# Population and **Activity of On -**  **ro ad** 

**Ve hicles in MOVES2014** 

June 201 5



## U nited S tates Environm

ental Protection Agency Of fice of Tr ansportation and Air Quality Assessm ent and Standards Division12



## Table of Contents 1. Introduction
................................ ................................ ................................ ............................. 4 2. MOVES Vehicle and Activity Classifications
................................ ................................ ........ 6 2.1. HPMS Class
................................ ................................ ................................ .................... 6 2.2. Source Use T yp es ................................ ................................ ................................ ........... 6 2.3. Regulatory

Classes
................................ ................................ ................................ .......... 7 2.4. Fuel T ypes ................................ ................................ ................................ ....................... 8 2.5. Road Ty

pes ................................ ................................ ................................ ..................... 9 2.6. Source Classification Codes (SCC)
................................ ................................ .............. 10 2.7. Source Bins
................................ ................................ ................................ ................... 11 2.8. Allowable Vehicle Modeling Combinations
................................ ................................ 12 2.9. Default Inputs and Fleet and Activity Generators
................................ ........................ 14 3. Data Sources
................................ ................................ ................................ ......................... 18 3.1. VIUS
................................ ................................ ................................ ............................. 18 3.2. Polk NVPP® and TIP®
................................ ................................ ................................ 18 3.3. EPA Sample Vehicle Counts
................................ ................................ ........................ 18 3.4. FHWA Highway Statistics
................................ ................................ ............................ 18 3.5. FTA National Transit Database
................................ ................................ .................... 19 3.6. School Bus Fleet Fact Book
................................ ................................ .......................... 19 3.7. MOBILE6
................................ ................................ ................................ ..................... 19 3.8. Annual Energy Outlook & National Energy Modeling Sy stem ................................ .... 20 3.9. Transportation Energy Data B ook
................................ ................................ ................ 20 3.10. FHWA Weigh
- in
- Motion
................................ ................................ .............................. 20 3.11. Motorcycle Industry Counc il Statistical Annual
................................ ........................... 20 4. VMT by

Calendar Year and Vehicle Ty pe ................................ ................................ ........... 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 Ty pe Populations (1990 and 1999
- 2011)
................................ ............ 25 5.2. Projected Vehicle Populations (2012
- 2050)
................................ ................................ . 31 6. Fleet Characteristics
................................ ................................ ................................ .............. 33 6.1. Source T ype Definiti ons ................................ ................................ ............................... 33 6.2. Sample Vehicle Population
................................ ................................ ........................... 36 7. Vehicle Characteristics that Vary by Age
................................ ................................ ............. 50 7.1. Age Distributions
................................ ................................ ................................ .......... 50 7.2. Relative Mileage A ccumulation Rate
................................ ................................ ........... 61 8. VMT Distribution of Source Ty pe by Road Ty pe ................................ ............................... 66 9. Average Speed Distributions
................................ ................................ ................................ 68 9.1. Li ght
- Dut y Average Speed Distributions
................................ ................................ ..... 68 9.2. Heav y- Duty

Average Speed Distributions
................................ ................................ .... 72 10. Driving Schedules and Ramps
................................ ................................ .......................... 77 10.1. Driving Schedules
................................ ................................ ................................ ......... 77 10.2. Ramp Activity
................................ ................................ ................................ ............... 82 11. Hotelling Activity ................................ ................................ ................................ ............. 83 11.1. National Default Hotelling Rate
................................ ................................ ................... 833

11.2. Hotelling Activity Distribution
................................ ................................ ..................... 85 12. Temporal Distributions
................................ ................................ ................................ ..... 87 12.1. VMT Distribution by Month of the Year
................................ ................................ ...... 88 12.2. VMT Distribution by Type of Day
................................ ................................ ............... 89 12.3. VMT Distribution by Hour of the Day
................................ ................................ ......... 90 12.4. Engine Starts and Parking
................................ ................................ ............................. 91 12.5. Hourly

Hotelling Activity
................................ ................................ ............................. 94 12.6. Single and Multiday Diurnals
................................ ................................ ....................... 97 13. Geographical Allocation of Activity
................................ ................................ ................. 98 13.1. Source Hours Operating Allocation to Zones
................................ ............................... 98 13.2. Engine Start Allocations to Zones
................................ ................................ ................ 99 13.3. Parking Hours Allocat ion to Zones
................................ ................................ ............. 100 14. Vehicle Mass and Road Load Coefficients
................................ ................................ ..... 101 14. 1. Source Mass and Fixed Mass Factor
................................ ................................ .......... 102 14.2. Road L oad Coefficients
................................ ................................ .............................. 103 15. Air Conditioning Activity Inputs ................................ ................................ .................... 106 15.1. ACPenetrationFraction
................................ ................................ ............................... 106 15.2. FunctioningACFraction
................................ ................................ .............................. 107 15.3. ACActivityTerms
................................ ................................ ................................ ........ 108 16. Conclusion and Areas for Future Research
................................ ................................ .... 1 10 17. Appendix A: Projected Source Ty pe Populations by

