Population and Activity of On-road 

Vehicles in MOVES2014

June 2015

United States Environmental Protection Agency

Office of Transportation and Air Quality

Assessment and Standards Division
1
2

Table of Contents

1. Introduction............................................................................................................................. 4

2. MOVES Vehicle and Activity Classifications........................................................................ 6

2.1. HPMS Class.................................................................................................................... 6

2.2. Source Use Types ........................................................................................................... 6

2.3. Regulatory Classes.......................................................................................................... 7

2.4. Fuel Types....................................................................................................................... 8

2.5. Road Types ..................................................................................................................... 9

2.6. Source Classification Codes (SCC) .............................................................................. 10

2.7. Source Bins................................................................................................................... 11

2.8. Allowable Vehicle Modeling Combinations ................................................................ 12

2.9. Default Inputs and Fleet and Activity Generators ........................................................ 14

3. Data Sources ......................................................................................................................... 18

3.1. VIUS ............................................................................................................................. 18

3.2. Polk NVPP® and TIP®................................................................................................ 18

3.3. EPA Sample Vehicle Counts........................................................................................ 18

3.4. FHWA Highway Statistics............................................................................................ 18

3.5. FTA National Transit Database .................................................................................... 19

3.6. School Bus Fleet Fact Book.......................................................................................... 19

3.7. MOBILE6 ..................................................................................................................... 19

3.8. Annual Energy Outlook & National Energy Modeling System.................................... 20

3.9. Transportation Energy Data Book ................................................................................ 20

3.10. FHWA Weigh-in-Motion.............................................................................................. 20

3.11. Motorcycle Industry Council Statistical Annual........................................................... 20

4. VMT by Calendar Year and Vehicle Type ........................................................................... 21

4.1. Historic Vehicle Miles Traveled (1990 and 1999-2011).............................................. 21

4.2. Projected Vehicle Miles Traveled (2012-2050)............................................................ 22

5. Vehicle Populations by Calendar Year................................................................................. 25

5.1. Historic Source Type Populations (1990 and 1999-2011)............................................ 25

5.2. Projected Vehicle Populations (2012-2050)................................................................. 31

6. Fleet Characteristics.............................................................................................................. 33

6.1. Source Type Definitions............................................................................................... 33

6.2. Sample Vehicle Population........................................................................................... 36

7. Vehicle Characteristics that Vary by Age............................................................................. 50

7.1. Age Distributions.......................................................................................................... 50

7.2. Relative Mileage Accumulation Rate ........................................................................... 61

8. VMT Distribution of Source Type by Road Type ............................................................... 66

9. Average Speed Distributions ................................................................................................ 68

9.1. Light-Duty Average Speed Distributions..................................................................... 68

9.2. Heavy-Duty Average Speed Distributions.................................................................... 72

10. Driving Schedules and Ramps.......................................................................................... 77

10.1. Driving Schedules......................................................................................................... 77

10.2. Ramp Activity............................................................................................................... 82

11. Hotelling Activity ............................................................................................................. 83

11.1. National Default Hotelling Rate ................................................................................... 83
3

11.2. Hotelling Activity Distribution..................................................................................... 85

12. Temporal Distributions..................................................................................................... 87

12.1. VMT Distribution by Month of the Year...................................................................... 88

12.2. VMT Distribution by Type of Day ............................................................................... 89

12.3. VMT Distribution by Hour of the Day ......................................................................... 90

12.4. Engine Starts and Parking............................................................................................. 91

12.5. Hourly Hotelling Activity............................................................................................. 94

12.6. Single and Multiday Diurnals....................................................................................... 97

13. Geographical Allocation of Activity................................................................................. 98

13.1. Source Hours Operating Allocation to Zones............................................................... 98

13.2. Engine Start Allocations to Zones ................................................................................ 99

13.3. Parking Hours Allocation to Zones............................................................................. 100

14. Vehicle Mass and Road Load Coefficients..................................................................... 101

14.1. Source Mass and Fixed Mass Factor .......................................................................... 102

14.2. Road Load Coefficients .............................................................................................. 103

15. Air Conditioning Activity Inputs.................................................................................... 106

15.1. ACPenetrationFraction ............................................................................................... 106

15.2. FunctioningACFraction .............................................................................................. 107

15.3. ACActivityTerms........................................................................................................ 108

16. Conclusion and Areas for Future Research .................................................................... 110

17. Appendix A: Projected Source Type Populations by Year............................................. 112

18. Appendix B: Fuel Type and Regulatory Class Fractions for 1960-1981........................ 114

19. Appendix C: 1990 Age Distributions ............................................................................. 117

19.1. Motorcycles................................................................................................................. 117

19.2. Passenger Cars............................................................................................................ 117

19.3. Trucks ......................................................................................................................... 117

19.4. Intercity Buses ............................................................................................................ 118

19.5. School Buses and Motor Homes................................................................................. 118

19.6. Transit Buses............................................................................................................... 118

20. Appendix D: Driving Schedules and SCC Mappings..................................................... 120

20.1. SCC Mappings............................................................................................................ 120

20.2. Driving Schedules....................................................................................................... 121

21. Appendix E: MOVES2010b Source Masses .................................................................. 124

21.1. Motorcycles................................................................................................................. 125

21.2. Passenger Cars............................................................................................................ 126

21.3. General Trucks............................................................................................................ 126

21.4. Buses........................................................................................................................... 128

21.5. Refuse Trucks ............................................................................................................. 130

21.6. Motor Homes.............................................................................................................. 131

References................................................................................................................................... 133
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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 

many 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. 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 scientifically.

a

 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 rates, correction 

factor values, and information for nonroad equipment in the default database are described in 

other MOVES technical reports.1

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. 

 

a More detail on emission measurement is provided in the respective emission rate reports. 
5

MOVES has been specifically designed to accommodate the input of alternate, user supplied 

activity data for the most important parameters. EPA’s Technical Guidance2provides more 

information on customizing MOVES with local inputs.

Population and activity data is 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, 

development of fleet and activity inputs (including projections) will continue to be an important 

area of focus and improvement.
6

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: 

motorcycles (HPMSVTypeID 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. 

HPMSVTypeID 25 is new for MOVES2014 and replaces HPMSVTypeID 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 HPMSVTypeID 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. Onroad 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 5.2. 
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Table 2-1 MOVES2014 On-road Source Types

sourceTypeID Source Type Name HPMSVTypeID Description

11 Motorcycles 10 Motorcycles

21 Passenger Cars 25 Light-Duty Vehicles

31 Passenger Trucks (primarily personal use) 25 Light-Duty Vehicles

32 Light Commercial Trucks (primarily nonpersonal use) 25 Light-Duty Vehicles

41 Intercity Buses (non-school, non-transit) 40 Buses

42 Transit Buses 40 Buses

43 School Buses 40 Buses

51 Refuse Trucks 50 Single Unit Trucks

52 Single Unit Short-haul Trucks 50 Single Unit Trucks

53 Single Unit Long-haul Trucks 50 Single Unit Trucks

54 Motor Homes 50 Single Unit Trucks

61 Combination Short-haul Trucks 60 Combination Trucks

62 Combination Long-haul Trucks 60 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 (sourceTypeID 11, 21, 31, and 32) use vehicle 

specific power (VSP), which is dependent on the measured mass of the test vehicle. Heavy-duty 

vehicles (sourceTypeID 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 heavyduty 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.
8

Table 2-2 Regulatory Classes in MOVES2014

regClassID Regulatory Class Name Description

0 Doesn't Matter Doesn't Matter

10 MC Motorcycles

20 LDV Light-Duty Vehicles

30 LDT Light-Duty Trucks

40 LHD<=10k

Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < 

GVWR <= 10,000 lbs)

41 LHD<=14k Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 

Trucks (8,500 lbs < GVWR <= 14,000 lbs)

42 LHD45 Class 4 and 5 Trucks (14,00 lbs < GVWR <= 19,500 lbs)

46 MHD Class 6 and 7 Trucks (19,500 lbs < GVWR < =33,000 lbs)

47 HHD Class 8a and 8b Trucks (GVWR > 33,000 lbs)

48 Urban Bus Urban Bus (see CFR Sec. 86.091_2)

The GVWR distinction between LD and HD trucks falls in the midst of FHWA GVWR Class 2. 

Trucks of 6,001-8,500 lbs GVWR are sorted into Class 2a, which are considered light-duty 

vehicles, while vehicles of 8,500-10,000 lbs GVWR are sorted into Class 2b, which are 

considered heavy-duty vehicles. 

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 the US EPA, but the VMT from Class 2b vehicles with two axles and 

four tires are included in the light-duty vehicles categories by the FHWA 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 vehicles 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 

explained in the MOVES2014 technical report on the fuel supply.

5

 The table below summarizes 

the fuel types available in MOVES.
9

Table 2-3 A list of allowable fuel types to power vehicles/equipment in MOVES2014

fuelTypeID defaultFormulationID Description

1 10 Gasoline

2 20 Diesel Fuel

3 30 Compressed Natural Gas (CNG)

4 40 Liquefied Petroleum Gas (LPG)*

5 50 Ethanol (E-85) Capable

9 90 Electricity

 * MOVES2014 only models LPG use 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. Transit buses are 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

roadTypeID Description FHWA Functional Types

1 Off Network Off Network

2 Rural Restricted Access Rural Interstate

3 Rural Unrestricted Access Rural Principal Arterial, Minor Arterial, Major 

Collector, Minor Collector & Local

4 Urban Restricted Access Urban Interstate & Urban Freeway/Expressway

5 Urban Unrestricted Access Urban Principal Arterial, Minor Arterial, Collector 

& Local

6 Rural Restricted without Ramps

7 Urban Restricted without Ramps

8 Rural Restricted only Ramps

9 Urban Restricted only Ramps

100 Nonroad

The MOVES roadtypes 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 
10

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

  indicates mobile source (this has a value of 220 for both on-road and nonroad),

  indicates the MOVES fuelTypeID value,

  indicates the MOVES sourceTypeID value,

  indicates the MOVES roadTypeID value, and

  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 sourceTypeID, fuelTypeID, roadTypeID, and processID of emissions reported by

SCC without needing a decoding table. Refer to tables in the MOVES User Guide for the 

descriptions of the sourceTypeID, fuelTypeID, roadTypeID, 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 

sourceTypeID, fuelTypeID, roadTypeID, 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 

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

database.
11

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:

10000000000, where

 1 is a placeholder,

  is a MOVES fuelTypeID,

  is a MOVES engTechID,

b

  is a MOVES regClassID,

  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 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.

 

b

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.
12

Table 2-5 Data Tables Used to Allocate Source Type to Source Bin

Generator Table Name Key Fields* Additional Fields Notes

SourceTypePolProcess sourceTypeID

polProcessID

isRegClassReqd

isMYGroupReqd

Indicates which pollutant-processes the 

source bin distributions may be applied 

to and indicates which discriminators 

are relevant for each sourceTypeID and 

polProcessID (pollutant/process 

combination)

PollutantProcessModelYear polProcessID

modelYearID

modelYearGroupID Assigns model years to appropriate 

model year groups for each 

polProcessID.

SampleVehiclePopulation sourceTypeID

modelYearID

fuelTypeID

engTechID

regClassID

stmyFuelEngFraction

stmyFraction

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 defaults for the Alternative 

Vehicle Fuel & Technology (AFVT) 

importer, and is used to determine reg 

class fractions when users modify 

alternative fuel vehicle fleet fractions.

* In these tables, the sourceTypeID and modelYearID are combined into a single sourceTypeModelYearID.

While details of the SourceTypePolProcess and PollutantProcessModelYear tables are discussed 

in the reports on the development of the light- and heavy-duty emission rates, the 

SampleVehiclePopulation (SVP) table is a topic for this report and is discussed in Section 5.2. 

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.
13

Table 2-6 Matrix of the allowable source type-fuel type combinations in MOVES2014 (allowable 

combinations are marked with an X)

Source Use Types

Motorcycles

Passen

ger Cars

Passen

ger Trucks

Lig

ht Com

mercial

Trucks

Intercity

B

uses

Transit B

uses

School B

uses

Refuse Trucks

Short-Haul Sin

gle U

nit

Trucks

Lon

g-Haul Sin

gle U

nit

Trucks

Motor Homes

Short-Haul

Com

bination

Trucks

Lon

g-Haul

Com

bination

Trucks

Fuel Types 11 21 31 32 41 42 43 51 52 53 54 61 62

Gasoline 1 X X X X X X X X X X X

Diesel 2 X X X X X X X X X X X X

CNG 3 X

E85 5 X X X

Electricity 9 X X X

Table 2-7 Matrix of the allowable source type-regulatory class combinations in MOVES2014 (allowable 

combinations are marked with an X)

Source Use Types

Motorcycles

Passen

ger Cars

Passen

ger Trucks

Lig

ht Com

mercial

Trucks

Intercity

B

uses

Transit B

uses

School B

uses

Refuse Trucks

Short-Haul Sin

gle

U

nit Trucks

Lon

g-Haul Sin

gle

U

nit Trucks

Motor Homes

Short-Haul

Com

bination

Trucks

Lon

g-Haul

Com

bination

Trucks

Regulatory Classes 11 21 31 32 41 42 43 51 52 53 54 61 62

MC 10 X

LDV 20 X

LDT 30 X X

LHD<=10k 40 X X

LHD<=14k 41 X X X X X X

LHD45 42 X X X X X X X

MHD67 46 X X X X X X X X X

HHD8 47 X X X X X X X X X

Urban Bus 48 X
14

Table 2-8 A summary of source type, fuel type, and regulatory class combinations in MOVES2014

sourceTypeID fuelTypeID regClassID

11 1 10

21 1, 2, 5, 9 20

31 1, 2, 5, 9 30, 40

32 1, 2, 5, 9 30, 40

41 2 41, 42, 46, 47

42 1 42, 46, 47

2, 3 48

43 1, 2 41, 42, 46, 47

51 1, 2 41, 42, 46, 47

52 1, 2 41, 42, 46, 47

53 1, 2 41, 42, 46, 47

54 1, 2 41, 42, 46, 47

61 1, 2 46, 47

62 2 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. A separate version of the TAG creates inputs 

for the runs that only generate emission rates.

The Source Bin Distribution Generator (SBDG) uses information on fuel type fractions, 

regulatory class distributions, and similar information to estimate the number of vehicles 
15

belonging to each source bin as a function of source type and model year. The SBDG maps the 

activity data (by source types) to sourcebins which map directly to the MOVES emission rates.

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.
16

Table 2-9 MOVES Database Elements Covered in this Report

Database Table Name Content Summary Report Sections

AvgSpeedDistribution Distribution of time among average speed bins Section 0

DayVMTFraction Distribution of VMT between weekdays and 

weekend days

Section 12. 

DriveSchedule Average speed of each drive schedule Section 0

DriveScheduleAssoc Mapping of which drive schedules are used for each 

combination of source type and road type

Section 0

DriveScheduleSecond Speed for each second of each drive schedule Section 0

FuelType Broad fuel categories that indicate the fuel vehicles 

are capable of using.

Section 2. 

HotellingActivityDistribution Distribution of hotelling activity to the various

operating modes

Section 0

HotellingCalendarYear Rate of hotelling hours per rural restricted access 

VMT

Section 0

HourVMTFraction Distribution of VMT among hours of the day Section 12. 

HPMSVtypeYear Annual VMT by HPMS vehicle types Section 4. 

MonthGroupHour Coefficients to calculate air conditioning demand as 

a function of heat index

Section 15. 

MonthVMTFraction Distribution of annual VMT among months Section 12. 

PollutantProcessModelYear Assigns model years to appropriate groupings, 

which vary by pollutant and process

Section 4. 

RegulatoryClass Sorts vehicles into weight-rating based groups in 

which emission regulations are applied

Section 2. 

RoadOpModeDistribution Operating mode distributions by source type, road 

type, and speed bin

Section 0

RoadType Distinguishes roadways by population density of 

geographic area and by type of access, particularly 

the use of ramps for entrance and exit.

Section 2

RoadTypeDistribution Distribution of VMT among road types Section 8. 

SampleVehicleDay Identifies vehicles in the SampleVehicleTrip table Section 12. 

SampleVehiclePopulation Fuel type and regulatory class distributions by 

source type and model year. 

Section 4. 

SampleVehicleTrip Trip start and end times used to determine vehicle 

start and soak times

Section 12. 

SCC Source Classification Codes that identify the vehicle 

type, fuel type, road type and emission process in 

MOVES output.

Section 2. 

SourceBinDistribution Distribution of population among different vehicle 

sub-types (source bins)

Section 4. 
17

Table 2-9 MOVES Database Elements Covered in this Report

Database Table Name Content Summary Report Sections

SourceTypeAge Rate of survival to subsequent age, relative mileage 

accumulation rates, and fraction of functional air 

conditioning equipment

Section 7. 

Section 15. 

SourceTypeAgeDistribution Distribution of vehicle population among ages Section 7. 

SourceTypeHour The distribution of total daily hotelling among 

hours of the day

Section 12. 

SourceTypeModelYear Prevalence of air conditioning equipment Section 15. 

SourceTypePolProcess Indicates which sourcebin discriminators are 

relevant for each source type and pollutant/process

Section 4. 

SourceTypeYear Vehicle counts by year Section 0

SourceUseType Mapping from HPMS class to source type, 

including source type names

Section 2. 

SourceUseTypePhysics Road load coefficients and vehicle masses for each 

source type used to calculate Vehicle Specific 

Power and Scaled Tractive Power

Section 14. 

