Appendix E
Data 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 1999, the adjusted threshold
for Class I railroads was $258.5 million. Declassification from Class I status
occurs when a railroad falls below the applicable threshold for three consecutive
years. Although Class I railroads encompasses only 2 percent of the number of
railroads in the country, they account for over 71 percent of the industry's
mileage operated.
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.
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 data are based on information in the U.S. Department of Transportation,
Federal Transit Administration (FTA), National Transit Database (NTD). The legislative
requirement for the NTD is found in Title 49 U.S.C. 5335(a). Transit agencies
receiving funds through the Urbanized Area Formula Program are generally required
to report 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 Urbanized Area Formula 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 Information System (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 1-2. 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 2000, the adjusted threshold for Class I railroads was $261.9 million. Declassification
from Class I status occurs when a railroad falls below the applicable threshold
for three consecutive years. Although Class I railroads encompasses only 1 percent
of the number of railroads in the country, they account for over 71 percent
of the industry's mileage operated.
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 also
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 as soon as information is provided and
verified, and periodic archives are made. Historical data are available from
summary information previously prepared, including tables and reports. MCMIS
began operations in 1980. Safety data since 1990 are available to the public.
Marine Vessel Operators
The U.S. Army Corps of Engineers (USACE) provides the data for marine vessel
operators through 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 1-3. 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. ADA Accessible Rail Transit Stations
by Agency
TABLE 1-9. ADA Lift- or Ramp-Equipped Transit
Buses
These data are based on information in the U.S. Department of Transportation,
Federal Transit Administration (FTA), National Transit Database (NTD). The legislative
requirement for the NTD is found in Title 49 U.S.C. 5335(a). Transit agencies
receiving funds through the Urbanized Area Formula Program are generally required
to report financial and operating data, including certain aspects of station
and vehicle accessibility. Transit operators that do not report to FTA are those
that do not receive Urbanized Area Formula 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 1-10. 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 Transportation
study 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 1-11. Number of U.S. Aircraft, Vehicles,
Vessels, and Other Conveyances
TABLE 1-12. 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 Bike Council
estimated 1997 through 2001 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 2000, the threshold for Class I railroads
was $261.9 million. Although Class I railroads encompasses only 2 percent of
the number of railroads in the country, they account for over 71 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 ship owners 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 1-13. Active Air Carrier and General Aviation
Fleet by Type of Aircraft
Air Carrier, Certificated, All Services
Prior to 1995, data originated from the U.S. Department of Transportation,
Federal Aviation Administration (FAA), FAA Statistical Handbook of Aviation.
Later data are from the Aerospace Industries Association (AIA), Aerospace
Facts and Figures. However, Aerospace Facts and Figures is compiled from
the 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 nonĀresponse 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 2000, the adjusted threshold for Class I railroads was $ 261.9
million. Declassification from Class I status occurs when a railroad falls below
the applicable threshold for three consecutive years. Although Class I railroads
encompasses only 2 percent of the number of railroads in the country, they account
for over 71 percent of the industry's mileage operated.
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 reports
after 1996. Some jurisdictions fail to report by publication deadlines, and
the USCG provided estimates based on the previous year's estimate.
TABLE 1-14. 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 1-15. Annual U.S. Motor Vehicle Production
and Factory (Wholesale) Sales
TABLE 1-16. Retail New Passenger Car Sales
TABLE 1-17. New and Used Passenger Car Sales and
Leases
TABLE 1-18. 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 1-19 and 1-20. 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.
TABLE1-21. Number of Trucks by Weight
These data are derived from the Vehicle Inventory and Use Survey (VIUS) conducted
in 1997 by the U.S. Bureau of the Census. This survey, formerly known as the
Truck Inventory and Use Survey (TIUS), has been conducted every 5 years since
1963. 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.
TABLE 1-22. 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 1-23. 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 ship owners 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 1-24. 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 1-25. 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 1-26. 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 1-27. 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 1-28. 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. The legislative
requirement for the NTD is found in Title 49 U.S.C. 5335(a). Transit agencies
receiving funds through the Urbanized Area Formula Program are generally required
to report financial and operating data, including vehicle inventories. Transit
operators that do not report to FTA are those that do not receive Urbanized
Area Formula 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 1-29. 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 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 2000, the threshold for Class
I railroads was $261.9 million. Declassification from Class I status occurs
when a railroad falls below the applicable threshold for three consecutive years.
