Research, Development, and Application of Methods to Update Freight Analysis Framework Out-of-Scope Commodity Flow Data and Truck Payload Factors
Chapter 15. Summary
This chapter is divided into two sections. The first section focuses on the methods to estimate commodity flow data that are Out-of-Scope (OOS) to Commodity Flow Survey (CFS) and the second section details the summary and future research for payload factors.
Out of Scope Commodities
At the outset of this task, the study identified limitations/opportunities for improvement of the Freight Analysis Framework Version 4 (FAF4) regarding the data and methodologies used to develop the out-of-scope commodity flows. These were in the areas including the sufficiency of current data, the future availability of data, and the appropriateness of the methodological approach. One of the most significant issues related to data quality is that the estimation processes for farm-based and service commodities rely on the Vehicle Inventory and Use Survey (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 results of the literature review also found that other research efforts into modeling commodity flows offer alternative approaches to current FAF4 methods for estimating commodity flow data that are OOS to CFS. Notably, the University of Texas (UT) Austin Center for Transportation Research and the National Cooperative Freight Research Program examined the movements of several out-of-scope commodities. The preliminary investigation determined that aspects of those methodologies could be used to develop alternative approaches for farm-based, fishery, and logging OOS shipments and potentially yield benefits to future versions of the Freight Analysis Framework (FAF).
Following the preliminary investigation, the project team went on to develop and test alternative estimation methodologies for that select group of OOS commodities. The alternative methodologies each share the same basic structure of distributing productions to attractions based on the physical locations of the facilities that comprise the nodes of the out-of-scope portion of the commodities’ respective supply chains. These locations were determined primarily using information from the U.S. Census Bureau County Business Patterns database. The effectiveness of the methodological approach at the national level was demonstrated for the select group of OOS commodities. Overall, the methodological approach largely results in OOS commodity flows being distributed to FAF4 zones that contain greater numbers of agricultural facilities that represent the first step in the supply chain, which is the portion of the supply chain that is not currently captured by the CFS. Importantly, the results also demonstrate that the approach can be applied to the national level, which is critical for the Freight Analysis Framework.
It is important to note that the most direct approach to address many of the challenges of estimating OOS commodity flows is to either expand the sampling frame of the CFS so that those commodities are within-scope or to start a new information collection aimed at those establishments that determines from where they receive goods (i.e., a receiver survey as opposed to a shipper survey). For example, a new information collection could survey sawmills to ask from which counties their logs are sourced. The same could be asked of grain elevators and other agricultural storage establishments (see table 53 for relevant establishments for the OOS commodities included in this report). This would obviate the need to model OOS commodity flows. However, the approaches developed in this research effort provide an improvement in the estimation of OOS commodity flows until these and other longer-term improvements can take place. While the results demonstrate that the methodological approach can effectively model OOS commodity flows, it is not without its limitations. These limitations are primarily in the areas of data limitations and calibration and validation of results. These limitations are discussed in greater detail below.
Table 53. Crosswalk of out-of-scope commodity and establishment North American industry classification system code.
| OOS Commodity |
In-Scope Establishment (NAICS) |
| Corn |
Grain elevators (NAICS 493130 and 424510) |
| Chickens |
Poultry processing plants (NAICS 311615) |
| Logs |
Sawmills (NAICS 321113) |
| Fish |
Seafood Product Preparation and Packaging (NAICS code 3117) |
(Source: Federal Highway Administration.)
Limitations and Future Improvements
Data Availability
The main premise of the methodological approach is that by using information on the counties in which productions of OOS commodities occur and the counties containing facilities that attract those productions (representing the OOS component of the supply chain), the Freight Analysis Framework (FAF) can shift away from approaches that rely on the VIUS and those that assume that all flows are within-zone. However, county-level data was not available for all the commodities considered in this research which limits the efficacy of the proposed approach. For example, the number of broilers hatched was only available at the state level which required further assumptions on where those productions occurred at the county level. The necessity of assumptions on county-level production limits the ability of the proposed method to yield improvements over current FAF4 methods.
