically signifi -

cant. Each variable has the proper positive 

sign indicating that rail passenger travel 

rises as a result of the effects of the previous time period and the effects of increases 

src/index.xml and National Transportation Statistics, 

http://www.bts.gov/publiications/national_transportation_statistics//2005/index.html, 2005.

12 See William Greene, Econometric Analysis, 2003.

in income per driver. The Durbin-Watson 

statistic revealed no serial correlation. 

DISCUSSION

Economic Demand Elasticities Comparison

Often an evaluation of econometric equations includes comparisons to other estimates using the estimated coeffi cients to 

develop economic elasticities. Demand 

elasticities measure the degree, in percentage change, to which one economic variable 

changes based on the percentage change of 

another economic variable. Table 7 shows 

a comparison between demand elasticities from the BTS 

study versus Carol 

Dahl’s compilation 

of various model 

elasticities.13 All of 

the BTS elasticities 

were calculated at the 

mean and are very 

close or within the 

range of Carol Dahl’s 

fi ndings, possibly 

with the exception of the aviation income 

elasticity, which appears to be larger than the 

13 Carol Dahl, Professor, Director of CSM/IFP 

Joint International Degree Program in Petroleum 

Economics and Management, Colorado School of 

Mines, Golden, Colorado. Dahl has compiled demand equation elasticities for 20 authors, many with 

multiple estimations over time. They are categorized 

according to equation specifi cations and type of 

data used. Elasticities are derived from equation 

coeffi cients in the form of a percentage change in 

demand for a percentage change in price or income.

Table 6: Log of Seasonally Adjusted Passenger-Miles for Rail per 

Driving Age 16+ Population Equation Estimation

Dependent variable: Log Rail Passenger-miles per Population Aged 16+ (seasonally adjusted)

Method: Least Squares

Sample (adjusted): February 2000 to June 2003

Included observations: 41 after adjustments

Variable Coeffi cient Standard error t-statistic Probability 

Log of seasonally adjusted rail 

passenger miles per population 

aged 16+ lagged one period 0.767 0.105 7.301 0.000

Log of income per population aged 

16+ 0.175 0.079 2.223 0.032

R-squared 0.537 

Adjusted R-squared 0.525 

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.
Estimated Impacts of September 11th on U.S. Travel 27

higher range of the estimates (Dahl, 1995). 

It should also be noted that Dahl’s elasticities 

are measured with respect to transportation 

fuel consumption and are not as specifi c to a 

particular mode as the BTS study. Therefore, 

the elasticities should be judged in a more 

general manner than viewed as exact comparisons by mode.

The results indicate that there is a strong 

statistical relationship between the events 

of 9/11 and aviation and highway travel, 

but not rail revenue passenger miles. Given 

that the 9/11 dummy variables are statistically signifi cant, it can be concluded that the 

negative effect of 9/11 on travel declines in 

a more arithmetic fashion for air travel over 

a 6 month period, in which there is a decline 

in the intercept. Similarly, highway travel has 

a tendency to decline in a more exponential 

pattern over a 4-month period. These conclusions do not attempt to determine when 

travel will return to a more normal historical 

level, but rather only the declining portion 

of the travel. Furthermore, it can be concluded that there is a statistically signifi cant 

substitution relationship between air travel 

and highway travel, although the cross elasticity is small at -0.041. This cross elasticity 

was derived from the air travel coeffi cient 

and indicates that for each 100 percent drop 

in air travel, highway travel will increase at 

approximately 4.1 percent. 

The conclusions of this study do not equate 

to assuming that future catastrophic events 

would yield the same results because the 

degree of damage and the impending effect 

on behavior may not be the same for future 

events. The severity of catastrophic events 

and their impact on future behavior is not 

easily quantifi able, and yet is essential for 

future analysis of catastrophic events. Hopefully future research and analysis will lead 

to some kind of indicator of the degree of 

severity of catastrophes.

Table 7: Economic Demand Elasticities 

Aviation Highway Rail Dahl

Income 

elasticity 1.74 1.04 0.18 0.09 to 0.85

Fuel price 

elasticity -0.38 - - -0.01 to -0.36

SOURCE: Dahl, Carol A. “Demand for Transportation Fuels: A Survey of Demand Elasticities and Their Components,” Journal of Energy 

Literature, 1(2), Fall, 1995. and U.S. Department of Transportation, 

Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.

29

APPENDIX A

Methodology 

SOURCE AND ACCURACY

The fi ndings from the 2001 NHTS survey are based on travel data collected 

from a random digit dial sample of telephone interviews conducted with 

over 60,000 individuals in approximately 26,000 nationally representative 

households. Interviews were conducted between March 2001 and May 2002. 

Individuals in the NHTS sample were asked to complete a travel diary for a 

specifi ed day, known as the travel day, and were also asked to report on the 

characteristics of long-distance trips of 50 miles or more from home made 

during a 4-week period, known as the travel period.

Estimates reported here are based on weighted data to account for selection 

probabilities at the household and individual level, and are further adjusted for 

household and individual nonresponse. Comparisons made in this report are 

statistically signifi cant at a 0.05 level. 

CREATION OF PRE-9/11 AND POST-9/11 DATA FILES 

The 2001 NHTS was conducted from March 2001 through May 2002. The 

NHTS person-level dataset was divided into two parts: pre-9/11 data and 

post-9/11 data. The pre-9/11 dataset includes trips taken in the period from 

March 2001 to September 2001, a period of 5 1/2 months, and includes the 

summer season in which a large proportion of long-distance trips are taken. 

