&EPA
United States
Environmental Protection
Agency
EPA-600/R-05/090b
August 2005
Heavy-Duty Diesel Vehicle
Modal Emission Model
(HDDV-MEM) Volume II:
Model Components and
Outputs
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EPA-600/R-05/090b
August 2005
Heavy-Duty Diesel Vehicle Modal
Emission Model (HDDV-MEM)
Volume II: Model Components and
Outputs
by
Randall Guensler
Seungju Yoon
Vetri Venthan Elango
School of Civil and Environmental Engineering
Georgia Institute of Technology
Atlanta, GA
Contract No: 4C-R022-NAEX
EPA Project Officer: Sue Kimbrough
U.S. Environmental Protection Agency
National Risk Management Research Laboratory
Air Pollution Prevention and Control Division
Research Triangle Park, NC 27711
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Abstract
The research reported in this document outlines a proposed heavy-duty Diesel vehicle modal
emission modeling framework (HDDV-MEMF) for heavy-duty diesel-powered trucks and
buses. The heavy-duty vehicle modal modules being developed under this research effort,
although different from the structure within the motor vehicle emissions simulator (MOVES)
model, should be compatible with it. In the proposed HDDV-MEMF, emissions from
heavy-duty vehicles are predicted as a function of hours of on-road operation at specific engine
horsepower loads. Hence, the basic algorithms and matrix calculations in the new heavy-duty
diesel vehicle modeling framework should be transferable to MOVES. The specific
implementation approach employed by the research team to test the model in Atlanta is
somewhat different from other approaches in that an existing geographic information system
(GIS) based modeling tool is being adapted to the task. The new model implementation is
similar in general structure to the previous modal emission rate model known as the Mobile
Assessment System for Urban and Regional Evaluation (MEASURE) model.
Sponsored by the U.S. Environmental Protection Agency, this exploratory framework is
designed to be applied to a variety of policy assessments. The model can be used to evaluate
policies aimed at reducing the emission rates from heavy-duty vehicles as well as policies
designed to change the on-road operating characteristics to reduce emissions.
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Foreword
The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting
the Nation's land, air, and water resources. Under a mandate of national environmental laws,
the Agency strives to formulate and implement actions leading to a compatible balance
between human activities and the ability of natural systems to support and nurture life. To meet
this mandate, EPA's research program is providing data and technical support for solving
environmental problems today and building a science knowledge base necessary to manage
our ecological resources wisely, understand how pollutants affect our health, and prevent or
reduce environmental risks in the future.
The National Risk Management Research Laboratory (NRMRL) is the Agency's center for
investigation of technological and management approaches for preventing and reducing risks
from pollution that threaten human health and the environment. The focus of the Laboratory's
research program is on methods and their cost-effectiveness for prevention and control of
pollution to air, land, water, and subsurface resources; protection of water quality in public
water systems; remediation of contaminated sites, sediments and ground water; prevention
and control of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with
both public and private sector partners to foster technologies that reduce the cost of
compliance and to anticipate emerging problems. NRMRL's research provides solutions to
environmental problems by: developing and promoting technologies that protect and improve
the environment; advancing scientific and engineering information to support regulatory and
policy decisions; and providing the technical support and information transfer to ensure
implementation of environmental regulations and strategies at the national, state, and
community levels.
This publication has been produced as part of the Laboratory's strategic long-term research
plan. It is published and made available by EPA's Office of Research and Development to
assist the user community and to link researchers with their clients.
Sally Gutierrez, Director
National Risk Management Research Laboratory
in
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EPA Review Notice
This report has been peer and administratively reviewed by the U. S. Environmental Protection
Agency and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
This document is available to the public through the National Technical Information Service,
Springfield, Virginia 22161.
IV
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TABLE OF CONTENTS
Section Page
Abstract ii
List of Figures vi
List of Tables vi
Acronyms vii
Acknowledgments viii
Overview 1
Vehicle Technology Groups 3
Vehicle Activity Module 5
Engine Power Module 7
Roadway Characteristics 8
GDOT HPMS 9
Road Grade via GIS Spatial Analysis 9
Average Speed 9
Speed/Acceleration Matrices 10
Vehicle Weight Profiles 11
Auxiliary Power 11
Environmental Conditions 12
Vehicle Frontal Areas and Aerodynamic Drag Coefficients 12
Effective Inertia 12
Emission Rate Module 15
Baseline Diesel Emission Rates 16
Diesel Registration Fractions 16
Annual Mileage Accumulation Rates 16
Processing Link-based Emissions and Air Toxics Ratios 17
Model Outputs 18
Conclusions 19
References 21
Appendix A: Link-Based HDDV-MEM Modeling Results on the GIS Roadway
Network 23
Appendix B: HDDV-MEM System Operations 27
v
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List of Figures
Figure
1. Heavy-Duty Diesel Vehicle Modal Emissions Modeling Process
2. Horsepower Distributions for 4 axle and 5+ axle Vehicles 3
3. Vehicle Activity Module Process 6
4. Engine Power Module Process 8
5. Freeway HDV Speed-Acceleration Profile 10
6. UDDS Speed/Acceleration Profile from UDDS 11
7. Emission Rate Module Process 15
8. Emissions Rate Simulation and Air Toxic Emissions Rate Process 17
A-l. HDDV-MEM Modeling Area (inside of Red Box) in Downtown Atlanta, GA . . . . 23
A-2. NOX Emissions (g/h) on Selected Links inside 1-285 24
A-3. NOX Emissions (g/h) on Links in Downtown Atlanta, GA 25
B-l. Example View of the HDDV-MEM Run Screen 28
List of Tables
Table Page
1. Vehicle Technology Grouping Map 4
2. Parameters in the Roadway Characteristics Table 8
3. Typical Vehicle Frontal Areas and Aerodynamic Drag Coefficients 12
4. Typical Tire Radius and Inertia Values 13
5. Example Baseline Emissions Rates for Year 1980 and 2004 16
6. Example of Normalized Diesel Registration Fractions 16
7. An Example Output File for HC Emissions 18
B-l. FIPS Codes for 13 Atlanta Metropolitan Area Counties 27
VI
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Acronyms
AADT
ahp
bhp
bhp-hr
CARS
CO
CPC
DOT
EMFAC2002
FHWA
g/mi
g/s
GDOT
GIS
HDDV-MEM
HDDVs
HDV
HPMS
MOBILE6
MOVES
mph
mph/s
NOX
PM
U.S. EPA
HDDS
VMT
VOC
ZML
annual average daily traffic
axle horsepower
brake-horsepower
brake-horsepower hour
California Air Resources Board
carbon monoxide
Climate Prediction Center
Department of Transportation
California's mobile source emissions model
Federal Highway Administration
grams per mile
grams per second
Georgia Department of Transportation
geographic information system
Heavy-Duty Diesel Vehicle Modal Emission Model
heavy-duty diesel vehicles
heavy-duty vehicle
Highway Performance Monitoring System
EPA's mobile source emission factor model
motor vehicle emission simulator
miles per hour
miles per hour per second
oxides of nitrogen
particulate matter
U.S. Environmental Protection Agency
urban dynamometer driving schedule
vehicle miles traveled
volatile organic compounds
zero-mile level
vn
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Heavy-Duty Diesel Vehicle
Acknowledgements
The authors wish to acknowledge the EPA Region 4 management and staff who have
contributed to the success of this project, specifically Beverly Bannister, Director, Air,
Pesticides and Toxics Management Division, Kay Prince, Chief, Air Planning Branch,
Thomas L. Baugh, and Dale Aspy.