Data limitations exist with estimating attractions at the county level as well. The approach relies on the locations of facilities that represent the first step in the supply chain and information on payroll, as a proxy for capacity, to determine county-level attractions. While the U.S. Census Bureau County Business Patterns database is enough for determining the locations of facilities, payroll information may not be a sufficient proxy for capacity. Furthermore, for some agricultural activities, such as the locations and capacities of broiler farms at the county level, neither the U.S. Census Bureau County Business Patterns database nor the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) provide information at the desired level of detail for the approach.
Calibration and Validation
Related to limitations in data availability is the ability to calibrate and validate the methodological approach. Without observed data on the actual amount of tonnage produced and attracted to each county, it is impossible to calibrate the methodological approach and validate its results. This is evident in the trip length distributions resulting from the methodological approach. Shipments of OOS commodities are generally assumed to be local movements with few shipments traveling distances of 500 miles or greater. While the majority flows across commodities examined in this research effort are estimated to travel distances of 200 miles or less, there are occurrences where commodities are estimated to travel distances greater than 500 miles. This is generally more pronounced in western states than others as can be seen in the results included in appendix B. Trip Length Distributions by Commodity and Zone. While calibration and validation is also a limitation of current FAF4 methods, further effort in this area would be needed for the proposed approach to move forward.
Definition of Production-Consumption Zones
One technique the methodological approach uses to develop reasonable estimates of commodity flows is the defining of production-consumption zones. OOS commodity flows are balanced within these zones so that commodity flows do not travel across zones. This is analogous to current FAF4 methods for some commodities which require commodity flows to begin and end in the same FAF4 zone (e.g., fish and logs), but expands that assumption to a broader geography. The goal of the technique is to allow movements of these commodities across state lines, but to retain reasonable trip lengths. The definition of these zones presents an opportunity for future improvements. The zones were defined by observing where productions of commodities appeared to be clustered and in some cases using zones as defined by United States Department of Agriculture (USDA) or other agencies with expertise in a particular commodity. A future improvement could be taking a more rigorous approach to defining these zones by undertaking a formal cluster analysis, for example. In addition, the zones could be further refined to place a ceiling on trip lengths of commodity flows.
Truck Payload Factors
The payload factors computed by revisiting U.S. VIUS, for Single Unit (SU) and Combination Unit (CU) trucks, as updated by changes in miles from Highway Statistics table VM-1 and payloads from Vehicle Travel Information System (VTRIS) table W-3, as shown for 2012 in table 43, and for 2017 in table 44, could be used in any new FAF assignments. These payload factors consider the standard error in VIUS and are computed using a minimum of calculations. These payload factors do not exceed the typical legal payload, even if all the miles and ton-miles are assumed to be by Gross Vehicle Weight (GVW) 8, Combination Unit (CU), trucks.
While 2002 U.S. VIUS is dated, the payload factors that can be computed using the more recent California Vehicle Inventory and Use Survey (CA-VIUS) are within acceptable error ranges from the proposed payload factors from 2002 VIUS. Revisiting 2002 VIUS and expressing the payloads by Standard Classification of Transported Goods (SCTG) 2 using miles and ton-miles, allows the FAF assignment to be reported for SU and CU trucks, and allows the payload factors to be updated to more current years, if it is assumed that the overall changes in miles and payloads, apply to each payload factor by Standard Classification of Transported Goods 2 (SCTG2).
As VIUS-like surveys are conducted by additional States, their findings with respect to payloads could be compared with the proposed payload factors, with error ranges.
The VIUS remains the best source of information about cargo carried by trucks and remains the most viable source to determine the payload factors, tons per truck, that are needed for the FAF. While considerable advances have been made in passive detection of truck weight, those methods still only infer truck body types and total payload weights but are unable to determine the commodity carried by the trucks.
Advances in electronic clearance and electronic logs for drivers could be monitored and if commodity information is collected, efforts may be undertaken to make the classification system that is used compatible with the SCTG2 system used by the FAF.
Research could be undertaken to determine the bias and usage of these passive detections system so that any findings can be expanded to produce the payload factors for the universe of all trucks
