DEMAND 

Freight Transportation Demand: 

Energy-Efficient Scenarios for a 

Low-Carbon Future 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES: 

Freight Transportation Demand:
 

Energy-Efficient Scenarios for a Low-Carbon Future
 

A Study Sponsored by
 

U.S. Department of Energy
 

Office of Energy Efficiency and Renewable Energy
 

March 2013 

Prepared by 

CAMBRIDGE SYSTEMATICS
 

Cambridge, MA 02140
 

under subcontract DGJ-1-11857-01
 

Technical monitoring performed by 

NATIONAL RENEWABLE ENERGY LABORATORY
 

Golden, Colorado 80401-3305
 

managed by
 

Alliance for Sustainable Energy, LLC
 

for the
 

U.S. DEPARTMENT OF ENERGY
 

Under contract DC-A36-08GO28308
 

This report was prepared as an account of work sponsored by an agency of the United States 

Government. Neither the United States Government nor any agency thereof, nor any of their 

employees, makes any warranty, expressed or implied, or assumes any legal liability or 

responsibility for the accuracy, completeness, or usefulness of any information, apparatus, 

product, or process disclosed, or represents that its use would not infringe privately owned rights. 

Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States Government or any agency thereof. The views 

and opinions of authors expressed herein do not necessarily state or reflect those of the United 

States Government or any agency thereof. 
 

 

 

 

 

 

 

 

ABOUT THE TRANSPORTATION ENERGY FUTURES PROJECT 

This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) 

project, a U.S. Department of Energy (DOE)-sponsored multi-agency project initiated to identify 

underexplored strategies for abating greenhouse gases (GHG) and reducing petroleum 

dependence related to transportation. The project was designed to consolidate existing 

transportation energy knowledge, advance analytic capacity-building, and uncover opportunities 

for sound strategic action. 

Transportation currently accounts for 71% of total U.S. petroleum use and 33% of the nation’s 

total carbon emissions. The TEF project explores how combining multiple strategies could 

reduce GHG emissions and petroleum use by 80%. Researchers examined four key areas – lightduty vehicles, non-light-duty vehicles, fuels, and transportation demand – in the context of the 

marketplace, consumer behavior, industry capabilities, technology and the energy and 

transportation infrastructure. The TEF reports support DOE long-term planning. The reports 

provide analysis to inform decisions about transportation energy research investments, as well as 

the role of advanced transportation energy technologies and systems in the development of new 

physical, strategic, and policy alternatives. 

In addition to the DOE and its Office of Energy Efficiency and Renewable Energy, TEF 

benefitted from the collaboration of experts from the National Renewable Energy Laboratory and 

Argonne National Laboratory, along with steering committee members from the Environmental 

Protection Agency, the Department of Transportation, academic institutions and industry 

associations. More detail on the project, as well as the full series of reports, can be found 

at http://www.eere.energy.gov/analysis/transportationenergyfutures. 

Contract Nos.
 

DC-A36-08GO28308 and DE-AC02-06CH11357
 

iv 
v 

AVAILABILITY

This report is available electronically at http://www.osti.gov/bridge

Available for a processing fee to 

U.S. Department of Energy and its 

contractors, in paper form, from: 

U.S. Department of Energy 

Office of Scientific and Technical 

Information 

P.O. Box 62 

Oak Ridge, TN 37831-0062 

phone: 865.576.8401 

fax: 865.576.5728 

email: reports@adonis.osti 

Available for sale to the public, 

in paper form, from: 

U.S. Department of Commerce 

National Technical Information Service 

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Springfield, VA 22161 

phone: 800.553.6847 

fax: 703.605.6900 

email: orders@ntis.fedworld.gov 

online ordering: 

http://www.ntis.gov/help/ordermethods.aspx

CITATION

Please cite as follows: 

Grenzeback, L.R.; Brown, A.; Fischer, M.J.; Hutson, N.; Lamm, C.R.; Pei, Y.L.; Vimmerstedt, 

L.; Vyas, A.D.; Winebrake, J.J. (March 2013). Freight Transportation Demand: Energy-Efficient 

Scenarios for a Low-Carbon Future. Transportation Energy Futures Series. Prepared by 

Cambridge Systematics, Inc., and the National Renewable Energy Laboratory (Golden, CO) for 

the U.S. Department of Energy, Washington, DC. DOE/GO-102013-3711. 82 pp. 

 
REPORT CONTRIBUTORS AND ROLES 

National Renewable Energy Laboratory 

Austin Brown Co-lead 

Laura Vimmerstedt Co-lead 

Argonne National Laboratory 

Anant D. Vyas Co-lead and contributing author 

Cambridge Systematics 

Lance R. Grenzeback Primary author 

Michael J. Fischer Contributing author 

Nathan Hutson Contributing author 

Yi Lin Pei Contributing author 

Christopher R. Lamm Contributing author 

Energy & Environmental Research Associates 

Dr. James J. Winebrake Contributing author 

vi 
 

 

 

 

 

 

 

ACKNOWLEDGMENTS 

We are grateful to colleagues who reviewed portions or the entirety of this report in draft form, 

including: 

Sarah Froman, Policy Advisor, Office of Transportation and Air Quality, U.S. Environmental 

Protection Agency 

Dr. Gerard McCullough, Associate Professor, Department of Applied Economics, University of 

Minnesota 

Dr. Thomas O’Brien, Director of Research, Center for International Trade and Transportation, 

California State University, Long Beach 

Art Rypinski, Economist, Office of the Secretary, U.S. Department of Transportation 

Dr. Frank Southworth, Principal Research Scientist, Civil and Environmental Engineering, 

Georgia Institute of Technology 

Participants in an initial Transportation Energy Futures scoping meeting in June 2010 – 

representing the U.S. Department of Energy and national laboratories – assisted by formulating 

innovative and timely ideas to consider for the project. Steering Committee members and 

observers offered their thoughtful perspective on transportation analytic research needs as well as 

insightful comments on an initial Transportation Energy Futures work plan in a December 2010 

meeting, and periodic teleconferences through the project. 

Many analysts and managers at the U.S. Department of Energy played important roles in 

sponsoring this work and providing valuable guidance. From the Office of Energy Efficiency and 

Renewable Energy, Sam Baldwin and Carla Frisch provided leadership in conceptualizing the 

project. A core team of analysts collaborated closely with the national lab team throughout 

implementation of the project. These included: 

Jacob Ward and Philip Patterson (now retired), Vehicle Technologies Office 

Tien Nguyen and Fred Joseck, Fuel Cell Technologies Office 

Zia Haq, Kristen Johnson, and Alicia Lindauer-Thompson, Bioenergy Technologies Office 

The national lab project management team consisted of Austin Brown, Project Lead, and Laura 

Vimmerstedt, Project Manager (from the National Renewable Energy Laboratory); and Tom 

Stephens, Argonne Lead (from Argonne National Laboratory). Data analysts, life cycle 

assessment analysts, managers, contract administrators, administrative staff, and editors at both 

labs offered their dedication and support to this effort. 

vii 

 

 

 

 

 

 

 

 

 

 

TABLE OF CONTENTS
 

List of Figures ............................................................................................................................................. x
 

List of Tables .............................................................................................................................................. xi
 

Acronyms................................................................................................................................................... xii
 

Executive Summary.................................................................................................................................... 1
 

1.0 Introduction........................................................................................................................................... 5
 

2.0 Freight Transportation Demand.......................................................................................................... 7
 

2.1. Current/Historical Freight Demand.................................................................................................. 7
 

2.2. Freight Transportation Demand Factors ....................................................................................... 18
 

2.3. Economic Factors and Trends....................................................................................................... 20
 

2.4. Logistics Factors and Trends ........................................................................................................ 30
 

2.5. Transportation Factors and Trends ............................................................................................... 33
 

2.6. Policy and Regulatory Factors and Trends ................................................................................... 37
 

2.7. Summary ....................................................................................................................................... 41
 

3.0 Policy Actions..................................................................................................................................... 46
 

3.1. Fuel Taxes..................................................................................................................................... 46
 

3.2. Low-Carbon Fuel Standards.......................................................................................................... 46
 

3.3. Vehicle Efficiency Standards......................................................................................................... 47
 

3.4. Road Pricing .................................................................................................................................. 48
 

3.5. Freight Operations......................................................................................................................... 48
 

3.6. Infrastructure Investment............................................................................................................... 49
 

3.7. Trade Policy................................................................................................................................... 50
 

3.8. Land Use ....................................................................................................................................... 50
 

3.9. Opportunity Matrix ......................................................................................................................... 51
 

4.0 Freight Transportation Demand Projection Methods ..................................................................... 52
 

4.1. Overview........................................................................................................................................ 52
 

4.2. Macroeconomic/Commodity Demand Models............................................................................... 53
 

4.3. Time Series Models....................................................................................................................... 58
 

4.4. Behavioral/Choice Models............................................................................................................. 63
 

5.0 Additional Analysis ............................................................................................................................ 66
 

6.0 Conclusions ........................................................................................................................................ 67
 

Appendix: Literature Review.................................................................................................................... 70

References................................................................................................................................................. 77
 

ix 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF FIGURES 

Figure 2.1. Freight transportation demand, 2010 ......................................................................................... 8
 

Figure 2.2. Domestic freight ton-miles of travel by distance group, 2007 all modes (except pipeline)....... 10
 

Figure 2.3. Freight truck traffic on the National Highway System, all commodities, 2007.......................... 11
 

Figure 2.4. Lumber, wood, and paper products flows................................................................................. 12
 

Figure 2.5. Petroleum fuel consumption by U.S. domestic transportation mode........................................ 12
 

Figure 2.6. Freight transportation demand, 2040 ....................................................................................... 14
 

Figure 2.7. Trucking marginal operating costs per mile, Q1 2010.............................................................. 15
 

Figure 2.8. Rail operating costs, Q1 2012 .................................................................................................. 16
 

Figure 2.9. Highway funding sources by governmental division, 2008 (in billions) .................................... 17
 

Figure 2.10. Federal revenue sources for highways, 2010......................................................................... 18
 

Figure 2.11. Population growth and rail freight ton-miles, 1980 to 2030 .................................................... 21
 

Figure 2.12. Freight transportation demand forecasts................................................................................ 24
 

Figure 2.13. Value of trade by U.S. border region, 1860-2005................................................................... 27
 

Figure 2.14. Mega-regions and “GDP” of major cities ................................................................................ 28
 

Figure 2.15. Change in trucking industry employment, 1990–2010 ........................................................... 29
 

Figure 2.16. U.S. business logistics cost, 2001-2010................................................................................. 31
 

Figure 2.17. Transportation as a percentage of gross regional product for major Oregon and 

Washington state industries ................................................................................................... 31
 

Figure 2.18. Freight transportation systems ............................................................................................... 34
 

Figure 2.19. Projected miles per gallon for freight trucks, 2010 and 2030 ................................................. 35
 

Figure 2.20. U.S. railroad performance, 1964 to 2007 ............................................................................... 40
 

x 
 

 

 

 

 

LIST OF TABLES 

Table ES.1. Key Findings: Opportunity Matrix for Freight Transportation Energy Use and 

Greenhouse Gas Emissions Initiatives .................................................................................... 1
 

Table 2.1. Freight Tonnage and Value by Commodity Type, 2007 .............................................................. 9
 

Table 2.2. Transportation Demand Factors ................................................................................................ 19
 

Table 2.3. Summary of Trends and Freight Demand Implications by Factor ............................................. 43
 

Table 3.1. Opportunity Matrix for Freight Transportation Energy Use and GHG Emissions Initiatives ...... 51
 

xi 
ACRONYMS 

BEA Bureau of Economic Analysis 

BMI Business Market Insights 

BTM Business Transaction Matrix 

CFS Commodity Flow Survey 

CO2 carbon dioxide 

EPA U.S. Environmental Protection Agency 

FAF Freight Analysis Framework 

FHWA Federal Highway Administration 

GDP gross domestic product 

GHG greenhouse gas 

GVW gross vehicle weight 

IO Input-Output 

IP intellectual property 

LCFS low-carbon fuel standards 

NAICS North American Industry Classification System 

NREL National Renewable Energy Laboratory 

RFID radio-frequency identification 

RUBMZIO Random-Utility-Based Multi-Zonal Input-Output Model 

SCTG Standard Classification of Transported Goods 

TEF Transportation Energy Futures 

TRB Transportation Research Board 

U.S. DOT U.S. Department of Transportation 

VDOT Virginia Department of Transportation 

VMT vehicle miles traveled 

xii 
 

 

 

 

 

 

 

 

 

 

 

EXECUTIVE SUMMARY 

30 Billion Tons of Freight by 2050 

Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and by extrapolation, to 

nearly 30.2 billion tons in 2050, requiring ever-greater amounts of energy. This report describes the 

current and future demand for freight transportation in terms of tons and ton-miles of commodities moved 

by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, 

and policy and regulatory factors that shape freight demand; the possible trends and 2050 outlook for 

these factors, and their anticipated effect on freight demand and related energy use. The report draws 

upon a variety of sources, including published literature and unpublished perspectives based on authors’ 

expertise. After describing federal policy actions that could influence freight demand, the report then 

summarizes the available analytical models for forecasting freight demand, and identifies possible areas 

for future action. This is not intended to propose or promote particular policy actions. 

The U.S. Department of Transportation (U.S. DOT) Federal Highway Administration (FHWA) Freight 

Analysis Framework (FAF) estimates that 18.3 billion tons of goods were moved in the United States in 

2007, generating 5.4 trillion ton-miles of travel (U.S. DOT FHWA Undated). Trucks moved about 72% 

of all freight tonnage, accounting for 42% of all ton-miles. Rail accounted for 11% of tons moved, but 

28% of ton-miles. Domestic waterborne and air freight transportation shares were considerably smaller.1 

While all modes of domestic freight transportation are expected to experience significant growth in the 

coming decades, trucking’s share – when measured in tons and ton-miles – is projected to continue to 

grow at the expense of rail and waterborne freight. This reflects changes in the U.S. economy that are 

anticipated to favor the production and shipment of higher-value-added and time-sensitive goods, as well 

as an established preference among many freight shippers for using trucks to move such goods. Even as 

freight needs grow, more accurate demand forecasting, paired with effective policymaking, can help 

minimize this sector’s energy consumption and emissions. As shown in Table ES.1, increasing heavyduty engine efficiency and emission standards and imposing low-carbon fuel standards were identified as 

the policy options with the greatest probability of implementation and the highest potential for energy use 

and GHG emissions reduction. The assessments are based on available data, historic trends, and the 

authors’ professional insights on this industry. 

Table ES.1. Key Findings: Opportunity Matrix for Freight Transportation Energy Use and 

Greenhouse Gas Emissions Initiatives
 

Potential Reduction in Freight Transportation Energy Use and GHG Emissions 

Low Moderate High 

Probability of Implementation

High

Increase heavy-duty engine 

efficiency and emission 

standards. 

Moderate

Increase investment in freight 

infrastructure. 

Increase federal motor fuel tax. 

Implement road pricing 

(vehicle-miles-traveled user fees). 

Impose low-carbon 

fuel standards. 

Low 

Provide tax incentives for colocation of freight generators and 

terminals. 

Deregulate U.S. coastal shipping. 

Restructure U.S. trade policies to 

promote in- and near-sourcing. 

1 The FAF demand projections are described further in Section 2. 

1 
 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

Influential Factors and Policies 

The factors expected to have the most influence on freight demand between now and 2050 are: 

Economic Factors 

•	 Population and income 

•	 Economic growth rate (Gross Domestic Product) 

•	 Trade volume and partners 

•	 Urbanization and mega-region concentration. 

Logistics Factors 

•	 Supply chain restructuring. 

Transportation Factors 

•	 Petroleum fuel availability and cost. 

Policy and Regulatory Factors 

•	 Truck engine efficiency and greenhouse gas (GHG) emissions standards 

• Federal funding for freight infrastructure. 

A range of federal policy actions could influence freight transportation demand. They include: 

•	 Increasing heavy-duty engine efficiency and emission standards 

•	 Imposing low-carbon fuel standards 

•	 Increasing investment in freight infrastructure 

•	 Increasing federal motor fuel tax 

•	 Implementing road pricing (vehicle miles traveled user fees) 

•	 Restructuring U.S. trade policies to promote in- and near-sourcing 

•	 Providing tax incentives for the co-location of freight generators and terminals 

•	 Deregulating U.S. coastal shipping. 

The first half of this report takes a closer look at how these demand factors, trends and policies have been 

assessed using the existing knowledge base. 

Selecting the Most Effective Models 

A half-dozen types of analytical models can be used to project and assess the potential impact of these 

policy actions on freight transportation demand. They include: 

•	 Macroeconomic/Commodity Models, which estimate current and future freight traffic by linking 

economic activity to freight flows 

•	 Time Series Models, which extrapolate future demand based on historical freight volumes 

•	 Behavioral Models, which include both choice- and survey-based demand models, and capture 

how shippers select modes 

•	 Demand/Supply Equilibrium Models, which estimate freight demand based on balancing demand 

against supply of transportation services that have acceptable characteristics 

•	 Microsimulation and Agent-Based Models, which simulate large numbers of individual freight 

shipments and sum them to produce total freight flow estimates 

•	 Supply Chain and Logistics Models, which are intended to capture the relationship between 

suppliers and customers, as well as the decisions made by participants in the supply chain that 

affect freight demand. 

2
 
 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

The second half of this report explores how these models might be applied to expand understanding of 

future freight transportation demand. None of the available demand models addresses all the factors that 

shape freight transportation demand. Macroeconomic input/output models and behavioral (price elasticity 

of demand models)—coupled with national-level freight flow assignment network models—can provide 

direct estimates of the impacts of key factors—such as population, economic activity, and trade patterns, 

on tons and ton-miles of freight demand—which can then be translated into fuel use and GHG emissions 

estimates. While most of the other models also address population, economic activity, and trade patterns, 

extensive data collection and calibration requirements make them less suited to a first approximation of 

impacts of national-level changes in freight demand on fuel use and GHG emissions. 

Using appropriate models to project freight demand, based on a deep understanding of the diverse set of 

factors that influence it, can help assess the effects of possible actions to reduce petroleum-based fuels 

and emissions. 

3
 

 

 

 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

1.0 INTRODUCTION 

Freight accounts for about 26% of all petroleum-based fuels consumed in the U.S. transportation sector. 

This report is designed to help decision makers better understand the factors that shape freight 

transportation demand and gauge how federal actions might influence demand in ways that change 

petroleum-based fuel use. 

Freight demand is typically estimated using historical trend data and macroeconomic forecasts of industry 

production and household consumption. These traditional models are limited in their ability to consider 

the potential impact of shifts in fuel prices and transportation modes. At the same time, energy demand 

projections often neglect to factor in changes in freight demand, relying primarily on distance and fuel 

consumption data. This report provides a bridge between the existing methodologies and possible new 

approaches with an analysis of the current knowledge base, examination of potential policy actions, and 

recommendations for future analysis. 

Existing literature on freight demand tends to view rapid growth in demand as a result of a burgeoning 

consumer base, the expansion of global trade, and changing logistics patterns. Research reviewed for this 

report—from agencies including the Transportation Research Board (TRB), U.S. Department of 

Transportation (U.S. DOT), the American Association of State Highway and Transportation Officials, and 

the U.S. Army Corps of Engineers—addresses the impact of demographic trends, off-shoring and nearshoring production, supply chain customization, transportation network capacity, and government 

regulations. Drawing from this literature, the report presents trends with the potential to affect energy use 

and greenhouse gas (GHG) emissions. These are not intended as predictions, but as explorations of 

important interactions within the freight sector. The report draws on a range of evidence, including 

empirically verifiable statements of fact and quantitative findings from published studies, as well as 

interpretations and judgments. The authors used expert judgment based on the literature to select and 

describe possible trends. 

The report is structured as follows: 

•	 Freight Transportation Demand. Section 2 describes how freight transportation demand is 

measured and summarizes the current and projected demand for freight transportation. The section 

then discusses four broad groups of key factors that affect freight transportation demand: 

economic factors, logistics factors, transportation industry factors, and public policy and 

regulatory factors. 

•	 Policy Actions. Section 3 outlines potential federal government policy actions that could influence the 

demand for freight transportation. 

•	 Freight Transportation Demand Projection Methods. Section 4 describes the methods that can 

be used to estimate the impact of policy actions on freight transportation demand.2 

•	 Additional Analysis. Section 5 suggests areas of research and development where additional work 

could significantly improve understanding and anticipate the effects of trends and policies on 

freight transportation demand and energy use. 

•	 A Literature Review is provided in Appendix A. 

This report also provides background for the development of the National Renewable Energy Laboratory 

(NREL) Freight Energy Analysis Tool. The tool makes it possible to evaluate the transportation energy 

and GHG impacts of tonnage, location, and mode scenarios at a national level through 2050. Information 

from the literature reviewed for this report informed the choice of datasets, variables, and analytical 

2 The section mentions but does not describe in detail the methods available to project changes in mode shares (e.g., the potential 

to shift freight from truck to rail or water). Those methods are covered elsewhere in the Transportation Energy Futures project. 

5
 
 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

methods to develop this tool. This tool can be accessed through the NREL Transportation Energy Futures 

website: http://www.nrel.gov/analysis/transportation_futures. 

This report focuses on the structure and dynamics of freight transportation demand. Companion 

Transportation Energy Futures reports describe other transportation demand issues, including freight 

modal choice; strategies for trip reduction and efficient driving in personal transportation; and effects of 

the built environment on transportation demand (Porter, Brown, Dunphy et al. 2013; Porter, Brown, 

DeFlorio et al. 2013; Brogan et al. 2013). 

6
 
 

 

 

 

 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

2.0 FREIGHT TRANSPORTATION DEMAND 

This section presents current and historical freight demand, drawing from public data, especially the 

Federal Highway Administration’s (FHWA’s) Freight Analysis Framework (FAF). It also summarizes 

important factors that influence freight demand, drawing from the literature and selected by the authors 

based on expert judgment. Possible future trends are identified and discussed to illustrate interactions of 

various factors within the freight sector and how those could influence its development. Trends and 

projections are not precise predictions; a range of possible future outcomes is possible. The authors also 

use expert judgment based on the literature to select and describe possible trends. 

2.1. Current/Historical Freight Demand 

Freight transportation demand is typically measured in tons, ton-miles, and value (dollars) of goods 

moved by the freight sector.3 The FAF estimates that 18.5 billion tons of goods were moved in the 

United States in 2007, generating 5.4 trillion ton-miles of travel, and with a value approaching $16.7 

trillion (U.S. DOT FHWA Undated).4 

The ton, ton-mile, and value data can be broken down by mode, as shown in Figure 2.1 (Cambridge 

Systematics, Inc. analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool). In 2007, 

trucks moved about 72% of all freight tonnage, accounting for 42% of all ton-miles and 70% of freight 

commodity value.5 Rail accounted for only 11% of tons moved, but 28% of ton-miles and 3.5% of total 

value, reflecting rail’s cost effectiveness in hauling heavier, but generally lower-value, commodities, such 

as coal and grain, over long distances. Excluding international maritime shipments, waterborne 

transportation accounted for a smaller percentage of tons and ton-miles. Air freight transportation 

constituted an even smaller share, except when measured by value. 

3 A ton of goods moved one mile is counted as one ton-mile. There is some double-counting in the reporting of aggregate 

national freight transportation statistics because data are compiled by mode, not specific shipment. A ton of goods transported 

two miles by truck is counted as one ton of freight and two ton-miles of freight movement; however, a ton of goods transported 

the one mile by truck, then transferred to rail and transported one more mile by rail is counted as two tons of freight (one by 

truck, one by rail) and two ton-miles of freight movement. 4 Unless otherwise noted, the freight demand statistics are based on FAF versions 3.1 and 3.2. 

