NEMS Freight Transportation 

Module Improvement Study

February 2015

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | NEMS Freight Transportation Module Improvement Study i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the U.S. Department of Energy 

or other Federal agencies.
February 2015

U.S. Energy Information Administration | NEMS Freight Transportation Module Improvement Study 1

NEMS Freight Transportation Module Improvement Study

The U.S. Energy Information Administration (EIA) contracted with IHS Global, Inc. (IHS) to 

analyze the relationship between the value of industrial output, physical output, and freight 

movement in the United States for use in updating analytic assumptions and modeling structure 

within the National Energy Modeling System (NEMS) freight transportation module, including 

forecasting methodologies and processes to identify possible alternative approaches that would 

improve multi-modal freight flow and fuel consumption estimation. 

IHS identified primary issues with EIA’s NEMS freight transportation module with respect to 

state-of-the-art modeling, organized a workshop of experts to discuss alternatives to improving 

the NEMS transportation module, and performed independent research to ascertain 

improvements to the NEMS model structure. Based on the input from EIA, external experts, and 

further research, the final report from IHS makes the following recommendations:

• Replacing the use of the Commodity Flow Survey records with Freight Analysis 

Framework freight flow records as part of the ton-mile historic and projection metric 

development

• Application of a basic network assignment using geographic information system (GIS)

estimation of truck ton-mile shares across census divisions and commodity groups

• Disaggregation of industry classifications used in NEMS to the Standard Classification 

of Transported Goods (SCTG) level

In addition, IHS recommended study of other issues but did not otherwise recommend immediate 

action:

• Exploring the potential of creating a multi-modal component to NEMS freight 

transportation module

• Determining if other mode-split models can be leveraged

• Study how major shifts in the economy and industry technology and management 

practices affect forecasts of ton-miles

EIA is now using the Freight Analysis Framework in place of the Commodity Flow Survey in 

the determination of historical census division and commodity ton-mile data, including the 

derivation of the ton-mile per dollar of industrial output (a key metric used in the travel demand 

projection methodology). Further, these data include a GIS modeling estimation of the share of 

freight truck travel between origin and destination points through intermediate census divisions. 

EIA expects to explore disaggregation of industry into SCTG categories in the future. 

EIA will take close note of the potential for creating a multi-modal component to NEMS freight 

transportation module as recommended, as well as continuing to monitor and understand the 

changing nature of the economy and industry. IHS did not offer any recommendation on the 

outstanding issue of modeling the declining historical ton-miles in domestic marine shipping.
February 2015

U.S. Energy Information Administration | NEMS Freight Transportation Module Improvement Study 2

Appendix A
Final Report

NEMS Freight Transportation Module 

Improvement Study

Prepared for:

Energy Information Administration

Submitted by:

IHS, INC.

24 Hartwell Avenue

Lexington, MA 02421

Tel: 781-301-9072

Fax: 781-301-9409

June 20, 2014
2

Executive Summary

This report summarizes findings of a review of the Energy Information Administration’s (EIA) freight 

transportation energy consumption forecasting module conducted by IHS Global, Inc. (IHS). EIA 

contracted with IHS to analyze the relationship between the value of industrial output, physical 

output, and freight movement in the United States for use in updating analytic assumptions and 

modeling structure within the NEMS freight transportation module, including forecasting 

methodologies and processes to identify possible alternative approaches that would improve multimodal freight flow and fuel consumption estimation.

1

The NEMS freight transportation module estimates freight flows by census division, mode of 

transportation, and commodity type. NEMS inputs industry output in dollars by census division, 

commodity group, and mode into the freight transportation module, which reports energy consumption 

forecasts back into NEMS using these same units of evaluation. NEMS uses nine census divisions, ten 

aggregated commodity groups, and four modes of transportation: truck, rail, marine (domestic barge 

and intra-coastal as well as international), and air. Coal moving on rail is modeled separately, as this 

procedure has proven to improve model accuracy.

The primary mechanism of the freight transportation module is a “ton-mile metric” which estimates for 

each census division, mode, and commodity group the average ton-miles traveled2per unit of industrial 

output. For truck, rail, and domestic marine, this metric is the product of a simple division of base-year 

industrial output by base-year ton mileage (or modal demand forecasting metric equivalent) reported by 

various U.S. Government agencies at the national level. This data is disaggregated to the census division 

and modal level leveraging the Bureau of Transportation Statistics (BTS) and Census Bureau’s

Commodity Flow Survey (CFS). EIA then essentially calculates a simple weighted average from CFS as a 

proxy for modal and route assignment, which is used to disaggregate national-level commodity flow 

forecasts into NEMS units.

IHS identified nine primary issues with EIA’s NEMS freight transportation module with respect to stateof-the-practice transportation demand modeling. Possible areas of concern include:

 The use of sub-optimal input data for the specific objectives of the model (i.e., the CFS survey),

 Inaccurate accounting of the effects of time in between CFS base-year surveys,

 Levels of aggregation by commodity and geography might fail to capture system dynamics,

 The ton-mile metric possibly does not adequately account for fluctuations in commodity prices, 

all else equal,

 The ton-mile metric does not account for changes in product content (hence, weight-to-unit 

ratios), all else equal,

 

1

IHS is a global leader in transportation, energy, and trade modeling,

2

Freight transportation output is generally measured in ton-miles for all modes except for air cargo, which is 

measured in freight revenue ton miles, and truck freight, which is measured in both ton-miles and vehicle miles 

traveled (VMT). 
3

 Proxy methods for disaggregating ton-miles employ macroeconomic techniques, while the state 

of the practice employs more disaggregate microeconomic techniques,

 Mode share is static and does not take into account possible mode shifts over time,

 The ton-mile metric procedure does not accurately forecast the directionality of domestic 

marine demand,

 Proxy measures for network assignment and distance estimation are too simplistic and do not 

employ more rigorous state-of-the-art network assignment techniques.

IHS then organized a workshop with EIA and experts in freight modeling from government and the 

private sector to discuss alternatives for improving the EIA NEMS freight transportation module. 

Although state-of-the-art practices offer greater precision in transportation demand modeling, the most 

rigorous, time-consuming, and costly techniques may not be appropriate for EIA given the more highlevel objectives of the NEMS framework. Hence, the benefits of precision must be weighed against the 

costs in time and human and financial resources. Leveraging its knowledge of the NEMS freight 

transportation module and its industry experience with freight transportation demand modeling, IHS 

presented the workshop participants with four over-arching alternatives for debate and consideration. 

These included:

 Incremental improvements to existing NEMS freight transportation, involving primarily the use 

of additional variables and minor changes to processes but otherwise retaining the existing data, 

methodology, and approach,

 Employing econometric techniques to forecast the ton-mile metric,

 Leveraging the BTS Freight Analysis Framework (FAF) data in place of the CFS survey and 

possibly leveraging other BTS and FHWA resources and processes to improve model forecasting 

precision,

 Developing a new EIA freight forecasting module explicitly employing more micro-level 

approaches and more rigorous network assignment modeling techniques.

Feedback from EIA and workshop participants suggested that replacing the CFS survey with FAF would 

be the most practical approach to improving the NEMS freight transportation module, as it would 

improve data input accuracy with minimum changes to existing processes. Suggestions were made 

regarding how to better use base-year data to estimate flows in between FAF publication years. 

Participants also suggested that lack of precision in distance estimation of freight flows across census 

divisions could be improved by developing an index of average distance traveled through each census 

division zone by origin/destination pair. This could improve precision sufficient to EIA’s needs without 

adding the complexity and cost of a full-fledged network assignment model. The meeting participants all 

acknowledged that employing a dynamic mode share process would be very difficult and, in fact, FHWA 

is in the process of exploring how to incorporate such a procedure in its own freight demand 

forecasting. FHWA and EIA agreed to work together to investigate the possibilities of adding these 

capabilities to their respective models in the future.
4

Based on the results of the workshop and consultation with its internal freight modeling experts, IHS has 

put forth recommendations on improvements to the current NEMS freight transportation ton-mile 

forecasting process. Included in this document are discussions about steps to implement priority 

changes and examples and analyses of improvements from implementing new procedures. 

Documentation of spreadsheet calculations, procedures and related support materials will be delivered 

to EIA separately.

The primary recommendations for immediate improvement are:

 Replace CFS survey records with FAF freight flow records as part of the ton-mile metric 

development,

 Apply a basic network assignment developed by IHS using GIS modeling capabilities to the 

estimation of truck ton-mile shares across census division zones and commodity group types, 

and study the potential for the development of a similar process for rail,

 Disaggregate industry classifications used in the NEMS freight transportation module to the 

Standard Classification of Transported Goods (SCTG) level where sufficient sample sizes exist, 

and study logical aggregations of smaller industry categories.

IHS also recommends that EIA undertake studies to address several other issues with the NEMS freight 

transportation module, but does not otherwise recommend immediate action. The studies include:

 Exploring the potential benefits and costs of building a multi-modal component to the NEMS 

freight transportation module based on a conceptual process outlined in this study,

 Work with FHWA to determine if current research on integrating mode-split models in FAF 

forecasting could be leveraged by EIA and integrated into the NEMS freight transportation 

module,

 On an as-needed basis, study how major shifts in the economy and industry technology and 

management practices affect forecasts of individual or groups of industry classifications to 

develop potential adjustments to ton-mile metric calculations in between CFS/FAF base years.
5

Table of Contents

Executive Summary.......................................................................................................................................2

Contextual Description of the NEMS Freight Module ..................................................................................7

Heavy-duty Truck ......................................................................................................................................8

Rail.............................................................................................................................................................8

Domestic Marine.......................................................................................................................................8

Issues with the Freight Transportation Module .........................................................................................10

Conceptual Solutions..................................................................................................................................12

Incremental Improvements....................................................................................................................12

Econometric Approaches........................................................................................................................12

Leveraging the FHWA Freight Analysis Framework (FAF).......................................................................13

Network Assignment Models..................................................................................................................13

Industry Workshop Summary .....................................................................................................................15

Primary Discussion Topics.......................................................................................................................15

Decisions and Next Steps........................................................................................................................17

Addressing Issues with Modeling Freight Demand in the NEMS Environment..........................................18

NEMS Freight Transportation Module Process Overview ......................................................................18

Availability and Use of Data: FAF as a NEMS Solution............................................................................25

Employing a Route Optimization Proxy to Improve Network Assignment.............................................34

Disaggregating Census Divisions and TSIC Categories............................................................................42

Adjusting for Prices Using Commodity-Specific Inflation Factors...........................................................49

Modeling Multi-modal Goods Movement and Introducing a Dynamic Mode-Share Process ...............49

Accounting for Changes in Product Content and Industry Operations and the Effects of Time ............52

Correcting NEMS Forecasts for Domestic Marine ..................................................................................53

Changes to other Data Inputs to NEMS ..................................................................................................53

Conclusions.................................................................................................................................................54

Appendix A: Workshop Participants...........................................................................................................58

Appendix B: Additional Comparison Tables for FAF Versus CFS.................................................................59
6
7

Contextual Description of the NEMS Freight Module

The National Energy Modeling System (NEMS) is a general equilibrium model that calculates energy 

consumption for different end users and from different sources. Energy supplies are calculated in four 

modules: oil and gas, natural gas, coal, and renewable fuels. Similarly, four modules represent demand 

for energy: residential, commercial, transportation, and industrial. Other modules of the integrated 

model include international energy and macroeconomic activity, as well as two conversion modules 

(electricity and liquid fuels). Demand for energy for freight movement is included as part of the 

transportation demand module.

Figure 1: NEMS Model Schematic

The NEMS freight module forecasts consumption of different fuel types by mode (heavy-duty truck, rail, 

domestic and international marine, and air), geography (census division), and commodity group.

Whereas heavy-duty truck demand is reported in vehicle miles traveled (VMT), rail and domestic marine 

are reported in ton-miles. Air is represented in freight revenue ton-miles, and international marine is not 

reported as travel demand. Freight travel demand is driven by the value of industrial output generated 

by NEMS for heavy-duty truck, rail, and domestic marine. Imports and exports drive air freight and 

international marine. This report does not explore freight movement by aircraft or international marine 

vessels. Coal transport (by rail) is informed by a separate coal market module in NEMS.
8

Heavy-duty Truck

EIA organizes historic freight movement data by truck from two sources: VMT from the Federal Highway 

Administration (FHWA) and ton-miles from the Surface Transportation Board (STB), both at the national 

level. VMT and ton-mile truck movement data reported by EIA are also organized at the census division 

level. The Commodity Flow Survey (CFS) produced by the Bureau of Transportation Statistics (BTS)

provides ton-mile by origin/destination state and commodity every five years (1997, 2002, and 2007

with a pending full release for 2012).

NEMS assumes direct relationships between VMT, ton-miles, and industrial output. National VMT data is 

disaggregated by commodity ton-mile data estimated from the CFS for each census division. For

intermediate years between CFS updates, shares are calculated by assuming a linear pattern of growth.

A ton-miles per dollar metric is developed and publicly reported by EIA for each commodity and census 

division by dividing industrial output estimates in NEMS by ton-miles associated with each division and 

commodity (again, reported by census division and commodity group by the most recent CFS estimates; 

currently, CFS 2007). VMT projections are calculated directly by the change in ton-miles forecasted by 

these ton-miles per dollar output variables by commodity and census division. The assumption of a 

static relationship between VMT, ton-miles, and industrial output means that the VMT forecasts by 

census division have a direct linear relationship to the ton-mile per dollar of output metric and EIA’s 

industrial output forecasts by commodity group. In other words, cargo content and prices are assumed 

to be constant.

Rail

EIA forecasts rail freight flows following a similar process employed for heavy-duty truck, except that no 

conversion is made from ton-miles to VMT and that certain cargo is estimated independently. For freight 

moving by rail, national ton-mile data is provided by the Surface Transportation Board (STB) for Class I 

Railroads only. EIA then separates coal and non-coal ton-mile data at the national level prior to the 

calculation of census division and commodity shares. Otherwise, like the heavy-duty truck forecast, tonmiles origin/destination data are extracted from the CFS for years in which these values are available to 

calculate shares by census division. Intermediate year forecasts are estimated as a linear function of CFS

historical data and the ton-miles per dollar estimates by commodity and census division are created and 

publicly reported by dividing NEMS industrial output by CFS 2007 ton-miles. A static relationship 

between ton-miles and industrial output is assumed in this case.

Coal shipments are kept separate from the broader mining category in this model. Instead, the separate

coal markets module is used to project growth in coal ton-miles by rail. 

Domestic Marine

Similar to EIA’s rail freight forecasts, domestic marine freight forecasts are reported in ton-miles by 

census division and commodity, except that coal is not excluded. Historical commodity ton-mile 

movement data for domestic marine (primarily river and intra-coastal barge shipments) comes from the 

U.S. Army Corps of Engineers, specifically Waterborne Commerce. Ton-mile shares are taken from the 

CFS for years in which these values are available. In intermediate between CFS publication years, shares 
9

are calculated between following a linear pattern. Ton-miles per dollar by commodity and census 

division are created and publicly reported using CFS 2007 ton-miles by mode and commodity in 

conjunction with industrial output from NEMS.

Unlike the rail forecasts, however, EIA calibrates the ton-mile forecasts downward to account for recent 

divergence in industrial output and domestic marine freight transportation demand. Essentially, demand 

for domestic marine freight is declining despite increased industrial output on account of factors outside 

the NEMS modeling environment. EIA adjusts ton-miles forecasts between 2007 and 2011 by an average 

of -3.12%, owing to this historical decline in the use of domestic waterborne shipment. This adjustment 

is held forward until 2020, then adjusted by -1.56% through 2030 and -0.78% between 2030 and 2040. 
10

Issues with the Freight Transportation Module

Preliminary potential issues with the ton-mile/VMT per value-of-output metric were identified and were 

explored as part of the NEMS freight transportation module. These issues include, but are not limited to:

 Availability and use of data: Like any model, the quality of outputs is related to the quality of 

inputs. Alternative data sources exist. Their appropriateness needs to be evaluated with respect 

to costs of acquisition, terms of use for EIA public disclosure requirements, and compatibility 

with other NEMS model data and processes.

 The effects of time: The freight transportation module in NEMS relies heavily on the CFS, which 

is published every five years and with a lag. Thus, the accuracy of the module is highly 

vulnerable to interim changes occurring, for example, in goods manufacturing and supply 

chains; freight technology and operations; global, national, and regional trade and logistics; and 

economic volatility.

 Levels of disaggregation: The current aggregation of regions and commodity groups may not 

fully capture the dynamics within each region and across various industries.

 Changes in price: There is not necessarily a direct correspondence between aggregate value and 

aggregate weight. Changes in the pricing of goods would increase or decrease the value of 

industrial output, but not necessarily the tonnage of output or the distance traveled. Thus, the 

ton-mile metric might overestimate or underestimate freight demand.

 Changes in product content: Similar to price changes, changes in the design and content of 

certain products (e.g., consumer electronics, automobiles, etc.) may alter the ratio of value of 

output to unit weight. Hence, the ratio of output value to tonnage of a newly designed product 

may be different than in previous editions, all else equal.

 O/D weights: The weighting of goods movement by region and commodity may be flawed, 

either in execution or perhaps more fundamentally. Flaws in execution may result from issues 

previously discussed, including levels of disaggregation by commodity and region and failure to 

capture dynamic changes over the course of time. Moreover, it may be that other variables 

need to be considered. Concerns about fundamental approaches center on the validity of using 

highly-aggregated macroeconomic data to capture travel behavior. Travel demand modeling 

tends to employ microeconomic techniques at the most disaggregate levels possible, and then 

aggregating this output up to the desired geographic level. The issue is whether or not the 

current proxy measures of flows are appropriate given time, budget, and data constraints and, if 

so, how current processes could be re-designed to increase accuracy.

 Modal shares of value: EIA uses a static measure of mode share for each commodity group 

based on the historical value of an industrial output metric associated with each mode. This 

means calculating a ton-mile per dollar industrial output for each mode based solely on each 

mode’s historical ton-miles and total industrial value. Additionally, goods movement across 

multiple modes is a challenge. One possible enhancement would be employing a more dynamic 

mode share model. This may or may not be possible or advantageous but it warrants

consideration.
11

 Linear year calculations: The domestic marine mode’s forecast is currently replaced with a 

negative linear growth rate regardless of NEMS industrial output trends. The rate of decline, in 

this case, is -3.12% through 2020, -1.56% through 2030, and -0.78% between 2030 and 2040. 

This assumption is based on declining historic domestic ton-mile trends.