Year
................................ ............. 112 18. Appendix B: Fuel Ty pe and Regulatory

Class Fractions for 1960
- 1981
........................ 114 19. Appendix C: 1990 Age Distributions
................................ ................................ ............. 117 19.1. Motorcycles
................................ ................................ ................................ ................. 117 19.2. Passenger Cars
................................ ................................ ................................ ............ 117 19.3. Trucks
................................ ................................ ................................ ......................... 117 19.4. Intercity Buses
................................ ................................ ................................ ............ 118 19.5. School Buses and Motor Homes
................................ ................................ ................. 118 19.6. Transit Buses
................................ ................................ ................................ ............... 118 20. Appendix D: Driving Schedules and SCC Mappings
................................ ..................... 120 20.1. SCC Mappings
................................ ................................ ................................ ............ 120 20.2. Driving Schedules
................................ ................................ ................................ ....... 121 21. Appendix E: MOVES2010b Source Masses
................................ ................................ .. 124 21.1. Motorcycles
................................ ................................ ................................ ................. 125 21.2. Passenger Cars
................................ ................................ ................................ ............ 126 21.3. General Trucks
................................ ................................ ................................ ............ 126 21.4. Buses
................................ ................................ ................................ ........................... 128 21.5. Refuse Trucks
................................ ................................ ................................ ............. 130 21.6. Motor Homes
................................ ................................ ................................ .............. 131 References
................................ ................................ ................................ ................................ ... 1334



## 1. **Introduction** The United States Environmental Protection Agency ’s Motor Vehicle Emission Simulator, commonly

referred to as MOVES, is a set of modeling tools for estimating emissions produced by on - road (cars, trucks, motorcycles, etc .) and nonroad (backhoes, lawnmowers, etc.) mobile sources. MOVES estimates the emissions of greenhouse gases (GHG), criteria pollutants and selected air toxics. The MOVES model is currently the official model for use for state implementation plan (SIP) submissions to EPA and for transportation conformity anal ys es outside of California
. The model is also the primary modeling tool to estimate the impact of many mobile source regulations on emission inventories
. The MOVES model calculates emission inventori es by multiplying emission rates by the appropriate emission
- related activity , applying correction (adjustment) factors as needed to simulate specific situations , and then adding up the emissions from all sources (populations) and regions. A useful analogy is that an inventory can be picture d as a stool ; the three legs of the stool are the emission rates, activity , and populations. The emission rates are inputs to the model specified for various “processes” including running exhaust, start exhaust, and a number of evaporative processes , among others
. The processes are largel y chosen to be causal such that the phy sical or engineering principles involved in generating those emissions are isolated , which in turn allows research test programs to measure them s cientifically
. a These processes also define the activity , populations , and technology inputs required. This report describes the sources and derivation for on - road vehicle population and activity information and associated adjustments as stored in the M OVES2014 default database s . Th is data has been extensively updated from previous versions of MOVES . Emission rates, correction factor values , and information for nonroad equipment in the default database are described in other MOVES technical reports. 1 T he MOVES2014 default database has a domain that encompasses all on
- road (highway) vehicle and nonroad equipment activity and emissions for the entire United States, Puerto Rico , and the Virgin Islands. Properl y characterizing emissions from the on
- road vehicle subset requires a detailed understanding of the cars and trucks that make up the vehicle fleet and their patterns of operation. The national default activity information in MOVES2014 provides a re asonable basis for estimating national emissions. Th e most important of these inputs, such as VMT and population estimates, come from long
- term s ys tematic national measurements. However , the uncertainties and variability in the default data contribute to the uncertainty in the resulting emission estimates. Inparticular, when modellers estimate emissions for specific geographic locations, EPA guidance recommend s replac ing man y of the MOVES fleet and activity defaults with local data. This is especially true for inputs that vary geographically

and for inputs where local data is more detailed or up
- to - date than that provided in the MOVES defaults.