5 The FAF captures most Interstate and intercity freight traffic and some but not all local freight traffic. As an example, the FAF 

will capture and report the shipment of food products by truck from Iowa to the Washington, D.C. metropolitan area, but may not 

capture all the local truck movements involved in the final delivery of those food products to local supermarkets, restaurants, and 

neighborhood stores. Overall, the FAF accounts for almost all ton-miles of travel by air, water, and rail, but only about half of all 

ton-miles of travel by truck because the FAF does not survey or report local truck freight trips. This qualification is important 

when attempting to estimate freight transportation energy consumption and greenhouse gas emissions by mode based on the FAF 

data. 

7
 
 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

Share 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Tons Ton-Miles Value 

Truck

Rail

Water

Air (include truck-air) 

Multiple modes and mail 

Pipeline

Other and unknown

Truck

Rail

Water

Air (include truck-air) 

Multiple modes and mail 

Pipeline

Other and unknown

Truck

Rail

Water

Air (include truck-air) 

Multiple modes and mail 

Pipeline

Other and unknown 

Figure 2.1. Freight transportation demand, 2010

(tons, ton-miles, and value by mode)
 

(Source: Cambridge Systematics, Inc. analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool)
 

Freight demand can also be broken down by commodity. Table 2.1 shows freight tons associated with 

industries defined by two-digit Standard Classification of Transported Goods (SCTG) commodity codes, 

the level of disaggregation most commonly used in national-scale freight and policy studies.6 

6 The SCTG is used to classify commodities for the Commodity Flow Survey, a periodic survey of manufacturers and other 

major freight shippers conducted by the U.S. DOT Bureau of Transportation Statistics and the U.S. Department of Commerce 

Bureau of the Census. For a description of the SCTG classifications (from two-digit to five-digit levels), see FHWA and U.S. 

DoC (undated). 

8
 
 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Table 2.1. Freight Tonnage and Value by Commodity Type, 2007 

2007 Value 

2007 Tons (Millions

SCTG2 SCTG2 Commodity (Thousands) Dollars) 

01 Live animals/fish 106,851 $144,883 

02 Cereal grains 1,475,408 $196,227 

03 Other agriculture products 436,348 $267,929 

04 Animal feed 270,333 $96,486 

05 Meat/seafood 116,145 $327,074 

06 Milled grain products 136,903 $159,745 

07 Other foodstuffs 539,309 $553,699 

08 Alcoholic beverages 140,872 $191,629 

09 Tobacco products 4,624 $94,746 

10 Building stone 57,469 $9,795 

11 Natural sands 569,898 $8,015 

12 Gravel 2,263,771 $23,523 

13 Nonmetallic minerals 375,185 $20,747 

14 Metallic ores 120,451 $32,108 

15 Coal 1,444,753 $40,222 

16 Crude petroleum 836,581 $365,905 

17 Gasoline 1,005,614 $688,780 

18 Fuel oils 744,791 $417,475 

19 Coal-n.e.c. 1,295,016 $512,413 

20 Basic chemicals 463,156 $340,388 

21 Pharmaceuticals 20,912 $880,159 

22 Fertilizers 230,038 $59,623 

23 Chemical products 148,078 $403,550 

24 Plastics/rubber 224,336 $578,252 

25 Logs 517,410 $22,180 

26 Wood products 416,536 $231,531 

27 Newsprint/paper 162,616 $134,600 

28 Paper articles 105,157 $142,865 

29 Printed products 55,453 $205,763 

30 Textiles/leather 70,178 $696,089 

31 Nonmetal mineral products 1,392,666 $247,564 

32 Base metals 422,247 $556,705 

33 Articles-base metal 205,439 $572,938 

34 Machinery 185,914 $1,759,415 

35 Electronics 78,108 $1,429,934 

36 Motorized vehicles 190,635 $1,267,189 

37 Transport equipment 8,361 $228,458 

38 Precision instruments 7,440 $386,806 

39 Furniture 50,046 $233,481 

40 Miscellaneous manufacturing products 111,720 $691,728 

41 Waste/scrap 1,324,523 $130,846 

43 Mixed freight 342,411 $1,061,482 

99 Unknown 205,034 $237,649 

(Source: Cambridge Systematics, Inc. analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool) 

9 
 

 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

Freight demand can be further described by length of trip. Figure 2.2 (Cambridge Systematics, Inc. 

analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool) shows the distribution of 

domestic freight ton-miles of travel by distance group for all modes (except pipeline) in 2007. The 

segments within each column show the relative contribution of each mode to the ton-miles of travel in 

each distance group. Most shipments moving less than 100 miles and majority of shipments moving less 

than 500 miles are handled by truck. Rail dominates the longer-haul markets between 500 and 

1,500 miles. 

Millions 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

1,000 

<50 50-100 100-250 250-500 500-750 750-1000 1000-1500 1500-2000 >2000 

Truck Rail Water Air (include truck-air) Multiple modes and mail Other and unknown 

Figure 2.2. Domestic freight ton-miles of travel by distance group, 2007 all modes (except pipeline) 

(Source: Cambridge Systematics, Inc. analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool) 

Finally, freight demand can be mapped as freight flows. Figure 2.3 shows estimated truck freight flows 

measured in daily truck traffic in 2007 [FAF version 3.1 (FHWA 2010b)]. (Wider bandwidth lines 

indicate greater flows.) 

10
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Figure 2.3. Freight truck traffic on the National Highway System, all commodities, 2007 

[Source: FAF version 3.1. FHWA (2010b)] 

Freight flows can be further broken down by commodity type (e.g., chemicals, auto parts), by origin and 

destination (typically economic region, county, or major port), and by mode. As an example, Figure 2.4 

maps the flows of lumber, wood, and paper products moving by rail through Portland, Oregon (Oregon 

Department of Transportation 2003). 

The information about freight transportation demand can be paired with information about fuel sales to 

estimate fuel consumption by mode, as shown in Figure 2.5 (TRB 2011b). The data show that freight 

transportation accounts for about 26% of all petroleum-based fuels (gasoline, diesel, etc.) consumed in the 

transportation sector, which itself is 97% dependent on petroleum-based fuels. 

11
 
 

 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

Origins and Destinations of
 

Lumber, Wood, and Paper
 

Products Shipped via
 

Portland-Vancouver Rail
 

Triangle, 1998
 

0 0.05 0.25 1.0 2.5 10 

(million tons)
 

Volume of Lumber, Wood, and 

Paper Products on PortlandVancouver Rail Triangle 

Access Routes, 1998
 

0 0.25 0.75 2.5 5.0 33.0 

(million tons) 

Figure 2.4. Lumber, wood, and paper products flows 

(Oregon Department of Transportation 2003) 

Single-Unit 

Trucks, Freight 5.1% Railroads, 

2.1% 

Combination 

Trucks, 

14.2% 

Cars and Light Trucks 

Combination Trucks 

Passenger Airlines 

Single-Unit Trucks 

Freight Railroads 

Cargo Airlines 

Domestic Waterways 

General Aviation 

Transit Bus and Rail 

Motor Coach 

Commuter Rail 

Intercity Passenger Rail 

Figure 2.5. Petroleum fuel consumption by U.S. domestic transportation mode 

[Source: (TRB 2011b)] 

Note: The total represents consumed gallons of gasoline, diesel, and jet fuel, irrespective of energy density. Mode totals were 

calculated through various government and industry sources for the most recent year in the period covered. Fuel used by pipelines, 

international aviation, and international maritime are excluded. 

12
 
 

 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Freight transportation demand is projected to grow from 18.9 billion tons in 2007 to 27.5 billion tons in 

2040, and by extrapolation, to nearly 30.2 billion tons in 2050.7 The FAF projections are based on macroeconomic projections of production, consumption and trade by industry sector and reflect post-recession 

expectations about domestic and global economic growth rates. The freight projections are demand 

driven; they are not constrained by transportation capacity or supply. They assume that if demand grows 

for a commodity that is shipped by truck today, then the additional tonnage will also be shipped by truck. 

This is a conservative approach to freight demand forecasting that allows analysts to apply different 

assumptions about how the future supply and pricing of freight transportation services might shift freight 

shares among the modes. 

Demand-drive forecasts are used as a starting point for freight transportation policy studies because, as a 

general rule, freight transportation is primarily a derived demand. Growth in freight transportation 

demand tracks the projected growth in population and economic activity. More people and more 

economic activity translate almost directly into increased demand for food, clothing, housing, energy, as 

well as manufactured goods, and the subsequent transportation of these goods to consumers. 

Improvements in transportation technologies and changes in the relative quality and pricing of truck, rail 

and other modal services can affect overall demand, but these effects are typically second order and 

redistributive. 

The projected mode shares in 2040 are shown in Figure 2.6 (Cambridge Systematics, Inc. analysis of 

2007 FAF3.2 data using the NREL Freight Energy Analysis Tool). While all modes of transportation will 

experience significant growth in this scenario, trucking’s share of freight transportation – when measured 

in tons and ton-miles – is projected to continue to grow at the expense of rail and waterborne freight, 

reflecting anticipated structural changes in the U.S. economy that favor the production and shipment of 

higher-value-added and more time-sensitive goods and a preference for using trucks to move such goods. 

If these projections are realized, the trucking industry as a whole (heavy-, medium- and light-duty trucks) 

could experience increases in fuel consumption ranging from 11% to nearly 30% between 2009 and 2030, 

depending on assumptions made about the rate of improvement in truck engine fuel efficiency.8 Changes 

in truck fuel efficiency will increase or decrease overall demand. Deteriorating fuel efficiency that drives 

up the cost of trucking services will tend to dampen demand and shift freight to other modes. Conversely, 

improving fuel efficiency that reduces the cost of truck services will tend to increase overall demand. 

Both trends will also be greatly influenced by consumer preferences for service and shippers’ and freight 

carriers’ strategies for managing total logistics costs through redesign and relocation of supply chains and 

distribution networks. The fuel and emissions impacts of the projected demand and modal shares will also 

be affected by shippers’ and carriers’ approaches to minimizing their corporate carbon emissions in 

response to consumer preferences and market competition. The FAF projections do not explicitly account 

for these evolving shifts in business and consumer behavior. 

As will be discussed further in Section 4, which deals with freight demand projection methods, national 

freight demand is typically estimated using historical trend data and macroeconomic forecasts of industry 

sector production and household consumption. These models have limited capability to consider the effect 

of substantial shifts in fuel prices and transportation mode selection. As noted, if a commodity is shipped 

by truck today, freight demand models assume that the commodity will continue to be shipped by truck in 

the future regardless of changes in fuel prices and fuel types. Conversely, the national energy demand 

projections have limited capability to consider the effects of changes in freight demand, relying primarily 

7 The FAF provides projections to 2040. We extrapolated the FAF projections to 2050 for the NREL Freight Energy Analysis
 

Tool.
 8 The recently published Transportation Research Board Special Report 307 (TRB 2011b) projects increases in the range of 11% 

if engine efficiencies are improved significantly. The Energy Information Administration’s Annual Energy Outlook 2009 and 

Annual Energy Outlook 2010 reports project increases in the range of 30% or more with limited improvements to engine fuel
 

efficiencies (U.S. EIA 2009, 2010).
 

13
 
 

 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

on historical trend data and projections of truck-miles of travel and engine fuel consumption rates (gallons 

per ton-mile). These shortcomings are well recognized, but have not yet been addressed. 

Share 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Truck

Rail

Water

Air 

Multiple Modes

Pipeline

Other and Unknown

Truck

Rail

Water

Air 

Multiple Modes

Pipeline

Other and Unknown

Truck

Rail

Water

Air 

Multiple modes

Pipeline

Other and unknown 

Tons Ton-Miles Value 

Figure 2.6. Freight transportation demand, 2040

(tons, value, and ton-miles by mode)
 

(Source: Cambridge Systematics, Inc. analysis of 2007 FAF3.2 data using the NREL Freight Energy Analysis Tool)
 

2.1.1. Breakdown of Operating Costs 

Figure 2.7 shows the marginal cost per mile of operating a truck in early 2010 (American Transportation 

Research Institution 2011). Figure 2.8 shows the industry-wide estimate of railroad operating costs in 

early 2012 (Association of American Railroads 2012a).

9 The data are not directly comparable because of 

the differences in the dates and the underlying accounting and reporting procedures. However, the data 

provide a general indication of the relative importance of fuel costs in the operation of truck and rail 

services. 

9 The category “Other” includes purchased services, portions of administrative expenses and property taxes. 

14
 
 

 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Premiums 

3% 

Repair & 

Maintenance 

8% 

Truck/ Trailer 

Lease or 

Purchase 

Payments 

16% 

Driver Wages, 

Benefits 

36% 

Fuel, Oil 

31% 

Truck Insurance 

Permits, Licenses 

2% 

Tires 

2% 

Tolls 

2% 

Figure 2.7. Trucking marginal operating costs per mile, Q1 2010 

(Source: American Transportation Research Institution 2011) 

For trucking, fuel costs in early 2010 accounted for 31% of marginal operating costs per mile, driver labor 

costs for 36%. These proportions have varied considerably in recent years, with fuel accounting for 38% 

in 2008 and 28% in 2009. The cost and volatility of fuel prices in the past decade as well as increasing 

interest by shippers in decreasing fuel costs and carbon emissions from goods movement have been major 

factors pushing the motor carrier industry to search for more fuel-efficient operational and technical 

solutions. As reported by the U.S. Environmental Protection Agency’s (EPA’s) SmartWay Transport 

Partnership program, which works with the shipping and trucking community to reduce fuel use and 

emissions from goods movement, these solutions can include cleaner and more efficient engines and 

transmissions, more aerodynamically clean truck shapes (including nose cones, skirts and gap fairings), 

idle reduction technologies, low rolling resistant and single-wide tires, lower weight components and 

aluminum wheels, driver training, and more efficient head-haul and back-haul routing and dispatching 

(EPA 2012a, 2012b).

10 Energy efficiency opportunities for trucks and other non-light-duty vehicles are 

reviewed in another report in the Transportation Energy Futures series. 

10 EPA’s SmartWay Transport Partnership is a government-industry collaboration to accelerate the adoption of advanced 

technologies and strategies to improve fuel efficiency and reduce GHGs and other emissions from the freight sector, including 

trucks. 

15
 
 

 

 

 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

Transfers and Other
 

Income
 

32%
 

Gasoline Tax 

45% 

Federal Use Tax
 

2%
 

Trucks and Trailers 

3% Tires
 

1%
 

Diesel Tax 

17% 

Figure 2.8. Rail operating costs, Q1 2012 

(Source: Association of American Railroads 2012a) 

Note: “Other” includes purchased services, casualties and insurance, loss and damage, taxes other than income and payroll, and 

general and administrative expense. 

Railroads spend relatively less on fuel, reflecting the economies of scale and corresponding fuel savings 

that are achieved by hauling very large volumes of freight over long distances. In 2008, railroads 

consumed approximately 320 Btu per ton-mile, compared to trucking, which used approximately 1,390 

Btu per ton-mile for heavy-duty truck operations.11 The difference in fuel use is reflected in the generally 

higher price of trucking services and the generally lower price of rail services, but the services provided 

by truck and rail also differ substantially in load capacity, routes and destinations served, frequency of 

service, transit time and reliability of travel time. 

The other major difference between trucking and rail operating costs is the accounting of infrastructure 

costs—for trucking, the cost of construction and maintenance of highways, bridges and tunnels, and for 

rail, the cost of construction and maintenance of rail lines, bridges, and tunnels. Trucking pays for its 

highway infrastructure through motor fuel taxes, registration fees, vehicle and tire sales taxes, and tolls. 

Trucking shares these costs with automobiles and light trucks. Both trucks and passenger cars “pay as 

they go” because the public sector generally finances the initial construction and ongoing refurbishment 

and pays off the costs over time with annual fuel tax receipts. 

By contrast, the railroads finance construction and refurbishment of the rail infrastructure through their 

own revenues and private sector bonds. Freight railroads are very capital intensive. The railroad industry 

estimates that 17% of revenues are invested in capital, about five times the amount invested by 

manufacturing industries (Association of American Railroads 2012b). One consequence is that railroads 

tend to be very conservative about investing in new rail lines and expanded services, attempting to 

balance the risk of long-term capital costs against fluctuating short-term market demand and revenues. 

11 Btu (British thermal units) per ton-mile is a measure of energy intensity. These estimates are from Energy and Environmental 

Research Associates for rail based on data reported in Bureau of Transportation Statistics, Table 4-25 (RITA 2012); and for 

trucks based on data reported in Transportation Energy Data Book, Table 2.16 (Davis, Diegel, and Boundy 2009). The truck 

estimates assume 16 tons payload per truck (FHWA 2007). 

16
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Figure 2.9 shows the sources of highway funding by level of government (FHWA 2008). Federal funding, 

largely from federal motor fuel taxes, accounts for about quarter of highway funding; state funding, from 

state motor fuel, vehicle excise and sales taxes, accounts for about half; and local government funding, 

from property and local sale taxes, for the remaining quarter. This distribution, which evolved out of the 

Interstate highway program, has been relatively stable over the past several decades. 

State 

$97.69 

51% 

Local 

$53.13 

27% 

Federal 

$41.91 

22% 

Figure 2.9. Highway funding sources by governmental division, 2008 

(in billions) 

(Source: FHWA 2008) 

Figure 2.10 provides a breakdown of the federal revenue sources for highway funding (FHWA 2010a). In 

2010, approximately two-thirds of federal spending on highways was funded from gasoline and diesel 

fuel taxes and one-third from transfers from general revenues (e.g., derived from income and other taxes). 

The large proportion of funding allocated from general revenues is relatively new and reflects the 

inability of current motor fuel taxes revenues to cover spending needs in an environment of diminished 

fuel consumption, higher spending on infrastructure, and resistance to higher motor fuel taxes. The 

federal motor fuel tax rate has not been increased since the last Congressional action in 1993; and because 

the federal rate is not indexed to inflation, the purchasing power of each federal motor fuel tax dollar has 

dropped by about one-third since 1993. This slow decoupling of fuel taxes from federal highway spending 

has several long-term implications. It makes it somewhat easier to pursue energy efficiency and GHG 

reduction policies because the loss of motor fuel tax revenues—the consequence of improved engine 

efficiency—is compensated for by increased funding from other non-transportation revenue sources. But 

it also means greater pressure on other federal revenues sources (e.g., income and sales taxes) as well as 

pressure to develop new revenue sources, such as vehicle-miles-of-travel-based user fees. 

17
 
 

 

 

Gasoline Tax 

45% 

Diesel Tax 

Tires 

1% 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

Transfers and 

Other Income
 

32%
 

Federal Use Tax 

2% 

Trucks and 

Trailers 

3% 

17% 

Figure 2.10. Federal revenue sources for highways, 2010 

(Source: FHWA 2010a) 

The next sections describe the factors that shape freight transportation demand and the models available 

to estimate the effects of broad shifts in energy use, fuels, and engine efficiency on freight demand. 

2.2. Freight Transportation Demand Factors 

Table 2.2 sets out a general taxonomy of transportation demand factors. The factors are grouped into four 

broad categories: 

1.	 Economic factors, which are the primary determinants of the types and quantities of freight 

generated, and the location of freight production and consumption 

2.	 Logistics factors, which shape how industries and individual firms source, make, route, and sell 

their products 

3.	 Transportation factors, which affect how freight demand and business logistics plans are executed 

over highways, rail lines, and waterborne shipping lanes, including the infrastructure, 

technologies, and fuels that are used to move goods 

4.	 Policy and regulatory factors, which set the “rules of the game” governing the economic,
 

logistics, and transportation elements.
 

The taxonomy is useful for describing the factors that shape freight transportation demand and tracing 

their interactions. The taxonomy is not a model, only a framework. It has been constructed to highlight 

the factors of most relevance to a national-level study of the interaction between freight demand, fuel use, 

and GHG emissions, based on the authors’ professional judgment. A narrower and more detailed 

taxonomy might be appropriate for regional- or corridor-level freight studies. 

The subsections describe each of these demand factors, their anticipated trends, and the effects of changes 

in the factors on freight transportation demand and energy use. The assessments of the trends draw upon 

available data, literature, and the opinions of informed experts and are presented as possible and probable, 

but not definitive outcomes. 

18
 
 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Table 2.2. Transportation Demand Factors 

Economic Factors Consumption Population 

Immigration/Migration 

Households 

Income 

Lifestyle 

Production Economic Activity (GDP) 

Industries/Commodities 

Technology 

Labor 

Energy 

Trade Trade 

Trade Partners/Trade 

Lanes 

Economic Geography Land Use 

Logistics Factors Supply Chains Strategies 

Sourcing 

Packaging 

Staging/Networks 

Transportation Factors Motor Carriers/Highways Firms 

Services 

Volumes 

Trucks 

Highways 

Facilities and Access 

Railroads/Rail Lines Firms 

Services 

Volumes 

Locomotives/Rail Cars 

Rail Lines 

Facilities and Access 

Shipping Lines/Marine Firms 

Ports Services 

Volumes 

Ships 

Terminals 

Facilities and Access 

Air Cargo Carrier/Airports Firms 

Services 

Volumes 

Planes 

Airports 

Facilities and Access 

Policy and Regulatory 

Factors 

International 

National 

Policy 

Plans and Programs 

State Regulation 

Regional Taxation 

City/Local 

19
 
 

 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

2.3. Economic Factors and Trends 

This subsection discusses the impact of changes in consumption, production, trade, and economic 

geography on freight demand. Freight demand is commonly viewed as a dependent variable that is driven 

by changes in broader factors, such as population and economic activity. But the freight sector itself is 

also an important driving force behind these economic factors. Economic activity in the United States is 

largely dependent on the ability of the freight sector to deliver goods efficiently. For example, the 

U.S. agriculture industry, which has a high ratio of tonnage per unit of gross domestic product (GDP), has 

thrived in large part because of the ability of the freight system to move massive amounts of crops and 

animal and forest products inexpensively. Other industries, such as retailing and high-tech manufacturing, 

have thrived because of the ability of the U.S. freight system to reliably deliver goods and industrial parts 

within very narrow delivery windows. The freight system of the future will respond to the economy, but 

the economy will also be built around the capabilities of the freight transportation system. This section 

focuses of changes in the economy that create freight demand; Section 2.5 focuses on changes in the 

transportation system that affect economic activity. Freight demand implications of anticipated trends are 

authors’ interpretations based on professional expertise and knowledge of the literature. In describing 

these trends, as shorthand the text often uses the word “will” to describe possible future events; these 

events are conditional and must be understood within the context of the trends and implications described. 

2.3.1. Consumption 

Consumption is impacted by the size of the population and the structure and changing needs of different 

population cohorts. 

2.3.1.1. Population 

Freight transportation demand and population growth are closely correlated. As an example, Figure 2.11 

compares trends in population and ton-miles of railroad freight transportation (Association of American 

Railroads projections based on railroad traffic data and U.S. Census Bureau population data).12 Similar 

patterns—but at different proportional rates—are found between population and truck, waterborne, and 

air freight tonnage, ton-miles, and revenues. 

•	 Trend: In 2010, the U.S. population was 308 million. The U.S. Census midpoint projection for 

2050 anticipates that the U.S. population will grow to 439 million. 

–	 Freight Demand Implications: A larger population will consume more food, clothing, 

housing, and energy, increasing the demand for freight transportation. According to the 

FHWA’s FAF, population growth—and the corresponding growth in economic activity and 

trade—will translate into a 72% increase in freight transportation tonnage by 2050. 

