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· The potential divertible market, including only loads with · The converted truck trip table is valuable in identifying and
a distance and commodity that is likely to divert, is 2.1 mil- planning for major regional freight corridors and termi-
lion annual tons presently carried by trucks. nals. In addition, the complete statewide freight model can
· The estimated annual truck tonnage that would be identify the routing and demand for regional trucks on the
diverted to rail if rail costs decreased by 10% is 300,000, or entire Ohio system. For example, the relative importance
15% of the total divertible market segment and 2.2% of all of I-71 in Cleveland to trucking in the MORPC region can
freight truck loads in the corridor. be identified.
· The diversion analysis would not be possible without the
commodity and O-D information available from Ohio's The freight-truck trip table and assignment represent only
TRANSEARCH database. a small portion of the total truck movement in a region. They
· Because most of the divertible market had origins and des- do not include local delivery, construction truck, service
tination outside of the state, Ohio should form coalitions trucks, etc. The need to forecast these truck trips at the
with other states to address rail and trucking issues. regional level will remain.
Mid-Ohio Regional Planning Commission Performance Measures and Evaluation
Case Study
Performance measures were not developed in the Ohio
OVERVIEW Interim Freight Model.
This case study examined how statewide freight-truck in-
formation might be applied in improving the travel demand
8.5 Case Study Freight Analysis
models at a regional and metropolitan level.
Framework
MPO-supported travel demand models in Ohio generally Background
forecast truck trips at external stations by extending the trend
of observed historical growth. This method of forecasting the Context
external-external truck trips passing through the MPO or the The FHWA's Office of Freight Management and Opera-
external-internal truck trips between the MPO and areas out- tions has developed the FAF as a policy tool to estimate com-
side the MPO suffers from an important weakness: It is not modity flows and related freight activity at national, state, and
sensitive to economic changes outside of the MPO's bound- county levels. FAF not only covers domestic freight move-
aries. The Microsoft Access-supported TRANSEARCH ments, but major international freight movements as well.
freight-truck database was examined to determine whether The tool has been developed to provide an accurate, compre-
the forecasts of truck traffic in that database could be used to hensive forecast of commodity flows and freight activity for
improve the model's forecasts of truck trips. In order to test
the analysis years 1998, 2010, and 2020. These forecasts are
this process, the Mid-Ohio Regional Planning Commission
sensitive to changes in economic conditions, the transporta-
(MORPC), the MPO for the Columbus urban area, was
tion system, and other factors.
selected to evaluate such a process.
Objective and Purpose of the Model
CONCLUSIONS
The FAF provides the U.S. Department of Transportation
· Freight-truck trip tables can be converted to a standard with a policy analysis tool to help it understand commodity
travel demand model package, such as TRANPLAN, and flows and the pressures these flows place on the transporta-
the information can be extracted for a specific region. tion system. A better understanding of goods movement
· Reasonable expansion factors can be developed to convert helps the agency identify deficiencies in the transportation
the county-level trip table to the TAZ system supported by infrastructure and formulate the means to address them.
a metropolitan region. The FAF was developed initially for use as a national pol-
· The truck forecast is particularly valuable for external sta- icy analysis tool but has proven to be useful at other levels as
tions, which are generally problematic in regional forecast- well. Although it can never replace more detailed analysis
ing processes and often are forecast based only on tools developed for states and metropolitan planning organ-
historical trends. However, because the number of exter- izations, FAF can assist by:
nal stations that have substantial volumes in the subarea
freight truck trip table is fairly limited, the most appropri- · Providing a benchmark for state and local freight planning;
ate use of the freight truck forecasts may be to qualitatively · Identifying current and future congested links on a national,
guide the adjustment of the model's external forecasts. corridor, and regional scale;
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· Providing nationally consistent forecasts of freight growth It estimates truck freight production by subtracting the
by commodity type and mode; other major modes--rail, water, pipeline, and air--from the
· Understanding nationwide flows and their potential total.
impact at the local level, thus allowing state and local agen- FAF splits truck productions into two major groups, pri-
cies to identify crucial freight connections to serve external vate and for-hire, dividing the for-hire trucks into truckload
markets; and less-than-truckload. Payload factors are used to convert
· Establishing a framework for converting and consolidating tons of commodity into trucks. The payload factors vary
multistate and multi-agency transportation, traffic, and depending upon the type of truck, the type of commodity,
freight information; and and the distance of the trip.