 Distance as a critical driver: Assignment of commodity flows to census divisions—currently 

calculated as 50% to the origin and 50% to the destination—does not accurately account for 

shares of distance traveled in each census division. Furthermore, intermediate census divisions 

are excluded that may account for a substantial portion of a given origin/destination pair’s 

flows.
12

Conceptual Solutions

This section outlines IHS’s preliminary evaluation of alternative methodologies or approaches, the 

potential benefits, and the potential challenges for improving the NEMS freight transportation module. 

Issues of concern include model accuracy and reliability, data availability and cost, human resource 

availability and cost, and other NEMS and DOE constraints. IHS also acknowledges that there may be 

other alternatives or hybrids of the following alternatives, which will be addressed.

The purpose of this preliminary analysis was to develop an initial framework for evaluating alternative 

approaches to forecasting freight movement in NEMS. That framework was used to direct more indepth research of these alternatives, structure a workshop composed of experts to provide advice and 

recommendations to the EIA and IHS project teams, and to facilitate dialogue between EIA, IHS, and 

outside experts on the path forward.

Incremental Improvements

Using incremental improvements to curtail some of the problems in the NEMS freight module would 

allow EIA to retain as much of the current forecast process and metrics as possible, as well as most of 

the key input data. One possible improvement could be employing different aggregation or 

disaggregation of input and output data in commodity and regional groupings. Alternatively, additional 

process steps and/or variables could be integrated into the freight transportation module to calibrate 

forecasts more accurately to historical trends and provide flexibility to address future uncertainties.

There are numerous advantages to making incremental changes to the freight module. Most 

importantly, this would probably be the lowest-effort alternative, whereby many processes would 

remain the same but could be enhanced and refined. With regard to explanation and publication, this 

strategy would be simpler. Existing data feeding the model would be retained, whose sources and use in 

NEMS are already documented. New process development would be minimal as many of the existing 

processes would remain the same.

The drawbacks to this approach are also clear. The criticism remains of the freight module as a topdown approach using aggregate macroeconomic data rather than a bottom-up approach using 

microeconomic techniques. The fundamental weaknesses of the ton-mile approach are not directly 

addressed, but instead numerous short-term fixes are implemented that may not stand the test of time. 

Accurately forecasting freight movement as origin/destination “flows” will be a challenge regardless of 

refinements given the processes in place.

Econometric Approaches

A more sophisticated econometric model could be implemented to forecast ton-miles (and energy 

consumption) using output from the NEMS integrated model. The benefit of this approach is that it 

could offer a more reliable predictor of VMT or ton-miles at a given level of disaggregation (by 

commodity or region) using publicly available data.
13

Although an econometric approach could provide a more robust model of ton-miles and VMT, the 

modeling process would still require some method of disaggregation by region (and possibly by 

commodity and mode) given that the forecasts are derived from national-level data. If the forecasts 

were developed at a less aggregate level (e.g., separate models for each region), there could be 

challenges in maintaining an internally coherent model once aggregated. Developing regional-level 

forecasts independent of one another could lead to a situation where the flows across regions would 

not “match up”. Therefore additional constraints and processes might be included to ensure that inflows 

and outflows across regions are internally consistent and roll up to an accurate national forecast.

Leveraging the FHWA Freight Analysis Framework (FAF)

One methodological option could be leveraging the Freight Analysis Framework (FAF) from FHWA. FAF, 

which covers gaps in some goods movement not included in CFS, could replace the latter to enhance the 

accuracy of EIA forecasts. These gaps include, for example, some agriculture freight flows that are 

generally not covered in the CFS survey. There is an additional benefit to using FAF, as FHWA and BTS 

staff members have expressed a willingness to provide assistance to EIA on how to best use the data set 

for energy consumption forecasting applications.

FHWA also routes FAF flows on the muti-modal transportation network; however, the routed data set is 

confidential. EIA could choose to develop or contract with a third party to route the FAF data in a 

manner similar to the one employed by FHWA, thus replacing the current origin/destination weights 

process with a more explicit and disaggregate accounting of freight flows. EIA could then consider 

altering the ton-mile metric to a more reliable measure relating industrial output (or some other 

economic measure(s)) to freight transportation demand. The advantages of fully employing processes 

similar to FHWA’s routing model will be described in the next section on network assignment models. 

Using FAF forecasts does not, however, solve one other key problem with the current process. As FAF is 

updated with the CFS, adjustments need to be made within five-year intervals. Thus, intermediate years 

must still be estimated. Other issues, such as a lack of a mode share model, would also persist.

Network Assignment Models

A final alternative approach to the NEMS freight module could be the use of an actual network 

assignment model. This would enhance the current origin/destination matrix and ton-mile metric 

process with either a new internal freight forecasting process or the purchase of commercial data.

This network assignment model could employ a dataset that explicitly forecasts freight flows in the U.S. 

using more disaggregate, microeconomic approaches, similar to the approach that FHWA employs to 

route FAF forecasts onto the multi-modal transportation network. Depending upon the quality of the 

model or dataset purchase, it may be possible to improve upon the limitations of FAF. This could lead to 

a model that is more explicitly driven by state-of-the-practice transportation demand modeling and may 

be more flexible in addressing some of the acknowledged weaknesses of the current NEMS process.
14

Although this approach would more directly employ transportation modeling theory and common 

practice, it is likely that it would be impractical for EIA. There is a possibly that costs (including 

development time) would outweigh any potential benefits in model robustness and forecast accuracy. 

Challenges might include new skill development within EIA, contracting for additional commercial 

services, and possibly altering the manner in which NEMS industrial forecasts are fed into the freight 

transportation module and how demand metrics are reported back into the model framework. 

Furthermore, any use of commercial data would be challenging due to issues of transparency, in that 

proprietary data may come with restrictions on release to the public.

Table 1: Conceptual Alternative Solutions

Advantages Disadvantages

Incremental 

Improvements to 

Existing Processes

 Most likely the lowest-effort alternative

 Retains simplicity of approach for explanation 

and publication

 Retains most of the existing data sources 

feeding the model, whose sources and use in 

NEMS are already documented

 Retains as much of the existing processes as 

possible to minimize new process 

development

 May be criticized for retaining a topdown approach using aggregate 

macroeconomic data rather than a 

bottom-up approach

 Does not directly address 

fundamental weaknesses of the tonmile approach; instead replaces with 

numerous “patches” that may not 

stand the test of time

 Capturing O/D “flows”, especially, 

will be a challenge regardless of 

refinements

Employ Econometrics 

to the Ton-mile 

Calculation

 Could offer a more reliable predictor of 

VMT/ton-miles at a given level of 

disaggregation (by commodity or region) 

using other publicly available data

 If at the national level, the modeling 

process would still require some 

method of disaggregation.

 If at a more disaggregate level, there 

may be challenges with maintaining 

an internally coherent model once 

aggregated.

Leveraging US 

Department of 

Transportation FAF 

Forecasts

 Employs a dataset that explicitly forecasts 

freight flows in the US using more 

disaggregate, microeconomic approaches 

(e.g., O/D surveys of actual carriers, 123 FAF 

regions rather than nine Census Divisions, 

etc.)

 Makes use of free data, owned and produced 

by the US DOT

 Otherwise maintains the data, processes, and 

institutional knowledge embedded in the 

existing NEMS freight module

 FAF is updated with the CFS, so 

adjustments need to be made within 

five-year intervals

 Employing FHWA freight routing 

processes, if pursued, could be 

expensive and time consuming while 

possibly also requiring new skill 

development within EIA
15

Advantages Disadvantages

Develop a New EIA 

Network Freight 

Forecasting Module

 Could employ a dataset that explicitly 

forecasts freight flows in the US using more 

disaggregate microeconomic approaches

 Depending upon the data quality, it may be 

possible to improve upon the limitations of 

FAF

 Leads to a model that is more explicitly 

driven by state-of-the-practice transportation 

modeling, and may be more flexible for 

addressing some acknowledged weaknesses 

of the current process

 While this approach more directly 

employs theory and common 

practice, it may be impractical for EIA 

(e.g., the costs may outweigh the 

benefits)

 Any use of commercial data will 

challenge transparency issues

 It may be challenging to develop the 

new module within the NEMS 

architecture

Industry Workshop Summary

A workshop was held at the IHS office in Washington, DC to foster a discussion with experts in freight 

transportation modeling. In attendance at the workshop were representatives from:

 American Transportation Research Institute (ATRI),

 Energy Information Administration (EIA),

 IHS Global, Inc. (IHS),

 United States Department of Transportation (U.S. DOT): Federal Highway Administration 

(FHWA), Bureau of Transportation Statistics (BTS), and the Volpe National Transportation 

System Center.

First, EIA presented the current state of the freight module in NEMS to the workshop participants. IHS 

made a short presentation on the issues identified with the freight module and potential solutions.

Finally, a group discussion moderated by IHS explored ways to improve the freight module. This section 

summarizes the workshop discussion topics and recommendations from the participants.

Primary Discussion Topics

The greater part of the discussion focused on the possibility of leveraging FAF for the NEMS freight 

module. This was due in part to the abundance of representatives from FHWA, who were able to assist 

with practical considerations in using FAF, but also because the advantages of leveraging FAF quickly 

became apparent.

FAF began in its first generation as a dataset developed from proprietary data, but has evolved into one 

using public data so that it can be disseminated to the general public. There are two aspects of FAF: an 

origin/destination matrix of commodity flows for all modes and a dataset of those flows assigned to the 

network. The challenges with the development of the origin/destination matrix are different from those 

concerning network flows modeling at FHWA, the latter of which is not available for dissemination 

outside of FHWA.

The workshop participants agreed that, at the very least, EIA should use FAF instead of CFS for ton-miles 

inputs to the NEMS freight module. This would improve the freight module because FAF uses CFS as a 
16

base (about 2/3 of the freight flows) but estimates additional commodity movements not reported in 

CFS. These additional data include shipments originating on farms and international flows, including 

truck and rail to and from Canada and Mexico.

Like CFS, FAF reports commodity flows in ton-miles, and EIA could use very simple methods to sum up 

from FAF zones to the census division level. EIA could also backcast FAF to provide intermediary years of 

data rather than simply using linear trends between FAF years. Additionally, customer demand for FAF 

has grown in recent years, and customers are pushing for greater levels of disaggregation and detail in 

both the origin/destination and network datasets, which suggests that FAF could be improved over the 

next few years.

There are disadvantages to using FAF, though most of these are also true of CFS. FAF has some 

weaknesses on muti-modal commodity flows. The freight demand and network assignment models are 

the weakest aspects of FAF, and network assignment is done using proprietary data. Also, FHWA’s 

models are not currently very strong with respect to adjusting for policy decisions. In terms of updating 

FAF, the gap between 2007 and the hypothetical 2012 dataset will have completely missed major shifts 

in industry production and logistics (e.g., Bakken shale oil, Great Recession, etc.). Further weaknesses 

exist in multi-modal and mail categories.

FHWA suggested that it faces similar challenges in the development of FAF as EIA experiences with the

NEMS freight transportation module. With this in mind, a closer collaboration between the two agencies 

could yield improvements to the transportation modules in NEMS as well as to FHWA’s output.

A secondary topic of conversation in the workshop concerned mode choice and how this dynamic is 

represented in the NEMS freight module. EIA currently does not model modal shifts for given 

commodities other than what is implicit as changes in industrial output. This is particularly problematic 

for truck vs. rail freight movement, which is interchangeable to a larger extent than other mode pairs.

EIA expressed concern that a full-fledged mode choice model as part of the freight module would 

increase processing time. External research could be used to apply mode share to the NEMS freight 

module. One example cited in the workshop was the Intermodal Transportation and Inventory Cost 

Model (ITIC) from FHWA.

ATRI collects data from trucking companies on freight movements. These data could serve as primary 

inputs to the NEMS freight module or as a way of validating modeled movements based on FAF. ATRI is 

also working on a calibration tool for FAF to account for samples that are biased to larger carriers. This 

collaborative research could also contribute to EIA’s estimation of truck movements, as there are ways 

of linking truck GPS data used by ATRI to commodity flow volumes; although ATRI recognized that this 

could be very arduous. 

Diagrams of the NEMS freight module and further discussion of replacing CFS with FAF can be found in 

the next major section of this report, “In-depth Analysis of FAF as a NEMS Solution”.
17

Finally, workshop participants discussed how high-level metrics could be used to estimate distances 

traveled within each zone (e.g., percentage of ton-miles between origin/ destination pairs falling in each 

census division). Hence, some of the precision of employing network assignment models could be 

partially gained without the expense in time and costs associated with building a full-fledged model. The 

“O/D distance model” sub-section in the “Exploration of Future Improvements” section of this report 

details potential alternative methodological approaches. 

Decisions and Next Steps

Members of the workshop agreed that at the very least EIA can incorporate FAF instead of CFS into the 

NEMS freight module. This change would require minimal effort and it is believed that this change 

would greatly improve accuracy. Although it will not address all of the existing issues with the freight 

module, it will address some of them without creating major new ones. This is because, at its core, FAF 

is the same as CFS with the addition of freight flows for some industry segments that CFS does not 

sample (e.g., international freight and truck shipments from farms).

After implementation of FAF as an input instead of CFS, some additional relatively-low-effort, highpotential-benefit modifications will be explored. Using a more sophisticated origin/ destination matrix 

could improve the accuracy in aggregating flows to the census division level. FAF origin-destination 

matrices merged with routing and distance-traveled assumptions developed in a GIS program could 

allow a more accurate ton-mile share calculation across census divisions without requiring a full-fledged 

network assignment model.
18

Addressing Issues with Modeling Freight Demand in the NEMS 

Environment 

The preliminary analysis by IHS and the feedback received from experts at the workshop suggests a 

pathway to improving the NEMS freight transportation demand module at minimal cost. The pathway

would consist of maintaining the existing overarching NEMS environment architecture, while making the 

following improvements to the preparation of data fed into the freight transportation module:

1. Replacing CFS data with FAF data for the calculation of ton-mile shares,

2. Employing a separate route optimization process to estimate the ton-mile shares attributable to 

each of the nine census divisions for each origin-destination pair,

3. Disaggregating commodity groups or, more precisely, eliminating the aggregation of some or all 

of the 43 SCTG industry classifications for which NEMS forecasts industry output,

4. Employing a commodity-group-specific inflation correction matrix to adjust for changes in 

prices.

This section will also address other issues identified in this study that are considered lower-priority due 

to the high costs and uncertain benefits of potential solutions. For example, IHS will make 

recommendations on future studies on the possibility of addressing dynamic mode share analyses and 

muti-modal goods movement, which we believe to be potentially feasible but which would require 

substantial investment and, moreover, would present challenges to reconciliation with the NEMS 

framework. Included in this section will be conceptual descriptions of potential solutions and discussions 

about potential costs and benefits.

NEMS Freight Transportation Module Process Overview

It is important to elaborate further on the existing methodologies and procedures in the NEMS Freight 

Transportation Module to provide context for the suggested improvements and other recommendations 

in this section of the report. These processes, the challenges, and potential solutions have thus far been 

described at a high level. This section attempts to provide more in-depth analysis and guidance for 

updates and improvements.

As previously described freight transportation ton-miles estimated in NEMS are aggregated to the 

census division level. There are nine census divisions, and Figure 2 illustrates the states organized at this 

geographical scale. Currently, ton-miles between two different regions are attributed 50% to their origin 

and 50% to their destination. As the map illustrates, a 50-50% split of ton mileage between regions fails 

not only to account for the relative distances traveled within the respective origin and destination 

regions, but also all of the census divisions in between. The possibility of introducing a network 

assignment procedure to the freight module will be discussed in detail later in this section.
19

Figure 2: Census Divisions

For the purposes of reporting census-division ton-miles, EIA aggregates the 43 SCTG commodity 

classifications used in CFS (and FAF) to the ten TSIC (e.g., EIA-defined aggregated industry classifications) 

categories shown in Table 2. SCTG 99 (unknown commodity) is not represented in these groupings. 

Furthermore, SCTG 16 (crude petroleum), which was previously omitted from the aggregated CFS 

inputs, is included in TSIC 5 (petroleum products). For the rail module, SCTG 15 (coal) is removed from 

the broader mining category and treated on its own. This is due to the fact the NEMS model has a 

module estimating coal production and demand.
20

Table 2: EIA Commodity Crosswalk

TSIC (NEMS) SCTG (FAF)

1. Chemicals, rubber, and 

plastic

20. Basic chemicals

21. Pharmaceutical products

22. Fertilizers

23. Chemical products and preparations

24. Plastics and rubber

2. Primary metals

32. Base metal in primary or semi-finished forms and in finished basic shapes

33. Articles of base metal

41. Waste and scrap

3. Processed food

5. Meat, fish, seafood, and their preparations

6. Milled grain products and preparations, and bakery products

7. Other prepared foodstuffs and fats and oils

4. Paper products 27. Pulp, newsprint, paper, and paperboard

28. Paper or paperboard articles

5. Petroleum products

16. Crude petroleum

17. Gasoline and aviation turbine fuel

18. Fuel oils

19. Coal and petroleum products

6. Stone, clay, glass, concrete 31. Nonmetallic mineral products

7. Metal durables

34. Machinery

35. Electronic and other electrical equipment and components and office 

equipment

36. Motorized and other vehicles (including parts)

37. Transportation equipment

38. Precision Instruments and Apparatus

8. Other manufacturing

8. Alcoholic beverages

9. Tobacco Products

25. Logs and other wood in the rough

26. Wood products

29. Printed products

30. Textiles, leather, and articles of textiles or leather

39. Furniture, mattresses and mattress supports, lamps, lighting fittings, and 

illuminated signs

40. Miscellaneous manufactured products

43. Mixed freight

9. Agriculture

1. Live animals and live fish

2. Cereal grains

3. Other agricultural products

4. Animal feed and products of animal origin

10. Mining

10. Monumental or building stone

11. Natural sands

12. Gravel and crushed stone

13. Nonmetallic minerals 

14. Metallic ores and concentrates

15. Coal
21

EIA then employs a series of steps to calculate the ton-miles metric by census division and TSIC code. 

Figure 3, Figure 4, and Figure 5 diagram the current processes for converting raw data to the ton-mile 

metrics for each respective mode, including truck, rail, and domestic marine. These process flowcharts 

are similar among the modes—with the exception of some inputs—but their calculations are done 

entirely separately. The appropriateness of the complete separation of these modes, particularly 

between truck and rail, will be further explored later in this section.

The primary input of interest in this study is the CFS share, which appears in the top right corner of each 

of the modal diagrams. As detailed previously, the CFS sample is used as proxy or benchmark for the 

distribution of ton-mile shares by commodity and census division. This study has already documented 

the limitations of CFS for informing this analysis, and the replacement of CFS with FAF will be detailed 

later in this section. 