•	 Trend: The number of older adults and the elderly is projected to grow more rapidly than the 

number of children and working age adults. Under this trend, children and working age adults will 

still make up the majority of the U.S. population, but the number of children below age 21 will 

grow at a compound annual rate below 1% and the number of working age adults age 16 to 54 will 

grow at a slightly lower rate. In contrast, the number of adults age 65 to 84 will grow at about 2% 

and the number of adults age 85 and above will grow at about 3%. 

–	 Freight Demand Implications: Older adults preparing for retirement purchase less food, 

clothing, and housing than young adults forming households and raising children. With an 

aging population, there will be relatively less demand for housing-related commodities and 

freight transportation and more demand for service-related goods. 

12 Revenue ton-miles are loaded railcar ton-miles as opposed to nonrevenue or empty railcar ton-miles. 

20
 
 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

Population 

(Millions) 

400 

Ton-Miles 

(Billions) 

2,800 

375 2,500 

350 2,200 

325 1,900 

300 1,600 

275 1,300 

250 1,000 

225 700 

200 

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 

400 

Population (left axis) 

Ton-miles (right axis) 

Correlation from Actual Projection 

1980-2010: 97% 

Figure 2.11. Population growth and rail freight ton-miles, 1980 to 2030 

(Source: Association of American Railroads projections based on railroad traffic data and U.S. Census Bureau population data) 

•	 Trend: Global population is projected to reach 9.3 billion by the year 2050 (ESA 2010). 

–	 Freight Demand Implications: Given the global reach of U.S. businesses and the 

opportunities to export U.S. agricultural and manufactured products, world population growth 

may be the ultimate driver of U.S. freight transportation demand. We are seeing this today in 

the increase of agricultural and natural resource exports to China and are likely to see 

increases in exports to South America and possibly Africa in the longer-term future. 

2.3.1.2. Immigration/Migration 

•	 Trend: Immigration rates are down because of the recession, and substantial reforms to 

immigration policy are unlikely in the near future. The U.S. Census Bureau projects that the 

annual net in-migration rates will increase gradually but consistently from 1.3 million in 2010 to 

over 2 million per year in 2050. 

–	 Freight Demand Implications: Immigration rates are not expected to have a significant effect 

on freight demand in the near term. However, with an aging population, policies that 

encourage immigration may become a necessary catalyst to future economic growth. If the 

United States were to encourage a higher inflow of educated, working-age immigrants with 

children, this could reshape the population profile, partially counteracting the projected drop 

in working age adults, and accelerate demand for production of housing and the freight 

transportation of construction and related commodities. 

•	 Trend: Domestic migration—from rural to urban areas, and from the Northeast and Midwest to the 

South and Southwest—is projected to continue through 2050, but at rates less dramatic than those 

of the previous 50 years. 

–	 Freight Demand Implications: Internal migration will not significantly affect total freight 

demand, but changing migration patterns (e.g., the possibility of slower migration to the 

Southwest because of persistent, long-term water shortages) would shift freight production 

21
 
 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

and consumption patterns and impact freight volumes at international gateways and along 

freight corridors. 

2.3.1.3. Households 

•	 Trend: Since the end of World War II, the United States has experienced very high rates of 

household formation, substantially outpacing the population growth rate. Demographers expect 

that the number households will continue to increase (and the number of people per household will 

continue to decline) through 2050. However, multigenerational households, which are common in 

many other cultures, may become somewhat more prevalent in the United States because of the 

growth in the Hispanic and Asian-American population, which grew by 30% between 2000 and 

2010 (Taylor et al. 2010). 

–	 Freight Demand Implications: More, albeit smaller, households would increase the demand 

for the production and transportation of building materials and housing goods. A substantial 

shift toward larger households, driven by cultural preferences or economic hardship, would 

slow the rate of household formation and demand for transportation of building materials, etc. 

2.3.1.4. Income 

•	 Trend: U.S. household and personal income is projected to increase through 2050 as GDP growth 

outpaces population growth. 

–	 Freight Demand Implications: In general, increasing affluence means increasing consumption 

of housing and consumer goods; however, several trends may temper this trend. An older 

population will invest less in housing and merchandise, and more in travel, services, and 

health care (Gale 1997). And smaller households, even with rising per capita income, 

typically invest less money in housing and merchandise. Uncertainty about the future of 

Social Security could lead to higher personal and family savings rates, leaving less income to 

spend on housing and goods.13 A strong countervailing influence would be public policy that 

more aggressively redistributes wealth, reversing the trends of past several decades. A 

wealthier middle class and a significant lowering of the U.S. poverty rate from the 15% 

recorded in 2010 could boost consumption and freight demand. 

2.3.1.5. Lifestyle 

•	 Trend: The culture of thrift that marked the Depression-era generation gave way to more 

aggressive consumption in the post-World War II generations that followed. It would appear that 

each generation since World War II has been more environmentally aware than the generation that 

preceded it, but the long-term impact of a greener, more environmentally centered lifestyle on 

consumption and freight demand is not clear. Forecasts to 2050 must make assumptions for at least 

two generations whose patterns are not yet formed, but with more of the U.S. population settling in 

urban areas, it is highly likely that the lifestyles of the 2050s will be different than today’s. As 

evidence of a societal trend toward sustainability that can impact businesses, consumers, and 

communities, the Retail Industry Leaders Association, a trade association representing top U.S. 

retailers, recently issued (with contributions by BSR), its first sustainability report, highlighting 

the increased consideration that sustainability is garnering within the retail industry.14 

–	 Freight Demand Implications: The trend toward a “green” lifestyle today is expressed as a 

preference for more fuel-efficient cars and buildings, more locally produced food and retail 

goods, and more recycling. If these lifestyle preferences become more firmly ingrained by 

13 The United States has long had extremely low individual and household savings rate by world standards. In 2008, the average
 

savings rate of U.S. households was 2.7%, compared to 5.8% for the EU. See, for example, “Household Net Savings Rate by
 

Country” (Census Bureau 2012). 14 See RILA (2012) for a discussion of the 2012 Retail Sustainability Report.
 

22
 
 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

2050, then they could modestly reduce the demand for long-haul freight transportation. 

However, the strength of this trend is not clear. The trend toward a greener lifestyle could 

also mean more, but smaller, shipments moving among more origins and destinations as 

consumers seek to purchase higher-quality, lower-cost, and individually tailored “green” 

products. This may further accelerate web-based shopping and increase the demand for 

higher-service and more-energy-intensive truck and air cargo transportation, thereby limiting 

new opportunities for rail transportation. An increasingly urban population and lifestyle 

would also mean a more urban consumption pattern, including more income spent on housing 

and other location-related amenities and less income available for nondurable goods. Finally, 

recycling – at volumes sufficient to achieve significant economies of scale – typically 

requires development of “reverse” supply chains and creates new, additional truck and rail 

miles of travel. One of the highest-volume commodities “exported” from urban areas today is 

municipal solid waste, much of it moving in medium-distance truck hauls and long-distance 

rail moves. 

2.3.2. Production 

Production factors quantify the impact of industrial and business activity on freight demand. 

2.3.2.1. Economic Activity/GDP 

•	 Trend: Current projections suggest that the national economy will expand at a compound annual 

growth rate of 2.5% to 2.6% through 2050, with rates of 3% to 4% through 2015 and lower rates 

thereafter.15 The long-term average rates are considerably less than the average of about 3% 

experienced over the previous 30 years. 

–	 Freight Demand Implications: Short-term freight demand tracks economic activity very 

closely – so closely that freight indices, such as the Ceridian UCLA-Pulse of Commerce 

Index, are used as a proxy for overall economic performance. However, predicting freight 

demand based on anticipated economic growth rates is no more accurate than the underlying 

short- and long-term economic growth forecasts. Figure 2.12 shows four freight demand 

projections developed between 1998 and 2009 (Cambridge Systematics, Inc.).16 The forecasts 

illustrate both the severe impact of the recession on both short- and long-term freight demand 

and the limited ability of macroeconomic models to predict the future. 

15 The growth rates reflect recent estimates by IHS-Global Insight and other macro-economic forecasters. The rates incorporate 

the near-term effects of the recession and the longer-term effects of slowing population and workforce growth rates on the 

economy.

16 The macroeconomic forecasts were prepared by IHS-Global Insight and are similar to the IHS-Global macroeconomic 

forecasts underpinning the FHWA’s FAF freight demand forecasts. 

23
 
 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

Billions of Tons 

0 

5 

10 

15 

20 

25 

30 

2000 2005 2010 2015 2020 2025 2030 2035 

1998 2004 2007 2009 

Figure 2.12. Freight transportation demand forecasts 

(Source: Cambridge Systematics, Inc., based on IHS-Global Insight, Inc., Transearch data, multiple years 1998 to 2009) 

2.3.2.2. Industries/Commodities 

•	 Trend: The U.S. economy today is dominated by the service industry. The service sector accounts 

for 76.6% of GDP, industry for 22.2%, and agriculture for 1.2%. There are strong indicators that 

the growth of the service sector will continue, propelled by the continuing expansion of 

knowledge-based professional, scientific, and technical services, and by the expansion of health 

care and social assistance for an aging population. 

–	 Freight Demand Implications: Moderate expansion or contraction of the service sector will 

have a modest impact on overall freight demand because the service sector generates 

relatively little freight directly. However, the service industry is very dependent on the freight 

services provided by other sectors for the delivery of building materials, food, energy, office 

equipment, supplies, etc. The price and cost-effectiveness of freight transportation has a 

direct impact on the economic well-being and productivity of the service sector. 

•	 Trend: Despite the overall dominance of the service sector in the economy, there has been a 

sustained effort on the part of the both political parties in the United States to prioritize exports of 

manufactured goods and agricultural and natural resource products. A resurgence of 

manufacturing will likely be concentrated in areas in which the United States has a long-term 

comparative economic advantage: specialized and high-tech manufacturing, agricultural chemical 

24
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

manufacturing (fertilizers), pharmaceutical and medicine manufacturing, and petroleum and coal 

production. 

–	 Freight Demand Implications: The agriculture sector, which accounts for a modest portion of 

GDP, will be the single largest source of freight tonnage growth through 2040, with a 

projected increase of 1.1 billion tons (Oklahoma State University undated). The growth will 

be driven by rising standards of living in China, which will drive up demand for exports of 

meat and animal feeds from the United States (Kemmsies 2011). The freight demand impacts 

will be felt as increased rail shipments from the Midwest to Pacific Northwest ports and 

increased rail and barge shipments through New Orleans and other Gulf ports. There is also 

the long-term potential to increase exports and trade with food-importing regions such as 

Africa. 

Five other commodity groups are also projected to experience total tonnage growth of over 

150%: precision instruments, pharmaceuticals, miscellaneous manufacturing products, 

chemical products, and transport equipment. Higher-than-projected growth in these areas 

would generate new demand for domestic interplant freight transportation as well as export 

freight transportation. Miniaturization and the use of composite materials in these products 

will continue to reduce the average weight of the products, making shipment by truck and air 

more cost effective. In all cases, the expansion would likely increase the demand for truck 

and air transportation. 

2.3.2.3. Technology 

•	 Trend: Business analysts anticipate continued and rapid innovation in manufacturing, 

telecommunications, computing, health and biological sciences, and energy production that will 

create new businesses and accelerate automation in many industries. Although still speculative, 

some see the current trend toward “mass customization” (e.g., assembling mass produced 

components to create products tailored for individual consumers) evolving into “distributed, 

flexible manufacturing at the point of consumption” (e.g., low-cost, small-batch, custom-made 

fabrication of products at the point of use accomplished by precision “printing” using 

nanotechnology materials). 

–	 Freight Demand Implications: The majority of emergent industries will be producing highvalue products, which implies increased demand for timely and secure delivery by truck and 

air. Increasing automation will reduce the number of people employed in manufacturing, but 

will increase output and freight demand. If realized, the introduction of entirely new methods 

of production using nanotechnology materials has the potential to trigger significant 

restructuring of supply chains for raw materials, parts, and finished products. 

2.3.2.4. Labor 

•	 Trend: Labor productivity is forecast to increase with more automation, partially offsetting slower 

growth in the number of working age adults. Public policy actions to raise the Social Security 

retirement age or prolonged periods of slow economic growth may also keep older workers in the 

labor force longer. 

–	 Freight Demand Implications: Higher productivity per worker will reduce labor costs, making 

U.S. industries and products more competitive in global markets. This would reinforce the 

growth in export traffic and the demand for marine shipping. 

2.3.2.5. Energy 

•	 Trend: The U.S. Energy Information Administration’s Annual Energy Outlook projections and 

published industry estimates anticipate a steady increase in energy costs along with continued 

25
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

volatility in energy prices.17 The long-term response to higher and more volatile costs is uncertain 

but will likely lead to diversification of fuels and sources (e.g., shift from coal to natural gas, 

expanded use of hydro- and nuclear-generated electricity, and development of biofuels as 

substitutes for petroleum-based fuels). 

–	 Freight Demand Implications: Increasing energy costs would have different effects on each 

mode. For long-haul freight demand, rising fuel costs would tend to shift demand from air and 

truck to rail, and from rail to ship (where waterborne transport can provide competitive service). 

For short-haul freight demand, rising costs would shift demand from conventional diesel and 

gasoline powered trucks to hybrid-power trucks. A large shift away from coal would 

significantly reduce railroad revenues and could potentially hinder the ability of the railroads to 

make large-scale capital investments to expand intermodal and carload rail services. A shift 

away from conventional sources of ethanol to cellulosic sources would greatly expand the 

potential origins and destinations for renewable fuel. And finally, a consistent increase in all 

fuel costs would trigger a broad restructuring of supply chains and markets, as well as possibly 

dampening consumer spending on non-energy goods and services. Businesses would do more 

near- or in-sourcing, reposition plants and distribution centers, and in some cases, drop 

products and markets that were not profitable. 

2.3.3. Trade Factors and Trends 

The volume and value of international trade has grown and the number and location of major U.S. trade 

partners has shifted. This has redirected trade flows and forced the restructuring of ports, border 

crossings, and freight transportation trade corridors. 

2.3.3.1. Trade 

•	 Trend: Current data suggest resumption—following the recent recession—of the long-term trend 

toward globalization of trade and higher trade volumes serving the expanding economies and 

increasing affluence countries, such as Brazil, Russia, India, and China. 

–	 Freight Demand Implications: The United States can expect to see increased export of 

agricultural and natural resource products as well as high-end manufacturing products and 

services. Agricultural and natural resource exports will be supported primarily by rail and 

marine transportation, mostly through West Coast (especially the Pacific Northwest) and Gulf 

Coast ports. Trade with Brazil would expand demand for north-south freight transportation 

services; and trade with Russia, India, and Africa will likely flow through U.S. East Coast 

and Gulf ports. 

2.3.3.2. Trade Partners/Trade Lanes 

•	 Trend: Trade with China will continue to dominate the Pacific ports, but trade with South America 

and India will bring more import freight to the East and Gulf Coasts. This will augment the effect 

of the widening of the Panama Canal, which will give shippers and carriers the option of an allwater route from the Pacific Rim to the U.S. Gulf and East Coast.18 As these emerging economies 

grow, they will also import more goods and services from the United States, driving up total trade 

traffic. Because of its slower population growth rates and aging population, trade with Europe (via 

the Atlantic) in not expected to grow significantly. In the North American Free Trade Agreement 

region, harmonization of freight inspection technologies to ensure secure supply chains should 

17 See for example, the recent U.S. Energy Information Administration’s Annual Energy Outlook for 2009 and 2010. 18 The FAF and other freight demand forecasts already assume that some Pacific Rim to U.S. West Coast port traffic will be 

diverted through the Panama Canal to U.S. Gulf and East Coast ports. The magnitude of the shift in the longer term is not yet 

clear because U.S. railroads will likely lower their transcontinental rates to compete with the ocean carriers using the widened 

Panama Canal, and the ocean carriers will need to find sufficient export cargo at U.S. Gulf and East Coast ports to justify the cost 

of the Panama Canal transit fees. 

26
 
 

 

100,000 

200,000 

300,000 

400,000 

500,000 

600,000 

1860

1880

1900

1920

1940

1960

1980

2000

2003

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

help move the region toward seamless borders. Due to the superior fuel efficiency performance of 

maritime transportation, carriers will attempt to land trade cargo at ports that are closer to their 

inland destinations and will seek to limit reliance on long-haul, overland distribution. 

–	 Freight Demand Implications: In the past 40 years, the primary international trade flows have 

been through the East Coast, the U.S.-Canada border, and the Pacific Coast, as illustrated in 

Figure 2.13 (based on U.S. Department of Commerce trade statistics, various years). The 

chart shows the approximate value of trade across each of the major border regions between 

1860 and 2005. With continued globalization of trade, flows through the Gulf Coast and 

across the Mexican border are likely to increase with concomitant pressure to improve freight 

capacity at ports and the highway, rail, and inland waterway routes serving them. Trade 

through the Ports of Los Angeles and Long Beach will continue to grow and drive intermodal 

rail traffic to the Midwest, but a growing proportion of total U.S. trade will move through the 

East and Gulf Coasts. 

Millions of Real (2000) Dollars 

-

1860 

1880 

1900 

1920 

1940 

1960 

2000 

2003 

1980 

200,000 

100,000 

400,000 

300,000 

600,000 

500,000 

Figure 2.13. Value of trade by U.S. border region, 1860-2005 

(Based on U.S. Department of Commerce trade statistics, various years) 

2.3.4. Economic Geography 

Economic geography refers to the physical location and distribution of economic activity. Freight systems 

adjust over time to mirror the economic geography of the overall economy. 

2.3.4.1. Land Use 

•	 Trend: The U.S. population will be more urbanized by 2050, with corresponding population 

declines in rural areas. In 2010, 82% of the population lived in urban areas. The annual rate of 

urbanization is projected to continue at 1.5% through 2015. Growth will occur in the urban cores, 

which will likely be the catalysts for technical innovation as they become both denser and more 

27
 
 

 

 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

specialized. However, much of the overall population growth and economic activity will occur in 

the secondary cities and exurban areas between them, creating mega-regions that are large 

agglomerations of multiple metropolitan areas (Gifford et al. undated). Mega-regions in the United 

States – and their global counterparts – will be the competitive trade blocs of the future. 

Figure 2.14 is a generalized illustration of the anticipated U.S. mega-regions along with estimates 

of the metropolitan GDP of the major cities in each region (Berube 2007). 

St. Louis (17) Seattle (15) Minneapolis (14) $116,215,000 $182,170,000 $171,361,000	 Chicago (3) 

$461,374,000 Detroit (12) Boston (8) $198,630,000 $261,086,000 

New York (1) San Francisco (11) $1,056,381,000 $268,300,000 

Philadelphia (6) 

$295,236,000 

Baltimore (18) Los Angeles (2) $118,063,000 $632,407,000 

Washington, D.C. (4) 

San Diego (16) $347,631,000 

$146,341,000 Atlanta (10) Riverside (19) $242,382,000 $101,561,000 Phoenix (13) Houston (9) $160,028,000 Miami (7) $316,332,000 $231,806,000 Dallas (5) Tampa (20) 

$315,544,000 $100,952,000 

Figure 2.14. Mega-regions and “GDP” of major cities 

(Source: Berube 2007) 

–	 Freight Demand Implications: The consolidation of mega-regions could accelerate the use of 

rail (and some water) for long-haul shipping between regions and reinforce the dominance of 

trucking within mega-regions and the urban cores. It will also focus transportation services 

around major import/export gateways for each region and sharpen competition among 

national freight gateways. This effect can be seen in Figure 2.15, which shows the shift in 

trucking industry employment between 1990 and 2010 in response to emergence of the Ports 

of Los Angeles and Long Beach as the primary freight import gateway for the Southwest 

mega-region and the nation (based on U.S. Bureau of Labor Statistics, Employment by 

County and Industry for 1990–2010). 

•	 Trend: High land costs, highway congestion, siting concerns of affected neighbors, and 

opportunities for economies of agglomeration will make it more attractive for complementary 

businesses and industries to co-locate in “freight villages” or clusters of businesses sharing 

common freight transportation facilities. In the 19th and early 20th century, high transportation 

costs made it necessary to co-locate manufacturing factories and housing. But by the mid-20th 

century, improved trucks and highways, greater industrial specialization, and lower tolerance for 

factory emissions and noise led to increasing separation of factories, offices, and homes. The 

current trend points to the continued separation of factories and homes, but reintegration of 

complementary factories, distribution centers, and freight terminals as travel times across 

metropolitan and mega-region areas increase. 

28
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

Figure 2.15. Change in trucking industry employment, 1990–2010 

(Based on U.S. Bureau of Labor Statistics, Employment by County and Industry for 1990–2010) 

–	 Freight Demand Implications: The geographic concentration of industries and distribution 

centers will create opportunities for consolidation of compatible deliveries with some 

reduction of freight transportation demand, energy use, and emissions. However, high land 

costs and relatively low transportation costs will likely maintain the observed, long-term 

trend toward ex-urbanization of warehousing and distribution centers. This will generate 

more truck-miles of travel on the major distribution corridors leading into and within 

metropolitan areas. 

•	 Trend: The National Research Council, in its 2008 Special Report 290, Potential Impacts of 

Climate Change on U.S. Transportation, identifies five climate changes of particular importance 

the transportation: Increases in very hot day and heat waves; increases in Arctic temperatures; 

rising sea levels; increases in intense precipitation events; and increases in hurricane intensity. The 

report concludes that the impacts of these changes will vary by mode of transportation and region 

of the country, but they will be widespread and costly in both human and economic terms and will 

require significant changes in the planning, design, construction, operation, and maintenance of 

transportation systems (TRB and NRC 2008). 

–	 Freight Demand Implications: The National Research Council report anticipates that the 

greatest impact of climate change for North America’s transportation systems will be 

flooding of coastal roads, railways, transit systems, and airport runways because of global 

rising sea levels, coupled with storm surges and exacerbated in some locations by land 

subsidence. This implies an on-going need to reinforce or relocate freight transportation 

facilities, which will affect decisions about routing, supply chain design, and capital 

investment. At a minimum, this has the potential to reallocate freight transportation demand 

within and among economic regions. It also has the potential to shift demand for 

commodities, such as agricultural and natural resource products, if climate change affects the 

costs and productivity of regions differentially. 

29
 
 

 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

2.4. Logistics Factors and Trends 

Logistics describe the strategies that businesses adopted to organize the chain of make-buy-sell 

transactions for their products and services. Four aspects of logistics are covered here: supply chains, 

sourcing, packaging/containerization, and networks. Selection of major factors and freight demand 

implications of anticipated trends are authors’ interpretations based on professional expertise and 

knowledge of the literature. Descriptions of trends provide an understanding of the complex and interrelated nature of the freight transportation system. Trends and implications are uncertain and conditional. 

2.4.1. Supply Chain Strategies 

•	 Trend: Thirty years ago, most businesses operated push supply chains. Suppliers delivered 

materials to a manufacturer, who pushed products to a distributor or retailer, and then to the 

customer. Each business maintained a large and expensive inventory of critical materials and 

products to protect against stockouts. Today, most businesses are moving toward pull or 

on-demand supply chains (just-in-time manufacturing and retailing), replenishing whatever the 

customer consumes as soon as it is sold. To ensure that inventory is available, businesses are 

tracking customer purchases as they occur, reducing and centralizing inventory at fewer locations, 

and managing in-transit inventory closely. Industries that once held large inventories of products 

and could tolerate delays in shipment and receipt of goods now demand greater reliability and 

visibility, or knowledge of current status of shipments, from their freight carriers. The trend 

toward pull or replenishment operations is expected to continue with some adjustment for the 

higher risk involved in operating long and complex supply chains (“just-in-case” supply chains). 

