Forecasting Statewide Freight Toolkit (2008) / Chapter Skim
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From page 42... ...
If it will be part of a larger process, how will the freight model be included? Flow Units What flow units will the model be expected to report (annual tons, daily trucks, daily trucks by truck type, etc.)
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Are these aggregations of existing commodity classification schemes? For truck models, what truck types will be separately included?
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The IRC Management Plan process included a comprehensive technical analysis and public involvement process in order to evaluate existing and future travel conditions, identify deficiencies, and weigh the various improvement alternatives. Current and future truck activity in the TH 10 corridor was studied through analysis of historical truck data and development of a truck traffic forecasting methodology that utilized historical truck count data, regional employment data, FHWA truck trip generation rates, and local truck trip-making activity.
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This method is appropriate for corridors where no network-based truck forecasting models exist. Flow Units The TH 10 Truck Trip Forecasting Model estimates daily truck trips in the corridor.
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Based on future employment at these businesses and the adjusted FHWA truck trip generation rates, the number of truck trips associated with each employer were estimated. By geocoding the employment locations and the associated truck trips, highway segments with high truck volumes could be identified.
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8.3 Case Study – The Heavy Truck Freight Model for Florida Ports Background Context Ports are usually considered special generators of truck traffic in transportation planning models, in that they do not produce or attract truck trips proportionate to the employment or other socioeconomic variables at the port. Instead they generate truck traffic proportionate to the shipment of freight traffic through the port, which typically originates or terminates at an unspecified international location.
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The research project developed equations using linear and ARIMA regressions of time series data to produce forecasts of future year truck volumes. The Heavy Truck Freight Model was originally developed to estimate the truck trips produced from and attracted 48 Employment Growth Internal Truck Growth 2020 Projections Location 2000-2020 2000-2020 Based On From To County Annual Total Annual Total 1999 1995a MN25 MN24 (Becker)
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However, because ports often are considered special generators, the model can be used to estimate the production and attraction of truck trips from the port for inclusion as a part of a statewide or regional model. Flow Units The model starts with the monthly imported/exported freight units, and finally estimates the hourly volume of total trucks.
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The network covers the following roads: 50 Source of Data Resolution Period Terminal Company Gate Movements Daily Truck Movements January 1996-December 1997 Port of Miami Gate Passes Individual Truck Movements January 1997-May 1997a Video Counts Individual Truck Movements October 31, November 3, and November 6, 1997 Gantry Crane Activities Start Time and End Time January 1996-December 1997 Dock Reports Individual Vessel Arrival and Departure Times January 1996-December 1997 Trailer/Container Reports Daily Trailer/Container Totals January 1996-December 1997 Monthly Performance Reports Monthly Trailer/Container Totals October 1978-April 1998 a Only 57 days were collected. Table 8.4.
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The dependent variables are the daily inbound and outbound loaded truck volumes, and the independent variables are the total number of exported and imported freight units. The team also developed equations for forecasting future year inbound and outbound freight units, which are required to estimate future year truck trips.
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and outbound (production) models indicate that the Heavy Truck Model explains almost 80% of the variability in the number of inbound loaded truck movements, and almost 70% of the variability in the number of outbound loaded truck movements (dependent variable)
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Estimate the daily number of inbound and outbound loaded freight trucks by multiplying the regression model results for the number of loaded trucks for each group by the average of truck movement percentage for each day of the week.
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The interim freight study was designed to provide information and tools to assess freight trends and impacts on Ohio's roadways.18 The data developed was used in four individual Ohio case studies each addressing a different aspect of freight movement. The model associated with the study is referred to here as the Ohio Interim Model.
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, origin, and destination. Additionally conversion factors were applied to convert tonnage by trucks into annual trucks and then to daily truck trips.
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The resulting network flows were then mapped as a roadway network using the ArcView GIS software. The Ohio Interim Freight Model developed facility freight flows by directly using a method of freight forecasting described as O-D table factoring and assignment.