There were approximately 22,000 persons responding about travel prior to 

9/11. The post-9/11 dataset includes trips taken in the period from September 2001 to May 2002, a period of roughly 8 months, and includes Thanksgiving and Christmas—a traditionally heavy season for long-distance trips. The 

survey had responses from approximately 38,000 persons on their long-distance trips after 9/11. The composition of persons who took long-distance 

trips prior to 9/11 and those that took long-distance trips following 9/11 was 

not the same. A more detailed description of how the pre-9/11 and post-
30 Estimated Impacts of September 11th on U.S. Travel

9/11 data fi les were created is contained in 

Chapter 1 of “National Household Travel 

Survey—Pre- and Post-Data Documentation” found on www.bts.gov.

Additional steps were taken to make each 

of the pre-9/11 and post-9/11 groups a 

nationally representative sample. This is 

achieved by constructing new weights using 

statistically sound methods briefl y described 

below. 

REWEIGHTING OF PRE-9/11 

AND POST-9/11 DATA FILES 

Weights are needed to produce valid population-level estimates so that the results of a 

survey of the population are representative 

of the population as a whole. For example, 

if in the survey 47 percent of the respondents were male and in the U.S. population 

49 percent of the population was known to 

be male, then the male survey respondents 

would be weighted stronger so that their 

data and travel information would count for 

49 percent of the population’s travel patterns. Adjustment and post-stratifi cation are 

performed on collected data to reduce bias 

of estimates. Post-stratifi cation reweights 

the data so that the characteristics of the respondents are the same as the characteristics 

of the population. 

To see whether the pre- and post- 9/11 

groups were representative samples of the 

national population it was necessary to 

construct population control totals for key 

survey variables using Census 2000 numbers. Tolerance levels were used in the fi nal 

reweighting program to determine which 

combination of variables to keep. An example of a tolerance level would be for Hispanics to be within 4 persons of a census 

generated control total that would represent 

the total Hispanics in the United States for 

a designated timeframe. A more detailed 

description of how the pre-9/11 and post9/11 data fi les were reweighted is contained 

in chapter 2 of “National Household Travel 

Survey- Pre- and Post-Data Documentation” found on www.bts.gov. 
31

APPENDIX B 

NHTS Tables

NOTE: Cells with a small sample size (< 30) or that have a covariance or c.v. 

(> 0.3) are shaded blue to indicate that the weighted estimates are not reliable. 

Cells with bold type indicate signifi cant differences (p<0.05) between pre-9/11 

and post-9/11 estimates.

Table B1: Distribution of Pre- and Post-9/11 Long-Distance Trips by Trip 

Purpose, for Each Mode

Percentage trips

(standard error)

Trip purpose

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Commute 13.3 13.8 2.6 3.6 0.7 4.3 6.2 30.1 12.1 13.1

(1.47) (0.88) (0.73) (1.49) (0.46) (2.25) (3.66) (11.05) (1.34) (0.82)

Business 13.2 15.0 37.0 41.1 6.7 6.6 36.6 30.3 15.1 16.8

(0.87) (0.74) (2.05) (1.92) (2.21) (2.47) (6.70) (10.18) (0.82) (0.68)

Pleasure 56.4 56.0 49.5 48.4 55.0 58.7 46.4 31.9 55.7 55.2

(1.13) (0.84) (1.95) (1.78) (5.08) (3.31) (6.40) (7.06) (1.01) (0.78)

Personal 

business 13.4 12.2 9.4 6.4 37.3 29.4 6.9 5.2 13.5 12.1

(0.45) (0.45) (0.97) (0.86) (4.51) (3.11) (2.47) (2.09) (0.43) (0.43)

Other 3.8 3.1 1.5 0.6 0.4 1.1 3.9 2.5 3.5 2.8

(0.26) (0.16) (0.80) (0.20) (0.26) (0.74) (2.61) (1.83) (0.23) (0.15)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
32 Estimated Impacts of September 11th on U.S. Travel

Table B2: Distribution of Pre- and Post-9/11 Long-Distance Trips by Age Group, 

for Each Mode

Percentage trips

(standard error)

Age group

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

0 - 24 26.6 24.6 20.5 14.5 46.2 51.9 6.8 25.0 26.4 24.5

(0.75) (0.68) (1.38) (1.23) (5.05) (3.44) (2.44) (10.50) (0.70) (0.63)

25 - 54 53.6 55.4 61.7 70.0 21.4 26.8 75.4 58.4 53.8 55.8

(0.99) (0.84) (1.70) (1.50) (3.68) (3.34) (4.72) (10.03) (0.91) (0.76)

55 - 64 11.7 11.0 11.7 9.6 7.0 8.7 8.0 10.7 11.6 10.8

(0.72) (0.44) (1.03) (0.80) (2.45) (2.19) (2.52) (4.51) (0.65) (0.42)

65 - 74 5.6 6.4 4.2 3.5 16.0 8.2 8.0 4.6 5.8 6.2

(0.26) (0.34) (0.87) (0.47) (7.03) (1.74) (2.76) (1.61) (0.29) (0.31)

75 + 2.4 2.7 2.0 2.5 9.4 4.5 1.8 1.2 2.5 2.7

(0.18) (0.22) (0.42) (0.45) (1.88) (0.97) (0.91) (0.66) (0.17) (0.21)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Table B3: Distribution of Pre- and Post-9/11 Long-Distance Trips by Gender, 

for Each Mode

Percentage trips

(standard error)