–	 Freight Demand Implications: Eliminating inventory and replenishing everything in real time 

results in smaller shipment sizes (since units are consumed one by one) and more individual 

products per shipment (to make lot sizes economical to ship). This approach increases the 

importance of transportation over warehousing and favors the use of faster and more reliable 

trucking, intermodal, and air shipments over carload rail and waterborne freight shipments. 

•	 Trend: Logistics costs declined during through the 1990s, rose through the mid-2000s, and then 

dropped sharply during the recession, as shown in Figure 2.16 (Wilson 2011). Logistics costs 

(which include the transportation, warehousing, administration, insurance, and inventory carrying 

costs) are projected to rise again as the economy recovers, as freight demand again presses the 

capacity of the freight transportation system, and as interest rates increase from the near-zero 

levels experienced during the recession. 

–	 Freight Demand Implications: Transportation costs are a relatively small percentage of the 

delivered price of most goods and services, constituting 1% to 2% of the gross regional 

product for most industries. Only in industries such as agriculture, mining, and construction 

do transportation expenditures approach 10%, as illustrated in Figure 2.17, which shows 

transportation as a percentage of GDP for the major industries in Oregon and Washington 

states (Oregon Department of Transportation 2003). However, rising transportation costs are 

closely linked to inventory carrying costs and regulatory requirements. When interest rates 

are high, the combined effect on total logistics costs will trigger redesign of supply chains. 

This effect was noticeable in the mid-2000s, when higher fuel costs, congestion, and interest 

rates encouraged major retailers and others to reposition distribution centers so that they 

could take advantage of lower-cost transportation (primarily rail) and minimize their use of 

more expensive long-haul truck moves. 

Increasingly, shipping customers are also looking for ways to cut carbon emissions as part of 

a longer term sustainability approach and finding freight strategies that cut carbon also reduce 

transportation costs. For example, American Shipper magazine, in partnership with the 

Council for Supply Chain Management, Eye for Transport, and the National Retail 

30
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

Federation, published a benchmark study highlighting how businesses can assess the returnon-investment (ROI) case for sustainable goods movement (Blaeser and Whiting 2012). 

Dollars (Trillions) 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

1.6 

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

Figure 2.16. U.S. business logistics cost, 2001-2010 

(Source: Wilson 2011) 

GRP (in Billions) 

0 

10 

20 

30 

40 

50 

60 

70 

Agriculture 

Construction 

Transportation

and Utilities 

Wholesale and 

Retail Trade 

Manufacturing 

Government 

Services 

7.7% 7.4% 7.1% 4.7% 3.2% 2.7% 0.6% 

% Transportation Costs as a Percent of Each Sector’s Output 

Finance, 

Insurance, 

Real Estate 

0.8% 

Figure 2.17. Transportation as a percentage of gross regional product for major Oregon and 

Washington state industries
 

Based on U.S. Bureau of Economic Analysis data and Transportation Satellite Account data. 

(Source: Oregon Department of Transportation 2003). 

2.4.2. Sourcing 

•	 Trend: Outsourcing (that is, purchasing goods and services outside the continental United States to 

pursue lower labor, energy, and raw material costs) will continue as countries take advantage of 

31
 
 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

comparative economic advantage and opportunities for global trade. Countries such as China have 

greatly expanded the variety of commodity types they can successfully export, which has 

increased opportunities for U.S. outsourcing. In 2011, the United States imported more than 

700 additional types of commodities from China (HS-6) that were not imported in significant 

volumes in 2003.19 For certain commodities, changes in labor costs, transportation costs, and 

network congestion will lead to selective in-sourcing (renewed production within the continental 

United States) and near-sourcing (increased production in Mexico and Latin America). In many 

cases, labor costs and legal issues [such as protection of patents and intellectual property (IP) 

rights] will take priority over transportation cost considerations. 

–	 Freight Demand Implications: Changes in near-sourcing and in-sourcing are likely to have a 

modest impact on total freight demand, but will affect the origins, destinations (including 

trade gateways), and mode shares of domestic transportation. In general, near-sourcing and 

in-sourcing will expand the use of truck and air transportation at the expense of rail and 

waterborne transport. 

2.4.3. Packaging/Containerization 

•	 Trend: The introduction of intermodal containers in the 1950s and their widespread adoption in the 

1980s and 1990s revolutionized the handling of freight, especially international shipments. Most 

manufactured goods, including automobile parts, electronics, processed foods, specialty chemicals, 

paints, and household and consumer goods are now moved in containers. This pattern will 

continue with more bulk commodities being containerized to ensure, as examples, the quality of 

specialized agricultural products, prevent genetically modified grains from intermixing with nongenetically modified grains, and protect the security of chemical shipments. This trend will be 

accelerated by the growing obsolescence of dry bulk ships and terminals. 

A parallel trend is to reduce the weight of products and the space taken up by their packaging. 

This improves the weight-to-volume ratio, making truck and rail moves more cost-effective, and 

reduces the volume and cost of recycling. 

The final trend in packaging is toward ubiquitous use of barcoding and radio-frequency 

identification (RFID) transponders to identify products and track shipments. “Nested” tracking 

systems – where product RFID tags “talk” to box tags, which talk to pallet tags, which talk to 

container tags, which talk to truck or rail vehicle tags, which relay information about the status 

and location of the shipment to shippers, carriers, receivers, and customs and security officials 

over cellular and satellite telecommunications systems – will be commonplace. 

–	 Freight Demand Implications: Advances in packaging and containerization will improve the 

efficiency of the freight system, reducing the energy consumed in carrying extra packaging 

and fielding extra shipments to make up for lost and damaged goods. 

2.4.4. Networks 

•	 Trend: Supply chains are executed over a series of links (e.g., rail lines, highways, and shipping 

lanes) and nodes (e.g., farms, factories, terminals, distribution centers, and other freight staging 

points). With improvements in the range and reliability of trucks, trains, planes, and ships and in 

the communications systems to control and synchronize them, the number of different supply 

chains in operation has increased, and supply chains have become longer and more complex. This 

trend is expected to continue. 

19 Commodities with imports valued at over $1 million. Gross regional product calculated from U.S. Census, Foreign Trade 

Statistics. The Harmonized System six-digit level (HS-6) refers to the commodity description and coding system’s tariff 

nomenclature at a highly disaggregated level. This level of disaggregation allows for the identification of recognizable 

commodity types instead of broad groupings. 

32
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

–	 Freight Demand Implications: Higher population densities in metropolitan areas and megaregions will make possible more economies of scale in freight shipments. Moderately 

populated areas that were once served by truck may now justify more efficient rail or 

waterborne transportation. Transloading yards, where commodities like lumber and plastic 

pellets are transferred from rail cars to truck, and transloading centers, where consumer 

merchandise is unloaded from smaller international shipping containers and reloaded in larger 

domestic containers/trailers in order to increase the productivity and cost-efficiency of freight 

moves, will grow in prominence. As shipment density grows, shippers will press for more 

efficient loading of containers and all-water routing to allow one-box, door-to-door deliveries 

and reduced re-handling costs. Distribution centers will be repositioned to reduce 

transportation costs, accommodate higher urban development densities, and allow for 

“postponement.”20 Traditional, single product warehousing will be outsourced and integrated 

with value-added services in order to justify a higher-skilled workforce. Access to rail will 

become a more important feature when siting new distribution centers. 

2.4.5. Supply Chain Carbon Accounting and Reporting 

•	 Trend: Freight carriers and shippers are increasingly under pressure to address carbon risk and 

operational costs associated with freight. Shareholders, customers, and other stakeholders are 

pressuring shippers to quantify, track, report, and reduce their carbon emissions. Freight shippers 

are looking to their transportation providers to reduce carbon in the freight supply chain, 

increasingly encouraging or requiring their carriers to participate in the EPA’s SmartWay 

Transport Partnership program (EPA 2012b) or to engage in other carbon reporting and report card 

exercises. SmartWay provides a uniform, integrated framework for shippers, carriers, and logistics 

companies to assess and track emissions and fuel use from goods movement. SmartWay has nearly 

3,000 industry partners, and is being used as a model to establish similar programs in many top 

U.S. trade partner regions, including China, Canada, Mexico, and the European Union. The trend 

of assessing and tracking carbon emissions from freight transportation and goods movement is 

expected to continue. 

–	 Freight Demand Implications: If industry works to optimize carbon efficiency and improve 

freight performance it could affect freight demand and accelerate mode shifting. Shippers 

working to optimize performance could also drive carriers to accelerate efficiency gains 

through advanced technologies and operational practices. Efforts to reduce the carbon 

footprint of supply chains will likely lead to renewed consideration of near-sourcing and insourcing and further accelerate efforts to introduce engines that use less carbon-intensive 

fuels. 

2.5. Transportation Factors and Trends 

The following section provides an overview of factors and trends specific to transportation systems and 

modes that will impact the ability of the future network to accommodate freight growth. Selection of 

major factors and freight demand implications of anticipated trends are authors’ interpretations based on 

professional expertise and knowledge of the literature. Descriptions of trends provide an understanding of 

the complex and inter-related nature of the freight transportation system. Trends and implications are 

uncertain and conditional; the use of the word “will” does not assert certainty about future outcomes, but 

is merely used as shorthand (instead of “if...then”). 

20 Postponement refers to the practice of importing a shipment of goods, storing all or most of the goods in a large, centrally 

located warehouse or distribution center near the port of entry, and then later allocating and forwarding portions of the shipment 

to different final destinations as market demand dictates. The objective is to reduce the transportation costs of shuffling and 

repositioning small lots of goods among widely dispersed markets. 

33
 
 

 

 

 

 

 

Intermodal

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

2.5.1. Freight Systems 

•	 Trend: The successful introduction of a new mode of freight transportation is a rare event. Rail and 

steamships were both introduced in the early 1800s. Modern trucking began in the early 1900s, 

high-volume air cargo services in the mid-1900s, and containerization in the late 1900s, as 

illustrated in Figure 2.18. There have been no new freight transportation modes successfully 

deployed since that time. Nevertheless, the freight transportation industry is in the midst of a 

radical technological shift, this time driven by adoption of information and communications 

technologies that have revolutionized how shippers and carriers monitor and control the movement 

of freight shipments, freight vehicles, and freight networks. 

–	 Freight Demand Implications: The continued evolution and application of RFID shipment 

tags, on-board vehicle and trailer/container sensors, traffic monitoring systems, database 

technologies, global positioning systems, telecommunications, and precision dispatching and 

routing systems will significantly improve the productivity of all freight transportation systems. The benefits will be seen in less down time for maintenance, more efficient routing and 

fewer vehicle miles traveled (VMT) per delivery, more timely and accurate intermodal 

transfers, less damage and loss, and fewer injuries and fatalities. 

Air 

Sail 

Rail 

Truck 

Information 

• Digital 

communications 

• Onboard computers 

• GPS 

• RFID 

• ... 

Figure 2.18. Freight transportation systems 

2.5.2. Motor Carriers/Highways 

•	 Trend: The trucking industry will see further consolidation and restructuring. 

–	 Freight Demand Implications: The industry has been aggressive in incorporating global 

positioning systems and other tracking and shipment management technology into their 

operations; however, most trucking companies are small (approximately 80% of motor carrier 

firms own 5 to 10 trucks) and effective use of the technology requires sophisticated staff and 

managers. Small, independent trucking companies will continue to exist; however, they will 

contract to large carriers or subscribe to dispatching or load matching services to ensure that 

capital is utilized effectively. Information-technology-intensive firms will generally prosper 

at the expense of less information-technology-intensive firms – a trend that will favor large 

firms. Structural shifts in the economy that generate more high-value, lower-weight, timesensitive goods should mean that the overall demand for trucking will be high. Driver 

34
 
 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

shortages are not expected to be an intractable problem, but will be a recurring issue given the 

unregulated economic entry and boom-and-bust nature of the industry. Nevertheless, price 

competition with rail (because of the higher fuel cost and labor shortages incurred by longhaul trucking) will squeeze some transcontinental truckload operations out of business. 

•	 Trend: There will be a continuing move towards greater specialization of trucking equipment with 

hybrids and eventually all-electric trucks used for local pickup and delivery trips and drayage (i.e., 

trucking of containers from port terminals to warehouses), with longer and heavier diesel-powered 

trucks reserved for intercity and heavy-load movements (i.e., over-the-road, truckload and lessthan-truckload operations). All classes of truck will see improvement in miles traveled per gallon. 

–	 Freight Demand Implications: In the light- and medium-truck classes [under 10,000 pounds 

gross vehicle weight (GVW), and 10,000 to 26,000 pounds GVW, respectively], there will be 

a greater penetration of hybrids and all-electric vehicles. Heavy-duty, long-haul trucks 

(26,000 pounds GVW to 80,000+ pounds GVW) will see relatively slower improvement in 

productivity, gallons per mile, and emission reductions because of technological barriers and 

the need to maintain a high load-to-vehicle weight ratio. There will be a push for use of more, 

longer combination vehicles (a single tractor hauling two trailers) along with demands for 

higher weight-capable trucks (e.g., 97,000 pounds GVW and higher, with the weight 

distributed over more axles and tires). The push for larger vehicles will face state and local 

resistance to the operation of longer and heavier trucks on local streets because of safety and 

pavement damage concerns. Figure 2.19 shows one estimate of the projected miles traveled 

per gallon for freight trucks (TRB 2011b). 

Miles per Gallon 

0 

2 

4 

6 

8 

10 

12 

Single-Unit Combination-Unit 

Trucks Trucks 2010 2030 

Figure 2.19. Projected miles per gallon for freight trucks, 2010 and 2030 

(Source: TRB 2011b) 

Note: Chart is based on data from “Figure 4.8, Projected Growth (Miles per Gallon) for New Trucks and the Overall Fleet of SingleUnit and Combination Trucks, 2010-2030.” 

• Trend: There will be adequate funds for preservation and maintenance of existing highways, 

limited funds for capacity expansion, and almost no funds for major new Interstates that are not 

35
 
 

 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

constructed as toll roads. While congestion has abated because of the economic recession, severe 

highway congestion bottlenecks will reemerge as the economy and freight demand recover. 

–	 Freight Demand Implications: Carriers will invest heavily in dispatching and routing systems 

to avoid congestion, and reposition urban truck terminals, service areas, and truck stops to 

improve delivery efficiency and offset rising land costs and local impacts. However, higher 

overall network congestion will likely offset the gains in fuel efficiency and routing and 

dispatching, driving up the delivery cost of many goods and services. Some existing service 

lanes (trip origin/destination pairs and connecting routes) will become uneconomic due to 

transportation costs. 

2.5.3. Railroads/Rail Lines 

•	 Trend: The railroad industry has realized steady productivity improvements since the economic 

deregulation of the industry in the 1980s. The improvements have been achieved by restructuring 

the rail system and creating new business lines serving long-haul intermodal freight demand and 

coal movements out of the Powder River Basin in Montana and Wyoming, and by increasing the 

number of tons carried per railcar, the number of railcars moved per train, and – with more sidings 

and better signal systems – the number of trains moved over a line. The railroads are also 

upgrading track to handle heavier cars along many lines, thereby allowing more tonnage to be 

handled over existing corridors. These changes have resulted in much improved intermodal service 

along high-volume traffic lanes (e.g., between the ports of Los Angeles and the distribution centers 

of Chicago and Atlanta), but a slow erosion of carload service for low-volume shippers and 

shippers located on underserved branch and secondary rail lines. Improved intermodal service has 

taken long-haul trucks off the road; but the deterioration of carload and local services has put 

freight back into trucks and onto the road. 

The rail industry has largely recovered from the recession, is profitable, and has been investing 

heavily to increase capacity and improve service reliability. And although unpopular with the 

freight railroads, the congressionally mandated system of positive train controls to reduce the risk 

of train crashes is a bellwether of an industry-wide shift toward more automation and greater 

productivity in freight rail routing and dispatching operations. 

–	 Freight Demand Implications: The FAF projections show railroads losing market share 

because of structural changes in the economy that will generate more higher-value freight by 

2050. However, the FAF projections do not attempt to anticipate price and service changes 

that might affect market shares. Current business forecasts anticipate that the freight railroads 

will retain their market share and perhaps capture more of the long-haul freight demand 

market. This would help reduce energy consumption in the long-haul freight market. 

Electrification is possible in some rail freight lanes, but the capital costs are so high that only 

a few freight rail lines may be electrified by 2050. 

•	 Trend: Railroads have achieved financial success by rationalizing their service and tailoring 

operations to a select number of high-volume customers. However, flat (and possibly declining) 

demand for coal and limited opportunities to expand in the long-haul freight market could threaten 

railroad revenues and pressure the railroads to develop new services. 

–	 Freight Demand Implications: The freight railroads will push to expand intermodal services 

into 400- to 700-mile freight transportation markets. Long-haul intermodal service (over 

700 miles) is profitable because the railroads can achieve considerable economies of scale in 

long-distance moves; however, shorter distances are less profitable and the reliability of 

transit times is harder to maintain. Building new services will be a significant challenge, 

involving redesign and repositioning of older yards as intermodal terminals and development 

of new, scheduled intermodal services. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

2.5.4. Shipping Lanes/Maritime Ports 

•	 Trend: Ships continue to grow in size as shipping lines seek economies of scale to reduce the unit 

cost of moving containers and other commodities. An expansion of the Panama Canal is underway 

to accommodate these larger ships. Eventually, however, the capacity of harbors to accommodate 

the larger, deep-draft ships will slow the growth in ship size. The supersizing of ships has already 

reached equilibrium in the tanker industry and a similar trend may emerge for the container fleet. 

Nevertheless, large investments are being made at ports along the Eastern seaboard to deepen 

channels and expand terminal capacities in anticipation of “New Panamax” ships that take 

advantage of the canal’s expansion in 2014. 

–	 Freight Demand Implications: The trend toward larger ships will concentrate freight 

movements into deep water ports with the largest ships making the highest volume ports their 

first port of call (both because of market demand and because offloading at the first port of 

call allows access to second ports of call that may have shallower channels and berthing 

areas). Expansion of the Panama Canal will trigger some diversion of West Coast traffic from 

the Ports of Los Angeles and Long Beach to U.S. Gulf Coast and East Coast ports, but the 

railroads will likely lower rates for transcontinental intermodal service, counteracting some of 

the potential diversion. By 2050, the expansion of global trade among all countries and the 

growth of U.S. trade with Brazil, Russia, India and China will support dedicated, all-Atlantic 

and all-Pacific liner (container ship) operations, making Panama Canal transits a less 

dominant factor in shipping costs. 

•	 Trend: One of the more dramatic changes triggered by the rise in fuel prices and the recession is 

the trend toward “slow steaming” in the liner industry. Slow steaming refers to the practice of 

operating a liner vessel below its design speed in order to save fuel. This practice was initiated 

during the mid-2000s when the industry faced high fuel costs, surplus ship capacity, and dropping 

demand. Current indications are that the liner industry may make slow steaming a permanent 

feature of marine operations by integrating slower-design speeds into the construction of new 

vessels (Maersk Line 2011). 

–	 Freight Demand Implications: A consequence of slower shipping transit times is that shippers 

and receivers must keep more goods in inventory and more goods moving through the entire 

logistics chain. This increases the volume of commodities in “pipeline stock” (goods in 

transit) as well as in “cycle stock” and “safety stock” (goods in warehouses and distribution 

centers). With lower transportation costs and greater visibility of freight movements, shippers 

have substituted more and faster transportation for traditional warehousing, essentially 

turning trucks, railcars, and ships into (relatively fast) moving warehouses. This has been 

particularly attractive when interest rates and, therefore, the cost of holding inventory are 

high. If the added costs of slow steaming and port congestion are not offset by the lower costs 

in operating large ships, then supply chains will be reset to compensate, either by changing 

sources or restructuring the location and operation of U.S. domestic distribution networks. 

2.6. Policy and Regulatory Factors and Trends 

Policies and regulations established at the national, state, and local levels all have a direct impact on 

freight transportation demand, through policies and taxes that subsidize the growth of some industries and 

transportation modes over others, through regulations that affect the relative prices of freight 

transportation, and through programs that invest in transportation infrastructure. Selection of major 

factors and freight demand implications of anticipated trends are authors’ interpretations based on 

professional expertise and knowledge of the literature. Descriptions of trends provide an understanding of 

the complex and inter-related nature of the freight transportation system, including how relationships 

could change with policy and regulation. The use of the word “will” does not assert certainty about future 

outcomes, but is merely used as shorthand; trends and implications are uncertain and conditional, and 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

descriptions should be understood to mean “if this apparent trend continues and if system relationships 

work as expected...then likely outcomes are... “. 

2.6.1. Policy 

•	 Trend: As the economy recovers, demand for freight transportation will again press the capacity of 

the freight transportation system. The resulting congestion will undermine the reliability and 

connectivity of freight movements, which are essential to the nation’s economic well-being, and 

renew calls for more investment in transportation infrastructure. Federal policy recognized the 

importance of the Interstate Highway System program to economic development and freight 

transportation in the 1960s; and, in the 1980s, federal policy supported deregulation of the freight 

transportation industry as a means of restructuring the industry and reestablishing market rates for 

freight transportation services. Starting with the reauthorization of the federal funding for surface 

transportation by the Intermodal Surface Transportation Efficiency Act of 1991, successive 

reauthorizations have recognized the need for a more explicit and detailed national freight 

transportation policy, but made limited headway toward enacting specific policies and programs 

until the Moving Ahead for Progress in the 21st Century Act, which was enacted in 2012 (U.S. 

DOT FMCSA 2012). The Moving Ahead for Progress in the 21st Century Act mandated that 

U.S. DOT develop a national freight policy and goals, designate a national freight network, and 

produce a periodic report on the condition and performance of the national freight systems. 

–	 Freight Demand Implications: Congress may undertake a broad policy debate about the role 

of the federal government in transportation and the importance of maintaining national freight 

transportation capacity and connectivity. The Moving Ahead for Progress in the 21st Century 

Act is a start and foundation for more comprehensive national freight policy and supporting 

program. Some observers expect that within one or two reauthorization cycles (6 to 12 years) 

the nation will have a freight transportation policy and one or several freight investment 

programs in place targeted at projects of national and regional significance. Given the 

dominant role of trucking and highways in the U.S. freight transportation system, the policies 

will likely favor continued investment to maintain highway capacity for trucking. 

2.6.2. Taxation 

•	 Trend: There is a broad recognition that an increase in private and public investment in the freight 

transportation systems to keep pace with economic growth and demand is required. Funding for 

freight transportation improvements has lagged demand. The federal motor fuel tax was last 

increased in 1993, but because it is not indexed to inflation, motor-fuel-tax revenues have lost 

about one-third of their purchasing power. Tolling and congestion pricing may help manage 

demand on the most congested roadways and generate revenue to expand capacity, but tolling and 

pricing revenues will not be sufficient to fund projected highway preservation and maintenance 

nor to ensure connectivity across the national freight network since tolling and pricing are only 

economically viable on a small percentage of Interstate and National Highway System roads. The 

vast majority of freight highways do not have sufficient volumes and intensity of use to generate 

significant revenues through tolling. 

Politically challenging fuel-tax increases and sales taxes may bridge the funding gap for a short 

time, but energy policies and GHG emission regulations will reduce the long-term yield from fuel 

taxes. 

