Population and Activity of On-road
            Vehicles in MOVES2014


            Draft Report
&EPA
United States
Environmental Protection
Agency

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                    Population and Activity of On-road
                           Vehicles  in  MOVES2014

                                     Draft Report
                                 Assessment and Standards Division
                                Office of Transportation and Air Quality
                                 U.S. Environmental Protection Agency
                  NOTICE

                  This technical report does not necessarily represent final EPA decisions or
                  positions. It is intended to present technical analysis of issues using data
                  that are currently available. The purpose in the release of such reports is to
                  facilitate the exchange of technical information and to inform the public of
                  technical developments.
&EPA
United States
Environmental Protection
Agency
EPA-420-D-15-001
July 2015

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Table of Contents

1.   Introduction	3
2.   MOVES Vehicle and Activity Classifications	5
  2.1.    HPMS Class	5
  2.2.    Source Use Types	5
  2.3.    Regulatory Classes	6
  2.4.    Fuel Types	7
  2.5.    Road Types	8
  2.6.    Source Classification Codes (SCC)	9
  2.7.    Source Bins	10
  2.8.    Allowable Vehicle Modeling Combinations	11
  2.9.    Default Inputs and Fleet and Activity Generators	13
3.   Data Sources	17
  3.1.    VIUS	17
  3.2.    Polk NVPP® and TIP®	17
  3.3.    EPA Sample Vehicle Counts	17
  3.4.    FHWA Highway Statistics	17
  3.5.    FTA National Transit Database	18
  3.6.    School Bus Fleet Fact Book	18
  3.7.    MOBILE6	18
  3.8.    Annual Energy Outlook & National Energy Modeling System	19
  3.9.    Transportation Energy Data Book	19
  3.10.   FHWA Weigh-in-Motion	19
  3.11.   Motorcycle Industry Council Statistical Annual	19
4.   VMT by Calendar Year and Vehicle Type	20
  4.1.    Historic  Vehicle Miles  Traveled (1990 and 1999-2011)	20
  4.2.    Projected Vehicle Miles Traveled (2012-2050)	21
5.   Vehicle Populations by Calendar Year	23
  5.1.    Historic  Source Type Populations (1990 and 1999-2011)	23
  5.2.    Projected Vehicle Populations (2012-2050)	28
6.   Fleet Characteristics	30
  6.1.    Source Type Definitions	30
  6.2.    Sample Vehicle Population	33
7.   Vehicle Characteristics that Vary by Age	47
  7.1.    Age Distributions	47
  7.2.    Relative Mileage Accumulation Rate	58
8.   VMT Distribution of Source Type by Road Type	64
9.   Average Speed Distributions	66
  9.1.    Light-Duty Average Speed Distributions	66
  9.2.    Heavy-Duty Average Speed Distributions	70
10.    Driving Schedules and Ramps	75
  10.1.   Driving  Schedules	75
  10.2.   Ramp Activity	80
11.    Hotelling Activity	81
  11.1.   National Default Hotelling Rate	81

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  11.2.   Hotelling Activity Distribution	83
12.    Temporal Distributions	85
  12.1.   VMT Distribution by Month of the Year	86
  12.2.   VMT Distribution by Type of Day	87
  12.3.   VMT Distribution by Hour of the Day	88
  12.4.   Engine Starts and Parking	89
  12.5.   Hourly Hotelling Activity	92
  12.6.   Single and Multiday Diurnals	95
13.    Geographical Allocation of Activity	96
  13.1.   Source Hours Operating Allocation to Zones	96
  13.2.   Engine Start Allocations to Zones	97
  13.3.   Parking Hours Allocation to Zones	98
14.    Vehicle Mass and Road Load Coefficients	99
  14.1.   Source Mass and Fixed Mass Factor	100
  14.2.   Road Load Coefficients	100
15.    Air Conditioning Activity Inputs	104
  15.1.   ACPenetrationFraction	104
  15.2.   FunctioningACFraction	105
  15.3.   ACActivityTerms	106
16.    Conclusion and Areas for Future Research	108
17.    Appendix A: Projected Source Type Populations by Year	110
18.    Appendix B: Fuel Type and Regulatory Class Fractions for 1960-1981	112
19.    Appendix C: 1990 Age Distributions	115
  19.1.   Motorcycles	115
  19.2.   Passenger Cars	115
  19.3.   Trucks	115
  19.4.   Intercity Buses	116
  19.5.   School Buses and Motor Homes	116
  19.6.   Transit Buses	116
20.    Appendix D: Driving Schedules and SCC Mappings	118
  20.1.   SCC Mappings	118
  20.2.   Driving Schedules	119
21.    Appendix E: MOVES201 Ob Source Masses	122
  21.1.   Motorcycles	123
  21.2.   Passenger Cars	124
  21.3.   General Trucks	124
  21.4.   Buses	126
  21.5.   Refuse Trucks	128
  21.6.   Motor Homes	129
References	131

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1. Introduction

The United States Environmental Protection Agency's Motor Vehicle Emission Simulator,
commonly referred to as MOVES, is a set of modeling tools for estimating emissions produced
by on-road (cars, trucks, motorcycles, etc.) and nonroad (backhoes, lawnmowers, etc.) mobile
sources. MOVES estimates the emissions of greenhouse gases (GHG), criteria pollutants and
selected air toxics. The MOVES model is currently the official model for use for state
implementation plan (SIP) submissions to EPA and for transportation conformity analyses
outside of California. The model is also the primary modeling tool to estimate the impact of
mobile source regulations on emission inventories.

The MOVES model calculates emission inventories by multiplying emission rates by the
appropriate emission-related activity, applying correction (adjustment) factors as needed to
simulate specific situations,  and then adding up the emissions from all sources (populations) and
regions. A useful analogy is that an inventory  can be pictured as a stool; the three legs of the
stool are the emission rates,  activity, and populations, while the seat is the inventory. The
emission rates are inputs to the model specified for various "processes" including running
exhaust, start exhaust,  and a number of evaporative processes, among others. The processes are
largely chosen to be causal such that the physical or engineering principles involved in
generating those emissions are isolated, which in turn allows research test programs to measure
them empirically. These processes also define the activity, populations, and technology inputs
required.

This report describes the sources and derivation for on-road vehicle population and activity
information and associated adjustments as stored in the MOVES2014 default databases. This
data has been extensively updated from previous versions of MOVES. Emission measurement
and rates, correction factor values, and information for nonroad equipment in the default
database are described in other MOVES technical reports.l

The MOVES2014 default database has a domain that encompasses all  on-road (highway) vehicle
and nonroad equipment activity and emissions for the entire United States, Puerto Rico, and the
Virgin Islands. Properly characterizing emissions from the on-road vehicle subset requires a
detailed understanding of the cars and trucks that make up the vehicle fleet and their patterns of
operation. The national default activity information in MOVES2014 provides a reasonable basis
for estimating national emissions. The most important of these inputs,  such as VMT and
population estimates, come from long-term systematic national measurements.

However, the uncertainties and variability in the default data contribute to the uncertainty in the
resulting emission estimates. In particular, when modellers estimate emissions for specific
geographic locations, EPA guidance recommends replacing many of the MOVES fleet and
activity defaults with local data. This is especially true for inputs that vary geographically and for
inputs where local data is  more detailed or up-to-date than that provided in the MOVES defaults.
MOVES has been specifically designed to accommodate the input of alternate, user supplied
activity data for the most important parameters. EPA's Technical Guidance2 provides more
information on customizing  MOVES with local inputs.

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Population and activity data are ever changing. As part of EPA's MOVES development process,
the model undergoes major updates and review every few years. As we progress with MOVES,
the development of fleet and activity inputs (including projections) will continue to be an
important area of focus and improvement.

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2. MOVES Vehicle and Activity Classifications

EPA has developed some terminology that is specific to MOVES, particularly related to vehicle
classification, such as source use types and regulatory classes. The MOVES terms introduced in
this section will be used throughout the report and will be discussed in later sections.

   2.1.      HPMS Class
In this report, MOVES HPMS class refers to one of five categories derived from the categories
used in the US Department of Transportation (DOT) Highway Performance Monitoring System
(HPMS) vehicle classes used by the Federal Highway Administration (FHWA) in the VM-1
Table of their annual Highway Statistics report.  The five HPMS classes used in MOVES are as
follows: motorcycles (HPMSVTypelD 10), light-duty vehicles (25), buses (40), single unit
trucks (50), and combination trucks (60).

Note that in MOVES2014, what we call the HPMS class for light-duty vehicles (25) denotes the
sum of the VM-1 values for long wheelbase and short wheelbase light-duty vehicles.
HPMSVTypelD 25 is new for MOVES2014 and replaces HPMSVTypelD 20 (passenger cars)
and 30 (other two-axle four-tire vehicles) in MOVES2010. As such, in MOVES2014 any VMT
input by HPMS class for passenger cars and light-duty trucks must be entered as a combined
value in the new HPMSVTypelD 25. This change in HPMS classes  has come about as passenger
vehicles have evolved over time with the physical characteristics of "cars" and "trucks"
becoming less distinct. In response, DOT has changed the HPMS classification system and
MOVES has evolved to reflect this change.


   2.2.      Source Use Types
The primary vehicle classification in MOVES is source use type, or, more simply, source type.
Source types are intended to be groups of vehicles with similar activity and usage patterns. On-
road source types were categorized from the HPMS classes, but the HPMS vehicle classes were
further differentiated into MOVES source types using vehicle characteristics from the US Census
Bureau's Vehicle Inventory and Use Survey (VIUS).  The MOVES2014 source types are listed in
Table 2-1 along with the associated HPMS classes. More detailed source type definitions are
provided in Section 6.1.

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                          Table 2-1 MOVES2014 on-road source types
sourceTypelD
11
21
31
32
41
42
43
51
52
53
54
61
62
Source Type Name
Motorcycles
Passenger Cars
Passenger Trucks (primarily personal use)
Light Commercial Trucks (primarily non-
personal use)
Intercity Buses (non-school, non-transit)
Transit Buses
School Buses
Refuse Trucks
Single Unit Short-haul Trucks
Single Unit Long-haul Trucks
Motor Homes
Combination Short-haul Trucks
Combination Long-haul Trucks
HPMSVTypelD
10
25
25
25
40
40
40
50
50
50
50
60
60
Description
Motorcycles
Light-Duty Vehicles
Light-Duty Vehicles
Light-Duty Vehicles
Buses
Buses
Buses
Single Unit Trucks
Single Unit Trucks
Single Unit Trucks
Single Unit Trucks
Combination Trucks
Combination Trucks
In MOVES, the distinction between light-duty (LD) and heavy-duty (HD) source types is
essential because light- and heavy-duty operating modes were developed differently based on
vehicle power and speed. Light-duty vehicles (sourceTypelD 11,21,31, and 32) use vehicle
specific power (VSP), which is dependent on the measured mass of the test vehicle. Heavy-duty
vehicles (sourceTypelD 41, 42, 43, 51, 52, 53, 54, 61, and 62) use scaled tractive power (STP)
which is scaled by a fixed mass factor since their emission rates correlates better with absolute
vehicle power than vehicle specific power. For more discussion on VSP and STP definitions,
please refer to Section 14 of this report and the MOVES2014 reports on light-duty and heavy-
duty vehicle emission rate development, respectively.3'4
   2.3.      Regulatory Classes
In contrast to source types, regulatory classes are used to group vehicles subject to similar
emission standards. The EPA regulates vehicle emissions based on groupings of technologies
and classifications that do not necessarily correspond to DOT activity and usage patterns. To
properly estimate emissions, it is critical for MOVES to account for these emission standards,
despite the fact that the activity data often uses a different classification scheme. Thus, we must
map the two schemas.

The regulatory classes used in MOVES are summarized in Table 2-2 below. The "doesn't
matter" regulatory class is used internally in the model if the emission rates for a given pollutant
and process are independent of regulatory class. The motorcycle (MC) and light-duty vehicle
(LDV) regulatory classes have a one-to-one correspondence with source type.  Other source types
are allocated between regulatory classes based on gross vehicle weight rating (GVWR), which is
a set of eight classes developed by FHWA according to the combined weight of the vehicle and
its load. Urban buses have their own regulatory definition, and therefore have an independent
regulatory class.

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                          Table 2-2 Regulatory classes in MOVES2014
regClassID
0
10
20
30
40
41
42
46
47
48
Regulatory Class Name
Doesn't Matter
MC
LDV
LOT
LHD<=10k
LHD<=14k
LHD45
MHD
HHD
Urban Bus
Description
Doesn't Matter
Motorcycles
Light-Duty Vehicles
Light-Duty Trucks
Class 2b Trucks with 2 Axles and 4 Tires (8,500
GVWR <= 10,000 Ibs)
Class 2b Trucks with 2 Axles and at least 6 Tires
Trucks (8,500 Ibs < GVWR <= 14,000 Ibs)
Class 4 and 5 Trucks (14,00 Ibs < GVWR <= 19,
lbs<
or Class 3
500 Ibs)
Class 6 and 7 Trucks (19,500 Ibs < GVWR < =33,000 Ibs)
Class 8a and 8b Trucks (GVWR > 33,000 Ibs)
Urban Bus (see CFR Sec. 86.091 2)
The GVWR distinction between light-duty (LD) and heavy-duty (HD) trucks falls in the midst of
FHWA GVWR Class 2. Trucks of 6,001-8,500 Ibs. GVWR are sorted into Class 2a, which are
considered light-duty trucks in regulatory class 30, while vehicles of 8,500-10,000 Ibs. GVWR
are sorted into Class 2b, which are considered light heavy-duty vehicles (LHD) in regulatory
classes 40 or 41.

In MOVES2014, we have introduced a new regulatory class 40 for vehicles that are in Class 2b,
but are classified as passenger truck or light-commercial trucks. These vehicles are regulated as
heavy-duty vehicles by EPA, but the VMT from Class 2b vehicles with two axles and four tires
are included in the light-duty vehicles categories of FHWA's Highway Statistics report. MOVES
assigns operating modes for source types 31  and 32 according to VSP. As such, we created
regulatory class 40, so that regulatory class 40 models the emission rates of Class 2b trucks
according to VSP-based operating modes, and regulatory class 41 models the emission rates of
Class 2b trucks according to STP-based operating modes. Class 2b trucks with two axles and at
least six tires (colloquially known as "dualies") and Class 3 trucks fall into regulatory class 41
and are only modeled in the heavy-duty source types.

In summary, the light-duty truck source types (31  and 32) map only to regulatory classes 30 and
40 in MOVES2014, while the heavy-duty vehicle source types (41 and above)  map to regulatory
classes 41 and above.
   2.4.       Fuel Types
MOVES2014 models vehicles and equipment powered by following fuel types: gasoline, diesel,
E-85 (a blend of 85 percent ethanol and 15 percent gasoline), compressed natural gas (CNG),
electricity, and liquefied petroleum gas (LPG, only available for nonroad equipment). Note that
in some cases, a single vehicle can use more than one fuel; for example, flexible fuel vehicles are
capable of running on either gasoline or E-85. Thus, fuel type refers to the capability of the
vehicle capability rather than the fuel in the tank. In MOVES, the fuel actually used depends on
a number of factors including the location, year, and month in which the fuel was purchased, as
                                            7

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explained in the MOVES2014 technical report on the fuel supply.5  Table 2-3 below summarizes
the fuel types available in MOVES.

          Table 2-3 A list of allowable fuel types to power vehicles/equipment in MOVES2014
fuelTypelD
1
2
3
4
5
9
defaultFormulationID
10
20
30
40
50
90
Description
Gasoline
Diesel Fuel
Compressed Natural Gas (CNG)
Liquefied Petroleum Gas (LPG)*
Ethanol (E-85) Capable
Electricity
               * MOVES2014 models LPG use only in nonroad equipment.

It is important to note that not all fuel type/source type combinations can be modeled in
MOVES. That is, MOVES2014 will not model gasoline fueled long-haul combination trucks,
gasoline intercity buses, or diesel motorcycles. Though there are other source types in the real
world that can fuel with CNG, transit buses are the most common and thus are currently the only
on-road source type that may be modeled using CNG. Similarly, flexible fuel (E85-compatible)
and electric vehicles are only modeled for passenger cars, passenger trucks, and light commercial
trucks. None of the on-road (highway) source types can be modeled as fueled by LPG. For more
information on how MOVES models the impact of fuels on emissions, please see the MOVES
documentation on fuel effects.6
   2.5.      Road Types
MOVES calculates emissions separately for each of four road types and for "off-network"
activity when the vehicle is not moving. It also allows separate output for ramp and non-ramp, as
described in Section 10.2 below. The road type codes used in MOVES are listed in Table 2-4.
The four MOVES road types (2-5) are aggregations of FHWA functional facility types.

                          Table 2-4 Road type codes in MOVES2014
roadTypelD
1
2
3
4
5
6
7
8
9
100
Description
Off Network
Rural Restricted Access
Rural Unrestricted Access
Urban Restricted Access
Urban Unrestricted Access
Rural Restricted without Ramps
Urban Restricted without Ramps
Rural Restricted only Ramps
Urban Restricted only Ramps
Nonroad
FHWA Functional Types
Off Network
Rural Interstate
Rural Principal Arterial, Minor Arterial, Major
Collector, Minor Collector & Local
Urban Interstate & Urban Freeway/Expressway
Urban Principal Arterial, Minor Arterial, Collector
& Local





                                           8

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The MOVES road types are based on two important distinctions in how FWHA classifies roads:
1) urban versus rural roadways are distinguished based on land use and human population
density, and 2) unrestricted versus restricted are distinguished based on roadway access—
restricted roads require the use of ramps. The urban/rural distinction is used primarily for
national level calculations. It allows different default speed distributions in urban and rural
settings. Of course, finer distinctions are possible. Users with more detailed information on
speeds and acceleration patterns may choose to create their own additional road types, or may
run MOVES at project level where emissions can be calculated for individual links.


   2.6.      Source Classification Codes (SCC)
Source Classification Codes (SCC) are used in air quality models, such as the UNC SMOKE7
model, to unambiguously identify the source of the emissions when generating emission
inventories. In MOVES, SCCs are single numerical codes that identify the vehicle type, fuel
type, road type, and emission process. The SCCs were redesigned for MOVES2014 to directly
relate to the source use types and road types used by MOVES.

The new SCCs retain the previous 10-digit design, but use different numerical combinations to
avoid conflicts with existing codes. The new codes use MOVES numerical identification (ID)
codes in the following form:

       AAAFVVRRPP, where

   •   AAA indicates mobile source (this has a value of 220 for both on-road and nonroad),
   •   F indicates the MOVES fuelTypelD value,
   •   VV indicates the MOVES sourceTypelD value,
   •   RR indicates the MOVES roadTypelD value, and
   •   PP indicates the MOVES emission processID value.

Building the new SCC  values in this way will allow additional source types, fuel types, road
types, and emission processes to be easily added to the list of SCC values as changes are made to
future versions of MOVES. Using the mapping described above, modelers can also easily
identify the sourceTypelD, fuelTypelD, roadTypelD, and processID of emissions reported by
SCC without needing a decoding table. Refer to tables in the MOVES User Guide2 for the
descriptions of the sourceTypelD, fuelTypelD, roadTypelD, and processID values currently used
by MOVES.

The explicit coding of fuel type, source type, road type, and emission process also allows the
MOVES SCCs to indicate aggregations. For example, a zero code (00) for any of the
sourceTypelD, fuelTypelD, roadTypelD, and processID strings that make up the SCC indicates
that the reported emissions are an aggregation of all categories of that type.

The SCC values used in previous versions of MOVES do not have a one-to-one correspondence
with the new SCC values. However, MOVES2014 has the capability to report results by
regulatory class as well as by SCC, which will aid in comparing SCC results from earlier

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versions of MOVES. All feasible SCC values are listed in the SCC table within the default
database.


   2.7.       Source Bins
To estimate emissions, MOVES must know the emission-related characteristics of the vehicle
such as the type of fuel that it is designed to use and the emission standards it is subject to.
Therefore, we group vehicles into source bins that classify a vehicle by discriminators relevant
for emissions and energy calculations, including fuel type, regulatory class, and model year
group. Each sourceBinID is a unique 19-digit identifier in the following form:

       IFFEERRMMOOOOOOOOOO, where

   •   lisa placeholder,
   •   FF is a MOVES fuelTypelD,
   •   EE is a MOVES engTechID,a
   •   RRisa. MOVES regClassID,
   •   MM is a MOVES shortModYrGroupID, and
   •   10 trailing zeros for future characteristics.

A mapping of model year to model year groups is stored in the PollutantProcessModelYear
table. Distributions of fuel and engine technologies and regulatory class are stored by model
year in the SampleVehiclePopulation table. The MOVES Source Bin Distribution Generator
combines information from these two tables (see Table 2-5) to create a detailed
SourceBinDistribution. These bins may vary by pollutant and process as indicated in the
SourceTypePolProcess table. In general, fuel type and model year group are relevant for all
emission calculations, but the relevance of regulatory class and model year group depend on the
pollutant and process being modeled.  Since MOVES2014 can produce results by various vehicle
classifications—source type, SCC, or regulatory class—the mapping between
SourceBinDistribution and SampleVehiclePopulation differs depending on the output selected.
a In MOVES2014, engTechID 1 is used for all fuel types except electric vehicles, where engTechID 30 is used
instead. Thus, in this version, engTechID is somewhat redundant with fuel type and adds no new information when
determining source bin distributions or calculating emissions.
                                           10

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Table 2-5 Data tables used to allocate source tyi
Generator Table Name
SourceTypePolProcess
PollutantProcessModelYear
Sample VehiclePopulation
Key Fields*
sourceTypelD
polProcessID
polProcessID
modelYearlD
sourceTypelD
modelYearlD
fuelTypelD
engTecMD
regClassID
Additional Fields
isRegClassReqd
isMYGroupReqd
modelYearGroupID
stmyFuelEngFraction
stmyFraction
)e to source bin
Notes
Indicates which pollutant-processes the
source bin distributions may be applied
to and indicates which discriminators
are relevant for each sourceTypelD and
polProcessID (pollutant/process
combination)
Assigns model years to appropriate
model year groups for each
polProcessID.
Includes fuel type and regulatory class
fractions for each source type and
model year, even for some source
type/fuel type combinations that do not
currently have any appreciable market
share (i.e. electric cars). This table
provides default fuel type fractions for
the Alternative Vehicle Fuel &
Technology (AFVT) importer.
* In these tables, the sourceTypelD and modelYearlD are combined into a single sourceTypeModelYearlD.

While details of the SourceTypePolProcess and PollutantProcessModelYear tables are discussed
in the reports on the development of the light- and heavy-duty emission rates3'4, the
SampleVehiclePopulation (SVP) table is a topic for this report and is discussed in Section 6.2.


   2.8.      Allowable Vehicle Modeling Combinations
MOVES2014 allows users to model most combinations of source type, regulatory class, and fuel
type. However,  each combination must have accompanying emission rates; combinations that
lack emissions testing or have negligible market share cannot be directly modeled in
MOVES2014. Table 2-6 is a matrix summarizing the allowable source type-fuel type
combinations. Most of the gasoline and diesel combinations exist with a few notable exceptions,
but options for alternative fuels are limited, as discussed earlier in Section 2.4.

MOVES also stores regulatory class distributions by source type in the
SampleVehiclePopulation table. Table 2-7 summarizes the allowable source type-regulatory
class combinations in MOVES2014.  Any vehicles in regulatory class 40 and less are considered
light-duty while any vehicles in regulatory class 41 and greater are considered heavy-duty.
Similarly, source types 32 and less are considered light-duty and source types 41 and above are
considered heavy-duty.

Table 2-8 joins together the information in the two matrices about source type, fuel type, and
regulatory class combinations in MOVES2014. Each source type-fuel type combination contains
all regulatory classes listed, except for gasoline transit buses, which have been called out
separately.
                                           11

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   Table 2-6 Matrix of the allowable source type-fuel type combinations in MOVES2014
                   (Allowable combinations are marked with an X)





Fuel Types
Gasoline
Diesel
CNG
ESS-Capable
Electricity
1
2
3
5
9
Source Use Types


Motorcycles

11
X






Passenger Cars

21
X
X

X
X

^
assenger Truck

31
X
X

X
X

r
ight CommerciE
Trucks

32
X
X

X
X


Intercity Buses

41

X





Transit Buses

42
X
X
X




in
0
|
Cd
VI

43
X
X





Refuse Trucks

51
X
X



in
g*
oT
i
52
X
X



r
o
B
OQ
ffi
i
53
X
X





Motor Homes

54
X
X




P
Short-Haul
mbination True
Sf
61
X
X




o
o
Long-Haul
mbination True
Sf
62

X



Table 2-7 Matrix of the allowable source type-regulatory class combinations in MOVES2014
                   (Allowable combinations are marked with an X)

Regulatory Classes
MC
LDV
LOT
LHD<=10k
LHD<=14k
LHD45
MHD67
HHD8
Urban Bus
10
20
30
40
41
42
46
47
48
Source Use Types
Motorcycles
11
X








Passenger Cars
21

X







Passenger Trucks
31


X
X





Light Commercial
Trucks
32


X
X





Intercity Buses
41




X
X
X
X

Transit Buses
42





X
X
X
X
in
0
g"
o_
Cd
1
VI
43




X
X
X
X

Refuse Trucks
51




X
X
X
X

in
^
B *
5- m
§"!.
S in
% B'
OQ
tr
52




X
X
X
X

r
o
i^cg
- X
31
*%
'" 
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     Table 2-8 A summary of source type, fuel type, and regulatory class combinations in MOVES2014
sourceTypelD
11
21
31
32
41
/LO

43
51
52
53
54
61
62
fuelTypelD
1
1,2,5,9
1,2,5,9
1,2,5,9
2
1
2,3
,2
,2
,2
,2
,2
,2
2
regClassID
10
20
30,40
30,40
41, 42, 46, 47
42, 46, 47
48
41, 42, 46, 47
41, 42, 46, 47
41, 42, 46, 47
41, 42, 46, 47
41, 42, 46, 47
46,47
46,47
   2.9.       Default Inputs and Fleet and Activity Generators
Population and activity data are critical inputs for calculating emission inventories from
emissions processes such as running exhaust, start exhaust, and evaporative emissions. In
MOVES, most running emissions are distinguished by operating modes, depending on road type
and vehicle speed. Start emissions are determined based on the time a vehicle has been parked
prior to the engine starting ("soak"). Evaporative emissions modes are affected by vehicle
operation and the time that vehicles are parked. Emission rates are further categorized by source
bins with similar fuel type, regulatory classification, and other vehicle and activity
characteristics.

Because of these distinctions, MOVES calculators require information on vehicle population and
activity at a very fine scale. In project-level modeling, this detailed information may be
available and manageable. However, in other cases the fleet and activity data used in the
MOVES calculators must be generated from inputs in a condensed or more readily available
format. MOVES uses "generators" to create fine-scale information from user inputs and MOVES
defaults.

The MOVES Total Activity Generator (TAG) estimates hours of vehicle activity using vehicle
miles travelled (VMT) and speed information to transform VMT into source hours operating
(SHO). Other types of vehicle activity are generated by applying appropriate factors to vehicle
populations. Vehicle starts, extended idle hours, and source hours (including hours operating and
not-operating) are also generated. The default database for MOVES2014 contains national
estimates for VMT, vehicle population, and vehicle age distributions for every possible analysis
year (1990 and 1999-2050). For national inventory runs, annual  national activity is distributed
temporally and spatially using allocation factors.

The Source Bin Distribution Generator (SBDG) uses information on fuel type fractions,
regulatory class distributions, and similar information to estimate the number of vehicles
belonging to each source bin as a function of source type and model year. The  SBDG maps the
activity data (by source types) to source bins which map directly to the MOVES emission rates.

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There are a number of MOVES modules that generate operating mode distributions based on
vehicle activity inputs.  The Rates Operating Mode Distribution Generator and the Link
Operating Mode Distribution Generator use information on speed distributions and driving
patterns (driving schedules) to develop operating mode fractions for each source type, road type,
and time of day.  Similarly, the Evaporative Emissions Operating Mode Generator and the Start
Operating Mode Distribution Generator use MOVES inputs to develop operating mode
distributions for starts and vapor venting.  The details of each these generators and other
MOVES2014 algorithms are described in the MOVES2014 Module Reference.8

This report documents the sources and calculations used to produce the default population and
activity data in the MOVES2014 database used to compute national level emissions based on
defaults for individual counties, months, day types, and hours of the day. In particular, this report
will describe the data used to fill the tables listed in Table 2-9.

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Table 2-9 MOVES database elements covered in this report
Database Table Name
AvgSpeedDistribution
DayVMTFraction
Drive Schedule
Drive Schedule Assoc
Drive Schedule Second
FuelType
HotellingActivityDistribution
HotellingCalendarYear
HourVMTFraction
HPMSVtypeYear
MonthGroupHour
Month VMTFraction
PollutantProcessModelYear
RegulatoryClass
RoadOpModeDistribution
RoadType
RoadTypeDistribution
Sample VehicleDay
Sample VehiclePopulation
Sample VehicleTrip
sec
SourceBinDistribution
Content Summary
Distribution of time among average speed bins
Distribution of VMT between weekdays and
weekend days
Average speed of each drive schedule
Mapping of which drive schedules are used for
each combination of source type and road type
Speed for each second of each drive schedule
Broad fuel categories that indicate the fuel
vehicles are capable of using.
Distribution of hotelling activity to the various
operating modes
Rate of hotelling hours per rural restricted access
VMT
Distribution of VMT among hours of the day
Annual VMT by HPMS vehicle types
Coefficients to calculate air conditioning demand
as a function of heat index
Distribution of annual VMT among months
Assigns model years to appropriate groupings,
which vary by pollutant and process
Sorts vehicles into weight-rating based groups in
which emission regulations are applied
Operating mode distributions by source type, road
type, and speed bin
Distinguishes roadways by population density of
geographic area and by type of access, particularly
the use of ramps for entrance and exit.
Distribution of VMT among road types
Identifies vehicles in the Sample VehicleTrip table
Fuel type and regulatory class distributions by
source type and model year.
Trip start and end times used to determine vehicle
start and soak times
Source Classification Codes that identify the
vehicle type, fuel type, road type and emission
process in MOVES output.
Distribution of population among different vehicle
sub -types (source bins)
Report Sections
Section 9
Section 12
Section 10
Section 10
Section 10
Section 2
Section 1 1
Section 1 1
Section 12
Section 4
Section 15
Section 12
Section 4
Section 2
Section 10
Section 2
Section 8
Section 12
Section 4
Section 12
Section 2
Section 4
                        15

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Table 2-9 MOVES database elements covered in this report
Database Table Name
SourceTypeAge
SourceTypeAgeDistribution
SourceTypeHour
SourceTypeModelYear
SourceTypePolProcess
SourceTypeYear
SourceUseType
SourceUseTypePhysics
Zone
ZoneRoadType
Content Summary
Rate of survival to subsequent age, relative
mileage accumulation rates, and fraction of
functional air conditioning equipment
Distribution of vehicle population among ages
The distribution of total daily hotelling among
hours of the day
Prevalence of air conditioning equipment
Indicates which source bin discriminators are
relevant for each source type and pollutant/process
Vehicle counts by year
Mapping from HPMS class to source type,
including source type names
Road load coefficients and vehicle masses for each
source type used to calculate vehicle specific
power (VSP) and scaled tractive power (STP)
Allocation of activity to zone (county)
Allocation of driving time to zone (county) and
road type
Report Sections
Section 7
Section 15
Section 7
Section 12
Section 15
Section 4
Section 5
Section 2
Section 14
Section 13
Section 13
                        16

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3. Data Sources

A number of organizations collect data relevant to this report. The most important sources used
to populate the vehicle population and activity portions of the MOVES database are described
here. These sources are referred to throughout this document by the abbreviated name given in
this description, but the reference citation is only given here.

   3.1.      VIUS
Until 2002, the US Census Bureau conducted the Vehicle Inventory and Use Survey (VIUS)9 to
collect data on the physical characteristics and activity of US trucks every five years. The survey
is a sample of private and commercial trucks that were registered in the United States as of July
of the survey year. The survey excludes automobiles, motorcycles, government-owned vehicles,
ambulances, buses, motor homes, and nonroad equipment.

For MOVES, VIUS provides information to characterize trucks by source type and to estimate
age, fuel type, and regulatory class distributions as well as relative mileage accumulation rates.
MOVES2014 uses data from both the 1997 and 200210 surveys. While the survey includes a
large number of vehicles and was designed to be representative of the US fleet, information on
model year is not available for many of the older trucks. Thus, the distribution data for many
older model years is sparse and sometimes erratic. Note that the Census Bureau discontinued
VIUS in 2002, although there has been discussion recently about reinitiating the survey.

   3.2.      Polk NVPP® and TIP®
Acquired by UTS in July 2013, R.L. Polk & Co. was a private company providing automotive
information services. The company maintained two databases relevant for MOVES: the National
Vehicle Population Profile (NVPP®)11 and the Trucking Industry Profile (TIP®Net) Vehicles in
Operation12 database. The first focused on light-duty cars and trucks, the second focused on
medium and heavy-duty trucks. Both compiled data from state vehicle registration lists. For
MOVES2014, EPA used NVPP® and TIP® datasets purchased for  1999 and 201 l._Polk/fflS
data was used in determining vehicles populations by age, fuel type, and regulatory class. At the
time of these EPA data purchases Polk was independently operated, so we will continue to refer
to these datasets under the Polk name in this report.