IHS will not recommend replacing any additional inputs to the current process. As referenced previously 

in this section, however, we will make suggestions and recommendation for the enhancements of some 

elements of the model, which may involve inputting new and additional data and/or updating and 

changing various processes.
22

Figure 3: NEMS Freight Module Diagram (Truck)
23

Figure 4: NEMS Freight Module Diagram (Rail)
24

Figure 5: NEMS Freight Module Diagram (Domestic Marine)
25

Availability and Use of Data: FAF as a NEMS Solution

The primary take-away from the workshop discussion was that, at the very least, EIA should replace the 

CFS inputs to the NEMS freight transportation module with corresponding inputs from FAF. This is due 

to the improvements made in producing FAF over and above the data that is collected in the CFS. As 

detailed in Figure 6, FAF supplements CFS with other external resources to cover a number of holes in 

the survey. The additional flows reported in FAF are primarily concentrated in three areas that are 

under-represented in CFS: truck flows for agriculture and many non-manufacturing goods; multi-modal 

crude petroleum, petroleum products, and natural gas flows; and any international flows. 

Figure 6: FAF Flow Matrix3

 

3

Source: Southworth, F., Peterson, B.E., Hwang, H., Chin, S., and Davidson, D. (2011). The Freight Analysis 

Framework Version 3 (FAF3). Prepared for the Federal Highway Administration, Washington, DC. Accessed 16 April 

2014 at: http://faf.ornl.gov/fafweb/Data/FAF3ODDoc611.pdf. 
26

Based on recommendations of the experts present at the workshop and the analysis of IHS’s freight 

transportation modeling experts, this section explores the use of FAF in place of CFS to improve the tonmile share metrics in the NEMS freight transportation module. Additionally, IHS provides a 

demonstration of the results by replicating the current NEMS freight transportation module data 

preparation process using FAF data rather than CFS.

The full FAF origin/destination datasets were used for 1997, 2002, and 2007 at the state level. The 

procedure for the development of these shares is as follows:

1. State-to-state flows at the SCTG commodity level are transposed into a state-by-commodity 

table, which also takes the step of assigning 50% of flows to the origin state and 50% of the 

flows to the destination state, according to the procedure for calculating CFS shares.4

2. FAF multi-modal flows5are distributed among the CFS multi-modal pair categories (e.g., rail-totruck, etc.) as a ratio consistent with the shares of multi-modal ton-miles associated with each 

relevant CFS bi-modal category. This is done because while CFS reports multi-modal flows by bimodal category, while FAF does not. IHS developed ratios by mode and commodity.

3. Multi-modal ton-miles are then distributed among each mode in each bi-modal category as a 

50-50% split, replicating the process employed by EIA for distributing CFS-derived bi-modal tonmile shares within each category. 

4. The tables are aggregated from the SCTG commodity level to the TSIC commodity according to 

the commodity group map in Table 2. Coal (SCTG 15) is omitted from TSIC 10 for the rail tables 

in each year6.

5. The tables are aggregated from the state level to the census division level according to the 

census division map in Figure 2.

6. Total commodity flows are divided by totals of all commodity groups and census divisions to

calculate ton-mile shares.

The census division and commodity group shares calculated from FAF are detailed in this report, as well 

as in a separate spreadsheet deliverable. Table 3, Table 4, and Table 5 contain the aggregated ton-mile 

shares using 1997 FAF data for truck, rail, and domestic marine, respectively. Table 6, Table 7, and Table 

8 contain the aggregated ton-mile shares using 2002 FAF for truck, rail, and domestic marine, 

respectively. Table 9, Table 10, and Table 11 contain the aggregated ton-mile shares using 2007 FAF for 

truck, rail, and domestic marine, respectively.

 

4

For the purposes of demonstration, the current 50-50% share is maintained. An improved network assignment 

process will be discussed later in this section.

5

FAF aggregates multi-modal flows as a single category, while CFS shares these out among multi-modal categories. 

CFS does not, however, distribute the ton mileage aboard each mode pair. In CFS, various multi-modal categories 

account for about 12.5% of total ton-miles. These flows account for only 6.5% of domestic ton-miles in FAF.

6

This report will recommend removing the process of aggregating to 10 TSIC groups. For the purposes of 

demonstrating the impacts of replacing CFS with FAF, however, we maintain the current commodity aggregation 

process.
27

Table 3: FAF Truck Shares (1997)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.46% 0.40% 0.26% 0.20% 0.26% 0.17% 0.20% 0.77% 0.25% 0.38% 3.35%

2 0.85% 1.26% 0.93% 0.40% 0.72% 0.67% 0.48% 1.21% 0.58% 1.75% 8.84%

3 1.46% 2.49% 1.36% 0.73% 1.11% 1.38% 1.13% 1.82% 2.03% 3.10% 16.61%

4 0.60% 0.85% 1.03% 0.23% 0.58% 0.60% 0.41% 1.23% 3.73% 1.47% 10.72%

5 1.48% 1.54% 1.23% 0.62% 1.15% 1.62% 0.79% 3.79% 1.38% 2.98% 16.57%

6 0.47% 0.86% 0.47% 0.28% 0.48% 0.61% 0.41% 1.45% 0.76% 1.71% 7.50%

7 1.45% 1.54% 0.96% 0.45% 2.06% 0.99% 0.76% 2.28% 2.19% 2.13% 14.81%

8 0.42% 0.55% 0.43% 0.13% 0.83% 0.59% 0.35% 1.02% 0.98% 1.13% 6.45%

9 1.40% 1.42% 1.30% 0.58% 2.18% 0.95% 1.05% 2.75% 1.65% 1.86% 15.14%

∑ 8.59% 10.91% 7.98% 3.61% 9.38% 7.59% 5.59% 16.31% 13.54% 16.51% 100%

Table 4: FAF Rail Shares (1997)

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.15% 0.28% 0.08% 0.14% 0.10% 0.06% 0.03% 0.25% 0.10% 0.15% 0.08% 1.42%

2 0.65% 0.64% 0.39% 0.28% 0.49% 0.08% 0.16% 0.37% 0.19% 0.43% 1.28% 4.95%

3 1.72% 1.42% 0.81% 0.80% 0.60% 0.20% 0.74% 0.62% 2.10% 1.58% 5.53% 16.11%

4 1.15% 0.53% 0.91% 0.25% 0.85% 0.11% 0.20% 0.50% 4.35% 1.37% 3.19% 13.41%

5 2.21% 0.57% 0.51% 0.52% 0.33% 0.22% 0.22% 0.79% 1.00% 1.24% 6.81% 14.43%

6 0.54% 0.51% 0.22% 0.39% 0.31% 0.13% 0.08% 0.36% 0.39% 0.32% 3.53% 6.79%

7 3.06% 0.96% 0.43% 0.44% 0.83% 0.22% 0.31% 0.83% 1.97% 1.04% 3.87% 13.96%

8 1.18% 0.41% 0.20% 0.37% 0.32% 0.23% 0.20% 0.76% 0.45% 0.76% 14.08% 18.95%

9 1.47% 0.80% 0.85% 0.48% 0.89% 0.32% 0.34% 1.40% 2.93% 0.34% 0.17% 9.98%

∑ 12.12% 6.11% 4.40% 3.67% 4.72% 1.57% 2.27% 5.88% 13.48% 7.23% 38.53% 100%

Table 5: FAF Domestic Marine Shares (1997)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.08% 0.03% 0.02% 0.01% 0.27% 0.02% 0.03% 0.06% 0.07% 0.08% 0.67%

2 0.21% 0.17% 0.09% 0.03% 1.13% 0.04% 0.11% 0.16% 0.25% 2.41% 4.59%

3 0.90% 0.26% 0.23% 0.05% 0.79% 0.55% 0.21% 0.17% 5.44% 4.96% 13.56%

4 0.18% 0.07% 0.09% 0.01% 0.84% 0.09% 0.07% 0.08% 3.42% 1.76% 6.63%

5 1.11% 0.13% 0.17% 0.07% 3.27% 0.17% 0.14% 0.23% 0.55% 3.17% 9.01%

6 0.47% 0.20% 0.10% 0.06% 2.55% 0.23% 0.08% 0.39% 0.91% 4.27% 9.25%

7 3.29% 0.35% 0.35% 0.05% 11.03% 0.26% 0.08% 0.29% 9.26% 2.24% 27.20%

8 0.03% 0.03% 0.01% 0.01% 0.84% 0.01% 0.04% 0.06% 0.03% 0.89% 1.95%

9 0.52% 0.42% 0.42% 0.15% 22.47% 0.17% 0.37% 0.64% 1.56% 0.43% 27.14%

∑ 6.79% 1.66% 1.48% 0.43% 43.19% 1.54% 1.13% 2.08% 21.49% 20.22% 100%
28

Table 6: FAF Truck Shares (2002)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.39% 0.28% 0.29% 0.20% 0.22% 0.15% 0.12% 0.57% 0.22% 0.26% 2.70%

2 1.41% 1.21% 1.10% 0.40% 0.66% 0.56% 0.53% 1.40% 0.56% 1.40% 9.23%

3 1.59% 2.48% 1.73% 0.60% 1.05% 1.22% 1.69% 2.05% 1.69% 2.86% 16.97%

4 0.75% 0.77% 1.25% 0.20% 0.51% 0.86% 0.50% 1.01% 3.18% 1.39% 10.42%

5 1.65% 1.49% 1.41% 0.69% 0.87% 1.28% 0.85% 3.44% 1.20% 2.66% 15.55%

6 0.72% 0.92% 0.60% 0.31% 0.69% 0.56% 0.55% 1.58% 0.69% 1.12% 7.74%

7 1.85% 1.71% 1.15% 0.36% 1.75% 1.13% 0.98% 2.03% 1.80% 1.86% 14.63%

8 0.60% 0.53% 0.61% 0.14% 0.89% 0.50% 0.34% 1.16% 0.88% 1.10% 6.75%

9 1.44% 1.42% 1.66% 0.46% 1.87% 1.28% 1.09% 2.97% 1.69% 2.13% 16.00%

∑ 10.41% 10.81% 9.80% 3.35% 8.51% 7.55% 6.67% 16.22% 11.92% 14.78% 100%

Table 7: FAF Rail Shares (2002)

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.10% 0.09% 0.04% 0.16% 0.03% 0.01% 0.02% 0.22% 0.07% 0.16% 0.08% 0.98%

2 0.62% 0.48% 0.33% 0.21% 0.45% 0.06% 0.16% 0.37% 0.53% 0.46% 1.93% 5.59%

3 1.68% 1.23% 0.69% 0.41% 0.63% 0.18% 0.82% 0.86% 2.11% 1.70% 5.80% 16.10%

4 0.88% 0.53% 1.02% 0.26% 0.32% 0.16% 0.19% 0.70% 4.44% 1.21% 2.96% 12.66%

5 1.80% 0.60% 0.41% 0.42% 0.39% 0.30% 0.22% 0.61% 1.15% 1.28% 5.02% 12.21%

6 0.67% 0.33% 0.18% 0.32% 0.51% 0.30% 0.15% 0.25% 0.37% 0.54% 3.73% 7.36%

7 2.47% 0.60% 0.42% 0.36% 0.66% 0.30% 0.41% 0.59% 2.18% 0.90% 5.32% 14.21%

8 0.64% 0.29% 0.17% 0.12% 0.32% 0.25% 0.06% 0.56% 0.38% 0.68% 16.62% 20.09%

9 1.03% 0.68% 0.81% 0.28% 0.86% 0.24% 0.47% 1.97% 3.17% 0.64% 0.65% 10.80%

∑ 9.89% 4.83% 4.07% 2.53% 4.17% 1.79% 2.49% 6.13% 14.41% 7.56% 42.12% 100%

Table 8: FAF Domestic Marine Shares (2002)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.05% 0.02% 0.01% 0.01% 0.13% 0.01% 0.03% 0.05% 0.08% 0.03% 0.40%

2 0.23% 0.77% 0.08% 0.02% 2.57% 0.06% 0.17% 0.28% 0.17% 1.32% 5.67%

3 1.09% 1.08% 0.29% 0.05% 1.20% 0.44% 0.47% 0.39% 5.14% 6.41% 16.56%

4 0.23% 0.69% 0.07% 0.01% 0.40% 0.15% 0.07% 0.11% 7.21% 1.95% 10.89%

5 0.45% 0.21% 0.21% 0.07% 2.82% 0.15% 0.19% 0.32% 0.44% 1.41% 6.26%

6 0.51% 1.09% 0.16% 0.03% 1.99% 0.20% 0.11% 0.15% 0.69% 2.43% 7.35%

7 2.99% 1.16% 0.41% 0.04% 9.56% 0.24% 0.30% 0.21% 12.84% 2.13% 29.87%

8 0.05% 0.03% 0.02% 0.01% 1.18% 0.02% 0.04% 0.07% 0.03% 1.12% 2.56%

9 0.50% 0.53% 0.59% 0.12% 14.88% 0.22% 0.62% 0.95% 1.21% 0.82% 20.44%

∑ 6.10% 5.58% 1.85% 0.35% 34.73% 1.47% 1.98% 2.53% 27.79% 17.62% 100%
29

Table 9: FAF Truck Shares (2007)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.44% 0.32% 0.31% 0.17% 0.30% 0.14% 0.14% 0.46% 0.22% 0.34% 2.84%

2 1.03% 1.52% 1.18% 0.39% 0.72% 0.40% 0.55% 1.53% 0.69% 1.40% 9.42%

3 1.78% 2.58% 1.57% 0.65% 0.72% 0.83% 1.41% 1.86% 2.29% 2.27% 15.95%

4 0.81% 0.97% 1.14% 0.20% 0.46% 0.58% 0.51% 0.94% 3.85% 1.42% 10.87%

5 1.86% 1.73% 1.49% 0.71% 0.96% 1.29% 0.89% 3.00% 1.28% 2.56% 15.77%

6 0.65% 1.02% 0.54% 0.30% 0.50% 0.46% 0.48% 1.39% 0.53% 1.85% 7.71%

7 1.97% 1.96% 1.20% 0.41% 1.85% 0.93% 1.00% 1.80% 1.69% 1.84% 14.65%

8 0.58% 0.65% 0.60% 0.15% 0.51% 0.67% 0.33% 1.07% 1.24% 1.19% 7.00%

9 1.50% 1.86% 1.95% 0.56% 0.96% 1.19% 1.34% 2.96% 1.99% 1.47% 15.79%

∑ 10.62% 12.61% 9.98% 3.53% 6.99% 6.47% 6.66% 15.02% 13.78% 14.33% 100%

Table 10: FAF Rail Shares (2007)

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.16% 0.10% 0.03% 0.12% 0.03% 0.01% 0.02% 0.11% 0.03% 0.09% 0.02% 0.72%

2 0.78% 0.50% 0.24% 0.19% 0.34% 0.03% 0.10% 0.39% 0.36% 0.22% 0.97% 4.12%

3 1.64% 0.90% 0.45% 0.37% 0.66% 0.15% 0.57% 0.54% 1.79% 1.54% 6.67% 15.28%

4 1.06% 0.44% 0.85% 0.22% 0.29% 0.11% 0.09% 0.62% 4.07% 1.05% 4.33% 13.14%

5 1.50% 0.44% 0.27% 0.37% 0.38% 0.15% 0.13% 0.38% 0.72% 0.65% 6.48% 11.47%

6 0.78% 0.42% 0.11% 0.25% 0.18% 0.06% 0.10% 0.17% 0.32% 0.49% 4.32% 7.22%

7 3.03% 0.66% 0.40% 0.42% 1.40% 0.11% 0.29% 0.43% 1.92% 1.37% 5.50% 15.54%

8 1.37% 0.34% 0.18% 0.11% 0.51% 0.16% 0.10% 0.41% 0.53% 0.65% 19.90% 24.26%

9 1.07% 0.57% 0.70% 0.38% 0.83% 0.17% 0.35% 1.15% 2.35% 0.31% 0.35% 8.25%

∑ 11.41% 4.38% 3.25% 2.44% 4.62% 0.94% 1.76% 4.21% 12.09% 6.37% 48.54% 100%

Table 11: FAF Domestic Marine Shares (2007)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.02% 0.02% 0.01% 0.03% 0.28% 0.00% 0.02% 0.05% 0.01% 0.02% 0.46%

2 0.77% 0.17% 0.06% 0.05% 1.57% 0.02% 0.04% 0.12% 0.05% 1.61% 4.46%

3 0.96% 0.42% 0.09% 0.08% 2.50% 0.50% 0.14% 0.27% 5.04% 6.15% 16.14%

4 0.17% 0.05% 0.17% 0.02% 0.87% 0.19% 0.05% 0.12% 6.36% 2.10% 10.09%

5 0.58% 0.14% 0.07% 0.10% 2.49% 0.06% 0.08% 0.16% 0.36% 1.35% 5.39%

6 0.58% 0.62% 0.03% 0.05% 1.79% 0.14% 0.04% 0.06% 1.51% 2.55% 7.36%

7 3.63% 0.75% 0.16% 0.07% 9.86% 0.30% 0.06% 0.23% 12.41% 2.09% 29.56%

8 0.06% 0.04% 0.03% 0.02% 0.11% 0.07% 0.04% 0.10% 0.02% 0.37% 0.87%

9 0.26% 0.15% 0.34% 0.13% 21.43% 0.24% 0.18% 0.61% 0.76% 1.57% 25.67%

∑ 7.03% 2.37% 0.95% 0.55% 40.90% 1.51% 0.67% 1.72% 26.52% 17.79% 100%
30

Figure 7, Figure 8, and Figure 9 illustrate shares of flows by census division for truck, rail, and domestic 

marine modes, respectively, for 1997, 2002, and 2007. These figures show that, generally, FAF shares 

are stable across survey years. The most noticeable exception is domestic marine in the Pacific census 

division, which has an extreme dip in 2002. Rail flows in the Mountain census division (CD 8) are the 

most prominent out of all other census divisions due to the coal extraction in that region, but there are 

virtually no domestic marine flows in CD 8 due to lack of commercially navigable inland waterways.

Figure 7: Truck Flows by Census Division

Figure 8: Rail Flows by Census Division

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

CD 1 CD 2 CD 3 CD 4 CD 5 CD 6 CD 7 CD 8 CD 9

Percent of Ton-miles

Census Division

FAF 1997 FAF 2002 FAF 2007

0%

5%

10%

15%

20%

25%

30%

CD 1 CD 2 CD 3 CD 4 CD 5 CD 6 CD 7 CD 8 CD 9

Percent of Ton-miles

Census Division

FAF 1997 FAF 2002 FAF 2007
31

Figure 9: Domestic Marine Flows by Census Division

Table 12, Table 13, and Table 14 illustrate the change in percent ton-mile shares attributable to each 

census division and TSIC classification using FAF instead of CFS for 2007. The purpose of these tables is 

to illustrate how the use of FAF changes the relative allocation of shares of ton-mileage across 

commodities and census divisions compared to the calculation of ton-mile shares using CFS. These 

changes in relative shares do not address the absolute total ton-miles reported by EIA, which are derived 

from other sources (e.g., the FHWA National Transportation Statistics for highway VMT). Hence, 

replacing the CFS-derived ton-mile shares in the NEMS freight transportation module with the FAFderived ton-mile shares will simply re-allocate the national ton-mile/VMT forecast by commodity and 

census division. The use of alternative data sets for NEMS for the top-line national freight figures will be 

addressed in the conclusion of this report.

