Office of Operations Freight Management and Operations

Quick Response Freight Manual II

5.0 Commodity Models

5.1 Introduction

In Section 4.0, the methods to forecast freight demand that were discussed involve the creation of flows of freight between zones, and trip tables, using trip generation and distribution steps. For urban models, trip tables (generally just for trucks) are created by trip generation and distribution equations that are created from trip diaries or surveys of commercial vehicles or using the coefficients of others that have been developed from such surveys. Those statewide models that deal with commodity freight develop trip generation and distribution equations from surveys of commodity flows, such as the Commodity Flow Survey, the Freight Analysis Framework or TRANSEARCH. Urban commercial vehicle surveys will always only be a statistical sample of all truck trips. However, even if commodity flow surveys are developed from statistical samples, they are generally expanded into complete flow tables, typically for an entire year. Since these commodity flow tables are themselves trip tables, if freight flow patterns are expected to be fairly stable, instead of using the commodity flows surveys as a means of developing trip generation and distribution equations, these commodity flow surveys themselves can be used as trip tables. This section discusses how commodity flow surveys can be used directly as trip tables in freight forecasting.

Although the organization of a commodity flow database might not look like a trip table to those who are familiar with travel demand models, its data fields easily can be reorganized into a trip table of freight flows. It contains as attributes origins and destinations, commodity type (purpose), and units of flow by mode. A sample frame of the TRANSEARCH database as used in the Tennessee Freight Model is shown in Figure 5.1, where the records are identified by the origin, the destination, and the commodity (purpose). The flow for each of these records by mode is specified in annual tons.

Figure 5.1 Tennessee Freight Model TRANSEARCH Database Sample Frame

Figure 5.1 shows a sample of a TRANSEARCH database. The first column shows the Origin Region’s code, the second shows the actual name for the Origin (the third and fourth show the same for the destination), the fifth lists the Commodity Code (in STCC4), followed by the commodity description. The last four columns show the freight flows in by Air (tons), Truck (tons), Water (tons), and Truckloads (truck units).

The use of a commodity table in place of one developed through a trip-generation and trip-distribution process as described in Section 4.0 does have limitations. These forecasts are not easily modified in response to changes in employment forecast by industry or by specific units of geography. The freight flows will not change in response to changes in the transportation system that might result in new distribution patterns. The use of a fixed table for freight may represent a different paradigm than that used for passenger travel. The use of commodity tables directly for freight flows is often part of a less sophisticated model, where simplifications were for the passenger trip table. The direct use of commodity flow tables in transportation forecasts is typically done in state forecasting, since the internal truck movements that are of interest in urban travel forecasting are not represented in most commodity databases. The direct use of a commodity trip table may be considered for the external portion of the forecasting as described in the Hybrid Approach for metropolitan areas that is discussed in Section 6.0.

5.2 Acquiring Commodity Tables

In order to be useful in freight forecasting, a commodity flow table must represent all of the flows in the geographic area, not be just a sample of selected flows. There are a number of public and private commercial commodity flow databases. The database that best serves as a complete representation of commodity flows will be discussed in more detail in later sections. They are the publicly available Commodity Flow Survey, discussed in Section 5.7; commercially available TRANSEARCH database, discussed in Section 5.9; and the Freight Analysis Framework, discussed in Section 5.9. The publicly available databases are available for no cost but, due to sampling and disclosure agreements, have limited levels of data availability by commodity, mode, and most importantly geography. To be useful in forecasting applications, these data typically need to be disaggregated in some fashion. This effort is labor intensive and requires detailed information for the disaggregated unit of geography that will support the disaggregation process. Most often industry employment that can be related to commodity classifications is used to disaggregate flows. For the commercial TRANSEARCH database, this information is available at smaller units of geography, but supporting information on how flows at smaller units of geography is proprietary and is not available to those acquiring this database. The price of the TRANSEARCH data is related to the number of records delivered. Since the records are uniquely defined by origin, destination, and commodity, additional zones and commodity detail will increase the number of records and the cost of the database. A method to limit the number of zones is to use detailed geography in the study area, for example counties, and to use increasingly less detailed units of geography at increasing distances from a study area, progressing to states and census regions, as shown in Figure 5.2 for Tennessee.

