Improved Estimates of Ton-Miles
SCOTT M. DENNIS *
ABSTRACT
This paper describes recent improvements in measuring ton-miles
for the air, truck, rail, water, and pipeline modes. Each modal
estimate contains a discussion of the data sources used and
methodology employed, presents a comparison with well-known existing
estimates for reference purposes, and discusses the limitations of
the data. The resulting estimates provide more comprehensive
coverage of transportation activity than do existing estimates,
especially with respect to trucking and natural gas pipelines.
KEYWORDS: Transportation measurement, ton-miles.
INTRODUCTION
The Bureau of Transportation Statistics (BTS) is improving some
of its basic estimates of transportation activity. This paper
describes proposed ton-mile estimates for the air, truck, rail,
water, and pipeline modes. Each modal estimate contains a discussion
of the data sources used and methodology employed, presents a
comparison with well-known existing estimates for reference
purposes, and discusses the limitations of the data. This paper
should be viewed as part of a continuing series of steps forward.
Additional planned work will allow BTS to further improve its basic
estimates of transportation activity.
CONCEPT
Ton-miles is the primary physical measure of freight
transportation output. A ton-mile is defined as one ton of freight
shipped one mile, and therefore reflects both the volume shipped
(tons) and the distance shipped (miles). Ton-miles provides the best
single measure of the overall demand for freight transportation
services, which in turn reflects the overall level of industrial
activity in the economy. In addition, a ton-mile estimate is
necessary in order to construct other estimates of transportation
system performance, such as energy efficiency and accident, injury,
and fatality rates.
Domestic ton-mile estimates are usually developed by aggregating
data for individual freight transportation modes. Data for air
freight, railroad, and water transportation are readily available as
a result of government provision of infrastructure or residual
economic regulation. Comprehensive pipeline data are difficult to
obtain, because a significant percentage of pipeline traffic is
"in-house" transportation for companies that produce and refine oil.
Ton-mile data for the trucking sector are even more problematic due
to the large number of shippers, receivers, and trucking firms, as
well as the substantial percentage of in-house trucking traffic. All
data sources suffer, at least to some degree, from gaps in the
desired scope of coverage.
The Eno Transportation Foundation has published historical
ton-mile estimates for many years (Eno 2002, p. 42), but no longer
does so. In more recent years, BTS has provided alternative
estimates in National Transportation Statistics (NTS)
(USDOT BTS 2003). But due to the problems described above, these
well-known sources do not appear to provide complete, reliable
estimates of this basic transportation measure. BTS, therefore,
undertook a research program to address these shortcomings. This
paper presents the results of this research.
DATA SOURCES
BTS developed its improved estimates of domestic ton-miles
(traffic within and between the 50 states, the District of Columbia,
Puerto Rico, and the U.S. Virgin Islands) to maintain compatibility
with other U.S. Department of Transportation Strategic Plan data.
These annual ton-mile estimates illustrate long-term trends.
Comprehensive coverage is achieved by combining reported data from
established sources, estimates from surveys, and calculations based
on certain assumptions. Table
1 briefly compares the scope of the improved BTS ton-mile
estimates with the NTS and Eno estimates, while figure
1 presents all three estimates for all modes (also see appendix
table
A1). The NTS and Eno estimates are not available for the
most recent years.
Air
Figure
2 shows air freight ton-mile data from the three datasets (also
see appendix table
A2). The improved BTS data are compiled in Air Carrier
Traffic Statistics Monthly (USDOT BTS 1990–2003), which presents
the results of the T-100 reporting system, supplemented by special
tabulations of data on domestic all-cargo operators from the Federal
Aviation Administration (FAA).
The T-100 data represent the population of all domestic freight
traffic for Section 401 air carriers, which operate planes with a
passenger seating capacity of more than 60 seats or a maximum
payload capacity of more than 18,000 pounds. These data include the
vast majority of all domestic air freight traffic. As a result of a
BTS rulemaking, data for smaller carriers have been included in this
source starting with the fourth quarter of 2003. The inclusion of
smaller carriers does not substantially affect the value of the data
series. Domestic all-cargo operators (Section 418 carriers) have
been gradually integrated into Air Carrier Traffic Statistics
Monthly. The FAA data captured those carriers who had not yet
reported in Air Carrier Traffic Statistics Monthly, thus
allowing representation of the full population of domestic all-cargo
operators.
