h. christopher frey,a nagui m. rouphail,a,b haibo zhaia,c · 2000 baseline no 1.23 39.0 4.36 995...

Measurement and Modeling of the Real-World Activity, Fuel Use, and Emissions of OnroadVehicles: Policy Implications of Fuels, Technologies, and Infrastructure

2010 TRB Energy and Environment Research ConferenceJune 6-9, 2010Raleigh, NC

H. Christopher Frey,a Nagui M. Rouphail,a,b

Haibo Zhaia,c

a Department of Civil, Construction, and

Environmental Engineeringb Institute for Transportation Research and

Education

North Carolina State University

Raleigh, NC 27695

c Now at Carnegie Mellon University

Key Questions

• What are the real-world energy use and

emissions of the transportation system?

• How sensitive are emissions to infrastructure,

vehicle technology, fuels, driving cycles, and

landuse?

• How can fuel consumption be decreased?

• How can emissions be reduced?

Estimating Vehicle Fuel Use Based on Vehicle Specific Power (VSP)

a = vehicle acceleration (m/s2)

A = vehicle frontal area (m2)

CD = aerodynamic drag coefficient (dimensionless)

CR = rolling resistance coefficient (dimensionless, ~ 0.0135)

g = acceleration of gravity (9.8 m/s2)

m = vehicle mass (in metric tons)

r = road grade

v = vehicle speed (m/s)

VSP = Vehicle Specific Power (kw/ton)

ε = factor accounting for rotational masses (~ 0.1)

ρ = ambient air density (1.207 kg/m3 at 20 ºC)

ACvgCgravVSP D

Frey, H.C., K. Zhang, and N.M. Rouphail, “Vehicle-Specific Emissions Modeling

Based Upon On-Road Measurements,” Environmental Science and Technology,

in press (published online 4/10/10)

Portable Emission Measurement System

• OEM-2100 Montana System

– Clean Air Technologies International, Inc.

– Carry-on Luggage size

– Weight: 35 lbs.

– Global Positioning System (GPS)

• Gas Analyzer

– NO and O2 from electro-chemical sensors

– HC, CO, and CO2 from non-dispersive infrared (NDIR)

– PM from laser light scattering detection

• Global Positioning System (GPS)

– GPS system measures vehicle location

CO2 Emissions versus Vehicle Specific Powerfor a Typical Light Duty Gasoline Vehicle

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

Vehicle Specific Power(kW/ton)

sion (

VSP Mode Based Emissions Model for a 2005 Caravan

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

NOx , Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

HC, Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

CO, Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

CO2, Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

NOx , Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

HC, Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

CO, Caravan 3.3 L

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

CO2, Caravan 3.3 L

Frey, H.C., K. Zhang, and N.M. Rouphail, “Fuel Use and Emissions Comparisons for Alternative

Routes, Time of Day, Road Grade, and Vehicles Based on In-Use Measurements,” Environmental

Science and Technology, 42(7):2483–2489 (April 2008)

Example of a Real World Field Study:Multiple Routes and Roadway Types

Route A

Route B

Route C

Route 1

Route 2

Route 3

Six Forks Rd

Wake Forest

RTPNorth

Raleigh

NC State

O/D Pair: NC State to North RaleighRoutes A, B, C

O/D Pair: North Raleigh to RTPRoutes 1, 2, 3

Frey, H.C., K. Zhang, and N.M. Rouphail, “Fuel Use and Emissions Comparisons for

Alternative Routes, Time of Day, Road Grade, and Vehicles Based on In-Use

Measurements,” Environmental Science and Technology, 42(7):2483–2489 (April 2008).

Example: Quantifying Activity for Primary Arterials for a Speed Range of Average Speed

Distance (km)

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

120Average Speed: 30-40 km/h

9 Runs

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

9 Runs

Distance (km)

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

9 Runs

VSP Bin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

9 Runs

Frey, H.C., N.M. Rouphail, and H. Zhai, “Speed- and Facility-Specific Emission

Estimates for On-Road Light-Duty Vehicles based on Real-World Speed Profiles,”

Transportation Research Record, 1987:128-137 (2006)

Link-based Average Emission Rates for Light Duty Gasoline Vehicles on Principal Arterials

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Speed (km/h)

10-20 20-30 30-40 40-50 >50

Link-based Average Emission Rates of NOx for LDGVs for Selected Roadway Types and Speeds

* Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.

