Research, Development, and Application of Methods to Update Freight Analysis Framework Out-of-Scope Commodity Flow Data and Truck Payload Factors
Chapter 13. Bundling of Commodities and Implementation
The proposed method supports the development of truck payload factors (TPF), for individual Standard Classification of Transported Goods (SCTG) 2 commodities. However, the sampling plan in Vehicle Inventory and Use Survey (VIUS) was developed to provide statistically valid responses over an entire subgroup, e.g., Single Unit and Combination Unit trucks, and not for the SCTG2 commodities carried by those subgroups. The Standard Deviation, Number of Records, Means, etc., for the ton-miles and miles that are used to compute payloads can have a reasonable standard error when aggregated over all commodities, but a less desirable standard error when computed separately for each commodity. Tables 34 through 36 report the Relative Standard Error for ton-miles, miles, and payloads for each SCTG2 commodity, as well as for all commodities combined.
Payload factors for the individual 43 SCTG2 commodities may not only have large relative standard errors, this large number of payload factors can itself create data management problems. It is common practice to group or bundle commodities before using their payloads. However, the grouping of commodities is dependent on how they will be used. A grouping of commodities that support infrastructure analysis may not be useful in supporting economic analysis. Some common applications are shown in table 45.
Table 45. Bundling of commodities by selected applications.
| Application |
Bundling |
Issues |
| Infrastructure |
Group commodities with similar impacts (e.g., tons per truck). |
Infrastructure impacts are related to link usage, i.e., assignments. Payloads will be applied to trip tables prior to assignment. Groupings that support assignment may not be appropriate for trip tables. |
| Economic |
Group commodities that are inputs to, or outputs of, important industries. |
Important industries will vary by jurisdiction. |
| Modeling |
Group commodities with similar behavior: e.g., tons per truck, correlation with industry employment, average trip lengths, etc. |
Similarity will be based on trips beginning or ending in a specific modeling area. |
| Vehicle Impacts (e.g., environmental, energy, etc.) |
Group commodities that use equipment with similar impacts. |
Usage of equipment may be specific to an area. |
(Source: Federal Highway Administration.)
The grouping of the 43 SCTG2 commodities into 15 bundles of those commodities for the California Statewide Freight Forecasting Model is shown in table 37. The resulting payloads and their Relative Standard Error for Combination Unit trucks from VIUS are shown in table 38. These statistics are for the behaviors that were found to be significant in freight modeling in California.
Additionally, the SCTG2 commodities themselves were developed in a way that support groupings based on the characteristics of similar commodities. The Commodity Flow Survey, (CFS), and the definition of SCTG2 commodities includes a grouping of SCTG2 commodities. As noted, this is not the only or best grouping of commodities, only an example of how commodities can be grouped. However, this grouping can show how the information in table 34 and table 35 can be used to compute initial payload factors for any grouping of the SCTG2 commodities. The CFS grouping of SCTG2 commodities is shown in table 46.
Table 46. Commodity flow survey bundling of standard classification of transported goods 2 commodities.
| SCTG |
Description |
Bundles |
| 01 |
Live Animals and Fish |
01-05 Agriculture products and fish |
| 02 |
Cereal Grains (including seed) |
01-05 Agriculture products and fish |
| 03 |
Other Agricultural Products, except for Animal Feed |
01-05 Agriculture products and fish |
| 04 |
Animal Feed and Products of Animal Origin, n.e.c. |
01-05 Agriculture products and fish |
| 05 |
Meat, Fish, and Seafood, and Their Preparations |
01-05 Agriculture products and fish |
| 06 |
Milled Grain Products and Preparations, and Bakery Products |
06-09 Grains, alcohol, and tobacco products |
| 07 |
Other Prepared Foodstuffs, and Fats and Oils |
06-09 Grains, alcohol, and tobacco products |
| 08 |
Alcoholic Beverages |
06-09 Grains, alcohol, and tobacco products |
| 09 |
Tobacco Products |
06-09 Grains, alcohol, and tobacco products |
| 10 |
Monumental or Building Stone |