· Supporting policy development at all levels, including the Three different truck types are used to allocate the freight
Federal transportation reauthorization process. to trucks:
· Single units trucks;
General Approach
· Combination tractor-trailer trucks; and
Model Class · Double tractor-trailer trucks.
As a commodity flow factoring class of model, the FAF is a
FAF highway freight movements capture only intercounty
comprehensive estimate of origins and destinations for
flows, not intracounty. However, the 1997 CFS indicates that
freight moving by truck, rail, water, and air. Freight flows are
intracounty freight flows are a substantial component of the
assigned to the transportation system to evaluate or deter-
overall highway freight market.
mine current and future deficiencies. The general approach
of the FAF is to estimate the flows of commodities at the four-
digit STCC level for each mode at the county level for the WATERBORNE FREIGHT
entire United States. This county-level flow table is then con- Waterborne freight is estimated using data from the U.S.
verted to transport units of each mode and assigned to a Army Corps of Engineers. The Corps collects data on all U.S.
network. A detailed description of the commodity O-D flow waterway shipments, which it reports at the aggregate state-
factoring method is provided in Section 6.2. to-state level by commodity group. The data is disaggregated
for use in FAF by using individual port data and data for both
Modes private and public facilities. Domestic, international, and
total waterborne movements are listed in Table 8.13.
The county-level flow table consists of four primary After estimating flows, FAF assigns waterborne freight to
modes, with various subsets, for a total of seven modes as waterways based on the shortest path between an origin and
listed in Table 8.12. a destination. It does not capture the drayage portion of
Freight moved by truck is the most difficult of the major waterborne freight.
freight modes to estimate due to the extent of the service
markets and the lack of a cohesive dataset. FAF estimates
AIR FREIGHT
truck production volumes by first estimating total freight
production by state using the U.S. Census Bureau's Annual In terms of tonnage carried, air freight is the smallest of the
Survey of Manufactures and the Census of Manufactures. major modes included in FAF. In 1998, air freight accounted
Table 8.12. Modes included in the Freight Analysis Framework.
Primary Mode Subset Mode
Truck Private
For-Hire Truckload
For-Hire Less than Truckload
Rail Conventional Rail
Rail/Truck Intermodal
Water Water
Air Air
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Table 8.13. Freight Analysis Framework waterborne freight shipments by ton
and value.
Tons (Millions) Value (Billions of Dollars)
1998 2010 2020 1998 2010 2020
Domestic
Waterborne 1,082 1,345 1,487 146 250 358
Total 13,484 18,820 22,537 7,876 15,152 24,075
International
Waterborne 136 199 260 17 34 57
Total 1,787 2,556 3,311 1,436 3,187 5,879
Domestic and International
Waterborne 1,218 1,544 1,747 163 284 415
Total 15,271 21,376 25,848 9,312 18,339 29,954
Source: Federal Highway Administration, Freight News, October 2002.
for just nine million tons (0.1%) of domestic freight included ties. Using the commodity flow table, each airport market
in the FAF. While the overall tonnage carried by air is low, the area is examined to further refine the flow of commodities.
value is considerably higher, almost 7% of the total in 1998. Similar to the rail freight portion, the truck drayage portion
The Bureau of Transportation Statistics Airport Activity of air freight flows is included in the FAF.