   3.3.      EPA Sample Vehicle Counts
 Neither VIUS nor the Polk dataset contained enough information separately to develop
 distributions by regulatory class, fuel type, and age for each vehicle source type in MOVES, so
 EPA combined these datasets, and incorporated additional data sources to cover vehicles types,
 such as motorcycles, buses, and motor homes that were excluded from either the VIUS or Polk
 datasets. The resulting sample vehicle counts dataset is the basis for the MOVES2014
 SampleVehiclePopulation table and the 2011 age distributions. More details on how we
 constructed the Sample Vehicle Counts dataset can be found in Section 6.2.

   3.4.      FHWA Highway Statistics
Each year the US DOT Federal Highway Administration's (FHWA) Office of Highway Policy
Information publishes Highway Statistics. This volume summarizes a vast amount of roadway
and vehicle data from the Highway Performance Monitoring System,  a national information

                                          17

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system that collects data from states and other sources on many facets of the US roadway
system.
In MOVES2014, vehicle miles traveled (VMT) and vehicle population data for the historic years
1990 and 1999-2011 come from four tables in Highway Statistics: MV-113, MV-1014, VM-115,
and VM-216, which we will reference by table name. For some years, the VMT values were
revised by FHWA in subsequent publications. Table 3-1 summarizes the data source and revision
date we used for each historical year.
able 3-1 Corresponding Highway Statistics data source for historical yeai
Year
1990
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
FHWA Publication Source (Publication/Revision Date)
Highway Statistics 1991
Highway Statistics 1999
Highway Statistics 2000
Highway Statistics 2001
Highway Statistics 2002
Highway Statistics 2003
Highway Statistics 2004
Highway Statistics 2005
Highway Statistics 2006
Highway Statistics 2007
Highway Statistics 2008
Highway Statistics 2010
Highway Statistics 2010
Highway Statistics 2011
(October 1992)
(October 2000)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(April 2011)
(December 20 12)
(December 20 12)
(March 2013)
   3.5.
FTA National Transit Database
The US DOT, Federal Transit Administration (FTA) summarizes financial and operating data
from mass transit agencies across the country in the National Transit Database (NTD).1? For
MOVES2014, we used 1999-2011 vehicle counts from the NTD Revenue Vehicle Inventory for
motor buses (MB) to determine fuel type distributions and populations.
   3.6.
School Bus Fleet Fact Book
The School Bus Fleet Fact Book includes estimates, by state, of the number of school buses and
total miles traveled.18 The Fact Book is published by Bobit Publications. School bus mileage
accumulation rates came from the 1997 Fact Book, originally used in MOBILE6. We have used
1999-2011 sales data from the 2009 and 2012 Fact Book to calculate age distributions.
   3.7.
MOBILE6
MOBILE6 was a precursor to MOVES used to estimate highway vehicle emissions. In some
cases, we have used estimates from MOBILE6 model with only minor adaptation. In particular,
we used MOBILE6 data for some relative mileage accumulation rates, air conditioning usage
rates, and driving schedules.
                                         18

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The MOBILE6 data is documented in technical reports, particularly M6.FLT.002, Update of
Fleet Characterization Data for Use in MOBILE6 - Final Report.19 Additional MOBILE6
documentation is available online.20

   3.8.      Annual Energy Outlook & National Energy Modeling System
The Annual Energy Outlook (AEO)21 describes Department of Energy forecasts for future energy
consumption. The National Energy Modeling System (NEMS) is used to generate these
projections based on economic and demographic forecasts. Vehicle sales and miles travelled are
included in the projections because they strongly influence fuel consumption. Therefore, the
AEO is an important source of future projections in MOVES. For MOVES2014, we used
AEO2014 to forecast VMT and vehicle populations in years 2012-2050.

   3.9.      Transportation Energy Data Book
Each year Oak Ridge National Laboratory produces the annual Transportation Energy Data Book
(TEDB) for the Department of Energy. This book summarizes transportation and energy data
from a variety of sources, including EPA, FHWA, Polk, and Ward's Automotive, Inc. For
MOVES2014 we used information for estimating vehicle sales and survival fractions for historic
years 1990 and 1999-2011 from TEDB Edition 32, published in 2013.22

   3.10.     FHWA Weigh-in-Motion
FHWA compiles truck weight data by axle configuration and roadway type from individual
states' Weigh-in-Motion (WEVI) programs.23 The average weight for single unit trucks and
combination trucks was determined from FHWA's Vehicle Travel Information System (VTRIS)
W-3 Tables using data collected in 2011.

   3.11.     Motorcycle Industry Council Statistical Annual
The Motorcycle Industry Council (MIC) collects data on sales, ownership, and activity trends
each year. MIC's Statistical Annual summarizes this data,24 which we used in MOVES2014,
particularly the 1999-2011 sales of highway motorcycles.

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 4. VMT by Calendar Year and Vehicle Type

 For national level calculations, MOVES calculates source operating hours from national VMT
 by vehicle type. The default database contains national VMT estimates for all analysis years,
 which include 1990 and 1999-2050. Years 1991-1998 are excluded because there is no
 regulatory requirement to analyze them and including them would increase model complexity.
 Calendar year 1990 continues to be a base year because of the Clean Air Act Amendments of
 1990.

 The national VMT estimates are stored in the HPMSVTypeYear table, which includes three data
 fields: HPMSBaseYearVMT (discussed below), baseYearOffNetVMT, and VMTGrowthFactor.
 Off network VMT refers to the portion of activity that is not included in travel demand model
 networks or any VMT that is not otherwise reflected in the other four road types. The field
 baseYearOffNetVMT is provided in case it is useful for modeling local areas. However, the
 reported FIPMS VMT values, used to calculate the national averages discussed here, are intended
 to include all VMT. Thus, for MOVES2014 national defaults, the baseYearOffNetVMT is zero
 for all vehicle types. Additionally, the VMTGrowthF actor field is not used in MOVES2014  and
 is set to zero for all vehicle types.


4.1.   Historic Vehicle Miles Traveled (1990 and 1999-2011)
 The HPMSBaseYearVMT field  stores the total national VMT for each HPMS vehicle type for all
 analysis years. For historical  years 1990 and 1999-2011, the VMT is derived from the FHWA
 VM-1 tables. In reporting years 2007 and later, the VM-1 data use an updated methodology  with
 different HPMS vehicle type categories. The current HPMS categories are light-duty short
 wheelbase, light-duty long wheelbase, motorcycles, buses, single unit trucks,  and combination
 trucks. Because MOVES categorizes light-duty source types based on vehicle type and not
 wheelbase length, the short and long wheelbase categories are combined into  a single category of
 light-duty vehicles (HPMSVTypelD 25). Internally, the MOVES Total Activity Generator8
 allocates this VMT to MOVES source types and ages using vehicle populations, age distributions
 and relative mileage accumulation rates.

 For years prior to 2007, the VM-1 data with historical vehicle type groupings needed to be
 adjusted for consistency. In early 2011, the FHWA released such adjusted VMT data for years
 2000-2006 to match the new category definitions. Shortly afterward, the agency replaced these
 adjusted numbers with the unadjusted VMT data stating, "[FHWA] determined that it is more
 reliable to retain the original  2000-2006 estimates because the information available for those
 years does not fully meet the requirements of the new methodology."b However, needing
 consistent VMT and lacking  a better adjustment methodology, we used the FHWA-adjusted
 values as the VMT for 2000-2006.

 This left two years, 1990 and 1999, that needed to be adjusted to be consistent with the new
 HPMS vehicle categories. These adjustments were made using the average ratio of the
 b This text appears in a footnote to FHWA's Highway Statistics Table VM-1 for publication years 2000-2009.
                                           20

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methodology change for each vehicle category. This was found by dividing the FHWA-adjusted
VMT for each vehicle category by the original VMT for each year 2000-2006 and then
calculating the average ratio for each category. This ratio was then applied to the corresponding
VMT values reported in VM-1 for 1990 and 1999. Since FHWA's adjustments conserved the
original total VMT estimates, we normalized our adjusted values such that the original total
VMT for the years were unchanged.

The resulting values for historic years by HPMS vehicle class are listed in Table 4-1. The VMT
for 1990 and 1999 were EPA-adjusted from VM-1, 2000-2006 were FHWA-adjusted, and 2007-
2011 were unadjusted, other than the simple combination of the short and long wheelbase classes
into light-duty vehicles.

               Table 4-1 Historic year VMT by HPMS vehicle class (millions of miles)
Year
1990

1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Motorcycles
11,404

13,619
12,175
11,120
11,171
11,384
14,975
13,773
19,157
21,396
20,811
20,822
18,513
18,500
Light-Duty
Vehicles
1,943,197

2,401,408
2,458,221
2,499,069
2,555,467
2,579,194
2,652,092
2,677,641
2,680,535
2,691,034
2,630,213
2,633,248
2,648,457
2,646,641
Buses
10,279

14,853
14,805
12,982
13,336
13,381
13,523
13,153
14,038
14,516
14,823
14,387
13,770
13,783
Single Unit
Trucks
70,848

100,534
100,486
103,470
107,317
112,723
111,238
109,735
123,318
119,979
126,855
120,207
110,738
103,515
Combination
Trucks
108,624

160,921
161,238
168,969
168,217
173,539
172,960
175,128
177,321
184,199
183,826
168,100
175,789
163,692
       4.2.  Projected Vehicle Miles Traveled (2012-2050)
The previous section describes historic fleet VMT. This section presents how EPA projected
those values into the future. The VMT growth in years beyond 2011 is based on the VMT
projections as described in AEO2014. Due to differences in methodology, the absolute VMT
values presented in AEO differ slightly from the HPMS values in VM-1 where the analysis years
overlap. Therefore, the projections in AEO were not used directly. Instead, percent changes from
year to year in the projected values were calculated and applied to the HPMS data. Since
AEO2014 only projects out to 2040, VMT for years 2041-2050 were assumed to continue to
grow at the average growth rate over 2031-2040.

A mapping between the two data sources was necessary because the vehicle categories differed
between AEO and HPMS. AEO's light-duty category was mapped to both the combined HPMS
light-duty and the motorcycle categories. Motorcycles were included here because they were not
explicitly accounted for elsewhere in AEO. Since buses span a large range of heavy-duty
vehicles and activity, the combination of AEO's light medium, medium, and heavy heavy-duty
categories was mapped to the HPMS bus category. AEO's light medium and medium heavy-duty
                                          21

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categories were combined for mapping to the HPMS single unit truck category, and AEO's
heavy heavy-duty category was mapped to the HPMS combination truck category.
The percent growth changes over time from the groupings described above were calculated and
applied by HPMS category to the 2011 base year VMT from the VM-1 Table. The resulting
values are presented in Table 4-2 below.

              Table 4-2 VMT projections for 2012-2050 by HPMS class (millions of miles)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Motorcycles
18,776
19,030
19,073
19,162
19,375
19,590
19,756
19,931
20,107
20,284
20,454
20,627
20,807
20,997
21,205
21,426
21,662
21,897
22,133
22,378
22,625
22,867
23,086
23,293
23,493
23,687
23,880
24,060
24,217
24,436
24,657
24,880
25,105
25,332
25,561
25,792
26,025
26,261
26,498
Light-Duty
Vehicles
2,686,152
2,722,469
2,728,546
2,741,392
2,771,828
2,802,578
2,826,337
2,851,349
2,876,481
2,901,914
2,926,116
2,950,908
2,976,667
3,003,914
3,033,572
3,065,195
3,099,033
3,132,690
3,166,361
3,201,376
3,236,805
3,271,436
3,302,691
3,332,329
3,360,885
3,388,760
3,416,287
3,442,035
3,464,551
3,495,877
3,527,485
3,559,380
3,591,563
3,624,036
3,656,804
3,689,868
3,723,230
3,756,894
3,790,863
Buses
13,384
13,954
14,374
14,991
15,612
16,036
16,325
16,609
16,906
17,222
17,550
17,877
18,173
18,495
18,799
19,052
19,277
19,509
19,765
20,005
20,198
20,429
20,725
21,017
21,308
21,600
21,887
22,146
22,417
22,701
22,989
23,280
23,575
23,874
24,176
24,483
24,793
25,107
25,425
Single Unit
Trucks
103,284
108,811
113,054
118,343
123,348
126,693
128,737
130,692
132,833
135,237
137,759
140,171
142,243
144,418
146,389
147,999
149,382
150,824
152,391
153,916
155,034
156,435
158,246
159,910
161,452
162,945
164,353
165,603
166,905
168,431
169,970
171,524
173,091
174,673
176,270
177,881
179,507
181,147
182,803
Combination
Trucks
157,396
163,467
167,837
174,804
181,988
186,928
190,433
193,905
197,484
201,214
205,076
208,983
212,579
216,551
220,329
223,510
226,348
229,268
232,509
235,518
237,990
240,929
244,678
248,437
252,265
256,123
259,948
263,426
267,050
270,775
274,552
278,381
282,264
286,201
290,193
294,241
298,345
302,507
306,726
                                           22

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5. Vehicle Populations by Calendar Year

MOVES uses vehicle populations to characterize emissions activity that is not directly dependent
on VMT. These data are also used to allocate VMT from HPMS class to source type and age.
(For more details, see Section 7.) The default database stores historic estimates and future
projections of total US vehicle populations in 1990 and 1999-2050 by source type. All of these
values have been updated in MOVES2014 with improved data sources. The MOVES database
stores this information in the SourceTypeYear table, which has three data fields:
sourceTypePopulation, salesGrowthFactor, and migrationRate. However,  the salesGrowthFactor
and migrationRate fields are not used in MOVES2014.


   5.1.      Historic Source Type Populations (1990 and 1999-2011)
MOVES populations for calendar years 1990 and 1999-2011 are derived top-down from
registration data in Table MV-1 of the Federal Highway Administration's annual Highway
Statistics report. In this table, vehicles are separated into four general vehicle categories:
motorcycles, passenger cars, trucks, and buses. These categories include government vehicles
and vehicles in Puerto Rico but do not account for vehicles in the Virgin Islands due to their
relatively small effects on national population estimates. Motorcycle  and car data were used
without adjustment, but since MOVES populations are input by source type, allocations within
the general categories of trucks and buses were necessary, as shown in Figure 5-1.

Figure 5-1 Conceptual map of allocating FHWA MV-1 vehicle registration estimates to MOVES source types
                           FHWA (MV-1)
MOVES Source Types
          Registrations
           including
          government
          vehicles and
           vehicles in
          Puerto Rico
                             Source type
                            distributions
                            derived from
                            interpolation
                            between CY
                            1999 and 2011

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Trucks were separated into single unit and combination trucks using registration data in the
Highway Statistics VM-1 Table. The remaining MV-1 truck registrations were allocated to the
light-duty trucks. Single unit and combination trucks were further allocated among their
respective source types using the EPA sample vehicle counts data. Since we only had sample
vehicle counts for calendar years 1999 and 2011, the 2000-2010 distributions among source
types within the general truck categories were linearly interpolated between 1999 and 2011
rather than using the predictions for these years as in MOVES2010b. This ensured that every
source type population would more or less track its general MV-1 population, as shown for
example in for allocating between short-haul and long-haul combination trucks below (see
Figure 5-2). This linear interpolation method was also used for single unit (see Figure 5-3) and
light-duty truck source types (see Figure 5-4). Car and motorcycle populations are reported
directly in the MV-1 Table and thus were not subject to linear interpolation adjustments.

            Figure 5-2 Combination truck source type populations interpolated for 1999-2011

   3,000,000
   2,500,000
 o
 i 2,000,000
 -2
 3 1,500,000
 | 1,000,000
     500,000
D Long-Haul
 Combination
 Trucks (62)
D Short-Haul
  Combination
  Trucks (61)
           1999 2000 2001 2002 2003  2004  2005  2006  2007  2008  2009 2010 2011
                                          Year

-------
             Figure 5-3 Single unit truck source type populations interpolated for 1999-2011
•a
—
i
sS
z
9,000,000

8,000,000

7,000,000

6,000,000

5,000,000

4,000,000

3,000,000

2,000,000

1,000,000
D Motor Homes
 (54)


• Long-Haul
 Single Unit
 Trucks (53)

D Short-Haul
 Single Unit
 Trucks (52)

D Refuse Trucks
 (51)
            1999 2000  2001 2002  2003 2004  2005 2006 2007  2008 2009  2010 2011
                                             Year


      Figure 5-4 Light-duty vehicle source type populations; light trucks interpolated for 1999—2011

   300,000,000  -,
a

"wa
•a
0)

^
~S
National \


200


130

100
50


,000


,000

,000
,000
,

,000


,000

,000
,000








	 ' ~~~
— — ' — HH— 	 —
__ — - — •


	 	

	

• Light
Commercial
Trucks (32)
n Passenger
Trucks (31)

D Passenger
Cars (21)
D Motorcycles
(11)
              1999 2000 2001  2002 2003  2004 2005 2006  2007 2008  2009 2010 2011
                                              Year

-------
                      Figure 5-5 Bus source type populations in MOVES2014
     900,000

     800,000 -
  g  700,000
i  600,000

(2  500,000
—
2  400,000
>
|  300,000
.0
|  200,000

   100,000
                                                                             D School Buses
                                                                               (43)

                                                                             D Transit Buses
                                                                               (42)

                                                                             D Intercity
                                                                               Buses (41)
           1999 2000 2001 2002  2003  2004  2005  2006 2007 2008 2009 2010 2011
                                          Year

Buses were allocated in a similar fashion as trucks, but using different data sources (see Figure
5-5). School bus estimates for all years 1999-2011 were taken from the Highway Statistics Table
MV-10 and transit bus estimates for these years were taken from the National Transit Database
(NTD) compiled by the  Federal Transit Administration. The remainder of MV-1  bus
registrations were allocated to the intercity bus source type. Since school  and transit bus
registrations in Puerto Rico were not readily available, we estimated them by multiplying the US
transit or school bus registrations by the ratio of bus registrations in Puerto Rico to the total MV-
1 bus registrations. Note that the precipitous drop in bus populations from 2010 to 2011 is
reflected in the MV-1 bus registration data published by FFEWA, which has been used in
MOVES2014 without adjustment.
                                            26

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                       Table 5-1 Historic source type populations for calendar years 1990 and 1999-2011 (in thousands)
Year
1990

1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Motorcycle
4,281

4,174
4,368
4,925
5,026
5,392
5,813
6,259
6,770
7,254
7,869
8,046
8,125
8,553
Passenger
Car
145,112

134,480
135,670
139,709
137,996
137,745
138,642
138,779
137,742
138,354
139,501
138,743
133,313
128,078
Passenger
Truck
27,700

55,472
58,930
62,685
63,789
65,651
69,860
72,980
76,321
78,443
78,596
79,219
79,641
87,030
Light
Commercial
Truck
9,903

18,532
19,217
19,947
19,801
19,873
20,616
20,987
21,380
21,398
20,868
20,464
20,007
21,252
Intercity
Bus
60

81
81
81
79
81
83
85
88
91
96
94
89
18
Transit
Bus
59

56
60
61
65
65
65
65
66
67
65
67
68
66
School
Bus
511

595
609
611
620
634
650
660
672
680
687
684
694
587
Refuse
Truck
67

105
106
116
120
126
132
141
152
164
172
178
180
176
Single
Unit Short-
haul Truck
3,870

5,312
5,123
5,416
5,396
5,452
5,528
5,703
5,948
6,208
6,322
6,356
6,234
5,915
Single
Unit Long-
haul Truck
145

314
296
305
297
292
288
289
293
297
293
286
271
248
Motor
Home
927

,073
,055
,137
,155
,189
,228
,290
,370
,456
,509
,544
,540
,487
Combination
Short-haul
Truck
1,177

,361
,368
,384
,335
,307
,293
,309
,353
,364
,319
,317
,266
,198
Combination
Long-haul
Truck
705

,008
,043
,087
,080
,088
,108
,155
,228
,274
,268
,303
,289
,255
Note that the decline in sales seen in the 2008 recession results in a flattening of total population growth rates, and eventually a decline
in total population for passenger cars and long-haul combination trucks as shown in Table 5-1. This suggests that the decline in sales
was accompanied by a delay in the scrappage of older vehicles. The dynamic vehicle survival rates in MOVES and their impact on
age distributions are discussed in Section 7.1.2.
                                                              27

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   5.2.       Projected Vehicle Populations (2012-2050)
The previous section described the historic fleet as it appeared in the data. This section presents
how EPA projected those vehicle populations into the future. This work is inherently dependent
on projections of both vehicle sales and scrappage. While future vehicle sales are commonly
included in economic forecasts, there are no reliable sources for projected national vehicle
scrappage. Therefore, we decided to use projected VMT growth as a surrogate for vehicle
population growth. In examining VMT per vehicle by HPMS  class over the historic years
presented above, this surrogate appears reasonable.  Table 5-2  shows the VMT values of Table
4-1 divided by the vehicle populations of Table 5-1  grouped by HPMS classification. At this
level of aggregation,  VMT per vehicle is relatively constant with no clear trends over time.

                      Table 5-2 MOVES2014 VMT per vehicle by HPMS class
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Motorcycles
3,263
2,787
2,258
2,223
2,111
2,576
2,201
2,830
2,950
2,645
2,588
2,279
2,163
Light-Duty Vehicles
11,518
11,497
11,240
11,533
11,552
11,575
11,505
11,385
11,298
11,007
11,044
11,369
11,197
Buses
20,291
19,740
17,240
17,455
17,155
16,946
16,238
16,995
17,322
17,480
17,026
16,181
20,541
Single Unit Trucks
14,776
15,271
14,837
15,401
15,969
15,501
14,783
15,885
14,767
15,291
14,372
13,464
13,227
Combination Trucks
67,928
66,876
68,381
69,655
72,459
72,037
71,075
68,702
69,825
71,058
64,160
68,802
66,731
Therefore, the AEO growth factors used to project future VMT as described in Section 4.2 were
used to project populations. Motorcycle growth was calculated using factors from light-duty
vehicles. Since these growth factors are by HPMS class, the 2011  source type populations were
aggregated by HPMS class before the growth factors were applied to the base populations. The
resulting HPMS class population projections are presented in Table 5-3. However, MOVES
cannot use populations in this format as it requires them to be disaggregated by source type. The
distribution projected HPMS class populations to source type was calculated with the same
algorithm used to produce age distributions. Please see Section 7.1.2.2 for a detailed discussion
on this topic. The resulting projected source type populations are tabulated in Section 17
(Appendix A).
                                           28

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Table 5-3 Projected HPMS class populations for 2012-2050 (in thousands)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Motorcycles
8,571
8,687
8,706
8,747
8,844
8,943
9,018
9,098
9,178
9,260
9,337
9,416
9,498
9,585
9,680
9,781
9,888
9,996
10,103
10,215
10,328
10,439
10,538
10,633
10,724
10,813
10,901
10,983
11,055
11,155
11,256
11,357
11,460
11,564
11,668
11,774
11,880
11,988
12,096
Light-Duty Vehicles
236,285
239,479
240,028
241,178
243,868
246,584
248,692
250,904
253,126
255,371
257,508
259,695
261,966
264,368
266,983
269,767
272,745
275,707
278,670
281,752
284,871
287,918
290,669
293,277
295,790
298,244
300,667
302,932
304,914
307,671
310,453
313,260
316,092
318,951
321,835
324,745
327,681
330,642
333,632
Buses
704
734
757
789
822
844
860
875
890
906
923
941
956
974
990
,004
,015
,027
,041
,053
,063
,075
,091
,106
,122
,137
,152
,166
,180
,196
,210
,226
,241
,257
,273
,289
,304
,322
,338
Single Unit Trucks
8,198
8,637
8,973
9,393
9,790
10,056
10,218
10,373
10,543
10,733
10,934
11,126
11,290
11,463
11,620
11,747
11,858
11,978
12,107
12,234
12,335
12,454
12,606
12,745
12,877
13,007
13,129
13,238
13,346
13,472
13,599
13,731
13,864
13,998
14,135
14,273
14,411
14,550
14,691
Combination Trucks
2,471
2,566
2,635
2,745
2,857
2,935
2,990
3,045
3,100
3,159
3,220
3,281
3,338
3,400
3,459
3,510
3,554
3,600
3,650
3,698
3,737
3,783
3,842
3,901
3,961
4,021
4,081
4,136
4,193
4,251
4,311
4,371
4,432
4,494
4,556
4,620
4,684
4,750
4,816
                                29

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6. Fleet Characteristics

MOVES categorizes vehicles into thirteen source use types as described in Section 2.1, which
are defined using physical characteristics, such as number of axles and tires, and travel behavior
characteristics, such as typical trip lengths. This section describes the defining characteristics of
the source types in greater detail and explains how source type is related to fuel type and
regulatory class, primarily through the SampleVehiclePopulation table.

   6.1.      Source Type Definitions
MOVES source types are intended to further divide HPMS vehicle classifications into groups of
vehicles with similar activity patterns For example, passenger trucks and light commercial trucks
are expected to have different daily trip patterns. VIUS was our main source of information for
distinguishing these vehicles. Table 6-1 summarizes how the VIUS2002 parameters were used to
delineate the light-duty, single unit, and combination truck source types for MOVES2014.

Axle arrangement (AXLE_CONFIG) was used to define four categories: straight trucks with two
axles and four tires (codes 1, 6, 7, 8), straight trucks with two axles and six tires (codes 2, 9, 10,
11), all straight trucks (codes 1-21), and all tractor-trailer combinations (codes 21+). Primary
distance of operation (PRIMARY_TRIP) was used to define short-haul (codes 1-4) for vehicles
with primary operation distances less than 200 miles and long-haul (codes 5-6) for 200 miles and
greater. The VIN-decoded gross vehicle weight (ADM_GVW) and survey weight (VIUS_GVW)
were used to distinguish vehicles less than 10,000 Ibs. as light-duty and vehicles greater than or
equal to 10,000 Ibs. as heavy-duty. Any vehicle with two axles and at least six tires was
considered a single unit truck regardless of weight. We also note that refuse trucks have their
own VIUS vocational category (BODYTYPE 21) and that MOVES distinguishes between
personal (OPCLASS 5) and non-personal use.

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          Table 6-1 VIUS2002 parameters used to distinguish truck source types in MOVES2014
Source
Type
Passenger
Trucks
Light
Commercial
Trucks
Refuse
Trucks*
Single Unit
Short-Haul
Trucks*
Single Unit
Long-Haul
Trucks*
Combination
Short-Haul
Trucks
Combination
Long-Haul
Trucks
Axle
Arrangement
AXLE CONFIG
in(l,6/7,8)t
AXLE CONFIG
in(l,6,7,8)t
AXLE CONFIG
in (2,9,10,11)
AXLE CONFIG
<=21
AXLE CONFIG
in (2,9,10,11)
AXLE CONFIG
<=21
AXLE CONFIG
in (2,9,10,11)
AXLE CONFIG
<=21
AXLE CONFIG
>=21
AXLE CONFIG
>=21
Primary Distance
of Operation
Any
Any
TRIP PRIMARY
in (1,2,3,4)
TRIP PRIMARY
in (1,2,3,4)
TRIP PRIMARY
in (1,2,3,4)
TRIP PRIMARY
in (1,2,3,4)
TRIP PRIMARY
in (5,6)
TRIP PRIMARY
in (5,6)
TRIP PRIMARY
in (1,2,3,4)
TRIP PRIMARY
in (5,6)
Weight
ADM GVWin(l,2)&
VIUS GVW in (1,2,3)
ADM GVW in (1,2) &
VIUS_GVW in (1,2,3)
Any
ADM GVW>2&
VIUS GVW>3
Any
ADM GVW>2&
VIUS GVW>3
Any
ADM GVW>2&
VIUS GVW>3
Any
Any
Body Type
Any
Any
BODYTYPE
=21
BODYTYPE
=21
BODYTYPE
#1
BODYTYPE
#1
Any
Any
Any
Any
Operator
Classificat
ion
OPCLASS
=5
OPCLASS
^5
Any
Any
Any
Any
Any
Any
Any
Any
    t In the MOVES20 14 analysis, we did not constrain axle configuration of light-duty trucks, so there are some,
    albeit very few, light duty trucks that have three axles or more and/or six tires or more. These vehicles are
    classified as light-duty trucks based primarily on their weight. Only 0.27 percent of light-duty trucks have such
    tire and/or axle parameters and they have a negligible impact on vehicle populations and emissions.
    * For a source type with multiple rows, the source type is applied to any vehicle with either set of parameters.

Motorcycles and passenger cars in MOVES borrow vehicle definitions from the FHWA
Highway Performance Monitoring System (HPMS) classifications from the Highway Statistics
MV-1 Table. Source type definitions for intercity, transit, and school buses are taken from
various US Department of Transportation sources. While refuse trucks were identified and
separated from other single unit trucks in VIUS, motor homes were not.

          6.1.1.     Motorcycles
According to the HPMS vehicle description, motorcycles (sourceTypelD  11) are,  "all two- or
three-wheeled motorized vehicles, typically with saddle seats and steered by handlebars rather
than a wheel."25 This category usually includes any registered motorcycles, motor scooters,
mopeds, and motor-powered bicycles.  Neither the 201 1 Polk dataset nor VIUS contain any
information on motorcycles. As noted  in Section 5.1 information on motorcycle populations
comes from HPMS MV-1 registrations.
           6.1.2.     Passenger Cars
Passenger cars are defined as any coupes, compacts, sedans, or station wagons with the primary
purpose of carrying passengers.25 All passenger cars (sourceTypelD 21) are categorized in the

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light-duty vehicle regulatory class (regClassID 20). Cars were not surveyed in VIUS, but Polk
has a robust yet proprietary dataset of car registrations from all fifty states.

          6.1.3.     Light-Duty Trucks
Light-duty trucks include pickups, sport utility vehicles  (SUVs), and vans.25 Depending on use
and GVWR, we categorize them into two different MOVES source types: 1) passenger trucks
(sourceTypelD 31), and 2) light commercial trucks (sourceTypelD 32). According to 2011 VM-1
vehicle classifications from FHWA, light-duty vehicles  are those weighing less than 10,000
pounds, specifically vehicles with a GVWR in Class 1 and 2,  except Class 2b trucks with two
axles or more and at least six tires are assigned to the single unit truck category.

VIUS contains many survey questions on weight; we chose to use both a VIN-decoded gross
vehicle weight rating (ADM_GVW) and a respondent self-reported GVWR (VIUS_GVW) to
differentiate between light-duty and single unit trucks. For the passenger trucks, there is a final
VIUS constraint that the most frequent operator classification (OPCLASS) must be personal
transportation. Inversely, light commercial trucks (sourceTypelD 32) have a VIUS constraint
that their most frequent operator classification must not  be personal transportation.

          6.1.4.     Buses
MOVES has three bus source types: intercity (sourceTypelD 41), transit (sourceTypelD 42),  and
school buses (sourceTypelD 43). Buses were not included in either VIUS or the Polk dataset, so
supplementary data sources were necessary. MOVES uses various US Department of
Transportation definitions for buses.

Transit buses are defined in the Federal Transit Administration's National Transit Database
(NTD), which states that they are buses owned by a public transit organization for the primary
purpose of transporting passengers on fixed routes and schedules.26 According to FHWA, school
buses are defined as vehicles designed to carry more than ten  passengers, used to transport K-12
students between their home and school.27 Intercity buses  are, as defined by the Bureau of
Transportation Statistics, "interstate motor carrier of passengers with an average annual gross
revenue of at least one million dollars,"28 but MOVES also considers any bus that cannot be
categorized as either a transit or school bus to be an intercity bus.

          6.1.5.     Single Unit Trucks
The single unit HPMS class in MOVES consists of refuse trucks (sourceTypelD 51), short-haul
single unit trucks (sourceTypelD 52), long-haul single unit trucks (sourceTypelD  53), and motor
homes (sourceTypelD 54). With 2013 VM-1 updates to  vehicle classifications, FHWA now
defines a single unit truck as a single-frame truck with a gross vehicle weight rating of greater
than 10,000 pounds or with two axles and at least six tires—colloquially known as a "dualie." As
with light-duty truck source types, single unit trucks are sorted using VIUS parameters, in this
case that includes axle configuration (AXLE_CONFIG) for straight trucks (codes 1-21), vehicle
weight (both ADM_GVW and VIUS_GVW), most common trip distance (TRIP_PRIMARY),
and body type (BODYTYPE). All short-haul single unit trucks must have a primary trip distance
of 200 miles or less and must not be refuse trucks and all long-haul trucks must have  a primary
trip distance of greater than 200 miles. Refuse trucks are short-haul single unit trucks with a

                                          32

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body type (code 21) for trash, garbage, or recyclable material hauling. Motor homes are not
included in VIUS.