BTS's proposed estimates of air freight ton-miles are essentially
the same as the Eno estimates. Neither estimate includes private
carriage of air freight or air freight forwarders who do not use
T-100 reporting carriers. These exceptions account for well under 5%
of all air freight traffic. The substantial difference between the
two data series in 2001 is due apparently to Eno's use of
preliminary data.
Truck
Oak Ridge National Laboratory produced estimates of truck
ton-miles based on the 1993 and 1997 Commodity Flow Surveys (CFS)
(USDOT and USDOC 1993 and 1997), supplemented with data on
farm-based shipments and imports arriving by truck from Canada and
Mexico. Transportation Statistics Annual Report provides this
1997 estimate of truck ton-miles (USDOT BTS 2000, p. 124). To
produce the improved BTS estimate, the 1993 and 1997 estimates were
updated and backdated using intercity and intracity vehicle
miles-traveled (VMT) for single-unit and combination trucks, as
reported in Highway Statistics (USDOT FHWA 1990–2003). Figure
3 presents the resulting estimates (also see appendix table
A3). The trend in both series is the same, because the same VMT
data were used to update each series. After making these adjustments
for different time periods and population coverage, the difference
between the 1993 and 1997 estimates is less than 2%.1
The CFS captures export movements, as well as movements of
imports once they reach their first domestic destination, such as a
warehouse. In order to provide a more complete estimate of truck
traffic, the data in figure 3 were further adjusted to reflect truck
ton-miles from maritime movements prior to reaching their first
domestic destination. The number of loaded 20-foot equivalent unit
(TEU) containers shipped through U.S. ports is reported in U.S.
Waterborne Container Traffic by Port (U.S. Army Corp of
Engineers WCSC 2003). These figures were then divided by 2.4 to
convert them to an equivalent number of 48-foot trucks. Estimates of
the percentage of import traffic, the truck share of import traffic,
miles to the first domestic destination, and tons per truck for
East, Gulf, and West Coast ports were obtained through interviews
with port personnel in New York, Houston, and Los Angeles,
respectively. The resulting estimates added between 7 billion and 12
billion truck ton-miles each year. This represents approximately 1%
of all truck ton-miles currently estimated.
Figure
4 shows trucking ton-mile estimates (also see appendix table
A4). The improved BTS estimates are based on the Oak Ridge
National Laboratory supplement to the 1997 study, which is the more
recent of the two studies. The improved BTS estimate is about 10%
higher than the NTS and Eno estimates, each of which reflects
only intercity truck traffic. Therefore, the improved BTS estimate
provides a more comprehensive estimate of truck traffic.
The CFS data used to construct the improved trucking ton-miles
estimate exclude shipments by households and retail, service,
utility, and government establishments (including the U.S. Postal
Service); and certain noncommercial freight shipments, such as
construction traffic and municipal solid waste. The existing
NTS and Eno estimates do not include intracity traffic.
Therefore, it appears that a significant percentage of truck VMT and
a somewhat smaller percentage of truck ton-miles are not included in
any of these estimates. Clearly more work is needed in this area.
Railroad
BTS developed its improved railroad ton-miles estimates using
data from the Carload Waybill Sample (USDOT STB 1990–2003).
The population estimate in this source is based on a 500,000 record
sample of all traffic terminating on all railroads in the United
States. The sample implicitly includes traffic originating on U.S.
railroads and terminating on Mexican railroads, because almost all
such traffic is rebilled to U.S. border crossings.2
Population data on the tonnage of railroad shipments originating
in the United States and terminating in Canada come from
Transportation in Canada (Transport Canada 1990–2002) for
years prior to 2003. The average length of haul for U.S. railroad
shipments was applied to this tonnage to obtain an estimate of U.S.
railroad ton-miles for shipments terminating in Canada. This
assumption seems reasonable, because even though much of this
traffic originates in states bordering Canada, more distant states
such as California, Texas, and Georgia are also among the 10 largest
originating states. The Carload Waybill Sample's improved
coverage of Canadian terminations of rail shipments originating in
the United States allows estimates of all railroad ton-miles for
2003 and subsequent years.