Vlll
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Modal Modeling Components
Overview
A Georgia Institute of Technology (Georgia Tech)
research team in the School of Civil and Environmen-
tal Engineering has recently completed development
of a new heavy-duty diesel vehicle (HDDV) modal
emission modeling framework (Guensler et al.,
2005). The HDDV modal emission model (HDDV-
MEM) first predicts second-by-second engine power
demand as a function of on-road vehicle operating
conditions and then applies brake-specific emission
rates to these activity predictions (Yoon et al.,
2005a). On-road operating modes (cruise, accelera-
tion, deceleration, and motoring/idling) are integral in
the power demand functions, as are other relevant
factors such as vehicle weight, road grade, road
surface type, and so forth (Feng et al., 2005).
The HDDV modal emissions model (HDDV-MEM)
consists of three modules: a vehicle activity module
(with vehicle activity tracked by vehicle technology
group), an engine power module, and an emission
rate module. Each module performs a series of
routines designed to estimate on-road vehicle activity
and operating conditions for each vehicle technology
group, estimates engine horsepower demand for each
technology group and roadway link, and then calcu-
lates resulting emissions from these on-road activi-
ties. The three modules are initiated with modeling
parameters defined in model input command lines.
Once modeling parameters are defined in the com-
mand window, each module processes in parallel and
serial to predict HDDV emissions on each roadway
link (Figure 1).
In the vehicle activity module, on-road HDDV
volumes in vehicles-hours per hour (veh-h/h) are
estimated using total vehicle volumes obtained from
the highway performance monitoring system (HPMS)
daily volumes, vehicle-miles-traveled (VMT) frac-
tions for each vehicle class, diesel fractions, and
hourly vehicle volume profiles.
In the engine power module, the engine horsepower
demand (brake-horsepower) for each roadway link is
calculated for each technology group. Power demand
is predicted by applying speed-acceleration matrices,
vehicle weight distributions, auxiliary power require-
ment estimates, environmental conditions, roadway
link characteristics, and a variety of applicable
parameters associated with vehicle physical charac-
teristics. Power demand includes tractive power
demand plus auxiliary power demand associated with
running refrigeration units and other equipment
onboard the heavy-duty vehicles. On-road activity
with positive tractive power demand (and all activity
with auxiliary power demand) is linked directly to
work-related emission rates (grams per brake-horse-
power-hour per vehicle). Activity for which tractive
power demand is less than or equal to zero, which a
vehicle is under the motoring mode, is linked to idle
emission rates (grams per hour per vehicle).
The emission rate module provides work-related
emission rates (grams per brake-horsepower-hour per
vehicle) and idle emission rates (grams per hour per
vehicle) for each technology group. Work-related
emission rates are derived from EPA's baseline
running emission rate data, and idle emission rates
are derived from the California mobile source emis-
sions model EMFAC2002 idling emission rate test
data. Diesel vehicle registration fractions and annual
1
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Heavy-Duty Diesel Vehicle
Emission Rate Module
• Running Emission Rates
(g/bhp-hr/veh)
• Idling/Motoring Emission
Rates (g/hr/veh)
Specify Modeling Parameters
Modeling Year, Month, Hour
&
Road Characteristics File
Engine Power Module
• Tractive and Auxiliary
Engine Powers (bhp)
• Idling/Motoring Fractions
Vehicle Activity Module
• Running Vehicle Volumes
(veh-hr/hr)
• Idling/Motoring Vehicle
Volumes (veh-hr/hr)
Processing Emissions
• Link-Specific Emissions (g/hr)
Air Toxic Emissions
• Apply MOBILE6.2 Air ToxicA/OC Ratio
Link-Specific Emissions Output
Figure 1. Heavy-Duty Diesel Vehicle Modal Emission Modeling
Process.
mileage accumulation rates are employed to develop
calendar year emission rates for each technology
group.
Vehicle volume estimates, engine horsepower de-
mand, and calendar year (modeling year) emission
rates are joined to calculate total mass emissions in
grams per hour (g/h) for volatile organic compounds
(VOC), carbon monoxide (CO), oxides of nitrogen
(NOX), and particulate matter (PM) for each vehicle
type on each roadway link. Toxic air contaminant
emissions rates (benzene, 1,3-butadiene, formalde-
hyde, acetaldehyde, and acrolein) are also estimated
in grams per hour for each vehicle type using the
MOBILE6.2-modeled ratios of air toxics to VOC for
each calendar year.
HDDV-MEM output files provide not only hourly
emissions, but also aggregated total daily emissions
(in accordance with input command options). The
structure of the output files are such that hourly
emissions predictions can be directly incorporated
with roadway network features in geographic infor-
mation systems (GIS) environment for use in interac-
tive air quality analysis (regional and microscale).
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Modal Modeling Components
Vehicle Technology Groups
To properly link baseline diesel emission rates with
on-road vehicle class, the research team has devel-
oped a new vehicle technology grouping methodol-
ogy, which associates EPA engine size classes, EPA
heavy duty vehicle (HDV) classes, Federal Highway
Administration (FHWA) truck classes, and field
observation X classes (Yoon et al., 2004). Three
engine classes (light, medium, and heavy) were
allocated into each EPA FIDV class. To translate
FHWA truck classes into EPA HDV classes, X
classes were modified to cross-link between both the
EPA HDV and FHWA truck classes. The XI class
was separated to XIA and X1B classes for light and
medium engines because baseline emission rates
were significantly different across these engine
classes. The X2 class was separated into X2A and
X2B classes for 3-axle single unit and double unit
trucks. This was because 3-axle double unit trucks
travel more than 2.5 times further than 3-axle single
unit trucks (Yoon, 2005b). X3 class was also sepa-
rated to X3A and X3B classes for 4-axle FHWA
truck and greater than 4-axle FHWA truck classes,
because engine horsepower ranges were statistically
different across these two classes (Figure 2, Ahanotu,
1999). School and urban buses were assigned to X4
and X5 classes, respectively.
•JW
450-
400 -
350 -
fe 300 -
|
ft 250 -
£
I 200 -
150-
100-
50-
n -
x £
* * -yj**-1* ^
X ^ 3^ ^r * *Fjt,TM?-
X*&
%
+ X K K ^™ X^x Xttt
•*• J»»5j XII J:<5|B>:
f * I % Jji^ * ^+ ^'e
«%* < * X
«
• «*
500-
400-
S.300-
4)
0
0200-
10
100-
20000 40000 60000 80000 100000 120000 140000
Weight (pounds)
Axles
Figure 2. Horsepower Distributions for 4-axle and 5+axle Vehicles.
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Heavy-Duty Diesel Vehicle
With the creation of sub-groups and additional groups
from the original X-scheme, a new vehicle technol-
ogy grouping map was created (Table 1). Using this
map, vehicle classes can be translated from FHWA
truck classes to EPA HDV classes and vice versa.
