Appendix C. Source and Accuracy StatementsData Source and
Accuracy Statements Chapter
1 Extent, Condition, and Performance TABLE 1-1.System Mileage Within the United States
Highway The Highway Performance
Monitoring System (HPMS) is the source of road mileage data and is considered
reliable.(See box 1-1 for detailed
information about the HPMS.)The
Federal Highway Administration (FHWA) of the U.S. Department of Transportation
(USDOT) collects and reviews state-reported HPMS data for completeness,
consistency, and adherence to specifications.
Some inaccuracy may arise from variations across states in their
adherence to federal guidelines in the Traffic Monitoring Guide and the Highway Performance Monitoring System Field
Manual for the Continuing Analytical and Statistical Database. Beginning with the 1997 issue of Highway Statistics, FHWA instituted a
new method for creating mileage-based tables derived from the HPMS.Previously, adjustments to tables developed
from sample data were made using area-wide mileage information provided by states.These adjustments are now being made using
universe totals from the HPMS dataset.
In addition, FHWA has discontinued the process of spreading rounding and
other differences across table cells.
Thus, users may note minor differences in table-to-table totals.FHWA considers mileage totals from table
HM-20, “Public Road Length, Miles by Functional System” to be the controlling
totals should a single value be required. Reliability may be diminished for
comparisons with pre-1980 data, which were collected via different methods and
special national studies.For instance,
pre-1980 mileage data included some nonpublic roadways (95,000 miles in 1979)
while post-1980 data reports only public road mileage (roads or streets
governed and maintained by a public authority and open to public travel). Class I Rail These data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR data are based on 100-percent reporting by Class I railroads to the
Surface Transportation Board (STB) via Schedule 700 of the R1 Annual Report.The STB
defines Class I railroads as having operating revenues at or above a threshold
indexed to a base of $250 million (1991) and adjusted annually in concert with
changes in the Railroad Freight Rate Index published by the Bureau of Labor
Statistics.In 1998, the threshold for
Class I railroads was $259.4 million.
Declassification from Class I status occurs when a railroad falls below
the applicable threshold for three consecutive years.Although Class I railroads represent only 2 percent of railroads
in the country, they account for over 70 percent of the industry’s
mileage. To obtain railway mileage, AAR
subtracts trackage rights from miles of rail traveled on line 57 in the
Schedule 700 report.Historical
reliability may vary due to changes in the railroad industry, including
bankruptcies, mergers, and declassification by the STB.Small data errors may also exist because of
because of independent rounding of this series by AAR.
Box 1-1. Highway Performance
Monitoring System Sampling Frame Construction The Highway Performance Monitoring System (HPMS) sample is a
stratified simple random sample of highway links (small sections of roadway)
selected from state inventory files.
The 1997 sample consists of about 120,000 samples.Each state maintains an independent
inventory of highway road links for those roads that the state is responsible
for (in some cases this can be a low percentage of total road miles within the
state).Lower jurisdictions (MPO’s,
counties, cities, national parks, Indian reservations, etc.) may also maintain
inventories of highway links under their jurisdiction.The HPMS sample was originally selected in
1978 based on guidelines provided by the FHWA for sampling highway systems
excluding those roads functionally classified as local.The sampling frame for the state systems
were the state inventories.The
estimates represent the highway systems of each state.The HPMS sample was designed as a fixed
sample to minimize data collection costs but adjustments to maintain
representativeness are carried out periodically.The HPMS also consists of universe reporting (a complete census)
for the Interstate and the National Highway System, and tabular summary
reporting of limited information.A
small number of data items (about 30) are reported for the complete
universe.The universe information
contains no sampling error.There are 4
tables reported as part of the summary. Stratification The HPMS sample (and universe) is stratified by state, type of
area (rural, urban, and individual urbanized areas), highway functional
classification, and traffic (annual average daily traffic (AADT) volume
groups).Complete information is provided
in the HPMS Field Manual. Weighting The HPMS sample expansion factors are the ratio of universe
mileage to sample mileage in each strata. Data Collection Data are collected independently by the 50 states, metropolitan
planning organizations (MPOs), and lower jurisdictions.Many of the geometric data items rarely
change, such as number of lanes.Others
change frequently, such as traffic.
Typically, the states maintain data inventories that are the repositories
of a wide variety of data items.The
HPMS data items are extracted from these inventories.For example, each State has a traffic volume counting
program. Typically, equipment is
installed or placed on the roads to measure traffic.The counts are then converted to annual average daily traffic
(AADT) and stored in the state databases.
AADT is one of the sample and universe items extracted from the
inventories and reported to the HPMS.
The FHWA provides guidelines for data collection in the HPMS Field Manual, which the states follow to
varying extents depending on issues such as staff, resources, state
perspective, uses of the data, state/MPO/local needs for data, etc.Traffic data collection, for example, is an
expensive and dangerous undertaking, particularly in high volume urban areas. State departments of transportation report HPMS data annually
to the FHWA.There are about 80 data
items reported for the sample component.
The reporting deadline is June 15.
Except for special cases where major problems occur, data items are
reported for each sample.There is no
provision for nonresponse since a number is available for each section in the
state inventories; however, states do leave items blank to indicate that no
data collection has taken place for a specific item (e.g., if no system to
measure pavement has been implemented in the state, the pavement condition item
may be left blank.)The HPMS has gone
through a major restructuring effort, and major data item reductions,
modifications, and other changes will begin to be implemented with the 1999
data reported by June 15, 2000. Sampling Error The sample size is estimated based on traffic volume (AADT)
within each stratum.Traffic volume is
the most variable data item.Sampling
error can be estimated directly based on the sample design for each stratum and
aggregated by stratified random sample methods to total values.This exercise was done originally in 1980
for some of the most variable data items including vehicle-miles traveled.It has not been repeated since due to the
work involved and the limited impact of sampling error as compared to
nonsampling error. Nonsampling Error This is a major item of concern for the HPMS.For some of the most variable and important
data items, such as AADT, guidelines for measurement and data collection have
been produced.States have the option
of using the guidelines or using their own procedures.Many data items are difficult and costly to
collect and are reported as estimates not based on direct measurement.The data are collected and reported by many
entities and individuals within the responsible organizations.Most do a reasonably good job, but staff
turnover, cost, equipment issues, etc., can create difficulties identifying
data problems.As mentioned before, a
response is usually provided for each link as included in state
inventories.Measurement errors are
unknown, but the difficulty of collecting some of the data items is well
known.For highway links not the
responsibility of states, metropolitan planning organizations and lower
jurisdictions using a wide variety of methods may collect the data.This a major area of concern and efforts are
underway within States to standardize data collection. The major effort with the HPMS is to insure
the collection and reporting of reliable annual data.The FHWA field offices in each state conduct annual verification
of the data reported.Computer software
is provided to build the database and conduct logic edits prior to
submittal.The reported data are
subjected to intense editing and comparison with previous reporting and a
written annual report is provided to each state to document problems found and
encourage correction.Data resubmittal
is requested in cases where major problems are found.The process involves many people and substantial resources, but
it provides extensive quality assurance.
Complete information on data items, edits, processing, expansion, sample
design, definitions, data reporting, etc., is included in the HPMS Field Manual.
Amtrak These statistics originate from
the Statistical Appendix to Amtrak’s
Annual Report. Amtrak estimates track mileage based on point-to-point city
timetables that railroad companies provide for engineers.The figures are estimates, but are
considered reliable. Transit These figures are based on
information in the U.S. Department of Transportation, Federal Transit
Administration (FTA), National Transit Database.Section 15 of the Federal Transit Act requires federally funded
transit agencies to provide detailed financial and operating data, including
vehicle inventories and directly operated mileage.Transit operators that do not report to FTA are those that do not
receive federal funding, typically private, small, and rural operators.The data are generally considered accurate
because FTA reviews and validates information submitted by individual transit
agencies.Reliability may vary because
some transit agencies cannot obtain accurate information or may misinterpret
certain data definitions. Navigable Channels These statistics originate from a
mid-1950s U.S. Army Corps of Engineers (USACE) estimate that there were
approximately 25,000 miles of commercially important navigable channels in the
United States. That number has been adjusted from time to time, for example, by
addition of the 234-mile Tennessee-Tombigbee Waterway in the early 1980s. The
25,000 plus mile number has been universally quoted for decades, but has
definitional and methodological uncertainties.
USACE is currently developing a rigorous, Global InformationSystem (GIS)-based approach to facilitate
tabulation of the lengths of shallow and deep-draft commercially navigable
waterways in the United States; this calculation will be available in several
years. Oil Pipeline The data are from Transportation in America, published by
the Eno Transportation Foundation, Inc. (Eno).
The numbers reprinted here for 1960, 1965, 1970, and 1975 are Eno
estimates from the U.S. Department of Energy (DOE) Energy Data Report issues labeled “Crude-oil and Refined Products
Mileage in the United States.”Eno
estimated the 1980 number based on the assumption that refinement of old, less
profitable, and smaller lines exceeded in mileage the construction of new,
larger, and more profitable lines.
Post-1985 data were calculated using a base figure reported in a 1982
USDOT study entitled Liquid Pipeline
Director and then combined with data from the Association of Oil Pipe Lines
and the Oil Pipeline Research Institute.
Lack of additional information raises definitional and methodological
uncertainties for the data’s reliability.
Moreover, the three different information sources introduce data
discontinuities, making time comparisons unreliable. Gas Pipeline These statistics originate from
annual editions of Gas Facts,
published by the American Gas Association (AGA).The data reported by the AGA are based on gas utilities
participation and reporting to the Uniform
Statistical Report.Utilities
reporting represented 98 percent of gas utility industry sales while the
remaining 2 percent was estimated for nonreporting companies based on recent
historical experience.Varying
percentages of nonreporters from year to year introduce minor reliability
problems for time-series comparisons. TABLE 1-3. Number of U.S. Airports The
Federal Aviation Administration (FAA), Office of Airport Safety and Standards Administrator’s Fact Book (annual
issues) furnished the data shown in this table and includes airports certified
for air carrier operations with aircraft that seat 30 or more passengers.These airports include civil and joint
civil-military use airports, heliports, STOLports (short takeoff and landing),
and seaplane facilities.The FAA obtained
this data via physical inspections and mail solicitations of all federally
regulated landing facilities.Since
this is a census of all U.S. airports, reliability should be high. Data,
however, may be subject to reporting errors typical of administrative
recordkeeping. TABLE 1-4.
Public Road and Street Mileage in the United States by Type of Surface TABLE 1-5. U.S. Public Road and Street
Mileage by Functional System TABLE 1-6. Estimated U.S. Roadway Lane-Miles
by Functional Class The Highway Performance
Monitoring System (HPMS) is the source of road mileage data and is considered
reliable.(See box 1-1 for detailed
information about the HPMS.)The U.S.
Department of Transportation, Federal Highway Administration collects and
reviews state-reported HPMS data for completeness, consistency, and adherence
to specifications.Some inaccuracy may
arise from variations across states in their adherence to federal guidelines in
the Traffic Monitoring Guide and the Highway
Performance Monitoring System Field Manual for the Continuing Analytical and
Statistical Database. Beginning with the 1997 issue of Highway Statistics, FHWA instituted a
new method for creating mileage-based tables derived from the HPMS.Previously, adjustments to tables developed
from sample data were made using area-wide mileage information provided by
states.These adjustments are now being
made using universe totals from the HPMS dataset.In addition, FHWA has discontinued the process of spreading
rounding and other differences across table cells.Thus, users may note minor differences in table-to-table totals.FHWA considers mileage totals from table
HM-20, “Public Road Length, Miles by Functional System” to be the controlling
totals should a single value be required. Lane-miles are calculated by
multiplying the centerline length by the number of through lanes.Because the HPMS requires that the number of
lanes be reported for all principal arterials, other National Highway System
(NHS) roads, and all standard samples, lane length can be computed for the
Interstate, other principal arterials, and the NHS on a 100-percent basis.For minor arterials, rural major collectors,
and urban collectors, lane length is calculated based on standard sample
sections using the reported number of through lanes, length of section, and an
expansion factor.FHWA uses the expanded
sample to check that the centerline length of a state’s functional system
matches the universe functional system length.
If the centerline length and functional system length do not match, FHWA
may ask a state to make adjustments. Reliability may be diminished for
comparisons with pre-1980 data, which were collected via different methods and
special national studies.For instance,
pre-1980 mileage data included some nonpublic roadways (95,000 miles in 1979)
while post-1980 data reports only public road mileage (roads or streets
governed and maintained by a public authority and open to public travel). TABLE 1-7. Number of Stations Served by
Amtrak and Rail Transit, Fiscal Year These
numbers originate from Amtrak’s Statistical Appendix to Amtrak’s Annual Report and the U.S. Department of Transportation,
Federal Transit Administration’s National Transit Database. Amtrak maintains a computer
database with a record of every station, locomotive, and car it operates.Those records include for each vehicle the
year built, its service status (operating or not on a daily basis), and
location.These data should be
considered very reliable. TABLE 1-8. U.S.
Oil and Gas Pipeline Mileage Oil Pipeline The data are from Transportation in America, published by the
Eno Transportation Foundation, Inc. (Eno).
The numbers reprinted here for 1960, 1965, 1970, and 1975 are Eno
estimates from the U.S. Department of Energy’s Energy Data Report issues labeled “Crude-oil and Refined Products
Mileage in the United States.”Eno
estimated the 1980 number based on the assumption that refinement of old, less
profitable, and smaller lines exceeded in mileage the construction of new,
larger, and more-profitable lines.
Figures from 1985 and later years are calculated from a base figure that
Eno obtained from the 1982 U.S. Department of Transportationstudy Liquid
Pipeline Director and then incorporated that figure with data from the
Association of Oil Pipe Lines and the Oil Pipeline Research Institute.Lack of additional information raises
definitional and methodological uncertainties for the data’s reliability.Moreover, the three different information
sources introduce data discontinuities making time comparisons less reliable. Gas Pipeline These statistics originate from annual
editions of Gas Facts published by
the American Gas Association (AGA).The
data reported by AGA are based on gas utilities participation and reporting to
the Uniform Statistical Report.
Utilities reporting in 1991 represented 98 percent of total gas utility
industry sales while the remaining 2 percent was estimated for the nonreporting
companies based on recent historical experience.Varying percentages of nonreporters from year to year introduce
minor reliability problems for time-series comparisons. TABLE 1-2.Number of Air Carriers, Railroads,
Interstate Motor Carriers, Marine Operators, and Pipeline Operators Air Carriers The data are from the Air Carrier Financial Statistics Quarterly,
published by the Office of Airline Information of the U.S. Department of
Transportation, Bureau of Transportation Statistics (BTS). The Alphabetical
List of Air Carriers by Carrier Group at the beginning of each fourth quarter
edition is used to determine the number of major air carriers and other air
carriers in operation at the end of each calendar year.The publication draws its data from the
T-100 and T-100(f) databases maintained by BTS.These databases include data obtained from a 100-percent census
of BTS Form 41 schedule submissions by large certificated air carriers, which
are carriers that hold a certificate issued under section 401 of the Federal
Aviation Act of 1958 and that (1) operate aircraft designed to have a maximum
passenger seating capacity of more than 60 seats or a maximum payload capacity
of more than 18,000 pounds or (2) that conduct international operations.Carriers are grouped as major, national,
large regional, or medium regional based on their annual operating revenues.The thresholds were last adjusted July 1,
1999 and the threshold for major air carriers is currently $1 billion.The table combines the number of national,
large regional, and medium regional air carriers into the other air carrier
category. Railroads The Association of American
Railroads (AAR)’s Railroad Ten-Year
Trends series is the source for the number of railroads.The number of Class I railroads is based on
100-percent reporting by Class I railroads to the Surface Transportation Board
(STB) via Schedule 700 of the R1 Annual
Report.The STB defines Class I railroads
as having operating revenues at or above a threshold indexed to a base of $250
million (1991) and adjusted annually in concert with changes in the Railroad
Freight Rate Index published by the Bureau of Labor Statistics.In 1998, the threshold for Class I railroads
was $259.4 million.Declassification
from Class I status occurs when a railroad falls below the applicable threshold
for three consecutive years.Although
Class I railroads represent only 2 percent of railroads in the country, they account
for over 70 percent of the industry’s mileage. The Association of American
Railroads determines the number of non-Class I railroads through an annual
survey sent to every U.S. freight railroad.
By following up with nonrespondents, the AAR obtains essentially a
census of railroads.Use of the current
survey instrument began in 1986. Interstate Motor Carriers The Motor Carrier Management
Information System (MCMIS), maintained by the U.S. Department of
Transportation, Federal Motor Carrier Safety Administration, contains
information on the safety of all commercial interstate motor carriers and
hazardous material (HM) shippers subject to the Federal Motor Carrier Safety
Regulations and the Hazardous Materials Regulations. All carriers operating in interstate or foreign commerce within
90 days of beginning operations must submit a Form MCS 150, Motor Carrier
Identification Report.Carriers may use
the form to update their information.
The Motor Carrier Safety Improvement Act of 1999 requires that reports
be periodically updated, but not more than once every two years.MCMIS is updated on a weekly basis, and
periodic archives are not made.
Historical data are only available from summary information previously
prepared, including tables and reports.
MCMIS began operations in 1980, but data prior to 1990 are not
available.Data since 1990 are
available on a fiscal year basis (October through September).MCMIS data are from a 100-percent census.
Marine Vessel Operators
The U.S. Army Corps of Engineers
(USACE) provides the data for marine vessel operatorsthrough the Waterborne
Transportation Lines of the United States.
Data are collected by the USACE’s Navigation Data Center (NDC) by
various means, including the U.S. Coast Guard’s registry, maritime service
directories, and waterway sector publications.
However, an annual survey of companies that operate inland waterway
vessels is the principle source of data.
More than 3,000 surveys are sent to these companies and response rates
are typically above 90 percent.
However, a USACE official did report that less than 10 percent of the
total number of companies operating inland water vessels either did not receive
or respond to the annual survey. Pipeline Operators The
Office of Pipeline Safety (OPS) in the U.S. Department of Transportation’s
Research and Special Programs Administration collects annual report data from
natural gas transmission and distribution operators as required by 49 CFR
191.17 and 191.11, respectively.Annual
data must be submitted by March 15 of the following calendar year.No annual report is required for hazardous
liquid pipeline operators.However,
information is available through the pipeline safety program.Since 1986, the program has been funded by
fees assessed to each OPS-regulated pipeline operator based on per-mile of
hazardous pipeline operated.Data for
each operator and each mile of pipeline are stored in the OPS user-fee
database, which is revised annually as updated fees are assessed. Totals
for pipeline operators in this table will differ from those in other tables due
to differences in the regulatory authority of USDOT and the Federal Energy
Regulatory Commission (FERC).FERC
regulates only interstate pipelines, whereas DOT regulates both interstate and
intrastate pipelines, except for rural gathering lines and some offshore
pipelines, which fall under jurisdiction of the U.S. Coast Guard or the U.S.
Department of the Interior’s Minerals Management Service.An OPS official stated that FERC regulates
about two-thirds the amount of pipeline mileage that USDOT regulates. TABLE 1-9. Number of U.S. Aircraft,
Vehicles, Vessels, and Other Conveyances TABLE 1-11. Active Air Carrier and General
Aviation Fleet by Type of Aircraft Air Carrier, Certificated, All Services Data are from the U.S. Department
of Transportation, Federal Aviation Administration (FAA), FAA Statistical Handbook of Aviation.U.S. air carrier fleet data are based on reports collected by FAA
field offices from carriers.The
reports include information on the number of aircraft by type used in air
carrier service.The FAA points out
that this information is not an inventory of the aircraft owned by air
carriers, but represents the aircraft reported to the FAA as being used in air
carrier fleet service.The reported
aircraft are all aircraft carrying passengers or cargo for compensation or hire
under 14 CFR 121 and 14 CFR 135. General Aviation The 1960-1980 figures originated
from the FAA Statistical Handbook of
Aviation.Later data are from FAA annual
issues of the General Aviation and Air
Taxi Activity (GAATA) Survey report, table 3.1.The FAA collects both aircraft registration data and voluntary
information about aircraft operation, equipment, and location.Before 1978, the FAA mandated owners to
annually register their aircraft for the Aircraft Registration Master
File.This was a complete enumeration
of operating aircraft.Registrants were
also asked to voluntarily report information on hours flow, avionics equipment,
base location, and use.The FAA changed
their data collection methodology in 1978.
The annual registration requirement became triennial and the General
Aviation Activity and Avionics Survey was initiated to sample aircraft
operation and equipment data. The General Aviation Activity and
Avionics Survey was renamed the General Aviation and Air Taxi Activity Survey
in 1993 to reflect the fact that the survey includes air taxi aircraft.This survey is conducted annually and
encompasses a stratified, systematic design from a random start to generate a
sample of all general aviation aircraft in the United States.It is based on the FAA registry as the
sampling frame.FAA established three
stratification design variables in the survey:
1) the average annual hours flown per aircraft by aircraft type, 2) the
aircraft manufacturer/model characteristics, and 3) the state of aircraft
registration. Data Reliability Because of the change in 1978,
the reliability of comparisons over time will be affected.The FAA asserted that the change to a
triennial registration deteriorated the Aircraft Registration Master File in
two ways.First, the resulting lag in
registration updates caused the number of undeliverable questionnaires to
steadily increase over the three-year period.
Second, inactive aircraft would remain in the registry, inflating the
general aviation fleet count.In
addition, a new regulation added two categories of aircraft to the general
aviation fleet.However, FAA concluded
that these changes resulted in no more than a five-percent error in the fleet
population estimate. The
reliability of the GAATA survey can be impacted by two factors:sampling and nonsampling error.A measure, called the standard error, is
used to indicate the magnitude of sampling error.Standard errors can be converted for comparability by dividing
the standard error value by the estimate (derived from sample survey results)
and multiplying it by 100.This
quantity, referred to as the percent standard error, totaled seven-tenths of a
percent in 1997 for the general aviation fleet.A large standard error relative to an estimate indicates lack of
precision and, inversely, a small standard error indicates precision. Nonsampling errors could include
problems such as nonresponse, respondent’s inability or unwillingness to
provide correct information, differences in interpretation of questions, and
data-entry mistakes.Readers should
note that nonresponse bias might be a component of reliability errors in the
data from 1980 to 1990.The FAA conducted
telephone surveys of nonrespondents in 1977, 1978, and 1979 and found no
significant differences or inconsistencies in respondents’ and nonrespondents’
replies.The FAA discontinued the
telephone survey of nonrespondents in 1980 to save costs.Nonresponse surveys were resumed in 1990,
and the FAA found notable differences and thus adjusted its fleet
estimates.The 1991 through 1996 data
have been revised to reflect nonresponse bias.
In 1997, a sample of 29,954 aircraft was identified and surveyed from an
approximate population of 251,571 registered general aviation aircraft.Just over 65 percent of the sample responded
to the survey. Highway, Total (registered vehicles) The 1960 to 1980 figures are from
the U.S. Department of Transportation, Federal Highway Administration (FHWA)
document, Highway Statistics, Summary to
1985, table MV-201 and related tables.
Data quality and consistency will be less reliable for these years
because of a diversity of registration practices from state to state.Users should recognize that motor vehicle
statistical information is not necessarily comparable across all states or
within a state from year to year.For
instance, the FHWA reported that separate data on single-unit trucks and
combinations was unobtainable from all states in 1990. After 1980, the FHWA began to use
the Highway Performance Monitoring System (HPMS) database, which improved data
reliability.FHWA reviews
state-reported HPMS data for completeness, consistency, and adherence to these
specifications.Some inaccuracy may
arise from variations across states in their adherence to federal guidelines in
the Highway Performance Monitoring System
Field Manual for the Continuing Analytical and Statistical Database. If choosing to compare state
data, the FHWA recommends that users carefully select a set of peer states that
have characteristics similar to the specific comparison. Improperly selected
peer states are likely to yield invalid data comparisons.Characteristics that a user needs to
consider in determining compatibility of a peer state include similarities and
differences in urban/rural areas, population densities, degrees of
urbanization, climate, geography, state laws and practices that influence data
definitions, administrative controls of public road systems, state economies,
traffic volumes, and degrees of centralization of state functions.The FHWA has developed a set of variables
that users may use to determine appropriate peer states. Other 2-Axle 4-Tire Vehicle (truck) Sources for these figures included FHWA’s Highway Statistics, Summary to 1995 (table VM-201A) and annual
issues of Highway Statistics (table
VM-1).FHWA compiles these figures from
the U.S. Bureau of the Census’ Truck Inventory and Use Survey (TIUS).Since 1963, Census has conducted the TIUS
every five years with the last survey completed in 1997.The Census Bureau changed the name of the
survey to the Vehicle Inventory and Use Survey (VIUS) in 1997.The VIUS collects data and the physical and
operational characteristics of the nation’s truck population.In 1997, 131,000 trucks were surveyed from
an estimated universe of over 75 million trucks.Chronological reliability may be diminished due to sampling
design changes in 1977, 1982, and 1992.
In 1977, the sampling universe was first stratified by the number of
trucks in a state:large (> 1.5
million trucks), medium (700,000 to 1.5 million), and small (< 700,000); and
then by two truck sizes. Stratification in 1982 was then
based on body type rather than vehicle weight.
In 1992 and 1997, the sampling universe was first subdivided
geographically and then into five strata:
1) pickups, 2) vans, 3) single-unit light, 4) single-unit heavy, and 5)
truck tractor.Cases were then selected
randomly within each stratum. Census delivered a
mail-out/mail-back survey to the owner identified in the vehicle registration
records. Data collection is staggered
as state records become available. Owners report data only for the vehicles selected.
In the 1992 survey, a method was employed to also collect data on new truck
purchases in the latter half of the year to estimate the fleet for the calendar
year.This adjustment in the sampling
frame had not been done in previous surveys and may diminish chronological
reliability.The sample for 1997 was
some 22,500 vehicles smaller than for 1992.
The 1997 VIUS had two sampling stages.
For the first stage, the Census Bureau surveyed about 131,000 trucks registered
as of July 1, 1997.The second stage
sampled a total of 3,000 truck owners with state mailing addresses different
from the state of truck registration. The accuracy and reliability of
the VIUS survey depends jointly on sampling variability and nonsampling
errors.Standard errors arising from
sampling variability can be converted for comparability by dividing the
standard error value by the estimate and multiplying it by 100.This quantity, referred to as the percent
standard error, totaled two-tenths of a percent in 1992 and 1997 for the VIUS
sample.A large standard error relative
to an estimate indicates lack of precision and, inversely, a small standard
error indicates precision.The 1992
TIUS achieved over 90.2 percent reporting and the 1997 response rate equaled
84.5 percent, thus reliability may have decreased in the most recent survey. Transit The American Public Transit
Association (APTA) provided these data, which are based on the Federal Transit
Administration (FTA), National Transit Database.These data are generally accurate because the FTA reviews and
validates information submitted by individual transit agencies.Reliability may vary because some transit
agencies cannot obtain accurate information or may misinterpret data.APTA conservatively adjusts FTA data to
include transit operators that do not report to the database (private, very
small, and rural operators). Railroad (all categories) The data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR data are based on 100-percent reporting by Class I railroads to the
Surface Transportation Board (STB) via Schedule 700 of the R1 Annual Report.Thus,
data estimates are considered very reliable.