–	 Freight Demand Implications: New revenue mechanisms such as mileage-based or VMT user 

fees (already a partial source of revenues from the trucking industry) will likely be introduced 

and expanded along with freight-related user fees and taxes (e.g., port facility charges, 

conveyance fees at terminals, and value-added taxes on shipments) to fund critical national 

and regional freight projects. Mileage-based or VMT user fees have the potential to generate 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

considerable revenue, but unlike today’s motor fuel taxes, which are collected from major oil 

distributors, VMT user fees must be collected from individual drivers. The cost of 

administering and enforcing VMT user fee programs may prove too high, limiting their 

effectiveness. These mechanisms will be paired with investment tax credits and other forms 

of public support of private sector investment to increase and accelerate private investment in 

rail systems and other freight infrastructure. Most of the cost will be passed along to shippers, 

receivers, and consumers, affecting the demand for specific commodities in ways that cannot 

be reliably predicted. 

2.6.3. Regulation 

•	 Trend: The EPA has moved to introduce new truck fuel-efficiency standards, and high fuel prices 

and consumer demand for “green” products have encouraged companies to adopt fuel savings 

strategies on their own. EPA’s SmartWay Transportation Partnership program and the experience 

of its partners in demonstrating the fuel-saving technologies and strategies that the program tests 

and promotes helped to inform EPA’s development of the new standards. Wal-Mart, for example, 

a SmartWay partner, set a goal several years ago of doubling the fuel economy of its truck fleet by 

2015, and had achieved a 25% fleetwide improvement by 2008.21 Given the anticipated increase in 

truck traffic, diesel fuel consumption, and GHG emissions, it is it is anticipated that the federal 

government will consider increased truck fuel-efficiency standards and possibly GHG emission 

standards by 2050 (EPA 2012a). 

–	 Freight Demand Implications: The impact of stricter truck fuel-efficiency standards on freight 

demand will depend somewhat on the ability of engine manufacturers to meet the standards 

without significantly increasing the cost of truck engines and fuels. If truck costs increase 

substantially, rail and water could compete for some of truck’s freight share, especially 

longer-haul freight. There will be less opportunity to shift mid-range and short-haul freight 

from truck to rail. If significant cost increases are persistent (lasting three to five years or 

more), businesses will redesign their supply chains to minimize total logistics costs, but will 

also pass the increased costs on to customers and consumers. Conversely, if the standards 

lead to technological breakthroughs and lower engine and fuel costs, then the pattern could 

reverse with some freight shifting back from rail to truck. It is important to note, however, 

that the degree of mode shift is dependent on the commodity, the availability of alternative 

modes, service performance, and general market behavior (e.g., how carriers respond to 

changes in costs, and how shippers respond to changes in rates). 

•	 Trend: In the 1980s, the federal government reduced its economic (but not safety) regulation of the 

freight industry. The response, which played out into the 1990s, resulted in a radical 

transformation of the aviation industry, the railroads, and eventually the motor carrier industry. 

The impact on the railroad industry, which had been sliding into bankruptcy in the 1960s and 

1970s, was pronounced, as illustrated by the trends in productivity, volume, revenue, and price 

shown in Figure 2.20. The realignment of freight transportation services and the introduction of 

more market- and demand-based pricing have benefited most shippers, receivers, and consumers, 

but it has triggered recurring proposals for reregulation from captive shippers, who are typically 

high-tonnage and high-volume shippers and receivers of grains, coal, and chemicals that are served 

by only one railroad. 

–	 Freight Demand Implications: Proposals for reregulation of the railroads have made little 

headway, but if they were eventually successful, would likely lead to higher rail prices and 

diversion of some freight traffic from rail back to truck. 

21 Wal-Mart collaborated with EPA on testing and evaluation of fuel efficient technologies. Wal-Mart adopted a range of these 

SmartWay technologies on its trucks in order to reach that sustainability goal. 

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2.6.4. Public-Private Partnership 

•	 Trend: EPA developed the SmartWay Transport Partnership program as a government-industry 

collaboration to accelerate the adoption of advanced technologies and strategies to improve fuel 

efficiency and reduce GHG and other emissions from the freight sector, including the trucking and 

rail sectors. The EPA reports that performance benchmarking tools, data assessment, and 

recognition for partner achievements have helped the freight industry to save over $6.5 B in fuel 

costs, cut oil use by 55 million barrels, and reduce carbon dioxide (CO2) by over 23.6 million 

metric tons since 2004. This trend is expected to continue as more shippers and carriers join this 

voluntary, market-based collaboration (EPA 2012a). 

–	 Freight Demand Implications: This program improves the energy and emissions efficiency of 

freight. If fuel cost savings are passed on to customers, a rebound effect could cause freight 

demand to increase. Participation in this benchmarking, reporting, and recognition program 

will increase the transparency of carbon performance in the freight sector and help the market 

optimize freight operations by enabling better decision making on mode and carrier choices. 

The data collected through the partner process and the government-industry collaboration are 

expected to further inform policy making and regulations in ways that ensure more costeffective and successful programs. 

Class I (Index 1981 = 100) 

300 

270 

240 

210 

180 

150 

120 

90 

60 

30 

0 

Revenue 

Volume 

Productivity 

Price 

Staggers Act 

(deregulation) 

October 1980 

1964 1974 1984 1994 2004 

Figure 2.20. U.S. railroad performance, 1964 to 2007 

(Source: Association of American Railroads) 

2.6.5. Metropolitan Freight Programs 

•	 Trend: The cost of picking up and delivering freight is being driven upwards by congestion and 

unreliable travel times in urban areas. Improving metropolitan freight mobility means preserving 

truck access and ensuring fast, safe, and reliable pickup and delivery operations ranging from local 

delivery to transcontinental truck trips. And it means restructuring some rail freight operations and 

removing rail bottlenecks in metropolitan areas. However, there will be few opportunities to divert 

freight from truck to rail within metropolitan areas. The infrastructure for urban rail freight 

operations has been largely torn up, and few communities are enthusiastic about having heavy40
 
 

 

 

 

 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

industry rail lines and terminals as neighbors. Trucking – albeit with cleaner and quieter engines 

and organized to make fewer and more efficient trips – will be delivering most food, clothing, 

merchandise, housing materials, and other goods within urban areas for the foreseeable future. 

–	 Freight Demand Implications: The failure of metropolitan freight transportation systems will 

be unacceptable because of the political and economic cost for the national economy. The 

primary opportunities for Congressional action would likely take the form of restructuring 

metropolitan planning organizations with a broadened mandate to deal with freight 

transportation, not just automobile and transit systems. With this will come efforts to 

recognize major highway bottlenecks as a national-scale problem that threatens to choke the 

highway freight system; introduction of roadway pricing for both automobiles and trucks to 

moderate demand, manage congestion, raise revenue to maintain transportation system, and 

reduce energy use and GHG emissions; and support for the rationalization of urban rail 

operations and consolidation of urban rail terminals to preserve rail service, reduce truck 

traffic, and minimize community impacts. 

•	 Trend: Within metropolitan areas, more freight transportation capacity is needed at ports and 

gateways, and better connections are needed between those port gateways and the national 

highway and rail networks. The quality of access to and from ports and other major international 

gateways is important because it affects the cost of moving freight, determines the market area that 

can be served cost-effectively from the gateway, and impacts the surrounding communities. The 

intermodal freight connectors of the National Highway System are the first and last miles of 

roadway used by truckers to travel between the major highways of the National Highway System 

and the nation’s ports, rail terminals, and air cargo hubs. They are usually local roads and often 

weave their way through older industrial and residential neighborhoods. They are critical links, but 

often the weakest links, in the freight transportation network. The need for intermodal connector 

improvements has been widely discussed since the Intermodal Surface Transportation Efficiency 

Act of 1991, but the issue still lacks focused attention. Without investment in the “last mile” of 

freight transportation on the intermodal connectors, the value of investment in national highway 

and rail connectivity will be much reduced. 

–	 Freight Demand Implications: The resumption of economic growth and the anticipated 

expansion of global trade may lead to federal policies and programs to partially fund 

improvements to intermodal port and border crossing connectors. An important catalyst will 

be the success or failure of U.S. export initiatives to prompt significant growth in trade. 

2.7. Summary 

Table 2.3 provides a summary of the trends and freight demand implications by freight demand factor. 

The trends are described by their anticipated direction and the certainty associated with the trend 

projection. For example, under “Population,” the anticipated direction is “growing [population] with 

relatively more elderly and fewer working adults” and the certainty of this projection is rated as “good.” 

As noted, the assessments of the trends draw upon available data, literature, and the opinions of informed 

experts, but reflect the authors’ best professional judgment about their likely direction, magnitude, 

incidence, and significance, and are not intended to suggest certainty about future outcomes. 

The freight demand implications have been collapsed to show the likely impact in four dimensions: 

1) freight tonnage; 2) freight commodity types (or mix of commodities); 3) freight VMT (as a proxy for 

fuel use and GHG emissions under the very broad assumption that an increase in VMT will generate 

some corresponding increase in fuel use and GHG emissions); and 4) freight mode. A blank cell indicates 

that the factor does not have a direct and readily discernible effect. For example, while it is clear that the 

ongoing shift in supply chain strategies from push to pull operations has the direct effect of increasing 

freight VMT and shifting modal shares, it is less clear that this shift has a direct impact on commodity 

41
 
 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

tonnage and mix. Over time, a change in supply chain strategies will influence tonnage and commodity 

mix, but the table does attempt to trace out the complexity of the secondary and equilibrium effects. 

The table should be read as a summary of trends that underpin the 2007 to 2040 FAF freight demand 

projections. As such, the trends represent the “most likely” trajectory for freight demand assuming the 

continuation of current policies, technologies, trade patterns, etc. Many other alternative futures are 

possible. 

For the purposes of the Transportation Energy Futures study and making first approximations of energy 

and GHG emission effects, the factors most likely to shape freight demand between now and 2050 were 

considered to include the following: 

•	 Economic Factors: 

–	 Population and income 

–	 Economic growth rate (GDP) 

–	 Trade volume and partners 

–	 Urbanization and mega-region concentration. 

•	 Logistics Factors: 

–	 Supply chain restructuring in response to changes in economic geography and differences in 

modal transportation costs. 

•	 Transportation Factors: 

–	 Petroleum fuel availability and cost. 

•	 Policy and Regulatory Factors: 

–	 Truck engine efficiency and GHG emissions standards 

–	 Federal funding for freight infrastructure. 

These major economic, logistic, transportation, and policy and regulatory factors will directly influence 

freight transportation demand, with national, state, and local levels of government each playing distinct 

roles. These factors provide the context in which efforts to reduce energy use and GHG emissions will 

operate, and these possible policy actions are considered next. 

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TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Table 2.3. Summary of Trends and Freight Demand Implications by Factor 

43
 
 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

Table 2.3. Summary of Trends and Freight Demand Implications by Factor (continued) 

44
 
 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Table 2.3. Summary of Trends and Freight Demand Implications by Factor (continued) 

45
 
 

 

 

 

 

 

 

 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

3.0 POLICY ACTIONS 

This section outlines potential federal government policy actions that could influence the demand for 

freight transportation as well as potential impacts, implementation issues, and assessment methods. This 

section is intended to inform, and is not intended to promote, propose, or oppose such actions. 

3.1. Fuel Taxes 

Federal Action. Increase federal motor fuel taxes on diesel and other petroleum-based fuels used in 

freight transportation. Because taxes are already imposed on fuels used in most transportation modes, 

higher fuel taxes would be straightforward to implement and administer. Moreover, the motor carrier 

industry is on record as favoring an increase in motor fuel taxes to support increased funding for highway 

and bridge maintenance and replacement that benefit freight (American Trucking Associations 2009). The 

major challenge to implementation would be to find innovative ways to engender and sustain broad public 

support for higher motor fuel taxes, which have been resisted during the past two decades. 

Impacts. Increasing the federal motor fuel tax will increase the cost of fuel and, because fuel cost 

constitutes a large portion of the cost of operating heavy-duty freight trucks, carriers will look for ways to 

minimize fuel costs – buying more fuel efficient engines and vehicles, establishing more fuel-efficient 

routing and dispatching practices, shifting longer-haul freight to rail and water where services are 

available, etc. However, federal motor fuel taxes are a decreasing percentage of total fuel cost as fuel 

prices rise. This means that changes in the federal motor fuel tax rates per se – unless the changes are 

very large – have a diminishing impact of carrier behavior. The impact of a change in federal motor fuel 

tax rates would be felt most directly by trucking and somewhat less so by rail because railroads are 

generally exempted from the fuel excise taxes paid by truckers.22 In general, increasing petroleum-based 

fuel costs can be expected to shift freight demand from air to truck to rail to water, but with the degree of 

diversion highly dependent on the commodity, the availability of alternative modes, and service 

performance. 

Implementation. Federal fuel taxes policies and rates were first set by Congress in 1932, and then 

formalized as part of the Federal-Aid Highway Act of 1956 (Weingroff 1996). The federal motor fuel tax 

is typically paid at the fuel distributor level, collected by the Internal Revenue Service, and deposited to 

the Highway Trust Fund administered by the FHWA. 

Assessment Methods. The primary method for assessing the impact of fuel tax increases is estimation of 

price elasticity of demand using historical mode share data, revealed-preference data, or stated-preference 

surveys. Other parts of the Transportation Energy Futures project address modal share estimation 

methods. There have been numerous studies of price elasticity of demand for freight transportation, but 

most were done in the 1970s and 1980s and are now of limited value given the structural changes in the 

economy and logistics since that time. Assessment of the policy impacts of increased fuel taxes would 

require better and more current data to re-estimate supply chain costs. Because the cost of freight 

transportation is generally passed through to the customer, macroeconomic models can be used to 

estimate sector- and industry-wide economic effects. 

3.2. Low-Carbon Fuel Standards 

Federal Action. Impose low-carbon fuel standards (LCFS) on the freight transportation sector. LCFS 

would help attract and sustain investment in alternative fuels, potentially lowering the cost of supplying 

22 The Railroad Revitalization and Regulatory Reform Act of 1976 specifically restricts the ability of state and local governments 

to levy taxes on rail carriers that would be discriminatory. This has implications for the types of taxes that can be levied on rail. 

The Supreme Court recently dealt with the interpretation of this statute in CSX Transportation, Inc. v. Alabama Department of 

Revenue, which held that a rail carrier could challenge a state law that imposed a sales tax on diesel fuel for railroads but not on 

motor carriers. 

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TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

them over time. If fuel prices remain high as a consequence, the challenge would be to maintain freight 

industry and public support for the program. 

Impacts. The main effect of LCFS is to reduce the GHGs emitted during fuel consumption and production 

by increasing the demand for and supply of alternative, low-carbon fuels. If fuel prices increase as a 

consequence, the standards could also cause some reduction in transportation activity and shift toward 

more energy-efficient freight vehicles and operations. 

Implementation. The prospects for federal implementation of a LCFS are unclear. If the standards raise 

the price of fuel, as would be expected, the implementation challenge will be similar to that of raising fuel 

taxes. As with other policies to control GHG emissions, the ability to account for and verify freight 

transportation emissions will affect the implementation potential. The California Air Resources Board 

approved a LCFS for California that went into effect in January 2011. The effects are being watched as a 

bellwether of the feasibility of a national program. 

Assessment Methods. There are no national-level studies that directly address the potential impact of a 

LCFS on the freight transportation industry and freight demand. As with motor fuel taxes, the key 

variables would be the amount of increase or decrease in fuel cost relative to future diesel fuel prices, the 

cost of adapting truck and rail engines to the alternative fuels, and the net cost difference by mode. 

3.3. Vehicle Efficiency Standards 

Federal Action. Increase heavy-duty truck engine efficiency standards to require more miles traveled and 

fewer GHG emissions per gallon. In August 2011, the EPA and National Highway Traffic Safety 

Administration announced the first regulations to reduce GHG emissions and improve fuel efficiency of 

heavy-duty trucks and buses. The regulations will apply to vehicles produced between 2014 and 2018; a 

second phase of regulations is planned for the truck model years beyond 2018 (EPA 2011). 

Impacts. The EPA and the National Highway Traffic Safety Administration estimate that the combined 

standards will reduce GHG emissions by about 270 million metric tons CO2-equivalent and save about 

530 million barrels of oil over the life of vehicles built for the 2014 to 2018 model years, providing $49 

billion in net program benefits. The reduced fuel use alone will enable $50 billion in fuel savings to 

accrue to vehicle owners, or $42 billion in net savings when considering the costs of developing and 

implementing the technology. In addition to fuel savings, the agencies have estimated monetized benefits 

from GHG reductions, improved energy security, reduced time spent refueling, as well as possible 

increased driving accidents, traffic congestion, and noise. Most of these effects are directly attributable to 

reduced freight cost and induced demand. 

Using technologies commercially available today, the majority of vehicles will see a payback period of 

less than one year, while others, especially those with lower annual miles, will experience payback 

periods of up to two years. For example, an operator of a semi-truck can pay for technology upgrades in 

under a year and have net savings up to $73,000 over the truck’s useful life. 

In addition to the benefits from reduced GHGs, the EPA has estimated the benefits of reduced ambient 

concentrations of particulate matter and ozone resulting from this program. Air quality will improve and 

health impacts from these air pollutants will be reduced, with estimated monetized health-related benefits 

ranging from $1.3 to $4.2 billion in 2030, discounted at 3%. 

In total, the combined standards will reduce GHG emissions from the U.S. heavy-duty fleet by 

approximately 76 million metric tons of CO2-equivalent annually in 2030 and in subsequent years. The 

potential impacts of the program that are not quantified and monetized in the analysis include the health 

and environmental impacts associated with changes in ambient exposures to toxic air pollutants and the 

benefits associated with avoided non-CO2 GHGs (methane, nitrous oxide, and hydrofluorocarbons). 

While these environmental benefits do not directly translate into higher freight demand, they mitigate 

against the future risk of community opposition, which can constrain the ability of freight facilities to 

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Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

grow. Many freight facilities in urban areas, for example, cannot gain community consent to expand 

without meeting environmental performance targets that far exceed federal or state standards. 

Implementation. EPA and the National Highway Traffic Safety Administration have worked jointly to 

implement this program. These agencies are expected to continue to work together when considering an 

expansion of the program beyond model year 2018. 

Assessment Methods. The assessment of changes in vehicle and engine efficiency standards is based on 

estimates of annual VMT by different classes of trucks, engine efficiency and emission performance, and 

fleet turnover rates. Improved short-term estimates could be gained with better information on the actual 

duty cycles of trucks in specific supply chain operations. Estimating longer-term impacts requires 

projection of the relative cost changes for each mode. 

3.4. Road Pricing 

Federal Action. Implement road pricing through the substitution of VMT user fees for some or all of the 

current motor fuel taxes and support wider application of road tolling. 

Impacts. Road user pricing is done today by taxing motor fuel consumption as a proxy for VMT on 

roadways. Interest in direct VMT user fees has grown as the revenue yield from motor fuel taxes has 

decreased – a function of the introduction of more fuel-efficient engines and a broad political reluctance 

to raise fuel taxes or index the tax rates to inflation. The impact of VMT user fees on freight demand 

would depend on whether the VMT fees were revenue neutral. A revenue-neutral shift would have little 

impact, but a shift to VMT fees that is accompanied by changes in allocation of cost responsibility could 

have a significant impact on trucking. National studies have long argued that heavy freight trucks 

operating in long-haul service pay less than their proportional share of road costs compared to light- and 

medium-duty trucks. A reallocation of cost responsibility to raise a greater share of fees from long-haul 

trucking could shift some long-haul freight from truck to rail. 

Implementation. Road user pricing will likely be implemented through the states because the states have 

the administrative capability in place to administer and collect federal and state motor fuel taxes along 

with state vehicle registration fees and various federal heavy-duty vehicle sales taxes. However, the costs 

for administration and enforcement of a road-user pricing mechanisms, such as a mile-based or VMT user 

tax, could be prohibitively high. Several states are experimenting to determine the cost-effectiveness and 

efficiency of road pricing. 

Assessment Methods. The U.S. DOT, many states, and several industry associations are conducting 

studies of the feasibility and economic impacts of road pricing. Most studies focus on road user and 

administrative costs and benefits; few have attempted to estimate the price elasticity of demand effects on 

freight transportation demand and supply chain operations. 

3.5. Freight Operations 

Federal Action. Deregulate U.S. domestic maritime shipping through repeal or modification of the Jones 

Act. 

Maritime shipping is generally the most fuel-efficient means of freight transport. For this reason, there 

has been significant interest in expanding the role of domestic maritime and inland waterway shipping 

between U.S. ports. However, attempts to increase short-distance maritime shipping have been hobbled 

by the Merchant Marine Act of 1920, commonly referred to as the Jones Act, which regulates all vessels 

engaged in the transportation of cargo or passengers between two points within the United States, its 

territories, and possessions, as well as vessels engaged in dredging, towing, salvage, fishing, and other 

marine operations. Under the Jones Act, vessels engaged in these activities are required to be U.S.-built, 

U.S.-documented, U.S.-owned and controlled, and U.S.-crewed. Because ships constructed in the United 

States are typically more expensive than those built in other countries (due to higher labor rates and 

unfavorable scale economies) and U.S. merchant mariners are typically paid more than their foreign 

48
 
 

 

 

 

TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

counterparts, there is very little domestic maritime freight shipping outside of barging. A relaxation of 

Jones Act requirements that would allow foreign-built vessels and non-U.S. crews to service U.S. 

domestic container or other freight demand would permit the introduction of a new mode to accommodate 

future freight demand to supplement trucks and rail. Provision of shipbuilding grants for the construction 

of a domestic fleet would be an alternative mechanism to develop this mode of transportation within the 

confines of the Jones Act. 

Impacts. Domestic maritime shipping is a niche freight transportation service today and serves a handful 

of corridors and commodity types. There have been numerous studies speculating about the potential for 

these services if the market were deregulated, but no definitive estimates have been made. Proponents 

argue that in situations where freight could be moved economically and reliably by domestic maritime 

operations, the increasing need for parallel truck or rail operations may be reduced, thereby helping to 

mitigate congestion and air quality impacts. However, it is unlikely that repealing or modifying the Jones 

Act, which restricts carriage of cargoes between U.S. ports to U.S.-built and -crewed ships, will result in 

meaningful diversion of truck or rail traffic in most locations, even those with existing access to domestic 

maritime services. The primary constraint to growth in short-sea shipping is the additional cost of 

handling the loading and off-loading of containers and trailers at ports. This makes many shipments 

uneconomical compared to direct truckload service. 

Implementation. Implementation of policies and programs to increase short-sea shipping would be under 

the jurisdiction of the Federal Maritime Administration. 

Assessment Methods. Estimates of the potential market for domestic maritime shipping are based on 

commodity flows between origin and destination pairs that can be efficiently served by waterborne 

shipping, market penetration rates for reasonably comparable services (e.g., in Europe), and anticipated 

operating costs. Estimates could be improved by more complete accounting of the role of coastal shipping 

in total supply chain costs. 

3.6. Infrastructure Investment 

Federal Action. Establish a national freight transportation policy; define a national freight system that 

delivers national connectivity; provide access to global markets and support a strong economy; and 

increase the public and private investment in highway, rail, and waterborne transportation systems. 

The key elements of a federal freight program could include: 1) providing financial support to projects of 

national and regional significance that maintain and improve national connectivity and capacity; 

2) separating freight and passenger movements on key national rail and highway corridors; 3) improving 

rail and truck productivity through new policies and investments, such as providing tax credits 

(previously extended to short-line railroads to help finance the upgrade of track for use with heavier, 

higher-capacity rail cars); 4) offering letters of credit and loan guarantees to railroads and state agencies 

to help finance the clearance of rail lines to accommodate higher, double-stack trains; and 5) allowing – 

where safe – the operation of larger and heavier trucks along key freight corridors and on connectors to 

ports and other key intermodal facilities. 

