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Research, Development, and Application of Methods to Update Freight Analysis Framework Out-of-Scope Commodity Flow Data and Truck Payload Factors

Chapter 4. Summary of Findings and Approaches for Improvement

The Freight Analysis Framework Version 4 (FAF4) relies on several different types of data gathered from various sources. However, the majority of data come from other Federal agencies, namely the U.S. Census Bureau, National Agricultural Statistics Service, National Oceanic Atmospheric Administration, U.S. Forest Service, Bureau of Transportation Statistics, U.S. Bureau of Economic Analysis, and the U.S. Energy Information Agency. Just as the majority of data for the FAF4 comes from a relatively small number of Federal agencies, it also uses data from relatively few sources within those agencies. The primary data sources are the Commodity Flow Survey, Economic Census, Census of Agriculture, Vehicle Inventory and Use Survey (VIUS), County Business Patterns, and the American Community Survey. These data sources are broadly used across out-of-scope (OOS) commodity methodologies.

In reviewing the data and methodologies used to develop the out-of-scope commodity flows, the project team identified limitations/opportunities for improvement as presented in table 13. Largely, these insights can be summarized into one of three categories: 1) Sufficiency of Current Data; 2) Availability of Data in the Future; and 3) Appropriateness of Methodological Approach. The first category, Sufficiency of Current Data, addresses the challenges that U.S. Department of Transportation (USDOT) faces in obtaining data that is of sufficient quality (in terms of accuracy, spatial resolution, and frequency of updates, among others). One of the most significant issues related to data quality is that the estimation processes for farm-based and service commodities rely on the VIUS. Given that the 2002 version represents the most recent VIUS, it is possible that the underlying industry-specific logistics patterns regarding vehicle types and operating distances that are captured in the VIUS have changed. The potential impact of this is large given that farm-based shipments represent a considerable amount of commodity flows in terms of tonnage and value as shown in table 10.

Regarding the second challenge, Availability of Data in the Future, some out-of-scope commodities rely on data inputs from sources with unclear plans for future data collection efforts. For instance, both municipal solid waste (MSW) and construction and demolition (C&D) commodity flows utilize data from the biannual BioCycle State of Garbage in America Survey conducted by the Earth Engineering Center at Columbia University. Prior to Columbia University, the survey was conducted by the BioCycle Journal. Given the transition and that some survey years were missed prior to the transition, the future status of the survey and its update frequency are unclear. The VIUS similarly represents an availability challenge as it is not feasible to use the 2002 results in perpetuity.

The last challenge to estimating out-of-scope commodity flows is the Appropriateness of Methodological Approach. These are fewer pressing challenges than those associated with data and primarily relate to assumptions made about the magnitude of retail and service commodity flows by truck and the shipment distances of logging and fishery commodities. For retail and service commodity flows, the FAF4 assumes that a portion of brick-and-mortar sales and services results in a truck shipment in an amount that varies by the specific type of good (e.g., furniture, clothing, etc.). However, no supporting information is provided to justify this assumption and the exact assumed shares by commodity group are given. Regarding fishery and logging shipments, the FAF4 assumes that all shipments occur within the FAF4 zone that the port or timber-producing site is located. Based on the case studies, though these commodities are likely to be transported over relatively short distances (as assumed in the FAF4), they may cross State lines as the political boundaries do not affect the supply chain decisions for these commodities.