10-14 Stones, nonmetallic minerals, and metallic ores |
| 11 |
Natural Sands |
10-14 Stones, nonmetallic minerals, and metallic ores |
| 12 |
Gravel and Crushed Stone |
10-14 Stones, nonmetallic minerals, and metallic ores |
| 13 |
Nonmetallic Minerals, n.e.c. |
10-14 Stones, nonmetallic minerals, and metallic ores |
| 14 |
Metallic Ores and Concentrates |
10-14 Stones, nonmetallic minerals, and metallic ores |
| 15 |
Coal |
15-19 Coal and petroleum products |
| 16 |
Crude Petroleum Oil |
15-19 Coal and petroleum products |
| 17 |
Gasoline and Aviation Turbine Fuel |
15-19 Coal and petroleum products |
| 18 |
Fuel Oils |
15-19 Coal and petroleum products |
| 19 |
Coal and Petroleum Products, n.e.c. |
15-19 Coal and petroleum products |
| 20 |
Basic Chemicals |
20-24 Pharmaceutical and chemical products |
| 21 |
Pharmaceutical Products |
20-24 Pharmaceutical and chemical products |
| 22 |
Fertilizers |
20-24 Pharmaceutical and chemical products |
| 23 |
Chemical Products and Preparations, n.e.c. |
20-24 Pharmaceutical and chemical products |
| 24 |
Plastics and Rubber |
20-24 Pharmaceutical and chemical products |
| 25 |
Logs and Other Wood in the Rough |
25-30 Logs, wood products, and textile and leather |
| 26 |
Wood Products |
25-30 Logs, wood products, and textile and leather |
| 27 |
Pulp, Newsprint, Paper, and Paperboard |
25-30 Logs, wood products, and textile and leather |
| 28 |
Paper or Paperboard Articles |
25-30 Logs, wood products, and textile and leather |
| 29 |
Printed Products |
25-30 Logs, wood products, and textile and leather |
| 30 |
Textiles, Leather, and Articles of Textiles or Leather |
25-30 Logs, wood products, and textile and leather |
| 31 |
Nonmetallic Mineral Products |
31-34 Base metal and machinery |
| 32 |
Base Metal in Primary or Semi-Finished Forms and in Finished Basic Shapes |
31-34 Base metal and machinery |
| 33 |
Articles of Base Metal |
31-34 Base metal and machinery |
| 34 |
Machinery |
31-34 Base metal and machinery |
| 35 |
Electronic and Other Electrical Equipment and Components, and Office Equipment |
35-38 Electronic, motorized vehicles, and precision instruments |
| 36 |
Motorized and Other Vehicles (including parts) |
35-38 Electronic, motorized vehicles, and precision instruments |
| 37 |
Transportation Equipment, n.e.c. |
35-38 Electronic, motorized vehicles, and precision instruments |
| 38 |
Precision Instruments and Apparatus |
35-38 Electronic, motorized vehicles, and precision instruments |
| 39 |
Furniture, Mattresses and Mattress Supports, Lamps, Lighting Fittings, and Illuminated Signs |
39-43 Furniture, mixed freight and misc. manufactured products |
| 40 |
Miscellaneous Manufactured Products |
39-43 Furniture, mixed freight and misc. manufactured products |
| 41 |
Waste and Scrap |
39-43 Furniture, mixed freight and misc. manufactured products |
| 42 |
Mail, Empty Containers and Other Special |
39-43 Furniture, mixed freight and misc. manufactured products |
| 43 |
Mixed Freight |
39-43 Furniture, mixed freight and misc. manufactured products |
(Source: Federal Highway Administration.)
This grouping reduces the 43 SCTG2 commodities to 9 bundles of commodities. The ton-miles and tons for the SCTG2 commodities can be obtained from table 34 and table 35. An example for one specific bundle, "35-38 Electronic, motorized vehicles, and precision instruments" is shown in table 47. The ton-miles and miles for the entire bundle is the sum of the values for its individual SCTG2 commodities.
Table 47. 2002 Payloads for an example bundle.
 |
SU Trucks |
RSE |
CU trucks |
RSE |
| 2002 VIUS Ton Miles (in billions) by SCTG2—35 |
1.54 |
14% |
17.28 |
36% |
| 2002 VIUS Ton Miles (in billions) by SCTG2—36 |
3.94 |
11% |
38.02 |
4% |
| 2002 VIUS Ton Miles (in billions) by SCTG2—37 |
0.35 |
43% |
11.47 |
4% |
| 2002 VIUS Ton Miles (in billions) by SCTG2—38 |
0.90 |
24% |
4.57 |
89% |
| 2002 VIUS Ton Miles (in billions) by SCTG2—Bundle of 35 through 38 |
6.73 |
N/A |
71.34 |
N/A |
| 2002 VIUS Miles (in billions) by SCTG2—35 |
0.69 |
0.09 |
2.00 |
7% |
| 2002 VIUS Miles (in billions) by SCTG2—36 |
1.41 |
0.1 |
2.18 |
6% |
| 2002 VIUS Miles (in billions) by SCTG2—37 |
0.04 |
0.2 |
0.53 |
15% |
| 2002 VIUS Miles (in billions) by SCTG2—38 |
0.35 |
0.13 |
0.24 |
18% |
| 2002 VIUS Miles (in billions) by SCTG2—Bundle 35 through 38 |
2.49 |
N/A |
4.95 |
N/A |
| 2002 TPF for Bundle |
2.70 |
N/A |
14.41 |
N/A |
| 2002 Share of Ton-Miles for Bundle |
8.6% |
N/A |
91.4% |
N/A |
(Source: Federal Highway Administration.)
