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

For

Working Group 2

September 25, 2013 | Washington, DC

By

Trisha Hutchins and Nicholas Chase

Office of Transportation Energy Consumption and Efficiency Analysis

Annual Energy Outlook 2014:

transportation modeling updates and 

preliminary results
Overview

2

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results

Washington, D.C., September 25, 2013

Discussion purposes only - Do not cite or circulate

• Macroeconomic drivers

– GDP, population, world oil price

• Light-duty vehicle

– New travel demand module including population demographics

– New region specific consumer behavior and E85 demand

– Updated battery electric vehicle cost, efficiency, and availability

• Heavy-duty vehicle, freight rail, and domestic marine

– New region, mode, and commodity specific freight travel demand

– Updated freight rail and domestic marine efficiency

– Added LNG as a fuel choice for freight locomotives
Real GDP is lower in the AEO2014

3

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

5

10

15

20

25

30

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

trillion 2005$

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
Population 16+ is lower in the AEO2014

4

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

150

175

200

225

250

275

300

325

350

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

millions

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
World oil price is lower in the AEO2014

5

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

20

40

60

80

100

120

140

160

180

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

2012$ per barrel

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
Light-duty vehicle

6

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate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

7

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 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

• New regional travel model

– VMT estimated by Census Division 

and aggregated to national level 

– Based on travel behavior and regional

licensing rates for males/females 

– 13 licensing rate age groups and 5 VMT age groups 
Growth in driver licensing by age cohort

8

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

-20 0 20 40 60 80 100

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

65+

55-64

35-54

30-34

25-29

20-24

<20

Source: AEO2014 preliminary

change in 

licensed driver 

population 

(thousand)

year
Driving population distribution by age group

9

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

85+

75-84

65-74

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

16-19

percent of population

Source: AEO2014 preliminary
Change in travel—16-19 year old age cohort

10

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

1

2

3

4

5

6

7

8

9

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Actual male

Model male

Model female

Actual female

VMT per licensed driver (thousands/year)

Source: AEO2014 preliminary

Male

Female
Change in travel—20-34 year old age cohort

11

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

14

16

18

20

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Actual male

Model male

Model female

Actual female

VMT per licensed driver (thousands/year)

Source: AEO2014 preliminary

Male

Female
Change in travel—35-54 year old age cohort

12

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

14

16

18

20

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Actual male

Model male

Model female

Actual female

VMT per licensed driver (thousands/year)

Source: AEO2014 preliminary

Male

Female
Change in travel—55-64 year old age cohort

13

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

14

16

18

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Actual male

Model male

Model female

Actual female

VMT per licensed driver (thousands/year)

Source: AEO2014 preliminary

Male

Female
Change in travel—65+ year old age cohort

14

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Actual male

Model male 

Model female

Actual female

VMT per licensed driver (thousands/year)

Source: AEO2014 preliminary

Male

Female
Licensed drivers increase over the projection 

period

15

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

150

175

200

225

250

275

300

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

licensed drivers (millions)

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
VMT per licensed driver decreases until 2024

16

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

11.0

11.5

12.0

12.5

13.0

13.5

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

VMT per licensed driver (thousand miles)

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
LDV travel lower in AEO2014

17

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

total LDV travel (billion miles)

AEO2013

AEO2014

Source: AEO2014 preliminary

History 2012 Projection
Total LDV energy use is lower in AEO2014 

due less travel demand 

18

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

14

16

18

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

quadrillion Btu

Source: AEO2014 preliminary

History 2012 Projection

AEO2013

AEO2014
Consumer preference for E85

19

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 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

• AEO2013 model assumed single consumer behavior across 

census divisions 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

– Potential issues related to inherent preference and habit formation
Consumer choice for E85

20

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 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

E85 market share

Assumes E85 is $0.10 cheaper per gallon of gasoline equivalent

AEO2013

AEO2014
E85 consumption greater in AEO2014

21

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0.0

0.5

1.0

1.5

2.0

2.5

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

billion gallons

Source: AEO2014 preliminary

AEO2013

AEO2014
Battery electric vehicle modeling updates

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

22

• Battery size (kWh)

– Updated using OEM manufacturer websites for model year 2012 and 2013

– Modified depth-of-discharge improvement

• 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 Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

23

• 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

• Fuel economy equivalent 

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

range
$0

$500

$1,000

$1,500

$2,000

$2,500

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

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

Battery cost to consumer ($/kWh)

24

2012$

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate
0

10

20

30

40

Near-term (2015) Medium-term (2025) Long-term (2040)