Zone Allocation of activity to zone (county) Section 0

ZoneRoadType Allocation of driving time to zone (county) and 

road type

Section 0
18

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)9to 

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 IHS 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 

Operation database.12 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 2011. Polk/IHS

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 5.2. 

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 
19

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-1

13, MV-1014, VM-115

, 

and VM-2

16, 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.

Table 3-1 Corresponding Highway Statistics Data Source for Historical Years

Year FHWA Publication Source (Publication/Revision Date)

1990 Highway Statistics 1991 (October 1992)

1999 Highway Statistics 1999 (October 2000)

2000 Highway Statistics 2000 (April 2011)

2001 Highway Statistics 2001 (April 2011)

2002 Highway Statistics 2002 (April 2011)

2003 Highway Statistics 2003 (April 2011)

2004 Highway Statistics 2004 (April 2011)

2005 Highway Statistics 2005 (April 2011)

2006 Highway Statistics 2006 (April 2011)

2007 Highway Statistics 2007 (April 2011)

2008 Highway Statistics 2008 (April 2011)

2009 Highway Statistics 2010 (December 2012)

2010 Highway Statistics 2010 (December 2012)

2011 Highway Statistics 2011 (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).17 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.
20

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 

MOVES 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 (WIM) 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.
21

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 HPMS 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 VMTGrowthFactor 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 (HPMSVTypeID 25). Internally, the MOVES Total Activity

Generatorcallocates 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.”d However, lacking a better 

adjustment methodology, we used the retracted FHWA-adjusted values as the VMT for 2000-

2006. 

 

c

For more information on the MOVES Total Activity Generator, please see the MOVES2014 Module Reference, 

available on the MOVES website: http://www.epa.gov/otaq/models/moves/#user

d This text appears in a footnote to FHWA’s Highway Statistics Table VM-1 for publication years 2000-2009.
22

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 

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 in Millions of Miles

Year Motorcycles Light-Duty

Vehicles Buses Single Unit Trucks

Combination 

Trucks

1990 11,404 1,943,197 10,279 70,848 108,624

...

1999 13,619 2,401,408 14,853 100,534 160,921

2000 12,175 2,458,221 14,805 100,486 161,238

2001 11,120 2,499,069 12,982 103,470 168,969

2002 11,171 2,555,467 13,336 107,317 168,217

2003 11,384 2,579,194 13,381 112,723 173,539

2004 14,975 2,652,092 13,523 111,238 172,960

2005 13,773 2,677,641 13,153 109,735 175,128

2006 19,157 2,680,535 14,038 123,318 177,321

2007 21,396 2,691,034 14,516 119,979 184,199

2008 20,811 2,630,213 14,823 126,855 183,826

2009 20,822 2,633,248 14,387 120,207 168,100

2010 18,513 2,648,457 13,770 110,738 175,789

2011 18,500 2,646,641 13,783 103,515 163,692

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 
23

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 

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 VM-1. The resulting values are 

presented in Table 4-2 below. 
24

Table 4-2 VMT projections for 2012-2050 by HPMS Vehicle Class in Millions of Miles

Year Motorcycles Light-Duty

Vehicles Buses Single Unit Trucks

Combination 

Trucks

2012 18,776 2,686,152 13,384 103,284 157,396

2013 19,030 2,722,469 13,954 108,811 163,467

2014 19,073 2,728,546 14,374 113,054 167,837

2015 19,162 2,741,392 14,991 118,343 174,804

2016 19,375 2,771,828 15,612 123,348 181,988

2017 19,590 2,802,578 16,036 126,693 186,928

2018 19,756 2,826,337 16,325 128,737 190,433

2019 19,931 2,851,349 16,609 130,692 193,905

2020 20,107 2,876,481 16,906 132,833 197,484

2021 20,284 2,901,914 17,222 135,237 201,214

2022 20,454 2,926,116 17,550 137,759 205,076

2023 20,627 2,950,908 17,877 140,171 208,983

2024 20,807 2,976,667 18,173 142,243 212,579

2025 20,997 3,003,914 18,495 144,418 216,551

2026 21,205 3,033,572 18,799 146,389 220,329

2027 21,426 3,065,195 19,052 147,999 223,510

2028 21,662 3,099,033 19,277 149,382 226,348

2029 21,897 3,132,690 19,509 150,824 229,268

2030 22,133 3,166,361 19,765 152,391 232,509

2031 22,378 3,201,376 20,005 153,916 235,518

2032 22,625 3,236,805 20,198 155,034 237,990

2033 22,867 3,271,436 20,429 156,435 240,929

2034 23,086 3,302,691 20,725 158,246 244,678

2035 23,293 3,332,329 21,017 159,910 248,437

2036 23,493 3,360,885 21,308 161,452 252,265

2037 23,687 3,388,760 21,600 162,945 256,123

2038 23,880 3,416,287 21,887 164,353 259,948

2039 24,060 3,442,035 22,146 165,603 263,426

2040 24,217 3,464,551 22,417 166,905 267,050

2041 24,436 3,495,877 22,701 168,431 270,775

2042 24,657 3,527,485 22,989 169,970 274,552

2043 24,880 3,559,380 23,280 171,524 278,381

2044 25,105 3,591,563 23,575 173,091 282,264

2045 25,332 3,624,036 23,874 174,673 286,201

2046 25,561 3,656,804 24,176 176,270 290,193

2047 25,792 3,689,868 24,483 177,881 294,241

2048 26,025 3,723,230 24,793 179,507 298,345

2049 26,261 3,756,894 25,107 181,147 302,507

2050 26,498 3,790,863 25,425 182,803 306,726
25

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.
26

Figure 5-1 Conceptual map of allocating FHWA MV-1 vehicle registration estimates to MOVES source types

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 MV-1 and thus were not subject to linear interpolation adjustments.
27

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

Figure 5-3 Single unit truck source type populations interpolated 1999-2011

 -

 500,000

 1,000,000

 1,500,000

 2,000,000

 2,500,000

 3,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

National Vehicle Registrations

Year

Long-Haul

Combination

Trucks (62)

Short-Haul

Combination

Trucks (61)

 -

 1,000,000

 2,000,000

 3,000,000

 4,000,000

 5,000,000

 6,000,000

 7,000,000

 8,000,000

 9,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

National Vehicle Registrations

Year

Motor Homes

(54)

Long-Haul

Single Unit

Trucks (53)

Short-Haul

Single Unit

Trucks (52)

Refuse Trucks

(51)
28

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

Figure 5-5 Bus source type populations in MOVES2014

Buses were allocated in a similar fashion as trucks, but using different data sources. 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 

 -

 50,000,000

 100,000,000

 150,000,000

 200,000,000

 250,000,000

 300,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

National Vehicle Registrations

Year

Light

Commercial

Trucks (32)

Passenger

Trucks (31)

Passenger

Cars (21)

Motorcycles

(11)

 -

 100,000

 200,000

 300,000

 400,000

 500,000

 600,000

 700,000

 800,000

 900,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

National Vehicle Registrations

Year

School Buses

(43)

Transit Buses

(42)

Intercity

Buses (41)
29

registrations by the ratio of bus registrations in Puerto Rico to the total MV-1 bus registrations. 

MOVES2014 bus populations are shown in Figure 5-5. Note that the precipitous drop in bus 

populations from 2010 to 2011 is reflected in the MV-1 bus registration data published by

FHWA, which has been used in MOVES2014 without adjustment.
30

Table 5-1 Historic Source Type Populations for Calendar Years 1990 and 1999-2011 (in thousands)

Year Motorcycle Passenger 

Car

Passenger 

Truck

Light 

Commercial 

Truck

Intercity 

Bus

Transit 

Bus

School 

Bus

Refuse 

Truck

Single 

Unit Shorthaul Truck

Single 

Unit Longhaul Truck

Motor 

Home

Combination 

Short-haul 

Truck

Combination 

Long-haul 

Truck

1990 4,281 145,112 27,700 9,903 60 59 511 67 3,870 145 927 1,177 705

...

1999 4,174 134,480 55,472 18,532 81 56 595 105 5,312 314 1,073 1,361 1,008

2000 4,368 135,670 58,930 19,217 81 60 609 106 5,123 296 1,055 1,368 1,043

2001 4,925 139,709 62,685 19,947 81 61 611 116 5,416 305 1,137 1,384 1,087

2002 5,026 137,996 63,789 19,801 79 65 620 120 5,396 297 1,155 1,335 1,080

2003 5,392 137,745 65,651 19,873 81 65 634 126 5,452 292 1,189 1,307 1,088

2004 5,813 138,642 69,860 20,616 83 65 650 132 5,528 288 1,228 1,293 1,108

2005 6,259 138,779 72,980 20,987 85 65 660 141 5,703 289 1,290 1,309 1,155

2006 6,770 137,742 76,321 21,380 88 66 672 152 5,948 293 1,370 1,353 1,228

2007 7,254 138,354 78,443 21,398 91 67 680 164 6,208 297 1,456 1,364 1,274

2008 7,869 139,501 78,596 20,868 96 65 687 172 6,322 293 1,509 1,319 1,268

2009 8,046 138,743 79,219 20,464 94 67 684 178 6,356 286 1,544 1,317 1,303

2010 8,125 133,313 79,641 20,007 89 68 694 180 6,234 271 1,540 1,266 1,289

2011 8,553 128,078 87,030 21,252 18 66 587 176 5,915 248 1,487 1,198 1,255

Note that the decline in sales seen in the 2008 recession results in a flattening of total population growth rates, and eventually a decline 

for Passenger Cars and Combination Long-haul 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. 
31

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 VMT per Vehicle by HPMS Classification

Year Motorcycles Light-Duty Vehicles Buses Single Unit Trucks Combination Trucks

1999 3,263 11,518 20,291 14,776 67,928

2000 2,787 11,497 19,740 15,271 66,876

2001 2,258 11,240 17,240 14,837 68,381

2002 2,223 11,533 17,455 15,401 69,655

2003 2,111 11,552 17,155 15,969 72,459

2004 2,576 11,575 16,946 15,501 72,037

2005 2,201 11,505 16,238 14,783 71,075

2006 2,830 11,385 16,995 15,885 68,702

2007 2,950 11,298 17,322 14,767 69,825

2008 2,645 11,007 17,480 15,291 71,058

2009 2,588 11,044 17,026 14,372 64,160

2010 2,279 11,369 16,181 13,464 68,802

2011 2,163 11,197 20,541 13,227 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).
32

Table 5-3 Projected HPMS Category Populations for 2012-2050 (in thousands)

Year Motorcycles Light-Duty Vehicles Buses Single Unit Trucks Combination Trucks

2012 8,571 236,285 704 8,198 2,471

2013 8,687 239,479 734 8,637 2,566

2014 8,706 240,028 757 8,973 2,635

2015 8,747 241,178 789 9,393 2,745

2016 8,844 243,868 822 9,790 2,857

2017 8,943 246,584 844 10,056 2,935

2018 9,018 248,692 860 10,218 2,990

2019 9,098 250,904 875 10,373 3,045

2020 9,178 253,126 890 10,543 3,100

2021 9,260 255,371 906 10,733 3,159

2022 9,337 257,508 923 10,934 3,220

2023 9,416 259,695 941 11,126 3,281

2024 9,498 261,966 956 11,290 3,338

2025 9,585 264,368 974 11,463 3,400

2026 9,680 266,983 990 11,620 3,459

2027 9,781 269,767 1,004 11,747 3,510

2028 9,888 272,745 1,015 11,858 3,554

2029 9,996 275,707 1,027 11,978 3,600

2030 10,103 278,670 1,041 12,107 3,650

2031 10,215 281,752 1,053 12,234 3,698

2032 10,328 284,871 1,063 12,335 3,737

2033 10,439 287,918 1,075 12,454 3,783

2034 10,538 290,669 1,091 12,606 3,842

2035 10,633 293,277 1,106 12,745 3,901

2036 10,724 295,790 1,122 12,877 3,961

2037 10,813 298,244 1,137 13,007 4,021

2038 10,901 300,667 1,152 13,129 4,081

2039 10,983 302,932 1,166 13,238 4,136

2040 11,055 304,914 1,180 13,346 4,193

2041 11,155 307,671 1,196 13,472 4,251

2042 11,256 310,453 1,210 13,599 4,311

2043 11,357 313,260 1,226 13,731 4,371

2044 11,460 316,092 1,241 13,864 4,432

2045 11,564 318,951 1,257 13,998 4,494

2046 11,668 321,835 1,273 14,135 4,556

2047 11,774 324,745 1,289 14,273 4,620

2048 11,880 327,681 1,304 14,411 4,684

2049 11,988 330,642 1,322 14,550 4,750

2050 12,096 333,632 1,338 14,691 4,816
33

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 4-6 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 three categories: 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 lbs. as light-duty and vehicles greater than or equal to 10,000 lbs. 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.
34

Table 6-1 VIUS2002 Parameters Used to Distinguish Truck Source Types in MOVES2014

Source 

Type

Axle 

Arrangement

Primary Distance 

of Operation Weight Body Type

Operator 

Classificat

ion

Passenger 

Trucks

AXLE_CONFIG 

in (1,6,7,8)† Any ADM_GVW in (1,2) &VIUS_GVW in (1,2,3) Any OPCLASS=5

Light 

Commercial 

Trucks

AXLE_CONFIG 

in (1,6,7,8)† Any ADM_GVW in (1,2) &VIUS_GVW in (1,2,3) Any OPCLASS≠5

Refuse 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (1,2,3,4) Any BODYTYPE=21 Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (1,2,3,4)

ADM_GVW > 2 &

VIUS_GVW > 3

BODYTYPE

=21 Any

Single Unit 

Short-Haul 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (1,2,3,4) Any BODYTYPE≠21

Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (1,2,3,4)

ADM_GVW > 2 &

VIUS_GVW > 3

BODYTYPE

≠21

Any

Single Unit 

Long-Haul 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (5,6) Any Any Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (5,6)

ADM_GVW > 2 &

VIUS_GVW > 3 Any Any

Combination 

Short-Haul 

Trucks

AXLE_CONFIG 

>=21

TRIP_PRIMARY 

in (1,2,3,4) Any Any Any

Combination 

Long-Haul 

Trucks

AXLE_CONFIG 

>=21

TRIP_PRIMARY 

in (5,6) Any Any Any

† In the MOVES2014 analysis, we did not constrain axle configuration of light-duty trucks, so there are some, 

albeit very few, 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% 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 (sourceTypeID 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 2011 Polk dataset nor VIUS contain any 

information on motorcycles. As noted in Section 0information 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 (sourceTypeID 21) are categorized in the 
35

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. Depending on use 

and GVWR, we categorize them into two different MOVES source types: 1) passenger trucks

(sourceTypeID 31), and 2) light commercial trucks (sourceTypeID 32). According to 2011 VM-1 

vehicle classifications from FHWA, light-duty vehicles are those weighing less than 10,000 

pounds (i.e. they are in weight class 1 and 2), except Class 2b trucks with two axles and 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 (sourceTypeID 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 (sourceTypeID 41), transit (sourceTypeID 42), and 

school buses (sourceTypeID 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. According to FHWA, school 

buses are defined as vehicles designed to carry more than 10 passengers, used to transport K-12 

students between their home and school.26 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,” 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 (sourceTypeID 51), short-haul 

single unit trucks (sourceTypeID 52), long-haul single unit trucks (sourceTypeID 53), and motor 

homes (sourceTypeID 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 
36

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 (sourceTypeID 61) and 

long-haul combination trucks (sourceTypeID 62). Like single unit trucks, short-haul and longhaul combination trucks are distinguished by their primary trip length (TRIP_PRIMARY) 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.

27

6.2. Sample Vehicle Population

To match source types to emission rates, MOVES must associate each source type with specific 

fueltypes 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 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, we define the stmyFraction as,

(),,, =

,,,

 ,,,

,

,

Equation 1

where the number of vehicles  in a given model year , regulatory class , fuel type , and 

source type  is divided by the sum of vehicles across the set of all regulatory classes  and all 

fuel types . 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.
37

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 as,

(),,, =

,,,

 ,,,



,

Equation 2

for number of vehicles , model year , regulatory class , fuel type , 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, E85-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
38

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

6.2.2. Sample Vehicle Counts

The SampleVehiclePopulation table fractions have been 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 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.
39

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 Error! Reference source not found. 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 for delineating trucks weighing more or less than 

8,500 lbs.

Table 6-1 Initial mapping from FHWA truck classes to MOVES regulatory classes.

Vehicle Category FHWA Truck Weight Class Weight Range (lbs) regClassID

Trucks 1 < 6,000 30

Trucks 2a 6,001 – 8,500 30*

Trucks 2b 8,501 – 10,000 41*

Trucks 3 10,001 – 14,000 41

Trucks 4 14,001 – 16,000 42

Trucks 5 16,001 – 19,500 42*

Trucks 6 19,501 – 26,000 46

Trucks 7 26,001 – 33,000 46

Trucks 8a 33,001 – 60,000 47

Trucks 8b > 60,001 47

Cars 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 have been 

reclassified into the new regulatory class 40 (see explanation in Section 2.3. ), any regulatory class 30 

vehicles in single unit truck source types have been reclassified into regulatory class 41, and any regulatory 

class 42 vehicles in combination truck source types have been reclassified into regulatory class 46.