Statistics (AAS) is the basis for the air freight component of
FAF. The AAS contains data on the total tonnage originat-
Markets
ing from airports. This data is combined with flow data also
provided by the AAS to determine the tonnage origins and FAF is designed to be a comprehensive database of freight
destinations for the nation's airports. Individual airports movement, and as such is intended to include all markets.
are aggregated to the county level for use in the FAF. FAF reports both national and international freight move-
Domestic, international, and total air movements are listed ments throughout the United States at the county level.
in Table 8.14. International freight is recorded as having an origin or
The commodity flow table is used to disaggregate the destination at the county in which it enters or exits the
county-to-county tonnage flows into individual commodi- United States.
Table 8.14. FAF air freight shipments by ton and value.
Tons (Millions) Value (Billions of Dollars)
1998 2010 2020 1998 2010 2020
Domestic
Air 9 18 26 545 1,308 2,246
Total 13,484 18,820 22,537 7,876 15,152 24,075
International
Air 9 16 24 530 1,182 2,259
Total 1,787 2,556 3,311 1,436 3,187 5,879
Domestic and International
Air
Total 15,271 21,376 25,848 9,312 18,339 29,954
Source: Federal Highway Administration, Freight News, October 2002.
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Framework on the baseline forecast. The economic forecasts address
growth in the supply side of commodity production.
FAF data is used in many regional, statewide, and urban
The WEFA forecast makes a number of long-term assump-
models. Since FAF is a national commodity flow model and
tions about the United States economy, including:
the output is public data, other freight models for any subre-
gion within the U.S. may use FAF as a data source. · The civilian labor force will grow more slowly;
FAF modeling procedure does not lend itself to forecasting · The manufacturing sector will continue to shrink and the
passenger vehicles and no complementary passenger model
service sector will continue to grow;
has been developed. · The gross domestic product (GDP) will grow more slowly
as a result of slower labor force growth;
Flow Units · The increase in the government sector's share of the GDP
will slow due to a decrease in defense spending;
Units of flow in FAF are in annual tons per commodity · The share of real total expenditures devoted to services and
type. Annual tons are reported for all four major modes in the
durable goods will rise, while the share of expenditures
FAF, truck, rail, water, and air.
devoted to nondurable goods, such as energy, will fall;
FAF also provides an assignment of the converted tonnage · The fastest growing sector of the economy for investment
flows for the highway freight component. These flows are rep-
will be producers' durable equipment; and
resented in the network as daily trucks for each of the forecast · Manufacturing of durable goods will grow faster than
years of 1998, 2010, and 2020. The trucks are identified as being
manufacturing of nondurable goods.
commodity-carrying trucks or noncommodity-carrying trucks.
WEFA's economic assumptions are posted on the Office of
Data Freight Management and Operations web site at: http://
www.ops.fhwa.gov/freight/adfrmwrk/index.htm.
As a comprehensive forecast of commodity flows, FAF draws
For forecasting the base year, data is aggregated into
upon many sets of data from both public and proprietary
Bureau of Economic Analysis Economic Areas and Census
sources. These data are used to create the Freight Analysis
Divisions. This reduces the number of areas for the forecasts
Framework Database (FAFD). FAFD contains county-to-
to be developed. The forecast goes through various steps
county freight flows for truck, rail, water, and air at the four-
required to determine the supply and demand of particular
digit STCC level.
commodities in the future. The forecast data is then disaggre-
The basis for the FAFD is Reebie Associates' TRANSEARCH
gated to the county and STCC four-digit codes.
visual database. The TRANSEARCH database is derived from,
but not limited to, the following sources:
EXTERNAL MARKETS
· Bureau of Transportation Statistics' 1997 CFS;
FAF accounts for external markets as well, primarily
· Surface Transportation Board's Railroad Waybill Sample;
Canada and Mexico. Asia, Europe, Latin America, and the
· U.S. Census Bureau's Annual Survey of Manufacturers and
rest of the world also are included in FAF. Only the portion
Census of Manufacturers;
of the trip on the U.S. domestic freight network is included,
· U.S. Census Bureau's VIUS;
with the international freight origin or destination taken as
· HPMS;
the U.S. county through which it crosses the border. This data
· FAF State to State Commodity Flow Database; and
is mostly based on proprietary data from the TRANSEARCH
· Data from a proprietary motor carrier traffic sample.
international database.
