Population and Activity of On-road 

Vehicles in MOVES2014 
Population and Activity of On-road 


Vehicles in MOVES2014 


Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments. 

EPA-420-R-16-003a 

March 2016 
Table of Contents
 

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

2 MOVES Vehicle and Activity Classifications........................................................................ 8
 

2.1 HPMS Class.................................................................................................................... 8
 

2.2 Source Use Types ........................................................................................................... 8
 

2.3 Regulatory Classes.......................................................................................................... 9
 

2.4 Fuel Types..................................................................................................................... 10
 

2.5 Road Types ................................................................................................................... 11
 

2.6 Source Classification Codes (SCC) .............................................................................. 12
 

2.7 Model Year Groups....................................................................................................... 13
 

2.8 Source Bins ................................................................................................................... 13
 

2.9 Allowable Vehicle Modeling Combinations ................................................................ 15
 

2.10 Default Inputs and Fleet and Activity Generators ........................................................ 16
 

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............................................................................................ 19
 

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

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

3.7 MOBILE6 ..................................................................................................................... 20
 

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 ........................................................................... 58
 

8 VMT Distribution of Source Type by Road Type ................................................................ 65
 

9 Average Speed Distributions ................................................................................................ 67
 

9.1 Light-Duty Average Speed Distributions ..................................................................... 67
 

9.2 Heavy-Duty Average Speed Distributions.................................................................... 71
 

10 Driving Schedules and Ramps.............................................................................................. 74
 

10.1 Driving Schedules......................................................................................................... 74
 

10.2 Ramp Activity............................................................................................................... 79
 

11 Hotelling Activity ................................................................................................................. 81
 

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11.1 National Default Hotelling Rate ................................................................................... 81
 

11.2 Hotelling Activity Distribution..................................................................................... 83
 

12 Temporal Distributions ......................................................................................................... 85
 

12.1 VMT Distribution by Month of the Year...................................................................... 86
 

12.2 VMT Distribution by Type of Day ............................................................................... 87
 

12.3 VMT Distribution by Hour of the Day ......................................................................... 88
 

12.4 Engine Starts and Parking............................................................................................. 89
 

12.5 Hourly Hotelling Activity............................................................................................. 92
 

12.6 Single and Multiday Diurnals ....................................................................................... 95
 

13 Geographical Allocation of Activity..................................................................................... 96
 

13.1 Source Hours Operating Allocation to Zones............................................................... 96
 

13.2 Engine Start Allocations to Zones ................................................................................ 97
 

13.3 Parking Hours Allocation to Zones............................................................................... 98
 

14 Vehicle Mass and Road Load Coefficients........................................................................... 99
 

14.1. Source Mass and Fixed Mass Factor .......................................................................... 100
 

14.2. Road Load Coefficients .............................................................................................. 100
 

15 Air Conditioning Activity Inputs........................................................................................ 104
 

15.1 ACPenetrationFraction ............................................................................................... 104
 

15.2 FunctioningACFraction .............................................................................................. 105
 

15.3 ACActivityTerms........................................................................................................ 106
 

16 Conclusion and Areas for Future Research ........................................................................ 108
 

17 Appendix A: Projected Source Type Populations by Year................................................. 110
 

18 Appendix B: Fuel Type and Regulatory Class Fractions for 1960-1981............................ 112
 

19 Appendix C: 1990 Age Distributions ................................................................................. 115
 

19.1 Motorcycles................................................................................................................. 115
 

19.2 Passenger Cars ............................................................................................................ 115
 

19.3 Trucks ......................................................................................................................... 115
 

19.4 Intercity Buses ............................................................................................................ 116
 

19.5 School Buses and Motor Homes................................................................................. 116
 

19.6 Transit Buses............................................................................................................... 116
 

20 Appendix D: Detailed Derivation of Age Distributions ..................................................... 118
 

20.1 2012-2050 Age Distribution Projections .................................................................... 118
 

20.2 1999-2010 Age Distributions...................................................................................... 120
 

21 Appendix E: SCC Mappings............................................................................................... 123
 

21.1 SCC Mappings between MOVES2014 and MOVES2010b....................................... 123
 

21.2 2011 SCC VMT Conversions ..................................................................................... 124
 

22 Appendix F: Calculation of Combination Truck Average Speed Distributions................. 127
 

23 Appendix G: Driving Schedules ......................................................................................... 130
 

24 Appendix H: MOVES2010b Source Masses...................................................................... 133
 

24.1 Motorcycles................................................................................................................. 134
 

24.2 Passenger Cars ............................................................................................................ 135
 

24.3 General Trucks............................................................................................................ 135
 

24.4 Buses ........................................................................................................................... 137
 

24.5 Refuse Trucks ............................................................................................................. 139
 

24.6 Motor Homes .............................................................................................................. 140
 

25 Peer Review of Draft Report............................................................................................... 143
 

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25.1 Kanok Boriboonsomsin, PhD, PE............................................................................... 143
 

25.2 Randall Guensler, PhD................................................................................................ 150
 

26 References........................................................................................................................... 160
 

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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 onroad and nonroad mobile sources. MOVES estimates the emissions of greenhouse gases 

(GHGs), criteria pollutants, and selected air toxics. The MOVES model is currently the official 

model for use for state implementation plan (SIP) submissions to EPA and for transportation 

conformity analyses outside of California. The model is also the primary modeling tool to 

estimate the impact of mobile source regulations on emission inventories. 

MOVES calculates emission inventories by multiplying emission rates by the appropriate 

emission-related activity, applying correction and adjustment factors as needed to simulate 

specific situations, and then adding up the emissions from all sources and regions. An inventory 

can be pictured as a stool; the three legs of the stool are the emission rates, activity, and 

populations, while the seat is the inventory. The emission rates are inputs to the model specified 

for various “processes” including running exhaust, start exhaust, and a number of evaporative 

processes, among others. These processes also define the activity, populations, and technology 

inputs required. 

Vehicle 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, known as a “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 characteristics. 

This report describes the sources and derivation for onroad vehicle population and activity 

information and associated adjustments as stored in the MOVES2014 and MOVES2014aa 

default databases. This data has been extensively updated from MOVES2010b and previous 

versions of MOVES. Emission measurement and rates, correction factor values, and information 

for nonroad equipment in the default database are described in other MOVES technical reports.1 

There have not been any major updates between this final report and the earlier released public 

draft in July 2015.2 However, this final report does have some notable revisions from the draft, 

namely some added or improved explanatory tables and figures, a new introductory subsection 

on vehicle model year groups, movement of content to different sections and appendices for 

better readability, clarifications to ambiguous descriptions, and a new appendix documenting 

peer review comments along with EPA’s responses to those comments. 

The MOVES2014 default database has a domain that encompasses all onroad (highway) vehicle 

and nonroad equipment activity and emissions for the entire United States, Puerto Rico, and the 

a In this report, “MOVES2014” refers to both MOVES2014 and MOVES2014a unless specified otherwise. 

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Virgin Islands. Properly characterizing emissions from the onroad 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. 

Given the availability of these national measurements when MOVES2014 was being developed, 

2011 was chosen as the base year for future year projections. Like in previous versions of 

MOVES, users may analyze emission inventories in 1990 to correspond with the last Clean Air 

Act amendments as well as every year from 1999 to 2050. 

In addition to uncertainties associated with projections, 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 where local data is more detailed or up-to-date than that provided in the MOVES 

defaults. MOVES has been specifically designed to accommodate the input of alternate, usersupplied activity data for the most important parameters. EPA’s Technical Guidance3 provides 

more information on customizing MOVES2014 with local inputs. 

This report documents the sources and calculations used to produce the default population and 

activity data in the MOVES2014 database for computing national-level emissions. In particular, 

this report will describe the data used to fill the tables listed below in Table 1-1. 

Population and activity data are ever changing. As part of the MOVES development process, the 

model undergoes major updates and review every few years. As MOVES progresses, the 

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

area of focus and improvement. 

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Table 1-1 MOVES database elements covered in this report 

Database Table Name Content Summary Report Sections 

AvgSpeedDistribution Distribution of time among average speed bins Section 9 

DayVMTFraction Distribution of VMT between weekdays and 

weekend days Section 12 

DriveSchedule Average speed of each drive schedule Section 10 

DriveScheduleAssoc Mapping of which drive schedules are used for 

each combination of source type and road type Section 10 

DriveScheduleSecond Speed for each second of each drive schedule Section 10 

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 11 

HotellingCalendarYear Rate of hotelling hours per rural restricted access 

VMT Section 11 

HourVMTFraction Distribution of VMT among hours of the day Section 12 

HPMSVtypeYear Annual VMT by HPMS vehicle types Section 4 

ModelYearGroup A list of years and groups of years corresponding 

to vehicles with similar emissions performance Section 2 

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 10 

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 

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Table 1-1 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 source bin discriminators are 

relevant for each source type and pollutant/process 

Section 4 

SourceTypeYear Source type vehicle counts by year Section 5 

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 (VSP) and scaled tractive power (STP) 

Section 14 

Zone Allocation of activity to zone (county) Section 12 

ZoneRoadType Allocation of driving time to zone (county) and 

road type Section 13 

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2 MOVES Vehicle and Activity Classifications 

In developing MOVES, we needed to pull together information on vehicle activity and 

emissions. We wanted to enter vehicle population and activity data in a form as close as possible 

to how this data is collected by highway departments and vehicle registrars, but we had to map 

this to existing information on emission standards and in-use emission rates. Thus, EPA 

developed MOVES-specific terminology classifying vehicles according to how they are 

operated, such as “source types,” and to emission-related characteristics, such as “regulatory 

classes” and “fuel types.” At the most detailed level, vehicles are classified into “source bins” 

which have a direct mapping to the rates in the MOVES emission rate tables. 

This section provides definitions of the various vehicle classifications used in MOVES. The 

MOVES terms introduced in this section will be used throughout the report. Later sections 

explain how default vehicle populations and activity are assigned and allocated to these 

classifications. 

2.1 HPMS Class 

In this report, MOVES HPMS class refers to one of five categories derived from the US 

Department of Transportation (DOT) Highway Performance Monitoring System (HPMS) based 

vehicle classes used by the Federal Highway Administration (FHWA) in the Table VM-1 of their 

annual Highway Statistics report. The five HPMS classes used in MOVES are as follows: 

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, US DOT changed the organization of HPMS classifications 

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. 

HPMS vehicle classes were differentiated into MOVES onroad source types. 

Source types cannot be fully determined using field observations and must be paired with 

additional information about the vehicle’s activity to determine whether it typically travels shortor long-haul routes (greater than 200 miles per day), whether it has specific travel routines such 

as a refuse truck, and whether it is a commercial or personal vehicle. Estimates for shorthaul/long-haul and commercial/personal distributions relied on information from the federal 

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Vehicle Inventory and Use Survey (VIUS). The MOVES2014 source types are listed in Table 

2-1 along with the associated HPMS classes. More detailed source type definitions are provided 

in Section 6.1. 

Table 2-1 MOVES2014 onroad 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 are assigned by source type and their 

calculation differs for light- and heavy-duty vehicles. Light-duty vehicles (sourceTypeID 11, 21, 

31, and 32) use vehicle specific power (VSP) operating modes, which are 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) operating modes which are scaled by a fixed mass 

factor since their emission rates correlates better with absolute vehicle power than vehicle 

specific power. For more discussion on VSP and STP definitions, please refer to Section 14 of 

this report and the MOVES2014 reports on light-duty and heavy-duty vehicle emission rate 

development, respectively.

4,5 

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. 

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 

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are allocated between regulatory classes based primarily on gross vehicle weight rating (GVWR) 

classification, which is a set of eight classes defined by FHWA based on the manufacturerdefined maximum combined weight of the vehicle and its load. Urban buses have their own 

regulatory definition, and therefore have an independent regulatory class. 

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 EPA regulatory distinction between light-duty (LD) and heavy-duty (HD) trucks falls in the 

midst of FHWA GVWR Class 2. Trucks of 6,001-8,500 lbs. GVWR are Class 2a; in MOVES 

they are considered light-duty trucks in regulatory class 30. Vehicles of 8,500-10,000 lbs. 

GVWR are Class 2b, and considered light heavy-duty vehicles (LHD) in regulatory classes 40 or 

41. 

In MOVES2014, we introduced a new regulatory class 40 for vehicles that are classified as lightduty by FHWA (because they have only two axles and four tires), and are thus mapped to source 

type 30 (passenger trucks) or 31 (light-commercial trucks) in MOVES, but have a GVWR that 

puts them in Class 2b, so are subject to heavy-duty emission standards. As described above, 

these regulatory class 40 vehicles use light-duty (VSP-based) operating modes because they are 

light-duty source types, but the new regulatory class maps them to emission rates that are more 

consistent in how these vehicles are regulated. Meanwhile, Class 2b trucks with two axles and at 

least six tires (colloquially known as “dualies”) and Class 3 trucks are considered single unit 

trucks by DOT, and therefore fall into regulatory class 41 and are modeled as the heavy-duty 

source types using STP-based operating modes. 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. Section 6.2 

provides more information on the distribution of vehicles among regulatory classes. 

2.4 Fuel Types 

MOVES also classifies vehicles by the fuel they are designed to use. MOVES2014 models 

vehicles and equipment powered by following fuel types: gasoline, diesel, E-85 (a nominal 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. In MOVES, fuel type refers to the capability of the vehicle 

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rather than the fuel in the tank. The fuel use actually modeled 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.6 Table 2-3 below summarizes the fuel types 

available in MOVES. 

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

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 models LPG use only in nonroad equipment. 

It is important to note that not all fuel type/source type combinations can be modeled in 

MOVES. That is, MOVES2014 will not model gasoline fueled long-haul combination trucks, 

gasoline intercity buses, or diesel motorcycles. Though other natural gas vehicles such as CNG 

refuse trucks can found in the US today, CNG transit buses are the most prevalent and welltested, and thus are currently the only onroad 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 onroad (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.7 

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. 

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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 road types are based on two important distinctions in how FWHA classifies roads: 

1) urban versus rural roadways are distinguished based on land use and human population 

density, and 2) unrestricted versus restricted are distinguished based on roadway access— 

restricted roads require the use of ramps. The urban/rural distinction is used primarily for 

national level calculations. It allows different default speed distributions in urban and rural 

settings. Of course, finer distinctions are possible. Users with more detailed information on 

speeds and acceleration patterns may choose to create their own additional road types, or may 

run MOVES at project level where emissions can be calculated for individual links. 

2.6 Source Classification Codes (SCC) 

Source Classification Codes (SCC) are used to group and identify emission sources in large-scale 

emission inventories. They are often used when post-processing MOVES output to further 

allocate emissions temporally and spatially when preparing inputs for air quality modeling. 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 for onroad vehicles use MOVES numerical 

identification (ID) codes in the following form: 

AAAFVVRRPP, where 

• AAAAAA indicates mobile source (this has a value of 220 for both onroad and nonroad), 

• FF indicates the MOVES fuelTypeID value, 

• VVVV indicates the MOVES sourceTypeID value, 

• RRRR indicates the MOVES roadTypeID value, and 

• PPPP indicates the MOVES emission processID value. 

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Building the 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 SCCs as changes are made to future 

versions of MOVES. The explicit coding of fuel type, source type, road type, and emission 

process also allows the new 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. Using the 

mapping described above, modelers can also easily identify the sourceTypeID, fuelTypeID, 

roadTypeID, and processID of emissions reported by SCC. Refer to tables in the MOVES User 

Guide3 or appropriate sections in this document for the descriptions of the sourceTypeID, 

fuelTypeID and roadTypeID values currently used by MOVES. A description of mapping 

between older SCCs in MOVES2010b and newer SCCs in MOVES2014 can be found in Section 

21 (Appendix E: SCC Mappings). Emission processes are discussed in other MOVES reports on 

emission rate development4,5 and are not described here. All feasible SCC values are listed in the 

SCC table within the default database. 

2.7 Model Year Groups 

MOVES uses model year groups to avoid unnecessary duplication of emission rates for vehicles 

with similar technology and similar expected emission performance. For example, there is a 

model year group for, “1980 and earlier.” In MOVES2014a, model year refers to the year in 

which the vehicle was produced, built, and certified as compliant with emission standards. 

The default ModelYearGroup table provides information on the model year group names, 

beginning and ending years, and a two-digit shorthand identifier (shortModelYrGroupID). 

However, the model year groups that are relevant for a given calculation can vary depending on 

pollutant and emission process as defined in the PollutantProcessModelYear table. For example, 

a 2011 vehicle belongs to the “2011” model year group for estimating hydrocarbon running 

exhaust emissions, but belongs to the “2011-2020” group for estimating nitrous oxide running 

emissions. Because these groupings are determined based on analysis of the actual or expected 

emissions performance, the rationale for each model year grouping is provided in the 

MOVES2014 emission rate reports.4,5 

2.8 Source Bins 

The MOVES default database identifies emission rates by emission-related characteristics such 

as the type of fuel that a vehicle uses and the emission standards it is subject to. These 

classifications are called “source bins.” They are named with a sourceBinID that is a unique 19-

digit identifier in the following form: 

1FFFFFFFFRRRRFFFF0000000000, where 

• 1 is a placeholder, 

• FFFF is a MOVES fuelTypeID, 

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• FFFF is a MOVES engTechID,b 

• RRRR is a MOVES regClassID, 

• FFFF is a MOVES shortModYrGroupID, and 

• 10 trailing zeros for future characteristics. 

The model allocates vehicle activity and population to these source bins as described below. 

A mapping of model year to model year groups is stored in the PollutantProcessModelYear 

table. Distributions of fuel and engine technologies and regulatory class are stored by model 

year in the SampleVehiclePopulation table. The MOVES Source Bin Distribution Generator 

combines information from these two tables (see Table 2-5) to create a detailed 

SourceBinDistribution. These bins may vary by pollutant and process as indicated in the 

SourceTypePolProcess table. In general, fuel type and model year group are relevant for all 

emission calculations, but the relevance of regulatory class and model year group depend on the 

pollutant and process being modeled. If desired, MOVES2014 can produce results by various 

vehicle classifications—source type, SCC, or regulatory class—and by fuel type and model year. 

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 default fractions for the 

Alternative Vehicle Fuel & Technology 

(AFVT) importer. 

* 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 rates4, 5, the 

SampleVehiclePopulation (SVP) table is a topic for this report and is discussed in Section 6.2. 

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. 

14
 
 

 

 

 

 

 

 

 

 

2.9 Allowable Vehicle Modeling Combinations 

In theory, the MOVES source bins would allow users to model any combination of source type, 

model year, 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 summarizes 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. Table 2-8 combines the information in the 

two preceding tables. Each source type-fuel type combination contains all regulatory classes 

listed, except for gasoline transit buses, which have been called out separately. Additional 

discussion about decisions to include and exclude certain types of vehicles can be found in 

Section 6. 

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

Passenger Cars

Passenger Trucks

Light Commercial 

Trucks

Intercity Buses

Transit Buses

School Buses

Refuse Trucks

Short-Haul Single Unit 

Trucks 

Long-Haul Single Unit 

Trucks

Motor Homes

Short-Haul 

Combination Trucks

Long-Haul 

Combination 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-Capable 5 X X X 

Electricity 9 X X X 

15
 
 

 

 

 

 

 

 

 

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

Passenger Cars

Passenger Trucks

Light Commercial 

Trucks

Intercity Buses

Transit Buses

School Buses

Refuse Trucks

Short-Haul Single 

Unit Trucks

Long-Haul Single 

Unit Trucks

Motor Homes

Short-Haul 

Combination Trucks

Long-Haul 

Combination 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 

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 

2.10 Default Inputs and Fleet and Activity Generators 

As explained in the introduction, vehicle population and activity data are critical inputs for 

calculating emission inventories and 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 

16
 
 

 

 

 

 

 

 

 

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 traveled (VMT) and speed information to transform VMT into source hours operating 

(SHO). Other types of vehicle activity are generated by applying appropriate factors to vehicle 

populations. Vehicle starts, extended idle hours, and source hours (including hours operating and 

not-operating) are also generated. The default database for MOVES2014 contains national 

estimates for VMT, vehicle population, and vehicle age distributions for every possible analysis 

year (1990 and 1999-2050). For national inventory runs, annual national activity is distributed 

temporally and spatially using allocation factors. 

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

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

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

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

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 

17
 
 

 

 

 

 

 

 

 

3 Data Sources 

A number of organizations collect data relevant to this report. The most important sources used 

to populate the national default 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. 

The MOVES Technical Guidance3 provides detailed information on recommended data sources 

for users developing their own inputs. 

3.1 VIUS 

Until 2002 the US Census Bureau conducted the Vehicle Inventory and Use Survey (VIUS)9 to 

collect data on the physical characteristics and activity of US trucks every five years. The survey 

is a sample of private and commercial trucks that were registered in the United States as of July 

of the survey year. The survey excludes automobiles, motorcycles, government-owned vehicles, 

ambulances, buses, motor homes, and nonroad equipment. 

For MOVES, VIUS provides information to characterize trucks by source type and to estimate 

age, fuel type, and regulatory class distributions as well as relative mileage accumulation rates. 

MOVES2014 uses data from both the 1997 and 200210 surveys. While the survey includes a 

large number of vehicles and was designed to be representative of the US fleet, information on 

model year is not available for many of the older trucks. Thus, the distribution data for many 

older model years is sparse and sometimes erratic. Note that the Census Bureau discontinued 

VIUS in 2002, although there has been discussion recently about reinitiating the survey. 

3.2 Polk NVPP® and TIP® 

Acquired by 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 

Operation12 database. The first focused on light-duty cars and trucks, the second focused on 

medium and heavy-duty trucks. Both compiled data from state vehicle registration lists. For 

MOVES2014, EPA used NVPP® and TIP® datasets purchased for 1999 and 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 6.2. 

18
 
 

 

 

 

 

 

 

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 

system that collects data from states and other sources on many facets of the US roadway 

system. 

In MOVES2014, vehicle miles traveled (VMT) and vehicle population data for the historic years 

1990 and 1999-2011 come from four tables in Highway Statistics: MV-113, MV-1014, VM-115 , 

and VM-216, which we will reference by table name. For some years, the VMT values were 

revised by FHWA in subsequent publications. Table 3-1 summarizes the data source and revision 

date we used for each historical year. 

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. 

19
 
 

 

 

 

 

 

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. 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 traveled are 

included in the projections because they strongly influence fuel consumption. Therefore, the 

AEO is an important source of future projections in MOVES. For MOVES2014, we used 

AEO2014 to forecast VMT and vehicle populations in years 2012-2050. 

3.9 Transportation Energy Data Book 

Each year Oak Ridge National Laboratory produces the annual Transportation Energy Data Book 

(TEDB) for the Department of Energy. This book summarizes transportation and energy data 

from a variety of sources, including EPA, FHWA, Polk, and Ward’s Automotive, Inc. For 

MOVES2014 we used information for estimating vehicle sales and survival fractions for historic 

years 1990 and 1999-2011 from TEDB Edition 32, published in 2013.22 

3.10 FHWA Weigh-in-Motion 

FHWA compiles truck weight data by axle configuration and roadway type from individual 

states’ Weigh-in-Motion (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. 