The STB defines Class I railroads as having operating revenues at or
above a threshold indexed to a base of $250 million (1991) and adjusted
annually in concert with changes in the Railroad Freight Rate Index published
by the Bureau of Labor Statistics.In
1998, the adjusted threshold for Class I railroads was $259.4 million.Declassification from Class I status occurs
when a railroad falls below the applicable threshold for three consecutive
years.Although Class I railroads
represent only 2 percent of railroads in the country, they account for over 70
percent of the industry’s mileage. AAR determines the number of non-Class
I railroads through an annual, comprehensive survey sent to every U.S. freight
railroad.By following up with
nonrespondents, the AAR obtains essentially a 100 percent census of all
railroads.Use of the current survey
instrument began in 1986. Amtrak Amtrak maintains a computer
database with a record of every locomotive and car it operates.For each vehicle, those records include the
year built, service status (operating or not operating on a daily basis), and location.This data should be considered very
reliable. Water Transportation The source for Inland
Nonself-Propelled Vessels, Self-Propelled Vessels, and flag passenger and cargo
vessels is the U.S. Army Corps of Engineers (USACE), Waterborne Transportation Lines of the United States, annual
issues. Data are collected by the USACE’s Navigation Data Center (NDC) by
various means, including the U.S. Coast Guard’s registry, maritime service
directories, and waterway sector publications.
However, an annual survey of companies that operate inland waterway
vessels is the principle source of data.
More than 3,000 surveys are sent to these companies, and response rates
are typically above 90 percent.
However, a USACE official did report that less than 10 percent of the
total number of companies operating inland vessels either did not receive or
respond to the annual survey. Oceangoing Steam Motor Ships Merchant Fleets of the World, published annually by the U.S.
Department of Transportation, Maritime Administration (MARAD), is the source of
these data. MARAD, which classifies vessels as merchant based on size and type,
compiles these figures from a data service provided by Lloyd’s Maritime
Information Service (LMIS).The parent
company, Lloyd’s Register (LR), collects data from 200 offices worldwide, from
data transfers and agreements with other classification societies, from
questionnaires to ship owners and ship builders, from feedback from government
agencies, and from input from port agents.
According to an LR official, consistent data-gathering methods have been
maintained for more than 30 years.The
same official did caution that there are sometimes inconsistencies in groupings
of ship types over time.For example,
propelled tank barges are now included in the tanker ship-type grouping. Recreational Boats Boating Statistics, published annually by the U.S. Coast Guard
(USCG), is the source.The USCG derives
these figures from state and other jurisdictional reporting of the actual count
of valid boat numbers issued.In
accordance with federal requirements, all 55 U.S. states and territories
require motor-powered vessels to be numbered.
However, over half the states do not require nonpowered vessels to be
numbered.Accuracy can also be
diminished by noncompliance of boat owners with numbering and registration
laws.In 1996, the USCG estimated that
approximately eight million recreational boats are not numbered and, thus, are
excluded from the reported number of recreational vessels.The USCG did not provide estimates for the
number of boats without numbering in their 1997 and 1998 reports.Some jurisdictions fail to report by
publication deadlines, and the USCG provided estimates based on the previous
year’s estimate. TABLE 1-10. Sales or Deliveries of New
Aircraft, Vehicles, Vessels, and Other Conveyances Civilian Aircraft The
Aerospace Industries Association (AIA) provided this data in their annual
issues Aerospace Facts and Figures,
“Civil Aircraft Shipments.”AIA
collects their data from aircraft company reports, the General Aviation
Manufacturers Association (GAMA), and the U.S. Department of Commerce’s (DOC)
International Trade Administration. DOC data provide total number of shipments
and exports, and the difference computed by AIA equals domestic shipments.DOC collects shipments data separately for
individual factories or establishments and not at the company level.A potential limitation of this approach is
when a factory producing aircraft for shipment also makes aircraft parts.If the establishment has 80 percent of its
production in aircraft and 20 percent in parts, all of the output is attributed
to aircraft shipments. Transport The Aerospace Industries
Association (AIA) is the source of these data.
AIA obtains quarterly data from Boeing Corp., now the sole U.S. manufacturer
of transport aircraft, and publicly available financial disclosure information
filed with the U.S. Securities and Exchange Commission (SEC) via Form
10-k.SEC requires a publicly traded
company to file an annual report 90 days after the end of the company’s fiscal
year to provide an overview of that business. Helicopters AIA surveyed and received data
from all 10 major helicopter manufacturers on their sales and deliveries. General Aviation The general aviation figures are
taken from the General Aviation
Statistical Databook published by the GAMA.General aviation refers usually to the small aircraft industry in
the United States.GAMA collects
quarterly data from the 10 to 14 manufacturers who nearly equal a census of the
general aviation sector. Passenger Car, Truck, Bus, and Recreational
Vehicles Ward’s Motor Vehicle Facts and Figures is the source of these data.Ward’s obtains sales data directly from
manufacturers.Readers should note that
automobile manufacturers have inflated sales figures in the past, but Ward’s
does contact companies to verify numbers that appear too high or low. Motorcycle The Motorcycle Industry Council,
Inc. (MIC) publishes the Motorcycle
Statistical Annual, which is the source for these data.MIC derived the estimate for new retail
motorcycle sales for each state from the MIC
Retail Sales Report, and adjusted for total retail sales.Motorcycle company reports provided sales
data.Prior to 1985, all-terrain
vehicles (ATVs) were included in the motorcycle total.In 1995, the Motorcycle Industry Council
revised its data for the years 1985 to present to exclude all terrain vehicles
from its totals. Bicycle The National Bicycle Dealers
Association (NBDA) reported these data, which are based on Bicycle Manufacturers
Association (BMA) information through 1996.
BMA stopped reporting members’ shipments in 1996.Moreover, BMA represents the largest bicycle
manufacturers (Huffy, Roadmaster, and Murray), and thus the data do not reflect
specialty bike makers or other manufacturers.
The Bicycle Council estimated 1997 and 1998 figures in the table.According to a Bicycle Council
representative, the estimates are a combination of domestic forecasts produced
by a panel of industry experts and import data from monthly U.S. census
databases. Transit The American Public Transit
Association provided these figures, which are based on information in the U.S.
Department of Transportation, Federal Transit Administration (FTA), National
Transit Database. These data are generally considered accurate because the FTA
reviews and validates information submitted by individual transit
agencies.Reliability may vary because
some transit agencies cannot obtain accurate information or misinterpret data.APTA conservatively adjusts FTA data to
include transit operators that do not report to the database (private, very
small, and rural operators). Class I Rail The data are from Railroad Facts,
published annually by the Association of American Railroads (AAR).AAR data are based on 100-percent reporting
by Class I railroads to the Surface Transportation Board (STB) via Schedule 700
of the R1 Annual Report.STB defines Class I railroads as having
operating revenues at or above a threshold indexed to a base of $250 million
(1991) and adjusted annually in concert with changes in the Railroad Freight
Rate Index published by the Bureau of Labor Statistics.In 1998, the threshold for Class I railroads
was $259.4 million.Although Class I
railroads encompass only 2 percent of the number of railroads in the country,
they account for over 70 percent of the industry’s mileage operated.Historical reliability may vary due to
changes in the railroad industry, including bankruptcies, mergers, and
declassification by the STB.Small data
errors may also have occurred because of independent rounding in this series by
the AAR. Amtrak Amtrak maintains a computer
database with a record of every locomotive and car it operates.For each vehicle, those records include the
year built, its service status (operating or not on a daily basis), and
location.These data should be
considered very reliable. Water Transportation U.S. Department of
Transportation, Maritime Administration (MARAD), which classifies vessels as
merchant based on size and type, reports these data in annual issues of its Merchant Fleets of the World.MARAD compiles these figures from a data
service provided by Lloyd’s Maritime Information Service.The parent company, Lloyd’s Register (LR),
collects data from several sources:its
200 offices worldwide, data transfers and agreements with other classification
societies, questionnaires to shipowners and shipbuilders, feedback from
government agencies, and input from port agents.According to an LR official, consistent data gathering methods
have been maintained for more than 30 years but cautioned that inconsistencies
may occur in groupings of ship types over time.For example, tank barges are now included in the tanker ship-type
grouping rather than the barge grouping. TABLE 1-12. U.S. Automobile and Truck
Fleets by Use These
statistics originate from two sources.
The R.L. Polk Co. provides numbers for commercial fleet vehicles from
state registrations.Bobit Publishing
Co. also obtains fleet vehicle sales data from automobile manufacturers.These two sources cover nearly 100 percent
of fleet vehicles in the United States.
Thus, the data should be very accurate.
TABLE 1-13. Annual U.S. Motor Vehicle
Production and Factory (Wholesale) Sales TABLE 1-14. Retail New Passenger Car Sales TABLE 1-15. New and Used Passenger Car
Sales and Leases TABLE 1-19. World Motor Vehicle
Production, Selected Countries Motor Vehicle Production, Factory Sales, and
New Passenger Car Retail Sales Ward’s Motor Vehicle Facts &
Figures is the source of these data.
Ward’s obtains sales data directly from manufacturers.Readers should note that automobile
manufacturers have inflated sales figures in the past, but Ward’s does contact
companies to verify numbers that appear too high or low. Used Passenger Car Sales and Leased
Passenger Cars ADT Automotive Used Car Market
Report is the source of these data.The
Wall Street Journal (WSJ) is the
original source of 1999 data.According
to an ADT representative, publishing deadlines require ADT to use WSJ numbers until they can be replaced
with National Automotive Dealers Association data.ADT Automotive’s Market Analysis Department also gathers figures
from CNW Marketing/Research and the R.L. Polk Co.CNW estimates used car sales volumes by collecting state title
transfer data and determining if a transaction was made between private
individuals or between a consumer and a franchised or independent dealer.This estimate is evaluated by comparing
total transactions with state automobile sales revenues.Polk,
an additional source of data, maintains a state vehicle registration
database.For 1998, the ADT
representative stated that Polk’s data were within 5 percentage points of CNW
estimates. TABLE 1-16. Retail Sales of New Cars by
Sector The U.S. Department of Commerce,
Bureau of Economic Analysis, uses data from Ward’s
Automotive Reports.The sectoral
break down is derived from registration data obtained from R.L. Polk.Ward’s obtains sales data directly from
manufacturers.Readers should note that
automobile manufacturers have inflated sales figures in the past, but Ward’s
does contact companies to verify numbers that appear too high or low. TABLES 1-17 and 1-18. Period Sales, Market
Shares, and Sales-Weighted Fuel Economies of New Domestic and Imported
Automobiles and Light Trucks, Selected Sales Periods These data originate from Oak
Ridge National Laboratory’s (ORNL) Light-Duty MPG and Market Shares System
database, which relies on information from monthly Ward’s Automotive
Reports.Comparisons and observations
are made on sales and fuel economy trends from one model year to the next.ORNL has adopted several conventions to
facilitate these comparisons, such as the use of sales-weighted average to
estimate fuel economy and vehicle characteristics.For example, “sales-weighted” miles per gallon refers to a
composite or average fuel economy based on the distribution of vehicle
sales.ORNL’s methodology for
sales-weighting can be found in the Appendix of the Highway Vehicle MPG and Market Shares Report:Model Year 1990 (the latest published report).The method was changed dramatically in 1983,
and data reliability prior to that year is questionable.This information is now published annually
in ORNL’s Transportation Energy Data Book. TABLE 1-20. Number and Size of the
U.S.Flag Merchant Fleet and Its Share
of the World Fleet The U.S. Department of
Transportation, Maritime Administration, which classifies vessels as merchant
based on size and type, compiles these figures from a data service provided by
Lloyd’s Maritime Information Service.
The parent company, Lloyd’s Register (LR), collects data from several
sources:its 200 offices worldwide,
data transfers and agreements with other classification societies,
questionnaires to shipowners and shipbuilders, feedback from government
agencies, and input from port agents.
According to an LR official, consistent data gathering methods have been
maintained for more than 30 years, but cautioned that inconsistencies may occur
in groupings of ship types over time.
For example, tank barges are now included in the tanker ship-type
grouping rather than the barge grouping. TABLE 1-21. U.S. Airport Runway Pavement
Conditions These data originate from the
U.S. Department of Transportation, Federal Aviation Administration (FAA),
National Plan of Integrated Airport Systems (NPIAS).The NPIAS includes all commercial service airports, all reliever
airports, and selected general aviation airports.It does not include more than 1,000 publicly owned public use
landing areas, privately owned public use airports, and other civil landing
areas not open to the general public.
NPIAS airports serve 92 percent of general aviation aircraft (based on
an estimated fleet of 200,000 aircraft).
In 1998, the NPIAS encompassed 3,344 of the 5,357 airports with public
access.Runway pavement condition is
classified as follows: Good: All cracks and joints are sealed. Fair: Mild surface cracking, unsealed joints, and slab edge
spalling. Poor:Large open cracks,
surface and edge spalling, vegetation growing through cracks and joints. On a rotating basis, the FAA
arranges annual inspections for about 2,000 of the approximately 4,700
public-use airports.The inspections
are based on funding availability and not on statistical criteria, and nearly
all runways are inspected every two years.
Inspections are primarily made to collect information for pilots on
airport conditions.The FAA relies on
state and local agencies to perform inspections, so some inaccuracy may arise
from variation in their adherence to federal guidelines regarding pavement
condition reporting.In 1998, the U.S.
General Accounting Office found that Pavement Condition Index information was
available for about 35 percent of NPIAS airports (GAO/RCED-98-226). TABLE 1-22. Median Age of Automobiles and
Trucks in Operation in the United States The R.L. Polk Co. is a private
enterprise that purchases state registration data to maintain a database of
operational vehicles.Its data
represent a near census of registered vehicles in the United States, and the
age estimate should be considered very reliable. TABLE 1-23. Condition of U.S. Roadways by
Functional System U.S. Department of
Transportation, Federal Highway Administration (FHWA) collects pavement
condition data from each state through the Highway Performance Monitoring
System.The FHWA uses two rating
schemes—the Present Serviceability Rating (PSR) and the International Roughness
Indicator (IRI).IRI is used to measure
the condition of Interstates, other principal arterials, rural minor arterials,
and other National Highway System roadways.
PSR is used to measure the condition of rural major collectors and urban
minor arterials and collectors.Rural
minor collectors are not measured.
Where IRI data are not reported for sampled sections, the PSR data are
collected. Using the PSR, values range
from 0.1 to 5.0, where 5.0 denotes new pavement in excellent condition and 0.1
denotes pavement in extremely poor condition. On the IRI scale however, lower
values indicate smoother roads (e.g., <60 for interstate pavement in very
good condition to >170 for interstate pavement in poor condition). The IRI is
an objective measure of pavement roughness developed by the World Bank.The PSR is a more subjective measure of a
broader range of pavement characteristics and therefore less comparable.Prior to 1993, all pavement conditions were
evaluated using PSR values.Beginning
with data published in Highway Statistics
1993, the FHWA began a transition to the IRI, which should eventually
replace the PSR.The change from PSR to
IRI makes comparisons between pre-1993 pavement condition data and 1993 and
later pavement condition data difficult.
Thus, trend comparisons should be made with care. FHWA
indicates that the protocol of measuring pavement roughness is not followed by
all states, and some did not report for all required mileage.Totals only reflect those states reporting
usable or partially usable data. Column percentages may not sum to 100 and may
differ slightly from percentages in source tables, which were adjusted so that
they would add to 100.FHWA believes
that the IRI data are of “reasonably good quality.” TABLE 1-24. Condition of U.S. Bridges These
figures are from the U. S. Department of Transportation, Federal Highway
Administration (FHWA),National Bridge
Inventory Database.State highway
agencies are required to maintain a bridge inspection program and inspect most
bridges on public roadways at a minimum of every two years.With FHWA approval, certain bridges may be
inspected less frequently.A complete
file of all bridges is collected and maintained, representing a very reliable
assessment of bridge conditions.
However, some inaccuracy may be attributable to variations in state
inspector’s adherence to the National Bridge Inspection Standards. TABLE 1-25. Average Age of Urban Transit
Vehicles These
figures are based on information in the U.S. Department of Transportation,
Federal Transit Administration (FTA), National Transit Database.Section 15 of the Federal Transit Act
requires federally funded transit agencies to provide detailed financial and
operating data, including vehicle inventories.
Transit operators that do not report to FTA are those that do not receive
federal funding, typically private, small, and rural operators.The data are generally considered accurate
because FTA reviews and validates information submitted by individual transit
agencies.Reliability may vary because
some transit agencies cannot obtain accurate information or may misinterpret
certain data definitions. TABLE 1-26. Class I Railroad Locomotive
Fleet by Year Built The data
are from Railroad Facts, published
annually by the Association of American Railroads (AAR).Figures reported by the AAR are based on
100-percent reporting by Class I railroads to the Surface Transportation Board
(STB) via Schedule 700 of the R1 Annual
Report.STB defines Class I
railroads as having operating revenues at or above a threshold indexed to a
base of $250 million (1991) and adjusted annually in concert with changes in
the Railroad Freight Rate Index published by the Bureau of Labor
Statistics.In 1998, the threshold for
Class I railroads was $259.4 million.
Declassification from Class I status occurs when a railroad falls below
the applicable threshold for three consecutive years.Although Class I railroads encompass only 2 percent of the number
of railroads in the country, they account for over 70 percent of the industry’s
mileage. TABLE 1-27. Age and Availability of Amtrak
Locomotive and Car Fleets Amtrak
maintains a computer database with a record of every locomotive and car it
operates.For each vehicle those
records include the year built, its service status (operating or not on a daily
basis), and location.These data should
be considered very reliable. TABLE 1-28. U.S. Flag Vessels by Type and
Age The data
are from the U.S. Army Corps of Engineers (USACE), Waterborne Transportation Lines of the United States (WTLUS),
annual issues. The WTLUS database contains information on vessel operators and
characteristics and descriptions for all domestic vessel operations.Data are collected by the USACE’s Navigation
Data Center, primarily through a survey of vessel operating companies.More than 3,000 surveys are sent to these
companies and response rates are typically above 90 percent.However, a USACE official did report that
less than 10 percent of the total number of companies operating inland vessel
fleets either did not receive and/or did not respond to the annual survey. TABLE 1-29. U.S. Vehicle-Miles TABLE 1-30. Roadway Vehicle-Miles Traveled
(VMT) and VMT per Lane-Mile by Functional Class TABLE 1-31. U.S. Passenger-Miles Air Carrier, Certificated, Domestic, All
Services The U.S. Department of
Transportation (USDOT), the Bureau of Transportation Statistics, Office of
Airline Information, reports aircraft revenue-miles andpassenger-miles in its publication Air Traffic Statistics.These numbers are based on 100-percent
reporting of passengers and trip length by large certificated air
carriers.Minor errors arise from
nonreporting but amount to less than 1 percent of all air carrier
passenger-miles.The figures do not
include data for all airlines, such as most scheduled commuter airlines and all
nonscheduled commuter airlines.These,
ifadded, may raise total air
passenger-miles by about 5 percent. General Aviation Passenger-mile numbers for 1975
to present are calculated by adjusting the Interstate Commerce Commission’s
1974 figure for air passenger-miles by the percentage change in annual hours
flown by general aviation aircraft as published in the USDOT, Federal Aviation
Administration (FAA),FAA Statistical Handbook of Aviation.Numbers in the handbook are based on the
General Aviation and Air Taxi Survey (GAATA).
In 1993, the GAATA stopped including commuter aircraft.Commuter-miles collected before 1993 by the
GAATA were, according to one FAA official, woefully underreported.Therefore, problems with the estimate of
general aviation aircraft include: a break in the series between 1992 and 1993,
a possible outdated factor used to calculate passenger-miles, and the
classification of commuter operations. Highway Highway vehicle-miles of travel
(vmt) are estimated using data from the Highway Performance Monitoring System
(HPMS), a database maintained by FHWA that contains information on highway
characteristics supplied by individual states.
Annual vmt by highway functional system is calculated as the product of
the annual average daily traffic (AADT) along each highway section, the
centerline length of each highway section, and the number of days in the
year.Also, expansion factors are used
for roadways that are sampled rather than continuously monitored.Vmt by vehicle type is estimated using
vehicle share estimates supplied by states. FHWA has established methods for
collecting, coding, and reporting HPMS data in two manuals:Traffic
Monitoring Guide (TMG) and Highway
Performance Monitoring System Field Manual.The prescribed sampling process for collecting highway volume
data, which is used to estimate AADT, is based on statistical methods.However, in practice, several factors affect
the ultimate quality of the data. FHWA discusses many of these issues in their
annual Highway Statistics report and
other publications.However, BTS is not
aware of any study or report that has statistically quantified the accuracy of
vmt estimates.Some of the primary
issues related to data quality are noted here. 1. The sampling procedures suggested in the
TMG and HPMS Field Manual are
designed to produce traffic volume estimates with an average precision level of
80-percent confidence with a 10-percent allowable error at the state level.
FHWA provides additional guidance to states through annual workshops and other
avenues to help them follow these procedures as closely as possible.However, the actual data quality and
consistency of HPMS information are dependent on the programs, actions, and
maintenance of sound databases by numerous data collectors, suppliers, and
analysts at the state, metropolitan, and other local area levels.Not all states follow the recommended
sampling, counting, and estimating procedures contained in the Traffic Monitoring Guide, and the exact
degree to which the states follow these guidelines overall is unknown.However, FHWA believes that most states
generally follow the guidelines. 2. Estimates for
higher level roadway systems are more accurate than those for lower level ones,
since traffic volumes on higher level roadways are sampled at a higher
rate.The TMG recommends that traffic
counts be collected for all Interstate and principal arterial sections on a
three-year cycle.Under this scheme,
about one-third of the traffic counts for these roadway sections in a given
year are actually measured, while volumes on the remainder are factored to
represent present growth.Although some
States collect data at all traffic count locations every year, most use some
variation of the TMG data collection guidelines.Volumes on urban and rural minor arterials, rural major
collectors, and urban collectors are collected using a sampling procedure.States are not required to report volumes
for rural/urban local systems and rural minor collectors, though most do so.However, the methods used to estimate travel
on these roadways vary from state to state since there are no standard
guidelines for calculating travel on these roadways. 3. Vmt estimates by vehicle type are less accurate than are
estimates for total motor vehicle vmt for several reasons:1) vehicle classification equipment can
frequently misclassify vehicles (see B.A. Harvey et al, Accuracy of Traffic Monitoring Equipment, GDOT 9210, (Georgia Tech
Research Institute:1995)); 2) vehicle
shares are often determined by methods or by special studies that are not
directly compatible with HPMS data definitions and/or purposes, and observed
local-level vehicle classification counts are difficult to apply on a statewide
basis; and 3) vehicle type definitions can vary among states. 4. Vmt estimates for combination trucks in HPMS differ from
survey-based estimates from the Truck Inventory and Use Survey (TIUS), as much
as 50 percent for some categories of combination trucks.Much of this discrepancy appears to be due
to differences in truck classification definitions and biases introduced by
data collection practices.See R.D.
Mingo et al.1995.Transportation
Research Record, No. 1511 (Washington, DC: National Academy Press), pp.
42-46. 5. FHWA adjusts questionable data using a variety of standard
techniques and professional judgement. For example, national average temporal
adjustment factors developed from HPMS and other national highway monitoring
programs are applied to State data, when necessary, to compensate for temporal
deficiencies in sampling practices. Also, in estimating vmt by vehicle type,
FHWA employs an iterative process to reconcile vmt, fuel economy (miles per
gallon), fuel consumption, and vehicle registration estimates. Fuel
consumption, total vmt by highway functional class, and registrations by
vehicle group are used as control totals. This process limits the size of
errors and ensures data consistency. 6.Passenger-miles of
travel (pmt) are calculated by multiplying vmt estimates by vehicle loading (or
occupancy) factors from various sources, such as the Nationwide Personal
Transportation Survey conducted by FHWA and TIUS.Thus, pmt data are subject to the same accuracy issues as vmt,
along with uncertainties associated with estimating vehicle loading factors. Transit The American Public Transit
Association (APTA) figures are based on information in USDOT, Federal Transit
Administration (FTA), National Transit Database.Transit data are generally considered accurate because FTA
reviews and validates information submitted by individual transit
agencies.However, reliability may vary
because some transit agencies cannot obtain accurate information or may
misinterpret data.APTA adjusts the FTA
data to include transit operators that do not report to the FTA database
(private, very small, and rural operators). Class I Rail (vehicle-miles) Data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR data are based on 100-percent reporting by Class I railroads to the
Surface Transportation Board (STB) via Schedule 700 of the R1 Annual Report required of Class I railroads.STB defines Class I railroads as having
operating revenues at or above a threshold indexed to a base of $250 million
(1991) and adjusted annually in concert with changes in the Railroad Freight
Rate Index published by the Bureau of Labor Statistics.In 1998, the threshold for Class I railroads
was $259.4 million.Declassification
from Class I status occurs when a railroad falls below the applicable threshold
for three consecutive years.Although
Class I railroads represent only 2 percent of railroads in the country, they
account for over 70 percent of the industry’s mileage. Intercity Train The AAR passenger-miles number is
based on an almost 100-percent count of tickets and, therefore, is considered
accurate. TABLE 1-33. Long-Distance Travel in the
United States by Selected Trip Characteristics: 1995 TABLE 1-34. Long-Distance Travel in the
United States by Selected Traveler Characteristics: 1995 The data presented in these
tables are estimates derived from the 1995 American Travel Survey (ATS)
conducted for the U.S. Department of Transportation, Bureau of Transportation
Statistics. The survey’s estimation procedure inflates unweighted sample
results to independent estimates of the total population of the United States.