Forecasting Data
Modal Networks
BASE AND FORECAST YEAR SOCIOECONOMIC DATA
FAF has four modal networks, one for each mode, with the
Forecasts of the base year data are based primarily on eco- rail and air modes also using the highway network for the
nomic forecasts, as the economy and freight movement are drayage portion of their movements. Of the four networks,
integrally tied to each other. The Macroeconomic Service the highway network is the most complex. The rail network
Long-Term Trend Scenario prepared by WEFA, Inc. (now is the second most complex, but is not nearly as intricate as
Global Insights, Inc.) is used as the basis for the freight flow the highway network.
forecasts. WEFA has three forecasts: a baseline and lower and The waterways network consists of the nation's navigable
higher versions of the baseline. The freight forecasts are based waterways and uses a shortest distance path to determine the
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route of the movement. The air freight network is based on Model Development Data
the straight-line distance between airports.
The commodity table eliminates the need to develop trip
generation or trip distribution parameters or coefficients.
HIGHWAY NETWORK The use of existing (circa 1998) mode splits for future mode
FAF highway network has its origins in the NHPN. NHPN splits also does not require the development of a mode choice
is a national planning network that consists of approximately model.
450,000 miles of roadway, including:
Conversion Data
· Interstate Highway System;
· NHS; A series of conversions is required to transform the com-
· National Network (NN); modity flow tonnages by STCC code to number of trucks.
· National Truck Network; and The FAF uses these procedures to convert the tonnages into
· Other state highways. trucks, but the specifics of the procedures are proprietary.
The conversion process utilizes the data from VIUS, TIUS,
FAF network is basically a subset of the NHPN. Additional the Comprehensive Truck Size Weight Study, as well as
highway links are added to FAF network for connectivity pur- adjustments from industry experts.
poses. Counties not adequately served by NHPN have addi- The conversion process is a four-step process. First, each
tional urban streets and rural minor arterials added to them. commodity is allocated to a truck body type. Several truck types
FAF network is shown in Figure 8.5. are considered in the allocation process. Some commodities are
allocated to only one truck type, while others are allocated to
many types. Secondly, distributions by truck configuration for
INTERMODAL TERMINAL DATA
each body type are developed. The distributions are based on
FAF highway network has centroid connectors coded for the VIUS data for the state of origin. Third, the tons are con-
the intermodal terminals identified by the Bureau of Trans- verted to trucks, based on VIUS data, for payload weight distri-
portation Statistics. No information is provided for O-D butions for each body type, STCC code, and configuration.
flows at these terminals. These flows may be separated from Finally, an estimate is made for the number of empty tucks. By
the county-to-county flows in subsequent FAF updates. definition, empty trucks are not commodity-carrying trucks,
Source: Freight Analysis Framework Highway Capacity Analysis Methodology Report, April 2002, Figure 2.
Figure 8.5. The Freight Analysis Framework highway network.
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but they must be considered in the number of trucks needed to Trip Generation
ship freight.
Not applicable for this model class.
Validation Data
Trip Distribution
No validation data was used in FAF.
Not applicable for this model class.
Model Development
Software Commodity Trip Table
FAF highway assignment process utilizes the TransCAD Flows are estimated for a base year of 1998 and the forecast
modeling software package. Networks with the assigned years of 2010 and 2020. This section describes the methods
volumes are available in TransCAD, ESRI Inc.'s shape file used to estimate domestic and international freight flows for
and database formats at the Office of Freight Management each mode and the procedures used to map them to the
and Operations web site at: http://ops.fhwa.dot.gov/freight/ transportation network.
freight_analysis/faf/faf_highwaycap.htm.
FAF nonhighway assignment uses the proprietary fixed
RAIL FREIGHT
path routing files in TRANSEARCH. These routing files are
use in Microsoft Access to develop DBF files of water and rail- Rail freight flows are estimated using the STB's confiden-
road network flows. These network flow files can be mapped tial data set, the Carload Waybill Sample. The Waybill Sam-
using FAF railroad and waterway network shapefiles in ESRI's ple is a stratified sample of carload waybills for terminated
ArcGIS family of software. shipments by railroad carriers, encompassing 62 railroad sys-
tems (including all Class I and II railroads) and the major
short lines.