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57 Commodity Group Code Name STCC Codes in Commodity Group 1998 Annual Tonnage by All Modes 1998 Annual Tonnage by Truck 1 Agriculture 1, 7, 8, 9 28,898,426 6,679,545 2 Metallic Ores 10 43,887,516 – 3 Coal 11 132,797,767 11,135,211 4 Other Minerals 13, 14, 19 26,096,634 – 5 Food 20 96,036,220 76,781,243 6 Nondurable Manufacturing 21, 22, 23, 25, 27 13,311,467 12,646,266 7 Lumber 24 27,041,926 22,128,079 8 Paper 26 31,175,374 24,416,542 9 Chemicals 28 94,527,499 66,666,943 10 Petroleum 29 46,791,003 29,842,434 11 Rubber/Plastics 30 18,797,786 18,442,466 12 Durable Manufacturing 31, 36, 38, 39 23,187,380 22,128,609 13 Clay, Concrete, Glass 32 70,984,985 64,114,794 14 Primary Metals 33 87,342,217 62,115,438 15 Fabricated Metal Products 34 27,871,702 27,107,319 16 Transportation Equipment 37 47,048,025 31,064,887 17 Miscellaneous Freight 40-48, 5020, 5030 43,143,468 – 18 Warehousing 5010 82,420,938 82,420,938 Table 8.9. Commodity groups used in the Ohio Interim Freight Model.
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1 Farm Products 12.04 18.37 19.10 18.71 17.67 8 Forest Products 13.36 11.64 13.27 13.27 13.27 9 Fresh Fish or Marine Products 8.20 8.13 14.42 15.89 16.11 10 Metallic Ores 16.98 18.81 25.77 25.77 25.77 11 Coal 16.98 18.81 25.77 25.77 25.77 13 Crude Petroleum or Natural Gas 14.43 19.58 17.84 17.84 17.84 14 Nonmetallic Minerals 16.98 18.81 25.77 25.77 25.77 19 Ordnance or Accessories 7.05 4.42 11.47 9.84 11.30 20 Food or Kindred Products 8.20 8.13 14.42 15.89 16.11 21 Tobacco Products 11.50 16.25 16.03 11.47 15.96 22 Textile Mill Products 1.34 3.57 18.18 18.16 17.48 23 Apparel or Related Products 1.34 3.57 18.18 18.16 17.48 24 Lumber or Wood Products 10.33 12.35 17.50 17.61 17.83 25 Furniture or Fixtures 2.92 3.25 11.02 11.26 11.38 26 Pulp, Paper, or Allied Products 4.07 7.67 15.66 15.17 14.59 27 Printed Matter 4.07 7.67 15.66 15.17 14.59 28 Chemicals or Allied Products 5.18 15.39 19.55 19.25 19.25 29 Petroleum or Coal Products 14.43 19.58 17.84 17.84 17.84 30 Rubber or Miscellaneous Plastics 7.05 4.42 11.47 9.84 11.30 31 Leather or Leather Products 1.34 3.57 18.18 18.16 17.48 32 Clay, Concrete, Glass, or Stone 10.69 14.47 18.53 18.63 18.81 33 Primary Metal Products 11.82 14.73 19.96 20.14 20.13 34 Fabricated Metal Products 4.00 11.33 14.49 14.49 14.49 35 Machinery 6.97 12.55 17.42 17.21 17.21 36 Electrical Equipment 4.05 7.42 14.81 14.62 14.62 37 Transportation Equipment 2.48 14.12 17.21 16.92 14.18 38 Instruments, Photo Equipment, Optical Equipment 6.97 12.55 17.42 17.21 17.21 39 Miscellaneous Manufacturing Products 5.48 5.40 11.63 13.04 14.23 50 Drayage, Warehousing, Distribution 7.05 9.67 14.85 14.98 14.93 Source: Derived from Vehicle Inventory and Usage Survey records for Ohio. Table 8.10.
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The Ohio model converted the observed tonnages to values and annual trucks. The Ohio model used 306 working days per year to convert from annual to daily trucks.
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60 STCC Code Description Value Per Ton (1998$) 1 Farm Products $1,147 8 Forest Products $40 9 Fresh Fish or Other Marine Products $5,493 10 Metallic Ores $50 11 Coal $24 13 Crude Petroleum, Natural Gas, or Gasoline $31 14 Nonmetallic Minerals $19 19 Ordnance or Accessories $11,590 20 Food or Kindred Products $1,408 21 Tobacco Products, Excluding Insecticides $32,610 22 Textile Mill Products $6,735 23 Apparel or Other Finished Textile Products $25,732 24 Lumber or Wood Products, Excluding Furniture $2,363 25 Furniture or Fixtures $5,465 26 Pulp, Paper, or Allied Products $1,333 27 Printed Matter $3,054 28 Chemicals or Allied Products $2,064 29 Petroleum or Coal Products $239 30 Rubber or Miscellaneous Plastics Products $7,290 31 Leather or Leather Products $29,268 32 Clay, Concrete, Glass, or Stone Products $205 33 Primary Metal Products $1,273 34 Fabricated Metal Products $3,544 35 Machinery, Excluding Electrical $21,980 36 Electrical Machinery, Equipment, or Supplies $28,724 37 Transportation Equipment $13,904 38 Instruments, etc.