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

MOVES has been specifically designed to accommodate the input of alternate, user supplied activity data for the most important parameters. EPA’s Technical Guidance 2 provides more information on customizing MOVES with local inputs. Population and activity

data is ever changing. As part of EPA’s MOVES development process, the model undergoes major updates and review every few years . As we progress with MOVES, developm ent of fleet and activity inputs (including projections) will continue to be an important area of focus and improvement.6



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

### 2.1. **HPMS Class** In this report, MOVES HPMS class refers to one of five categories derived from the categories used in the US Department of Transportation (DOT) Highway Performance Monitoring Sy stem (HPMS) vehicle classes used by the Federal Highway

Administration (FHWA) i n the VM
- 1 Table of their annual Highway Statistics report . The five HPMS classes used in MOVES are: motorcycles (HPMSVTypeID 10), light
- duty vehicles (25), buses (40), single unit trucks (50), and combination trucks (60). Note that in MOVES2014, what we call the HPMS class for light
- duty vehicles (25) denotes the sum of the VM
- 1 values for long wheelbase and short wheelbase light
- duty vehicles. HPMSVTy

peID 25 is new for MOVES2014 and replaces HPMSVTy peI D 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 HPMSVTy peI D 25. This change in HPMS classes has come about as passenger vehicles have evo lved over time with the phy sical characteristics of “cars” and “ trucks” becoming less distinct. In response, DOT has changed the HPMS classification sy stem and MOVES has evolved to reflect this change. 

### 2.2. **Source Use**  **Type s**  The primary

vehicle classification in MOVES is source use typ e, or , more simply , source type. Source ty pes are intended to be groups of vehicles with similar activity and usage patterns. On
road source ty

pe s were categorized from the HPMS c lasses, but the HPMS vehicle classes were further differentiate d into MOVES source types using vehicle characteristics from the US Census Bureau’s Vehicle Inventory and Use Survey

(VIUS). The MOVES 2014 source ty pe s are listed in Table 2
- 1 along with the associated HPMS classes . M ore detailed source type definitions are provided in Section 5.2.7 **Table 2**  **- 1**  **MOVES2014 On**  **- road**  **Source Types**  **s ourceTypeID**  **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 non
personal 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 Com

bination Short
- haul Trucks 60 Com bination Trucks 62 Com

bination Long
- haul Trucks 60 Com bination Trucks In MOVES, the distinction between light
- duty (LD) and heav y- duty (HD) source types is essential because light
- and heav y - dut y operating modes were developed differently based on vehicle power and speed. L ight
- duty vehicles ( sourceTy peI D 11, 21, 31, and 32) use vehicle s pecific power (VSP), which is dependent on the measured mass of the test vehicle. Heav y- duty vehicles ( sourceTy

peI D 41, 42, 43, 51, 52, 53, 54, 61, and 62) use scaled tractive power (STP) which is scaled b y a fixed mass factor since their emission rates co rrelates 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 heav y duty vehicle emission rate development, respectively
. 3 , 4 

### 2.3. **Regulatory Classes** In contrast to source ty

pes, r egulatory

classes are used to group vehicles subject to similar emission standards. The EPA regul ates vehicle emissions based on groupings of technologies and classifications that do not necessaril y correspond to DOT activity and usage patterns. To properl y estimate emissions, i t is critical for MOVES to account for these emission standards , despite the fact that the activity data often use s a different classification scheme. Thus, w e 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

cla sses have a one
- to - one correspondence with source ty pe. Other source ty

pes are allocated between regulatory classes based on gros s vehicle weight rating (GVWR), which is a set of eight c lasses developed by FHWA according to the combined weight of the vehic le and its load. Urban buses have their own regulatory definition , and therefore have an independent regulatory

class.8 **Table 2**  **- 2**  **Regulatory Classes in MOVES 2014**  **r egClassID**  **Reg ulatory**  **Class Nam**  **e Description**  0 Doesn't Matter Doesn't Matter 10 MC Motorcycles 20 LDV Light
- Duty Vehicles 30 LDT Light
- Duty Trucks 4 0 LHD<=1 0 k Class 2b Trucks with 2 Axles and 4 Tires (8 , 500 lbs < GVWR <= 1 0,000 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 , 5 00 lbs < GVWR < =33 ,000 lbs) 47 HHD Class 8a and 8b Trucks (GVWR > 33 ,000 lbs) 48 Urban Bus Urban Bus (see CFR Sec. 86.091_2) The GVWR distinction between LD and HD trucks falls in the midst of FHWA GVWR Class 2. Trucks of 6,001
- 8,500 lbs GVWR are sorted into Class 2a , which are considered light - duty vehicles, while vehicles of 8,500
- 10,000 lbs GVWR are sorted into Class 2b , which are considered heav y - duty vehicles
. In MOVES2014 , w e have introduced a new regulatory class 40 for vehicles that are in Class 2b, but are classified as passenger truck or light
- commercial trucks. These vehicles are regulated as heav y- duty vehicles b y the US EPA, but the VMT from Clas s 2b vehicles with two axles and four tires are included in the light
- duty vehicles categories b y the FHWA Highway Statistics Report. MOVES as signs operating modes for source t ypes 31 and 32 according to VSP. As such, we created regulatory