Commodity Groups/Truck Types
The Waybill Sample contains detailed information about
The commodity groups used in the derivation of the FAF each sampled movement. Included in these data are the type
commodity truck trip table are listed in Table 8.15. of commodity and volume being carried as well as the origin
Truck types considered in the trip table are single units and and destination of the trip.
combination tractor trailers, as listed in Table 8.16. The rail volumes and types of commodities being carried
While commodity groups and truck types are factored into are classified as carloads, and the rail intermodal volumes are
the truck traffic assigned to the network, they are not assigned classified as trailer-on-flatcar or container-on-flatcar. The
separately. FAF reports only commodity-carrying trucks. trailer-on-flatcar and container-on-flatcar freight move-
Table 8.15. Commodity types.
STCC 2 Product STCC 2 Product
1 Farm 32 Clay/Concrete/Glass/Stone
8 Forest 33 Primary Metal
9 Fish/Marine 34 Fabricated Metal
10 Metallic Ores 35 Machinery except Electrical
11 Coal 36 Electrical Mach/Equip/Supp
13 Crude Petroleum/Natural Gas 37 Transportation Equipment
14 Nonmetallic Minerals 38 Instruments/Optical/Watches/Clocks
19 Ordnance/Accessories 39 Miscellaneous Manufacturing
20 Food/Kindred 40 Waste/Scrap Materials
21 Tobacco 41 Miscellaneous Shipping
22 Textile Mill 42 Shipping Containers
23 Apparel 43 Mail
24 Lumber/Wood 44 Freight Forwarder
25 Furniture/Fixtures 45 Shipper Association
26 Pulp/Paper/Allied 46 Freight All Kind
27 Printed Matter 47 Small Package
28 Chemicals/Allied 48 Hazardous Waste
29 Petroleum/Coal 49 Hazardous Materials
30 Rubber/Plastics 50 Secondary Moves
31 Leather 99 Less-than-Truckload-General Cargo
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Table 8.16. Truck types.
Truck Body Types Truck Configurations
Dry Van Single Unit
Reefer Combination tractor semi-trailer or double trailer
Flat Combination tractor semi-trailer or double trailer
Automobile Combination tractor semi-trailer or double trailer
Bulk (Including hoppers and open-top gondolas) Combination tractor semi-trailer or double trailer
Tank Combination tractor semi-trailer or double trailer
Livestock Combination tractor semi-trailer or double trailer
ments consist of a long rail movement with short truck The forecasted commodity flow tables are based largely on the
drayage on both ends of the rail trip. Domestic, international, WEFA's Macroeconomic Service Long-Term Trend Scenario.
and total rail movements are listed in Table 8.17.
Mode Split
HIGHWAY FREIGHT
The FAF does not have a policy-sensitive mode split com-
Of the modes covered by FAF, highway freight is the great- ponent. Mode shares are defined and forecasted using growth
est in terms of both tonnage and value. As shown in Table rates based on historical freight movement. Differences in
8.18, highway freight accounted for 10.4 billion of the 13.5 mode shares for future years may be reflected in the aggregate
billion domestic tons estimated for the year 1998. due to different growth rates for particular commodities. At
With some exceptions, the commodity flow table used in a disaggregate level, the mode shares do not change for each
the FAF is approximately at the county level. While this table O-D pair by commodity.
is proprietary and is not available to the public, an aggrega-
tion is available at the state-to-state level online at: http://ops.
Flow Unit and Time Period Conversion
fhwa.dot.gov/freight/freight_analysis/faf/fafstate2state.htm.