Table 12 – 2007 Difference in Ton-mile Shares Using FAF Versus CFS, Truck

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.22% 0.14% -0.14% -0.03% 0.14% 0.01% 0.06% 0.04% 0.14% 0.18% 0.77%

2 -0.19% 0.45% -0.82% -0.15% 0.22% 0.00% 0.02% -0.01% 0.23% 0.33% 0.08%

3 -0.72% -0.54% -1.44% -0.50% 0.11% -0.22% -0.50% -0.50% 1.46% 0.64% -2.22%

4 -0.02% 0.33% -0.76% -0.08% 0.06% -0.03% -0.06% -0.07% 2.08% 0.11% 1.57%

5 -0.48% 0.42% -0.91% -0.40% 0.17% -0.16% -0.03% -0.43% 0.46% 0.65% -0.71%

6 -0.20% -0.01% -0.41% -0.26% 0.17% -0.19% -0.17% -0.09% 0.34% 0.50% -0.32%

7 -0.50% 0.08% -0.89% -0.28% 0.35% -0.39% -0.10% -0.40% 0.98% 0.31% -0.84%

8 0.01% 0.29% -0.22% -0.02% 0.09% -0.03% 0.15% -0.15% 0.83% 0.36% 1.30%

9 -0.02% 1.01% -1.61% -0.36% 0.05% 0.12% 0.33% -0.30% 0.66% 0.49% 0.37%

∑ -1.90% 2.17% -7.19% -2.08% 1.37% -0.88% -0.29% -1.92% 7.16% 3.57% 0.00%

0%

5%

10%

15%

20%

25%

30%

35%

40%

CD 1 CD 2 CD 3 CD 4 CD 5 CD 6 CD 7 CD 8 CD 9

Percent of Ton-miles

Census Division

FAF 1997 FAF 2002 FAF 2007
32

Table 13 – 2007 Difference in Ton-mile Shares Using FAF Versus CFS, Rail

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.06% 0.08% 0.02% 0.04% 0.02% 0.01% 0.02% 0.04% 0.01% 0.03% 0.02% 0.35%

2 0.32% 0.36% 0.05% 0.12% 0.07% 0.02% 0.09% 0.20% 0.11% 0.18% -0.07% 1.46%

3 0.45% 0.39% 0.19% 0.16% 0.25% 0.06% 0.38% 0.32% 0.91% 0.72% -2.57% 1.26%

4 0.72% 0.29% 0.03% 0.16% 0.19% 0.03% 0.06% 0.37% 1.61% 0.33% -1.58% 2.20%

5 0.51% 0.30% 0.16% 0.14% 0.26% 0.05% 0.03% 0.25% 0.26% 0.32% -2.29% -0.02%

6 0.10% 0.17% 0.07% 0.05% 0.05% 0.03% 0.08% 0.13% 0.17% 0.16% -1.23% -0.20%

7 -0.13% 0.33% 0.07% 0.04% 0.26% 0.05% 0.24% 0.22% 0.66% 0.15% -3.12% -1.23%

8 1.06% 0.29% 0.11% 0.05% 0.12% 0.07% 0.08% 0.16% 0.30% 0.44% -9.98% -7.28%

9 0.57% 0.37% 0.09% 0.03% 0.47% 0.15% 0.24% 0.41% 1.34% 0.11% -0.30% 3.48%

∑ 3.67% 2.59% 0.79% 0.79% 1.67% 0.46% 1.23% 2.10% 5.37% 2.45% -21.12% 0.00%

Table 14 – 2007 Difference in Ton-mile Shares Using FAF Versus CFS, Domestic Marine

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.02% 0.02% 0.01% 0.03% 0.28% 0.00% 0.02% 0.05% 0.01% 0.02% 0.46%

2 0.77% 0.17% 0.06% 0.05% 1.56% 0.02% 0.03% 0.10% 0.05% -1.40% 1.42%

3 0.44% -0.02% 0.09% 0.08% 2.31% 0.15% 0.14% -0.17% -4.60% -3.34% -4.92%

4 -0.16% 0.05% 0.17% 0.02% 0.64% -0.07% 0.04% 0.12% -7.46% -3.69% -10.35%

5 0.58% 0.07% 0.07% 0.09% 0.73% 0.01% 0.08% 0.12% 0.36% 0.85% 2.97%

6 0.38% 0.13% 0.03% 0.05% 0.60% -0.09% 0.04% 0.06% -0.54% -1.79% -1.12%

7 1.96% 0.45% 0.00% 0.07% 3.77% 0.28% 0.06% -0.23% -12.88% -2.21% -8.73%

8 0.05% 0.04% 0.02% 0.02% 0.11% 0.07% 0.04% 0.02% 0.02% 0.37% 0.77%

9 0.18% 0.08% -0.50% 0.10% 20.14% -0.20% -0.22% -0.05% 0.70% -0.74% 19.49%

∑ 4.21% 1.00% -0.05% 0.52% 30.14% 0.17% 0.25% 0.02% -24.34% -11.92% 0.00%

Using FAF in place of CFS for calculating ton-mile shares makes a non-trivial impact on the resulting 

matrices, particularly for certain TSIC groups. For trucks, the most notable shifts are in the increased 

overall shares attributable to agriculture (TSIC 9), mining products (TSIC 10), and primary metals (TSIC 2). 

This conforms to the hypothesis that FAF better captures non-manufactured goods moving on trucks, 

which are under-represented in CFS. Interestingly, much of the percentage share increases if agriculture 

appears to come at the relative expense of processed foods and beverages (TSIC 3). It is important to 

reiterate, however, that a decline in relative share is not synonymous with a decline in absolute tonmiles when comparing the CFS and FAF forecast data sets. Again, FHWA estimates that the FAF forecast

counts 50 percent greater ton-miles than CFS. If EIA continues to use the same source in the NEMS 

freight transportation module for total national ton-miles and VMT; however, the FAF ton-mile metrics 

will reduce or increase the total freight figures for each census division-commodity combination by the 

percentage shares deltas.
33

For rail, a dramatic shift occurs in the share of ton-mile shares from coal to all other TSIC groups. 

Moreover, and intuitively, a large geographic shift in ton-mile shares occurs from the Mountain Region 

(CD 8) where much of the coal is mined to other parts of the country, especially the East and West 

coasts. Interestingly, the absolute total ton-mile forecasts for coal, specifically, in CFS and FAF are nearly 

identical. Therefore, the lower shares of coal in CFS are entirely due to increases in ton-mile estimates 

for other TSIC groups. Again, agriculture, mining products, and primary metals enjoy large share gains,

conforming to the assumption that FAF captures these freight flows better than CFS alone. Chemicals 

(TSIC 1), many of which are imported and exported, also not surprisingly see relative gains.

Domestic marine ton-mile shares experience dramatic shifts in three TSIC categories and three census 

division regions. With respect to commodities, agriculture (TSIC 9) and mining products (TSIC 10, 

including coal) ton-mile shares fall dramatically, while crude and petroleum products (TSIC 5) rise 

substantially. Most of the relative gains are in the Pacific census division (CD 9), while most of the 

relative losses are in census divisions adjacent to and immediately west of the Mississippi River (CDs 4 

and 7). IHS hypothesizes that FAF is capturing a higher quantity of marine flows of crude oil from Alaska 

to other West Coast refineries. This is plausible given that the US West Coast generally receives 

substantial portions of feedstocks from Alaska and given CFS’s weaknesses in capturing crude. Since 

agriculture and coal constitute such a high percentage of freight flows recorded on U.S. inland 

waterways, especially via the Mississippi River, it is logical that these commodities and regions would fall 

the greatest by increasing crude and petroleum volumes, all else equal.

In the previous tables, IHS reviewed relative differences in ton-mile shares using FAF versus CFS for all 

modes in 2007. Approximately similar trends are apparent in previous year estimates. These 

comparisons are included in Appendix B.

Without complete knowledge of how FHWA develops FAF, IHS cannot validate the appropriateness of 

using FAF versus CFS for the NEMS freight transportation module ton-mile share calculation. Our 

analysis, however, identifies specific weaknesses that are possibly related to using CFS data and 

demonstrates how some of these weaknesses appear to be at least partially addressed with FAF. First, 

the FAF dataset is larger and richer, while it is generally acknowledged that CFS poorly captures at least 

some commodity types and flows. This may distort ton-mile share calculations. Second, EIA can benefit 

from working with professional staff at FHWA who are familiar with both data sets and, after having 

attended the workshop, have an understanding of EIA’s specific needs and objectives. Third, employing 

FAF in place of CFS to calculate the ton-mile metric is a simple solution, whereby EIA need only change 

the source data but otherwise maintains all existing NEMS freight transportation module processes.

Given the low level-of-effort required to run the NEMS freight transportation module with FAF data as 

the source of the ton-mile share, IHS recommends that EIA employ (at least internally) both approaches 

for a period of time to determine which data set offers more accurate forecasts. If EIA determines that 

fundamental problems remain that may be related to the use of either CFS or FAF for the ton-mile share 

calculation, the agency can reassess whether to develop a different data set or modeling procedure.
34

Employing a Route Optimization Proxy to Improve Network Assignment

There are many possible ways to improve upon the current 50-50% assignment of ton-mileage between 

origin and destination census divisions. As previously described, state-of-the-practice techniques would 

likely require a more fundamental change to the current design of the NEMS freight transportation 

module and, possibly, to the NEMS model itself. In the workshop, however, experts suggested possible 

approaches that could improve accuracy with minimal additional effort and resources and without 

fundamentally altering NEMS or the NEMS freight transportation module.

In this sub-section, IHS demonstrates a relatively simple approach to improving the network assignment 

for truck ton-mileage. This approach involves developing a general (not commodity-specific) “optimal” 

assignment of goods movement onto the road network for all state-by-state origin/destination pairs. For 

each of these origin/destination pairs, the process calculates the total mileage within each state along

the optimized route and divides this figure by the total route mileage. The result is a percentage of 

distance traveled through each state, which can be used as a proxy for ton-mileage. State-by-state tonmile shares can then be aggregated to the census division levels using a weighted average of the market 

shares of each corresponding state-by-state pair therein.7

In this study, IHS provides an example for truck freight only. There are several reasons why a simple 

network assignment model as described in the previous paragraph may work for truck. First, there are 

numerous truck movements in every state over a dense roadway network such that the designing of a 

single “average” route might offer a close enough approximation of overall behavior. Second, truck 

freight movements tend to follow relatively simple rules, especially for long-haul shipments. IHS 

believes, essentially, that a shortest path routing approximates the typical decision-making paradigm for 

truckers, or at least sufficiently for the purposes of estimating ton-mile allocations to the network. 

IHS applied the following procedure to develop a network assignment matrix to estimate ton-mile 

allocations across each census division for each state-by-state origin/destination pair:

1. Load a transportation network shape file into a GIS-based software with a transportation 

network analysis capability8,

2. Develop centroids9for each state,

3. Map the centroids onto the transportation network, (See Figure 10)

4. Estimate the shortest paths between state-by-state centroids (See example in Figure 11, which 

illustrates, as an example, the shortest path for all Alabama origin-destination truck flows) 10,

 

7

Total FAF ton-mileage associated with each corresponding state-by-state pair is a logical benchmark to apply for 

the weighted average calculation.

8

For the first three steps in this process, IHS used ArcGIS software. 

9

Centroids are essentially a central location in a given area which is used as the proxy origin or destination point 

for all flows. For this example, IHS used the geographic center of each state as the centroid. More complex 

analyses might include a weighted measure by population or origination of freight activity, etc.

10 For this study, IHS applied a shortest-path algorithm using TransCAD software. Other algorithms could also be 

used (e.g., lowest, cost, etc.).
35

5. Overlay a map of the shortest paths with the census divisions to determine the share of goods 

movement between any two states in each census division. 

Figure 10: Lower 48 States with Geographic Centroids Mapped with the Primary Road Network
36

Figure 11: Commodity Agnostic Shortest Path Estimate for all Alabama Origin/Destination Truck Flows

IHS then joined the network assignment matrix results to the FAF data sets for 1997, 2002, and 2007 to 

estimate new ton-mile shares. The procedure is as follows:

1. Create a data set using the FAF ton-mile records of each instance of a freight shipment between 

each state,

2. Join the census division allocations estimated in the network assignment matrix (e.g., nine new 

fields) to each shipment record,

3. For each record, create nine new fields containing the products of the total ton-mileage

reported and the origin/destination-specific network assignment allocations associated with 

each of the nine census divisions,

4. For all records, sum the total ton-miles associated with each census division and TSIC group,

5. Divide each census-division-TSIC ton-mileage datum by the sum of all ton-mileage in FAF.

The following tables illustrate the results of the new network assignment procedure for each of the 

three most recent FAF base years and compare these to the FAF-based estimates calculated with 50-

50% census division network assignments. All previous model steps and updates, including the 

procedures for allocating FAF multi-modal flows to bi-modal classifications and allocating these tonmiles to individual modes on a 50-50% basis, are maintained. 
37

Table 15: 1997 FAF Truck Ton-Mile Shares Calculated by Roadway Shortest Path

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.15% 0.22% 0.12% 0.09% 0.22% 0.12% 0.07% 0.47% 0.13% 0.25% 1.84%

2 0.60% 1.04% 0.69% 0.34% 0.65% 0.70% 0.35% 1.11% 0.45% 1.71% 7.64%

3 1.50% 2.59% 1.44% 0.74% 1.16% 1.46% 1.16% 2.29% 2.32% 3.41% 18.08%

4 0.99% 1.09% 1.27% 0.40% 0.70% 0.77% 0.72% 1.64% 3.70% 1.50% 12.80%

5 1.33% 1.49% 1.12% 0.57% 1.13% 1.36% 0.61% 3.26% 1.28% 2.91% 15.06%

6 0.90% 1.16% 0.66% 0.38% 0.55% 0.72% 0.48% 1.63% 0.97% 1.81% 9.25%

7 1.32% 1.36% 0.89% 0.38% 2.01% 0.95% 0.78% 2.26% 2.00% 2.01% 13.96%

8 1.20% 0.97% 1.07% 0.37% 0.86% 0.80% 0.95% 1.75% 1.57% 1.29% 10.83%

9 0.60% 0.99% 0.71% 0.33% 2.09% 0.72% 0.47% 1.90% 1.12% 1.61% 10.55%

∑ 8.59% 10.91% 7.98% 3.61% 9.38% 7.59% 5.59% 16.31% 13.54% 16.51% 100%

Table 16: 1997 FAF Truck Ton-Mile Shares – Comparing Shortest Path Assignment to 50-50% Split 

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 -0.31% -0.18% -0.14% -0.11% -0.05% -0.05% -0.12% -0.30% -0.12% -0.13% -1.51%

2 -0.25% -0.21% -0.24% -0.06% -0.07% 0.03% -0.13% -0.10% -0.13% -0.04% -1.20%

3 0.03% 0.10% 0.09% 0.02% 0.05% 0.08% 0.03% 0.47% 0.30% 0.31% 1.46%

4 0.40% 0.24% 0.25% 0.17% 0.12% 0.16% 0.31% 0.41% -0.03% 0.04% 2.08%

5 -0.15% -0.05% -0.11% -0.05% -0.01% -0.26% -0.18% -0.53% -0.10% -0.06% -1.51%

6 0.43% 0.30% 0.18% 0.11% 0.07% 0.10% 0.07% 0.18% 0.21% 0.10% 1.75%

7 -0.13% -0.18% -0.07% -0.06% -0.05% -0.05% 0.02% -0.02% -0.19% -0.12% -0.85%

8 0.78% 0.42% 0.64% 0.24% 0.03% 0.21% 0.59% 0.73% 0.59% 0.15% 4.38%

9 -0.80% -0.43% -0.60% -0.25% -0.09% -0.22% -0.59% -0.84% -0.53% -0.25% -4.60%

∑ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Table 17: 2002 FAF Truck Ton-Mile Shares Calculated by Roadway Shortest Path

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.11% 0.16% 0.13% 0.09% 0.18% 0.08% 0.06% 0.33% 0.11% 0.21% 1.45%

2 0.72% 0.91% 0.81% 0.34% 0.59% 0.43% 0.37% 1.05% 0.38% 1.21% 6.82%

3 1.66% 2.55% 1.88% 0.65% 1.10% 1.26% 1.64% 2.20% 1.95% 3.25% 18.14%

4 1.19% 1.03% 1.54% 0.31% 0.57% 1.01% 0.87% 1.62% 3.24% 1.40% 12.79%

5 1.79% 1.43% 1.21% 0.65% 0.85% 1.19% 0.69% 3.13% 1.07% 2.63% 14.63%

6 1.49% 1.16% 0.79% 0.39% 0.68% 0.63% 0.61% 1.75% 0.90% 1.22% 9.62%

7 1.47% 1.57% 1.09% 0.34% 1.77% 1.11% 0.97% 2.08% 1.64% 1.79% 13.84%

8 1.36% 1.08% 1.55% 0.33% 1.08% 0.96% 0.97% 2.30% 1.52% 1.13% 12.29%

9 0.63% 0.90% 0.79% 0.24% 1.69% 0.87% 0.49% 1.76% 1.09% 1.95% 10.42%

∑ 10.41% 10.81% 9.80% 3.35% 8.51% 7.55% 6.67% 16.22% 11.92% 14.78% 100%
38

Table 18: 2002 FAF Truck Ton-Mile Shares – Comparing Shortest Path Assignment to 50-50% Split 

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 -0.28% -0.11% -0.16% -0.11% -0.04% -0.07% -0.07% -0.24% -0.11% -0.05% -1.25%