          6.1.6.     Combination Trucks
A combination truck is any truck-tractor towing at least one trailer according to VIUS. MOVES
divides these tractor-trailers into two MOVES source types: short-haul (sourceTypelD 61) and
long-haul combination trucks (sourceTypelD 62). Like single unit trucks, short-haul and long-
haul combination trucks are distinguished by their primary trip length (TRIP_PRTMARY) in
VIUS. If the tractor-trailer's primary trip length is equal to or less than 200 miles, then it is
considered short-haul. If the tractor-trailer's primary trip length is greater than 200 miles, then it
is considered long-haul. Short-haul combination trucks are older than long-haul combination
trucks and these short-haul trucks often purchased in secondary markets, such as for drayage
applications, after being used primarily for long-haul trips.29


    6.2.      Sample Vehicle Population
To match source types to emission rates, MOVES must associate each source type with specific
fuel types and regulatory classes. Much of default the information on fleet characteristics is
stored in the SampleVehiclePopulation table, which contains two fractions: 1) stmyFraction, and
2) stmyFuelEngFraction. The former fraction defines the default fuel type distribution, which can
be modified by the user through the Alternative Fuel Vehicle and Technology (AVFT) table, and
the  latter fraction forms the default regulatory class distribution. Both SVP fractions are
computed through the EPA sample vehicle counts dataset that joins 2011  national R.L. Polk
vehicle registration data with 2002 Vehicle Inventory and Use Survey (VIUS) classifications.

          6.2.1.     Fuel Type and Regulatory Class Distributions

The stmyFraction is the default national fuel type and regulatory class allocation for each source
type and model year. Written out mathematically in Equation 1, we define the stmyFraction as,
                                              Ni,j,k,l
                                                                             Equation 1
where the number of vehicles N in a given model year i, regulatory class j, fuel type /c, and
source type / is divided by the sum of vehicles across the set of all regulatory classes/ and all
fuel types K. That is, the denominator only differs by source type and model year. For example,
model year 2010 passenger trucks have stmyFractions that indicate the distribution of these
vehicles between gasoline, diesel, E85, and electricity and regulatory classes 30 and 40. These
values must  sums to one for each source type and model year. A value of zero indicates that the
MOVES default  population of vehicles of that source type, model year, fuel type, and regulatory
class is negligible or does not exist.
                                           33

-------
While stmyFraction indicates MOVES default values, the stmyFuelEngFraction allows the
modeling of non-default fuel type distributions.  For each allowable combination of source type,
model year and fuel type, the StmyFuelEngFraction indicates the expected regulatory class
distribution, whether or not these vehicles exist in the default. Similar to the stmyFraction above,
we define StmyFuelEngFraction in Equation 2 as,
                                                 Ntjki
                       f(stmyfueleng)iijikil =	—	,
                                              *""•                            Equati
for number of vehicles N, model year i, regulatory classy, fuel type /c, source type /, and the set
of all regulatory classes/. The denominator differs by source type, model year, and fuel type in
this case. For example, for model year 2010 gasoline passenger trucks, the table will list a
StmyFuelEngFraction for regulatory class 30 and another for regulatory class 40. These fractions
sum to one for each combination of source type, model year and fuel type.

For example, while the stmyFraction indicates that the MOVES defaults assign zero fraction of
model year 2010 passenger trucks to the electricity fuel type, the  StmyFuelEngFraction indicates
a default (hypothetical) regulatory class distribution  if these vehicles existed. In this case,
MOVES would model them all as belonging to regulatory class 30. The stmyFraction is
particularly important because users can edit fuel type distributions using the Alternative Vehicle
Fuel and Technology (AVFT) importer. For instance, a user can create a future scenario in which
there is a high penetration of electric passenger trucks. The StmyFuelEngFraction allows
MOVES to assign vehicles to regulatory class without requiring this input from the user. This
means an allowed StmyFuelEngFraction must never  be zero.

As an example, Figure 6-1 shows the national default fuel type fractions for all light-duty
vehicles among the different MOVES fuel types. As noted in Section 2.4 these fuel type
fractions indicate the fuel capability of the vehicle and not the fuel being used by the vehicle. In
this report's nomenclature, ESS-capable and flexible fuel vehicles are synomous—meaning they
can accept either gasoline or E85  fuel. Although these vehicle are capable of running on E85, the
fuel type distributions do not have any information on how often they actually use E85.
Discussion on fuel usage can be found in the MOVES2014 Fuel Supply Report.5
                                           34

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 Figure 6-1 Default fuel type fractions for light-duty source types in MOVES2014, where being E85-capable
       indicates flexible fuel vehicle populations and all default electric vehicle populations are zero
     1.00-

     0.75-

     0.50 -

     0.25-

     0.00-
     1.00-

     0.75-

     0.50 -

  I 0.25 H
   o
   2
  f-o.oo-
   &1.00-
  1*0.75-
     0.50 -

     0.25-

     0.00-
     1.00-

     0.75-

     0.50 -

     0.25-

     0.00-
                                            Source Types
                                     	Passenger Cars

                                     	Passenger Tracks

                                     	Light Commercial Trucks
                  1975
 2000          2025
Model Year
2050
           6.2.2.     Sample Vehicle Counts

The SampleVehiclePopulation table fractions were developed by EPA using the sample vehicle
counts dataset referenced in Section 3, which primarily joins calendar year 2011 registration data
from R.L. Polk and the 2002 Vehicle Inventory and Use Survey (VIUS) results. The sample
vehicle counts dataset was generated by multiplying the 2011 Polk vehicle populations by the
source type allocations from VIUS.
                                             35

-------
While VIUS provide source type classifications, we relied primarily on the 2011 Polk vehicle
registration dataset to form the basis of the fuel type and regulatory class distributions in the
SampleVehiclePopulation table. We purchased the Polk dataset in April 2012, so it did not have
complete registration records for model year 2012 vehicles, and, therefore, model year 2012
vehicles were omitted from the SVP analysis. The Polk data was provided with the following
fields: vehicle type (cars or trucks), fuel type, gross vehicle weight rating (GVWR) for trucks,
household vehicle counts, and work vehicle counts. We combined the household and work
vehicle  counts. The MOVES distinction between personal and commercial travel for light-duty
trucks comes from VIUS.

The Polk records by FHWA truck weight class were grouped into MOVES GVWR-based
regulatory classes, as shown in Table 6-2 below. As stated above, all passenger cars were
assigned to regClassID 20. The mapping of weight class to regulatory class is straightforward
with one notable  exception: delineating trucks weighing more or less than  8,500 Ibs.
Table 6-2 Initial mapping from FHWA truck classes to MOVES regulatory classes
Vehicle Category
Trucks
Trucks
Trucks
Trucks
Trucks
Trucks
Trucks
Trucks
Trucks
Trucks
Cars
FHWA Truck Weight Class
1
2a
2b
3
4
5
6
7
8a
8b

Weight Range (Ibs)
< 6,000
6,001-8,500
8,501 - 10,000
10,001 - 14,000
14,001 - 16,000
16,001 - 19,500
19,501-26,000
26,001-33,000
33,001-60,000
> 60,001

regClassID
30
30*
41*
41
42
42*
46
46
47
47
20
       *After the Polk data has been sorted into source types (described later in this section), some regulatory
       classes were merged or divided. Any regulatory class 41 vehicles in light-duty truck source types were
       reclassified into the new regulatory class 40 (see explanation in Section 2.3), any regulatory class 30
       vehicles in single unit truck source types were reclassified into regulatory class 41, and any regulatory class
       42 vehicles in combination truck source types were reclassified into regulatory class 46.

Since the Polk dataset did not distinguish between Class 2a (6,001-8,500 Ibs) and Class 2b
(8,501-10,000 Ibs) trucks, but MOVES regulatory classes 30, 40, and 41 all fall within Class 2,
we needed a secondary data source to allocate the Polk gasoline and diesel trucks between Class
2a and 2b. We derived information from an Oak Ridge National Laboratory (ORNL) paper30
summarized in Table 6-3 to allocate the Polk Class 2 gasoline and diesel trucks into the
regulatory classes. Class 2a trucks fall in regulatory class 30 and Class 2b trucks fall in either
regulatory class  40 or 41.

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                    Table 6-3 Fractions used to distribute Class 2a and 2b trucks
Fuel Type
Gasoline
Diesel
Truck Class
2a
0.975
0.025
2b
0.760
0.240
Additionally, the Polk dataset includes a variety of fuels, some that are included in MOVES and
others that are not. Only the Polk gasoline and diesel vehicles were included in our analysis; all
other alternative fuel vehicles were omitted. While MOVES2014 does model light-duty E-85 and
electric vehicles, and compressed natural gas (CNG) transit buses, these relative penetrations of
alternative fuel vehicles have been developed from secondary data sources rather than Polk
because Polk excludes some government fleets and retrofit vehicles that could potentially be
large contributors to these alternative fuel vehicle populations. Instead we used flexible fuel
vehicle sales data reported for EPA certification, and dedicated CNG bus populations from the
National Transit Database. The Table 6-4 illustrates how Polk fuels were mapped to MOVES
fuel types, and which Polk fuels were not used in MOVES.

The "N/A" mapping shown in Table 6-4 led us to discard 0.22 percent, roughly 530,000 vehicles
(mostly dedicated or aftermarket alternative fuel vehicles), of Folk's 2011 national fleet in
developing the default fuel type fractions. However, because the MOVES national population is
derived top-down from FHWA registration data, as outlined in Section 5.1, the total population
is not affected. We considered the Polk vehicle estimates to be a sufficient sample for the fuel
type and regulatory class distributions in the SampleVehiclePopulation table.
                                           37

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       Table 6-4 A list of fuels from the Polk dataset used to develop MOVES fuel type distributions
Polk Fuel Type
Unknown
Undefined
Both Gas and Electric
Gas
Gas/Elec
Gasoline
Diesel
Natural Gas
Compressed Natural Gas
Natr.Gas
Propane
Flexible (Gasoline/Ethanol)
Flexible
Electric
Cnvrtble
Conversion
Methanol
Ethanol
Convertible
MOVES fuelTypelD
N/A
N/A
1
1
1
1
2
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
MOVES Fuel Type


Gasoline
Gasoline
Gasoline
Gasoline
Diesel












Next we transformed the VIUS dataset into MOVES format. The VIUS vehicle data was first
assigned to MOVES source types using the constraints in Table 6-1 and then to MOVES
regulatory classes using the mapping described in Table 6-2, including the allocation between
Class 2a and 2b trucks from the ORNL study in Table 6-3.  Similar to our fuel type mapping of
the Polk dataset, we chose to omit alternative fuel vehicles, as summarized below in Table 6-5.

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                Table 6-5 Mapping of VIUS2002 fuel types to MOVES2014 fuel types
VIUS Fuel Type
Gasoline
Diesel
Natural gas
Propane
Alcohol fuels
Electricity
Gasoline and natural gas
Gasoline and propane
Gasoline and alcohol fuels
Gasoline and electricity
Diesel and natural gas
Diesel and propane
Diesel and alchol fuels
Diesel and electricity
Not reported
Not applicable
VIUS Fuel Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
MOVES fuelTypelD
1
2
N/A
N/A
N/A
N/A
1
1
1
1
2
2
2
2
N/A
N/A
MOVES Fuel Type
Gasoline
Diesel




Gasoline
Gasoline
Gasoline
Gasoline
Diesel
Diesel
Diesel
Diesel


This process yielded VIUS data by MOVES source type, model year, regulatory class, and fuel
type. The VIUS source type distributions were calculated in a similar fashion to the
SampleVehiclePopulation fractions discussed above for each regulatory class-fuel type-model
year combination. Stated formally, for any given model year i, regulatory class j,  and fuel type
k, the source type population fraction / for a specified source type / will be the number of VIUS
trucks N in that source type divided by the sum of VIUS trucks across the set of all source types
L. The source type population fraction is summarized in Equation 3:
                                               Ł—>lFl     '
                                                                            Equation 3
The VIUS data in our analysis spanned model year 1986 to 2002. The 2002 source type
distribution has been used for all distributions after MY 2002 and the 1986 distribution for all
prior to MY 1986.

From there the source type distributions from VIUS were multiplied by the Polk vehicle
populations to generate the sample vehicle counts by source type, as shown schematically in
Figure 6-2. Expressed in Equation 4, the sample vehicle counts are,
                                     = P(Polk\jikil •
Equation 4
                                          39

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where N is the number of vehicles used to generated the SampleVehiclePopulation table, P is the
2011 Polk vehicle populations, and / is the source type distributions from VIUS.

 Figure 6-2 A schematic overview of how the 2011 Polk dataset and VIUS 2002 were joined to create EPA's
 sample vehicle counts for MOVES2014. Note that data on buses, motorcycles, and motor homes was pulled
                                    from other sources.
                                                                        VIUS 2002
    Polk 2011
                                         Interim VIUS
                         Interim Polk
                                             sourceTypelD
                                             modelYearlD
                        modelYearlD
                        fuelTypelD
                        regClassID
                        totalCounts
                                             fuelTypelD
                                             regClassID
                                             sourceTypeFractions
Household Units
     INTERCITY BUSES
                                      source jype
                                      modelYearlD
                                      fuelTypelD
                                      regClassID
                                      vehicleCounts
SAMPLEJD
AXLE_COKFIO
TRIP_PRIMARY
OPCLASS
FUEL
VTUS_GVW
ADM_MODELYEAR
ADM_GVW
TAB TRUCKS
                                                                MOTORCYCLES
          J

SCHOOL BUSES
j
                                                                   MOTOR HOMES
                              Sample Vehicle Counts

These sample vehicle counts by source type were then utilized to calculate the SVP fractions,
stmyFraction and stmyFuelEngFraction, as defined above. Due to a small sample size of vehicles
30 years old and older in both the Polk and VIUS datasets, MOVES2010b SVP fractions were
used for MY 1981 and earlier, which were generated following roughly the same procedure
outlined above but using a 1999 Polk vehicle registration dataset joined with VIUS. These
MOVES2010b SVP fractions for MY 1960-1981 are described in Section 18 (Appendix B).
MOVES2014 assumes no changes to fuel type distributions after model year 2011 except for
flexible-fuel (E85-capable) vehicles, which are assumed to displace gasoline vehicles based on
sales estimates as described below. MOVES2014 estimates  any other population growth by
source type, as described earlier in Section  5.2 rather than growth for specific fuel types within a
source type.

All Class 2b and 3 trucks were initially assigned to regulatory class 41 until vehicles were sorted
into source types. Once the sample vehicle  counts were available by source type, any light-duty
trucks (sourceTypelD 31 or 32) in the original LHD regulatory class less than 14,000 Ibs
(regClassID 41) were reclassified in the new LHD regulatory class less than 10,000 Ibs
(regClassID 40), whereas any heavy-duty vehicles (sourceTypelD 41 and above) remained in
regClassID 41. Similarly, any  single unit trucks (sourceTypelD 52 and 53) in the LDT regulatory
class (regClassID 30) were reclassified in regClassID 41 as  heavy-duty vehicles. We also moved
any regClassID 42 vehicles in combination truck source types to regClassID 46 because tractor-
trailers must be either Class 7 or 8 trucks. This ensures a clean break between light- and heavy-
                                           40

-------
duty emission results and that the emission calculations use the appropriate fixedMassFactor
when calculating vehicle-specific power (VSP) for light-duty vehicles and scaled tractive power
(STP) for heavy-duty vehicles.

As noted above, the initial sample vehicle counts dataset did not contain motorcycles, buses, or
motor homes, so information on these source types was appended. Motor homes—even though
they are considered single unit vocational vehicles—cannot be identified in VIUS. In the
subsections below, we have provided more detailed descriptions by source type.

          6.2.2.1.    Motorcycles
The representation of motorcycles in the  SampleVehiclePopulation table is straightforward. All
motorcycles fall into the motorcycle regulatory class (regClassID 10) and must be fueled by
gasoline. We acknowledge that some alternative fuel motorcycles have been prototyped and may
even be in small production, but they account for a negligible fraction of total US motorcycle
sales and cannot be modeled in MOVES2014.

          6.2.2.2.    Passenger Cars
Any passenger car is  considered to be in the light-duty vehicle regulatory class (regClassID 20).
Cars were included in the Polk dataset purchased in 2012, and EPA's subsequent sample vehicle
counts dataset, which provided the split between gasoline and diesel cars in the
SampleVehiclePopulation table. Flexible fuel (ESS-capable) cars were also included in the SVP
fuel type distributions but added after the sample vehicle counts analysis. We assume that a
flexible fuel vehicle would directly displace its gasoline counterpart. For model years 2011 and
earlier, we used manufacturer reported sales to EPA in order to calculate the fraction of sales of
flexible fuel cars among sales of all gasoline and flexible fuel cars and added those penetrations
as the fraction of E85 (fuelTypelD 5) vehicles and deducted them from the gasoline  cars in the
Polk dataset.

Similarly, for model years 2012 and later, we used Department of Energy car sales projections
from AEO2014's table labeled "Light-Duty Vehicle Sales by Technology Type" to derive
flexible fuel vehicle penetrations and applied them to the SVP fractions for regulatory class 20.31
All other alternative fueled cars were determined to have insignificant market shares now and
into the future.

While MOVES can model electric vehicles (fuelTypelD 9), the current market share of electric
cars is sufficiently  small that we have set the default electric car population to zero. Users can
model an electric vehicle population by using the AVFT tool to redistribute market share.
Electric vehicles do not have any tailpipe emissions, but MOVES2014 has electric vehicle rates
for energy consumption, brakewear, and tirewear (electric vehicle brake and  tirewear rates are
copied from gasoline vehicles). Please consult the MOVES2014 documentation on greenhouse
gases32 and brake and tirewear33, respectively, for more information on the development of the
energy and emission rates themselves.

          6.2.2.3.    Light-Duty Trucks
Since passenger and light commercial trucks are defined as light-duty vehicles, they  are
constrained to regulatory class 30 and 40. Within the sample vehicle counts,  GVWR Class 1 and

-------
2a trucks were classified as regulatory class 30 and Class 2b trucks with two axles and four tires
were classified as regulatory class 40. Both light-duty truck source types are divided between
gasoline and diesel using the underlying splits in the sample vehicle counts data. Passenger
trucks and light commercial trucks have similar but distinct distributions.  Similar to cars, a
penetration of flexible fuel (E-85-capable) light-duty trucks was calculated using EPA
certification sales for historic years (MY 2011 and earlier) and AEO light truck projections for
future years (MY 2012 and later) from the AEO2014 table on light-duty vehicle sales.31 The
flexible fuel vehicle penetration was applied to regClassID 30 for both E-85 (fuelTypelD 5)
passenger and light commercial trucks and then deducted from their gasoline counterparts in the
same regulatory class.

          6.2.2.4.   Buses
In line with the US Energy Information Administration (EIA) assumptions, all intercity buses in
MOVES are powered by diesel fuel.34 The following non-school bus regulatory class distribution
for intercity buses was applied to all model years based on 2011 FHWA data, as shown in Table
6-6.35

        Table 6-6 Regulatory class fractions of school and non-school buses using 2011 FHWA data
Vehicle Type
Non-School Buses
School Buses
MOVES regClassID
41
0.1856
0.0106
42
0.0200
0.0070
46
0.1214
0.9371
47
0.6730
0.0453
Total
1
1
The National Transit Database (NTD) Revenue Vehicle Inventory (Form 408) closely tracks the
number of motor buses (MB) by fuel type each year and those statistics are used to develop the
MOVES fuel type distributions for transit buses. The mapping from NTD fuel types to MOVES
fuel types is summarized in Table 6-7.
                                           42

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             Table 6-7 Mapping National Transit Database fuel types to MOVES fuel types
NTD code
BD
BF
CN
DF
DU
EB
EP
ET
GA
GR
HD
HG
KE
LN
LP
MT
OR
NTD description
Bio-diesel
Bunker fuel
Compressed natural gas
Diesel fuel
Dual fuel
Electric battery
Electric propulsion
Ethanol
Gasoline
Grain additive
Hybrid diesel
Hybrid gasoline
Kerosene
Liquefied natural gas
Liquefied petroleum gas
Methanol
Other
fuelTypelD
2
N/A
o
J
2
1
N/A
N/A
N/A
1
N/A
2
1
N/A
3
N/A
N/A
N/A
MOVES Fuel
Description
diesel

CNG
diesel
diesel



gasoline

diesel
gasoline

CNG



While some other MOVES fuel types are included in the NTD, the transit bus fuel type
distributions were allocated between diesel, CNG, and gasoline only. Together these three fuel
types account for more than 99 percent of all transit buses in 2011, so no other alternative fuels
are allowed within the transit bus source type due to negligible market shares.

Biodiesel does not appear in the SampleVehiclePopulation table—in MOVES it is considered a
fuel subtype rather than a fuel type—so biodiesel buses were added to the diesel buses from the
NTD. Liquefied natural gas (LNG) comprises less than ten percent of all natural gas transit buses
and only about 1.5 percent of the whole transit bus fleet in 2011. Without any readily available
emission rate data on LNG buses, we grouped all natural gas fueled transit buses together. This
means we effectively model LNG buses as if they were powered by CNG. Due to limited data,
we assume that gasoline has a one-percent market share prior to model year 2000 and that diesel
has a 99 percent market share prior to MY 1990. All other market shares of transit bus fuel types
are derived using the NTD, as shown in Table 6-8.  MOVES modelers can adjust these
distributions between the fuel types using the AVFT tool.
                                           43

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           Table 6-8 Fuel type market shares by model year for transit buses in MOVES2014
Model Year
1982-1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011+
MOVES Fuel Type
Gasoline
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
1.00%
0.85%
0.88%
0.91%
0.94%
0.89%
1.05%
1.18%
1.29%
1.61%
1.89%
2.14%
2.46%
Diesel
99.00%
98.30%
97.20%
94.40%
91.40%
90.50%
83.70%
89.20%
81.60%
84.10%
87.70%
91.57%
90.51%
89.09%
88.06%
86.85%
85.61%
84.73%
83.99%
82.91%
82.55%
81.96%
81.75%
CNG
0.00%
0.70%
1.80%
4.60%
7.60%
8.50%
15.30%
9.80%
17.40%
14.90%
11.30%
7.58%
8.60%
10.00%
10.99%
12.27%
13.34%
14.09%
14.72%
15.49%
15.56%
15.90%
15.79%
Urban transit buses are regulated separately from other heavy-duty vehicles, under 40 CFR
86.091-2.36 For this reason, CNG and diesel transit buses are each categorized in regulatory class
48. Lacking better data, we used a single regulatory class distribution from a study of diesel and
CNG transit buses, highlighted in the MOVES2014 FID Emissions Rates Report4, for gasoline
transit buses as shown in Table 6-9 below.

              Table 6-9 Regulatory class fractions of gasoline transit buses in MOVES2014
MOVES Source Type & Fuel Type
Gasoline Transit Buses
MOVES regClassID
42
0.2683
46
0.0976
47
0.6341
Total
1
The MOVES2014 school bus fuel type distribution is based on MOBILE6 estimates, originally
calculated from 1996 and 1997 Polk bus registration data, for model years 1982-1996 are
summarized in Table 6-10. The Union of Concerned Scientists estimates that roughly one
percent of school buses run on non-diesel fuels, so we have assumed that one percent of school
                                           44

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buses are gasoline fueled in MY 1997 and later.37 The school bus regulatory class distribution
was also derived from the 2011 FHWA data in Table 6-6.

           Table 6-10 Fuel type market shares by model year for school buses in MOVES2014
Model Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997+
MOVES Fuel Type
Gasoline
67.40%
67.62%
61.55%
48.45%
32.67%
26.55%
24.98%
22.90%
12.40%
8.95%
1.00%
12.05%
14.75%
11.43%
4.15%
1.00%
Diesel
32.60%
32.38%
38.45%
51.55%
67.33%
73.45%
75.02%
77.10%
87.60%
91.05%
99.00%
87.95%
85.25%
88.57%
95.85%
99.00%
          6.2.2.5.    Single Unit Trucks
The fuel type and regulatory class distributions for the single unit trucks are calculated directly
from the EPA's sample vehicle counts datasets, except motor homes. The single unit source
types are split between gasoline and diesel only.  Single unit vehicle are distributed among the
heavy-duty regulatory classes (regClassIDs 41, 42, 46, and 47) based on the underlying sample
vehicle data. Motor home was not included as a VIUS body type response, so their fuel type and
regulatory class distributions have been developed through supplementary data sources. The fuel
type distribution for motor homes is unchanged from MOVES2010b (see  Table 6-11), originally
based on interpolating information from the Recreation Vehicle Industry Association (RVIA) on
fuel type market shares.38
                                           45

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Table
6-11 Fuel type market shares for motor homes in MOVES2014
Model Year
1982-1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010+
Percent of Diesel
15%
18%
21%
23%
26%
29%
32%
34%
37%
40%
41%
43%
44%
46%
47%
49%
50%
50%
Percent of Gasoline
85%
82%
79%
77%
74%
71%
68%
66%
63%
60%
59%
57%
56%
54%
53%
51%
50%
50%

The motor home regulatory class distribution, shown below in Table 6-12, is used across all
model years based on the same 2011 FHWA dataset35 referenced above for school and non-
school buses.

             Table 6-12 Regulatory class fractions of motor homes using 2011 FHWA data
MOVES Source Type
Motor Homes
MOVES regClassID
41
0.2697
42
0.3940
46
0.2976
47
0.0387
Total
1
          6.2.2.6.
Combination Trucks
Combination trucks consist mostly of Class 8 trucks in the MOVES HHD regulatory class
(regClassID 47) but also contain some Class 7 trucks in the MHD regulatory class (regClassID
46), predominantly in short-haul. Similarly, almost all combination trucks are diesel fueled.
MOVES does not model gasoline long-haul combination trucks. Even for the short-haul source
type, gasoline combination trucks are being phased out rapidly. After model year 2005,
MOVES2014 assumes no gasoline combination trucks sales. These fuel type and regulatory class
trends come out of the sample vehicle counts dataset. There has been growing interest in natural
gas for freight transportation but currently this remains largely in the planning stages. There has
not been sufficient testing of these trucks to develop MOVES emission rates yet. We will
consider adding natural gas combination trucks as they become more prevalent and their
emissions are more thoroughly tested.

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7. Vehicle Characteristics that Vary by Age

Age is an important factor in calculating vehicle emission inventories, identifying high emitters,
and characterizing travel behavior. MOVES employs a number of different age dependent
factors, including deterioration of engine and emission after-treatment technology due to
tampering and malmaintenance, vehicle scrappage and fleet turnover, and mileage accumulation
over the lifetime of the vehicle. Deterioration effects are detailed in the MOVES2014 reports on
the development of light-duty and heavy-duty emission rates.3'4 In this section, there is
discussion of vehicle age distributions, survival rates, and relative mileage accumulation rates by
source type.

   7.1.       Age Distributions
A vehicle's age is simply the difference between its model year and the year of analysis. Age
distributions in MOVES vary by source type and range from zero to 30+ years, so that all
vehicles 30 years and older are modeled together. As such, an age distribution is comprised of 31
fractions, where each fraction represents the number of vehicles present at a certain age divided
by the vehicle population for all ages, as summarized later in this section in Equation 9. Since
sales and scrappage rates are not constant, these distributions vary by calendar year. The age
distribution for each source type is stored in the SourceTypeAgeDistribution table, and fractions
from each source type's age distribution sum to one across a calendar year. MOVES age
distributions were compiled from a variety of data sources, which are discussed below. Age
distributions for the 2011 base year are summarized in Table 7-1; all other years are available in
the MOVES2014 default database  SourceTypeAgeDistribution table.

          7.1.1.     Age Distributions from Registration Data
Ideally all historic age distributions could be derived from registration data sources for each
analysis year available in MOVES. However, acquiring such data is prohibitively costly, so
MOVES2014 only contains registration-based  age distributions for two analysis years: 1990 and
2011. The following sections detail how these data were analyzed and used in MOVES2014.

          7.1.1.1.    1990 Age Distributions
MOVES2014 age distributions for calendar year 1990 have not been updated since the last
model release. Please refer to Section 19 (Appendix C) for more information on the 1990 age
distributions.

          7.7.7.2.    2011 Age Distributions
The 2011 age distributions for cars and trucks were derived from the sample vehicle counts
dataset, as discussed earlier in Section 6.2.2.  This sample vehicle data includes eight of the
thirteen source types: passenger cars (21), passenger trucks (31), light commercial trucks (32),
refuse trucks (51), short-haul single unit trucks (52),  long-haul single unit trucks (53), short-haul
combination trucks (61), and long-haul combination  trucks (62). We were able to develop  zero to
30+ year age distributions in 2011  for the eight source types mentioned.

For the source types that were not included in the sample vehicle data—specifically motorcycles,
motor homes, and buses—we calculated the 2011 age distributions by running MOVES2010b
                                           47

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with the latest sales data available. That is, MOVES2010b used 1999 populations, sales, and
scrappage forecasts to project future populations by model year, which we then used to compute
age distributions. This approach kept the MOVES2010b base populations and scrappage rates
but substituted in MY 1999-2011 sales. We pulled sales for motorcycles (11) from the
Motorcycle Industry Council's Statistical Annual report24, for transit buses (42) from internal
EPA estimates based on manufacturer reporting, and for school buses (43) from the School Bus
Fleet Fact Bookn.  Since 2011 age distributions were calculated independently, intercity bus (41)
and motor home (54) sales data were based on slightly different assumptions. Both of these
source types used an average of Ward's Class 3-8 truck sales in Oak Ridge's Transportation
Energy Data Book22, transformed into MOVES source types using the allocation of sample
vehicle counts described in Section 6. For more information on these data sources, please revisit
Section 3.

Figure 7-1 and Table 7-1 show the fraction of vehicles by age (0-30+ years) and source type for
calendar year 2011. These 2011 age distributions became the basis for all the forecast age
distributions in Section 7.1.2.2 and all backcast age distributions in Section 7.1.2.3.

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           Figure 7-1 2011 age distributions by source type in MOVES2014
0.00-
                                                        Source Type
                                                        — Combination Long-haul Truck
                                                        ~~ Combination Short-haul Truck
                                                        ^ Intercity Bus
                                                          " Light Commercial Truck
                                                          • Motor Home
                                                          ' Motorcycle
                                                            Passenger Car
                                                          • Passenger Truck
                                                        ~~ Refuse Truck
                                                        — School Bus
                                                            Single Unit Long-haul Truck
                                                            Single Unit Short-haul Truck
                                                            Transit Bus
                    10
30
                         Ase
                                       49

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Table 7-1 2011 age fractions by MOVES source type
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30+
11
0.0585
0.0565
0.0614
0.1088
0.0968
0.0917
0.0803
0.0682
0.0583
0.0514
0.0436
0.0348
0.0263
0.0224
0.0215
0.0188
0.0142
0.0163
0.0133
0.0111
0.0088
0.0071
0.0053
0.0045
0.0044
0.0037
0.0031
0.0028
0.002
0.0016
0.0025
21
0.042
0.0472
0.043
0.0545
0.0597
0.0562
0.0562
0.0526
0.0551
0.055
0.0534
0.0575
0.05
0.0441
0.042
0.0354
0.0367
0.029
0.0249
0.0209
0.0178
0.015
0.0124
0.0097
0.008
0.0065
0.0053
0.0042
0.0025
0.0017
0.0016
31
0.0496
0.044
0.0335
0.0587
0.0626
0.0644
0.0677
0.0686
0.0638
0.0624
0.0562
0.0545
0.0504
0.0424
0.0372
0.0284
0.0274
0.025
0.0175
0.0142
0.012
0.0106
0.0108
0.0092
0.007
0.0071
0.0049
0.004
0.0024
0.0019
0.0016
32
0.0557
0.0482
0.0372
0.0668
0.0703
0.0743
0.077
0.0781
0.0724
0.0702
0.0647
0.055
0.0433
0.0273
0.0305
0.0203
0.0219
0.0137
0.0136
0.0073
0.007
0.0075
0.008
0.0073
0.0057
0.0053
0.0037
0.0031
0.0019
0.0015
0.0012
41
0.0477
0.0421
0.0353
0.0458
0.0601
0.0617
0.0638
0.062
0.0574
0.0538
0.0517
0.0492
0.0478
0.0362
0.0295
0.0244
0.0317
0.0244
0.0201
0.0148
0.0168
0.0188
0.0187
0.0174
0.018
0.0151
0.0132
0.0104
0.0041
0.0035
0.0047
42
0.0628
0.0385
0.0393
0.0555
0.0539
0.0389
0.0607
0.0498
0.0488
0.0495
0.057
0.0385
0.0374
0.0439
0.0401
0.0369
0.0303
0.0264
0.0219
0.019
0.0192
0.0281
0.0214
0.0168
0.0156
0.0131
0.0113
0.0088
0.0083
0.0045
0.0039
43
0.0368
0.0403
0.048
0.0529
0.0548
0.0644
0.0574
0.0565
0.0487
0.0511
0.0467
0.0508
0.047
0.0371
0.0345
0.0298
0.038
0.0184
0.0219
0.0177
0.0226
0.0255
0.0145
0.0173
0.0175
0.0153
0.0131
0.0101
0.0037
0.0027
0.0047
51
0.0334
0.0265
0.0351
0.0273
0.0956
0.0718
0.0677
0.0407
0.04
0.029
0.0357
0.0488
0.0702
0.0645
0.0312
0.0406
0.0521
0.0367
0.0167
0.0149
0.0233
0.0166
0.0256
0.0147
0.0132
0.0068
0.0068
0.0056
0.0025
0.0029
0.0035
52
0.035
0.0216
0.0231
0.0479
0.0629
0.0666
0.0577
0.0506
0.0438
0.0393
0.0427
0.0697
0.0591
0.0334
0.0459
0.0308
0.0423
0.0323
0.0225
0.0179
0.0162
0.022
0.0211
0.0188
0.0171
0.0154
0.0132
0.0113
0.0067
0.0067
0.0066
53
0.0237
0.015
0.0176
0.031
0.0544
0.0486
0.045
0.0333
0.0284
0.0238
0.059
0.1457
0.1267
0.0213
0.0175
0.0198
0.0338
0.0279
0.0777
0.0137
0.0213
0.0132
0.0535
0.017
0.0061
0.0064
0.0055
0.0048
0.0028
0.0028
0.0027
54
0.046
0.0406
0.034
0.0442
0.0579
0.0594
0.0615
0.0597
0.0553
0.0518
0.0498
0.0474
0.0461
0.0271
0.0417
0.0258
0.0305
0.0291
0.02
0.0175
0.013
0.0171
0.0221
0.0196
0.0191
0.0141
0.015
0.0152
0.0098
0.0057
0.0039
61
0.0219
0.0164
0.0213
0.0192
0.0629
0.0468
0.0455
0.0288
0.0256
0.0199
0.0391
0.0535
0.0482
0.049
0.0398
0.0556
0.0628
0.0524
0.038
0.0292
0.0272
0.0337
0.0343
0.0317
0.025
0.0174
0.0177
0.0145
0.0062
0.0073
0.0089
62
0.0478
0.0378
0.0501
0.0392
0.1371
0.1028
0.0971
0.0584
0.057
0.0415
0.0482
0.0766
0.0572
0.0381
0.0215
0.0234
0.0209
0.0127
0.0086
0.0052
0.004
0.0031
0.0031
0.0019
0.0032
0.0009
0.0009
0.0007
0.0003
0.0004
0.0004
                     50

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          7.1.2.     Forecasting and Backcasting Age Distributions
Since purchasing registration data for all calendar years is prohibitively costly for historic years
and impossible for future years, an algorithm was developed to forecast and backcast age
distributions from the 2011 age distribution described above for all other calendar years in the
model. In prior versions of MOVES, these age distributions were calculated during the model
run using sales estimates and assuming a constant survival rate. In MOVES2014, age
distributions for national level runs were pre-calculated using updated sales estimates and
assuming a dynamic survival rate. However, while sales data for historic years are well known
and projections for future years are common in economic modeling, historic and projected
vehicle survival are not well studied. For MOVES2014,  a generic survival rate was scaled up or
down for each calendar year based on our assumptions of sales and changes in total populations.
The following three sections detail the derivation of the generic survival rate and the algorithms
used to forecast and backcast age distributions using an adjusted survival rate in each year.