Options for financing increased investment in more energy-efficient freight transportation would include 

short-term increases in the federal motor fuels tax, indexing the tax to inflation, introduction of a VMT 

user fee to fund transportation improvements, and consideration of a palette of freight-specific user fees, 

such as container conveyance fees to finance freight-specific improvements. 

Along with investments in physical infrastructure capacity and connectivity, federal investments in traffic 

management and traffic information can make freight operations more efficient by reducing delays and 

detours. 

Impacts. The intent of a federal freight program would be to focus federal aid and investment in critical 

commerce corridors; to leverage state investments to relieve congestion at urban Interstate interchanges 

(the locus of most truck hours of delay); and to improve access to port and rail intermodal terminals. The 

49
 
 

 

 

 

Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

expected outcomes would be to maintain or reduce travel times, increase the reliability of freight trip 

travel times, and broadly keep pace with the increases expected from population growth and economic 

development. The caveat that must accompany all infrastructure and operational improvements is that 

faster and more reliable travel times will generally induce additional trips, trips that previously were not 

made because they were not economically viable at the margin. 

Implementation. The U.S. DOT/FHWA and the Federal Railroad Administration would be the likely 

organizational units involved in implementing this program. 

Assessment Methods. The FHWA FAF3 national network, state highway models, and localized 

interchange traffic flow simulation models can be used to approximate the reductions in delay and 

improvements in travel time and reliability of infrastructure improvements. However, the current 

generation of models is geared for urban transportation analysis and especially to assess peak-period 

congestion. The trip tables that drive the models do not do a good job of accounting for off-peak and 

nighttime truck travel, which is common with many trucking operations. The models also do not deal well 

with assigning multiday trips, which are the norm for long-haul trucking operations. Methods are 

available, but are seldom applied, to approximate truck time-of-day dispatching patterns and anticipate 

the decisions made by drivers and dispatchers in scheduling and routing around peak-period congestion. 

3.7. Trade Policy 

Federal Action. Restructure U.S. trade policies to promote in- and near-sourcing of goods and 

commodities. Examples of trade policies aimed at redirecting make-buy-sell supply chains are “Buy 

America” statutes and the more recent and broader effort to expand domestic petroleum and natural gas 

production and alternative clean energy sources (solar, wind, geothermal, etc.) to reduce the United 

States’ dependence on foreign energy providers. 

Impacts. The freight demand impacts of policies directed at sourcing will vary by commodity. For 

example, substituting U.S.-manufactured consumer goods for similar products manufactured in China 

would reduce the VMT and emissions associated with the manufacturing process, consolidation, and 

container ship movement across the Pacific, but might not reduce VMT and emissions associated with the 

distribution of the consumer goods within the United States. The net “life cycle” effect on fuel use and 

GHG emissions will also be affected by the location of raw materials and parts suppliers. If they are 

within the United States, the impact of these policies may be positive in reducing freight transportation 

demand and associated energy use and GHG emissions; if outside, the net impact might be to increase 

overall freight transportation demand, as parts and raw materials would need to be imported to meet 

demand. 

Implementation. The responsibility for implementation would fall primarily with the Department of 

Commerce and the Department of State. 

Assessment Methods. Regional input/output models could be used to approximate the first-order effects of 

major changes in sourcing of commodities. Estimating the longer-term effects would require a detailed 

economic analysis of the industries involved and their supply chains. 

3.8. Land Use 

Federal Action. Provide federal tax incentives for the co-location of complementary manufacturing firms, 

distribution centers, and freight transportation terminals. 

Proximity to highways has been a critical factor in industrial real estate development since the initiation 

of the Interstate Highway program, but not until demand outgrew highway capacity in the mid-2000s did 

industrial real estate developers begin to pay serious attention to the effects of congestion on travel time, 

travel reliability, and cost in site selection. Tax incentives could make development (or redevelopment) of 

industrial and freight staging areas that are closer to city centers more economically attractive. Likewise, 

location-efficient mortgages, which give lower interest rates to homeowners who live in closer proximity 

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TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

to their places of employment, could help reshape urban areas over time, providing greater economies of 

scale in freight operations and fewer truck-miles of travel. 

The location of freight facilities has historically been determined at the local level. More centralized 

planning at the regional or state level might help promote the establishment of concentrations of freight 

activity (freight villages) serving multiple industries. Another key factor is the ability of local and state 

governments, with federal assistance, to preserve existing freight assets and freight corridors from being 

displaced by non-freight uses. 

Impacts. As long as freight transportation costs remain low relative to other production factors, such as 

capital, labor, and technology, land-rent economics will generally favor the migration of industrial and 

transportation logistics uses to the edges of metropolitan areas. The short-term impacts are likely to be 

modest, but retrospective studies of the impact of federal home ownership policies since World War II, 

coupled with highway funding programs, are strong illustrations of the longer-term potential of tax 

incentives to influence the spatial form of urban areas. 

Implementation. This action would likely be implemented through the Department of Treasury and the 

Internal Revenue Service. 

Assessment Methods. Metropolitan area land use and transportation models can be used to project the 

impact of land use, zoning, truck parking, and pricing on settlement patterns and transportation demand. 

For example, the Metropolitan Washington Council of Governments (and others) has explored policy 

impacts to 2040 and 2050. The models do not generally address freight demand and transportation, but 

could be extended and calibrated to do so. 

3.9. Opportunity Matrix 

The opportunity matrix in Table 3.1 arrays potential federal policy actions along two dimensions: the 

probability of their implementation, and the likely payoff measured in terms of reduction in freight 

transportation energy use and GHG emissions. The allocation of the actions is based on best professional 

judgment, and is not intended to promote or oppose particular actions. 

Table 3.1. Opportunity Matrix for Freight Transportation Energy Use and GHG Emissions
 

Initiatives
 

Potential Reduction in Freight Transportation Energy Use and GHG Emissions 

Low Moderate High 

Probability of Implementation

High

Increase heavy-duty engine 

efficiency and emission 

standards. 

Moderate 

Increase investment in freight 

infrastructure. 

Increase federal motor fuel 

tax. 

Implement road pricing 

(VMT user fees). 

Impose low-carbon 

fuel standards. 

Low 

Provide tax incentives for the 

co-location of freight 

generators and terminals. 

Deregulate U.S. coastal 

shipping. 

Restructure U.S. trade 

policies to promote in- and 

near-sourcing. 

51
 
 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

4.0 FREIGHT TRANSPORTATION DEMAND PROJECTION METHODS
 

4.1. Overview 

Freight demand projection methods have received much attention over the past decade and have 

employed increasingly sophisticated forecasting methodologies. These methods have helped generate 

better and more accurate estimations of freight demand both in terms of flow volumes as well as mode 

shifts. At the same time, the models have also become very “data-intensive.” The lack of current and 

accessible data, not analytical capabilities, is the larger problem today in estimating future freight 

demand. Available freight demand projection methods and models fall under the following categories: 

•	 Macroeconomic/Commodity Models, which estimate current and future forecast freight traffic by 

linking economic activity to freight flows 

•	 Time Series Models, which estimate future demand based upon extrapolations of historical freight 

demand data 

•	 Behavioral Models, which include both choice- and survey-based demand models and capture how 

shippers select modes 

•	 Demand/Supply Equilibrium Models, which model freight demand based on balancing demand 

against supply, with the costs of supply associated with acceptable levels of service 

•	 Microsimulation and Agent-Based Models, which simulate large numbers of individual freight 

shipments and sum them to produce total freight flow estimates 

•	 Supply Chain and Logistics Models, which aim to capture the relationship between suppliers and 

customers and the decisions made by participants in the supply chain that affect freight demand. 

Because freight is a derived demand and very responsive to changes in economic activity, estimates of 

freight demand generally correlate with economic activity, and macroeconomic models frequently form 

the basis for most advanced freight forecasting models. Time series models, though crude, provide an 

easy way to estimate general growth of freight flows, especially when data are lacking; however, the 

major shortcoming of time series models is that they are not sensitive to policy changes, changes in modal 

services, or major investments that reduce the cost of transportation. Behavioral models provide an 

improvement relative to time series models and can offer the capability to estimate the shares between 

competing modes. Microsimulation models and supply chain/logistics models are specialized behavioral 

models with the former tending to be more focused on vehicle utilization decisions (equipment types and 

payloads and routing and dispatch patterns), whereas the latter tend to capture decisions across an entire 

supply chain. Demand/supply equilibrium models explicitly account for supply constraints in the 

transportation system that would tend to dampen demand. 

This section provides detailed descriptions of freight demand projection methods, covering those that are 

in current use or have been developed in the past decade. It includes methods used by national 

transportation agencies, states, and other regional agencies from around the world. It also includes 

methods discussed in academic papers as well as methods used by commercial vendors. However, only 

methodologies that are applicable to the goals of this report, to help develop alternative future scenarios, 

are included. Thus the focus of this review is on models that are effective at characterizing aggregate 

demand at the international, national, or state level. Most of the recent work on microsimulation and 

agent-based models has focused on smaller area forecasting that is not appropriate to national-level 

demand forecasting. Supply chain and logistics models are relative newcomers to the field of aggregate 

demand forecasting and so most of the models that are described in the literature are conceptual and have 

seen limited application in practice. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

The methods reviewed are: 

Macroeconomic/Commodity Demand Models 

•	 Input/Output Accounts 

•	 FAF3 

•	 Transearch 

•	 Random-Utility-Based Multi-Zonal Input-Output Model (RUBMZIO). 

Time Series Models 

•	 American Association of Railroads, American Trucking Association, PIERS 

•	 Freight Forecasting Using Economic Indices 

•	 FTR Associates’ Freight Forecasting Model 

•	 New Brunswick Intermodal Freight Projections 

•	 Virginia Department of Transportation (VDOT) I-81 Freight Projections 

•	 National Freight Demand Modeling using Simple Regression. 

Behavioral (Choice) Models 

•	 Neural Network Model for Multimodal Freight Network Modeling 

•	 Great Britain Freight Model (MDS Transmodal and UK Department for Transport 2012). 

The review shows that none of the available generation of freight transportation demand models addresses 

all the factors that shape freight transportation demand. However, for the purposes of the study, 

macroeconomic input/output models and behavioral (price elasticity of demand models)—coupled with 

national-level freight flow assignment network models—will be the most useful because they can provide 

direct estimates of the impacts of key factors, such as population, economic activity, and trade patterns, 

on tons and ton-miles of freight demand, which can then be translated in fuel use and GHG emissions 

estimates. Most of the other models also address population, economic activity, and trade patterns, but 

execution of these models requires extensive data collection and calibration that make them less 

appropriate for a first approximation of impacts of national-level changes in freight demand on fuel use 

and GHG emissions. 

The NREL Freight Energy Analysis Tool provides the capabilities to explore the first-order effects on 

fuel use and GHG emissions of changing: 

•	 Commodity tonnage and mix (approximating input/output effects) 

•	 Commodity shipment origins and destinations (approximating shifts in the origins and destinations 

of networks flows) 

•	 Mode of transportation (approximating modal price elasticity effects). 

4.2. Macroeconomic/Commodity Demand Models 

4.2.1. Input-Output Industry Accounts 

•	 Method name: Input/Output Industry Accounts. 

•	 Developer/owner: Bureau of Economic Analysis (BEA) (U.S. Department of Commerce BEA 

2010). 

•	 IP rights and transparency of method and data: Interactive data freely available online. Published 

data methodology is available online, but dated (1992). 

•	 Contact person: Carol E Moylan, (202) 606-9612. 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

•	 Overview of methodology: The Input-Output (IO) accounts are economic accounts that display the 

interdependencies among different economic sectors. The accounts are widely used as input data 

for freight-flow forecasting methodologies. The BEA IO accounts contain the make table, the use 

table, and direct requirement and total requirement tables that show the inter-industry 

relationships within the U.S. economy. Benchmark IO accounts are published every five years and 

include most detailed data from the Economic Census, with the latest one released in 2007. 

Another type of IO account, the Annual IO accounts, is available every year and has information 

on 65 industries rather than the 425 industries in the benchmark accounts. 

The benchmark IO accounts are based primarily on data collected from the economic census every 

five years. The Economic Census data contain information on industry and commodity production, 

materials consumed, operating expenses, etc. Annual IO accounts employ data from a plethora of 

sources for each industry, which are generally industry-specific survey data (such as agriculture, 

retail trade), or component-specific (such as employment, tax data). 

The benchmark IO accounts are prepared as follows: 

–	 Industry and commodity outputs for the IO make and use tables are estimated with gaps in the 

data filled from other sources 

–	 Commodity inputs required by an industry to produce its output are estimated 

–	 Value added by all industries is estimated. “Value added” consists of compensation of 

employees, indirect business tax, nontax liability, etc. 

–	 Detailed final-use categories are completed. 

Using the IO accounts, the direct and indirect effects of changes in final uses on industries and 

commodities can be determined. For instance, the effect of increases in energy prices can cause a 

decrease in demand for driving, and therefore reduction in motor vehicle production, which can 

reduce production of steel, chemicals, iron, limestone, etc., and therefore ultimately reduces the 

commodity flow on the transportation network. 

•	 Major inputs: Economic Census. Other data are drawn from National Income and Product 

Accounts, Bureau of Labor Statistics, U.S. Department of Treasury, U.S. Department of Energy, 

Office of Management and Budget, among others. 

•	 Major outputs: Make, use, direct requirement, and total requirement tables. Output requirements 

per dollar of delivery to final demand. 

•	 Duration: No forecast data. 

•	 Time steps: Historic benchmark data available every five years. Annual data are also available 

until 2010. 

•	 Geographic scope: U.S. national level; however, when supplemented with regional data from 

BEA, IO analysis can show economic effects by region. Other commercial software packages 

(e.g., IMPLAN) can be used for regional analysis at the county level. 

•	 Commodity: Available in North American Industry Classification System (NAICS) code-based 

industries covering 17 broad commodity groups (U.S. Census Bureau 2012). 

•	 Strengths: The IO tables provide inter-industry relationships and can help show the effect of 

changes in one industry on others. For example, if a future scenario specifies a change in the 

energy commodity mix, the IO tables can determine higher-order changes in flows of all input 

commodities that result from the initial change in energy commodity demand. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

•	 Weaknesses: Application of IO to forecast freight flow, especially at the subnational level, is a 

major effort, requiring large amounts of regional data to obtain statistically robust IO coefficients. 

While the BEA IO tables do provide information about the value of goods produced and consumed 

in the United States (by industry sector and commodity), they do not provide information about 

tonnage or origin-destination information for the commodity flows. Thus, it is impossible to 

directly determine ton-mile or vehicle-mile freight demand from IO tables. An extension of the 

national IO accounts is the multiregional input-output approach. An active example of this type of 

model is offered by MIG, Inc. (formerly the Minnesota IMPLAN Group) as part of its IMPLAN 

v3 software. The IMPLAN multiregional input-output was developed by MIG and the U.S. Forest 

Service. In addition to many standard IO data inputs, it uses information from the U.S. Commodity 

Flow Survey (see Freight Analysis Framework below) to develop the origin-destination 

characteristics of commodity flows. 

4.2.2. Freight Analysis Framework 3 

•	 Method name: FAF3. 

•	 Developer/owner: FHWA (U.S. DOT FHWA). 

•	 IP rights and transparency of method and data: The data are freely available online in raw format 

as well as summary formats. The methodology used for both the base and future cases are detailed 

and available online. 

•	 Contact person: Michael Sprung, Michael.Sprung@dot.gov. 

•	 Overview of methodology: FAF3 is a comprehensive database of freight movements among states 

and major metropolitan areas by all modes of transportation. Base year (2007) data primarily 

comes from the 2007 Commodity Flow Survey (CFS) by the Bureau of Transportation Statistics. 

To overcome data suppression issues from CFS, log-linear modeling combined with iterative 

proportional fitting is used to provide estimates for missing cell values and reconcile estimated 

flows. Supplemental data from Waterborne Commerce Data, STB Public Use Carload Waybill 

Sample Carload, and older U.S. Commodity Flow data and other commodity-specific data (where 

available) are used to further improve accuracy. 

In addition to overcoming the issue of data suppression, freight flows for industries that are out of 

scope for CFS are estimated using other data sources (primarily through existing survey data) and 

methods. IO tables are used in combination with these data sources to come up with final freight 

flows. 

For forecasts, the tonnage and value forecasts are based on the long-term projection of the U.S. 

economy produced by IHS Global Insight. Baseline, high, and low scenarios are developed using 

different assumptions of future industry productions and employment for each industry drawn 

from IHS Global Insight’s U.S. macroeconomic forecasting model . 

A multistep approach was used to develop the baseline forecasts: 

1. Establish national control totals by commodity. 

2. Apply specific shipment growth by market and commodity. 

3. Apply specific purchasing and consumption growth by market and commodity. 

4. Summarize and compare the results from Steps 2 and 3 with national controls. 

5. Adjust the resulting freight flows so that volumes correspond with the national control levels. 

For previous domestic forecasts, IHS Global Insight’s Business Market Insights database (BMI) 

was used to provide output and employment forecasts to 2040 using annual growth rates for 

region-to-region shipment forecasts. For region-to-region purchase forecasts, IO tables from IHS 

55
 
 

 

 

 

 

 

Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

Global Insight’s Business Transaction Matrix (BTM) were used. The base year volumes obtained 

are then forecast using growth rates in BTM. 

•	 Major inputs: CFS, output, and employment data (BMI), IO commodity data (BTM). 

•	 Major outputs: Annual tonnages and dollars of goods transported. 

•	 Duration: From 2007 to 2040. 

•	 Time steps: Every five years starting from 2010. 

•	 Geographic scope: Flows between all U.S. states and 123 FAF zones (which approximate 

metropolitan areas) are available, as well as the remainder of state totals. In addition, export and 

import flows between United States and eight foreign regions are also available. 

•	 Commodity: Commodity information available at two-digit SCTG codes level. 

•	 Mode: Truck, rail, water, air, pipeline, multiple modes and mail, other and unknown, and no
 

domestic mode.
 

•	 Strengths: The method is detailed and linked to long-term macroeconomic forecasts. It is the only 

dataset that attempts to fully link multimodal trips.23 Base year data are perhaps the most 

extensively used open-source commodity flow data. 

•	 Weaknesses: It does not account for the effects of energy, fuels, and the environment on the input 

factors. General assumptions must be made on how such factors will impact employment, IO and 

so on. The FAF base data are updated only every five years, and, by the time of its release, three 

years have passed since the data were collected. 

4.2.3. Transearch 

•	 Method name: Transearch. 

•	 Developer/owner: IHS Global Insight (2012). 

•	 IP rights and transparency of method and data: The data must be purchased and used for specific 

projects with use governed by specific contract terms. Data forecasting methodology must be 

requested; it is not publicly available. 

•	 Contact person: Marc Venditti, (781) 301-9325. 

•	 Overview of methodology: Transearch is an annually published commodity flow database that can 

be customized for regions and provides freight flows by Origin/Destination, commodity, and 

mode. Base year production data start with IO tables from BEA to determine output volumes, as 

well as BMI databases from Global Insight. The BMI county-level sales information is used in 

conjunction with BEA IO tables to estimate the value of production and consumption for each 

commodity at the county level. The tonnages at the county level can then be determined from 

control totals obtained from governmental agency sources. 

Various supplemental data sources are used for development of modal freight flows. Rail traffic 

data come from the Surface Transportation Board’s Carload Waybill Sample data, and waterborne 

traffic from the U.S. Army Corps of Engineers. Truck flows, being the most complicated to 

determine, are calculated by subtracting the other modal flows from total flows. These flows are 

then fed into a gravity model for determination of Origin/Destination pairs. The results are 

supplemented with actual truck freight flow data collected from large national motor carriers who 

23The calculation of multiple modes and mail in the FAF3 is incorrect. The decision to rectify this issue or wait until the next 

FAF release has not yet been made as this is being written. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

provide data to IHS Global Insight in a data exchange program from which the carriers obtain 

access to the final Transearch product. 

For the forecasts, projections of supply (originating flows) and demand (destination flows) by 

county and four-digit STCC are developed and then constrained to a national total. The forecast 

uses supply and demand-side factors, including employment, output, and purchases by industry 

and county. It focuses on freight volumes, but a value forecast is also produced that holds the base 

year price as fixed. The county-to-county commodity shipments are forecast by using proprietary 

information from a variety of sources within IHS Global Insight, listed under major inputs below. 

•	 Major inputs: U.S. macroeconomic data, energy service data, automotive service data, world trade 

service, BMI and, BTM IO tables (all from IHS Global Insight). 

•	 Major outputs: Annual tonnages and dollars of goods transported, and also units (such as truck 

loads), VMT, and ton-miles. 

•	 Time steps: Base year and future year value are usually provided. 

•	 Geographic scope: Flows are available at county level for each of the counties within the United 

States and also for regions in Mexico and Canada. 

•	 Commodity: Commodity information is available at four-digit STCC code level for more than 450 

commodities. 

•	 Mode: For-hire truckload, for-hire less-than-truckload, private truck, conventional rail carload, 

rail/highway intermodal, air, water. 

•	 Vehicle and fuel categories: Trucks are broken into for-hire truckload, for-hire less-than-truckload, 

and private truck. No information is available by fuel type. 

•	 Strengths: Forecasts are detailed both in terms of geography and commodity at a level not 

available elsewhere. Furthermore, Transearch is the only available source that categorizes truck 

traffic by type. 

•	 Weaknesses: The complex nature of the forecasting methodology means it is not easy to determine 

the effect of energy and other factors on freight demand. In addition, the actual forecast tools that 

underlie the models are proprietary and not necessarily peer reviewed. Linkage between 

multimodal trips is limited to rail/highway intermodal. 

4.2.4. Random-Utility-Based Multi-Zonal Input-Output Model (RUBMZIO) 

•	 Method name: Random-Utility-Based Multi-Zonal Input-Output Model (RUBMZIO). 

•	 Developer/owner: Lin Jing, Kara Kockelman, Yong Zhao, The University of Texas at Austin. 

•	 IP rights and transparency of method and data: IP rights unknown, methodology transparent and 

detailed in published papers. 

•	 Reference: Kockelman et al. (2005). Tracking land use, transport, and industrial production using 

random-utility multizonal input-output models: applications for Texas trade. Journal of Transport 

Geography 13(3), pp. 275–286. 

•	 Overview of methodology: The RUBMZIO model combines a random-utility model with a 

multizonal IO model to estimate production, trade, and travel using Texas data. This model 

simulates trade patterns of labor and commodities among zones based on different export 

demands, production technologies, travel modes, and network routing options. It can be used to 

explore changes in location choices, production, and trade flow patterns due to different export 

demand and travel costs. 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

The structure of the RUBMZIO model consists of disutility functions between zones, production 

functions, and trade flow functions. Disutility functions are based on acquiring some commodity 

from one zone and consuming it in another zone, dependent on transportation cost. A nested logit 

model structure is used to determine choices between mode and origin. For this purpose, the 1997 

CFS data were used. IMPLAN’s industry-by-industry transaction tables are used to generate 

technical coefficients for estimating parameters as well. The functions are solved iteratively 

together to achieve an equilibrium trade pattern. Behavioral data, such as mode preferences, are 

required to calibrate the model, and various input data, such as export demands, are required to run 

the model. 

One of the most important relationships determined from this model is the effect of transportation 

costs on trade volumes. The model predicted that a 10% travel cost increase caused a 9.8% 

decrease in a trade-weighted average highway travel distances and 10.1% decrease in railway 

travel distances. A 10% decrease in costs resulted in a 10.1% increase in trade-weighted average 

highway travel distance and a 10.3% increase in trade weighted average railway travel distance. 