Since the Polk dataset did not distinguish between Class 2a (6,001-8,500 lbs) and Class 2b

(8,501-10,000 lbs) trucks, but MOVES regulatory classes 30, 40, and 41 all fall somewhere in

Class 2, we needed to use a secondary data source to allocate the Polk gasoline and diesel trucks 

between Class 2a and 2b. According an Oak Ridge National Laboratory (ORNL) paper28, just 

over 25 percent of diesel Class 2 trucks are Class 2a and about 81 percent of gasoline Class 2 

trucks are in Class 2b. These fractions are summarized in Table 6-2. We used these fractions to 

allocate the Polk Class 2 gasoline and diesel trucks into the appropriate regulatory classes (Class 

2a are in regulatory class 30, and Class 2b are in regulatory class 40/41). 
40

Table 6-2 Fractions of Class 2a and 2b trucks fueled by gasoline and diesel

Fuel Type

Truck Class

2a 2b

Gasoline 0.975 0.760

Diesel 0.025 0.240

1.000 1.000

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. 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 6-3 illustrates how Polk fuels were mapped to MOVES fuel types, and 

which Polk fuels were not used in MOVES.

This mapping in Table 6-3 led us to discard 0.22 percent, roughly 530,000 vehicles (mostly

dedicated or aftermarket alternative fuel vehicles), of Polk’s 2011 national fleet in developing the 

default fuel type fractions. However, because the MOVES national population is derived topdown 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.
41

Table 6-3 A list of fuels from the Polk dataset used to develop MOVES fuel type distributions.

Polk Fuel Type MOVES fuelTypeID MOVES fuelType

Unknown N/A

Undefined N/A

Both Gas and Electric 1 Gasoline

Gas 1 Gasoline

Gas/Elec 1 Gasoline

Gasoline 1 Gasoline

Diesel 2 Diesel

Natural Gas N/A

Compressed Natural Gas N/A

Natr.Gas N/A

Propane N/A

Flexible (Gasoline/Ethanol) N/A

Flexible N/A

Electric N/A

Cnvrtble N/A

Conversion N/A

Methanol N/A

Ethanol N/A

Convertible N/A

Next we transformed the VIUS dataset into MOVES format. The VIUS vehicle data was first

assigned to MOVES source types using the constraints in Error! Reference source not found.

and then to MOVES regulatory classes using the mapping described in Error! Reference source 

not found., including the allocation between Class 2a and 2b trucks from the ORNL study in

Table 6-2. Similar to our fuel type mapping of the Polk dataset, we chose to omit alternative fuel 

vehicles, as summarized below in Table 6-4.Table 6-1 VIUS2002 Parameters Used to 

Distinguish Truck Source Types in MOVES2014
42

Source 

Type

Axle 

Arrangement

Primary Distance 

of Operation Weight Body Type

Operator 

Classificat

ion

Passenger 

Trucks

AXLE_CONFIG 

in (1,6,7,8)† Any ADM_GVW in (1,2) &VIUS_GVW in (1,2,3) Any OPCLASS=5

Light 

Commercial 

Trucks

AXLE_CONFIG 

in (1,6,7,8)† Any ADM_GVW in (1,2) &VIUS_GVW in (1,2,3) Any OPCLASS≠5

Refuse 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (1,2,3,4) Any BODYTYPE=21 Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (1,2,3,4)

ADM_GVW > 2 &

VIUS_GVW > 3

BODYTYPE

=21 Any

Single Unit 

Short-Haul 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (1,2,3,4) Any BODYTYPE≠21

Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (1,2,3,4)

ADM_GVW > 2 &

VIUS_GVW > 3

BODYTYPE

≠21

Any

Single Unit 

Long-Haul 

Trucks*

AXLE_CONFIG 

in (2,9,10,11)

TRIP_PRIMARY 

in (5,6) Any Any Any

AXLE_CONFIG 

<=21

TRIP_PRIMARY 

in (5,6)

ADM_GVW > 2 &

VIUS_GVW > 3 Any Any

Combination 

Short-Haul 

Trucks

AXLE_CONFIG 

>=21

TRIP_PRIMARY 

in (1,2,3,4) Any Any Any

Combination 

Long-Haul 

Trucks

AXLE_CONFIG 

>=21

TRIP_PRIMARY 

in (5,6) Any Any Any

† In the MOVES2014 analysis, we did not constrain axle configuration of light-duty trucks, so there are some, 

albeit very few, 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% 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.
43

Table 6-4 Mapping of VIUS2002 fuel types to MOVES2014 fuel types

VIUS Fuel Type VIUS Fuel Code MOVES fuelTypeID MOVES fuelType

Gasoline 1 1 Gasoline

Diesel 2 2 Diesel

Natural gas 3 N/A

Propane 4 N/A

Alcohol fuels 5 N/A

Electricity 6 N/A

Gasoline and natural gas 7 1 Gasoline

Gasoline and propane 8 1 Gasoline

Gasoline and alcohol fuels 9 1 Gasoline

Gasoline and electricity 10 1 Gasoline

Diesel and natural gas 11 2 Diesel

Diesel and propane 12 2 Diesel

Diesel and alchol fuels 13 2 Diesel

Diesel and electricity 14 2 Diesel

Not reported 15 N/A

Not applicable 16 N/A

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 , regulatory class , and fuel type

, the source type population fraction  for a specified source type  will be the number of VIUS 

trucks  in that source type divided by the sum of VIUS trucks across the set of all source types 

. The source type population fraction is summarized in the following formula:

(),,, =

,,,

 ,,,



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 form, the sample vehicle counts are,

(),,, = (),,,∙ (),,,, Equation 4
44

where  is the number of vehicles used to generated the SampleVehiclePopulation table,  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.

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 roughly follows the same procedure outlined above but 

instead uses 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 (sourceTypeID 31 or 32) in the original LHD regulatory class less than 14,000 lbs 

(regClassID 41) were reclassified in the new LHD regulatory class less than 10,000 lbs

(regClassID 40), whereas any heavy-duty vehicles (sourceTypeID 41 and above) remained in 

regClassID 41. Similarly, any single unit trucks (sourceTypeID 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 tractortrailers must be either Class 7 or 8 trucks. This ensures a clean break between light- and heavyduty emission results and that the emission calculations use the appropriate fixedMassFactor 
45

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 (E85-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 (fuelTypeID 5) vehicles and deducted them from the gasoline cars in the 

Polk dataset.

Similarly, for model years 2012 and later, we used Annual Energy Outlook (AEO) 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 Sample Vehicle Population, 

exclusively for regulatory class 20.29 All other alternative fueled cars were determined to have

insignificant market shares now and into the future.

While MOVES can model electric vehicles (fuelTypeID 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 

gases30 and brake and tirewear31, 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 
46

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 AEO2014’s Table 64. The flexible fuel vehicle 

penetration was applied to regClassID 30 for both E-85 (fuelTypeID 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.32 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 

6-5.

33

Table 6-5 Regulatory class fractions of school and non-school buses using 2011 FHWA data

Vehicle Type

MOVES regClassID

41 42 46 47 Total

Non-School Buses 0.1856 0.0200 0.1214 0.6730 1

School Buses 0.0106 0.0070 0.9371 0.0453 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 6-6. 

Table 6-6 Mapping National Transit Database Fuel Types to MOVES Fuel Types

NTD code NTD description fuelTypeID MOVES Fuel 

Description

BD Bio-diesel 2 diesel

BF Bunker fuel N/A

CN Compressed natural gas 3 CNG

DF Diesel fuel 2 diesel

DU Dual fuel 2 diesel

EB Electric battery N/A

EP Electric propulsion N/A

ET Ethanol N/A

GA Gasoline 1 gasoline

GR Grain additive N/A

HD Hybrid diesel 2 diesel

HG Hybrid gasoline 1 gasoline

KE Kerosene N/A

LN Liquefied natural gas 3 CNG

LP Liquefied petroleum gas N/A

MT Methanol N/A

OR Other N/A
47

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 6-7. MOVES modelers can adjust these 

distributions between the fuel types using the AVFT tool.
48

Table 6-7 Fuel type market shares by model year for transit buses in MOVES2014

Model Year

MOVES Fuel Type

Gasoline Diesel CNG

1982-1989 1.00% 99.00% 0.00%

1990 1.00% 98.30% 0.70%

1991 1.00% 97.20% 1.80%

1992 1.00% 94.40% 4.60%

1993 1.00% 91.40% 7.60%

1994 1.00% 90.50% 8.50%

1995 1.00% 83.70% 15.30%

1996 1.00% 89.20% 9.80%

1997 1.00% 81.60% 17.40%

1998 1.00% 84.10% 14.90%

1999 1.00% 87.70% 11.30%

2000 0.85% 91.57% 7.58%

2001 0.88% 90.51% 8.60%

2002 0.91% 89.09% 10.00%

2003 0.94% 88.06% 10.99%

2004 0.89% 86.85% 12.27%

2005 1.05% 85.61% 13.34%

2006 1.18% 84.73% 14.09%

2007 1.29% 83.99% 14.72%

2008 1.61% 82.91% 15.49%

2009 1.89% 82.55% 15.56%

2010 2.14% 81.96% 15.90%

2011+ 2.46% 81.75% 15.79%

Urban transit buses are regulated separately from other heavy-duty vehicles, under 40 CFR

86.091-2.34 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 HD Emissions Rates Report4, for gasoline 

transit buses as shown in Table 6-8 below. 

Table 6-8 Regulatory class fractions of gasoline transit buses in MOVES2014

MOVES Source Type & Fuel Type

MOVES regClassID

42 46 47 Total

Gasoline Transit Buses 0.2683 0.0976 0.6341 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 6-9. 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 buses are 
49

gasoline fueled in MY 1997 and later.35 The school bus regulatory class distribution was also 

derived from the 2011 FHWA data in Table 6-5.

Table 6-9 Fuel type market shares by model year for school buses in MOVES2014

Model Year MOVES Fuel Type

Gasoline Diesel

1982 67.40% 32.60%

1983 67.62% 32.38%

1984 61.55% 38.45%

1985 48.45% 51.55%

1986 32.67% 67.33%

1987 26.55% 73.45%

1988 24.98% 75.02%

1989 22.90% 77.10%

1990 12.40% 87.60%

1991 8.95% 91.05%

1992 1.00% 99.00%

1993 12.05% 87.95%

1994 14.75% 85.25%

1995 11.43% 88.57%

1996 4.15% 95.85%

1997+ 1.00% 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, originally based on 

interpolating information from the Recreation Vehicle Industry Association (RVIA) on fuel type 

market shares.36 
50

Table 6-10 Fuel type market shares for motor homes in MOVES2014

Model Year Percent of Diesel Percent of Gasoline

1982-1993 15% 85%

1994 18% 82%

1995 21% 79%

1996 23% 77%

1997 26% 74%

1998 29% 71%

1999 32% 68%

2000 34% 66%

2001 37% 63%

2002 40% 60%

2003 41% 59%

2004 43% 57%

2005 44% 56%

2006 46% 54%

2007 47% 53%

2008 49% 51%

2009 50% 50%

2010+ 50% 50%

The motor home regulatory class distribution, shown below in Table 6-11, is used across all

model years based on the same 2011 FHWA dataset referenced above for school and non-school 

buses.

Table 6-11 Regulatory class fractions of motor homes using 2011 FHWA data

MOVES Source Type

MOVES regClassID

41 42 46 47 Total

Motor Homes 0.2697 0.3940 0.2976 0.0387 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. We will consider 

adding natural gas combination trucks as they become more prevalent and their emissions are 

more thoroughly tested.
51

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.3,4In 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 

distributions.

7.1.1.2. 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, 

motorhomes, and buses—we calculated the 2011 age distributions by running MOVES2010b 
52

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, transit buses (42) from internal EPA 

estimates based on manufacturer reporting, and school buses (43) from the School Bus Fleet Fact 

Book18. 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, revisit Section 3. 

Table 7-1 shows 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. 
53

Figure 7-1 2011 Age Distributions for MOVES Source Types
54

Table 7-1 2011 Age Fractions for MOVES Source Types

Age 11 21 31 32 41 42 43 51 52 53 54 61 62

0 0.0585 0.042 0.0496 0.0557 0.0477 0.0628 0.0368 0.0334 0.035 0.0237 0.046 0.0219 0.0478

1 0.0565 0.0472 0.044 0.0482 0.0421 0.0385 0.0403 0.0265 0.0216 0.015 0.0406 0.0164 0.0378

2 0.0614 0.043 0.0335 0.0372 0.0353 0.0393 0.048 0.0351 0.0231 0.0176 0.034 0.0213 0.0501

3 0.1088 0.0545 0.0587 0.0668 0.0458 0.0555 0.0529 0.0273 0.0479 0.031 0.0442 0.0192 0.0392

4 0.0968 0.0597 0.0626 0.0703 0.0601 0.0539 0.0548 0.0956 0.0629 0.0544 0.0579 0.0629 0.1371

5 0.0917 0.0562 0.0644 0.0743 0.0617 0.0389 0.0644 0.0718 0.0666 0.0486 0.0594 0.0468 0.1028

6 0.0803 0.0562 0.0677 0.077 0.0638 0.0607 0.0574 0.0677 0.0577 0.045 0.0615 0.0455 0.0971

7 0.0682 0.0526 0.0686 0.0781 0.062 0.0498 0.0565 0.0407 0.0506 0.0333 0.0597 0.0288 0.0584

8 0.0583 0.0551 0.0638 0.0724 0.0574 0.0488 0.0487 0.04 0.0438 0.0284 0.0553 0.0256 0.057

9 0.0514 0.055 0.0624 0.0702 0.0538 0.0495 0.0511 0.029 0.0393 0.0238 0.0518 0.0199 0.0415

10 0.0436 0.0534 0.0562 0.0647 0.0517 0.057 0.0467 0.0357 0.0427 0.059 0.0498 0.0391 0.0482

11 0.0348 0.0575 0.0545 0.055 0.0492 0.0385 0.0508 0.0488 0.0697 0.1457 0.0474 0.0535 0.0766

12 0.0263 0.05 0.0504 0.0433 0.0478 0.0374 0.047 0.0702 0.0591 0.1267 0.0461 0.0482 0.0572

13 0.0224 0.0441 0.0424 0.0273 0.0362 0.0439 0.0371 0.0645 0.0334 0.0213 0.0271 0.049 0.0381

14 0.0215 0.042 0.0372 0.0305 0.0295 0.0401 0.0345 0.0312 0.0459 0.0175 0.0417 0.0398 0.0215

15 0.0188 0.0354 0.0284 0.0203 0.0244 0.0369 0.0298 0.0406 0.0308 0.0198 0.0258 0.0556 0.0234

16 0.0142 0.0367 0.0274 0.0219 0.0317 0.0303 0.038 0.0521 0.0423 0.0338 0.0305 0.0628 0.0209

17 0.0163 0.029 0.025 0.0137 0.0244 0.0264 0.0184 0.0367 0.0323 0.0279 0.0291 0.0524 0.0127

18 0.0133 0.0249 0.0175 0.0136 0.0201 0.0219 0.0219 0.0167 0.0225 0.0777 0.02 0.038 0.0086

19 0.0111 0.0209 0.0142 0.0073 0.0148 0.019 0.0177 0.0149 0.0179 0.0137 0.0175 0.0292 0.0052

20 0.0088 0.0178 0.012 0.007 0.0168 0.0192 0.0226 0.0233 0.0162 0.0213 0.013 0.0272 0.004

21 0.0071 0.015 0.0106 0.0075 0.0188 0.0281 0.0255 0.0166 0.022 0.0132 0.0171 0.0337 0.0031

22 0.0053 0.0124 0.0108 0.008 0.0187 0.0214 0.0145 0.0256 0.0211 0.0535 0.0221 0.0343 0.0031

23 0.0045 0.0097 0.0092 0.0073 0.0174 0.0168 0.0173 0.0147 0.0188 0.017 0.0196 0.0317 0.0019

24 0.0044 0.008 0.007 0.0057 0.018 0.0156 0.0175 0.0132 0.0171 0.0061 0.0191 0.025 0.0032

25 0.0037 0.0065 0.0071 0.0053 0.0151 0.0131 0.0153 0.0068 0.0154 0.0064 0.0141 0.0174 0.0009

26 0.0031 0.0053 0.0049 0.0037 0.0132 0.0113 0.0131 0.0068 0.0132 0.0055 0.015 0.0177 0.0009

27 0.0028 0.0042 0.004 0.0031 0.0104 0.0088 0.0101 0.0056 0.0113 0.0048 0.0152 0.0145 0.0007

28 0.002 0.0025 0.0024 0.0019 0.0041 0.0083 0.0037 0.0025 0.0067 0.0028 0.0098 0.0062 0.0003

29 0.0016 0.0017 0.0019 0.0015 0.0035 0.0045 0.0027 0.0029 0.0067 0.0028 0.0057 0.0073 0.0004

30+ 0.0025 0.0016 0.0016 0.0012 0.0047 0.0039 0.0047 0.0035 0.0066 0.0027 0.0039 0.0089 0.0004
55

7.1.2. Forecasting and Backcasting Age Distributions

Since purchasing registration data for all historic years is prohibitively costly 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.37 Survival rates for 

passenger cars, passenger trucks and light commercial trucks came from NHTSA's survivability

Table 3 and Table 4.38 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 ageID 2, 

so the survival fractions were shifted accordingly.

 Because MOVES requires survival rates for ageIDs < 2, these values were linearly 

interpolated with the assumption that the survival rate prior to ageID 0 is 1. Effectively,

this results in a near constant survival rate until ageID 3 for light-duty vehicles and until 

ageID 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.
56

Because MOVES ageID 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 ageID 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,  represents the MOVES survival rate at age , and  represents the NHTSA survival 

rate at age . When this generic survival rate is discussed below, the shorthand notation 

⃗ will 

represent a one-dimensional array of  values at each permissible age  as described below.