Table 13. Limitations and opportunities for improvement in current out-of-scope methods.
OOS Commodity Data Data Source Limitations/Opportunities for Improvement
Farm-Based Shipments
  • Value of agricultural production at the statewide and county levels.
  • Volume-to-weight conversion factors.
  • Commodity Flow Survey zones originating agricultural shipments.
  • Distribution of average shipment distances by truck and commodity type.
  • Census of Agriculture.
  • Agricultural Statistics.
  • Vehicle Inventory and Use Survey.
  • Among other data sources, the estimation of farm-based Origin-Destination (O-D) flows rely on the Vehicle Inventory and Use Survey (VIUS), which is discontinued. This creates the possibility that while the overall analytical estimation process may be sound, the underlying industry logistics practices (in terms of the average distances farm-based shipments are transported) that are reflected in the VIUS may have changed. If those patterns have changed, then the FAF4 does not estimate for these O-D flows that are as accurate as would be given more recent data.
  • For each farm-based commodity, the FAF4 assumes that the destination regions for a commodity are those that originate a product derived from that farm-based commodity. An alternative methodology may be to base the destinations of farm-based commodities on the locations of out-of-scope facilities within the supply chain.
Fishery Shipments
  • Value and tonnage of fishery landings at the statewide level.
  • Value and tonnage of fishery landings at the top 104 ports.
  • Fisheries of the United States.
  • The FAF4 assumes that all fishery shipments are local (i.e., within a FAF4 zone) as processing facilities tend to be proximate to ports. While this is likely an accurate assumption, there may be port areas that straddle State boundaries and contain local processing facilities in two or more States. An alternative methodology is to allow fishery shipments to cross state lines.
Logging Shipments
  • Board feet of timber produced at the county level.
  • Board feet of timber produced at the county level in the States of California and Nevada.
  • Board feet-to-tons conversion factors.
  • Various State reports from the Forest Inventory Data Online database.
  • Timber Product Output.
  • Various State and Region Price Reports.
  • The FAF4 assumes that all logging shipments are local (i.e., within a FAF4 zone) as processing facilities tend to be proximate to timber producing sites. While the literature confirms this assumption, it also revealed that there are timber-producing areas that straddle State boundaries and contain processing facilities in two or more States. In those areas, freshly harvested logs may be transported over state lines.
  • An alternative methodology is to base the destinations of freshly harvested logs on the locations of mills in the timber-producing region and to allow logging shipments to cross state lines.
Municipal Solid Waste (MSW) Shipments
  • Tonnage of MSW produced at the county and statewide levels for reporting States.
  • Tonnage of MSW moved across State borders.
  • Destinations of MSW moved across State borders.
  • Tonnage of MSW produced at the county and statewide levels for nonreporting States.
  • Population growth.
  • County-level population.
  • Various State Annual MSW Reports.
  • BioCycle State of Garbage in America Survey.
  • U.S. Census American Community Survey.
  • Not all States produce municipal solid waste reports. Furthermore, the current status of the State of Garbage in America Survey is unclear. Determining if this data source will continue to be available in future years is important for modeling MSW commodity flows.
Construction and Demolition (C&D) Debris Shipments
  • Tonnage of C&D produced at the county and statewide levels for reporting States.
  • Tonnage of C&D moved across State borders.
  • Destinations of C&D moved across State borders.
  • Tonnage of C&D produced at the county and statewide levels for nonreporting States.
  • Population growth.
  • County-level population.
  • C&D recycling rates.
  • Various State Annual MSW Reports
  • BioCycle State of Garbage in America Survey
  • U.S. Census American Community Survey.
  • The Benefits of Construction and Demolition Materials Recycling in the United States.
  • Not all States produce municipal solid waste reports. Furthermore, the current status of the State of Garbage in America Survey is unclear. Determining if this data source will continue to be available in future years is important for modeling C&D debris commodity flows.
  • Census data on new housing construction is potentially a new source of data on the locations of productions of C&D debris. Currently, the methodology for estimating these flows relies on a factor applied to the magnitude of MSW flows and assumes the same distribution patterns as MSW flows.
Retail Shipments
  • Total retail sales.
  • Sales receipts by retail-related North American Industry Classification System (NAICS) industry sector.
  • Commodity value-to-weight ratios.
  • Payroll shares by retail-related NAICS industry sector.
  • Annual Retail Trade Survey.
  • Economic Census.
  • Commodity Flow Survey Public Use Microdata.
  • County Business Patterns.