2 -0.69% -0.30% -0.29% -0.06% -0.07% -0.13% -0.16% -0.35% -0.18% -0.19% -2.42%

3 0.07% 0.07% 0.15% 0.05% 0.05% 0.04% -0.05% 0.15% 0.26% 0.39% 1.17%

4 0.45% 0.26% 0.29% 0.12% 0.06% 0.15% 0.36% 0.61% 0.06% 0.01% 2.37%

5 0.13% -0.05% -0.20% -0.04% -0.02% -0.09% -0.16% -0.32% -0.13% -0.03% -0.92%

6 0.77% 0.24% 0.20% 0.08% -0.02% 0.07% 0.05% 0.17% 0.21% 0.10% 1.88%

7 -0.38% -0.13% -0.06% -0.02% 0.01% -0.02% -0.01% 0.04% -0.16% -0.07% -0.79%

8 0.76% 0.55% 0.94% 0.20% 0.19% 0.45% 0.62% 1.14% 0.65% 0.03% 5.53%

9 -0.81% -0.53% -0.87% -0.21% -0.17% -0.41% -0.60% -1.21% -0.60% -0.18% -5.58%

∑ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Table 19: 2007 FAF Truck Ton-Mile Shares Calculated by Roadway Shortest Path

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.13% 0.20% 0.16% 0.08% 0.29% 0.10% 0.05% 0.25% 0.08% 0.26% 1.61%

2 0.76% 1.22% 0.81% 0.32% 0.58% 0.33% 0.34% 1.08% 0.54% 1.37% 7.35%

3 1.78% 2.69% 1.70% 0.66% 0.83% 0.87% 1.36% 2.05% 2.53% 2.52% 16.99%

4 1.25% 1.16% 1.49% 0.35% 0.45% 0.56% 0.96% 1.56% 3.91% 1.33% 13.02%

5 1.60% 1.69% 1.29% 0.63% 0.88% 1.25% 0.66% 2.68% 1.12% 2.50% 14.31%

6 1.22% 1.32% 0.78% 0.41% 0.69% 0.54% 0.59% 1.64% 0.73% 2.00% 9.94%

7 1.77% 1.78% 1.14% 0.37% 1.77% 0.91% 0.99% 1.76% 1.60% 1.76% 13.84%

8 1.43% 1.10% 1.58% 0.39% 0.60% 0.94% 1.15% 2.20% 1.98% 1.25% 12.62%

9 0.68% 1.45% 1.04% 0.31% 0.89% 0.96% 0.56% 1.80% 1.29% 1.35% 10.33%

∑ 10.62% 12.61% 9.98% 3.53% 6.99% 6.47% 6.66% 15.02% 13.78% 14.33% 100%

Table 20: 2007 FAF Truck Ton-Mile Shares – Comparing Shortest Path Assignment to 50-50% Split 

(Red – Increase; Green – Decrease; Blue – Unchanged)

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 -0.31% -0.13% -0.15% -0.09% -0.01% -0.04% -0.10% -0.21% -0.13% -0.08% -1.24%

2 -0.27% -0.30% -0.38% -0.07% -0.14% -0.07% -0.21% -0.45% -0.15% -0.03% -2.07%

3 0.00% 0.11% 0.13% 0.02% 0.11% 0.04% -0.05% 0.19% 0.24% 0.25% 1.04%

4 0.44% 0.19% 0.35% 0.15% -0.01% -0.01% 0.45% 0.61% 0.06% -0.09% 2.15%

5 -0.26% -0.04% -0.20% -0.07% -0.08% -0.03% -0.23% -0.32% -0.16% -0.06% -1.46%

6 0.57% 0.31% 0.24% 0.11% 0.19% 0.08% 0.11% 0.26% 0.20% 0.15% 2.23%

7 -0.20% -0.18% -0.06% -0.04% -0.08% -0.02% -0.01% -0.05% -0.09% -0.08% -0.81%

8 0.85% 0.44% 0.97% 0.24% 0.09% 0.27% 0.82% 1.13% 0.74% 0.06% 5.62%

9 -0.82% -0.42% -0.91% -0.25% -0.07% -0.22% -0.78% -1.16% -0.70% -0.12% -5.46%

∑ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
39

A pattern emerges that is generally consistent across all three base years. The ton-mile shares drop 

along the census divisions touching the Atlantic, Pacific, and most of the Gulf coasts and increase in the 

central census divisions. The following figures illustrate these results vividly.

Figure 12: Shifts in Truck Ton-mile Share Percentages Using Network Shortest Path Network 

Assignment, 1997

Figure 13: Shifts in Truck Ton-mile Share Percentages Using Network Shortest Path Network 

Assignment, 2002
40

Figure 14: Shifts in Truck Ton-mile Share Percentages Using Network Shortest Path Network 

Assignment, 2007

These results are consistent with the hypothesis that the 50-50% network assignment of ton-mileage 

between census division origins and destinations fails to account for geographic (and energy 

consumption) movements through intermediate regions. While Census Division 7 is a centrally-located 

region, it is also a coastal region and a destination for many goods shipped across the country. These 

two offsetting forces result in a minimal impact. 

The extent to which this adjustment to the ton-mile metric calculation improves other weaknesses of 

the current NEMS freight transportation network assignment process is beyond the scope of this study. 

However, EIA can test the results over time to determine the extent to which this process generates 

greater accuracy. Other options might include further breaking down the initial route optimization to 

the FAF region (see Figure 15) rather than the state level. IHS recommends that EIA at minimum

implement and test the procedures outlined in this section.
41

Figure 15: Geographic Areas for FAF and CFS

IHS recommends further study of an improved approach to rail network assignment that can be applied 

in the NEMS environment. Rail freight network assignment is more complicated than truck because of 

the structure of the industry, where routing decisions must consider competition (or lack thereof) 

between multiple railroads with different network and operational configurations, trackage rights and 

other operational agreements, and other complex criteria. It would be difficult to estimate an “average” 

path using simple rules, but EIA might consider further study. Possibly a similar methodology used for 

truck could be applied to rail shipments, but with straight-line distances between state (or FAF-zone) 

centroids rather than network distances. This would strike a balance between capturing intermediate 

zones and avoiding venturing into rail assignment which would be a virtual black box. Nonetheless, IHS 

first recommends exploring the feasibility and benefits of a more in-depth model.

For domestic marine freight, while a 50-50% split does not accurately represent distance traveled, the 

estimate likely serves as a reasonable proxy from the standpoint of energy consumption. Presumably 

barges and maritime vessels fuel at origins at the beginning of trips and again upon making return trips 

from the destination back to the origin. Of course, vessels might be loaded in both directions, but over 

the course of numerous trips the averages may work out. Otherwise, assigning actual ton-miles to the 

network will be extraordinarily challenging. For example, nearly 40% of marine transportation tonmileage in NEMS is assigned to the Lower Mississippi census divisions (6 and 7). The Mississippi River 
42

actually forms the border between these regions, complicating the task of physically assigning tonmileage. The same issue occurs for transport along the Ohio River, which also forms a census division 

border. Ton-miles between far-away census divisions, such as the Pacific and South Atlantic, move over 

international waters for large portions of most trips. As described later, the domestic marine forecasts 

face more fundamental challenges; therefore, IHS does not recommend any changes to the process of 

domestic marine network assignment until these other issues are addressed. 

Disaggregating Census Divisions and TSIC Categories

The NEMS freight module is highly aggregated, using only the nine census divisions as geographical units 

and ten TSIC categories as commodity groupings. The level of aggregation is not necessarily believed to 

be one of the more critical problems identified in the freight module, but less aggregated inputs could 

improve model accuracy.

Disaggregation could be particularly beneficial for the accuracy of NEMS if applied to the commodity 

grouping scheme. Some of the TSIC commodity groupings contain combinations of commodities that—

while theoretically related—are not necessarily similar in terms of shipping practices. For example, 

refined petroleum products and crude petroleum are both included in TSIC 5, but are shipped from 

different states and locations, and by different modes.

For the geographical unit of the NEMS freight module output, the census division level is appropriate. 

First, the desired output for the full NEMS general equilibrium model is at the census division level. 

Second, a great degree of precision can be added to state-level estimates prior to census-division 

aggregation, which will occur before the freight module is implemented into the full iterative process. 

Disaggregating to the SCTG level is relatively simple, requiring just the removal of the TSIC aggregation 

step from the preparation of data for the NEMS freight transportation freight model. Table 21, Table 22, 

and Table 23 illustrate results of full disaggregation by SCTG level for 2007 truck, rail, and domestic 

marine ton-miles, respectively, after also employing FAF data in place of CFS for ton-mile metric 

development (for the truck example, we have used the updated ton-mile metric data after also applying 

the network assignment process described in the previous sub-section).

State-of-the-art transportation modeling employs micro-level approaches that tend to favor 

disaggregating data as much as possible; however, an argument for aggregation is to reduce potential 

estimation error and model sensitivity by aggregating small sample groups with similar behavioral 

attributes. It is challenging to generalize with small sample sizes, and there may be instances where it 

would be preferable to aggregate SCTG commodities where the total quantity shipped is very small.

It is beyond the scope of this study to recommend specific groupings of SCTG codes and, again, it is IHS 

position that disaggregation is preferable when reasonable sample sizes are available. EIA might 

consider further study of what constitutes too small a sample size so as to warrant aggregation with like 

commodities.
43

Table 21: 2007 Truck Ton-mile Shares Disaggregated by SCTG Code (with FAF Data and Shortest Path Network Assignment)

Census Division

1 2 3 4 5 6 7 8 9 ∑

SCTG Code

1 0.00% 0.02% 0.09% 0.21% 0.14% 0.08% 0.17% 0.08% 0.06% 0.85%

2 0.02% 0.17% 1.55% 2.36% 0.31% 0.26% 0.78% 0.72% 0.39% 6.56%

3 0.04% 0.25% 0.57% 0.77% 0.42% 0.24% 0.34% 0.91% 0.67% 4.21%

4 0.02% 0.10% 0.33% 0.57% 0.25% 0.15% 0.31% 0.27% 0.17% 2.16%

5 0.02% 0.11% 0.27% 0.34% 0.26% 0.18% 0.29% 0.26% 0.14% 1.86%

6 0.02% 0.14% 0.37% 0.32% 0.19% 0.16% 0.18% 0.30% 0.17% 1.84%

7 0.13% 0.57% 1.05% 0.83% 0.84% 0.44% 0.67% 1.02% 0.73% 6.28%

8 0.02% 0.11% 0.19% 0.16% 0.20% 0.06% 0.16% 0.21% 0.17% 1.27%

9 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.00% 0.00% 0.00% 0.03%

10 0.00% 0.03% 0.04% 0.01% 0.04% 0.01% 0.03% 0.05% 0.05% 0.26%

11 0.03% 0.13% 0.29% 0.25% 0.29% 0.10% 0.40% 0.15% 0.24% 1.87%

12 0.15% 0.61% 1.32% 0.61% 1.52% 1.24% 0.82% 0.48% 0.84% 7.59%

13 0.04% 0.34% 0.36% 0.18% 0.38% 0.13% 0.23% 0.28% 0.19% 2.11%

14 0.00% 0.04% 0.22% 0.06% 0.03% 0.05% 0.13% 0.06% 0.02% 0.61%

15 0.03% 0.23% 0.29% 0.22% 0.26% 0.47% 0.14% 0.24% 0.01% 1.89%

16 0.00% 0.00% 0.01% 0.01% 0.00% 0.00% 0.03% 0.03% 0.02% 0.10%

17 0.12% 0.13% 0.25% 0.11% 0.35% 0.13% 0.52% 0.14% 0.35% 2.10%

18 0.10% 0.17% 0.17% 0.12% 0.24% 0.14% 0.47% 0.15% 0.15% 1.70%

19 0.08% 0.27% 0.41% 0.21% 0.29% 0.41% 0.74% 0.28% 0.38% 3.08%

20 0.06% 0.28% 0.51% 0.32% 0.44% 0.41% 0.65% 0.46% 0.18% 3.29%

21 0.00% 0.03% 0.04% 0.04% 0.05% 0.03% 0.04% 0.04% 0.02% 0.27%

22 0.01% 0.03% 0.22% 0.22% 0.32% 0.16% 0.20% 0.14% 0.10% 1.41%

23 0.02% 0.18% 0.41% 0.28% 0.32% 0.24% 0.34% 0.33% 0.16% 2.28%

24 0.04% 0.24% 0.60% 0.40% 0.46% 0.38% 0.55% 0.47% 0.23% 3.37%

25 0.04% 0.08% 0.16% 0.06% 0.58% 0.51% 0.26% 0.09% 0.21% 1.99%

26 0.07% 0.22% 0.45% 0.39% 0.69% 0.39% 0.40% 0.60% 0.65% 3.86%
44

Census Division

1 2 3 4 5 6 7 8 9 ∑

27 0.06% 0.19% 0.41% 0.19% 0.45% 0.28% 0.21% 0.22% 0.18% 2.19%

28 0.02% 0.13% 0.25% 0.16% 0.18% 0.13% 0.16% 0.17% 0.13% 1.34%

29 0.02% 0.10% 0.19% 0.13% 0.11% 0.07% 0.06% 0.11% 0.05% 0.85%

30 0.01% 0.11% 0.17% 0.16% 0.25% 0.15% 0.18% 0.27% 0.11% 1.42%

31 0.10% 0.33% 0.87% 0.56% 1.25% 0.54% 0.91% 0.94% 0.96% 6.47%

32 0.05% 0.44% 1.28% 0.44% 0.49% 0.62% 0.67% 0.34% 0.19% 4.52%

33 0.03% 0.16% 0.45% 0.28% 0.33% 0.27% 0.46% 0.35% 0.19% 2.53%

34 0.02% 0.12% 0.40% 0.34% 0.23% 0.20% 0.34% 0.35% 0.15% 2.14%

35 0.01% 0.09% 0.23% 0.23% 0.17% 0.14% 0.32% 0.34% 0.14% 1.67%

36 0.01% 0.12% 0.68% 0.35% 0.25% 0.23% 0.29% 0.42% 0.25% 2.60%

37 0.00% 0.01% 0.03% 0.02% 0.01% 0.01% 0.02% 0.01% 0.01% 0.12%

38 0.00% 0.01% 0.02% 0.02% 0.01% 0.01% 0.02% 0.03% 0.02% 0.12%

39 0.01% 0.06% 0.15% 0.14% 0.14% 0.09% 0.13% 0.22% 0.11% 1.04%

40 0.02% 0.11% 0.28% 0.22% 0.22% 0.13% 0.19% 0.30% 0.14% 1.62%

41 0.11% 0.62% 0.95% 0.45% 0.87% 0.43% 0.66% 0.40% 1.06% 5.56%

43 0.07% 0.28% 0.46% 0.29% 0.49% 0.24% 0.37% 0.38% 0.36% 2.94%

∑ 1.61% 7.35% 16.99% 13.02% 14.31% 9.94% 13.84% 12.62% 10.33% 100%
45

Table 22: 2007 Rail Ton-mile Shares Disaggregated by SCTG Code (with FAF Data and 50-50% Origin-Destination Split)

Census Division

1 2 3 4 5 6 7 8 9 ∑

SCTG Code

1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

2 0.01% 0.26% 1.33% 3.21% 0.49% 0.20% 1.57% 0.35% 1.91% 9.31%

3 0.01% 0.04% 0.16% 0.35% 0.08% 0.07% 0.12% 0.05% 0.22% 1.09%

4 0.02% 0.06% 0.30% 0.52% 0.14% 0.05% 0.23% 0.13% 0.22% 1.68%

5 0.00% 0.01% 0.00% 0.01% 0.01% 0.00% 0.01% 0.00% 0.04% 0.08%

6 0.01% 0.05% 0.10% 0.21% 0.07% 0.01% 0.16% 0.08% 0.13% 0.83%

7 0.02% 0.18% 0.34% 0.63% 0.19% 0.10% 0.24% 0.10% 0.54% 2.34%

8 0.01% 0.12% 0.15% 0.22% 0.05% 0.01% 0.12% 0.08% 0.14% 0.90%

9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

10 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%

11 0.00% 0.02% 0.21% 0.05% 0.02% 0.02% 0.29% 0.11% 0.03% 0.75%

12 0.04% 0.04% 0.41% 0.15% 0.21% 0.22% 0.69% 0.05% 0.11% 1.91%

13 0.05% 0.10% 0.15% 0.22% 0.40% 0.11% 0.26% 0.25% 0.09% 1.63%

14 0.00% 0.05% 0.76% 0.63% 0.03% 0.15% 0.13% 0.24% 0.07% 2.06%

15 0.02% 0.97% 6.67% 4.33% 6.48% 4.32% 5.50% 19.90% 0.35% 48.54%

16 0.00% 0.01% 0.00% 0.00% 0.01% 0.00% 0.01% 0.00% 0.01% 0.04%

17 0.01% 0.01% 0.00% 0.00% 0.03% 0.01% 0.04% 0.00% 0.08% 0.17%

18 0.00% 0.00% 0.04% 0.01% 0.03% 0.01% 0.07% 0.05% 0.04% 0.25%

19 0.02% 0.33% 0.61% 0.28% 0.31% 0.16% 1.28% 0.46% 0.71% 4.17%

20 0.07% 0.50% 0.78% 0.40% 0.67% 0.56% 1.71% 1.02% 0.63% 6.34%

21 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.03%

22 0.00% 0.06% 0.39% 0.43% 0.51% 0.09% 0.34% 0.28% 0.18% 2.28%

23 0.02% 0.04% 0.11% 0.07% 0.06% 0.02% 0.14% 0.01% 0.06% 0.53%

24 0.06% 0.18% 0.37% 0.17% 0.26% 0.11% 0.83% 0.07% 0.19% 2.23%

25 0.00% 0.00% 0.01% 0.01% 0.01% 0.00% 0.01% 0.01% 0.00% 0.06%

26 0.09% 0.17% 0.27% 0.35% 0.24% 0.14% 0.25% 0.28% 0.81% 2.60%
46

Census Division

1 2 3 4 5 6 7 8 9 ∑

27 0.10% 0.13% 0.27% 0.17% 0.32% 0.22% 0.36% 0.08% 0.29% 1.93%

28 0.02% 0.07% 0.10% 0.06% 0.05% 0.03% 0.06% 0.03% 0.09% 0.51%

29 0.00% 0.01% 0.02% 0.01% 0.01% 0.00% 0.01% 0.00% 0.02% 0.07%

30 0.00% 0.06% 0.01% 0.01% 0.02% 0.00% 0.01% 0.02% 0.06% 0.19%

31 0.01% 0.03% 0.15% 0.11% 0.15% 0.06% 0.11% 0.16% 0.17% 0.94%

32 0.04% 0.22% 0.68% 0.21% 0.19% 0.30% 0.38% 0.24% 0.38% 2.65%

33 0.00% 0.03% 0.07% 0.07% 0.03% 0.04% 0.14% 0.07% 0.05% 0.51%

34 0.00% 0.01% 0.05% 0.01% 0.02% 0.01% 0.04% 0.01% 0.02% 0.17%

35 0.01% 0.01% 0.03% 0.01% 0.03% 0.01% 0.01% 0.01% 0.06% 0.18%

36 0.01% 0.07% 0.48% 0.05% 0.07% 0.08% 0.21% 0.07% 0.26% 1.30%

37 0.00% 0.01% 0.01% 0.01% 0.00% 0.00% 0.03% 0.01% 0.00% 0.09%

38 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.02%

39 0.00% 0.01% 0.01% 0.00% 0.01% 0.00% 0.01% 0.00% 0.02% 0.07%

40 0.01% 0.01% 0.02% 0.02% 0.02% 0.00% 0.02% 0.01% 0.04% 0.16%

41 0.06% 0.25% 0.14% 0.16% 0.22% 0.09% 0.14% 0.03% 0.14% 1.22%

43 0.00% 0.01% 0.05% 0.01% 0.03% 0.00% 0.01% 0.01% 0.06% 0.17%

∑ 0.72% 4.12% 15.28% 13.14% 11.47% 7.22% 15.54% 24.26% 8.25% 100%
47

Table 23: 2007 Domestic Marine Ton-mile Shares Disaggregated by SCTG Code (with FAF Data and 50-50% Origin-Destination Split)