Figure 5.2 Tennessee Freight Model Regions and District Geography

Figure 5.2 displays a map of the United States with the regions used for the Tennessee freight model. There are four regions in Tennessee (1-4), and five other aggregate regions covering the Southeast (5), the Midwest (6), Northeast (7), the West South Central (8), and the Mountain and Pacific regions (9).

The publicly available databases also are national databases, and without assigning the database to a network, the through traffic for a particular jurisdiction cannot be easily established. For example, from the CFS or FAF2 databases, it cannot be determined what portion of the flows from California to Pennsylvania pass through Illinois. The TRANSEARCH database does include an assignable fixed path network, as described in Section 4.2.9. The inclusion of a network means that TRANSEARCH purchase can exclude external-to-external freight flows that do not pass through a study area.

In summary, the first tradeoff to consider is fixed price for a commercial private database versus labor and data costs to disaggregate a free public database. The second tradeoff to consider is the ability to easily include external through traffic, which are of interests to a study area, in commercial databases versus the lack of a process to include these trips in public free databases. The third tradeoff to consider is the transparency of the process and the ability to modify the processing of the free public database versus the lack of transparency and ability to modify the records in a private commercial database. The final consideration is the use of the databases. The free public databases, the CFS and the FAF2, are linked mode trip tables that easily can provide mode share information on complete trips. However, they cannot be easily routed on modal networks. The unlinked trip table that can be produced from the TRANSEARCH database cannot easily be used to analyze modal share changes for trips that use several modes, but since it identifies the zones where trips change modes as an origin or destination, it is ideally suited for assignment to modal networks.

5.3 Forecasting

Forecasts of the commodity flow tables are produced by applying economic forecasts of the industries consuming and producing freight to the related commodity flows. These forecasts are applied directly to observed base year commodity flows, rather than being used in trip generation and distribution methods. The Georgia Freight Model did not prepare an independent set of forecasts. It applied the growth rates that already had been used in preparing the FAF1 state-to-state commodity flow table by commodity as shown in Table 5.1.

Table 5.1 Georgia Freight Model Freight Analysis Framework Annual Percentage Rate of Growth