  • The FAF4 methodology for estimating out-of-scope retail shipments assumes that a share of retail sales by commodity result in a truck delivery. The method by which these assumed shares and the data supporting them are unclear. Industry outreach to retail sectors that historically generate home deliveries, such as furniture and home appliances, could provide more information on the magnitude of retail home deliveries.
Service Shipments
  • Total service sales.
  • Sales receipts by service-related NAICS industry sector.
  • Commodity value-to-weight ratios.
  • Payroll shares by service-related NAICS industry sector.
  • Distribution of average shipment distances by truck and commodity type.
  • Service Annual Survey
  • Economic Census
  • Commodity Flow Survey Public Use Microdata
  • County Business Patterns.
  • Vehicle Inventory and Use Survey.
  • The FAF4 methodology for estimating out-of-scope service shipments assumes that a share of service sales by commodity result in a truck delivery. The method by which these assumed shares and the data supporting them are unclear.
  • The FAF4 approach also assumes that service shipments associated with NAICS industry subsectors 7111, 7112, and 71211, are destined for nearby major metropolitan areas only.
Household and Business Moves (HH&B)
  • County-level migration flows.
  • Average household size by county.
  • Percentage of total moves that are business or self-moves.
  • Consumer durable goods involved in HH&B moves and their per-move average value.
  • County-to-County Migration Flows, American Community Survey.
  • American Community Survey.
  • American Moving and Storage Association website.
  • Consumer Durable Goods Current Cost Net Stock.
  • Commodity Flow Survey Public Use Microdata.
  • In the FAF4 methodology, all intracounty moves are assumed to be self-moves that do not involve trucks. This assumption could be investigated further to determine if it is accurate. Data from the American Community Survey or Current Population Survey could be used to estimate the household size of movers which may help to verify this assumption.
  • The average shipment value is based on national, as opposed to regional, averages.
  • Furthermore, the average value per household and business move is adjusted to remove items that are not likely to be transported by truck. However, the FAF4 documentation is unclear on what these items are.
Crude Petroleum Shipments
  • Petroleum Administration for Defense District (PADD)-to-PADD movements.
  • Locations, operating capacities, and crude petroleum input to refineries.
  • Payroll shares for NAICS 211111 (Crude Petroleum and Natural Gas Extraction) industry sector.
  • Origins and destinations of transborder crude petroleum shipments.
  • Total amount of crude petroleum imported by U.S. companies.
  • Barrels of exported crude petroleum by destination country.
  • Barrels of exported crude petroleum by PADD.
  • PADD Movements.
  • Refinery Net Input.
  • County Business Patterns.
  • Carload Waybill Sample.
  • Company-Level Imports.
  • Exports by Destination Country.
  • Exports by PADD.
  • Refinery capacity is a proxy for actual county-level data on consumption, which requires that production-to-consumption flows be estimated via a gravity model.
Natural Gas Shipments
  • Interstate and intrastate natural gas movements.
  • Locations and operating capacities natural gas receipt/delivery points.
  • Payroll shares for NAICS 211111 (Crude Petroleum and Natural Gas Extraction) industry sector.
  • Natural gas consumption by State and end-use sector.
  • Volume and value of natural gas imports and exports by State.
  • Locations of natural gas processing plants.
  • Population data, vehicle population data, and electric generating units.
  • Natural Gas Annual.
  • Natural Gas Receipt/Delivery Points Database.
  • County Business Patterns.
  • U.S. Energy Information Administration (EIA) Natural Gas website.
  • Natural Gas Processing Plants Database
  • Ancillary Databases.
  • The FAF4 uses several ancillary data sources that are not well-documented, including the locations and capacities of natural gas receipt/delivery points, vehicle population data, and electric generating units. Furthermore, it is not detailed in how mode is assigned, especially for domestic movements. Generally, the FAF4 seems to assign most natural gas flows to pipeline.
Foreign Trade Shipments
  • Commodity shares for 1-Digit Standard Classification of Transported Goods (SCTG) groups.
  • Volume and weight of foreign trade by water and air.
  • Value and weight of foreign trade by all modes and State-level origins and destinations.
  • Volume-to-weight conversion factors.
  • Border crossings by mode.
  • Domestic mode distributions of freight shipments
  • Domestic mode for waterborne foreign shipments.
  • Payroll shares by NAICS industry sector.
  • Domestic destinations for foreign airborne shipments.
  • USA Trade Online.
  • Special Tabulation of Foreign Trade Public Data.
  • Transborder Surface Freight Data.
  • Commodity Flow Survey.
  • Port Import/Export Reporting Service (PIERS): Bill of Lading Data for U.S. Imports and Exports.
  • County Business Patterns.
  • T-100 Market and Segment Data.
  • Generally, foreign trade flow data on the domestic leg of shipments is lacking, requiring numerous assumptions and the use of professional judgment.
(Source: Federal Highway Administration.)