          7.1.2.1.    Generic Survival Rates
The survival rate describes the fraction of vehicles of a given source type and age that remain on
the road from one year to the next. Although this rate changes from year to year, a single  generic
rate was  calculated from available data. While the use of this generic rate is described in the next
couple of sections, its derivation is specified here.

Survival  rates for motorcycles were calculated based on  a smoothed curve of retail sales and
2008 registration data as described in a study conducted  for the EPA.39 Survival rates for
passenger cars, passenger trucks and light commercial trucks came from NHTSA's survivability
Table 3 and Table 4.40 These survival rates are based on a detailed analysis of Polk vehicle
registration data from 1977 to 2002. We modified these rates to consistent with the MOVES
format using the following guidelines:

    •   NHTSA rates for light trucks were used for both  the MOVES passenger truck and light
       commercial truck source types.
    •   MOVES calculates emissions for vehicles up to age 30 (with all older vehicles lumped
       into the age 30 category), but NHSTA car survival rates were available only to age 25.
       Therefore, we extrapolated car rates to age 30 using the estimated survival rate equation
       in Section 3.1 of the NHTSA report. When converted to MOVES format, this caused a
       striking discontinuity at age 26 which we removed by interpolating between ages  25 and
       27.
    •   According to the NHTSA methodology, NHTSA age 1 corresponds to MOVES agelD 2,
       so the survival fractions were shifted accordingly.
    •   Because MOVES requires survival rates  for agelDs < 2, these values were linearly
       interpolated with the assumption that the survival rate prior to agelD 0 is 1. Effectively,
       this results in a near constant survival rate until agelD 3 for light-duty vehicles and until
       agelD 4 for heavy-duty vehicles.
    •   NHTSA defines survival rate as the ratio of the number of vehicles remaining in the fleet
       at a given year as compared to a base year. However, MOVES defines the survival rate as
       the ratio of vehicles remaining from one year to the next, so we transformed the NHTSA
       rates accordingly.

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Because MOVES agelD 30 is intended to represent all vehicles 30 years old and greater, this age
category can grow quite large as our age distribution algorithm eventually transfers all vehicles
to this age group. To assure that the population of very old vehicles does not grow excessively,
the generic survival  rate for agelD 30 was set to 0.3. The actual survival rate of these age 30+
vehicles is unknown.

Quantitatively, the following piecewise formulas were used to derive the MOVES survival rates.
In them, sa represents the MOVES survival rate at age a, and oa represents the NHTSA survival
rate at age a. When this generic survival rate is discussed below, the shorthand notation 50 will
represent a one-dimensional array of sa values at each permissible age a as described in
Equations 5 through 8 below:

                                              1-0-7
                Age 0:               s0 = 1	                         Equation 5
                 A    1                        2(1-oz)                       ^    .    ,
                Age 1:              s-i = 1	—                       Equation 6
                                                   -
                Age 2-29:           sa = s2 29 =	                       Equation 7
                                                 °a-2
                Age 30:                  s30 = 0.3                            Equation 8
With limited data available on heavy-duty vehicle scrappage, survivability for all other source
types came from the Transportation Energy Data Book. We used the heavy-duty vehicle survival
rates for model year 1980 (TEDB32, Table  3.14). The 1990 model year rates were not used
because they were significantly higher than rates for the other model years in the analysis (i.e. 45
percent survival rate for 30 year-old trucks), and seemed unrealistically high. While limited data
exists to confirm this judgment, a snapshot of 5-year survival rates can be derived from VIUS
1992 and 1997 results for comparison. According to VIUS, the average survival rate for model
years 1988-1991 between the 1992 and 1997 surveys was 88 percent. The comparable survival
rate for 1990 model year heavy-duty vehicles from TEDB was 96 percent, while the rate for
1980 model year trucks was 91 percent. This comparison lends credence to the decision that the
1980 model year survival rates are more in line with available data. TEDB does not have
separate survival rates for medium-duty vehicles, so it was necessary to apply the heavy-duty
rates uniformly across the bus, single unit truck, and combination truck categories. The TEDB
survival rates were transformed into MOVES format in the same way as the NHTSA rates,
including setting age 30+ survival rates to 0.3 for all source types.

The resulting survival rates are listed in the default database's SourceTypeAge table, shown
below in Table 7-2. Please note that since MOVES2014 does not calculate age distributions
during a run, these survival rates are  not actively used by MOVES. However, they were used in

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the development of the national age distributions stored in the SourceTypeAgeDistribution table,
and remain in the default database for reference.
                      Table 7-2 MOVES survival rate by age and HPMS class
Age
0
1
2
o
J
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Motorcycles
1.000
0.979
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.940
0.300
Light-Duty Vehicles
Passenger
Cars
0.997
0.997
0.997
0.993
0.990
0.986
0.981
0.976
0.971
0.965
0.959
0.953
0.912
0.854
0.832
0.813
0.799
0.787
0.779
0.772
0.767
0.763
0.760
0.757
0.757
0.754
0.754
0.567
0.752
0.752
0.300
Passenger Trucks
Light Comm. Trucks
0.991
0.991
0.991
0.986
0.981
0.976
0.970
0.964
0.958
0.952
0.946
0.940
0.935
0.929
0.913
0.908
0.903
0.898
0.894
0.891
0.888
0.885
0.883
0.880
0.879
0.877
0.875
0.875
0.873
0.872
0.300
Buses
1.000
1.000
1.000
1.000
0.990
0.980
0.980
0.970
0.970
0.970
0.960
0.960
0.950
0.950
0.950
0.940
0.940
0.930
0.930
0.920
0.920
0.920
0.910
0.910
0.910
0.900
0.900
0.900
0.890
0.890
0.300
Single Unit
Trucks
1.000
1.000
1.000
1.000
0.990
0.980
0.980
0.970
0.970
0.970
0.960
0.960
0.950
0.950
0.950
0.940
0.940
0.930
0.930
0.920
0.920
0.920
0.910
0.910
0.910
0.900
0.900
0.900
0.890
0.890
0.300
Combination
Trucks
1.000
1.000
1.000
1.000
0.990
0.980
0.980
0.970
0.970
0.970
0.960
0.960
0.950
0.950
0.950
0.940
0.940
0.930
0.930
0.920
0.920
0.920
0.910
0.910
0.910
0.900
0.900
0.900
0.890
0.890
0.300
          7.7.2.2.     2012-2050 Age Distributions
The 2012-2050 age distributions were derived from the 2011 age distribution described above
using population, survival, and sales projections. Age distributions are easily calculated from
population counts,  if the populations are known by age:
                                      f   ~-
                                      lay — n
Equation 9
Here in Equation 9, fay is the age fraction to be calculated, pa is the population of vehicles at
age a, and Py is the total population in calendar year y. In this section, arrow notation will be
used if the operations are to be performed at the individual age level. For example, fy would be
                                           53

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used to represent all age fractions in calendar year y.  Another example is Py; it represents an
array of pa values at each permissible age in calendar year y. In contrast, Py represents the total
population in year y.

Intuitively, projecting an age distribution forward one year involves removing the vehicles
scrapped in the base year and adding the new vehicles sold in the next year, as shown in
Equation 10:

                               Py+i = P^ - P^ + Ny+i                        Equation 10

where Py+1 is the population (known at each age) of the next year, Py is the population in the
base year, Ry is the population of vehicles removed in the in the base year, and JVy+1 is new
vehicles sold in the next year. Please note that the final term only includes new vehicles at age 0;
if the equation is evaluated for  any a > 0, the sales term is zero. Equation 10 can be used
algorithmically to forecast a known population distribution as follows:

    1.  Starting with the base population distribution (Py), remove the number of vehicles that
       did not survive (Ry) at each age level.
    2.  Increase the population age index by one (for example, 3 year old vehicles are
       reclassified as 4 year old vehicles).
    3.  Add new vehicle sales (Ny+i) as the age 0 cohort.
    4.  Combine the new age 30 and 31 vehicles into a single age 30 group.
    5.  This results in the next year population distribution (Py+i). If this algorithm is to be
       repeated, Py+1 becomes Py for the next iteration.

Unfortunately, as described in the section above, the only survival information we have is a
single snapshot. Because vehicle populations and new sales change differentially (for example,
the historic populations shown in Section 5.1 level off during the recent recession; at the same
time, sales of most vehicle types plummeted), it is important to adjust the survival curve in
response to changes in population and sales. We did so by defining a scalar adjustment factor ky
that can be algebraically calculated from population and sales estimates. Its use in determining
the population of vehicles removed and its relationship to the generic survival rate 50 is given by
Equation 11. Note that the open circle operator (°) represents entrywise product; that is, each
element in an array is multiplied by the corresponding element in the other one, and it results in
an array with the same number of elements.
                               Ry = ky-(l- S0) ° Py                        Equation 11


Substituting Equation 11 into Equation 10 yields Equation 12:

                            ' = K - ky- (1 - SQ*) ° K + Wy+i                  Equation 12
                                           54

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Since both the value of the scalar adjustment factor and the actual distribution of the next year's
population are unknown, Equation 12 can't be used yet. However, by using an estimate of next
year's total population, it can be transformed into Equation 13:
                     Py+1 = Py - ky ^  ((1 - Sj) ° Pj)  + Wy+1               Equation 13

This was algebraically solved for ky and evaluated for each HPMS category0 using the following
information:

    •  Total populations Py and Py+1 by HPMS category. For analysis year 201 1, this
       information is described source type in Section 5.1 and simply needs to be summed by
       HPMS category for use here. For years 2012+, this information is described in Section
       5.2.
    •  Survival 50 by HPMS category, which is described in Section 7. 1 .2. 1 .
    •  Population distribution Py by HPMS category. For analysis year 201 1, this information
       came from combining the total populations  described in Section 5.1 with the age
       distributions described in Section 7.1.1.2 and summing by HPMS category. For years
       2012+, this comes from Py+1 of the previous year.
    •  New vehicle sales Ny+-L by HPMS category, which are derived from AEO2014. The
       projection of sales was calculated as a percentage of the total population using the vehicle
       category mapping discussed in Section 4.2;  this  is converted to the number of new
       vehicles by multiplying by the HPMS category population.

After determining ky by HPMS category, Equation 12 was used with the following information
to compute the next year's population and then age distribution by source type:

    •  Population distribution Py by source type. For analysis year 201 1, this information came
       from combining the total populations described  in Section 5.1 with the age distributions
       described in Section 7.1.1.2. For years 2012+, this comes from Py+1 of the previous year.
    •  The scalar adjustment factor ky and generic survival rate 50 applied by source type using
       the HPMS to source type mapping described by Table 2-1. Please note that limits were
       placed on the /cy(l — 50) term of Equation  12: the value of this term for each age was
       restricted to being between 0 and 1 .
    •  New vehicle sales Wy+1 determined as a percentage of the total population in AEO2014
       as discussed above; this is converted to the number of new vehicles by multiplying by the
       total source type population.
0 Because vehicle survival rates use the categories of motorcycles, passenger cars, light-duty trucks, buses, single
unit trucks, and combination trucks, these were the categories used for determining the scalar adjustment factor.
Since these are essentially the HPMS categories used by MOVES with the additional subcategories of passenger car
and light-duty trucks, the term "HPMS category" is used here for simplicity.

                                            55

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With all of this information, the population distributions were algorithmically determined for
years 2012-2050. The resulting total source type populations (Py) are stored in the
SourceTypeYear table of the default database. The resulting age distributions are stored in the
SourceTypeAgeDistribution table.

In addition to producing the 2012-2050 default age distributions, a version of this algorithm was
implemented in the Age Distribution Projection Tool for MOVES2014.41 This tool can be used
to project future  local age distributions from user-supplied baseline distributions, provided that
the baseline year is 2011 or later. This requirement ensures that the 2008-2009 recession is fully
visible in the baseline. The differences between the default algorithm described above and the
algorithm used in the tool are as follows:

   •  In the tool,  the generic survival rate for all vehicle types at age 30 is set to one (1.0).
   •  Step 4 was  modified so that in the tool, the new age 30 fraction is set equal to the new
       age 31  fraction. The new age 31 fraction is then discarded.
   •  In the tool,  the age distribution for ages 1-29 is then normalized such that the full
       distribution (ages 0-30) sums to one (1.0).

The first two bullets were implemented to retain the fraction of 30+ year old vehicles in the user-
inputted baseline distribution. This was done because local data frequently indicates a sizeable
fraction in this age bin. Since the default scrappage curve was designed to prevent this bin from
growing too large,  the default algorithm would reduce this fraction in most cases. Therefore, the
age 30+ fraction is not modified and the resulting age distribution in each iteration of the
algorithm is normalized in the final step so that the full distribution sums to one. The sales rates
and scrappage assumptions  are the same in the tool as they are in the national case. In general,
projections made with the tool tend to converge with the national age distributions the farther out
the projection year becomes. This is because local projections of sales and scrappage are
generally unavailable, and the national trends are the best available data.

          7.1.2.3.     1999-2010 Age Distributions

The method used to backcast the 1999-2010 age distributions from the 2011  distribution is very
similar to the forecasting method described above. For backcasting an age distribution one year,
Equation 10 of the previous section can be rewritten as Equation 14:

                               Py_[ = Py* - JVy + Ry_i                       Equation 14

Essentially, this can be thought of as taking the base year's population distribution, removing the
vehicles sold (or added to the population) in that year, and then adding the vehicles that were
removed in the year before. This can be represented algorithmically as follows:

    1.  Starting with the base population distribution (Py), remove the age 0 vehicles (JVy).
   2.  Decrease the population age index by one (for example, 3 year old vehicles are
       reclassified as 2 year old vehicles).
   3.  Add the vehicles that were removed in the previous year (Ry-i).

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   4.  This results in the previous year population distribution (Py_i). If this algorithm is to be
       repeated, Py-\ becomes Py for the next iteration.

However, without detailed historical data for every year, we needed to estimate vehicle
removals. The equation governing vehicle removal discussed the previous section is also
applicable here. Taking careful note of the subscripts, Equations 11 and  14 can be combined into
Equation 15:

                                                         VT               Equation 15

As in the forecasting situation, the value of the scalar adjustment factor and the actual
distribution of the previous year's population are unknown. With a similar strategy of using the
previous year's known total population, Equation 15 can be transformed into Equation 16:

                    Py-i = Py - Ny + ky-j. V  ((1 - 5^) ° P^)             Equationl6


However, this still leaves a Py-\ term, which is unavoidable because the total number of vehicles
removed is dependent on the age distribution of those vehicles. To properly solve Equation 16
for /ty-i and Py_i, a numerical method of approximation could be employed. However, due to
lack of resources, Py was used as a simple approximation of Py-\ on the left hand side of
Equation 16. The following sources were used to determine /ty-i by HPMS category:

   •  Total populations Py and Py-\ by HPMS category. For all historic analysis years, this
       information is described  source type in Section 5.1 and simply needs to be summed by
       HPMS category across all ages for use here.
   •  Survival 50 by HPMS category, which is described in Section 1'. 1.2.1.
   •  Population distribution Py by HPMS category. For analysis year 2011, this information
       came from combining the total populations described in Section 5.1 with the age
       distributions described in Section 7.1.1.2 and summing by HPMS category. For other
       years, this comes from Py-\ of the previous iteration.
   •  New vehicle sales Ny+i data, which was collected by source type from  a variety of
       sources. Each of these was summed by HPMS category. Motorcycles sales comes from
       the Motorcycle Industry Council; sales data for passenger cars, passenger trucks, light
       commercial trucks, refuse trucks, short-haul and long-haul single unit trucks, and short-
       haul and long-haul combination trucks comes from TEDB and VIUS; transit buses
       production estimates are based on EPA certification data; and school bus sales came from
       the School Bus Fleet Fact Book. No sales data were available for intercity buses,  so the
       other bus categories were used as a surrogate. That is, the total transit bus production and
       school bus sales as a percentage of the transit and school  bus populations in each year
       were applied to the intercity bus populations to estimate their sales. Similarly, no sales
       data were available for motor homes, so a sales fraction was estimated by averaging the
       sales of refuse, short-haul, and long-haul single unit trucks as a fraction of their total
       population.
                                           57

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After determining ky_l by HPMS category, Equation 15 was used with the following
information to compute the previous year's age distribution by source type:

   •   Population distribution Py by source type. For analysis year 201 1, this information came
       from combining the total populations described in Section 5.1 with the age distributions
       described in Section 7.1.1.2. For other years, this comes from Py-\ of the previous
       iteration.
   •   The scalar adjustment factor ky-\ and generic survival rate 50 applied by source type
       using the HPMS to source type mapping described by Table 2-1. As with before, limits
       were placed on the /cy(l — 50) term, such that the value of this term for each age was
       restricted to being between 0 and 1. Also, the Py-\ term used when calculating the
       number of vehicles removed was approximated by Py.
   •   New vehicle sales Wy+1, from the sources listed above and applied by source type.
With all of this information, the population distributions were algorithmically determined for
years 1999-2010. The resulting age distributions are stored in the SourceTypeAgeDistribution
table.
   7.2.       Relative Mileage Accumulation Rate

MOVES uses a relative mileage accumulation rate (RMAR) in combination with source type
populations (see Section 5.1) and age distributions described earlier in this section to distribute
the total annual miles driven by each HPMS vehicle type (see Section 4) to each source type and
age group. Using this approach, the vehicle population and the total annual vehicle miles traveled
(VMT) can vary from calendar year to calendar year, but the proportional travel by an individual
vehicle of each age will not vary.

VMT is provided, either by default values or by user input, by the five Highway Performance
Monitoring System (HPMS) vehicle classifications. These classifications are further broken
down into the groupings of the MOVES source use types, as described in Section 2.1.

The RMAR is determined within each HPMS vehicle classification such that the annual mileage
accumulation for a single vehicle of each age of a source type is relative to the mileage
accumulation of all of the source types and ages within the HPMS vehicle classification.  For
example, passenger cars, passenger trucks and light commercial trucks are all within the same
HPMS vehicle classification.  By definition, new (age 0) passenger trucks and light commercial
trucks have a RMAR of one (1.0).d Based on the data, new passenger cars have a RMAR of
d Within each HPMS vehicle class, an RMAR value of one is assigned to the source type and age with the highest
annual VMT accumulation. Because we use the same mileage accumulation data for passenger trucks and light
commercial trucks, they both have a value of one.

                                           58

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0.885.  This means that when the VMT assigned to the HPMS class 25 is allocated to passenger
cars, passenger trucks and light commercial trucks, a passenger car of age 0 will be assigned only
88.5 percent of the annual VMT assigned to a passenger truck or light commercial truck of age 0.

The RMAR values for MOVES2014 for the source types 11 (motorcycles), 41 (intercity buses),
42 (transit buses), 43 (school buses) and 54 (motor homes) were not changed from the values
used in MOVES2010b. Passenger car and light-duty truck RMAR values were recalculated to
reflect the change in the HPMS vehicle classifications used for VMT input and the remaining
heavy-duty vehicle classifications were updated with data from the 2002 Vehicle Inventory and
Use Survey (VIUS) and recalculated.

          7.2.1.     Motorcycles
The RMAR values for motorcycles in MOVES2014 were not changed from MOVES2010b
estimates. The MOVES2010b RMAR values were calculated from MARs for motorcycles
(sourceTypelD 11) based on the model years and odometer readings listed in motorcycle
advertisements. A stratified sample of about 1,500 ads were examined. A modified Weibull
curve was fit to the data to develop the relative mileage accumulation rates used in MOVES.39

          7.2.2.     Passenger Cars, Passenger Trucks and Light Commercial Trucks
In MOVES2010b, passenger cars had their own HPMS  vehicle classification. In MOVES2014,
they are grouped with passenger trucks and light commercial trucks. For MOVES2014, the
MOVES2010b passenger car RMAR values were adjusted to reflect the relative difference in
annual mile accumulation between passenger cars and the light trucks. Analysis of the data
determined that new passenger cars (age 0) accumulate  only 88.5  percent of the annual miles
accumulated by new light trucks.  Thus, all of the RMAR values for passenger cars were
adjusted to be 88.5 percent of their previous values.

The MOVES2010b RMAR values for passenger cars, passenger trucks and light commercial
trucks (sourceTypelD 21,  31 & 32) were taken from the NHTSA report on survivability and
mileage schedules.40 In the NHTSA analysis, annual mileage by age was determined for cars and
for trucks using data from the 2001 National Household Travel Survey. In this NHTSA analysis,
vehicles that were less than one year old at the time of the survey  were classified as "age 1", etc.
NHTSA used cubic regression to smooth the VMT by age estimates.

We used NHTSA's regression coefficients to extrapolate mileage to ages 26 through 30 not
covered by the report. Since passenger trucks had the highest MAR in what was then the light-
duty truck HPMS class, each source type's mileage by age was divided by passenger truck
mileage at age 1 to determine a relative MAR. For consistency with MOVES age categories, we
then shifted the relative MARs such that the NHTSA age 1 ratio was used for MOVES age 0,
etc. We used NHTSA's light truck VMT to determine relative MARs for both passenger trucks
and light commercial trucks.

Since a newer version of the National Household Travel Survey was available, we conducted a
preliminary analysis of the impact of updating the MARs based on the 2009 National Household
Travel Survey. This resulted in changes to the MOVES  allocation of VMT by one percent or less
                                          59

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for each of the vehicle categories covered by the survey. As such, we feel that the MARs
developed from the 2001 survey are still reasonable for use in MOVES2014.

          7.2.3.     Buses
The RMAR values for all bus categories in MOVES2014 were not changed from MOVES2010b
estimates. The intercity bus (sourceTypelD 41) annual mileage accumulation rate is taken from
Motorcoach Census 2000.42 The data did not distinguish vehicle age, so the same MAR (59,873
miles per year) was used for each age. The school bus (sourceTypelD 43) annual mileage
accumulation rate (9,939 miles per year) is taken from the 1997 School Bus Fleet Fact Book.
The MOVES  model assumes the same annual mileage accumulation rate for each age. The
Transit Bus (category 42) annual mileage accumulation rate are taken from the MOBILE6 values
for diesel transit buses (FtDDBT). This mileage data was obtained from the  1994 Federal
Transportation Administration survey of transit agencies.43 The MOBILE6 equation was
extended to calculate values for ages 26 through 30.

          7.2.4.     Other Heavy-Duty Vehicles
The RMAR values for source types 51 (refuse trucks), 52 (short-haul single unit trucks), 53
(long-haul single unit trucks), 61 (short-haul combination trucks) and 62 (long-haul combination
trucks) were updated from MOVES2010b using the data from the 2002 Vehicle Inventory and
Use Survey (VIUS). The total reported  annual miles traveled by truck in each source type, as
shown in Table 7-3, was divided by the  vehicle population to determine the average annual miles
traveled per truck by source type.
                                          60

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                       Table 7-3 VIUS2002 annual mileage by vehicle age
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0-3
Model
Year
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1999-2002
Average
Single Unit Trucks
Refuse
(51)
26,703
32,391
31,210
31,444
31,815
28,450
25,462
30,182
20,722
25,199
23,366
18,818
12,533
15,891
19,618
12,480
12,577
30,437
Short-haul
(52)
21,926
22,755
24,446
23,874
21,074
21,444
16,901
15,453
13,930
13,303
11,749
13,675
11,332
9,795
9,309
9,379
4,830
23,250
Long-haul
(53)
40,538
28,168
30,139
49,428
33,266
23,784
21,238
27,562
21,052
11,273
18,599
15,140
13,311
9,796
12,067
16,606
8,941
37,069
Combination Trucks
Short-haul
(61)
119,867
114,983
110,099
105,215
100,331
95,447
90,563
85,679
80,795
75,911
71,026
66,142
61,258
56,374
51,490
46,606
41,722
61,240
Long-haul
(62)
109,418
128,287
117,945
110,713
99,925
94,326
85,225
85,406
71,834
71,160
67,760
80,207
48,562
64,473
48,242
58,951
35,897
116,591
For each source type, in the first few years, the data showed only small differences in the annual
miles per vehicle and no trend.  After that, the average annual miles per vehicle declined in a
fairly linear manner, at least until the vehicles are at age 16 (the limit of the data). MOVES,
however, requires mileage accumulation rates for all ages to age 30. For MOVES2014, we
assumed that the mileage accumulation rate at age 30 would be the same as used for
MOVES2010b.

Mileage accumulation rates for these vehicles were determined for each age from 0 to 30 using
the following method:

       1)  Ages 0 through 3 use the same average annual mileage accumulation rate for age 0-3
          vehicles of that source type.
       2)  Ages 4 through 16 use mileage accumulation rates calculated using a linear regression
          of the VIUS data for ages three through 16 summarized in Table 7-4,
       3)  Ages 17 through 29 use values from interpolation between the values in age 16 and
          age 30.
       4)  Age 30 uses the MOVES2010b mileage accumulation rate for age 30. These rates
          were allocated to MOVES source types from MOBILE6 mileage accumulation rates,
          which were derived from the  1992 TIUS as documented in the Arcadis report.44
                                          6l

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     Table 7-4 Regression statistics for heavy-duty truck annual mileage accumulation rates (ages 4-16)
Measurement
Average*
Refuse
Truck (51)
30,437
Single Unit
Short-haul (52)
23,250
Single Unit
Long-haul (53)
37,069
Combination
Short-haul (61)
61,240
Combination
Long-haul (62)
116,591

Intercept
Slope
36,315
-1,510
25,442
-1,209
36,305
-1,794
65,773
-3,447
119,867
-4,884

Age 30
0.0320
0.0518
0.1025
0.0320
0.0571
* Average sample annual miles traveled for ages 0 through 3.
The resulting relative mileage accumulation rates are shown in Table 7-5 below. Note that the
first four values are identical and then decline linearly to age 16 and then linearly to age 30 with
a different slope.
          7.2.5.
Motor Homes
Motor home relative mileage accumulation rates for MOVES2014 are unchanged from
MOVES2010b. For motor homes (sourceTypelD 54), the initial MARs were taken from an
independent research study45 conducted in October 2000 among members of the Good Sam
Club. The members are active recreation vehicle (RV) enthusiasts who own motor homes,
trailers and trucks. The average annual mileage was estimated to be 4,566 miles. The data did not
distinguish vehicle age, so the same MAR was used for each age.
                                          62

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Table 7-5 Relative mileage accumulation rates for heavy-duty trucks in MOVES2014
agelD
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Refuse (51)
1.0000
1.0000
1.0000
1.0000
0.9525
0.9050
0.8575
0.8099
0.7624
0.7149
0.6674
0.6199
0.5724
0.5249
0.4773
0.4298
0.3823
0.3573
0.3323
0.3073
0.2822
0.2572
0.2322
0.2072
0.1821
0.1571
0.1321
0.1071
0.0820
0.0570
0.0320
Short Haul
Single Unit (52)
0.6864
0.6864
0.6864
0.6864
0.6484
0.6103
0.5723
0.5343
0.4962
0.4582
0.4202
0.3821
0.3441
0.3061
0.2680
0.2300
0.1920
0.1808
0.1696
0.1585
0.1473
0.1361
0.1249
0.1138
0.1026
0.0914
0.0802
0.0691
0.0579
0.0467
0.0355
Long Haul
Single Unit
(53)
0.9729
0.9729
0.9729
0.9729
0.9165
0.8601
0.8036
0.7472
0.6908
0.6343
0.5779
0.5215
0.4650
0.4086
0.3522
0.2957
0.2393
0.2293
0.2194
0.2094
0.1994
0.1894
0.1795
0.1695
0.1595
0.1496
0.1396
0.1296
0.1197
0.1097
0.0997
Motor Home
(54)
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
0.0590
Short Haul
Combination
(61)
0.5269
0.5269
0.5269
0.5269
0.4941
0.4613
0.4286
0.3958
0.3631
0.3303
0.2975
0.2648
0.2320
0.1993
0.1665
0.1338
0.1010
0.0950
0.0890
0.0830
0.0770
0.0710
0.0649
0.0589
0.0529
0.0469
0.0409
0.0349
0.0289
0.0229
0.0169
Long Haul
Combination
(62)
1.0000
1.0000
1.0000
1.0000
0.9536
0.9072
0.8607
0.8143
0.7679
0.7215
0.6751
0.6286
0.5822
0.5358
0.4894
0.4430
0.3965
0.3723
0.3481
0.3238
0.2996
0.2753
0.2511
0.2268
0.2026
0.1783
0.1541
0.1298
0.1056
0.0814
0.0571
                                    63

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8. VMT Distribution of Source Type by Road Type

For each source type, the RoadTypeVMTFraction field in the RoadTypeDistribution table stores
the fraction of total VMT for each vehicle class that is traveled on each of the MOVES five road
types. Users may supply the distribution VMT to vehicle classes for each road type for individual
counties when using County Scale, however, for National Scale, the default distribution is
applied to all locations.

The national default distribution of VMT to vehicle classes for each road type in MOVES2014
were derived to reflect the VMT data included in the 2011 National Emission Inventory (NEI)
Version I46 (July 31, 2013). This data is provided by states every three years as part of the NEI
project and is supplemented by EPA estimates, based on Federal Highway Administration
(FHWA) statistics47, when state supplied estimates are not available.

The 2011 NEI VI data48 is grouped by the Source Classification Code (SCC) used at that time
and these older classifications do not cleanly map to the source types used by MOVES. The
VMT was mapped to the source types used by MOVES by calculating the fraction of VMT for
each source type found in each SCC classification result in a national MOVES2010b run for
calendar year 2011. The factors calculated from the MOVES201b run are shown in Section 20
(Appendix D).  The first seven digits of the 10 digit SCC (SCC7) indicate the vehicle
classification.

The SCC road types map cleanly to the MOVES road types. The eighth and ninth digits of the
10-digit SCC (SCC89) indicate the road  type, as shown below in Table 8-1.