STCC2

Commodity Description

Truck APR

Rail APR

Water APR

Air APR

Total APR

01

Farm Products

1.3%

2.0%

4.1%

2.1%

1.4%

08

Forest Products

1.5%

3.0%

Not Applicable

0.5%

1.6%

09

Fresh Fish

4.7%

8.0%

Not Applicable

-0.5%

4.0%

10

Metallic Ores

1.5%

4.9%

Not Applicable

5.1%

4.7%

11

Coal

5.0%

0.9%

Not Applicable

3.2%

1.1%

13

Crude Petroleum

3.1%

0.0%

Not Applicable

4.7%

1.1%

14

Nonmetallic Minerals

1.1%

0.8%

-1.5%

3.8%

1.0%

20

Food Products

4.4%

4.1%

3.0%

3.2%

4.3%

21

Tobacco Products

1.6%

Not Applicable

Not Applicable

2.3%

1.6%

22

Textile Mill Products

1.5%

3.4%

Not Applicable

2.7%

1.5%

23

Apparel

4.3%

5.3%

Not Applicable

4.9%

4.4%

24

Lumber or Wood

3.1%

3.2%

5.5%

4.0%

3.1%

25

Furniture or Fixtures

3.8%

6.5%

Not Applicable

4.3%

4.0%

26

Pulp and Paper

2.6%

3.0%

2.8%

2.3%

2.7%

27

Printed Matter

3.7%

3.5%

Not Applicable

2.6%

3.7%

28

Chemicals

2.4%

2.3%

1.5%

2.5%

2.4%

29

Petroleum or Coal

2.3%

1.9%

1.3%

1.8%

2.2%

30

Rubber and Plastics

3.0%

3.7%

Not Applicable

2.6%

3.0%

31

Leather

4.3%

Not Applicable

Not Applicable

3.4%

4.3%

32

Clay, Concrete, Glass, Stone

3.8%

3.6%

5.2%

3.2%

3.7%

33

Primary Metal Products

3.2%

3.3%

5.5%

2.8%

3.2%

34

Fabricated Metal

3.5%

3.8%

3.4%

2.7%

3.5%

35

Nonelectrical Machinery

5.9%

4.7%

7.1%

5.2%

5.8%

36

Electrical Machinery

5.1%

6.4%

Not Applicable

5.5%

5.2%

37

Transportation Equipment

3.4%

2.7%

4.8%

3.2%

3.1%

38

Instruments

4.9%

4.1%

Not Applicable

4.4%

4.9%

39

Miscellaneous Manufacturing

3.8%

4.1%

Not Applicable

3.2%

3.8%

40

Waste or Scrap Materials

4.2%

3.1%

2.3%

4.6%

3.0%

41

Miscellaneous Freight Shipment

Not Applicable

1.1%

Not Applicable

Not Applicable

1.1%

42

Containers Returned Empty

Not Applicable

2.9%

Not Applicable

Not Applicable

2.9%

43

Mail

4.8%

5.9%

Not Applicable

5.8%

5.2%

44

Freight Forwarder

Not Applicable

4.9%

Not Applicable

Not Applicable

4.9%

45

Shipper Association

Not Applicable

-7.8%

Not Applicable

Not Applicable

-7.8%

46

Freight all Kinds

3.4%

3.6%

1.2%

4.0%

3.5%

47

Small Packages

Not Applicable

5.0%

Not Applicable

Not Applicable

5.0%

48

Hazardous Materials

Not Applicable

Not Applicable

Not Applicable

Not Applicable

Not Applicable

50

Secondary and Drayage

5.1%

Not Applicable

Not Applicable

Not Applicable

5.1%

Total

All Commodities

3.0%

2.4%

1.7%

4.7%

2.9%

The Tennessee Freight Model applied growth rates for industries available from economic development agencies. It applied those factors differently to industries producing freight than to industries consuming freight. The relationship between commodities and producing industries is shown in Table 5.2. In almost every case 100 percent of the growth in the outbound shipment of commodities is related to the industry producing that commodity. Table 5.3 shows the relationship of the inbound (consumption) shipment of commodities to the employment industry groups used in the model. These will be quite different from the industry producing that commodity. For example, 58 percent of the agricultural shipments are consumed by manufacturing, 19 percent are consumed by populations, and 14 percent are consumed by the agricultural industry, with the balance in service and government. The growth in the outbound shipment of commodities is the application of the growth in each of these industries, weighted by the percentages shown in Table 5.2.

Table 5.2 Tennessee Freight Model Commodity Production to Employment Relations by Model Commodity Group

NAICS Employment Type

Commodity Group:
Agriculture

Commodity Group:
Timber and Lumber

Commodity Group:
Construction

Commodity Group:
Food and Kindred Products

Commodity Group:
Household Goods and Other

Commodity Group:
Paper Products

Commodity Group:
Chemicals

Commodity Group:
Primary Metals

Commodity Group:
Machinery

Commodity Group:
Mixed Shipments and Warehouse

Farm

100%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Agriculture

0%

100%

0%

0%

0%

0%

0%

0%

0%

0%

Construction and Mining

0%

0%

100%

0%

0%

0%

0%

0%

0%

0%

Manufacturing

0%

0%

0%

100%

100%

100%

100%

100%

100%

0%

Trade

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Transportation and Public Utilities

0%

0%

0%

0%

0%

0%

0%

0%

0%

70%

Service

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Government

0%

0%

0%

0%

0%

0%

0%

0%

0%

30%

Table 5.3 Tennessee Freight Model Commodity Consumption to Employment Relations by Model Commodity Group