The payload is computed by dividing the total of ton-miles for SU trucks, e.g., 6.73 billion ton‑miles, by the total of miles for SU trucks, e.g., 2.50 billion miles. For this bundle "SCTG 35 through 38" is the payload factor is 2.70 tons per SU truck. The share of the ton-miles, which is used to allocate total tons among truck sizes, is found by dividing the share of ton-miles for SU trucks, 6.73 billion ton miles for that bundle by the total of SU and CU trucks ton-miles for that bundle, 6.73 billion plus 71.34 billion ton‑miles. The resulting payload factors for these bundles is shown in table 48. The allocation of tons between SU and CU trucks, based on the share of ton-miles, is in table 49. The Relative Standard Errors for the bundles are shown as Not Available, N/A, because these cannot be computed without examining all the relevant records in the 2002 VIUS microdata.
Table 48. Payloads, tons per truck, for 2002 commodity flow survey bundles.
| CFS Bundle |
SU trucks |
CU trucks |
| 01-05 Agriculture products and fish |
5.24 |
17.82 |
| 06-09 Grains, alcohol, and tobacco products |
3.52 |
15.95 |
| 10-14 Stones, nonmetallic minerals, and metallic ores |
13.91 |
22.46 |
| 15-19 Coal and petroleum products |
5.82 |
23.36 |
| 20-24 Pharmaceutical and chemical products |
3.56 |
11.03 |
| 25-30 Logs, wood products, and textile and leather |
3.65 |
15.18 |
| 31-34 Base metal and machinery |
4.92 |
19.88 |
| 35-38 Electronic, motorized vehicles, and precision instruments |
2.70 |
14.41 |
| 39-43 Furniture, mixed freight and misc. manufactured products |
4.85 |
16.32 |
| Grand Total |
5.21 |
16.47 |
(Source: Federal Highway Administration.)
Table 49. 2002 allocation of total tons to single unit and combination unit trucks for commodity flow survey bundles.
| CFS Bundle |
SU trucks |
CU trucks |
| 01-05 Agriculture products and fish |
5.7% |
94.3% |
| 06-09 Grains, alcohol, and tobacco products |
4.9% |
95.1% |
| 10-14 Stones, nonmetallic minerals, and metallic ores |
36.5% |
63.5% |
| 15-19 Coal and petroleum products |
16.3% |
83.7% |
| 20-24 Pharmaceutical and chemical products |
8.0% |
92.0% |
| 25-30 Logs, wood products, and textile and leather |
5.0% |
95.0% |
| 31-34 Base metal and machinery |
16.4% |
83.6% |
| 35-38 Electronic, motorized vehicles, and precision instruments |
8.6% |
91.4% |
| 39-43 Furniture, mixed freight and misc. manufactured products |
8.1% |
91.9% |
| Grand Total |
10.2% |
89.8% |
(Source: Federal Highway Administration.)
The values in table 48 and table 49 are the values for the bundles of commodities according to 2002 U.S. VIUS. This replaces the bundling of the tables in Step 1 and Step 2 of the proposed method. These values still must be adjusted to later years using the methods described in Step 3, but this application is the same regardless of whether the adjustment is applied to individual SCTG2 commodities or to bundles of those SCTG2 commodities. It is noted that if all of the commodities are combined into a single bundle, those proposed values for payloads, allocation of tons to SU and CU trucks, updated to 2012 and 2017 are already reported for all SCTGs in table 43 and table 44. Table 50 shows the payloads per truck and allocation of total tons (based on the share of ton-miles) for each CFS commodity group shown in table 46 for 2012 and 2017.