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As such, the TRANSEARCH flows are best considered general flows along a corridor rather than actual facility flows. Model Application The Ohio Interim Freight Model freight data was used in four case studies to address various freight operations and policy issues.
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The truck data and forecasts can provide detailed information about the industries served and commodities carried on I-75, both now and in the future. CONCLUSIONS • Analysis indicates that of the top five commodities carried ranked by value, four are industrial commodities (transportation equipment, general machinery, electrical machinery, and fabricated metal)
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Performance Measures and Evaluation Performance measures were not developed in the Ohio Interim Freight Model. 8.5 Case Study – Freight Analysis Framework Background Context The FHWA's Office of Freight Management and Operations has developed the FAF as a policy tool to estimate commodity flows and related freight activity at national, state, and county levels.
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Three different truck types are used to allocate the freight to trucks: • Single units trucks; • Combination tractor-trailer trucks; and • Double tractor-trailer trucks. FAF highway freight movements capture only intercounty flows, not intracounty.
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Freight Analysis Framework waterborne freight shipments by ton and value. Table 8.14.
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Only the portion of the trip on the U.S. domestic freight network is included, with the international freight origin or destination taken as the U.S.
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FAF network is shown in Figure 8.5. INTERMODAL TERMINAL DATA FAF highway network has centroid connectors coded for the intermodal terminals identified by the Bureau of Transportation Statistics.
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The rail volumes and types of commodities being carried are classified as carloads, and the rail intermodal volumes are classified as trailer-on-flatcar or container-on-flatcar. The trailer-on-flatcar and container-on-flatcar freight move68 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 Table 8.15.
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The annual tonnage 69 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 Tons (Millions)
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Commodities in the TRANSEARCH database are aggregated to 14 basic commodity groupings. VIUS is used to develop payload factors by commodity group and by length of haul groups, and these payload factors are applied to the tonnage flows to convert to truck trips.
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Single Unit Trucks Semi-Trailer Double Trailers Triples Commodity STCC Initial Refined Percent Difference Initial Refined Percent Difference Initial Refined Percent Difference Initial Refined Percent 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 Products 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 Fresh Fish or Marine Products 9 6.1 21.3 28.1 9.8 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 Accessories 19 7.6 26.7 35.2 12.3 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 Products 23 4.6 16.2 21.3 7.4 Lumber and Fabricated Products 24 7.7 8.3 -8.07 27.1 37.1 -36.86 35.7 48.1 -34.58 12.5 100 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 Products 31 4.2 14.6 19.3 6.7 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 Table 8.19. Payload factors by STCC and truck type.
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Single Unit Trucks Semi-Trailer Double Trailers Triples Commodity STCC Initial Refined Percent Difference Initial Refined Percent Difference Initial Refined Percent Difference Initial Refined Percent Difference Primary Metal Products 33 7.3 6.5 10.49 25.7 37.9 -47.15 33.8 54.2 -60.27 11.8 100 Fabricated Metal Products 34 5.2 5.0 5.3 18.5 35.3 -90.99 24.3 26.1 -7.53 8.5 100 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 Equipment 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 Instruments, Photo Equipment, Optical 38 3.6 12.5 16.5 5.8 Miscellaneious products of Manufacturing 39 5.4 5.6 -3.21 19.1 33.4 -75.06 25.1 28.9 -15.06 8.8 100 Scrap, Refuse or Garbage 40 6.0 13.2 -121.23 21.1 36.6 -73.63 27.7 45.9 -65.38 9.7 100 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. Table 8.19.
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8.6 Case Study – New Jersey Statewide Model Truck Trip Table Update Project Background Context Geographically, New Jersey is among the smallest states in the union, yet it ranks ninth in terms of total population and first in terms of population density. New Jersey's density is even greater than that of the Netherlands, the most densely populated country in Europe.
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A major impact on regional truck trips was expected after the completion of I-287 in northern New Jersey and the completion of the remaining section of I-295 in the Greater Trenton Area. The revised New Jersey Truck Model is an update of the previously existing truck trip model.20 General Approach Model Class As a truck model, the New Jersey Truck Model develops highway freight truck flows by assigning an O-D table of freight truck flows to a highway network.