Age 0:  = 1 −

1 − 

3

Equation 5

Age 1:  = 1 −

2(1 − )

3

Equation 6

Age 2-29:  = ... =





Equation 7

Age 30:  = 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. Please note 

that since MOVES2014 does not calculate age distributions during a run, these values are not 

actively used by MOVES. However, they were used in the development of the national age 
57

distributions stored in the SourceTypeAgeDistribution table, and remain in the default database 

for reference.

Table 7-2 Survival Rate by Age and HPMS Class

Age Motorcycles

Light-Duty Vehicles

Buses Single Unit 

Trucks

Combination 

Trucks Passenger 

Cars

Passenger Trucks

Light Comm. Trucks

0 1.000 0.997 0.991 1.000 1.000 1.000

1 0.979 0.997 0.991 1.000 1.000 1.000

2 0.940 0.997 0.991 1.000 1.000 1.000

3 0.940 0.993 0.986 1.000 1.000 1.000

4 0.940 0.990 0.981 0.990 0.990 0.990

5 0.940 0.986 0.976 0.980 0.980 0.980

6 0.940 0.981 0.970 0.980 0.980 0.980

7 0.940 0.976 0.964 0.970 0.970 0.970

8 0.940 0.971 0.958 0.970 0.970 0.970

9 0.940 0.965 0.952 0.970 0.970 0.970

10 0.940 0.959 0.946 0.960 0.960 0.960

11 0.940 0.953 0.940 0.960 0.960 0.960

12 0.940 0.912 0.935 0.950 0.950 0.950

13 0.940 0.854 0.929 0.950 0.950 0.950

14 0.940 0.832 0.913 0.950 0.950 0.950

15 0.940 0.813 0.908 0.940 0.940 0.940

16 0.940 0.799 0.903 0.940 0.940 0.940

17 0.940 0.787 0.898 0.930 0.930 0.930

18 0.940 0.779 0.894 0.930 0.930 0.930

19 0.940 0.772 0.891 0.920 0.920 0.920

20 0.940 0.767 0.888 0.920 0.920 0.920

21 0.940 0.763 0.885 0.920 0.920 0.920

22 0.940 0.760 0.883 0.910 0.910 0.910

23 0.940 0.757 0.880 0.910 0.910 0.910

24 0.940 0.757 0.879 0.910 0.910 0.910

25 0.940 0.754 0.877 0.900 0.900 0.900

26 0.940 0.754 0.875 0.900 0.900 0.900

27 0.940 0.567 0.875 0.900 0.900 0.900

28 0.940 0.752 0.873 0.890 0.890 0.890

29 0.940 0.752 0.872 0.890 0.890 0.890

30 0.300 0.300 0.300 0.300 0.300 0.300

7.1.2.2. 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:

, =





Equation 9

Here, , is the age fraction to be calculated,  is the population of vehicles at age , and  is 

the total population in calendar year . In this section, arrow notation will be used if the 

operations are to be performed at the individual age level. For example, 

⃗ would be used to 
58

represent all age fractions in calendar year . Another example is 

⃗; it represents an array of 

values at each permissible age in calendar year . In contrast,  represents the total population 

in year .

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:



⃗ = ⃗ − ⃗ + ⃗ Equation 10

where 

⃗ is the population (known at each age) of the next year, ⃗ is the population in the 

base year, 

⃗ is the population of vehicles removed in the in the base year, and ⃗ 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  > 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 (

⃗), remove the number of vehicles that 

did not survive (

⃗) 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 (

⃗) 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 (

⃗). If this algorithm is to be 

repeated, 

⃗ becomes ⃗ 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 

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 

⃗ 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.



⃗ =  ∙ 1 − 

⃗ ∘ 

⃗ Equation 11

Substituting Equation 11 into Equation 10 yields:



⃗ = ⃗ −  ∙ (1 − 

⃗) ∘ 

⃗ + ⃗ Equation 12
59

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:

 =  −   1 − 

⃗ ∘ 

⃗



+  Equation 13

This was algebraically solved for  and evaluated for each HPMS categoryeusing the following 

information:

 Total populations  and  by HPMS category. For analysis year 2011, 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 

⃗ by HPMS category, which is described in Section 7.1.2.1. 

 Population distribution 

⃗ 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 years 

2012+, this comes from 

⃗ of the previous year.

 New vehicle sales  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  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 

⃗ by source type. 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. For years 2012+, this comes from 

⃗ of the previous year.

 The scalar adjustment factor  and generic survival rate 

⃗ applied by source type using 

the HPMS to source type mapping described by Table 2-1. Please note that limits were 

placed on the (1 − 

⃗) term of Equation 12: the value of this term for each age was 

restricted to being between 0 and 1.

 

e 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.
60

 New vehicle sales 

⃗, 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.

With all of this information, the population distributions were algorithmically determined for 

years 2012-2050. The resulting total source type populations () are stored in the 

SourceTypeYear table of the default database. The resulting age distributions are stored in the 

SourceTypeAgeDistribution table.

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

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 (

⃗), remove the age 0 vehicles (⃗).

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 (

⃗).

4. This results in the previous year population distribution (

⃗). If this algorithm is to be 

repeated, 

⃗ becomes ⃗ for the next iteration.

The equation governing vehicle removal discussed the previous section is also applicable here. 

Taking careful note of the subscripts, Equation 11 and Equation 14 can be combined:



⃗ = ⃗ − ⃗ +  ∙ 1 − 

⃗ ∘ 

⃗ 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:

 =  −  +  1 − 

⃗ ∘ 

⃗



Equation 16

However, this still leaves a 

⃗ 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  and 

⃗, a numerical method of approximation could be employed. However, due to 
61

lack of resources, 

⃗ was used as a simple approximation of ⃗ on the left hand side of 

Equation 16. The following sources were used to determine  by HPMS category:

 Total populations  and  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 

⃗ by HPMS category, which is described in Section 7.1.2.1. 

 Population distribution 

⃗ 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 

⃗ of the previous iteration.

 New vehicle sales  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 shorthaul 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.

After determining  by HPMS category, Equation 15 was used with the following 

information to compute the previous year’s age distribution by source type:

 Population distribution 

⃗ by source type. 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. For other years, this comes from 

⃗ of the previous 

iteration.

 The scalar adjustment factor  and generic survival rate 

⃗ applied by source type 

using the HPMS to source type mapping described by Table 2-1. As with before, limits 

were placed on the (1 − 

⃗) term, such that the value of this term for each age was 

restricted to being between 0 and 1. Also, the 

⃗ term used when calculating the 

number of vehicles removed was approximated by 

⃗.

 New vehicle sales 

⃗, 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.
62

7.2. Relative Mileage Accumulation Rate

MOVES uses a relative mileage accumulation rate (RMAR) in combination with source type 

populations (see Section 0and 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).

f

 Based on the data, new passenger cars have a RMAR of 

0.885. This means that when the VMT assigned to the HPMS vehicle classification (25) is 

allocated to passenger cars, passenger trucks and light commercial trucks, a passenger car of age 

0 will be assigned only 88.5% of the 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 (motorhomes) 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 

(category 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.

37

 

f 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.
63

7.2.2. Passenger Cars, Passenger Trucks and Light Commercial Trucks

In MOVES2010b, passenger cars were not included in the same HPMS vehicle classification as 

for 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% of the annual miles accumulated by new light 

trucks. All of the RMAR values for passenger cars were adjusted to be 88.5% of their previous 

values.

The MOVES2010b RMAR values for passenger cars, passenger trucks and light commercial 

trucks (categories 21, 31 & 32) were taken from the NHTSA report on survivability and mileage 

schedules.38 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 LightDuty Truck HPMS category, 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 in MOVES’ allocation of VMT by one percent or less 

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 (category 41) annual mileage accumulation rate is taken from 

Motorcoach Census 2000.39 The data did not distinguish vehicle age, so the same MAR (59,873 

miles per year) was used for each age. The School Bus (category 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 (HDDBT). This mileage data was obtained from the 1994 Federal 

Transportation Administration survey of transit agencies.40 The MOBILE6 equation was 

extended to calculate values for ages 26 through 30.
64

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 using the data from the 2002 Vehicle Inventory and Use Survey (VIUS). 

The total reported annual miles traveled by trucks in each source type was divided by the vehicle 

population to determine the average annual miles traveled per truck by source type. 

Table 7-3 2002 VIUS Annual Mileage Accumulation Rates

Age Model 

Year

Single Unit Trucks Combination Trucks

Refuse 

(51)

Short-haul 

(52) Long-haul (53) Short-haul (61) Long-haul (62)

0 2002 26,703 21,926 40,538 119,867 109,418

1 2001 32,391 22,755 28,168 114,983 128,287

2 2000 31,210 24,446 30,139 110,099 117,945

3 1999 31,444 23,874 49,428 105,215 110,713

4 1998 31,815 21,074 33,266 100,331 99,925

5 1997 28,450 21,444 23,784 95,447 94,326

6 1996 25,462 16,901 21,238 90,563 85,225

7 1995 30,182 15,453 27,562 85,679 85,406

8 1994 20,722 13,930 21,052 80,795 71,834

9 1993 25,199 13,303 11,273 75,911 71,160

10 1992 23,366 11,749 18,599 71,026 67,760

11 1991 18,818 13,675 15,140 66,142 80,207

12 1990 12,533 11,332 13,311 61,258 48,562

13 1989 15,891 9,795 9,796 56,374 64,473

14 1988 19,618 9,309 12,067 51,490 48,242

15 1987 12,480 9,379 16,606 46,606 58,951

16 1986 12,577 4,830 8,941 41,722 35,897

0-3

1999-2002 

Average 30,437 23,250 37,069 61,240 116,591

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,
65

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.41

Table 7-4 Regression Statistics for Heavy-Duty Truck Annual Mileage Accumulation Rates Ages 4-16

Measurement Refuse 

Truck (51)

Single Unit 

Short-haul (52)

Single Unit 

Long-haul (53)

Combination 

Short-haul (61)

Combination 

Long-haul (62)

Average* 30,437 23,250 37,069 61,240 116,591

Intercept 36,315 25,442 36,305 65,773 119,867

Slope -1,510 -1,209 -1,794 -3,447 -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 (sourceTypeID 54), the initial MARs were taken from an 

independent research study42 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.
66

Table 7-5 Relative Mileage Accumulation Rates for Heavy-Duty Trucks

ageID Refuse (51) Short Haul

Single Unit (52)

Long Haul 

Single Unit 

(53)

Motor Home 

(54)

Short Haul

Combination

(61)

Long Haul 

Combination 

(62)

0 1.0000 0.6864 0.9729 0.0590 0.5269 1.0000

1 1.0000 0.6864 0.9729 0.0590 0.5269 1.0000

2 1.0000 0.6864 0.9729 0.0590 0.5269 1.0000

3 1.0000 0.6864 0.9729 0.0590 0.5269 1.0000

4 0.9525 0.6484 0.9165 0.0590 0.4941 0.9536

5 0.9050 0.6103 0.8601 0.0590 0.4613 0.9072

6 0.8575 0.5723 0.8036 0.0590 0.4286 0.8607

7 0.8099 0.5343 0.7472 0.0590 0.3958 0.8143

8 0.7624 0.4962 0.6908 0.0590 0.3631 0.7679

9 0.7149 0.4582 0.6343 0.0590 0.3303 0.7215

10 0.6674 0.4202 0.5779 0.0590 0.2975 0.6751

11 0.6199 0.3821 0.5215 0.0590 0.2648 0.6286

12 0.5724 0.3441 0.4650 0.0590 0.2320 0.5822

13 0.5249 0.3061 0.4086 0.0590 0.1993 0.5358

14 0.4773 0.2680 0.3522 0.0590 0.1665 0.4894

15 0.4298 0.2300 0.2957 0.0590 0.1338 0.4430

16 0.3823 0.1920 0.2393 0.0590 0.1010 0.3965

17 0.3573 0.1808 0.2293 0.0590 0.0950 0.3723

18 0.3323 0.1696 0.2194 0.0590 0.0890 0.3481

19 0.3073 0.1585 0.2094 0.0590 0.0830 0.3238

20 0.2822 0.1473 0.1994 0.0590 0.0770 0.2996

21 0.2572 0.1361 0.1894 0.0590 0.0710 0.2753

22 0.2322 0.1249 0.1795 0.0590 0.0649 0.2511

23 0.2072 0.1138 0.1695 0.0590 0.0589 0.2268

24 0.1821 0.1026 0.1595 0.0590 0.0529 0.2026

25 0.1571 0.0914 0.1496 0.0590 0.0469 0.1783

26 0.1321 0.0802 0.1396 0.0590 0.0409 0.1541

27 0.1071 0.0691 0.1296 0.0590 0.0349 0.1298

28 0.0820 0.0579 0.1197 0.0590 0.0289 0.1056

29 0.0570 0.0467 0.1097 0.0590 0.0229 0.0814

30 0.0320 0.0355 0.0997 0.0590 0.0169 0.0571
67

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 five roadway

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 143 (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) reports44, when state supplied estimates are not available. 

The 2011 NEI v1 data45 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 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: Driving Schedules and SCC Mappings). 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) SCC Road Type

MOVES Road 

Type ID MOVES Road Type

11 Rural Interstate 2 Rural Restricted Access

13 Rural Other Principal Arterial 3 Rural Unrestricted Access

15 Rural Minor Arterial 3 Rural Unrestricted Access

17 Rural Major Collector 3 Rural Unrestricted Access

19 Rural Minor Collector 3 Rural Unrestricted Access

21 Rural Local 3 Rural Unrestricted Access

23 Urban Interstate 4 Urban Restricted Access

25 Urban Other Freeways & Expressways 4 Urban Restricted Access

27 Urban Other Principal Arterial 5 Urban Unrestricted Access

29 Urban Minor Arterial 5 Urban Unrestricted Access

31 Urban Collector 5 Urban Unrestricted Access

33 Urban Local 5 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 (roadTypeID=1) is not used and 

is allocated none of the VMT.
68

Table 8-2 MOVES2014 Road Type Distribution by Source Type

Source 

Type Description

Road Types

Off 

Network

Rural 

Restricted

Rural 

Unrestricted

Urban 

Restricted

Urban 

Unrestricted

1 2 3 4 5 All

11 Motorcycle 0 0.0804768 0.3019230 0.1913280 0.4262730 1.000

21 Passenger Car 0 0.0847394 0.2344520 0.2374280 0.4433810 1.000

31 Passenger Truck 0 0.0859437 0.2753580 0.2178360 0.4208630 1.000

32 Light Commercial 

Truck

0 0.0866643 0.2755600 0.2180390 0.4197360 1.000

41 Intercity Bus 0 0.1409270 0.2811960 0.2195920 0.3582850 1.000

42 Transit Bus 0 0.1384440 0.2813130 0.2196020 0.3606420 1.000

43 School Bus 0 0.1383910 0.2813150 0.2196020 0.3606920 1.000

51 Refuse Truck 0 0.2396390 0.2717580 0.2524620 0.2361420 1.000

52 Single Unit Shorthaul Truck0 0.1635030 0.2869150 0.2345890 0.3149930 1.000

53 Single Unit Longhaul Truck0 0.1638220 0.2869700 0.2346570 0.3145510 1.000

54 Motor Home 0 0.1233290 0.2876100 0.2255300 0.3635310 1.000

61 Combination Shorthaul Truck0 0.2366730 0.2744240 0.2516600 0.2372430 1.000

62 Combination Longhaul Truck0 0.2476010 0.2705480 0.2543110 0.2275400 1.000
69

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 Speed Bin Categories

Bin Average Speed (mph) Average Speed Range (mph)

1 2.5 speed < 2.5 mph

2 5 2.5 mph <= speed < 7.5 mph

3 10 7.5 mph <= speed < 12.5 mph

4 15 12.5 mph <= speed < 17.5 mph

5 20 17.5 mph <= speed < 22.5 mph

6 25 22.5 mph <= speed < 27.5 mph

7 30 27.5 mph <= speed < 32.5 mph

8 35 32.5 mph <= speed < 37.5 mph

9 40 37.5 mph <= speed < 42.5 mph

10 45 42.5 mph <= speed < 47.5 mph

11 50 47.5 mph <= speed < 52.5 mph

12 55 52.5 mph <= speed < 57.5 mph

13 60 57.5 mph <= speed < 62.5 mph

14 65 62.5 mph <= speed < 67.5 mph

15 70 67.5 mph <= speed < 72.5 mph

16 75 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.

g

TomTom makes in-vehicle GPS navigation devices as well as 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, which “virtually all” use 

them in light-duty cars, trucks and vans. 

 

g Much of the following text and tables are excerpted from the ERG Work Plan (EPA-121019), submitted to US 

EPA on January 11, 2012.
70

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 that 

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 over represents 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 shortcoming of 

MOVES, 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 US 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.
71

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 MOVESh. 

Table 9-2 Correspondence between TomTom Functional Class, Census Information, and MOVES Road

Types.

MOVES Road Type 

Description

Census Information for the 

TomTom Roadway Segment

TomTom Functional 

Road Class

Rural Restricted Access Rural 0 and 1

Rural Unrestricted Access Rural 2 through 7

Urban Restricted Access Urban 0 and 1

Urban Unrestricted Access Urban 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. 