                   Table 8-1 Mapping of SCC road types to MOVES road types
SCC Road Type
Code (SCC89)
11
13
15
17
19
21
23
25
27
29
31
33
SCC Road Type
Rural Interstate
Rural Other Principal Arterial
Rural Minor Arterial
Rural Major Collector
Rural Minor Collector
Rural Local
Urban Interstate
Urban Other Freeways & Expressways
Urban Other Principal Arterial
Urban Minor Arterial
Urban Collector
Urban Local
MOVES
Road Type ID
2
o
J
3
3
o
5
o
5
4
4
5
5
5
5
MOVES Road Type
Rural Restricted Access
Rural Unrestricted Access
Rural Unrestricted Access
Rural Unrestricted Access
Rural Unrestricted Access
Rural Unrestricted Access
Urban Restricted Access
Urban Restricted Access
Urban Unrestricted Access
Urban Unrestricted Access
Urban Unrestricted Access
Urban Unrestricted Access
Once the SCC VMT values have been mapped to MOVES source types and road types, the
national distribution of road type VMT by source type can be calculated from the NEI VMT
estimates, summarized in Table 8-2. The off network road type (roadTypelD 1) is not used and is
allocated none of the VMT.
                                         64

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Table 8-2 MOVES2014 road type distribution by source type
Source
Type
11
21
31
32
41
42
43
51
52
53
54
61
62
Description
Motorcycle
Passenger Car
Passenger Truck
Light Commercial
Truck
Intercity Bus
Transit Bus
School Bus
Refuse Truck
Single Unit Short-
haul Truck
Single Unit Long-
haul Truck
Motor Home
Combination Short-
haul Truck
Combination Long-
haul Truck
Road Types
Off
Network
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Rural
Restricted
2
0.0804768
0.0847394
0.0859437
0.0866643
0.1409270
0.1384440
0.1383910
0.2396390
0.1635030
0.1638220
0.1233290
0.2366730
0.2476010
Rural
Unrestricted
3
0.3019230
0.2344520
0.2753580
0.2755600
0.2811960
0.2813130
0.2813150
0.2717580
0.2869150
0.2869700
0.2876100
0.2744240
0.2705480
Urban
Restricted
4
0.1913280
0.2374280
0.2178360
0.2180390
0.2195920
0.2196020
0.2196020
0.2524620
0.2345890
0.2346570
0.2255300
0.2516600
0.2543110
Urban
Unrestricted
5
0.4262730
0.4433810
0.4208630
0.4197360
0.3582850
0.3606420
0.3606920
0.2361420
0.3149930
0.3145510
0.3635310
0.2372430
0.2275400

All
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
                         65

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9. Average Speed Distributions
Average speed is used in MOVES to convert VMT inputs into the source hours operating (SHO)
units that MOVES uses for internal calculations. It is also used to select appropriate driving
cycles, which are then used to calculate exhaust running operating mode distributions at the
national, county (and sometimes project) level. Instead of using a single average speed in these
tasks, MOVES2014 uses a distribution of average speeds by bin. The AvgSpeedDistribution
table lists the default fraction of driving time for each source type, road type, day, and hour in
each average speed bin. The  fractions sum to one for each combination of source type, road type,
day, and hour. The MOVES  average speed bins are defined in Table 9-1.
Table 9-1 MOVES s
Bin
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Average Speed (mph)
2.5
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
peed bin categories
Average Speed Range (mph)
speed < 2. 5 mph
2.5 mph <= speed < 7.5 mph
7.5 mph<= speed < 12.5 mph
12.5 mph <= speed < 17.5 mph
17.5 mph <= speed < 22.5 mph
22.5 mph <= speed < 27.5 mph
27.5 mph <= speed < 32.5 mph
32.5 mph <= speed < 37.5 mph
37.5 mph <= speed < 42.5 mph
42.5 mph <= speed < 47.5 mph
47.5 mph <= speed < 52.5 mph
52.5 mph <= speed < 57.5 mph
57.5 mph <= speed < 62.5 mph
62.5 mph <= speed < 67.5 mph
67.5 mph <= speed < 72.5 mph
72.5 mph<= speed
   9.1.      Light-Duty Average Speed Distributions
For MOVES2014, the light-duty average speed distributions are based on in-vehicle global
position system (GPS) data. The data was obtained through a contract with Eastern Research
Group (ERG), who subcontracted with TomTom to provide summarized vehicle GPS data.6
TomTom makes in-vehicle GPS navigation devices and supports cell-phone navigation
applications. ERG provided the US EPA with updated values for the AvgSpeedDistribution
calculated from the TomTom delivered data based on their consumers, where "virtually all" use
them in light-duty cars, trucks, and vans.
e Much of the following text and tables are excerpted from the ERG Work Plan (EPA-121019), submitted to US
EPA on January 11, 2012.
                                          66

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Some of the characteristics of the TomTom GPS data to consider are as follows:

    •  Data is self-selective. Data is only recorded from users of TomTom GPS units and an
       iPhone application. Additionally, TomTom data is only collected when the units are on.
       This creates bias not only for users, but also for types of driving. Anecdotally, drivers
       who own GPS units are less likely to use them when they drive in familiar areas in
       comparison with unfamiliar areas. Compared to the default VMT by road type
       information in MOVES, TomTom overrepresents behavior on rural restricted access
       roads, which suggests the higher usage of GPS on vacations and business trips.
    •  No information on vehicle type is available. TomTom suggests that "virtually all" the
       vehicles are light-duty cars, trucks, and vans.  MOVES allows for separate average speed
       distributions for each source type. However, due to a lack of information on other source
       types, the average speed distribution derived from the TomTom light-duty GPS data is
       applied to all source types, although the combination long-haul trucks distribution was
       adjusted as described at the end of this section. Other heavy-duty source types such as
       single unit long-haul trucks were not adjusted. We recognize this as a potential
       shortcoming, and look to incorporate source type specific average speed information in
       the future.
    •  The MOVES average speed distributions are based on the average speed in each roadway
       segment, not second-by-second speed measurements.
    •  Only data that is associated with the vehicle network is included in the average speed
       delivery. As part of the quality control methods, TomTom excludes data that does not
       "snap to the roadway grid" to remove points caused by loss of satellite signal and errors
       while  the TomTom unit is trying to acquire the satellite signal. TomTom uses data quality
       control techniques to minimize data arising from non-light-duty-vehicle use, such as from
       pedestrians, bicycles, and airplanes.

Under direction of ERG, TomTom queried its database of historic traffic probes to produce a
table of total distance and total time as a function of road type, weekday/weekend, hour of the
day, and average speed bin for the calendar year 2011 for the 50 states and the District of
Columbia. TomTom delivered a  table  identifying the total distance and total time of vehicles
travelling at an average  speed interval  for all combinations of:

    1. Identifier for Average Speed Bin (20 levels): average speeds were binned in 5 mph
    increments, starting at 2.5mph: 0-2.5mph; 2.5mph-7.5mph; 7.5mph-12.5mph; ...
    92.5mph-97.5mph.

    2. Identifier for Month of the Year (12 levels).

    3. Identifier for Day of the Week (2 levels): the period for weekday is Monday,
    00:00:00 to Friday, 23:59:59, and the period for weekend is Saturday, 00:00:00 to
    Sunday, 23:59:59.

    4. Identifier for Time of Day (24 levels): times are binned in one hour increments,
    starting at midnight: 00:00:00 to 00:59:59; 01:00:00 to 01:59:59, ..., 23:00:00 to
    23:59:59.

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   5. Identifier for Road Type (4 levels): TomTom used the information in Table 9-2 to
   classify between the TomTom Functional Classes and the MOVES road type
   description. TomTom also categorized the road types as rural or urban, according to
   the Census definitions used in MOVESf

 Table 9-2 Correspondence between TomTom functional class, census information, and MOVES road types
MOVES Road Type
Description
Rural Restricted Access
Rural Unrestricted Access
Urban Restricted Access
Urban Unrestricted Access
Census Information for the
TomTom Roadway Segment
Rural
Rural
Urban
Urban
TomTom Functional
Road Class
Oandl
2 through 7
Oandl
2 through 7
TomTom first "snapped" their data points onto road segments. Off-network driving data was not
obtained from the TomTom data. Much of the TomTom data that does not "snap to the roadway
grid" is caused by loss of satellite signal and errors while the TomTom unit is trying to acquire
the satellite signal. Therefore, a difficult analysis would be required to separate real off-network
data from GPS error data, and even if the analysis  could be done, the reliability of the results
would probably be unknown. As such,  only data that was associated with the roadway grid was
used in the analysis.

Table 9-3 shows the method for using the internal  TomTom data (Columns E through I) to
produce the desired output, which ERG used to produce the MOVES2014 tables. The example in
the table uses 16 observations that might have been recorded on two urban unrestricted roadway
segments (Column E) during TomTom personal navigation device use between  14:00:00 and
14:59:59 on a weekday in April  2011. Column F is an internal ID (1-5 occur on  Segment A, and
11-21 occur on Segment B).  Column G gives the length of the segment.  Column H gives the
time that the device spent on the segment. Column I gives the average speed of the device on the
segment. The 16 observations are sorted by the average speed bin, which is given in Column J.
The total distance traveled and the total time spent in each combination of road type, month,
weekday/weekend, hour of the day, and average speed bin are given in Columns K and L.
TomTom provided Columns A, B, C, D,  J, K, and L to ERG. The data in those columns was
purchased by ERG from TomTom and  is provided under license terms that permit free
distribution to EPA and the public. The raw data in Columns E,  F, G,  H, and I were not provided
to ERG and the US  EPA.
f http://www.census.gov/geo/www/ua/2010urbanruralclass.html

                                          68

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     Table 9-3 Example of accumulating total distance and total time for the TomTom deliverable table
A
Road Type
(4 levels)
B
Month
(12
levels)
C
Weekday/
Weekend
(2 levels)
D
Hour
of the
Day
(24
levels)
E
Segment
F
Data
Point
G
Segment
Length
(feet)
H
Time
in
Segment
(s)
I
Average
Speed
in
Segment
(mph)
J
Average
Speed
Bin
(mph)
(20
levels)
K
Total of
Segment
Lengths
for this
Speed
Bin
(feet)
L
Total of
Segment
Times
for this
Speed
Bin (s)
Urban
Unrestricted
April
Weekday
14:00:00
to
14:59:59
A
B
A
B
B
B
B
B
B
A
A
A
B
B
B
B
5
16
1
11
12
15
18
20
21
2
3
4
13
14
19
17
300
250
300
250
250
250
250
250
250
300
300
300
250
250
250
250
15
12
10
8
9
8
8
9
8
9
8
9
7
7
7
6
13.64
14.20
20.45
21.31
18.94
21.31
21.31
18.94
21.31
22.73
25.57
22.73
24.35
24.35
24.35
28.41
15
20
25
30
550
1800
1650
250
27
60
47
6
Using the table delivered by TomTom, ERG calculated the time-based average speed distribution
for each road type, day, and hour of the day using the average speed bin (Column J) and the total
of segment times (Column L)g. ERG calculated the average speed distribution according to the
16 speed bins used in MOVES. Figure 9-1 plots the average speed distribution for one hour
(5pm) stored in the averageSpeedDistribution table in MOVES, which contains average speed
distributions for  each hour of the day (24 hours). We are using the TomTom data to represent
national default average speed distribution in MOVES.
g MOVES uses time-based speed because the emission rates are time-based (e.g. gram/hour).

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  Figure 9-1 Average speed distribution for 5pm (hourlD 17) for source types (11 through 54) stored in the
                          AvgSpeedDistribution table in MOVES2014
                                                                        Dav
                                                                           Weekday

                                                                           Weekend
                                      40           60           SO
                               Average Bin Speed

   9.2.       Heavy-Duty Average Speed Distributions
It has been shown that combination trucks travel at approximately 92 percent of the speed of
light-duty vehicles on restricted access roads49. Since the TomTom data was developed from
light-duty vehicles, the average speed distribution for both short-haul and long-haul combination
trucks was adjusted on rural and urban restricted road types.
                                           70

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The average speed for each roadway type, day type, and hour can be calculated by multiplying
the average speed of each bin by the corresponding distribution of time as shown in Equation 17.
Here, v is the average speed of the distribution, Vi is the average speed of bin i, and pi is the
proportion of time spent in bin i.
                       v =    vi' Pi
                                                                            Equation 17
                         = 2.5 • P! + 5 • p2 + ••• + 70 • p15 + 75 • p16

To adjust the average speed for heavy-duty combination trucks, we redistributed the proportion
of time spent in each speed bin such that its contribution to the average speed was 92 percent of
the light-duty speed, as shown in Equation 18. This redistributed proportion of time in each
speed bin is given by p[.

                            ^combination  = (0-92) Alight-duty
                                                 ,                          Equation 18
                                              t • Pi
-I
To perform this redistribution, we defined two new variables, a and /?, where at is the fraction of
Pi that is shifted down one speed bin, and /?j is the fraction of PJ shifted down two speed bins.
The new distribution at speed bin i (given by p[) starts with the original distribution (pt\ gains
the proportions moved down from the higher speed bins (tfj+1 • PJ+I and /?j+2 • Pi+zX and loses
the proportion that is moved to a lower speed bin (at • pt and /?j • PJ). This is shown in Equation
19:

                 Pi = Pi + Oi+i ' Pi+i) + (A+2 ' Pi+2~) ~ Oi ' Pi)  - (A ' Pi)     Equation 19

For speed bins with an average speed of less than or equal to 60 mph, we only needed to shift
distributions using a fraction of one speed bin (or 5 mph). Thus we only calculated a^ and
set Pi = 0. Mathematically, reducing a bin's average speed by a certain fraction (77) can be
expressed with Equation 20:

                         (1 — 77)  • PI = at • (vt — 5)  + (1 — «j) • PI            Equation 20

Essentially, the fraction that is moved to the next slower bin («j) is multiplied by the slower
speed (note that each of the speed bins are 5 mph apart, so this is Vi — 5), and the fraction that
remains (1 — at) is multiplied by the original speed vt.  Since the average speed of the
combination trucks is 92 percent of cars,  (1 — 77) =  92% and 77 = 0.08.
By rearranging terms from Equation 20, and solving for at we obtain Equation 21 :
                                                                            Equation 21
However, for speed bins > 65 mph, Equation 21 yields at greater than 1. Since that logically
can't happen, some of the distribution needed to be moved to the second next slower speed bin to

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fully account for the 8 percent speed reduction. This is mathematically shown in Equation 22,
which is the logical extension of Equation 20:
(1 - 77) • vi = Pi • (vt - 10) + «i • Oj - 5) + (1 - «i - fa)
                                                                              Equation 22
The difference between Equations 20 and 22 is that an additional fraction (fa) is removed from
the original speed bin and is given the speed of two speed bins slower (or 10 mph slower). With
this additional factor, there is an infinite combination of solutions that could satisfy Equation 22.
We solved this problem with a linear equation solver by setting Equation 22 to a constraint (see
Equation 23), adding the  constraint that a^ + fa are less than or equal to 1 (Equation 24), and
choosing the solution that minimized fa.
                          t - 5) + fa • (vt - 10)
                                                              Equation 23
                                       at+fa
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An additional adjustment was made for the highest speed bins because we assumed that the
maximum speed bin had a triangular distribution with an average speed of 75 mph (see Figure
9-2). In the new distribution, all of the maximum speed bin fraction was redistributed to the 65
and 70 mph bins. Therefore, the new maximum speed bin (70 mph) was also assumed to have a
triangular distribution. Geometrically, 1/9* of a triangular distribution averaging 70 mph is faster
than 72.5 mph. Since the 75 mph speed bin is defined as any speed >72.5 mph, l/9th of the new
70 mph fraction (p{5) was reclassified as the new fraction for the 75 mph bin.

This process was repeated for both short- and long-haul combination trucks on restricted access
road types for every hour and day type combination. Figure 9-3 illustrates the results of this
analysis.
                                           73

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Figure 9-3 Average weekday speed distribution for 5pm (hourlD 17) by source type stored in the
                        AvgSpeedDistribution table in MOVES
                                                              Source

                                                              ~**~ All other source types

                                                                   Combination trucks
                 20          40          60
                     Average Bin Speed
SO
                                        74

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10.   Driving  Schedules and Ramps
Drive schedule refers to a second-by-second vehicle speed trajectory. A drive schedule typically
includes all vehicle operation from the time the engine starts until the engine is keyed off, both
driving (travel) and idling time. Drive schedules are used in MOVES to determine the operating
mode distribution for most MOVES running processes for calculation of emissions and energy
consumption.

In brief, there is an emission rate (in grams per hour of vehicle operation) for each operating
mode of vehicle operation. Each second of vehicle operation is assigned to an operating mode as
a function of vehicle velocity in each second and the specific power (VSP), or scaled tractive
power (STP) for heavy-duty vehicles, is calculated from the driving schedules. This distinction
between VSP and STP is discussed in Section 14. The average speed distribution is used to
weight the operating mode distributions determined from driving schedules with different
average speeds into a composite operating mode distribution that represents overall travel by
vehicles.  The distribution of operating modes is used by MOVES to weight the emission rates to
account for the vehicle operation.


    10.1.    Driving Schedules
A key feature of MOVES is the capability to accommodate a number of drive schedules to
represent driving patterns across source type, road type, and average speed. For the national
default case, MOVES2014 employs 49 drive schedules with various average speeds, mapped to
specific source types and road types.

MOVES  stores all of the drive schedule information in four database tables. DriveSchedule
provides the drive schedule name, identification number, and the average speed of the drive
schedule. DriveScheduleSecond contains the second-by-second vehicle trajectories for each
schedule. In some cases the vehicle trajectories are not contiguous; as detailed below, they may
be formed from several unconnected microtrips that overall represent driving behavior.
DriveScheduleAssoc defines the set of schedules which are available for each combination of
source use type and road type. Ramps use operating mode distributions directly and do not use
drive schedules to calculate operating modes. The RoadOpModeDistribution table lists
operating mode distributions used for ramps for each source use type, road type and speed bin,
discussed in further detail later in this section.

Tables 10-1 through 10-6 below list the driving schedules used in MOVES2014. Some driving
schedules are used for both restricted access (freeway) and unrestricted access (non-freeway)
driving. In most cases, these represent atypical conditions, such as extreme congestion or
unrealistic free flow speeds. In these conditions, we assume that the road type itself has little
impact on the expected driving behavior (driving schedule). Normally, these conditions
represent only a small portion of overall driving.   Similarly, some driving schedules are used for
multiple source types where vehicle specific information was not available.

In the past, if there was no appropriate driving schedule to use for modeling an average speed
bin, MOVES would use the nearest schedule. MOVES2014 now requires driving schedules that
                                          75

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can be used as the upper bound and the lower bound for all average speed bins.  New default
driving schedules have been added to assure that all average speed bins have appropriate driving
schedules for all the MOVES average speed bins.

 Table 10-1 MOVES  driving cycles for motorcycles, passenger cars, passenger trucks, and light commercial
                                     trucks (11,21,31,32)
ID
101
1033
1043
1041
1021
1030
153
1029
1026
1020
1011
1025
1019
1024
1018
1017
1009
158
Cycle Name
LD Low Speed 1
Final FC14LOSF
Final FC19LOS AC
Final FC17LOSD
Final FC11LOSF
Final FC14LOSC
LD LOS E Freeway
Final FC14LOSB
Final FC12LOSE
Final FC11LOSE
Final FC02LOSDF
Final FC12LOSD
Final FC11LOSD
Final FC12LOSC
Final FC11LOSC
Final FC11LOSB
Final FC01LOSAF
LD High Speed Freeway 3
Average
Speed
2.5
8.7
15.7
18.6
20.6
25.4
30.5
31.0
43.3
46.1
49.1
52.8
58.8
63.7
64.4
66.4
73.8
76.0
Unrestricted Access
Rural
X


X

X

X


X


X


X
X
Urban
X


X

X

X
X


X

X


X
X
Restricted access
Rural
X
X
X

X

X


X


X

X
X
X
X
Urban
X
X
X

X

X


X


X

X
X
X
X
                      Table 10-2 MOVES driving cycles for intercity buses (41)
ID
398
404
201
405
202
203
204
205
206
251
252
253
254
255
397
Cycle Name
CRC E55 HHDDT Creep
New York City Bus
MD 5 mph Non-Freeway
WMATA Transit Bus
MD lOmph Non-Freeway
MD 15mph Non-Freeway
MD 20mph Non-Freeway
MD 25mph Non-Freeway
MD 3 Omph Non-Freeway
MD 3 Omph Freeway
MD 40mph Freeway
MD 50mph Freeway
MD 60mph Freeway
MD High Speed Freeway
MD High Speed Freeway Plus 5 mph
Average
Speed
1.8
3.7
4.6
8.3
10.7
15.6
20.8
24.5
31.5
34.4
44.5
55.4
60.4
72.8
77.8
Unrestricted access
Rural
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Urban
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Restricted access
Rural
X

X

X
X
X
X
X
X
X
X
X
X
X
Urban
X

X

X
X
X
X
X
X
X
X
X
X
X
                                             76

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                  Table 10-3 MOVES driving cycles for transit and school buses (42,43)
ID
398
201
404
202
405
401
203
204
205
402
206
251
252
403
253
254
255
397
Cycle Name
CRC E55 HHDDT Creep
MD 5 mph Non-Freeway
New York City Bus
MD lOmph Non-Freeway
WMATA Transit Bus
Bus Low Speed Urban*
MD 15mph Non-Freeway
MD 20mph Non-Freeway
MD 25mph Non-Freeway
Bus 30 mph Flow*
MD 3 Omph Non-Freeway
MD 3 Omph Freeway
MD 40mph Freeway
Bus 45 mph Flow*
MD 50mph Freeway
MD 60mph Freeway
MD High Speed Freeway
MD High Speed Freeway Plus 5 mph
Average
Speed
1.8
4.6
3.7
10.7
8.3
15
15.6
20.8
24.5
30
31.5
34.4
44.5
45
55.4
60.4
72.8
77.8
Unrestricted access
Rural
X

X

X
X



X



X
X
X
X
X
Urban
X

X

X
X



X



X
X
X
X
X
Restricted access
Rural
X
X

X


X
X
X

X
X
X

X
X
X
X
Urban
X
X

X


X
X
X

X
X
X

X
X
X
X
* To be consistent with the speed distributions described in Section 9, this speed represents the average
for the traffic the bus is traveling in, not the average speed of the bus, which is lower due to stops.
                        Table 10-4 MOVES driving cycles for refuse trucks (51)
ID
398
501
301
302
303
304
305
306
351
352
353
354
355
396
Cycle Name
CRC E55 HHDDT Creep
Refuse Truck Urban
HD 5 mph Non-Free way
HD 1 Omph Non-Freeway
HD 15mph Non-Freeway
HD 20mph Non-Freeway
HD 25mph Non-Freeway
HD 3 Omph Non-Freeway
HD 3 Omph Freeway
HD 40mph Freeway
HD 50mph Freeway
HD 60mph Freeway
HD High Speed Freeway
HD High Speed Freeway Plus 5 mph
Average
Speed
1.8
2.2
5.8
11.2
15.6
19.4
25.6
32.5
34.3
47.1
54.2
59.4
71.7
77.8
Unrestricted access
Rural

X

X
X
X
X
X
X
X
X
X
X
X
Urban

X

X
X
X
X
X
X
X
X
X
X
X
Restricted access
Rural
X

X
X
X
X
X
X
X
X
X
X
X
X
Urban
X

X
X
X
X
X
X
X
X
X
X
X
X
                                                77

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          Table 10-5 MOVES driving cycles for single unit trucks and motor homes (52,53,54)
ID
398
201
202
203
204
205
206
251
252
253
254
255
397
Cycle Name
CRC E55 HHDDT Creep
MD 5 mph Non-Freeway
MD lOmph Non-Freeway
MD 15mph Non-Freeway
MD 20mph Non-Freeway
MD 25mph Non-Freeway
MD 3 Omph Non-Freeway
MD 3 Omph Freeway
MD 40mph Freeway
MD 50mph Freeway
MD 60mph Freeway
MD High Speed Freeway
MD High Speed Freeway Plus 5 mph
Average
Speed
1.8
4.6
10.7
15.6
20.8
24.5
31.5
34.4
44.5
55.4
60.4
72.8
77.8
Unrestricted access
Rural
X
X
X
X
X
X
X
X
X
X
X
X
X
Urban
X
X
X
X
X
X
X
X
X
X
X
X
X
Restricted access
Rural
X
X
X
X
X
X
X
X
X
X
X
X
X
Urban
X
X
X
X
X
X
X
X
X
X
X
X
X
                 Table 10-6 MOVES driving cycles for combination trucks (61,62)
ID
398
301
302
303
304
305
306
351
352
353
354
355
396
Cycle Name
CRC E55 HHDDT Creep
HD 5 mph Non-Free way
HD 1 Omph Non-Freeway
HD 15mph Non-Freeway
HD 20mph Non-Freeway
HD 25mph Non-Freeway
HD 3 Omph Non-Freeway
HD 3 Omph Freeway
HD 40mph Freeway
HD 50mph Freeway
HD 60mph Freeway
HD High Speed Freeway
HD High Speed Freeway Plus 5 mph
Average
Speed
1.8
5.8
11.2
15.6
19.4
25.6
32.5
34.3
47.1
54.2
59.4
71.7
77.8
Unrestricted access
Rural
X
X
X
X
X
X
X
X
X
X
X
X
X
Urban
X
X
X
X
X
X
X
X
X
X
X
X
X
Restricted access
Rural
X
X
X
X
X
X
X
X
X
X
X
X
X
Urban
X
X
X
X
X
X
X
X
X
X
X
X
X
The default drive schedules for light-duty vehicles listed in the tables above were developed
from several sources. "LD LOS E Freeway" and "HD High Speed Freeway" were retained from
MOBILE6 and are documented in report M6.SPD.001.50   "LD Low Speed 1" is a historic cycle
used in the development of speed corrections for MOBILES and is meant to represent extreme
stop-and-go "creep" driving. "LD High Speed Freeway 3" was developed for MOVES to
represent very high speed restricted access driving. It is a 580-second segment of restricted
access driving from an in-use vehicle instrumented as part of EPA's On-Board Emission
Measurement Shootout program,51 with an average speed of 76 mph and a maximum speed of 90
mph.  Fifteen new light-duty "final" cycles were developed by a contractor for MOVES based on
urban and rural data collected in California in 2000 and 2004.39 The new cycles were selected to
best cover the range of road types and average speeds modeled in MOVES.

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Most of the driving schedules used for buses are borrowed directly from driving schedules used
for single unit trucks (described below).  The "New York City Bus"52 and "WMATA Transit
Bus"53 drive schedules are included for urban driving that includes transit type bus driving
behavior. The "CRC E55 HHDDT Creep" 54 cycle was included to cover extremely low speeds
for heavy-duty trucks. The "Bus 30 mph Flow" and "Bus 45 mph Flow" cycles used for transit
and school buses were developed by EPA based on Ann Arbor Transit Authority buses
instrumented in Ann Arbor, Michigan.55 The bus "flow" cycles were developed using selected
non-contiguous snippets of driving from one stop to the next stop, including idle, to create cycles
with the desired average driving speeds.  The bus "flow" cycles have a nominal speed used for
selecting the driving cycles that does not include the idle time and only considers the free-flow
speed between stops. The actual average speed of the cycle (including stops) are shown in
Section 20 (Appendix D). Note that the "Bus Low Speed Urban" bus cycle is the last 450
seconds of the standard  New York City Bus cycle.

The "Refuse Truck Urban" cycle represents refuse truck driving with many stops and a
maximum speed of 20 mph but an average speed of 2.2 mph.  This cycle was developed by West
Virginia University for the State of New York.  The CRC E55 HHDDT Creep cycle was used
instead for restricted access driving of refuse trucks at extremely low speeds. All of the other
driving cycles used for refuse trucks were borrowed from driving cycles developed for heavy-
duty combination trucks, described below.

Single unit and combination trucks use driving cycles developed specifically for MOVES, based
on work performed for EPA by Eastern Research Group (ERG), Inc. and documented in the
report "Roadway-Specific Driving Schedules for Heavy-Duty Vehicles."56 ERG analyzed data
from 150 medium and heavy-duty vehicles instrumented to gather instantaneous speed and GPS
measurements. ERG segregated the driving into restricted access and unrestricted access driving
for medium and heavy-duty vehicles, and then further stratified vehicles trips according the pre-
defined ranges of average speed covering the range of vehicle operation.  The medium duty
cycles are used with single unit trucks and heavy-duty cycles are used with combination trucks.

The schedules developed by ERG are not contiguous schedules which could be run on a chassis
dynamometer, but are made up of non-contiguous "snippets" of driving (microtrips) meant to
represent target distributions.  For use with MOVES, we modified the schedules' time field in
order to signify when one microtrip ended and one began. The time field of the driving schedule
table increments two seconds (instead of one) when each new microtrip begins. This two-second
increment signifies that  MOVES should not regard the microtrips as contiguous operation when
calculating accelerations.

Both single unit and combination trucks use the CRC E55 HHDDT Creep cycle for all driving at
extremely low speeds. At the other end of the distribution, none of the existing driving cycles
for heavy-duty trucks included average speeds sufficiently high to cover the highest  speed bin
used by MOVES. To construct such cycles, EPA started with the highest speed driving cycle
available from the ERG analysis and added 5 mph to each point, effectively increasing the
average speed of the driving cycle without increasing the acceleration rate at any point.  We have
checked the feasibility of these new driving cycles (396 and 397) using simulations with the
EPA's Greenhouse Gas  Emissions Model (GEM)57 for medium- and heavy-duty vehicle
                                          79

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compliance. GEM is a forward-looking full vehicle simulation tool that calculates fuel economy
and GHG emissions from an input drive trace and series of vehicle parameters. One of the
aspects of forward-looking models is that the driver model is designed to demand torque until the
vehicle drive trace is met. Our results indicate that the simulated vehicles were able to follow the
speed demands of the proposed driving cycles without exceeding maximum torque or power.

None of the driving schedules used to represent restricted access (freeway) driving contain
vehicle operation on entrance or exit ramps. The effect of ramp operation is added separately in
MOVES.


    10.2.      Ramp Activity
Ramp activity is the driving behavior of vehicles that occurs on entrance and exit ramps as
vehicles enter or leave restricted access roads. It includes all of the activity between operation
on the unrestricted road and operation on the restricted road.

None of the driving schedules used to represent restricted access (freeway) driving contain
vehicle operation on entrance or exit ramps. The effect of ramp operation is calculated
separately.  Instead of using driving schedules to generate operating mode distributions for
ramps, each average speed bin has an associated operating mode distribution that reflects the
power demand expected from  ramp operation associated with each nominal average speed for
each of the source types.  The  operating mode distributions used for ramps in MOVES2014 were
estimated to represent the driving connecting to and from  a freeway with the  given average
speed. These operating mode distributions (i.e. the fractions of time spent in each of the
operating modes for each source type on each road  type at each average speed) can be found in
the in the default MOVES2014 database (RoadOpModeDistribution table).

Each set of ramp operating modes is associated with a corresponding average speed that does not
include ramp operation.  Since operating modes for ramp emissions are affected by the
distribution of the average speed bins on the surrounding roads, the determination of average
speeds for restricted access roads (both urban and rural) should not include the time or distance
of vehicles on ramps. However, the VMT on ramps should be included with restricted access
VMT.

The emission impact of ramp activity is combined with the other driving activity found in the
restricted access (freeway) driving cycles using a ramp fraction. This fraction defines the
fraction of all time spent on a road that occurs on entrance and exit ramps. The fraction used (8
percent) in MOVES2014  is derived from the ramp fraction value developed originally for the
MOBILE6 model.58
                                           80

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11.   Hotelling Activity

MOVES2014 defines "hotelling" as any long period of time that drivers spend in their vehicles
during mandated down times during long distance deliveries by tractor/trailer combination
heavy-duty trucks. During the mandatory down time, drivers can stay in motels or other
accommodations, but most of these trucks have sleeping spaces built into the cab of the truck and
drivers stay with their vehicles. Hotelling hours are included in MOVES2014 in order to account
for use of the truck engine (referred to as "extended idling") to power air conditioning, heat, and
other accessories and account for the use of auxiliary power units (APU), which are small on-
board power generators.

In MOVES2014, only the long-haul combination truck source use type (sourceTypelD 62) is
assumed to have any hotelling activity. All of the long-haul combination trucks are diesel
fueled.  All source use types other than long-haul combination trucks have hotelling activity
fractions set to zero.


    11.1.      National Default Hotelling Rate
Federal law limits long-haul truck drivers to ten hours driving followed by a mandatory eight
hour rest period. These regulations  are described in the Federal Register.59 In long-haul
operation, drivers will stop periodically along their routes. For MOVES, the total hours of
hotelling are estimated by using the national estimate of VMT by long-haul combination trucks
divided an estimated average speed to calculate total hours of driving. The total hours of driving
divided by ten gives the number of eight-hour rest periods needed and thus the national total
hotelling hours.

A method is needed to allocate these total hotelling hours to locations.  For MOVES2014, we
decided to determine a "hotelling rate" (hours of hotelling per mile of travel) that could be used,
in combination with VMT information to allocate the hotelling hours, described in Equations 25
to 28. We calculate a hotelling rate  as the national total hours of hotelling divided by the national
total miles driven by long-haul trucks on rural restricted access (freeways) roads.  Driving time
on all roads contributes to the total hotelling hours calculation.  However, most locations used
for hotelling are located near the roadways (restricted access) most travelled by long-haul trucks.
In order to prevent large amounts of hotelling to be allocated to congested urban areas, we
decided to only use the VMT on rural restricted roads as the surrogate for allocating the total
hotelling hours.

The hotelling rate (hotelling hours per mile of rural restricted access travel by long-haul
combination trucks) is applied to the estimate of rural restricted access VMT by long-haul
combination trucks to estimate the default hotelling hours for any location, month or day. The
allocation of hotelling to specific hours of the day is described below in Section 12.5.