Approaches for Further Testing and Implementation

Table 14 identifies short- and long-term data improvement activities for further testing and implementation. Short-term activities are those efforts that can be started relatively quickly and whose results can be readily applied to the OOS commodity flows without an extensive data collection and/or modeling effort. Long-term activities are those that require a more substantial effort in terms of data collection and analysis. Short-term activities are candidates for implementation and are detailed in chapter 5.

Table 14. Limitations and opportunities for improvement in current out-of-scope methods.
OOS Commodity Limitations/Opportunities for Improvement Approaches for Improvement Timeframe
Farm-Based Shipments
  • Among other data sources, the estimation of farm-based O-D flows rely on the Vehicle Inventory and Use Survey (VIUS), which is discontinued. This creates the possibility that while the overall analytical estimation process may be sound, the underlying industry logistics practices (in terms of the average distances farm-based shipments are transported) that are reflected in the VIUS may have changed. If those patterns have changed, then the FAF4 does not estimates for these O-D flows that are as accurate as would be given more recent data.
  • Federal Highway Administration (FHWA) could consider using components of the National Cooperative Freight Research Program (NCFRP) Report 26 and the Center for Transportation Research approaches to modeling farm-based commodity flows. Both of these methodologies use a supply chain approach where acreage/yield data (i.e., productions) is combined with processing facility location data (i.e., attractions) to model the initial movement in the supply chain. Importantly, this initial movement is equivalent to the out-of-scope movement that the Commodity Flow Survey (CFS) does not capture. Due to the age of the VIUS, a short-term improvement effort is to apply this methodology to a few farm-based commodities at the national level in order to determine its feasibility at that geographic scope.
 Short-Term
Fishery Shipments
  • The FAF4 assumes that all fishery shipments are local (i.e., within a FAF4 zone) as processing facilities tend to be proximate to ports. While this is likely an accurate assumption, there may be port areas that straddle State boundaries and contain local processing facilities in two or more States.
  • The supply chain approach taken by the Center for Transportation Research (CTR) and NCFRP Report 26 methodologies could also be applied to fishery shipments. The out-of-scope movement, ports to processing facilities, could be modeled using facility location data rather than shipment distance distributions. This may be especially useful for ports near State borders if the FAF4 methodology is revised to allow these shipments to travel between FAF4 zones in different States. A short-term improvement effort is to test this methodology at the national level to determine its feasibility and if it produces more accurate results than the current FAF4 approach.
 Short-Term
Logging Shipments
  • The FAF4 assumes that all logging shipments are local (i.e., within a FAF4 zone) as processing facilities tend to be proximate to timber producing sites. While the literature confirms this assumption, there may be timber-producing areas that straddle State boundaries and contain processing facilities in two or more States.
  • Like the FAF4 methodology, the CTR approach relies heavily on U.S. Forest Service data, especially for determining total production for logs. Researchers at both the CTR and the National Center for Freight and Infrastructure Research and Education (CFIRE) observed that while freshly harvested logs traveled relatively shorts distances for processing, those facilities could be located in other States. This is especially important for timber-producing regions that straddle State borders. A short-term improvement effort is to employ a methodology that relies on processing facility location data at the national level to determine its feasibility and if it produces more accurate results than the current FAF4 approach. Importantly, this approach would allow log shipments to travel nearby FAF4 zones in other States, consistent with what was observed by the CTR and CFIRE researchers.
 Short-Term
Municipal Solid Waste (MSW) Shipments
  • Not all States produce municipal solid waste reports. Furthermore, the current status of the State of Garbage in America Survey is unclear.
  • Also, the FAF4 documentation states that MSW flows are assumed to have no value. However, in the FAF4 commodity flows corresponding to waste have an associated value.
  • Confirm the future availability of the BioCycle State of Garbage in America Survey with the Earth Engineering Center at Columbia University. If it may no longer be available or be available inconsistently, then a suitable replacement is needed, or a new data collection effort begun. One possibility is to reach out to States that do not produce annual reports to determine if there are internal reports that could be made available for the Freight Analysis Framework (FAF).
 Long-Term
Construction and Demolition (C&D) Debris Shipments
  • Not all States produce municipal solid waste reports. Furthermore, the current status of the State of Garbage in America Survey is unclear.
  • Confirm the future availability of the BioCycle State of Garbage in America Survey with the Earth Engineering Center at Columbia University. If it may no longer be available or be available inconsistently, then a suitable replacement is needed, or a new data collection effort begun. One possibility is to reach out to States that do not produce annual reports to determine if there are internal reports that could be made available for the FAF.