 

h

http://www.census.gov/geo/www/ua/2010urbanruralclass.html
72

Table 9-3 Example of Accumulating Total Distance and Total Time for the TomTom Deliverable Table

A B C D E F G H I J K L

Road Type 

(4 levels)

Month 

(12 

levels)

Weekday/

Weekend

(2 levels)

Hour 

of the 

Day

(24 

levels)

Segment Data

Point

Segment 

Length 

(feet)

Time 

in 

Segment 

(s)

Average 

Speed 

in

Segment

(mph)

Average 

Speed 

Bin

(mph)

(20 

levels)

Total of 

Segment 

Lengths 

for this 

Speed 

Bin 

(feet)

Total of 

Segment 

Times 

for this 

Speed 

Bin (s)

Urban 

Unrestricted April Weekday

14:00:00 

to

14:59:59

A 5 300 15 13.64

15 550 27

B 16 250 12 14.20

A 1 300 10 20.45

20 1800 60

B 11 250 8 21.31

B 12 250 9 18.94

B 15 250 8 21.31

B 18 250 8 21.31

B 20 250 9 18.94

B 21 250 8 21.31

A 2 300 9 22.73

25 1650 47

A 3 300 8 25.57

A 4 300 9 22.73

B 13 250 7 24.35

B 14 250 7 24.35

B 19 250 7 24.35

B 17 250 6 28.41 30 250 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)i. 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 (5 pm) 

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. 

 

i MOVES uses time-based speed because the emission rates are time-based (e.g. gram/hour). 
73

Figure 9-1 Average speed distribution for hour = 17 (5 pm) 

for source types (11 through 54) stored in the AvgSpeedDistribution table in MOVES

9.2. Heavy-Duty Average Speed Distributions

It has been shown that combination trucks travel at approximately 92% of the speed of light-duty

vehicles on restricted access roads46. 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.
74

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 29. 

Here, ̅is the average speed of the distribution, is the average speed of bin , and is the 

proportion of time spent in bin .

̅= ∙ 

= 2.5 ∙  + 5 ∙  + ⋯ + 70 ∙  + 75 ∙ 

Equation 17

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% of the 

light-duty speed, as shown in Equation 18. This redistributed proportion of time in each speed 

bin is given by 



.

̅combination = (. 92) ̅light-duty

= ∙ 

 Equation 18

To perform this redistribution, we defined two new variables,  and , where is the fraction of 

that is shifted down one speed bin, and is the fraction of shifted down two speed bins. 

The new distribution at speed bin  (given by 



) starts with the original distribution (), gains 

the proportions moved down from the higher speed bins ( ∙  and  ∙ ), and loses 

the proportion that is moved to a lower speed bin (∙  and ∙ ). This is shown in Equation 

31:



 =  + ( ∙ 

) + ( ∙ ) − (

∙ 

) − (

∙ 

) 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  and 

set  = 0. Mathematically, reducing a bin’s average speed by a certain fraction () can be 

expressed with Error! Reference source not found.:

(1 − ) ∙  = 

∙ ( − 5) + (1 − 

) ∙  Equation 20

Essentially, the fraction that is moved to the next slower bin () is multiplied by the slower 

speed (note that each of the speed bins are 5 mph apart, so this is  − 5), and the fraction that 

remains (1 − ) is multiplied by the original speed . Since the average speed of the 

combination trucks is 92% slower, (1 − ) = 92% and  = 0.08.

By rearranging terms from equation 20, and solving for  we obtain:

 =

∙ 

5

Equation 21

However, for speed bins ≥65 mph, Error! Reference source not found. yields  greater than 1. 

Since that logically can’t happen, some of the distribution needed to be moved to the second next 
75

slower speed bin to fully account for the 8% speed reduction. This is mathematically shown in 

Error! Reference source not found., which is the logical extension of Equation 20:

(1 − ) ∙  = 

∙ ( − 10) + ∙ ( − 5) + (1 −  − 

) ∙  Equation 22

The difference between Error! Reference source not found. and Error! Reference source not 

found. is that an additional fraction () 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 Error! Reference source not 

found.), adding the constraint that  +  are less than or equal to 1 (Equation 24), and choosing

the solution that minimized .

∙ ( − 5) + ∙ ( − 10) + ∙ ( −  − 

) = 0 Equation 23

 +  ≤ 1 Equation 24

This linear program was used to solve for  and for each speed bin between 65 and 75 mph. 

With  and  known for each bin, the new distributions 

 were calculated.

Figure 9-2 An illustration of adjustments made to the average speed bin 55 mph. Here, the original speed 

distribution is shown in light gray. The darker gray is the proportion of speed bin 55 that is moved out to the 

slower speed bin 50 mph, and the black areas are the distributions from speed bin 60 and 65 that are moved 

in to speed bin 55 mph.
76

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, 1/9th 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, 1/9

th of the new 

70 mph fraction (



) 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. See Figure 9-3 for an example illustration 

of the results of this analysis.
77

Figure 9-3 Average weekday speed distribution for hour = 17 (5 pm) 

by source type stored in the avgSpeedDistribution table in MOVES.
78

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. 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, 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. 

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.

The tables 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 

can be used as the upper bound and the lower bound for all average speed bins. New default 
79

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 Driving Cycles for Motorcycles, Cars, Passenger Cars and Light Commercial Trucks (11,21,31,32)

ID Cycle Name

Average 

Speed

Unrestricted Access Restricted access

Rural Urban Rural Urban

101 LD Low Speed 1 2.5 X X X X

1033 Final FC14LOSF 8.7 X X

1043 Final FC19LOSAC 15.7 X X

1041 Final FC17LOSD 18.6 X X

1021 Final FC11LOSF 20.6 X X

1030 Final FC14LOSC 25.4 X X

153 LD LOS E Freeway 30.5 X X

1029 Final FC14LOSB 31.0 X X

1026 Final FC12LOSE 43.3 X

1020 Final FC11LOSE 46.1 X X

1011 Final FC02LOSDF 49.1 X

1025 Final FC12LOSD 52.8 X

1019 Final FC11LOSD 58.8 X X

1024 Final FC12LOSC 63.7 X X

1018 Final FC11LOSC 64.4 X X

1017 Final FC11LOSB 66.4 X X

1009 Final FC01LOSAF 73.8 X X X X

158 LD High Speed Freeway 3 76.0 X X X X

Table 10-2 Driving Cycles for Intercity Buses (41)

ID Cycle Name

Average 

Speed

Unrestricted access Restricted access

Rural Urban Rural Urban

398 CRC E55 HHDDT Creep 1.8 X X X X

404 New York City Bus 3.7 X X

201 MD 5mph Non-Freeway 4.6 X X X X

405 WMATA Transit Bus 8.3 X X

202 MD 10mph Non-Freeway 10.7 X X X X

203 MD 15mph Non-Freeway 15.6 X X X X

204 MD 20mph Non-Freeway 20.8 X X X X

205 MD 25mph Non-Freeway 24.5 X X X X

206 MD 30mph Non-Freeway 31.5 X X X X

251 MD 30mph Freeway 34.4 X X X X

252 MD 40mph Freeway 44.5 X X X X

253 MD 50mph Freeway 55.4 X X X X

254 MD 60mph Freeway 60.4 X X X X

255 MD High Speed Freeway 72.8 X X X X

397 MD High Speed Freeway Plus 5 mph 77.8 X X X X
80

Table 10-3 Driving Cycles for Transit and School Buses (42,43)

ID Cycle Name

Average 

Speed

Unrestricted access Restricted access

Rural Urban Rural Urban

398 CRC E55 HHDDT Creep 1.8 X X X X

201 MD 5mph Non-Freeway 4.6 X X

404 New York City Bus 3.7 X X

202 MD 10mph Non-Freeway 10.7 X X

405 WMATA Transit Bus 8.3 X X

401 Bus Low Speed Urban* 15 X X

203 MD 15mph Non-Freeway 15.6 X X

204 MD 20mph Non-Freeway 20.8 X X

205 MD 25mph Non-Freeway 24.5 X X

402 Bus 30 mph Flow* 30 X X

206 MD 30mph Non-Freeway 31.5 X X

251 MD 30mph Freeway 34.4 X X

252 MD 40mph Freeway 44.5 X X

403 Bus 45 mph Flow* 45 X X

253 MD 50mph Freeway 55.4 X X X X

254 MD 60mph Freeway 60.4 X X X X

255 MD High Speed Freeway 72.8 X X X X

397 MD High Speed Freeway Plus 5 mph 77.8 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 Driving Cycles for Refuse Trucks (51)

ID Cycle Name

Average 

Speed

Unrestricted access Restricted access

Rural Urban Rural Urban

398 CRC E55 HHDDT Creep 1.8 X X

501 Refuse Truck Urban 2.2 X X

301 HD 5mph Non-Freeway 5.8 X X

302 HD 10mph Non-Freeway 11.2 X X X X

303 HD 15mph Non-Freeway 15.6 X X X X

304 HD 20mph Non-Freeway 19.4 X X X X

305 HD 25mph Non-Freeway 25.6 X X X X

306 HD 30mph Non-Freeway 32.5 X X X X

351 HD 30mph Freeway 34.3 X X X X

352 HD 40mph Freeway 47.1 X X X X

353 HD 50mph Freeway 54.2 X X X X

354 HD 60mph Freeway 59.4 X X X X

355 HD High Speed Freeway 71.7 X X X X

396 HD High Speed Freeway Plus 5 mph 77.8 X X X X
81

Table 10-5 Driving Cycles for Single Unit Trucks and Motor Homes (52,53,54)

ID Cycle Name

Average 

Speed

Unrestricted access Restricted access

Rural Urban Rural Urban

398 CRC E55 HHDDT Creep 1.8 X X X X

201 MD 5mph Non-Freeway 4.6 X X X X

202 MD 10mph Non-Freeway 10.7 X X X X

203 MD 15mph Non-Freeway 15.6 X X X X

204 MD 20mph Non-Freeway 20.8 X X X X

205 MD 25mph Non-Freeway 24.5 X X X X

206 MD 30mph Non-Freeway 31.5 X X X X

251 MD 30mph Freeway 34.4 X X X X

252 MD 40mph Freeway 44.5 X X X X

253 MD 50mph Freeway 55.4 X X X X

254 MD 60mph Freeway 60.4 X X X X

255 MD High Speed Freeway 72.8 X X X X

397 MD High Speed Freeway Plus 5 mph 77.8 X X X X

Table 10-6 Driving Cycles for Combination Trucks (61,62)

ID Cycle Name

Average 

Speed

Unrestricted access Restricted access

Rural Urban Rural Urban

398 CRC E55 HHDDT Creep 1.8 X X X X

301 HD 5mph Non-Freeway 5.8 X X X X

302 HD 10mph Non-Freeway 11.2 X X X X

303 HD 15mph Non-Freeway 15.6 X X X X

304 HD 20mph Non-Freeway 19.4 X X X X

305 HD 25mph Non-Freeway 25.6 X X X X

306 HD 30mph Non-Freeway 32.5 X X X X

351 HD 30mph Freeway 34.3 X X X X

352 HD 40mph Freeway 47.1 X X X X

353 HD 50mph Freeway 54.2 X X X X

354 HD 60mph Freeway 59.4 X X X X

355 HD High Speed Freeway 71.7 X X X X

396 HD High Speed Freeway Plus 5 mph 77.8 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.001.47 “LD Low Speed 1” is a historic cycle 

used in the development of speed corrections for MOBILE5 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,48 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.

37 The new cycles were 

selected to best cover the range of road types and average speeds modeled in MOVES.
82

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”

49 and “WMATA Transit 

Bus”

50 drive schedules are included for urban driving that includes transit type bus driving 

behavior. The “CRC E55 HHDDT Creep” 51 cycle was included to cover extremely low speeds. 

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.

52

 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: 

Driving Schedules and SCC Mappings). 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 EFF 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.”53 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 predefined 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 EFF 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)54 for medium- and heavy-duty vehicle 
83

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%) in MOVES2014 is derived from the ramp fraction value developed originally for the 

MOBILE6 model.

55
84

11. Hotelling Activity

MOVES2014 defines "hotelling" as any long period of time that drivers spend at 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 onboard power generators.

In MOVES2014, only the long haul combination truck source use type (sourceTypeID=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 10 hours driving followed by a mandatory 8 hour 

rest period. These regulations are described in the Federal Register.56 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 

10 gives the number of 8-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. 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.
85

  =

 h  

  Equation 25

  =

 

10 h  

Equation 26

  =   ∗ 8 h   Equation 27

  =

 

    

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 Annual Value units

Rural Restricted 31392300000 miles

Rural Unrestricted 34301700000 miles

Urban Restricted 32243100000 miles

Urban Unrestricted 28848900000 miles

Total annual VMT 126786000000 miles

Hours (58.3 mph) 2174716981 hours

Trips (10 hrs per trip) 217471698 trips

Hotelling hours (8 hrs per trip) 1739773585 hours

Hotelling hours per mile for rural restricted roads 0.055414 rate
86

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 2011 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

OpModeID Description

200 Extended Idling of Main Engine

201 Hotelling Diesel Auxiliary Power Unit (APU)

203 Hotelling Battery or AC (plug in)

204 Hotelling All Engines and Accessories Off

The HotellingActivityDistribution table contains the MOVES default values for the distribution 

of hotelling activity to the operating modes.

Table 11-3 Default Hotelling Activity Distributions

beginModelYearID endModelYearID opModeID opModeFraction

1960 2009 200 1

1960 2009 201 0

1960 2009 203 0

1960 2009 204 0

2010 2050 200 0.7

2010 2050 201 0.3

2010 2050 203 0

2010 2050 204 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 
87

use extended idle 70 percent of the time and use APUs 30% of the time based on EPA’s 

assessment of technologies used by tractor manufacturers to comply with the Heavy-Duty

Greenhouse Gas standards.
88

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 

(distributions), 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 (OHIM)57. The report describes analysis of a 

sample of 5,000 continuous traffic counters distributed through 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 OHIM 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 SampleVehicle and SampleVehicleTrip 

tables of the MOVES database. These tables contain a set of vehicle trip activity information 

from over 37,000 trips, taken from a sample of vehicles intended to be representative of 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 samples take no trips.

Table 12-1 Sample Vehicle Day Table Sample Sizes

Source Type Vehicle Sample

sourceTypeID Description Weekday (dayID=5) Weekend (dayID=2)

11 Motorcycle 2214 983

21 Passenger Car 821 347

31 Passenger Truck 834 371

32 Light Commercial Truck 773 345

41 Intercity Bus 190 73

42 Transit Bus 110 14

43 School Bus 136 59

51 Refuse Truck 205 65

52 Single Unit Short-haul Truck 112 58

53 Single Unit Long-haul Truck 123 50

54 Motor Home 5431 2170

61 Combination Short-haul Truck 130 52

62 Combination Long-haul Truck 122 49
89

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 assume 

a non-leap year (365 days).

Table 12-2 MonthVMTFraction

Month Normalized 

VMT/day

MOVES

Distribution

January 1.0000 0.0731

February 1.0560 0.0697

March 1.1183 0.0817

April 1.1636 0.0823

May 1.1973 0.0875

June 1.2480 0.0883

July 1.2632 0.0923

August 1.2784 0.0934

September 1.1973 0.0847

October 1.1838 0.0865

November 1.1343 0.0802

December 1.0975 0.0802

Sum 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 201058. 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 for motorcycles are only a national average and do not 

reflect the strong regional differences that would be expected due to climate.
90

Table 12-3 MonthVMTFraction for Motorcycles

Month Month ID Distribution

January 1 0.0262

February 2 0.0237

March 3 0.0583

April 4 0.1007

May 5 0.1194

June 6 0.1269

July 7 0.1333

August 8 0.1349

September 9 0.1132

October 10 0.0950

November 11 0.0442

December 12 0.0242

Sum 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. The OHIM report explains that data for “3am” refers to data collected 

from 3am to 4am. Thus data labeled “midnight” belongs to and was summed with the upcoming 

day. 

Table 12-4 DayVMTFractions

Fraction Rural Urban

Weekday 0.72118 0762365

Weekend 0.27882 0.237635

Sum 1.00000 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 onefifth of the weekday fraction and the fraction of weekly VMT for a single weekend day will be 

one-half the weekend fraction.
91

12.3. VMT Distribution by Hour of the Day

HourVMTFraction uses the same data as for DayVMTFraction. 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 DayVMTFraction. The OHIM report explains that 

data for “3am” refers to data collected from 3am to 4am. Thus data labeled “midnight” belongs 

to and was included with the upcoming day.

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. 