20
 
 

 

 

 

 

 

 

 

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 estimatesfor 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 tablec

, 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 methodology25 , 

which implements state-reported data directly rather than a modeled approach and which has 

different vehicle categories. The current HPMS-based VM-1 categories are 1) light-duty short 

wheelbase, 2) light-duty long wheelbase, 3) motorcycles, 4) buses, 5) single unit trucks, and 6) 

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 

Generator

8 allocates this VMT to MOVES source types and ages using vehicle populations, age 

distributions, and relative mileage accumulation rates. 

For years prior to 2007, the VM-1 data with historical vehicle type groupings are inconsistent 

with the current VM-1 vehicle categories used in MOVES and cannot be used as they are 

currently reported. However, in early 2011, FHWA released revised VMT data for years 2000-

2006 to match the new category definitions. Shortly afterward, the agency replaced these revised 

numbers with the previously published VMT data stating, “[FHWA] determined that it is more 

c In MOVES2014a, users can enter VMT estimates using four different input methods: annual miles by HPMS class, 

annual miles by source type, annual average daily miles by HPMS class, and annual average daily miles by source 

type. As in MOVES2014, the default table, HPMSVTypeYear, continues to use annual miles by HPMS class in 

MOVES2014a. Considering the default table has not changed in MOVES2014a, any discussion in this report on 

annual VMT estimates will be in the context of annual miles traveled by HPMS class. 

21
 
 

 

 

 

 

 

 

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, needing 

continuity of the VM-1 vehicle categories, we used these FHWA-revised values by the new 

categories as the VMT for 2000-2006. 

This left two years, 1990 and 1999, that needed to be adjusted to be consistent with the new 

HPMS vehicle categories. Since the methodology that FHWA used to revise the 2000-2006 data 

is unknown, we adjusted 1990 and 1999 using the average ratio of the change for each vehicle 

category. This was found by dividing the FHWA-adjusted VMT for each vehicle category by the 

original VMT for each year 2000-2006 and then calculating the average ratio for each category. 

This ratio was then applied to the corresponding VMT values reported in VM-1 for 1990 and 

1999. Since FHWA’s adjustments conserved the original total VMT estimates, we normalized 

our adjusted values such that the original total VMT for the years were unchanged. 

The resulting values for historic years by HPMS vehicle class are listed in Table 4-1. The VMT 

for 1990 and 1999 were EPA-adjusted from VM-1, 2000-2006 were FHWA-adjusted, and 2007-

2011 were unadjusted, other than the simple combination of the short and long wheelbase classes 

into light-duty vehicles. 

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

Year 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 explains 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 

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

22
 
 

 

 

 

 

 

 

 

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 percent growth was mapped to both the combined 

HPMS light-duty and the motorcycle categories. Motorcycles were included here because they 

were not explicitly accounted for elsewhere in AEO. Since buses span a large range of heavyduty vehicles and activity, the combination of AEO’s light medium, medium, and heavy heavyduty growth rates was mapped to the HPMS bus category. AEO’s light medium and medium 

heavy-duty growth rates were combined for mapping to the HPMS single unit truck category, 

and AEO’s heavy heavy-duty growth was mapped to the HPMS combination truck category. We 

acknowledge that using VMT growth estimates from different vehicle types as surrogates for 

motorcycles and buses in particular will introduce additional uncertainty into these projections. 

The percent growth over time was calculated for each of the groups described above and applied 

by HPMS category to the 2011 base year VMT from the Table VM-1. The resulting values are 

presented in Table 4-2 below. 

23
 
Table 4-2 VMT projections for 2012-2050 by HPMS class (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 

24
 
 

 

 

 

5 Vehicle Populations by Calendar Year 

MOVES uses vehicle populations to characterize emissions activity that is not directly dependent 

on VMT. These data are also used to allocate VMT from HPMS class to source type and age. 

(For more details, see Section 7) The default database stores historic estimates and future 

projections of total US vehicle populations in 1990 and 1999-2050 by source type. All of these 

values have been updated in MOVES2014 with improved data sources. The MOVES database 

stores this information in the SourceTypeYear table, which has three data fields: 

sourceTypePopulation, salesGrowthFactor, and migrationRate. However, the salesGrowthFactor 

and migrationRate fields are not used in MOVES2014. 

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

MOVES populations for calendar years 1990 and 1999-2011 are derived top-down from 

registration data in Table MV-1 of the Federal Highway Administration’s annual Highway 

Statistics report. In this table, vehicles are separated into four general vehicle categories: 

motorcycles, passenger cars, trucks, and buses. These categories include government vehicles 

and vehicles in Puerto Rico but do not account for vehicles in the Virgin Islands due to their 

relatively small effects on national population estimates. Motorcycle and car data were used 

without adjustment, but since MOVES populations are input by source type, allocations within 

the general categories of trucks and buses were necessary, as shown in Figure 5-1. 

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

25
 
 

 

 

 

 

 

 

 

 

 

 

Trucks were separated into single unit and combination trucks using registration data in the 

Highway Statistics Table VM-1. 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 (see Section 6.2.2). Since we 

only had sample vehicle counts for calendar years 1999 and 2011, the 2000-2010 source type 

allocations within the general truck categories were linearly interpolated between 1999 and 2011 

rather than using the predictions for these years as in MOVES2010b. For example, we fit a linear 

regression of the fraction of long-haul combination trucks out of total combination trucks 

between 1999 and 2011 and then fit another regression for the short-haul combination truck 

fraction. Regressions were fit in a similar fashion to allocate source type populations among 

light-duty and single unit trucks. For reference, the interpolated fractions for MOVES2014 that 

distribute the populations of light-duty, single unit, and combination trucks to the MOVES 

source types by calendar year are shown below in Table 5-1. 

Table 5-1 MOVES2014 linearly interpolated fractions to allocate truck populations to source types, such as 

refuse trucks (sourceTypeID 51) among all single unit trucks (50s), by calendar year* 

Year 31/30s 32/30s 51/50s 52/50s 53/50s 54/50s 61/60s 62/60s 

1999 0.7496 0.2504 0.0155 0.7807 0.0462 0.1577 0.5744 0.4256 

2000 0.7541 0.2459 0.0161 0.7786 0.0450 0.1604 0.5673 0.4327 

2001 0.7586 0.2414 0.0166 0.7765 0.0438 0.1631 0.5601 0.4399 

2002 0.7631 0.2369 0.0172 0.7745 0.0426 0.1657 0.5529 0.4471 

2003 0.7676 0.2324 0.0178 0.7724 0.0414 0.1684 0.5457 0.4543 

2004 0.7721 0.2279 0.0184 0.7703 0.0402 0.1711 0.5386 0.4614 

2005 0.7767 0.2233 0.0190 0.7682 0.0390 0.1738 0.5314 0.4686 

2006 0.7812 0.2188 0.0196 0.7662 0.0378 0.1765 0.5242 0.4758 

2007 0.7857 0.2143 0.0201 0.7641 0.0366 0.1792 0.5171 0.4829 

2008 0.7902 0.2098 0.0207 0.7620 0.0354 0.1819 0.5099 0.4901 

2009 0.7947 0.2053 0.0213 0.7600 0.0341 0.1846 0.5027 0.4973 

2010 0.7992 0.2008 0.0219 0.7579 0.0329 0.1873 0.4955 0.5045 

2011 0.8037 0.1963 0.0225 0.7558 0.0317 0.1900 0.4884 0.5116 

*Some fractions shown in this table may not sum exactly to one due to rounding. Fractions used to 

calculate source type populations had more significant digits than shown and sum precisely to one. 

These interpolated fractions were then multiplied by the FHWA populations of light-duty, single 

unit, and combination trucks, respectively. This ensured that every source type population would 

more or less track its Highway Statistics population, as shown for combination trucks in Figure 

5-2, for single unit trucks in Figure 5-3, and light-duty trucks in Figure 5-4. Car and motorcycle 

populations are reported directly in the Table MV-1 and thus were not subject to linear 

interpolation adjustments. Note that 1990 source type fractions were not interpolated and were 

instead retained from MOVES2010b. 

26
 
Figure 5-2 Combination truck source type populations interpolated for1999-2011 

National Vehicle Registrations National Vehicle Registrations 

 3,000,000 

 2,500,000

 2,000,000

 1,500,000

 1,000,000

 500,000

 -

Long-Haul 

Combination 

Trucks (62) 

Short-Haul 

Combination 

Trucks (61)

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

Year 

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

 9,000,000 

 8,000,000

 7,000,000

 6,000,000

 5,000,000

 4,000,000

 3,000,000

 2,000,000

 1,000,000

 -

Motor Homes 

(54) 

Long-Haul 

Single Unit 

Trucks (53) 

Short-Haul 

Single Unit 

Trucks (52) 

Refuse Trucks 

(51) 

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

Year 

27
 
 

 

 

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

National Vehicle Registrations 

 300,000,000 

 250,000,000

 200,000,000

 150,000,000

 100,000,000

 50,000,000

 -

Light 

Commercial 

Trucks (32) 

Passenger 

Trucks (31) 

Passenger 

Cars (21) 

Motorcycles 

(11)

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

Year
 

Figure 5-5 Bus source type populations in MOVES2014
 

-

 100,000

 200,000

 300,000

 400,000

 500,000

 600,000

 700,000

 800,000

 900,000 

National Vehicle Registrations 

School Buses 

(43) 

Transit Buses 

(42) 

Intercity 

Buses (41) 

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

Year 

Buses were allocated in a similar fashion as trucks, but using different data sources (see Figure 

5-5). School bus estimates for all years 1999-2011 were taken from the Highway Statistics Table 

MV-10 and transit bus estimates for these years were taken from the National Transit Database 

(NTD) compiled by the Federal Transit Administration. The remainder of MV-1 bus 

registrations were allocated to the intercity bus source type. Since school and transit bus

28
 
registrations in Puerto Rico were not readily available, we estimated them by multiplying the US 

transit or school bus registrations by the ratio of bus registrations in Puerto Rico to the total MV1 bus registrations. Note that the precipitous drop in bus populations from 2010 to 2011 is 

reflected in the MV-1 bus registration data published by FHWA, which has been used in 

MOVES2014 without adjustment. 

29
 
 

 

Table 5-2 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 

in total population for passenger cars and long-haul combination trucks as shown in Table 5-2. 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. 

30
 
 

 

 

 

 

 

 

 

 

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 in historic years, this 

surrogate appears reasonable. Figure 5-6 shows the VMT values of Table 4-1 divided by the 

vehicle populations of Table 5-2 grouped by HPMS classification. At this level of aggregation, 

VMT per vehicle is relatively constant with no clear trends over time. 

Figure 5-6 MOVES2014 annual miles traveled per vehicle by HPMS class, 1999-2011 

Annual Miles Travelled Per Vehicle 

80,000 

70,000 

60,000 

50,000 

40,000 

30,000 

20,000 

10,000 

0 

Motorcycles 

Light-Duty Vehicles 

Buses 

Single Unit Trucks 

Combination Trucks 

1999 2001 2003 2005 2007 2009 2011 

Year 

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). 

31
 
 

 

Table 5-3 Projected HPMS class 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 

32
 
 

 

 

 

 

 

 

 

 

 

 

6 Fleet Characteristics 

Despite the availability of vehicle registration databases, comprehensive surveys for 

characterizing travel pattern, and sophisticated sensors and cameras for measuring vehicle 

activity, it is still difficult to estimate vehicle populations in the categories needed for emissions 

inventory modeling. Differentiating, for example, between passenger car and trucks, or between 

light-duty and heavy-duty trucks presents substantial modeling challenges since the 

characteristics that are important for emissions are not always readily observable.26, 27 To 

develop MOVES defaults, we have merged registration and survey data with activity 

measurements in an effort identify key vehicle parameters such as weight, axle and tire 

configuration, and typical trip range. 

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, explains how source type is related to fuel type and regulatory 

class, primarily through the SampleVehiclePopulation table, and how MOVES2014 estimates 

and projects the number of vehicles in each category. 

6.1 Source Type Definitions 

MOVES source types are intended to further divide HPMS vehicle classifications into groups of 

vehicles with similar activity patterns For example, passenger trucks and light commercial trucks 

are expected to have different daily trip patterns. VIUS was our main source of information for 

distinguishing these vehicles. Table 6-1 summarizes how the VIUS2002 parameters were used to 

delineate the light-duty, single unit, and combination truck source types for MOVES2014. 

Axle arrangement (AXLE_CONFIG) was used to define four categories: straight trucks with two 

axles and four tires (codes 1, 6, 7, 8), straight trucks with two axles and six tires (codes 2, 9, 10, 

11), all straight trucks (codes 1-21), and all tractor-trailer combinations (codes 21+). Primary 

distance of operation (PRIMARY_TRIP) was used to define short-haul (codes 1-4) for vehicles 

with primary operation distances less than 200 miles and long-haul (codes 5-6) for 200 miles and 

greater. The VIN-decoded gross vehicle weight (ADM_GVW) and survey weight (VIUS_GVW) 

were used to distinguish vehicles less than 10,000 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. 

33
 
 

 

 

 

 

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, light-duty trucks that have three axles or more and/or six tires or more. These vehicles are 

classified as light-duty trucks based primarily on their weight. Only 0.27 percent of light-duty trucks have such 

tire and/or axle parameters and they have a negligible impact on vehicle populations and emissions. 

* For a source type with multiple rows, the source type is applied to any vehicle with either set of parameters. 

Motorcycles and passenger cars in MOVES borrow vehicle definitions from the FHWA 

Highway Performance Monitoring System (HPMS) classifications from the Highway Statistics 

Table MV-1. 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.”28 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 5.1 information on motorcycle populations 

comes from HPMS MV-1 registrations. 

34
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.1.2 Passenger Cars 

Passenger cars are defined as any coupes, compacts, sedans, or station wagons with the primary 

purpose of carrying passengers.28 All passenger cars (sourceTypeID 21) are categorized in the 

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.28 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, specifically vehicles with a GVWR in Class 1 and 2, except Class 2b trucks with two 

axles or more and at least six tires are assigned to the single unit truck category. 

VIUS contains many survey questions on weight; we chose to use both a VIN-decoded gross 

vehicle weight rating (ADM_GVW) and a respondent self-reported GVWR (VIUS_GVW) to 

differentiate between light-duty and single unit trucks. For the passenger trucks, there is a final 

VIUS constraint that the most frequent operator classification (OPCLASS) must be personal 

transportation. Inversely, light commercial trucks (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.29 According to FHWA, school 

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

students between their home and school.30 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,”31 but MOVES also considers any bus that cannot be 

categorized as either a transit or school bus to be an intercity bus, such as motor coaches and 

airport shuttles. 

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 

35
 
 

 

 

 

 

 

 

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 

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.32 

6.2 Sample Vehicle Population 

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

fuel types and regulatory classes. As vehicle markets shift, this mapping changes with model 

year. 

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. The latter fraction forms the default 

regulatory class distribution. Both fractions are computed using the EPA sample vehicle counts 

dataset that joins 2011 national R.L. Polk vehicle registration data with 2002 Vehicle Inventory 

and Use Survey (VIUS) classifications. 

6.2.1 Fuel Type and Regulatory Class Distributions 

The stmyFraction is the default national fuel type and regulatory class allocation for each source 

type and model year. Written out mathematically in Equation 1, we define the stmyFraction as, 

NNii,jj,kk,ll ff(ssssssss)ii,jj,kk,ll = , Equation 1 ෍	 NNii,jj,kk,ll

jjjjjj,kkjjkk

36
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

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

fuel types KK. That is, the denominator is the total for a given 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. 

While stmyFraction indicates MOVES default values, the stmyFuelEngFraction allows the 

modeling of non-default fuel type distributions. For each allowable combination of source type, 

model year and fuel type, the stmyFuelEngFraction indicates the expected regulatory class 

distribution, whether or not these vehicles exist in the default. Similar to the stmyFraction above, 

we define stmyFuelEngFraction in Equation 2 as, 

NNii,jj,kk,ll ff(ssssssssffssssllssssss)ii,jj,kk,ll = , Equation 2 ෍	 NNii,jj,kk,ll

jjjjjj

for number of vehicles NN, model year ii, regulatory class jj, fuel type kk, source type ll, and the set 

of all regulatory classes JJ. In this case, the denominator is the total for a given source type, model 

year, and fuel type.. 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 any electric passenger trucks 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 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.

e In this report’s nomenclature, E85-capable and flexible 

e MOVES allocates fuel to specific vehicles in a two-step process: 1) vehicles are classified by the type of fuel they 

can use in the fuel type fraction, and then 2) fuels are distributed according to how much of each fuel is used relative 

to the vehicles’ total fuel consumption in the fuel usage fraction. For example, Figure 6-1 below shows the national 

default fuel type fractions for all light-duty vehicles among the different MOVES fuel types. 

37
 
 

 

 

 

 

 

 

fuel vehicles are synomous—meaning they can accept either gasoline or E85 fuel. The amount of 

E85 versus the amount of gasoline used out of all the fuel consumed by the vehicle is stored in 

the fuel usage fraction. Discussion on fuel usage can be found in the MOVES2014 Fuel Supply 

Report.

6 MOVES2014 does not explicitly model hybrid electric cars but accounts for these 

vehicles in calculating fleet-average energy consumption and CO2 rates.f 

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 

f While we have considered creating a separate category for hybrid vehicles, modeling their emissions separately is 

not required for regulatory purposes and presents a number of challenges, including obtaining representative detailed 

data on hybrid vehicle emissions and usage, and accounting for offsetting emissions allowed under the fleetaveraging provisions of the relevant emissions standards. 

38
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.2.2 Sample Vehicle Counts 

The SampleVehiclePopulation table fractions were developed by EPA using the sample vehicle 

counts dataset referenced in Section 3, which primarily joins calendar year 2011 registration data 

from R.L. Polk and the 2002 Vehicle Inventory and Use Survey (VIUS) results. The sample 

vehicle counts dataset was generated by multiplying the 2011 Polk vehicle populations by the 

source type allocations from VIUS. 

While VIUS provide source type classifications, we relied primarily on the 2011 Polk vehicle 

registration dataset to form the basis of the fuel type and regulatory class distributions in the 

SampleVehiclePopulation table. We purchased the Polk dataset in April 2012, so it did not have 

complete registration records for model year 2012 vehicles, and, therefore, model year 2012 

vehicles were omitted from the SVP analysis. The Polk data was provided with the following 

fields: vehicle type (cars or trucks), fuel type, gross vehicle weight rating (GVWR) for trucks, 

household vehicle counts, and work vehicle counts. We combined the household and work 

vehicle counts. The MOVES distinction between personal and commercial travel for light-duty 

trucks comes from VIUS. 

The Polk records by FHWA truck weight class were grouped into MOVES GVWR-based 

regulatory classes, as shown in Table 6-2 below. As stated above, all passenger cars were 

assigned to regClassID 20. The mapping of weight class to regulatory class is straightforward 

with one notable exception: delineating trucks weighing more or less than 8,500 lbs. 

Table 6-2 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 had been sorted into source types (described later in this section), some regulatory 

classes were merged or divided. Any regulatory class 41 vehicles in light-duty truck source types were 

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

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

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

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

(8,501-10,000 lbs) trucks, but MOVES regulatory classes 30, 40, and 41 all fall within Class 2, 

we needed a secondary data source to allocate the Polk gasoline and diesel trucks between Class 

39
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2a and 2b. We derived information from an Oak Ridge National Laboratory (ORNL) paper33 

summarized in Table 6-3 to allocate the Polk Class 2 gasoline and diesel trucks into the 

regulatory classes. Class 2a trucks fall in regulatory class 30 and Class 2b trucks fall in either 

regulatory class 40 or 41. 

Table 6-3 Fractions used to distribute Class 2a and 2b trucks 

Fuel Type 

Truck Class 

2a 2b 

Gasoline 0.975 0.760 

Diesel 0.025 0.240 

Additionally, the Polk dataset includes a variety of fuels, some that are included in MOVES and 

others that are not. Only the Polk gasoline and diesel vehicles were included in our analysis; all 

other alternative fuel vehicles were omitted. While MOVES2014 does model light-duty E-85 and 

electric vehicles, and compressed natural gas (CNG) transit buses, these relative penetrations of 

alternative fuel vehicles have been developed from secondary data sources rather than Polk 

because Polk excludes some government fleets and retrofit vehicles that could potentially be 

large contributors to these alternative fuel vehicle populations. Instead we used flexible fuel 

vehicle sales data reported for EPA certification, and dedicated CNG bus populations from the 

National Transit Database. The Table 6-4 illustrates how Polk fuels were mapped to MOVES 

fuel types, and which Polk fuels were not used in MOVES. 

The “N/A” mapping shown in Table 6-4 led us to discard 0.22 percent, roughly 530,000 vehicles 

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

developing the default fuel type fractions. However, because the MOVES national population is 

derived top-down from FHWA registration data, as outlined in Section 5.1, the total population 

is not affected. We considered the Polk vehicle estimates to be a sufficient sample for the fuel 

type and regulatory class distributions in the SampleVehiclePopulation table. 

40
 
 

 

 

 

Table 6-4 A list of fuels from the Polk dataset used to develop MOVES fuel type distributions 

Polk Fuel Type MOVES fuelTypeID MOVES Fuel Type 

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 Table 6-1 and then to MOVES 

regulatory classes using the mapping described in Table 6-2, including the allocation between 

Class 2a and 2b trucks from the ORNL study in Table 6-3. Similar to our fuel type mapping of 

the Polk dataset, we chose to omit alternative fuel vehicles, as summarized below in Table 6-5. 

41
 
 

 

 

 

 

 

 

Table 6-5 Mapping of VIUS2002 fuel types to MOVES2014 fuel types 

VIUS Fuel Type VIUS Fuel Code MOVES fuelTypeID MOVES Fuel Type 

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 ii, regulatory class jj, and fuel type 

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

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

LL. The source type population fraction is summarized in Equation 3: 

NNii,jj,kk,ll ff(VVVVVVVV)ii,jj,kk,ll = Equation 3 ෍	 NNii,jj,kk,ll

lljjll

The VIUS data in our analysis spanned model year 1986 to 2002. The 2002 source type 

distribution has been used for all distributions after MY 2002 and the 1986 distribution for all 

prior to MY 1986. 

From there the source type distributions from VIUS were multiplied by the Polk vehicle 

populations to generate the sample vehicle counts by source type, as shown schematically in 

Figure 6-2. Expressed in Equation 4, the sample vehicle counts are, 

NN(VVVVPP)ii,jj,kk,ll = PP(PPPPllkk)ii,jj,kk,ll ∙ ff(VVVVVVVV)ii,jj,kk,ll, Equation 4 

42
 
 

 

 

 

 

 

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

2011 Polk vehicle populations, and ff 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 were generated following roughly the same procedure 

outlined above but using a 1999 Polk vehicle registration dataset joined with VIUS. These 

MOVES2010b SVP fractions for MY 1960-1981 are described in Section 18 (Appendix B). 

MOVES2014 assumes no changes to fuel type distributions after model year 2011 except for 

flexible-fuel (E85-capable) vehicles, which are assumed to displace gasoline vehicles based on 

sales estimates as described below. MOVES2014 estimates any other population growth by 

source type, as described earlier in Section 5.2 rather than growth for specific fuel types within a 

source type. 

All Class 2b and 3 trucks were initially assigned to regulatory class 41 until vehicles were sorted 

into source types. Once the sample vehicle counts were available by source type, any light-duty 

trucks (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 heavy43
 
 

 

 

 

 

 

 

 

duty emission results and that the emission calculations use the appropriate fixedMassFactor 

when calculating vehicle-specific power (VSP) for light-duty vehicles and scaled tractive power 

(STP) for heavy-duty vehicles. 