Census Division

1 2 3 4 5 6 7 8 9 ∑

SCTG Code

1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

2 0.00% 0.02% 3.57% 4.89% 0.31% 1.20% 9.32% 0.01% 0.62% 19.94%

3 0.00% 0.01% 1.27% 1.40% 0.02% 0.29% 2.93% 0.01% 0.07% 6.01%

4 0.01% 0.01% 0.21% 0.06% 0.03% 0.02% 0.16% 0.01% 0.06% 0.57%

5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.10% 0.12%

6 0.00% 0.01% 0.02% 0.06% 0.02% 0.01% 0.05% 0.01% 0.05% 0.22%

7 0.01% 0.04% 0.06% 0.12% 0.05% 0.02% 0.11% 0.02% 0.19% 0.61%

8 0.01% 0.02% 0.14% 0.04% 0.02% 0.00% 0.13% 0.01% 0.12% 0.49%

9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

10 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%

11 0.01% 0.03% 0.16% 0.06% 0.01% 0.08% 0.13% 0.08% 0.01% 0.57%

12 0.00% 0.16% 2.43% 1.25% 0.21% 0.74% 1.08% 0.02% 0.05% 5.95%

13 0.00% 0.01% 0.63% 0.10% 0.03% 0.08% 0.63% 0.07% 0.14% 1.69%

14 0.00% 1.40% 0.47% 0.50% 0.00% 0.01% 0.12% 0.00% 1.36% 3.86%

15 0.00% 0.01% 2.46% 0.19% 1.09% 1.63% 0.13% 0.20% 0.00% 5.70%

16 0.00% 0.15% 0.00% 0.00% 0.10% 0.07% 2.06% 0.00% 12.90% 15.30%

17 0.00% 0.33% 0.05% 0.00% 0.77% 0.47% 1.05% 0.00% 0.34% 3.01%

18 0.05% 0.18% 0.11% 0.09% 0.44% 0.32% 1.68% 0.02% 1.11% 4.00%

19 0.22% 0.91% 2.33% 0.77% 1.18% 0.93% 5.07% 0.09% 7.09% 18.59%

20 0.00% 0.71% 0.68% 0.03% 0.17% 0.49% 2.89% 0.01% 0.04% 5.02%

21 0.00% 0.01% 0.00% 0.00% 0.01% 0.00% 0.01% 0.00% 0.01% 0.05%

22 0.00% 0.00% 0.19% 0.10% 0.28% 0.06% 0.53% 0.02% 0.04% 1.22%

23 0.00% 0.01% 0.03% 0.01% 0.06% 0.01% 0.08% 0.01% 0.07% 0.28%

24 0.01% 0.04% 0.06% 0.02% 0.07% 0.02% 0.13% 0.02% 0.09% 0.45%

25 0.00% 0.00% 0.00% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% 0.03%

26 0.01% 0.02% 0.04% 0.02% 0.03% 0.03% 0.05% 0.04% 0.25% 0.49%
48

Census Division

1 2 3 4 5 6 7 8 9 ∑

27 0.03% 0.04% 0.08% 0.01% 0.09% 0.04% 0.06% 0.02% 0.10% 0.48%

28 0.00% 0.01% 0.01% 0.00% 0.01% 0.01% 0.01% 0.00% 0.03% 0.08%

29 0.00% 0.01% 0.02% 0.01% 0.01% 0.00% 0.01% 0.01% 0.02% 0.09%

30 0.01% 0.02% 0.02% 0.01% 0.03% 0.01% 0.01% 0.01% 0.04% 0.15%

31 0.00% 0.02% 0.50% 0.19% 0.06% 0.14% 0.30% 0.07% 0.24% 1.51%

32 0.01% 0.14% 0.28% 0.03% 0.08% 0.32% 0.44% 0.02% 0.07% 1.41%

33 0.00% 0.01% 0.06% 0.01% 0.01% 0.01% 0.05% 0.01% 0.04% 0.20%

34 0.00% 0.01% 0.02% 0.01% 0.02% 0.02% 0.01% 0.01% 0.03% 0.12%

35 0.01% 0.02% 0.03% 0.01% 0.03% 0.01% 0.02% 0.01% 0.06% 0.20%

36 0.00% 0.01% 0.09% 0.02% 0.03% 0.02% 0.02% 0.02% 0.08% 0.29%

37 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.02%

38 0.00% 0.01% 0.01% 0.00% 0.01% 0.00% 0.00% 0.00% 0.01% 0.04%

39 0.00% 0.01% 0.00% 0.00% 0.01% 0.00% 0.01% 0.00% 0.01% 0.05%

40 0.01% 0.02% 0.03% 0.02% 0.03% 0.01% 0.02% 0.01% 0.05% 0.20%

41 0.00% 0.02% 0.09% 0.01% 0.05% 0.28% 0.25% 0.00% 0.04% 0.75%

43 0.00% 0.01% 0.03% 0.01% 0.02% 0.00% 0.01% 0.01% 0.11% 0.21%

∑ 0.46% 4.46% 16.14% 10.09% 5.39% 7.36% 29.56% 0.87% 25.67% 100%
49

Adjusting for Prices Using Commodity-Specific Inflation Factors

IHS investigated the concern that the ton-mile metric may inadequately address fluctuations in product 

prices. Since the ton-mile metric assumes a static relationship between price and unit weight, any 

changes in the price-to-weight ratio will adversely affect the accuracy of the ton-mile metric. Hence, use 

of nominal dollars in industrial output forecasts would distort ton-mile metric calculations derived from 

base-year FAF (or CFS) dollar-per-unit ratios.

Correcting for changing prices can typically be achieved by applying an inflation index to forecasts. This 

is not necessary for NEMS, however, as the industry output forecast data used in the freight 

transportation model is entered in real dollars by commodity groups. Therefore, changes in commodity 

prices are accounted for in the ton-mile metric, at least on a year-by-year basis. 

Since constant dollars are used in NEMS, concerns about the affect of pricing are mostly moot. There is 

still a potential issue concerning the volatility of prices within periods of a given year. Given that the EIA 

publishes NEMS forecasts annually, however, use of annual real-dollar industry output metrics is likely 

sufficient to address the impact of changing prices on model accuracy.

Modeling Multi-modal Goods Movement and Introducing a Dynamic ModeShare Process

This section explores two issues concerning modal alternatives for freight. These issues were identified 

as challenges within the NEMS freight transportation module whose potential solutions might require 

substantial levels-of-effort with uncertain likelihood of success or significant improvement. Nonetheless, 

conceptual solutions are discussed at a high level and maintaining the following boundaries:

 Any updates to the methodology must fit into the NEMS model framework,

 A balance must be struck between increased sophistication and added complexity and 

processing time.

EIA expressed concern about the current inability to accurately account for freight moving from its origin 

to its ultimate destination across multiple modes of transportation in the NEMS freight transportation 

module ton-mile calculation. As previously discussed, both CFS and FAF account for mixed-mode 

shipments in separate modal classifications, with the former providing additional details about which 

combination of modes are utilized. 

IHS believes that it is possible to more accurately account for multi-modal freight movement in the 

NEMS ton-mile metric calculation and offers a conceptual procedure. This procedure assumes the 

adoption of FAF in place of CFS in the NEMS freight transportation module and the development by EIA 

of capabilities (or budget to procure) to build network assignment models in GIS-based software. It also 

assumes that EIA is able to develop a network assignment procedure for the national railway network. 

EIA would continue to process all uni-modal freight data as previously, except excluding adjustments for 

multi-modal classified ton-mileage, while adding the following key conceptual steps: 
50

1. For each state-by-state (of FAF zone, if preferred) origin-destination combination develop a

multi-modal network assignment model11 for all types of “mixed-mode” classifications in FAF 

(e.g., truck-barge, rail-barge, etc.). EIA would also need to develop rules by which the network 

routing rationalization would follow (e.g., some combination of impedance factors by mode, or 

certain rules for the way commodities travel on different modes, etc.),

2. Assign each FAF mixed-mode record to the corresponding minimum impedance factors, rules, 

etc., developed in the previous step and estimate the number of ton-miles traveled across each 

state (or FAF zone) by mode,

3. Develop three separate tables for truck, rail, and domestic marine that display ton-miles 

allocated to each mode from the mixed-mode records. The tables should display 56 rows (if 

using a state-by-state matrix) and as many columns as necessary for all commodity 

classifications (10 TSIC codes, all 43 SCTG codes, or whichever level of aggregation EIA deems 

most appropriate),

4. Merge the three new modal tables associated with the separated mixed-mode records to the 

three tables already developed from uni-modal ton-mile records.

For this last step, EIA would have several options to integrate the uni-modal and mixed mode ton-mile 

estimates in the ton-mile metric calculations. The two major options include:

1. Sum uni-modal and mixed mode ton-mile calculations for all FAF records (preferably before 

aggregating to the census-division level to improve accuracy) and then calculate modal ton-mile 

shares including all data. This would result in a table for each of three modes with nine rows 

(census division) and as many columns as needed to report commodity classifications,

2. Calculate ton-mile shares for uni-modal and mixed mode records separately in the same table. 

This will result in a table for each of three modes with nine rows (census division) and double 

the amount of columns as the previous alternative (i.e., there would be a uni-modal and a mixed 

mode field associated with each commodity classification). 

The above procedure would allow EIA to account for multi-modal goods movement in the forecasting of 

freight energy demand. For the final step, the second option would allow for greater flexibility to 

account for the impacts of industrial output on uni-modal and multi-modal goods flows separately. Since 

all commodity classifications roll-up to the census-division level, the solution should be feasible in 

NEMS. The only modification would be the application of industry output forecasts twice, once for unimodal commodity forecasts and once for multi-modal commodity forecasts. These could later be 

summed to represent total ton-mileage associated with each mode, census division, and commodity.

Integrating multi-modal goods movement into the NEMS freight transportation module offers 

opportunities for greater forecasting accuracy, but the degree of improvement is yet unknown. 

Moreover the costs in resources to develop this procedure are high. Freight modelers at IHS are 

 

11 Similar to the network assignment solution proposed in this report for uni-modal goods movement, the simplest 

approach would be a commodity-agnostic network assignment, but some accuracy might be compromised.
51

challenged to estimate the level-of-effort precisely, but believe that it could be a multi-month effort 

employing one or more freight network modeling experts.

Even if EIA were to integrate multi-modal goods movement into its NEMS freight transportation module, 

the shares of ton-miles assigned by mode would still be static. That is, the dynamics of changing 

logistical patterns for the movement of goods and the choice of modes would not be accounted for 

during the intervals between CFS/FAF base years. With regard to mode choice, one of the most 

problematic aspects of the NEMS freight module is that rail, truck, and domestic marine forecasts are 

modeled separately. Therefore, it is assumed that goods shipped by truck will continue to be shipped by 

truck, goods shipped by rail will continue to be shipped by rail, and goods shipped by domestic marine 

will continue to move on water. In the case of multi-modal freight, it is assumed that whatever share of 

goods movement across each mode will continue in that proportion. In reality, most commodities can 

be shipped by either truck or rail, and a decision is made whether to ship by truck or rail based on 

various considerations including but not limited to cost, volume, and time sensitivity.

In transportation modeling, the state of the practice is to include a mode choice model as a discrete 

step. It is typical to employ a multinomial logit model to estimate the probability of any shipment 

choosing a given mode among a set of alternatives, and simulating the flows on the network. However, 

in this case, adding a full-scale mode share model to the NEMS freight module would drastically increase

complexity and, possibly, NEMS processing time.

Mode choice is particularly challenging for freight modeling at a national scale. IHS understands that EIA 

previously attempted to build such a model but encountered many problems. Such an option could be 

explored again in the future. An alternative is coordinating with other federal agencies currently 

conducting mode share modeling research such as FHWA. Existing mode share forecasts could be used 

to rebalance ton-mile shares across modes in between CFS/FAF base years. The Intermodal 

Transportation and Inventory Cost Model12 mentioned in the workshop could serve as a useful resource 

in rebalancing mode share.

Modeling intermodal freight movements and developing dynamic mode share models for the NEMS 

freight transportation module carry high costs, but development of such innovations potentially come

with added benefits. Although EIA does not directly model inter-modal transfers, the existing model 

indirectly accounts for all freight VMT and/or ton-miles as top-line national forecasts are applied. The 

loss in fidelity is in the development of accurate forecasts of ton-mileage using industry-output data 

based on the ton-mile shares allocated by mode and census division. If EIA were to adopt a more 

disaggregate, microeconomic approach to freight demand forecasting based on trips generated at the 

firm level (i.e., state-of-the-practice techniques in transportation demand modeling), incorporating 

inter-modal transfers and modal shifts might be more straightforward. 

Given the limitations of resources and the constraints of NEMS environment, IHS recommends that EIA 

instead consider studying the proposed process outlined in this sub-section to account for multi-modal 

 

12 https://www.fhwa.dot.gov/policy/otps/061012/iticst_info.htm
52

goods movement, revisit the development of an internal EIA freight mode-share model, and/or explore 

opportunities to leverage research on freight mode share models being developed by federal 

government colleagues at FHWA.

Accounting for Changes in Product Content and Industry Operations and the 

Effects of Time

IHS and EIA discussed other issues identified in the NEMS freight transportation module for which 

potential solutions may be feasible, but which are likely to be challenging to address systematically. 

Similar to the issue concerning the impacts on ton-mile forecasts due to price changes, changes in 

product content and changes in manufacturing, logistics, and supply chain practices primarily impact 

ton-mile forecasts in intermediate periods between CFS/FAF base years. Unlike changes in price, 

however, it would be difficult to systematically apply adjustment metrics (e.g., real dollars) to the NEMS 

freight transportation module to correct for industry changes in technology and management of 

production and distribution.

While subtle shifts may occur across all industries, the most significant changes in industry production 

and distribution landscape are often tied primarily to one or several industries. For example, since 2007 

the landscape for US oil and gas production, distribution, and refining has shifted dramatically. The U.S. 

has become a net producer and net exporter, or at least a less voluminous importer, of numerous 

petroleum and petrochemical products. This shift has altered the geography of production and 

distribution, much of which would not be reflected accurately in forecasts calculated from CFS (or FAF) 

2007 ton-mile shares.

In terms of product content, the industries potentially experiencing significant changes within any fiveyear period in relative weight-to-unit ratios are limited. Automotive, aviation, and various high 

technology industries would be some of the likely candidates, as innovation in product design continues 

to drive lighter and more efficient output. Many industries, however, are unlikely to see major shifts in 

weight-to-unit ratios such as agriculture, oil, steel, wood products, etc. 

IHS recommends that EIA consider intermediate-year adjustments on a case-by-case basis. It is likely 

that major problems will be easily identified by users of EIA forecasts and/or from following trends in 

the wider economy. EIA can then try to address these issues surgically rather than making drastic 

changes to NEMS transportation freight module and/or ton-mile share forecasts for unaffected 

industries. Those minor “patch” solutions can then carry EIA’s NEMS freight transportation module 

forecasts forward until the next CFS/FAF base year when changes in industry practices in intermediate 

years should become fully incorporated into the model.
53

Correcting NEMS Forecasts for Domestic Marine

Based on a review of the challenges of forecasting domestic marine, where positive industry output 

growth coincides with declining ton-miles recorded, IHS concludes that the ton-mile metric may be 

inadequate for accurate estimations in NEMS. It appears that other factors besides industry output are 

influencing domestic marine ton-mileage. These might include, but are not limited to, increased pipeline 

investment reducing barge traffic, a lack of domestic marine terminal investment to handle containers

leading to diversion to truck and rail, and shifting centers of production. 

EIA has, essentially, come to the same conclusion as IHS and has for a period of time overwritten the 

ton-mile forecasts for domestic marine with a trend-line analysis of historical demand. This solution will 

probably suffice for the time being. It is entirely possible that such a trend may reverse, for example, if 

increased containerization is paired with successful investment in container intermodal services at US 

inland ports. Therefore, EIA should continue to monitor these trends. Moreover, IHS would recommend 

that EIA also explore the development of a systematic, valid, transparent, and well-defended process for 

estimating and applying the ton-mile override.

Changes to other Data Inputs to NEMS

For the purposes of this study, IHS explored mostly process changes (e.g., new network assignment 

proxies, disaggregation of TSICs, etc.). The only input change explored in detail was replacing CFS with 

FAF data in the ton-mile metric calculation. There may be opportunities, however, for exploring the use 

of other input data to improve model accuracy.

Data inputs that may especially warrant further scrutiny are the top-line national ton-mile and VMT 

data. EIA uses CFS (and now, potentially, FAF) for calculating only the shares of total national ton-miles 

and VMT allocated to census divisions and commodity groups. For trucks and rail, the top-line data 

comes from FHWA National Transportation Statistics, while domestic marine top-line national ton-mile 

forecasts come from U.S. Army Corps of Engineers Waterborne Commerce data. These data provide EIA 

with more up-to-date national-level forecasts (2009 for truck, 2012 for rail, and 2010 for domestic 

marine) than CFS or FAF. Nonetheless, EIA may consider exploring replacing National Transportation 

Statistics and Waterborne Commerce data with other sources such as FAF forecasts, at least for rail and 

waterborne (truck metrics would still need to be adjusted for VMT output). 

It is possible that using FAF as both the primary top-line freight volume data input as well as for the tonmile metric calculation may improve model accuracy. Moreover, it could make the model more 

internally coherent. Nonetheless, while there is evidence to suggest that FAF may provide an 

improvement over CFS for the ton-mile metric calculation, it is not clear whether FAF forecasts of total 

national ton-miles would be any more or less accurate than existing FHWA and U.S. Army Corps of 

Engineers data. Certainly, truck VMT calculations would be a challenge. 