Table 12-5 Distribution of VMT by Hour of the Day

hourID Description

Urban Rural

Weekday Weekend Weekday Weekend

1 Hour beginning at 12:00 midnight 0.0098621 0.0214739 0.0107741 0.0164213

2 Hour beginning at 1:00 AM 0.00627248 0.0144428 0.0076437 0.0111921

3 Hour beginning at 2:00 AM 0.00505767 0.0109684 0.0065464 0.0085415

4 Hour beginning at 3:00 AM 0.00466686 0.0074945 0.0066348 0.00679328

5 Hour beginning at 4:00 AM 0.00699469 0.0068385 0.0095399 0.00721894

6 Hour beginning at 5:00 AM 0.018494 0.0103588 0.0200551 0.0107619

7 Hour beginning at 6:00 AM 0.0459565 0.0184303 0.0410295 0.01768008

8 Hour beginning at 7:00 AM 0.0696444 0.0268117 0.0579722 0.0268751

9 Hour beginning at 8:00 AM 0.0608279 0.0363852 0.0534711 0.0386587

10 Hour beginning at 9:00 AM 0.0502862 0.0475407 0.0525478 0.0522389

11 Hour beginning at 10:00 AM 0.0499351 0.0574664 0.0550607 0.0631739

12 Hour beginning at 11:00 AM 0.0543654 0.0650786 0.0576741 0.0699435

13 Hour beginning at 12:00 Noon 0.0576462 0.0713228 0.0591429 0.0729332

14 Hour beginning at 1:00 PM 0.0580319 0.0714917 0.0608019 0.0731218

15 Hour beginning at 2:00 PM 0.0622554 0.0717226 0.0652985 0.0736159

16 Hour beginning at 3:00 PM 0.0710049 0.0720061 0.0726082 0.0744608

17 Hour beginning at 4:00 PM 0.0769725 0.0711487 0.0773817 0.0742165

18 Hour beginning at 5:00 PM 0.077432 0.0678874 0.0754816 0.0700091

19 Hour beginning at 6:00 PM 0.059783 0.0617718 0.0587059 0.0614038

20 Hour beginning at 7:00 PM 0.0443923 0.0516882 0.0439864 0.0505043

21 Hour beginning at 8:00 PM 0.0354458 0.0428658 0.0357309 0.0412072

22 Hour beginning at 9:00 PM 0.031824 0.0380302 0.0307428 0.0336373

23 Hour beginning at 10:00 PM 0.0249419 0.0322072 0.0238521 0.0262243

24 Hour beginning at 11:00 PM 0.0179068 0.0245677 0.0173177 0.0191666

Sum of All Fractions 1.000 1.000 1.000 1.000
92

Figure 12-1 Hourly VMT Fractions by Day Type and Road Type

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. 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 (sourceTypeID), 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.

59 This change is described in greater detail in the 

report describing evaporative emissions in MOVES2014.

60

The second table, SampleVehicleTrip, lists the trips in a day made by each of the vehicles in the 

SampleVehicleDay table. It records the vehID, dayID, a trip number (tripID), the hour of the trip 

(hourID), 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 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fractio

n

of D

aily

Ve

hicle

Miles Tra

v

ele

d

Hour of the Day

Rural Weekend

Urban Weekend

Rural Weekday

Urban Weekday
93

midnight of the day of the trip. 439 trips (about 1.1%) 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 (dayID 

5) and weekends (dayID 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.61,62 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 Study Area Study 

Years Vehicle Types Vehicle Count

3-City FTP 

Study

Atlanta, GA; Baltimore, MD; 

Spokane, WA 1992 Passenger cars & trucks 321

Minneapolis Minneapolis/St. Paul, MN 2004-2005 Passenger cars & trucks 133

Knoxville Knoxville, TN 2000-2001 Passenger cars & trucks 377

Las Vegas Las Vegas, NV 2004-2005 Passenger cars & trucks 350

Battelle California, statewide 1997-1998 Heavy-duty trucks 120

TxDOT Houston, TX 2002 Diesel dump trucks 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. 
94

Table 12-7 Synthesis of Sample Vehicles for Source Types Lacking Data

Source Type Based on 

Direct Data? Synthesized From

Motorcycles No Passenger Cars

Passenger Cars Yes n/a

Passenger Trucks Yes n/a

Light Commercial Trucks No Passenger Trucks

Intercity Buses No Combination long-haul trucks

Transit Buses No Single unit short-haul trucks

School Buses No Single unit short-haul trucks

Refuse Trucks No Combination short-haul trucks

Single unit short-haul trucks Yes n/a

Single unit long-haul trucks No Combination long-haul trucks

Motor homes No Passenger Cars

Combination short-haul trucks Yes n/a

Combination long-haul trucks Yes 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 per Day by Source Type

Source Type

MOVES2014

Weekday

MOVES2014

Weekend

Motorcycles 0.78 0.79

Passenger Cars 5.89 5.30

Passenger Trucks 5.80 5.06

Light Commercial Trucks 6.05 5.47

Intercity Buses 2.77 0.88

Transit Buses 4.58 3.46

School Buses 5.75 1.26

Refuse Trucks 3.75 0.92

Single unit short-haul trucks 6.99 1.28

Single unit long-haul trucks 4.29 1.29

Motor homes 0.57 0.57

Combination short-haul trucks 5.93 1.16

Combination long-haul trucks 4.29 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).3,4The 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. 
95

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 track 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,

53 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

hourID Hour of the Day Trip Starts Trip Ends

1 Hour beginning at 12:00 midnight 78 171

2 Hour beginning at 1:00 AM 76 167

3 Hour beginning at 2:00 AM 65 144

4 Hour beginning at 3:00 AM 94 98

5 Hour beginning at 4:00 AM 107 71

6 Hour beginning at 5:00 AM 131 73

7 Hour beginning at 6:00 AM 194 71

8 Hour beginning at 7:00 AM 230 52

9 Hour beginning at 8:00 AM 279 85

10 Hour beginning at 9:00 AM 267 48

11 Hour beginning at 10:00 AM 275 78

12 Hour beginning at 11:00 AM 240 76

13 Hour beginning at 12:00 Noon 201 65

14 Hour beginning at 1:00 PM 211 94

15 Hour beginning at 2:00 PM 171 107

16 Hour beginning at 3:00 PM 167 131

17 Hour beginning at 4:00 PM 144 194

18 Hour beginning at 5:00 PM 98 230

19 Hour beginning at 6:00 PM 71 279

20 Hour beginning at 7:00 PM 73 267

21 Hour beginning at 8:00 PM 71 275

22 Hour beginning at 9:00 PM 52 240

23 Hour beginning at 10:00 PM 85 201

24 Hour beginning at 11:00 PM 48 211
96

An estimate of the distribution of truck hotelling duration times is derived from a 2004 CRC 

paper63 based on a survey of 365 truck drivers at 6 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

Duration (hours) Fraction of Trucks

2 0.227

4 0.135

6 0.199

8 0.191

10 0.156

12 0.057

14 0.014

16 0.021

Total 1.000

We assume that all hotelling activity begins at the trip ends shown in Table 12-9. The 

distribution of hotelling durations from Table 12-10 is applied to these trip ends. These 

calculations are shown in 

Table 12-11. The column “2 hours” adds the number of trip ends in the previous 2 hours, 

assuming all trips will have 2 hours of hotelling after their trip ends, the column “4 hours” adds 

the trips ends in the previous 4 hours, etc. The first hour of hotelling in each category is reduced 

by 60% to account for the fact that not all of the trips actually ended at the beginning of the hour. 

The total hotelling hours is the product of the total hours in each column multiplied by the 

fraction of trucks with each duration from Table 12-10.
97

Table 12-11 Calculation of Hourly Distributions of Hotelling Activity

hourID Trip Starts Trip Ends* 2 hours 4 hours 6 hours 8 hours 10 hours 12 hours 14 hours 16 hours Total Hours Distribution

1 78 171 255.4 679 1204.8 1736 2120.4 2343.6 2495.4 2638.2 1276 0.0628

2 76 167 235.4 629.4 1100 1643.6 2118.6 2408.8 2593 2739.2 1234 0.0611

3 65 144 210.8 566.4 990 1515.8 2047 2431.4 2654.6 2806.4 1166 0.0577

4 94 98 155.6 477.4 871.4 1342 1885.6 2360.6 2650.8 2835 1056 0.0526

5 107 71 110.2 379.8 735.4 1159 1684.8 2216 2600.4 2823.6 930 0.0458

6 131 73 101.4 299.6 621.4 1015.4 1486 2029.6 2504.6 2794.8 823 0.0407

7 194 71 100.2 254.2 523.8 879.4 1303 1828.8 2360 2744.4 728 0.0357

8 230 52 80.4 224.4 422.6 744.4 1138.4 1609 2152.6 2627.6 630 0.0306

9 279 85 105.8 237.2 391.2 660.8 1016.4 1440 1965.8 2497 581 0.0289

10 267 48 82 213.4 357.4 555.6 877.4 1271.4 1742 2285.6 507 0.0255

11 275 78 97.2 231.8 363.2 517.2 786.8 1142.4 1566 2091.8 479 0.0238

12 240 76 107.2 236 367.4 511.4 709.6 1031.4 1425.4 1896 457 0.0221

13 201 65 95.4 238.2 372.8 504.2 658.2 927.8 1283.4 1707 434 0.0221

14 211 94 120 266.2 395 526.4 670.4 868.6 1190.4 1584.4 447 0.0221

15 171 107 144.6 296.4 439.2 573.8 705.2 859.2 1128.8 1484.4 476 0.0238

16 167 131 173.8 358 504.2 633 764.4 908.4 1106.6 1428.4 526 0.0255

17 144 194 246.4 469.6 621.4 764.2 898.8 1030.2 1184.2 1453.8 635 0.0323

18 98 230 307.6 597.8 782 928.2 1057 1188.4 1332.4 1530.6 767 0.0374

19 71 279 371 755.4 978.6 1130.4 1273.2 1407.8 1539.2 1693.2 933 0.0458

20 73 267 378.6 853.6 1143.8 1328 1474.2 1603 1734.4 1878.4 1068 0.0526

21 71 275 381.8 913 1297.4 1520.6 1672.4 1815.2 1949.8 2081.2 1194 0.0594

22 52 240 350 893.6 1368.6 1658.8 1843 1989.2 2118 2249.4 1268 0.0628

23 85 201 297 822.8 1354 1738.4 1961.6 2113.4 2256.2 2390.8 1289 0.0645

24 48 211 291.4 762 1305.6 1780.6 2070.8 2255 2401.2 2530 1308 0.0645

Totals 3428 3428 4799 11655 18511 25367 32223 39079 45935 52791 20213 1.0000

* Assumes all trip ends occur 10 hours after trips start and all trips are 10 hours long.

The first hour of hotelling in each hour bin column sum is reduced by 60% to account for trip ends in a column that are not a full hour.
98

The distribution calculated using this method is similar to the behavior observed in a 

dissertation64 at the University of Tennessee, Knoxville. This study observed the trucks parking 

at the Petro truck travel center located at the I40/I75 and Watt Road interchange between midDecember 2003 and August 2004. Rather than use a 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

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 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.62

0%

1%

2%

3%

4%

5%

6%

7%

12:00 AM 12:00 PM 12:00 AM

Percent of Fleet Hotelling

Hours of Day
99

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 MOVES 

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 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 roadtype 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.

43 These estimates are based on the 

Highway Performance Monitoring System (HPMS) state level data collected by the Federal 

Highway Administration65 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 

SHOAllocFactor in MOVES2014 were calculated using the VMT estimates obtained from 

Version 1 of the 2011 NEI66 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 
100

distributions from the MOVES default database tables, DayMVTFraction and 

HourVMTFraction.

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 then calculated operating

hours by dividing the VMT in each average speed bin by the corresponding nominal average 

speed value.

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 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 2011.

66

VMT estimates for each county in each state and the allocation is calculated using the following 

formula, where  represents each individual county and  is the set of all US counties.
101

CountyAllocation = CountyVMT CountyVMT



 Equation 31

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 StartAllocFactor 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.
102

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 11-32) and STP is used for heavyduty vehicles (source types 41-62).

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 the equations:

 = 





 ∙  + 





 ∙ 

 + 





 ∙ 

 + ( +  ∙ sin ) ∙  Equation 32

 =

 + 

 +  +  ∙  ∙ ( +  ∙ )



Equation 33

where , , and  are the road load coefficients in units of kW-s⁄m, kW-s

 m ⁄ , and

kW-s

 m ⁄ respectively.  is associated with tire rolling resistence,  with mechanical rotating 

friction as well as higher order rolling resistance losses, and  with aerodynamic drag.  is the 

source mass for the source type in metric tons,  is the acceleration due to gravity 9.8 m⁄s



, v

is the instantaneous vehicle speed in m⁄s,  is the instantaneous vehicle acceleration in m⁄s



, 

sin  is the (fractional) road grade, and  is a scaling factor. 

When mapping actual emissions data to VSP bins with Error! Reference source not found., 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 Error! Reference source not found., 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 ( = ). For heavy-duty vehicles using STP, 

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  = 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  =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-duty3and heavy-duty4emission rate 

reports, respectively.
103

In both cases, operating mode distributions are derived from 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 Error! Reference source not found.

and Error! Reference source not found..

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 (in the equations above), and 

the fixed mass factor represents the STP scaling factor ( in the equations above).

While the source masses for light-duty were unchanged from MOVES2010b, all of the heavyduty source masses were updated with newer data. Please see Section 21. (Appendix E: 

MOVES2010b Source Masses) for a discussion of the MOVES2010b source masses. The heavyduty 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 (WIM) 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 WIM HPMS mass and the average 

MOVES2010b HPMS massj. 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 WIM data was then applied to the 

source masses of Single Unit Short-haul Trucks, Single Unit Long-haul Trucks, Refuse Trucks, 

and Motor Homes. Likewise, the percentage difference between the average combination truck 

mass in MOVES2010b and the WIM data was applied to the source masses of Combination 

Short-haul Trucks and Combination Long-haul Trucks, including the 2014+ model year groups. 

These differences are shown in Error! Reference source not found., and the resulting source 

type masses are presented in Error! Reference source not found..

Table 14-1 Weigh-in-Motion (WIM) Masses Weighted by VMT

HPMS Category Average Weight (lbs) % Change from

MOVES2010b

Single Unit Trucks 20,107 11.7%

Combination Trucks 52,907 -21.7%

 

j

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. 
104

14.2. Road Load Coefficients

The information available on road load coefficients varied by regulatory class. Motorcycle road 

load coefficients were parameterized, in accordance with standard practice, using the following 

emperical equations67,68:

 = 0.088 ∙  Equation 34

 = 0 Equation 35

 = 0.00026 + 0.000194 ∙  Equation 36

For light-duty vehicles, the road load coefficients were calculated according to the following 

empirical equations:69

 =

0.7457

50 ∙ 0.447 ∙ 0.35 ∙ @

Equation 37

 =

0.7457

(50 ∙ 0.447)



∙ 0.10 ∙ @

Equation 38

 =

0.7457

(50 ∙ 0.447)



∙ 0.55 ∙ @

Equation 39

In each of the above equations, the first factor is the appropriate unit conversion to allow , , 

and  to be used in Error! Reference source not found. and Error! Reference source not 

found., 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 , , and  for 

source types 21, 31, and 32 were derived from applying TRLHP values recorded in the Mobile 

Source Observation Database (MSOD)70 to Error! Reference source not found.-Error! 

Reference source not found.. 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.

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,71 as shown in Error! Reference source not found.. 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 Error! 

Reference source not found.. 
105

Table 14-2 Road Load Coefficients for Heavy-Duty Trucks, Buses, and Motor Homes for 1960-2013 Model 

Year Vehicles

Coefficient

8500 to 14000 lbs

(3.855 to 6.350 

metric ton)

14000 to 33000 lbs

(6.350 to 14.968 

metric ton)

>33000 lbs

(>14.968 metric ton)

Buses and Motor 

Homes

 

-



 0.0996 ∙  0.0875 ∙  0.0661 ∙  0.0643 ∙ 

 

-





 0 0 0 0

 

-







0.00289 +

5.22 × 10̇  ∙ 

0.00193 +

5.90 × 10∙ 

0.00289 +

4.21 × 10∙ 

0.0032 +

5.06 × 10∙ 

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.

72 Error! Reference source not found. 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 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
106

Table 14-3 Estimated Reductions in Rolling Resistance and Aerodynamic Drag Coefficients from Reference 

Case for Alternative 3 (Model Years 2014 and Later)

Truck Type

Reduction In Tire Rolling 

Resistance Coefficient From 

Baseline

Reduction In 

Aerodynamic Drag 

Coefficient From Baseline

Weight 

Reduction 

(lbs)

Combination long-haul 9.6% 12.1% 400

Combination short-haul 7.0% 5.9% 321

Vocational vehicles (Singleunit trucks, refuse trucks, 

motor homes, buses, and 

light commercial trucks) 

5.0% 0% 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 beginModelYearID and endModelYearID fields in the 

SourceUseTypePhysics table.