The commodity flow table includes flows for truck, rail, The FAF flow table is not adjusted for time period.
water, and air freight for the years 1998, 2010, and 2020. Commodity-based trip generation models typically start
The assemblage of this data is described online at: http:// with an estimate of commodity flow tonnage, generally
ops.fhwa.dot.gov/freight/freight_analysis/faf/index.htm. county-to-county or state-to-state flows. The annual tonnage
Table 8.17. Freight Analysis Framework rail freight shipments by ton and value.
Tons (Millions) Value (Billions of Dollars)
1998 2010 2020 1998 2010 2020
Domestic
Rail 1,954 2,528 2,894 530 848 1,230
Total 13,484 18,820 22,537 7,876 15,152 24,075
International
Rail 358 518 699 166 248 432
Total 1,787 2,556 3,311 1,436 3,187 5,879
Domestic and International
Rail 2,312 3,046 3,593 696 1,096 1,662
Total 15,271 21,376 25,848 9,312 18,339 29,954
Source: Federal Highway Administration, Freight News, October 2002.
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Table 8.18. Freight Analysis Framework highway freight shipments by ton
and value.
Tons (Millions) Value (Billions of Dollars)
1998 2010 2020 1998 2010 2020
Domestic
Highway 10,439 14,930 18,130 6,656 12,746 20,241
Total 13,484 18,820 22,537 7,876 15,152 24,075
International
Highway 419 733 7,069 722 1,724 3,131
Total 1,787 2,556 3,311 1,436 3,187 5,879
Domestic and International
Highway 10,858 15,663 25,199 7,378 14,470 23,372
Total 15,271 21,376 25,848 9,312 18,339 29,954
Source: Federal Highway Administration, Freight News, October 2002.
flows are then converted to daily truck trips using payload fac- Trip Distribution
tors. These payload factors may come from local survey or
Not applicable.
from national data, such as VIUS. Commodities in the
TRANSEARCH database are aggregated to 14 basic commod-
ity groupings. VIUS is used to develop payload factors by com- Mode Choice
modity group and by length of haul groups, and these payload
factors are applied to the tonnage flows to convert to truck Since the mode choice is based on the surveyed existing
trips. mode shares, validation of the mode choice is not applicable.
Payload factors developed in the FAF using the four steps
described in the Conversion section of this case study are Modal Assignment
summarized in Table 8.19. The resulting payload factors are
adjusted for observed vehicle weights from VIUS. While there is no validation of the assignment of FAF,
freight flows in terms of trucks may be compared to observed
Assignment trucks on the network. This can only serve as an indicator of
the performance of the FAF because there is no way to know
Network attributes on the FAF highway network are from how many of the total trucks are actually commodity-carrying
the HPMS, NHPN, and state department of transportation trucks, the only type accounted for by FAF.
data. Each highway link contains, at a minimum, a travel time No data is available to validate the railroad or waterway
and a capacity. The highway capacity is used in the evaluation assignments because no source of independent observations
of routes used, but not in the assignment process. Since all- exists that can be used in validation.
or-nothing assignments assume that all trips are assigned to
the shortest path and do not reflect congestion and other mit-
igating effects, the assignments were carefully checked. Model Application
The assignment uses a preload process for nonfreight FAF is a comprehensive national freight flow model. As
(local) trucks and passenger traffic to account for congestion such, it is used at all levels of government. FAF provides
as a result of non-commodity-carrying trucks. Figure 8.6 information for Federal, state, and local transportation agen-
illustrates the results of assigning the 1998 base truck table to cies to allow them to determine which transportation corridors
the highway network. will become heavily congested in the future and to better plan
congestion relief measures.
Model Validation Federal applications of FAF utilize the commodity flow data
between states, major urban centers, major ports, and border
Trip Generation
crossings. Some states use the state-to-state flows to estimate
Not applicable. the through-movement of freight (the county-to-county
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Table 8.19. Payload factors by STCC and truck type.