Local &

Collector

Arterial Freeway Off-Ramp On-Ramp

10-20 km/h

20-30 km/h

30-40 km/h

40-50 km/h

50-60 km/h

60-70 km/h

90-100 km/h

Real World CO2 Emission Rates (and Fuel Use) for Selected Roadway Types and Speeds

* Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.

Local &

Collector

Arterial Freeway Off-Ramp On-Ramp

10-20 km/h

20-30 km/h

30-40 km/h

40-50 km/h

50-60 km/h

60-70 km/h

90-100 km/h

FRAMEWORK

Trucks Cars Buses

Vehicle ClassLink Type Link Speed

Link-based Emission Factors (EF) per veh-sec

Link Volume

Emission Inventory =

Link Travel

- Diesel

- Biodiesel

- Gasoline

- Diesel

- Ethanol85

- Hybrid

- Electric

- Fuel cell

- Diesel

Conventional Technology

Alternative Technology

ional T

VolumeTimeTravelEFlink classveh.

Link-based Emissions Model for a Pollutant

= basic emission rate (g/sec);

= cycle correction factor for real-world link-based cycle at FTP average speed versus FTP cycle

= emission factor (g/sec);

= relative humidity correction (dimensionless);

= pressure correction factor (dimensionless);

= link-based speed correction factor, ratio of emissions at speed V to a baseline speed;

= technology correction factor, ratio of emissions for technology T to conventional technology T’

(=1 for conventional fuels and technologies);

= temperature correction factor (dimensionless).

= facility type (freeway, arterial, ramp, local & collector);

= technology class (gasoline, diesel, E85, HEV, CNG cars, etc.);

= index of conventional fuels and technologies (gasoline or diesel);

= average driving cycle speed (19.6 mph for LDGV and 20.0 mph for HDDV);

= average link-based speed (mph);

= calendar year (CY2005, CY2030).

Where:

Subscripts:

VfTTTTvfTYVfTY SCFTCFCCFPCFHCFTECFBEREF ,,,,,,,,,

Parameter Database

Parameter Vehicle Fuel & Technology Source

Basic Emission

RatesLDGV, LDDV, HDDT, HDDB MOBILE6

Speed Correction

Factors

LDGV, HDDT NCSU PEMS

HDDB EPA PEMS

LDDV Portugal PEMS

Fuel Economy

LDGV EPA

LDDV, HEV, CNG CarsFuel Economy Guide by

EPA & DOE

Technology

Correction Factors

E85, HEV, CNG Cars EPA Certification Tests

B20 trucks, CNG Buses NCSU PEMS, Literature*

Traffic Demand Triangle Region Model ITRE, NCSU

* TCFs derived emission comparison studies for B20 versus diesel heavy-duty trucks, and from

reported comparisons of CNG versus diesel buses.

Example of Link-based Tailpipe Emission Factors: Light Duty Vehicles, Arterials, CY 2005

10-20 20-30 30-40 40-50 50-60

Speed (km/h)H

LDGV E85 CNG LDDV HEV

10-20 20-30 30-40 40-50 50-60

Speed (km/h)

10-20 20-30 30-40 40-50 50-60

Speed (km/h)

10-20 20-30 30-40 40-50 50-60

Speed (km/h)

10-20 20-30 30-40 40-50 50-60

Speed (km/h)

Emission Inventory Scenarios & Fleet Characterization

Vehicle

ClassFuel & Tech.