The technology group classes employed in the current
version of the model are statistically based and can be
modified as warranted when supplemental analyses
of emission rate data and field observations indicate
that such modifications would serve to further refine
the model. There is no practical limit to the number
of technology groups that can be employed in the
model. Each technology group simply needs to be
coded for tracking in the vehicle activity and emis-
sion rate modules. Addition of technology groups
does add more complexity and model computation
time, so the basic goal is to ensure that further refine-
ment of technology groups provides improvements in
model accuracy without significantly sacrificing
model efficiency. This is one of the basic goals of the
ongoing data analysis being undertaken by the model
development team in the phase II study, 2005.
Table 1. Vehicle Technology Grouping Map.
EPA Class
A. ^iass rnwA^iass
Engine Size HP Range HDV Class
X1A
X1B
X2A
X2B
X3A
X3B
X4
X5
Light 70-170 HDDV2b
HDDV3
HDDV4
HDDV5
Medium 170-250 HDDV6
HDDV7
Heavy >250 HDDVSa
HDDVSa
HDDVSb
HDDVSb
SCHBUS
URBUS
3
5
6
8
7
9
4
4
(HDV)
(3 axle)
, 8 (4 axle)
to 13
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Modal Modeling Components
Vehicle Activity Module
The vehicle activity module provides hourly vehicle
volumes for each vehicle technology group on each
transportation link in the modeled transportation
system (Equation 1). The annual average daily traffic
(AADT) estimate for each system link (coded by road
and adjusted to link direction) is processed to yield
vehicle-hours of operation per hour for each technol-
ogy group (using truck percentages, VMT fraction by
vehicle technology group, diesel fraction, hourly
volume apportionment of daily travel, link length,
and average vehicle speed).
vAvMf = AADTS x (NL,/WL)X HVFVJ, x
VFV*DFVX(SL,IASV}
(1)
where VA is the estimated vehicle activity in vehi-
cle hour per hour,
v is the vehicle technology group,
//is the hour of day,
s is the transportation link,
/is the road type for the link,
AADT is the annual average daily traffic for
the link,
NL is the number of lanes in the specific link
direction,
TNL is the total number of lanes on the link,
HVF is the hourly vehicle fraction,
VF is the VMT fraction for each vehicle
technology group,
DF is the diesel vehicle fraction for each
technology group,
SL is the link length (miles), and
AS is the link average speed of the technol-
ogy group in miles per hour.
To estimate on-road running emissions from each
link, two sets of calculations are performed. On-road
vehicle activity (vehicle-hour) for each hour is
multiplied by engine power demand for observed link
operations (positive tractive power demand plus
auxiliary power demand), and then by baseline
emission rates (grams per brake-horsepower-hour per
vehicle). As discussed in the engine power module
section, these calculations are processed separately
for each speed/acceleration matrix cell. Emissions
from motoring activity are calculated by first deter-
mining the vehicle-hours of motoring activity on each
link for each hour (Equation 2) and multiplying by
the baseline idle emission rate (grams per hour per
vehicle). The fraction of motoring activity is calcu-
lated in the engine power module. Figure 3 outlines
the modeling procedure.
MVA . = VA >fxMFvh
v,h,s f v,h,s\f v-"
(2)
where MVA is the motoring vehicle activity in
vehicle hour per hour, and
MF is the motoring activity fraction deter-
mined in the power demand module.
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Heavy-Duty Diesel Vehicle
Input Parameters
Vehicle Activity Module
• Running Vehicle Volume
(veh-hr/hr)
Motoring Vehicle Volume
(veh-hr/hr)
External Files
Hourly_VMT_Profile.csv
VMT_Distribution,csv
Diesel_Fraction.csv
Processing
Emissions Rates
Figure 3. Vehicle Activity Module Process.
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Modal Modeling Components
Engine Power Module
In the engine power module, engine power demand to
overcome external forces (rolling resistance, aerody-
namic drag, and so forth) and to move a vehicle
forward can be estimated for each vehicle class and
for each vehicle year. Engine power demand consists
of two terms: instantaneous tractive power demand
and continuous auxiliary power demand. Instanta-
neous tractive power demand is calculated with
acceleration force, rolling resistance, gravitational
force, aerodynamic drag, and rotational inertial loss.
Continuous auxiliary power demand is tabulated from
duty-cycle-based average heavy HDV auxiliary
power requirements obtained from the 2004 Automo-
tive Handbook (SAE, 2004), by season (summer and
winter), and by time of day (day and night). Equation
3 expresses the estimation of overall required engine
power (unit conversions omitted).
VxAFFxl \—xa\ + FR +
(3)
Fw
W
F,)) ,
' >>i,j\v,h,f,S
AP
where P is the engine power demand in brake-
horsepower,
/' is the speed bin from the applicable speed/
acceleration matrix,
j is the acceleration bin from the applicable
speed/acceleration matrix,
Fis the vehicle speed for each speed/accel-
eration bin in miles per hour,
AFF is the acceleration frequency fraction
for each speed/acceleration bin,
W is the actual vehicle weight in pounds
force,
gis the gravitational acceleration (32.2 ft/s2),
a is the vehicle acceleration in feet per sec-
ond squared,
FR is the rolling resistance in pounds force,
Fwis the gravitational force in pounds force,
FD is the aerodynamic drag in pounds force,
Fj is the drive train rotational inertial loss in
pounds force, and
AP is the auxiliary power demand in brake-
horsepower.
To integrate Equation 3 into the model, a series of
tables was generated for the various parameters
(roadway characteristics, speed/acceleration matrices,
vehicle weight profiles, auxiliary power, vehicle
characteristics, and environmental conditions). Figure
4 illustrates the general calculation process and
identifies the external data sources required for
estimation of engine horsepower demand for each
vehicle class on a specific roadway link.
The deceleration rate of a vehicle depends upon the
drivers' driving habits and roadway geometry (inter-
sections and road grades). In the absence of proven
models to predict driver behavior, the factors causing
positive and negative vehicle acceleration cannot be
readily identified (i.e., it is not easy to determine
whether deceleration events are the result of road
grade, driver response to external traffic conditions,
or natural driving variability). However, observed
speed/acceleration profiles can be employed to
represent the combined effects noted under certain
roadway operating conditions such as vehicle class,
time of day, congestion level, and so forth.
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Heavy-Duty Diesel Vehicle
Input Parameters
Engine Power Module
Rolling Resistance Coefficient
Aerodynamic Drag Coefficient
Surface Roughness Coefficient
Vehicle Frontal Area
External Files
Speed_ Acceleration^ Matrix.csv
Weight Distribution,csv
Effective Jnertia.csv
EnvironmentaLParameter.csv
ligpUT_Road_Segment.csv
Figure 4. Engine Power Module Process.
Using Equation 3, the tractive power demand and
auxiliary power demand are summed to obtain the
instantaneous engine power demand for activity
represented in each speed/acceleration matrix cell.
Note, however, that negative or zero tractive power
demand can result while a vehicle is decelerating and
will result when the vehicle is idling.