NAICS Employment Type

Commodity Group:
Agriculture

Commodity Group:
Timber and Lumber

Commodity Group:
Construction

Commodity Group:
Food and Kindred Products

Commodity Group:
Household Goods and Other

Commodity Group:
Paper Products

Commodity Group:
Chemicals

Commodity Group:
Primary Metals

Commodity Group:
Machinery

Commodity Group:
Mixed Shipments and Warehouse

Farm

14%

21%

1%

3%

0%

0%

2%

0%

1%

1%

Agriculture

0%

23%

0%

0%

0%

0%

0%

0%

0%

0%

Construction and Mining

0%

0%

13%

0%

9%

1%

9%

4%

7%

4%

Manufacturing

58%

39%

26%

13%

36%

43%

34%

90%

37%

12%

Trade

1%

0%

3%

1%

2%

5%

2%

1%

4%

3%

Transportation and Public Utilities

0%

0%

1%

0%

1%

1%

3%

0%

3%

1%

Service

6%

9%

19%

14%

14%

34%

14%

2%

8%

18%

Government

1%

0%

13%

4%

4%

6%

6%

0%

7%

3%

Population

19%

8%

23%

64%

33%

9%

28%

1%

34%

60%

The Virginia Freight Model applies a similar method of applying growth factors. As shown in Figure 5.3, the growth in employment by industry is obtained from a commercial vendor Woods and Poole. These employment forecasts are associated with producing and consuming industries using state provided information and national Input-Output tables and are then related to the STCC commodities. Increases in labor productivity that would account for increases in freight shipment that are greater than the growth in employment are obtained and included in the forecast. The resulting growth rates in commodity consumption and attraction by county are applied to the base year, TRANSEARCH, commodity flow table.

Figure 5.3 Virginia Freight Model Commodity Flow Forecast Methodology

Figure 5.3 shows a flowchart outlining the methodology used to forecast commodity flow in Virginia’s freight model.  The process starts with gathering data from the 1) Woods & Poole database (it contains employment projections by industry and county). After applying 2) National Productivity Coefficients, 3) Output Levels by Industry and County are calculated. Then, using an Input-Output Table Matrix, the 4) Total Consumption by industry and county is calculated. The industries are then 5) Mapped to the Commodity Code structure in TRANSEARCH (STCC2), and 6) the growth rates are computed by commodity and region. Finally, 7) these forecasts are applied to the original TRANSEARCH database.

5.4 Mode Choice

The use of a commodity table is typically associated with a simplified level of effort. Therefore, it is not surprising to account for mode share in forecasting by simply assuming that the existing mode share, by origin, destination, and commodity, remains the same in the future. Since the relative flow by origin, destination, and mode can change, this simplified constant mode share can result in changes of modes, but only because of changes in the mix of the flow table. The modal shares by commodity used in the Georgia Freight Model, which even though they are applied by origin and destination, are averaged for the state and shown in Table 5.4.