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Male 57.2 57.6 56.6 60.1 35.9 54.8 52.3 67.0 56.6 57.8

(1.00) (0.88) (2.14) (1.60) (4.50) (3.63) (7.87) (7.50) (0.91) (0.82)

Female 42.8 42.4 43.5 40.0 64.1 45.2 47.7 33.0 43.4 42.2

(1.00) (0.88) (2.14) (1.60) (4.50) (3.63) (7.87) (7.50) (0.91) (0.82)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Table B4: Distribution of Pre- and Post-9/11 Long-Distance Trips by Income, 

for Each Mode

Percentage trips

(standard error)

Personal vehicle Air Bus Other All modes

Household 

income ($) Pre Post Pre Post Pre Post Pre Post Pre Post

0-24,999 13.1 13.6 5.9 4.4 32.1 18.5 11.0 19.3 12.9 13.1

(0.79) (0.60) (0.87) (0.63) (6.92) (3.00) (5.18) (11.24) (0.76) (0.55)

25-49,999 32.7 30.5 14.3 15.8 30.3 28.6 35.6 15.5 31.2 29.3

(1.00) (0.87) (1.27) (1.29) (4.51) (3.06) (7.59) (4.61) (0.91) (0.82)

50,000+ 54.3 55.9 79.7 79.8 37.6 52.9 53.4 65.2 55.9 57.6

(1.04) (1.00) (1.55) (1.39) (4.88) (3.85) (8.57) (10.49) (0.97) (0.92)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Estimated Impacts of September 11th on U.S. Travel 33

Table B5: Distribution of Pre- and Post-9/11 Long-Distance Trips by Trip Length, 

for Each Mode

Percentage trips

(standard error)

Trip length

one-way (miles)

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

50 - 99 49.0 53.9 0.5 0.3 22.7 35.8 43.6 64.8 44.6 49.9

(1.04) (0.77) (0.32) (0.17) (3.22) (3.96) (7.41) (7.14) (0.99) (0.73)

100 - 249 35.3 33.3 5.1 2.4 47.0 40.2 33.1 18.9 33.1 31.1

(0.96) (0.65) (1.16) (0.51) (3.97) (3.28) (6.53) (4.05) (0.88) (0.58)

250 - 499 10.9 8.9 13.8 16.1 15.1 17.2 5.8 5.9 11.1 9.5

(0.38) (0.37) (1.61) (1.43) (2.52) (2.82) (1.81) (2.67) (0.39) (0.36)

500 - 999 3.3 2.6 30.4 27.9 10.0 3.5 7.0 2.3 5.6 4.4

(0.21) (0.15) (1.92) (1.35) (2.22) (0.90) (4.08) (0.89) (0.26) (0.16)

1,000+ 1.6 1.3 50.1 53.4 5.1 3.2 10.4 8.1 5.6 5.0

(0.14) (0.11) (1.95) (1.57) (2.11) (1.13) (3.67) (2.48) (0.26) (0.19)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Table B6: Distribution of Pre- and Post-9/11 Long-Distance Trips by Trip Destination, 

for Each Mode

Percentage trips

(standard error)

Trip destination 

category

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Same state 66.8 68.1 9.6 5.5 43.9 62.7 33.4 33.3 61.5 63.2

(0.87) (0.71) (1.73) (0.66) (4.94) (3.92) (6.90) (7.53) (0.86) (0.65)

Same census 

division 16.8 18.0 10.2 8.9 18.8 21.6 31.6 45.7 16.4 17.8

(0.59) (0.63) (1.29) (0.91) (3.06) (3.38) (7.39) (10.50) (0.55) (0.58)

Same census 

region 7.5 6.4 11.5 17.6 10.5 7.3 7.9 4.7 7.9 7.1

(0.63) (0.30) (1.37) (1.28) (2.83) (1.57) (2.60) (1.58) (0.59) (0.28)

Different region 7.5 6.7 52.3 56.4 24.5 6.7 19.7 8.6 11.6 10.2

(0.34) (0.32) (1.95) (1.72) (6.78) (1.51) (4.64) (3.21) (0.44) (0.30)

International 1.4 0.8 16.4 11.7 2.3 1.7 7.4 7.8 2.6 1.7

(0.14) (0.09) (1.52) (0.97) (0.74) (0.80) (2.63) (2.36) (0.17) (0.11)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
34 Estimated Impacts of September 11th on U.S. Travel

Table B7: Distribution of Pre- and Post-9/11 Long-Distance Trips by Area Type, 

for Each Mode

Percentage trips

(standard error)

Area type

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Urban 72.0 69.8 89.7 90.5 83.5 72.4 91.8 73.9 73.9 71.3

(1.08) (0.97) (1.03) (0.81) (2.80) (3.01) (4.54) (8.45) (0.97) (0.88)

Rural 28.0 30.2 10.3 9.5 16.5 27.6 8.2 26.1 26.2 28.7

(1.08) (0.97) (1.03) (0.81) (2.80) (3.01) (4.54) (8.45) (0.97) (0.88)

All trips 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Table B8: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Trip Purpose

Percentage trips

(standard error)

Trip purpose

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Commute 97.7 94.7 1.7 1.9 0.1 0.7 0.5 2.6 100.0 100.0

(0.56) (1.37) (0.52) (0.81) (0.09) (0.40) (0.28) (1.12)