Because separate emission rates are not currently
available for motoring activity, the model currently
operates under the assumption that motoring emission
rates are not significantly different from emissions
under idle conditions (this ignores any potential fuel
control issues for motoring). If the tractive power
demand in a matrix cell is greater than zero, the
auxiliary power demand is added to the tractive
power demand, and then the combined engine power
demand in the cell is weighted by the speed/accel-
eration activity frequency in the cell to provide the
weighted contribution to power emissions. If the
instantaneous engine power demand is less than or
equal to zero, the vehicle activity is defined as motor-
ing activity, and only the auxiliary power demand is
weighted by the activity frequency in the cell. Hence,
if the tractive power demand in a cell is less than
zero, the power demand for the activity in the cell is
set to the auxiliary power demand. For motoring
activity fractions, the idle emission rate is also
applied, so that the net hourly emissions associated
with motoring activity is the sum of the auxiliary
power demand emissions and idle emissions.
Roadway Characteristics
The roadway characteristics table contains selected
infrastructure parameters that were obtained from
Georgia Department of Transportation (GDOT)
HPMS database (GDOT, 2004a). These parameters
are required for power demand estimation and for use
in GIS analysis. Table 2 summarizes the roadway
data elements. A critical roadway parameter is the
assigned speed/acceleration matrix, which establishes
the cumulative distribution function of vehicle speed
Table 2. Parameters in the Roadway Characteristics Table.
From GDOT HPMS Through GIS Spatial Analysis
From Speed/Acceleration Matrices
County code
RcLink ID3
Mile (link) point
Speed limit
Number of left lanes
Number of right
lanes
Surface material type
Road type
Annual average daily
traffic (AADT)
Truck percent
Link start and end X- and 7-coordi-
nates
Link length (miles)
Left and right lane slopes
percent of vehicle activity by
speed/acceleration bin
Average speed
1 RcLink = record link, a unique record number.
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Modal Modeling Components
and acceleration in a binned format. Speed/accel-
eration matrices can be assigned to specific vehicle
technology groups, operating on a specific roadway
link, by time of day if such data resolution is avail-
able. Alternatively, aggregate speed acceleration
profiles can be assigned to groups of technology
groups, groups of hours, and roadway classifications.
GDOTHPMS
• For roadway links, the number of lanes in each
direction (left and right) is identified for use in
estimating vehicle volume in each direction, to
differentiate between inbound and outbound
facility volumes. Vehicle volumes in different
directions also differ by time of day (this requires
local information outside of the HPMS data).
Unless specific directional volumes are available,
the total HPMS vehicle volume is separated
directionally using the directional lane ratio.
• The surface material codes, classified by four
surface material types, address typical road
surface material coefficients in the calculation of
rolling resistance. The four surface material types
are good-smooth concrete, worn concrete or good
asphalt, brick, and worn asphalt with codes of
"I", "J", "K", and blank, respectively. Road
surface material coefficients of 1.0, 1.2, 1.2, and
1.5 are assigned for surface material codes of "I",
"J", "K", and blank, respectively (Gillespie,
1992).
• FHWA road functional classes, as well as EPA
road functional classes (freeway, arterial, local,
and ramp) are created based on the FHWA road
functional classes and speed limits (Guensler et
al., 2004). These FHWA and EPA road func-
tional classes provide the bridge to applicable
model parameter tables.
• AADT is used to estimate HDDV volumes for
each EPA HDDV vehicle class or for each X
vehicle class.
• By definition, truck percent from the HPMS
includes only the FHWA truck classes 5 to 13,
and does not include 2-axle, 4-tire heavy-duty
vehicles such as the EPA HDDV class 2b. There-
fore, AADT weighted by truck percent are only
apportioned into EPA HDDV classes 3 to 8b and
buses. HDDV2b VMT was estimated by AADT
and HDDV2b VMT fractions estimated from
1993 to 1999 Highway Statistics Series.
Road Grade via GIS Spatial Analysis
To estimate the roadway slopes for each link on the
HPMS road network, 2-dimensional and 3-dimen-
sional distances were calculated through the GIS
spatial and 3-dimensional analyst tools. Because the
HPMS road network does not have elevation infor-
mation, the 2-dimensional roadway network was
converted to the 3-dimensional roadway network
through the interpolation process with Georgia State
Digital Elevation Model in l:24,000-scale, down-
loaded from GDOT GIS Clearinghouse (GDOT,
2004b). After the interpolation process, 2-dimen-
sional and 3-dimensional distances between a firom-
node and a to-node were calculated for each link.
Then, link slopes in degrees were calculated with
distances. Because a roadway link is represented
with a single line without considering directions (left
or right lanes), slopes calculated for one direction
(right lanes) were used for left lane slopes with
addition of a negative sign. To avoid GIS slope
calculation errors, slopes greater than +5.4° (+12%)
or -5.4° (-5.4%) were suppressed to +5.4° or -5.4°.
Cut and fill sections are present in the network,
especially for freeway links. Hence, the current slope
estimation approach will overestimate the contribu-
tion of grade to regional emissions. The freeway
slopes will be updated with field-measured values in
the next model iteration.
Average Speed
The average speed for each link is used to estimate
total vehicle-hours of travel on a link for each hour,
given the predicted traffic volumes. The average
speed for each roadway link can be taken directly
from the speed/acceleration matrix applied to each
link. The median speed of each speed/acceleration
matrix cell is weighted by the activity frequency in
the call and summed across the speed/acceleration
matrix. In the current application of the model in
Atlanta, HDDV on-road operating data are not
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Heavy-Duty Diesel Vehicle
sufficient to develop link-specific speed/acceleration
matrices. In the current model application, the re-
search team has applied road-type speed/acceleration
matrices. Because the average speeds would be the
same on every freeway link regardless of posted
speed limit (because they would be calculated using
the same matrix), the calculated average speeds for
use in the model are shifted up or down as a function
of the posted speed limit. The research team acknowl-
edges that the modeling method is very sensitive to
the speed/acceleration operating profiles and that the
current approach in the Atlanta application does not
provide a realistic representation of speed/accelera-
tion matrices across roads (nor a realistic estimate of
the average speed). However, these matrices and
average speeds will be employed in Atlanta until
road-specific and link-specific matrices are devel-
oped.
Speed/Acceleration Matrices
In the current version of the model, the team has
focused on the model structure and implementation.
The model employs speed/acceleration matrices in
the estimation of tractive engine power demand
(Yoon et al., 2005c). Speed and acceleration both
contribute to tractive load and therefore engine load,
as outlined in Equation 3. Only two speed/accelera-
tion matrices have been integrated into the beta test
version of the model. One matrix was created with
data obtained from Grant's speed and acceleration
data (Grant, 1998) and the other represents the EPA
Urban Dynamometer Driving Schedule (UDDS) for
heavy-duty vehicles (U.S. EPA, 2004). Because
Grant's speed and acceleration data were obtained
from freeways, the data were used to develop a
freeway speed/acceleration matrix. For the other
roadways (arterials, locals, and ramps), the speed/
acceleration matrix developed from UDDS was used.
Figure 5 and 6 show speed-acceleration frequency
profiles for freeways and the other roadways.
Freeway Speed-Acceleration Profile
Acceleration (mph/s)
Frequency
• 009010
• 0 08-0 09
DO. 07-0.08
• 006-007
Q 0 05-0 06
• 004-005
DO 03-0 04
DO 02 003
• 001-002
DO 00-0 01
Speed (mph)
Figure 5. Freeway HDV Speed-Acceleration Profile (Grant, 1998).