As noted above, the initial sample vehicle counts dataset did not contain motorcycles, buses, or 

motor homes, so information on these source types was appended. Motor homes—even though 

they are considered single unit vocational vehicles—cannot be identified in VIUS. In the 

subsections below, we have provided more detailed descriptions by source type. 

6.2.2.1 Motorcycles 

The representation of motorcycles in the SampleVehiclePopulation table is straightforward. All 

motorcycles fall into the motorcycle regulatory class (regClassID 10) and must be fueled by 

gasoline. We acknowledge that some alternative fuel motorcycles have been prototyped and may 

even be in small production, but they account for a negligible fraction of total US motorcycle 

sales and cannot be modeled in MOVES2014. 

6.2.2.2 Passenger Cars 

Any passenger car is considered to be in the light-duty vehicle regulatory class (regClassID 20). 

Cars were included in the Polk dataset purchased in 2012, and EPA’s subsequent sample vehicle 

counts dataset, which provided the split between gasoline and diesel cars in the 

SampleVehiclePopulation table. Flexible fuel (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 Department of Energy car sales projections 

from AEO2014’s table labeled “Light-Duty Vehicle Sales by Technology Type” to derive 

flexible fuel vehicle penetrations and applied them to the SVP fractions for regulatory class 20.34 

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 

gases35 and brake and tirewear36, 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 

44
 
 

 

 

 

 

 

 

 

 

 

2a trucks were classified as regulatory class 30 and Class 2b trucks with two axles and four tires 

were classified as regulatory class 40. Both light-duty truck source types are divided between 

gasoline and diesel using the underlying splits in the sample vehicle counts data. Passenger 

trucks and light commercial trucks have similar but distinct distributions. Similar to cars, a 

penetration of flexible fuel (E-85-capable) light-duty trucks was calculated using EPA 

certification sales for historic years (MY 2011 and earlier) and AEO light truck projections for 

future years (MY 2012 and later) from the AEO2014 table on light-duty vehicle sales.

34 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.37 The following non-school bus regulatory class distribution 

for intercity buses was applied to all model years based on 2011 FHWA data, as shown in Table 

6-6.38 

Table 6-6 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 6-7. 

45
 
 

 

 

 

 

 

 

Table 6-7 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 

While some other MOVES fuel types are included in the NTD, the transit bus fuel type 

distributions were allocated between diesel, CNG, and gasoline only. Together these three fuel 

types account for more than 99 percent of all transit buses in 2011, so no other alternative fuels 

are allowed within the transit bus source type due to negligible market shares. 

Biodiesel does not appear in the SampleVehiclePopulation table—in MOVES it is considered a 

fuel subtype rather than a fuel type—so biodiesel buses were added to the diesel buses from the 

NTD. Liquefied natural gas (LNG) comprises less than ten percent of all natural gas transit buses 

and only about 1.5 percent of the whole transit bus fleet in 2011. Without any readily available 

emission rate data on LNG buses, we grouped all natural gas fueled transit buses together. This 

means we effectively model LNG buses as if they were powered by CNG. Due to limited data, 

we assume that gasoline has a one-percent market share prior to model year 2000 and that diesel 

has a 99 percent market share prior to MY 1990. All other market shares of transit bus fuel types 

are derived using the NTD, as shown in Table 6-8. MOVES modelers can adjust these 

distributions between the fuel types using the AVFT tool. 

46
 
 

 

 

 

 

 

 

 

Table 6-8 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 

86.091-2.39 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 heavy-duty emission rates report5

, for 

gasoline transit buses as shown in Table 6-9 below. 

Table 6-9 Regulatory class fractions of gasoline transit buses in MOVES2014 

MOVES Source Type & Fuel Type 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 6-10. The Union of Concerned Scientists estimates that roughly one 

percent of school buses run on non-diesel fuels, so we have assumed that one percent of school 

47
 
 

 

 

 

buses are gasoline fueled in MY 1997 and later.40 The school bus regulatory class distribution 

was also derived from the 2011 FHWA data in Table 6-6. 

Table 6-10 Fuel type market shares by model year for school buses in MOVES2014 

Model Year 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 (see Table 6-11), originally 

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

fuel type market shares.41 

48
 
 

 

Table 6-11 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 6-12, is used across all 

model years based on the same 2011 FHWA dataset38 referenced above for school and nonschool buses. 

Table 6-12 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 yet. We will 

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

emissions are more thoroughly tested. 

49
 
 

 

 

 

 

 

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.4,5 In this section, there is 

discussion of vehicle age distributions, survival rates, and relative mileage accumulation rates by 

source type. 

7.1 Age Distributions 

A vehicle’s age is simply the difference between its model year and the year of analysis. Age 

distributions in MOVES vary by source type and range from zero to 30+ years, so that all 

vehicles 30 years and older are modeled together. As such, an age distribution is comprised of 31 

fractions, where each fraction represents the number of vehicles present at a certain age divided 

by the vehicle population for all ages, as summarized later in this section in Equation 9. Since 

sales and scrappage rates are not constant, these distributions vary by calendar year. The age 

distribution for each source type is stored in the SourceTypeAgeDistribution table, and fractions 

from each source type’s age distribution sum to one across a calendar year. MOVES age 

distributions were compiled from a variety of data sources, which are discussed below. Age 

distributions for the 2011 base year are summarized in Table 7-1; all other years are available in 

the MOVES2014 default database SourceTypeAgeDistribution table. 

7.1.1 Age Distributions from Registration Data 

Ideally all historic age distributions could be derived from registration data sources for each 

analysis year available in MOVES. However, acquiring such data is prohibitively costly, so 

MOVES2014 only contains registration-based age distributions for two analysis years: 1990 and 

2011. The following sections detail how these data were analyzed and used in MOVES2014. 

7.1.1.1 1990 Age Distributions 

MOVES2014 age distributions for calendar year 1990 have not been updated since the last 

model release. Please refer to Section 19 (Appendix C) for more information on the 1990 age 

distributions. 

7.1.1.2 2011 Age Distributions 

The 2011 age distributions for cars and trucks were derived from the sample vehicle counts 

dataset, as discussed earlier in Section 3.3. This sample vehicle data includes eight of the thirteen 

source types: passenger cars (21), passenger trucks (31), light commercial trucks (32), refuse 

trucks (51), short-haul single unit trucks (52), long-haul single unit trucks (53), short-haul 

combination trucks (61), and long-haul combination trucks (62). We were able to develop zero to 

30+ year age distributions in 2011 for the eight source types mentioned. 

For the source types that were not included in the sample vehicle data—specifically motorcycles, 

motor homes, and buses—we calculated the 2011 age distributions from the MOVES2010b 

50
 
 

 

 

default 1999 distributions with the latest sales data available. This approach kept the 

MOVES2010b base populations and scrappage rates but substituted in MY 1999-2011 sales. We 

pulled sales for motorcycles (11) from the Motorcycle Industry Council’s Statistical Annual 

report24, for transit buses (42) from internal EPA estimates based on manufacturer reporting, and 

for school buses (43) from the School Bus Fleet Fact 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, please revisit Section 3. 

Figure 7-1 and Table 7-1 show the fraction of vehicles by age (0-30+ years) and source type for 

calendar year 2011. These 2011 age distributions became the basis for all the forecast age 

distributions in Section 7.1.2.2 and all backcast age distributions in Section 7.1.2.3. 

Figure 7-1 2011 age distributions by source type in MOVES2014 

51
 
 

5

10

15

20

25

Table 7-1 2011 age fractions by MOVES source type 

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 

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 

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 

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 

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 

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 

52
 
 

 

 

 

 

 

7.1.2 Forecasting and Backcasting Age Distributions 

Since purchasing registration data for all calendar years is prohibitively costly for historic 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, national trends in projected vehicle survival for every MOVES source type 

at all ages 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 national registration data as described in a study conducted for the EPA.42 Survival rates 

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

survivability Table 3 and Table 4.43 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. 

53
 
 

 

 

 

 

 

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, ssaa represents the MOVES survival rate at age aa, and σσaa represents the NHTSA survival 

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

0

⃗ will 

represent a one-dimensional array of ssaa values at each permissible age aa as described in 

Equation 5 through Equation 8 below: 

= 1 − 1 − σσ2 Age 0: ss0 Equation 5 3 

= 1 − 2(1 − σσ2) Age 1: ss1 Equation 6 3 

σσaa−1 Age 2-29: ssaa = ss2...29 = Equation 7 σσaa−2

Age 30: ss30 = 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; the heavy-duty rates were applied uniformly 

across the bus, single unit truck, and combination truck categories. The TEDB survival rates 

were transformed into MOVES format in the same way as the NHTSA rates, including setting 

age 30+ survival rates to 0.3 for all source types. 

The resulting survival rates are listed in the default database’s SourceTypeAge table, shown 

below in Table 7-2. Please note that since MOVES2014 does not calculate age distributions 

during a run, these survival rates are not actively used by MOVES. However, they were used in 

54
 
 

the development of the national age distributions stored in the SourceTypeAgeDistribution table, 

and remain in the default database for reference. 

Table 7-2 MOVES survival rate by age and HPMS class 

Age 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 calculated from 

population counts, if the populations are known by age: 

55
 
 

 

 

 

 

 

ሬሬሬ ሬሬሬሬሬሬሬሬ

ሬሬ ሬ

ሬሬ ሬ

ሬሬሬሬሬሬሬሬ ሬሬ

ppaa ffaa,yy = Equation 9 PPyy

Here in Equation 9, ffaa,yy is the age fraction to be calculated, ppaa is the population of vehicles at 

age aa, and PPyy is the total population in calendar year ss. In this section, arrow notation will be 

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

yy

ሬ⃗ would be 

used to represent all age fractions in calendar year ss. Another example is PPሬሬ

yy

ሬ⃗; it represents an 

array of ppaa values at each permissible age in calendar year ss. In contrast, PPyy represents the total 

population in year ss. 

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: 

ሬ

PPሬሬሬሬሬሬሬሬ ሬሬ ሬሬሬ ሬሬሬሬሬሬሬሬሬ⃗ Equation 10 yy+1

⃗ = PPሬ yy⃗ − RRሬ yy⃗ + NNyy+1

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

yy

ሬ⃗ where PP is the population in the ሬ yy+1

base year, RRሬ yy

⃗ is the population of vehicles removed in the in the base year, and ሬ

NNyy+1⃗ 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 aa > 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 (PPሬሬ

yy

ሬ⃗), remove the number of vehicles that 

did not survive (RRሬሬሬ

yy

ሬ⃗) 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 (NNሬሬ

yy+1 ሬሬሬሬ⃗) 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 (PPሬሬ

yy+1 ሬሬሬሬ⃗). If this algorithm is to be 

repeated, PPሬ yy+1⃗ becomes PPሬ yy

⃗ for the next iteration. 

Please see Section 20 (Appendix D: Detailed Derivation of Age Distributions) for more 

information on how this algorithm was applied to derive the projected national default age 

distributions in MOVES. The resulting age distributions are stored in the 

SourceTypeAgeDistribution table. 

In addition to producing the 2012-2050 default age distributions, a version of this algorithm was 

implemented in the Age Distribution Projection Tool for MOVES2014.44 This tool can be used 

to project future local age distributions from user-supplied baseline distributions, provided that 

the baseline year is 2011 or later. This requirement ensures that the 2008-2009 recession is fully 

56
 
 

 

 

 

 

 

 

 

 

 

 

ሬሬሬሬሬሬሬሬ

ሬሬ ሬ

ሬሬሬሬሬሬሬሬ ሬሬ

visible in the baseline. The differences between the default algorithm described above and the 

algorithm used in the tool are as follows: 

•	 In the tool, the generic survival rate for all vehicle types at age 30 is set to 1.0. 

•	 Step 4 was modified so that in the tool, the new age 30 fraction is set equal to the new 

age 31 fraction. The new age 31 fraction is then discarded. 

•	 In the tool, the age distribution for ages 1-29 is then normalized such that the full
 

distribution (ages 0-30) sums to 1.0.
 

The first two bullets were implemented to retain the fraction of 30+ year old vehicles in the userinputted baseline distribution. This was done because local data frequently indicates a sizeable 

fraction in this age bin. Since the default scrappage curve was designed to prevent this bin from 

growing too large, the default algorithm would reduce this fraction in most cases. Therefore, the 

age 30+ fraction is not modified and the resulting age distribution in each iteration of the 

algorithm is normalized in the final step so that the full distribution sums to one. The sales rates 

and scrappage assumptions are the same in the tool as they are in the national case. In general, 

projections made with the tool tend to converge with the national age distributions the farther out 

the projection year becomes. This is because local projections of sales and scrappage are 

generally unavailable, and the national trends are the best available data. 

7.1.2.3 1999-2010 Age Distributions 

The method used to backcast the 1999-2010 age distributions from the 2011 distribution is very 

similar to the forecasting method described above. For backcasting an age distribution one year, 

Equation 10 of the previous section can be rewritten as Equation 11: 

PPሬሬሬ

yy

ሬሬሬ

−

ሬሬሬ

1

⃗ = PPሬሬ

yy

ሬ⃗ − NNሬሬሬሬ

yy

ሬ⃗ + RRሬሬሬ

yy

ሬሬሬ

−

ሬሬሬ

1

⃗	 Equation 11 

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 (PPሬሬ

yy

ሬ⃗), remove the age 0 vehicles (NNሬሬሬሬ

yy

ሬ⃗). 

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 (RRሬ yy−1⃗).
 

4.	 This results in the previous year population distribution (PPሬሬ

yy−1 ሬሬሬሬ⃗). If this algorithm is to be 

repeated, PPሬ yy−1⃗ becomes PPሬ yy

⃗ for the next iteration. 

Please see Section 20 Appendix D: Detailed Derivation of Age Distributions) for more 

information on how this algorithm was applied to derive the historic national default age 

distributions in MOVES. The resulting age distributions are stored in the 

SourceTypeAgeDistribution table. 

57
 
 

 

 

 

 

 

 

 

7.2 Relative Mileage Accumulation Rate 

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

populations (see Section 5.1) and age distributions described earlier in this section to distribute 

the total annual miles driven by each HPMS vehicle type (see Section 4) to each source type and 

age group. Using this approach, the vehicle population and the total annual vehicle miles traveled 

(VMT) can vary from calendar year to calendar year, but the proportional travel by an individual 

vehicle of each age will not vary. 

VMT is provided, either by default values or by user input, by the five Highway Performance 

Monitoring System (HPMS) vehicle classifications. These classifications are further broken 

down into the groupings of the MOVES source use types, as described in Section 2.1. 

The RMAR is determined within each HPMS vehicle classification such that the annual mileage 

accumulation for a single vehicle of each age of a source type is relative to the mileage 

accumulation of all of the source types and ages within the HPMS vehicle classification. For 

example, passenger cars, passenger trucks and light commercial trucks are all within the same 

HPMS vehicle classification. By definition, new (age 0) passenger trucks and light commercial 

trucks have a RMAR of one (1.0).g Based on the data, new passenger cars have a RMAR of 

0.885. This means that when the VMT assigned to the HPMS class 25 is allocated to passenger 

cars, passenger trucks and light commercial trucks, a passenger car of age 0 will be assigned only 

88.5 percent of the annual VMT assigned to a passenger truck or light commercial truck of age 0. 

The RMAR values for MOVES2014 for the source types 11 (motorcycles), 41 (intercity buses), 

42 (transit buses), 43 (school buses) and 54 (motor homes) were not changed from the values 

used in MOVES2010b. Passenger car and light-duty truck RMAR values were recalculated to 

reflect the change in the HPMS vehicle classifications used for VMT input and the remaining 

heavy-duty vehicle classifications were updated with data from the 2002 Vehicle Inventory and 

Use Survey (VIUS) and recalculated. 

7.2.1 Motorcycles 

The RMAR values for motorcycles in MOVES2014 were not changed from MOVES2010b 

estimates. The MOVES2010b RMAR values were calculated from MARs for motorcycles 

(sourceTypeID 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.

42 

g Within each HPMS vehicle class, an RMAR value of one is assigned to the source type and age with the highest 

annual VMT accumulation. Because we use the same mileage accumulation data for passenger trucks and light 

commercial trucks, they both have a value of one. 

58
 
 

 

 

 

 

 

 

 

 

7.2.2 Passenger Cars, Passenger Trucks and Light Commercial Trucks 

In MOVES2010b, passenger cars had their own HPMS vehicle classification. In MOVES2014, 

they are grouped with passenger trucks and light commercial trucks. For MOVES2014, the 

MOVES2010b passenger car RMAR values were adjusted to reflect the relative difference in 

annual mile accumulation between passenger cars and the light trucks. Analysis of the data 

determined that new passenger cars (age 0) accumulate only 88.5 percent of the annual miles 

accumulated by new light trucks. Thus, all of the RMAR values for passenger cars were 

adjusted to be 88.5 percent of their previous values. 

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

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

mileage schedules.43 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 a simple 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 class, each source type’s mileage by age was divided by passenger truck 

mileage at age 1 to determine a relative MAR. For consistency with MOVES age categories, we 

then shifted the relative MARs such that the NHTSA age 1 ratio was used for MOVES age 0, 

etc. We used NHTSA's light truck VMT to determine relative MARs for both passenger trucks 

and light commercial trucks. 

Since a newer version of the National Household Travel Survey was available, we conducted a 

preliminary analysis of the impact of updating the MARs based on the 2009 National Household 

Travel Survey. This resulted in changes to the MOVES allocation of VMT by one percent or less 

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. However, the 2009 

values may not fully represent current trends in vehicle usage due to the recent economic 

downturn. A more complete analysis of all available mileage accumulation information in recent 

years will be necessary to truly update these values. 

59
 
 

 

Table 7-3 NHTSA Vehicle Miles Traveled from 2001 

Annual Vehicle Miles Traveled 

Vehicle Age Passenger Cars Light Trucks 

1 14,417 15,806 

2 13,803 15,683 

3 13,692 15,859 

4 13,415 15,302 

5 13,183 14,762 

6 12,301 13,836 

7 12,253 13,542 

8 11,709 13,615 

9 11,893 12,875 

10 11,855 12,203 

11 10,620 11,501 

12 9,986 10,815 

13 10,248 11,391 

14 9,515 10,843 

15 9,168 10,378 

16 8,636 9,259 

17 8,941 8,358 

18 7,267 9,371 

19 8,890 7,352 

20 8,759 8,363 

21 6,878 6,999 

22 7,242 7,327 

23 6,350 6,969 

24 5,745 6,220 

25 4,130 6,312 

26 6,745 

27 9,515 

28 6,635 

29 12,108 

30 5,067 

31 4,577 

32 6,923 

7.2.3 Buses 

The RMAR values for all bus categories in MOVES2014 were not changed from MOVES2010b 

estimates. The intercity bus (sourceTypeID 41) annual mileage accumulation rate is taken from 

Motorcoach Census 2000.45 The data did not distinguish vehicle age, so the same MAR (59,873 

miles per year) was used for each age. The school bus (sourceTypeID 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 

60
 
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.46 The MOBILE6 results were extended 

to calculate values for ages 26 through 30. 

Table 7-4 Annual mileage accumulation of transit buses from 1994 Federal Transit Administration data 

Age Miles Age Miles Age Miles 

1 * 11 32,540 21 19,588 

2 * 12 32,605 22 22,939 

3 46,791 13 27,722 23 26,413 

4 41,262 14 28,429 24 23,366 

5 42,206 15 32,140 25 11,259 

6 39,160 16 28,100 26 23,228 

7 38,266 17 24,626 27 21,515 

8 36,358 18 23,428 28 25,939 

9 34,935 19 22,575 29 20,117 

10 33,021 20 23,220 30 17,515 

* Insufficient data 

7.2.4 Other Heavy-Duty Vehicles 

The RMAR values for source types 51 (refuse trucks), 52 (short-haul single unit trucks), 53 

(long-haul single unit trucks), 61 (short-haul combination trucks) and 62 (long-haul combination 

trucks) were updated from MOVES2010b using the data from the 2002 Vehicle Inventory and 

Use Survey (VIUS). The total reported annual miles traveled by truck in each source type, as 

shown in Table 7-5, was divided by the vehicle population to determine the average annual miles 

traveled per truck by source type. 

61
 
 

 

 

 

 

Table 7-5 VIUS2002 annual mileage by vehicle age 

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 relative 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 the average of ages 0 to 3 as age 3 with ages 4 through 16 from 

the data summarized in Table 7-6, 

3)	 Ages 17 through 29 use values from interpolation between the values in age 16 and 

age 30. 

4)	 Age 30 uses the MOVES2010b relative 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.

46 

62
 
 

 

Table 7-6 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 0-3* 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 RMAR 0.0320 0.0518 0.1025 0.0320 0.0571 

* Average sample annual miles traveled for ages 0 through 3. 

** Intercept and slope from ages 4 through 16. 

The resulting relative mileage accumulation rates are shown in Table 7-7 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 study47 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. 

63
 
 

Table 7-7 Relative mileage accumulation rates for heavy-duty trucks in MOVES2014 

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 

64
 
 

 

 

 

 

 

 

 

8 VMT Distribution of Source Type by Road Type 

For each source type, the RoadTypeVMTFraction field in the RoadTypeDistribution table stores 

the fraction of total VMT for each vehicle class that is traveled on each of the MOVES five road 

types. Users may supply the distribution VMT to vehicle classes for each road type for individual 

counties when using County Scale, however, for National Scale, the default distribution is 

applied to all locations. 

The national default distribution of VMT to vehicle classes for each road type in MOVES2014 

were derived to reflect the VMT data included in the 2011 National Emission Inventory (NEI) 

Version 148 (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) statistics49, when state supplied estimates are not available. 

The 2011 NEI V1 data50 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. As discussed 

in Section 2.6, SCCs are now formed as a 10-digit concatenated string, including existing 

identification codes for MOVES fuel type, source type, road type, and emission process. For 

reference, we have included a comparison of the MOVES2010b SCCs and MOVES2014 fuel 

types and regulatory classes in Section 21 (Appendix E: 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. The VMT was mapped to the 

source types used by MOVES by calculating the fraction of VMT for each source type found in 

each SCC classification result in a national MOVES2010b run for calendar year 2011. The 

factors calculated from the MOVES2010b run are also shown in Section 21 (Appendix E: SCC 

Mappings). 

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 

65
 
 

 

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. 

Table 8-2 MOVES2014 road type distribution by source type 

Road Type* 

Source 

Type Description Rural Restricted 

Rural 

Unrestricted 

Urban 

Restricted 

Urban 

Unrestricted 

2 3 4 5 All 

11 Motorcycle 0.0805 0.3019 0.1913 0.4263 1.000 

21 Passenger Car 0.0847 0.2345 0.2374 0.4434 1.000 

31 Passenger Truck 0.0859 0.2754 0.2178 0.4209 1.000 

32 Light Commercial Truck 0.0867 0.2756 0.2180 0.4197 1.000 

41 Intercity Bus 0.1409 0.2812 0.2196 0.3583 1.000 

42 Transit Bus 0.1384 0.2813 0.2196 0.3607 1.000 

43 School Bus 0.1384 0.2813 0.2196 0.3607 1.000 

51 Refuse Truck 0.2396 0.2718 0.2525 0.2361 1.000 

52 Single Unit Short-Haul Truck 0.1635 0.2869 0.2346 0.3150 1.000 

53 Single Unit Long-Haul Truck 0.1638 0.2870 0.2346 0.3146 1.000 

54 Motor Home 0.1234 0.2876 0.2255 0.3635 1.000 

61 Combination Short-Haul Truck 0.2367 0.2744 0.2517 0.2372 1.000 

62 Combination Long-Haul Truck 0.2476 0.2705 0.2543 0.2276 1.000 

* RoadTypeID = 1 (Off Network) is assigned no VMT. 