Values for missing data are estimated through imputation procedures. Since ATS estimates come from a
sample, they are subject to two possible types of error: nonsampling and
sampling. Sources of nonsampling errors include inability to obtain information
about all sample cases, errors made in data collection and processing, errors
made in estimating values for missing data, and undercoverage. The accuracy of an estimate
depends on both types of error, but the full extent of the nonsampling error is
unknown. Consequently, the user should be particularly careful when
interpreting results based on a relatively small number of cases or on small
differences between estimates. Standard errors for ATS estimates
that indicate the magnitude of sampling error as well as complete documentation
of the source and reliability of the data may be obtained from detailed ATS
reports.Because of methodological
differences, users should use caution when comparing these data with data from
other sources. TABLE 1-35. U.S. Air Carrier Departures,
Enplaned Revenue Passengers, and Enplaned Revenue Tons The Airport Activity Statistics of Certificated
Air Carriers (AAS) is the source of these data.Published annually by the U.S. Department of Transportation,
Bureau of Transportation Statistics, Office of Airline Information (OAI), the
AAS presents traffic statistics for all scheduled and nonscheduled service by
large certificated U.S. air carriers for each airport served within the 50 states,
the District of Columbia, and other U.S. areas designated by the Federal
Aviation Administration.The
publication draws its data from the T-100 and T-3 databases maintained by OAI.These data are based on a 100-percent
reporting of enplanements, departures, and tonnage information by large
certificated U.S. air carriers via BTS Form 41. Prior to 1993, the AAS included
all scheduled and some nonscheduled enplanements for certificated air carriers
but did not include enplanements for air carriers offering charter service
only.Prior to 1990, the freight
category was divided into both freight and express shipments and the mail
category was divided into U.S. mail (priority and nonpriority) and foreign
mail.Beginning in 1990, only aggregate
numbers were reported for freight and mail. Air traffic hubs are designated
as geographical areas based on the percentage of total passengers enplaned in
the area.A hub may have more than one
airport.This definition of hub should
not be confused with the definition used by airlines in describing their
“hub-and-spoke” route structures. TABLE 1-36. Passengers Boarded at the Top
50 U.S. Airports The Airport Activity Statistics of Certificated Air Carriers (AAS), is
the source of these data.Data for 1998
are from the U.S. Department of Transportation (USDOT), Federal Aviation
Administration’s Statistical Handbook of
Aviation.Published by USDOT,
Bureau of Transportation Statistics, Office of Airline Information (OAI), the
AAS presents traffic statistics for all scheduled and nonscheduled service by
large certificated U.S. air carriers for each airport served within the 50
states, the District of Columbia, and other U.S. areas designated by the
Federal Aviation Administration.The
publication draws its data from the T-100 and T-3 databases maintained by
OAI.These data are based on a
100-percent reporting of enplanements, departures, and tonnage information by
large certificated U.S. air carriers via BTS Form 41. Prior to 1993, the AAS included
all scheduled and some nonscheduled enplanements for certificated air carriers
but did not include enplanements for air carriers offering charter service
only.Prior to 1990, the freight
category was divided into both freight and express shipments and the mail
category was divided into U.S. mail (priority and nonpriority) and foreign
mail.Beginning in 1990, only aggregate
numbers were reported for freight and mail. TABLE 1-37. Air Passenger Travel Arrivals
in the United States from Selected Foreign Countries TABLE 1-38. Air Passenger Travel
Departures from the United States to Selected Foreign Countries The
International Trade Administration in the U. S. Department of Commerce
publishes these data, which are based on information collected from 100,000
international visitors. TABLE 1-41. U.S. Ton-Miles of Freight Air Carrier Air Carrier Traffic Statistics, published by the U.S. Department of
Transportation, Bureau of Transportation Statistics (BTS), Office of Airline
Information (OAI), is the source of these data.Large certificated U.S. air carriers report domestic freight
activities to OAI via BTS Form 41.The
information reported in the table represents transportation of freight
(excluding passenger baggage), U.S. and foreign mail, and express mail within
the 50 states, the District of Columbia, Puerto Rico, and the Virgin
Islands.It also covers transborder
traffic to Canada and Mexico by U.S. carriers.
The data does not include information on small certificated air
carriers, which represent less than 5 percent of freight ton-miles. Intercity Truck The data are estimates from Transportation in America, published by
the Eno Transportation Foundation, Inc. (Eno).
Eno’s estimates of intercity truck ton-miles are based on historic data
from the former Interstate Commerce Commission (ICC), estimates from the
American Trucking Association, and other sources.Eno supplements its estimates by using additional information on
vehicle-miles of truck travel published in Highway
Statistics by the Federal Highway Administration.Users should note that truck estimates in the tables do not
include local truck movements. Class I Rail The data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR data are based on 100-percent reporting by Class I railroads to the
Surface Transportation Board (STB).The
data represent all revenue freight activities of the Class I railroads and are
not based on information from the Rail Waybill Sample. The STB defines Class I
railroads as having operating revenues at or above a threshold indexed to a
base of $250 million (1991) and adjusted annually in concert with changes in
the Railroad Freight Rate Index published by the Bureau of Labor
Statistics.In 1998, the threshold for
Class I railroads was $259.4 million.
Declassification from Class I status occurs when a railroad falls below
the applicable threshold for three consecutive years.Although the Class I railroads represented only 2 percent of the
number of railroads in the country, they account for over 90 percent of the
rail industry’s freight revenues. Domestic Water
Transport The data are from Waterborne Commerce of the United States,
published by the U.S. Army Corps of Engineers (USACE).All vessel operators of record report their
domestic waterborne traffic movements to USACE via ENG Forms 3925 and
3925b.Cargo movements are reported
according to points of loading and unloading.
Certain cargo movements are excluded: 1) cargo carried on general
ferries, 2) coal and petroleum products loaded from shore facilities directly
into vessels for fuel use, 3) military cargo moved in U.S. Department of
Defense vessels, and 4) cargo weighing less than 100 tons moved on government
equipment.USACE calculates ton-miles
by multiplying the cargo’s tonnage by the distance between the points of
loading and unloading. Oil Pipeline The data for 1960, 1965, and 1970
are from Transportation in America,
published by the Eno Transportation Foundation, Inc., and the data for 1975 to
1998 are from Shifts in Petroleum
Transportation, by the Association of Oil Pipe Lines (AOPL).Eno’s data are based on information from the
former Interstate Commerce Commission’s Transport
Economics.Common carrier oil
pipelines reported all freight activities to the ICC. AOPL obtains barrel-miles from
the Federal Energy Regulatory Commission (FERC), which requires petroleum
shippers to report annual shipments.
AOPL then coverts barrel-miles to ton-miles using conversion figures in
the American Petroleum Institute’s (API’s) Basic
Petroleum Data Book.Since 16
percent of pipeline shipments are intrastate and not subject to FERC reporting
requirements, AOPL makes adjustments to FERC data. TABLE 1-42. Average Length of Haul:Domestic Freight and Passenger Modes Freight Air Carrier and Truck The Eno Transportation
Foundation, Inc. estimated these figures. Class I Rail The data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR data are based on 100-percent reporting by Class I railroads to the
Surface Transportation Board (STB) via Schedule 700 of the R1 Annual Report
required of Class I railroads.The STB
defined Class I railroads as having operating revenues at or above a threshold
indexed to a base of $250 million (1991) and adjusted annually in concert with
changes in the Railroad Freight Rate Index published by the Bureau of Labor
Statistics.In 1998, the threshold for
Class I railroads was $259.4 million.
Declassification from Class I status occurs when a railroad falls below
the applicable threshold for three consecutive years.Although Class I railroads represent only 2 percent of railroads
in the country, they account for over 70 percent of the industry’s mileage. Water The data are from Waterborne Commerce of the United States,
published by the U.S. Army Corps of Engineers (USACE).All vessel operators of record report their
domestic waterborne traffic movements to USACE via ENG Forms 3925 and
3925b.Cargo movements are reported
according to points of loading and unloading.
Certain cargo movements are excluded: 1) cargo carried on general
ferries, 2) coal and petroleum products loaded from shore facilities directly
into vessels for fuel use, 3) military cargo moved in U.S. Department of
Defense vessels, and 4) cargo weighing less than 100 tons moved on government
equipment.USACE calculates ton-miles
by multiplying the cargo’s tonnage by the distance between points of loading
and unloading. Oil Pipeline The Eno Transportation
Foundation, Inc., provided these figures, which are estimates based on U.S.
Department of Energy and Association of Oil Pipe Lines reports.Figures are derived by dividing estimated
pipeline ton-miles by estimated crude and petroleum products tonnage. Passenger Air Carrier The U.S. Department of Transportation
(USDOT), the Bureau of Transportation Statistics, Office of Airline
Information, reports average trip length in its publication Air Traffic Statistics.These numbers are based on 100-percent
reporting of passengers and trip length by large certificated air carriers via
BTS Form 41.The figures do not include
data for all airlines, such as most scheduled commuter airlines and all
nonscheduled commuter airlines. Bus The Eno Transportation
Foundation, Inc. estimated these figures based on Class I carrier passenger
data and vehicle-miles data from Highway
Statistics, an annually published report of the USDOT, Federal Highway
Administration. Commuter Rail The American Public Transit
Association (APTA) provided these data, which are based on the USDOT, Federal
Transit Administration’s (FTA’s), National Transit Database.Transit data are generally accurate because
the FTA reviews and validates information submitted by individual transit
agencies.Reliability may vary because
some transit agencies cannot obtain accurate information or may misinterpret
data.APTA conservatively adjusts FTA
data to include transit operators that do not report to the database (private,
very small, and rural operators). Intercity/Amtrak The Statistical Appendix to the Amtrak Annual Report is the source of
these data.Amtrak data are based on
100 percent of issued tickets, and thus should be accurate. TABLE 1-43. Top
U.S. Foreign Trade Freight Gateways by Value of Shipments: 1998 The value
of U.S. air, maritime, and land imports and exports are captured from
administrative documents required by the U.S. Departments of Commerce and
Treasury.In 1990, the United States
entered into a Memorandum of Understanding with Canada concerning the exchange
of import data.As a consequence, each
country is using the other’s import data to replace its own export data.U.S. international merchandise trade
statistics, therefore, are no longer derived exclusively from the
administrative records of the Departments of Commerce and Treasury, but from
Revenue Canada.Import value is for
U.S. general imports, customs value basis.
Export value is FAS (free along ship) and represents the value of
exports at the U.S. port of export, including the transaction price and inland
freight, insurance, and other charges.
Trade levels reflect the mode of transportation as a shipment entered or
exited a U.S. Customs port. Truck, rail pipeline, mail, and
miscellaneous modes are included in the total for land modes. Data present
trade activity between the United States, Puerto Rico, and the U.S. Virgin
Islands and Canada and Mexico.These
statistics do not include traffic between Guam, Wake Island, and America Samoa
and Canada and Mexico.These statistics
also exclude imports that are valued at less than $1,250 and for exports that
are valued at less than $2,500. TABLE 1-46. U.S. Waterborne Freight The data
are from Waterborne Commerce of the
United States, published by the U.S. Army Corps of Engineers (USACE). All
vessel operators of record report their domestic waterborne traffic movements
to USACE via ENG Forms 3925 and 3925b.
Cargo movements are reported according to points of loading and
unloading.Certain cargo movements are
excluded: 1) cargo carried on general ferries, 2) coal and petroleum products
loaded from shore facilities directly into vessels for fuel use, 3) military
cargo moved in U.S. Department of Defense vessels, and 4) cargo weighing less
than 100 tons moved on government equipment.
USACE calculates ton-miles by multiplying the cargo’s tonnage by the
distance between points of loading and unloading. Foreign waterborne statistics are
derived from Census Bureau and U.S. Customs data, which excludes traffic
between Guam, Wake Island, and American Samoa and any other foreign country,
and imports and exports used by U.S. Armed Forces abroad.Individual vessel movements with origins and
destinations at U.S. ports, traveling via the Panama Canal are considered
domestic traffic. TABLE 1-47. Tonnage of Top 50 U.S. Water
Ports, Ranked by Total Tons Data on
the weight of U.S. maritime imports and exports are captured from
administrative documents required by the U.S. Departments of Commerce and
Treasury.In 1990, the United States
entered into a Memorandum of Understanding with Canada concerning the exchange
of import data.As a consequence, each
country is using the other’s import data to replace its own export data.The United States’ merchandise trade statistics,
therefore, are no longer derived exclusively from U.S. government administrative
records, but from Revenue Canada.
Maritime weight data are initially processed and edited by the Foreign
Trade Division, U.S. Census Bureau (Census) as part of the overall edits and
quality checks performed on all U.S. international merchandise trade data.After Census processing, the U.S. Army Corps
of Engineers (USACE) and the Maritime Administration (MARAD) perform additional
maritime-specific processing and quality edits on maritime-related data elements,
including the weight of maritime imports and exports.The USACE and MARAD began performing this function in October
1998 after the Foreign Waterborne Trade data program was transferred from the
Census Bureau.Prior to October 1998,
the USACE historically performed additional specialized edits at the port
level, including reassignment of some tonnage data to the actual waterborne
port rather than the reported U.S. Customs port. TABLE 1-43.
Modal Shares of Freight Shipments within the United States by Domestic
Establishments:1993 and 1997 TABLE 1-49.
Value, Tons, and Ton- Miles of Freight Shipments within the United States by
Domestic Establishment, 1997 TABLE 1-52. U.S. Hazardous Materials
Shipments by Mode of Transportation, 1997 TABLE 1-53. U.S. Hazardous Materials
Shipments by Hazard Class, 1997 These data
are collected via the 1997 Commodity Flow Survey (CFS) undertaken through a
partnership between the U.S. Department of Commerce, Census Bureau (Census),
and the U.S. Department of Transportation, Bureau of Transportation Statistics.For the 1997 CFS, Census conducted a sample
of 100,000 domestic establishments randomly selected from a universe of about
800,000 multiestablishment companies in the mining, manufacturing, wholesale
trade, and selected retail industries.
It excluded establishments classified as farms, forestry, fisheries,
governments, construction, transportation, foreign, services, and most retail. Reliability of the Estimates An estimate based on a sample
survey potentially contains two types of errors—sampling and nonsampling.
Sampling errors occur because the estimate is based on a sample, not on the
entire universe.Nonsampling errors can
be attributed to many sources in the collection and processing of the data and
occur in all data, not just those from a sample survey.The accuracy of a survey result is affected
jointly by sampling and nonsampling errors. Sampling Variability Because the estimates are derived
from a sample of the survey population, results are not expected to agree with
those that might be obtained from a 100-percent census using the same
enumeration procedure.However, because
each establishment in the Standard Statistical Establishment List had a known
probability of being selected for sampling, estimating the sampling variability
of the estimates is possible.The
standard error of the estimate is a measure of the variability among the values
of the estimate computed from all possible samples of the same size and
design.Thus, it is a measure of the
precision with which an estimate from a particular sample approximates the
results of a complete enumeration.The
coefficient of variation is the standard error of the estimate divided by the
value being estimated.It is expressed
as a percent.Note that measures of
sampling variability, such as the standard error or coefficient of variation,
are estimated from the sample and are also subject to sampling
variability.Standard errors and
coefficients of variation for CFS data presented in this report are given in
Appendix B of the 1997 Economic Census report, and are available online
www.census.gov/econ/wwwse0700.html. Nonsampling Errors In the CFS, as in other surveys,
nonsampling errors can be attributed to many sources, including 1) nonresponse;
2) response errors; 3) differences in the interpretation of questions; 4)
mistakes in coding or recoding the data; and 5) other errors of collection,
response, coverage, and estimation. A
potentially large source of nonsampling error is due to nonresponse, which is
defined as the inability to obtain all intended measurements or responses from
selected establishments.Nonresponse is
corrected by imputation. TABLE 1-50. Value of U.S. Land Exports to
and Imports from Canada and Mexico by Mode The
Transborder Surface Freight Data (TSFD) is derived from official U.S.
international merchandise import and export data. (For a description of U.S.
merchandise trade statistics, see
www.census.gov/foreign-trade/www/index.html.)
As of December 1995, about 96 percent of the value of all U.S. imports
has been collected electronically by the Automated Broker Interface System.
About 55 percent of the value of all U.S. exports is collected electronically
through the U.S./Canada Data Exchange and the Automated Export Reporting
Program.The balance is collected from
administrative records required by the U.S. Departments of Commerce and
Treasury. The TSFD
incorporates all data, by surface mode, on shipments entering or exiting the
United States from or to Canada or Mexico.
Prior to January 1997, this dataset also included
transshipments—shipments entering or exiting the United States by way of U.S.
Customs ports on the northern or southern borders even when the actual origin
or final destination of the goods was other than Canada or Mexico.(In other U.S. Bureau of the Census trade
statistics, transshipments through Canada and Mexico are credited to the true
country of origin or final destination.)
To make this dataset more comparable to other U.S. Census Bureau trade
statistics, detailed information on transshipments has been removed.The TSFD presents a summary oftransshipments by country, direction of
trade, and mode of transportation.
Shipments that neither originate nor terminate in the United States
(i.e., intransits) are beyond the scope of this dataset because they are not
considered U.S. international trade shipments. In general, the reliability of
U.S. foreign trade statistics is very good.
Users should be aware that trade data fields (e.g., value and commodity
classification) are typically more rigorously reviewed than transportation data
fields (e.g., the mode of transportation and port of entry/exit).Users should also be aware that the use of
foreign trade data to describe physical transportation flows may not be
accurate.For example, this dataset provides
surface transportation information for individual U.S. Customs districts and
ports on the northern and southern borders.
However, because of filing procedures for trade documents, these ports
may or may not record where goods physically cross the border.This is because the information filer may
choose to file trade documents at one port while shipments actually enter or
exit at another port.The TSFD,
however, is the best publicly available approximation for analyzing transborder
transportation flows. Since the dataset was introduced
in April 1993, it has gone through several refinements and improvements.When improbabilities and inconsistencies
were found in the dataset, extensive analytical reviews were conducted and
improvements made.However, accuracy
varies by direction of trade and individual field.For example, import data are generally more accurate than export
data.This is primarily because the
U.S. Customs Bureau uses import documents for enforcement purposes while it
performs no similar function for exports.
For additional information on TSFD, the reader is referred to the U.S.
Department of Transportation, Bureau of Transportation Statistics Internet site
at www.bts.gov/transborder. TABLE 1-51. Crude
Oil and Petroleum Products Transported in the United States by Mode Pipelines The Association of Oil Pipelines
(AOPL) obtains barrel-miles from the Federal Energy Regulatory Commission
(FERC), which requires petroleum shippers to report annual shipments.AOPL then coverts barrel-miles to ton-miles
using conversion figures in the American Petroleum Institute’s (API’s) Basic Petroleum Data Book.Since 16 percent of pipeline shipments are
intrastate and not subject to FERC reporting requirements, AOPL makes
adjustments to FERC data to include intrastate shipments.AOPL also conducts periodic studies to
estimate intrastate shipments. Water Carriers Data are from Waterborne Commerce of the United States,
published by the U.S. Army Corps of Engineers (USACE).All vessel operators of record report
domestic freight and tonnage information to USACE via ENG Forms 3925 and
3925b.Cargo movements are reported
according to points of loading and unloading.
Certain cargo movements are excluded:
1) cargo carried on general ferries, 2) coal and petroleum products
loaded from shore facilities directly into vessels for fuel use, 3) military
cargo moved in U.S. Department of Defense vessels, and 4) cargo weighing less
than 100 tons moved on government equipment.
USACE calculates ton-miles by multiplying the cargo’s tonnage by the
distance between the points of loading and unloading. Motor Carriers AOPL estimates ton-miles by
multiplying tons by the average length of haul.For crude, the tonnage of the prior year is projected by using a
growth rate established by data from the U.S. Department of Energy, Energy
Information Administration’s Petroleum
Supply Annual, vol. 1, table 37.
For products, the same calculation is made but with a growth rate
estimated by the American Trucking Association in Financial and Operating Statistics, Class I and II, Motor Carriers, Summary table VI-B.Average length of haul is determined from
the prior six years of data for ton-miles and tonnage of crude and petroleum
products moved by motor carriers. Railroad AOPL calculates ton-miles by
multiplying tonnage by average length of haul.
Tonnage data for crude and products comes from the Association of
American Railroad’s Freight Commodity
Statistics, U.S. Class I Railroads.
The U.S. Department of Transportation, Federal Railroad Commission
provides the average length of haul for crude and products in its Carload Way Bill Statistics. TABLE 1-54. Worldwide Commercial Space
Launches The U.S.
Department of Transportation, Federal Aviation Administration, Associate Administrator
for Commercial Space Transportation (AST) licenses and regulates U.S.
commercial space launches as authorized by the Commercial Space Launch Act of
1984 and Executive Order 12465.Every
commercial space launch must be approved and monitored by AST.Thus, data reliability is high. TABLE 1-55. Passengers Denied Boarding by
the Largest U.S. Air Carriers TABLE 1-56. Mishandled-Baggage Reports
Filed by Passengers with the Largest U.S. Air Carriers TABLE 1-57. Flight Operations Arriving On
Time for the Largest U.S. Air Carriers These numbers are based on data
filed with the U.S. Department of Transportation on a monthly basis by the
largest U.S. air carriers – those that have at least one percent of total
domestic scheduled-service passenger revenues.
Data cover nonstop scheduled service flights between points within the
United States (including territories).
The largest U.S. carriers account for more than 90 percent of domestic
operating revenues.They include Alaska
Airlines, America West Airlines, American Airlines, Continental Airlines, Delta
Air Lines, Northwest Airlines, Trans World Airlines, Southwest Airlines, United
Airlines, and US Airways.However,
there are other carriers offering domestic scheduled passenger service that are
not required to report.In some cases,
major airlines sell tickets for flights that are actually operated by a smaller
airline that is not subject to the reporting requirement. TABLE 1-58. U.S. Air Carrier Delays
Greater than 15 Minutes by Cause The source
of these data, the U.S. Department of Transportation (USDOT), Federal Aviation
Administration (FAA), counts a flight as delayed if it departed or arrived more
than 15 minutes after its scheduled gate departure and arrival times.FAA calculates delayed departures based on
the difference between the time a pilot requests FAA clearance to taxi and the
time an aircraft’s wheels lift off the runway, minus the airport’s standard
unimpeded taxi-out time.Users should
note that taxi-out time varies by airport due to differences in
configurations.The cause of delay is
also recorded, e.g., weather, terminal volume, closed runways, etc. USDOT guidance defines departure
as the time the aircraft parking brake is released and gate arrival as the time
the brake is set.According to the
USDOT’s Office of the Inspector General (OIG), FAA’s omission of part of a
plane’s ground movement compromises the data’s validity.A recent OIG report noted that the FAA
tracks ground time only after a pilot requests clearance and fails to track a
plane’s time in the ramp area.OIG
found that ramp time comprised 28.7 percent to 40.5 percent of the average
taxi-out time at the three major New York area airports (OIG Audit Report
CR-2000-112), and would not be counted as an FAA delay. Reliability Several data collection changes
complicate comparisons over time.For
example, FAA modified its method for calculating volume-related delays that
resulted in a 17 percent drop in such delays.
Decreases in volume-related delays from 1998 to 1999 totaled less than
one percent.Moreover, prior to 1999,
USDOT did not provide a clear definition of what a departure was.An OIG Audit (CE-1999-054) report noted that
air carriers used four different departure events: 1) rolling of aircraft
wheels; 2) release of parking brake; 3) closure of passenger and/or cargo
doors; and 4) a combination of door closures and release of the parking
break.The same report also noted
errors in the reporting of departure times by the air carriers. Data are now manually entered in
FAA’s Operations Network (OSPNET) database, and reporting errors may arise and
decrease reliability.The FAA monitors
data quality assurance by spot checking the reported delay data and requesting
that discrepancies be reviewed by the responsible facility.According to an OIG Audit (CR-2000-112),
however, mistakes are not reliably corrected and many air traffic controllers
suggested that delays are underreported sometimes by as much as 30 percent. TABLE 1-59. Major U.S. Air Carrier Delays,
Cancellations, and Diversions A second
data source for air-carrier delay is the USDOT, Bureau of Transportation
Statistics, Office of Airline Information (OAI).This information originates from the Airline Service Quality
Performance data. These figures are collected from the largest airlines—those
that have at least one percent or more of total domestic scheduled service
passenger revenues. Delays are categorized by phase of flight (i.e., gate-hold,
taxi-out, airborne, or taxi-in delays).
These data differ from FAA’s OSPNET information due to differences in
definition of delay. While the FAA tracks delays on
the taxiway, runway, and in the air, BTS tracks delays at the departure and
arrival gates.OAI calculates delays as
the difference between scheduled and actual gate departure.If a flight leaves the gate within 15
minutes of its scheduled time, then OAI would record it as departed on-time
even if it sat for several hours on the ramp or runway, in which case the delay
would be accounted for as a late arrival.
TABLE 1-60. Annual Person-Hours of Delay
Per Eligible Drive TABLE 1-61.Roadway Congestion Index TABLE 1-62.Congestion Index and Cost Values The Texas
Transportation Institute’s (TTI) Urban
Roadway Congestion Annual Report provided figures for tables 1-54 through
56.TTI relies on data from the U.S.
Department of Transportation, Federal Highway Administration, Highway
Performance Monitoring System database (HPMS).
TTI utilizes these data as inputs to its congestion estimation
model.Detailed documentation for the
TTI model and estimations can be found at this website
http://mobility.tamu.edu/study/index.stm.
Structure, Assumptions, and Parameters Urban roadway congestion levels
are estimated using a formula measuring traffic density. Average travel volume
per lane on freeways and principal arterial streets are estimated using area
wide estimates of vehicle-miles of travel (vmt) and lane miles of roadway. The
resulting ratios are combined using the amount of travel on each portion of the
system so that the combined index measures conditions on the freeway and
principal arterial street systems. Values greater than one are indicative of
undesirable congestion levels.Readers
seeking the algorithm for the congestion index should examine this websitehttp://mobility.tamu.edu/study/numbers.stm. Annual person-hours of delay
results from the multiplication of daily vehicle-hours of incident and
recurring delay times 250 working days per year times 1.25 persons per
vehicle.Two types of costs are
incurred due to congestion: time delay and fuel consumption.Delay costs are the product of passenger
vehicle hours of delay times $12.00 per hour person time value times 1.25
occupants per vehicle.Fuel costs are
calculated for passenger and commercial vehicles from the multiplication of
peak period congestion speeds, the average fuel economy, fuel costs, and
vehicle-hours of delay. In previous reports, the TTI
methodology assumed that 45 percent of all traffic, regardless of the urban
location, occurred in congested conditions.
TTI indicated that this assumption overestimated travel in congested
periods.Thus, their 1999 estimates now
vary by urban area anywhere from 21 percent to 50 percent of travel that occurs
in congestion.TTI’s model structure
applies to two types of roads: freeways and principal arterial streets.The model derives estimates of vehicle
traffic per lane and traffic speed for an entire urban area.Based on variation in these amounts, travel
is then classified under 5 categories: uncongested, moderately congested,
heavily congested, severely congested, and extremely congested (a new category
in 1999).The threshold between
uncongested and congested was changed in 1999.
Previous editions classified congested travel when areawide traffic
levels reached 13,000 vehicles per lane per day on highways and 5,000 vehicles
per lane per day on principal arterial streets.These thresholds were raised in the latest report to 14,000 and
5,500 vehicles per lane per day respectively.
Comparisons across time will be questionable due to these changes.For instance, TTI applied the new
methodology to 1996 data that resulted in lower congestion levels.Readers should refer to the TTI Internet
site for more detailed algorithms and estimation procedures at http://mobility.tamu.edu/estimating_mobility. TTI reviews and adjusts the data
used in their models.State and local
officials also review the TTI data and estimations.Some of the limitations acknowledged in the TTI report include
the macroscopic character of the index. Thus, it does not account for local
variations in travel patterns that may affect travel times.The index also does not account for local
improvements, such as ramp metering or travel speed advantages obtained with
transit or carpool lanes. TABLE 1-63.Amtrak On-Time Performance Trends and Hours of Delay by Cause Amtrak
determines on-time performance through its computer system maintained at the
National Operations Center (NOPS) in Wilmington, Delaware.If a train is delayed, a call is made to the
NOPS for recordkeeping.These data can
be supplemented with computer entries made for locomotive or car malfunctions
that cause delays.These data should be
considered reliable. Chapter 2Safety AIR DATA TABLE 2-1. Transportation
Fatalities by Mode TABLE 2-2. Injured Persons by
Transportation Mode TABLE 2-3. Transportation Accidents by
Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-7. Transportation-Related
Occupational Fatalities TABLE 2-9. U.S. Air Carrier Safety Data TABLE 2-10. U.S. Commuter Air Carrier
Safety Data TABLE 2-11. U.S. Air Carrier Fatal
Accidents by First Phase of Operation TABLE 2-12. U.S. Commuter Air Carrier
Fatal Accidents by First Phase of Operation TABLE 2-13. U.S. On-Demand Air Taxi Safety
Data TABLE 2-14. U.S. General Aviation Safety
Data National Transportation Safety
Board investigators perform onsite and offsite investigations of all accidents
involving U.S. registered air carriers operating under 14 CFR 121, 14 CFR 135,
and general aviation U.S. Department of Transportation (USDOT), Federal
Aviation Administration (FAA) regulations.