•	 Major inputs: 1997 CFS data, 1997 IMPLAN industry transaction tables by county, TransCAD 

shortest inter-county path distances by mode. 

•	 Major outputs: Freight flow volumes on routes. 

•	 Geographic scope: Texas’s 254 counties. 

•	 Commodity: Eighteen economic sectors. 

•	 Mode: Truck and rail. 

•	 Strengths: This is a highly detailed but aggregate model that combined traditional IO models with 

logit-based utility maximizing approach. It provides linkages between transportation costs and 

freight demand. 

•	 Weaknesses: The model is calibrated using Texas-specific data that may not be applicable on a 

nationwide level. It is also very complex and data-intensive. 

4.3. Time Series Models 

Time series models forecast freight demand by using historical freight demand trends or by regression 

analysis of those trends and correlated economic variables. While there are many methods used, modespecific forecasts are also provided by associations such as the American Trucking Association and the 

American Association of Railroads. 

For the most part, the forecasts provided by these associations are straightforward extrapolations of 

historic trends. IHS Global Insight that uses Transearch data, government data, including the CFS, and 

historic industry data to develop transportation demand reports for the American Trucking Association. 

The forecast covers primary shipments and domestic moves only with a time horizon of 10 years. The 

forecast is reported by tonnage and revenue for each mode of freight transportation. 

The American Association of Railroads also produces a report based on 10-year historical rail volume 

data and the trends of various economic indicators. By extrapolating the relationship between real-time 

GDP and rail volumes, a straight line forecast can be made. Coal and grain volumes must be excluded 

because they vary with international market conditions and are influenced strongly by external demands 

not directly related to GDP. PIERS, a commercial product that reports container shipping volumes also 

provides short-term forecasts on container volumes, carriers, and ports. 

4.3.1. Freight Forecasting Using Economic Indices 

•	 Method name: Freight Forecasting Using Economic Indices. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

•	 Developer/owner: Fite et al. (2002). 

•	 IP rights and transparency of method and data: IP rights are not known. Description of
 

methodology available.
 

•	 Contact person: Jonathon T. Fite, Accenture Consulting. 

•	 Reference: Fite, J.T., Taylor, G.D., Usher, J.S., English, J.R., Roberts, J.N. (2002) “Forecasting 

freight demand using economic indices,” International Journal of Physical Distribution and 

Logistics Management, 32(4), pp. 299–308. 

•	 Overview of methodology: This is a short-term forecast model used to predict future freight 

volumes in the truckload industry. The approach involves the use of stepwise multiple linear 

regression models that relate freight volume to a variety of economic indicators. The models are 

built using a large set of actual freight data provided by JB Hunt Transport for three years. The 

data were first analyzed using a set of national data, and then data on specific industrial and 

regional segments. The model development steps are outlined below: 

–	 Historic monthly freight demand is obtained from JB Hunt. 

–	 Economic and industrial indices from 107 different sources (e.g., commodity price index) for 

the same time period are obtained. 

–	 Multi-linear regression formula is used to model the cause and effect relationships between 

the monthly values of indicators, and monthly freight volume. 

–	 Indicators with the best correlation are identified and used in constructing a multivariable 

model linking the indicators to freight demand. 

–	 The model is validated, and industry- and regional-level models are developed using similar 

methods. 

•	 Major inputs: Historic monthly freight demand data (JB Hunt), 107 economic and industrial
 

indices (from various sources).
 

•	 Major outputs: Near-term freight demand in the truckload industry. 

•	 Duration: Near-term forecast (mostly suitable for monthly predictions). 

•	 Time steps: Monthly. 

•	 Geographic scope: U.S. national level, regional level. 

•	 Commodity: Represented by 13 large industrial segments. 

•	 Mode: Truckload. 

•	 Strengths: The model is straightforward and useful in predicting short-term freight demand in the 

truckload industry. 

•	 Weaknesses: The forecast is only available for the short term and is limited to the truckload mode. 

Because it depends solely on the values of several economic indices to predict demand, the effect 

of different energy futures cannot be readily predicted using this model. 

4.3.2. FTR Associates Freight Forecasting Model 

•	 Method name: FTR Associates Freight Forecasting Model. 

•	 Developer/owner: FTR Associates (FTR 1999). 

•	 IP rights and transparency of method and data: Forecast data in the form of reports are available 

for purchase by the public. Methodology of forecast data development is briefly outlined online. 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

•	 Contact person: Eric Starks, estarks@ftrassociates.net. 

•	 Overview of methodology: The FTR Freight Forecast Model is a short-term forecast model used to 

forecast U.S. freight tonnage movements based on industry economic trends. This model was first 

constructed in the mid-1980s and was re-estimated during 1997–1999. It is based on an estimation 

of the movement of heavier goods in the U.S. economy. The development of the base data and 

forecasts follows the steps below: 

–	 Total tonnage movements for different commodity/industry categories are determined 

regardless of mode of transportation. 

–	 Tonnages are moved to different levels of the country’s economic infrastructure, including 

extraction, basic manufacturing, and finished manufacturing, wholesale, retail, and 

waste/recycling. 

–	 Moves are distinguished by types of movements, including to the next-stage, intra-stage, 

imports, and exports. 

–	 Tonnages are then assigned to three-digit commodity categories using various data sources, 

including the CFS. 

–	 Rail, truck, pipeline, and water modes are assigned to move the tonnage. Lengths of haul are 

set to determine ton-mile values. 

–	 Annual tons and ton-miles estimates are turned into quarterly values and forecast using the 

historical pattern of economic forecast data, which is provided by the Center for Econometric 

Research of Indiana University. Modal shares are kept constant for forecast values. 

•	 Major inputs: Economic data from the Center for Econometric Model Research (Indiana 

University), Economic Census Data (Census Bureau), 1993 and 1997 CFS and industry reports 

(U.S. Department of Commerce). Other data sources are public data sources pertaining to rail, 

energy, minerals fisheries, and agriculture. 

•	 Major outputs: Near-term reports on freight volumes (tons and ton-miles) from shippers, trucking, 

intermodal, rail, and driver supply perspectives. 

•	 Duration: Near-term forecast (less than five years). 

•	 Time steps: Monthly, quarterly data, annual (up to five years). 

•	 Geographic scope: U.S. national level. 

•	 Commodity: Available at three-digit STCC Code level. 

•	 Mode: Truck (includes short-haul), rail, water, and pipeline. 

•	 Vehicle and fuel categories: Trucks are divided into truck-load, less-than-truckload. 

•	 Strengths: The model and data are comprehensive (in terms of both modes and commodities) and 

provide realistic short-term forecasts based on real-time economic trends. 

•	 Weaknesses: The method provides only short-term forecasts as market information for specific 

industry sectors; therefore, it is not suited for long-term planning purposes. The forecast of the 

model is based exclusively on industry economic forecasts and does not take into consideration 

shifts in mode choices, which is an important consideration for long-term forecasts. 

4.3.3. New Brunswick Intermodal Freight Projections 

•	 Method name: New Brunswick Intermodal Freight Projections. 

•	 Developer/owner: J. Robert G. Brander and Frank R. Wilson. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

•	 IP rights and transparency of method and data: IP rights not applicable; model outlined in
 

published paper.
 

•	 Reference: Brander and Wilson (2001). Regional intermodal freight transport flows and 

projections. Transportation Research Record 1763: pp. 20–26.
 

•	 Overview of methodology: This methodology involves a trend-based extrapolation of the most 

likely growth scenario using a freight database, constructed for the study through data collected 

from truck, rail, water and marine centers, and a shipper survey. Because the scope of the study is 

limited to a region, current trends for each component of intermodal transport were analyzed. 

Uncertainties about the future planned expansions were used to determine the growth trend for 

intermodal traffic, and trade and GDP data for the province and nation were used as expansion 

proxies. By determining the relative growth of the region to the nation, a factor was determined 

that can be multiplied with trade and GDP growth factors to obtain adjusted regional factors. This 

resulted in six growth factors, covering each traffic type (container, trucking, and intermodal rail) 

and trade/GDP combination. The most likely scenario factor was then chosen and the factor used 

for straight-line extrapolation (e.g., one half the growth of intermodal rail using GDP as a proxy). 

This generated freight forecasts for all Origin/Destination movement pairs (out of nation, within 

nation, international, between Atlantic Canada). 

•	 Major inputs: Provincial and national trade and GDP historic data, base case freight flow data. 

•	 Major outputs: Freight flows in number of intermodal units. 

•	 Duration: Near to medium term (1998 to 2005). 

•	 Geographic scope: New Brunswick, Canada. 

•	 Commodity: Represented by 13 large industrial segments. 

•	 Mode: Railroad intermodal. 

•	 Strengths: This is a relatively simple forecasting model that uses trade and GDP data, which are 

readily available. Different growth factors can be easily assumed and tested, and modal shifts can 

be predicted by using different modal growth rates. 

•	 Weaknesses: This model is most suitable for regional databases because the effects of recent 

events on growth must be evaluated, which can be too burdensome at the national level. 

4.3.4. VDOT I-81 Freight Forecasting 

•	 Method name: VDOT I-81 Freight Forecasting (VDOT 2007). 

•	 Developer/owner: Virginia Department of Transportation (VDOT). 

•	 IP rights and transparency of method and data: IP rights unknown; forecasting methodology is 

transparent and outlined in report. 

•	 Overview of methodology: This methodology involves forecasting freight traffic along the I-81 

corridor to investigate truck-to-rail diversion potential in the future. The forecast for 2035 truck 

movements are developed within a Truck Trip Analyzer framework by applying a variety of 

economic growth rates to existing traffic counts. It is based on the premise that increases in 

industrial output create increases in freight demand, which translates to increases in freight traffic. 

The Truck Trip Analyzer model combines Virginia-specific commodity flow data with economic 

growth forecasts by industry developed by Regional Economic Models Inc. The development of 

the forecasts includes the following steps: 

– Processing existing VDOT truck counts. 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

– Forecasting single-unit truck trips. 

– Processing state/industry output growth factors for forecasting combination truck trips. 

– Developing state/industry commodity flow weights for combination truck trips. 

– Forecasting combination truck trips. 

– Estimating county/city truck trips. 

Once the tonnage values are determined, a truck trip table is built using the 1998 Transearch 

database to estimate 2035 truck trips using Origin/Destination information. 

•	 Major inputs: Regional Economic Models Inc. economic data, VDOT Truck Count Data, 1998 

VDOT Transearch Database. 

•	 Major outputs: Freight movements in commodity tonnages and truck trips. 

•	 Duration: 2035. 

•	 Time steps: Annually. 

•	 Geographic scope: I-81 Study Area in Virginia. 

•	 Mode: Truck. 

•	 Strengths: This is a relatively simple methodology for forecasting freight tonnage as well as truck 

trips using economic data. 

•	 Weaknesses: Effects of energy prices and patterns on growth rates must be determined before this 

method can be applied. This method works only with the Truck Trip Analyzer, but this tool can be 

replaced with another analyzer or modeling software. 

4.3.5. National Freight Demand Modeling Using Simple Regression 

•	 Method name: National Freight Demand Modeling Using Simple Regression. 

•	 Developer/owner: Shi-Miao Chin, Ph.D. and Ho-Ling Hwang, Ph.D. of Oak Ridge National
 

Laboratory.
 

•	 IP rights and transparency of method and data: IP rights unknown; model outlined in published 

paper. 

•	 Reference: Chin and Hwang (2007). National Freight Demand Modeling – Bridging the Gap 

between Freight Flow Statistics and U.S. Economic Patterns. Oak Ridge, Tennessee: Oak Ridge 

National Laboratory. 

•	 Overview of methodology: This methodology involves using simple regression-based estimation 

for quantifying national freight production/consumption by industry based on business patterns 

and population. It is based on the assumption that national freight demands are consistent with 

business patterns, and that a simple relationship exists between freight production/consumption 

and business patterns. It uses the 2002 CFS as the freight flow data source, the County Business 

Patterns data for business location and employment, etc., and the BEA IO tables to relate interindustry transactions. 

This methodology consists of estimation of regression equations for NAICS industry sectors at the 

state level for production and attraction. For freight production, two simple models – a linear and a 

power equation – are used to estimate the relationship between state-level freight shipped (y 

variable) and state-level annual payroll (x variable). For freight attraction, the same two equations 

can be used. To determine annual payroll values for freight receiving establishments (since this is 

not available in County Business Patterns), the IO make and use tables are utilized. By applying 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

“use” shares of industry sectors that use commodities produced by a given “make” industry sector, 

the annual payroll from industry sectors that “use” the commodities “made” by each industry can 

be estimated. The resulting freight demand models for the 27 industry sectors can then be used to 

estimate freight demand in the short run given projected annual payroll information. 

•	 Major inputs: 2002 CFS, 2002 County Business Patterns, 2002 IIO Accounts. 

•	 Major outputs: Freight production and demand by weight and value. 

•	 Duration: Short term (e.g., less than five years). 

•	 Time steps: Continuous. 

•	 Geographic scope: National and state level; can also be disaggregated to county or zip code level. 

•	 Commodity: Defined according to the 27 NAICS sectors. 

•	 Mode: Truck, rail, water, air, multiple modes and mail, pipeline, other and unknown, and no
 

domestic mode (2002 CFS modes).
 

•	 Strengths: This is a relatively simple forecasting model that estimates short-run freight flows at the 

national and state level. Its concept can be easily adopted and formulated using different economic 

factors. 

•	 Weaknesses: This model relies on the availability of U.S. business forecasts, which may not be 

linked to energy demand in the future. It is not suitable for long-term scenario planning purposes 

and cannot predict future mode shifts. 

4.4. Behavioral/Choice Models 

One of the shortcomings of time series modeling of freight demand evident from the models reviewed 

above is its inability to model dynamic changes in improved technologies that impact relevant factors, 

such as cost. Behavioral-based models try to use more disaggregate data and methods that allow the 

incorporation of a wider range of explanatory variables using improved econometric methods. 

Clark et al. (2005) provided a detailed review of aggregate and disaggregate behavioral freight demand 

models. Aggregate demand models use data that describe the behavioral aspects of a large group of 

shippers. The more advanced aggregate models are formulated using functions based on the relation 

between production and cost functions. Using the functions, elasticities of mode substitution and elasticity 

of demand for a mode can be estimated; these are found to vary across commodity groups. Nevertheless, 

it is found that these estimates may be understating the sensitivity of demand to price because using 

aggregate data can suppress a significant amount of information regarding variation in decision-making 

characteristics. 

Disaggregate models on the other hand use data on individual decision-makers and may yield better 

elasticity estimates. Disaggregate models can be classified into inventory- and behavior-based models, 

with behavior models the most widely developed and discussed. The behavior models deal primarily with 

modal choice, and include discrete or joint choice and simultaneous equation, random utility models. 

These choice models can help analyze spatial policy issues, interaction between mode and shipment 

decisions, and generate elasticity estimates, but they require extensive data. 

4.4.1. Neural Network Model for Multimodal Freight Network Modeling 

•	 Method name: Neural Network Model for Multimodal Freight Network Modeling. 

•	 Developer/owner: Peter Nijkamp, Aura Reggiani, Wai Fai Tsang. 

•	 IP rights and transparency of method and data: IP rights unknown; method outlined in published 

paper. 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

•	 Reference: Nijkamp, Reggiani, and Tsang (1999). Comparative modeling of interregional 

transport flows. Applications to multimodal European freight transport. Research Memorandum 

1999-2. Free University of Amsterdam, Netherlands. 1999. 

•	 Overview of methodology: This methodology is based on the concept of artificial neural networks, 

which has been adapted from other fields and applied to transportation engineering. A neural 

network is a network based on the workings of neurons in the brain, where neurons are linked with 

each other through a predetermined pattern of connections. Values are sent through connections 

with weights. After the neural network is formulated, it goes through a learning process where the 

network adjusts the weights on the connections. This learning process can help the model 

eliminate “noise” in the data and hence reduce error associated with incorrect data. Time, cost, and 

distance are used as variables. 

European transport data are applied to the model, which defines 108 regions in Europe and two 

modes (truck and rail). The same data set is also fed into a logit model and the performance by the 

neural network and the logit model are compared. It was found that neural network models in 

general are more accurate than logit models. When a sensitivity analysis between a neural network 

and a logit model is carried out, it was shown that neural network models were more suitable for 

spatial forecasting of freight flows, while logit models were more suitable for temporal 

forecasting. 

•	 Major inputs: Freight flow data for Europe. 

•	 Major outputs: Future freight flow data. 

•	 Geographic scope: 108 regions in Europe. 

•	 Commodity: Food and chemical products. 

•	 Mode: Truck and rail. 

•	 Strengths: Neural networks generate somewhat more accurate spatial freight forecasts. 

•	 Weaknesses: Overall, neural networks do not offer significantly better results than logit models, 

and their complex architecture cannot be easily adapted for determining relationships between 

energy outlook and demand. 

4.4.2. The Great Britain Freight Model 

•	 Method name: The Great Britain Freight Model (MDS Transmodal and UK DOT 2012). 

•	 Developer/owner: MDS Transmodal/UK Department for Transport. 

•	 IP rights and transparency of method and data: Model developed and used by UK Department for 

Transport. Detailed methodology of various versions available online. 

•	 Contact person: UK Department for Transport. 

•	 Overview of methodology: This highly evolved model is the UK’s national freight travel demand 

forecasting model. Unlike other modeling systems, the software has been designed to adapt the 

inexpensive program to the expensive data, and not the other way around. Therefore, the model 

only works with one data set. The model can be used to do macro-forecasting; for example, 

estimating the effect of changes in GDP on freight ton-kilometers by mode in the future. It can 

also be used to understand national policy impacts, regional analysis, ports and port hinterlands, 

inland rail freight terminals, road and rail network impacts, and so on. A user-friendly interface 

allows users to carry out “what-if” tests related to policy variables, trend-based forecasts and 

changes to land use and employment. 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

The model follows the traditional four-step approach to modeling, but condenses it to two steps, 

which include matrix building and path/mode choice assignment. In addition, it has a forecasting 

component that combines trend- and scenario-based forecasting, where the latter is designed by the 

user. Economic forecasting trends provided by MDS Transmodal are used as input data for the 

trend forecasts. At the center of the model is the use of an F-Logit approach to choice modeling, 

where a route can win traffic if it is cheaper but not dominated by a similar, better alternative. This 

logit approach recognizes the effects of common routing elements and uses a similarity matrix to 

avoid selection of irrelevant alternatives to which other logit models are subject. 

•	 Major inputs: World trade trends data model (MDS Transmodal), imports and exports by 

commodity, CFSs, and other modal data from various international and domestic sources. 

•	 Major outputs: Freight flows on transportation networks in terms of tons, ton-kilometers. 

•	 Geographic scope: UK national level. 

•	 Mode: Truck, rail, water. 

•	 Strengths: This is a comprehensive freight travel demand model that is based on economic, policy 

and trade data, and dynamic mode choice. Users can manipulate the model to produce various 

“what-if” scenarios on freight traffic flow total volumes as well as mode choices. 

•	 Weaknesses: The method is highly complex and only adopted for the UK freight data. 

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Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

5.0 ADDITIONAL ANALYSIS 

This section provides brief descriptions of additional research that could produce better freight demand 

projections supporting U.S. Department of Energy policy and programmatic analysis. This report does not 

advocate for or against undertaking this additional research, but is intended only to identify research that 

might advance freight energy analysis to inform U.S. Department of Energy long-term planning. 

Examine a broader range of scenarios through a more comprehensive and structured scenario analysis 

approach. In such an approach, “scenario landscapes” could be constructed that cover a wide range of 

possible values for key input variables (e.g., population, economics, technology, etc.). Some of these 

variables may also have probability distributions associated with their values. By combining these inputs 

with quantitative modeling tools, scenario landscapes can show an extensive range of possible futures and 

how these futures may be influenced by the policy interventions discussed in this issue paper. 

Develop sketch-level maps of major industry-level supply chains. The FAF provides a snapshot of 

commodity flows by origin/destination and freight transportation mode; however, the FAF does not 

describe the underlying supply chains. Sketch-level descriptions of supply chains and distribution 

networks would help inform scenario development. 

Disaggregate the FAF truck trip tables to create time-of-day truck trip tables. The FAF assignment 

process assumes that all trips are completed within a day and assigns trips to roadways assuming peakperiod congestion. As a result, longer-distance trips are often routed on circuitous routes, which increase 

the calculated VMT and GHG emissions. In practice, many industries and carriers dispatch trucks, 

especially long-haul trucks, at night or at carefully engineered periods during the day so as to aggressively 

avoid being delayed in peak-period traffic, while using more direct routes than the model would assign. 

Collect more complete and accurate data on urban freight flows and urban trucking operations. About 

half of all truck VMT is accrued in urban freight transportation and related construction and service 

operations, yet information about the patterns of urban truck freight flows, duty cycles, etc., is very 

limited. The national Vehicle Use and Inventory Survey (VIUS), now discontinued, covered only intercity 

truck trips with the latest data release in 2002. Prior to 1997, the survey was known as the Truck 

Inventory and Use Survey (TIUS). VIUS/TIUS were panel surveys done with the same vehicles/fleets 

over time and using registration data. 

Collect better information on truck duty cycles to improve estimates of fuel use and GHG emissions. 

Trucks are used in a much wider range of operations than passenger vehicles and pick-ups, and most 

trucks are customized with specific types of engines, transmissions, and body elements to match specific 

types of freight operations (local pickup and delivery versus long-haul over-the-road transport). Better 

information on truck duty cycles linked to operational patterns and commodity flows would significantly 

improve the estimates of fuel use and GHG emissions. 

Develop mega-region (multi-state) freight demand projections to more accurately reflect diverging 

economic and policy trends. The economies of the U.S. mega-regions differ significantly; consider, for 

example, the different structure and growth rates of the Northeast (Boston to DC) mega-region compared 

to the Pacific Northwest or the Upper Midwest regions. The NREL Freight Energy Analysis Tool follows 

the FAF convention of defining freight origin and destination zones by economic region. This makes it 

possible to tailor freight demand and modal share projections by mega-region and more accurately reflect 

differences among economic regions as well as differences in fuel use policy, regulation of freight 

transportation, and investment in freight transportation facilities. 

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6.0 CONCLUSIONS 

As global populations and economic activity continue to grow, consumer demand for food, clothing, and 

manufactured goods keeps rising, requiring ever-expanding freight transportation services. This report 

examined existing research on factors and trends that shape freight demand, and their anticipated effect on 

freight demand and related energy use. The most significant factors in shaping freight demand between 

now and 2050 were identified as follows. 

Economic Factors 

•	 A projected U.S. population of 439 million and a global population of 9.3 billion by the year 2050 

will translate into a 72% increase in U.S. freight transportation, as well as the potential for 

significant increases in exports to China, South America, and possibly Africa. 

•	 “Green” lifestyle trends, including preferences for fuel-efficient cars and buildings, locally 

produced food and retail goods, and recycling, could modestly reduce the demand for long-haul 

freight transportation. However, these trends could also result in more small shipments with 

multiple origins and destinations, increasing the demand for energy-intensive truck and air cargo 

transportation. 

•	 The consolidation of mega-regions could accelerate the use of rail (and some water) for long-haul 

shipping between regions and reinforce the dominance of trucking within mega-regions and the 

urban cores. 

•	 High land costs and relatively low transportation costs are likely to maintain ex-urban 

warehousing and distribution centers, generating more truck-miles of travel on major distribution 

corridors leading into and within metropolitan areas. 

•	 The agriculture sector, which currently accounts for a modest portion of GDP, is expected to be 

the single largest source of freight tonnage growth through 2040, with a projected increase of 

1.1 billion tons. 