10
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Modal Modeling Components
UDDS Speed-Acceleration Profile
•
Acceleration (mph/s)
Speed (mph)
Figure 6. UDDS Speed/Acceleration Profile from UDDS.
Vehicle Weight Profiles
For the vehicle weight profiles, the research team
used actual vehicle weights measured nationwide in
1999 and 2000 (Lindhjem et al., 2004). From mea-
sured vehicle weights, hourly vehicle weight profiles
were generated for road functional classes and
vehicle classes. Weight data for minor collectors and
local roads were inadequate and did not provide 24-h
vehicle weight distributions. So, hourly vehicle
weight profiles for rural major collectors and urban
minor arterial roads were used for rural minor collec-
tors and local roads and for urban collectors and local
roads. Because vehicle weights were provided for
FHWA truck classes, EPA vehicle classes were
assigned into FHWA truck classes through the
X-classification scheme. Double-unit, 3- or 4-axle
trucks (FHWA truck class 8) were separated with the
3-axle/4-axle ratio of 0.12 into X2 and X3A classes
(Yoon et al., 2004). For weights by road type, vehicle
class, and hour of day, measured vehicle volumes
were multiplied by each weight bin and divided by
total measured hourly volumes. Because 2-axle trucks
correspond to EPA HDDV2b to 7 and their weights
can not be separated into each EPA HDDV classes
properly, the highest weight category bins in
Lindhjem's (2004) weight data for each EPA HDDV
class were used for their vehicle weights. Weight
category bin values of 9,500,12,000,15,000,17,750,
22,750, and 29,500 Ibs were assigned to HDDV2b,
HDDV3, HDDV4, HDDV5, HDDV6, and HDDV7
classes, respectively.
Auxiliary Power
Estimated auxiliary power demand for on-road
vehicle operations was obtained from the SAE 2004
Automotive Handbook (SAE, 2004). The SAE
Handbook provides typical auxiliary power demand
for various accessories, but only for heavy HDVs and
buses. Auxiliary power demand for each accessory
was classified by season (winter and summer) and by
time of day (day and night). Months from November
to April were assigned to winter, and the other
11
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Heavy-Duty Diesel Vehicle
months were assigned to summer. Daytime is as-
sumed to be from 9 a.m. to 5 p.m. during winter
months and 8 a.m. to 6 p.m. during summer months.
However, because the SAE Handbook does not
provide light and medium HDV auxiliary power
requirements, the research team used U.S. DOE
study results (U.S. DOE, 2000), which indicate that
a typical medium HDV requires approximately 50%
of heavy HDV auxiliary power. Therefore, 50% of
heavy HDV auxiliary power demand was used for
light and medium HDDV auxiliary power demand.
Environmental Conditions
Hourly temperatures and atmospheric pressures were
created for the calculation of air density. Hourly
environmental parameters observed at Atlanta Harts-
field International Airport Meteorology Station from
2002 and 2004 were downloaded from National
Climatic Data Center (NCDC, 2004). All same-month
and hour data points were summed and then divided
by the total number of data points to create a set of
3-year average hourly temperatures and atmospheric
pressures.
Vehicle Frontal Areas and Aerody-
namic Drag Coefficients
Typical vehicle frontal areas for EPA HDDV classes
were estimated using vehicle specifications published
by vehicle manufacturers (Truck Index, 1997). The
research team investigated typical vehicle front and
side shapes and assigned typical aerodynamic drag
coefficients to each vehicle class. Due to the lack of
vehicle configuration data (bobtail, single-unit,
trailer, double trailer, tanker, flat-top, etc.), a single
aerodynamic drag coefficient for each vehicle class
was used (in later model versions, multiple configura-
tions could be accommodated). Table 3 shows the
typical vehicle frontal areas and aerodynamic drag
coefficients.
Table 3. Typical Vehicle Frontal Areas and
Aerodynamic Drag Coefficients.
X Class
X1A
X1B
X2A
X2B
X3A
X3B
X4
X5
EPA Class
HDDV2b
HDDV3
HDDV4
HDDV5
HDDV6
HDDV7
HDDVSa
HDDVSa
HDDVSb
HDDV8B
SCHBUS
URBUS
Frontal
Area
(ft2)
26.2
28.3
26.7
37.5
40.6
44.0
46.3
46.3
51.0
51.0
80.0
80.0
Aerodymanic
Drag Coeffi-
cient
0.45
0.45
0.45
0.45
0.60
0.60
0.99
0.99
0.99
0.99
1.17
1.17
Effective Inertia
For the estimation of drive train inertial loss (required
for acceleration of moving engine and transmission
parts as well as the axles and tires), the research team
used vehicle speed and engine speed (revolution per
minute, or rpm) data from EPA HDV test results
(U.S. EPA, 200la) to approximate combined trans-
missions gear ratio and differential gear ratio. For
wheel and engine inertia, typical inertia values
suggested by Gillespie (2004) were used. For trans-
missions and drive train inertia values, the research
team used basic information from a parts manufac-
turer (ZELU, 2004). The inertia values suggested by
ZELU were assigned into each EPA HDV classes
using vehicle information obtained from the Diesel
Truck Index (Truck Index, 1997). Tire radius data for
each HDDV class were also obtained from the Diesel
Truck Index. Table 4 shows typical rotational inertia
for parts.
12
-------
Modal Modeling Components
Table 4. Typical Tire Radius and Inertia Values. HDDVSa 1.30 15.7 2.0 2.46 2.46
HDDVSb 1.30 15.7 2.0 2.46 2.46
Tire Rotational Inertia SCHBUS 1.30 15.7 2.0 2.46 2.46
EPA Class Radius „,, , v . Trans- Drive URBUS 1.30 15.7 2.0 2.46 2.46
Wheel Engine . . „ . ^^^^^^^^^^^^^^^^^^^^^^^^^^^^~
(ft) ,-, -^ ,?^ mission Iram
\
w)
dr) do)
HDDV2b 0.95 6.0 2.0 0.93 0.93 These values will be significantly improved and
HDDV3/4/5 1 00 80 20 0 93 0 93 refined as new data become available from on-road
HDDV6/7 125 157 20 173 173 vehicle and in-laboratory drive train testing.
13
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Heavy-Duty Diesel Vehicle
14
-------
Modal Modeling Components
Emission Rate Module
In the emission rate module, baseline diesel emission
rates for each engine certification group are aggre-
gated according to diesel registration fractions and
annual mileage accumulation rates by vehicle age
(Figure 7). Each zero-mile emission rate is multiplied
Input Parameters
Emission Rate Module
Running Emissions Rates
(g/bhp-hr/veh)
Motoring Emissions Rates
(g/hr/veh)
External Files
Base!ine_Emissions,csv
Diesel_Regist_ Distribution.csv
Accumulation_Mileage.csv
Processing
Emissions Rates
Figure 7. Emission Rate Module Process.
by the diesel registration fraction by vehicle age, and
each deterioration emission rate is multiplied by the
diesel registration fraction and by the annual mileage
accumulation rate by vehicle age. Twenty-five
weighted zero-mile emission rates and deterioration
emission rates are aggregated to a calendar year
emission rate (Equation 4).