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9 Average Speed Distributions 

Average speed is used in MOVES to convert VMT inputs into the source hours operating (SHO) 

units that MOVES uses for internal calculations. It is also used to select appropriate driving 

cycles, which are then used to calculate exhaust running operating mode distributions at the 

national, county (and sometimes project) level. Instead of using a single average speed in these 

tasks, MOVES2014 uses a distribution of average speeds by bin. The AvgSpeedDistribution 

table lists the default fraction of driving time for each source type, road type, day, and hour in 

each average speed bin. The fractions sum to one for each combination of source type, road type, 

day, and hour. The MOVES average speed bins are defined in Table 9-1. The default average 

speed distributions in MOVES201051 were based on much more limited data and travel demand 

model output, and have been substantially updated in MOVES2014.52 

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.

h 

TomTom makes in-vehicle GPS navigation devices and supports cell-phone navigation 

applications. ERG provided the US EPA with updated values for the AvgSpeedDistribution 

calculated from the TomTom delivered data based on their consumers, where “virtually all” use 

them in light-duty cars, trucks, and vans. 

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

EPA on January 11, 2012. 

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Some of the characteristics of the TomTom GPS data are: 

•	 Data is self-selective. Data is only recorded from users of TomTom GPS units and an 

iPhone application. Additionally, TomTom data is only collected when the units are on. 

This creates bias not only for users, but also for types of driving. Anecdotally, drivers 

who own GPS units are less likely to use them when they drive in familiar areas in 

comparison with unfamiliar areas. Compared to the default VMT by road type 

information in MOVES, TomTom 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 potential 

shortcoming, and look to incorporate source type specific average speed information in 

the future. 

•	 The average speed distributions are based on the average speed in each roadway segment, 

not the average of all 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. 

Some of the data characteristics present concerns regarding their representativeness of real-world 

driving. Despite these concerns, the TomTom data presented a great improvement to the speed 

distribution information used in previous versions of MOVES. 

Under direction of EPA’s contractor, ERG, TomTom queried its database of historic traffic 

probes to produce a table of total distance and total time as a function of road type, 

weekday/weekend, hour of the day, and average speed bin for the calendar year 2011 for the 50 

states and the District of Columbia. TomTom delivered a table identifying the total distance and 

total time of vehicles travelling at an average speed interval for all combinations of: 

1. Identifier for Average Speed Bin (20 levels): average speeds were binned in 5 mph 

increments, starting at 2.5mph: 0-2.5mph; 2.5mph-7.5mph; 7.5mph-12.5mph; ... 

92.5mph-97.5mph.
 

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

3. Identifier for Day of the Week (2 levels): the period for weekday is Monday, 

00:00:00 to Friday, 23:59:59, and the period for weekend is Saturday, 00:00:00 to 

Sunday, 23:59:59.
 

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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. 

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 MOVESi. 

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. 

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

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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)j. ERG calculated the average speed distribution according to the 

16 speed bins used in MOVES. Figure 9-1 plots the average speed distribution for one hour 

(5pm) stored in the averageSpeedDistribution table in MOVES, which contains average speed 

distributions for each hour of the day (24 hours). We are using the TomTom data to represent 

national default average speed distribution in MOVES. 

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

70
 
 

 

Figure 9-1 Average speed distribution for 5pm (hourID 17) for source types (11 through 54) stored in the 

AvgSpeedDistribution table in MOVES2014 

9.2 Heavy-Duty Average Speed Distributions 

It has been shown that combination trucks travel at approximately 92 percent of the speed of 

light-duty vehicles on restricted access roads53. 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 to have an 8% lower average speed 

than the respective TomTom average speed for light-duty vehicles. The equations and 

assumptions used to adjust the combination truck average speed distributions are located in 

71
 
 

 

Section 22 (Appendix F: Calculation of Combination Truck Average Speed Distributions). 

Figure 9-2 illustrates the results of this analysis. 

Figure 9-2 Average weekday speed distribution for 5pm (hourID 17) by source type stored in the 

AvgSpeedDistribution table in MOVES 

72
 
 

 

In the absence of additional data, all other heavy-duty vehicles (including single unit heavy-duty 

vehicles) and all heavy-duty vehicles operating on unrestricted access roads, use the same 

average speed distributions as light-duty vehicles. We recognize that these assumptions are less 

than ideal, and we hope to update the heavy-duty average speed distributions using heavy-duty 

data in the future. Nonetheless, MOVES energy consumption and emission estimates from 

heavy-duty appear to be only moderately sensitive to changes in the average speed distribution. 

The 8% speed decrease in average speed distribution on restricted access roadways for 

combination trucks caused the total onroad predicted NOx emissions to decrease by only ~0.5% 

and the national onroad diesel fuel consumption to decrease by only ~1.3%. Other researchers54 

have found that other local inputs are significantly more important for emissions inventories than 

average speed distributions, including population, age distribution, and the combination truck 

fraction of heavy-duty VMT. Nonetheless, we strongly encourage MOVES users to use local 

average speed distributions when using MOVES at the regional and county-level. 

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10 Driving Schedules and Ramps 

Drive schedule refers to a second-by-second vehicle speed trajectory. A drive schedule typically 

includes all vehicle operation from the time the engine starts until the engine is keyed off, both 

driving (travel) and idling time. Drive schedules are used in MOVES to determine the operating 

mode distribution for most MOVES running processes for calculation of emissions and energy 

consumption. 

In brief, there is an emission rate (in grams per hour of vehicle operation) for each operating 

mode of vehicle operation. Each second of vehicle operation is assigned to an operating mode as 

a function of vehicle velocity in each second and the specific power (VSP), or scaled tractive 

power (STP) for heavy-duty vehicles, is calculated from the driving schedules. This distinction 

between VSP and STP is discussed in Section 14. The average speed distribution is used to 

weight the operating mode distributions determined from driving schedules with different 

average speeds into a composite operating mode distribution that represents overall travel by 

vehicles. The distribution of operating modes is used by MOVES to weight the emission rates to 

account for the vehicle operation. 

10.1 Driving Schedules 

A key feature of MOVES is the capability to accommodate a number of drive schedules to 

represent driving patterns across source type, road type, and average speed. For the national 

default case, MOVES2014 employs 49 drive schedules with various average speeds, mapped to 

specific source types and road types. 

MOVES stores all of the drive schedule information in four database tables. DriveSchedule 

provides the drive schedule name, identification number, and the average speed of the drive 

schedule. DriveScheduleSecond contains the second-by-second vehicle trajectories for each 

schedule. In some cases the vehicle trajectories are not contiguous; as detailed below, they may 

be formed from several unconnected microtrips that overall represent driving behavior. 

DriveScheduleAssoc defines the set of schedules which are available for each combination of 

source use type and road type. Ramps use operating mode distributions directly and do not use 

drive schedules to calculate operating modes. The RoadOpModeDistribution table lists 

operating mode distributions used for ramps for each source use type, road type and speed bin, 

discussed in further detail later in this section. 

Table 10-1 through Table 10-6 MOVES driving cycles for combination trucks (61, 62) 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 unimpeded high 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. 

74
 
 

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 requires driving schedules that can 

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

schedules have been added to assure that all average speed bins have appropriate driving 

schedules for all the MOVES average speed bins. 

Table 10-1 MOVES driving cycles for motorcycles, passenger cars, passenger trucks, and light commercial 

trucks (11, 21, 31, 32) 

ID 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 

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Table 10-2 MOVES 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 

Table 10-3 MOVES 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. 

76
 
 

 

Table 10-4 MOVES 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 

Table 10-5 MOVES 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 

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Table 10-6 MOVES 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.001.55 “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,56 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 2004.42 The new cycles were selected to 

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

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”57 and “WMATA Transit 

Bus”58 drive schedules are included for urban driving that includes transit type bus driving 

behavior. The “CRC E55 HHDDT Creep” 59 cycle was included to cover extremely low speeds 

for heavy-duty trucks. The “Bus 30 mph Flow” and “Bus 45 mph Flow” cycles used for transit 

and school buses were developed by EPA based on Ann Arbor Transit Authority buses 

instrumented in Ann Arbor, Michigan.60 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 23 (Appendix G: Driving Schedules). Note that the “Bus Low Speed Urban” bus cycle is 

the last 450 seconds of the standard New York City Bus cycle. 

The “Refuse Truck Urban” cycle represents refuse truck driving with many stops and a 

maximum speed of 20 mph but an average speed of 2.2 mph. This cycle was developed by West 

Virginia University for the State of New York. The CRC E55 HHDDT Creep cycle was used 

instead for restricted access driving of refuse trucks at extremely low speeds. All of the other 

78
 
 

 

 

 

 

 

 

driving cycles used for refuse trucks were borrowed from driving cycles developed for heavyduty 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.”61 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 E55 HHDDT Creep cycle for all driving at 

extremely low speeds. At the other end of the distribution, none of the existing driving cycles 

for heavy-duty trucks included average speeds sufficiently high to cover the highest speed bin 

used by MOVES. To construct such cycles, EPA started with the highest speed driving cycle 

available from the ERG analysis and added 5 mph to each point, effectively increasing the 

average speed of the driving cycle without increasing the acceleration rate at any point. We have 

checked the feasibility of these new driving cycles (396 and 397) using simulations with the 

EPA’s Greenhouse Gas Emissions Model (GEM)62 for medium- and heavy-duty vehicle 

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. 

The driving schedules used to represent restricted access (freeway) driving in MOVES2014 are 

not intended to represent vehicle operation on entrance or exit ramps. Activity that occurs on the 

79
 
 

 

 

 

 

freeway in anticipation of ramps or occurring after entry (including activity on “weaving lanes”) 

is included in the non-ramp freeway driving schedules. 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 highway 

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 highway speed that 

does not include ramp operation. Since operating modes for ramp emissions are affected by the 

distribution of the average speed bins on the surrounding roads, the determination of average 

speeds for restricted access roads (both urban and rural) should not include the time or distance 

of vehicles on ramps. However, the VMT on ramps should be included with restricted access 

VMT. 

The emission impact of ramp activity is combined with the other driving activity found in the 

restricted access (freeway) driving cycles using a ramp fraction. This fraction defines the 

fraction of all time spent on a road that occurs on entrance and exit ramps. The fraction used (8 

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

MOBILE6 model.63 

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11 Hotelling Activity 

MOVES2014 defines "hotelling" as any long period of time that drivers spend in their vehicles 

during mandated down times during long distance deliveries by tractor/trailer combination 

heavy-duty trucks. During the mandatory down time, drivers can stay in motels or other 

accommodations, but most of these trucks have sleeping spaces built into the cab of the truck and 

drivers stay with their vehicles. Hotelling hours are included in MOVES2014 in order to account 

for use of the truck engine (referred to as “extended idling”) to power air conditioning, heat, and 

other accessories and account for the use of auxiliary power units (APU), which are small 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 ten hours driving followed by a mandatory eight 

hour rest period. These regulations are described in the Federal Register.64 In long-haul 

operation, drivers will stop periodically along their routes. For MOVES, the total hours of 

hotelling are estimated by using the national estimate of VMT by long-haul combination trucks 

divided an estimated average speed to calculate total hours of driving. The total hours of driving 

divided by ten gives the number of eight-hour rest periods needed and thus the national total 

hotelling hours. 

A method is needed to allocate these total hotelling hours to locations. For MOVES2014, we 

decided to determine a “hotelling rate” (hours of hotelling per mile of travel) that could be used, 

in combination with VMT information to allocate the hotelling hours, described in Equation 12 

to Equation 15. 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 traveled by 

long-haul trucks. In order to prevent large amounts of hotelling to be allocated to congested 

urban areas, we decided to only use the VMT on rural restricted roads as the surrogate for 

allocating the total hotelling hours. 

The hotelling rate (hotelling hours per mile of rural restricted access travel by long-haul 

combination trucks) is applied to the estimate of rural restricted access VMT by long-haul 

combination trucks to estimate the default hotelling hours for any location, month or day. The 

allocation of hotelling to specific hours of the day is described below in Section 12.5. 

The MOVES2014 default hotelling rate was calculated using default national total VMT 

estimates for calendar year 2011 shown in Table 11-1. 

81
 
 

 

 

TTPPssaall VVsshiiiillss FFiillssss TTHHaaTTssllssTT

TTPPssaall HHPPssHHss =	 Equation 12 AATTssHHaassss VVppssssTT

TTPPssaall HHPPssHHss

TTPPssaall TTHHiippss =	 Equation 13 10 hPPssHHss ppssHH ssHHiipp

HHPPsssslllliissss HHPPssHHss = TTPPssaall TTHHiippss ∗ 8 hPPssHHss ppssHH ssHHiipp Equation 14 

HHPPsssslllliissss HHPPssHHss

HHPPsssslllliissss RRaassss =	 Equation 15 TTPPssaall RRssHHaall RRssssssHHiiiissssTT FFiillssss TTHHaaTTssllssTT

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 31,392,300,000 miles 

Rural Unrestricted 34,301,700,000 miles 

Urban Restricted 32,243,100,000 miles 

Urban Unrestricted 28,848,900,000 miles 

Total annual VMT 126,786,000,000 miles 

Hours (58.3 mph) 2,174,716,981 hours 

Trips (10 hrs per trip) 217,471,698 trips 

Hotelling hours (8 hrs per trip) 1,739,773,585 hours 

Hotelling hours per mile on rural restricted roads 0.055414 hours/mile 

82
 
 

 

 

 

 

 

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 (see Table 11-3 below) contains the MOVES default 

values for the distribution of hotelling activity to the operating modes. 

Table 11-3 Default hotelling activity distributions 

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 

83 
 

 

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

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

Greenhouse Gas standards. 

84
 
 

 

 

 

12 Temporal Distributions 

MOVES is designed to estimate emissions for every hour of every day type in every month of 

the year. The vehicle miles traveled (VMT) are provided for MOVES2014 in terms of annual 

miles. These miles are allocated to months, days and hours using allocation factors, either 

default values or values provided by users. 

Default values for most temporal VMT allocations are derived from a 1996 report from the 

Office of Highway Information Management (OHIM).65 The report describes analysis of a 

sample of 5,000 continuous traffic counters distributed throughout the United States. EPA 

obtained the data used in the report and used it to generate the VMT temporal distribution inputs 

in the form needed for MOVES2014. 

The 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 SampleVehicleDay and 

SampleVehicleTrip tables of the MOVES database, shown below in Table 12-1. These tables 

contain a set of vehicle trip activity information constructed to represent activity for each source 

type. Evaporative emissions are also affected by the time of day and the duration of parking. 

Some of the vehicles in the tables take no trips. 

Table 12-1 SampleVehicleDay table 

Source Type Number of Records 

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 

85
 
 

 

12.1 VMT Distribution by Month of the Year 

In MOVES, VMT is entered as an annual value and allocated to month using the 

MonthVMTFraction table. For MOVES, we use the data from the OHIM report, Figure 2.2.1 

“Travel by Month, 1970-1995,” but modified to fit MOVES specifications. The table shows 

VMT/day taken from the OHIM report, normalized to one for January. For MOVES, we need the 

fraction of total annual VMT in each month. The report values of VMT per day were used to 

calculate the VMT in a month using the number of days in each month. The calculations in 

Table 12-2 assume a non-leap year (365 days). 

Table 12-2 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 2010.66 This allocation increases motorcycle activity (and 

emissions) in the summer months and decreases them in the winter compared to the other source 

types. These default values in Table 12-3 for motorcycles are only a national average and do not 

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

86
 
 

 

 

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, as shown in Table 12-4. 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. 

87
 
 

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. There are separate sets of 

HourVMTFractions for "urban" and "rural" road types, but unrestricted and unrestricted roads 

use the same HourVMTFraction distributions. All source types use the same HourVMTFraction 

distributions, and Table 12-5 and Figure 12-1 summarize these default values. 

Table 12-5 MOVES distribution of VMT by hour of the day 

hourID Description 

Urban Rural 

Weekday Weekend Weekday Weekend 

1 Hour beginning at 12:00 midnight 0.00986 0.02147 0.01077 0.01642 

2 Hour beginning at 1:00 AM 0.00627 0.01444 0.00764 0.01119 

3 Hour beginning at 2:00 AM 0.00506 0.01097 0.00655 0.00854 

4 Hour beginning at 3:00 AM 0.00467 0.00749 0.00663 0.00679 

5 Hour beginning at 4:00 AM 0.00699 0.00684 0.00954 0.00722 

6 Hour beginning at 5:00 AM 0.01849 0.01036 0.02006 0.01076 

7 Hour beginning at 6:00 AM 0.04596 0.01843 0.04103 0.01768 

8 Hour beginning at 7:00 AM 0.06964 0.02681 0.05797 0.02688 

9 Hour beginning at 8:00 AM 0.06083 0.03639 0.05347 0.03866 

10 Hour beginning at 9:00 AM 0.05029 0.04754 0.05255 0.05224 

11 Hour beginning at 10:00 AM 0.04994 0.05747 0.05506 0.06317 

12 Hour beginning at 11:00 AM 0.05437 0.06508 0.05767 0.06994 

13 Hour beginning at 12:00 Noon 0.05765 0.07132 0.05914 0.07293 

14 Hour beginning at 1:00 PM 0.05803 0.07149 0.06080 0.07312 

15 Hour beginning at 2:00 PM 0.06226 0.07172 0.06530 0.07362 

16 Hour beginning at 3:00 PM 0.07100 0.07201 0.07261 0.07446 

17 Hour beginning at 4:00 PM 0.07697 0.07115 0.07738 0.07422 

18 Hour beginning at 5:00 PM 0.07743 0.06789 0.07548 0.07001 

19 Hour beginning at 6:00 PM 0.05978 0.06177 0.05871 0.06140 

20 Hour beginning at 7:00 PM 0.04439 0.05169 0.04399 0.05050 

21 Hour beginning at 8:00 PM 0.03545 0.04287 0.03573 0.04121 

22 Hour beginning at 9:00 PM 0.03182 0.03803 0.03074 0.03364 

23 Hour beginning at 10:00 PM 0.02494 0.03221 0.02385 0.02622 

24 Hour beginning at 11:00 PM 0.01791 0.02457 0.01732 0.01917 

Sum of All Fractions 1.00000 1.00000 1.00000 1.00000 

88
 
 

 

Figure 12-1 Hourly VMT fractions by day type and road type 

Fraction of Daily Vehicle Miles Traveled 

0.09 

0.08 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

Rural Weekend 

Urban Weekend 

Rural Weekday 

Urban Weekday 

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

Hour of the Day 

12.4 Engine Starts and Parking 

To properly estimate engine start emissions and evaporative fuel vapor losses, it is important to 

estimate the number of starts by time of day, and the duration of time between vehicle trips. The 

time between trips with the engine off is referred to as “soak time”. To determine typical patterns 

of trip starts and ends, MOVES uses information from instrumented vehicles. This data is stored 

in two tables in the MOVES default database, as discussed earlier. We have made only minor 

changes for MOVE2014. 

The first table, SampleVehicleDay, lists a sample population of vehicles, each with an identifier 

(vehID), an indication of vehicle type (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.

67 This change is described in greater detail in the 

report describing evaporative emissions in MOVES2014.68 

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 

89
 
 

 

 

 

midnight of the day of the trip. 439 trips (about 1.1 percent) were added to this table to assure 

that at least on trip is done by a vehicle from each source type in each hour of the day to assure 

that emission rates will be calculated in each hour. Light-duty vehicle trip and soak data was 

copied to all the other source types (11, 41, 42, 43, 51, 52, 53, 54, 61, and 62) for both weekdays 

(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.69,70 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. 

90
 
 

 

 

 

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).68 The distribution of operating modes in each hour of 

the day is part of the calculation used to determine the engine start emissions for that hour of the 

day. 

91
 
 

 

 

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.4,5 

12.5 Hourly Hotelling Activity 

The hotelling hours in each day should not directly correlate with the miles traveled in each hour, 

since hotelling occurs only when drivers are not driving. Instead, the fraction of hours spent 

hotelling by time of day can be derived from other sources. In particular, the report, RoadwaySpecific Driving Schedules for Heavy-Duty Vehicles61 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 

92
 
 

 

 

 

 

 

 

 

 

 

 

 

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

paper71 based on a survey of 365 truck drivers at six different locations. Table 12-10 lists the 

fraction of trucks in each duration bin. Some trucks are hotelling for more than the required 

eight hours, but some are hotelling for less than eight hours. 

Table 12-10 Distribution of truck hotelling activity duration 

Hotelling Duration 

(hours) 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. But not all trip 

ends have the same number of hotelling hours. The distribution of hotelling durations from 

Table 12-10 is applied to the hotelling that occurs at each of these trip ends. 

Table 12-11 illustrates the hotel activity calculations based on the number of trip starts and trip 

ends. The hours of hotelling in any hour of the day is the number of trip ends in the current hour 

plus the trip ends from the previous hours that are still hotelling. However, since not all trips 

begin and end precisely on the hour, we have discounted the oldest hour included in the 

calculation by 60 percent to account for those unsynchronized trips. 

For example, there are 171 trip ends in hourID 1. If all trip ends idle for two hours, the number 

of hours is 171 (for hourID 1) and 40 percent of 211 (for hourID 24), and thus 171 + (0.4*211) = 

255.4 hours of hotelling. Similarly, the number of hours can be calculated for other hotelling 

time periods. For four hour hotelling periods, the hotelling hours would be 171 + 211 + 201 + 

(0.4*240) = 679. Only the oldest hour of the day is discounted. 

This calculation accounts for the time in the current hour of the day which is a result of hotelling 

from trips that ended in the current hour and trips that ended in previous hours. This approach 

assumes that all hotelling begins at the trip end. For example, in the hour of the day 1 for the 

four hours hotelling bin, the trip ends in hourID 22 contribute to the hours of hotelling in hourID 

1, since these trip ends are still hotelling (four hours) after the trip end. The trip ends in hourID 

21 do not contribute to the four hours hotelling bin, since it has been more than four hours since 

the trip ends occurred. 

The initial calculated hours assume that all trucks idle the same amount of time, indicated by the 

hotelling hours bin. The distribution (weight) from Table 12-10 is applied to the hour estimate in 

each hotelling hours bin to calculate the weighted total idle hours for each hour of the day. 

93
 
 

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 

Weighted Total 

Idle 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 

Weight 0.227 0.135 0.199 0.191 0.156 0.057 0.014 0.021 

*Assumes every trip ends 10 hours after it starts, such that all trips are 10 hours long. The first hour of hotelling in each hour bin 

column sum is reduced by 60 percent to account for trip ends in a column that are not a full hour. 

94 
 

 

 

 

 

 

 

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

dissertation72 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 results from a single study at a specific 

location, MOVES2014 uses the more generic simulated values to determine the diurnal 

distribution of hotelling behavior. The distribution of total hotelling hours to hours of the day is 

calculated from the total hotelling hours and stored in the SourceTypeHour table of the default 

MOVES2014 database. 

