U.S. Energy Information Administration Independent Statistics & Analysis www.eia.gov

John Maples, Patricia Hutchins, Nicholas Chase

Office of Energy Analysis; Energy Consumption and Efficiency Analysis

July 23, 2013 | Washington, DC

Annual Energy Outlook 2014:

Modeling Updates in the Transportation Sector
Overview

2

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

• Light-duty vehicle

– Vehicle miles traveled by age cohort, update modeling parameters, employment and VMT

– E85 demand

– Battery electric vehicle cost, efficiency, and availability

• Heavy-duty vehicle, rail, marine

– Regionalization of freight movement (vehicle miles traveled and ton-mile) by commodity

– Rail and marine fuel efficiency update

• Potential future updates

– Heavy-duty vehicle market by regional, technology, and representation of HD national 

program

– Regionalization (U.S.) of international shipping

– Natural gas as a fuel choice for locomotives and marine vessels
Light-duty vehicle

3

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
Light-duty vehicle travel

4

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

• Recent studies indicate possible structural shift in travel behavior

– Decoupled link between travel behavior and economic growth

– Population shifts to urban areas

– Telecommuting, e-commerce, etc.

– Travel by age cohort and the aging population

• Data limitations restrict ability to analyze current activity and 

behavior

• Goal will be to include age related travel element to projection
Growth trends in travel, income, and 

employment

5

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1975 1980 1985 1990 1995 2000 2005 2010

Travel

Income

Employment

Indexed to 1995
Licensing rates by age cohort

6

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990 1995 2000 2005 2010

<20 (-)

20-34 (-)

35-54 (-)

55-64 (+)

65+ (+)

Percent of age cohort with a drivers license

Source: FHWA Highway Statistics
Annual vehicle miles traveled by licensed 

drivers

7

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

20%

30%

40%

50%

60%

70%

0

2

4

6

8

10

12

14

16

18

1975 1980 1985 1990 1995 2000 2005 2010

Average

<20

20-34

35-54

55-64

65+

Emp. Rate

Miles (thousand)

Source: NHTS and FHWA Highway Statistics

Employment rate 
Growth in driver licensing by age cohort

8

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

-20 -10 0 10 20 30 40 50 60 70 80

1980

1985

1990

1995

2000

2005

2010

<20

20-24

25-29

30-34

35-54

55-64

65+

Source: FHWA Highway Statistics

Change in 

licensed driver 

population 

(thousand)
Driver licenses by age cohort

9

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

0

50

100

150

200

250

1980 1985 1990 1995 2000 2005 2010

65+

55-64

35-54

20-34

<20

Source: FHWA Highway 

million
Consumer preference for E85

10

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

• E85 demand determined using a probability model developed by 

Greene at ORNL

– Market share determine by fuel prices and E85 availability

• Current model assumes single consumer behavior across census 

divisions the with differences in fuel availability and fuel prices 

determining demand 

• New model will reflect differences in consumer behavior across 

census divisions

– Model developed by Greene at ORNL

– Market share determined by fuel prices and E85 availability
Consumer choice for E85

11

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

0%

10%

20%

30%

40%

50%

60%

0 10% 20% 30% 40% 50%

E85 Fuel Availability

AEO2014

AEO2013

E85 market share

Assumes E85 is $0.10 cheaper per gallon of gasoline equivalent
Battery electric vehicle modeling updates

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

12

• Battery size (kWh)

– OEM manufacturer websites have model year 2012 and 2013 data on battery size (kWh): 

• HEV: Toyota Prius hybrid (1.7 kWh NiMH)

• Volkswagen Jetta hybrid (1.1 kWh lithium-ion)

• PHEV10: Toyota Prius plug-in hybrid (4.4 kWh lithium-ion) 

• PHEV40: Chevy Volt plug-in hybrid (16.5 kWh lithium-ion)

• EV100: Nissan Leaf plug-in electric (24.0 kWh lithium-ion)

• EV200: Tesla Model S 200 mile plug-in electric (60.0 kWh lithium-ion)

• Non-battery systems cost

– EPA OMEGA model provides total cost for 2012 through 2025 (by vehicle type and by size 

class)

– EPA/NHTSA 2017-2025 Final Rule JTSD provide near and long-term learning rates

– These data used to develop non-battery systems cost by vehicle type and size class
Battery electric vehicle modeling updates 

(continued)

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

13

• Battery cost ($/kWh)

– Battery costs vary by vehicle type (HEV, PHEV10, PHEV40, EV100, EV200)