(4)
where ER is the emission rate in grams per brake-
horsepower-hour,
v is the vehicle technology group,
y is the vehicle age,
ZML is the baseline zero-mile emissions rate
in grams per brake-horsepower-hour,
DRFis the diesel vehicle registration fraction
by vehicle age,
DET is the baseline deterioration emissions
rate in grams per brake-horsepower-hour per
10,000 mile, and
AMR is the annual mileage accumulation rate
in miles per 10,000miles.
Emission rates for motoring (absorbing power as the
vehicle and engine slow) are set to idle, and emission
rates for vehicle idling are obtained from the data set
used by the California Air Resources Board (CARB)
in the development of EMFAC2002 emission rates
(CARB, 2004). Baseline idling emission rates for
each vehicle type and model year are multiplied by
the diesel vehicle registration fraction for each year.
The twenty-five idle emission rates are then aggre-
gated for each calendar year to create a diesel vehicle
idling (or motoring) emission rate for each vehicle
type (Equation 5).
24
(5)
MERV = £ (lDLvy x DRFvy
v=0
(DETvyxDRFvyxAMRvy)]
where MER is the idling (or motoring) emission rate
in grams per hour, and
IDL is the baseline idling emission rate in
grams per hour.
15
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Heavy-Duty Diesel Vehicle
Table 5. Example Baseline Emission Rates for Year 1980 and 2004.
vr,, ,70 A /-i HP Model HC ZML
X Class EPA Class „ _, , „ , - , , , ,
Range Year (g/bhp-h/veh)
X1A
X1B
X2A
X2B
X3A
X3B
X4
X5
HDD V2b, HDDV3 , HDDV4, HDDV5 70-170
HDDV6, HDDV7 170-250
HDDVSa 250+
HDDVSa 250+
HDDVSb 250+
HDDVSb 250+
SCHBUS
URBUS
1980
2004
1980
2004
1980
2004
1980
2004
1980
2004
1980
2004
1980
2004
1980
2004
0.664
0.140
0.660
0.170
0.470
0.170
0.470
0.170
0.470
0.170
0.470
0.170
0.660
0.170
0.470
0.080
HC_DET
(g/bhp-h/veh)
0.002
0.001
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.002
0.001
0.001
0.000
HC IDL
(g/h/veh)
23
8
23
8
23
8
23
8
23
8
23
8
23
8
23
8
Baseline Diesel Emission Rates
Baseline diesel emission rates (grams per brake-
horsepower-hour) for each engine certification group
are derived from the EPA engine dynamometer test
results used in the MOBILE6.2 emission rate model
development (U.S. EPA, 2002). As indicated above,
because EPA does not provide idling emission rates
by engine age group or engine certification group,
idling emission rates (grams per hour) are obtained
from chassis dynamometer test results, which were
used for the EMFAC2002 model (CARS, 2004). As
an example, Table 5 shows hydrocarbon baseline
emission rates obtained from the MOBILE6.2 and
EMFAC2002.
Diesel Registration Fractions
Diesel registration fractions are calculated from
default diesel fractions and default registration
fractions (sum of gasoline and diesel vehicles) for
each vehicle type and age, which were used in
MOBILE6.2 (U.S. EPA, 200Ib). Each calendar
year's diesel fraction is multiplied by the correspond-
ing year registration fractions. To calculate diesel
registration fractions, diesel fractions multiplied by
registration fractions are normalized for each vehicle
class (Table 6).
Table 6. Example of Normalized Diesel Registration
Fractions.
Age
0
EPA
Class
HDDV2b
HDDV2b
Regis.
Fraction
0.046
Diesel
Fraction
0.200
1 HDDV2b 0.059 0.258
Rest of data not shown for brevity
24
HDDV2b
0.000
0.000
Sum of
Diesel
Fraction
0.223
Normal.
Diesel
Regis.
Fraction
0.042
0.069
0.000
Annual Mileage Accumulation Rates
For the estimation of calendar year deterioration
emissions rates, MOBILE6.2 default annual mileage
accumulation rates are used for the annual mileage
accumulation rates (U.S. EPA, 2001b). The default
annual mileage accumulation rates are used without
the correction of off-cycle effect.
16
-------
Modal Modeling Components
Processing Link-Based Emissions and Air Toxics Ratios
Baseline zero-mile emission rates, baseline deteriora-
tion emission rates, tractive engine power, motoring
fraction, running vehicle volume, and motoring
vehicle activity estimated from the three modules are
incorporated to estimate emissions (grams per hour)
for HC, CO, NOX, and PM (Equation 6, Figure 8).
HEV = (VAV xPvxERv) + (MVAV x MERV] (6)
where HE is the estimated hourly emissions from a
vehicle class a link in grams per hour,
VA is the estimated vehicle activity in vehi-
cle-hour per hour,
P is the engine power demand in brake
horsepower,
ER is the work-related emission rate in grams
per brake horsepower-hour,
MVA is the motoring vehicle activity in vehi-
cle-hour per hour, and
MER is the motoring (idling) emission rate in
grams per hour.
From the estimated HC emissions, air toxic emissions
are also estimated by multiplying the ratios of air
toxics to HC for each calendar year. Air toxics
include benzene (BENZ), 1,3-butadiene (BUTA),
formaldehyde (FORM), acetaldehyde (ACET), and
acrolein (ACRO). To develop the ratios of air toxics
to HC, the research team ran MOBILE6.2 from
calendar year 1995 to 2020 with Atlanta air quality
planning region default modeling parameters. Each of
the five air toxic emissions rates was divided by HC
emissions rates for each calendar year. Estimated
ratios were directly multiplied to hourly modal
emission rates estimated with the interim outputs
from the three modules (see Equation 6).
External Files
Air Toxic Fraction.cSv
Output
HC. CO, NOX, PM, BENZ,
BUTA, FORM, ACET, and
ACRO Emissions Rate (g/hr)
Figure 8. HC, CO, NOX, PM, and Air Toxic
Emissions Estimation Process.
17
-------
Heavy-Duty Diesel Vehicle
Model Outputs
Output file names are automatically created using the
pre-defined modeling options such as
INPUT_Road_Segment file name, county code,
calendar year, modeling month, and modeling hour
(i.e.,"Atlanta_Downtown.csv_121_2004_7_7.csv").
Output files contain road characteristic information
taken from HPMS information and estimated link-
based emissions for each pollutant and for each
vehicle type. Table 7 shows an example output file
for HC emissions rates.
Output files contain not only hourly emissions for
each pollutant and for each vehicle, as well as road-
way characteristic information such as county codes,
state roadway codes, link identifications by mile
point, roadway classifications, and so forth. Positive
roadway identification can be used to import link-
based emissions for regional or county level emission
inventories and for use in local level air quality
impact assessment. For instance, county-level emis-
sions inventory data can be readily developed by
aggregating emissions across county link identifica-
tion codes. Local air quality impact assessment
analyses can be undertaken by extracting the hourly
emissions from specified roadway links identified by
State DOT Roadway Characteristic Link ID and
mile-point. For spatial and temporal emissions
analysis, output files are connected to the original
GIS roadway network through the creation of unique
link identifiers. Using relink IDs and link mile-points,
unique link IDs can be created and joined back into
the GIS roadway networks. Appendix A shows the
how the results can be transferred back to the GIS
interfaced to view emissions analysis results.
Table 7. An Example Output File for HC Emissions.