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The trip generation process estimated truck trips generated within the five region study area as well as in the adjacent regions. Internally, trip generation was performed at the zonal level using employment, households, and truck terminals as 75
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Special generators, in the form of truck terminals, warehouses, and pipeline terminals, were utilized for conditions where the typical employment relationships would poorly estimate truck trips. In addition, the truck terminals served as attractors for a portion of the long-haul truck trips entering the study area from the adjacent regions.
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. The revised truck trip generation process requires employment data by type and household data for each of the internal study area zones.
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Internal trip distribution was performed using a synthetic data set derived from the 1991 Phoenix Truck Model Update Project. This data was as an observed distribution, adjusted as necessary to establish a reasonable target for the calibration process for both medium and heavy truck trips.
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Toll links for each vehicle type also were coded in the network for all toll facilities in New Jersey. Model Validation Trip Generation Using the Phoenix values and definitions as a starting point, truck trips were estimated and summed together with the truck terminal special generators.
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As shown in Table 8.23, comparisons by area and facility type 80 Central Business District 1 Urban 2 Suburban 3 Rural 4 Heavy Truck Percentages Freeway 1 8.5 11.0 12.0 10.5 Expressway 2 7.5 8.0 11.5 8.0 Principal Divided 3 6.0 10.0 6.0 7.5 Principal Undivided 4 5.8 6.0 5.5 6.0 Major Divided 5 4.7 7.0 5.0 6.0 Major Undivided 6 4.6 7.0 4.0 5.0 Minor 7 4.5 8.0 5.0 4.0 Collector-Local 8 4.5 8.0 5.0 4.0 Medium Truck Percentages Freeway 1 1.1 1.4 1.6 1.4 Expressway 2 1.0 1.0 1.5 1.0 Principal Divided 3 1.6 2.6 1.6 2.0 Principal Undivided 4 1.5 1.6 1.4 1.6 Major Divided 5 1.2 1.8 1.3 1.6 Major Undivided 6 1.2 1.8 1.0 1.3 Minor 7 1.5 2.6 1.7 1.3 Collector-Local 8 1.5 2.6 1.7 1.3 Total Truck Percentages Freeway 1 7.4 9.6 10.4 9.1 Expressway 2 6.5 7.0 10.0 7.0 Principal Divided 3 4.4 7.4 4.4 5.5 Principal Undivided 4 4.3 4.4 4.1 4.4 Major Divided 5 3.5 5.2 3.7 4.4 Major Undivided 6 3.3 5.2 3.0 3.7 Minor 7 3.0 5.4 3.3 2.7 Collector-Local 8 3.0 5.4 3.3 2.7 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999. Table 8.23.
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The before and 81 Volume Group Number of Observations Average Observations Average Estimate R-Squared RMS Percent Percent Deviation Total Traffic > 80,000 30 90,270 88,224 0.5812 7.8 6.0 70,001-80,000 12 71,989 70,937 0.7864 26.9 20.0 60,001-70,000 43 64,724 67,357 0.1050 22.5 18.2 50,001-60,000 54 55,209 57,900 0.0055 22.8 18.5 40,001-50,000 94 44,963 48,682 0.1177 32.6 24.3 30,001-40,000 159 34,295 38,763 0.0063 41.8 30.5 20,001-30,000 232 25,323 26,359 0.0002 44.9 26.9 10,001-20,000 485 13,955 15,718 0.1684 51.9 35.9 1-10,000 1,077 5,211 5,863 0.3159 78.5 50.8 Total 2,185 17,050 18,411 0.8334 48.4 29.4 Total Trucks > 8,000 32 10,738 10,840 0.5336 21.1 13.9 7,001-8,000 13 7,455 5,639 0.0312 39.2 33.4 6,001-7,000 55 6,493 5,778 0.1891 31.7 23.4 5,001-6,000 56 5,446 4,576 0.0585 28.8 25.0 4,001-5,000 82 4,464 4,271 0.0179 27.6 21.8 3,001-4,000 122 3,438 3,078 0.0244 37.9 29.0 2,001-3,000 107 2,501 2,788 0.0068 78.1 44.6 1,001-2,000 285 1,414 1,585 0.0771 65.1 45.4 1-1,000 1,373 368 440 0.3820 105.4 65.1 Total 2,125 1,442 1,447 0.8100 64.0 35.0 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999. Table 8.24.
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Overall, the model performs reasonably well and produces results reasonable for policy testing. 8.7 Case Study – SCAG Heavy-Duty Truck Model Background Context The SCAG is the largest association of governments in the United States.