The MOVES2014 default hotelling rate was calculated using default national total VMT
estimates for calendar year 2011 shown in Table 11-1.
                                           81

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            Total Hours =
Total Vehicle Miles Traveled
       Average Speed
Equation 25
                  Total Trips =
        Total Hours
     10 hours per trip
Equation 26
          Hotelling Hours = Total Trips * 8 hours per trip
                                        Equation 27
      Hotelling Rate =
                                  Hotelling Hours
                       Total Rural Restricted Miles Traveled
                                        Equation 28
Where:
       Total Hours is the calculated time long-haul combination trucks spend driving.
       Total Vehicle Miles Traveled is the total miles traveled by diesel long-haul
       combination trucks in the nation in calendar year 2011 on all road types taken
       from MOVES defaults.
       Average Speed is an estimate of the average speed (distance divided by time) for
       diesel long-haul combination trucks on all road types while operating.
       Total Trips is the calculated number of trips by long-haul combination trucks.
       Hotelling Hours is the calculated amount of rest time for long-haul combination
       trucks.
       Rural Restricted Miles is the total miles traveled by diesel long-haul combination
       trucks on only rural  restricted access roads (freeways) in calendar year 2011 using
       MOVES defaults.
Table 11-1 Calculation of hotelling hours from long-haul combination truck VMT
Description
Rural Restricted
Rural Unrestricted
Urban Restricted
Urban Unrestricted
Total annual VMT
Hours (58.3 mph)
Trips (10 hrs per trip)
Hotelling hours (8 hrs per trip)
Hotelling hours per mile on rural restricted roads
Annual Value
31,392,300,000
34,301,700,000
32,243,100,000
28,848,900,000
126,786,000,000
2,174,716,981
217,471,698
1,739,773,585
0.055414
units
miles
miles
miles
miles
miles
hours
trips
hours
hours/mile
                                   82

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For the MOVES default, all hotelling activity is assumed to occur in counties with travel on rural
restricted access roads, and thus will occur primarily in rural areas of states.
The national rate of hotelling hours per mile of rural restricted access roadway VMT is stored in
the HotellingCalendarYear table for each calendar year.  The same value calculated for 201 1 is
used as the default for all calendar years. The County Data Manager includes the
HotellingActivityDistribution table which provides the opportunity for states to provide their
own estimates of hotelling hours specific to their location and time. Whenever possible states
and local areas should obtain and use more accurate local estimates of hotelling hours when
modeling local areas.

   11.2.     Hotelling Activity Distribution
Hotelling differs from simple parking. In MOVES, hotelling hours are divided into operating
modes which define the emissions associated with the type of hotelling activity. Long-haul
trucks are often equipped with sleeping berths and other amenities to make the drive rest periods
more comfortable. These amenities require power for operation.  This power can be obtained by
running the main truck engine (extended idle) or by use of smaller on-board power generators
(auxiliary power units, APU). Some truck stop locations include power hookups (truck stop
electrification) to  allow use of amenities without running either the truck engines or APUs.
Some of rest time may occur without use of amenities at all.  Table 11-2 shows the hotelling
operating modes used in MOVES.

                          Table 11-2 Hotelling activity operating modes
OpModelD
200
201
203
204
Description
Extended Idling of Main Engine
Hotelling Diesel Auxiliary Power Unit (APU)
Hotelling Battery or AC (plug in)
Hotelling All Engines and Accessories Off
The HotellingActivityDistribution table (see Table 11-3 below) contains the MOVES default
values for the distribution of hotelling activity to the operating modes.

                        Table 11-3 Default hotelling activity distributions
beginModelYearlD
1960
1960
1960
1960
2010
2010
2010
2010
endModelYearlD
2009
2009
2009
2009
2050
2050
2050
2050
opModelD
200
201
203
204
200
201
203
204
opModeFraction
1
0
0
0
0.7
0.3
0
0
All of the hotelling hours for long-haul trucks of model years before 2010 are assumed to use
extended idle to power accessories. Starting with the 2010 model year, the trucks are assumed to

-------
use extended idle 70 percent of the time and use APUs 30 percent of the time based on EPA's
assessment of technologies used by tractor manufacturers to comply with the Heavy-Duty
Greenhouse Gas standards.

-------
12.   Temporal Distributions
MOVES is designed to estimate emissions for every hour of every day type in every month of
the year. The vehicle miles traveled (VMT) are provided for MOVES2014 in terms of annual
miles.  These miles are allocated to months, days and hours using allocation factors, either
default values or values provided by users.

Default values for most temporal VMT allocations are derived from a 1996 report from the
Office of Highway Information Management (OHEVI).60  The report describes analysis of a
sample of 5,000 continuous traffic counters distributed throughout the United States. EPA
obtained the data used in the report and used it to generate the VMT temporal distribution inputs
in the form needed for MOVES2014.

The OHEVI report does not specify VMT by vehicle type, so MOVES uses the same values for
all source types, except motorcycles, as described below. In MOVES, daily truck hotelling hours
are calculated as proportional to source hours operating (SHO) calculated by MOVES from the
VMT and speed distributions for long-haul combination trucks. However, the hours of hotelling
activity in each hour of the day are not proportional to VMT, as described in Section 12.5.

The temporal distribution for engine start and corresponding engine soak (parked) distributions
are calculated from vehicle activity data stored in the SampleVehicleDay and
SampleVehicleTrip tables of the MOVES database, shown below in Table 12-1 These tables
contain a set of vehicle trip activity information constructed to represent activity for each source
type. Evaporative emissions are also affected by the time of day and the duration of parking.
Some of the vehicles in  the tables take no trips.

                             Table 12-1 SampleVehicleDay table
Source Type
sourceTypelD
11
21
31
32
41
42
43
51
52
53
54
61
62
Description
Motorcycle
Passenger Car
Passenger Truck
Light Commercial Truck
Intercity Bus
Transit Bus
School Bus
Refuse Truck
Single Unit Short-haul Truck
Single Unit Long-haul Truck
Motor Home
Combination Short-haul Truck
Combination Long-haul Truck
Number of Records
Weekday (daylD 5)
2214
821
834
773
190
110
136
205
112
123
5431
130
122
Weekend (daylD 2)
983
347
371
345
73
14
59
65
58
50
2170
52
49
                                          85

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    12.1.     VMT Distribution by Month of the Year
In MOVES, VMT is entered as an annual value and allocated to month using the
MonthVMTFraction table. For MOVES, we use the data from the OHIM report, Figure 2.2.1
"Travel by Month, 1970-1995," but modified to fit MOVES specifications.  The table shows
VMT/day taken from the OHIM report, normalized to one for January. For MOVES, we need the
fraction of total  annual VMT in each month. The report values of VMT per day were used to
calculate the VMT in a month using the number of days in each month.  The calculations in
Table 12-2 assume a non-leap year (365 days).

                               Table 12-2 Month VMTFraction
Month
January
February
March
April
May
June
July
August
September
October
November
December
Sum
Normalized
VMT/day
1.0000
1.0560
1.1183
1.1636
1.1973
1.2480
1.2632
1.2784
1.1973
1.1838
1.1343
1.0975

MOVES
Distribution
0.0731
0.0697
0.0817
0.0823
0.0875
0.0883
0.0923
0.0934
0.0847
0.0865
0.0802
0.0802
1.0000
       FHWA does not report monthly VMT information by vehicle classification. But it is
clear that in many regions of the United States, motorcycles are driven much less frequently in
the winter months. For MOVES2014 an allocation for motorcycles was derived using monthly
national counts of fatal motorcycle crashes from the National Highway Traffic Safety
Administration Fatality Analysis System for 2010.61 This allocation increases motorcycle
activity (and emissions) in the summer months and decreases them in the winter compared to the
other source types.  These default values in Table 12-3 for motorcycles are only a national
average and do not reflect the strong regional differences that would be expected due to climate.
                                          86

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                        Table 12-3 MonthVMTFraction for motorcycles
Month
January
February
March
April
May
June
July
August
September
October
November
December
Sum
Month ID
1
2
3
4
5
6
7
8
9
10
11
12

Distribution
0.0262
0.0237
0.0583
0.1007
0.1194
0.1269
0.1333
0.1349
0.1132
0.0950
0.0442
0.0242
1.0000
    12.2.     VMT Distribution by Type of Day
The DayVMTFraction distribution divides the weekly VMT into two day types.  The OHIM
report provides VMT percentage values for each day and hour of a typical week for urban and
rural roadway types for various regions of the United States. Since the day-of-the-week data
obtained from the OHIM report is not disaggregated by month or source type, the same values
were used for every month and source type. MOVES uses the 1995 data displayed in Figure
2.3.2 of the OHIM report.

The DayVMTFraction needed for MOVES has only two categories; week days (Monday,
Tuesday, Wednesday, Thursday and Friday) and weekend (Saturday and Sunday) days. The
OHIM reported percentages for each day of the week were summed in their respective categories
and converted to  fractions, as shown in Table 12-4. The OHIM report explains that data for
"Sam" refers to data collected from Sam to 4am.  Thus data labeled "midnight" belongs to and
was summed with the upcoming day.

                               Table 12-4 DayVMTFractions
Fraction
Weekday
Weekend
Sum
Rural
0.72118
0.27882
1.00000
Urban
0762365
0.237635
1.000000
We assigned the "rural" fractions to the rural road types and the "urban" fractions to the urban
road types. The fraction of weekly VMT reported for a single weekday in MOVES will be one-
fifth of the weekday fraction and the fraction of weekly VMT for a single weekend day will be
one-half the weekend fraction.

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    12.3.     VMT Distribution by Hour of the Day
HourVMTFraction uses the same data as for Day VMTFraction. We converted the OHIM
report's VMT data by hour of the day in each day type to percent of day by dividing by the total
VMT for each day type, as described for the Day VMTFraction. There are separate sets of
HourVMTFractions for "urban" and "rural" road types, but unrestricted and unrestricted roads
use the same HourVMTFraction distributions. All source types use the same HourVMTFraction
distributions, and Table 12-5 and Figure 12-1 summarize these default values.

                    Table 12-5 MOVES distribution of VMT by hour of the day
hourlD
1
2
o
5
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Description
Hour beginning at 12:00 midnight
Hour beginning at 1:00 AM
Hour beginning at 2:00 AM
Hour beginning at 3 : 00 AM
Hour beginning at 4:00 AM
Hour beginning at 5:00 AM
Hour beginning at 6:00 AM
Hour beginning at 7:00 AM
Hour beginning at 8:00 AM
Hour beginning at 9:00 AM
Hour beginning at 10:00 AM
Hour beginning at 1 1 :00 AM
Hour beginning at 12:00 Noon
Hour beginning at 1:00 PM
Hour beginning at 2:00 PM
Hour beginning at 3 : 00 PM
Hour beginning at 4:00 PM
Hour beginning at 5:00 PM
Hour beginning at 6:00 PM
Hour beginning at 7:00 PM
Hour beginning at 8:00 PM
Hour beginning at 9:00 PM
Hour beginning at 10:00 PM
Hour beginning at 1 1 :00 PM
Sum of All Fractions
Urban
Weekday
0.0098621
0.00627248
0.00505767
0.00466686
0.00699469
0.018494
0.0459565
0.0696444
0.0608279
0.0502862
0.0499351
0.0543654
0.0576462
0.0580319
0.0622554
0.0710049
0.0769725
0.077432
0.059783
0.0443923
0.0354458
0.031824
0.0249419
0.0179068
1.000
Weekend
0.0214739
0.0144428
0.0109684
0.0074945
0.0068385
0.0103588
0.0184303
0.0268117
0.0363852
0.0475407
0.0574664
0.0650786
0.0713228
0.0714917
0.0717226
0.0720061
0.0711487
0.0678874
0.0617718
0.0516882
0.0428658
0.0380302
0.0322072
0.0245677
1.000
Rural
Weekday
0.0107741
0.0076437
0.0065464
0.0066348
0.0095399
0.0200551
0.0410295
0.0579722
0.0534711
0.0525478
0.0550607
0.0576741
0.0591429
0.0608019
0.0652985
0.0726082
0.0773817
0.0754816
0.0587059
0.0439864
0.0357309
0.0307428
0.0238521
0.0173177
1.000
Weekend
0.0164213
0.0111921
0.0085415
0.00679328
0.00721894
0.0107619
0.01768008
0.0268751
0.0386587
0.0522389
0.0631739
0.0699435
0.0729332
0.0731218
0.0736159
0.0744608
0.0742165
0.0700091
0.0614038
0.0505043
0.0412072
0.0336373
0.0262243
0.0191666
1.000
                                          88

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    0.09
                   Figure 12-1 Hourly VMT fractions by day type and road type
                                                 Rural Weekend
                                                 Urban Weekend
                                                 Rural Weekday
                                                 Urban Weekday
           123456789
10 11 12 13  14  15  16  17 18 19 20 21  22  23  24
  Hour of the Day
    12.4.     Engine Starts and Parking
To properly estimate engine start emissions and evaporative fuel vapor losses, it is important to
estimate the number of starts by time of day, and the duration of time between vehicle trips. The
time between trips with the engine off is referred to as "soak time". To determine typical patterns
of trip starts and ends, MOVES uses information from instrumented vehicles. This data is stored
in two tables in the MOVES default database,  as discussed earlier. We have made only minor
changes for MOVE2014.

The first table, SampleVehicleDay, lists a sample population of vehicles, each with an identifier
(vehID), an indication of vehicle type (sourceTypelD), and  an indication (dayID) of whether the
vehicle is part of the weekend or weekday vehicle population.  Some vehicles were added to this
table to increase the number of vehicles in each day which do not take any trips to better match a
recent study of vehicle activity in Georgia.62 This change is described in greater detail in the
report describing evaporative emissions in MOVES2014.63

The second table, SampleVehicleTrip, lists the trips in a day made by each of the vehicles in the
SampleVehicleDay table. It records the vehID, day ID, a trip number (tripID), the hour of the trip
(hourlD), the trip number of the prior trip (priorTripID), and the times at which the engine was
turned on and off for the trip. The keyOnTime and keyOffTime are recorded in minutes since

-------
midnight of the day of the trip. 439 trips (about 1.1 percent) were added to this table to assure
that at least on trip is done by a vehicle from each source type in each hour of the day to assure
that emission rates will be calculated in each hour. Light-duty vehicle trip and soak data was
copied to all the other source types (11, 41, 42, 43, 51, 52, 53, 54, 61, and 62) for both weekdays
(daylD 5) and weekends (daylD 2) for hours with no trips.

To account for overnight soaks, many first trips reference a prior trip with a null value for
keyOnTime and a negative value for keyOffTime. The SampleVehicleDay table also includes
some vehicles that have no trips in the SampleVehicleTrip table to account for vehicles that sit
for one or more days without driving at all.

The data and processing algorithms used to populate these tables are detailed in two contractor
reports.64'65 The data comes from a variety of instrumented vehicle studies, summarized in Table
12-6. This data was cleaned, adjusted, sampled and weighted to develop a distribution intended
to represent average urban vehicle activity.

                    Table 12-6 Source data for sample vehicle trip information
Study
3 -City FTP
Study
Minneapolis
Knoxville
Las Vegas
Battelle
TxDOT
Study Area
Atlanta, GA; Baltimore, MD;
Spokane, WA
Minneapolis/St. Paul, MN
Knoxville, TN
Las Vegas, NV
California, statewide
Houston, TX
Study
Years
1992
2004-
2005
2000-
2001
2004-
2005
1997-
1998
2002
Vehicle Types
Passenger cars & trucks
Passenger cars & trucks
Passenger cars & trucks
Passenger cars & trucks
Heavy-duty trucks
Diesel dump trucks
Vehicle
Count
321
133
377
350
120
4
For vehicle classes that were not represented in the available data, the contractor synthesized
trips using trip-per-operating hour information from the EPA MOBILE6 model and soak time
and time-of-day information from source types that did have data. The application of synthetic
trips is summarized in Table 12-7.
                                           90

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                Table 12-7 Synthesis of sample vehicles for source types lacking data
Source Type
Motorcycles
Passenger Cars
Passenger Trucks
Light Commercial Trucks
Intercity Buses
Transit Buses
School Buses
Refuse Trucks
Single unit short-haul trucks
Single unit long-haul trucks
Motor homes
Combination short-haul trucks
Combination long-haul trucks
Based on
Direct Data?
No
Yes
Yes
No
No
No
No
No
Yes
No
No
Yes
Yes
Synthesized From
Passenger Cars
n/a
n/a
Passenger Trucks
Combination long-haul trucks
Single unit short-haul trucks
Single unit short-haul trucks
Combination short-haul trucks
n/a
Combination long-haul trucks
Passenger Cars
n/a
n/a
The resulting trip-per-day estimates are summarized in Table 12-8.  The same estimate for trips
per day is used for all ages of vehicles in any calendar year.
Table 12-8 Starts i
Source Type
Motorcycles
Passenger Cars
Passenger Trucks
Light Commercial Trucks
Intercity Buses
Transit Buses
School Buses
Refuse Trucks
Single unit short-haul trucks
Single unit long-haul trucks
Motor homes
Combination short-haul trucks
Combination long-haul trucks
per day by source type
MOVES2014
Weekday
0.78
5.89
5.80
6.05
2.77
4.58
5.75
3.75
6.99
4.29
0.57
5.93
4.29
MOVES2014
Weekend
0.79
5.30
5.06
5.47
0.88
3.46
1.26
0.92
1.28
1.29
0.57
1.16
1.29
MOVES2014 now has inputs in the County Data Manager that allows users to specify the
number of engine starts in each month, day type and hour of the day, as well as by source type
and vehicle age.  These user inputs override the default values provided by MOVES.

The same trip information that is used to determine the number of engine starts is also used to
determine the vehicle soak time. "Soak time" is the time between trips when the engine is off.
The soak times are used to estimate the activity in each of the operating modes for engine start
emissions.  The base emission rate for engine starts is based on a  12-hour soak period. All
engine soaks greater than 12 hours assume the same engine start emission rate as for 12 hours.
However, for all engine soaks less than 12 hours, the base engine start emission rate is adjusted
based on soak time bins (operating modes).3'4 The distribution of operating modes in each hour
of the day is part of the calculation used to determine the engine start emissions for that hour of
the day.

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A more complete discussion of the relationship between engine soak time and emissions will be
found in the MOVES report covering engine start emission rates used in MOVES.3
    12.5.      Hourly Hotelling Activity
The hotelling hours in each day should not directly correlate with the miles traveled in each hour,
since hotelling occurs only when drivers are not driving. Instead, the fraction of hours spent
hotelling by time of day can be derived from other sources. In particular, the report, Roadway-
Specific Driving Schedules for Heavy-Duty Vehicles?6 combines data from several instrumented
truck studies and contains detailed information about truck driver behavior. While none of the
trucks were involved in long-haul interstate activity, for lack of better data, we have assumed
that long-haul truck trips have the same hourly truck trip distribution as the heavy heavy-duty
trucks that were studied.

For each hour of the day, we estimated the number of trips that would end in that hour, based on
the number of trips that started 10 hours earlier. The hours of hotelling in that hour is the number
that begin in that hour, plus the number that began in the previous hour, plus the number that
began in the hour before that, and so on, up to the required eight hours of rest time.  Table 12-9
shows the number of trip starts and inferred trip ends over the hours of the day in the sample of
trucks assuming all trips are 10 hours long. For example, the number of trip ends in hour 1 is the
same as the number of trip starts 10 hours earlier in hour 15 of the previous day.

             Table 12-9 Hourly distribution of truck trips used to calculate hotelling hours
hourlD
1
2
o
J
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Hour of the Day
Hour beginning at 12:00 midnight
Hour beginning at 1 :00 AM
Hour beginning at 2:00 AM
Hour beginning at 3 : 00 AM
Hour beginning at 4:00 AM
Hour beginning at 5:00 AM
Hour beginning at 6:00 AM
Hour beginning at 7:00 AM
Hour beginning at 8:00 AM
Hour beginning at 9:00 AM
Hour beginning at 10:00 AM
Hour beginning at 1 1 :00 AM
Hour beginning at 12:00 Noon
Hour beginning at 1:00 PM
Hour beginning at 2:00 PM
Hour beginning at 3 :00 PM
Hour beginning at 4:00 PM
Hour beginning at 5:00 PM
Hour beginning at 6:00 PM
Hour beginning at 7:00 PM
Hour beginning at 8:00 PM
Hour beginning at 9:00 PM
Hour beginning at 10:00 PM
Hour beginning at 1 1 :00 PM
Trip Starts
78
76
65
94
107
131
194
230
279
267
275
240
201
211
171
167
144
98
71
73
71
52
85
48
Trip Ends
171
167
144
98
71
73
71
52
85
48
78
76
65
94
107
131
194
230
279
267
275
240
201
211
                                           92

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An estimate of the distribution of truck hotelling duration times is derived from a 2004 CRC
paper66 based on a survey of 365 truck drivers at six different locations. Table 12-10 lists the
fraction of trucks in each duration bin. Some trucks are hotelling for more than the required
eight hours, but some are hotelling for less than eight hours.

                    Table 12-10 Distribution of truck hotelling activity duration
Hotelling Duration
(hours)
2
4
6
8
10
12
14
16
Total
Fraction of Trucks
0.227
0.135
0.199
0.191
0.156
0.057
0.014
0.021
1.000
We assume that all hotelling activity begins at the trip ends shown in Table 12-9.  But not all trip
ends have the same number of hotelling hours.  The distribution of hotelling durations from
Table 12-10 is applied to the hotelling that occurs at each of these trip ends.

Table 12-11 illustrates the hotel activity calculations based on the number of trip starts and trip
ends. The hours of hotelling in any hour of the  day is the number of trip ends in the current hour
plus the trip ends from the previous hours that are still hotelling.  However, since not all trips
begin and end precisely on the hour, we have discounted the oldest hour included in the
calculation by 60 percent to account for those unsynchronized trips.

For example, there are 171 trip ends in hourlD 1.  If all trip ends idle for two hours, the number
of hours is 171 (for hourlD  1) and 40 percent of 211 (for hourlD 24), and thus 171 + (0.4*211) =
255.4 hours of hotelling.  Similarly, the number of hours can be calculated for other hotelling
time periods. For four hour hotelling periods, the hotelling hours would be 171 +211 +201 +
(0.4*240) = 679. Only the oldest hour of the day is discounted.

This calculation accounts for the time in the current hour of the day which is a result of hotelling
from trips that ended in the current hour and trips that ended in previous hours. This approach
assumes that all hotelling begins at the trip end. For example, in the hour of the day 1 for the
four hours hotelling bin, the trip ends in hourlD 22 contribute to the hours of hotelling in hourlD
1, since these trip ends are still hotelling (four hours) after the trip end.  The trip ends in hour ID
21 do not contribute to the four hours hotelling bin, since it has been more than four hours since
the trip ends occurred.

The initial calculated hours assume that all trucks idle the same amount of time, indicated by the
hotelling hours bin. The distribution (weight) from Table 12-10 is applied to the hour estimate in
each hotelling hours bin to calculate the weighted total idle hours for each hour of the day.
                                           93

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                                        Table 12-11 Calculation of hourly distributions of hotelling activity
hourlD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Totals
Weight
Trip
Starts
78
76
65
94
107
131
194
230
279
267
275
240
201
211
171
167
144
98
71
73
71
52
85
48
3428

Trip
Ends*
171
167
144
98
71
73
71
52
85
48
78
76
65
94
107
131
194
230
279
267
275
240
201
211
3428

2
hours
255.4
235.4
210.8
155.6
110.2
101.4
100.2
80.4
105.8
82
97.2
107.2
95.4
120
144.6
173.8
246.4
307.6
371
378.6
381.8
350
297
291.4
4799
0.227
4
hours
679
629.4
566.4
477.4
379.8
299.6
254.2
224.4
237.2
213.4
231.8
236
238.2
266.2
296.4
358
469.6
597.8
755.4
853.6
913
893.6
822.8
762
11655
0.135
6
hours
1204.8
1100
990
871.4
735.4
621.4
523.8
422.6
391.2
357.4
363.2
367.4
372.8
395
439.2
504.2
621.4
782
978.6
1143.8
1297.4
1368.6
1354
1305.6
18511
0.199
8
hours
1736
1643.6
1515.8
1342
1159
1015.4
879.4
744.4
660.8
555.6
517.2
511.4
504.2
526.4
573.8
633
764.2
928.2
1130.4
1328
1520.6
1658.8
1738.4
1780.6
25367
0.191
10
hours
2120.4
2118.6
2047
1885.6
1684.8
1486
1303
1138.4
1016.4
877.4
786.8
709.6
658.2
670.4
705.2
764.4
898.8
1057
1273.2
1474.2
1672.4
1843
1961.6
2070.8
32223
0.156
12
hours
2343.6
2408.8
2431.4
2360.6
2216
2029.6
1828.8
1609
1440
1271.4
1142 .4
1031.4
927.8
868.6
859.2
908.4
1030.2
1188.4
1407.8
1603
1815.2
1989.2
2113.4
2255
39079
0.057
14
hours
2495.4
2593
2654.6
2650.8
2600.4
2504.6
2360
2152.6
1965.8
1742
1566
1425.4
1283.4
1190.4
1128.8
1106.6
1184.2
1332.4
1539.2
1734.4
1949.8
2118
2256.2
2401.2
45935
0.014
16
hours
2638.2
2739.2
2806.4
2835
2823.6
2794.8
2744.4
2627.6
2497
2285.6
2091.8
1896
1707
1584.4
1484.4
1428.4
1453.8
1530.6
1693.2
1878.4
2081.2
2249.4
2390.8
2530
52791
0.021
Weighted Total
Idle Hours
1276
1234
1166
1056
930
823
728
630
581
507
479
457
434
447
476
526
635
767
933
1068
1194
1268
1289
1308
20213

Distribution
0.0628
0.0611
0.0577
0.0526
0.0458
0.0407
0.0357
0.0306
0.0289
0.0255
0.0238
0.0221
0.0221
0.0221
0.0238
0.0255
0.0323
0.0374
0.0458
0.0526
0.0594
0.0628
0.0645
0.0645
1.0000

*Assumes every trip ends 10 hours after it starts, such that all
column sum is reduced by 60 percent to account for trip ends
trips are 10 hours long. The first hour of hotelling in each hour bin
in a column that are not a full hour.
                                                                       94

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The distribution calculated using this method is similar to the behavior observed in a
dissertation67 at the University of Tennessee, Knoxville. This study observed the trucks parking
at the Petro truck travel center located at the 140/175 and Watt Road interchange between mid-
December 2003 and August 2004. Rather than use results from a single study at a specific
location, MOVES2014 uses the more generic simulated values to determine the diurnal
distribution of hotelling behavior. The distribution of total hotelling hours to hours of the day is
calculated from the total hotelling hours and stored in the SourceTypeHour table of the default
MOVES2014 database.

MOVES2014 uses this same default hourly distribution from Table 12-11 for all days and
locations, as shown below in Figure 12-2. Note this distribution of hotelling by hour of the day
is similar to the inverse of the VMT distribution used for these trucks by hour of the day.

              Figure 12-2 Truck hotelling distribution by hour of the day in MOVES2014
            7%
            12:00 AM                    12:00 PM                    12:00 AM
                                     Hours of Day

    12.6.      Single and Multiday Diurnals
The evaporative vapor losses from gasoline vehicle fuel tanks are affected by many factors,
including the number of hours a vehicle is parked without an engine start, referred to as engine
soak time. Most modern gasoline vehicles are equipped with emission control systems designed
to capture most evaporative vapor losses and store them. These stored vapors are then burned in
the  engine once the vehicle is operated. However, the vehicle storage capacity for evaporative
vapors is limited and multiple days of parking (diurnals) will overload the storage capacity of
these systems, resulting in larger losses of evaporative vapors in subsequent days.

The soak time calculations are discussed earlier in Section  12.4. The detailed description of the
calculation for the number of vehicles that have been soaking for more than a day and the
amount of time that the vehicles have been soaking can be  found in the MOVES technical report
on evaporative emissions.65
                                           95

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13.   Geographical Allocation of Activity

MOVES is designed to model activity at a "domain" level and then to allocate that activity to
"zones." The MOVES2014 default database is populated for a domain of the entire United States
(including Puerto Rico and the Virgin Islands), and the default zones correspond to individual
counties. The MOVES design only allows for one set of geographic allocations to be stored in
the default database. While geographic allocations clearly change over time, the MOVES2014
defaults were developed using the data from calendar year 2011, and are used for all calendar
years. For this reason, the MOVES default allocation of activity is rarely used for any official
purpose by either EPA or local areas. National-level emissions can be generated with calendar
year specific geographical information by running each year separately, with different user-input
allocations for each run. County- and Project-level calculations do not use the default
geographical allocation factors at all. Instead, County and Project scales require that the user
input local total activity for each individual year being modeled. The MOVES geographic
allocation  factors are stored in two tables, Zone and ZoneRoadType.


   13.1.     Source Hours Operating Allocation to Zones

Most of the emission rate calculations in MOVES2014 are based on emission rates by time units
(hour). Using time units for emissions is the most flexible approach, since the activity for some
processes (like leaks and idling) and some source types (like nonroad generators) are more
naturally in units of time.  As a result, MOVES converts activity data to hours in many cases in
order to produce the hours needed for emissions calculations.

The national total source hours of operation (SHO) are calculated from the estimates of VMT
and speed  as described in sections above. This total  VMT for each road type is allocated to
county using the SHOAllocFactor field in the ZoneRoadType table. The allocation factors are
derived using 2011 VMT and MOVES default VMT.

In particular, the MOVES2014 default estimates for the VMT by county come from Version 1 of
the 2011 National Emission Inventory (NEI) analysis.46 These estimates are based on the
Highway Performance Monitoring System (HPMS) state level data collected by the Federal
Highway Administration68 annually for use in transportation planning. The HPMS state level
VMT is distributed to the individual counties in each state as part of the NEI analysis.  This data
is reviewed and updated by the states as necessary prior to use in the NEI. The default inputs for
SHO AllocF actor in MOVES2014 were calculated using the VMT estimates obtained from
Version 1  of the 2011 NEI69 for each county by road type.

Vehicle miles traveled can be converted to hours of travel using average speeds. The average
speed estimates were taken directly from the AvgSpeedDistribution table of the MOVES default
database. The default average speed distributions do not vary by county or source type, but do
vary by road type, day type (weekday and weekend  day) and hour of the day. The 2011 NEI
VMT was aggregated into the four MOVES road types in each county. The VMT by road type
in each county was then allocated to day type and hour of the day using the day type and hour

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distributions from the MOVES default database tables, DayMVTFraction and
HourVMTFracti on.

Using the nominal speeds for each average speed bin in the AvgSpeedDistribution table for each
hour of each day type and the corresponding VMT, the hours of vehicle operation (SHO) can be
calculated for each hour of the day on each road type for each day type in each county.  The
average speed distribution is in units of time, so the distribution must be converted to units of
distance to be applied to the VMT values. For this step, we  multiplied each value of each
distribution (in terms of time) by the corresponding nominal average speed value for that average
speed bin to calculate distance (hours * miles/hour). Then we divided each distance value in the
distribution by the sum of all distance values in that distribution to calculate the average speed
distribution in terms of distance.

Finally, we multiplied the total VMT corresponding to each average speed distance distribution
(by road type, by day type, by hour of the day) by each of the values in the distribution to
calculate the VMT corresponding to each average speed bin. We then calculated operating  hours
by dividing the VMT in each average speed bin by the corresponding nominal average speed
value, shown in Equation 29.
                   SHO = VMT (miles) / Speed (miles per hour)            Equation 29
Once the hours of operation have been calculated, the hours in each county were summed by
road type. The allocation factor for each county in Equation 30 was calculated by dividing the
county hours for each road type by the national total hours of operation for each road type.


                   SHOAllocFactor  = County SHO / National SHO           Equation 30


The county allocation values for each roadway type sum to one (1.0) for the nation.  The same
SHOAllocFactor set is the default for all calendar years at the National scale. County- and
Project-level calculations do not use the default SHOAllocFactor allocations at all. Instead,
County and Project scales require that the user input all local activity.


   13.2.    Engine Start Allocations to Zones
The allocation of the domain-wide count of engine starts to zones is stored in the
StartAllocFactor in the Zone table. In the default database for MOVES2014, the domain is the
nation and the zones are counties. There is no national source for data on the number of trip
starts by county, so for MOVES2014,  we have used VMT to determine this allocation.  VMT for
each county was taken from the most recent National Emission Inventory analysis for calendar
year 20II.69

VMT estimates for each county in each state and the allocation is calculated using Equation 31,
where i represents each individual  county and / is the set of all US counties.
                                           97

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                 CountyAllocatiorij = CountyVMTj/^ CountyVMTj         Equation 31
                                                   iel

The county allocation values sum to one (1.0) for the nation.  The same StartAllocFactor set is
the default for all calendar years at the National scale.  County- and Project-level calculations do
not use the default StartAllocF actor allocations at all. Instead, County and Project scales require
that the user input all local activity.


   13.3.     Parking Hours Allocation to Zones
The allocation of the domain-wide hours of parking (engine off) to zones is stored in the
SHPAllocFactor in the Zone table. In the default database for MOVES2014, the domain is the
nation and the zones are the counties. There is no national source for hours of parking by county,
so for MOVES2014, we have used the same VMT-based allocation as used for the allocation  of
starts in the StartAllocFactor (see above).