Table 5.4 Georgia Freight Model TRANSEARCH Tonnage Mode Split

STCC2

Commodity Description

Truck

Carload

Intermodal

Water

Air

01

Farm Products

46%

49%

1%

4%

0%

08

Forest Products

0%

23%

77%

0%

0%

09

Fresh Fish

0%

0%

7%

47%

47%

10

Metallic Ores

0%

80%

0%

20%

0%

11

Coal

1%

94%

0%

4%

0%

13

Crude Petroleum

0%

100%

0%

0%

0%

14

Nonmetallic Minerals

0%

86%

0%

14%

0%

19

Ordnance

0%

98%

2%

0%

0%

20

Food or Kindred Products

83%

15%

2%

0%

0%

21

Tobacco Products

98%

1%

1%

0%

0%

22

Textile Mill Products

100%

0%

0%

0%

0%

23

Apparel

94%

0%

4%

0%

2%

24

Lumber or Wood Products

85%

14%

0%

0%

0%

25

Furniture or Fixtures

97%

1%

2%

0%

0%

26

Pulp and Paper

65%

33%

2%

0%

0%

27

Printed Matter

94%

0%

4%

0%

2%

28

Chemicals

64%

30%

1%

5%

0%

29

Petroleum or Coal Products

77%

9%

0%

14%

0%

30

Rubber and Plastics

97%

1%

2%

0%

0%

31

Leather

97%

0%

2%

0%

1%

32

Clay, Concrete, Glass, or Stone Products

78%

22%

0%

0%

0%

33

Primary Metal Products

76%

20%

0%

4%

0%

34

Fabricated Metal Products

94%

0%

1%

5%

0%

35

Nonelectrical Machinery

93%

2%

1%

0%

3%

36

Electrical Machinery

94%

1%

2%

0%

3%

37

Transportation Equipment

60%

39%

1%

0%

1%

38

Instruments

93%

0%

1%

0%

7%

39

Miscellaneous Manufacturing

91%

3%

5%

0%

1%

40

Waste or Scrap Materials

0%

40%

2%

58%

0%

41

Miscellaneous Freight Shipment

0%

41%

5%

54%

0%

42

Containers Returned Empty

0%

4%

96%

0%

0%

43

Mail

0%

0%

25%

0%

75%

44

Freight Forwarder

0%

0%

100%

0%

0%

45

Shipper Association

0%

0%

100%

0%

0%

46

Freight All Kinds

0%

11%

87%

0%

2%

47

Small Packages

0%

0%

100%

0%

0%

48

Hazardous Materials

0%

96%

4%

0%

0%

50

Secondary and Drayage

100%

0%

0%

0%

0%

Grand Total

All Commodities

70%

24%

3%

3%

0%

Note: Percentages may not sum to 100 percent across rows due to rounding.

Even with this simplified approach, qualitative changes can be made to the mode shares. Target mode shares can be established by commodity. Origin-destination records where the existing mode share falls below this amount can be identified and adjusted upwards towards the target level as sensitivity tests. The qualitatively changed mode shares can then be applied to the forecast to determine how changes in mode share can be reflected through the system. In applying this method, sometimes referred to as “market segmentation” since the target mode share has been applied to segmented origin, destination, and commodity markets, care must be taken to recognize that some modes, for a variety of reasons are virtually captive to certain modes and that no qualitative change should be made. For example in Table 5.4 for Georgia, 93 percent of Precision Instruments (STCC 38) move by truck with the remainder by air. The captive market should be recognized and diversion to other modes, for example to rail, should be considered carefully in forecasting.

5.5 Vehicle Conversion

The methods to convert commodity trip tables are very similar to the methods used to convert freight trip distribution tables to vehicles discussed in Section 4.3.8. The Tennessee Freight Model used payload factors that were purchased as part of the TRANSEARCH database. Payloads across commodity groups were increased since the majority of trucks were multiunit, long-haul trucks. The adjusted payload factors also were compared against Federal regulations for truck weight and size. Those values are shown in Table 5.5. Truck movements were derived from commodity flows and, as such, did not reflect the presence of “empty trucks.” Empty trucks, however, contribute to truck VMT and affect consumption of highway capacity. It was assumed that the most efficient truckers operate at 20 percent empty or less. An empty-truck adjustment was made for each type of movement based on its internal-to-internal (I‑I), internal-to-external (I‑E), and external-to-external (E‑E) orientation. Relatively short-haul I‑I trips account for the highest proportion of empty truck trips, while E‑E trips accounted for the lowest share. These percentages were then applied to each of the loaded movements as an estimate of empty truck trips. An assumption also was made that empty movements were depicted as partial reverse trips dependent on the loaded direction.

Table 5.5 Tennessee Freight Model Estimated Payload for Commodity Groups

Commodity Group

Tons per Load

Agriculture

22

Chemicals

21

Construction and Mining

17

Food and Kindred Products

23

Household Goods and Other Manufactures

17

Machinery

15

Mixed Miscellaneous Shipments, Warehouse, Rail Intermodal Drayage, and Secondary Traffic

7

Paper Products

22

Primary Metal

25

Timber and Lumber

26

Waste Materials

Not Applicable

Source: TRANSEARCH 2001, Reebie Associates.

Georgia developed payload factors from VIUS in the same manner as Wisconsin and Florida that is shown in Section 4.3.8.

For Virginia, the TRANSEARCH commodity flow tables report annual commodity flows by STCC type by ton, with the origin and destination as a state or BEA. For truck trip flows, only Truck, less-than-load (LTL), and private truck trips were used at this step. The commodity flow tables were first converted into truck trips using truck load factors according to the STCC type. The load factors, as shown in Table 5.6, were borrowed from those developed by Reebie Associates for Texas.