AEO2014 AEO2013

Price of midsize plug-in hybrid electric vehicle with 40 mile 

range higher in AEO2014

25

thousand 2012$

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate
Gasoline LDVs (including with micro hybridization) account 

for 82% of sales in 2040

26

U.S. light car and truck new sales

millions 

0

5

10

15

20

2000 2012 2015 2020 2025 2030 2035 2040

Gasoline without hybridization

Gasoline with micro hybridization

E85 flex fuel

Diesel

Hybrid electric

Plug-in hybrid electric

Electric

Gaseous and fuel cell

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

History Projections

Source: AEO2014 preliminary
LDV energy consumption by fuel remains predominantly motor 

gasoline with only small shares of other fuels, mostly diesel

27

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

2

4

6

8

10

12

14

16

18

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

quadrillion Btu

Source: AEO2014 preliminary

History 2012 Projection

motor gasoline

diesel

E85

gaseous, hydrogen, electricity

98% 93%

2%

4%

2%

1%
Heavy-duty vehicle, freight rail, and 

domestic marine

28

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

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

commodity 

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

29

• 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 by commodity 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

• Historic heavy-duty truck ton-mile and vehicle miles traveled 

data show direct relationship
Projecting regional freight movement by 

mode and commodity

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

30

• Ton-mile per dollar of industrial output 

– by census division and commodity derived from historical data (CFS2007) and 

NEMS Macro model value of industrial output

• Heavy-duty truck 

– ton-miles (vehicle miles traveled) projected using ton-mile per dollar value of 

output, by census division and commodity

• Freight rail 

– ton-miles split into non-coal and coal; non-coal projected using ton-mile per 

dollar value of output, by census division and commodity; while coal ton-miles 

use growth rate of coal ton-miles from NEMS coal module

• Domestic marine 

– ton-miles projected using ton-mile per dollar value of output by census division 

and commodity, with relationship showing phased-out historical rate of decline 
Heavy-duty freight vehicle miles traveled lower in AEO2014 

due to lower macroeconomic growth and new methodology 

31

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

50

100

150

200

250

300

350

400

450

500

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

vehicle miles traveled

(billion)

Source: AEO2014 preliminary

History 2012 Projection

AEO2013

AEO2014
Rail ton-miles higher and domestic marine ton-miles lower in 

AEO2014 due to methodology change

32

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

500

1,000

1,500

2,000

2,500

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

AEO2013

AEO2014

AEO2013

AEO2014

Freight rail

Domestic marine

ton-miles traveled

(billion)

Source: AEO2014 preliminary

History 2012 Projection
Freight rail and domestic marine efficiency 

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

33

• 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 (1/2 historic rate)

• 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 (1/2 historic rate)
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 AEO2013

Rail AEO2014

Marine AEO2013

Marine AEO2014

34

Btu / 1,000 ton-miles

Freight rail and domestic marine efficiencies 

improves at 1⁄2 the historic rate

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

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate
LNG Class I freight locomotives

35

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

• Price differential between LNG and diesel fuel has raised 

interest (fuel cost is 23% of total operating expense)

• BNSF will acquire 6 line-haul locomotives (3 from GE, 3 from 

EMD) in pilot program

– Testing will begin in late 2013 and continue for at least 1 year

– BNSF would “move quickly” if pilot program proves a success

• Canadian National Railways line-haul locomotive pilot 

program testing 2 ECI conversion kits (3,000 HP) and will 

acquire 2 line-haul locomotives from EMD (4,300 HP) with 

Caterpillar/EMD HPDI technology and Westport tender car

– Conversion kit testing in Canada began in late 2012

– Experiencing some mechanical and logistical challenges but too early to tell 

success/failure
Modeling LNG as a fuel choice for freight 

locomotives

36

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

• LNG fuel choice based on endogenous fuel economics 

calculation

– Incremental cost of LNG engine + fuel tender = $1,000,000 

– Annual ton-miles travelled per locomotive = 70,868,670

– Efficiency (Btu/ton-mile) is fuel neutral

– Discount rate = 11.5% (Class I Railroad average return on equity)

– Payback period = 15 years

– LNG locomotives available as fuel starting in 2015

– Class I Railroads pay about 80% of retail price of transportation diesel fuel

– Phase-in of new/rebuild LNG locomotives over 5 years each for BNSF/GT; 

CSX,NS,UPRR; KCS/Soo
Total transportation energy consumption

37

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

20

21

22

23

24

25

26

27

28

29

30

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

quadrillion Btu

Source: AEO2014 preliminary

AEO2014

AEO2013

History 2012 Projection
Transportation energy consumption declines across projection, 