CNTY RcLink — HC2b HC3
— HC8a HCX3A HCX3B HCX4
121
121
121
121
1211- —
1211- —
1211- —
1211- —
2.10
0.22
0.55
0.75
0.08 —
0.01 —
0.02 —
0.02 —
0.18
0.06
0.05
0.05
0.42
0.06
0.11
0.21
1.03
0.09
0.27
0.21
0.00
0.00
0.00
0.00
Rest of data not shown for brevity.
121 1211- —
7.29
0.28
0.65
1.46
3.60
0.00
18
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Modal Modeling Components
Conclusions
Over the next year, the research team will be evaluat-
ing the sensitivity of the HDD V-MEM and evaluating
additional sources of data that can be used to refine
the model. The HDD V-MEM uses several rule-of-
thumb parameters and assumed criteria such as
motoring activity, inertial loads, coefficients related
to vehicle characteristics, and so forth, which may
yield significant changes in emissions predictions
when input data are improved. The model sensitivity
analysis will identify the model parameters that are
the most important to improve first.
Although the HDD V-MEM has uncertainties at the
current stage of development, this model has out-
standing potential for applications of mobile source
emissions inventory development and microscale air
quality assessment only if parameters causing uncer-
tainties are examined and improved. For the examina-
tion and improvement of the HDD V-MEM, extensive
data should be collected such as on-road emissions
data as a function of engine load, vehicle technology
subgroup development (data and statistical grouping),
modal activity characterization, and so forth.
19
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Heavy-Duty Diesel Vehicle
20
-------
Modal Modeling Components
References
Ahanotu, D. (1999). Heavy-Duty Vehicle Weight and
Horsepower Distributions: Measurement of Class-specific
Temporal and Spatial, Thesis, Georgia Institute of Tech-
nology.
CARB (2004). Development of Idling Emissions Factors
(http://www.arb.ca.gov/regact/hdvidle/appc.doc). Califor-
nia Air Resources Board.
Feng, C., S. Yoon, and R. Guensler (2005). Data Needs
for a Proposed Modal Heavy-Duty Diesel Vehicle Emis-
sion Model, Proceedings of the 97th Air and Waste Man-
agement Association Annual Meeting, Minneapolis, MN.
GDOT (2004a). Georgia Statewide Highway Performance
Monitoring System, Georgia Department of Transporta-
tion.
GDOT (2004b). Georgia GIS Clearinghouse, Map Data &
Aerial Photography (http://gis.dot.state.ga.us/). Georgia
Department of Transportation.
Gillespie, T (1992). Fundamentals of Vehicle Dynamics,
ISBN 1-56091-199-9, Society of Automotive Engineers,
Inc., Warrendale, PA.
Gillespie, T (2004). Typical inertia values for wheel and
engine, Personal communication, November, 2004.
Grant, C. (1998). Modeling Speed/Acceleration Profiles
on Freeways, Thesis, Georgia Institute of Technology.
Guensler, R., K. Dixon, V. Elango, and S. Yoon (2004).
MOBILE-Matrix: Georgia Statewide MTPT Application
for Rural Areas, Transportation Research Record: Journal
of Transportation Research Board, No. 1880, Transporta-
tion Research Board, National Research Council, Wash-
ington, DC, pp. 83-89.
Guensler, R., S. Yoon, C. Feng, H. Li, and J. Jun (2005).
Heavy-Duty Diesel Vehicle Modal Emission Model
(HDDV-MEM): Volume I: Modal Emission Modeling
Framework, EPA-600/R-05/090a, U.S. Environmental
Protection Agency, Washington, DC.
Lindhjem, C., and S. Stella (2004). Development Work for
Improved Heavy-Duty Vehicle Modeling Capability Data
Mining-FHWA Datasets, Phase II: Final Report, ENVI-
RON International Corporation, Novato, CA.
NCDC (2004). National Climate Date Center, Atlanta
Hartsfield International Airport (Atlanta, Georgia)
(http://www.ncdc.noaa.gov/oa/ncdc.html) (accessed
August 2005).
SAE (2004). Information Relating to Duty Cycles and
Average Power Requirement of Truck and Bus Engine
Accessories, SAE J1343 (August 2000). Society of
Automotive Engineers, Inc., Warrendale, PA.
Truck Index (1997). 1997 Diesel Truck Index, Truck
Index, Inc.
U.S. DOE (2000). Technology Roadmap for the 21st
Century Truck Program, 21CT-001, Oak Ridge National
Laboratory, U.S. Department of Energy.
U.S. EPA (200la). Heavy Duty Diesel Fine Particulate
Matter Emissions: Development and Application of
On-Road Measurement Capabilities, EPA-600/R-01/079
(NTIS PB2002-100140), National Risk Management
Research Laboratory, U.S. Environmental Protection
Agency, Washington, DC.
U.S. EPA (2001b). Fleet Characterization Data for
MOBILE6.2, EPA-420/R-01/047, Office of
Transportation and Air Quality, U.S. Environmental
21
-------
Heavy-Duty Diesel Vehicle
Protection Agency, Washington, DC.
U.S. EPA (2002). Update of Heavy-Duty Emission Levels
(Model Years 1998-2004+) for Use in MOBILE6.2,
EPA-420/R-02/018, Office of Transportation and Air
Quality, U.S. Environmental Protection Agency, Washing-
ton, DC.
U.S. EPA (2004). EPA Urban Dynamometer Driving
Schedule for Heavy-Duty Vehicles, 40CFR86 Appendix
I (c). U.S. Government Printing Office, Washington, DC.
Yoon, S., P. Zhang, J. Pearson, R. Guensler, and M.
Rodgers (2004). A Heavy-Duty Vehicle Visual Classifica-
tion Scheme: Heavy-Duty Vehicle Reclassification
Method for Mobile Source Emissions Inventory Develop-
ment. Proceedings of the 97th AWMA Annual Meeting.
Indianapolis, IN.
Yoon, S., H. Li, J. Jun, R. Guensler, and M. Rodgers
(2005a). Transit Bus Engine Power Simulation: Compari-
son of Speed-Acceleration-Road Grade Matrices to
Second-by-Second Speed, Acceleration, and Road Grade
Data. Proceedings of the 97th Air and Waste Management
Association Annual Meeting, Minneapolis, MN.
Yoon, S. (2005b). A New Heavy-Duty Vehicle Visual
Classification and Activity Estimation Method for Re-
gional Mobile Source Emissions Modeling, Thesis,
Georgia Institute of Technology.
Yoon, S., H. Li, J. Jun, J. Ogle, R. Guensler, and M.
Rodgers (2005c). A Methodology for Developing Transit
Bus Speed-Acceleration Matrices to be Used in Load-
Based Mobile Source Emissions Models, Transportation
Research Record: Journal of Transportation Research
Board, Transportation Research Board, National Research
Council, Washington, D.C. (in Press).
ZELU (2004). Industrias Zelu,S.L. Inertia values for
rotating transmissions and drive trains
(http: //www .navarrainfo. com/zelu/application .htm)
22
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Modal Modeling Components
Appendix A: Link-Based HDDV-MEM
Modeling Results on the GIS Roadway Network
The heavy-duty diesel vehicle modal emission model
(HDDV-MEM) was run for a portion of the Atlanta
downtown area along I-75/I-85 downtown connector.