MOVES2014 uses this same default hourly distribution from 

Table 12-11 for all days and locations, as shown below in Figure 12-2. Note this distribution of 

hotelling by hour of the day is similar to the inverse of the VMT distribution used for these 

trucks by hour of the day. 

Figure 12-2 Truck hotelling distribution by hour of the day in MOVES2014 

7% 

6% 

5% 

4% 

3% 

2% 

1% 

0% 

Hours of Day 

12.6 Single and Multiday Diurnals 

The evaporative vapor losses from gasoline vehicle fuel tanks are affected by many factors, 

including the number of hours a vehicle is parked without an engine start, referred to as engine 

soak time. Most modern gasoline vehicles are equipped with emission control systems designed 

to capture most evaporative vapor losses and store them. These stored vapors are then burned in 

the engine once the vehicle is operated. However, the vehicle storage capacity for evaporative 

vapors is limited and multiple days of parking (diurnals) will overload the storage capacity of 

these systems, resulting in larger losses of evaporative vapors in subsequent days. 

The soak time calculations are discussed earlier in Section 12.4. The detailed description of the 

calculation for the number of vehicles that have been soaking for more than a day and the 

amount of time that the vehicles have been soaking can be found in the MOVES technical report 

on evaporative emissions.70 Percent of Fleet Hotelling 

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

95
 
 

 

 

 

 

 

13 Geographical Allocation of Activity 

MOVES is designed to model activity at a “domain” level and then to allocate that activity to 

“zones.” The MOVES2014 default database is populated for a domain of the entire United States 

(including Puerto Rico and the Virgin Islands), and the default zones correspond to individual 

counties. The MOVES design only allows for one set of geographic allocations to be stored in 

the default database. While geographic allocations clearly change over time, the MOVES2014 

defaults were developed using the data from calendar year 2011, and are used for all calendar 

years. For this reason, the MOVES default allocation of activity is rarely used for any official 

purpose by either EPA or local areas. National-level emissions can be generated with calendar 

year specific geographical information by running each year separately, with different user-input 

allocations for each run. County- and Project-level calculations do not use the default 

geographical allocation factors at all. Instead, County and Project scales require that the user 

input local total activity for each individual year being modeled. The MOVES geographic 

allocation factors are stored in two tables, Zone and ZoneRoadType. 

13.1 Source Hours Operating Allocation to Zones 

Most of the emission rate calculations in MOVES2014 are based on emission rates by time units 

(hour). Using time units for emissions is the most flexible approach, since the activity for some 

processes (like leaks and idling) and some source types (like nonroad generators) are more 

naturally in units of time. As a result, MOVES converts activity data to hours in many cases in 

order to produce the hours needed for emissions calculations. 

The national total source hours of operation (SHO) are calculated from the estimates of VMT 

and speed as described in sections above. This total VMT for each road type is allocated to 

county using the SHOAllocFactor field in the ZoneRoadType table. The allocation factors are 

derived using 2011 VMT and MOVES default VMT. 

In particular, the MOVES2014 default estimates for the VMT by county come from Version 1 of 

the 2011 National Emission Inventory (NEI) analysis.48 These estimates are based on the 

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

Highway Administration73 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 NEI74 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 

96
 
 

 

 

 

 

 

 

 

 

 

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 calculated operating hours 

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

value, shown in Equation 16. 

SHO = VMT (miles) / Speed (miles per hour) Equation 16 

Once the hours of operation have been calculated, the hours in each county were summed by 

road type. The allocation factor for each county in Equation 17 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 17 

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 2011.74 

VMT estimates for each county in each state and the allocation is calculated using Equation 18, 

where ii represents each individual county and VV is the set of all US counties. 

97
 
 

 

 

 

CountyAllocationii = CountyVMTii൘෍CountyVMTii Equation 18 

iijjii

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. 

98
 
 

 

 

 

 

 

 

 

 

14 Vehicle Mass and Road Load Coefficients 

The MOVES model calculates emissions using a weighted average of emisson rates by operating 

mode. This level of detail is required for microscale modeling, which in MOVES is called 

project level analysis. For running exhaust emissions, the operating modes are defined by either 

vehicle specific power (VSP) or scaled tractive power (STP). Both VSP and STP are calculated 

based on a vehicle’s speed and acceleration but differ in how they are scaled (or normalized). 

VSP is used for light-duty vehicles (source types 11 through 32) and STP is used for heavy-duty 

vehicles (source types 41 through 62). 

The SourceUseTypePhysics table describes the vehicle characteristics needed for the VSP and 

STP calculations, including average vehicle mass, a fixed mass factor, and three road load 

coefficients for each source type averaged over all ages. MOVES uses these to calculate VSP 

and STP for each source type according to Equation 19 and Equation 20: 

VVVVPP = ൬

AA ∙ TT + ൬BB ∙ TT2 + ൬CC ∙ TT3 + (aa + ss ∙ sin θθ) ∙ TT Equation 19 FF൰ FF൰ FF൰

AATT + BBTT2 + CCTT3 + FF ∙ TT ∙ (aatt + ss ∙ ssiissθθ) VVTTPP = Equation 20 ffssssaallss

⁄ ⁄

kW-s3⁄m3 respectively. AA is associated with tire rolling resistence, BB with mechanical rotating 

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

source mass for the source type in metric tons, ss is the acceleration due to gravity ൫9.8 m s

where AA, BB, and CC are the road load coefficients in units of kW-s m, kW-s2 m2, and 

⁄ 2

൯, v 

is the instantaneous vehicle speed in m s ⁄ 2 ⁄ , aa is the instantaneous vehicle acceleration in m s ,

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

When mapping actual emissions data to VSP bins with Equation 19, the vehicle’s measured 

weight is used as the source mass factor. In contrast, when calculating average VSP distributions 

for an entire source type with MOVES, the average source type mass is used instead. STP is 

calculated with Equation 20, 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 (ffssssaallss = 

FF). For heavy-duty vehicles using STP, ffssssaallss 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 ffssssaallss = 2.06, which is equal to the mass of 

source type 32 in metric tons. This is because operating modes for passenger trucks and lightcommercial 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 heavyduty source types (buses, single unit, and combination trucks) are assigned using STP with 

ffssssaallss =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-duty4 and heavy-duty5 emission rate reports, respectively. 

99
 
 

 

 

 

 

 

 

 

 

In both cases, operating mode distributions are derived by combining second-by-second speed 

and acceleration data from a specific drive schedule with the proper coefficients for a specific 

source type. More information about drive schedules can be found in Section 10.1. The 

following sections detail the derivation of values used in Equation 19 and Equation 20. 

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 (FF in the equations above), and 

the fixed mass factor represents the STP scaling factor (ffssssaallss 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 24 (Appendix H: 

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 mass.k 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 short-haul single unit trucks, long-haul single unit trucks, refuse trucks, and 

motor homes. Likewise, the percentage difference between the average combination truck mass 

in MOVES2010b and the WIM data was applied to the source masses of short-haul and longhaul combination trucks, including the 2014+ model year groups. These differences are shown in 

Table 14-1, and the resulting source type masses are presented in Table 14-4. 

Table 14-1 Weigh-in-Motion (WIM) masses weighted by VMT 

HPMS Category Average Weight (lbs) Percent Change from 

MOVES2010b 

Single Unit Trucks 20,107 11.7% 

Combination Trucks 52,907 -21.7% 

14.2. Road Load Coefficients 

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

load coefficients, given in Equation 21 through Equation 23, were empiricially derived in 

accordance with standard practice75,76: 

k 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. 

100
 
 

 

 

 

 

 

 

 

 

AA = 0.088 ∙ FF Equation 21 

BB = 0 Equation 22 

CC = 0.00026 + 0.000194 ∙ FF Equation 23 

For light-duty vehicles, the road load coefficients were calculated according to Equation 24 

through Equation 26:77 

0.7457 Equation 24 AA = ∙ 0.35 ∙ TTRRLLHHPP@50mph 50 ∙ 0.447 

0.7457 Equation 25 BB = ∙ 0.10 ∙ TTRRLLHHPP@50mph (50 ∙ 0.447)2

0.7457 Equation 26 CC = ∙ 0.55 ∙ TTRRLLHHPP@50mph (50 ∙ 0.447)3

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

and CC to be used in Equation 19 and Equation 20, 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 AA, BB, and CC for source types 21, 31, and 32 were derived from 

applying TRLHP values recorded in the Mobile Source Observation Database (MSOD)78 to 

Equation 24 through Equation 26. While we expect light-duty road load coefficients to improve 

over time due to the Light-Duty Greenhouse Gas Rule, the impact of these changes have been 

directly incorporated into the emission and energy rates. Therefore, these coefficients remain 

constant over time in the MOVES (if not in the real-world) to avoid double counting the impacts 

of actual road load improvements in the fleet. 

For the heavier vehicles, no road load parameters were available in the MSOD. For these source 

types, relationships of road load coefficent to vehicle mass came from a study done by V.A. 

Petrushov,79 as shown in Table 14-2. These relationships are grouped by regulatory class; source 

type values were determined by weighting the combination of MOVES2010b weight categories 

that comprise the individual source types. The final SourceMass, FixedMassFactor and road load 

coefficients for all source types are listed in Table 14-4. 

101
 
 

 

 

 

 

 

Table 14-2 Road load coefficients for heavy-duty trucks, buses, and motor homes for MY 1960-2013 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 

AA ൬

kkkk-ss

ss ൰ 0.0996 ∙ FF 0.0875 ∙ FF 0.0661 ∙ FF 0.0643 ∙ FF

BB ቆ

kkkk-ss2

ss2 ቇ 0 0 0 0 

CC ቆ

kkkk-ss3

ss3 ቇ 0.00289 + 

5.22 × 10−5̇ ∙ FF

0.00193 + 

5.90 × 10−5 ∙ FF

0.00289 + 

4.21 × 10−5 ∙ FF

0.0032 + 

5.06 × 10−5 ∙ FF

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.80 Table 14-3 contains the combination long-haul tractor and vocational vehicle tire 

rolling resistance, coefficient of drag, and weight reductions expected from the technologies 

which could be used to meet the standards. The value in the table reflects a 400 pound mass 

reduction. As discussed in the regulatory impact analysis for the final rulemaking, EPA used a 

sales mix of 10 percent Class 7 low roof, 10 percent Class 7 high roof, 45 percent Class 8 low 

roof, and 35 percent Class 8 high roof based on feedback from the manufacturers. 

The values in the table reflect a modeling assumption that 8 percent of all tractors (19.7 percent 

of short-haul tractors) would be considered vocational tractors and therefore will only be 

required to meet the vocational vehicle standards and not show any aerodynamic or weight 

improvement. The weight reduction applied to short-haul tractors is 321 pounds, which is 

calculated from the 400 pound weight reduction assumed for non-vocational tractors, reduced by 

19.7 percent. The tire rolling resistance reduction is assumed to be 5 percent based on the data 

derived in the tire testing program conducted by EPA. Comparatively tire rolling resistance is 

reduced by 9.6 percent for long-haul tractors and 7 percent for short-haul tractors while 

aerodynamic drag is reduced 12.1 percent for long-haul tractors and 5.9 percent for short-haul 

tractors in model year 2014 and later. Further details on the rule’s assumptions about reductions 

to source mass and road load coefficients in the SourceUseTypePhysics table and 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.5 

102
 
 

Table 14-3 Estimated reductions in rolling resistance and aerodynamic drag coefficients from HD GHG 

Phase 1 Rule for model years 2014 and later
 

Truck Type 

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 (Single 

unit 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 MOVES2014 SourceUseTypePhysics table 

sourceTypeID 

Begin 

Model 

Year 

End 

Model 

Year 

Rolling 

Term A 

(kW-s/m) 

Rotating 

Term B 

(kW-s2/m2) 

Drag 

Term C 

(kW-s3/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 

103
 
 

 

 

 

 

 

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.81 

15.1 ACPenetrationFraction 

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

source type and model year were taken from MOBILE6.82 Market penetration data by model 

year were gathered from Ward’s Automotive Handbook for light-duty vehicles and light-duty 

trucks for model years 1972 through the 1995 for cars and 1975-1995 for light trucks. Rates in 

the first few years of available data are quite variable, so values for early model years were 

estimated by applying the 1972 and 1975 rates for cars and trucks, respectively. Projections 

beyond 1995 were developed by calculating the average yearly rate of increase in the last five 

years of data and applying this rate until a predetermined cap was reached. A cap of 98 percent 

was placed on cars and 95 percent on trucks under the assumption that there will always be 

vehicles sold without air conditioning, more likely trucks than cars. No data was available on 

heavy-duty trucks. While VIUS asks if trucks are equipped with A/C, “no response” was coded 

the same as “no,” making the data unusable for this purpose. For MOVES, the light-duty vehicle 

rates were applied to passenger cars, and the light-duty truck rates were applied to all other 

source types (except motorcycles, for which A/C penetration is assumed to be zero), summarized 

in Table 15-1. 

104
 
 

 

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 (see Table 15-2) indicates the 

fraction of the air-conditioning equipped fleet with fully functional A/C systems, by source type 

and vehicle age. A value of 1 means all systems are functional. This is used in the calculation of 

total energy to account for vehicles without functioning A/C systems. Default estimates were 

developed for all source types using the “unrepaired malfunction” rates used for 1992-and-later 

model years in MOBILE6. The MOBILE6 rates were based on the average rate of A/C system 

failure by age reported in a consumer study and assumptions about repair frequency during and 

after the warranty period. The MOBILE6 rates were applied to all source types except 

motorcycles, which were assigned a value of zero for all years. 

105
 
 

 

 

 

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.

82 They are based on 

analysis of air conditioning usage data collected in Phoenix, Arizona, in 1994. 

In MOVES, ACActivityTerms are allowed to vary by monthGroup and Hour, in order to provide 

the possibility of different A/C activity demand functions at a given heat index by season and 

time of day (this accounts for differences in solar loading observed in the original data). 

However, for MOVES2014, the default data uses one set of coefficients for all MonthGroups and 

Hours. These default coefficients represent an average A/C activity demand function over the 

course of a full day. The coefficients are listed in Table 15-3. 

106
 
 

 

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) 

107
 
 

 

 

 

 

 

 

 

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 prevalent vehicle classes are well-known; base year age distributions 

are well-measured, and driving activity has been the subject of much study in recent years. 

Still, the fleet and activity inputs do have significant limitations, and the uncertainties and 

variability in this local data can contribute significant uncertainty in resulting emission estimates. 

Thus it is often appropriate to replace many of the MOVES fleet and activity defaults with local 

data as explained in EPA’s Technical Guidance.3 

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. The inputs 

for MOVES2014 were developed for a 2011 base year and much of the source data is from 2011 

and earlier. This information needs to be updated periodically to assure that the model defaults 

reflect the latest available data and projections on the US fleet. 

Updating the vehicle fleet data will be complicated by the fact that one of the primary data 

sources for this document, the Census Bureau’s Vehicle Inventory and Use Survey, has been 

discontinued. EPA is currently working with DOT and other federal agencies to revive this 

survey. Doing so becomes more important as the data gathered from the last survey (2002) ages. 

A related complication is the cost of data. Collecting data on vehicle fleet and activity is 

expensive, especially when the data is intended to accurately represent the entire United States. 

Even when EPA does not generate data directly (for example, compilations of state vehicle 

registration data), obtaining the information needed for MOVES can be costly and, thus, 

dependent on budget choices. 

In addition to these general limitations, there are also specific MOVES data elements that could 

be improved with additional research, including: 

• real-world highway driving cycles and operating mode distributions;
 

• off-network behavior including vehicle starts and soaks;
 

•	 truck hotelling, particularly extended engine idling, and APU use; 

•	 idling while loading/unloading, in traffic queues (i.e. tolls), or elsewhere; 

•	 VSP/STP adjustments for speed, road grade, and loading; 

•	 activity changes with age, such as mileage accumulation rates, start activity, and soak 

distributions; 

•	 updated estimates of vehicle scrappage rates used to project vehicle age distributions; 

•	 further incorporation of data from instrumented vehicle studies; 

•	 summaries from large-scale instrumented vehicle studies; 

108
 
 

 

 

 

 

 

 

 

• vehicle identification and sorting by size, sector, and vocation; 

• activity weighting of source mass averages; 

• air conditioning system usage, penetration, and failure rates; 

• vehicle type distinctions in temporal activity; 

• heavy truck and bus daily trip activity patterns; and 

• ramp activity and operating mode distributions. 

We expect many of these MOVES data limitations can be addressed through analysis of data 

captured on instrumented vehicles. The recent emergence and availability of large streams of 

activity data from GPS devices, data loggers, and other onboard diagnostic systems will likely 

lead to a better understanding of travel behavior. These data streams often provide frequent 

sampling of real-world driving for a large number of vehicles, so, while imperfect, they are 

useful for improving the nationally representative default inputs in MOVES. EPA is actively 

acquiring such data for future MOVES updates. 

Future updates to vehicle population and activity defaults will need to continue to focus on the 

most critical elements required for national fleet-wide estimates, namely gasoline light-duty cars 

and trucks, and diesel heavy-duty trucks. Information collection on motorcycles, refuse trucks, 

motor homes, diesel light-duty vehicles and gasoline heavy-duty vehicles will be a lower 

priority. In addition to updating the model defaults, we will need to consider whether the current 

MOVES design continues to meet our modeling needs. Simplifications to the model to remove 

categories, such as source types or road types, might make noticeable improvements in run time 

without affecting the validity of fleet-wide emission estimates. EPA hopes to perform further 

validation of MOVES activity data using fuel volumes reported from US Department of 

Transportation in a separate technical report. This type of fuel volume validation and other 

MOVES2014 validation work was initially presented to the MOVES Federal Advisory 

Committee Act (FACA) Work Group.83 

At the same time, the fundamental MOVES assumption that vehicle activity varies by source 

type and not by fuel type or other source bin characteristic may be challenged by the growing 

market share of alternative fuel vehicles, such as electric vehicles, which may have distinct 

activity patterns. As we progress with MOVES, the development of vehicle population and 

activity inputs will continue to be an essential area of research. 

109
 
17 Appendix A: Projected Source Type Populations by Year 

Table 17-1: Source type populations (in thousands), as derived from HPMS populations in §5.2and the age distribution algorithm in §7.1.2.2 

Year Motorcycle 

(11) 

Passenger 

Car 

(21) 

Passenger 

Truck 

(31) 

Light 

Comm. 

Truck 

(32) 

Intercity 

Bus 

(41) 

Transit 

Bus 

(42) 

School 

Bus 

(43) 

Refuse 

Truck 

(51) 

Single 

Unit 

ShortHaul 

(52) 

Single 

Unit 

LongHaul 

(53) 

Motor 

Home 

(54) 

Combination 

Short-Haul 

(61) 

Combination 

Long-Haul 

(62) 

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 

110
 
Year Motorcycle 

(11) 

Passenger 

Car 

(21) 

Passenger 

Truck 

(31) 

Light 

Comm. 

Truck 

(32) 

Intercity 

Bus 

(41) 

Transit 

Bus 

(42) 

School 

Bus 

(43) 

Refuse 

Truck 

(51) 

Single 

Unit 

ShortHaul 

(52) 

Single 

Unit 

LongHaul 

(53) 

Motor 

Home 

(54) 

Combination 

Short-Haul 

(61) 

Combination 

Long-Haul 

(62) 

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

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 

111
 
 

 

 

 

18 Appendix B: Fuel Type and Regulatory Class Fractions 

for 1960-1981 

As noted in the text, the fuel type and regulatory class distributions in the 

SampleVehiclePopulation table for model year 1981 and earlier have not changed from 

MOVES2010b. Those fuel type distributions between 1960 and 1981 for each source type have 

been summarized in Table 18-1 and Table 18-2. Many of the data sources for the fuel type 

fractions are the same in MOVES2010b and MOVES2014. Truck diesel fractions in Table 18-1 

are derived using a MOVES2010b sample vehicle counts dataset—similar to the MOVES2014 

one—but with 1999 Polk vehicle registrations and the 1997 VIUS, except for refuse trucks and 

motor homes. We assumed 96 percent of refuse trucks were manufactured to run on diesel fuel in 

1980 and earlier according to the average diesel fraction from VIUS across all model years. We 

also assumed that 15 percent of these motor homes are diesel powered based on information 

from the Recreation Vehicle Industry Association (RVIA), as noted above in Section 6.2.2.5. 

Table 18-1 Diesel fractions for truck source types* 

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 

As in MOVES2010b, lacking both emission rate and population data, we assume in 

MOVES2014 that all motorcycles will be gasoline powered, all intercity buses will be diesel 

powered over all model years, and all transit buses will be run on diesel from 1960 to 1981. 

School bus fuel type fractions are reused from MOBILE6, originally based on 1996 and 1997 

Polk data. Passenger cars are split between gasoline and diesel for 1960-1981 using the 

MOVES2010b sample vehicle counts dataset. 

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

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 

112 
 

 

 

The 1960-1981 regulatory class distributions have been derived from the MOVES2010b sample 

vehicle counts dataset. 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. The 1960-1981 regulatory class distributions for dieselfueled single unit and combination trucks have been summarized in Table 18-4 below. All 1960-

1981 gasoline-fueled single unit and combination trucks fall into the medium heavy-duty (MHD) 

regulatory class (regClassID 46). 

113
 
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). 

114
 
 

 

 

 

 

 

 

19 Appendix C: 1990 Age Distributions 

19.1 Motorcycles 

The motorcycle age distributions are based on Motorcycle Industry Council estimates of the 

number of motorcycles in use, by model year, in 1990. However, data for individual model years 

starting from 1978 and earlier were not available. A logarithmic regression curve (R2 value = 

0.82) was fitted to available data, which was then used to extrapolate age fractions for earlier 

years beginning in 1978. 

19.2 Passenger Cars 

To determine the 1990 age fractions for passenger cars, we began with Polk NVPP® 1990 data 

on car registration by model year. However, this data presents a snapshot of registrations on July 

1, 1990, and we needed age fractions as of December 31, 1990. To adjust the values, we used 

monthly data from the Polk new car database to estimate the number of new cars registered in 

the months July through December 1990. Model Year 1989 cars were added to the previous 

estimate of “age 1” cars and Model Year 1990 and 1991 cars were added to the “age 0” cars. 

Also the 1990 data did not detail model year for ages 15+. Hence, regression estimates were used 

to extrapolate the age fractions for individual ages 15+ based on an exponential curve (R2 value 

=0.67) fitted to available data. 

19.3 Trucks 

For the 1990 age fractions for passenger trucks, light commercial trucks, refuse trucks, short-haul 

and long-haul single unit trucks and short-haul and long-haul combination trucks, we used data 

from the TIUS92 (1992 Truck Inventory and Use Survey) database. Vehicles in the TIUS92 

database were assigned to MOVES source types as summarized in Table 19-1. Like VIUS97, 

TIUS92 does not include a model year field and records ages as 0 through 10 and 11-and-greater. 

Because we needed greater detail on the older vehicles, we followed the practice used for the 

1999 fractions and determined the model year for some of the older vehicles by using the 

responses to the questions “How was the vehicle obtained?” (TIUS field “OBTAIN”) and “When 

did you obtain this vehicle?” (TIUS field “ACQYR”) and we adjusted the age-11-and-older 

vehicle counts by dividing the original count by model year by the fraction of the older vehicles 

that were coded as “obtained new.” 

