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

Chapter 5. Implementation Overview

The first four chapters of this report evaluated current Freight Analysis Framework Version 4 (FAF4) methods for integrating Commodity Flow Survey out-of-scope data and identified alternative methodological approaches and data modeling these flows. Using the results of the examination of alternative methodologies, this and subsequent chapters develop and test alternative methodologies that potentially offer short-term improvements for estimating out-of-scope (OOS) commodity flows.

Background

The project team conducted a review of efforts made by other researchers to model the movements of out-of-scope commodities. This section of the report contains an overview of the findings of that review. While the search focused on research efforts whose primary goal was the development of a commodity flow database for the out-of-scope commodities, it also included those efforts that attempted to capture the movements of out-of-scope commodities for other purposes. Though these studies did not explicitly attempt to model commodity flows, the insights gained from the modeling of vehicle movements is potentially useful to the FAF4. This is especially true considering the FAF4's reliance on the discontinued Vehicle Inventory and Use Survey (VIUS) for similar information.

National Cooperative Freight Research Program (NCFRP) Report 26: Guidebook for Developing Subnational Commodity Flow Data provided guidance for developing subnational commodity flow databases to meet transportation planning needs at the regional level. In developing a sub-national commodity flow database, the Guidebook argues that it is important to understand the supply chain associated with a commodity, including facilities involved in the processing of a commodity and the modes used in transporting a commodity across the supply chain. One example in the Guidebook is the development of a commodity flow database for potatoes in Washington State where, among other data, the locations of potato processing facilities to estimate the destinations of farm-based potato shipments. Data from stakeholder interviews were used to estimate the share of potatoes shipped by each mode.

The Center for Transportation Research (CTR) at the University of Texas at Austin (UT Austin), in cooperation with the Texas Department of Transportation and Federal Highway Administration (FHWA), released a study that developed a commodity-based approach for evaluating the value of a select group of commodities moved on the Texas freight network. The UT Austin researchers obtained unique data sources for the select commodities through online investigations and communication with industry representatives. From that data, the researchers estimated the quantity of commodities moved from their origins to their destinations, as well as the routes, transportation modes, and vehicle types used. The selected commodities included: cattle, grain sorghum and corn, chickens, and timber, among others. Importantly, movements of cattle, grain sorghum and corn, chickens, and timber from farms (or forests in the case of timber) all represent out-of-scope commodity movements.

Overall, the various methodologies developed as part of the UT Austin CTR study can be described as taking a supply chain approach to estimating OOS commodity flows. With this approach, OOS movements are based on the locations of the facilities that represent the first step in supply chain upon leaving the farm or forest (in the case of timber). For example, The UT Austin CTR study modeled grain sorghum and corn commodity flows from farms to grain elevators and them from grain elevators to cattle feedyards. The OOS movement of grain sorghum and corn was estimated by first determining the number of elevators in each county and estimating their capacity based on the number of employees. Flows were then distributed to each county using an algorithm that allocated county-level grain productions to its closest elevator until capacity was reached. Once capacity at the nearest elevator was reached, the algorithm then allocated the remaining production to the next closest elevator.

Similar procedures were developed for estimating flows logs from forests to sawmills and of chickens from farms to processing facilities and from farms to farms. The latter movement of chickens is important because in the context of the FAF4, it represents two OOS movements. Also important, the UT Austin CTR study observed that the first point of processing for timber harvested in Texas was often in counties in Louisiana and Arkansas near those states borders with Texas. This has implications for the FAF4 assumption that movements of logging shipments are internal to FAF4 zones, which do not cross state boundaries.

The next relevant study was conducted by researchers at the National Center for Freight and Infrastructure Research Education at the University of Wisconsin-Superior. This study collected data on the movements of log and chip trucks in the Upper Peninsula region of Michigan using global positioning systems (GPS) data. Its primary purpose was to identify opportunities to increase the efficiency of these movements in order to lower the overall transportation costs to shippers. An important observation of the study was that though the timber-harvesting sites were centered in the State of Michigan, there was overlap into the northeast portion of Wisconsin. This overlap includes the processing facilities to which the harvested timber was delivered. As observed in the review of the UT Austin CTR study, this has implications for the assumption in the FAF4 that movements of harvested timber occur within a FAF4 zone.

Opportunities for Alternative Approaches

Of the literature reviewed, the NCFRP Report 26 and the UT Austin CTR methodologies were the most applicable for developing alternative methodologies for the FAF4. Both methodologies can be viewed as supply chain-based approaches for modeling commodity flows as both relied on knowledge of commodity supply chains gathered from industry trade groups or academic literature. While the NCFRP> Report 26 employed an origin-destination survey, the UT Austin CTR methodology primarily relied on third-party data sources.

Based on these two studies in particular, there are opportunities for alternative approaches based on the limitations of the data used and certain assumptions taken by the FAF4. For farm-based shipments, one of the most significant issues related to data quality is that the estimation process relies on the VIUS, which has been discontinued since 2002. It is possible that the underlying industry-specific logistics patterns regarding vehicle types and operating distances that are captured in the VIUS have changed. Both the UT Austin CTR and the NCFRP> Report 26 offer alternative methodologies for farm-based shipments as neither rely on the VIUS.

The challenge with applying these methodologies at the national scale are the number of distinct farm-based commodities. There are 117 farm-based commodities included in the FAF4. Applying the NCFRP> and UT Austin CTR methodologies of augmenting national data with local and supply chain data would require that the process be extended to all 117 of these commodities. Thus, recreating this type of analysis at the national level for all farm-based commodities would require an extensive new data collection given the vast number of crops that are included in farm-based shipments. An alternative is to apply a similar supply chain-based methodology to a smaller number of farm-based commodities that are large in magnitude relative to the scale of OOS flows, or that are deemed economically important from a national perspective. This approach is demonstrated in this report as a potential short-term improvement for the FAF4.

The literature review also revealed additional insight into the distribution patterns of freshly harvested logs. The FAF4 assumes that all shipments occur within the FAF4 zone that the port or timber-producing site is located. However, the results of the research conducted by the UT Austin CTR and the National Center for Freight and Infrastructure Research and Education (CFIRE) implied that though logging shipments from timber-producing sites are primarily local, they are not limited to FAF4 zones that end at State borders. In the case of timber producing regions such as Upper Peninsula of Michigan/North Wisconsin, southeastern Texas and western Louisiana and Arkansas, and South Georgia/North Florida, logging shipments may cross State lines into neighboring FAF4 zones to access processing facilities and/or rail spurs. Though the magnitude of these movements relative to other OOS commodity flows is relatively small, they are important for State and regional partner-agencies that utilize the FAF4 for statewide and regional freight planning.