Although Class I railroads encompasses only 2 percent of the number of railroads
in the country, they account for over 71 percent of the industry's mileage operated.
TABLE 1-30. 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 1-31. 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 1-32. U.S. Vehicle-Miles
TABLE 1-33. Roadway Vehicle-Miles Traveled (VMT)
and VMT per Lane-Mile by Functional Class
TABLE 1-34. 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 and
passenger-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, if added, 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 1999, the adjusted
threshold for Class I railroads was $258.5 million. Declassification from Class
I status occurs when a railroad falls below the applicable threshold for three
consecutive years. Although Class I railroads encompasses only 2 percent of
the number of railroads in the country, they account for over 71 percent of
the industry's mileage operated.
Intercity Train
The AAR passenger-miles number is based on an almost 100-percent count of
tickets and, therefore, is considered accurate.
TABLE 1-36. Long-Distance Travel in the United
States by Selected Trip Characteristics: 1995
TABLE 1-37. 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 1-38. 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 1-39. Passengers Boarded at the Top 50 U.S.
Airports
The Airport Activity Statistics of Certificated Air Carriers (AAS) is the
source of these data. 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 1-40. Air Passenger Travel Arrivals in the
United States from Selected Foreign Countries
TABLE 1-41. Air Passenger Travel Departures from
the United States to Selected Foreign Countries
The International Trade Administration in the U.S. Department of Commerce
publishes the U.S. International Air Travel Statistics Report annually.
The passenger data is based on information collected by the U.S. Immigration
and Naturalization Service using the INS Form I-92. All passengers on international
flights must complete the I-92 form with the exception of those passengers on
flights arriving or departing from Canada.
The international passenger arrivals
and departures data for Canada is obtained from Air Carrier Traffic at Canadian
Airports, which is published by Statistics Canada. Three surveys are conducted
by Statistics Canada in order to collect the necessary passenger data. Since
all data is not received by the time of publication and data is occasionally
updated or resubmitted by the participating carriers, data should be considered
preliminary for the years referenced in the source publication.
TABLE 1-44. 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 2000, the adjusted threshold
for Class I railroads was $ 261.9 million. Declassification from Class I status
occurs when a railroad falls below the applicable threshold for three consecutive
years. Although Class I railroads encompasses only 1 percent of the number of
railroads in the country, they account for over 71 percent of the industry's
mileage operated.
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 1-45. 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 2000, the
adjusted threshold for Class I railroads was $ 261.9 million. Declassification
from Class I status occurs when a railroad falls below the applicable threshold
for three consecutive years. Although Class I railroads encompasses only 1percent
of the number of railroads in the country, they account for over 71 percent
of the industry's mileage operated.
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 1-46. Top U.S. Foreign Trade Freight Gateways
by Value of Shipments: 2001
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 1-49. 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 1-50. 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 1-46. Modal Shares of Freight Shipments
within the United States by Domestic Establishments: 1993 and 1997
TABLE 1-52. Value, Tons, and Ton- Miles of Freight
Shipments within the United States by Domestic Establishment, 1997
TABLE 1-55. U.S. Hazardous Materials Shipments
by Mode of Transportation, 1997
TABLE 1-56. 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 1-53. 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 of transshipments 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 1-54. 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 1-57. 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 1-58. Passengers Denied Boarding by the
Largest U.S. Air Carriers
TABLE 1-59. Mishandled-Baggage Reports Filed by
Passengers with the Largest U.S. Air Carriers
TABLE 1-60. 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 1-61. 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 1-62. 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 1-63. Annual Person-Hours of Delay Per -
Person
TABLE 1-64. Roadway Congestion Index
TABLE 1-65. Congestion Index and Cost Values
The Texas Transportation Institute's (TTI) Urban Roadway Congestion Annual
Report provided figures for tables 1-60through 62.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.
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 website
http://mobility.tamu.edu.
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 $12.85 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 2002 estimates now vary by urban area anywhere from 18
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 2002. Previous editions classified
congested travel when areawide traffic levels reached 14,000 vehicles per lane
per day on highways and 5,500vehicles per lane per day on principal arterial
streets. For the current edition, these values are 15,500 and 5,500 vehicles
per lane per day, respectively. Previous years values have been re-estimated
based on these new assumptions. Readers should refer to the TTI Internet site
for more detailed algorithms and estimation procedures at
http://mobility.tamu.edu.
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 1-66. 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.
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