Figure
5 illustrates the railroad ton-mile estimates (also see appendix
table
A5). From 1998 to 2001 (the most recent years for which all
three estimates are available), the improved BTS estimates are about
5% greater than the NTS estimates, which include only Class I
railroads; about 1% greater than the Waybill estimates, which
do not include Canadian terminations; and almost identical to the
Eno estimates, which include both non-Class I railroads and Canadian
terminations. The increase in the improved BTS estimates relative to
the other estimates is probably due to better coverage of the
rapidly growing railroad shipments originating in the United States
and terminating in Canada. Further, while Eno's ton-mile estimates
for non-Class I railroads are based on financial survey data, the
improved BTS estimates are based on actual ton-mile data and should
be considered more reliable.
Finally, railroad ton-mile data may not include shipments
originating in Mexico and terminating in Canada. Based on data from
Transport Canada, it appears that these shipments account for less
than one tenth of one percent of all U.S. railroad traffic.
Water
Domestic waterborne ton-mile estimates are presented in figure
6 (also see appendix table
A6). The current NTS estimates of annual water
transportation ton-miles were taken from Waterborne Commerce of
the United States (U.S. Army Corps of Engineers 2003). Data in
this source are developed from lock data and individual trip reports
that must be filed with the U.S. Coast Guard. Therefore, this source
represents the entire population of all domestic water traffic,
including inland waterways, coastwise, Great Lakes, and intraport
traffic, along with traffic to and from Alaska, Hawaii, and Puerto
Rico. The NTS and Eno estimates differ substantially, because
NTS includes coastwise (domestic ocean) traffic and Eno does
not. Thus, the current NTS data, which are proposed for use
here, are more comprehensive than Eno's estimates.
Pipeline
Figure
7 shows pipeline ton-miles (also see appendix tables
A7(a) and A7(b)). Annual oil and oil products pipeline ton-miles
were obtained from Shifts in Petroleum Transportation
(Association of Oil Pipelines 2003). These data represent the entire
population of crude petroleum and petroleum products carried in
domestic transportation by both federally regulated and nonfederally
regulated pipelines. Both NTS and Eno currently use these
data, which we also propose for use here.
Natural gas pipeline ton-miles are also presented in figure 7.
These new estimates are based on natural gas deliveries reported in
the Annual Energy Review (USDOE 2003a). BTS first converted
the gas deliveries, measured in cubic feet, to metric tons and then
to tons using a standard conversion factor of 48,700 cubic feet per
metric ton as reported in the International Energy Annual
(USDOE 2001).
There are no data available on the length of haul for natural gas
shipments, because natural gas is drawn from a common pipeline
rather than shipped to a specific consignee. Origination and
termination data indicate that natural gas has a distribution
pattern similar to oil and oil products (USDOE 2003b and 2003c). The
oil and natural gas pipeline networks are also very similar.
Therefore, the length of haul for oil and oil products was applied
to the tonnage of natural gas to estimate natural gas ton-miles in
transmission lines.3
Natural gas ton-miles in distribution lines (i.e., local
utilities) were estimated using 5% of transmission length of haul,
which is approximately half the diameter of a major metropolitan
area. Natural gas ton-miles in gathering lines (i.e., from well to
processing plant) were estimated using the same length of haul as in
distribution lines. The ton-miles for gathering, transmission, and
distribution lines were then summed to provide an estimate of total
natural gas ton-miles. Natural gas ton-miles, which have not to our
knowledge been previously estimated, represent nearly as much
traffic as that carried on the inland waterway system. These new
estimates fill a substantial gap in the existing ton-mile data.
The natural gas pipeline data do not include gas used to
repressurize gas fields or power the pipeline itself, because these
uses do not represent gas carried in revenue transportation. The
pipeline data also exclude coal slurry, ammonia, and other types of
pipelines. There are only a few such pipelines, which tend to have
either short haul or low volume, and appear to account for well
under 1% of all pipeline ton-miles. BTS will investigate the recent
decline in the oil pipeline ton-mile data and the resulting
reduction in the estimate of natural gas ton-miles in order to
improve the most recent estimates.