Table 14-4 SourceUseTypePhysics Table

sourceTypeID

Begin 

Model 

Year 

End

Model 

Year

Rolling

Term A

(kW-s/m)

Rotating

Term B

(kW-s

2

/m2)

Drag

Term C

(kW-s

3

/m3)

Source Mass

(metric tons)

Fixed Mass 

Factor (metric 

tons)

11 1960 2050 0.0251 0 0.0003 0.2850 0.2850

21 1960 2050 0.1565 0.0020 0.0005 1.4788 1.4788

31 1960 2050 0.2211 0.0028 0.0007 1.8669 1.8669

32 1960 2050 0.2350 0.0030 0.0007 2.0598 2.0598

41 1960 2013 1.2952 0 0.0037 19.5937 17.1

41 2014 2050 1.2304 0 0.0037 19.5937 17.1

42 1960 2013 1.0944 0 0.0036 16.5560 17.1

42 2014 2050 1.0397 0 0.0036 16.5560 17.1

43 1960 2013 0.7467 0 0.0022 9.0699 17.1

43 2014 2050 0.7094 0 0.0022 9.0699 17.1

51 1960 2013 1.5835 0 0.0036 23.1135 17.1

51 2014 2050 1.5043 0 0.0036 23.1135 17.1

52 1960 2013 0.6279 0 0.0016 8.5390 17.1

52 2014 2050 0.5965 0 0.0016 8.5390 17.1

53 1960 2013 0.5573 0 0.0015 6.9845 17.1

53 2014 2050 0.5294 0 0.0015 6.9845 17.1

54 1960 2013 0.6899 0 0.0021 7.5257 17.1

54 2014 2050 0.6554 0 0.0021 7.5257 17.1

61 1960 2013 1.5382 0 0.0040 22.9745 17.1

61 2014 2050 1.4305 0 0.0038 22.8289 17.1

62 1960 2013 1.6304 0 0.0042 24.6010 17.1

62 2014 2050 1.4739 0 0.0037 24.4196 17.1
107

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.73

15.1. ACPenetrationFraction

The ACPenetrationFraction is a field in the SourceTypeModelYear table. Default values, by

source type and model year were taken from MOBILE6.74 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).
108

Table 15-1 AC Penetration Fractions in MOVES2014

Motorcycles Passenger Cars All Trucks and Buses

1972-and-earlier 0 0.592 0.287

1973 0 0.726 0.287

1974 0 0.616 0.287

1975 0 0.631 0.287

1976 0 0.671 0.311

1977 0 0.720 0.351

1978 0 0.719 0.385

1979 0 0.694 0.366

1980 0 0.624 0.348

1981 0 0.667 0.390

1982 0 0.699 0.449

1983 0 0.737 0.464

1984 0 0.776 0.521

1985 0 0.796 0.532

1986 0 0.800 0.544

1987 0 0.755 0.588

1988 0 0.793 0.640

1989 0 0.762 0.719

1990 0 0.862 0.764

1991 0 0.869 0.771

1992 0 0.882 0.811

1993 0 0.897 0.837

1994 0 0.922 0.848

1995 0 0.934 0.882

1996 0 0.948 0.906

1997 0 0.963 0.929

1998 0 0.977 0.950

1999+ 0 0.980 0.950

15.2. FunctioningACFraction 

The FunctioningACFraction field in the SourceTypeAge table indicates the fraction of the airconditioning 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.
109

Table 15-2 FunctioningACFraction by Age (All Source Types except Motorcycles)

ageID functioningACFraction

0 1

1 1

2 1

3 1

4 0.99

5 0.99

6 0.99

7 0.99

8 0.98

9 0.98

10 0.98

11 0.98

12 0.98

13 0.96

14 0.96

15 0.96

16 0.96

17 0.96

18 0.95

19 0.95

20 0.95

21 0.95

22 0.95

23 0.95

24 0.95

25 0.95

26 0.95

27 0.95

28 0.95

29 0.95

30 0.95

15.3. ACActivityTerms

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 in MOBILE6.

74 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.
110

Table 15-3 Air Conditioning Activity Coefficients

A B C

-3.63154 0.072465 -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

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

70 75 80 85 90 95 100 105 110

Heat Index (F)
111

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 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.

75

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 and 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 large-scale 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,

 VSP/STP adjustments for speed, road grade, and loading,

 Age effects such as mileage accumulation and scrappage,

 Summaries of real-time GPS and on-board diagnostic device data streams, 

 Vehicle identification and sorting by size, sector, and vocation.

 Activity weighting of source mass averages.

 Air Conditioning system penetration and failure rates.
112

 Vehicle type distinctions in temporal activity, 

 Heavy truck and bus daily trip activity patterns (particularly at night).

Future updates to vehicle population and activity defaults will need to continue 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 electric vehicles and other vehicles that 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.
113

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 Motorcycle Passenger 

Car

Passenger 

Truck

Light 

Comm.

Truck

Intercity 

Bus

Transit 

Bus

School 

Bus

Refuse 

Truck

Single 

Unit 

Shorthaul

Single 

Unit 

Longhaul

Motor 

Home

Combination 

Short-haul

Combination 

Long-haul

2012 8571 128033 86859 21393 18 69 617 185 6194 260 1559 1191 1280

2013 8687 129764 87924 21791 19 72 643 195 6525 274 1643 1234 1332

2014 8706 130054 88014 21960 20 74 663 203 6777 285 1708 1258 1377

2015 8747 130666 88345 22167 21 77 691 213 7093 299 1788 1306 1439

2016 8844 132117 89259 22492 22 80 720 223 7392 312 1863 1354 1503

2017 8943 133583 90198 22803 22 82 740 230 7589 322 1915 1380 1555

2018 9018 134715 90934 23043 23 84 753 235 7709 328 1946 1390 1600

2019 9098 135907 91718 23279 23 86 766 239 7824 333 1977 1400 1645

2020 9178 137105 92513 23508 23 87 780 243 7953 335 2012 1410 1690

2021 9260 138317 93324 23730 24 88 794 247 8093 340 2053 1422 1737

2022 9337 139471 94098 23939 24 90 809 252 8242 345 2095 1437 1783

2023 9416 140653 94892 24150 25 92 824 256 8385 351 2134 1453 1828

2024 9498 141880 95725 24361 25 93 838 260 8510 352 2168 1466 1872

2025 9585 143179 96598 24591 26 95 853 264 8638 357 2204 1482 1918

2026 9680 144593 97557 24833 26 97 867 267 8752 362 2239 1495 1964

2027 9781 146100 98575 25092 27 98 879 269 8846 366 2266 1505 2005

2028 9888 147713 99664 25368 27 99 889 272 8927 371 2288 1514 2040

2029 9996 149317 100741 25649 27 100 900 274 9017 375 2312 1527 2073

2030 10103 150922 101823 25925 28 101 912 277 9114 376 2340 1546 2104

2031 10215 152591 102952 26209 28 103 922 280 9209 377 2368 1567 2131

2032 10328 154280 104098 26493 28 104 931 283 9286 381 2385 1585 2152

2033 10439 155930 105216 26772 28 105 942 286 9378 385 2405 1609 2174

2034 10538 157420 106225 27024 29 106 956 290 9493 391 2432 1639 2203

2035 10633 158833 107181 27263 29 108 969 293 9599 396 2457 1669 2232
114

2036 10724 160194 108102 27494 30 109 983 296 9698 401 2482 1701 2260

2037 10813 161523 109001 27720 30 111 996 299 9795 405 2508 1733 2288

2038 10901 162835 109888 27944 30 113 1009 301 9887 409 2532 1766 2315

2039 10983 164062 110717 28153 31 114 1021 304 9968 413 2553 1794 2342

2040 11055 165135 111441 28338 31 115 1034 306 10051 416 2573 1822 2371

2041 11155 166628 112449 28594 32 117 1047 309 10147 420 2596 1849 2402

2042 11256 168135 113466 28852 32 118 1060 312 10243 424 2620 1876 2435

2043 11357 169655 114490 29115 32 120 1074 315 10342 428 2646 1901 2470

2044 11460 171189 115523 29380 33 121 1087 318 10442 432 2672 1925 2507

2045 11564 172737 116567 29647 33 123 1101 321 10543 436 2698 1950 2544

2046 11668 174299 117620 29916 34 124 1115 324 10646 440 2725 1975 2581

2047 11774 175875 118683 30187 34 126 1129 328 10749 445 2751 2001 2619

2048 11880 177465 119756 30460 34 127 1143 331 10853 449 2778 2028 2656

2049 11988 179069 120838 30735 35 129 1158 334 10958 453 2805 2055 2695

2050 12096 180688 121931 31013 35 131 1172 337 11064 458 2832 2083 2733
115

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 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 Trucks*

Source Type

Model 

Year

Passenger 

Trucks

(31)

Light 

Commercial 

Trucks

(32)

Refuse 

Trucks

(51)

Single Unit

Trucks

(52 & 53)

Motor Homes 

(54)

Short-haul 

Combination 

Trucks

(61)

Long-haul 

Combination 

Trucks

(62)

1960-1979 0.0139 0.0419 0.96 0.2655 0.15 0.9146 1.0000

1980 0.0124 0.1069 0.96 0.2950 0.15 0.9146 1.0000

1981 0.0178 0.0706 0.96 0.3245 0.15 0.9146 1.0000

* All other trucks are assumed to be gasoline powered

Like 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*

Source Type

Model 

Year

Motorcycles

(11)

Passenger 

Cars

(21)

Intercity Buses

(41)

Transit Buses

(42)

School Buses 

(43)

1960-1974 0 0.0069 1.000 1.000 0.0087

1975 0 0.0180 1.000 1.000 0.0087

1976 0 0.0165 1.000 1.000 0.0086

1977 0 0.0129 1.000 1.000 0.0240

1978 0 0.0151 1.000 1.000 0.0291

1979 0 0.0312 1.000 1.000 0.0460

1980 0 0.0467 1.000 1.000 0.0594

1981 0 0.0764 1.000 1.000 0.2639

* All other vehicles are assumed to be gasoline powered
116

The 1960-1981 regulatory class distributions are difficult to represent succinctly, but they have 

been derived from the MOVES2010b sample vehicle counts dataset as well, with a few 

exceptions. Motorcycles (sourceTypeID 11 and regClassID 10) and passenger cars 

(sourceTypeID 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 (sourceTypeID 31) and light commercial trucks (sourceTypeID 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 (sourceTypeID 31) and light 

commercial truck (sourceTypeID 32)

Passenger Trucks (31) Light Commercial Trucks (32)

Gasoline Diesel Gasoline Diesel

Model Year

LDT 

(30)

LHD

(40)

LDT 

(30)

LHD

(40)

LDT 

(30)

LHD

(40)

LDT 

(30)

LHD

(40)

1960-1966 81% 19% 38% 62% 24% 76% 7% 93%

1967 90% 10% 38% 62% 72% 28% 7% 93%

1968 88% 12% 38% 62% 67% 33% 7% 93%

1969 100% 0% 38% 62% 91% 9% 7% 93%

1970 99% 1% 38% 62% 80% 20% 7% 93%

1971 96% 3% 38% 62% 94% 6% 7% 93%

1972 96% 4% 38% 62% 75% 25% 7% 93%

1973 95% 5% 38% 62% 59% 41% 7% 93%

1974 95% 5% 38% 62% 65% 35% 7% 93%

1975 97% 3% 38% 62% 72% 28% 7% 93%

1976 95% 5% 38% 62% 88% 12% 7% 93%

1977 89% 11% 38% 62% 79% 21% 7% 93%

1978 85% 15% 38% 62% 81% 19% 7% 93%

1979 87% 13% 38% 62% 78% 22% 7% 93%

1980 90% 10% 38% 62% 74% 26% 40% 60%

1981 96% 4% 38% 62% 89% 11% 12% 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).
117

Table 18-4 Percentage of MHD Trucks (regClassID 46) among Diesel-fueled Single Unit and Combination 

Trucks*

Source Type

Model Year

Refuse Trucks

(51)

Single Unit 

Trucks

(52&53)

Short-haul 

Comb. Trucks

(61)

Long-haul Comb.

Trucks

(62)

1960-1972 100% 0% 0% 0%

1973 100% 3% 8% 0%

1974 0% 6% 30% 0%

1975 0% 14% 3% 0%

1976 0% 44% 13% 0%

1977 0% 43% 31% 0%

1978 0% 36% 18% 0%

1979 0% 34% 16% 0%

1980 0% 58% 29% 5%

1981 0% 47% 31% 6%

*For these source types, all remaining trucks are in the HHD regulatory class (regClassID 47).
118

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 (R2value = 

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 (R2value 

=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.”
119

Table 19-1 VIUS 1997 Codes Used for Distinguishing Truck Source Types

Source Type Axle Arrangement Primary Area of 

Operation

Body Type Major Use

Passenger Trucks 2 axle/4 tire 

(AXLRE= 1,5,6,7)

Any Any personal 

transportation 

(MAJUSE=20)

Light Commercial 

Trucks

2 axle/4 tire 

(AXLRE= 1,5,6,7)

Any Any any but personal 

transportation

Refuse Trucks Single Unit 

(AXLRE=2-4,

8-16)

Off-road, local or 

short-range 

(AREAOP <=4)

Garbage hauler 

(BODTYPE=30)

Any

Single Unit Shorthaul TrucksSingle Unit 

(AXLRE=2-4,

8-16)

Off-road, local or 

short-range 

(AREAOP<=4)

Any except garbage 

hauler

Any

Single Unit Longhaul TrucksSingle Unit 

(AXLRE=2-4,

8-16)

Long-range 

(AREAOP>=5)

Any Any

Combination Shorthaul TrucksCombination 

(AXLRE>=17)

Off-road, local or 

medium 

(AREAOP<=4)

Any Any

Combination Longhaul TrucksCombination 

(AXLRE>=17)

Long-range 

(AREAOP>=5)

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 the1990 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 MOBILE 6 age fractions since 1990 data on 

transit buses was not available from the Federal Transit Administration database. MOBILE 6 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% of the year’s sales will have occurred by July 1st

of a given calendar year.
120

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 

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 by Age

Vehicle 

Age Equation

1-17  = 3462 ∗ e





.

.

18-25+ 24987.0776 ∗ e

.∗
121

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 Mapping of Previous SCC Vehicle Classifications to MOVES Source Types for Calculation of 

Road Type Distributions

SCC

(7 digits) Description

Source 

Type Description

2011 

Fractions

2201001 Gasoline Light-Duty Vehicles (Passenger Cars) 21 Passenger Car 1.000000

2201020 Gasoline Light-Duty Trucks (0-6,000 lbs. GVWR) 31 Passenger Truck 0.779270

2201020 Gasoline Light-Duty Trucks (0-6,000 lbs. GVWR) 32 Light Commercial 

Truck

0.220730

2201040 Gasoline Light-Duty Trucks (6,001-8,500 lbs. GVWR) 31 Passenger Truck 0.779269

2201040 Gasoline Light-Duty Trucks (6,001-8,500 lbs. GVWR) 32 Light Commercial 

Truck

0.220731

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

31 Passenger Truck 0.450274

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

32 Light Commercial 

Truck

0.267803

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

42 Transit Bus 0.000664

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

43 School Bus 0.002476

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

51 Refuse Truck 0.000509

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

52 Single Unit Shorthaul Truck0.221958

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

53 Single Unit Longhaul Truck0.030154

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

54 Motor Home 0.025802

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. and 

greater GVWR)

61 Combination Shorthaul Truck0.000359

2201080 Gasoline Motorcycles 11 Motorcycle 1.000000

2230001 Diesel Light-Duty Vehicles (Passenger Cars) 21 Passenger Car 1.000000

2230060 Diesel Light-Duty Trucks (0-8,500 lbs. GVWR) 31 Passenger Truck 0.343599

2230060 Diesel Light-Duty Trucks (0-8,500 lbs. GVWR) 32 Light Commercial 

Truck

0.656401

2230071 Diesel Class 2b Heavy-Duty Vehicles (8501-10,000 lbs. 

GVWR)

31 Passenger Truck 0.364691

2230071 Diesel Class 2b Heavy-Duty Vehicles (8501-10,000 lbs. 

GVWR)

32 Light Commercial 

Truck

0.635309

2230072 Diesel Class 3, 4 & 5 Heavy-Duty Vehicles (10,001-

19,500 lbs. GVWR)

31 Passenger Truck 0.305092
122

SCC

(7 digits) Description

Source 

Type Description

2011 

Fractions

2230072 Diesel Class 3, 4 & 5 Heavy-Duty Vehicles (10,001-

19,500 lbs. GVWR)

32 Light Commercial 

Truck

0.694908

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

51 Refuse Truck 0.001726

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

52 Single Unit Shorthaul Truck0.623978

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

53 Single Unit Longhaul Truck0.086570

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

54 Motor Home 0.025294

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

61 Combination Shorthaul Truck0.194650

2230073 Diesel Class 6 & 7 Heavy-Duty Vehicles (19,501-

33,000 lbs. GVWR)

62 Combination Longhaul Truck0.067783

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

51 Refuse Truck 0.008531

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

52 Single Unit Shorthaul Truck0.100296

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

53 Single Unit Longhaul Truck0.013800

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

54 Motor Home 0.000328

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

61 Combination Shorthaul Truck0.323425

2230074 Diesel Class 8a & 8b Heavy-Duty Vehicles (33,001 lbs. 

and greater GVWR)