LDV energy share falls while HDV energy share rises 

38

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

5

10

15

20

25

30

35

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

quadrillion Btu

Source: AEO2014 preliminary

History 2012 Projection

light-duty vehicle

rail

60%

20%

9%

3%

3%

3%

heavy-duty vehicle

air

marine

pipeline other 2%

48%

29%

11%

4%

3%

3%

2%
Motor gasoline declines as share of transportation fuel 

consumed while diesel fuel rises 

39

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

0

5

10

15

20

25

30

35

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

quadrillion Btu

Source: AEO2014 preliminary

History 2012 Projection

61% motor gasoline

22%

11%

diesel

jet fuel

47%

31%

13%

all other fuels

2012: 1% 2040: 4% (CNG/LNG: 2%, E85: 1%)

3%

3%

pipeline natural gas

residual oil

2%

2%
Discussion/questions

40

Nicholas Chase | phone: 202-586-8851 

| email: nicholas.chase@eia.gov

Trisha 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 Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate
Annual vehicle miles traveled by licensed 

drivers

41

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 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

65+

55-64

35-54

20-34

<20

Emp. Rate

miles (thousand)

Source: NHTS and FHWA Highway Statistics

employment rate 
Decline in licensing rates for age cohorts under 54 years old 

while increase for age cohorts above

42

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990 1995 2000 2005 2010

65+ (+)

55-64 (+)

35-54 (-)

20-34 (-)

<20 (-)

driver licensing rate

Source: NHTS
Driver licenses by age cohort

43

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 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
Example: heavy-duty truck ton-mile distribution by 

census division and commodity, CFS2007

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

44

census

division

chemicals 

rubber

plastic

primary 

metals proc food paper products

petroleum 

products

stone, 

clay, 

glass, 

concrete

metal 

durables

other 

mfg agri mining

1 0.2% 0.2% 0.5% 0.2% 0.2% 0.1% 0.1% 0.4% 0.1% 0.2%

2 1.2% 1.1% 2.0% 0.5% 0.5% 0.4% 0.5% 1.5% 0.5% 1.1%

3 2.5% 3.1% 3.0% 1.1% 0.6% 1.1% 1.9% 2.4% 0.8% 1.6%

4 0.8% 0.6% 1.9% 0.3% 0.4% 0.6% 0.6% 1.0% 1.8% 1.3%

5 2.3% 1.3% 2.4% 1.1% 0.8% 1.5% 0.9% 3.4% 0.8% 1.9%

6 0.8% 1.0% 0.9% 0.6% 0.3% 0.7% 0.7% 1.5% 0.2% 1.3%

7 2.5% 1.9% 2.1% 0.7% 1.5% 1.3% 1.1% 2.2% 0.7% 1.5%

8 0.6% 0.4% 0.8% 0.2% 0.4% 0.7% 0.2% 1.2% 0.4% 0.8%

9 1.5% 0.9% 3.6% 0.9% 0.9% 1.1% 1.0% 3.3% 1.3% 1.0%
Example: heavy-duty truck ton-mile per dollar of 

industrial output

AEO2014 Transportation Working Group 2: Modeling updates and preliminary results 

Washington, D.C., September 25, 2013 

Discussion purposes only - Do not cite or circulate

45

census

division

chemicals 

rubber

plastic

primary 

metals proc food paper products

petroleum 

products

stone, 

clay, 

glass, 

concrete

metal 

durables

other 

mfg agri mining

1 0.07 0.33 0.25 0.27 1.63 0.31 0.01 0.10 0.02 3.68 

2 0.09 0.28 0.32 0.25 0.93 0.26 0.03 0.18 0.19 1.00 

3 0.14 0.43 0.26 0.31 1.23 0.54 0.04 0.23 0.25 1.22 

4 0.13 1.31 0.22 0.14 2.04 0.70 0.04 0.21 0.58 1.44 

5 0.14 0.53 0.24 0.39 4.66 0.56 0.04 0.21 0.16 0.83 

6 0.15 0.35 0.24 0.37 2.85 0.75 0.04 0.34 0.07 0.65 

7 0.14 0.57 0.28 0.38 2.26 0.85 0.03 0.39 0.17 0.06 

8 0.23 0.75 0.22 0.33 21.62 0.61 0.01 0.29 0.18 0.11 

9 0.13 0.76 0.44 0.29 1.87 0.67 0.02 0.32 0.17 0.15