The investigators compile information on fatalities and injuries for all
accidents.The counts for fatalities
and serious injuries are expected to be extremely accurate.(See glossary for serious injury
definition.) Exposure data (aircraft-miles, aircraft-hours, and
aircraft-departures) are obtained from the FAA, which in turn gets some of its
exposure data from the USDOT, Bureau of Transportation Statistics, Office of
Airline Information (OAI) and other exposure data from its own General Aviation
and Air Taxi Activity and Avionics (GAATAA) Survey.The OAI data represent 100 percent reporting by airlines.Tables that include air carriers (14 CFR
121, scheduled and nonscheduled service) and commuter air carriers (14 CFR 135,
scheduled service only) use OAI exposure data.
Tables that include on-demand air taxi (14 CFR 135, nonscheduled service)
and general aviation use GAATAA Survey results.For information about the GAATA Survey, please refer to the
chapter 1 data accuracy statement for table 1-9. The coefficients of variation for aircraft-hours vary by
year, but are usually in the 9 to 10 percent range for on-demand air taxi and are
approximately 2 percent for general aviation. TABLE 2-15. Number of Pilot-Reported Near
Midair Collisions by Degree of Hazard Near Midair Collision reports are
provided voluntarily by air carriers, general aviation companies, and the
military, and this information is added to the Near Midair Collisions System
database.Factors that may influence
whether or not a near midair collision is reported include the pilot’s or other
crew member’s perception of whether a reportable near midair collision occurred,
which in turn can depend on factors such as visibility conditions; the
reporter’s flying experience; or the size of the aircraft involved.A reportable incident is one in which an
aircraft is within 500 feet of another aircraft and a possibility of collision
existed. TABLE 2-16. Airline Passenger Screening
Results by Type of Weapons Detected, Persons Arrested, and Bomb Threats
Received Federal Aviation Regulations
(FARs) mandate that passenger screening be performed by each air carrier
required to implement an approved security program.The USDOT, Federal Aviation Administration, monitors the
records of passenger screening in accordance with FAR, and oversees compliance
with the carriers’ security programs through, for example, scheduled and unscheduled
inspections.FAR requires the reporting
of information on bomb threats. HIGHWAY DATA TABLE 2-1.Transportation Fatalities by Mode TABLE 2-2. Transportation Injuries by Mode TABLE 2-3. Transportation Accidents by Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-5. Highway-Rail Grade-Crossing Safety
Data and Property Damage TABLE 2-7. Transportation-Related Occupational
Fatalities TABLE 2-17. Motor Vehicle Safety Data TABLE 2-18. Motor Vehicle Fatalities,
Vehicle-Miles, and Associated Rates by Highway Functional System TABLE 2-19. Occupant Fatalities by Vehicle
Type and Nonoccupant Fatalities TABLE 2-21. Passenger Car Occupant Safety Data TABLE 2-22. Motorcycle Ride Safety Data TABLE 2-23. Truck Occupant Safety Data TABLE 2-24. Bus
Occupant Safety Data TABLE 2-25.
Fatalities by Highest Blood Alcohol Concentration in Highway Crashes TABLE 2-27. Motor
Vehicle Fatal Crashes by Day of Week, Time of Day, and Weather and Light
Conditions TABLE 2-28. Motor
Vehicle Fatal Crashes by Posted Speed Limit
TABLE 2-20.
Occupant and Nonmotorist Fatalities in Crashes by Number of Vehicles and
Alcohol Involvement Fatalities Highway fatality data come from the Fatality Analysis
Reporting System (FARS), which is compiled by trained FARS analysts at USDOT,
National Highway Traffic Safety Administration (NHTSA) regional offices. Data
are gathered from a census of police accident reports (PARs), state vehicle
registration files, state drivers licensing files, state highway department
data, vital statistics, death certificates, coroner/medical examiner reports,
hospital medical reports, and emergency medical service reports.A separate form is completed for each fatal
crash.Blood alcohol concentration
(BAC) is estimated when not known.
Statistical procedures used for unknown data in FARS can be found in the
NHTSA report A Method for Estimating
Posterior BAC Distributions for Persons Involved in Fatal Traffic Accidents,
DOT HS 807 094 (Washington, DC: July 1986).
Data are collected from relevant state agencies and
electronically submitted for inclusion in the FARs database on a continuous
basis.Cross-verification of PARs with
death certificates ensures that undercounting is rare.Moreover, when data are entered, they are
checked automatically for acceptable range values and consistency, enabling
quick corrections when necessary.
Several programs continually monitor the data for completeness and
accuracy.Periodically, sample cases
are analyzed for accuracy and consistency. Note that the FARS data do not include motor vehicle
fatalities on nonpublic roads.However,
previous NHTSA analysis found that these fatalities account for 2 percent or
fewer of the total motor vehicle fatalities per year. (See glossary for highway
fatality definition.) Injuries and Crashes NHTSA’s General Estimates System (GES) data are a
nationally representative sample of police-reported crashes that contributed to
an injury or fatality or resulted in property damage, and involved at least one
motor vehicle traveling on a trafficway.
Trained GES data collectors randomly sample PARs and forward copies to a
central contractor for coding into a standard GES system format.Documents such as police diagrams or
supporting text provided by the officers may be further reviewed to complete a
data entry. NHTSA suggests that about half of motor vehicle crashes
in the United States are not reported to police and that the majority of these
unreported crashes involve minor property damage and no significant personal
injury.A NHTSA study ofinjuries from motor vehicle crashes
estimated the total count of nonfatal injuries at over 5 million compared with
the GES’s estimate of 3.2 million in 1998.
(See glossary for highway crash and injury definitions.) (See U.S. Department of Transportation, National Highway
Traffic Safety Administration, Traffic
Safety Facts, 1998, DOT HS 808 983 (Washington, DC: October 1999),
appendices B and C for further information on the GES, including a table of
standard errors applicable to GES data.) TABLE 2-29. Safety Belt and Motorcycle
Helmet Use The National Occupant Protection Use Survey (NOPUS),
conducted in 1994, 1996, and 1998 by the U.S. Department of Transportation,
National Highway Traffic Safety Administration is the source for these data. In 1994 and 1996, NOPUS consisted of three separate
studies: 1) the Moving Traffic Study, which provides information on overall
shoulder belt use, 2) the Controlled Intersection Study, which provides more
detailed information about shoulder belt use by type of vehicle,
characteristics of the belt users and child restraint use, and 3) the Shopping
Center Study, which provides information on rear-seat belt use and shoulder
belt misuse.In 1998, the Shopping
Center Study was dropped from the survey.
The Controlled Intersection Study includes the collection of license
plate information to link seat belt use to vehicle type.As the results of the Controlled
Intersection Study for 1998 were not available prior to publication, only the
Moving Traffic Study data were used in this table. In 1998, the NOPUS separated pickups from the light truck
category, thereby creating three categories of passenger vehicles:passenger cars, pickup trucks, and other
passenger vehicles.Other passenger
vehicles include vans, minivans, and sport utility vehicles.In this table, 1998 data for pickup trucks
and other vehicles are combined into the light truck category to allow
comparison to data from the earlier surveys. In 1994, operators and riders wearing any type of helmet
were counted as helmeted.In 1996 and
1998, motorcycle helmets that meet USDOT standards are counted as valid
protection, whereas those that do not meet USDOT standards were treated as if
the operator/rider were not wearing a helmet. Data collection from the moving traffic study was
conducted at over 3,800 sites across the country.Shoulder belt use was obtained for drivers and right-front
passengers only.Three observers (two observers
in 1994 and 1996) were stationed for 30 minutes at interstate/highway exit
ramps, controlled (intersections with stop signs or traffic signals), and
uncontrolled intersections.Every day
of the week and all daylight hours (8 a.m. to 6 p.m.) were covered in each
survey.Commercial and emergency
vehicles were excluded. NOPUS was designed as a multistage probability sample to
ensure that the results would represent occupant protection use in the
country.In the first stage, counties
were grouped by region (northeast, midwest, south, west), level of urbanization
(metropolitan or not), and level of belt use (high, medium, or low).Fifty counties or groups of counties were
selected based on the vehicle miles of travel in those locations.In the next stage, roadways were selected
from two categories: major roads and local roads. Finally, approximately 4,000 intersections or exit ramps were
chosen on these roadways. Of the originally selected sites, some were found to
be ineligible during mapping and data collection, and at some sites no vehicles
were observed.In 1998, a total of
199,412 passenger cars, 135,505 light trucks (of which 76,004 were other
vehicles and 59,501 were pickup trucks), and 1,444 motorcycles were observed. Each reported estimate has been statistically weighted
according to the sample design. Two kinds of error can be attributed to all
survey research: sampling and nonsampling. A measure, called the standard
error, is used to indicate the magnitude of sampling error.The source information provides two standard
errors along with each estimate.
Nonsampling errors could include problems such as vehicles not counted,
incorrect determination of restraint use, and data entry mistakes, among
others. TABLE 2-30. Estimated Number of Lives
Saved by Use of Restraints The U.S. Department of
Transportation, National Highway Traffic Safety Administration (NHTSA) uses
data obtained from the Fatality Analysis Reporting System to calculate the
number of lives saved by the use of restraints.The methodology used is outlined in a NHTSA report, Research Note, Estimating Lives Saved by
Restraint Use in Potentially Fatal Crashes (Washington, DC: June
1995).The general approach is to
adjust the observed number of fatalities by a determined effectiveness rate for
each type of restraint.This equates to
subtracting the actual fatalities from the potential fatalities to determine
the number of lives saved.This method
is more accurate than earlier estimation methods since all calculations are
derived from NHTSA’s count of fatalities in which restraints were used.Reported restraint use is believed to be
accurate for fatalities. The key to NHTSA’s calculations is the effectiveness
estimate for preventing fatalities for each type of restraint.With the exception of an adjustment in the
effectiveness estimate for front outboard air bag-only restraint use in
passenger cars (NHTSA, Fourth Report to
Congress, Effectiveness of Occupant Protection Systems and Their Use,
Washington, DC, May 1999), a list of effectiveness estimates can be found in a
NHTSA report, Estimating Alcohol Involvement
in Fatal Crashes in Light of Increases in Restraint Use, published in March
1998.This report also includes
additional references describing the determination of these effectiveness
estimates. TRANSIT DATA TABLE 2-1.
Transportation Fatalities by Mode TABLE 2-2. Transportation Injuries by Mode TABLE 2-3. Transportation Accidents by
Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-31. Transit Safety and Property
Damage Data TABLE 2-32. Transit Safety Data by Mode
for All Reported Accidents TABLE 2-33.
Transit Safety Data by Mode for All Reported Incidents TABLE 2-34.
Reports of Violent Crime, Property Crime, and Arrests by Transit Mode The data for this report are obtained from the U.S.
Department of Transportation, Federal Transit Administration’s (FTA’s) National
Transit Database (NTD) Reporting System.
Transit agencies are required to file an NTD report at regular intervals
if they are recipients ofUrbanized
Area Formula Funds. In 1998, 575 agencies reported to the NTD.Of that total, 60 transit agencies received
exemptions from detailed reporting because they operated 9 or fewer vehicles,
and 6 were deleted because their data were incomplete.Thus, 509 individual reporters were included
in the NTD, accounting for 90 to 95 percent of passenger-miles traveled on
transit.Of the transit agencies
reporting, 56.2 percent contract for some or all of their transportation from
private or public companies or organizations.
Transit operators report fatalities, injuries, accidents,
incidents, and property damage in excess of $1,000. Electronic reporting has
recently been implemented for the NTD.
Certification from a company’s Chief Executive Officer must accompany
all NTD reports along with an independent auditor’s statement. Upon receipt, an NTD report is reviewed and
outstanding items noted in writing to the agency that submitted the form.(See glossary for transit fatality, injury,
and accident definitions.) Four major categories of transit safety are collected: 1)
collisions, 2) derailments/buses going off the road, 3) personal casualties,
and 4) fires.These major categories
are divided into subcategories.The
collisions category comprises collisions with vehicles, objects, and people
(except suicides).Of the four major
categories, only the first two are included in the definition of transit
accidents adopted in this report (see glossary). Understanding this definition of accident is relevant to
understanding how double counting is removed in the grand total of U.S. transportation
fatalities and injuries.(See cross
modal comments in box 2-1.) Transit data submitted to the NTD are generally
considered accurate because the FTA reviews and validates information submitted
by individual transit agencies.
However, reliability may vary because some transit agencies cannot
obtain accurate information or misinterpret data. Security FTA collects security data from transit agencies serving
urbanized areas of over 200,000 in population, using Form 405, and manages it
in the National Transit Database (NTD).
The reporting of security data follows the FBI Uniform Crime Reporting Handbook (Washington, DC:1984) and is divided into two
categories:1) Reported Offenses,
including violent and property crime, and 2) Arrests, consisting of less
serious crimes.The figures for violent
and property crime are based on records of calls for service, complaints,
and/or investigations.They do not
reflect the findings of a court, coroner, jury, or decision of a
prosecutor.Security data were first
reported in 1995 and were not compiled for earlier years. In 1998, the number of agencies reporting to this
database was 575.Of that, 60 transit
agencies received exemptions from detailed reporting because they operated nine
or fewer vehicles, and six were deleted because their data were
incomplete.Thus, 509 individual
reporters are included in the full database in 1998.Of the transit agencies reporting, 56.2 percent contract for some
or all of their transportation from private or public companies or
organizations.
Box 2-1. Cross-Modal Comparisons Caution must be exercised in comparingfatalities (and injuries) across modes
because different definitions for reportable events are used among the
modes.In particular, rail and transit
facilities and injuries include deaths and injuries that are not, strictly
speaking, caused by transportation accidents, but are caused by such events as
a fall on a transit station escalator; or for railroad employees, a fire in a
workshed.Similar fatalities for the
air and highway modes (death at airports not caused by moving aircraft, or
fatalities from accidents in automobile repair shops) are not counted towards
the totals for these modes. Total fatalities (injuries) in the tables are less than the sum
of the modal totals because some deaths (injuries) are reported and counted in
more than one mode. To avoid double
counting, adjustments have been made to fatality totals (see table 2-4).
RAILROAD DATA TABLE 2-1.
Transportation Fatalities by Mode TABLE 2-2. Transportation Injuries by Mode TABLE 2-3. Transportation Accidents by
Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-5. Highway-Rail Grade-Crossing
Safety Data and Property Damage TABLE 2-7. Transportation-Related
Occupational Fatalities TABLE 2-35. Railroad and Grade-Crossing
Fatalities by Victim Class TABLE 2-36. Railroad and Grade-Crossing
Injured Persons by Victim Class TABLE 2-37. Train Fatalities, Injuries,
and Accidents by Type of Accident TABLE 2-38. Railroad Passenger Safety Data TABLE 2-39. Railroad System Safety and
Property Damage Data TABLE 2-40. Fatalities and Injuries of
On-Duty Railroad Employees Railroads are required to file a report for each train
accident resulting in property damage in excess of $6,600, each highway-rail
accident, and each incident involving the operation of a railroad resulting in
a fatality or a reportable injury.(See
glossary for reportable injury, train accident and incident, and nontrain
incident definitions.) Reporting requirements, which are fixed in law, are very
broad and encompass events not strictly related to transportation.For example, if a passenger falls on a
staircase and breaks a leg in the station while going to a train, the injury
would be reported and appear in the data as a rail injury. WATERBORNE TRANSPORTATION
DATA TABLE 2-1.
Transportation Fatalities by Mode TABLE 2-2. Transportation Injuries by Mode TABLE 2-3. Transportation Accidents by
Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-7. Transportation-Related
Occupational Fatalities TABLE 2-41. Waterborne Transportation
Safety Data and Property Damage Related to Vessel Casualties TABLE 2-42. Waterborne Transportation
Safety Data Not Related to Vessel Casualties U.S. waterborne fatality and injury data are based on
reports required by CFR Part 4.05-10. This code requires that the owner, agent,
master, operator, or person in charge file a written report of any marine
casualty or accident within five days of the accident.Reports must be delivered to Investigative
Officers (IOs) at a U.S. Coast Guard Marine Safety Office or Marine Inspection
Office at the U.S. Department of Transportation, who use these reports as
guides to investigate the marine casualty or accident.The IO ensures that all the entries on the
forms are filled out and errors are corrected.
Regulations require IO notification of marine casualties for certain
circumstances, including loss of life; injuries that require medical treatment
beyond first aid; and, for individuals engaged or employed onboard a vessel in
commercial service, injuries that render a person unfit to perform routine
duties. Incidents requiring an investigation include death,
injury resulting in substantial impairment, and other incidents determined
important to promoting the safety of life or property or to protect the marine
environment.These incidents are
investigated in accordance with procedures set forth in the regulations.Furthermore, the Federal Water Pollution
Control Actmandates that certain
incidents be reported to the U.S. Coast Guard.
The reports are entered into the Marine Safety Information System, which
is later analyzed and transferred to the Marine Safety Management System
maintained in Washington, DC. RECREATIONAL BOATING DATA TABLE 2-1.
Transportation Fatalities by Mode TABLE 2-2. Transportation Injuries by Mode TABLE 2-3. Transportation Accidents by
Mode TABLE 2-4. Distribution of Transportation
Fatalities by Mode TABLE 2-43. Recreational Boating Safety,
Alcohol Involvement, and Property Damage Data TABLE 2-44. Personal Watercraft Safety
Data TABLE 2-45. U.S. Coast Guard Search and
Rescue Statistics, Fiscal Years Operators of boats involved in an accident resulting in
1) a fatality, 2) an injury requiring medical treatment beyond first aid, 3)
damage to the vessel or other property greater than $500 or complete loss of
vessel, or 4) the disappearance of a person from the vessel under circumstances
indicating death or injury are required to file a report with the U.S. Coast
Guard.If a person dies within 24 hours
of the occurrence, requires medical treatment beyond first aid, or disappears
from the vessel, reports must be made within 48 hours of the occurrence.In cases involving only damage to the vessel
and/or property, reports are to be submitted within 10 days of the
occurrence.Although there is no
quantitative estimate of the response rate, there may be considerable underreporting,
especially of nonfatal accidents, because of the difficulty of enforcing the
requirement and because boat operators may not always be aware of the law. NATURAL GAS AND LIQUID PIPELINE
DATA TABLE 2-1.
Transportation Fatalities by Mode TABLE 2-2. Transportation
Injuries by Mode TABLE 2-3.
Transportation Accidents by Mode TABLE 2-4.
Distribution of Transportation Fatalities by Mode TABLE 2-46.Hazardous Liquid and Natural Gas Pipeline
Safety and Property Damage Data U.S. fatality and injury data for natural gas pipelines
are based on reports filed with the U.S. Department of Transportation (USDOT),
Office of Pipeline Safety (OPS).
Accidents must be reported as soon as possible, but no later than 30
days after discovery.Reports are sent
to the Information Systems Manager at the OPS.
Possible sources of error include a release going undetected; even if
subsequently detected and reported, it may not be possible to accurately
reconstruct the accident.Property
damage figures are estimates.(See glossary
for gas and liquid pipeline fatality data and injury definitions.) TABLE 2-6. Hazardous Materials Safety Data
and Property Damage Data Incidents resulting in certain
unintentional releases of hazardous materials must be reported under 49 CFR
171.16.Each carrier must submit a
report to the U.S. Department of Transportation, Research and Special Programs
Administration (RSPA) within 30 days of the incident, including information on
the mode of transportation involved, results of the incident, and a narrative
description of the accident.These
reports are made available on the incident database within 60 days of receipt. Fatalities and injuries are counted only if they are
directly due to a hazardous material.
For example, a truck operator killed by impact forces during a motor
vehicle crash would not be counted as a hazardous-material fatality.RSPA verifies all reported fatalities and
injuries by telephone with the carrier submitting the report. Possible sources of error include a release going undetected;
even if subsequently detected and reported, it may not be possible to
accurately reconstruct the accident.
Although RSPA acknowledges that there is some level of underreporting,
it believes that the underreporting is limited to small, nonserious
incidents.As incident severity
increases, it is more likely that the incident will come to RSPA’s attention
and will ultimately be reported.
Additionally, the reporting requirements were extended to intrastate
highway carriers on October 1, 1998, and the response rate from this new group
is expected to increase over time.
Property damage figures are estimates determined by the carrier prior to
the 30-day reporting deadline and are generally not subsequently updated.Property damage figures, therefore, may
underestimate actual damages. Chapter 3Transportation and the Economy TABLE 3-1a & 3-1b. U.S. Gross Domestic
Product Attributed to For-Hire Transportation Services (Current and chained
1996 dollars) TABLE 3-2a & 3-2b. U.S. Gross Domestic
Product Attributed to Transportation-Related Final Demand (Current and chained
1996 dollars) TABLE 3-3a & 3.3b. U.S. Gross Domestic
Demand Attributed to Transportation-Related Final Demand (Current and chained
1996 dollars) TABLE 3-4a & 3-4b. Contributions to
Gross Domestic Product:Selected
Industries (Current and chained 1996 dollars) TABLE 3-5. Gross Domestic Product by Major
Social Function Tables 3-1 through 3-5 present
data on transportation's contributions to the economy through consumption (or
the money spent on transportation activity).
The Survey of Current Business
(SCB) published by the U.S. Department of Commerce, Bureau of Economic Analysis
(BEA).The SCB is a monthly journal
that contains estimates of U.S. economic activity, including industry contributions
to the Gross Domestic Product (GDP).
GDP is defined as the net value of the output of goods and services
produced by labor and property located in the United States.BEA constructs two complementary measures of
GDP-one based on income and the other on expenditures (product).Together, they represent the National Income
and Product Accounts (NIPA), our nation's principle framework for macroeconomic
estimates.The product side results
from the addition of labor, capital, and taxes for producing output.Consumption derives from household,
business, and government expenditures and net foreign purchases. Table 3-3 presents
transportation's economic impact in a different form, Gross Domestic Demand
(GDD).Also derived from the national
accounts, GDD is the sum of personal consumption, gross private domestic
investment, and government purchases.
GDD includes imports, but excludes exports, thus counting only what is
consumed, purchased, or invested in the United States. GDP Methodology The 1960 through 1985 data in
table 3-1 are from the November 1993 issue of the SCB.The 1990 through 1991 data and 1992 through
1996 data are from an August 1996 and November 1997 SCB issue respectively.The October 1999 issue introduced a revised
methodology for GDP estimates (Yuskavage 1996).This section describes BEA's methodology for estimating
transportation's share of GDP. BEA's current-dollar estimates of
GDP by industry rely on several sources, including the Bureau of Labor
Statistics (BLS), the Health Care Financing Administration, and the Internal
Revenue Service (IRS).Some of the
tables in this chapter report chained-dollar figures.BEA derived chained dollars by using the Fisher Ideal Quantity
Index to calculate changes between adjacent years (Parker and Triplett 1996;
Landerfeld and Parker 1997).Annual
changes are then chained to form a time series that incorporates the effects of
relative price and output composition changes.
Please refer to page 142 of the August 1996 issue of the Survey of Current Business for the
mathematical formulas (Yuskavage 1996).
This method produced separate estimates of gross output and intermediate
inputs for a sector's GDP calculation.
BEA updated the reference year for the chained-dollar estimates from
1992 to 1996. Transportation GDP in chained
dollars was estimated using the double-deflation method, which relies on a
chain-type quantity index formula, and requires gross output and intermediate
input information.Principal source
data for the transportation categories include:1) operating revenues of air carriers and Federal Express from
the U.S. Department of Transportation and public sources (air); 2) operating
revenues for Class I motor carriers from historical records of the Interstate
Commerce Commission and Census Bureau annual surveys (trucking and
warehousing); 3) BEA personal consumption expenditures (PCE), BLS, and trade
sources (local and interurban passenger transit); 4) operating revenues for
Class I railroads and Amtrak (rail); and 5) other trade sources
(pipelines).Data sources for water
were not provided (Yuskavage, 1996). Table 3-1 reported current dollar
estimates from various SCB issues.BEA
derived the 1991 data and subsequent years in four steps: 1.
BEA's benchmark input-output (I-O) tables produced input compositions for 1977,
1982, and 1987. 2.BEA estimated 1978 through 1981 and 1983
through 1986 input compositions by interpolating the 1977, 1982, and 1987
figures. 3.BEA estimates the 1977 through 1987 imported
and domestically imported shares of each detailed input. 4.BEA estimates the 1988 through 1994 input
compositions based on the 1987 figures and the Economic Censuses of 1992. For intermediate input
estimations, BEA deflates each of the current-dollar inputs.(BEA deflates import and domestic production
separately.)For deflation, quantities
are approximated by real values (expressed at present with 1996 as the base
period) that are calculated by dividing the current-dollar value of the
component by its price index.BEA
develops estimates for import prices with data from a variety of sources, but
primarily from the BLS import price series Reliability and Accuracy BEA views GDP as a reliable
measure of output because of the source data underlying the estimates.The following reliability comments are based
on the Valliant (1993) SCB article and Ritter (2000).GDP data originate from three types of sources.The foundational data come first from the
economic censuses conducted every five years.
These approach complete enumerations of sectoral activity in state and
local governments, manufacturing, services, retail trade, wholesale trade,
construction, transportation, communications and utilities, mining, finance,
insurance, and real estate.Annual
estimates form the second tier of GDP data and emanate form sources such as IRS
tax returns and smaller surveys of establishments.The Annual Retail Trade Survey, for instance, forms one of the
major components of the annual estimates.
The U.S. Census Bureau collects sales and end-of-year inventory data
from about 22,000 retail firms totaling $2 trillion of the $8.8 trillion GDP
amount.While considered reliable by
many economists, sampling variability may introduce errors into these annual
estimates.Moreover, the Census Bureau
imputes (substitutes estimates for missing or clearly incorrect data) about 11
percent of reported national annual retail sales because of accounting
inconsistencies or raw survey data errors.