•	 Five other commodity groups are also projected to experience total tonnage growth of over 150%: 

precision instruments, pharmaceuticals, miscellaneous manufacturing products, chemical 

products, and transport equipment. Higher-than-projected growth in these areas would generate 

new demand for domestic interplant freight transportation as well as export freight transportation. 

•	 The majority of emergent industries produce high-value products, so demand will continue to 

grow for timely and secure delivery by truck and air. 

Logistic Factors 

•	 A shift toward on-demand supply chains will result in smaller shipment sizes and more individual 

products per shipment, which favors the use of faster and more reliable trucking, intermodal, and 

air shipments over rail and waterborne freight shipments. 

•	 Higher fuel costs, congestion, and interest rates will likely continue to encourage major retailers 

and others to reposition distribution centers so that they can take advantage of lower-cost 

transportation (primarily rail) and minimize use of more expensive long-haul trucking. 

•	 Advances in packaging and containerization will improve the efficiency of the freight system, 

reducing the energy consumed in carrying extra packaging and fielding extra shipments to make 

up for lost and damaged goods. 

•	 Higher population densities in metropolitan areas and mega-regions will make possible more 

economies of scale in freight shipments. 

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Transportation Factors 

•	 For long-haul freight demand, rising fuel costs will tend to shift demand from air and truck to rail. 

For short-haul freight demand, rising costs will shift demand from conventional diesel and 

gasoline powered trucks to hybrid-power trucks. 

•	 The continued evolution and application of RFID shipment tags, on-board vehicle and 

trailer/container sensors, traffic monitoring systems, database technologies, global positioning 

systems, telecommunications, and precision dispatching and routing systems will significantly 

improve the productivity of all freight transportation systems. 

•	 In the light- and medium-truck classes, there will be a greater penetration of hybrids and allelectric vehicles. Heavy-duty, long-haul trucks will see relatively slower improvement in 

productivity, gallons per mile, and emission reductions because of technological barriers and the 

need to maintain a high load-to-vehicle weight ratio. 

•	 Freight railroads may be expected to retain their market share and perhaps capture more of the 

long-haul freight demand market. 

Policy and Regulatory Factors 

•	 Given the dominant role of trucking and highways in the U.S. freight transportation system, 

federal policies will likely favor continued investment to maintain highway capacity for trucking. 

•	 Projected increases in truck traffic, diesel fuel consumption, and GHG emissions make it likely 

that EPA will consider tightening truck fuel-efficiency and emission standards by 2050. The 

impact of stricter standards on freight demand will depend somewhat on the ability of engine 

manufacturers to meet the standards without significantly increasing the cost of truck engines and 

fuels. 

A review of possible federal policy actions determined that increasing heavy-duty engine efficiency and 

emission standards and imposing low-carbon fuel standards have the greatest potential for influencing 

freight demand and the highest probability of implementation. Newly enacted EPA and National Highway 

Traffic Safety Administration standards for medium- and heavy-duty vehicles are projected to reduce 

GHG emissions by about 270 million metric tons CO2-equivalent and save about 530 million barrels of 

oil over the life of 2014 to 2018 model-year vehicles. More stringent low-carbon fuel would help attract 

and sustain investment in alternative fuels, potentially lowering the cost. 

Policies that are likely to face greater challenges include: 

•	 Increasing investment in freight infrastructure 

•	 Increasing federal motor fuel tax 

•	 Implementing road pricing (VMT user fees) 

•	 Restructuring U.S. trade policies to promote in- and near-sourcing 

•	 Providing tax incentives for the co-location of freight generators and terminals 

•	 Deregulating U.S. coastal shipping. 

None of the available models addresses all the factors this study identifies as vital in determining freight 

transportation demand. A combination of macroeconomic input/output, behavioral, and national-level 

freight flow assignment network models proved to be the most useful, with the capability to provide 

direct estimates of the impacts of key factors, such as population, economic activity, and trade patterns, 

on tons and ton-miles of freight demand, which can then be translated in fuel use and GHG emissions 

estimates. Although most of the other models address population, economic activity, and trade patterns, 

running these models requires extensive data collection and calibration that make them less appropriate 

for a first approximation of impacts of national-level changes in freight demand on fuel use and GHG 

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TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

emissions. While this report provides a strong foundation of existing freight demand intelligence, many 

areas remain for future action. Examining a broader range of scenarios through a more comprehensive 

and structured analysis approach; collecting more complete and accurate data on urban freight flows, 

urban trucking operations, and truck duty cycles; and developing mega-region freight demand projections 

to more accurately reflect diverging economic and policy trends are just a few strategies for continued 

improvement. Accurately projecting demand and formulating sound policy will be crucial in meeting 

freight demand with energy efficient and low-emission freight transportation services. 

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APPENDIX: LITERATURE REVIEW 

A.1 Overview 

This literature review covers the following topics: 

•	 Factors that shape the demand for freight transportation, including population demographics, 

economic activity, logistics strategies and practices, freight transportation modes and operation, 

and regulation 

•	 Freight transportation demand projection methods, including data requirements, model types, and 

outputs. 

A.2 Freight Demand 

Authors of literature on the topic of freight demand generally agree that rapid growth in freight demand 

has been fueled by growth populations of consumers, global trade, and ever-changing logistics patterns. 

Research cited in this literature review address the impacts of demographic trends, such as aging 

population and urbanization, off-shoring and near-shoring production, customization of supply chain 

activities to meet customer specifications, transportation network capacity, and federal state and local 

government regulations on current and future freight demand in the United States. 

The literature reviewed includes TRB publications and reports and studies completed by U.S. DOT, the 

American Association of State Highway and Transportation Officials (AASHTO), and the U.S. Army 

Corps of Engineers, most of which include a list and/or assessment of key factors and trends. The review 

also examined several journal publications and conference proceedings that provide further insight into 

one or more of the factors, trends, and potential future scenarios. 

American Association of State Highway and Transportation Officials. Transportation: Invest in America: 

Freight-Rail Bottom Line Report (AASHTO 2002). 

This report describes the nation’s freight-rail system, issues, needs, and the estimated cost of addressing 

those needs. The Freight Rail Bottom Line Report is part of a series of AASHTO Bottom Line reports 

that deal with all of the major modes of freight and passenger transportation. The report discusses recent 

changes in demand for freight rail and identifies a number of trends and issues affecting the nation’s 

economy and logistics networks in sum. 

Beagan, Fischer, and Kuppam (2007). Quick Response Freight Manual II. Federal Highway 

Administration, Report No. FHWA-HOP-08-010. 

This report provides background information on the freight transportation system and factors affecting 

freight demand and identifies sources of data and freight-related forecasts, and how to apply this 

information in developing forecasts for specific facilities and developing truck trip tables. The report 

provides a list and discussion of key logistics issues and tradeoffs. 

Bingham (2006). “Freight Transportation Megatrends.” Freight Demand Modeling: Tools for PublicSector Decision-Making. Conference Proceedings 40, Transportation Research Board, National 

Academies Press, Washington, D.C. 

This presentation reviews a comprehensive list of megatrends in demographics, consumer habits and 

demands, global trade, and regulations that are likely to impact demand for freight transportation in 

coming decades. 

Case and Newman (2010). Advances in Manufacturing Technology, VIII: Proceedings of the Tenth 

National Conference on Manufacturing Research. University of Technology, Loughborough, United 

Kingdom. 

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This report provides a synopsis of discussions of current and future technological advancements in 

engineering, fabrication, assembly, and logistics processes, and the impacts those developments have on 

production capacity and freight transportation demand at the National Conference on Manufacturing 

Research held in the United Kingdom in 2010. 

Castro and Kuse (2005). “Impacts of Large Truck Restrictions in Freight Carrier Operations in Metro 

Manila.” Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, 2005. 

This document assesses the impacts of peak-period large truck restrictions on the operating behaviors of 

motor carriers in Manila, Philippines, and discusses some of the unintended impacts of regulations. The 

study found that because many receivers refused to shift delivery windows, motor carriers changed their 

vehicle fleets, purchasing smaller trucks that were not subject to the restrictions so they could continue to 

make deliveries during the peak period. This resulted in lower productivity and more vehicles on the 

roads during peak periods. 

Corbett and Winebrake (2008). The Impacts of Globalisation on International Maritime Transport 

Activity: Past Trends and Future Activity. Organisation for Economic Cooperation and Development. 

This report discusses the symbiotic relationship between globalization and maritime shipping, projected 

growth in maritime, and the energy and environmental consequences that maritime shipping has on 

global, regional, and local ecosystems. The report presents suggestions on how the environmental impacts 

of maritime shipping may be limited without hindering economic activity. 

Deitz and Orr (2006). “A Leaner, More Skilled U.S. Manufacturing Workforce.” Federal Reserve Bank 

of New York Current Issues in Economics and Finance, Volume 12, No. 2, 2010. 

This article highlights the contraction of employment in the manufacturing sector and the rise of 

technology and productivity between 1980 and the mid-2000s in the United States. 

Knight, K. (2008). The Implications of Panama Canal Expansion to U.S. Ports and Coastal Navigation 

Economic Analysis. Institute of Water Resources, United States Army Corps of Engineers. 

Demonstrating the impacts of infrastructure capacity enhancements on freight transportation demand, this 

report presents anticipated impacts on maritime cargo volumes, port traffic, and potential bottlenecks in 

the landside transportation networks that may occur on the U.S. East Coast when the Panama Canal 

expansion is completed. 

Rodrigue (2006). “Transportation and the Geographical and Functional Integration of Global Production 

Networks.” Growth and Change, Vol. 37, No. 4, July 2006. 

This article examines the trends of globalization and geographic decentralization of sourcing, gateways, 

and distribution hubs and discusses impacts of these trends on primary and secondary trade lanes. 

Sheiner, Sichel, and Slifman (2006). A Primer on the Macroeconomic Implications of Population Aging. 

Finance and Economics Discussion Series, Divisions of Research and Statistics and Monetary Affairs, 

Federal Reserve Board, Washington, D.C. 

This paper examines the aging trend in the U.S. population and discusses the macroeconomic 

consequences. The paper concludes that population aging will lead to a reduction in per capita 

consumption and workforce participation. 

TRB (2003b). Freight Capacity for the 21st Century: Committee for the Study of Freight Capacity for the 

Next Century. Transportation Research Board Special Report 271. 

This report contains a summary of the proceedings of the Committee for the Study of Freight Capacity for 

the Next Century in 2002. The committee outlined factors contributing to growing freight transportation 

demand, such as new railroad and port infrastructure, productivity growth, increasing population 

urbanization, and wealth. The report cautions that, if infrastructure capacity addition lags traffic in the 

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long term, a system breakdown could occur. The report advocates for a comprehensive federal 

government freight program that adheres to a list of guiding principles. 

A.3 Freight Demand Projection Methods 

The literature indicates that agencies at all levels of government have been working to develop analytical 

capabilities to estimate demand for freight transportation in their jurisdictions. These agencies have 

adopted different approaches, depending upon the geographic scope, variables they wish to track and data 

availability. These approaches vary somewhat with each application, but generally align with one of the 

following groups of models: 

•	 Time series models that estimate demand based upon extrapolations of freight demand based upon 

historic trends (Cohen, Horowitz, and Pendyala 2008; Veenstra and Haralambides 2001). 

•	 Behavioral models, which include both choice- and survey-based demand models, and capture 

how companies perceive and select from the many available freight choices (de Jong, Gunn, and 

Walker 2004). 

•	 Commodity-based and IO models that estimate current and forecasted freight traffic generation 

and distribution by linking economic activity to associated commodity flows, and converting those 

flows to truck and rail trips (Kim et al., 2004) (Sorratini 2000; Lawrence and Kleinman 1997). 

•	 Supply chain and logistics models that aim to capture the relationships between suppliers and 

customers and the decisions made by participants in the supply chain that affect freight demand. 

This approach is based largely upon proprietary information regarding private-sector supply chain 

and operations data (Jinghua, Hancock, and Southworth 2003). 

•	 Network design models that private sector companies use for locating factories, distribution
 

centers, warehouses, and other freight-generating facilities. Network design planning is very 

challenging given its scale, complexities, and decision interdependencies.
 

•	 Hybrid models that combine aspects of two or more of these approaches. (Cambridge Systematics, 

Inc. 2007b) (Fischer et al. 2005). 

•	 This literature review summarizes the major recent meta or summary studies, primarily published 

by the Transportation Research Board, that offer comparative analysis of the types of approaches 

listed above, descriptions of data sources that currently are available, and describe existing future 

needs for freight data and modeling tool capabilities. This review also contains summaries of 

academic journal articles and research papers that describe representative applications of modeling 

approaches, particularly those that have experimented with incorporation of atypical analysis tools 

and data sources. 

TRB (1997). A Guidebook for Forecasting Freight Transportation Demand. National Collaborative 

Highway Research Program (NCHRP) Report 388. Transportation Research Board, National Academy 

Press, Washington, D.C. 

This report provides reference information on freight transportation planning processes, techniques, tools, 

data, and applications. The guidebook appendices include, among other resources, a list of factors that 

impact freight demand, freight demand forecasting studies, freight data sources, descriptions of survey 

procedures, statistical forecasting techniques. 

FHWA (1999). Guidebook and Statewide Travel Forecasting. Prepared for the Federal Highway 

Administration by the Center for Urban Transportation Studies University of Wisconsin-Milwaukee. 

This guidebook reviews the state of the practice of statewide travel forecasting current in 1999. The 

guidebook stresses that it is not possible to create a one-size-fits-all model to deal with every possible 

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TRANSPORTATION ENERGY FUTURES SERIES	 Freight Transportation Demand 

situation, and that sometimes a simple application of a growth factor based on historic trends is more 

effective than an elaborate network analysis. 

Clark et al. (2005). A Survey of the Freight Transportation Demand Literature and a Comparison of 

Elasticity Estimates. Navigation Economics Technologies Program (NETS) of the Army Corps of 

Engineers. 

This report reviews several model approaches that estimate freight demand elasticities and finds that 

elasticities vary greatly by commodity and by region of the country. The study concludes that the 

functional form of models impacts the elasticity estimates. 

Cohen, Horowitz, and Pendyala (2008). Forecasting Statewide Freight Toolkit. National Collaborative 

Highway Research Program (NCHRP) Report 606. Transportation Research Board, National Academy 

Press, Washington, D.C. 

This report reviews the component steps of statewide freight models – generation, distribution, mode 

choice, and assignment. The report provides public and proprietary data sources for economic forecasting, 

commodity flow forecasts, and truck VMT data. The report suggests collecting on-the-ground data 

samples, such as truck counts and origin-destination surveys, to validate freight model outputs. 

de Jong and Ben-Akiva (2007). “A Microsimulation Model of Shipment Size and Transport Chain 

Choice.” Transportation Research Record Part B: Methodological, Volume 41, No. 9. 

This report summarizes an effort to develop a disaggregated behavioral model that simulates logistics 

decisions regarding shipment size and mode of transportation. The report argues that the traditional fourstep modeling approach has difficulty capturing the factors, such as firm-to-firm relationships and 

distribution center requirements, which influence shipper and carrier decisions. 

Fischer et al. (2005). “Innovative Framework for Modeling Freight Transportation in Los Angeles, 

California.” Journal of the Transportation Research Board, No. 1906, 2005. 

This report proposes a modeling approach for Los Angeles County that combines logistics chain 

modeling and tour-based truck modeling elements to evaluate freight transportation demands and the 

impacts of transportation investments. The authors argue that logistics chain models improve basic 

commodity flow models because choice models are built into every step of the logistics chain. The report 

concludes with a suggestion that adding a tour-based model capability to capture service vehicles and 

local pickup and delivery activities would be a desirable feature for future development. 

Fite et al. (2002). “Forecasting Freight Demand Using Economic Indices.” International Journal of 

Physical Distribution and Logistics Management, Vol. 32, No. 4, 2002. 

In this article, the authors develop an approach to predict future freight volume and trade imbalances in 

the truckload trucking industry using stepwise linear regression models that relate historic freight data 

from a large motor carrier fleet to a variety of economic factors. 

TRB (2006). Proceedings of the Freight Demand Modeling: Tools for Public-Sector Decision-Making: 

Transportation Research Board Conference Proceedings 40. 2006 September 25–27, Washington, D.C. 

Transportation Research Board, National Academies Press, Washington, D.C. 

This report provides a summary of presentations and breakout sessions on several topics concerning 

current practices in freight demand modeling, emerging developments and international examples of 

modeling methodologies, analytical needs of public-sector agencies, and roles for developing those 

needed capabilities. Highlights of interest to this research include: 

•	 A presentation by Paul Bingham that lists key megatrends in consumer demands, global trade, and 

regulations that will influence future freight demand 

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Freight Transportation Demand	 TRANSPORTATION ENERGY FUTURES SERIES 

•	 A series of case studies of international stateside freight demand modeling efforts, including a 

model for Oregon that incorporates a land use model 

•	 Findings that frequently or continuously collected national freight data and modeling tools that 

link econometric, transportation, logistics decisions, and economic geography are desired to 

support decision-makers. 

Holguín-Veras and Patil (2008). A Multicommodity Integrated Freight Origin-Destination Synthesis 

Model. Springer Science and Media. 

This paper presents an effort to develop and test an origin-destination synthesis model that combines a 

commodity-based model and a complementary model of empty trips. The results of the study show that 

the integration of the two models produced results that closely matched observed traffic counts. The 

authors suggest that the model could be used to estimate origin-destination matrices. 

Holguin-Veras et al. (2001). An Assessment of Methodological Alternatives for a Regional Freight Model 

in the NYMTC Region. New York Metropolitan Transportation Council, New York, New York. 

This report assesses various urban freight transportation modeling methodologies, including data 

requirements, staff requirements needed to maintain the models, computer hardware requirements, cost to 

develop and maintain, and conceptual validity. 

Jinghua, Hancock, and Southworth (2003). Simulation of Regional Freight Movement with Trade and 

Transportation Multinetworks. Transportation Research Record No. 1854. 

This report presents a microsimulation model that incorporates three simulation components: 1) a freight 

traffic simulator; 2) a supply chain decision-making simulator; and 3) a pseudo-real-time simulator. The 

model was designed to evaluate the effects of information technologies and logistic strategies on freight 

demand. The discussion of conclusions speculates that trends in technology, such as Internet auctioning of 

vehicle space, could be an influence that increases load factors, despite the decline in load factors brought 

on by just-in-time logistics. 

Kornhauser and Morris (2003). Urban Goods Movement Model for Manhattan. New York Metropolitan 

Transportation Council, New York, New York. 

The authors of this report describe the development of an urban freight model that takes a “land use-based 

approach,” using building characteristics, such as rentable square footage, to develop trip generation 

estimates. 

Lawrence and Kleinman (1997). Development of a Freight Forecasting Model to Forecast Truck Flow 

Between NJ Counties Themselves and Between NJ Counties and Other States. New Jersey Department of 

Transportation. 

This report summarizes an effort to develop a freight forecasting model to estimate truck flows at the 

county-to-county level. The effort used demand factors, including population, employment, earned 

income, total personal income, and commodity flow data to develop a gravity model based not on 

population alone, rather on a combination of many consumption variables. 

Mizutani et al. (2006). “International Freight Transport Demand Forecasting by SCGE Model.” 2006 

Intermediate Input-Output Meeting, Sendai, Japan, July 26–28, 2006. 

This paper highlights two steps to forecasting international freight demand: first, forecast trade; and 

second, calculate corresponding freight transport volume. To forecast international trade, this paper 

discusses a concept to append a “Capital Flow” model to a standard equilibrium model to track changes in 

international investments. The theory argued is that investments are constantly shifting to countries which 

offer higher returns on investment. 

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TRANSPORTATION ENERGY FUTURES SERIES Freight Transportation Demand 

Pearson et al. (2005). A Comprehensive Commodity/Freight Movement Model for Texas. Texas 

Transportation Institute, on behalf of Texas Department of Transportation. 

This report summarizes an effort to develop a statewide commodity flow model in the State of Texas. A 

key element of the approach required the disaggregation of statewide commodity flow data to the county 

level, based upon demographics. Travel surveys were administered and the data were used to estimate 

load factors and truck trip generation. 

Regan and Garrido (2002). Modeling Freight Demand and Shipper Behavior: State of the Art, Future 

Directions. University of California, Irvine Institute for Transportation Studies. 

This report serves as a synthesis of research in freight demand and an assessment of the advantages and 

disadvantages of several aggregate and disaggregate demand estimation methods at the urban, 

interregional, and international levels. The report also reviews research in shipper behavior modeling and 

evaluate the opposing forces of disintermediation and disintegration of supply chains. 

Chin and Hwang (2007). National Freight Demand Modeling: Bridging the Gap Between Freight Flow 

Statistics and U.S. Economic Patterns. Transportation Research Board 86th Annual Meeting Compendium 

of Papers (2007). 

This paper describes the national freight demand models covered by the 2002 Commodity Flow Survey. 

The CFS model estimation framework hinges largely on the assumption that a relatively simple 

relationship exists between freight production/consumption and business patterns for each industry 

defined by the three-digit NAICS industry codes. The paper concludes that the CFS freight demand 

models could be enhanced with capabilities to disaggregate origin-destination tables to county or zip code 

levels or to assess regional economic impacts associated with service interruptions. 

Sorratini (2000). “Estimating Statewide Truck Trips Using Commodity Flows and Input-Output 

Coefficients.” Journal of Transportation and Statistics, Vol. 3, No. 1, 2000. 

This document describes an effort to model statewide truck trips using commodity flow data and inputoutput coefficients to derive freight production and attraction. The summary concludes with the 

acknowledgment that empty movements are absent from the analysis, and hence, results do not calibrate 

well to observed truck counts. 

Southworth et al. (2010). The Freight Analysis Framework, Version 3: Overview of the FAF3 National 

Freight Flow Tables. Federal Highway Administration. 

This report provides an overview of the data products included in the FAF3 database, improvements to 

the commodity flow matrix over previous versions of FAF, lists of geographic zones and transportation 

modes, a summary of the flow matrix construction process, and a summary of the methodological 

approaches to estimating flows of specific commodities and international flows. 

TRB (2003a). A Concept for A National Freight Data Program. Transportation Research Board Special 

Report 276. 

This report advocates for the establishment of a national freight data program that is reliable, consistent, 

and comprehensive. The program’s purpose would be to support decision-making through pinpointing 

problems and prioritizing policies and investments. The committee envisioned a modular database that 

leverages existing data sources to the extent possible. The database would include data on: origin and 

destination; commodity characteristics, weight and value; modes of shipment; routing and time of day; 

and vehicle/vessel types and configurations. The report suggests roles for the U.S. DOT and other 

stakeholders in implementing and maintaining the program. 

TRB (2011a). How We Travel: A Sustainable National Program for Freight Data. Transportation 

Research Board Special Report 304. 

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Freight Transportation Demand TRANSPORTATION ENERGY FUTURES SERIES 

This report presents existing sources of freight and passenger data at the federal, state, and local levels 

and proposes a travel data system that can support public and private decision-making. Within this 

document is a comprehensive list of commodity flow, foreign trade, air traffic, highway performance, and 

commercial vehicle and use data available in the public and private domains. The report presents the cost 

of maintaining public data and the frequency with which the data are updated. 

Veenstra and Haralambides (2001). “Multivariate Autoregressive Models for Forecasting Seaborne Trade 

Flows.” Transportation Research Part E, Volume 37, 2001. 

This article describes the use of multivariate autoregressive time series models to produce long-term trade 

flow forecasts of four commodities: crude oil, iron ore, grain, and coal. 

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77 

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