Single Unit Trucks Semi-Trailer Double Trailers Triples
Percent Percent Percent Percent
Commodity STCC Initial Refined Difference Initial Refined Difference Initial Refined Difference Initial Refined Difference
Farm Products 1 6.1 12.2 -101.81 21.3 39.7 -85.78 28.1 49.3 -75.72 9.8 41.3 -320.03
Forestry and Other 8 7.7 12.5 -62.56 27.1 46.8 -72.44 35.7 60.9 -70.52 12.5 61.5 -392.48
Products
Fresh Fish or Marine 9 6.1 21.3 28.1 9.8
Products
Metallic Ores 10 8.6 30.4 40.0 14.0
Coal 11 8.6 30.4 40.0 14.0
Mining Products 14 8.6 20.5 -138.04 30.4 45.3 -49.06 40.0 20.5 48.65 14.0 100
Ordnance or 19 7.6 26.7 35.2 12.3
Accessories
Processed Foods 20 6.5 7.7 -17.89 23.1 33.5 -45.3 30.4 35.9 -18.15 10.6 100
Tobacco Products 21 6.2 21.8 28.7 10.0
Textile Mill Products 22 6.1 4.7 22.15 21.3 30.2 -41.51 28.1 38.3 -36.33 9.8 100
Apparel or Related 23 4.6 16.2 21.3 7.4
Products
Lumber and Fabricated 24 7.7 8.3 -8.07 27.1 37.1 -36.86 35.7 48.1 -34.58 12.5 100
Products
Furniture or Hardware 25 4.2 4.0 5.35 14.8 28.3 -91.6 19.4 35.0 -80.08 6.8 100
Paper Products 26 6.8 7.4 -8.15 24.0 34.3 -43.26 31.5 31.8 -0.68 11.0 12.5 -13.33
Printed Matter 27 5.1 17.9 23.5 8.2
Chemicals 28 6.2 10.4 -67.59 21.8 38.9 -78.03 28.7 50.3 -74.98 10.0 100
Petroleum 29 7.9 12.5 -57.81 27.8 47.3 -69.79 36.6 52.3 -42.67 12.8 100
Plastics and/or Rubber 30 3.4 5.8 -72.44 11.9 32.6 -173.37 15.7 29.4 -87.07 5.5 54.0 -883.92
Leather or Leather 31 4.2 14.6 19.3 6.7
Products
Building Materials 32 5.2 18.8 -257.85 18.5 42.1 -127.72 24.3 48.5 -99.23 8.5 62.4 -633.18
(continued on next page)
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Table 8.19. (Continued).
Single Unit Trucks Semi-Trailer Double Trailers Triples
Percent Percent Percent Percent
Commodity STCC Initial Refined Difference Initial Refined Difference Initial Refined Difference Initial Refined Difference
Primary Metal 33 7.3 6.5 10.49 25.7 37.9 -47.15 33.8 54.2 -60.27 11.8 100
Products
Fabricated Metal 34 5.2 5.0 5.3 18.5 35.3 -90.99 24.3 26.1 -7.53 8.5 100
Products
Machinery 35 4.0 6.5 -63.52 14.0 33.1 -136.51 18.4 35.4 -91.74 6.4 100
Electrical Equipment 36 4.7 16.7 21.9 7.7
Transportation 37 4.1 5.3 -28.72 14.6 33.3 -128.36 19.2 31.9 -66.54 6.7 12.5 -86.48
Equipment
Instruments, Photo 38 3.6 12.5 16.5 5.8
Equipment, Optical
Miscellaneious 39 5.4 5.6 -3.21 19.1 33.4 -75.06 25.1 28.9 -15.06 8.8 100
products of
Manufacturing
Scrap, Refuse or 40 6.0 13.2 -121.23 21.1 36.6 -73.63 27.7 45.9 -65.38 9.7 100
Garbage
Mixed cargo 41 5.9 5.5 5.56 20.7 33.3 -60.79 27.3 32.4 -18.85 9.5 16.1 -68.88
Average payload 6.0 8.9 -50.53 21.1 36.6 -80.38 27.7 39.2 -47.2 9.7 37.2 -63.07
Source: Freight Analysis Framework Highway Capacity Analysis Methodology Report, April 2002, Table 4-3.