The third component of the GDP flows from quarterly estimates. In the October 1993 SCB, Valliant
described the reliability and accuracy of the quarterly estimates of GDP,
providing insights into the pre-1985 data in terms of dispersion and bias.BEA followed a schedule that produced three
successive "current" estimates; advanced, preliminary, and final. BEA
analysts developed a dispersion and bias measure based on the difference
between these three estimates. Dispersion is the average of the
absolute values of the revisions, or, the difference between P, representing the percentage change in
the current estimates, and L
representing the percentage change in the latest available estimates, divided
by n, representing the number of
quarterly changes.Bias is the average
of the revisions.According to the October
1993 SCB, dispersion averaged 1.6 percent from 1958 to 63 and dropped to 1.1
percent for 1968 to 1972. BEA stated that these declines in dispersion
correspond with more accurate initial and final estimates subsequent to the
late 1950s.For years after 1973 until
1991, the BEA concluded that more accurate source data for preliminary and
final estimates did not improve reliability by much.BEA also determined that bias was not large enough from 1978 to
1991 to be significant under normality assumptions at the five- percent
confidence level.Overall, for the
period beginning in 1978 and covering the 1985 data from table 3-1, the BEA
concluded there was no evidence of reliability increases.BEA also questioned its own estimating
procedures and, in particular, the use of disparate sources of data, which may
explain why reliability levels have not increased. The NIPA framework also undergoes
major updates referred to as comprehensive, or benchmark revisions.Eleven of these have been completed
including one in 1996 and most recently on October 28, 1999 that provided the
data for tables 3-1 through 3-5.The
major change encompassed a definitional change reflecting our evolving economic
system.Software became a business
investment rather than just a "purchased input," or the equivalent of
raw material.Unless the company
increased the price of its product to cover software purchases, no impact
registered in the GDP.With this
benchmark revision, the Census Bureau increased the 1996 estimate by $115 billion,
or 1.5 percent--the amount of software investments made in that year.Another change involved the Census Bureau's
interpretation of the value of "unpriced" banking services such as
ATM (automatic teller machine) contributions to an establishment's productivity.Previously, banking service productivity
relied only on an index constructed from labor input.Economists argued that this ignored productivity gains from
technological improvements such as ATMs and electronic banking.The BLS developed a productivity based
instead of bank transactions, and this was used in the 1999 revision.For more detail, readers should refer to
Moulton and Seskin (1999). Sources of Error for GDP Estimates The GDP estimates can contain
several kinds of error. One source of error arises from estimates based on
preliminary or incomplete tabulations of source data or BEA judgment in the
absence of data.Errors may also arise
because of sampling errors and biases in monthly, quarterly, annual, or
periodic tabulations.Another source of
potential error may arise when data are seasonally adjusted.Readers should refer to the October 1993 SCB
issue for more detail (Young 1993). NIPA and Transportation-Related Final Demand For table 3-2,
transportation-related final demand (TRFD) is from NIPA reported in the
SCB.It represents the sum of all
consumer and government expenditures for transportation purposes, plus the
value of goods and services purchased by business as investment for transportation
purposes.Since TRFD includes only
expenditures on the final products of the economy, it is comparable to GDP and
provides a measure of transportation's importance from a consumption
perspective. NIPA tables report the
composition of production and the distribution of incomes earned in
production.The totals of these produce
a GDP estimate that should theoretically be equal, but there is always a
difference referred to as the "statistical discrepancy."NIPA is based on four subaccounts of
national economic activity.These
include 1) the personal income and outlay account, 2) the gross savings and
investment account, 3) the government receipts and expenditures account, and 4)
the foreign transactions account. Personal Consumption Expenditures
(PCE) for transportation include 1) road motor vehicles, such as new and used
automobiles, and motorcycles; 2) motor vehicle parts, such as tires, tubes,
accessories; 3) motor fuels and lubricants; and 3) transportation services,
such as repair, greasing, washing, parking, storage, rental, leasing, tolls,
insurance, and purchased local and intercity transportation services.Motor vehicles used primarily for
recreation, boats, noncommercial trailers, and aircraft are excluded. Gross private domestic fixed
investment in transportation includes private purchases of transportation
structures and equipment.Transportation
structures include railroads and petroleum pipelines.Transportation equipment consists of automobiles, trucks, buses,
truck trailers, aircraft, ships and boats, and railroad equipment. Goods and services that are
counted as part of transportation-related exports include 1) civilian aircraft,
engines, and parts; 2) road motor vehicles, engines, and parts; 3) passenger
fares, including receipts of U.S. air and ocean/cruise carriers for
transporting non-U.S. residents between the United States and foreign countries
or between two foreign points; and 4) other transportation.The total for road motor vehicles, engines
and parts excludes boats, aircraft, and noncommercial trailers.Other transportation includes 1) the freight
revenues of U.S.-operated ocean, air, and other carriers (e.g., rail, pipeline,
and Great Lakes shipping) for international transport of U.S. exports and for
transporting foreign freight between foreign points; 2) port expenditure
receipts (representing payments for goods and services purchased in the United
States by foreign-operated carriers); and 3) receipts of U.S. owners from
foreign operators for the charter of vessels and rental of freight cars and containers. Goods and services that are
counted as part of transportation-related imports include 1) civilian aircraft,
engines, and parts;2) road motor
vehicles, engines, and parts; 3) passenger fares, including payments to foreign
air and ocean/cruise carriers for the transportation ofU.S. residents between the United States and
foreign countries or between two foreign points; and 4) other
transportation.The total for road motor
vehicle, engines and parts excludes boats, aircraft, and non-commercial trailers.Other transportation includes 1) freight
revenues offoreign-operated ocean,
air, and other carriers (e.g., rail, pipeline, and Great Lakes shipping) for
international transport of U.S. imports and for the transportation of foreign
freight between foreign points; 2) port expenditure receipts (representing
payments for goods and services purchased in foreign countries by U.S.-operated
carriers); and 3) payments to foreign owners from U.S. operators for the
charter of vessels and rental of freight cars and containers. Transportation-related government
purchases include federal, state, and local purchases of transportation
services, and government expenditures on transportation-related structures and
equipment.Federal, state, and local
purchases represent the sum of consumption expenditures and gross
inventory.Defense-related purchases
include expenditures on the transportation of materials (care and movement of
goods by water, rail, truck, and air); the rental of trucks and other
transportation equipment and warehousing fees; and travel of persons (care and
movement of Department of Defense military civilian employees), including
tickets for all modes of travel, per diem, taxi fares, automobile rental, and
mileage allowances for privately owned vehicles. Further References This data source and accuracy
statement is based on several papers that have appeared in the SCB.Data users who desire more methodological
detail can refer to the list of references at the end of this chapter. TABLE 3-6. National Transportation and
Economic Trends The Statistical Abstract of the United States published by the U.S.
Department of Commerce, Census Bureau, is the source of the population
data.The Current Population Reports are the source of the Abstract's data that are collected
through the Current Population Survey
(CPS).This is a monthly survey
administered by the Census Bureau of a scientifically selected sample
representative of the noninstitutional civilian population in 754 areas
covering every state and the District of Columbia.Like other surveys, the CPS is subject to sampling error.Readers should note that estimates based on
the CPS may not agree with census counts because different procedures are
used.Changes in the CPS also mean that
annual comparisons must be made with caution.
For instance, in 1994, the CPS methodology was dramatically changed, and
the estimates began to incorporate 1990 census population controls, adjusted
for the estimated undercount. Industrial production data come
from the Industrial Production Index, produced by the Board of Governors of the
Federal Reserve System and published in the Economic
Report of the President.For annual
figures, individual industrial production (IP) indexes are constructed from a
variety of sources, including the quinquennial Censuses of Manufactures and
Mineral Industries; the Annual Survey of Manufactures, prepared by the Census
Bureau; the Minerals Yearbook, prepared by the U.S. Department of the Interior;
and publications of the U.S. Department of Energy.The Federal Reserve Board (FRB) uses these data in a modeling
framework to produce estimates of industrial production.Below are brief discussions on three major
sources for the IP indexes; the survey of manufactures, the census of manufactures,
and the electric utility survey. Annual Survey of Manufacturers The Census Bureau conducts a mail
survey of approximately 55,000 manufactures with three different sample
strata.The sampling frame is based on
previously surveyed firms and is updated annually based partially on IRS
administrative records and other sources.
Large manufactures (shipments > $500 million, and > 250
employees), some computer manufacturing firms, and all remaining firms with at
least 250 employees are selected.Establishments
with employment generally ranging from 20 to 250 employees are sampled with a
probability proportional to a composite measure of establishment size.Approximately 5,000 of the smallest firms (5
to 20 employees) are also sampled and receive a shorter survey instrument.Additional information on the survey,
readers should refer to www.census.gov/econ/www/ma0300.html. Census of Manufacturers The Census of Manufactures
collects data through mail surveys from approximately 237,000 multiunit and single-unit
firms with a minimum payroll figure.
This census is supplemented by IRS administrative data from over 142,000
firms not contacted by mail.For
additional information on the census, readers should refer to www.census.gov/econ/www/ma0100.html. Electric Utility Survey Since 1971, the FRB has conducted
the Monthly Survey of Industrial
Electricity Use based on responses from utilities and manufacturing and
mining firms that are cogenerators.
This survey is the basis for estimates of the amount of electricity
power used by 120 industrial sectors.
More than 40 industrial production series estimates are based on data
from this survey and compose 28 percent of the Industrial Production Index in
1994 value-added proportions. Survey responses are voluntary
and are gathered from a panel of 175 utilities and 186 cogenerating companies
with a monthly response rate near 95 percent.
In 1992, an additional 71 new cogenerators joined the panel.This resulted, according to an FRB
statistical analysis, in a decrease of the standard deviation of errors for
electricity growth rates from 3.0 to 1.9 percentage points.Overall, the estimates for total power use
produce a standard error of about 0.5 percentage points.The panel accounts for approximately 73
percent of industrial electric power use in the United States. The Survey of Current Business, published by the U.S. Department of
Commerce, Bureau of Economic Analysis, is the source of GDP estimates.Readers should refer to the source and
accuracy statement for tables 3-1 through 3-5 for information on GDP
estimates. TABLE 3-7.Passenger and Freight Transportation Expenditures Detailed
information from the source was not available at the time of publication.Readers should contact the Eno
Transportation Foundation, Inc. directly for information about methodologies
and reliability. TABLE 3-8.Sales Price of Transportation Fuel to End-Users The U.S.
Department of Energy, Energy Information Administration's (EIA's) Annual Energy Review 1999, tables 5.20
and 5.21, provided price data, except for railroad fuel.Pre-1981 data were reported by the EIA from
Bureau of Labor Statistics reports.
Beginning in 1983, the EIA administered a series of surveys to collect
data on petroleum prices, market distribution, supply, and demand.The EIA-782 series encompasses three
surveys:1) Form EIA-782A,
Refiners'/Gas Plant Operators' Monthly Petroleum Product Sales Report; 2) Form
EIA-782B, Resellers'/Retailers' Monthly Petroleum Product Sales Reports; and 3)
Form EIA-782C, Monthly Report of Prime Supplier Sales of Petroleum Products
Sold for Local Consumption. EIA developed a method for
comparing data from the new surveys with older information gathered by various
methods.As a result, a number of
adjustment factors were developed and used to "backcast" price
estimates.Readers who require a more
detailed description of this methodology should refer to EIA's petroleum data
publications web page (www.eia.doe.gov/oil_gas/petroleum/pet_frame.html) and
the explanatory notes section. Changes in sample elements or
collection methods may affect data continuity.
Two regulatory changes affected data collection in October 1993.The Clean Air Act Amendments of 1990
required that oxygenated gasoline be sold in the winter months in ozone
nonattainment areas.Thus, the EIA-782
forms were modified to collect information on fuels divided among conventional,
oxygenated, and reformulated categories.
Second, requirements for the production and selling of low-sulfur diesel
were required and necessitated the separation of diesel fuel into high- and
low-sulfur categories.Moreover,
surveys prior to October 1993 did not include propane.The EIA followed several different sampling
designs during two periods in the 1980s and thus, there may be some price
estimate discontinuity for periods between December 1983 and January 1984 as
well as between August and September of 1988.
Data Collection The 782 series occurs on a
monthly schedule via mail.The 782A and
782C surveys reflect a census of about 115 and 190 firms, respectively.The 782B samples about 2,000 firms.The EIA first stratifies by sales volume for
the form 782B survey to ensure that dealers with 5 percent or more of the
market are captured with certainty.The
remaining elements of the frame were assigned a probability of selection to
form a 2,200 firm survey.These
"noncertainty" companies were poststratified by geographic area and
type of sales category Data Reliability EIA has studied its sampling
effects on reliability and determined that the sample size of 2,000 should
yield a less than 1-percent price coefficient of variation in its
estimates.Errors can arise because of
nonresponse, but an EIA official indicated that the response rates for the
1997-1999 782A, B, and C surveys averaged 95 percent, 86 percent, and 96
percent, respectively.Because survey
data invariably contain incomplete data (because of reporting errors or
nonresponse), EIA estimates or "imputes" missing data. Readers requiring imputation algorithms
should refer to the 782 series explanatory notes referred to above. TABLE 3-9. Price Trend of
Gasoline v. Other Consumer Goods and Services Data in this table were
reproduced from the American Petroleum Institute's (API) Basic Petroleum Data Book.
API noted that data reported prior to 1981 was obtained from Platt's Oil Price Handbook and Oilmanac.Platt's is part of Standard and Poor's, and an independent third
party organization that tracks the petroleum industry.Platt's reported the retail price of
gasoline based on telephone interviews with gas stations in 55 cities.More detailed historical information on
their data collection methods could not be ascertained and the data's
reliability is uncertain.API reported
the Bureau of Labor Statistics (BLS) as its data source for 1981 to 1998 retail
gasoline prices.The remainder of this
section discusses the BLS Consumer Price Index (CPI) data collection and
estimation methods used to derive the average retail price of gasoline. BLS uses the CPI as a measure of
average price changes paid by urban consumers for a fixed basket of goods and
services.BLS estimates the CPI with a
survey-based approach.Survey results
define a categorization of goods and services, a representative sample of items
to track, and weights according to the consumption of an average consumer
during a base period. Sample Design BLS relies on two sampling frames
for their CPI estimates.One represents
the universe of retail outlets from which households may purchase defined
groups of commodities and services including gasoline.A second represents households across urban
areas.Moreover, the household frame is
based on an "urban-consumer" population and consists of households in
Metropolitan Statistical Areas (MSA's) and in urban places with more than 2,500
inhabitants.This "all urban"
CPI (CPI-U) provides the estimates for retail gasoline prices shown in table
3-9.Thus, this frame does not
represent non-urban consumers. For the retail outlet sampling
frame, BLS relies on the Point-of-Purchase Survey (CPOPS) conducted by the
Census Bureau in 94 Primary Sampling Units (PSUs) identified by BLS.PSUs are based on urban counties, groups of
contiguous urban counties, or MSAs.For
the household sample, a noncompact clustering procedure was employed which
dispersed households evenly within a Census enumeration district (ED).More detailed sampling design information
can be found in BLS's Handbook of Methods
at http://stats.bls.gov/opub/hom/homhome.htm. Prices for the goods and services
used to calculate the CPI are collected in 91 PSUs located in 85 urban areas
throughout the country.The sample size
for the CPOPS totals about 21,000 retail and service
establishments-supermarkets, department stores, gasoline stations, hospitals,
etc.Food, fuels, and a few other items
are priced monthly in all 85 locations.
BLS field representatives collect all price information through visits
or telephone calls in the household surveys.
Price changes are computed based on a sample of outlets selected from
locations identified by consumers.
Specific sample items are then selected from each sample outlet to
ensure that the market basket is representative of where households shop. Estimation BLS routinely updates its price
estimates for specific items among the collection of goods and services, for
example, a new car model year.BLS
employs three techniques to produce new price estimates.First, an item that is directly comparable
to the previous discontinued good will be used to provide a price
estimate.However, a substitute item
may be inappropriate when goods change slightly in their characteristics.BLS relies on Hedonic regression modeling as
a second "quality adjustment" for price estimates.This statistical technique can model the
importance of various quality characteristics that add value to a particular
good (the fiber content and construction of apparel products for
instance).A researcher can estimate a
Hedonic regression model that identifies the factors most important is
determining the price of a good, and BLS field representatives will note these
in their data collection.Imputation is
a third quality adjustment used for "noncomparable" substitutions
where BLS estimates the price change from previous averages.Detailed algorithms can be found in chapter
17 of the BLS Handbook of Methods at
http://stats.bls.gov/opub/hom/homhome.htm. Effective January 1999, BLS began
using a new formula for calculating the basic components of the Consumer Price
Index for all Urban Consumers (CPI-U) and the Consumer Price Index for Urban
Wage Earners and Clerical Workers (CPI-W).
The new formula, the geometric mean estimator, is used in index
categories that comprise approximately 61 percent of total consumer spending
represented by the CPI-U.Based on BLS
research, it is expected that use of the new formula will reduce the annual
rate of increase in the CPI by approximately 0.2 percentage point per
year.Additional information on this
change was published in the April 1998 CPI Detailed Report and is available on
the Internet at http://stats.bls.gov/cpihome.htm. Accuracy One of the CPI's limitations is
that it represents price movements for urban residents and may not correctly
represent nonurban consumption patterns.
The CPI may also contain sampling error because it is estimated from a
sample of consumer purchases.
Nonsampling error may occur if respondents provide BLS field
representatives with inaccurate or incomplete information.Another potential source of error identified
by BLS may occur because of a time lag between the Point-of-Purchase Survey and
the initiation of price collection for commodities and services at resampled
outlets. Because of the time lag, the products offered by the outlet at the
time pricing is initiated may not coincide with the set from which the CPOPS
respondents were purchasing. The CPI is also subject to
response error when data are not collected because of non-response.BLS established a nonresponse auditing
program in 1986.It reported that response
rates in 1990 for transportation commodities and services were above 90
percent. Bias Four categories of bias were
identified in the BLS report, Measurement
Issues in the Consumer Price Index, published in 1997.First, because of the fixed-weight nature of
the index, the CPI creates substitution bias by placing too much weight on
items measured in previous surveys from which consumers may have shifted
away.Second, the study found that the
index did not account for consumers switching to discount stores.Third, a quality change bias was also
identified when the differences between goods priced in two different periods
cannot be accurately measured nor deduced from the accompanying price
difference between the goods.Finally,
the report noted that the CPI also had a new product bias because the index
inadequately reflected consumer value of products introduced into the
market.The commission concluded that
the CPI overstated the true cost-of-living change by 1.1 percentage points per
year. TABLE 3-10. Producer Price Indices for
Transportation Services TABLE 3-11. Producer Price Indices for
Transportation Equipment Data shown in these tables are
drawn from annual issues of The
Supplement to Producer Price Indexes published by the Bureau of Labor
Statistics (BLS) in the U.S. Department of Labor.These indexes represent a measure of outputs in all
goods-producing American industries as well as partial coverage of service
industries including transportation.
BLS defines a price as the net revenue accrued to a specified production
establishment from a specified kind of buyer for a specific product shipped
under specific transaction terms on a specified day of the month.BLS collects this data series through surveys
of a sample of establishments that report their prices from economic
transactions. Data Collection A BLS field economist visits an
establishment or cluster of establishments selected for price sampling.The economist uses a disaggregation
procedure to select a sample of transactions from all the establishment's
revenue-producing activities.This
disaggregation procedure assigns a probability of selection to each shipping or
receipt category proportionate to its value within a reporting unit.In most cases, the final price index produced
by the BLS requires that 1) there are at least three different respondents to a
survey, 2) at least two reporting units provide price information in a given
month, and 3) no single respondent accounts for 50 percent or more of the
weight for a given item. BLS regional offices review field
data for consistency and completeness.
The national office then conducts a final review and a survey is then
tailored specifically to establishments or clusters of establishments.BLS refers to these as repricing schedules
and sends them to reporting establishments on a regular basis.Most prices refer to a reporting schedule on
a particular day of the month, usually, the first Tuesday or the 13th of a
month. Estimation BLS collects prices for over
100,000 items.It utilizes several
different weighting schemes for the numerous indexes produced because some
products will have a greater effect on the movement of groupings of individual
products.BLS utilizes the net output
of shipment values as weights for the 4-digit SIC industries.Net output values include only shipments
from establishments in one industry to other industry establishments and, thus,
differ from gross shipment values.The
latter would include shipments among establishments in the same industry, even
if those establishments are separate firms.
BLS also makes seasonal adjustments if statistical tests and economic
rationale justify them, and imputes data when a participating company does not
deliver a price report.BLS bases the
missing price estimation on the average of price changes for similar products
reported by other establishments. Accuracy As in all surveys, the accuracy
of producer price indexes depends on the quality of information voluntarily
provided by participating establishments.
One of the accuracy concerns of BLS revolves around the preferred use of
realistic transaction prices (including discounts, premiums, rebates,
allowances, etc.) rather than list or book prices.Before BLS fully changed its data collection method in 1986, a survey
indicated that about 20 percent of traditional commodity indexes were based on
list prices.The newer and more
systematic methodology decreased the use of list prices.BLS documentation (available at
http://stats.bls.gov/opub/hom) provided no more details on sampling error,
response rates, or the availability of generalized variance parameters or
techniques for estimating them. TABLE 3-12: Personal Expenditures by
Category TABLE 3-13: Personal Consumption
Expenditures on Transportation by Subcategory Data used
in these tables are from the Bureau of Labor Statistics, Annual Report of Consumer Expenditure Survey.The Consumer Expenditure Survey (CEX)
collects information from U.S. households and families on their buying habits
(expenditures), income, and consumer characteristics. The strength of the
survey is that it allows data users to relate the expenditures and income of
consumers to the characteristics of those consumers.BLS uses 11 standard characteristics to classify consumers,
including income, before-tax income class, age, size of the consumer unit,
composition of the consumer unit, number of earners, housing tenure, race, type
of area (urban or rural), region, and occupation. The CEX is a national probability
sample of households.The sampling
frame (i.e., the list from which housing units are chosen) for this survey is
generated from the 1990 census 100-percent detail file, which is augmented by a
sample drawn from new construction permits.
Coverage improvement techniques are also utilized to eliminate
recognized deficiencies in the census. Data Collection The current survey consists of
two separate surveys (Interview and Diary), each utilizing a different data
collection technique and sample.Data
is collected for each survey from approximately 5,000 households.In the Interview survey, each consumer unit
(CU) in the sample is interviewed every three months over five calendar
quarters.The interviewer uses a
structured questionnaire to collect both the demographic and expenditure data
in the Interview survey.The
interviewer collects the demographic data in the Diary survey whereas the
respondent enters the expenditure data on the diary form.Both surveys accept proxy responses from any
eligible household member who is at least 16 years old if an adult is not
available after a few attempts to contact that person.The respondent family completes the Diary
(or recordkeeping) survey at home for two consecutive one-week periods. A reinterview program for the CEX
provides quality control. The program provides a means of evaluating individual
interviewer performance to determine how well the procedures are being carried
out in the field.A member of the
supervisory staff conducts the reinterview. Subsamples of approximately 6 percent
of households in the Interview survey and 17 percent in the Diary survey are
reinterviewed on an ongoing basis.A
new diary form with more categories and expanded use of cues for respondents
was introduced in 1991, based on results from earlier field and laboratory
studies. Estimation Missing or invalid data on
demographic or work experience are imputed.
No imputation is done for missing data on expenditures or income.Selected portions of the Diary data are also
adjusted by automated imputation and allocation routines when respondents
report insufficient detail to meet publication requirements.These procedures are performed annually on
the data.The imputation routines
assign qualifying information to data items when there is clear evidence of invalid
nonresponse. The statistical estimation of the
population quantities of interest, such as the average expenditure on a
particular item by a CU or the total number of CUs in a particular demographic
group, is conducted via a weighting scheme.
Each CU included in the survey is assigned a weight that is interpreted
as representing the number of similar families in the universe of interest, the
U.S. civilian noninstitutional population.
Readers should refer to http://stats.bls.gov/opub/hom/homch16_c.htm for
the detailed weighting method. Beginning with 1997 data, BLS
introduced a new calibration method to compute weights in the Consumer
Expenditure Survey.The weights are
calculated using a model-assisted, design-based regression estimator. Accuracy The Consumer Expenditures Survey
is a sample survey and hence is subject to two types of errors, nonsampling and
sampling. Nonsampling errors can be attributed to many sources, such as
differences in the interpretation of questions, inability or unwillingness of
the respondent to provide correct information, mistakes in recording or coding
the data obtained, and other errors of collection, response, processing,
coverage, and estimation for missing data.
The full extent of nonsampling error is unknown. Sampling errors occur
because the survey data are collected from a sample and not from the entire
population.Tables with coefficients of
variation and other reliability statistics are available on request from the national
office.However, because the statistics
are shown at the detailed item level, the tables are extensive. TABLE 3-14. Cost of Owning and Operating
an Automobile Your Driving Costs produced by the American Automobile Association
(AAA) provided the data for this table.
Prior to 1985, the cost figures are for a mid-sized, current model,
American car equipped with a variety of standard and optional accessories.After 1985, the cost figures are for a
composite of three current model American cars: 1. a 1999 Chevrolet Cavalier LS, 2. a 1999 Ford Taurus GL, and 3. a 1999 Mercury Grand Marquis GS.
Thus, the estimates are not
reliable estimates for all cars. Fuel costs were based on an
average price of $1.34 per gallon of regular unleaded gasoline, weighted 20
percent full-serve and 80 percent self-serve.
Insurance figures were based on personal use of vehicles driven less
than 10 miles to or from work, with no young drivers.Normal depreciation costs were based on the vehicle's trade-in
value at the end of four years or at 60,000 miles.American Automobile Association (AAA) analysis covers vehicles
equipped with standard and optional accessories, including automatic
transmission, air conditioning, power steering, power disc brakes, AM/FM
stereo, driver-and passenger side air bag, anti-lock brakes, cruise control,
tilt steering wheel, tinted glass, emission equipment and rear window defogger. TABLE 3-15a & 3-15b. Average Passenger
Fare (Current and chained 1996 dollars) TABLE 3-18. Total Operating Revenues Air The U.S. Department of Transportation,
Bureau of Transportation Statistics (BTS), Office of Airline Information,
reports passenger fares and operating revenues in its publication Air Carrier Financial Statistics.These numbers are based on 100 percent
reporting by large certificated air carriers.
Minor errors from nonreporting may occur but amount to less than one
percent of all passenger or freight activity. The figures do not include data
for all airlines; such as most scheduled commuter airlines and all nonscheduled
commuter airlines. Class I Bus Class I passenger motor carriers
are required to report financial and operating information to BTS using form
MP-1.(Prior to 1996, Class I carriers
were required to report to the Interstate Commerce Commission.)Class I passenger motor carriers are defined
as those having annual gross operating revenues, as adjusted for inflation, of
$5,000,000 or more.This table does not
include Class I carriers whose data had not been received at the time of
publication.Thus, these data do not represent
total Class I passenger motor carrier activity. Transit The American Public Transit
Association (APTA) reports these figures, which are based on the annual
National Transit Database report published by the USDOT, Federal Transit Administration
(FTA).Section 15 of the Federal
Transit Act requires federally funded transit agencies to provide detailed
financial and operating data including capital expenditures, revenues and
expenses. These data are generally considered accurate because the FTA reviews
and validates information submitted by individual transit agencies.Reliability may vary because some transit
agencies cannot obtain accurate information or misinterpret certain data
definitions.APTA conservatively
adjusts FTA data to include transit operators that do not report to the
database(private and very small
operators and rural operators). Rail Data are from Railroad Facts published annually by the Association of American
Railroads (AAR).AAR figures are based
on 100-percent reporting by all nine Class I railroads to the Surface
Transportation Board (STB) via Schedule 700 of the R1 Annual Report.STB defines Class I railroads as having
operating revenues at or above a threshold indexed to a base of $250 million in
1991 and adjusted annually in concert with changes in the "Railroad
Freight Rate Index" published by the Bureau of Labor Statistics.In 1998, the threshold for Class I railroads
was $259.4 million.Declassification
from Class I status occurs when a railroad falls below the applicable threshold
for three consecutive years.Although
Class I railroads represent only 2 percent of the number of railroads in this
country, they account for over 90 percent of the industry's freight revenues. Intercity/Amtrak Average passenger fare data are
based on 100 percent of issued tickets, and thus should be accurate.Created as a publicly-owned for-profit
corporation, Amtrak collects its own financial data and reports this
information in its annual report.