The county allocation values for parking hours sum to  one (1.0) for the nation.  The same
SHPAllocFactor set is the default for all calendar years at the National scale.  County- and
Project-level calculations do not use the default SHPAllocFactor allocations at  all. Instead,
County and Project scales require that the user input all local  activity.

In MOVES2014, hotelling hours (including extended idling and auxiliary power unit usage) are
calculated from long-haul combination truck VMT in each location and does have its own
allocation factors.

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14.   Vehicle Mass and Road  Load Coefficients

The MOVES model calculates emissions using a weighted average of emisson rates by operating
mode. This level of detail is required for microscale modeling, which in MOVES is called
project level analysis. For running exhaust emissions, the operating modes are defined by either
vehicle specific power (VSP) or scaled tractive power (STP). Both VSP and STP are calculated
based on a vehicle's speed and acceleration but differ in how they are scaled (or normalized).
VSP is used for light-duty vehicles (source types 11 through 32) and STP is used for heavy-duty
vehicles (source types 41 through 62).

The SourceUseTypePhysics table describes the vehicle characteristics needed for the VSP and
STP calculations, including average vehicle mass, a fixed mass factor, and three road load
coefficients for each source type averaged over all ages. MOVES uses these to calculate VSP
and STP for each source type according to Equations  32 and 33:

                VSP = (—)-v + (—} •v2  + (—]-v3 + (a + g- sin 0) • v       Equation 32
                      \M/      \M/       \M/


                          Av + Bv2 + Cv3 + M  • v • (at + g • sinO}
                    STP =	——	           Equation 33
                                           Jscale

where A, 5, and C are the road load coefficients in units of kW-s/m, kW-s2/m2, and
kW-s3/m3 respectively. A is  associated with tire rolling resistence, B with mechanical rotating
friction as well as higher order rolling resistance losses, and C with aerodynamic drag. M is the
source mass for the source type in metric tons, g is the acceleration due to gravity (9.8 m/s ), v
                                                                                  j
is the instantaneous vehicle speed in m/s, a is the instantaneous vehicle acceleration in m/s ,
sin 9 is the (fractional) road grade, and fscaie  is  a scaling factor.

When mapping actual emissions data to VSP bins with Equation 32, the vehicle's measured
weight is used  as the source mass factor. In contrast, when calculating average VSP distributions
for an entire source type with MOVES, the average source type mass is used instead. STP is
calculated with Equation 33, which is very similar to the VSP equation except the denominators
are different. In the case of VSP, the power is normalized by the mass of the vehicle (fscaie =
M). For heavy-duty vehicles using STP, fscaie depends on their regulatory class and is used to
bring the numerical range of tractive power into the same numerical range as the VSP values
when assigning operating modes. Class 40 trucks use  fscaie = 2.06, which is equal  to the mass of
source type 32  in metric tons. This is because operating modes for passenger trucks and light-
commercial trucks are assigned operating modes using VSP, and using a fixed mass factor of
2.06 essentially calculates VSP-based emission rates.  Running operating modes for all the heavy-
duty source types (buses, single unit, and combination trucks) are  assigned using STP with
fscaie =17.1, which is roughly equivalent to the average running weight in metric tons of all
heavy-duty vehicles. Additional discussion regarding  VSP and STP are provided in the MOVES
light-duty3 and heavy-duty4 emission rate reports, respectively.
                                          99

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In both cases, operating mode distributions are derived by combining second-by-second speed
and acceleration data from a specific drive schedule with the proper coefficients for a specific
source type. More information about drive schedules can be found in Section 10.1.  The
following sections detail the derivation of values used in Equations 32 and 33.

    14.1.    Source Mass and Fixed Mass Factor
The two mass factors stored in the SourceUseTypePhysics table are the source mass and fixed
mass factor. The source mass represents the average weight of a given source type, which
includes the weight of the vehicle, occupants, fuel, and payload (M in the equations above), and
the  fixed mass factor represents the STP scaling factor (fscaie m the equations above).

While the source masses for light-duty were unchanged from MOVES2010b, all of the heavy-
duty source masses were updated with newer data. Please see Section 21 (Appendix E) for a
discussion of the MOVES2010b source masses. The heavy-duty source masses for 2014+ model
year vehicles heavy-duty vehicles were first updated to account for the 2014 Medium and
Heavy-Duty Greenhouse Gase Rule as discussed in Section 14.2. Then the heavy-duty source
masses were updated with 2011 Weigh-in-Motion (WEVI) data made available through FHWA's
Vehicle Travel Information System (VTRIS). These data are available from FHWA by state,
road type, and HPMS truck type (single unit or combination). The average national mass by
truck type was calculated by weighting the masses with VMT by state and road type using
FHWA's Highway Statistics VM-2 Table. These average values then needed to be allocated from
the  HPMS truck classification to source types. This allocation was performed using the percent
difference between the average WEVI HPMS mass and the average MOVES2010b HPMS mass.11
The MOVES2010b average masses were calculated by weighting the source type masses with
the  updated 2011 VMT. The percentage difference between the average single unit truck mass in
MOVES2010b and the WEVI data was then applied to the source masses of short-haul single unit
trucks, long-haul single unit trucks, refuse trucks, and motor homes. Likewise, the percentage
difference between the average combination truck mass in MOVES2010b and the WEVI data was
applied to the source masses of short-haul and long-haul combination trucks, including the
2014+ model year groups. These differences are shown in Table 14-1, and the resulting source
type masses are presented in Table 14-4.

                   Table 14-1 Weigh-in-Motion (WIM) masses weighted by VMT
HPMS Category
Single Unit Trucks
Combination Trucks
Average Weight (Ibs)
20,107
52,907
Percent Change from
MOVES2010b
11.7%
-21.7%
    14.2.     Road Load Coefficients
The information available on road load coefficients varied by regulatory class. Motorcycle road
load coefficients, given in Equations 34 through 36, were empiricially derived in accordance
with standard practice70'71:
h For the WIM analysis, we only compared to the MOVES2010b masses because the 2014 Medium and Heavy-Duty
Rule impact is not assumed to begin phase-in until 2014.


                                          1OO

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                                 A = 0.088 • M                           Equation 34


                                     5 = 0                              Equation 35


                          C = 0.00026 + 0.000194 • M                    Equation 36

For light-duty vehicles, the road load coefficients were calculated according to Equations 37
through 39:72

                         _   0-7457  ,Q^,TRIHp                       Equation 37
                           50 • 0.447        1RLHr@5omPh


                        _    0.7457   .QW.TRJHp                     Equation 38
                          (50 • 0.447)2       1RLHr@5omPh


                        _    0.7457                                      Equation 39
                          (50 • 0.447)3       1KLHr@50mph

In each of the above equations, the first factor is the appropriate unit conversion to allow A, B,
and C to be used in Equations 32 and 33, the second factor is the power distribution into each of
the three load categories, and the third is the tractive road load horsepower rating (TRLHP).
Average values for A, B, and C for source types 21, 31, and 32 were derived from applying
TRLHP values recorded in the Mobile Source Observation Database (MSOD)73 to Equations 37
through 39. While we expect light-duty road load coefficients to improve over time due to the
Light-Duty Greenhouse Gas Rule, the impact of these changes have been directly incorporated
into the emission and energy rates. Therefore, these coefficients remain constant over time in the
MOVES (if not in the real-world) to avoid double counting the impacts of actual road load
improvements in the fleet.

For the heavier vehicles, no road load parameters were available in the MSOD. For these source
types, relationships of road load coefficent to vehicle mass came from a study done by V.A.
Petrushov,74 as shown in Table 14-2. These relationships are grouped by regulatory class; source
type values were determined by weighting the combination of MOVES2010b weight categories
that comprise the individual source types. The final SourceMass, FixedMassFactor and road load
coefficients for all source types are listed in Table 14-4.
                                         101

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 Table 14-2 Road load coefficients for heavy-duty trucks, buses, and motor homes for MY 1960-2013 vehicles
Coefficient
(kW-s\
A 1 1
\ m J
(kW-s2\
p 1 I
( -2 )
fkW-s3\
r 1 1
( -3 )
8500 to 14000 Ibs
(3.855 to 6.350
metric ton)
0.0996 -M
0
0.00289 +
5.22 X id-5 • M
14000 to 33000 Ibs
(6.350 to 14.968
metric ton)
0.0875 -M
0
0.00193 +
5.90xlQ-5-M
>33000 Ibs
(>14.968 metric ton)
0.0661 -M
0
0.00289 +
4.21 XlO-5 -M
Buses and Motor
Homes
0.0643 -M
0
0.0032 +
5.06xlQ-5-M
In MOVES2014, the vehicle mass and road load coefficient were updated for 2014 and later
model year heavy-duty vehicles to account for the 2014 Medium and Heavy-Duty Greenhouse
Gase Rule.75 Table 14-3 contains the combination long-haul tractor and vocational vehicle tire
rolling resistance, coefficient of drag, and weight reductions expected from the technologies
which could be used to meet the standards. The value in the table reflects a 400 pound mass
reduction. As discussed in the regulatory impact analysis for the final rulemaking, EPA used a
sales mix of 10 percent Class 7 low roof, 10 percent Class 7 high roof, 45 percent Class 8 low
roof, and 35 percent Class 8 high roof based on feedback from the manufacturers.

The values in the table reflect a modeling assumption that 8 percent of all tractors (19.7 percent
of short-haul tractors) would be considered vocational tractors and therefore will only be
required to meet the vocational vehicle standards and not show any  aerodynamic or weight
improvement. The weight reduction applied to short-haul tractors is 321 pounds, which is
calculated from the 400 pound weight reduction assumed for non-vocational tractors, reduced by
19.7 percent.  The tire rolling resistance reduction is assumed to be 5 percent based on the data
derived in the tire testing program conducted by EPA. Comparatively tire rolling resistance is
reduced by 9.6 percent for long-haul tractors and 7 percent for short-haul tractors while
aerodynamic drag is reduced 12.1 percent for long-haul tractors and 5.9 percent for short-haul
tractors in model year 2014 and later.  For further details on these assumptions about reductions
in source mass and road  load coefficients, please see the rulemaking documents. Discussion of
incorporating the rule's energy reductions from engine technology improvements into MOVES
can be found in the MOVES2014 Heavy-Duty Emission Rate Report.4
                                          1O2

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   Table 14-3 Estimated reductions in rolling resistance and aerodynamic drag coefficients from HD GHG
                          Phase 1 Rule for model years 2014 and later
Truck Type
Combination long-haul
Combination short-haul
Vocational vehicles (Single-
unit tracks, refuse tracks,
motor homes, buses, and
light commercial tracks)
Reduction In Tire Rolling
Resistance Coefficient From
Baseline
9.6%
7.0%
5.0%
Reduction In
Aerodynamic Drag
Coefficient From Baseline
12.1%
5.9%
0%
Weight
Reduction
(Ibs)
400
321
0
       These changes are represented in MOVES2014 through new aerodynamic coefficients
and weights, and they primarily affect short- and long-haul combination truck source types
beginning in MY 2014. The average vehicle mass and road load coefficients are updated by
source type through the beginModelYearlD and endModelYearlD fields in the
SourceUseTypePhysics table.

                       Table 14-4 MOVES2014 SourceUseTypePhysics table
sourceTypelD
11
21
31
32
41
41
42
42
43
43
51
51
52
52
53
53
54
54
61
61
62
62
Begin
Model
Year
1960
1960
1960
1960
1960
2014
1960
2014
1960
2014
1960
2014
1960
2014
1960
2014
1960
2014
1960
2014
1960
2014
End
Model
Year
2050
2050
2050
2050
2013
2050
2013
2050
2013
2050
2013
2050
2013
2050
2013
2050
2013
2050
2013
2050
2013
2050
Rolling
Term A
(kW-s/m)
0.0251
0.1565
0.2211
0.2350
1.2952
1.2304
1.0944
1.0397
0.7467
0.7094
1.5835
1.5043
0.6279
0.5965
0.5573
0.5294
0.6899
0.6554
1.5382
1.4305
1.6304
1.4739
Rotating
TermB
(kW-s2/m2)
0
0.0020
0.0028
0.0030
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Drag
TermC
(kW-s3/m3)
0.0003
0.0005
0.0007
0.0007
0.0037
0.0037
0.0036
0.0036
0.0022
0.0022
0.0036
0.0036
0.0016
0.0016
0.0015
0.0015
0.0021
0.0021
0.0040
0.0038
0.0042
0.0037
Source Mass
(metric tons)
0.2850
1.4788
1.8669
2.0598
19.5937
19.5937
16.5560
16.5560
9.0699
9.0699
23.1135
23.1135
8.5390
8.5390
6.9845
6.9845
7.5257
7.5257
22.9745
22.8289
24.6010
24.4196
Fixed Mass
Factor (metric
tons)
0.2850
1.4788
1.8669
2.0598
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
17.1
                                          103

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15.   Air Conditioning Activity Inputs

This report describes three inputs used in determining the impact of air conditioning on
emissions. The ACPenetrationFraction is the fraction of vehicles equipped with air conditioning.
FunctioningACFraction describes the fraction of these vehicles in which the air conditioning
system is working correctly. The ACActivityTerms relate air conditioning use to local heat and
humidity. More information on air conditioning effects is provided in the MOVES technical
report on adjustment factors.76


    15.1.     ACPenetrationFraction
The ACPenetrationFraction is a field in the SourceTypeModelYear table. Default values, by
source type and model year were taken from MOBILE6.77 Market penetration data by model
year were gathered from Ward's Automotive Handbook for light-duty vehicles and light-duty
trucks for model years 1972 through the 1995 for cars and 1975-1995 for light trucks. Rates in
the first few years of available data are quite variable, so values for early model years were
estimated by applying the 1972 and 1975 rates for cars and trucks, respectively. Projections
beyond 1995 were developed by calculating the average yearly rate of increase in the last five
years of data and applying this rate until a predetermined cap was reached. A cap of 98 percent
was placed on cars and 95 percent on trucks under the assumption that there will always be
vehicles sold without air conditioning, more likely trucks than cars. No data was available on
heavy-duty trucks. While VIUS asks if trucks are equipped with A/C, "no response" was coded
the same  as "no," making the data unusable for this purpose. For MOVES, the light-duty vehicle
rates were applied to passenger cars, and the light-duty truck rates were applied to all other
source types (except motorcycles, for which A/C penetration is assumed to be zero), summarized
in Table 15-1.
                                         104

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                       Table 15-1 AC penetration fractions in MOVES2014

1972-and-earlier
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999+
Motorcycles
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Passenger Cars
0.592
0.726
0.616
0.631
0.671
0.720
0.719
0.694
0.624
0.667
0.699
0.737
0.776
0.796
0.800
0.755
0.793
0.762
0.862
0.869
0.882
0.897
0.922
0.934
0.948
0.963
0.977
0.980
All Trucks and Buses
0.287
0.287
0.287
0.287
0.311
0.351
0.385
0.366
0.348
0.390
0.449
0.464
0.521
0.532
0.544
0.588
0.640
0.719
0.764
0.771
0.811
0.837
0.848
0.882
0.906
0.929
0.950
0.950
    15.2.     FunctioningACFraction
The FunctioningACFraction field in the SourceTypeAge table (see Table 15-2) indicates the
fraction of the air-conditioning equipped fleet with fully functional A/C systems, by source type
and vehicle age. A value of 1 means all systems are functional. This is used in the calculation of
total energy to account for vehicles without functioning A/C systems. Default estimates were
developed for all source types using the "unrepaired malfunction" rates used for 1992-and-later
model years in MOBILE6. The MOBILE6 rates were based on the average rate of A/C system
failure by age reported in a consumer study and assumptions about repair frequency during and
after the warranty period. The MOBILE6 rates were applied to all source types except
motorcycles, which were assigned a value of zero for all years.
                                          105

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            Table 15-2 FunctioningACFraction by age (all source types except motorcycles)
agelD
0
1
2
o
J
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
functioningACFraction
1
1
1
1
0.99
0.99
0.99
0.99
0.98
0.98
0.98
0.98
0.98
0.96
0.96
0.96
0.96
0.96
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
    15.3.     AC Activity Terms
In the MonthGroupHour table, ACActivityTerms A, B, and C are coefficients for a quadratic
equation that calculates air conditioning activity demand as a function of the heat index. These
terms are applied in the calculation of the A/C adjustment in the energy consumption calculator.
The methodology and the terms themselves were originally derived for MOBILE6 and are
documented in the report, Air Conditioning Activity Effects inMOBILE611 They are based on
analysis of air conditioning usage data collected in Phoenix, Arizona, in 1994.

In MOVES, ACActivityTerms are allowed to vary by monthGroup and Hour, in order to provide
the  possibility of different A/C activity demand functions at a given heat index by season and
time of day (this accounts for differences in solar loading observed in the original data).
However, for MOVES2014, the default data uses one set of coefficients for all MonthGroups and
Hours. These default coefficients represent an average A/C activity demand function over the
course of a full day. The coefficients are listed in Table 15-3.
                                          106

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                          Table 15-3 Air conditioning activity coefficients
A
-3.63154
B
0.072465
C
-0.000276
The A/C activity demand function that results from these coefficients is shown in Figure 15-1. A
value of 1 means the A/C compressor is engaged 100 percent of the time; a value of 0 means no
A/C compressor engagement.

                Figure 15-1 Air conditioning activity demand as a function of heat index
     0.9
     0.8

  I  0.7

  Ł  0.6
     0.5
     0.4
     0.3
     0.2
     0.1
70       75       80       85       90       95
                               Heat Index (F)
                                                              100
105
110
                                            1O7

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16.   Conclusion and Areas for  Future Research

Properly characterizing emissions from vehicles requires a detailed understanding of the cars and
trucks that make up the vehicle fleet and their patterns of operation. The national default
information in MOVES2014 provide a reliable basis for estimating national emissions. The most
important of these inputs are well-established: base year VMT and population estimates come
from long-term, systematic national measurements by US Department of Transportation.  The
emission characteristics for the most prevalent vehicle classes are well-known; base year age
distributions are well-measured, and driving activity has been the subject of much study in recent
years.

Still, the fleet and activity inputs do have significant limitations, and the uncertainties and
variability in this local data can contribute significant uncertainty in resulting emission estimates.
Thus it is often appropriate to replace many of the MOVES fleet and activity defaults with local
data as explained in EPA's Technical Guidance.2

The fleet and activity defaults also are limited by the necessity of forecasting future emissions.
EPA utilizes annual US Department of Energy forecasts of vehicle sales and activity,  but the
inputs for MOVES2014 were developed for a 2011 base year, and much of the source data is
from 2011 and earlier. This information needs to be updated periodically to assure that the model
defaults reflect the  latest available data and projections on the US fleet.

Updating the vehicle fleet data will be complicated by the fact that one of the primary data
sources for this  document, the Census Bureau's Vehicle Inventory and Use Survey, has been
discontinued. EPA is currently working with DOT and other federal agencies to revive this
survey.  Doing so becomes more important as the data gathered from the last survey (2002) ages.

A related complication is the cost of data. Collecting data on vehicle fleet and activity is
expensive, especially when the  data is intended to accurately represent the entire United States.
Even when EPA does not generate data directly (for example, compilations of state vehicle
registration data) obtaining the information needed for MOVES can be costly and, thus,
dependent on budget choices.

In addition to these general limitations, there are also specific MOVES data elements  that could
be improved with additional research, including:

       •  real-world highway  driving cycles and operating mode distributions;
       •  off-network behavior including vehicle starts and soaks;
       •  truck hotelling, particularly extended engine idling and APU use;
       •  idling while loading/unloading, in traffic queues (i.e. tolls), or elsewhere;
       •  VSP/STP adjustments for speed, road grade, and loading;
       •  activity changes with age, such as mileage accumulation rates, start activity, and  soak
          distributions;
       •  updated estimates of vehicle scrappage rates used to project vehicle age distributions;
       •  further incorporation of data from instrumented vehicle studies;
       •  summaries from large-scale instrumented vehicle studies;

                                          108

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       •  vehicle identification and sorting by size, sector, and vocation;
       •  activity weighting of source mass averages;
       •  air conditioning system usage, penetration and failure rates;
       •  vehicle type distinctions in temporal activity;
       •  heavy truck and bus daily trip activity patterns; and
       •  ramp activity and operating mode distributions.

We expect many of these MOVES data limitations can be addressed through analysis of data
captured on instrumented vehicles. The recent emergence and availability of large streams of
activity data from GPS devices, data loggers, and other onboard diagnostic systems will likely
lead to a better understanding of travel behavior. These data streams often provide frequent
sampling of real-world driving for a large number of vehicles, so they are ideally suited for
improving the nationally representative default inputs in MOVES. EPA is actively acquiring
such data for future MOVES updates.

Future updates to vehicle population and activity defaults will need to continue to focus on the
most critical elements required for national fleet-wide estimates, namely gasoline light-duty cars
and trucks, and diesel heavy-duty trucks. Information collection on motorcycles, refuse trucks,
motor homes, diesel light-duty vehicles and gasoline heavy-duty vehicles will be a lower
priority. In addition to updating the model defaults, we will need to consider whether the current
MOVES design continues to meet our modeling needs. Simplifications to the model to remove
categories, such as source types or road types, might make noticeable improvements in run time
without affecting the validity of fleet-wide emission estimates.

At the  same time, the fundamental MOVES assumption that vehicle activity varies by source
type and not by fuel type  or other source bin characteristic may be challenged by the growing
market share of alternative fuel vehicles, such as electric vehicles, which may have  distinct
activity patterns. As we progress with MOVES, the development of vehicle population and
activity inputs will continue to be an essential area of research.
                                           109

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17.  Appendix A: Projected Source Type Populations by Year
Table 17-1; Source type populations (in thousands), as derived from HPMS populations in §5.2. and the age distribution algorithm in §7.1.2.2.
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Motorcycle
(11)
8571
8687
8706
8747
8844
8943
9018
9098
9178
9260
9337
9416
9498
9585
9680
9781
9888
9996
10103
10215
10328
10439
10538
Passenger
Car
(21)
128033
129764
130054
130666
132117
133583
134715
135907
137105
138317
139471
140653
141880
143179
144593
146100
147713
149317
150922
152591
154280
155930
157420
Passenger
Truck
(31)
86859
87924
88014
88345
89259
90198
90934
91718
92513
93324
94098
94892
95725
96598
97557
98575
99664
100741
101823
102952
104098
105216
106225
Light
Comm.
Truck
(32)
21393
21791
21960
22167
22492
22803
23043
23279
23508
23730
23939
24150
24361
24591
24833
25092
25368
25649
25925
26209
26493
26772
27024
Intercity
Bus
(41)
18
19
20
21
22
22
23
23
23
24
24
25
25
26
26
27
27
27
28
28
28
28
29
Transit
Bus
(42)
69
72
74
77
80
82
84
86
87
88
90
92
93
95
97
98
99
100
101
103
104
105
106
School
Bus
(43)
617
643
663
691
720
740
753
766
780
794
809
824
838
853
867
879
889
900
912
922
931
942
956
Refuse
Truck
(51)
185
195
203
213
223
230
235
239
243
247
252
256
260
264
267
269
272
274
277
280
283
286
290
Single
Unit
Short-
haul
(52)
6194
6525
6777
7093
7392
7589
7709
7824
7953
8093
8242
8385
8510
8638
8752
8846
8927
9017
9114
9209
9286
9378
9493
Single
Unit
Long-
haul
(53)
260
274
285
299
312
322
328
333
335
340
345
351
352
357
362
366
371
375
376
377
381
385
391
Motor
Home
(54)
1559
1643
1708
1788
1863
1915
1946
1977
2012
2053
2095
2134
2168
2204
2239
2266
2288
2312
2340
2368
2385
2405
2432
Combination
Short-haul
(61)
1191
1234
1258
1306
1354
1380
1390
1400
1410
1422
1437
1453
1466
1482
1495
1505
1514
1527
1546
1567
1585
1609
1639
Combination
Long-haul
(62)
1280
1332
1377
1439
1503
1555
1600
1645
1690
1737
1783
1828
1872
1918
1964
2005
2040
2073
2104
2131
2152
2174
2203
                                                no

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Year
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Motorcycle
(11)
10633
10724
10813
10901
10983
11055
11155
11256
11357
11460
11564
11668
11774
11880
11988
12096
Passenger
Car
(21)
158833
160194
161523
162835
164062
165135
166628
168135
169655
171189
172737
174299
175875
177465
179069
180688
Passenger
Truck
(31)
107181
108102
109001
109888
110717
111441
112449
113466
114490
115523
116567
117620
118683
119756
120838
121931
Light
Comm.
Truck
(32)
27263
27494
27720
27944
28153
28338
28594
28852
29115
29380
29647
29916
30187
30460
30735
31013
Intercity
Bus
(41)
29
30
30
30
31
31
32
32
32
33
33
34
34
34
35
35
Transit
Bus
(42)
108
109
111
113
114
115
117
118
120
121
123
124
126
127
129
131
School
Bus
(43)
969
983
996
1009
1021
1034
1047
1060
1074
1087
1101
1115
1129
1143
1158
1172
Refuse
Truck
(51)
293
296
299
301
304
306
309
312
315
318
321
324
328
331
334
337
Single
Unit
Short-
haul
(52)
9599
9698
9795
9887
9968
10051
10147
10243
10342
10442
10543
10646
10749
10853
10958
11064
Single
Unit
Long-
haul
(53)
396
401
405
409
413
416
420
424
428
432
436
440
445
449
453
458
Motor
Home
(54)
2457
2482
2508
2532
2553
2573
2596
2620
2646
2672
2698
2725
2751
2778
2805
2832
Combination
Short-haul
(61)
1669
1701
1733
1766
1794
1822
1849
1876
1901
1925
1950
1975
2001
2028
2055
2083
Combination
Long-haul
(62)
2232
2260
2288
2315
2342
2371
2402
2435
2470
2507
2544
2581
2619
2656
2695
2733
111

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18.  Appendix B: Fuel Type and Regulatory Class Fractions

   for 1960-1981

As noted in the text, all the fuel type and regulatory class distributions in the
SampleVehiclePopulation table for model year 1981 and earlier have not changed from
MOVES2010b. Those fuel type distributions between 1960 and 1981 for each source type have
been summarized in Table 18-1 and Table 18-2. Many of the data sources for the fuel type
fractions are the same in MOVES2010b and MOVES2014. Truck diesel fractions in Table 18-1
are derived using a MOVES2010b sample vehicle counts dataset—similar to the MOVES2014
one—but with 1999 Polk vehicle registrations and the 1997 VIUS, except for refuse trucks and
motor homes. We assumed 96 percent of refuse trucks were manufactured to run on diesel fuel in
1980 and earlier according to the average diesel fraction from VIUS across all model years. We
also assumed that 15 percent of these motor homes are diesel powered based on information
from the Recreation Vehicle Industry Association (RVIA), as noted above in Section 6.2.2.5.

                       Table 18-1 Diesel fractions for truck source types*

Model
Year
1960-1979
1980
1981
Source Type
Passenger
Trucks
(31)
0.0139
0.0124
0.0178
Light
Commercial
Trucks
(32)
0.0419
0.1069
0.0706
Refuse
Trucks
(51)
0.96
0.96
0.96
Single Unit
Trucks
(52 & 53)
0.2655
0.2950
0.3245
Motor Homes
(54)
0.15
0.15
0.15
Short-haul
Combination
Trucks
(61)
0.9146
0.9146
0.9146
Long-haul
Combination
Trucks
(62)
1.0000
1.0000
1.0000
* All other trucks are assumed to be gasoline powered

As in MOVES2010b, lacking both emission rate and population data, we assume in
MOVES2014 that all motorcycles will be gasoline powered, all intercity buses will be diesel
powered over all model years, and all transit buses will be run on diesel from 1960 to 1981.
School bus fuel type fractions are reused from MOBILE6, originally based on 1996 and 1997
Polk data. Passenger cars are split between gasoline and diesel for 1960-1981 using the
MOVES2010b sample vehicle counts dataset.

                     Table 18-2 Diesel fractions for non-truck source types*

Model
Year
1960-1974
1975
1976
1977
1978
1979
1980
1981
Source Type
Motorcycles
(11)
0
0
0
0
0
0
0
0
Passenger
Cars
(21)
0.0069
0.0180
0.0165
0.0129
0.0151
0.0312
0.0467
0.0764
Intercity Buses
(41)
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Transit Buses
(42)
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
School Buses
(43)
0.0087
0.0087
0.0086
0.0240
0.0291
0.0460
0.0594
0.2639
         All other vehicles are assumed to be gasoline powered
                                         112

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The 1960-1981 regulatory class distributions have been derived from the MOVES2010b sample
vehicle counts dataset. Motorcycles (sourceTypelD 11 and regClassID 10) and passenger cars
(sourceTypelD 21 and regClassID 20) have one-to-one relationships between source types and
regulatory classes for all model years for both MOVES2010b and MOVES2014. Passenger
trucks (sourceTypelD 31) and light commercial trucks (sourceTypelD 32) are split between fuel
type and regulatory class (regClassID 30 and 40) as shown in Table 18-3.

  Table 18-3 Percentage by regulatory class and fuel type for passenger trucks (sourceTypelD 31) and light
commercial truck (source lyi

Model Year
1960-1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
Passenger Trucks (31)
Gasoline
LDT
(30)
81%
90%
88%
100%
99%
96%
96%
95%
95%
97%
95%
89%
85%
87%
90%
96%
LHD
(40)
19%
10%
12%
0%
1%
3%
4%
5%
5%
3%
5%
11%
15%
13%
10%
4%
Diesel
LDT
(30)
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
38%
LHD
(40)
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
62%
pelD 32)
Light Commercial Trucks (32)
Gasoline
LDT
(30)
24%
72%
67%
91%
80%
94%
75%
59%
65%
72%
88%
79%
81%
78%
74%
89%
LHD
(40)
76%
28%
33%
9%
20%
6%
25%
41%
35%
28%
12%
21%
19%
22%
26%
11%
Diesel
LDT
(30)
7%
7%
7%
7%
7%
7%
7%
7%
7%
7%
7%
7%
7%
7%
40%
12%
LHD
(40)
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
93%
60%
88%
The bus and motor home source types each have a single regulatory class distribution for all
model years, as described in Section 6.2.2. The 1960-1981 regulatory class distributions for
diesel-fueled single unit and combination trucks have been summarized in Table 18-4 below. All
1960-1981 gasoline-fueled single unit and combination trucks fall into the medium heavy-duty
(MHD) regulatory class (regClassID 46).
                                           113

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   Table 18-4 Percentage of MHD trucks (regClassID 46) among diesel-fueled single unit and combination
                                               trucks*

Model Year
1960-1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
Source Type
Refuse Trucks
(51)
100%
100%
0%
0%
0%
0%
0%
0%
0%
0%
Single Unit Trucks
(52&S3)
0%
3%
6%
14%
44%
43%
36%
34%
58%
47%
Short-haul Comb.
Trucks
(61)
0%
8%
30%
3%
13%
31%
18%
16%
29%
31%
Long-haul Comb.
Trucks
(62)
0%
0%
0%
0%
0%
0%
0%
0%
5%
6%
*For these source types, all remaining trucks are in the HHD regulatory class (regClassID 47).
                                                 114

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19.  Appendix C: 1990 Age Distributions

    19.1.     Motorcycles
The motorcycle age distributions are based on Motorcycle Industry Council estimates of the
number of motorcycles in use, by model year, in 1990. However, data for individual model years
starting from 1978 and earlier were not available. A logarithmic regression curve (R2 value =
0.82) was fitted to available data, which was then used to extrapolate age fractions for earlier
years beginning in 1978.

    19.2.     Passenger Cars
To determine the 1990 age fractions for passenger cars, we began with Polk NVPP® 1990 data
on car registration by model year. However, this data presents a snapshot of registrations on July
1, 1990, and we needed age fractions as of December 31, 1990. To adjust the values, we used
monthly data from the Polk new car database to estimate the number of new cars registered in
the  months July through December 1990. Model Year 1989 cars were added to the previous
estimate of "age 1" cars and Model Year 1990 and 1991 cars were added to the "age 0" cars.
Also the 1990 data did not detail model year for ages 15+. Hence, regression estimates were used
to extrapolate the age fractions for  individual ages 15+ based on an exponential curve (R2 value
=0.67) fitted to available data.