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Figure 8.8. Impact of I-287 opening.
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Figure 8.9. Impact of Trenton Complex opening.
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Objective and Purpose of the Model The HDT Model provides a methodology that can be integrated with the SCAG Regional Model to forecast HDT activity and associated VMT for the SCAG region. The main objectives of the HDT Model are as follows: • To characterize truck activity in terms of truck trips linked to goods movement, intermodal facilities, interregional truck traffic, regional distribution traffic, and intraregional truck traffic; • To understand and develop the relationships between truck trip generation and different types of economic activity and develop appropriate forecasts of future truck activity at the TAZ and facility level; • To develop model outputs for HDTs including traffic volumes, VMT, speeds on links, transit times between specific O-D points, etc., to be used to compute mobility performance indicators; and • To implement a simultaneous traffic assignment procedure using the TRANPLAN software system.
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Model Development Data The model coefficients and parameters are specifically developed for the HDT Model. While the internal truck trip generation involves deriving truck trip rates from truck surveys, the distribution model is based on gravity model parameters unique to this model that are calibrated to observed truck trip length distributions.
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Adjustments then were made to calibrate truck movements in the distribution model based on K-factors. The final trip distribution yielded average internal truck trip lengths of 5.592 miles for light-heavy trucks, 12.827 miles for medium-heavy, and 23.914 miles for heavy-heavy trucks.
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Flow Unit and Time Period Conversion Commodity flows are converted from annual tonnage to truck trips by truck weight class by using the TIUS data and O-D surveys performed at cordon points around the SCAG region. The California Department of Transportation's weigh-inmotion stations collect data from along the state highway system that are used for deriving truck time of day factors by truck class and by direction.
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89 Heavy-Duty Vehicle Passenger Car Equivalent Values by Vehicle Type, Terrain, and Percent Trucks Percent Grade Percent Trucks Length (Miles) 0-2 3-4 5-6 >6 Light-Heavy 0 5 <1 1.2 2 3.6 3.6 0 5 1-2 1.2 2 3.6 3.6 0 5 >2 1.2 2 3.6 3.6 5 10 <1 1.2 2 3.6 3.6 5 10 1-2 1.2 2 3.6 3.6 5 10 >2 1.2 2 3.6 3.6 10 100 <1 1.2 2 3.6 3.6 10 100 1-2 1.2 2 3.6 3.6 10 100 >2 1.2 2 3.6 3.6 Medium-Heavy 0 5 <1 1.5 2.5 4.5 4.5 0 5 1-2 1.5 2.5 4.5 4.5 0 5 >2 1.5 2.5 4.5 4.5 5 10 <1 1.5 2.5 4.5 4.5 5 10 1-2 1.5 2.5 4.5 4.5 5 10 >2 1.5 2.5 4.5 4.5 10 100 <1 1.5 2.5 4.5 4.5 10 100 1-2 1.5 2.5 4.5 4.5 10 100 >2 1.5 2.5 4.5 4.5 Heavy-Heavy 0 5 <1 2 3.3 6 6 0 5 1-2 2 3.3 6 6 0 5 >2 2 3.3 6 6 5 10 <1 2 3.3 6 6 5 10 1-2 2 3.3 6 6 5 10 >2 2 3.3 6 6 10 100 <1 2 3.3 6 6 10 100 1-2 2 3.3 6 6 10 100 >2 2 3.3 6 6 Passenger Car Equivalent Value Adjustment Factors for Highway Congestion Percent Trucks V/C Ratio L-H M-H H-H 0 5 0.0 0.5 1.0 1.0 1.0 0 5 0.5 1.0 1.0 1.0 1.2 0 5 1.0 1.5 1.1 1.2 1.3 0 5 1.5 2.0 1.0 1.2 1.2 0 5 2.0 99.0 1.0 1.2 1.3 5 10 0.0 0.5 1.0 1.0 1.0 5 10 0.5 1.0 1.0 1.0 1.2 5 10 1.0 1.5 1.2 1.3 1.3 5 10 1.5 2.0 1.0 1.2 1.3 5 10 2.0 99.0 1.0 1.2 1.3 10 100 0.0 0.5 1.0 1.0 1.0 10 100 0.5 1.0 1.0 1.0 1.2 10 100 1.0 1.5 1.2 1.3 1.3 10 100 1.5 2.0 1.0 1.2 1.3 10 100 2.0 99.0 1.0 1.2 1.3 Source: Southern California Association of Governments Heavy-Duty Truck Model.
