The Freight Analysis Framework (FAF):

Leveraging Federal Statistical Programs to 

Understand US Goods Movement

Monique Stinson

Freight Estimation, Forecasting and Analysis Manager

Stephanie Lawrence

Director

Office of Statistical and Economic Analysis

10/23/2023

Presented to the Federal Committee on Statistical Methodology (FCSM) Conference
Presentation Topics

• FAF

– Product overview

– Applications 

– Development, including CFS and other inputs

• Limitations of FAF

• Future directions

– Impact of expanded CFS sample

– Other possibilities

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FAF Provides Estimates of US Freight Flows

Product 1: Database of flows by Origin (O), 

Destination (D), Commodity, & Mode 

• Includes foreign O/D and mode (if applicable)

• Volumes: tons, value, and ton-miles

Product 2: Network flows by Truck, Rail and 

Water (e.g., estimated volumes on interstates)

*FAF5 is developed by the Bureau of Transportation Statistics (BTS) in partnership with Federal Highway Administration (FHWA)
Beyond the Present Day

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• Annual Estimates

• Historic Series

• Forecasts

Illustration for FAF4
Web-based data 

tabulation tool (DTT)

OD Flows (current & 

future years): 

Region-to-region (132 

regions)

&

State-to-state (also has 

historical to 1997)

Network Flows (truck)

Other (summary 

statistics, 

documentation, ...)

Access FAF at: https://www.bts.gov/faf
Data Tabulation Tool (DTT): Create visual or tabular summaries 

of OD flows by mode, commodity, region, and more
Level of Detail: Geographic

132 domestic regions 8 foreign regions

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https://www2.census.gov/programs-surveys/cfs/technical-documentation/methodology/2017cfsarea.pdf 2017cfsarea.pdf (census.gov)

FAF ZONE Foreign Region

801 Canada

802 Mexico

803 Rest of Americas

804 Europe

805 Africa

806 Southern, Central, and Western Asia

807 Eastern Asia

808 Southeastern Asia and Oceania

https://www.bts.gov/archive/subject_areas/freight_transportation/faf/faf4/app_d
Level of Detail: Commodity

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SCTG2: 2-digit Standard Classification of Transported Goods (numbered 1 to 43)
Applications: Local, Regional, and Statewide 

Freight Transportation Analysis

• Sectors

– Public: transportation planning agencies

– Private

• Real estate development

• Transportation & warehousing companies, e.g., third- party logistics providers (3PLs)

– Academic researchers

• Transportation planning applications

– Assess current system performance

– Plan for the future, esp. identifying funding needs

– Highway: bottleneck analysis  understand what 

goods are impacted

– Asset management (infrastructure) – e.g., 

pavement, bridges

• Supply chain analysis

– Top domestic and foreign trading partners

– Top commodities

– Mode shares

• Climate resilience

• Et cetera...

• Examples:

– Connecticut Statewide Freight Plan https://portal.ct.gov/-

/media/DOT/FASTLANE/Freight_Plan/CTDOTFreightPlanFinal111617pdf.pdf?la

=en

– District (DC) Freight Plan – update: 

https://ddot.dc.gov/sites/default/files/dc/sites/ddot/TAMP_

Master_SlideDeck_v3.pdf

– Oregon Statewide Model:

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Example Application

Economic impacts 

by state:
Purpose of FAF: Transportation Analysis  Data and 

Processes Focus on Capturing Freight Trips

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AUTO PARTS

MANUFACTURER

MANUFACTURER

ORIGIN #1 DESTINATION #1

1 Record

2 Records

MANUFAC WAREHOUSE -

TURER

ORIGIN #1 DESTINATION #1

AUTO PARTS

MANUFACTURER

ORIGIN #2 DESTINATION #2

AUTO PARTS
PRODUCT #1: 

OD FLOWS DATABASE

METHODS: 

CONSTRUCTION; 

MODELING

Development: Commodity Flow Estimates

Main Input: the BTS-Census Commodity Flow Survey (CFS)

IPF

Various

Processes

CFS SHIPMENT 

DATA

Origin DestinationSCTG2 Tons Value TonmilesDomestic 

Mode

Foreign 

Mode

WyomingChicago 

region

Coal xx xx xx xx xx

... ... ... ... ... ...

FLOWS or PRODUCTION 

VOLUME DATA:

11 OOS (Out of Scope) Data Sources:

Farm-based agriculture, fisheries, logging, crude 

petroleum, natural gas, retail, service, moving, 

foreign trade, construction and demolition, 

waste [from USDA, NOAA, EPA, Census FTD / 

USATO, BEA, EIA, USACE, states, industries]

IPF: Iterative Proportional Fitting 
PRODUCT #1: 

OD FLOWS DATABASE

Development: Network Flow Estimates

Main Input: OD flow estimates

FAF REGIONS

PRODUCT #2: NETWORK FLOWS

METHOD: ASSIGNMENT

NETWORKS
About 2/3 of FAF Volumes Are Based on CFS

For more detail, see FAF Base Year Methodology Report: https://doi.org/10.2172/1844893 14

Value by SCTG2 Commodity by Data Source

01, 02, 03... ... 40, 41, 43 SCTG 16 (Crude Oil) SCTG 43 (Mixed Freight)
Additional Uses of the CFS Data 

• Attraction for farm shipments

• Value-to-weight ratios – Construction and Demolition

– Retail 

– Services

– Household and business moves

• Industry contributions in the annual estimates

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Limitations of FAF

• Geographic granularity  solution (in progress): disaggregate from 

FAF/CFS regions to county level

– However, CFS input matrix is still very sparse

• Multimodal is one category  solution (in progress): develop 

method (using CFS records) to infer more path detail

• Less confidence in estimates for some Out-Of-Scope (OOS) 

commodities

• Weak coverage of “last mile” flows in general

– Customer pickup: historically was bundled with delivery categories

– E-commerce: unknown, but important for estimating vehicle miles traveled 

(and is needed for statewide freight plans)
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Impacts of Recent CFS on FAF

• FAF6 release (Fall 2025)

– Will use CFS 2022

• Geographic granularity  denser CFS input matrix  more 

confidence in high-fidelity (county level) estimates

• Multimodal is one category  more shipment records will improve 

our detailed path estimates

• Larger CFS sample may also improve our supplemental CFS inputs 

to the OOS estimation (e.g., value-to-weight ratios)

• Improved coverage of “last mile” flows

– Customer pickup
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Future Directions

• E-commerce: update CFS? Look to other Census surveys? Integrate 

data from BTS’ planned Household Logistics Data Program?

• SCTG2: update with more and/or different categories

– E.g., SCTG 19 contains both Asphalt and Liquefied Natural Gas

• Modernization: integrate richer set of data and potentially new 

methodologies

– Example: model instead of construction for some processes; additional 

features to support supply chain analysis

• Fill key data gaps - e.g., cost of shipping
THANK YOU

monique.stinson@dot.gov

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FAF Development Process

2017 Commodity 

Flow Survey Exports Directly reportable estimates

Processed data

FAF5

ODCM

Base Year

Dataset

Forecasting

Harmonization

Updates

Latest

FAF5

ODCM

Matrix

FAF Highway 

Network Assignment

FAF5

Network

Flows

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Census Foreign

Trade Data

USDA

NOAA

EPA

Census

BEA

EIA

USACE

data from states

data from industries

Processed data

Processed data

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