Business 77.3 80.1 19.4 17.0 1.0 0.9 2.3 2.1 100.0 100.0

(1.80) (1.48) (1.72) (1.36) (0.33) (0.33) (0.59) (0.78)

Pleasure 89.9 90.9 7.0 6.1 2.2 2.4 0.8 0.7 100.0 100.0

(0.49) (0.31) (0.32) (0.22) (0.35) (0.21) (0.12) (0.09)

Personal business 87.7 90.4 5.5 3.7 6.3 5.4 0.5 0.5 100.0 100.0

(0.88) (0.73) (0.55) (0.49) (0.66) (0.56) (0.16) (0.18)

Other 95.4 96.8 3.3 1.4 0.2 0.8 1.0 1.0 100.0 100.0

(1.89) (1.02) (1.80) (0.48) (0.17) (0.59) (0.70) (0.72)

All trips 88.9 89.7 7.9 7.0 2.3 2.2 0.9 1.1 100.0 100.0

(0.45) (0.37) (0.35) (0.25) (0.22) (0.15) (0.13) (0.20)

Table B9: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Age Group

Percentage trips

(standard error)

Age group

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

0 - 24 89.6 90.0 6.2 4.1 4.0 4.7 0.2 1.2 100.0 100.0

(0.62) (0.84) (0.47) (0.37) (0.39) (0.37) (0.08) (0.54)

25 - 54 88.7 89.0 9.1 8.8 0.9 1.1 1.3 1.2 100.0 100.0

(0.64) (0.53) (0.53) (0.41) (0.16) (0.16) (0.23) (0.26)

55 - 64 90.0 90.9 8.0 6.2 1.4 1.8 0.7 1.1 100.0 100.0

(1.08) (0.89) (0.89) (0.51) (0.48) (0.48) (0.18) (0.46)

65 - 74 86.7 92.3 5.7 3.9 6.3 2.9 1.3 0.9 100.0 100.0

(2.88) (0.96) (1.19) (0.54) (2.90) (0.64) (0.42) (0.24)

75 + 84.6 89.4 6.3 6.4 8.4 3.7 0.7 0.5 100.0 100.0

(1.90) (1.41) (1.29) (1.13) (1.57) (0.86) (0.34) (0.26)

All trips 88.9 89.7 7.9 7.0 2.3 2.2 0.9 1.1 100.0 100.0

(0.45) (0.37) (0.35) (0.25) (0.21) (0.15) (0.13) (0.20)
Estimated Impacts of September 11th on U.S. Travel 35

Table B10: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Gender

Percentage trips

(standard error)

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Male 89.8 89.3 7.9 7.2 1.4 2.1 0.9 1.3 100.0 100.0

(0.64) (0.52) (0.55) (0.34) (0.18) (0.20) (0.18) (0.33)

Female 87.7 90.1 8.0 6.6 3.4 2.4 1.0 0.9 100.0 100.0

(0.67) (0.45) (0.43) (0.35) (0.45) (0.25) (0.20) (0.15)

All trips 88.9 89.7 7.9 7.0 2.3 2.2 0.9 1.1 100.0 100.0

(0.45) (0.37) (0.35) (0.25) (0.21) (0.15) (0.13) (0.20)

Table B11: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Income Group

Percentage trips

(standard error)

Household 

income ($)

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

0-24,999 90.2 93.1 3.6 2.4 5.5 3.1 0.8 1.4 100.0 100.0

(1.69) (1.00) (0.51) (0.35) (1.45) (0.54) (0.39) (0.87)

25-49,999 93.2 93.5 3.6 3.8 2.2 2.2 1.1 0.5 100.0 100.0

(0.53) (0.38) (0.35) (0.29) (0.33) (0.26) (0.28) (0.13)

50,000+ 86.6 87.2 11.1 9.7 1.5 2.0 0.9 1.1 100.0 100.0

(0.64) (0.56) (0.58) (0.44) (0.17) (0.22) (0.16) (0.21)

All trips 89.1 89.8 7.8 7.0 2.2 2.2 0.9 1.0 100.0 100.0

(0.46) (0.36) (0.36) (0.26) (0.22) (0.16) (0.14) (0.16)

Table B12: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Trip Distance

Percentage trips

(standard error)

Trip length one-way 

(miles)

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

50 - 99 98.0 96.9 0.1 0.0 1.1 1.6 0.9 1.5 100.0 100.0

(0.22) (0.46) (0.06) (0.02) (0.15) (0.23) (0.18) (0.38)

100 - 249 94.9 95.9 1.2 0.5 3.0 2.9 0.9 0.7 100.0 100.0

(0.47) (0.33) (0.29) (0.12) (0.36) (0.28) (0.23) (0.12)

250 - 499 86.9 83.5 9.8 11.7 2.8 4.0 0.5 0.7 100.0 100.0

(1.22) (1.29) (1.14) (1.14) (0.53) (0.67) (0.15) (0.29)

500 - 999 52.1 53.5 43.1 44.2 3.7 1.8 1.1 0.6 100.0 100.0

(2.70) (2.07) (2.60) (2.07) (0.83) (0.44) (0.68) (0.20)

1,000+ 24.9 22.9 71.5 73.9 1.9 1.4 1.7 1.8 100.0 100.0

(1.81) (1.67) (1.81) (1.82) (0.80) (0.49) (0.57) (0.41)

All trips 89.1 89.7 7.9 7.0 2.1 2.2 0.9 1.1 100.0 100.0

(0.43) (0.37) (0.36) (0.25) (0.16) (0.15) (0.13) (0.20)
36 Estimated Impacts of September 11th on U.S. Travel