Auditing should ensure the accuracy of the operating revenue
figures. Trucking and Courier Services (except air) The Census Bureau's
Transportation Annual Survey (formerly known as the Motor Freight
Transportation and Warehousing Survey) is the source of this information.The sample survey represents all employer
firms with one or more establishments engaged primarily in providing commercial
motor freight transportation or public warehousing services. It excludes motor
carriers that operate as auxiliary establishments to nontransportation
companies, as well as independent owner-operators with no paid employees.Thus, the data do not represent the total
trucking industry. As with all sample surveys, two
types of errors are possible:sampling
and nonsampling.Nonsampling errors
may include response errors and mistakes in coding or keying data.For additional information about the survey
and data reliability, the reader is referred to the Census Bureau website at
www.census.gov. Water (Domestic) Eno Transportation Foundation,
Inc. is the source of these data.Eno
estimates these figures by multiplying ton-mile figures by estimated revenue
per ton-mile.The U.S. Army Corps of
Engineers reports the ton-mile figures in its publication Waterborne Commerce of the United States, and the revenue per
ton-miles figures are estimated by Eno.. Oil Pipeline Eno Transportation Foundation,
Inc., publishes these data, which are based on Federal Energy Regulatory
Commission (FERC) data and reported by the Oil Pipeline Research Institute for
years 1977 to the present.FERC data
originates from required quarterly reports filed by pipeline companies.Prior to 1977, the data are based on the
former Interstate Commerce Commission data for regulated pipelines, and
estimated to be 16 percent of the total of nonregulated pipelines. Gas Pipeline These statistics originate from Gas Facts, published annually by the
American Gas Association (AGA).AGA
data are based on gas utilities participation and reporting to the Uniform
Statistical Report and estimates for those companies not reporting based on
recent historical experience.Varying
percentages of nonreporters from year to year introduce minor reliability
problems for time-series comparisons. TABLE 3-19. Employment in For-Hire
Transportation and Selected Transportation-Related Industries Employment data by industry are
from the National Employment, Hours, and Earnings estimates published by the
Bureau of Labor Statistics (BLS), U.S. Department of Labor.These estimates originate from the Current
Employment Statistics (CES) survey program. The CES is a monthly survey
conducted by state employment security agencies in cooperation with the BLS.
The survey provides employment, hours, and earnings estimates based on payroll
records of nonfarm business establishments, including government. BLS uses a stratified sample
based on a sector's employment size, or the degree of variability among its
establishments, or both.This ensures
that BLS captures a more representative survey from employers with large payrolls.Thus, large establishments are certain of
selection while smaller ones have less of chance. Data Collection Data are collected electronically
from about two-thirds of the respondents and by mail or fax from the
remainder.The primary type of electronic
reporting is touch-tone phone self-response; others are computer-assisted phone
interviews and phone voice recognition technology.Increasingly, data are collected through electronic data
interchange from a small but growing number of companies that have a large
number of establishments across the country.
Mail respondents submit Form 790 to the BLS each month.It is then edited and returned to the
respondent for use again the following month.
All firms with 250 employees or more are asked to participate in the
survey, as well as a sample of smaller firms.
Estimation Employment estimates are made at
what is termed the basic estimating cell level and aggregated upward to broader
levels of industry detail by simple addition.
Basic cells are defined by industry (usually at the 3- or 4-digit SIC
level) and are stratified within industry by geographic region and/or size
class in the majority of cases. Within the wholesale trade, retail trade, and
services divisions, most industries are stratified into three to five size
classes (beginning in 1984). Most national employment
estimates are multiplied by bias adjustment factors to produce the monthly
published estimates. Bias adjustment factors are used primarily to compensate
for the inability to capture the entry of new firms on a timely basis. New
firms contribute a substantial amount to employment growth each year, but there
is a lag between the creation of a firm and its inclusion on the sample frame
(i.e., the Unemployment Insurance universe file). It is, therefore, necessary
to use modeling techniques to capture this segment of the population.BLS also performs seasonal adjustments for
certain SIC industries. Accuracy BLS does not publish data
reliability information along with estimates. Instead, it provides estimation
formula and the necessary parameters so that users can estimate standard
errors.For additional information, see
the "Explanatory Notes and Estimates of Error" in the BLS monthly
publication Employment and Earnings. The CES survey, which began over
50 years ago, predates the introduction of probability sampling as the
internationally recognized standard for sample surveys. Instead, a quota sample
has been used since its inception.
Quota samples are at risk for potentially significant biases, and
recently completed BLS research suggests that, despite the large CES sample
size, employment estimates based on that sample at times diverge substantially
from those that a more representative sample would have been expected to
produce.This leads to an over-reliance
on bias adjustment in the estimation procedure.Because bias adjustment is primarily based on past experience, it
is limited in its ability to accurately reflect changing economic conditions on
a timely basis. Government Employment The Office of the Secretary
provides employment figures for the U.S. Department of Transportation.State and local highway department
employment figures are from the' State
and Local Government Employment and Payroll Estimates, published by the U.S.
Department of Commerce, Bureau of the Census.
The data are for the 50 states and the District of Columbia. Employment
and payroll data pertain to the month of October.At present, data are collected for one pay period that includes
October 12 (regardless of the period's length) through the Public Employment
Survey (PES). Employment refers to all persons
gainfully employed by and performing services for a government.Employees include all persons paid for
personal services performed from all sources of funds, including persons paid
from federally funded programs, paid elected officials, persons in a paid leave
status, and persons paid on a per meeting, annual, semiannual, or quarterly
basis.Excluded from employment
statistics are unpaid officials, pensioners, persons whose work is performed on
a fee basis, and contractors and their employees. The Census Bureau derives
full-time equivalent(FTE) employment
by summing the number of full-time employees reported and converting the number
of hours worked by part-time employees to a full-time equivalent amount.Up until 1985 data, the method used to
calculate FTEs was based solely on payroll data.Effective with 1986 data, the annual employment survey started
collecting data on the number of hours worked by part-time employees in order
to provide a more accurate representation of full-time equivalent
employment.No October 1985 FTE
employment data are available. Beginning in 1999, the Public
Employment Survey (PES) was conducted using a separate sample of approximately
11,000 government units to improve data accuracy and survey efficiency.Government units meeting any of the
following criteria are included in the survey:
1) counties with populations greater than 100,000; 2) cities with
populations greater than 75,000; 3) townships in New England and Mid-Atlantic
with populations greater than 50,000; 4) special districts with FTEs greater
than 1000; 5) independent school districts with enrollment greater than 10,000;
and 6) all dependent and independent schools providing college level
education.In 1999, government units
were sampled to obtain a relative standard error of 3 percent or less for FTE
and total payroll for each of the states by type of government groups. Prior to 1993, the PES used a
joint sample of approximately 24,000 units for both employment and
finance.From 1993 to 1998, the sample
size was reduced to around 14,000 units.
The standard error for the PES prior to 1999 was designed to be around 3
percent for major state- or county-level estimates of finance variables
(state-level for 1993-1998 and county-level prior to 1993).Employment estimates are made using
regression, except when the number of noncertainty cases contributing to the
estimate is less than 20, where a simple unbiased estimate is used. TABLE 3-20. Employment in Transportation
Occupations TABLE 3-22. Median Weekly Earnings of
Full-Time Wage and Salary Workers in Transportation by Detailed Occupation Employment by detailed
transportation occupation data are from the Occupational Employment Statistics
(OES) survey, collected by the Bureau of Labor Statistics (BLS).The OES is a periodic mail survey of nonfarm
establishments that collects occupational employment data on workers by
industry.The OES program surveys approximately
725,000 establishments in 400 detailed industries.The average response rate for the last three years, according to
a BLS official, averaged about 70 percent. The sample is selected primarily
from the list of business establishments reporting to the state unemployment
insurance program. The OES sample initially stratifies the universe of
establishments by three-digit industry code and size- class code.
Establishments employing 250 employees or more are sampled with certainty.
Establishments employing fewer than 250 employees but more than 4 employees are
sampled with probability proportional to the size class employment within each
three-digit industry. Establishments employing four or fewer employees (i.e.,
size class 1 establishments) are not sampled. Instead, the employment for these
establishments are accounted for by assigning a larger sampling weight to
establishments employing five to nine employees (i.e., size-class 2
establishments).Within each three-digit
industry/size- class cell, establishments are systematically selected into the
sample through a single random start. Data Collection Employers are the source of
occupational data. Within establishments, the main source of occupational data
reported by respondents is personnel records.
Data are collected from respondents primarily by mail. Occasionally,
visits are made to large employers and to other respondents who indicate
particular difficulty in completing the questionnaires. Ordinarily, two
mailings follow the initial mailing.After
the third mailing, a subsample of the remaining nonrespondents is drawn and
contacted by telephone.The OES survey
follows a 3-year cycle.Three surveys
are conducted alternately for manufacturing, nonmanufacturing, and the balance
of nonmanufacturing industries. Estimation During the sample selection
process, each sampled establishment is assigned a sampling weight that is equal
to the reciprocal of its probability of selection. For example, if an
establishment on the sampling frame had a 1 in 10 chance of being selected into
the sample, then its sampling weight is 10.
For establishments that did not respond to the survey, a nonresponse
adjustment factor is calculated and applied against the sampling weights of the
responding establishments within each state/3-digit industry/size-class
cell.Multiplying these adjustment
factors by sampling weights increases the weight of the responding
establishments so they can account for the missing employment data of the
nonresponding establishments. Accuracy The OES survey uses a subsample
replication technique to estimate variances in occupational employment at the
3-digit industry/size-class level.For
additional information on occupational employment estimates and measurements of
sampling error associated with the estimates, the reader is referred to
http://stats.bls.gov/oeshome.htm. TABLE 3-21. Average Wage and Salary
Accruals per Full-Time Equivalent Employee by Transportation Industry TABLE 3-23. Total Wage and Salary Accruals
by Transportation Industry The Survey of Current Business (tables 6.3c and 6.6c) published by the
U.S. Department of Commerce, Bureau of Economic Analysis, is the source of
transportation wage and salary data.
These estimates are based on BLS tabulations of employee wages that are
covered by State unemployment insurance.
As a component of the income side of National Income and Product
Account, wages and salaries comprise part of the GDP calculation.These data reflect the monetary remuneration
of employees in terms of wage accruals less disbursements.It is defined as the difference between
wages and salaries on a "when-earned" basis, or accrued, and wages
and salaries on a "when-paid," or disbursed basis.This computation was instituted in 1992
because a significant portion of bonus payments were missed in previous
calculations.Readers should also refer
to the earlier discussion of GDP methods and reliability for more detail. TABLE 3-24. Labor Productivity Indices for
Selected Transportation Industries The Bureau of Labor Statistic's
(BLS) Industry Productivity Measures
is the source of transportation labor productivity data.BLS develops industry productivity measures
based on various data sources. For rail, BLS uses freight
ton-mile and passenger miles that are collected by the Surface Transportation
Board (STB), the Association of American Railroads (AAR), and Amtrak.BLS also aggregates four different air
transportation outputs to form a single productivity index: domestic
passenger-miles, domestic freight ton-miles, international passenger-miles, and
international freight ton-miles.Air
transportation data come from Air Carrier Traffic Statistics and Air Carrier Financial Statistics,
published by the U.S. Department of Transportation, Bureau of Transportation Statistics.For petroleum pipeline, BLS relies on data
from the Association of Oil Pipelines and derived an output index based on
trunkline barrel-miles.A barrel-mile
is one barrel of petroleum moved through one mile of pipeline. Estimation BLS generally calculates labor
productivity by dividing an index of output (in this case, ton-miles) by an
index of hours.Output is derived with
a weight adjusted Tornqvist formula that produces an output ratio for one year.BLS then combines these in a series that
produces a chained output index.The
hour indexes are developed from data in BLS's Current Employment Statistics
(CES; see discussion above for table 3-12) and are the results of dividing the
annual aggregate hours for each year by a base-period figure.Readers who need more detail, such as
mathematical specifications or equations, should refer to Kunze and Jablonski
(Kunze and Jablonski 1998) or call the Office of Productivity and Technology at
BLS. Accuracy BLS provides no measures of
reliability. However, BLS makes an
assumption that transportation outputs should be measured using the production
of passenger-miles or freight-miles.
Another school of thought might assume that many transportation firms or
facilities are actually providing capacity rather than actual use.Thus, an argument can be made that
productivity should be based on capacity rather than use.In fact, this is how BEA measures
transportation output.To evaluate the
BLS assumption, one study compared the two approaches by examining the
different growth rates produced by BLS and BEA and found that in 25 of 35
service industries, the differences are within one percentage point.For transportation, differences in growth
rates across BLS and BEA estimates were two percentage points or less (Kunze
and Jablonski 1998). Beginning with 1997 data, the
indices for bus and petroleum pipelines did not meet BLS publication standards
and are considered less reliable than those for other modes.These industries had between 14,000 and 15,000
employees, far below the 50,000-employee threshold established for
transportation industries by BLS.
However, they both met a basic test of variability of the annual percent
changes in the output per hour measure. GOVERNMENT REVENUES AND
EXPENDITURES TABLE 3-25a &3-25b. Federal, State,
and Local Government Transportation-Related Revenues and Expenditures, Fiscal
Year (Current and constant 1996 dollars) TABLE 3-26a & 3-26b. Federal
Transportation-Related Revenues, Fiscal Years (Current dollars and constant
1996 dollars) TABLE 3-27a & 3-27b. Federal
Transportation-Related Expenditures by Mode, Fiscal Year (Current and constant
1996 dollars) TABLE 3-28. Cash Balances of the
Transportation-Related Federal Trust Funds, Fiscal Year The main sources for federal-level
data are the Budget of the United States
and the Appendix to the Budget.These data are the "actual"
figures as reported for the various transportation-related programs in the
appendices of each year's budget document.1 The figures
are consistent from year to year and follow the definitional structure required
by the Office of Management and Budget (OMB).
1 The federal budget is broken down
into 20 functional categories, of which one is transportation (function
400). Function 400 isnot tied to any one department or agency,
but instead aggregates transportation functions wherever in the federal
government they occur. Thus, the transportation function may include many
activities, such as highway construction and safety, airways and airports,
maritime subsidies, U.S. Coast Guard operations, railroads, and mass transit.
It also covers grants-in-aid programs to support state and local activities. A
good summary of the federal budget process can be found in Collender, Stanley
E., The Guide to the Federal Budget,
Fiscal 1996 (Washington, DC: Urban Institute Press. 1995). |
Primary sources for state and
local transportation-related revenues and expenditures data are censuses and
surveys collected by the U.S. Census Bureau.
All units of government are included in the Census of Governments, which
is taken at five-year intervals for years ending in "2" or
"7," and these data are "full counts," and not subject to
sampling error. State and local government data
for noncensus years are obtained by annual surveys, which are subject to
sampling error.For the U.S. totals of
local government revenues and expenditures in this report, the sampling
variability is in most cases small (less than 2 percent). The federal figures in this
report correspond to the federal fiscal year, which begins on October 1, while
state and local data are for fiscal years that generally start in July.While this may create a small error in
totals for any given year, the data are suitable for illustrating trends in
public transportation finance.Programs
that were terminated before 1985 are excluded from the tables.The totals for transportation revenues and
expenditures in this report are the sum of the Census Bureau's state and local
figures plus the total of the federal figures. The source of the constant dollar
deflators is The Survey of Current
Business, August 1998, Bureau of Economic Analysis, table 3, "Chain
Type Price Indexes."All
inflation-adjusted data are for the base year 1992, instead of 1987 as in the
previous editions of the NTS.Note that
different deflators are used for the federal data and the state and local
data.Thus, if expenditures are totaled
across different levels of government in constant dollars before and after
Federal grant transfers, the totals do not match. Limitations of the Source Data Sets Some federal agencies, such as
the U.S. Department of Health and Human Services, have substantial
transportation activities, but do not distinguish these activities as individual
programs and do not report transportation revenues, obligations, and
expenditures as separate items.There
is reason to believe that the effect of omitting the transportation activities
in those agencies and programs with missing data is relatively small (less than
10 percent). The same is true in the case of
Census Bureau data at the state and local levels.It is known, for example, that the states expend funds for
intercity rail and bus services and pipeline safety programs, but the Census
Bureau does not report these outlays at the state and local government levels
separately.BTS has collected data from
other sources or estimated data using assumptions about ratios between federal,
state, and local funds.Data from other
sources include the Federal Highway Administration’s (FHWA’s) Highway Statistics report for
federal-level highway data, the National Aeronautics and Space Administration
(NASA) aeronautics expenditures data from the Aeronautics and Space Report of the President, and pipeline expenditures
data from direct agency contacts. The Census Bureau's database also
does not include detailed modal information on interest earnings and bond issue
proceeds on the revenue side nor bond retirement and interest payments on the
expenditure side.In addition, the
Census Bureau's highway expenditures data, in particular, do not include
highway law enforcement expenditures, which form a part of the state and local
highway expenditures published in Highway
Statistics.However, to maintain
consistency between the different modes regarding the types of revenues and
expenditures included, these additional data from the Highway Statistics report have not
been used. Transportation Revenues Transportation revenue estimates
include transportation-related user charges, taxes, or fees earmarked for
transportation-related expenditures, and funds that support federal
transportation programs through the U.S. government's General Fund.Estimates include transit fares from systems
owned and operated by state and local governments, including those systems
operated under contract by a private firm while the government maintains
day-to-day financial oversight. Not all transportation-related
revenues are included, however. Other funds exist that could be categorized as transportation-related
revenues, such as local government property taxes on vehicles, equipment, and
streets, and state income taxes to support rail and intercity bus
services.However, it is impossible to
identify these revenues because they are not shown as such in the source
materials used to compile the database in this report. In addition, taxes collected from
users of the transportation system that go into the General Fund are not
included as transportation revenues.
This occurred in 1981 and 1982 when the Airport and Airway Trust Fund
(AATF) revenues were assigned to the General Fund of the Treasury rather than
the AATF. The reader should note that in
the case of rail transportation, revenue estimates do not exist since both
freight and passenger rail yield no revenues to federal, state, or local
governments. Federal transportation revenues
generally consist of trust-fund collections from user charges, such as fuel
taxes, vehicle taxes, registration and licensing fees, and air passenger ticket
taxes.Interest earned on fund balances
are added to these funds, along with any damage payments made by private
parties and deposited in the funds to reimburse the government for related fund
expenditures. The five transportation-related
Federal trust funds are established by law: 1. Highway Trust
Fund (HTF) (which includes both highway and transit accounts), 2. Airport and Airway
Trust Fund (AATF), 3. Harbor Maintenance
Trust Fund (HMTF), 4. Inland Waterways
Trust Fund (IWATF), and 5. Oil Spill Liability
Trust Fund (OSLTF). These tables also contain data
relating to the Pipeline Safety Fund, which has not been designated by law as a
trust fund, but has been set up to record revenues and disbursements of fees
earmarked to support the pipeline safety program.A status report of each of these funds made annually in the Appendix to the Budget shows their
revenues, expenditures, and interest earnings. Air Revenues Federal air revenues are derived
from the AATF, which includes a passenger ticket tax and other taxes paid by
airport and airway users on air cargo and general aviation fuel.Most of this trust fund is devoted to
airport grants and capital improvements, such as new radar and traffic control
towers.Within certain limits set by
Congress, some of the remaining money can be used to cover the Federal Aviation
Administration's (FAA) operation and maintenance expenses.The portion of the FAA's operation and
maintenance expenses not paid from the trust fund revenues are financed by
general funds of the Treasury. State and local revenues from the
air mode are derived from airport charges.
Beginning in 1992, local governments began collecting passenger facility
charges and spending these revenues (both subject to FAA approval) to finance
capital programs.The collection of
passenger facility charges was authorized by the Aviation Safety and Capacity
Expansion Act of 1990.2
2 Public Law 101-508, 104 Stat. 1388
(November 5, 1990). |
Highway Revenues The major source of Federal
highway revenues is the Highway Trust Fund (HTF).HTF revenues are derived from various excise taxes on highway
users (e.g., motor fuel, motor vehicles, tires, and parts and accessories for
trucks and buses).The money paid into
the fund is earmarked primarily for the Federal-aid highway program.The excise tax on gasoline is the greatest
individual source of HTF revenues.
Although the excise tax per gallon changed several times during the 1985
through 1995 period, the amount dedicated to the HTF only increased once during
that time.Portions of the gasoline
excise tax per gallon were dedicated to budget deficit reduction and to the
Leaking Underground Storage Tank Trust Fund. State and local highway revenues
include state and local taxes on motor fuels, motor vehicle licenses, and motor
vehicle operator licenses, along with state and local charges for regular toll
highways and local parking charges.
Regular highway charges (revenues) include reimbursements for street
construction and repairs; fees for curb cuts and special traffic signs; and
maintenance assessments for street lighting, snow removal, and other highway or
street services unrelated to toll facilities.
Local governments finance local road and street programs with special
assessments and property taxes that may be commingled with other local revenue
in a general fund.Consistent with
federal revenues, state and local transportation revenues in this report do not
include general funds that may be allocated to transportation. Transit Revenue Effective April 1983, one cent
per gallon of the federal excise tax on gasoline sales was set aside for the
Mass Transit Account of the Highway Trust Fund; on December 1, 1990, this was
increased to 1.5 cents per gallon.
Although highway users pay the taxes, these funds are treated as federal
transit budget revenues in calculating user coverage. State and local transit revenues
include revenues from operations of public mass transportation systems (rapid
transit, subway, bus, street,railway,
and commuter rail services), such as fares, charter fees, advertising income,
and other operations revenues.They
exclude subsidies from other governments to support either operations or
capital projects. Waterway and Marine Revenues Federal water revenues come from
four primary sources: the Inland Waterways Trust Fund, the Harbor Maintenance
Trust Fund, the Oil Spill Liability Trust Fund, and tolls and other charges
collected by the Panama Canal Commission. Established by the Inland
Waterways Revenue Act of 1978, the Inland Waterways Trust Fund has been in
effect since fiscal year 1981. The source for the fund is a fuel tax paid by
freight carriers on inland waterways. From this tax of 24.2 cents per
gallon,4.3 cents goes for deficit
reduction, and a statutory maximum of 20 cents (raised to that level from the
previous maximum of 19 cents at the beginning of 1995) flows to the Trust
Fund.Funds are earmarked for 50
percent of the construction and rehabilitation costs of specified inland
waterway projects. Rail Revenues There are no governmental
transportation revenues for rail.(Rail
generates fuel taxes that are designated for deficit reduction and, thus, are
not considered transportation revenues in these tables.Rail also pays substantial taxes because it
does not have a publicly maintained infrastructure.) Pipeline Revenues The Pipeline Safety Program is
funded by user fees assessed on a per-mile basis.The assessments are made on each pipeline operator regulated by
the Office of Pipeline Safety (OPS) of the Research and Special Programs
Administration in the U.S. Department of Transportation.There are no state and local revenues for
pipeline. General Support Revenues General Support revenues come
from the Emergency Preparedness Fund, which is generated from fees paid by
registered shippers of hazardous materials.
RSPA administers and distributes the revenues to states, territories,
and tribes through the Hazardous Materials Emergency Preparedness (HMEP) grant
program, which is authorized by the Federal Hazardous Materials Transportation
Law. Transportation Expenditures Expenditures, rather than
obligations, are used in these tables because they represent the final, actual
costs to the government, by year, for capital goods and operating services
required by the transportation programs.
Obligations suggest government commitment to future transportation
expenditures, but do not indicate when the funds will actually be disbursed or
even if the amounts obligated will be spent. It is important to recognize that
in some accounts in the Budget of the
United States Government, expenditures for a particular year understate
total government disbursements.This is
because certain offsetting collections of fees and assessments from the public
are not treated as government revenues, but deducted from disbursements to
determine expenditures.These collections
are those mandated, by statute, to be applied directly to fund agency
expenditures rather than being transferred to the Treasury.For this reason, expenditures do not
necessarily indicate how much the Federal government actually spends on
transportation each year. Air Expenditures Federal expenditures reported
here consist of all FAA expenditures, such as those associated with
constructing, operating, and maintaining the national air traffic system;
administration of the airport grant program; safety regulation; and research
and development.NASA expenses related
to air transportation are also included. State and local expenditures for
air include the operation and maintenance of airport facilities, as administered
by local airport and port authorities quasi-government agencies with
responsibilities for promoting safe navigation and operations forair modes. Highway Expenditures FHWA expenditures include funds
for Federal Aid Highways (financed from the HTF) and the Interstate
Substitution and Railroad Crossing Demonstration (financed from the general
fund).The National Highway Traffic
Safety Administration (NHTSA) expenditures include: operations, research, and
highway traffic safety grants.Federal
highway expenditures also include road construction activities managed by the
Department of the Interior's National Park Service, Bureau of Indian Affairs,
Bureau of Reclamation, and Bureau of Land Management; the Department of
Agriculture's Forest Service; the Department of Housing and Urban Development;
and other federal agencies. State and local governments'
highway expenditures reported by the Census Bureau are generally slightly lower
than those reported in FHWA's Highway
Statistics because the FHWA includes some highway expenditure data, such as
law enforcement activities and patrols, and policing of streets and highways
not included in the Census data.Box
3-1 outlines the major differences in Census Bureau and FHWA calculation of
state and local highway transportation financial statistics. Transit Expenditures Federal expenditures include
grants to states and local agencies for the construction, acquisition, and
improvement of mass transportation facilities and equipment and for the payment
of operating expenses. Several other
items are also included: Federal Railroad Administration (FRA) commuter rail
subsidies related to the transition of Conrail to the private sector; research
and administrative expenses of the Federal Transit Administration (FTA); and
Federal interest payment contribution to the Washington Metropolitan Area
Transportation Authority (WMATA). Waterway and Marine Expenditures Federal expenditures comprise
those parts of U.S. Coast Guard's expenses that are transportation-related,
such as aids to navigation, marine safety, and marine environmental
protection.All expenses of the U.S.
Maritime Administration are included, such as subsidies for construction and
operation of vessels by U.S.-flag operators, research and development, and
training of ship officers.Also included
are those expenses of the U.S. Army Corps of Engineers for construction and
operations and maintenance of channels, harbors, locks and dams; protection of
navigation; the salaries and expenses of the Federal Maritime Commission; and
the expenses of the Panama Canal Commission. State and local governments incur
water transportation expenditures by operating and maintaining water terminal
facilities within ports and harbors. Rail Expenditures Federal rail transportation
expenditures include: 1. expenses for rail safety enforcement; 2. inspection and program administration; 3. railroad research and development; 4. financial assistance to states for
planning, acquisition, rail facility construction, and track rehabilitation
with respect to low volume freight lines; 5. grants to Amtrak, including funds to upgrade
the high-speed line between Boston, MA, and Washington, DC, owned by Amtrak
(the Northeast Corridor Improvement Program); annual appropriations to cover
operating losses; and funds to invest in new equipment and facilities; 6. the purchase of redeemable preference shares
for track rehabilitation and line acquisition; and 7. loan guarantee defaults for railroad
rehabilitation and improvement and Conrail labor protection.3
3
Funds in the Conrail Labor Protection Program were provided for benefits
to Conrail employees deprived of employment because of work force reductions
and other actions.This program no
longer exists since Conrail has been returned to the private sector.In 1988, the unobligated balances available
from this program were transferred to the USCG and in 1990 they were returned
to the U.S. Treasury |
The local rail freight assistance
program, a program of FRA grants to state governments, has had a70:30 percent federal-state funding share
since in 1982. Pipeline Expenditures The OPS reimburses state agencies
up to 50 percent of their costs to carry out the state's pipeline safety
program.Federal expenditures are for
the enforcement programs, research and development, and grants for state
pipeline safety programs. General Support Expenditures General Fund expenditures include
all of the expenses of the following agencies: Office of Inspector General, National Transportation Safety
Board, all expenses of RSPA (except pipeline expenditures) andthe Office of the Secretary of
Transportation (except for payments to Air Carriers and the Commission on
Aircraft Safety).