IHS makes no immediate recommendation for changing other data inputs to the NEMS freight 

transportation module. However, experimentation with different data sets can be a worthwhile exercise 

in quality control and consideration for future model development updates.
54

Conclusions

EIA NEMS freight transportation module demand and energy consumption forecasts can be significantly 

improved with relatively low-effort changes to existing processes. The resulting methodologies may not 

conform to state-of-the-art or state-of-the-practice transportation demand modeling techniques, but 

they offer potentially useful and relatively accurate solutions for the scale and objectives of the NEMS 

model. Many of these suggested improvements can be implemented with a combination of additional 

government data, while others may require either a combination of software license procurement and 

staff training or assistance from an outside vendor. 

The following table lists the key issues identified with NEMS, the recommended courses of action, 

summaries of implementation processes, and a high-level, initial summary of the resources required to 

implement each proposed solution.
55

Table 24: Summary of Recommendations

Recommendation Summary of Implementation Level of Effort/Resource Requirements

Availability and 

Use of Data

 Replace CFS with FAF as the primary 

survey data for estimating the ton-mile 

metric

 Replace CFS data input into ton-mile 

spreadsheet calculations with FAF 

records

 Update any cells to ensure the accurate 

capture and calculation of FAF records 

within the existing framework

 Make adjustments to break-out FAF 

“mixed-mode” ton-miles by bi-modal 

category

 EIA staff should be able to download FAF 

and update files 

 IHS has provided sample spreadsheet 

calculations that can be leveraged by EIA 

staff to update spreadsheet calculations

Network Assignment

 Employ a GIS-based network assignment 

proxy for estimating truck ton-mile 

distances across states for each census 

division origin/destination pair

 Study alternatives for building a similar 

network assignment procedure for rail

 Maintain 50-50% origin/destination split 

for domestic marine

 Develop a shortest-path GIS model for 

truck freight goods movement at a stateby-state (or FAF region) level using 

geographic centriods and transportation 

network shapefiles

 Intersect the FAF records with the GIS 

model to estimate ton-miles attributable 

to each state for each census-division

origin/destination pair

 Re-calculate ton-miles for each 

commodity using the updated censusdivision shares

 IHS has developed a proxy network 

assignment for trucks using FAF 1997, 

2002, 2007 data and has provided

documentation of the procedure

 EIA would need to identify an internal 

GIS resource, license GIS and/or GISbased transportation demand

 Development of a process for rail should 

involve a more in-depth study, but 

relatively simple solutions (a similar 

process as that with IHS employed for

trucks may be feasible)

Levels of 

Disaggregation

 Disaggregate commodity groups to the 

SCTG level when sample sizes are 

determined to be large enough 

 For small sample sizes, conduct a study 

of a logical re-mapping of commodity 

groupings

 For disaggregation, eliminate one 

aggregation procedure in the NEMS 

freight transportation module

 For any new aggregations, develop new 

spreadsheet formulas in the freight-ton 

metric calculation spreadsheets

 IHS has provided a full disaggregation by 

STCG code for all modes using 2007 FAF 

data and a new network assignment 

process for truck

 Time and resources will be needed to 

study logical groupings, but the 

procedures for updating the NEMS 

freight transportation module are 

straightforward spreadsheet exercises
56

Recommendation Summary of Implementation Level of Effort/Resource Requirements

Intermodal and Mode Share 

Models

 No immediate changes

 Study the possibility of incorporating a 

multi-modal route distance estimation

procedure and integrating the uni-modal 

ton-mile calculations with mixed-mode

ton-mile estimates

 Work with FHWA to determine if it may 

be possible to leverage current research 

on potential mode share models at 

USDOT to improve the NEMS freight 

transportation module

 The immediate implementation tasks are 

largely studies and coordination with 

other federal government agencies 

(FHWA)

 In the long-term, a potential 

implementation would include building 

complex models to account for ton-mile 

shares attributable to multi-modal

freight and synthesizing these estimates 

with uni-modal ton-mile metric 

calculations

 Conceptual mode share model 

procedures are difficult to delineate at 

this time

 Studies can be completed by EIA staff 

with or without the help of outside 

service vendors, depending upon scope

 The long-term implementation of mode 

share and multi-modal models within the 

NEMS freight transportation module 

would likely require a significant 

engagement with subject-matter and 

technical experts

Changes in Industry Technology 

and Management

 Track changes in industry technology and 

management that could impact product 

content, distribution patterns, and other 

factors affecting the accuracy of ton-mile 

forecasts in intermediate periods 

between CFS/FAF base years

 On a case-by-case basis explore periodic 

adjustments to individual or small 

groups of industry ton-mile forecasts 

when circumstances suggest dramatic 

inaccuracies

 Follow major changes in industry trends

 Create custom adjustments to ton-mile 

forecasts for specified industries until 

the next CFS/FAF base-year survey data 

becomes available

 EIA staff would need to track industry 

trends and develop spreadsheet 

calculation adjustments as needed
57

Recommendation Summary of Implementation Level of Effort/Resource Requirements

Domestic Marine Trends

 Continue to employ EIA’s current 

procedure for over-riding the ton-mile 

forecast with trend line estimates

 Observe if other recommended model 

changes improve the accuracy of the 

ton-mile estimate for domestic marine

freight flows

 Develop a systematic, valid, transparent,

and well-defended process for 

estimating and applying the ton-mile 

override

 Work to improve the trend-line 

estimation process using defensible 

assumptions, variables, and output

 EIA staff is already fully engaged in this 

process 

 Some supplementary work on improving 

and documenting methodology may be 

required
58

Appendix A: Workshop Participants

 American Transportation Research Institute (ATRI)

o Jeff Short

 Energy Information Administration (EIA)

o Nicholas Chase

o John Maples

 IHS Global, Inc. (IHS)

o Bob Brodesky, Transportation Consulting

o Rich Fullenbaum, Economic Consulting

o Chris Grillo, Transportation Consulting

o Tyler Kreider, Transportation Consulting

 United States Department of Transportation (U.S. DOT)

o Peter Bang, FHWA Freight Office

o Don Pickrell, Volpe Center

o Rolf Schmidt, Bureau of Transportation Statistics

o Mike Sprung, Bureau of Transportation Statistics

o Ed Strocko, FHWA Freight Office

o Coral Torres, FHWA Freight Office
59

Appendix B: Additional Comparison Tables for FAF Versus CFS

The following tables compare ton-mile metric calculations using FAF versus CFS for truck and rail for 

1997, 2002, and 2007 and domestic marine for 1997 and 2002.
60

Table 25 – 1997 FAF Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.46% 0.40% 0.26% 0.20% 0.26% 0.17% 0.20% 0.77% 0.25% 0.38% 3.35%

2 0.85% 1.26% 0.93% 0.40% 0.72% 0.67% 0.48% 1.21% 0.58% 1.75% 8.84%

3 1.46% 2.49% 1.36% 0.73% 1.11% 1.38% 1.13% 1.82% 2.03% 3.10% 16.61%

4 0.60% 0.85% 1.03% 0.23% 0.58% 0.60% 0.41% 1.23% 3.73% 1.47% 10.72%

5 1.48% 1.54% 1.23% 0.62% 1.15% 1.62% 0.79% 3.79% 1.38% 2.98% 16.57%

6 0.47% 0.86% 0.47% 0.28% 0.48% 0.61% 0.41% 1.45% 0.76% 1.71% 7.50%

7 1.45% 1.54% 0.96% 0.45% 2.06% 0.99% 0.76% 2.28% 2.19% 2.13% 14.81%

8 0.42% 0.55% 0.43% 0.13% 0.83% 0.59% 0.35% 1.02% 0.98% 1.13% 6.45%

9 1.40% 1.42% 1.30% 0.58% 2.18% 0.95% 1.05% 2.75% 1.65% 1.86% 15.14%

∑ 8.59% 10.91% 7.98% 3.61% 9.38% 7.59% 5.59% 16.31% 13.54% 16.51% 100%

Table 26 – 1997 CFS Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.21% 0.17% 0.28% 0.26% 0.08% 0.10% 0.10% 0.56% 0.08% 0.12% 1.96%

2 1.31% 1.46% 1.90% 0.73% 0.53% 0.84% 0.62% 1.42% 0.32% 1.38% 10.51%

3 2.42% 4.23% 3.32% 1.35% 0.84% 1.21% 2.41% 2.25% 0.91% 2.00% 20.93%

4 0.80% 0.78% 2.10% 0.33% 0.26% 0.66% 0.60% 0.98% 1.87% 0.73% 9.13%

5 1.98% 1.40% 2.42% 1.16% 0.94% 1.49% 1.02% 3.86% 0.64% 1.89% 16.81%

6 0.76% 1.16% 0.88% 0.55% 0.32% 0.65% 0.68% 1.66% 0.22% 1.00% 7.89%

7 2.07% 1.44% 2.01% 0.83% 0.78% 1.33% 0.93% 2.19% 0.81% 1.02% 13.40%

8 0.47% 0.34% 0.67% 0.13% 0.39% 0.52% 0.14% 0.73% 0.25% 0.34% 3.99%

9 1.45% 1.21% 2.65% 1.04% 0.91% 1.10% 1.06% 3.70% 1.26% 0.99% 15.37%

∑ 11.48% 12.18% 16.22% 6.39% 5.06% 7.92% 7.58% 17.35% 6.36% 9.45% 100%

Table 27 – 1997 Truck FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.25% 0.23% -0.02% -0.06% 0.18% 0.07% 0.10% 0.21% 0.17% 0.26% 1.39%

2 -0.46% -0.20% -0.97% -0.33% 0.19% -0.17% -0.14% -0.21% 0.26% 0.37% -1.67%

3 -0.96% -1.74% -1.96% -0.62% 0.27% 0.17% -1.28% -0.43% 1.12% 1.10% -4.32%

4 -0.20% 0.07% -1.07% -0.10% 0.32% -0.06% -0.19% 0.25% 1.86% 0.74% 1.59%

5 -0.50% 0.14% -1.19% -0.54% 0.21% 0.13% -0.23% -0.07% 0.74% 1.09% -0.24%

6 -0.29% -0.30% -0.41% -0.27% 0.16% -0.04% -0.27% -0.21% 0.54% 0.71% -0.39%

7 -0.62% 0.10% -1.05% -0.38% 1.28% -0.34% -0.17% 0.09% 1.38% 1.11% 1.41%

8 -0.05% 0.21% -0.24% 0.00% 0.44% 0.07% 0.21% 0.29% 0.73% 0.79% 2.46%

9 -0.05% 0.21% -1.35% -0.46% 1.27% -0.15% -0.01% -0.95% 0.39% 0.87% -0.23%

∑ -2.89% -1.27% -8.24% -2.78% 4.32% -0.33% -1.99% -1.04% 7.18% 7.06% 0.00%
61

Table 28 – 1997 FAF Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.15% 0.28% 0.08% 0.14% 0.10% 0.06% 0.03% 0.25% 0.10% 0.15% 0.08% 1.42%

2 0.65% 0.64% 0.39% 0.28% 0.49% 0.08% 0.16% 0.37% 0.19% 0.43% 1.28% 4.95%

3 1.72% 1.42% 0.81% 0.80% 0.60% 0.20% 0.74% 0.62% 2.10% 1.58% 5.53% 16.11%

4 1.15% 0.53% 0.91% 0.25% 0.85% 0.11% 0.20% 0.50% 4.35% 1.37% 3.19% 13.41%

5 2.21% 0.57% 0.51% 0.52% 0.33% 0.22% 0.22% 0.79% 1.00% 1.24% 6.81% 14.43%

6 0.54% 0.51% 0.22% 0.39% 0.31% 0.13% 0.08% 0.36% 0.39% 0.32% 3.53% 6.79%

7 3.06% 0.96% 0.43% 0.44% 0.83% 0.22% 0.31% 0.83% 1.97% 1.04% 3.87% 13.96%

8 1.18% 0.41% 0.20% 0.37% 0.32% 0.23% 0.20% 0.76% 0.45% 0.76% 14.08% 18.95%

9 1.47% 0.80% 0.85% 0.48% 0.89% 0.32% 0.34% 1.40% 2.93% 0.34% 0.17% 9.98%

∑ 12.12% 6.11% 4.40% 3.67% 4.72% 1.57% 2.27% 5.88% 13.48% 7.23% 38.53% 100%

Table 29 – 1997 CFS Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.03% 0.06% 0.05% 0.13% 0.00% 0.00% 0.00% 0.23% 0.00% 0.13% 0.00% 0.63%

2 0.48% 0.88% 1.31% 0.21% 0.02% 0.01% 0.05% 0.27% 0.07% 0.13% 2.17% 5.60%

3 1.23% 1.62% 1.13% 0.47% 0.11% 0.01% 0.44% 0.50% 1.01% 0.50% 7.77% 14.80%

4 0.31% 0.06% 0.78% 0.09% 0.03% 0.03% 0.13% 0.22% 2.89% 0.81% 5.36% 10.70%

5 1.54% 0.11% 0.32% 0.57% 0.07% 0.06% 0.13% 0.49% 0.82% 0.78% 8.31% 13.20%

6 0.55% 0.23% 0.41% 0.51% 0.05% 0.02% 0.03% 0.23% 0.09% 0.13% 4.07% 6.34%

7 3.38% 0.49% 0.34% 0.63% 0.46% 0.15% 0.11% 0.41% 0.42% 0.46% 7.25% 14.10%

8 0.73% 0.10% 1.59% 0.03% 0.13% 0.08% 0.02% 0.92% 0.00% 0.83% 22.67% 27.11%

9 0.88% 0.32% 0.84% 0.77% 0.27% 0.05% 0.17% 1.38% 2.34% 0.11% 0.39% 7.53%

∑ 9.14% 3.88% 6.77% 3.40% 1.14% 0.41% 1.08% 4.65% 7.64% 3.89% 58.00% 100%

Table 30 – 1997 Rail FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.12% 0.22% 0.03% 0.01% 0.10% 0.06% 0.03% 0.02% 0.10% 0.02% 0.08% 0.79%

2 0.17% -0.24% -0.92% 0.07% 0.47% 0.07% 0.11% 0.10% 0.12% 0.30% -0.89% -0.65%

3 0.49% -0.20% -0.32% 0.33% 0.49% 0.19% 0.30% 0.12% 1.09% 1.08% -2.24% 1.31%

4 0.84% 0.47% 0.13% 0.16% 0.82% 0.08% 0.07% 0.28% 1.46% 0.56% -2.17% 2.71%

5 0.67% 0.46% 0.19% -0.05% 0.26% 0.16% 0.09% 0.30% 0.18% 0.46% -1.50% 1.23%

6 -0.01% 0.28% -0.19% -0.12% 0.26% 0.11% 0.05% 0.13% 0.30% 0.19% -0.54% 0.45%

7 -0.32% 0.47% 0.09% -0.19% 0.37% 0.07% 0.20% 0.42% 1.55% 0.58% -3.38% -0.14%

8 0.45% 0.31% -1.39% 0.34% 0.19% 0.15% 0.18% -0.16% 0.45% -0.07% -8.59% -8.16%

9 0.59% 0.48% 0.01% -0.29% 0.62% 0.27% 0.17% 0.02% 0.59% 0.23% -0.22% 2.45%

∑ 2.98% 2.23% -2.37% 0.27% 3.58% 1.16% 1.19% 1.23% 5.84% 3.34% -19.47% 0.00%
62

Table 31 – 2002 FAF Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.39% 0.28% 0.29% 0.20% 0.22% 0.15% 0.12% 0.57% 0.22% 0.26% 2.70%

2 1.41% 1.21% 1.10% 0.40% 0.66% 0.56% 0.53% 1.40% 0.56% 1.40% 9.23%

3 1.59% 2.48% 1.73% 0.60% 1.05% 1.22% 1.69% 2.05% 1.69% 2.86% 16.97%

4 0.75% 0.77% 1.25% 0.20% 0.51% 0.86% 0.50% 1.01% 3.18% 1.39% 10.42%

5 1.65% 1.49% 1.41% 0.69% 0.87% 1.28% 0.85% 3.44% 1.20% 2.66% 15.55%

6 0.72% 0.92% 0.60% 0.31% 0.69% 0.56% 0.55% 1.58% 0.69% 1.12% 7.74%

7 1.85% 1.71% 1.15% 0.36% 1.75% 1.13% 0.98% 2.03% 1.80% 1.86% 14.63%

8 0.60% 0.53% 0.61% 0.14% 0.89% 0.50% 0.34% 1.16% 0.88% 1.10% 6.75%

9 1.44% 1.42% 1.66% 0.46% 1.87% 1.28% 1.09% 2.97% 1.69% 2.13% 16.00%

∑ 10.41% 10.81% 9.80% 3.35% 8.51% 7.55% 6.67% 16.22% 11.92% 14.78% 100%

Table 32 – 2002 CFS Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.22% 0.14% 0.26% 0.33% 0.12% 0.06% 0.06% 0.43% 0.08% 0.13% 1.82%

2 1.07% 1.34% 1.50% 0.71% 0.33% 0.45% 0.58% 1.66% 0.14% 0.79% 8.58%

3 2.22% 3.81% 2.83% 1.21% 0.81% 1.04% 2.76% 2.77% 0.31% 1.91% 19.67%

4 0.54% 0.75% 1.84% 0.30% 0.34% 0.34% 0.72% 1.15% 1.01% 0.83% 7.81%

5 1.85% 1.43% 2.70% 1.29% 0.81% 1.44% 1.02% 4.70% 0.47% 2.00% 17.70%

6 0.71% 1.15% 1.05% 0.54% 0.41% 0.28% 0.77% 1.77% 0.15% 0.92% 7.74%

7 2.04% 1.55% 1.87% 0.78% 1.50% 1.86% 1.27% 2.45% 0.61% 1.00% 14.92%

8 0.34% 0.34% 0.74% 0.14% 0.80% 0.58% 0.13% 1.01% 0.31% 0.48% 4.88%

9 1.46% 1.01% 3.34% 0.98% 1.30% 1.72% 0.93% 3.83% 1.03% 1.28% 16.87%

∑ 10.43% 11.51% 16.11% 6.29% 6.44% 7.76% 8.23% 19.77% 4.11% 9.35% 100%

Table 33 – 2002 Truck FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.17% 0.14% 0.03% -0.13% 0.10% 0.09% 0.06% 0.14% 0.14% 0.13% 0.88%