Table B13: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Trip Destination

Percentage trips

(standard error)

Trip destination category

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Same state 96.6 96.6 1.3 0.6 1.6 2.2 0.5 0.6 100.0 100.0

(0.32) (0.26) (0.25) (0.07) (0.17) (0.20) (0.12) (0.12)

Same census division 90.7 90.9 4.9 3.5 2.6 2.7 1.8 2.9 100.0 100.0

(0.91) (1.24) (0.67) (0.40) (0.40) (0.46) (0.54) (1.03)

Same census region 84.4 79.8 11.6 17.2 3.0 2.3 0.9 0.8 100.0 100.0

(1.69) (1.43) (1.38) (1.33) (0.87) (0.52) (0.29) (0.22)

Different region 57.8 59.1 35.8 38.5 4.8 1.5 1.6 1.0 100.0 100.0

(1.72) (1.87) (1.60) (1.76) (1.50) (0.35) (0.41) (0.33)

International 46.0 43.7 49.4 48.7 2.0 2.3 2.6 5.3 100.0 100.0

(3.55) (3.23) (3.69) (3.34) (0.61) (1.05) (0.91) (1.22)

All trips 88.9 89.7 7.9 7.0 2.3 2.2 0.9 1.1 100.0 100.0

(0.45) (0.37) (0.35) (0.25) (0.21) (0.15) (0.13) (0.20)

Table B14: Distribution of Pre- and Post-9/11 Long-Distance Trips by Mode, 

for Each Area Type

Percentage trips

(standard error)

Area type

Personal vehicle Air Bus Other All modes

Pre Post Pre Post Pre Post Pre Post Pre Post

Urban 86.6 87.7 9.6 8.8 2.6 2.3 1.2 1.2 100.0 100.0

(0.61) (0.50) (0.48) (0.36) (0.29) (0.18) (0.17) (0.25)

Rural 95.2 94.5 3.1 2.3 1.4 2.2 0.3 1.0 100.0 100.0

(0.44) (0.53) (0.33) (0.20) (0.21) (0.28) (0.16) (0.36)

All trips 88.9 89.7 7.9 7.0 2.3 2.2 0.9 1.1 100.0 100.0

(0.45) (0.37) (0.35) (0.25) (0.21) (0.15) (0.13) (0.20)
37

APPENDIX C 

The Time Series Data 

Table C1: Raw Data for Time Series Analysis

Air RPM (000s) Rail PM VMT (billions)

Jan-90 35,153,577 454,115,779 163.28

Feb-90 32,965,187 435,086,002 153.25

Mar-90 39,993,913 568,289,732 178.42

Apr-90 37,981,886 568,101,697 178.68

May-90 38,419,672 539,628,385 188.88

Jun-90 42,819,023 570,694,457 189.16

Jul-90 45,770,315 618,571,581 195.09

Aug-90 48,763,670 609,210,368 196.67

Sep-90 38,173,223 488,444,939 178.07

Oct-90 39,051,877 514,253,920 182.27

Nov-90 35,699,216 516,429,873 171.23

Dec-90 37,444,088 531,619,395 168.29

Jan-91 34,848,290 496,467,387 157.88

Feb-91 29,672,427 469,504,489 153.34

Mar-91 36,202,993 587,905,914 179.06

Apr-91 37,146,602 509,488,334 179.52

May-91 38,869,421 566,342,448 191.91

Jun-91 42,199,760 603,845,247 193.45

Jul-91 45,384,965 633,450,001 198.37

Aug-91 48,164,550 664,013,874 204.05

Sep-91 38,481,957 494,557,648 183.58

Oct-91 39,062,110 509,037,626 188.43

Nov-91 34,688,141 472,359,118 169.68

Dec-91 38,575,165 550,795,984 172.77

Jan-92 35,265,807 493,541,137 167.89

Feb-92 33,868,884 462,084,011 160.43

Mar-92 39,724,539 559,205,172 184.03

Table continues on the next page
38 Estimated Impacts of September 11th on U.S. Travel

Table C1 (continued)

Raw Data for Time Series Analysis

Air RPM (000s) Rail PM VMT (billions)