Figure A-l shows the selected roadway links (green links
in the red rectangle) inside 1-285 circle and links in the
middle of downtown (brown links in the blue rectangle).
The selected areas consisted of 3,853 roadway links, and
the HDDV-MEM model processing time per link was
approximately 2.5 seconds on a Pentium IV 2.4GHz PC
with 768 RAM. Four one-hour scenarios were run (7 A.M,
12 P.M., 5 P.M., and 10 P.M). When the process was
completed, a unique link ID for each link was created for
joining into the original roadway network feature in GIS
shape file format. The road characteristic link identifica-
tion number (RcLinkID), the start mile point, and the end
mile point for a link were combined as a unique link ID
and used in the joining process. Afterjoining the modeling
output to the roadway feature, the feature was converted
into 10 meter by 10 meter grid raster data with emission
values for each pollutant. Figures A-2 and A-3 show NOX
emissions in grams/hour for the four time periods on
selected roadway links.
As shown in Figures A-2 and A-3, roadway links on the
I-75/I-85 downtown connector show higher NOX emis-
sions than other arterial or local roads. That resulted from
those roadway links downtown experiencing much higher
HDDV volumes than other roadway links during the
simulation time periods.
Figure A-1. HDDV-MEM Modeling Area (inside of Red Box) in
Downtown Atlanta, GA.
23
-------
Heavy-Duty Diesel Vehicle
S$gS^-^P.
.- C3 1000 - 2000
id2000-3000 -
= • 3000 - 4000
1^000
Figure A-2. NOX Emissions (g/hr) on Selected Links (Red Rectangle) Inside
1-285.
24
-------
Modal Modeling Components
NOx (g/hr)
>500
- 1000
CD 1000 -2000
— CD 2000 -3000
"•3000-4000
<4000
Figure A-3. NOX Emissions (g/hr) on Links (Blue Rectangle) in Downtown
Atlanta, GA.
25
-------
Heavy-Duty Diesel Vehicle
26
-------
Modal Modeling Components
Appendix B: HDDV-MEM System Operations
The heavy-duty diesel vehicle modal emission model
(HDDV-MEM) estimates hourly emissions for each
simulated roadway segment. The time required to estimate
hourly emissions for a single road segment is approxi-
mately 2.5 seconds on a computer equipped with a
Pentium IV 2.4GHz CPU and 768 RAM. HDDV-MEM
estimates emissions using a set of modeling algorithms
calling data from tables containing a variety of model
parameters (in forms of internal data or external data
files). Internal data tables contain vehicle configuration
information (frontal areas in square feet, aerodynamic drag
coefficients, and so forth), road surface coefficients, and
other hard-coded data. External data files contain informa-
tion associated with vehicle activity, roadway characteris-
tics, environmental conditions, and other user-defined
variables. External data files include:
• Speed/acceleration matrices,
• Weight distributions,
• Environmental parameters,
• Auxiliary power demand,
• VMT distributions,
• Hourly VMT profiles,
• Diesel fraction,
• Effective inertia,
• Baseline emissions rates,
• Diesel registration distributions,
• Accumulation mileage rates,
• Air toxic emissions fractions, and
• Roadway characteristics.
The model is an ASPEN Perl executable and does not yet
have a true graphic user interface. Before running the
HDDV-MEM, "HDDVMEM_Vl.pl" all 13 external files
and should be in the same folder, and ASPEN Perl version
5.6 or higher version should be installed. The
MATH::ROUND Perl extension must be installed before
executing the model. This extension, "round.pm" should
be in folder C:\Perl\lib\Math. The "round.pm" extension
supplies functions that will round numbers in various
ways. The HDDV-MEM will be activated by double-
clicking the "HDDVMEMJV1 .pi". Then, a DOS window
will ask for the following:
• Modeling region (identified by FIPS code, see Table
B-l),
• Modeling year,
• Modeling month,
• Modeling hour, and
• Roadway characteristics file name .
Once all modeling parameters are entered, the HDDV-
MEM starts calculating hourly emissions from each
segment listed in the roadway characteristics file. If
"ALL" is typed for the modeling hour on the DOS win-
dow, daily emissions aggregated hourly emissions will be
outputted. Figure B-l shows an example of model execu-
tion window. If a user desires to estimate emissions for an
entire a region, users provide roadway characteristics files
in the same format with the roadway characteristics files
in the regional model.
Table B-1. FIPS Codes for 13 Atlanta Metropolitan
Area Counties.
County Name
Cherokee
Clayton
Cobb
Coweta
De Kalb
Douglas
Fayette
Forsyth
Fulton
Gwinnett
Henry
Paulding
Rockdale
FIPS Code
57
63
67
77
89
97
113
117
121
135
151
223
247
27
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Figure B-1. Example View of the HDDV-MEM Running Window.
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/R-05/090b
2.
3. RECIPIENTS ACCESSION NO.
4. TITLE AND SUBTITLE
Heavy-Duty Diesel Vehicle Modal Emission Model (HDDV-
MEM) Volume II: Modal Components and Output
5. REPORT DATE
August 2005
6. PERFORMING ORGANIZATION CODE
7. AUTHORS
R. Guensler, S. Yoon, C. Feng, H. Li, J. Jun
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
School of Civil and Environmental Engineering
Georgia Institute of Technology
Atlanta, GA
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
4C-R022-NAEX
12. SPONSORING AGENCY NAME AND ADDRESS
U. S. EPA, Office of Research and Development
Air Pollution Prevention and Control Division
Research Triangle Park, North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final; 11/03-02/05
14. SPONSORING AGENCY CODE
EPA/600/13
15. SUPPLEMENTARY NOTES
The EPA Project Officer is E. Sue Kimbrough, Mail Drop E305-02, phone (919) 541-2612, e-mail:
kimbrough.sue@epa.gov
16. ABSTRACT
The report outlines research of a proposed heavy-duty Diesel vehicle modal emission modeling framework
(HDDV-MEMF) for heavy-duty diesel-powered trucks and buses. Although the heavy-duty vehicle modal
modules being developed under this research are different from the motor vehicle emissions simulator
(MOVES) model, the HDDV-MEMF modules should be compatible with MOVES. In the proposed
HDDV-MEMF, emissions from heavy-duty vehicles are predicted as a function of hours of on-road operation
at specific engine horsepower loads. Hence, the basic algorithms and matrix calculations in the new
heavy-duty diesel vehicle modeling framework should be transferable to MOVES. The specific implementa-
tion approach employed by the research team to test the model in Atlanta is somewhat different from other
approaches in that an existing geographic information system (GIS) based modeling tool is being adapted
to the task. The new model implementation is similar in general structure to the previous modal emission
rate model known as the mobile assessment system for urban and regional evaluation (MEASURE) model.
This exploratory framework is designed to be applied to a variety of policy assessments, including those
aimed at reducing the emission rates from heavy-duty vehicles and those designed to change the on-road
operating characteristics to reduce emissions.
17.
KEYWORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Air Pollution
Highway Transportation
Trucks
Buses (vehicles)
Emissions
Transportation Models
Pollution Control
Stationary Sources
13B
15E
13F
14G
12A
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
36
Release to Public
20. SECURITY CLASS (This Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77 ) PREVIOUS EDITION IS OBSOLETE
forms/admin/techrpt.frm 7/8/99 pad
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