Table 50. Payloads and total tons allocations to single unit and combination unit trucks for commodity flow survey bundles (2012, 2017).
| Commodity Group |
2012
TPF: SU |
2012
TPF: CU |
2012
Share of Ton-miles: SU |
2012
Share of Ton-miles: CU |
2017
TPF: SU |
2017
TPF: CU |
2017
Share of Ton-miles: SU |
2017
Share of Ton-miles: CU |
| 01–05 Agriculture products and fish |
6.18 |
17.96 |
8% |
92% |
5.11 |
17.51 |
6% |
94% |
| 06–09 Grains, alcohol, and tobacco products |
4.16 |
16.08 |
7% |
93% |
3.44 |
15.68 |
5% |
95% |
| 10–14 Stones, nonmetallic minerals, and metallic ores |
16.42 |
22.64 |
44% |
56% |
13.57 |
22.07 |
39% |
61% |
| 15–19 Coal and petroleum products |
6.86 |
23.55 |
21% |
79% |
5.67 |
22.96 |
18% |
82% |
| 20–24 Pharmaceutical and chemical products |
4.20 |
11.12 |
11% |
89% |
3.47 |
10.84 |
9% |
91% |
| 25–30 Logs, wood products, and textile and leather |
4.31 |
15.30 |
7% |
93% |
3.56 |
14.92 |
5% |
95% |
| 31–34 Base metal and machinery |
5.80 |
20.04 |
21% |
79% |
4.79 |
19.54 |
18% |
82% |
| 35–38 Electronic, motorized vehicles, and precision instruments |
3.19 |
14.53 |
11% |
89% |
2.64 |
14.17 |
9% |
91% |
| 39–43 Furniture, mixed freight and misc. manufactured products |
5.72 |
16.45 |
11% |
89% |
4.72 |
16.04 |
9% |
91% |
| Grand Total |
6.15 |
16.60 |
13% |
87% |
5.08 |
16.19 |
11% |
89% |
(Source: Federal Highway Administration.)
Another potential bundling of commodities that is possible is to bundle commodities based on the type of commodity and the type of truck carrying the commodity. While this will be helpful from the perspective of the overall Freight Analysis Framework (FAF) program and truck assignment, some jurisdictions may not want these groupings and may want to separate out certain commodities for their own purposes because of their importance to their region.
Table 51 shows another potential bundling of commodities based on the type of commodity and type of truck carrying the commodity. Table 52 shows the 2002, 2012, and 2017 payload factors after bundling commodities like table 51.
The bundling in table 51 reflects both the commodity and the behavior of the trucks which are loaded with FAF Origin-Destination (O-D) annual commodity tons. These trucks will travel empty in the reverse direction back their origin or will be repositioned to accept another load of the same or potentially a different commodity. This is different than the behavior of empty trucks implied in the existing FAF methodology as shown in figure 66 . Not all trucks will travel empty on the same path as when it is loaded. When aggregated over all highway links and all directions, the magnitude of empty truck demand might be reasonable, but the assigned flows of empty trucks on individual highway links will not be correct. Some trucks and/or their trailers are specialized (e.g., refrigerated, beverage, log carriers, livestock, etc.) such that while the origin and destination might be reversed, one-way traffic, toll usage, or truck restrictions might require different paths in the loaded and reverse empty direction. Other truck/trailer types are generalized (e.g., dry-van, flat/platform, etc.) and can be repositioned to a new location to transport commodities of the same or a different SCTG2. For example, SCTG 36, which may use auto carriers when it is transporting SCTG 361, Automobiles, but dry-van equipment when it is transporting SCTG 364, Auto parts. Because the FAF does not support commodity classifications below STCG2, all a commodity must be assigned to only one bundle. Still other truck/trailers, while not specialized (e.g., dump, open, tank) carry bulk unpacked commodities that would require cleaning before they could be repositioned and are likely to be backhauled empty.
Table 51. Proposed commodity bundles.
| Bundled Commodity Name |
SCTG2 Code |
Commodity Type |
Truck/Trailer Type |
Empty Bundle Name |
| Farm Products |
1-5 |
Bulk |
Specialized |
Backhauled |
| Food, Beverage and Tobacco |
6-9 |
Bulk |
Specialized |
Backhauled |
| Solid Stone |
10 |
Bulk |
Specialized |
Backhauled |
| Sand, Gravel and Ores |
11-15 |
Bulk |
Specialized |
Backhauled |
| Liquid and Gases (except Chemicals) |
16-19 |
Bulk |
Specialized |
Backhauled |
| Chemicals (except Chemical Products n.e.c.) |
20-22 |
Bulk |
Specialized |
Backhauled |
| Logs |
25 |
Bulk |
Specialized |
Backhauled |
| Waste (Recyclables) |
41 |
Bulk |
Specialized |
Backhauled |
Consumer Manufacturing
(include Chemical Products n.e.c.) |
23-24, 26-30 |
Packaged |
General |
Repositioned |
Durable Manufacturing
(low tech) |
31-34, 39 |
Packaged |
General |
Repositioned |
Durable Manufacturing
(high tech) |
35, 37-38 |
Packaged |
General |
Repositioned |
| Vehicles |
36 |
Packaged |
General |
Repositioned |
| Mixed Freight |
40, 42-43 |
Packaged |
General |
Repositioned |
(Source: Federal Highway Administration.)