Apr-92 37,294,373 572,122,965 186.32

May-92 39,728,367 546,043,825 196.99

Jun-92 45,754,460 478,204,759 197.49

Jul-92 50,807,813 610,774,196 206.88

Aug-92 53,219,046 627,359,211 205.02

Sep-92 41,564,915 516,516,158 191.16

Oct-92 40,578,092 538,313,763 195.10

Nov-92 36,863,415 539,769,246 177.62

Dec-92 39,045,016 551,397,116 181.25

Jan-93 37,911,556 500,349,189 171.66

Feb-93 34,902,869 461,280,157 162.82

Mar-93 42,058,882 534,476,186 187.84

Apr-93 40,814,097 562,429,065 188.71

May-93 42,133,943 554,039,017 205.95

Jun-93 44,679,864 580,500,717 199.39

Jul-93 48,622,037 586,861,727 209.81

Sep-93 41,677,832 501,353,952 193.74

Oct-93 43,264,475 507,702,230 197.76

Nov-93 38,868,799 489,299,178 182.30

Dec-93 40,610,043 503,041,903 186.80

Jan-94 38,863,562 472,917,856 169.31

Feb-94 36,068,535 463,567,851 166.44

Mar-94 45,433,842 572,167,273 196.19

Apr-94 42,418,163 546,898,075 195.41

May-94 44,297,210 524,008,197 206.60

Jun-94 48,169,096 531,971,149 207.28

Jul-94 52,410,979 578,257,564 214.78

Aug-94 53,235,052 587,229,076 215.05

Sep-94 44,677,623 490,032,783 200.51

Oct-94 45,845,403 493,619,903 202.86

Nov-94 42,230,318 477,594,716 190.07

Dec-94 43,868,116 490,779,357 193.09

Jan-95 42,050,940 425,207,429 183.62

Feb-95 38,580,784 393,268,412 172.52

Mar-95 47,390,145 489,360,683 202.41

Apr-95 45,427,499 490,903,635 199.53

May-95 45,915,722 511,120,586 213.56

Jun-95 49,992,583 542,693,418 212.28

Jul-95 53,345,749 571,259,638 217.72

Table continues on the next page
Estimated Impacts of September 11th on U.S. Travel 39

Table C1 (continued)

Raw Data for Time Series Analysis

Air RPM (000s) Rail PM VMT (billions)

Aug-95 54,776,466 580,982,161 219.87

Sep-95 45,589,237 451,385,426 204.32

Oct-95 46,993,036 441,202,778 206.99

Nov-95 43,703,386 435,470,011 195.41

Dec-95 45,015,358 444,598,571 194.59

Jan-96 42,734,234 362,022,328 183.74

Feb-96 43,056,589 343,413,949 176.82

Mar-96 51,646,570 416,042,895 204.47

Apr-96 48,063,492 425,092,726 205.56

May-96 49,808,809 467,573,082 219.00

Jun-96 53,774,378 476,554,648 215.87

Jul-96 56,576,582 525,954,361 225.44

Aug-96 58,714,135 551,695,523 229.38

Sep-96 47,857,913 412,344,840 207.91

Oct-96 49,873,317 425,991,795 215.97

Nov-96 44,696,485 389,249,720 199.94

Dec-96 49,361,193 437,256,586 201.75

Jan-97 46,692,784 337,798,678 190.13

Feb-97 43,761,932 337,893,100 183.95

Mar-97 54,755,166 416,925,515 211.95

Apr-97 50,182,894 408,488,665 211.29

May-97 51,979,543 444,032,624 226.08

Jun-97 55,462,027 481,053,743 222.25

Jul-97 58,771,317 540,293,509 236.71

Aug-97 60,496,856 552,486,079 233.50

Sep-97 50,076,953 425,814,893 213.55

Oct-97 51,846,329 445,858,326 221.22

Nov-97 47,743,798 441,249,575 202.42

Dec-97 50,414,361 454,400,575 207.32

Jan-98 47,290,992 371,355,589 196.87

Feb-98 44,641,209 326,874,247 187.17

Mar-98 54,453,961 426,172,239 214.22

Apr-98 53,044,174 447,877,815 217.92

May-98 54,302,581 462,341,914 227.90

Jun-98 57,335,447 482,325,897 228.73

Jul-98 59,441,840 537,481,124 239.94

Aug-98 60,813,085 526,444,502 237.14

Sep-98 49,560,508 430,364,874 219.46

Oct-98 53,463,147 444,719,403 228.52

Nov-98 49,561,082 427,669,956 211.18

Table continues on the next page
40 Estimated Impacts of September 11th on U.S. Travel

Table C1 (continued)

Raw Data for Time Series Analysis

Air RPM (000s) Rail PM VMT (billions)

Dec-98 51,024,977 459,051,399 216.30

Jan-99 48,861,094 384,824,724 194.44

Feb-99 46,158,194 350,270,063 192.33

Mar-99 57,761,057 436,837,107 221.74

Apr-99 54,968,382 435,360,347 221.98

May-99 55,469,960 450,054,655 231.81

Jun-99 59,918,608 496,553,598 237.02

Jul-99 64,397,523 543,923,323 244.20

Aug-99 63,752,132 528,001,216 242.57

Sep-99 53,733,696 393,137,094 225.30

Oct-99 57,407,208 425,897,553 234.66

Nov-99 53,866,781 421,595,015 222.83

Dec-99 52,331,272 442,742,459 222.46

Jan-00 49,745,428 366,607,410 203.58

Feb-00 49,876,910 366,139,276 199.64

Mar-00 61,378,569 453,096,388 232.63

Apr-00 58,981,617 473,751,011 227.81

May-00 61,165,486 481,725,263 242.11

Jun-00 65,524,091 517,102,460 243.01

Jul-00 67,883,256 564,747,276 245.08

Aug-00 66,924,512 558,060,051 247.77

Sep-00 56,441,629 446,489,429 227.33

Oct-00 58,834,210 462,329,685 236.55

Nov-00 56,283,261 442,268,378 222.80

Dec-00 55,380,280 461,651,082 218.44

Jan-01 53,129,922 379,458,113 209.26

Feb-01 49,992,995 366,288,212 199.91

Mar-01 62,323,049 448,912,584 231.49

Apr-01 59,801,562 455,496,342 231.37

May-01 60,246,477 462,376,555 244.25

Jun-01 64,987,625 528,690,495 242.58

Jul-01 68,573,410 564,037,290 248.79

Aug-01 69,003,617 562,266,005 251.69

Sep-01 39,106,905 446,266,947 224.57

Oct-01 44,271,037 452,095,235 240.00

Nov-01 45,245,063 446,094,295 229.48

Dec-01 48,167,518 479,188,434 228.09

Jan-02 46,587,818 398,440,459 213.81

Feb-02 45,157,533 397,948,828 206.46

Mar-02 57,423,155 461,087,451 234.01

Table continues on the next page
Estimated Impacts of September 11th on U.S. Travel 41