62 Combination Longhaul Truck0.553619

2230075 Diesel Buses 41 Intercity Bus 0.430859

2230075 Diesel Buses 42 Transit Bus 0.122565

2230075 Diesel Buses 43 School Bus 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. 

T 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.
123

Table 20-2 MOVES2014 Default Driving Schedule Statistics

drive 

schedule id drive schedule name

avg 

speed

max 

speed

idle 

time 

(sec)

percent of 

time idling miles time (sec) minutes hours

101 LD Low Speed 1 2.5 10.00 280 46.5% 0.419 602.00 10.03 0.167

153 LD LOS E Freeway 30.5 63.00 5 1.1% 3.863 456.00 7.60 0.127

158 LD High Speed Freeway 3 76.0 90.00 0 0.0% 12.264 581.00 9.68 0.161

201 MD 5mph Non-Freeway 4.6 24.10 85 29.0% 0.373 293.00 4.88 0.081

202 MD 10mph Non-Freeway 10.7 34.10 61 19.6% 0.928 311.00 5.18 0.086

203 MD 15mph Non-Freeway 15.6 36.60 57 12.6% 1.973 454.00 7.57 0.126

204 MD 20mph Non-Freeway 20.8 44.50 95 9.1% 6.054 1046.00 17.43 0.291

205 MD 25mph Non-Freeway 24.5 47.50 63 11.1% 3.846 566.00 9.43 0.157

206 MD 30mph Non-Freeway 31.5 55.90 54 5.5% 8.644 988.00 16.47 0.274

251 MD 30mph Freeway 34.4 62.60 0 0.0% 15.633 1637.00 27.28 0.455

252 MD 40mph Freeway 44.5 70.40 0 0.0% 43.329 3504.00 58.40 0.973

253 MD 50mph Freeway 55.4 72.20 0 0.0% 41.848 2718.00 45.30 0.755

254 MD 60mph Freeway 60.1 68.40 0 0.0% 81.299 4866.00 81.10 1.352

255 MD High Speed Freeway 72.8 80.40 0 0.0% 96.721 4782.00 79.70 1.328

301 HD 5mph Non-Freeway 5.8 19.90 37 14.2% 0.419 260.00 4.33 0.072

302 HD 10mph Non-Freeway 11.2 29.20 70 11.5% 1.892 608.00 10.13 0.169

303 HD 15mph Non-Freeway 15.6 38.30 73 12.9% 2.463 567.00 9.45 0.158

304 HD 20mph Non-Freeway 19.4 44.20 84 15.1% 3.012 558.00 9.30 0.155

305 HD 25mph Non-Freeway 25.6 50.70 57 5.8% 6.996 983.00 16.38 0.273

306 HD 30mph Non-Freeway 32.5 58.00 43 5.3% 7.296 809.00 13.48 0.225

351 HD 30mph Freeway 34.3 62.70 0 0.0% 21.659 2276.00 37.93 0.632

352 HD 40mph Freeway 47.1 65.00 0 0.0% 41.845 3197.00 53.28 0.888

353 HD 50mph Freeway 54.2 68.00 0 0.0% 80.268 5333.00 88.88 1.481

354 HD 60mph Freeway 59.7 69.00 0 0.0% 29.708 1792.00 29.87 0.498

355 HD High Speed Freeway 71.7 81.00 0 0.0% 35.681 1792.00 29.87 0.498

396 HD High Speed Freeway Plus 5 mph 76.7 86.00 0 0.0% 38.170 1792.00 29.87 0.498

397 MD High Speed Freeway Plus 5 mph 77.8 85.40 0 0.0% 103.363 4782.00 79.70 1.328
124

Table 20-2 MOVES2014 Default Driving Schedule Statistics

drive 

schedule id drive schedule name

avg 

speed

max 

speed

idle 

time 

(sec)

percent of 

time idling miles time (sec) minutes hours

398 CRC E55 HHDDT Creep 1.8 8.24 107 42.3% 0.124 253.00 4.22 0.070

401 Bus Low Speed Urban (nominal 15 mph) 3.1 19.80 288 63.9% 0.393 451.00 7.52 0.125

402 Bus 30 mph Flow (nominal 30 mph) 11.5 33.80 109 37.5% 0.932 291.00 4.85 0.081

403 Bus 45 mph Flow (nominal 45 mph) 21.9 47.00 116 28.3% 2.492 410.00 6.83 0.114

404 New York City Bus 3.7 30.80 403 67.2% 0.615 600.00 10.00 0.167

405 WMATA Transit Bus 8.3 47.50 706 38.4% 4.261 1840.00 30.67 0.511

501 Refuse Truck Urban 2.2 20.00 416 66.9% 0.374 622.00 10.37 0.173

1009 Final FC01LOSAF Cycle (C10R04-

00854)

73.8 84.43 0 0.0% 11.664 569.00 9.48 0.158

1011 Final FC02LOSDF Cycle (C10R05-

00513)

49.1 73.06 34 5.0% 9.283 681.00 11.35 0.189

1017 Final FC11LOSB Cycle (C10R02-00546) 66.4 81.84 0 0.0% 9.567 519.00 8.65 0.144

1018 Final FC11LOSC Cycle (C15R09-00849) 64.4 78.19 0 0.0% 16.189 905.00 15.08 0.251

1019 Final FC11LOSD Cycle (C15R10-00068) 58.8 76.78 0 0.0% 11.922 730.00 12.17 0.203

1020 Final FC11LOSE Cycle (C15R11-00851) 46.1 71.50 1 0.1% 12.468 973.00 16.22 0.270

1021 Final FC11LOSF Cycle (C15R01-00876) 20.6 55.48 23 2.5% 5.179 905.00 15.08 0.251

1024 Final FC12LOSC Cycle (C15R04-00582) 63.7 79.39 0 0.0% 15.685 887.00 14.78 0.246

1025 Final FC12LOSD Cycle (C15R09-00037) 52.8 73.15 12 1.5% 11.754 801.00 13.35 0.223

1026 Final FC12LOSE Cycle (C15R10-00782) 43.3 70.87 0 0.0% 10.973 913.00 15.22 0.254

1029 Final FC14LOSB Cycle (C15R07-00177) 31.0 63.81 27 3.6% 6.498 754.00 12.57 0.209

1030 Final FC14LOSC Cycle (C10R04-00104) 25.4 53.09 41 8.0% 3.617 513.00 8.55 0.143

1033 Final FC14LOSF Cycle (C15R05-00424) 8.7 44.16 326 38.2% 2.066 853.00 14.22 0.237

1041 Final FC17LOSD Cycle (C15R05-00480) 18.6 50.33 114 16.1% 3.659 709.00 11.82 0.197

1043 Final FC19LOSAC Cycle (C15R08-

00267)

15.7 37.95 67 7.7% 3.802 870.00 14.50 0.242
125

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. Each source type’s source mass was 

calculated using an activity-weighted average of their associated source bins’ midpoint weights:

 =

∑  ∙ 

∑  ∙ 

∑ 

 

∑ 

Equation 40

where  is the source mass factor for the source type,  is the age fraction at age ,  is the 

source bin activity fraction for source bin , and  is the vehicle midpoint mass. Error! 

Reference source not found. 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. 
126

Table 21-1 MOVES Weight Classes

WeightClassID Weight Class Name Midpoint Weight

0 Doesn't Matter [NULL] 

20 weight < 2000 pounds 1000

25 2000 pounds <= weight < 2500 pounds 2250

30 2500 pounds <= weight < 3000 pounds 2750

35 3000 pounds <= weight < 3500 pounds 3250

40 3500 pounds <= weight < 4000 pounds 3750

45 4000 pounds <= weight < 4500 pounds 4250

50 4500 pounds <= weight < 5000 pounds 4750

60 5000 pounds <= weight < 6000 pounds 5500

70 6000 pounds <= weight < 7000 pounds 6500

80 7000 pounds <= weight < 8000 pounds 7500

90 8000 pounds <= weight < 9000 pounds 8500

100 9000 pounds <= weight < 10000 pounds 9500

140 10000 pounds <= weight < 14000 pounds 12000

160 14000 pounds <= weight < 16000 pounds 15000

195 16000 pounds <= weight < 19500 pounds 17750

260 19500 pounds <= weight < 26000 pounds 22750

330 26000 pounds <= weight < 33000 pounds 29500

400 33000 pounds <= weight < 40000 pounds 36500

500 40000 pounds <= weight < 50000 pounds 45000

600 50000 pounds <= weight < 60000 pounds 55000

800 60000 pounds <= weight < 80000 pounds 70000

1000 80000 pounds <= weight < 100000 pounds 90000

1300 100000 pounds <= weight < 130000 pounds 115000

9999 130000 pounds <= weight 130000

5 weight < 500 pounds (for MCs) 350

7 500 pounds <= weight < 700 pounds (for MCs) 600

9 700 pounds <= weight (for MCs) 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 Error! Reference source not found..
127

Table 21-2 Motorcycle Engine Size and Average Weight Distributions for Selected Model Years

Displacement 

Category

1969 MY 

distribution 

(assumed)

1990 MY 

distribution 

(MIC)

1998 MY 

distribution 

(MIC)

2000 MY

distribution

(certification 

data)

Weight distribution (EPA 

staff)

0-169 cc (1) 0.118 0.118 0.042 0.029 100%: <= 500 lbs

170-279 cc (2) 0.09 0.09 0.05 0.043 50%: <= 500 lbs

50%: 500lbs -700lbs

280+ cc (9) 0.792 0.792 0.908 0.928 30%: 500 lbs-700 lbs

70%: > 700lbs

21.2. Passenger Cars

Passenger car weights come from Polk. The weightClassID was assigned by adding 300 lbs 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 VIUS 1997 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 lbs or 

less, 6,001-10,000 lbs, 10,001-14,000lbs, 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.76

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):

 VIUS 1997 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 lbs, etc) VIUS 1997 broad categories 

were matched one-to-one with the MOVES weight classes.

 For trucks in the lower broad categories (6,000 lbs or less and 6001-10,000 lbs), 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”)
128

 We assigned trucks in the ORNL light-duty vehicle database to a weightClassID by

adding 300lbs to the recorded curb weight and determining the appropriate MOVES 

weight class.

 For the trucks with a VIUS 1997 average weight of 6,000 lbs or less, we multiplied the 

VIUS 1997 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 lbs 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 lbs, we did 

not use it to distribute the trucks with a VIUS 1997 average weight of 6,001-10,000 lbs. 

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 

VIUS 1997 Strata 1 and 2, thus a similar algorithm was applied.

 VIUS 1997 trucks of the source type in Strata 3, 4, and 5 were assigned to the appropriate 

MOVES weight class based on VIUS 1997 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 lbs, etc) VIUS 1997 broad categories 

were matched one-to-one with the MOVES weight classes.

 For trucks in the lower broad categories (6,000 lbs-or-less and 6001-10,000 lbs), 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”)

 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 lbs 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 lbs 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 VIUS1997. The 

VIUS 2002 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 
129

carrying a typical payload during 2002. These estimates were used to determine the MOVES 

weightClassID categories for these trucks. Table 4-5 shows the weight ranges used for each 

weightClassID. 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 FTA 2003 Report to Congress40

that specified the number of buses in various weight categories. This information is summarized 

in below in Error! Reference source not found.. Note the FTA 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 Estimate of Bus Weights

Weight (lbs) MOVES Weight

ClassID

MOVES Weight 

Range (lbs)

Number of 

buses (2000) Bus type

0-20,000 173,536 school & transit

20,000-30,000 392,345 school & transit

30,000-40,000 400 33,000-40,000 120,721 school & transit & intercity

40,000-50,000 500 40,000-50,000 67,905 intercity

total 754,509

Table 21-4 1999 Bus Population Comparisons

Data Source Total Buses Intercity Buses Transit Buses School Buses

FHWA MV-1 732,189

FHWA MV-10

(excludes PR)

728,777 592,029*

FHWA adjusted for PR 594,800

FTA NTD 55,706

APTA77 *** 75,087

Polk TIP® 460,178

School Bus Fleet Fact 

Book

429,086

Motorcoach Census39** 44,200

* Includes some church & industrial buses.

** Includes Canada.
130

*** Includes trolleybuses.

Using the 1999 bus population estimates in Error! Reference source not found., 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 * (84,454/(84,454+55,706+592,029)) = 87,028

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

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 FTA Report to 

Congress40 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 lbs) 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 data78 provided information on number of vehicles by gross 

vehicle weight class and fuel as detailed in Error! Reference source not found..

Table 21-5 California School Buses

Gas Diesel Other Total

LHDV 2740 4567 8 7315

MHDV 467 2065 2 2534

HHDV 892 11639 147 12678

Total 4099 18271 157

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 Error! Reference source not 

found.. 
131

Table 21-6 Weight Distributions for Buses by Fuel Type

Intercity Buses (41) Transit Buses (42) School Buses (43)

Weight Class Diesel Diesel & Gas Diesel Gas

70 0.0500 0.1337

80 0.0500 0.1337

90 0.0500 0.1337

100 0.0500 0.1337

140 0.0726 0.1565

160 0.0226 0.0228

195 0.1819 0.0772

260 0.1819 0.0772

330 0.1819 0.0772

400 0.2197 1.0000 0.1593 0.0544

500 0.7800

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. As for other trucks, the 

WeightClass was determined from the VIUS reported average weight. 
132

Table 21-7 Refuse Truck SizeWeight Fractions by Fuel Type

Gasoline

Engine Size Weight (lbs.) Pre-1997 1997 and

Newer

3-3.5L 5000-6000 0.009074 0

>5L 7000-8000 0.148826 0

>5L 9000-10000 0.070720 0

>5L 10000-14000 0.135759 0.324438

>5L 14000-16000 0.199961 0.593328

>5L 16000-19500 0.055085 0

>5L 19500-26000 0.205341 0

>5L 26000-33000 0.022105 0

>5L 33000-40000 0.153129 0

>5L 50000-60000 0 0.082234

Sum 1.000000 1.000000

Diesel

Engine Size Weight (lbs.) Pre-1998 1998 1999 2000 2001 2002 and 

Newer

3.5-4L 10000-14000 0.007758 0 0 0 0 0

4-5L 10000-14000 0 0 0 0 0 0.006614

4-5L 14000-16000 0 0 0 0.015505 0 0

4-5L 16000-19500 0 0 0 0 0.011670 0

>5L 9000-10000 0.006867 0.009593 0 0 0 0

>5L 10000-14000 0.011727 0 0 0 0.019438 0

>5L 14000-16000 0.022960 0 0 0 0 0

>5L 16000-19500 0.063128 0 0.011367 0.047200 0 0

>5L 19500-26000 0.099782 0.035378 0.026212 0.052132 0.018329 0.026079

>5L 26000-33000 0.102077 0.019625 0.067419 0.072106 0.043877 0

>5L 33000-40000 0.237485 0.103922 0.088975 0.085991 0.042678 0.046966

>5L 40000-50000 0 0.283642 0.275467 0.165624 0.266357 0.194716

>5L 50000-60000 0.336484 0.338511 0.326902 0.384612 0.315133 0.474469

>5L 60000-80000 0.111730 0.196424 0.193238 0.176831 0.282517 0.224995

>5L 80000-100000 0 0 0.010420 0 0 0.013081

>5L 100000-130000 0 0.012904 0 0 0 0.013081

Sum 1.000000 1.000000 1.000000 1.000000 1.000000 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 Error! Reference 

source not found. and Error! Reference source not found..
133

Table 21-8 Weight Fractions for Diesel Motor Homes by Model Year

Polk GVW bin 3 4 5 6 7 8

MOVES weight 

class 140 160 195 260 330 400

Model Year Diesel

1975-and-earlier 0.171431 0.792112 0.029828 0 0.006629 0

1976 0.637989 0.340639 0.018755 0.000436 0.002181 0

1977 0.68944 0.292308 0.012168 0.000277 0.005531 0.000277

1978 0.423524 0.574539 0 0.000387 0.00155 0

1979 0.096922 0.899344 0 0.001067 0.002667 0

1980 0.462916 0.537084 0 0 0 0

1981 0 0.941973 0 0.030174 0 0.027853

1982 0 0.868333 0 0.049 0.03 0.052667

1983 0 0.912762 0.000203 0.014845 0.030096 0.042094

1984 0 0.932659 0.000835 0.009183 0.036732 0.020592

1985 0 0.881042 0.001474 0.010761 0.083285 0.023438

1986 0 0.855457 0.013381 0.022962 0.089534 0.018667

1987 0 0.791731 0.085493 0.022498 0.087164 0.013113

1988 0 0.72799 0.148917 0.015469 0.093335 0.014289

1989 0 0.73298 0.128665 0.043052 0.082792 0.012511

1990 0 0.173248 0.614798 0.043628 0.149939 0.018387

1991 0 0 0.619344 0.063712 0.296399 0.020545

1992 0 0 0.551548 0.01901 0.385085 0.044356

1993 0 0 0.345775 0.471873 0.144844 0.037509

1994 0 0 0.45546 0.354386 0.159622 0.030531

1995 0 0 0.635861 0.163195 0.17468 0.026264

1996 0 0 0.553807 0.229529 0.184208 0.032456

1997 0 0 0.666905 0.193167 0.111299 0.028628

1998 0 0 0.267 0.335069 0.357508 0.040423

1999+ 0 0 0 0.736656 0.233886 0.029458
134

Table 21-9 Weight Fractions for Gasoline Motor Homes by Model Year

Polk GVW bin 3 4 5 6 7 8

MOVES weight class 140 160 195 260 330 400

Model Year Gasoline

1975-and-earlier 1 0 0 0 0 0

1976 1 0 0 0 0 0

1977 1 0 0 0 0 0

1978 1 0 0 0 0 0

1979 1 0 0 0 0 0

1980 1 0 0 0 0 0

1981 0.747723 0.252277 0 0 0 0

1982 0.732235 0.267765 0 0 0 0

1983 0.714552 0.285448 0 0 0 0

1984 0.641577 0.358423 0 0 0 0

1985 0.692314 0.307686 0 0 0 0

1986 0.720248 0.279752 0 0 0 0

1987 0.606635 0.393365 0 0 0 0

1988 0.459429 0.540571 0 0 0 0

1989 0.551601 0.448399 0 0 0 0

1990 0.543354 0.456646 0 0 0 0

1991 0.612025 0.322022 0.065952 0 0 0

1992 0.54464 0.373999 0.081361 0 0 0

1993 0.583788 0.361277 0.054935 0 0 0

1994 0.481099 0.361146 0.157755 0 0 0

1995 0.52997 0.198479 0.271551 0 0 0

1996 0.435959 0.289453 0.274588 0 0 0

1997 0.221675 0.433334 0.344991 0 0 0

1998 0.288222 0.581599 0.13018 0 0 0

1999+ 0.170133 0.392451 0.288411 0.149004 0 0

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59 Guensler, R., Yoon, S., Li, H., and Elango, V., Georgia Institute of Technology, Atlanta 

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66 Vehicle miles traveled obtained from Version 5 (“VMT_NEI_v1_2011_21aug2013_v5”) of 

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141

76 Ward's Automotive Inc., http://www.wardsauto.com.

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