115
 
 

 

 

 

 

 

 

 

Table 19-1 VIUS1997 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 Trucks Single Unit 

(AXLRE=2-4, 8-16) 

Off-road, local or 

short-range 

(AREAOP<=4) 

Any except garbage 

hauler 

Any 

Single Unit LongHaul Trucks Single Unit 

(AXLRE=2-4, 8-16) 

Long-range 

(AREAOP>=5) 

Any Any 

Combination ShortHaul Trucks Combination 

(AXLRE>=17) 

Off-road, local or 

medium 

(AREAOP<=4) 

Any Any 

Combination LongHaul Trucks Combination 

(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 MOBILE6 age fractions since 1990 data on 

transit buses was not available from the Federal Transit Administration database. MOBILE6 age 

fractions were based on fitting curves through a snapshot of vehicle registration data as of July 1, 

1996, which was purchased from R.L. Polk Company. To develop a general curve, the 1996 

model year vehicle populations were removed from the sample because it did not represent a full 

year, and a best-fit analysis was performed on the remaining population data. The best-fit 

analyses resulted in age distribution estimates for vehicles ages 1 through 25+. However, since 

the vehicle sales year begins in October, the estimated age 1 population was multiplied by 0.75 

to account for the fact that approximately 75 percent of the year’s sales will have occurred by 

July 1st of a given calendar year. 

Both Weibull curve fitting and exponential curve fitting were used to create the age distributions. 

The nature of the Weibull curve fitting formula is to produce an “S” shaped curve, which is 

116
 
relatively flat for the first third of the data, decreases rapidly for the next third, and flattens again 

for the final third. While using this formula resulted in a better overall fit for transit buses, the 

flatness of the final third for each curve resulted in unrealistically low vehicle populations for the 

older vehicle ages. For this reason, the original Weibull curve was used where it fit best, and 

exponential curves were fit through the data at the age where the Weibull curves began to flatten. 

Table 19-2 presents the equations used to create the age distribution and the years in which the 

equations were used. 

Table 19-2 Curve fit equations for registration distribution data by age 

Vehicle 

Age Equation 

1-17 ss = 3462 ∗ e

−൬ቀ age

17.16909475ቁ

12.53214119൰

18-25+ 24987.0776 ∗ e−0.2000∗age

117
 
 

 

 

 

 

ሬሬ ሬ

ሬሬሬሬሬሬሬሬ

ሬሬሬሬሬሬሬሬ ሬሬ

20 Appendix D: Detailed Derivation of Age Distributions 

20.1 2012-2050 Age Distribution Projections 

The base algorithm for forecasting age distributions is as follows: 

1.	 Starting with the base population distribution (PPሬሬ

yy

ሬ⃗), remove the number of vehicles that 

did not survive (RRሬሬሬ

yy

ሬ⃗) 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 (NNሬሬ

yy+1 ሬሬሬሬ⃗) 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 (PPሬ yy+1⃗). If this algorithm is to be 

repeated, PPሬ yy+1⃗ becomes PPሬ yy

⃗ for the next iteration. 

This is mathematically described with the following equation (reprinted from Section 7.1.2.2 for 

reference): 

ሬ

PPሬሬሬሬሬሬሬሬ ሬሬ ሬሬሬ ሬሬሬሬሬሬሬሬሬ⃗ Equation 10 yy+1

⃗ = PPሬ yy⃗ − RRሬ yy⃗ + NNyy+1

Unfortunately, as described in Section 7.1.2.1, 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 kkyy 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 ሬVVሬሬ

0

⃗ is given by 

Equation 27. 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. 

RRሬሬሬ

yy

ሬ⃗ = kkyy ∙ ൫1 − VVሬሬሬ

0

⃗൯ ∘ PPሬሬ

yy

ሬ⃗	 Equation 27 

Substituting Equation 27 into Equation 10 yields Equation 28: 

ሬሬ ሬ ሬ⃗ − kkyy (1 − VVሬ 0 ሬ⃗ + NNሬ yyሬ

+

ሬ

1 PPሬ ⃗ yy

ሬሬሬ

+

ሬሬ

1

⃗ = PPሬ yyሬ ∙ ሬሬ⃗) ∘ PPሬ yyሬ ሬሬሬሬሬሬ Equation 28 

Since both the value of the scalar adjustment factor and the actual distribution of the next year’s 

population are unknown, Equation 28 can’t be used yet. However, by using an estimate of next 

year’s total population, it can be transformed into Equation 29: 

PPyy+1 = PPyy − kkyy ෍ ቀ൫1 − VVሬሬሬ

0

⃗൯ ∘ PPሬሬ

yy

ሬ⃗ቁ + NNyy+1 Equation 29 aa

118
 
 

 

 

 

 

 

 

 

 

 

 

ሬሬሬ ሬ

ሬሬሬ ሬ

ሬሬ ሬ

This was algebraically solved for kkyy and evaluated for each HPMS categoryl using the following 

information: 

•	 Total populations PPyy and PPyy+1 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 VVሬሬሬ

0

⃗ by HPMS category, which is described in Section 7.1.2.1. 

•	 Population distribution PPሬሬ

yy

ሬ⃗ 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 PPሬሬ

yy+1 ሬሬሬ⃗ of the previous year. 

•	 New vehicle sales NNyy+1 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 kkyy by HPMS category, Equation 28 was used with the following information 

to compute the next year’s population and then age distribution by source type: 

•	 Population distribution PPሬሬ

yy

ሬ⃗ 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 PPሬሬ

yy+1 ሬሬሬ⃗

of the previous year. 

•	 The scalar adjustment factor kkyy and generic survival rate VVሬሬሬ

0

⃗ applied by source 

type using the HPMS to source type mapping described by Table 2-1. Please note 

that limits were placed on the kkyy(1 − VVሬሬሬ

0

⃗) term of Equation 28: the value of this 

term for each age was restricted to being between 0 and 1. 

•	 New vehicle sales NNሬሬ

yy+1 ሬሬሬሬ⃗ 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 (PPyy) are stored in the 

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

SourceTypeAgeDistribution table. An illustration of passenger car age distributions is presented 

in Figure 20-1. For clarity, only four years are shown: 2011, 2020, 2030, and 2040. The effects 

l 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. 

119
 
 

 

 

 ሬሬሬሬሬሬሬሬ

ሬሬሬሬሬሬሬሬ

ሬሬሬሬሬሬሬሬ ሬሬ

of the 2008-2009 recession are visible in the 0-3 year old cars in the 2011 age distribution. By 

2020, the recession dip is moved to the 9-11 year old cars as expected. 

Figure 20-1 Selected age distributions for passenger cars in MOVES2014 

20.2 1999-2010 Age Distributions 

The base algorithm for forecasting age distributions is as follows: 

1.	 Starting with the base population distribution (PPሬሬ

yy

ሬ⃗), remove the age 0 vehicles (NNሬሬሬሬ

yy

ሬ⃗). 

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 (ሬRRyy−1⃗).
 

4.	 This results in the previous year population distribution (PPሬ yy−1⃗). If this algorithm is to be 

repeated, PPሬ yy−1⃗ becomes PPሬ yy

⃗ for the next iteration. 

This is mathematically described with the following equation (reprinted from Section 7.1.2.3 for 

reference): 

ሬ

PPሬሬሬሬሬሬሬሬ ሬሬ ሬሬሬሬ ሬሬሬሬሬሬሬሬሬ⃗ Equation 30 yy−1

⃗ = PPሬ yy⃗ − NNሬ yy⃗ + RRyy−1

120
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

ሬሬሬ ሬ

ሬሬሬ ሬ

However, without detailed historical data for every year, we needed to estimate vehicle 

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

applicable here. Taking careful note of the subscripts, Equation 27 and Equation 30 can be 

combined into Equation 31: 

ሬ

PPሬሬሬሬሬሬሬሬ ሬሬ ሬሬሬሬ ሬሬ ሬሬሬሬሬሬሬሬሬ⃗ yy−1

⃗ = PPሬ yy⃗ − NNሬ yy⃗ + kkyy−1 ∙ ൫1 − VVሬ 0

⃗൯ ∘ PPyy−1	 Equation 31 

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 31 can be transformed into Equation 32: 

PPyy−1 = PPyy − NNyy + kkyy−1෍ ቀ൫1 − VVሬሬሬ

0

⃗൯ ∘ PPሬሬሬ

yy

ሬሬሬ

−

ሬሬሬ

1

⃗ቁ Equation 32 aa

However, this still leaves a PPሬሬ

yy−1 ሬሬሬ⃗ term, which is unavoidable because the total number of vehicles 

removed is dependent on the age distribution of those vehicles. To properly solve Equation 32 

ሬሬሬሬሬሬሬሬ⃗ for kkyy−1 and PP , a numerical method of approximation could be employed. However, due to ሬ yy−1

lack of resources, PPሬሬ

yy

⃗ was used as a simple approximation of PPሬሬ

yy−1 ሬ ሬሬሬሬሬሬሬ⃗ on the left hand side of 

Equation 32. The following sources were used to determine kkyy−1 by HPMS category: 

•	 Total populations PPyy and PPyy−1 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 VVሬሬሬ

0

⃗ by HPMS category, which is described in Section 7.1.2.1. 

•	 Population distribution PPሬሬ

yy

ሬ⃗ 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 PPሬሬ

yy−1 ሬሬሬ⃗ of the previous iteration. 

•	 New vehicle sales NNyy+1 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 kkyy−1 by HPMS category, Equation 31 was used with the following 

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

121
 
 

 

 

 

 

 

ሬሬሬሬሬሬሬሬ

ሬሬሬሬሬሬሬሬ

•	 Population distribution PPሬሬ

yy

ሬ⃗ 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 ሬPPሬሬሬሬሬሬሬሬ⃗ yy−1

of the previous iteration. 

•	 The scalar adjustment factor kkyy−1 and generic survival rate VVሬሬሬ

0

⃗ applied by source 

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

before, limits were placed on the kkyy(1 − ሬVVሬሬ

0

⃗) term, such that the value of this term 

for each age was restricted to being between 0 and 1. Also, the PPሬ yy−1⃗ term used 

when calculating the number of vehicles removed was approximated by ሬPPሬ

yy

ሬ⃗. 

•	 New vehicle sales NNሬ yy+1⃗, 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. 

122
 
 

 

 

21 Appendix E: SCC Mappings 

21.1 SCC Mappings between MOVES2014 and MOVES2010b 

The SCC values used in MOVES2010b and earlier versions of MOVES and MOBILE do not 

have a one-to-one correspondence with the MOVES2014 SCC values. This makes it difficult to 

compare results from MOVES2014 to those from earlier models. While MOVES2014 allows 

output by fuel type and regulatory class (which were the primary identifiers for the earlier 

SCCs), there are complications that prevent developing a simple mapping from the old to the 

new. The most important complication is that the distribution of regulatory classes and fuel 

types for each source type varies by model year, while typical inventories aggregate across 

model years. This means any mapping would have to vary with calendar year and with user 

vehicle population inputs. In addition, regulatory class groupings for light and light heavy-duty 

trucks do not line up exactly with the GVWR groupings used in the earlier SCCs. Table 21-1 

below compares MOVES2014 classification by fuel type and regulatory class to the older SCCs. 

123
 
 

 

Table 21-1 Comparison of MOVES2014 Fuel Types and Regulatory Classes to MOVES2010b Source 

Classification Code (SCC) Vehicle Classes
 

Fuel Type MOVES2014 Regulatory Class 

(RegClassID) 

MOVES2010b Source 

Classification Code (SCC) 

Motorcycles (10) Motorcycles (01080) 

Passenger Cars (20) Passenger Cars (01001) 

Gasoline Light-Duty Trucks (0-8500 lbs 

Light-Duty Trucks (0-6000 lbs 

GVWR) (01020) 

GVWR) (30) Light-Duty Trucks (6001-8500 lbs 

GVWR) (01040) 

Heavy-Duty Trucks and Buses (40, 

41, 42, 46 & 47) 

Heavy-Duty Trucks and Buses 

(01070) 

Passenger Cars (20) Passenger Cars (30001) 

Light-Duty Trucks (30) Light-Duty Trucks (30060) 

Heavy-Duty Vehicles 2b (8501-10000 

lbs GVWR) with four tires (40) 

Heavy Duty Vehicles 2b (8501-

10000 lbs GVWR) (30071) 

Heavy-Duty Vehicles 2b with 6 tires 

or more (8501-10000 lbs GVWR) 

Light Heavy-duty Vehicles (10001-

Diesel 19500 lbs GVWR) (30072) 

Heavy-duty Vehicles (10001-14000 

lbs GVWR) (41) 

Heavy-duty Vehicles (14001-19500 

lbs GVWR) (42) 

Medium Heavy-Duty Vehicles 

(19501-33000 lbs GVWR) (46) 

Medium Heavy-Duty Vehicles 

(19501-33000 lbs GVWR) (30073) 

Heavy Heavy-Duty Vehicles (33001+ 

lbs GVWR) (47) 

Heavy Heavy-Duty Vehicles 

(33001+ lbs GVWR) (30074) 

Transit Buses (48) Diesel Buses (30075) 

21.2 2011 SCC VMT Conversions 

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 

MOVES2010b run are shown in Table 21-2. 

124
 
 

Table 21-2 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 Truck 0.221958 

2201070 Gasoline Heavy-Duty Gasoline Vehicles (8501 lbs. 

and greater GVWR) 

53 Single Unit LongHaul Truck 0.030154 

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 Truck 0.000359 

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 

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 Truck 0.623978 

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

33,000 lbs. GVWR) 

53 Single Unit LongHaul Truck 0.086570 

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 Truck 0.194650 

125
 
SCC 

(7 digits) Description 

Source 

Type Description 

2011 

Fractions 

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

33,000 lbs. GVWR) 

62 Combination LongHaul Truck 0.067783 

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 Truck 0.100296 

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

lbs. and greater GVWR) 

53 Single Unit LongHaul Truck 0.013800 

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 Truck 0.323425 

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

lbs. and greater GVWR) 

62 Combination LongHaul Truck 0.553619 

2230075 Diesel Buses 41 Intercity Bus 0.430859 

2230075 Diesel Buses 42 Transit Bus 0.122565 

2230075 Diesel Buses 43 School Bus 0.446576 

126
 
 

 

 

 

 

 

22 Appendix F: Calculation of Combination Truck Average 

Speed Distributions 

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 33. 

Here, TT̅is the average speed of the distribution, TTii is the average speed of bin ii, and ρρii is the 

proportion of time spent in bin ii. 

TT̅= ෍TTii ∙ ρρii

Equation 33 

= 2.5 ∙ ρρ1 + 5 ∙ ρρ2 + ⋯ + 70 ∙ ρρ15 + 75 ∙ ρρ16

To adjust the average speed for heavy-duty combination trucks, we redistributed the proportion 

of time spent in each speed bin such that its contribution to the average speed was 92 percent of 

the light-duty speed, as shown in Equation 18. This redistributed proportion of time in each ′ speed bin is given by ρρii. 

TT̅

combination = (0.92) TT̅light-duty 

Equation 34 ′ = ෍TTii ∙ ρρii

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

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

The new distribution at speed bin ii (given by ρρii

′

) starts with the original distribution (ρρii), gains 

the proportions moved down from the higher speed bins (ααii+1 ∙ ρρii+1 and ββii+2 ∙ ρρii+2), and loses 

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

35: 

′ ρρii = ρρii + (ααii+1 ∙ ρρii+1) + (ββii+2 ∙ ρρii+2) − (ααii ∙ ρρii) − (ββii ∙ ρρii) Equation 35 

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 ααii and 

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

expressed with Equation 36: 

(1 − ηη) ∙ TTii = ααii ∙ (TTii − 5) + (1 − ααii) ∙ TTii Equation 36 

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

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

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

combination trucks is 92 percent of cars, (1 − ηη) = 92% and ηη = 0.08. 

By rearranging terms from Equation 20, and solving for ααii we obtain Equation 37: 

127
 
 

 

 

 

TTii ∙ ηη

ααii = Equation 37 5 

However, for speed bins ≥ 65 mph, Equation 37 yields ααii greater than 1. Since that logically 

can’t happen, some of the distribution needed to be moved to the second next slower speed bin to 

fully account for the 8 percent speed reduction. This is mathematically shown in Equation 38, 

which is the logical extension of Equation 36: 

(1 − ηη) ∙ TTii = ββii ∙ (TTii − 10) + ααii ∙ (TTii − 5) + (1 − ααii − ββii) ∙ TTii Equation 38 

The difference between Equation 36 and Equation 38 is that an additional fraction (ββii) 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 38. We solved this problem with a linear equation solver by setting Equation 38 

to a constraint (see Equation 39), adding the constraint that ααii + ββii are less than or equal to 1 

(see Equation 40), and choosing the solution that minimized ββii. 

ααii ∙ (TTii − 5) + ββii ∙ (TTii − 10) + TTii ∙ (ηη − ααii − ββii) = 0 Equation 39 

ααii + ββii ≤ 1 Equation 40 

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

With ′ ααii and ββii known for each bin, the new distributions ρρii were calculated. 

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 

22-1. In the figure, 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. 

128
 
 

Figure 22-1 An illustration of adjustments made to the average speed bin 55 mph for heavy-duty vehicles 

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, 1/9th of the new ′ 70 mph fraction (ρρ15) 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. 

129
 
 

 

23 Appendix G: Driving Schedules 

A key feature of MOVES is the capability to accommodate a number of drive schedules to 

represent driving patterns across source type, roadway type and average speed. For the national 

default case, MOVES2014 employs 49 drive schedules with various average speeds, mapped to 

specific source types and roadway types. 

Table 23-1 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. 

130
 
Table 23-1 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 

131
 
Table 23-1 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 

132
 
 

 

 

24 Appendix H: MOVES2010b Source Masses 

Light-duty source masses were unchanged from MOVES2010b. In addition, the heavy-duty 

source masses originally come from MOVES2010b, although they have been updated as 

described in Section 14.1. 

In MOVES2010b, weight data (among other kinds of information) were used to allocate source 

types to source bins using a field called weightClassID. As described in Equation 41, each source 

type’s source mass was calculated using an activity-weighted average of their associated source 

bins’ midpoint weights: 

∙ ss ∑ ൜ffaa ∙ ൬

∑bb ααbb aa ൰ൠ ∑bb ααbb Equation 41 FF = ∑aa ffaa

where FF is the source mass factor for the source type, ffaa is the age fraction at age aa, ααbb is the 

source bin activity fraction for source bin bb, and ss is the vehicle midpoint mass. Table 24-1 lists 

the vehicle midpoint mass for each weightClassID. The source bin activity fraction in 

MOVES2010b is a calculated value of activity based on fuel type, engine technology, regulatory 

class, model year, engine size, and weight class. This calculation is outside the scope of this 

document, but more information can be found in the MOVES2010b SDRM. 

133
 
 

 

 

 

Table 24-1 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. 

24.1 Motorcycles 

The Motorcycle Industry Council “Statistical Annual” provides information on displacement 

distributions for highway motorcycles for model years 1990 and 1998. These were mapped to 

MOVES engine displacement categories. Additional EPA certification data was used to 

establish displacement distributions for model year 2000. We assumed that displacement 

distributions were the same in 1969 as in 1990, and interpolated between the established values 

to determine displacement distributions for all model years from 1990 to 1997 and for 1999. 

Values for 2000-and-later model years are based on model year 2000 certification data. 

We then applied weight distributions for each displacement category as suggested by EPA 

motorcycle experts. The average weight estimate includes fuel and rider. The weight 

distributions depended on engine displacement but were otherwise independent of model year. 

This information is summarized in Table 24-2. 

134
 
 

 

 

 

 

Table 24-2 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 

24.2 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. 

24.3 General Trucks 

24.3.1 Light-Duty Trucks 

Determining weight categories for light trucks was fairly complicated. The VIUS1997 data 

combines information from two different survey forms. The first form was administered for 

VIUS “Strata” 1 and 2 trucks: pickup trucks, panel trucks, vans (including mini-vans), utility 

type vehicles (including jeeps) and station wagons on truck chassis. The second form was 

administered for all other trucks. While both surveys requested information on engine size, only 

the second form requested detailed information on vehicle weight. Thus for Strata 1 and 2 

trucks, VIUS classifies the trucks only by broad average weight category (AVGCK): 6,000 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.84 

data, to correlate engine size with vehicle weight distributions by model year. 

In particular, for source types 31 and 32 (Passenger Trucks and Light Commercial Trucks): 

•	 VIUS1997 trucks of the source type in Strata 3, 4, and 5 were assigned to the appropriate 

MOVES weight class based on VIUS detailed average weight information. 

•	 VIUS1997 trucks of the source type in Strata 1 and 2 were identified by engine size and 

broad average weight category. 

•	 Strata 1 and 2 trucks in the heavier (10,001-14,000 lbs, etc) VIUS1997 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 

VIUS1997 to determine the fraction of trucks by model year and fuel type that fell into 

each engine size/broad weight class combination (the “VIUS fraction”). 

135
 
 

 

 

 

•	 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 VIUS1997 average weight of 6,000 lbs or less, we multiplied the 

VIUS1997 fraction by the fraction of trucks with a given weightClassID among the 

trucks in the ORNL database that had the given engine size and an average weight of 

6,000 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 VIUS1997 average weight of 6,001-10,000 lbs. 

Instead these were distributed equally among the MOVES weightClassID 70, 80, 90 and 

100. 

24.3.2 Single Unit Trucks 

Source types 52 and 53 (long- and short-haul single unit trucks) also included some trucks in 

VIUS1997 Strata 1 and 2, thus a similar algorithm was applied. 

•	 VIUS1997 trucks of the source type in Strata 3, 4, and 5 were assigned to the appropriate 

MOVES weight class based on VIUS1997 detailed average weight information. 

•	 VIUS1997 trucks of the source type in Strata 1 and 2 were identified by engine size and 

broad average weight category. 

•	 Strata 1 and 2 trucks in the heavier (10,001-14,000 lbs, etc) VIUS1997 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 

VIUS1997 to determine the fraction of trucks by model year and fuel type that fell into 

each engine size/broad weight class combination (the “VIUS fraction”). 

•	 We did not believe the ORNL light-duty vehicle database adequately represented single 

unit trucks. Thus, the trucks with a VIUS1997 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 VIUS1997 average weight of 6,001-10,000 lbs were distributed equally 

among the MOVES weight classes 70, 80, 90 and 100. 

24.3.3 Combination Trucks 

Long- and short-haul combination trucks (source types 61 and 62) did not include any vehicles 

of VIUS1997 Strata 1 or 2. Thus we used the detailed VIUS1997 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 

VIUS2002 contains an estimate of the average weight (vehicle weight plus cargo weight) of 

136
 
 

 

 

 

 

1998-2002 model year vehicle or vehicle/trailer combination as it was most often operated when 

carrying a typical payload during 2002. These estimates were used to determine the MOVES 

weightClassID categories for these trucks. Any vehicles with a zero or missing value for the 

average weight and without a weight classification in the WeightAvgCK field were excluded 

from the analysis for determining the average weight distributions. 

Since there is a smaller number of gasoline trucks among the single unit and refuse trucks, all 

model years (1998-2002) were combined to determine a single weight distribution to use for 

these model years. The VIUS1997 based estimates were retained for light-duty trucks (source 

types 31 and 32) and for all model years prior to 1998. 