2 0.34% -0.13% -0.40% -0.31% 0.33% 0.11% -0.05% -0.26% 0.42% 0.61% 0.65%

3 -0.63% -1.33% -1.10% -0.61% 0.24% 0.18% -1.07% -0.72% 1.38% 0.95% -2.70%

4 0.21% 0.02% -0.59% -0.10% 0.17% 0.52% -0.22% -0.14% 2.17% 0.56% 2.61%

5 -0.20% 0.06% -1.29% -0.60% 0.06% -0.16% -0.17% -1.26% 0.73% 0.66% -2.15%

6 0.01% -0.23% -0.45% -0.23% 0.28% 0.28% -0.22% -0.19% 0.54% 0.20% 0.00%

7 -0.19% 0.16% -0.72% -0.42% 0.25% -0.73% -0.29% -0.42% 1.19% 0.86% -0.29%

8 0.26% 0.19% -0.13% 0.00% 0.09% -0.08% 0.21% 0.15% 0.57% 0.62% 1.87%

9 -0.02% 0.41% -1.68% -0.52% 0.57% -0.44% 0.16% -0.86% 0.66% 0.85% -0.87%

∑ -0.02% -0.70% -6.31% -2.94% 2.07% -0.21% -1.56% -3.55% 7.81% 5.43% 0.00%
63

Table 34 – 2002 FAF Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.10% 0.09% 0.04% 0.16% 0.03% 0.01% 0.02% 0.22% 0.07% 0.16% 0.08% 0.98%

2 0.62% 0.48% 0.33% 0.21% 0.45% 0.06% 0.16% 0.37% 0.53% 0.46% 1.93% 5.59%

3 1.68% 1.23% 0.69% 0.41% 0.63% 0.18% 0.82% 0.86% 2.11% 1.70% 5.80% 16.10%

4 0.88% 0.53% 1.02% 0.26% 0.32% 0.16% 0.19% 0.70% 4.44% 1.21% 2.96% 12.66%

5 1.80% 0.60% 0.41% 0.42% 0.39% 0.30% 0.22% 0.61% 1.15% 1.28% 5.02% 12.21%

6 0.67% 0.33% 0.18% 0.32% 0.51% 0.30% 0.15% 0.25% 0.37% 0.54% 3.73% 7.36%

7 2.47% 0.60% 0.42% 0.36% 0.66% 0.30% 0.41% 0.59% 2.18% 0.90% 5.32% 14.21%

8 0.64% 0.29% 0.17% 0.12% 0.32% 0.25% 0.06% 0.56% 0.38% 0.68% 16.62% 20.09%

9 1.03% 0.68% 0.81% 0.28% 0.86% 0.24% 0.47% 1.97% 3.17% 0.64% 0.65% 10.80%

∑ 9.89% 4.83% 4.07% 2.53% 4.17% 1.79% 2.49% 6.13% 14.41% 7.56% 42.12% 100%

Table 35 – 2002 CFS Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.02% 0.00% 0.01% 0.12% 0.00% 0.00% 0.00% 0.09% 0.00% 0.01% 0.05% 0.30%

2 0.33% 0.07% 0.25% 0.13% 0.00% 0.00% 0.02% 0.19% 0.02% 0.48% 2.68% 4.18%

3 0.72% 0.53% 0.47% 0.30% 0.08% 0.02% 0.25% 0.39% 0.18% 2.61% 11.30% 16.85%

4 0.27% 0.04% 0.70% 0.13% 0.01% 0.01% 0.07% 0.14% 1.07% 2.01% 6.24% 10.69%

5 0.79% 0.05% 0.17% 0.34% 0.01% 0.08% 0.09% 0.16% 0.05% 0.44% 9.08% 11.27%

6 0.40% 0.07% 0.16% 0.36% 0.00% 0.00% 0.02% 0.02% 0.10% 0.09% 5.95% 7.17%

7 2.73% 0.16% 0.24% 0.41% 0.56% 0.15% 0.10% 0.39% 0.07% 0.60% 7.81% 13.23%

8 0.14% 0.06% 0.00% 0.01% 0.12% 0.04% 0.01% 0.31% 0.03% 0.23% 31.31% 32.27%

9 0.30% 0.24% 0.40% 0.42% 0.09% 0.00% 0.10% 1.58% 0.56% 0.03% 0.36% 4.05%

∑ 5.69% 1.21% 2.40% 2.22% 0.88% 0.30% 0.66% 3.27% 2.07% 6.51% 74.78% 100%

Table 36 – 2002 Rail FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.08% 0.09% 0.03% 0.04% 0.03% 0.01% 0.02% 0.13% 0.07% 0.15% 0.03% 0.68%

2 0.29% 0.41% 0.08% 0.08% 0.45% 0.06% 0.14% 0.18% 0.51% -0.02% -0.75% 1.41%

3 0.96% 0.70% 0.22% 0.11% 0.55% 0.16% 0.57% 0.47% 1.93% -0.91% -5.50% -0.75%

4 0.61% 0.49% 0.32% 0.13% 0.31% 0.15% 0.12% 0.56% 3.37% -0.80% -3.28% 1.97%

5 1.01% 0.55% 0.24% 0.08% 0.38% 0.22% 0.13% 0.45% 1.10% 0.84% -4.06% 0.94%

6 0.27% 0.26% 0.02% -0.04% 0.51% 0.30% 0.13% 0.23% 0.27% 0.45% -2.22% 0.19%

7 -0.26% 0.44% 0.18% -0.05% 0.10% 0.15% 0.31% 0.20% 2.11% 0.30% -2.49% 0.98%

8 0.50% 0.23% 0.17% 0.11% 0.20% 0.21% 0.05% 0.25% 0.35% 0.45% -14.69% -12.18%

9 0.73% 0.44% 0.41% -0.14% 0.77% 0.24% 0.37% 0.39% 2.61% 0.61% 0.29% 6.75%

∑ 4.20% 3.62% 1.67% 0.31% 3.29% 1.49% 1.83% 2.86% 12.34% 1.05% -32.66% 0.00%
64

Table 37 – 2007 FAF Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.44% 0.32% 0.31% 0.17% 0.30% 0.14% 0.14% 0.46% 0.22% 0.34% 2.84%

2 1.03% 1.52% 1.18% 0.39% 0.72% 0.40% 0.55% 1.53% 0.69% 1.40% 9.42%

3 1.78% 2.58% 1.57% 0.65% 0.72% 0.83% 1.41% 1.86% 2.29% 2.27% 15.95%

4 0.81% 0.97% 1.14% 0.20% 0.46% 0.58% 0.51% 0.94% 3.85% 1.42% 10.87%

5 1.86% 1.73% 1.49% 0.71% 0.96% 1.29% 0.89% 3.00% 1.28% 2.56% 15.77%

6 0.65% 1.02% 0.54% 0.30% 0.50% 0.46% 0.48% 1.39% 0.53% 1.85% 7.71%

7 1.97% 1.96% 1.20% 0.41% 1.85% 0.93% 1.00% 1.80% 1.69% 1.84% 14.65%

8 0.58% 0.65% 0.60% 0.15% 0.51% 0.67% 0.33% 1.07% 1.24% 1.19% 7.00%

9 1.50% 1.86% 1.95% 0.56% 0.96% 1.19% 1.34% 2.96% 1.99% 1.47% 15.79%

∑ 10.62% 12.61% 9.98% 3.53% 6.99% 6.47% 6.66% 15.02% 13.78% 14.33% 100%

Table 38 – 2007 CFS Truck Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.22% 0.18% 0.45% 0.20% 0.16% 0.13% 0.08% 0.42% 0.08% 0.16% 2.07%

2 1.22% 1.07% 2.00% 0.55% 0.49% 0.40% 0.53% 1.54% 0.46% 1.08% 9.34%

3 2.51% 3.12% 3.01% 1.14% 0.61% 1.05% 1.91% 2.36% 0.84% 1.63% 18.17%

4 0.82% 0.64% 1.89% 0.28% 0.40% 0.60% 0.57% 1.01% 1.77% 1.31% 9.30%

5 2.34% 1.31% 2.39% 1.10% 0.79% 1.45% 0.92% 3.44% 0.82% 1.91% 16.48%

6 0.84% 1.03% 0.94% 0.56% 0.33% 0.65% 0.65% 1.48% 0.19% 1.34% 8.02%

7 2.47% 1.88% 2.09% 0.69% 1.50% 1.32% 1.10% 2.21% 0.71% 1.53% 15.49%

8 0.57% 0.36% 0.83% 0.17% 0.42% 0.69% 0.18% 1.23% 0.41% 0.82% 5.70%

9 1.52% 0.85% 3.56% 0.92% 0.91% 1.07% 1.01% 3.27% 1.34% 0.98% 15.42%

∑ 12.52% 10.44% 17.17% 5.61% 5.62% 7.36% 6.95% 16.94% 6.62% 10.76% 100%

Table 39 – 2007 Truck FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.22% 0.14% -0.14% -0.03% 0.14% 0.01% 0.06% 0.04% 0.14% 0.18% 0.77%

2 -0.19% 0.45% -0.82% -0.16% 0.23% 0.00% 0.02% -0.01% 0.23% 0.32% 0.08%

3 -0.73% -0.54% -1.44% -0.49% 0.11% -0.22% -0.50% -0.50% 1.45% 0.64% -2.22%

4 -0.01% 0.33% -0.75% -0.08% 0.06% -0.02% -0.06% -0.07% 2.08% 0.11% 1.57%

5 -0.48% 0.42% -0.90% -0.39% 0.17% -0.16% -0.03% -0.44% 0.46% 0.65% -0.71%

6 -0.19% -0.01% -0.40% -0.26% 0.17% -0.19% -0.17% -0.09% 0.34% 0.51% -0.31%

7 -0.50% 0.08% -0.89% -0.28% 0.35% -0.39% -0.10% -0.41% 0.98% 0.31% -0.84%

8 0.01% 0.29% -0.23% -0.02% 0.09% -0.02% 0.15% -0.16% 0.83% 0.37% 1.30%

9 -0.02% 1.01% -1.61% -0.36% 0.05% 0.12% 0.33% -0.31% 0.65% 0.49% 0.37%

∑ -1.90% 2.17% -7.19% -2.08% 1.37% -0.89% -0.29% -1.92% 7.16% 3.57% 0.00%
65

Table 40 – 2007 FAF Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.16% 0.10% 0.03% 0.12% 0.03% 0.01% 0.02% 0.11% 0.03% 0.09% 0.02% 0.72%

2 0.78% 0.50% 0.24% 0.19% 0.34% 0.03% 0.10% 0.39% 0.36% 0.22% 0.97% 4.12%

3 1.64% 0.90% 0.45% 0.37% 0.66% 0.15% 0.57% 0.54% 1.79% 1.54% 6.67% 15.28%

4 1.06% 0.44% 0.85% 0.22% 0.29% 0.11% 0.09% 0.62% 4.07% 1.05% 4.33% 13.14%

5 1.50% 0.44% 0.27% 0.37% 0.38% 0.15% 0.13% 0.38% 0.72% 0.65% 6.48% 11.47%

6 0.78% 0.42% 0.11% 0.25% 0.18% 0.06% 0.10% 0.17% 0.32% 0.49% 4.32% 7.22%

7 3.03% 0.66% 0.40% 0.42% 1.40% 0.11% 0.29% 0.43% 1.92% 1.37% 5.50% 15.54%

8 1.37% 0.34% 0.18% 0.11% 0.51% 0.16% 0.10% 0.41% 0.53% 0.65% 19.90% 24.26%

9 1.07% 0.57% 0.70% 0.38% 0.83% 0.17% 0.35% 1.15% 2.35% 0.31% 0.35% 8.25%

∑ 11.41% 4.38% 3.25% 2.44% 4.62% 0.94% 1.76% 4.21% 12.09% 6.37% 48.54% 100%

Table 41 – 2007 CFS Rail Share

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.10% 0.02% 0.02% 0.08% 0.01% 0.00% 0.00% 0.07% 0.02% 0.06% 0.00% 0.38%

2 0.46% 0.14% 0.19% 0.07% 0.28% 0.00% 0.01% 0.19% 0.25% 0.03% 1.04% 2.66%

3 1.19% 0.51% 0.26% 0.20% 0.40% 0.09% 0.20% 0.23% 0.88% 0.82% 9.24% 14.02%

4 0.34% 0.15% 0.82% 0.06% 0.11% 0.08% 0.04% 0.25% 2.46% 0.72% 5.92% 10.95%

5 0.99% 0.14% 0.12% 0.23% 0.12% 0.10% 0.09% 0.12% 0.46% 0.34% 8.78% 11.49%

6 0.68% 0.25% 0.05% 0.20% 0.13% 0.02% 0.03% 0.03% 0.15% 0.33% 5.55% 7.42%

7 3.16% 0.33% 0.33% 0.38% 1.14% 0.06% 0.05% 0.22% 1.26% 1.22% 8.63% 16.78%

8 0.31% 0.05% 0.07% 0.06% 0.40% 0.09% 0.01% 0.25% 0.23% 0.20% 29.87% 31.54%

9 0.50% 0.20% 0.61% 0.36% 0.37% 0.02% 0.11% 0.75% 1.01% 0.20% 0.65% 4.77%

∑ 7.73% 1.79% 2.46% 1.65% 2.95% 0.47% 0.53% 2.11% 6.72% 3.92% 69.66% 100%

Table 42 – 2007 Rail FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 Coal ∑

Census Division

1 0.06% 0.08% 0.01% 0.04% 0.02% 0.01% 0.02% 0.04% 0.01% 0.03% 0.02% 0.34%

2 0.32% 0.36% 0.05% 0.12% 0.06% 0.03% 0.09% 0.20% 0.11% 0.19% -0.07% 1.46%

3 0.45% 0.39% 0.19% 0.17% 0.26% 0.06% 0.37% 0.31% 0.91% 0.72% -2.57% 1.26%

4 0.72% 0.29% 0.03% 0.16% 0.18% 0.03% 0.05% 0.37% 1.61% 0.33% -1.59% 2.19%

5 0.51% 0.30% 0.15% 0.14% 0.26% 0.05% 0.04% 0.26% 0.26% 0.31% -2.30% -0.02%

6 0.10% 0.17% 0.06% 0.05% 0.05% 0.04% 0.07% 0.14% 0.17% 0.16% -1.23% -0.20%

7 -0.13% 0.33% 0.07% 0.04% 0.26% 0.05% 0.24% 0.21% 0.66% 0.15% -3.13% -1.24%

8 1.06% 0.29% 0.11% 0.05% 0.11% 0.07% 0.09% 0.16% 0.30% 0.45% -9.97% -7.28%

9 0.57% 0.37% 0.09% 0.02% 0.46% 0.15% 0.24% 0.40% 1.34% 0.11% -0.30% 3.48%

∑ 3.68% 2.59% 0.79% 0.79% 1.67% 0.47% 1.23% 2.10% 5.37% 2.45% -21.12% 0.00%
66

Table 43 – 2007 FAF Domestic Marine Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.02% 0.02% 0.01% 0.03% 0.28% 0.00% 0.02% 0.05% 0.01% 0.02% 0.46%

2 0.77% 0.17% 0.06% 0.05% 1.57% 0.02% 0.04% 0.12% 0.05% 1.61% 4.46%

3 0.96% 0.42% 0.09% 0.08% 2.50% 0.50% 0.14% 0.27% 5.04% 6.15% 16.14%

4 0.17% 0.05% 0.17% 0.02% 0.87% 0.19% 0.05% 0.12% 6.36% 2.10% 10.09%

5 0.58% 0.14% 0.07% 0.10% 2.49% 0.06% 0.08% 0.16% 0.36% 1.35% 5.39%

6 0.58% 0.62% 0.03% 0.05% 1.79% 0.14% 0.04% 0.06% 1.51% 2.55% 7.36%

7 3.63% 0.75% 0.16% 0.07% 9.86% 0.30% 0.06% 0.23% 12.41% 2.09% 29.56%

8 0.06% 0.04% 0.03% 0.02% 0.11% 0.07% 0.04% 0.10% 0.02% 0.37% 0.87%

9 0.26% 0.15% 0.34% 0.13% 21.43% 0.24% 0.18% 0.61% 0.76% 1.57% 25.67%

∑ 7.03% 2.37% 0.95% 0.55% 40.90% 1.51% 0.67% 1.72% 26.52% 17.79% 100%

Table 44 – 2007 CFS Domestic Marine Share

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.01% 0.00% 3.00% 3.03%

3 0.52% 0.44% 0.00% 0.00% 0.18% 0.35% 0.00% 0.44% 9.64% 9.48% 21.06%

4 0.32% 0.00% 0.00% 0.00% 0.23% 0.26% 0.01% 0.00% 13.82% 5.79% 20.44%

5 0.00% 0.07% 0.00% 0.00% 1.76% 0.05% 0.00% 0.04% 0.00% 0.49% 2.42%

6 0.20% 0.48% 0.00% 0.00% 1.19% 0.22% 0.00% 0.01% 2.05% 4.33% 8.49%

7 1.68% 0.29% 0.16% 0.00% 6.09% 0.01% 0.00% 0.46% 25.29% 4.29% 38.29%

8 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.08% 0.00% 0.00% 0.09%

9 0.08% 0.07% 0.84% 0.02% 1.30% 0.44% 0.40% 0.65% 0.06% 2.31% 6.18%

∑ 2.82% 1.36% 1.00% 0.03% 10.76% 1.34% 0.42% 1.69% 50.86% 29.71% 100%

Table 45 – 2007 Domestic Marine FAF-CFS Percent Shares

Commodity

1 2 3 4 5 6 7 8 9 10 ∑

Census Division

1 0.02% 0.02% 0.01% 0.03% 0.28% 0.00% 0.02% 0.05% 0.01% 0.02% 0.46%

2 0.77% 0.17% 0.06% 0.05% 1.57% 0.02% 0.03% 0.11% 0.05% -1.39% 1.43%

3 0.44% -0.02% 0.09% 0.08% 2.32% 0.15% 0.14% -0.17% -4.60% -3.33% -4.92%

4 -0.15% 0.05% 0.17% 0.02% 0.64% -0.07% 0.04% 0.12% -7.46% -3.69% -10.35%

5 0.58% 0.07% 0.07% 0.10% 0.73% 0.01% 0.08% 0.12% 0.36% 0.86% 2.97%

6 0.38% 0.14% 0.03% 0.05% 0.60% -0.08% 0.04% 0.05% -0.54% -1.78% -1.13%

7 1.95% 0.46% 0.00% 0.07% 3.77% 0.29% 0.06% -0.23% -12.88% -2.20% -8.73%

8 0.05% 0.04% 0.03% 0.02% 0.11% 0.07% 0.04% 0.02% 0.02% 0.37% 0.78%

9 0.18% 0.08% -0.50% 0.11% 20.13% -0.20% -0.22% -0.04% 0.70% -0.74% 19.49%

∑ 4.21% 1.01% -0.05% 0.52% 30.14% 0.17% 0.25% 0.03% -24.34% -11.92% 0.00%