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Trip length frequency curve (medium-heavy trucks)
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Trip length frequency curves (heavy-heavy trucks)
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Major commodities originating in Indiana by value included transportation equipment, metal products, food, electrical machinery, and chemicals. Major commodities by weight included petroleum or coal products, minerals, farm products, and metal products.
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Objective and Purpose of the Model InDOT's primary objective in supporting the research project was the creation of a model or forecasting tool capable of estimating future flows of commodities on Indiana's rail and highway networks, from which a general transportation model for the state could be developed. General Approach Model Class The Indiana Commodity Transport Model is a four-step commodity flow class of model based on the traditional fourstep transportation planning model commonly used for passenger and total truck forecasting applications.
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The study includes not only the 92 counties of Indiana but several major terminals outside the state including all of the remaining contiguous 47 states as well as additional nodes for the states bordering Indiana, for a total of 145 nodes or centers of freight activity. Framework The Indiana Commodity Transport Model was developed as a research project to prove the concepts presently being introduced into Indiana's Statewide model.
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Black, Transport Flows in the State of Indiana: Commodity Database Development and Traffic Assignment, Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana University, 1997. Figure 8.15.
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Table 8.30 shows the payload factors used for converting tonnage to truck volumes. Validation Data The 1993 CFS data were used to validate estimated commodity flow tables to, from, and within the 145 zones within the model.
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Farm Products 01 $5,794 39,902 Coal 11 281 10,759 Nonmetallic Minerals 14 463 57,341 Food and Kindred Products 20 16,958 21,039 Basic Textiles 22 275 93 Apparel 23 7,795 553 Lumber and Wood Products 24 3,235 4,131 Furniture and Fixtures 25 3,120 734 Pulp and Paper Products 26 3,194 2,814 Chemicals and Allied Products 28 11,474 11,957 Petroleum and Coal Products 29 9,008 62,500 Stone, Clay and Glass Products 32 2,748 21,972 Primary Metal Products 33 17,485 27,881 Fabricated Metal Products 34 10,363 4,572 Machinery (except Electrical) 35 9,504 1,023 Electrical Machinery 36 15,914 1,909 Transportation Equipment 37 34,401 6,731 Waste and Scrap Material 40 703 4,474 Other Manufactured Productsa 50 14,811 2,421 Source: Bureau of Transportation Statistics, 1993 Commodity Flow Survey.
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Black, Transport Flows in the State of Indiana: Commodity Database Development and Traffic Assignment, Phase 2, Bloomington, Indiana: Transportation Research Center, Indiana University, 1997. Table 8.32.
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99 Variable Name Description SIC Code Agser Employment in Agricultural Services 07 All Total Employment N/A App Employment in Apparel and Other Textile Products 23 Cash Gross Cash Receipts (in $1,000s) from Farming N/A Chem Employment in Chemicals and Allied Products 28 Coal Employment in Coal Mining 11 Elec Employment in Electrical and Electrical Equipment 36 Fab Employment in Fabricated Metal Products 34 Food Employment in Food and Kindred Products 20 Furn Employment in Furniture and Fixtures 25 Lum Employment in Lumber and Wood Products 24 Mac Employment in Industrial Machinery and Equipment 35 Man Employment in Manufacturing 02 and 03 Met Employment in Primary Metal Industries 33 Min Employment in Nonmetallic Minerals, except Fuels 14 Pet Employment in Petroleum and Coal Products 29 Pop Total Population N/A Pulp Employment in Paper and Allied Products 26 Tex Employment in Textile Mill Products 22 Tran Employment in Transportation Equipment 37 Source: W.R.
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Traffic density factors for rail cars and motor carriers by commodity.
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From page 101... ...
Model Application The Indiana Commodity Transport Model has not been applied to date, although the 1998 year trip tables crated by the model are being used as the basis for the development of InDOT's freight truck trip table in an update of the Indiana Statewide Travel Demand Model, now under development. Performance Measures and Evaluation No performance measures were developed for this research model.
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From page 102... ...
Florida has a statewide highway model in which total truck trips are forecasted based on total employment and are assigned together with auto trips. An existing four-step model for passenger auto and total truck traffic provided the state zone structure, highway network, and employment data that served as the structure for developing the commodity model.
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From page 103... ...
It captures large trucks moving on the FIHS, the shipment of commodities between regions in Florida, and the shipment of freight between Florida and the rest of North America. These truck trips currently represent about 25% of the total truck trips in Florida, but 45% of the total truck vehicle-miles traveled within the state.
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From page 104... ...