Table 52. Proposed commodity bundles: payload factors and share of ton-miles.
| Commodity Bundle Name |
2002
Payload Factors: SUs |
2002
Payload Factors: CUs |
2002
Share of Ton-miles: SUs |
2002
Share of Ton-miles: CUs |
2012
Payload Factors: SUs |
2012
Payload Factors: CUs |
2012
Share of Ton-miles: SUs |
2012
Share of Ton-miles: CUs |
2017
Payload Factors: SUs |
2017
Payload Factors: CUs |
2017
Share of Ton-miles: SUs |
2017
Share of Ton-miles: CUs |
| Farm Products |
5.24 |
17.82 |
6% |
94% |
6.18 |
17.96 |
8% |
92% |
6.02 |
17.65 |
6% |
94% |
| Food, Beverage and Tobacco |
3.52 |
15.95 |
5% |
95% |
4.16 |
16.08 |
7% |
93% |
4.05 |
15.80 |
5% |
95% |
| Solid Stone |
9.33 |
19.50 |
22% |
78% |
11.01 |
19.66 |
27% |
73% |
10.74 |
19.32 |
23% |
77% |
| Sand, Gravel and Ores |
14.41 |
23.11 |
36% |
64% |
17.00 |
23.29 |
43% |
57% |
16.58 |
22.90 |
38% |
62% |
| Liquid and Gases (except Chemicals) |
5.32 |
23.08 |
17% |
83% |
6.28 |
23.26 |
21% |
79% |
6.13 |
22.87 |
18% |
82% |
| Chemicals (except Chemical Products n.e.c.) |
4.30 |
10.20 |
11% |
89% |
5.07 |
10.28 |
15% |
85% |
4.94 |
10.11 |
12% |
88% |
| Logs |
7.82 |
23.35 |
7% |
93% |
9.23 |
23.53 |
9% |
91% |
9.00 |
23.13 |
7% |
93% |
| Waste (Recyclables) |
7.64 |
20.96 |
48% |
52% |
9.01 |
21.12 |
56% |
44% |
8.79 |
20.76 |
51% |
49% |
| Backhauled Subtotal |
7.06 |
17.62 |
14% |
86% |
8.33 |
17.76 |
18% |
82% |
8.12 |
17.46 |
15% |
85% |
| Consumer Manufacturing (and Chemical Products n.e.c.) |
3.16 |
13.73 |
5% |
95% |
3.73 |
13.84 |
7% |
93% |
3.64 |
13.60 |
5% |
95% |
| Durable Manufacturing (low tech) |
4.58 |
19.42 |
15% |
85% |
5.41 |
19.58 |
19% |
81% |
5.27 |
19.24 |
16% |
84% |
| Durable Manufacturing (high tech) |
2.58 |
12.03 |
8% |
92% |
3.05 |
12.13 |
10% |
90% |
2.97 |
11.92 |
8% |
92% |
| Vehicles |
2.79 |
17.44 |
9% |
91% |
3.30 |
17.58 |
12% |
88% |
3.21 |
17.28 |
10% |
90% |
| Mixed Freight |
3.41 |
16.05 |
4% |
96% |
4.02 |
16.18 |
5% |
95% |
3.92 |
15.91 |
4% |
96% |
| Repositioned Subtotal |
3.70 |
15.74 |
7% |
93% |
4.37 |
15.87 |
9% |
91% |
4.26 |
15.60 |
8% |
92% |
(Source: Federal Highway Administration.)
Table 52 supports the calculation of empty truck tables of those trucks transporting FAF tonnages. For those commodities designated as backhauled, the empty trucks O-D table would be merely the transpose of the loaded truck O-D table. For the commodities designated as repositioned, the payload factors support the sum of the production truck origin rows and the attraction truck columns which could be distributed by a model. The friction factor in the gravity model is a negative exponential equation of the distance between FAF regions. Using this gravity model trip distribution, the empty repositioned trucks are calculated.
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