In cases where distributions were missing (no survey information), distributions from a nearby 

model year with the same source type was used. Weight distributions for all 2003 and newer 

model years were set to be the same as for the 2002 model year for each source type. 

24.4 Buses 

For intercity buses, we used information from Table II-7 of the FTA 2003 Report to Congress46 

that specified the number of buses in various weight categories. This information is summarized 

in below in Table 24-3. 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 24-3 FTA estimates 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 24-4 1999 bus population comparisons by data source 

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 

APTA85 *** 75,087 

Polk TIP® 460,178 

School Bus Fleet Fact 

Book 

429,086 

Motorcoach Census45** 44,200 

* Includes some church & industrial buses.
 

** Includes Canada.
 

*** Includes trolleybuses.
 

137
 
 

 

 

 

 

 

 

 

 

 

Using the 1999 bus population estimates in Table 24-4, we were able to estimate the fraction of 

all buses that were intercity buses and then to estimate the fraction of intercity buses in each 

weight bin. In particular: 

Estimated number of intercity buses in 2000: 

754,509 * (84,454/(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 

Congress46 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 data86 provided information on number of vehicles by gross 

vehicle weight class and fuel as detailed in Table 24-5. 

Table 24-5 California school bus study weight classes and fuel types 

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 Table 24-6. 

138
 
 

Table 24-6 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 

24.5 Refuse Trucks 

Because the sample of Refuse Trucks in VIUS was small, the weight distributions were 

calculated for model year groups rather than individual model years, shown below in Table 24-7. 

As for other trucks, the WeightClass was determined from the VIUS reported average weight. 

139
 
 

 

Table 24-7 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 

24.6 Motor Homes 

No detailed information was available on average engine size and weight distributions for motor 

homes. We assumed all motor home engines were 5 L or larger. As a surrogate for average 

weight, we used information on gross vehicle weight provided in the Polk TIP® 1999 database 

by model year and mapped the Polk GVW Class to the MOVES weight bins. These values are 

likely to overestimate average weight. The Polk TIP® information did not specify fuel type, so 

we assumed that the heaviest vehicles in the Polk database were diesel-powered and the 

remainder were powered by gasoline. This led to the weight distributions in Table 24-8 and 

Table 24-9. 

140
 
Table 24-8 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 

141
 
Table 24-9 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 

142
 
 

 

 

 

 

 

 

 

 

25 Peer Review of Draft Report 

This section contains comments on the draft report of Population and Activity of On-road 

Vehicles in MOVES2014 from two peer reviewers and EPA’s responses to those comments. The 

reviewers were selected by a third-party contractor, ICF International, facilitating a peer review 

of the MOVES2014 technical reports. The submitted peer review comments are publicly 

available on the EPA Science Inventory database.87 

25.1 Kanok Boriboonsomsin, PhD, PE 

Dr. Boriboonsomsin has been a researcher at the University of California at Riverside since 

2005. He currently holds the position of Assistant Research Engineer at the College of 

Engineering’s Center for Environmental Research and Technology (CE-CERT) and received a 

PhD in Transportation Engineering from University of Mississippi in 2004. Dr. Boriboonsomsin 

previously reviewed the MOVES2010b Population and Activity Report. 

25.1.1 General Comments 

This is a review of the Draft Report on Population and Activity of On-road Vehicles in 

MOVES2014, referred to as the “Fleets Report”, prepared by the EPA Office of Transportation 

and Air Quality. I was also a peer reviewer of the Draft MOVES2009 Highway Vehicle 

Population and Activity Data, which helped me identify and understand changes made to the 

national default values for vehicle population and activity inputs in MOVES2014 during the time 

of this review. 

Overall, the Fleets Report is well written and organized, with sensible use of examples, tables, 

and figures. I appreciate the addition of Section 2 (MOVES Vehicle and Activity 

Classifications), which will help readers understand early on the various ways in which vehicles 

and their activities are classified in the context of MOVES. I find the description of analytical 

methods and procedures to be sufficiently clear with appropriate use of mathematical equations 

to help explain complex calculations such as in Section 9.2 (Heavy-Duty Average Speed 

Distributions) [Now Appendix F: Calculation of Combination Truck Average Speed 

Distributions]. I also appreciate the list of areas for future research in Section 16 (Conclusion and 

Areas for Future Research), which informs research directions for improving the vehicle 

population and activity data inputs in future updates of MOVES. 

In terms of the vehicle population and activity inputs, I find that the national default values in 

MOVES2014 have been appropriately updated by using more recent data from Polk (2011), 

AEO (2014), and TEDB (2013). Perhaps, the most important development in this vehicle 

population and activity update is the use of nationwide GPS dataset to develop average speed 

distributions for light-duty vehicles. This is an exciting time for vehicle activity research due to 

the increasing availability of large-scale, high-resolution instrumented vehicle data from a 

variety of sources. As indicated in the Fleets Report, many of the limitations in the current 

MOVES vehicle activity inputs can be addressed through analysis of such instrumented vehicle 

data. 

143
 
 

 

 

 

 

 

 

25.1.2 Detailed Comments 

Detailed comments and suggestions are provided below. These are made with the understanding 

of the challenges of developing nationally representative default values for MOVES vehicle 

population and activity inputs under the limited resources that the EPA has. 

25.1.2.1 Section 1 – Introduction 

An early explanation of the analysis years considered in this Fleets Report (e.g., 2011 being the 

base year) would be helpful to readers. 

Response: A short paragraph has been added to the Introduction to address the possible analysis 

years and 2011 base year. 

25.1.2.2 Section 2.3 – Regulatory Classes 

The mapping between multiple vehicle classification schemes has always been a challenging 

topic. The introduction of a new regulatory class 40 is well thought out, and the rationale for it is 

well explained. 

Response: Further explanation on regulatory class 40 can be found in the MOVES2014 report 

on emission rates of heavy-duty vehicles.5 

25.1.2.3 Section 2.4 – Fuel Types 

The population of CNG-fueled refuse trucks is growing and emissions test data of these trucks 

are increasingly available. This source type-fuel type combination may be considered for 

modeling in future versions of MOVES. 

Response: Yes, CNG refuse trucks have a rapidly growing market share. More than half of the 

refuse trucks sold today are manufactured to run on natural gas88; however, despite this rapid 

growth, there are about 8,800 natural gas-fueled refuse trucks according to 2014 industry 

estimates88, which constitutes less than 10 percent of the US refuse truck fleet. While developing 

MOVES2014, EPA decided not to include CNG refuse trucks but will consider adding them to 

future versions of MOVES. Report text has been edited to mention CNG refuse trucks. 

25.1.2.4 Section 2.8 – Allowable Vehicle Modeling Combinations 

Tables Table 2-6 and Table 2-7 provide a very good summary of allowable vehicle modeling 

combinations in MOVES2014. 

Where would shuttle buses (e.g., those used to pick up and drop off passengers at airports) fit in 

Table 2-7? 

Response: As discussed in Section 6.1.4, any buses in MOVES not utilized for urban public 

transit or service to and from a school are considered intercity buses. Since airport shuttles 

cannot be classified as either transit or school buses, they fall into the MOVES intercity bus 

source type. 

144
 
 

 

 

 

 

 

 

 

 

 

25.1.2.5 Section 4.1 – Historic Vehicle Miles Traveled (1990 and 1999-2011) 

Does FHWA publish the methodology used to adjust VMT data for 2000-2006? If not, the 

average ratio method used appears reasonable. 

Response: FHWA did not publish their methodology of adjusting the VMT data. The language in 

this section has been edited for clarity. 

25.1.2.6 Section 4.2 – Projected Vehicle Miles Traveled (2012-2050) 

The methods used to project VMT for future years are appropriate. 

25.1.2.7 Section 5.1 – Historic Source Type Populations (1990 and 1999-2011) 

It is described that “the 2000-2010 distributions among source types within the general truck 

categories were linearly interpolated between 1999 and 2011”. However, the 2000-2010 truck 

population distributions in Figures Figure 5-2, Figure 5-3, and Figure 5-4 do not show linear 

trends. Please clarify the linear interpolation that was performed. 

Response: We are not interpolating the populations but the fractions of each source type out of 

its general populations, such as long-haul combination trucks out of total combinations trucks, 

between 1999 and 2011. Those source type fractions were then multiplied by the general MV-1 

populations. Without interpolation of these source type allocations, source type populations 

would not have necessarily followed the MV-1 populations trends from 1999 to 2011, which 

would have implied that we knew more about the source type populations than we actually did. 

We have added clarifying text and Table 5-1 with these 1999-2011 source type population 

fractions to this section. 

25.1.2.8 Section 5.2 – Projected Vehicle Populations (2012-2050) 

The use of VMT growth as a surrogate for vehicle population growth is reasonable per the 

analysis of VMT per vehicle trends shown in [Figure 5-6, previously represented in a table]. 

25.1.2.9 Section 6.2.1 – Fuel Type and Regulatory Class Distributions 

Data on actual fuel type used by E85-capable vehicles are available for 100 vehicles in 

California, which may be used in future updates, 

(http://www.dot.ca.gov/research/researchreports/reports/2015/final_report_task_1919.pdf). 

Response: The use of E85 fuels varies geographically and is an area of great uncertainty and 

change. MOVES default values can be considered as our initial national estimate for E85 use. 

Users should consider using more locally-specific and up-to-date information when running 

MOVES. A more detailed discussion of E85 use in MOVES2014 can be found in the report on the 

default fuel supply.6 

According to AEO2014, hybrid electric and plug-in hybrid electric vehicles are projected to 

grow from 2.2% of total cars and light truck sales in 2011 to 6.1% in 2040. Would they warrant 

their own category with respect to fuel type in future versions of MOVES? 

145
 
 

 

Response: Hybrid electric and plug-in hybrid electric vehicles certainly present some modeling 

challenges in MOVES. We have added text to this section of the report discussing these 

challenges. 

25.1.2.10 Section 8 – VMT Distribution of Source Type by Road Type 

In Table 8-2, it is my personal opinion that some numbers are not intuitive. For example, I would 

think that refuse trucks are operated mostly in urban areas, but they are reported to have about 

the same VMT fraction in rural and urban areas. In another example, combination long-haul 

trucks have roughly the same VMT distribution as combination short-haul trucks although I 

would expect them to have higher VMT fraction on rural restricted access roads. The numbers in 

Table 8-2 are derived from the 2011 NEI V1, which is probably the most appropriate source of 

this type of data at this time. These numbers may be compared with numbers derived from largescale GPS datasets for each source type in the future. 

Response: It is difficult to find data on the activity of refuse trucks on a national basis and many 

states have trouble finding appropriate data for this category. Similarly, it is difficult to 

distinguish long-haul and short-haul trucks in most data sources. We are considering 

simplifying our source type categories in future versions of MOVES. 

25.1.2.11 Section 9.1 – Light-Duty Average Speed Distributions 

It may be of interest to compare some of the average speed distributions derived from TomTom 

dataset with those derived from traffic monitoring systems. For example, California has the 

Freeway Performance Measurement System or PeMS (http://pems.dot.ca.gov/). Average speed 

distributions can be derived using a subset of TomTom data on California freeways and compare 

to those derived from PeMS. This would help understand potential biases, if any, in TomTom 

data. It is understood this will incur additional analyses (and costs) by TomTom as the raw data 

are not provided to ERG and EPA. 

Response: Acquiring a California subset of the TomTom data and comparing it to PeMS data is 

an interesting idea to understand potential inherent bias—oversampling of long-distance trips 

and trips with routes unfamiliar to the driver. In the hope of making national comparisons, EPA 

is actively identifying other datasets like PeMS that represent typical driving in the US to 

compare against TomTom data. 

In Figure 9-1, it is observed that the highest average speed fraction for urban unrestricted access 

road is not in the lowest average speed bin (< 2.5 mph) although one would expect a significant 

amount of idle time at signalized intersections. This may be due to the length of intersection 

segments being much longer than a typical length of traffic queue, which causes the zero speed 

while idling in the queue to be canceled out by relatively higher speeds before joining the queue. 

I am not sure how much the shift in this average speed distribution would impact emission 

inventories at the national scale. If the impact would be significant enough, these intersection 

segments may be divided into shorter segments in future analyses. 

Response: Without the raw TomTom data, one can only speculate about the amount of time these 

GPS-equipped vehicles spent at urban intersections. We will consider how to improve this 

146
 
 

 

 

information in future data collection efforts. You are correct that the road segments used by 

MOVES are much larger than what might be used specifically for intersection modeling. All of 

the low speed driving cycles used in MOVES contain a significant amount of idling time that 

would reflect the time at idle at signalized intersections. 

25.1.2.12 Section 9.2 – Heavy-Duty Average Speed Distributions 

The adjustment made in this section is well done. 

Response: We have moved the details of the heavy-duty average speed adjustment to the 

appendix, and left a summary in the main text. 

25.1.2.13 Section 10.2 – Ramp Activity 

What data were used to estimate operating mode distributions for ramp activity? 

Response: Ramp operating modes in MOVES2014 were not based on data and were carried over 

from previous MOVES versions. The distributions were derived from existing operating mode 

distributions using engineering judgement. EPA has begun to analyze data from second-bysecond measurements of real world vehicle activity to develop new ramp operating mode 

distributions for the next major release of MOVES. 

The ramp fraction may be determined using either PeMS or TomTom data. It is understood that 

the latter will incur additional analyses (and costs) by TomTom as the raw data are not provided 

to ERG and EPA. 

Response: TomTom data was not provided with any geospatial coordinates, which would be 

necessary for identifying ramps, and acquiring this new level of detail would take additional 

analysis and cost beyond the original data purchase. 

25.1.2.14 Section 11.1 – National Default Hotelling Rate 

The assumptions made in this section can be validated using large-scale GPS datasets of 

commercial trucks, for example, the truck GPS dataset maintained by the American 

Transportation Research Institute (ATRI) (http://atri-online.org/2014/10/28/truck-gps-data-fortracking-freight-flows/). 

Response: Many states are also looking into truck hotelling and may be able to provide local 

estimates for truck hotelling hours. 

25.1.2.15 Section 11.2 – Hoteling Activity Distribution 

There are studies that provide data on APU and truck electrification usage that may be 

considered in future updates. For example: 

 Frey, H. C., P.-Y. Kuo, and C. Villa. (2008). Methodology for characterization of long-haul 

truck idling activity under real-world conditions. Transportation Research Part D, 13, 516-

523. 

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 National Renewable Energy Laboratory (NREL)’s Truck Stop Electrification Testing 

(http://www.nrel.gov/transportation/fleettest_truck_stop_electrification.html). 

Response: The current MOVES design does not currently allow for the application of countyby-county control programs that affect the amount of hotelling hours for national scale 

modeling. However, users may include specific hotelling hour estimates in the HotellingHours 

table in their county databases (CDBs). County specific CDBs are normally used for most 

official inventory estimates (such as the National Emission Inventory (NEI) and EPA rules, 

such as the recent light-duty vehicle Tier 3 rule), so providing appropriate hotelling hour 

estimates is done on a case-by-case basis using the available information at the time of the 

MOVES runs. EPA has used data provided by states during the NEI process from sources, such 

as you cite, to keep the CDBs we use and the inventory estimates generated up-to-date and 

accurate. 

25.1.2.16 Section 12 – Temporal Distributions 

Temporal distributions of VMT rely heavily on the 1996 OHIM report. Traffic monitoring 

systems, such as PeMS, may be considered for use as a source of more recent data, especially 

for restricted access roads. Note that in the case of PeMS, VMT are estimated separately for 

cars and trucks, which can be used to represent light-duty source types and heavy-duty source 

types, respectively. 

Response: EPA has begun analyzing data from instrumented vehicles as a source for much of 

the activity information used by MOVES, including temporal distributions, and would like to 

use this new data in future versions of MOVES. We also plan to investigate data from traffic 

monitoring systems, such as PeMS. 

25.1.2.17 Section 12.1 – VMT Distribution by Month of the Year 

Container volumes at ports around the US may be considered for use as a surrogate of VMT 

distribution by month of year for short-haul and long-haul combination trucks. For example, 

https://www.portoflosangeles.org/maritime/stats.asp. 

Response: This is a helpful suggestion, but we think instrumented vehicle data and traffic 

monitoring systems will likely be better sources for national average VMT temporal 

distributions. We intend to investigate these for a future version of MOVES. 

25.1.2.18 Section 12.2 – VMT Distribution by Type of Day 

Data from traffic monitoring systems may be used to estimate DayVMTFraction for each 

month. 

Response: States may and often provide their own data for modeling their areas, so updating 

the national average default distributions has not been a high priority. However, as traffic 

monitoring and instrumented vehicle data have become more available, we hope to make 

updates in the future. 

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25.1.2.19 Section 12.4 – Engine Starts and Parking 

More recent instrumented vehicle data are available on NREL’s Transportation Secure Data 

Center website (http://www.nrel.gov/transportation/secure_transportation_data.html) for 

passenger vehicles and on NREL’s Fleet DNA website 

(http://www.nrel.gov/transportation/fleettest_fleet_dna.html) for commercial vehicles. 

Response: EPA has gained access to the NREL FleetDNA dataset but has not yet determined 

its applicability for MOVES. EPA is also currently analyzing start and soak information for 

light-duty vehicles using a large telematics dataset. 

25.1.2.20 Section 12.5 – Hourly Hotelling Activity 

In future updates, the hourly hotelling activity may be estimated from large-scale GPS datasets 

of long-haul trucks such as ATRI’s. 

Response: We agree that data from instrumented vehicles will likely provide better estimates of 

truck hotelling activity than survey or self-reported data. EPA is exploring a variety of heavyduty vehicle activity datasets, including streams collected from GPS and/or OBD devices, to 

update MOVES default hotelling information such as hourly rates. 

25.1.2.21 Section 14.2 – Road Load Coefficients 

The road load coefficients for light-duty vehicles were set to remain constant over time despite 

the Light-Duty Greenhouse Gas Rule (because the improvements in these coefficients have 

already been incorporated into the energy and emission rates). However, the road load 

coefficients for 2014 and later model year heavy-duty vehicles were updated in light of the 

2014 Medium and Heavy-Duty Greenhouse Gas Rule. Shouldn’t the impact of the 2014 Rule 

be expected to reflect in [future energy and emission rates]? 

Response: Energy rates have been updated to reflect engine efficiency improvements set out in 

the 2014 Heavy-Duty Greenhouse Gas Rule, as described in the MOVES2014 technical report 

documenting the heavy-duty vehicle emission rates.

5 Please refer to Chapter 5 of the 2014 HD 

GHG Rule’s Regulatory Impact Analysis (RIA) for further information on modeling emissions 

from heavy-duty vehicles in future years.80 

25.1.2.22 Section 16 – Conclusion and Areas for Future Research 

The national default values for vehicle population and activity inputs in MOVES2014 were 

developed for the base year of 2011. It may be of interest to validate the 2012-2014 projections 

for some of these inputs with actual data that are available for those years. This will allow the 

assumptions made in the projections to be adjusted if necessary. 

Response: In the past EPA has validated MOVES default activity estimates according to fuel 

volumes reported through tax receipt data from FHWA.83 We hope to do a similar analysis for 

MOVES2014. 

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25.2 Randall Guensler, PhD 

Dr. Guensler is a Professor at Georgia Institute of Technology’s School of Civil and 

Environmental Engineering. He has served as chairmen of the Transportation Research Board 

Committee on Transportation and Air Quality (1997-2002) and as an advisor on EPA’s Mobile 

Sources Technical Review Subcommittee of the Clean Air Act Advisory Committee (1995-

2001). Dr. Guensler holds a PhD in Civil Engineering from the University of California at Davis. 

25.2.1 General Comments 

Thank you for the opportunity to participate in the peer review of the USEPA’s Population and 

Activity of On-road Vehicles in MOVES2014 Documentation. I have provided suggested edits 

using revision marks and comments in the margins of the Word document. At various points in 

the paper, I have suggested edits to move text explaining tables so that the text appears before the 

table is presented. There are a number of sections in the document that I suggest be summarized 

in a single paragraph, shipping the detailed text off to an Appendix, to improve readability. 

25.2.2 Detailed Comments 

The most important issues that I believe could be addressed in the document are summarized 

below. 

25.2.2.1 Section 1 – Introduction 

Somewhere up front in this paper a very brief overview of emissions sources and modeling goals 

should be added. How MOVES works, in a nutshell, and what data are needed to run MOVES. 

This can also differentiate between baseline emissions by source type and correction factors. VSP 

can be addressed here as well as internal driving cycles. Then, the document can refer back to the 

general discussion when needed. 

Response: We have updated the introduction to provide better context, to make the function of this 

report more clear, and to point users to other documents that address many of these questions. 

A big picture issue throughout the entire document is to set the stage for the reader as to why they 

should be using local-specific or regional-specific data. This is a common theme throughout my 

comments. 

Response: This report documents how EPA developed national-scale default data related to fleet 

characteristics and travel behavior. As explained in the introduction, information on customizing 

MOVES with local inputs is detailed in the MOVES2014 Technical Guidance.3 

25.2.2.2 Section 2 – MOVES Vehicle and Activity Classifications 

The MOVES Vehicle and Activity Classification section really needs an overview designed to 

introduce the reader to the content of the Chapter. This overview can help the reader understand 

that the emission rates need to be properly linked to the concepts of vehicle classes, vehicle 

source types, regulatory classes, etc. 

Response: We have added a paragraph to the beginning of this section describing the purpose of 

the section and the need to link emission rates to vehicle activity. 

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The paper could probably use a paragraph or two associated with the difficulty in mapping 

FHWA vehicle classes and EPA vehicle classes. Papers by Yoon (2006) and Liu (2015) offer 

some insight into these issues. Yoon discusses these in the context of visual classes for 

observational data, although that paper would need to be updated. Providing this in an Appendix 

might prove helpful to users. This applies in [Section] 3 as well. 

Response: We have added information in Section 2.2 and Section 6 that discussing the 

difficulties of vehicle classification for MOVES modeling and have added citations to the papers 

referenced. 

There is a problem with MOVES implementation at a higher level that, if resolved, would 

significantly improve modeling efforts. As outlined [in Section 2.2] and elsewhere, it is important 

to structure MOVES for users to enter mutually exclusive technology groups that can be derived 

from license plate observational data. Anything that can be added to the documentation to help 

users better classify their vehicle input based upon field observations will be appreciated by users. 

Comment 8 also suggests the development of a table to instruct users. 

Response: Source types cannot be fully resolved using field observations. Text has been added 

to Section 2.2 describing our use of a combination of field measurements along with survey and 

registration data. Guidance for users developing local data is provided in the Technical 

Guidance.3 

Comment 8: Another table here showing the mutually exclusive lines for source type, regulatory 

class, and fuel type might be helpful. 

Response: Section 2.8 describes the mutually exclusive combinations of source types, fuel types, 

and regulatory classes that can be modeled in MOVES2014. 

I suggest adding a new section to introduce the use of model year distributions. 

Response: An introduction to model year groups has been added, see Section 2.7 

There are a number of detailed explanations that probably belong in Appendices rather than in the 

text to improve readability (and initial clarity). 

Response: Both before and after this peer review, we have moved certain detailed explanations 

where appropriate to appendices for improved readability and clarity. In this final report, we 

have created appendices on SCC mappings between MOVES versions, forecast and backcast 

algorithms for age distributions, average speed distributions of combination trucks, and 

responses to peer review comments regarding this report. 