Table 5.6 Virginia Freight Model Truck 1 Load Factors

STCC

Commodity Type

Movement Type:
Intrastate

Movement Type:
Interstate

Movement Type:
Through

1

Farm Products

16.1

16.1

16.1

9

Fresh Fish or Marine Products

12.6

12.6

12.6

10

Metallic Ores

11.5

11.5

11.5

11

Coals

16.1

16.1

16.1

14

Nonmetallic Ores

16.1

16.1

16.1

19

Ordinance or Accessories

3.1

3.1

3.1

20

Food Products

17.9

17.9

17.9

21

Tobacco Products

9.7

16.4

16.8

22

Textile Mill Products

15.2

16.1

16.5

23

Apparel or Relented Products

12.4

12.4

12.5

24

Lumber or Wood Products

21.1

21.0

21.1

25

Furniture or Fixtures

11.3

11.3

11.4

26

Pulp, Paper, Allied Products

18.6

18.5

18.6

27

Printed Matter

13.8

13.6

13.9

28

Chemicals or Allied Products

16.9

16.9

16.9

29

Petroleum or Coal Products

21.6

21.6

21.6

30

Rubber or Miscellaneous Plastics

9.1

9.2

9.3

31

Leather or Leather Products

10.8

11.0

11.3

32

Clay, Concrete, Glass, or Stone

14.4

14.3

14.4

33

Primary Metal Products

19.9

19.9

2.00

34

Fabricated Metal Products

14.3

14.3

14.3

35

Machinery

10.8

10.8

10.9

36

Electrical Equipment

12.7

12.8

12.9

37

Transportation Equipment

11.3

11.3

11.3

38

Instruments, Photo Equipment, Optical Equipment

9.4

9.4

9.7

39

Miscellaneous Manufacturing Products

14.2

14.4

14.8

40

Waste or Scrap Metals

16.0

16.0

16.0

50

Secondary Traffic

16.1

16.1

16.1

 

5.6 Assignment

The ability to assign the commodity vehicle tables to modal network will in large part depend on the quality of the modal networks and the ability to consider traffic by vehicles other than those carrying freight. The choice to use a commodity table in freight forecasting in lieu of trip generation and distribution typically is done because a more sophisticated model transportation model is not available. This quite often is accompanied by the lack of an auto highway model. Although a commodity table can be assigned directly to a highway network, but without the interaction of auto traffic, the response to congestion cannot be considered. For that reason, the use of commodity models is often accompanied by simple auto highway models. For the Georgia and Tennessee Freight Models, auto trip tables were created through an Origin-Destination Matrix Estimation (ODME) process using only observed traffic counts. Although this table does not allow the consideration of behavioral changes, its inclusion at least ensures that the combined impact of auto and truck congestion is considered. Georgia and Tennessee also approached the inclusion of nonfreight trucks in the freight forecasting process differently. Tennessee made the assumption that commodity trucks can be considered the same as large combination tractor trailers and assumes that observed single unit trucks could be considered to be the same as nonfreight trucks. They estimated nonfreight truck trips through an ODME process. Georgia considered freight trucks to be a subset of all trucks. It calculated a total truck table from observed counts in an ODME process and then subtracted the commodity truck table from that total ODME truck table to calculate nonfreight trucks.

Virginia already had included autos in their Virginia State Model (VSM). It assumed that the commodity freight trucks could be considered to be identical to all trucks outside urban areas where the model would be used.

Even with these simplifications, the assignment results for commodity trucks can be produce acceptable results. The results for the validation of freight trucks in the Tennessee Model are shown in Table 5.7.

Table 5.7 Tennessee Freight Model Assignment Validation

VMT

VMT (Multiunit Daily Truck Traffic)

VMT (Assigned Daily Truck Volume)

Daily Truck Vehicle Miles of Travel by TDOT Regions: 1

27%

27%

Daily Truck Vehicle Miles of Travel by TDOT Regions: 2

20%

20%

Daily Truck Vehicle Miles of Travel by TDOT Regions: 3

33%

30%

Daily Truck Vehicle Miles of Travel by TDOT Regions: 4

20%

23%

Daily Truck Vehicle Miles of Travel by TDOT Regions: Total VMT

100% (13,087,821)

100% (14,382,402)