Box
3-1.
U.S. Census Bureau and Federal Highway
Administration calculations of state and local transportation financial
statistics differ in the following ways:
ITEM |
CENSUS |
FHWA |
Motor Fuel Tax Revenues |
Includes state and local tax
revenues on any fuel used in motor vehicles, and on gasoline used by
aircraft. |
Includes state and local fuel
tax revenues attributed to highway use of fuels, including diesel fuel,
gasohol and liquefied petroleum gas used by private and commercial highway
use motor vehicles and transit.Does
not include revenues on gasoline used by aircraft. |
Motor Vehicle License Tax
Revenues |
Includes vehicle mileage and weight taxes on motor
carriers; highway use taxes; or off-highway fees. |
Does not include vehicle mileage and weight taxes on motor
carriers; highway use taxes or off-highway fees. |
Local Parking Charges Revenues |
Includes local parking revenues. |
Not explicitly collected. |
Highway Expenditures |
Excludes patrols or policing of
streets and highways; traffic control activities of police or public safety
agencies; law enforcement and safety activities of vehicle inspection
enforcement, and vehicle size and weight enforcement; street cleaning
activities; and roads within parks maintained by a park agency. |
Includes patrols or policing of
streets and highways; traffic control activities of police or public safety
agencies; law enforcement and safety activities of vehicle inspection
enforcement, and vehicle size and weight enforcement; street cleaning
activities; and roads within parks maintained by a park agency. |
REFERENCES Corrado, C., C. Gilbert, et al.
(1997). "Industrial production and capacity utilization: historical
revision and recent developments." Federal
Reserve Bulletin 83(2): 67. Korn, E.L. and B.I.
Graubard.1991."A Note on the Large Sample Properties
of Linearization, Jackknife and Balanced Repeated Replication Methods for
Stratified Samples." The Annals of Statistics 19 (4):2275-2279. Krewski, D. and J.N. K.
Rao.1981."Inference from Stratified Samples:Properties of Linearization, Jackknife and
Balanced Repeated Replication Methods."
The Annals of Statistics
9(5):1010-1019. Kunze, K. and M. Jablonski
(1998). Productivity in service-producing
industries. Brookings Workshop on New Service-Sector Data, Washington, DC. Landerfeld, J. S. and R. P.
Parker (1997). "BEA's chain indexes, time series, and measures of
long-term economic growth." Survey
of Current Business 77(5): 58. Moulton, B.R. and Seskin,
E.P. (1999)."A preview of the
1999 comprehensive revision of the National Income and Product Accounts:statistical changes."Survey
of Current Business 79 (October 1999):
6-17. Parker, R. P. and J. E.
Triplett (1996). "Chain-type measures of real output and prices in the
U.S. national income and product accounts: an update." Business Economics 31(4): 37. Ritter, J.A. (2000)."Feeding the national
accounts."Federal Reserve Bank of
St. Louis Review.March/April:11-20. SCB (1991). "Gross
Domestic Product as a measure of U.S. Production." Survey of Current Business. Seskin, E. P. and R. P.
Parker (1998). "A guide to the NIPA's." Survey of Current Business 78(3): 26. U.S. Department of Labor,
Bureau of Labor Statistics.1997.Measurement Issues in the Consumer Price
Index.Referenced at http://stats.bls.gov/cpigm697.htm
on May 13, 1999. Valliant, R.1993.
"Poststratification and Conditional Vairance Estimation."Journal of the American Statistical
Association 88 (421):89-96. Young, A. H. (1993).
"Reliability and accuracy of the quarterly estimates of GDP." Survey of Current Business 73(10): 29. Young, A. H. and H. S. Tice
(1985). "An introduction to national economic accounting." Survey of Current Business 65: 59. Yuskavage, R. E. (1996).
"Improved estimates of gross product by industry, 1959-94." Survey of Current Business 76(8): 133. Chapter 4 Energy and the Environment PETROLEUM SUPPLY TABLE 4-1. Overview of U.S. Petroleum
Production, Imports, Exports, and Consumption The petroleum supply system is
extremely complicated, with many different processes, products, and entities
involved.Briefly, crude oil is
produced or imported, transported to refineries where it is refined into
various products, and then transported to markets.Imports and exports of crude oil and products must be accounted
for, as must be nonpetroleum components of final products, such as natural gas
plant liquids and ethanol for gasoline blending. The U.S. Department of Energy,
Energy Information Administration (EIA) collects extensive data at select
points in the petroleum supply system. Sixteen surveys are conducted by EIA's
Petroleum Supply Reporting System to track the supply and disposition of crude
oil, petroleum products, and natural gas plant liquids: · five weekly surveys cover refineries (form
EIA-800), bulk terminal stocks (form EIA-801), product pipelines (form
EIA-802), crude stocks (form EIA-803), and imports (form EIA-804). · eight monthly surveys
cover the same five points plus tanker and barge movement (form EIA-817), gas
processing facilities (form EIA-816), and oxygenates (form EIA-819M). · one survey (form
EIA-807) collects propane data on a monthly basis in the warmer months
(April-September) and on a weekly basis in the colder months. · one annual survey
determines production capacity of oxygenates and fuel ethanol (form EIA-819A),
and · one annual survey
determines refinery fuel use, capacity, and crude oil receipts by
transportation mode (form EIA-820). The five weekly surveys target
key points in the petroleum supply system.
They do not include all companies, but sample 90 percent of volume at each
selected point in the supply system.
EIA rank-orders the companies involved in the survey and sends surveys
as it scrolls down the list, stopping when it reaches the 90 percent level.Although 100 percent coverage is sacrificed,
this method keeps the level of incoming data manageable and avoids burdening
the smallest companies. All data are reviewed and anomalies checked. Monthly surveys provide data that
are used in the monthly and annual reports.
They are similar to the weekly surveys, but are more exhaustive in both
the range of data collected and the depth of the collection.Sample sizes and response rates for several
of the key points in the supply system are shown in table 4-1.The eight monthly surveys cover the industry
more accurately than the weekly surveys and provide some double-check points
that the other surveys do not.EIA
expends considerable effort to ensure that its data are as accurate as
possible.Revisions are made throughout
the year.For example, EIA's Annual Energy Review 1996, released in
July 1997, provided a preliminary 1996 number for total petroleum production of
8.30 million barrels per day (mmbd).
The Annual Energy Review 1997,
released a year later, revised that to 8.25 mmbd, and the 1999 Review reported 8.29 mmbd.
Table 4-1. Average
Response Rates for Monthly Surveys, 1998
Survey
site |
Average
universe site |
Average
number of respondents |
Percent |
Refinery |
252 |
243 |
96.3 |
Bulk terminal |
300 |
287 |
95.6 |
Pipeline |
81 |
80 |
99.3 |
Crude oil stocks |
174 |
169 |
99.1 |
NOTE:The
average response rate is calculated by summing individual monthly response
rates and dividing by 12. SOURCE: Tammy G. Heppner and
Carol L. French, Energy Information Administration, U.S. Department of Energy,
Accuracy of Petroleum Supply Data (Washington, DC:1998).
No complicated survey is likely
to be 100 percent accurate.EIA lists
four sources of potential systematic errors: 1.Some members of the
target population are missed.EIA
reports that it continually reviews the lists and searches industry periodicals
and newspapers to identify new actors.
Considering the nature of the petroleum industry, it is very unlikely
that companies with significant production are not surveyed. 2.Some members of the
target population do not respond.EIA
reports a 97 percent response rate for monthly surveys.For some points in the supply system, the
average response is over 99 percent.
Survey respondents are required by law to respond, but some nonresponse
is inevitable, especially among small companies.EIA assumes that the nonrespondent's value for that month is the
same as for the previous month except for imports.Since imports vary widely, with respondents frequently having no
imports, EIA assumes a nonresponse means zero imports.It can be assumed that EIA is good at
"filling in the blanks."
Assuming for illustration purposes that 0.5 percent of production does
not respond, and that EIA is 90 percent accurate in covering the gap, then
there is a possibility of a 0.05 percent error.Applying that to total production of 8.29 mmbd in 1999 suggests
that there could be an error of 0.0041 mmbd (4,100 barrels per day), which
would not affect the published number. 3.The most serious
problem may be response error. A company may have poor data, perhaps as a
result of imperfect measurements, or it may transmit the wrong number.EIA has no control over a company's data
quality.Companies have incentive to
measure their inputs and products accurately.
Otherwise, they may be cheating themselves or risking ill will with their
customers or suppliers. However, no instrumentation is perfectly accurate.The high throughput of, say, a refinery with
capacity of several hundred thousand barrels per day, with a variety of
products changing density and some lost or used on site, is very complicated to
measure.Instrumentation errors are
likely to be systematic at any one site, although they will be more nearly
random in the aggregate for all facilities.
There is potential for small but significant overall errors. Mistakes may be made in
recording and transferring the data.
EIA reviews the data and flags gross errors or missing data for review
by the respondent.However, not all
errors will be picked up by EIA and/or the respondent.Overall, response errors probably are
several times as large as nonresponse errors, but it is beyond the scope of
this profile to estimate them. 4.The final potential
source of systematic error is in the clarity of the survey form, i.e., whether
all respondents interpret it correctly.
No doubt errors and ambiguities can creep into a form, but at least for
petroleum supply, that does not appear to be a major risk.The supply system is not changing rapidly,
and EIA should be able to keep with it and the terminology.However the final digit of EIA's published
supply data is questionable. For additional information on
survey methodology and statistical reliability, the reader is referred to the
EIA reference cited in the tables or the EIA Internet site at www.eia.doe.gov. FUEL AND ENERGY CONSUMPTION TABLE 4-1. Overview of U.S. Petroleum
Production, Imports, Exports, and Consumption TABLE 4-2.U.S. Consumption of Energy from Primary Sources by Sector TABLE 4-3. Domestic Demand for Refined
Petroleum Products by Sector TABLE 4-4. U.S. Energy Consumption by the
Transportation Sector TABLE 4-7. Domestic Demand for Gasoline Petroleum consumption is far more
complex to measure than supply.Instead
of a few hundred companies at most measuring points in the supply system, there
are tens of millions of consumers.It
would be impossible for any survey of individual consumers to produce the high
rate of return of U.S. Department of Energy (DOE), Energy Information
Administration's (EIA's) supply surveys. EIA's transportation data collection
is further limited by the termination of the Residential Transportation Energy
Consumption Survey (RTECS).Therefore,
EIA uses surveys of sales of products (e.g., Form EIA-821:Annual Fuel Oil and Kerosene Sales Report)
or tax collection data from the U.S. Department of Transportation, Federal
Highway Administration (FHWA). EIA reviewed the accuracy of its
energy consumption data in a 1990 monograph Energy
Consumption by End-Use Sector, a Comparison of Measures by Consumption and
Supply Surveys.Unfortunately, this
monograph does not discuss the transportation sector because the consumption
and supply surveys were not comparable.
However, some of the results from other sectors indicate the
discrepancies between supply and consumption surveys.Table 4-2 shows the ratio of fuel supplied to the sector to
consumption reported by the sector in consumption surveys. In most cases, supply is reported
as substantially larger than consumption.
Supplies of fuel oil to the commercial sector are reported at almost
twice the level of consumption reported by that sector.Some of the discrepancies may be due to
definition differences (e.g., fuel oil for apartment buildings is included in
commercial supply surveys but not in consumption surveys.)Overall, however, the differences are too
large for great confidence in the accuracy of the data. If transportation had been
reviewed in the same format, it is likely that the discrepancies would have
been larger.Most transportation fuel
(gasoline for automobiles) is purchased in small quantities at irregular
intervals and cannot be checked simply by looking at a utility bill.Hence, highway transportation energy
consumption surveys must be extensive to avoid the risk of large uncertainties
in the data.But, with the termination
of the RTECS, EIA ceased conducting such surveys.Consumption data must be derived indirectly from sales of
petroleum products and tax collection data.
While petroleum supply may be accurate to one decimal place, it is
likely that disaggregating by sector use may be within plus or minus several
percentage points, or perhaps about half a quadrillion British thermal unit
(Btu) in table 4-1.
Table 4-2. Reported
Ratio of Fuel Supply to Reported Consumption
Sector |
Electricity |
Gas |
Oil |
Residential |
1.05 |
.92 |
.92 |
Commercial |
0.91 |
1.38 |
1.96 |
Industrial |
1.18 |
1.28 |
1.34 |
SOURCE:U.S. Department of Energy, Energy
Information Administration, Energy Consumption by End-Use Sector, A Comparison
of Measures by Consumption and Supply Surveys, DOE/EIA-0533 (Washington,
DC:1990).
Motor Gasoline Almost all gasoline is consumed in the transportation sector.Small amounts are used in the commercial
sector for nonhighway use and the industrial sector, which includes
agriculture, construction, and other uses.
Subtracting estimates of those uses from the known total sales yields
the transportation sector's total, which is further subdivided into highway and
marine use.Aviation gasoline is, of
course, used entirely in the transportation sector (for a very few
high-performance automobiles as well as small aircraft). Data on actual sales is collected
by the states for revenue purposes.
These data are forwarded to FHWA.
EIA uses the data from FHWA to allocate highway consumption of motor
gasoline among the states. For 1998, FHWA reported 124.7 billion gallons of
gasoline sold nationally for highway use.
EIA's table 5.12b of the Annual
Energy Review 1999 lists 8.13 mmbd of gasoline supplied for the
transportation sector, the same as 124.7 billion gallons. Such close agreement between
supply and demand is not totally convincing.
Definitions are unique to each state (e.g., whether gasohol is counted
as pure gasoline or part gasoline and part renewables), measurement points vary
from state to state, and each state handles losses differently.Hence, the total of all states' sales of
gasoline is not entirely consistent. Separation of highway from
nonhighway uses of gasoline is, by necessity, based in part on careful
estimates.Nevertheless, overall
gasoline sales are well documented, and the separation is probably fairly
accurate.Refinery output of motor
gasoline was 7.94 mmbd in 1999, which is probably accurate to the first decimal
place and maybe a little better.The
transportation sector's 8.13 mmbd would have about the same accuracy. Diesel Fuel Diesel fuelis used in highway vehicles, railroads,
boats, and military vehicles.Sales are
only about 30 percent of gasoline in the transportation sector, but
uncertainties are greater.More diesel
than gasoline is used for nonhighway purposes, especially agriculture and
construction.In addition, there has
been more potential for cheating to avoid the tax; heating oil is virtually the
same as diesel fuel and can easily be transferred to a vehicle.However, this is less significant now that
tracers have been added to fuel oil.After
the addition of tracers, the amount of transportation diesel fuel use jumped. To estimate diesel fuel sales by
mode, EIA starts with the total supply of distillate fuel and subtracts the
small amount sold to electric utilities (the most accurately known sector, as
measured by EIA Form EIA-759).The
remainder is divided among the other end-use sectors according to EIA's sales
surveys (Form EIA-821:Annual Fuel Oil
and Kerosene Sales Report, and Form EIA-863:
Petroleum Product Sales Identification Survey). This method introduces several
potential elements of inaccuracy.First,
the surveys of each sector are probably less accurate than the supply surveys
noted earlier.Companies and
individuals may inadvertently send incorrect data, or not respond at all.Then EIA has to determine what adjustment
factor to use for each end-use sector.
Since each sector will have a different response rate to the surveys,
the adjustments will be different.
Large adjustments can introduce large errors.EIA has not published its adjustments for the transportation
sector.As shown in table 2, the
adjustments in other sectors range from 5 to 96 percent of reported
consumption.Even a 20 percent
adjustment could introduce an error of one or two percentage points (plus or
minus) for any one sector. Overall, the accuracy of diesel
fuel use in the transportation sector should be viewed with some skepticism. Jet Fuel Jet fuel is the only other
petroleum-based fuel that is used in large quantities (over 1 million
barrels/day) in the transportation sector.
Virtually all of it is used by airlines.These data are accurate because airlines are required to report
usage, and because there are relatively few certificated air carriers, data
collection should be manageable. NONPETROLEUM FUELS CONSUMPTION TABLE 4-10. Estimated Consumption of
Alternative and Replacement Fuels for Highway Vehicles Collectively, oxygenates, natural
gas, electricity, and various alternative fuels amount to only about 3 percent
of all energy used in the transportation sector.While this may not be much greater than the error bars associated
with petroleum use, it is important to track changes in these fuels accurately.
Oxygenates Oxygenates, mostly methyl
tributyl ether (MTBE), which is derived from natural gas and ethanol, are part
of mainstream gasoline supply.They are
measured routinely with petroleum supply (forms EIA-819A and 819M).Consumption is estimated from production,
net imports, and stock changes.
Refineries and other entities are required to report data on oxygenates,
and EIA also monitors production capability to provide a crosscheck.Thus, oxygenates data are likely to be
reasonably accurate. Natural Gas Natural gas is used in the
transportation sector mainly as the fuel for compressor stations on natural gas
transmission lines.A small but growing
amount is used in compressed or liquefied form in vehicles.EIA collects data on natural gas much as it
does for petroleum, but the system is much simpler.Natural gas transmission companies may not know exactly how much
gas is used in compressor stations, but they have a good idea based on the size
of the equipment and the load on the line.
The reported numbers probably are reasonably accurate.Data on natural gas-fueled vehicles are
collected by DOE via Form-886, which is sent to fuel suppliers, vehicle manufacturers,
and consumers.In addition, private
associations and newslettersare
important sources of information on alternative vehicles and alternative fuels
use.Since most groups work
cooperatively with DOE, it is likely that the data reported are accurate.EIA tracks the number of natural gas
vehicles and the number of refueling stations to provide a cross check on
estimates of natural gas consumption. Electricity Electricity powers intercity
trains (Amtrak) and intracity rail systems.
In addition, the number of electric vehicles is growing.There is considerable uncertainty over the
energy consumed by these modes.Amtrak
no longer provides national totals of its electricity consumption.Data on intracity transit is based on U.S.
Department of Transportation, Federal Transit Administration's (FTA's) National
Transit Database that contains information for directly operated services by
federally funded transit agencies.
Section 15 of the Federal Transit Act requires that these agencies provide
detailed financial and operating information, including energy use. Although
the data is generally considered accurate because FTA reviews and validates
information submitted, reliability may vary because some transit agencies
cannot obtain accurate information or may misinterpret certain data. If electric vehicles become
important over the next decade or two, dedicated charging stations may become
commonplace, which could provide accurate data.Fleet owners (e.g., electric utilities) can keep accurate
records, but individuals who plug their vehicles in at home may not.Electricity use must be estimated from the
number of such vehicles and the expected driving cycles.Hence, data on electric power for
transportation must be viewed as an estimate. It should also be noted that
electricity is a form of work that usually is generated from heat with the loss
of about two-thirds of the energy.
Automobile engines are equivalent to electric generators in that they
convert chemical energy to heat and then to work, losing most of the energy as
waste heat.When electrical energy is
compared to petroleum in transportation, the waste heat must be included for
consistency. A kilowatt-hour of
electricity is equivalent to 3,413 British thermal units (Btu), but about
10,000 Btu of heat are required to produce it.
This factor is dropping as generators become more efficient.High efficiency gas turbines may require
8,000 Btu or less, but the average is much higher.It is usually impossible to tell where the power for a specific
use is generated, so average figures for a region are used to estimate the
waste energy, a factor that further reduces the accuracy of the data. Alternative Fuels In addition to oxygenates,
natural gas, and electricity, alternative fuels include ethanol and
methanol.EIA tracks the numbers of
such vehicles through Form-886, state energy offices, federal demonstration
programs, manufacturers, and private associations.These numbers probably are fairly accurate although it is
difficult to monitor retirements.Fuel
consumption is estimated from the types of vehicles in operation, vehicle miles
traveled, andexpected fuel efficiency.Adjustments are necessary for the
relatively few flexible-fuel vehicles.
Obviously, the reported data are estimates only. FUEL AND ENERGY CONSUMPTION
BY MODE TABLE 4-5. Fuel Consumption by Mode of
Transportation TABLE 4-6. Energy Consumption by Mode of
Transportation TABLE 4-8. Certificated Air Carrier Fuel
Consumption and Travel TABLE 4-9. Motor Vehicle Fuel Consumption
and Travel TABLE 4-11. Passenger Car and Motorcycle
Fuel Consumption and Travel TABLE 4-12. Other 2-Axle 4-Tire Vehicle
Fuel Consumption and Travel TABLE 4-13. Single-Unit 2-Axle 6-Tire or
More Truck Fuel Consumption and Travel TABLE 4-14. Combination Truck Fuel
Consumption and Travel TABLE 4-15. Bus Fuel Consumption and
Travel TABLE 4-16. Transit Industry Electric
Power and Primary Energy Consumption and Travel TABLE 4-17. Class I Rail Freight Service
Fuel Consumption and Travel TABLE 4-18. Amtrak Fuel Consumption and
Travel Fuel consumption data are
collected quite differently than supply data collected by the U.S. Department
of Energy, Energy Information Administration (EIA).Highway fuel consumption, for example, is based on U.S. Department
of Transportation, Federal Highway Administration (FHWA) data collected from
states in the course of revenue collection.
EIA starts from the fuel delivered to transportation entities. Highway Highway fuel data (tables 4-5,
4-9, and 4-11 through 4-15) are collected mainly by FHWA.All states plus the District of Columbia
report total fuel sold along with travel by highway category and vehicle
registration.Data typically flows from
state revenue offices to the state departments of transportation to FHWA.Even if reporting is reasonably accurate,
some data are always anomalous or missing and must be modified to fit expected
patterns.In addition, as discussed
earlier, there are some significant differences in methodology and definitions
among the states. In particular,
states differ in where the tax is applied in the fuel supply system, how
gasohol is counted, how nonhighway use is treated, and how losses are handled. Nonhighway use of gasoline and
diesel fuel is a particularly large source of potential error.Some states designate nonhighway users as
tax-exempt, others make the tax refundable.
In either case, many people won't bother to apply if the amount of money
is small.Nonhighway use of diesel fuel
is especially large because many construction and agricultural vehicles are
diesel powered.Thus, the fraction of
petroleum attributed to transportation could be overestimated.On the other hand, some nonhighway fuel finds
its way into the transportation system because heating oil can be used as diesel
fuel, evading the tax.Tracers are now
added to heating oil, which appears to have reduced the level of such tax
evasion-if found in a truck's fuel tank, the tracer indicates diversion from a
nontaxed source. Breaking fuel use down by class
of motor vehicle introduces the potential for error.FHWA must estimate the miles each class is driven and the fuel
economy.Estimation of miles is based
on the 1995 Nationwide Personal Transportation Survey (NPTS), administered by
FHWA, and the Vehicle Inventory and Use Survey (formerly known as the Truck
Inventory and Use Survey) conducted by the U.S. Census Bureau.For information about these two surveys, the
reader is referred to the technical appendix of Our Nation's Travel, available from the FHWA, Office of Highway
Information Management; and the 1997 Census of Transportation, available from
the Economics and Statistics Administration within the Census Bureau.Fuel economy is based on state-supplied
data, TIUS, and the National Highway Traffic Safety Administration data on new
car fuel economy, which must be reduced by about 15 percent to reflect actual
experience on the road.Overall, both
vehicle-miles of travel and fuel economy are estimates. Fuel consumption by buses is
particularly uncertain.FHWA collects
data on intercity buses, and the American Public Transit Association (APTA)
covers local travel.Very little data
are collected on school buses.APTA
figures are based on data from the USDOT, Federal Transit Administration's
(FTA's) National Transit Database, which covers about 90 to 95 percent of total
passenger-miles.These data are
generally accurate because FTA reviews and validates information submitted by
individual transit agencies.
Reliability may vary because some transit agencies cannot obtain
accurate information or may misinterpret data.
APTA conservatively adjusts the FTA data to include transit operators
that do not report to FTA, such as private and very small operators and rural
operators.Prior to 1984, APTA did not
include most rural and demand responsive systems. Air The U.S. Department of
Transportation, Bureau of Transportation Statistics, Office of Airline
Information (OAI) is the source of these data.
The numbers are based on 100-percent reporting of fuel use by large
certificated air carriers (those with revenues of more than $100 million
annually) via Form 41.The data are
probably reasonably accurate because the airlines report fuel use regularly,
and the limited number of airlines aids data management. Smaller airlines, such as medium
size regional and commuter air carriers, are not required to report energy
data.OAI estimates that about 8
percent would have to be added to the total of the larger airlines to account
for this use, but that has not been done in table 4-5 or 4-8. General aviation aircraft and air
taxis are covered in the General Aviation and Air Taxi and Avionics Survey,
conducted by the Federal Aviation Administration (FAA). The survey is conducted annually and
encompasses a stratified, systematic design from a random start to generate a
sample of all general aviation aircraft in the United States.It is based on the FAA registry as the
sampling frame.For instance, in 1997,
a sample of 29,954 aircraft was identified and surveyed from an approximate
population of 251,571 registered general aviation aircraft. The reliability of the GAATA
survey can be impacted by two factors:
sampling and nonsampling error.
A measure, called the standard error, is used to indicate the magnitude
of sampling error.Standard errors can
be converted for comparability by dividing the standard error by the estimate
(derived from the sample survey results) and multiplying it by 100.This quantity, referred to as the percent
standard error, totaled seven-tenths of a percent in 1997 for the general
aviation fleet.A large standard error
relative to an estimate indicates lack of precision, and inversely, a small
standard error indicates precision. Nonsampling errors could include
nonresponse, a respondent's inability or unwillingness to provide correct
information, differences in interpretation of questions, and data entry
mistakes.The reliability of general
aviation fleet data comparisons over time would decrease because of changes
implemented in 1978 and sampling errors discussed above.Readers should note that nonresponse bias
may be a componentof reliability
errors in the data from 1980 to 1990.
The FAA conducted telephone surveys of nonrespondents in 1977, 1978, and
1979 and found no significant differences or inconsistencies between respondent
and nonrespondent replies.The FAA
discontinued the telephone survey of nonrespondents in 1980 to save costs.Nonresponse surveys were resumed in 1990;
and the FAA found notable differences and make adjustments to its data to
reflect nonresponse bias. The U.S. Government, in
particular the Department of Defense (DOD), uses a large amount of jet fuel as
shown in table4-19 (see discussion on
government consumption below).However,
DOD reports all fuel purchased, including from foreign sources for operations
abroad.While the data may be accurate,
it is not comparable to EIA's overall U.S. supply and consumption figures on
jet fuel. International operations are
included in table 4-8 but not table 4-5.
The fuel use for international operations includes that purchased by
U.S. airlines for return trips.OAI
does not collect data on foreign airline purchases of fuel in the United
States.Thus, a significant use of U.S.
jet fuel is missed.However, these two
factors approximately balance each other out.