The SCC classes are another big picture issue with MOVES, in that these contribute to the 

mutually exclusive technology groups. The concept is complex and needs to be explained better 

in the text. I suggest the addition of a table for clarity. 

Response: The new SCCs do not add another grouping, but just clearly label the specific 

combination of technologies. We have changed Section 2.6 to improve the description of the 

SCC classes. A table has been added to Section 21 (Appendix E: SCC Mappings) to compare the 

MOVES regulatory classes to the old SCC categories to help map between them. 

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The audience needs a connection between SCC and regulatory class in Section 2.6. At the same 

time, Table 2-5 loses the audience due to complexity. An overview paragraph would help here. 

This is one of the most complicated sections and general improvements would help the audience. 

Specific comments are provided in the document markup. 

Response: The new SCCs are not based on regulatory class. The complexity comes when trying 

to use regulatory class to map back to the old SCCs. We have revised this section and Table 2-5 

to make the discussion clearer. 

Table 2-8 appears to be the key table for the entire chapter. If the text is rewritten, I would 

suggest pointing all of the explanations and discussions so that they result in the reader reaching 

the table with full understanding of the content of that table. A paragraph is needed after Table 

2-8 to let the reader know that everything they do from here on out is to generate the data that will 

be used by the 80 groups represented in this table. 

Response: We do not consider Table 2-8 to be of primary importance. We have revised the entire 

section to make it clearer that the aim of this section is to define MOVES terminology and that 

later sections talk about how we actually estimate populations and activity for the different 

categories. Table 2-8 simply identifies the vehicle technology combinations for which we have 

emissions data. 

Table 2-9 [now Table 1-1] is excellent and can be used to organize the presentation of materials 

before and after. Listing in order of use in the document, rather than alpha order, will help the 

structure. 

Response: We have moved this entire section, including the table to the introduction to help 

provide context for all of the paper. Listing the MOVES table names in Table 1-1 alphabetically 

makes it more useful for reference purposes. The table of contents will assist the reader in 

understanding the order of how topics will appear in the report. 

25.2.2.3 Section 3 – Data Sources 

Data sources introduction should be expanded significantly to inform the reader about what they 

need for modeling. Given the sensitivity and capabilities of MOVES, A goal here should be to 

shift users to locally-sourced data rather than national defaults. 

Response: The data sources described in Section 3 were used to develop national default values 

of activity-related inputs found in the MOVES2014 database. Users are encouraged to 

incorporate locally-source data for project- or county-scale modeling where possible. As 

indicated in the report’s introduction, there is separate technical guidance that provides details 

on developing locally-sourced inputs for project- and county-scale modeling.

3 We have now 

repeated that information at the beginning of Section 3. 

25.2.2.4 Section 4 – VMT by Calendar Year and Vehicle Type 

As indicated in Comment 34, buses and HD Trucks experience different growth rates. A 

separate data source should be found for the next set of updates. At the very least, local data 

should be recommended for buses of all types (these data can be obtained from transit agencies). 

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Comment 34: This seems a bit shaky. Bus activity growth will not parallel HDV truck growth. 

There are completely different causal factors in play. Single unit and large trucks are also likely 

to grow at different rates. Not sure I have a reasonable alternative to propose though. At least the 

uncertainty should be acknowledged here. 

Response: Lacking better information, it is unclear how VMT growth in buses will relate to 

growth in other heavy-duty vehicle types. We agree that the uncertainty of assuming bus growth 

follows heavy-duty freight growth should be acknowledged and have added text to that effect. 

25.2.2.5 Section 5 – Vehicle Populations by Calendar Year 

Changes in vehicle ownership and mileage accrual rates are generally different. These sources 

can be obtained from registration databases coupled with I/M programs. This would be a 

worthwhile small study to sponsor. 

Response: MOVES does not currently use vehicle registration databases from I/M areas for 

relative MARs or survival rates of light-duty vehicles. Only using data from a small sample of 

selected areas may generate issues of representativeness, but we are interested in reassessing the 

viability of registration data from I/M areas in the future. 

25.2.2.6 Section 6 – Fleet Characteristics 

The materials presented [in reference to modeling flexible fuel vehicles running on either 

gasoline or E85 in Section 6.2.1] (Comment 41) are very confusing for the reader and serve to 

reinforce the need for users to obtain their own regional/local input data. The discussion can be 

simplified for clarity or expanded with detail for clarity. 

Comment 41: This whole paragraph [discussing flexible fuel vehicles] is confusing (and 

reinforces the need to allow the user to provide direct inputs rather than relying on the internal 

algorithms for assignment), as noted earlier. If this paragraph remains, it should either be 

expanded to provide the exact details of the internal method, or reduced to avoid confusion. 

Response: The text has been revised to add a footnote that more clearly explain that information 

on MOVES vehicle fuel consumption comes from two separate fractions. The fuel type fraction 

delineates whether the vehicle is capable of running on single or multiple fuels and the fuel 

usage fraction describes how much of the total fuel consumed is a specific fuel, particularly for 

light-duty flexible fuel vehicles that are using E85 or gasoline. As noted in the text, discussion on 

fuel usage is in a separate technical report on the MOVES2014 fuel supply.6 Given the spatial 

variation of E85 use, user-supplied data would be preferable over national defaults for localized 

flexible fuel vehicle modeling. 

Comment 48 identifies an internal problem in MOVES that causes problems for users in matching 

local fleet composition. 

Comment 48: This is the problem [with the SampleVehiclePopulation table] identified earlier 

associated with single fixed assignments inside MOVES. The user cannot control these 

allocations later in the process. See the discussion in Liu et al., 2015. 

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Response: While the AVFT importer is designed to make it easy for users to change fuel type 

fractions by model year, we thought it unlikely that many users would have information on 

regulatory class distributions, so we did not create an importer for this data. 

25.2.2.7 Section 7 – Vehicle Characteristics that Vary by Age 

The discussion on survival modeling could be significantly improved (see comments) and 

caveats should be added. A number of comments are also provided on model year distribution 

values, especially for the oldest vehicle groups. Plus, the detailed text in this section would fit 

better as an appendix. A focused peer review of this section is probably warranted (see 

comments). 

Comment 55: This is a fairly weak justification.... If a method is applied later, stipulate the 

method and basis here. 

Response: To thoroughly model scrappage in MOVES, we need projections of vehicle retirement 

by source type and age for every calendar year 2012+. This is the justification for using base 

survival rates from TEDB and scaling them as necessary based on sales and VMT projections. 

The text here has been amended to include a more detailed justification. 

Comment 56: As noted below, a reader needs to see plots over time here to assess impact of 

assumptions. This can be done by overlaying future fleets by calendar year. The 30+ group 

should be growing slightly, or remaining stable, rather than shrinking over time, as folks hold 

onto vehicles longer. These are 1985 and older vehicles today. Vehicles in today’s fleet are more 

durable. Need to reassure users that the failure rate assumption is reasonable with an independent 

confirmation. 

Response: MOVES2014 has generic survival curves by vehicle classifications independent of 

model year. With this limited data, we chose to scale these generic survival curves to ensure that 

changes in population would be evident over time. We do not have enough data to adequately 

assess how 30+ year old vehicle populations are changing over time, but this is a topic of 

interest for EPA. We have added a figure towards the end of Section 20.1 (in Appendix D: 

Detailed Derivation of Age Distributions) with overlaying future fleets. Due to our assumptions 

in the national case, most source types do not see an uptick in 30+ vehicles (combination trucks 

being the exception). However, as noted in the paragraph regarding the user tool, projected 

local age distributions retain their 30+ population fractions. 

Comment 58: Need to be careful here. Differential retirements in model year groups could 

represent technology durability/acceptability issues. Need to double-check prior to discounting 

sources. Caveat whole paragraph by reassuring the audience that you did the best you could and 

that users can specify their own future fleets and ignore the retirement rates. 

Response: We agree that double-checking is necessary before discounting sources, which is why 

5-year survival rates derived from VIUS 1992 and 1997 were used to justify our choice. The 

paragraph is also caveated with the acknowledgement of how limited the data are. We provide 

user guidance in separate documents. 

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Comment 60: Showing a survival curve is a good idea. Survival curves can be developed 

separately for various technology groups if desired. 

Response: Vehicle survival rates by age and HPMS class are shown in Table 7-2. 

Comment 61: I suggest moving all of the text from this point down to the end of the section into 

an Appendix. It is not needed here and adds to reader confusion for something that is rarely 

needed by a user. The bottom line is that you have made adjustments to the rates to try to help 

the predictions match the data without adjusting rate parameters. You can say that here and refer 

the user to an appendix. 

Response: We agree and have moved the detailed description of implementing the algorithm to 

Appendix D (Section 20). 

Comment 62: Based on this text, it looks like the 0.3 value has been discarded, which is probably 

a good thing. This needs to be clarified and applicable text in this section corrected as needed. 

Response: In the Age Distribution Projection Tool, which users are encouraged to use to project 

local age distributions into the future, this is correct: the 0.3 value has been discarded. However, 

this is only true for the tool; the national analysis retains this assumption. 

Comment 63: It is not clear why [for] 1999-2010 this needs to be modeled with a survivor model 

at all, rather than simply interpolated between your 1990 and 2011 data sets, using sales figures 

for control given that there are no better data in between). Given that survival rates are so 

different across the country (e.g. New England vs. Arizona), and by technology as it entered the 

fleet, I’m not convinced that the detailed approach is warranted. 

Response: Our chosen approach provides a consistent method for generating age distributions 

across all analysis years in MOVES. Again, our user guidance encourages the use of local age 

distributions for regional or state-level analysis. 

Comment 64: Given the 140 pages to review, there is not enough time to perform a full technical 

analysis of Section 7.1. I would suggest that the equations be sent out for a separate and focused 

peer review... Finally, the users need to be reassured that they can specify the composition of 

the future fleet off model using their own sales and survival functions. 

Response: Thank you for the suggestion. Work on vehicle sales growth and retirement rates was 

presented as a poster at the 25th Coordinating Research Council (CRC) Real World Emissions 

Workshop in March 2015. EPA has discussed the possibility of publishing this research in a 

peer-reviewed journal sometime in the future. Our guidance documents allow users to employ 

different methodologies to generate age distributions. 

Mileage accrual for the older vehicles is also a potential issue (see comments). 

Comment 70: You have confounding effects from the recession in Section 7.2. The traffic 

volumes on freeways declined significantly during that period, but have been on the rise. MARs 

warrant a double check with post-2009 data. Given the vehicle purchase delays, the 

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accumulation rates will likely vary even more by model year cluster. I don’t have a better 

answer, but I question the stability assumption. 

Response: We agree with the assertion that the 2001 and 2009 data are similar does not address 

the issue of whether the 2009 data is anomalous in regards to a trend in mileage accumulation. 

However, without a full analysis of the trend, we are left with the choice of using the 2001 data 

or the 2009 data. Text has been added to the explanation of our choice to continue to use the 

2001 data—since it was unaffected by the 2008-2009 recession—in the report. 

Comment 72: These are fairly significant assumptions that cannot be verified from the 

information provided. Older vehicles are relegated to different service activities, so these are 

important assumptions to verify, especially given the age of the 1992 TIUS data. 

Response: Information about the oldest model years in the fleet are scarce, and due to the 

smaller number of vehicles, any data will have significant variability. It will be difficult to reduce 

the uncertainty in the mileage accumulation rates for the oldest vehicles without significant 

effort. Without clear information that the activity of these older vehicles has changed 

dramatically in recent years, updating this information will have a low priority. 

The Single unit long-haul truck distribution in Figure 7-1 is so different than the other curves that 

it warrants a detailed explanation. 

Response: All the age distributions for single unit trucks were constructed primarily from 

national registration data, but there was a particular small sample of long-haul trucks in 

VIUS2002, especially in more current years. Due to this small sample size, the long-haul single 

unit age distribution is probably affected greatly by minor changes in population. 

The Cubic Regression approach [in Section 7.2.2] is not clearly defined. 

Response: The text of Section 7.2.2 was updated to state clearly that the regression used was a 

simple cubic fit and not, for example, a spline fit. Given the very good fit reported by NHTSA 

and the use of the regression in their model, we did not see a need to reconsider this choice. 

Table 7-3 is good. Similar tables should be provided for other classes. 

Response: We have added a table to the report with the raw mileage accumulation rates for 

passenger cars and light-duty trucks copied from the NHTSA reference document. 

I could not replicate the data in Table 7-6. Please see comments. 

Response: We have updated the text explaining how the data was generated to include more 

details. The updated method will produce the reported statistics. 

25.2.2.8 Section 8 – Average Speed Distributions 

I have some expertise in the availability and resolution of TomTom data. The use of these data as 

outlined in the document appears problematic. Comments are provided throughout Section 9.1 

and 9.2I cannot recommend the use of these data in this fashion. I recommend that additional 

research in this area be undertaken. 

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Comment 79: You have nailed most of the potential biases in the bullets [in Section 9.1]. This 

was enough to keep us from using these data for our research efforts. There are significant 

vehicle class, lane choice, operating condition, and geographic biases that likely result. Given the 

tremendous sensitivity of MOVES to the selected duty cycle, I am not inclined to recommend the 

use of the derived average speed for hours or selection of driving cycle weightings without much 

more information to evaluate this effort and comparative studies with other data sources. 

Response: We agree that there are limitations to this dataset, which is why we listed the biases in 

the report. However, using the default nationally representative average speed distribution from 

TomTom GPS data is a substantial improvement over the previous default average speed 

distributions in MOVES2010. We added a sentence in the introduction paragraph of Section 9 

that discusses the state of the default average speed distribution in MOVES2010, and a citation 

to a MOVES FACA work group presentation that included comparisons of the updated the 

TomTom average speed distributions with the MOVES2010 average speed distributions. The 

MOVES2010 average speed distributions for urban areas were based on average speed 

estimates from a survey of urban travel demand models, and the rural speeds were based on 

chase-car studies conducted in California. Changing the average speed distribution does not 

always have a large impact on the vehicle emissions. We have added text in Section 9.2 that 

discusses cases in which MOVES is not strongly sensitive to changes in the average speed 

distributions. 

Comment 80: This [assumption of MOVES average speed distributions based on the average 

speed in each roadway segment, not second-by-second speed measurements] is even more 

troubling, because it indicates that the most disaggregate TomTom data were not employed. 

Response: The average speed distribution is intended to be the average distribution of the 

average link speeds within a modeling domain. It is not intended to be the average speed 

distribution of a collection of second-by-second speeds. 

Comment 81: I cannot recommend this [TomTom average speed analysis] or use of the results. 

Another independent data source is needed to verify these results. Naturalistic driving data 

[SHRP 2 Naturalistic Driving Study] or ATRI [truck GPS data from the American 

Transportation Research Institute] data. 

Response: We did not have another comparable telematics or data set available to compare the 

results to the TomTom dataset. We agree this would be a valuable comparison going forward. As 

we mention in the Introduction and the introductory paragraph of Section 3, local users have the 

option to (and should wherever possible) replace the default activity inputs, including average 

speed distributions, with their own local data. Please also see the comments above regarding the 

sensitivity of average speed distributions on results. 

Comment 82: A consistent method at the national level can have a significant bias and still be 

useful, as long as the bias is consistent over time. That is, you can look at percentage changes 

over time and even if the magnitude of the predicted value is consistently off by 20%, the results 

are useful. The problem here [with using TomTom data to represent national default average 

speed distribution in MOVES] is that regional agencies will likely use the same distributions in 

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county or regional EI development. I would suggest that the guidance here inform regional and 

project level users that they need to develop their own speed distributions. 

Response: The intention of this report is to document the default population and activity in 

MOVES. Recommended modeling approaches for regional and project level users is contained 

in the technical guidance documents.3 For example, in the development of SIP and regional 

transportation conformity analysis, a local speed distribution is a necessary input. 

Comment 83: Trucks operate in the two right-hand lanes. Field studies clearly show that the 

speed distributions in these lanes are very different than inside lanes, and trucks speed 

distributions can also differ in these lanes. A more appropriate data source is the ATA data set 

collected from trucks. I do not recommend “adjusting” the TomTom data for use here. You also 

do not need to show all of the equations below to tell the audience that you manually adjusted the 

values by [tell the audience in one paragraph of text]. You would be better off just showing the 

initial and shifted results in a comparative table. Providing all of these equations is an oversell of 

the quality of the data and the assumptions made. These equations can be moved to an Appendix 

if you decide move forward with this method. 

Response: We have added text to Section 9.2 that points out that it would be preferable to have 

heavy-duty specific data on heavy-duty trucks, including combination trucks. We also state that 

we will prioritize updating the heavy-duty specific average speed data in the future. We have 

also added information regarding the energy and emissions sensitivity of the average speed 

adjustment on combination truck speeds. In addition, we have moved the equations and detailed 

discussion to an appendix. 

25.2.2.9 Section 10 – Driving Schedules and Ramps 

The use of the driving cycle weighting is an issue in MOVES (see comment 92 and 93). Use of 

local driving cycles is preferable when such data are available. 

Comment 92: All of this assumes that the driving cycles are representative of these average 

speed cutpoints. I agree that the approach is probably better than the previous approach of using 

a “closed” cycle, but no compelling argument has been made that the weighting of the cycles 

employed in the latest algorithms matches real world composite driving for a facility. Some of 

the cycles were generated to make sure that we have adequate emission rate data for the model 

bins, not necessarily to be representative of onroad operations. [Tis] is not as big a deal at the 

national level (provided that all analyses backcast emissions for previous years and do not mix 

these outputs with the results of previous analyses that employed MOVES2010). However, there 

is no compelling reason to advocate that this default approach be used in regional or local 

analyses without corroboration. 

Response: The driving schedules used by MOVES are derived from real world driving behavior 

rather than emission certification cycles, and while it is difficult to assure they are representative 

of all driving in the associated bin, we feel they are reasonable estimates for national defaults. 

In addition, MOVES is specifically designed to accept the use of local driving schedules and 

operating modes when this type of data is available, and EPA guidance explains that this type of 

data is preferred, especially for project-level analysis.

89 

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Comment 93: The creep cycle was designed to assess emission rates for high inertial load lug 

operations required to get freight loads moving at low speeds (in freight yards as I recall). 

Matching this by average speed bin, based upon TomTom data, and weighting that bin may be a 

huge stretch and may even overstate emissions. Unfortunately, the only way to assess whether 

the method is viable is to do verification data collection, probably by extensive video analysis. 

Response: Trucks driving at extremely low speeds (similar to the average speed of the creep 

cycle) will experience inertial load lug operations similar to those occurring in the creep cycle, 

due to stop-and-go operation. At the national and regional level, most truck operation will occur 

at speeds much higher than the creep cycle, so that any differences between the creep cycle and 

actual low-speed truck behavior will have little effect on overall emission estimates for trucks. In 

cases where the focus of the analysis is specifically on low speed operation, EPA recommends 

using project-level analysis with user supplied driving schedules. 

It is not clear to users how they should handle activity on weaving and exit lanes. Comments [97-

99] address this issue. 

Comment 97: It is unclear whether the schedule includes any activity on weaving lanes (lanes that 

run between an entry ramp and the next exit ramp when ramps are close together). My assumption 

has always been (based upon Sierra Research presentation years ago) that weaving areas upstream 

of ramps were part of the freeway activity (and freeway driving cycles) and that ramps began at 

the gore area. Is there any way to confirm this and state it in the text? 

Response: The text in Section 10.2 has been updated to make is clear that the total operation of 

vehicles on restricted roads (freeways) was divided so that ramp activity could be separated. 

Activity that occurs on the freeway in anticipation of ramps or occurring after entry is included in 

the non-ramp freeway. 

Comment 98: It would be helpful to establish how these distributions were developed. A clear 

definition of start and end of ramp is warranted for user application. Perhaps some diagrams 

would support this. As I recall, the ramp cycles used car following data collected from gore area 

to the arterial and vice-versa, including any off-freeway weaving areas. It may be important to let 

the reader know that the HCM “area of influence” (about 450m upstream and downstream of the 

ramp) is not included in ramp activity but in freeway activity. 

Response: The ramp operating mode distributions used in MOVES2014 are not based on data 

collected from ramp activity. The existing set of operating modes for ramps were selected to 

represent the different average speed bins. Updating the handling of ramps related to freeway 

driving is a high priority for future versions of MOVES. 

25.2.2.10 Section 12 – Temporal Distributions 

Section 12.3 provides defaults for temporal distributions. Again, local data are preferred given 

the variability noted across urban areas. 

Response: EPA guidance generally advocates using local information in preference to MOVES 

defaults in almost all cases. MOVES specifically includes an importer for user inputs for 

temporal allocations in the County Data Manager and Project Data Manager to make it easier 

for users to provide their own data. 

159
 
 

 

 

 

26 References 

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26 Koupal, J., T. DeFries, C. Palacios, S. Fincher and D. Preusse (2014). Motor Vehicle Emissions 

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27 Yoon, S., Georgia Institute of Technology, A New Heavy-Duty Vehicle Visual Classification and 

Activity Estimation Method for Regional Mobile Source Emissions Modeling (student 

161
 
 

 

 

thesis), Atlanta, GA: August 2005, 

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41 Bryan, M., Recreational Vehicle Industry Association (personal communication), October 19, 2009, 

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42 US EPA, Use of Data from “Development of Emission Rates for the MOVES Model,” Sierra Research,
 

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43 National Highway Traffic and Safety Administration (NHTSA), "Vehicle Survivability and Travel
 

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44 The Age Distribution Projection Tool for MOVES2014 is a macro-enabled spreadsheet and is available 

for download at http://www.epa.gov/oms/models/moves/tools.htm#fleet-2014.
 

45 American Bus Association, Motorcoach Census 2000, conducted by R. L. Banks and Associates, Inc., 

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46 Browning, L., Chan, M., Coleman, D., and Pera, C., ARCADIS Geraghty & Miller, Inc., Update of
 

Fleet Characterization Data for Use in MOBILE6: Final Report, EPA420-P-98-016, Mountain View, 

CA: 11 May 1998, http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1001ZUK.PDF.
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48 US EPA, “The 2011 National Emission Inventory (NEI),” Technology Transfer Network:
 

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50 Data from EPA’s 2011 NEI Version 1 for source type VMT fractions by road type can be downloaded 

directly at ftp://ftp.epa.gov/EmisInventory/2011/doc from the zip file labeled,
 

“2011nei_supdata_or_VMT.zip,” last updated 21 August 2013.
 

51 US EPA, MOVES2010 Highway Vehicle Population and Activity Data, EPA-420-R-10-026, Ann 

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52 Brzezinski, D., Updates to MOVES Vehicle Activity, FACA MOVES Review Workgroup, 9 July 2013, 

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54 Koupal, J., T. DeFries, C. Palacios, S. Fincher and D. Preusse (2014). Motor Vehicle Emissions
 

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55 Sierra Research, Inc., Development of Speed Correction Cycles, M6.SPD.001, EPA 68-C4-0056, 

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59 Clark, N. and Gautam, M., West Virginia Research Corporation, Heavy-Duty Vehicle Chassis 

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71 Lutsey, N., Brodrick, C., Sperling, D., and Oglesby, C., "Heavy-Duty Truck Idling Characteristics: 

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165
 
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166