Trip Generation The FISHFM estimates the total freight tonnage by all modes -- truck, carload rail, intermodal rail, water, and air -- 104 Figure 8.17. Highway network for Florida Intermodal Statewide Highway Freight Model.
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From page 105... ...
The average trip lengths for 105 Code Description Standard Transportation Commodity Codes 1 Agricultural 1, 7, 8, 9 2 Nonmetallic Minerals 10, 13, 14, 19 3 Coal 11 4 Food 20 5 Nondurable Manufacturing 21, 22, 23, 25, 27 6 Lumber 24 7 Chemicals 28 8 Paper 26 9 Petroleum Products 29 10 Other Durable Manufacturing 30, 31, 33-39 11 Clay/Concrete/Glass 32 12 Waste 40 13 Miscellaneous Freight 41-47, 5020, 5030 14 Warehousing 5010 Table 8.36. Commodity groups.
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From page 106... ...
106 Code Name Coefficient Variable Coefficient Variable Commodity Groups 1 Agricultural 23.537 SIC20 2 Nonmetallic Minerals 1461.302 SIC28 3 Coal 178.639 SIC49 4 Food 109.51 SIC51 5 Nondurable Manufacturing 24.698 SIC51 6 Lumber 147.624 SIC25 0.448 Pop 7 Chemicals 83.247 SIC51 8 Paper 23.924 SIC51 9 Petroleum Products 0.228 Pop 10 Other Durable Manufacturing 46.762 SIC 50 11 Clay, Concrete, Glass 2.964 Pop 12 Waste 68.089 SIC33 13 Miscellaneous Freight 2.886 SUM (SIC42,44,45) 14 Warehousing 2.926 Pop CG Group Description Average Distance Deterrence Coefficient 1 Agricultural 1,260 0.00079 2 Nonmetallic Minerals 332 0.00301 3 Coal 764 0.00131 4 Food 681 0.00147 5 Nondurable Manufacturing 528 0.00189 6 Lumber 606 0.00165 7 Chemicals 790 0.00127 8 Paper 406 0.00246 9 Petroleum Products 768 0.00130 10 Other Durable Manufacturing 712 0.00140 11 Clay/Concrete/Glass 244 0.00410 12 Waste 1,034 0.00097 13 Miscellaneous Freight 748 0.00134 14 Warehousing 250 0.00400 Table 8.38.
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From page 107... ...
FISHFM develops daily truck assignments. It is therefore necessary to convert the annual truck table of tonnages to daily truck trips.
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From page 108... ...
The calibrated tons per daily truck by commodity group are shown in Table 8.41. In order to implement the mode split component and the conversion to daily truck trips in FSUTMS, a special program known as FMODESP was written in FORTRAN.
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From page 109... ...
Trip Assignment The truck volumes loaded in the model were validated against the truck counts on major corridors, across the screen lines and external stations. Estimates such as VMT, vehiclehours traveled by truck, and RMSE statistics were reviewed and compared with existing statewide freight models and urban freight/truck models.
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From page 110... ...
8.10 Case Study – Cross-Cascades Corridor Analysis Project Background Context Washington State depends heavily on trade for its economic well-being. Home to just 2% of the nation's population, the state accounts for 7% of the nation's exports.
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From page 111... ...
Modes The modes available to make freight trips and shipments include: • Air freight; • Rail freight; • Heavy truck freight; and • Medium truck freight. As an integrated passenger and freight model the following passenger modes also are included: • Air passenger; • Amtrak (rail passenger)
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From page 112... ...
Total employment by industry by county was allocated to subcounty, with zones based on 1990 Census data. MEPLAN MODEL COEFFICIENT Washington State's economic activity reflects through the MEPLAN model coefficient.
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From page 113... ...
Heavy truck load 113 Source: External Zones, Cross-Cascades Corridor Analysis Project Summary Report. Washington State Department of Transportation, 2001.
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From page 114... ...
Software MEPLAN software, developed and distributed by ME&P of Cambridge, England, is used to run the model.27 MEPLAN 114 Select Model Approach Designate Traffic Analysis Zones Build Highway, Railroad, Air Networks Insert Economic Data Process Traffic Count, Freight Movement, and Ridership Data Prepare OriginDestination Matrix Build in ArcView to View Output Run Model for Highway Modes Process Amtrak and Intercity Ridership Data Retrieve Results for Travel Time/Assignment, Modal Split, etc. Prepare Model User's Manual and Documentation Reference Test/Calibrate Model Improve Visual Output Display Source: Cross-Cascades Corridor Analysis Project Summary Report, Washington State Department of Transportation, 2001.