Table C1 (continued)

Raw Data for Time Series Analysis

Air RPM (000s) Rail PM VMT (billions)

Apr-02 53,013,066 453,147,413 234.81

May-02 55,663,570 461,648,459 249.89

Jun-02 60,224,150 499,751,748 246.39

Jul-02 62,570,110 564,046,073 254.19

Aug-02 62,881,793 516,103,068 256.24

Sep-02 48,925,997 385,097,846 230.60

Oct-02 52,779,975 399,248,774 243.29

Nov-02 48,902,677 375,996,408 228.39

Dec-02 55,355,434 448,246,102 231.26

Jan-03 50,602,700 393,287,373 216.60

Feb-03 46,455,943 390,041,481 201.90

Mar-03 56,033,121 472,573,962 236.70

Apr-03 51,146,721 483,653,074 238.10

May-03 53,591,384 489,484,678 253.20

Jun-03 59,426,357 531,138,943 252.00

Jul-03 65,041,408 578,767,927 261.30

Aug-03 64,095,456 557,897,322 259.60

Sep-03 51,347,366 427,807,444 236.00

Oct-03 55,810,667 463,025,617 253.40

Nov-03 53,249,129 448,181,812 233.30

Dec-03 57,795,908 489,403,554 237.60

Jan-04 53,447,972 410,338,691 217.30

Feb-04 52,608,801 389,778,365 210.40

Mar-04 63,600,019 453,014,590 247.50

Apr-04 61,887,720 471,116,666 245.40

May-04 62,074,299 472,861,212 253.00

Jun-04 67,833,476 514,365,919 253.70

43

APPENDIX D 

The Structural Time Series Model

Although terms such as “trend” and “seasonal” are intuitively appealing, they 

are mental constructs as we cannot observe them directly. Therefore, we use 

a structural modeling approach that treats them as unobserved components (Harvey, 1989; Harvey and Shephard, 1993). In the empirical work we used the 

STAMP (Structural Time Series Analyser, Modeller and Predictor) software in 

conjunction with GiveWin; for details, see Koopman et al. (2000). 

We defi ne the components at time t as follows: trend = μt

; slope = βt;

 

seasonal 

component = γt

; and irregular component = εt. We assume that the process is 

observed at unit time intervals (t, t+1,...) and that there are s such intervals in 

a year (e.g. s=12 for monthly data). We then allow each component to evolve 

over time according to the specifi cations:

μ + t = μt−1 + βt−1 ηt (1)

t t t β + = β ς −1 (2)

and

t t t s ωt γ = + γ + + γ −1 − +1 .... (3)

The quantities ηt, ζt, and ωt represent zero mean, random shifts in the corresponding component. We assume such shifts to be independent of one another and uncorrelated over time; we also assume that they are independent of 

the “irregular” component, εt

, seen in Equation (4) below. Equations (1)–(3) 

are known as the state or transition equations since they describe the underlying states of the process, or the transition of the components from one time 

period to the next.

Equations (1) and (2) provide a general framework for describing the evolution of the trend. If the process being modeled does not require all of these 
44 Estimated Impacts of September 11th on U.S. Travel

components, they can be dropped from the 

specifi cation. The components are tested in 

sequential fashion as follows (Harvey, 1989, 

pp. 248-56):

Does the slope disturbance term have 

positive variance? [Zero variance corresponds to the slope being fi xed over 

time]

Does the level disturbance have positive 

variance? [Zero variance corresponds to 

the mean level being fi xed over time]

If the slope disturbance is dropped, does 

the slope differ from zero?

If all three statistical tests produced negative 

outcomes, the trend term would be reduced 

to a constant.

When the time series is seasonal, we check:

Does the seasonal disturbance term have 

positive variance? [Zero variance corresponds to a stable seasonal pattern]

If the seasonal disturbance is dropped, 

are the seasonal components signifi -

cantly different from zero? [Is there any 

seasonal pattern?]

If we drop the seasonal disturbance term, 

we are left with a “classical” model with 

fi xed seasonal components. If the seasonal 

pattern is rejected completely, we reduce the 

model purely to its trend components.

The observed series is related to the states 

of the system by the observation (or measurement) equation:

t t t t y = μ + + γ ε (4) 

where εt denotes the ‘irregular’ component. 

•

•

•

•

•

The irregular component has zero mean and 

is assumed to be serially uncorrelated (i.e., 

not predictable) and independent of the 

disturbances in the state equations.

Estimation proceeds by maximum likelihood 

(Harvey, 1989, pp. 125-128). Operational 

details are provided in Koopman et al. (2000, 

section 8.3). The key parameters are the four 

variances corresponding to the disturbance 

terms [σε

2

, ση

2

, σζ

2 and σω2

]. Note that we 

assume these variances are constant over 

time; the time series may need to be transformed to justify this assumption, at least to 

a reasonable degree of approximation. The 

four variance terms control the form of 

the model, allowing each of level, slope and 

seasonal to be stochastic or fi xed; slope and 

seasonal elements may be present or absent. 

Table D1 illustrates the principal variations. 