Fleet Penetration by Vehicle Class (%)

Present Scenario (2005) Future Scenario (2030)

Baseline Alternative Baseline Alternative

LDGV 100 73 100 73

E85 0 9.9 0 9.9

HEV 0 9.9 0 9.9

LDDV 0 5.9 0 5.9

CNG 0 1.2 0 1.2

EV & Fuel

Cell0 0.1 0 0.1

TruckHDDT 100 73 100 73

B20 Trucks 0 27 0 27

Bus HDDB 100 73 100 73

CNG Bus 0 27 0 27

Effect of Vehicle Technology and Land-Use:Case Study for Mecklenburg County

Collaborative Project with UNC-CH Regional and Urban

Planning

Input-output model of Mecklenburg County’s economy with

12 sectors (UNC)

Cross-sectional land-market equilibrium model with three

sectors (UNC)

Multimodal behavioral travel forecasting, including non-

motorized modes and incorporating attributes of the built

environment (UNC and ITRE)

Modal approach to estimating emissions (NCSU)

Nominally looking at 2030 to 2050 time frame.

Vehicle Activity for Baseline and 2050 Future Scenarios

Roadway TypeBaseline

Scenario

Future Scenario

Business-as-

Growth

Freeways 649, 860 1,232,060 1,337,910

Arterials 1,470,760 2,930,120 2,640,750

Local roads 254,750 527,300 440,140

Ramps 65,260 130,400 136,900

Bus rapid

transit0 0 250

Light-rail 0 350 1,220

Commuter-rail 0 80 320

Entire network 2,440,640 4,820,310 4,557,480

Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, “Impact of Alternative Vehicle Technologies and Land

Use Patterns on Long-Term Regional On-Road Vehicle Emissions,” 12th World Congress on

Transportation Research, Lisbon, Portugal, July 11-15, 2010

Peak Hour Emissions for Baseline and 2050 Future Scenarios

Scenario Total emissions (tons)

Land use

Pattern

Alternative vehicle

TechnologiesHC CO NOx CO2

2000 Baseline No 1.23 39.0 4.36 995

Business-

as-usualNo 0.26 16.0 0.63 1700

Business-

as-usualYes 0.25 14.2 0.60 1640

Smart-

growthNo 0.24 15.0 0.60 1580

Smart-

growthYes 0.23 13.3 0.57 1530

Sensitivity of Emissions Reduction to Alternative Fuels and Technologies

0% 20% 40% 60% 80% 100%

Penetration Rate of Alternative Vehicle Technologies

Estimated On-Road 2050 Tailpipe Emissions

Pollutant Vehicle Fleet Land Use Pattern

Trend TOD

Hydrocarbons100% conventional Benchmark -7.8%

73% conventional + 27% alt. -6.0% -11.6%

Carbon monoxide

100% conventional Benchmark -6.3%

Carbon dioxide

TOD = Transit-Oriented Development

Percent Different in Link-Based NOx Emissions for Mecklenburg County: NCSU Link-Based Model vs. Mobile6 for Baseline

% ChangeSource: UNC

Key Findings from Mecklenburg Case Study

Fleet turnover to all Tier 2 compliant vehicles will

substantially reduce emissions of Hydrocarbons, Carbon

Monoxide, and Nitrogen Oxides by 50 percent or more even

with growth in vehicle miles travelled.

Modest deployment of alternative vehicles may reduce these

emission by an addition 5 to 10 percent.

CO2 emissions increase by approximately 70 percent with

conventional vehicles and 64 percent with modest market

penetration of alternative vehicles.

Compared to Business as Usual land use, Smart Growth

landuse may reduce emissions of HC, CO, NOx and CO2

emissions between 5.5 and 7.8%, and slightly more with

modest market penetration of alternative vehicles.

Conclusions

Improvements in vehicle technology likely to enable

continued reductions in emissions of some pollutants (HC,

CO, NOx) despite growth in energy use and miles travelled.

Changes in landuse patterns may lead to incremental

reductions in these emissions

Modest penetration of alternative vehicle technologies is not

enough to make a substantial difference – more aggressive

diffusion of such technologies should be pursued.

However, CO2 emissions are not abated and instead grow

significantly under the scenarios considered.

Acknowledgments

• Disclaimer: The contents of this presentation reflect the views of the author

and not necessarily the views of the sponsors. The author is responsible for

the facts and accuracy of the data presented herein. The contents do not

necessarily reflect the official views or policies of US EPA. This presentation

does not constitute a standard, specification, or regulation.