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From page 115... ...
Commodity Groups/Truck Types Exogenous production is production related to sales exported outside of the economic model area. Exogenous production is one of the inputs in the MEPLAN model, and is shown by industry in Washington State in Table 8.46.
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From page 116... ...
116 Economy and Land Use Transportation Component Trip Generation Distribution Transportation Availability and Cost • Structure of the economy • Location of the activity • Network • Costs • Mode Split • Trip Assignment Source: Special Input-Outputs, Cross-Cascades Corridor Analysis Project, Summary Report, Washington State Department of Transportation, 2001. Figure 8.23.
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From page 117... ...
. Modes available to make these freight trips and shipments include: • Air freight; • Rail freight; • Heavy truck freight; and • Medium truck freight.
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From page 118... ...
More direct representation of the various freight movements, rather than average cost and shipment size, can be made by using a statistical distribution to more accurately reflect actual freight diversity. 118 User Mode Flow Tons/Vehicle Light Truck Mid value-to-weight High value-to-weight 3.60 3.41 Medium Truck Low value-to-weight Mid value-to-weight High value-to-weight 15.50 14.41 13.64 Heavy Truck Low value-to-weight Mid value-to-weight 25.92 24.02 Freight Truck Low value-to-weight Mid value-to-weight 75.95 68.23 Table 8.49.
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From page 119... ...
The new Oregon Statewide Model can be used to 1) analyze and support land use and transportation decisionmaking; and 2)
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From page 120... ...
As shown in Figure 8.25, the Oregon model contains a set of seven separate but highly connected modules: regional economics and demographics; production allocations and interactions; household allocations; land development; commercial movements; household travel; and transportation supply. REGIONAL ECONOMICS AND DEMOGRAPHICS The regional economics and demographics module provides productions in each economic sector, imports and exports by economic sector, employment by labor category, and in-migration and payroll by sector for each year.
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From page 121... ...
TRANSPORT SUPPLY The transportation supply module is a hybrid of macroscopic and microscopic techniques. Equilibrium travel times 121 Data Store Regional Economics and Demographics Production Allocations and Interactions Household Allocations Land Development Commercial Movements Household Travel Transport Supply Source: J.D.
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From page 122... ...
The regional economic and demographic module determines the total production activity in all the economic sectors other than the households sector over the entire model area each year. The production sectors in the model are listed in Table 8.51.
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From page 123... ...
Table 8.51. Production sectors included in the Oregon model.
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From page 124... ...
Freight Shipments Containers, Carriers or Devices, Shipping, Returned Empty Waste Hazardous Materials or Waste Hazardous Substances Construction Services Pipeline Transportation Services Transportation and Storage Services Radio and Television Broadcasting Services Postal Services Utilities Services Wholesale Margins Retail Margins Other Finance, Insurance and Real Estate Services Business Services Education Services Health Services Amusement and Recreation Services Accommodation Services Food Services Other Personal and Miscellaneous Services Managerial Labor Professional Labor Grade-school Teaching Labor Clerical Labor Assembly and Fabrication Labor Agricultural Labor Semi-skilled Manual Labor Unskilled Manual Labor Retail Labor Health Care Labor Post-secondary Teaching Labor Other Labor Table 8.52. Commodity categories included in the Oregon Statewide Model.
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From page 125... ...
They are largely based on Census tracts and are nested into counties. The internal zones inside Oregon contain grid cells, while the internal zones outside Oregon do not.
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From page 126... ...
Both models are nested through composite costs. Trip Distribution The goods and services shipments flows are determined as part of the spatial distributions of activities and population, following the path from the production locations to the exchange locations and then to the consumption locations.
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From page 127... ...
These equilibrium travel times are then used in a microscopic assignment, which works at the level of individual vehicles, determining the network loadings from synthesized demands of the household travel and commercial movements. 127 Parameters Network Read/Verify Inputs Path Search by Mode Cost and Disutilities Trip Generation Modal Split Empty Returns Assignment of Trips Changes in Speeds and Waiting Times Coverage?
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From page 128... ...
Trip Generation The second generation Oregon Statewide model has not been validated. The trip generation step is also not validated.
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From page 129... ...
Model Application As of this writing, the second generation Oregon model has not been applied for any projects. However, the model will be used to analyze and support land use and transportation decision-making; and to make periodic, long-term economic, demographic, passenger, and commodity flow forecasts at the statewide and substate levels.
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Key Terms
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