Daily Truck Vehicle Miles of Travel by Functional Class: 1

49%

57%

Daily Truck Vehicle Miles of Travel by Functional Class: 2

7%

5%

Daily Truck Vehicle Miles of Travel by Functional Class: 6

6%

3%

Daily Truck Vehicle Miles of Travel by Functional Class: 11

27%

31%

Daily Truck Vehicle Miles of Travel by Functional Class: 12

1%

1%

Daily Truck Vehicle Miles of Travel by Functional Class: 14

7%

3%

Daily Truck Vehicle Miles of Travel by Functional Class: 16

2%

0%

Daily Truck Vehicle Miles of Travel by Functional Class: Total

100%

100%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-24

18%

16%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-240

2%

2%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-40

44%

45%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-55

1%

2%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-65

10%

9%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-75

18%

18%

Daily Truck Vehicle Miles of Travel by Interstate Systems: I-81

6%

8%

Daily Truck Vehicle Miles of Travel by Interstate Systems: Total

100%

100%

 

5.7 Commodity Flow Survey (CFS)

The CFS is undertaken as part of the Economic Census through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. The survey is undertaken approximately every five years, most recently in 2002. The survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail establishments. The commodity classification system used in the CFS has changed over time. The 2002 CFS uses the Standard Classification of Transported Goods (SCTG) commodity reporting system. It provides U.S. national data, data for all 50 states, and data for selected metropolitan areas plus remainder-of-state. The CFS is a “linked” trip table in that records between an origin and a destination report all modes used; for example, “truck-rail” rather than reporting each portion of the trip by mode aggregated to a separate record.

The CFS is available on a CD from BTS. Because the database is reported in 114 zones (state portion of major metropolitan areas and remainder of states), it is of limited use and would have to be disaggregated to smaller units of geography to be useful. The Indiana Freight Model was disaggregated in this fashion, but that effort was undertaken as a research project using primarily graduate student labor. Since the CFS contains essentially the same information included in the more complete FAF2 that is discussed in Section 5.6, it is not expected to be a common source of commodity adapted for use in freight forecasting.

5.8 TRANSEARCH

TRANSEARCH is a freight database that is available commercially from Global Insight. The databases had previously been available from Reebie Associates before they were acquired by Global Insight, and the database is often referred to as “Reebie” data. TRANSEARCH utilizes a multitude of mode-specific data sources to create a picture of the nation’s freight traffic flows on a market-to-market commodity basis. The national database from which purchases of TRANSEARCH are developed has U.S counties as the primary flow unit, although TRANSEARCH can use proprietary data to provide a more disaggregated level of geography. Each record in the TRANSEARCH database records the flow from an origin zone to a destination zone.

TRANSEARCH is created each year using:

  • The Annual Survey of Manufacturers (ASM) to establish production levels by state and industry;
  • The Surface Transportation Board (STB) Rail Waybill Sample to develop all market-to-market rail activity by industry;
  • The Army Corps of Engineers Waterborne Commerce data to develop all market-to-market water activity by industry;
  • The Federal Aviation Administration (FAA), Enplanement Statistics; and
  • Airport-to-airport cargo volumes …

in conjunction with information on commodity volumes moving by air from the BTS Commodity Flow Survey, to create detailed air flows; and the rail, water, and air freight flow data are deducted from the Bureau of Census ASM-based production data to establish preliminary levels of truck activity. The proprietary Motor Carrier Data Exchange Program provides information on actual market-to-market trucking industry movement activity. The Data Exchange Program includes carriers from both the private and for-hire segments of the industry and both the truckload (TL) and LTL sectors. The truckload sample covers about six percent of the market, and TRANSEARCH’s LTL sample is about 40 percent. In total, information is received on over 75 million individual truck shipments. By way of comparison, the government’s CFS covers about 12 million shipments, spread across all modes, and the Rail Waybill’s sample rate is about 2.5 percent of all rail freight moves.

TRANSEARCH’s county-to-county market detail is developed through the use of Global Insight’s Motor Carrier Data Exchange inputs and Freight Locator database of shipping establishments. The Freight Locator database provides information about the specific location of manufacturing facilities, along with measures of facility size (both in terms of employment and annual sales), and a description of the products produced. This information is aggregated to the county level and used in allocating production among counties.

Much of the Data Exchange inputs from the trucking industry are provided by zip code. The zip code information is translated to counties and used to further refine production patterns. A compilation of county-to-county flows and a summary of terminating freight activity are used to develop destination assignments.