Real-World Vehicle Activity, Fuel Use and Emissions Measurement Capability

• Portable Emission Measurement System (PEMS)

– Infrastructure Data: Vehicle location (GPS), road

grade (via altimeter and GPS, if applicable)

–Vehicle Technology and Fuels: Engine size, fuel

properties

–Behavior (Vehicle Dynamics): Speed,

Acceleration, Engine RPM

–Ambient conditions: temperature, humidity,

pressure

–Vehicle Fuel Use and Emissions: Gas analyzers

for NO, HC, CO, CO2 and opacity (Particulate

Matter)

Emission Rates Versus Vehicle Specific Power:CO2, NO, Hydrocarbons, and CO

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

sion (

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

sion (

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

NCSU VSP Driving Modes

Frey, H.C., A. Unal, J. Chen, S. Li, and C. Xuan, Methodology for Developing Modal Emission Rates

for EPA’s Multi-Scale Motor Vehicle and Equipment Emission Estimation System, EPA420-R-02-027,

Prepared by NC State University for U.S. Environmental Protection Agency, Ann Arbor, MI, Oct. 2002

VSP Mode VSP (kW/ton) VSP Mode VSP (kW/ton)

1 VSP < -2 2 -2 ≤ VSP < 0

3 0 ≤ VSP < 1 4 1 ≤ VSP < 4

5 4 ≤ VSP < 7 6 7 ≤ VSP < 10

7 10 ≤ VSP < 13 8 13 ≤ VSP < 16

9 16 ≤ VSP < 19 10 19 ≤ VSP < 23

11 23 ≤ VSP < 28 12 28 ≤ VSP < 33

13 33 ≤ VSP < 39 14 VSP ≥ 39

Comparing Fuel Use and Emission Rates for Different Roadway Types

Arteria

Freeway

On-Ram

Off-Ram

Arteria

Freeway

On-Ram

Off-Ram

Synthesizing the Micro-Scale Models into a Larger Framework

Develop link-based emissions models to couple with

transportation models for emission inventory estimates.

Characterize regional on-road mobile source emissions.

Evaluate the potential reductions in air pollutant emissions

associated with real-world operation of advanced fuel and

technology vehicles in comparison to conventional

vehicles.

Speed Correction Factors (SCFs)

SCF = ratio of link average emission rate at any speed to link

average emission rate at baseline speed range (e.g. 30 to 40

km/h).

Link average emission rates for a given technology are

estimated using field-measured second-by-second speed

profiles and Vehicle Specific Power (VSP)-based emission

rates.Frey, H.C., N.M. Rouphail, and H. Zhai, “Speed- and Facility-Specific Emission Estimates for On-Road Light-

Duty Vehicles based on Real-World Speed Profiles,” Transportation Research Record, 1987:128-137

(2006)

Zhai, H., H.C. Frey, and N.M. Rouphail, “A Vehicle-Specific Power Approach to Speed- and Facility-Specific

Emissions Estimates for Diesel Transit Buses,” Environmental Science and Technology, 42(21):7985-7991

(2008).

Frey, H.C., N.M. Rouphail, and H. Zhai, “Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on

Real-World Data,” Transportation Research Record, 2058:23-32 (2008).

Coelho, M., H.C. Frey, N.M. Rouphail, H. Zhai, and L. Pelkmans, “Assessing Methods for Comparing

Emissions from Gasoline and Diesel Light-Duty Vehicles Based on Microscale Measurements,”

Transportation Research – Part D, 14D(2):91-99 (March 2009).

Frey, H.C., H. Zhai, and N.M. Rouphail, “Regional On-Road Vehicle Running Emissions Modeling and

Evaluation for Conventional and Alternative Vehicle Technologies,” Environmental Science and Technology,

43(21):8449–8455 (2009).

Speed Correction Factors: Example for Light Duty Gasoline Vehicles on Arterials

10 20 30 40 50 60

Speed (km/h)

Speed C

ection F

10-20 20-30 30-40 40-50 50-60

Speed (km/h)N

Technology Correction Factors (TCFs)

TCFs account for differences in emissions rates when replacing

conventional with alternative vehicle technology

For HC, CO and NOx, TCFs for E85, HEV and CNG are estimated

based on average FTP emission rates from EPA’s annual certification

tests for alternative fuel versus gasoline from 2001 through 2007 (e.g.,

Frey et al., 2009).

