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

Chapter 1. Introduction

Methods for modeling freight demand and goods movement in the U.S. are evolving from aggregated methods to disaggregated methods. Emerging technologies are providing opportunities for more efficient data collection and new data collection that support more advanced freight modeling, analysis and data development environments. An improved method to estimate commodity flow data that are Out-of-Scope (OOS) to Commodity Flow Survey (CFS) along with an updated Truck Payload Factors (TPF) will further national freight transportation data and analysis capability and will allow for a more accurate analysis of transportation network performance for various freight flow scenarios.

In partnership with the Bureau of Transportation Statistics (BTS), Federal Highway Administration (FHWA) has developed and maintains the Freight Analysis Framework (FAF), a national, commodity-based, freight flow modeling tool. Originally designed by FHWA as a policy-support tool for the U.S. Department of Transportation (USDOT), the FAF is the only publicly available data source that provides a comprehensive resource of long distance national freight movement data across all modes of transportation. The FAF integrates data from a variety of sources to create a comprehensive national picture of freight movement. It estimates commodity flows and relates freight transportation activities among States, sub-State regions, and major international gateways. FAF then assigns those flows to the national highway network.

The FAF has been used in a variety of freight-related transportation and multimodal freight policy analyses. It has also become an important freight data source for transportation practitioners and researchers. State departments of transportation (DOT) and metropolitan planning organizations (MPO) regularly utilize FAF to understand regional and State freight transportation needs and initiatives. The latest version of FAF (Freight Analysis Framework Version 4 (FAF4)) is based on the 2012 Commodity Flow Survey (CFS) and provides future estimates to a horizon year of 2045 for freight flows, on a regional basis by Origin-Destination (O-D) pairs. It also provides estimates of long-haul truck flows along the nation's highway network.

Although FAF O-D commodity flow data is primarily based on the national CFS, the CFS sample frame excludes freight flows from specific industry sectors: farms, fisheries, transportation, construction and demolition, most retail and service industries, foreign establishments (imports), crude petroleum and natural gas shipments, municipal solid waste, logging, as well as household and business moves. These commodity flow data not captured through the CFS or CFS OOS data are available through various sources and differ in formats, reporting schedules and geographical representations. They are compiled and then modeled to supplement the FAF analysis framework to establish a comprehensive national FAF base year O-D matrix.

Finally, by pivoting off the base year FAF O-D matrix, FAF forecasts are prepared by applying mathematical models and macroeconomic data that are based on industry research knowledge. These forecasts are driven by the most up-to-date macroeconomic assumptions on short- and long-term U.S. economic trends at the time of FAF4 forecast development.

FAF also provides estimates of base year and future year long-haul truck traffic volume on the nation's highway network. This requires translation of commodity tonnage O-D moved by trucks into the O-D number of trucks needed to transport commodities. Once truck O-D are estimated, then network assignment modeling procedures are used to estimate freight truck traffic on the national highway system. In FAF, the truck payload factors (TPF) are used to convert O-D for truck tonnage flows to O-D for number of trucks.

The existing TPF is primarily based on the Vehicle Inventory and Use Survey (VIUS) 2002 database. VIUS provides data on the physical and operating characteristics of the nation's truck population such as: ownership, equipment type, truck configurations, dimensions, capacity, trip mileage, and commodities hauled. The first VIUS survey was conducted in 1963 and every five years thereafter beginning in 1967 and until 2002. TPF is also informed by the FHWA Vehicle Traveler Information System (VITRIS) Weigh-In-Motion (WIM) data.

This project is motivated by the need to provide an improved and detailed information regarding freight flow patterns to better support FHWA's current and future freight analysis needs through FAF. The objectives of this project are to provide:

• An improved method to integrate CFS OOS data into FAF.
• An updated TPF applicable within FAF to convert O-D flow of commodity weight to O-D flow of number of tucks.

This report documents the improved methods to integrate the CFS OOS into the FAF and provides an updated TPF applicable within FAF to convert annual tons to daily trucks.

Out of Scope Commodities

Out-of-scope commodities comprise 30 percent of the FAF4 by value.¹ Thus, improvements to the estimation of these commodity flows can substantially increase the quality of the FAF4. As a first step, the project team evaluated the FAF4 methods of integrating CFS OOS data, performed a comprehensive review of other available applicable techniques, and identified activities for further testing and implementation. Broadly, this initial evaluation technical approach consisted of three key steps:

1. Reviewed the existing OOS commodity methods employed by FAF4.
2. Reviewed more recent OOS commodity initiatives conducted as part of academic research or State and regional planning efforts with the goal of developing short- and long-term improvements.
3. Developed options for improvements of OOS commodity data that were reviewed by a technical panel of experts.

Following the evaluation of current FAF4 methods for integrating CFS OOS data and identifying alternative methodological approaches and data modeling these flows, the next step was to develop and test alternative methodologies that potentially offered short-term improvements for estimating OOS commodity flows. These improvements to the estimation of OOS commodity flows can substantially increase the quality of the FAF4 and improve its usefulness to the state and local transportation agencies that depend on the FAF4 to support freight planning initiatives.

Truck Payload Factors

The second objective of this project was to evaluate existing Truck Payload Factors (TPF), payload parameters and application approaches, explore the possibility of further analysis of available Vehicle Inventory and Use Survey (VIUS) data, comprehensive review of other available applicable techniques, identify new data sources, and identified activities for further testing and implementation. VIUS has provided the information for the current payload factors, TPFs, used in the FAF's highway assignment, but while VIUS had previously been collected concurrently with the FAF releases, the VIUS has not been collected or updated since 2002. Changes in the miles traveled by trucks and changes in truck technology have occurred in the ensuing 15 years. In order to properly reflect these changes in the truck assignments of FAF4, as well as subsequent releases of the FAF, a methodology was developed to make the truck payload factors more reflective and representative of current conditions. Broadly, the technical approach contained three key steps:

1. Reviewed the existing Truck Payload Factors (TPF), often known as payload factors, employed by FAF4.
2. Reviewed more recent TPF commodity initiatives conducted as part of research efforts with the goal of developing short- and long-term improvements.
3. Summarized the findings and developed a set of improvement for implementation.

The remainder of the report is organized as follows: chapter 2 performs a comprehensive review of the existing out-of-scope methods employed by the FAF4; chapter 3 reviews alternative methods and other research efforts that may provide improvements over current methods; chapter 4 presents a summary of findings from the review of existing and alternative methods and provides draft short- and long-term improvement activities to be implemented in chapters 5 to 10.

Chapters 11 through 14 focus on the truck payload factors, which includes the review of alternative methods, methods development, methods implementation, and validation.

The report concludes with a summary and potential future improvement activities for capturing OOS commodities.