CONCLUSION
The improved ton-mile estimates for the air, truck, rail, water,
and pipeline modes described in this paper are both more
comprehensive and more reliable than well-known existing estimates.
The improvements are most noticeable with respect to trucking and
natural gas pipelines. Additional work will allow BTS to further
improve these basic estimates of transportation activity.
BTS has already incorporated these improved estimates of domestic
freight ton-miles into the Transportation Statistics Annual
Report (USDOT BTS 2004, p. 213).4
BTS plans to extend the improved estimates back to 1980 in the fall
2005 update to National Transportation Statistics.5
Future research will be conducted to further extend the improved
estimates back to 1960 where data are available.
REFERENCES
Association of Oil Pipelines. 2003. Shifts in
Petroleum Transportation. Washington, DC. Table 1.
Eno Transportation Foundation. 2002. Transportation
in America. Washington, DC.
Transport Canada. 1990–2002. Transportation in
Canada. Ottawa, Ontario, Canada. Addendum, table A6-10.
U.S. Army Corps of Engineers. 2003. Waterborne
Commerce of the United States. Washington, DC. Part V, Section
1, Table 1-4, Total Waterborne Commerce.
U.S. Army Corps of Engineers, Waterborne Commerce
Statistics Center (WCSC). 2003. U.S. Waterborne Container Traffic
by Port. Washington, DC.
U.S. Department of Energy (USDOE), Energy Information
Administration. 2001. International Energy Annual.
Washington, DC. Table C-1.
_____. 2003a. Annual Energy Review. Washington,
DC. Table 6.5.
_____. 2003b. Natural Gas Annual. Washington,
DC. Table 12.
_____. 2003c. Petroleum Supply Annual, Volume
1. Washington, DC. Table 33.
U.S. Department of Transportation (USDOT), Bureau of
Transportation Statistics (BTS). 2000. Transportation Statistics
Annual Report. Washington, DC.
_____. 2003. National Transportation
Statistics. Washington, DC. Table 1-44.
_____. 2004. Transportation Statistics Annual
Report. Washington, DC.
U.S. Department of Transportation (USDOT), Bureau of
Transportation Statistics (BTS), Office of Airline Information.
1990–2003. Air Carrier Traffic Statistics Monthly.
Washington, DC. Freight, Express, and Mail Revenue Ton-Miles table,
p. 2, line 3.
U.S. Department of Transportation (USDOT), Bureau of
Transportation Statistics and U.S. Department of Commerce (USDOC),
Economics and Statistics Administration, U.S. Census Bureau. 1993.
Commodity Flow Survey. Washington, DC.
______. 1997. Commodity Flow Survey.
Washington, DC.
U.S. Department of Transportation (USDOT), Federal
Highway Administration (FHWA). 1990–2003. Highway Statistics.
Washington, DC. Table VM-1.
U.S. Department of Transportation (USDOT), Surface
Transportation Board (STB). 1990–2003. Carload Waybill
Sample. Washington, DC.
END NOTES
1. The VMT data include a substantial
amount of truck traffic that is outside the scope of the CFS, e.g.,
shipments by households and retail, service, utility, and government
establishments (including the U.S. Postal Service); and certain
noncommercial freight shipments, e.g., construction traffic and
municipal solid waste. The VMT data can, therefore, be taken to
provide a reasonable estimate of the trend in truck ton-miles, but
not the level, and should not be used to make inferences about
operational parameters such as empty mileage or average load per
truck.
2. Traffic originating on U.S. railroads
and terminating on Mexican railroads is treated for accounting
purposes as if it terminated at the U.S. border crossing, and is
therefore included in the Carload Waybill Sample. This
practice is known as rebilling.
3. The 2000 to 2003 oil pipeline length
of haul data are somewhat suspect; thus, the average length of haul
from 1990 to 1999 was used in place of these data.
4. Estimates presented in this paper
reflect subsequent revisions in the source data.
5. Available on the BTS website at http://www.bts.gov/.
ADDRESS FOR CORRESPONDENCE
* S. Dennis, Bureau of
Transportation Statistics, Research and Innovative Technology
Administration, U.S. Department of Transportation, 400 7th St. SW,
Room 3430, Washington, DC 20590. E-mail: scott.dennis@dot.gov
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