– Cost developed using current OEM price data, Argonne’s BatPaC model, and 

EPA/NHTSA’s 2017-2025 Final Rule JTSD

• Battery vehicle model year availability 

– Availability by size class reflect recent manufacturer offerings and product announcements, 

examples:

• Midsize: Toyota Prius Plug-in (PHEV10), 2013

• Compact: Ford Focus EV (EV100), 2012

• Large: Tesla Model S (EV200), 2013

• Fuel economy equivalent

– All-electric fuel efficiency calculated using battery size and vehicle all-electric range
$0

$2,000

$4,000

$6,000

Current (2012) Medium-term (2025) Long-term (2040)

PHEV

HEV

EV

Midsize passenger car non-battery incremental (net) cost to 

consumer

14

2010$

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
$0

$2,000

$4,000

$6,000

Current (2012) Medium-term (2025) Long-term (2040)

PHEV

HEV

EV

Small sports utility vehicle non-battery incremental (net) cost to 

consumer

15

2010$

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
$0

$500

$1,000

$1,500

$2,000

Current Medium-term (2025) Long-term (2040)

HEV (L) PHEV10 HEV (NiMH) PHEV40 EV100 EV200

Battery cost to consumer ($/kWh)

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2010$

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
Heavy-duty vehicle, rail, marine

17

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
Regionalizing freight movement by mode 

(truck, rail, marine) and commodity 

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

18

• Total freight ton-mile data available from

– Railroad (Class I): U.S. Department of Transportation, Surface Transportation Board, 

Annual Reports (R-1) (1995-2011)

– Domestic waterborne commerce: U.S. Army Corps of Engineers, Waterborne Commerce of 

the United States, Annual Editions (1995-2010)

– Truck: U.S. Department of Transportation, National Transportation Statistics (1995-2009); 

VMT, Federal Highway Administration (1995-2011)

• Regional ton-mile data available in Commodity Flow Survey (2007, 

2002, 1997), U.S. Department of Transportation and U.S. Census 

Bureau

– Commodity Flow Survey contains ton-mile data by origin and destination state by mode 

and by commodity 

• Ton-mile and truck vehicle miles projected using ton-mile/$output 

by mode, by census division, by commodity
0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

1995 2000 2005 2011

Historical ton-mile data by mode

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billion ton-miles

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

Rail

Truck

Domestic Waterborne
Updated freight modes to incorporate regionalization 

by commodity using CFS ton-mile shares: Rail 2007

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

20

Census

Division

chemicals 

rubber

plastic

primary 

metals

processed 

food

paper 

products

petroleum 

products

stone, 

clay, 

glass, 

concrete

metal 

durables

other 

manu

agriculture mining Total

1 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.4%

2 0.5% 0.1% 0.2% 0.1% 0.3% 0.0% 0.0% 0.2% 0.3% 1.1% 2.7%

3 1.2% 0.5% 0.3% 0.2% 0.4% 0.1% 0.2% 0.2% 0.9% 10.1% 14.0%

4 0.3% 0.1% 0.8% 0.1% 0.1% 0.1% 0.0% 0.3% 2.5% 6.6% 10.9%

5 1.0% 0.1% 0.1% 0.2% 0.1% 0.1% 0.1% 0.1% 0.5% 9.1% 11.5%

6 0.7% 0.3% 0.0% 0.2% 0.1% 0.0% 0.0% 0.0% 0.1% 5.9% 7.4%

7 3.2% 0.3% 0.3% 0.4% 1.1% 0.1% 0.0% 0.2% 1.3% 9.8% 16.8%

8 0.3% 0.0% 0.1% 0.1% 0.4% 0.1% 0.0% 0.2% 0.2% 30.1% 31.5%

9 0.5% 0.2% 0.6% 0.4% 0.4% 0.0% 0.1% 0.7% 1.0% 0.8% 4.8%

Total 7.7% 1.8% 2.5% 1.7% 2.9% 0.5% 0.5% 2.1% 6.7% 73.6%
Industrial output shares by census division 

and industrial sector in 2007 (2005$)

