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63 · 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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64 · 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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65 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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66 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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67 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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68 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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69 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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70 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.