If fi xed components are included in a model, 

the corresponding terms appear in the state 

equations (e.g. fi xed seasonal coeffi cients) 

but the variance term is zero. If the components are stochastic, the same terms appear 

in the model, but the variance is strictly 

positive. The most general form is the Basic 

Structural Model (BSM), in which all components are stochastic. The BSM forms the 

starting point for the model development 

process, and is the standard form employed 

in STAMP. The program then “tests down” 

to eliminate any components that are not 

required. 
Estimated Impacts of September 11th on U.S. Travel 45

Table D1: Some of the Principal Models in the Structural Framework

Type of model σε

2 ση2 σζ2 σω2

Level only

 Constant mean yes000

 Local level (LL) yes yes 0 0

 Random walk (RW) 0 yes 0 0

Trend only

 Deterministic yes000

 Local level with fi xed slope yes yes 0 0

 Random walk with fi xed drift 0 yes 0 0

 Local linear trend (Holt) yes yes yes 0

 Smooth trend yes 0 yes 0

 Second difference 0 0 yes 0

Seasonal (with selected trend)

 Fixed seasonals (yes or 0) (yes or 0) (yes or 0) 0

 Varying seasonals (yes or 0) (yes or 0) (yes or 0) yes

Basic Structural Model (BSM) yes yes yes yes

NOTE: Based on Koopman et al. (2000), page 141.

47

APPENDIX E 

Summary of Monthly Forecasts

Table E1: Forecasts and Forecast Errors for Air RPM

Air RPM Forecast of Air RPM before 9/11-01 Forecast error Absolute percent error

Mar-01 62,323,049

Apr-01 59,801,562

May-01 60,246,477

Jun-01 64,987,625

Jul-01 68,573,410

Aug-01 69,003,617

Sep-01 39,106,905 58,390,903.80 19,283,998.80 49.31%

Oct-01 44,271,037 61,261,140.50 16,990,103.50 38.38%

Nov-01 45,245,063 58,337,950.00 13,092,887.00 28.94%

Dec-01 48,167,518 57,977,550.10 9,810,032.10 20.37%

Jan-02 46,587,818 55,752,782.90 9,164,964.90 19.67%

Feb-02 45,157,533 52,754,058.80 7,596,525.80 16.82%

Mar-02 57,423,155 64,478,755.60 7,055,600.60 12.29%

Apr-02 53,013,066 61,791,637.40 8,778,571.40 16.56%

May-02 55,663,570 62,679,059.30 7,015,489.30 12.60%

Table E2: Forecasts and Forecast Errors for Rail RPM

Rail PM Forecasts of Rail PM before 9-11-01 Forecast error Absolute percent error

Mar-01 448,912,584

Apr-01 455,496,342

May-01 462,376,555

Jun-01 528,690,495

Jul-01 564,037,290

Aug-01 562,266,005

Sep-01 446,266,947 442,469,757.70 -3,797,189.30 0.85%

Oct-01 452,095,235 463,447,212.30 11,351,977.30 2.51%

Nov-01 446,094,295 448,090,467.10 1,996,172.10 0.45%

Dec-01 479,188,434 472,451,381.90 -6,737,052.10 1.41%

Jan-02 398,440,459 392,260,011.50 -6,180,447.50 1.55%

Feb-02 397,948,828 379,067,383.20 -18,881,444.80 4.74%

Mar-02 461,087,451 462,607,735.60 1,520,284.60 0.33%

Apr-02 453,147,413 472,183,198.80 19,035,785.80 4.20%

May-02 461,648,459 478,159,689.20 16,511,230.20 3.58%
48 Estimated Impacts of September 11th on U.S. Travel

Table E3: Forecasts and Forecast Errors for VMT

VMT Forecast of VMT prior to 9-11-01 Forecast error Absolute percent error

Mar-01 231.49

Apr-01 231.37

May-01 244.25

Jun-01 242.58

Jul-01 248.79

Aug-01 251.69

Sep-01 224.57 232.25 7.67 3.42%

Oct-01 240.00 240.33 0.33 0.14%

Nov-01 229.48 225.79 -3.69 1.61%

Dec-01 228.09 226.04 -2.05 0.90%

Jan-02 213.81 212.42 -1.38 0.65%

Feb-02 206.46 205.80 -0.67 0.32%

Mar-02 234.01 236.01 2.01 0.86%

Apr-02 234.81 235.29 0.48 0.21%

May-02 249.89 247.92 -1.97 0.79%
49

REFERENCES

Dahl, Carol A. 1995. “Demand for Transportation Fuels: A Survey of Demand 

Elasticities and their Components,” Journal of Energy Literature, 1(2).

Harvey, Andrew C. 1989. Forecasting, Structural Time Series Models, and The 

Kalman Filter. Cambridge, UK: Cambridge University Press.

Harvey, Andrew C. and Neil Shephard.1993. “Structural Time Series Models.” 

In Handbook of Statistics, Volume 11. Edited by G.S. Maddala, C.R. Rao and 

H.D. Vinod. Amsterdam, The Netherlands: Elsevier Science.

Koopman, Siem Jan, Andrew C. Harvey, Jurgen A. Doornik and Neil Shephard. 2000. STAMP: Structural Time Series Analyser, Modeller and Predictor. 

London: Timberlake Consultants Ltd.

Ord, Keith and Peg Young. 2004. “Estimating the Impact of Recent Interventions on Transportation Indicators”, Journal of Transportation and Statistics, 

Vol. 7, No. 1, pp.69-86.

Pindyck, Robert S., Daniel L. Rubinfeld. 1978. Econometric Models And Economic Forecasts. Lexington, Mass.: McGraw-Hill, 1st Edition.