As shown in table 1-34,foreign
carrier traffic is just slightly less than U.S. carrier international traffic,
so presumably the fuel purchased here by foreign carriers is very close to the
fuel purchased abroad by U.S. carriers. Rail The data are from Railroad Facts, published annually by
the Association of American Railroads (AAR).
AAR figures are based on 100 percent reporting by Class I railroads to
the Surface Transportation Board (STB) via Schedule 700 of the R1 Annual Report.Thus, the data are considered accurate.STB defines Class I railroads as having
operating revenues at or above a threshold indexed to a base of $250 million
(1991) and adjusted annually in concert with changes in the Railroad Freight Rate
Index published by the Bureau of Labor Statistics.In 1998, the adjusted threshold for Class I railroads was $259.4
million.Although Class I railroads
represent only 2 percent of the number of railroads in the country, they
account for over 70 percent of the industry's mileage operated and more than 90
percent of all freight revenue; energy consumption should be of the same
order.For passenger travel,
information is unavailable.Amtrak no
longer provides data on a national basis, and the regional data appears to be
inconsistent. Transit The APTA figures are based on
information in FTA's National Transit Data Base.APTA conservatively adjusts FTA data to include transit operators
that do not report to the FTA Database (private and very small operators and
rural operators), which accounts for about 90 to 95 percent of the total
passenger-miles.The data are generally
accurate because the FTA reviews and validates information submitted by
individual transit agencies.
Reliability may vary because some transit agencies cannot obtain
accurate information or misinterpret certain data definitions in federal
guidelines. Water The EIA collects data on residual
and distillate fuel oils and diesel through its Annual Fuel Oil and Kerosene Sales Report survey, form
EIA-821.The survey targets companies
that sell fuel oil and kerosene to end users.
This survey commenced in 1984 and data from previous years should be
used with caution. Sampling Frame and Design The sample's target universe
includes all companies that sell fuel oil and kerosene to end users.EIA derives the sampling frame from the
EIA-863 database containing identity information for approximately 22,300 fuel
oil and kerosene sellers.EIA
stratifies the sampling frame into two categories: companies selected with
certainty and uncertainty.Those in the
certainty category varied but included the end use "vessel
bunkering,"or sales for the fueling of commercial and private watercraft. Sampling Error, Imputation, and Estimates EIA reported a 91.3 percent
response rate for the 1999 survey.The
EIA also provides estimates of the sampling error for geographic areas and U.S.
averages are 1.3 for residential distillate fuel oil, 0.9 for nonresidential retail
distillate fuel oil, and 0.1 for retail residual fuel oil.Some firms inevitably ignore survey
requests, causing data gaps.EIA
estimates the volumes of these firm's sales by imputation; more detailed
information and the algorithm can be obtained at EIA's web site in the
technical notes for the Annual Fuel Oil and Kerosene Sales Report.See http://www.eia.doe.gov/oil_gas/petroleum/data_publications/fuel_oil_and_kerosene_sales/foks.html. TABLE 4-19. U.S. Government Energy
Consumption by Agency and Source Energy consumption data are collected
by DOE's Office of Federal Energy Management Programs in cooperation with most
departments and agencies.DOD is by far
the largest consumer, accounting for about 80 percent of the total.As discussed above, the data includes fuel
purchased abroad for military bases.
Since government agencies are required to report these data, they are
probably accurate.However, it is
possible that some consumption is missed.
For example, some agencies may report only fuel supplied directly,
missing consumption such as gasoline purchased by employees while on government
business for which they are then reimbursed.
In addition, smaller agencies were neglected.Overall, however, the data should provide a fairly good
approximation of government energy consumption. ENERGY EFFICIENCY TABLE 4-20. Energy Intensity of Passenger
Modes TABLE 4-21. Energy Intensity of
Certificated Air Carriers, All Services TABLE 4-22. Energy Intensity of Passenger
Cars, Other 2-Axle 4-Tire Vehicles, and Motorcycles TABLE 4-24. Energy Intensity of Transit
Motor Buses TABLE 4-25. Energy Intensity of Class I
Railroad Freight Service TABLE 4-26. Energy Intensity of Amtrak
Service Total energy consumed for each
mode can be estimated with reasonable accuracy.Miles traveled are known for some modes, such as air carriers,
but less accurately for others, most notably automobiles.When the numbers of passengers or tons are
required to calculate energy efficiency, another uncertainty is introduced.Again, air carriers and intercity buses know
how many passengers are on board and how far they travel, but only estimates
are available for automobiles and intracity buses. Thus, table 4-21 should be quite
accurate for certificated air carriers, though it is missing small airlines and
private aircraft.Table 4-22 is based
on FHWA fuel tax data, derived from state fuel tax revenues. VMT is as
discussed for tables 1-9 and 1-10.Data
for motorcycles must be adjusted significantly more than for automobiles
because less information is collected from the states or from surveys.Transit bus data (table 4-24) are very
uncertain because, unlike intercity buses, the distance each passenger travels
is not measured by ticket sales. The intermodal comparison of
passenger travel in table 4-20 must be viewed with considerable caution.Data for the different modes are collected
in different ways, and the preparation of the final results is based on
different assumptions. As noted above,
airlines accurately record passenger miles, but the data on occupancy of private
automobiles must be estimated from surveys.
Even relatively certain data, such as state sales of gasoline, must be
modified to resolve anomalies, and transit data are even harder to make
consistent.Furthermore, different
groups collect the data for the various modes, and they have different needs,
assumptions, and methodologies.Thus,
the comparisons are only approximate. Freight service data (table 4-25)
are from Railroad Facts, published
annually by the Association of American Railroads (AAR).AAR figures are based on 100 percent
reporting by Class I railroads to the Surface Transportation Board (STB) via
Schedule 700 of the R1 Annual Report.STB defines Class I railroads as having
operating revenues at or above a threshold indexed to a base of $250 million
(1991) and adjusted annually in concert with changes in the Railroad Freight
Rate Index published by the Bureau of Labor Statistics.In 1998, the adjusted threshold for Class I
railroads was $259.4 million.Although
Class I railroads comprise only 2 percent of the number of railroads in the
country, they account for over 70 percent of the industry's mileage and 90
percent of all freight revenue; energy data should be of the same order. TABLE 4-27. Annual Wasted Fuel Due to
Congestion TABLE 4-28. Wasted Fuel per Eligible
Driver The Texas Transportation
Institute's (TTI) Urban Roadway
Congestion Annual Report provided figures for tables 4-27 and 4-28.TTI relies on data from the U.S. Department
of Transportation, Federal Highway Administration, Highway Performance
Monitoring System database (HPMS). (See box 1-1 for detailed information about
the HPMS.)TTI utilizes these data as
inputs for its congestion estimation model.
Detailed documentation for the TTI model and estimations can be found at
http://mobility.tamu.edu/study/index.stm.
The sum of fuel wasted in typical
congestion (recurring delay) and incident related delays equal the annual
wasted fuel for an urban area. Recurring delay is the product of recurring
delay (annual hours in moderate, heavy, and severe delays) and average peak
period system speed divided by average fuel economy. Incident delay hours are
multiplied by the average peak period system speed and divided by the average
fuel economy to produce the amount of incident fuel wasted. Structure, Assumptions, and Parameters Urban roadway congestion levels
are estimated using a formula measuring traffic density. Average daily travel
volume per lane on freeways and principal arterial streets are estimated using
area wide estimates of vehicle-miles of travel and lane miles of roadway. The
resulting ratios are combined using the amount of travel on each portion of the
system (freeway and principal arterials) so that the combined index measures
conditions overall.This variable
weighting factor allows comparisons between areas such as Phoenix-where
principal arterial streets carry 50 percent of the amount of travel of
freeways-and cities such as Phoenix where the ratio is reversed.Values greater than one are indicative of undesirable
congestion levels.Readers seeking the
algorithm for the congestion index should examine http://mobility.tamu.edu/study/numbers.stm. In previous reports, TTI assumed
that 45 percent of all traffic, regardless of the urban location, occurred in
congested conditions.TTI indicated
that this presumption overestimated travel in congested periods.Its 1997 estimates now vary by urban area
anywhere from 21 to 50 percent of travel that occurs in congestion.TTI's model structure applies to two types
of roads: freeways and principal arterial streets.The model derives estimates of vehicle traffic per lane and
traffic speed for an entire urban area.
Based on variation in these amounts, travel is then classified under 5
categories: uncongested, moderately congested, heavily congested, severely
congested, and extremely congested (a new category in 1999).The threshold between uncongested and
congested was changed in 1999.Previous
editions classified congested travel when area wide traffic levels reached
13,000 vehicles per lane per day on highways and 5,000 vehicles per lane per
day on principal arterial streets.
These thresholds were raised in the latest report to 14,000 and 5,500
vehicles per lane per day respectively.
Comparisons across time will be questionable due to these changes.For example, TTI applied the new methodology
to 1996 data that resulted in lower congestion levels.Readers should refer to the TTI website for
more detailed information on its estimation procedures http://mobility.tamu.edu/estimating_mobility. TTI reviews and adjusts the data
used in its model, including statewide average fuel cost estimates (published
by the American Automobile Association) and the number of eligible drivers for
each urban area (taken from the Statistical Abstract of the United States,
published by the U.S. Department of Commerce, Bureau of the Census).The model has some limitations because it
does not include local variations (such as bottlenecks, local travel patterns,
or transportation improvements) that affect travel times.TTI documentation does not provide
information on peer-review, sensitivity analysis, or estimation errors for
their model.Information about
sensitivity analysis or external reviews of the model could not be obtained and
users should interpret the data cautiously. ENVIRONMENT TABLE 4-38. Estimated National Average
Vehicle Emissions Rates by Vehicle Type and Fuel TABLE 4-39. National Average Vehicle
Emissions Rates by Vehicle Type Using Reformulated Gasoline The U.S. Environmental Protection
Agency uses its Mobile Source Emissions Factor Model (MOBILE) to generate
average emissions factors for each vehicle and fuel type.The methods used in the model are
theoretically sound, the assumptions are reasonable, but the data vary in
quality, and no formal analysis of the accuracy of these estimates has been
performed.Emissions rate estimates for
light-duty vehicles are considered more reliable than those for heavy-duty
vehicles because in-use emissions tests are performed on a sample of vehicles
each year.Deterioration for heavy-duty
vehicles in the national fleet are based only on manufacturer's engine
deterioration tests.In addition,
because reformulated fuels (table 4-37) are newer than other gasoline fuels
(table 4-36), in-use emissions test data for reformulated fuels are not as
extensive. The estimates in the tables
represent average emissions rates taking into account the characteristics of
the nation's fleet, including vehicle type and age, and fuel used.The model also assumes Federal Test
Procedure conditions.The model does
not take into account actual travel distributions across different highway
types with their associated average speeds and operating mode fractions, nor do
they consider ambient local temperatures.
However, fleet composition and deterioration because of age are
considered.Thus, these rates
illustrate only trends due to vehicle emissions control improvements and their
increasing use in the national fleet and should not be used for other purposes. TABLES 4-40, 4-41, 4-42, 4-43, 4-44, 4-45
and 4-46. Estimates of National Emissions of Carbon Monoxide, Nitrogen Oxides,
Volatile Organic Compounds, Particular Matter, Sulfur Dioxide, and Lead Emissions by sector and source
are estimated using various models and calculation techniques and are based on
a number of assumptions and on data that vary in precision and
reliability.The methods used are
theoretically sound, the assumptions are reasonable, but the data vary in
quality, and no formal analysis of the accuracy of these estimates has been
performed. Carbon Monoxide (CO), Nitrogen Oxides (NOx),
and Volatile Organic Compounds (VOCs) Highway vehicle emissions of CO,
NOx, and VOC are generated by the U.S. Environmental
Protection Agency's (EPA's) Mobile Source Emissions Factor Model (MOBILE),
which uses per-mile vehicle emissions factors and vehicle travel
(vehicle-miles) to calculate county-level emissions.Emissions rates are then adjusted based on fuel characteristics,
vehicle fleet composition, emissions control measures, average vehicle speed,
and other factors that can affect emissions.
(Emissions rates used in MOBILE are based on vehicle certification
tests, emissions standards, and in-use vehicle tests and are updated
approximately every three years.)The
U.S. Department of Transportation, Federal Highway Administration's Highway
Performance Monitoring System is the source of vehicle travel estimates used in
the model.Although the methodology for
this survey data is sound and well documented, analyses have shown that
individual states vary in how rigorously they follow the established sampling
guidelines. Nonhighway vehicle emissions are
calculated by applying a growth factor (based on modal activity trends) to
modal emissions estimates from the most recently conducted state emissions
inventories.These emissions
inventories are typically estimated every three years in accordance with the
Clean Air Act of 1970,1but the methodology may vary among states
and by year.
1
Public Law 91-604, 84 Stat. 1705 (December 31, 1970). |
Particulate Matter Under 10 Microns (PM-10)
and 2.5 Microns (PM-2.5) in Size Highway vehicle emissions are
estimated using the U.S. Environments Protection Agency's PART model, which
estimates emissions factors for exhaust emissions and brake and tire wear by
vehicle type.Exhaust emissions factors
are based on certification tests, while brake wear (per vehicle) and tire wear
(per tire) are assumed values which are constant over all years.Per-mile emissions factors are multiplied by
vehicle travel (vehicle-miles) and adjusted to account for other factors that
effect exhaust emissions (e.g., fuel composition, weather, etc.).The U.S. Department of Transportation,
Federal Highway Administration's Highway Performance Monitoring System is the
source of vehicle-miles of travel (vmt) estimates used in the model.While the methodology for this survey data
is sound and well documented, analyses have shown that individual states vary
in how rigorously they follow the established sampling guidelines. Fugitive dust estimates for paved
and unpaved roads are calculated by multiplying vmt on each type of road by
emissions factors for each vehicle type and road type. Nonhighway vehicle emissions are
calculated by applying a growth factor (based on modal activity trends) to
modal emissions estimates from the most recently conducted state emissions
inventories.These emissions
inventories are typically estimated every three years in accordance with the
Clean Air Act of 1970, but the methodology may vary among states and by year. Sulfur Dioxide (SO2) Highway vehicle SO2
emissions are estimated by multiplying vehicle travel (for each vehicle type
and highway type) by an emissions factor reflecting each vehicle type and
highway type.Highway SO2
emissions factors are based on vehicle type and model year, sulfur content of
fuel by type and year, fuel density by fuel type, and vehicle fuel efficiency
by type and model year. In general, estimates for
nonhighway vehicles are calculated based on fuel consumption and sulfur content
of fuel, though other factors may be considered. Lead In general, lead emissions are
estimated by multiplying an activity level by an emissions factor that
represents the rate at which lead is emitted for the given source
category.This estimate is then
adjusted by a factor that represents the assumed effectiveness of control
technologies.For lead released during
combustion, a top-down approach is used to share national estimates of fuel
consumption by fuel type to each consumption category (e.g., motor fuel, electric
utility, etc.) and, subsequently, each source (e.g., passenger cars, light-duty
trucks, etc.). TABLE 4-47. Air Pollution Trends in
Selected Metropolitan Statistical Areas (MSAs) TABLE 4-48. Areas in Nonattainment of
National Ambient Air Quality Standards for Criteria Pollutants The U.S. Environmental Protection
Agency measures concentrations of pollutants in the ambient air at its air
quality monitoring sites, which are operated by state and local agencies.These sites conform to uniform criteria for
monitor siting, instrumentation, and quality assurance, and each site is
weighted equally in calculating the composite average trend statistics.Furthermore, trend sites must have complete
data for 8 of the 10 years in the trend time period to be included. However,
monitoring devices are placed in areas most likely to observe significant
concentrations of air pollutants rather than a random sampling of sites
throughout the nation. TABLE 4-49. U.S. Carbon Dioxide Emissions
from Energy Use by Sector The combustion of fossil fuels,
such as coal, petroleum, and natural gas, is the principal anthropogenic (human
caused) source of carbon dioxide (CO2)
emissions.Since fossil fuels are
typically 75 percent to 90 percent carbon by weight, emissions from the combustion
of these fuels can be easily measured in carbon units, as is shown in the
table. CO2 emissions data
are derived from estimates.The U.S.
Department of Energy, Energy Information Administration (EIA), estimates CO2emissions by multiplying energy consumption
for each fuel type by its carbon emissions coefficient, then subtracting carbon
that is sequestered by nonfuel use of fossil fuels.Carbon emissions coefficients are values used for scaling
emissions to specific activities (e.g., pounds of CO2emitted per barrel of oil consumed). Emissions estimates are based on
energy consumption data collected and published by EIA Several small
adjustments are made to its energy consumption data to eliminate double
counting or miscounting of emissions.For
example, EIA subtracts the carbon in ethanol from transportation gasoline
consumption because of its biological origin. Emissions coefficients are based
on the density, carbon content, and heat content of petroleum products.For many fuels, except liquefied petroleum
gas (LPG), jet fuel, and crude oil, EIA assumed coefficients to be constant
over time.For LPG, jet fuel, and crude
oil, EIA annualized carbon emissions coefficients to reflect changes in
chemical composition or product mix. Since the combustion of fossil
fuels is a major producer of CO2emissions, sources of uncertainty are
related to: 1) volumes of fuel consumed; 2) characteristics of fuel consumed;
3) emissions coefficients; and 4) coverage.
EIA notes that volumetric fuel data are fairly reliable in the 3 percent
to 5 percent range of uncertainty.The
density and energy content of fuels are usually estimated.According to EIA, the reliability of these estimates
vary.For example, estimates of the
energy content of natural gas are reliable to 0.5 percent, while estimates for
coal and petroleum products are lower because they are more heterogeneous
fuels. The reliability of emissions coefficients depends on whether the
characteristics of a fuel are difficult to measure accurately.Finally, uncertainties may result because
data may be excluded or unknown sources of emissions not included. EIA's estimation methods,
emissions coefficients, and the reliability of emissions estimates are
discussed in detail in U.S. Department of Energy, Energy Information
Administration, Emissions of Greenhouse
Gases in the United States, 1998 available on
www.eia.doe.gov/oiaf/1605/ggrpt/index.html. TABLE 4-50. Annual Oil Spills in U.S.
Navigable Waters by Vessel Type The U. S. Coast Guard's (USCG) Marine
Safety Information System (MSIS) is the source of these data.It includes data on all oil spills impacting
U.S. navigable waters and the Coastal Zone.
The USCG learns of spills through direct observation, reports from
responsible parties and third parties.
Responsible parties are required by law to report spills to the National
Response Center (NRC).Reports may be
made to the USCG or Environmental Protection Agency pre-designated On Scene
Coordinator for the geographic area where the discharge occurs if direct
reporting to the NRC is not practicable.
There is no standard format for these reports, but responsible personnel
face significant penalties for failing to do so.Most reports are made by telephone, and USCG personnel complete
investigations based on the information provided.The type and extent of an investigation conducted varies
depending on the type and quantity of the material spilled.Each investigation will determine as closely
as possible source of the pollutant, the quantity of the material spilled, the
cause of the accident, as well as whether there is evidence that any failure of
material (either physical or design) was involved or contributed to the
incident.These are so financial responsibility
may be properly assigned for the incidents, as well as proper recommendations
for the prevention of the recurrence of similar incidents may be made. Some
spills may not be entered into MSIS because they are either not reported to or
discovered by the USCG.The probability
of a spill not being reported is inversely proportional to its size.Large spills impact a large area and a large
number of people, resulting in numerous reports of such spills.Small spills are less likely to be reported,
particularly if they occur at night or in remote areas where persons other than
the responsible party are unlikely to detect them.Responsible parties are required by law to report spills and face
penalties for failing to do so, providing a strong incentive to report spills
that might be detected by others.
Experience with harbor patrols shows that the number of spills increases
as the frequency of patrols increases.
However, the volume of material spilled does not increase significantly,
indicating that the spills discovered through increased harbor patrols
generally involved very small quantities. Data Collection From 1973 to 1985, data were
collected on forms completed by the investigator and later entered into the
Pollution Incident Reporting System (PIRS) by data entry clerks at USCG headquarters.Since 1985, data have been entered directly
into MSIS by the investigator.From
1985 to 1991, data were entered into a specific electronic form that captured
information on the spilled substance and pollution response actions.Since 1995, a growing number of reports of
pollution incidents of 100 gallons or less of oil have been captured on a
Notice of Violation ticket form, which are then entered into MSIS. The information shown in this
table comes from the USCG Spill Compendium, which contains spill data from the
applications described above.The
Compendium contains summary data from 1969 through 1999 and is intended to
provide general information to the public, the maritime industry and other
interested persons about spills in and around U.S. waterways.For more information about spill data,
please refer to the USCG Internet sit at
http://www.uscg.mil/hq/g-m/nmc/response/stats/aa.htm. Nonsampling Errors According to the USCG,
nonsampling errors, such as nonreporting and mistakes made in data collection
and entry, should not have a major impact on most interpretations of the data,
but the impact will vary depending on the data used.The error rate for volume spilled is estimated to be less than 5
percent because larger spills, which account for most of the volume of oil
spilled, are thoroughly reviewed at several levels.The error rate for the number of spills is difficult to estimate
primarily due to low reporting rates for small spills.Most of the error in spill counts involves
spills of less than 100 gallons. TABLE 4-51. Leaking Underground Storage
Tank Releases and Cleanups A national inventory of reported
spills and corrective actions taken for leaking underground storage tanks is
compiled biannually based on state counts of leaking tanks reported by owners
as required by the Resource Conservation and Recovery Act of 1976.2 These data may be affected by general
accounting errors, some of which have changed semiannual counts by as many as
2,000 actions.
2
Public Law 94-580, 90 Stat. 2795 (October 21, 1976). |
TABLE 4-52. Highway Noise Barrier
Construction State highway agencies (SHAs)
provide data on highway noise barrier construction, extent, and costs to the
U.S. Department of Transportation, Federal Highway Administration.Individual SHA definitions of barriers and
costs may differ.This could lead to
nonuniformity and/or anomalies among state data, which will in turn affect
national totals. TABLE 4-53. Number of People Residing in
High-Noise Areas Around U.S. Airports The number of the people exposed
to aircraft noise around airports is estimated by computer modeling rather than
by actual measurements.The U.S.
Department of Transportation (USDOT), Federal Aviation Administration's (FAA's)
Integrated Noise Model (INM) has been the primary tool for assessing aircraft
noise around airports for nearly 30 years.
This model uses information on aircraft mix, average daily operations,
flight tracks, and runway distribution to generate and plot contours of Day
Night Sound Level (DNL).With the
addition of a digitized population census database, the model can estimate the
number of residents exposed to noise levels of 65 decibels (db) DNL. The U.S. Environmental Agency
(EPA) produced the first estimate of airport noise exposure in 1975.It reported that 7 million residents were
exposed to significant levels of aircraft noise in 1978.This number became the "anchor
point" for all future estimates of the nationwide noise impacts.In 1980, FAA developed another methodology
for estimating the change in the number of people impacted by noise (from the
1975 anchor value) as a function of changes in both the national fleet and in
the FAA's Terminal Area Forecast (TAF).
In 1990, the FAA created an improved method of estimating the change in
number of people impacted (relative to the 1980 estimates). In 1993, the FAA began using its
newly developed Nationwide Airport Noise Impact Model (NANIM) to estimate the
impact of airplane noise on residential communities surrounding U.S. airports
that support jet operations. FAA uses this model to determine the relative
changes in number of people and land area exposed to 65 db DNL as a result of
changes in nationwide aircraft fleet mix and operations.NANIM uses data on air traffic patterns
found in the Official Airline Guide (OAG), air traffic growth projections found
in FAA's TAF, population figures from the U.S. Census Bureau, and information
on noise contour areas for the top 250 U.S. civil airports with jet operations.
The methodology used in NANIM has
been peer reviewed and approved.
However, a formal evaluation of the model's accuracy has not been
conducted.Some data used in NANIM are
updated manually, thus the possibility of data entry errors does exist. Entries
are reviewed and then corrected as appropriate. The aircraft mix and operations
files from FAA's TAF and OAG are updated automatically.Changes to either of the sources could
introduce errors.For example, it was
recently discovered that OAG redefined some aircraft codes and altered some
data fields in its database.These
changes make it impossible for the NANIM utility program to accurately read the
current OAG database.A rewrite of the
source code is necessary to eliminate this error.Also, since airport authorities are not required to produce noise
exposure maps and reports unless they intend to apply for Federal grants, 14 of
the 50 busiest commercial airports, including JFK and LaGuardia, have not
produced (for public consumption) noise exposure maps in several years.In the absence of actual data, the NANIM
database contains approximations of the noise contours areas based on airports
of similar size and similar operation. Without actual airport data, it is
impossible to quantify the error introduced by the approximation. TABLE 4-54. Motor Vehicles Scrapped The Polk Company's Vehicles in
Operation database is the source of these data.This database is a census of vehicles that are currently
registered in all states within the United States.It is based on information from state department of motor
vehicles.Polk updates the database
quarterly (March, June, September, and December). Scrapped vehicles are those that
Polk removes from its database when: 1) States indicate registered vehicles
have suffered major damage (such as a flood or accident), or 2) No renewal
(reregistration) notice is received by Polk within a state's allotted time
(normally one year).In the latter
case, if a vehicle is subsequently reregistered, it is returned to the
database.The Polk data on motor
vehicles is broken down into passenger cars and trucks, and this identification
comes with the registration data from the DMV. REFERENCES U.S. Department of Energy, Energy
Information Administration. 1994. Accuracy
of Petroleum Supply Data. Tammy G. Heppner and Carol L. French, eds.Washington, DC. U.S. Department of Energy,
Energy Information Administration. 1990.
Energy Consumption by End-Use
Sector, A Comparison of Measures by Consumption and Supply Surveys,
DOE/EIA-0533. Washington, DC. U.S. Environmental Protection
Agency, Office of Mobile Sources.
1998.MOBILE5 Information Sheet #7: NOx Benefits of Reformulated
Gasoline Using MOBILE5a. Ann Arbor, MI.
September 30. U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards.1998.National Air Pollutant Emission Trends,
Procedure Document, 1900-1996.
EPA-454/R-98-008.Research
Triangle Park, NC.May. U.S. Environmental Protection
Agency, Office of Mobile Sources.
1996.Memorandum on Release of
MOBILE5b.(Revised Chapter 2 for the Users
Guide to MOBILE5).October 11. U.S. Environmental Protection
Agency, Office of Air Quality and Standards, Emission Factor and Inventory
Group.1995.Compilation of Air Pollutant
Emission Factors AP-42, Volume II: Mobile Sources. Appendix H. Fifth
ed.June 30. U.S. Environmental Protection
Agency, Office of Mobile Sources (OMS). 1995.
Draft User's Guide to PART5:A Program for Calculating Particle
Emissions from Motor Vehicles, EPA-AA-AQAB-94-2.Ann Arbor, MI.February. U.S. EPA, Office of Mobile
Sources.1994.Users
Guide to MOBILE5 (Mobile Source Emission Factor Model),EPA-AA-TEB-94-01.Ann Arbor, MI.May. U.S. Environmental Protection
Agency, Office of Air and Radiation.
1992.Procedures for Emission Inventory Preparation, Volume IV: Mobile
Sources, EPA-450/4-81-026d (Revised).
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