For CO2, TCFs for HEV and CNG are estimated based on fuel

economy comparisons for alternative fuel versus gasoline, and for E85

based on fuel combustion theoretical analysis.

For B20 biodiesel heavy-duty vehicles, TCFs are estimated from

previous studies at NCSU (e.g., Frey and Kim, 2006). Zhai, H., H.C. Frey, N.M. Rouphail, G. Goncalves, and T. Farias, “Comparison of Flexible Fuel Vehicle and

Life Cycle Fuel Consumption and Emissions of Selected Pollutants and Greenhouse Gases for Ethanol

85 Versus Gasoline,” Journal of the Air & Waste Management Association, 59(8):912-924 (August

2009).

Frey, H.C., and K. Kim, “Comparison of Real-World Fuel Use and Emissions for Dump Trucks Fueled with

B20 Biodiesel Versus Petroleum Diesel,” Transportation Research Record, 1987:110-117 (2006).

Frey, H.C., and K. Kim, “In-Use Measurement of Activity, Fuel Use, and Emissions of Cement Mixer Trucks

Operated on Petroleum Diesel and B20 Biodiesel,” Trans. Research – Part D. 14(8):585-592 (2009).

Emission Inventory

ctctct voltEFTE1

= combination of vehicle class and technology;

= link-based emission factor for vehicles of class / tech (ct) (g/sec);

= average link travel time for vehicles of class / tech (ct) (second);

= traffic volume on link for vehicles of class / tech (ct) (vehicles/hr);

= total emissions for a single link (g/hr).

Where TE reflects outputs for a SINGLE link

Vehicle activity (average speed, number and types of vehicles) for the RTP

road network estimated using ITRE’s Triangle Regional Model (TRM)

Data subsequently aggregated across all links in the network.

Triangle Regional Transportation Network

Present “Future”VMT growth (33%), average speed decrease (28%)

“Future”

No VMT growth

Durham

Chapel Hill

Raleigh

Regional Emissions on Weekday Morning Peak Hour

Scenario HC CO NOx CO2

Present: Baseline 0.85 34 4.6 1,380

Present: Alternative 0.79 30 4.5 1,330

Future, No Growth: Baseline 0.15 10 0.39 1,200

Future, No Growth: Alternative 0.15 8.4 0.37 1,170

Future, Growth: Baseline 0.24 14 0.60 1,850

Future, Growth: Alternative 0.24 13 0.56 1,780

Total Network Emissions (tons in peak hour)

Regional Emissions Relative Changes for Weekday Morning Peak (continued)

Scenario HC CO NOx CO2

Present: Alternative -8 -14 -3 -4

Future, No Growth: Baseline -82 -72 -92 -13

Future, No Growth: Alternative -83 -76 -92 -15

Future, Growth: Baseline -71 -58 -87 34

Future, Growth: Alternative -72 -64 -88 29

Difference in Emissions Relative to Present Baseline Scenario (%)

Change in Emissions for Future Alternative versus Future Baseline

Scenarios HC CO NOx CO2

Future, Growth, Alternative versus

Future, Growth, Baseline-3 -13 -7 -4

Spatial Characterization of Emissions During AM Peak Hour: Present Baseline Scenario

VMT Distribution

Total Network NOx

Emissions Distribution

121-328

26%39%

grams/mile/hr

Normalized NOx Link Emissions

28.8%Freeway+ Ramp

Arterial

Local and Collector

Impact of Vehicle Fleet Distribution on Regional Network

Emissions for Present and Alternative Scenarios

Distr.

Present

Baseline

Future

Alternative

(Growth)

HC CO NOx CO2

59%34%

99%61%

45%48%

3% 1%7%

h. christopher frey,a nagui m. rouphail,a,b haibo zhaia,c · 2000 baseline no 1.23 39.0 4.36 995...

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