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

21

Census

Division

chemicals 

rubber

plastic

primary 

metals

processed 

food

paper 

products

petroleum 

products

stone, 

clay, 

glass, 

concrete

metal 

durables

other 

manu

agriculture mining Total

1 0.5% 0.1% 0.3% 0.2% 0.0% 0.1% 1.9% 0.6% 0.1% 0.0% 3.7%

2 2.5% 0.7% 1.0% 0.3% 0.1% 0.2% 2.7% 1.4% 0.3% 0.2% 9.3%

3 3.6% 1.5% 2.1% 0.6% 0.1% 0.4% 10.5% 1.9% 0.6% 0.2% 21.5%

4 1.0% 0.1% 2.0% 0.2% 0.0% 0.2% 3.3% 0.8% 0.6% 0.2% 8.5%

5 3.0% 0.4% 1.5% 0.6% 0.0% 0.4% 4.4% 3.0% 0.8% 0.4% 14.5%

6 1.1% 0.6% 0.7% 0.4% 0.0% 0.2% 3.4% 0.9% 0.3% 0.4% 7.9%

7 3.6% 0.5% 1.1% 0.3% 0.1% 0.3% 4.1% 0.8% 0.6% 3.9% 15.4%

8 0.3% 0.1% 0.5% 0.1% 0.0% 0.2% 1.6% 0.6% 0.5% 1.4% 5.2%

9 1.3% 0.1% 1.5% 0.3% 0.1% 0.3% 5.5% 1.9% 1.7% 1.1% 13.9%

Total 16.8% 4.1% 10.8% 3.1% 0.5% 2.2% 37.4% 11.9% 5.4% 7.8%
Regionalizing historical ton-mile data by 

mode and by commodity 

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

22

• Commodity flow survey allows regionalization of ton-miles by mode 

and by commodity

• Historical industrial output data available by commodity from NEMS 

Macroeconomic Module (2005$)

• These data used to calculate ton-miles per dollar of industrial output 

by mode, by census division, and by commodity
Rail—ton-miles per dollar industrial output 

2007

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

23

Census

Division

Chemicals 

rubber

plastic

primary 

metals

processed 

food

paper 

products

petroleum 

products

stone, 

clay, 

glass, 

concrete

metal 

durables

other 

manu

agriculture mining

1 0.07 0.10 0.02 0.15 0.26 0.00 0.00 0.05 0.05 2.62

2 0.06 0.06 0.06 0.08 1.02 0.01 0.00 0.05 0.30 2.35

3 0.11 0.11 0.04 0.11 1.68 0.08 0.01 0.04 0.53 15.68

4 0.11 0.76 0.14 0.09 1.13 0.17 0.00 0.11 1.45 10.02

5 0.11 0.14 0.03 0.13 1.42 0.08 0.01 0.01 0.19 7.72

6 0.21 0.15 0.02 0.19 2.18 0.05 0.00 0.01 0.16 5.47

7 0.30 0.22 0.10 0.38 3.06 0.08 0.00 0.09 0.75 0.85

8 0.38 0.23 0.05 0.28 43.73 0.17 0.00 0.13 0.15 7.51

9 0.13 0.46 0.13 0.36 1.49 0.03 0.01 0.13 0.20 0.26
Projecting regional ton-miles by mode, 

census division, and commodity 

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

24

• For each mode, census division, and commodity grouping

– Ton-miles(year-1) + (Industrial Output(year) * ton-miles per $ output) 

• Ton-miles vary as industrial output rises or declines (ton-miles per $ 

output vary by mode)
Freight rail and domestic marine efficiency 

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate

25

• Freight rail efficiency (Btu/ton-mile)

– Railroad (Class I): U.S. Department of Transportation, Surface Transportation Board, 

Annual Reports (R-1) have ton-mile and fuel consumption data (1995-2011)

– Projected efficiency improves by 0.7% annually

• Domestic waterborne freight efficiency (Btu/ton-mile)

– Transportation Energy Data Book (31st edition), Waterborne Commerce on Taxed 

Waterways

– Projected efficiency improves by 0.8% annually
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Rail AEO 2013

Rail AEO 2014

Marine AEO 2013

Marine AEO 2014

26

Btu / 1,000 ton-miles

Proposed freight rail and domestic marine 

efficiencies

Source: EIA, Annual Energy Outlook 2013; USDOT Surface Transportation Board; Transportation Energy Data Book Ed. 31

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
Future modeling updates

27

• Further study and update to heavy-duty vehicle technology 

attributes and penetration, vehicle market representation, and 

modeling of HD National Program (Polk data)

• Regionalization of international shipping energy demand (U.S. Army 

Corps of Engineers PDSTATE data)

• Natural gas as a fuel choice for locomotives and marine vessels

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate
Discussion/questions

28

Nicholas Chase | phone: 202-586-8851 

| email: nicholas.chase@eia.gov

Patricia Hutchins | phone: 202-586-1029 

| email: patricia.hutchins@eia.gov

John Maples | phone: 202-586-1757 

| email: john.maples@eia.gov

U.S. Energy Information Administration home page | www.eia.gov

Annual Energy Outlook | www.eia.gov/forecasts/aeo

AEO2014 Transportation Model Updates 

Washington, D.C., July 2013 

Discussion purposes only - Do not cite or circulate