TRANSEARCH freight traffic flow data has limitations with respect to trucks:

  • Primary coverage of truck traffic is limited for nonmanufactured products. Supplemental purchases can provide for agricultural and mining resource extraction shipments from the source to a processing plant that are not ordinarily covered in commodity flow surveys.
  • Traffic movements originating in warehouses or distribution centers or drayage movements of intermodal rail or air freight are shown as STCC 5010. These are by definition truck movements. Movements to warehousing and distribution centers may be by other STCC codes and by any mode. Details on the types of items being moved are not available.
  • The inland or surface movements of import and export traffic volumes to locations outside of North America are included in the data. However, the flow patterns of this freight are based on the movement patterns of domestically sourced goods in the same market areas and are not the actual movements of the import/export freight.

Freight carried by trucks, based on the definitions used by the principal agencies collecting data, also typically excludes shipments to or from retail (excluding mail-order and warehousing), offices, service establishments, and residences. These local freight or goods deliveries are significantly different from those freight shipments that are included in terms of the distances traveled, the type of trucks used, the times of movement, and the routing of the shipment, but their exclusion does not detract from the larger freight-related issues.

5.9 Freight Analysis Framework (FAF)

The FAF, available from FHWA, integrates data from a variety of sources to estimate commodity flows and related freight transportation activity among states, regions, and major international gateways. The original version, FAF1, provides estimates for 1998 and forecasts for 2010 and 2020. The new version, FAF2, provides estimates for 2002 and the most recent year plus forecasts through 2035. The original FAF1 based its analysis on county-to-county freight flows; however the publicly available origin-destination database of freight flows was available only as state-to-state movements.

The FAF1 was developed in part from the TRANSEARCH database and uses the Standard Transportation Commodity Code (STCC) classification system at the two-digit hierarchy to report flows. The FAF1 also included a highway network, using the TransCAD format that was created from the National Highway Planning Network, the Highway Performance Monitoring System, and others adapted from state DOTs. The county-to-county freight flows were converted to trucks and assigned to the FAF1 network for 1998, 2010, and 2020. The FAF1 highway network included automobile and total truck counts and forecasts from the state DOTs. For the base year, nonfreight truck volumes were calculated for each link by subtracting the FAF1 freight truck assignment from the observed truck count. The forecast of nonfreight trucks was created by applying the state provided growth rate to this base nonfreight volume. While there was no opportunity to reassign auto or nonfreight trucks on the network, those volumes were considered in the capacity restrained assignment of the FAF1 trucks. The publicly available FAF1 highway network provided only totals of all freight truck volumes. These truck volumes were not disaggregated by commodity. Although the lack of a publicly available geography below the state level limited the direct use of the FAF1 origin-destination database, the growth factors provided a consistent set of forecasts for application in freight forecasting. The FAF1 highway network also serves as a valuable resource for developing the highway network portion of freight models for portions outside the primary study area.

The FAF2 was developed to address some of the shortcomings of the FAF1 database. The FAF2 origin destination table estimates commodity flows and related freight transportation activity among states, substate regions defined in the 2002 CFS, 17 additional international gateways, and 7 international regions. It also forecasts future flows among regions and relates those flows to the transportation network. In addition to the origin-destination database of commodity flows among regions, FAF2 includes a network database in which flows are converted to truck payloads and related to specific routes. The FAF2 commodity origin-destination database includes tons and value of commodity movements among regions by mode of transportation and type of commodity. The specific differences between the FAF1 and FAF2 are:

  • FAF2 contains projected commodity flow data ranging from 2010 to 2035 in five-year intervals, reported in the STCG commodity classification used in the CFS.
  • FAF2 excludes all foreign-to-foreign shipments via the United States.
  • The FAF2 2002 base year database is built entirely from public data sources. Key sources include the 2002 CFS; Foreign Waterborne Cargo data, developed by the U.S. Army Corps of Engineers; and a host of other sources. FAF2 statistics should not be compared with original FAF1 data because different methods and coverage are employed.
  • The FAF2 estimates commodity movements by truck and the volume of long-distance trucks over specific highways. The county share of truck VMT within a FAF2 region is used to disaggregate interregional flows from the Commodity Origin-Destination Database into flows among individual counties and assign the detailed flows to individual highways. Although the FAF provides reasonable estimates for national and multistate corridor analyses, FAF estimates are not a substitute for local data to support local planning and project development.

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