  • In addition, Census data on new housing construction is potentially a new source of data on the locations of productions of C&D debris. Currently, the methodology for estimating these flows relies on a factor applied to the magnitude of MSW flows and assumes the same distribution patterns as MSW flows.
 Long-Term
Retail Shipments
  • The FAF4 methodology for estimating out-of-scope retail shipments assumes that a share of retail sales by commodity result in a truck delivery. The method by which these assumed shares and the data supporting them are unclear.
  • Include technical details about exact shares that are applied in the FAF4 in the FAF technical publications. It would be beneficial to determine the magnitude of the impact of the truck delivery share assumption on the estimate of retail commodity flows by performing a sensitivity analysis. Lastly, the accuracy of the estimated could be improved by performing outreach to industry representatives, especially in retail sectors that have historically generated home truck deliveries such as furniture and appliances.
 Long-Term
Service Shipments
  • The FAF4 methodology for estimating out-of-scope service shipments assumes that a share of service sales by commodity result in a truck delivery. The method by which these assumed shares and the data supporting them are unclear.
  • The FAF4 approach also assumes that service shipments associated with NAICS industry subsectors 7111, 7112, and 71211, are destined for nearby major metropolitan areas only.
  • Include technical details about exact shares that are applied in the FAF4 in the FAF technical publications. It would be beneficial to determine the magnitude of the impact of the truck delivery share assumption on the estimate of retail commodity flows by performing a sensitivity analysis. Lastly, the accuracy of the estimate could be improved by performing outreach to industry representatives.
  • Determine the magnitude of the impact of the assumption of shipment distances for commodities associated with the 7111, 7112, and 71211 NAICS industry subsectors. Also, perform outreach to industry representatives to determine the accuracy of this assumption.
 Long-Term
Household and Business Moves (HH&B)
  • In the FAF4 methodology, all intracounty moves are assumed to be self-moves that do not involve trucks. This assumption could be investigated further to determine if it is accurate.
  • The average shipment value is based on national, as opposed to regional, averages.
  • Furthermore, the average value per household and business move is adjusted to remove items that are not likely to be transported by truck. However, the FAF4 documentation is unclear on what these items are.
  • Investigate the assumption that all intracounty moves are self-moves. Perform outreach to industry associations to gain a better understanding of its accuracy.
  • Developing region-specific average values of household and business moves could increase the accuracy of the FAF4 estimates. The sensitivity of HH&B commodity flow to value may be further explored to determine if region-specific values are a worthwhile pursuit.
  • A potential improvement may also be to use American Community Survey or Current Population Survey data to cross-tabulate migration flows with household size. The assumption would be that intracounty moves of small households are self-moves while those of larger households involve a truck.
 Long-Term
Crude Petroleum Shipments
  • Refinery capacity is a proxy for actual county-level data on consumption, which requires that production-to-consumption flows be estimated via a gravity model.
  • FHWA could consider coordinating with the U.S. EIA to determine if data on county-level consumption of crude petroleum is available through special tabulations that preserve confidentiality while providing greater information than what is currently available. This is similar to the existing collaborative effort that FHWA has with the U.S. Census Bureau Foreign Trade Division.
 Long-Term
Natural Gas Shipments
  • The FAF4 uses several ancillary data sources that are not well-documented, including the locations and capacities of natural gas receipt/delivery points, vehicle population data, and electric generating units. Furthermore, it is not detailed in how mode is assigned, especially for domestic movements. Generally, the FAF4 seems to assign most natural gas flows to pipeline.
  • In light of recent work by Pipeline and Hazardous Materials Safety Administration (PHMSA) and ongoing interest in transporting liquefied natural gas (LNG) by rail, there may need to be refinements to the natural gas methodology. Furthermore, work by PHMSA indicates that there are domestic movements of LNG by truck that may not be captured by the FAF4, though these numbers are likely small. Collaboration with PHMSA on the methodology to estimate natural gas flows could help to improve its accuracy.
 Long-Term
Foreign Trade Shipments
  • Generally, foreign trade flow data on the domestic leg of shipments is lacking, requiring numerous assumptions and the use of professional judgment.
  • Progress was made between Freight Analysis Framework Version 3 (FAF3) and FAF4 in the estimation of foreign trade shipments through collaboration with the U.S. Census Bureau Foreign Trade Division. However, there is still a gap in information on the domestic legs of foreign shipments. FHWA could consider a long-term investment in a new data collection as well as collaboration with the U.S. Census Bureau Foreign Trade Division to shed more light on these movements.
 Long-Term
(Source: Federal Highway Administration.)