    19.3.     Trucks
For the 1990 age fractions for passenger trucks, light commercial trucks, refuse trucks, short-haul
and long-haul single unit trucks and short-haul and long-haul combination trucks, we used data
from the TIUS92 (1992 Truck Inventory and Use Survey) database. Vehicles in the TIUS92
database were assigned to MOVES source types as summarized in Table 19-1. Like VIUS97,
TIUS92 does not include a model year field and records ages as 0 through 10 and 11-and-greater.
Because we needed greater detail on the older vehicles, we followed the practice used for the
1999 fractions and determined the  model year for some of the older vehicles by using the
responses to the questions "How was the vehicle obtained?" (TIUS field "OBTAIN") and "When
did you obtain this vehicle?" (TIUS field "ACQYR") and we adjusted the age-11-and-older
vehicle counts by dividing the original count by model year by the fraction of the older vehicles
that were coded as "obtained new."
                                         115

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                Table 19-1 VIUS1997 codes used for distinguishing truck source types
Source Type
Passenger Trucks
Light Commercial
Trucks
Refuse Trucks
Single Unit Short-
haul Trucks
Single Unit Long-
haul Trucks
Combination Short-
haul Trucks
Combination Long-
haul Trucks
Axle Arrangement
2 axle/4 tire (AXLRE=
1,5,6,7)
2 axle/4 tire (AXLRE=
1,5,6,7)
Single Unit
(AXLRE=2-4, 8-16)
Single Unit
(AXLRE=2-4, 8-16)
Single Unit
(AXLRE=2-4, 8-16)
Combination
(AXLRE>=17)
Combination
(AXLRE>=17)
Primary Area of
Operation
Any
Any
Off-road, local or
short-range
(AREAOP <=4)
Off-road, local or
short-range
(AREAOP<=4)
Long-range
(AREAOP>=5)
Off-road, local or
medium
(AREAOP<=4)
Long-range
(AREAOP>=5)
Body Type
Any
Any
Garbage hauler
(BODTYPE=30)
Any except garbage
hauler
Any
Any
Any
Major Use
personal
transportation
(MAJUSE=20)
any but personal
transportation
Any
Any
Any
Any
Any
    19.4.     Intercity Buses
For 1990, we were not able to identify a data source for estimating age distributions of intercity
buses. Because the purchase and retirement of these buses is likely to be driven by general
economic forces rather than trends in government spending, we will use the 1990 age
distributions that were derived for short-haul combination trucks, as described above.

    19.5.     School Buses and Motor Homes
To determine the age fractions of school buses and motor homes, we used information from the
Polk TIP® 1999 database. School bus and motor home counts were available by model year.
Unlike the Polk data for passenger cars, these counts reflect registration at the end of the
calendar year and, thus,  did not require adjustment. We converted model year to age and
calculated age fractions. Because we did not have access to 1990 data, these fractions were used
for  1990.

    19.6.     Transit Buses
For 1990 Transit Bus age distributions, we used the MOBILE6 age fractions since 1990 data on
transit buses was not available from the Federal Transit Administration  database. MOBILE6 age
fractions were based on  fitting curves through a snapshot of vehicle registration data as of July 1,
1996, which was purchased from R.L. Polk Company. To develop a general curve, the 1996
model year vehicle populations were removed from the sample because it did not represent a full
year, and a best-fit analysis was performed on the remaining population data. The best-fit
analyses resulted in age  distribution estimates for vehicles ages 1 through 25+. However, since
the  vehicle sales year begins in October, the estimated age 1 population was multiplied by 0.75
to account for the fact that approximately 75 percent of the year's sales will have occurred by
July 1st of a given calendar year.
Both Weibull curve fitting and exponential curve fitting were used to create the age distributions.
The nature of the Weibull curve fitting formula is to produce an "S" shaped curve, which is
                                         116

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relatively flat for the first third of the data, decreases rapidly for the next third, and flattens again
for the final third. While using this formula resulted in a better overall fit for transit buses, the
flatness of the final third for each curve resulted in unrealistically low vehicle populations for the
older vehicle ages. For this reason, the original Weibull curve was used where it fit best, and
exponential curves were fit through the data at the age where the Weibull curves began to flatten.
Table 19-2 presents the equations used to create the age distribution and the years in which the
equations were used.

                Table 19-2 Curve fit equations for registration distribution data by age
Vehicle
Age
1-17
18-25+
Equation
If age ,.12.53214119^
y = 3462 * e ^17.16909475^
24987.0776 * a'0-2000
)
*age
                                             117

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20.  Appendix D: Driving Schedules and SCC Mappings
   20.1.     SCC Mappings
The Source Classification Code (SCC) used before MOVES2014 do not cleanly map to the
source types used by MOVES.  In the 10-digit SCC, the first seven digits (SCC7) indicate the
vehicle classification. The SCC vehicle classifications were mapped to the source types used by
MOVES by calculating the fraction VMT for each source type found in each SCC classification
result in a national MOVES2010b run for calendar year 2011. The factors calculated from the
MOVES201b run are shown in Table 20-1.

 Table 20-1 Mapping of previous SCC vehicle classifications to MOVES source types for calculation of road
                                  type distributions
SCC
(7 digits)
2201001
2201020
2201020
2201040
2201040
2201070
2201070
2201070
2201070
2201070
2201070
2201070
2201070
2201070
2201080
2230001
2230060
2230060
2230071
2230071
2230072
Description
Gasoline Light-Duty Vehicles (Passenger Cars)
Gasoline Light-Duty Trucks (0-6,000 Ibs. GVWR)
Gasoline Light-Duty Trucks (0-6,000 Ibs. GVWR)
Gasoline Light-Duty Trucks (6,001-8,500 Ibs. GVWR)
Gasoline Light-Duty Trucks (6,001-8,500 Ibs. GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Heavy-Duty Gasoline Vehicles (8501 Ibs.
and greater GVWR)
Gasoline Motorcycles
Diesel Light-Duty Vehicles (Passenger Cars)
Diesel Light-Duty Trucks (0-8,500 Ibs. GVWR)
Diesel Light-Duty Trucks (0-8,500 Ibs. GVWR)
Diesel Class 2b Heavy-Duty Vehicles (8501-10,000
Ibs. GVWR)
Diesel Class 2b Heavy-Duty Vehicles (8501-10,000
Ibs. GVWR)
Diesel Class 3, 4 & 5 Heavy-Duty Vehicles (10,001-
19,500 Ibs. GVWR)
Source
Type
21
31
32
31
32
31
32
42
43
51
52
53
54
61
11
21
31
32
31
32
31
Description
Passenger Car
Passenger Truck
Light Commercial
Truck
Passenger Truck
Light Commercial
Truck
Passenger Truck
Light Commercial
Truck
Transit Bus
School Bus
Refuse Truck
Single Unit Short-
haul Truck
Single Unit Long-
haul Truck
Motor Home
Combination Short-
haul Truck
Motorcycle
Passenger Car
Passenger Truck
Light Commercial
Truck
Passenger Truck
Light Commercial
Truck
Passenger Truck
2011
Fractions
1.000000
0.779270
0.220730
0.779269
0.220731
0.450274
0.267803
0.000664
0.002476
0.000509
0.221958
0.030154
0.025802
0.000359
1.000000
1.000000
0.343599
0.656401
0.364691
0.635309
0.305092
                                       118

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sec
(7 digits)
2230072
2230073
2230073
2230073
2230073
2230073
2230073
2230074
2230074
2230074
2230074
2230074
2230074
2230075
2230075
2230075
Description
Diesel Class 3, 4 & 5 Heavy-Duty Vehicles (10,001-
19,500 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-
33,000 Ibs. GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001
Ibs. and greater GVWR)
Diesel Buses
Diesel Buses
Diesel Buses
Source
Type
32
51
52
53
54
61
62
51
52
53
54
61
62
41
42
43
Description
Light Commercial
Truck
Refuse Truck
Single Unit Short-
haul Truck
Single Unit Long-
haul Truck
Motor Home
Combination Short-
haul Truck
Combination Long-
haul Truck
Refuse Truck
Single Unit Short-
haul Truck
Single Unit Long-
haul Truck
Motor Home
Combination Short-
haul Truck
Combination Long-
haul Truck
Intercity Bus
Transit Bus
School Bus
2011
Fractions
0.694908
0.001726
0.623978
0.086570
0.025294
0.194650
0.067783
0.008531
0.100296
0.013800
0.000328
0.323425
0.553619
0.430859
0.122565
0.446576
   20.2.     Driving Schedules
A key feature of MOVES is the capability to accommodate a number of drive schedules to
represent driving patterns across source type, roadway type and average speed. For the national
default case, MOVES2014 employs 49 drive schedules with various average speeds, mapped to
specific source types and roadway types.

Table 20-2 below lists the driving schedules used in MOVES2014. Some driving schedules are
used for both restricted access (freeway) and unrestricted access (non-freeway) driving.  Some
driving schedules are used for multiple source types or multiple road types where vehicle
specific information was not available.
                                          119

-------
Table 20-2 MOVES2014 default driving schedule statistics
drive
schedule id
101
153
158
201
202
203
204
205
206
251
252
253
254
255
301
302
303
304
305
306
351
352
353
354
355
396
397
drive schedule name
LD Low Speed 1
LD LOS E Freeway
LD High Speed Freeway 3
MD 5mph Non-Freeway
MD lOmph Non-Freeway
MD 15mph Non-Freeway
MD 20mph Non-Freeway
MD 25mph Non-Freeway
MD 30mph Non-Freeway
MD 30mph Freeway
MD 40mph Freeway
MD 50mph Freeway
MD 60mph Freeway
MD High Speed Freeway
HD 5mph Non-Freeway
HD lOmph Non-Free way
HD 15mph Non-Freeway
HD 20mph Non-Freeway
HD 25mph Non-Freeway
HD 3 Omph Non-Free way
HD 3 Omph Freeway
HD 40mph Freeway
HD 50mph Freeway
HD 60mph Freeway
HD High Speed Freeway
HD High Speed Freeway Plus 5 mph
MD High Speed Freeway Plus 5 mph
avg
speed
2.5
30.5
76.0
4.6
10.7
15.6
20.8
24.5
31.5
34.4
44.5
55.4
60.1
72.8
5.8
11.2
15.6
19.4
25.6
32.5
34.3
47.1
54.2
59.7
71.7
76.7
77.8
max
speed
10.00
63.00
90.00
24.10
34.10
36.60
44.50
47.50
55.90
62.60
70.40
72.20
68.40
80.40
19.90
29.20
38.30
44.20
50.70
58.00
62.70
65.00
68.00
69.00
81.00
86.00
85.40
idle
time
(sec)
280
5
0
85
61
57
95
63
54
0
0
0
0
0
37
70
73
84
57
43
0
0
0
0
0
0
0
percent of
time idling
46.5%
1.1%
0.0%
29.0%
19.6%
12.6%
9.1%
11.1%
5.5%
0.0%
0.0%
0.0%
0.0%
0.0%
14.2%
11.5%
12.9%
15.1%
5.8%
5.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
miles
0.419
3.863
12.264
0.373
0.928
1.973
6.054
3.846
8.644
15.633
43.329
41.848
81.299
96.721
0.419
1.892
2.463
3.012
6.996
7.296
21.659
41.845
80.268
29.708
35.681
38.170
103.363
time (sec)
602.00
456.00
581.00
293.00
311.00
454.00
1046.00
566.00
988.00
1637.00
3504.00
2718.00
4866.00
4782.00
260.00
608.00
567.00
558.00
983.00
809.00
2276.00
3197.00
5333.00
1792.00
1792.00
1792.00
4782.00
minutes
10.03
7.60
9.68
4.88
5.18
7.57
17.43
9.43
16.47
27.28
58.40
45.30
81.10
79.70
4.33
10.13
9.45
9.30
16.38
13.48
37.93
53.28
88.88
29.87
29.87
29.87
79.70
hours
0.167
0.127
0.161
0.081
0.086
0.126
0.291
0.157
0.274
0.455
0.973
0.755
1.352
1.328
0.072
0.169
0.158
0.155
0.273
0.225
0.632
0.888
1.481
0.498
0.498
0.498
1.328
                           12O

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Table 20-2 MOVES2014 default driving schedule statistics
drive
schedule id
398
401
402
403
404
405
501
1009
1011
1017
1018
1019
1020
1021
1024
1025
1026
1029
1030
1033
1041
1043
drive schedule name
CRC E55 HHDDT Creep
Bus Low Speed Urban (nominal 15 mph)
Bus 30 mph Flow (nominal 30 mph)
Bus 45 mph Flow (nominal 45 mph)
New York City Bus
WMATA Transit Bus
Refuse Truck Urban
Final FC01LOSAF Cycle (C10R04-
00854)
Final FC02LOSDF Cycle (C10R05-
00513)
Final FC11LOSB Cycle (C10R02-00546)
Final FC11LOSC Cycle (C15R09-00849)
Final FC11LOSD Cycle (C15R10-00068)
Final FC11LOSE Cycle (C15R11-00851)
Final FC11LOSF Cycle (C15RO 1-00876)
Final FC12LOSC Cycle (C15R04-00582)
Final FC12LOSD Cycle (C15R09-00037)
Final FC12LOSE Cycle (C15R10-00782)
Final FC14LOSB Cycle (C15R07-00177)
Final FC14LOSC Cycle (C10R04-00104)
Final FC14LOSF Cycle (C15R05-00424)
Final FC17LOSD Cycle (C15R05-00480)
Final FC19LOSAC Cycle (C15R08-
00267)
avg
speed
1.8
3.1
11.5
21.9
3.7
8.3
2.2
73.8
49.1
66.4
64.4
58.8
46.1
20.6
63.7
52.8
43.3
31.0
25.4
8.7
18.6
15.7
max
speed
8.24
19.80
33.80
47.00
30.80
47.50
20.00
84.43
73.06
81.84
78.19
76.78
71.50
55.48
79.39
73.15
70.87
63.81
53.09
44.16
50.33
37.95
idle
time
(sec)
107
288
109
116
403
706
416
0
34
0
0
0
1
23
0
12
0
27
41
326
114
67
percent of
time idling
42.3%
63.9%
37.5%
28.3%
67.2%
38.4%
66.9%
0.0%
5.0%
0.0%
0.0%
0.0%
0.1%
2.5%
0.0%
1.5%
0.0%
3.6%
8.0%
38.2%
16.1%
7.7%
miles
0.124
0.393
0.932
2.492
0.615
4.261
0.374
11.664
9.283
9.567
16.189
11.922
12.468
5.179
15.685
11.754
10.973
6.498
3.617
2.066
3.659
3.802
time (sec)
253.00
451.00
291.00
410.00
600.00
1840.00
622.00
569.00
681.00
519.00
905.00
730.00
973.00
905.00
887.00
801.00
913.00
754.00
513.00
853.00
709.00
870.00
minutes
4.22
7.52
4.85
6.83
10.00
30.67
10.37
9.48
11.35
8.65
15.08
12.17
16.22
15.08
14.78
13.35
15.22
12.57
8.55
14.22
11.82
14.50
hours
0.070
0.125
0.081
0.114
0.167
0.511
0.173
0.158
0.189
0.144
0.251
0.203
0.270
0.251
0.246
0.223
0.254
0.209
0.143
0.237
0.197
0.242
                           121

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21.  Appendix E: MOVES2010b Source Masses

Light-duty source masses were unchanged from MOVES2010b. In addition, the heavy-duty
source masses originally come from MOVES2010b, although they have been updated as
described in Section 14.1.

In MOVES2010b, weight data (among other kinds of information) were used to allocate source
types to source bins using a field called weightClassID. As described in Equation 40, each source
type's source mass was calculated using an activity-weighted average of their associated source
bins' midpoint weights:
                              M =
                                              i ab • 77T
                                    ja]ja  \  v. „.   n                   Equation 40
                                           ia) a
where M is the source mass factor for the source type, fa is the age fraction at age a, ab is the
source bin activity fraction for source bin b, and m is the vehicle midpoint mass. Table 21-1 lists
the vehicle midpoint mass for each weightClassID. The source bin activity fraction in
MOVES2010b is a calculated value of activity based on fuel type, engine technology, regulatory
class, model year, engine size, and weight class. This calculation is outside the scope of this
document, but more information can be found in the MOVES2010b SDRM.
                                         122

-------
                             Table 21-1 MOVES weight classes
WeightClassID
0
20
25
30
35
40
45
50
60
70
80
90
100
140
160
195
260
330
400
500
600
800
1000
1300
9999
5
7
9
Weight Class Name
Doesn't Matter
weight < 2000 pounds
2000 pounds <= weight < 2500 pounds
2500 pounds <= weight < 3000 pounds
3000 pounds <= weight < 3500 pounds
3500 pounds <= weight < 4000 pounds
4000 pounds <= weight < 4500 pounds
4500 pounds <= weight < 5000 pounds
5000 pounds <= weight < 6000 pounds
6000 pounds <= weight < 7000 pounds
7000 pounds <= weight < 8000 pounds
8000 pounds <= weight < 9000 pounds
9000 pounds <= weight < 10000 pounds
10000 pounds <= weight < 14000 pounds
14000 pounds <= weight < 16000 pounds
16000 pounds <= weight < 19500 pounds
19500 pounds <= weight < 26000 pounds
26000 pounds <= weight < 33000 pounds
33000 pounds <= weight < 40000 pounds
40000 pounds <= weight < 50000 pounds
50000 pounds <= weight < 60000 pounds
60000 pounds <= weight < 80000 pounds
80000 pounds <= weight < 100000 pounds
100000 pounds <= weight < 130000 pounds
130000 pounds <= weight
weight < 500 pounds (for MCs)
500 pounds <= weight < 700 pounds (for MCs)
700 pounds <= weight (for MCs)
Midpoint Weight
[NULL]
1000
2250
2750
3250
3750
4250
4750
5500
6500
7500
8500
9500
12000
15000
17750
22750
29500
36500
45000
55000
70000
90000
115000
130000
350
600
700
The following sections detail how weight classes were assigned to the various source types in
MOVES.

   21.1.     Motorcycles
The Motorcycle Industry Council "Statistical Annual" provides information on displacement
distributions for highway motorcycles for model years 1990 and 1998. These were mapped to
MOVES engine displacement categories. Additional EPA certification data was used to
establish displacement distributions for model year 2000. We assumed that displacement
distributions were the same in 1969 as in 1990, and interpolated between the established values
to determine displacement distributions for all model years from 1990 to 1997 and for 1999.
Values for 2000-and-later model years are based on model year 2000 certification data.

We then applied weight distributions for each displacement category as suggested by EPA
motorcycle experts. The average weight estimate includes fuel  and rider. The weight
distributions depended on engine displacement but were otherwise independent of model year.
This information is summarized in Table 21-2.
                                         123

-------
       Table 21-2 Motorcycle engine size and average weight distributions for selected model years
Displacement
Category
0-169 cc (1)
170-279 cc (2)
280+ cc (9)
1969 MY
distribution
(assumed)
0.118
0.09
0.792
1990 MY
distribution
(MIC)
0.118
0.09
0.792
1998 MY
distribution
(MIC)
0.042
0.05
0.908
2000 MY
distribution
(certification
data)
0.029
0.043
0.928
Weight distribution (EPA
staff)
100%: <=5001bs
50%: <=5001bs
50%: 5001bs -7001bs
30%: 500 lbs-700 Ibs
70%: >7001bs
   21.2.     Passenger Cars
Passenger car weights come from Polk. The weightClassID was assigned by adding 300 Ibs to
the Polk curb weight and grouping into MOVES weight bins.  For each fuel type, model year,
engine size, and weight bin, the number of cars was summed and fractions were computed. In
general, entries for which data was missing were omitted from the calculations. Also, analysis
indicated a likely error in the Polk data (an entry for 1997 gasoline-powered Bentleys with
engine size 5099 and weight class 20). This  fraction was removed and the 1997 values were
renormalized. 1999 model year values were  used for all 2000-and-later model years.


   21.3.     General Trucks
          21.3.1.    Light-Duty Trucks
Determining weight categories for light trucks was fairly complicated.  The VIUS1997 data
combines information from two different survey forms. The first form was administered for
VIUS "Strata" 1 and 2 trucks:  pickup trucks, panel trucks, vans (including mini-vans), utility
type vehicles (including jeeps) and station wagons on truck chassis. The second form was
administered for all other trucks. While both surveys requested information on engine size, only
the second form requested detailed information on vehicle weight. Thus for Strata  1 and 2
trucks, VIUS classifies the trucks only by broad average weight category (AVGCK): 6,000 Ibs or
less, 6,001-10,000 Ibs, 10,001-14,0001bs, etc. To determine a more detailed average engine size
and weight distribution for these vehicles, we used an Oak Ridge National Laboratory (ORNL)
light-duty vehicle database, compiled from EPA test vehicle data and Ward's Automotive Inc.78
data, to correlate engine size with vehicle weight distributions by model year.

In particular, for source types 31 and 32 (Passenger Trucks and Light Commercial Trucks):
   •   VIUS1997 trucks of the source type  in Strata 3, 4, and 5 were assigned to the appropriate
       MOVES weight class based on VIUS detailed average weight information.
   •   VIUS 1997 trucks of the source type  in Strata 1 and 2 were identified by engine size and
       broad average weight category.
   •   Strata 1 and 2 trucks in the heavier (10,001-14,000 Ibs, etc) VIUS1997 broad categories
       were matched one-to-one with the MOVES weight classes.
   •   For trucks in the lower broad categories (6,000 Ibs or less and 6001-10,000  Ibs), we used
       VIUS 1997 to determine the fraction  of trucks by model year and fuel type that fell into
       each  engine size/broad weight class combination (the "VIUS fraction").
                                          124

-------
   •   We assigned trucks in the ORNL light-duty vehicle database to a weightClassID by
       adding SOOlbs to the recorded curb weight and determining the appropriate MOVES
       weight class.
   •   For the trucks with a VIUS1997 average weight of 6,000 Ibs or less, we multiplied the
       VIUS1997 fraction by the fraction of trucks with a given weightClassID among the
       trucks in the ORNL database that had the given engine size and an average weight of
       6,000 Ibs or less.  Note, the ORNL database did not provide information on fuel type, so
       the same distributions were used for all fuels.
   •   Because the ORNL database included only vehicles with a GVW up to 8500 Ibs, we did
       not use it to distribute the trucks with a VIUS1997 average weight of 6,001-10,000 Ibs.
       Instead these were distributed equally among the MOVES weightClassID 70, 80, 90 and
       100.

          21.3.2.    Single Unit Trucks
Source types 52 and 53 (long- and short-haul single unit trucks) also included some trucks in
VIUS1997 Strata 1 and 2, thus a similar algorithm was applied.

   •   VIUS1997 trucks of the source type in Strata 3, 4,  and 5 were assigned to the appropriate
       MOVES weight class based on VIUS1997 detailed average weight information.
   •   VIUS1997 trucks of the source type in Strata 1 and 2 were identified by engine size and
       broad average weight category.
   •   Strata 1 and 2 trucks in the heavier (10,001-14,000 Ibs, etc) VIUS1997 broad categories
       were matched one-to-one with the MOVES weight classes.
   •   For trucks in the lower broad categories (6,000 Ibs-or-less and 6001-10,000 Ibs), we used
       VIUS1997 to determine the fraction of trucks by model year and fuel type that fell into
       each engine size/broad weight class combination (the "VIUS fraction").
   •   We did not believe the ORNL light-duty vehicle database adequately represented single
       unit trucks. Thus, the trucks with a VIUS 1997 average weight of 6,000 Ibs or less  and an
       engine size less than 5 liters were distributed equally among the MOVES weight classes
       20, 25, 30, 35, 40, 45,  50, and 60. Because no evidence existed of very light trucks
       among the vehicles with larger engines (5 liter or larger), these were equally distributed
       among MOVES weight classes 40, 45, 50 and 60.
   •   The trucks with a VIUS 1997 average weight of 6,001-10,000 Ibs were distributed  equally
       among the MOVES weight classes 70, 80, 90 and  100.

          21.3.3.    Combination Trucks
Long- and short-haul  combination trucks (source types 61 and 62) did not include any vehicles
of VIUS 1997 Strata 1 or  2. Thus we used the detailed VIUS 1997 average weight information
and engine size information to assign engine size and weight classes for all of these trucks.

When VIUS2002 became available, we updated values that had been based on VIUS 1997. The
VIUS2002 contains an estimate of the average weight (vehicle weight plus cargo weight)  of
1998-2002 model year vehicle or vehicle/trailer combination as it was most often operated when

                                         125

-------
carrying a typical payload during 2002. These estimates were used to determine the MOVES
weightClassID categories for these trucks. Any vehicles with a zero or missing value for the
average weight and without a weight classification in the WeightAvgCK field were excluded
from the analysis for determining the average weight distributions.

Since there is a smaller number of gasoline trucks among the single unit and refuse trucks, all
model years (1998-2002) were combined to determine a single weight distribution to use for
these model years. The VIUS1997 based estimates were retained for light-duty trucks (source
types  31 and 32) and for all model years prior to 1998.

In cases where distributions were missing (no survey information), distributions from a nearby
model year with the same source type was used. Weight distributions for all 2003 and newer
model years were set to be  the same as for the 2002 model year for each source type.

   21.4.      Buses
For intercity buses, we used information from Table II-7 of the FT A 2003 Report to Congress43
that specified the number of buses in various weight categories. This information is summarized
in below in Table 21-3. Note the FT A uses the term "over-the-road bus" to refer to the class of
buses roughly equivalent to the MOVES intercity bus category. The FTA weight categories
were mapped to the equivalent MOVES weight classes.

                            Table 21-3 FTA estimates of bus weights
Weight (Ibs)
0-20,000
20,000-30,000
30,000-40,000
40,000-50,000
total
MOVES Weight
ClassID


400
500

MOVES Weight
Range (Ibs)


33,000-40,000
40,000-50,000

Number of
buses (2000)
173,536
392,345
120,721
67,905
754,509
Bus type
school & transit
school & transit
school & transit & intercity
intercity

                    Table 21-4 1999 bus population comparisons by data source
Data Source
FHWAMV-1
FHWA MV-10
(excludes PR)
FHWA adjusted for PR
FTANTD
APT A79 ***
Polk TIP®
School Bus Fleet Fact
Book
Motorcoach Census42**
Total Buses
732,189
728,777






Intercity Buses







44,200
Transit Buses



55,706
75,087



School Buses

592,029*
594,800


460,178
429,086

* Includes some church & industrial buses.
** Includes Canada.
*** Includes trolleybuses.
                                          126

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Using the 1999 bus population estimates in Table 21-4, we were able to estimate the fraction of
all buses that were intercity buses and then to estimate the fraction of intercity buses in each
weight bin. In particular:

  Estimated number of intercity buses in 2000:

                          754,509 * (84,4547(84,454+55,706+592,029)) = 87,028

  Estimated number of intercity buses 30,000-40,000 Ibs:

                                                     87,028-67,905 = 19,123
  Estimated intercity bus weight distribution:
                                             Class 400 = 19,123/87,028 = 22%
                                             Class 500 = 67,905/87,028 = 78%
This distribution was used for all model years.
For transit buses, we took average curb weights from Figure II-6 of the FT A Report to
Congress43 and added additional weight to account for passengers and alternative fuels. The
resulting in-use weights were all in the range from 33,850 to 40,850. Thus all transit buses were
assigned to the weight class "400" (33,000 - 40,000 Ibs) for all model years. This estimate could
be improved if more detailed weight information for transit buses becomes available.

For school buses, we used information from a survey of California school buses. While this data
is older and may not be representative of the national average distribution, it was the best data
source available. The California data80 provided information on number of vehicles by gross
vehicle weight class and fuel as detailed in Table 21-5.

                 Table 21-5 California school bus study weight classes and fuel types

LHDV
MHDV
HHDV
Total
Gas
2740
467
892
4099
Diesel
4567
2065
11639
18271
Other
8
2
147
157
Total
7315
2534
12678

To estimate the distribution of average weights among the MOVES weight classes, we assumed
that the Light Heavy-Duty (LHDV) school buses were evenly distributed among weightClassIDs
70, 80, 90, 100, and 140. Similarly, we assumed the Medium Heavy-Duty (MHDV) school buses
were evenly distributed among weightClassIDs 140, 160, 195, 260, and 330 and the Heavy
Heavy-Duty (HHDV) school buses were evenly distributed among weightClassIDs 195, 260,
330, and 440.

The final default weight distributions for buses are summarized in Table 21-6.
                                          127

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                      Table 21-6 Weight distributions for buses by fuel type

Weight Class
70
80
90
100
140
160
195
260
330
400
500
Intercity Buses (41)
Diesel









0.2197
0.7800
Transit Buses (42)
Diesel & Gas









1.0000

School Buses (43)
Diesel
0.0500
0.0500
0.0500
0.0500
0.0726
0.0226
0.1819
0.1819
0.1819
0.1593

Gas
0.1337
0.1337
0.1337
0.1337
0.1565
0.0228
0.0772
0.0772
0.0772
0.0544

   21.5.     Refuse Trucks
Because the sample of Refuse Trucks in VIUS was small, the weight distributions were
calculated for model year groups rather than individual model years, shown below in Table 21-7.
As for other trucks, the WeightClass was determined from the VIUS reported average weight.
                                         128

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Table 21-7 Refuse truck SizeWeight fractions by fuel type
Gasoline
Engine Size
3-3. 5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
Sum

Diesel
Engine Size
3.5-4L
4-5L
4-5L
4-5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
>5L
Sum

Weight (Ibs.)
5000-6000
7000-8000
9000-10000
10000-14000
14000-16000
16000-19500
19500-26000
26000-33000
33000-40000
50000-60000



Weight (Ibs.)
10000-14000
10000-14000
14000-16000
16000-19500
9000-10000
10000-14000
14000-16000
16000-19500
19500-26000
26000-33000
33000-40000
40000-50000
50000-60000
60000-80000
80000-100000
100000-130000


Pre-1997
0.009074
0.148826
0.070720
0.135759
0.199961
0.055085
0.205341
0.022105
0.153129
0
1.000000


Pre-1998
0.007758
0
0
0
0.006867
0.011727
0.022960
0.063128
0.099782
0.102077
0.237485
0
0.336484
0.111730
0
0
1.000000

1997 and
Newer
0
0
0
0.324438
0.593328
0
0
0
0
0.082234
1.000000


1998
0
0
0
0
0.009593
0
0
0
0.035378
0.019625
0.103922
0.283642
0.338511
0.196424
0
0.012904
1.000000















1999
0
0
0
0
0
0
0
0.011367
0.026212
0.067419
0.088975
0.275467
0.326902
0.193238
0.010420
0
1.000000















2000
0
0
0.015505
0
0
0
0
0.047200
0.052132
0.072106
0.085991
0.165624
0.384612
0.176831
0
0
1.000000















2001
0
0
0
0.011670
0
0.019438
0
0
0.018329
0.043877
0.042678
0.266357
0.315133
0.282517
0
0
1.000000















2002 and
Newer
0
0.006614
0
0
0
0
0
0
0.026079
0
0.046966
0.194716
0.474469
0.224995
0.013081
0.013081
1.000000
   21.6.     Motor Homes
No detailed information was available on average engine size and weight distributions for motor
homes. We assumed all motor home engines were 5 L or larger. As a surrogate for average
weight, we used information on gross vehicle weight provided in the Polk TIP® 1999 database
by model year and mapped the Polk GVW Class to the MOVES weight bins. These values are
likely to overestimate average weight. The Polk TIP® information did not specify fuel type, so
we assumed that the heaviest vehicles in the Polk database were diesel-powered and the
remainder were powered by gasoline. This led to the weight distributions in Table 21-8 and
Table 21-9.
                                         129

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Table 21-8 Weight fractions for diesel motor homes by model year
Polk GVW bin
MOVES weight
class
Model Year
1975-and-earlier
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999+
3
140
4
160
5
195
6
260
7
330
8
400
Diesel
0.171431
0.637989
0.68944
0.423524
0.096922
0.462916
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.792112
0.340639
0.292308
0.574539
0.899344
0.537084
0.941973
0.868333
0.912762
0.932659
0.881042
0.855457
0.791731
0.72799
0.73298
0.173248
0
0
0
0
0
0
0
0
0
0.029828
0.018755
0.012168
0
0
0
0
0
0.000203
0.000835
0.001474
0.013381
0.085493
0.148917
0.128665
0.614798
0.619344
0.551548
0.345775
0.45546
0.635861
0.553807
0.666905
0.267
0
0
0.000436
0.000277
0.000387
0.001067
0
0.030174
0.049
0.014845
0.009183
0.010761
0.022962
0.022498
0.015469
0.043052
0.043628
0.063712
0.01901
0.471873
0.354386
0.163195
0.229529
0.193167
0.335069
0.736656
0.006629
0.002181
0.005531
0.00155
0.002667
0
0
0.03
0.030096
0.036732
0.083285
0.089534
0.087164
0.093335
0.082792
0.149939
0.296399
0.385085
0.144844
0.159622
0.17468
0.184208
0.111299
0.357508
0.233886
0
0
0.000277
0
0
0
0.027853
0.052667
0.042094
0.020592
0.023438
0.018667
0.013113
0.014289
0.012511
0.018387
0.020545
0.044356
0.037509
0.030531
0.026264
0.032456
0.028628
0.040423
0.029458
                           130

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Table 21-9 Weight fractions for gasoline motor homes by model year
Polk GVW bin
MOVES weight class
Model Year
1975-and-earlier
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999+
3
140
4
160
5
195
6
260
7
330
8
400
Gasoline
1
1
1
1
1
1
0.747723
0.732235
0.714552
0.641577
0.692314
0.720248
0.606635
0.459429
0.551601
0.543354
0.612025
0.54464
0.583788
0.481099
0.52997
0.435959
0.221675
0.288222
0.170133
0
0
0
0
0
0
0.252277
0.267765
0.285448
0.358423
0.307686
0.279752
0.393365
0.540571
0.448399
0.456646
0.322022
0.373999
0.361277
0.361146
0.198479
0.289453
0.433334
0.581599
0.392451
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.065952
0.081361
0.054935
0.157755
0.271551
0.274588
0.344991
0.13018
0.288411
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.149004
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
                            131

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                                         133

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                                          134

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                                         135

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                                         136

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78
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