class 40, so that regulatory class 40 models the emission rates of Class 2b trucks according to VSP
- based operating modes, and regulatory class 41 models the emission rates of Class 2b trucks according to STP
- based operating modes. Class 2b trucks with two axles and at least six tires (colloquially known as dualies) and Class 3 trucks fall into regulatory class 41 and are onl y modeled in the heav y- duty vehicles source ty pes In summary , the light
- duty truck source ty

pes (31 and 32) map onl y to regulatory classes 30 and 40 in MOVES2014 , while the heav y- duty vehicle source types (41 and above) map to regulatory classes 41 and above. 

### 2.4. **Fuel Types** MOVES2014 models vehicles and equipment powered b y following fuel types: gasoline, diesel, E
- 85 ( a blend of 85 percent ethanol and 15 percent gasoline), compressed natural gas (CNG), electricity, and liquefied petroleum gas (LPG, only available for nonroad equipment)
. Note that in some case s, a single vehicle can use more than one fuel ; for example , flexible fuel vehicles are capable of running on either gasoline or E
- 85. Thus, fuel ty pe refers to the capability of the vehicle capability rather than the fue l in the tank
. In MOVES, the fuel actually used depends on a number of factors including th e location , year, and month in which the fuel was purchased, as explained in the MOVES2014 technical report on the fuel supply
. 5 The table below summarizes the fu el ty

pes available in MOVES
.9 **Table 2**  **- 3**  **A list of allowable fuel types to power vehicles /equipment**  **in MOVES 2014**  **fuelTypeID defaultForm**  **ulationID Description**  1 10 Gasoline 2 20 Diesel Fuel 3 30 Com pressed Natural Gas (CNG) 4 40 Liquefied Petroleum Gas (LPG) * 5 50 Ethanol (E
- 85) Capable 9 90 Electricity * MOVES2014 only

models LPG use in nonroad equipment. It is important to note that not all fuel ty pe/ source ty

pe combinations can be modeled in MOV ES. That is, MOVES2014 will not model gasoline fueled long
- haul combination trucks, gasoline intercity buses , or diesel motorcycles
. T ransit buses are the onl y on - road source type that may be model ed using CNG . Similarly , flexible fuel (E85
- compatible) and electric vehicles are onl y modeled for passenger cars, passenger trucks, and light commercial trucks
. N one of the on - road (highway) source ty pes can be modeled as fueled b y LPG. For more information on how MOVES models th e impact of fuels on emissions, please see the MOVES documentation on fuel effects. 6 

### 2.5. **Road Types** MOVES calculate s emissions separatel y for each of four road ty pe s and for “off

- network” activity when the vehicle is not moving
. Italso allows separate output for ramp and non
- ramp, as described in Section 10.2. below. The road ty pe codes used in MOVES are listed in Table 2
- 4
. The four MOVES road ty pes (2
- 5) are aggregations of FHWA functional facility types . **Table 2**  **- 4**  **Road Type Codes in MOVES 2014**  **r oadTypeID**  **Description FHWA**  **F unctional Types**  1 Off Network Off Network 2 Rural Restricted Access Rural Interstate 3 Rural Unrestricted Access Rural Principal Arterial, Minor Arterial, Major Collector, Minor Collector & Local 4 Urban Restricted Access Urban Interstate & Urban Freeway/Expressway 5 Urban Unrestricted Access Urban Principal Arterial, Minor Arterial, Collector & Local 6 Rural Restricted without Ramps 7 Urban Restricted without Ramps 8 Rural Restricted only Ramps 9 Urban Restricted only Ramps 100 Nonroad The MOVES roadtypes are based on two important distinctions in how FWHA classifies roads : 1) urban versus rural roadway s are distinguished based on land use and human population10

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 ty pes , or may run MOVES at project level where emissions can be calculated for individual links. 

### 2.6. **Source Classification Codes (SCC)** Source Classification Codes (SCC) are used in air quality models, such as the UNC SMOKE 7 model, to unambiguously identify the source of the emissions when generating emission inventories . In MOVES, SCCs are single numerical code s that identify the vehicle type, fuel ty pe, road type , and emission process
. T he SCCs were redesigned for MOVES2014 to directly relate to the source use types and road typ es used by MOVES. The new SCCs retain the previous 10
- digit design, but use different numerical combinations to avoid conflicts with existing codes. The new codes use MOVES numerical identification ( ID) codes in the following form : AAAFVVRRPP , where

 indicates mobile source (this has a value of 22 0 for both on
- road and nonroad) ,  indicates the MOVES fuel T yp eID value ,  indicates the MOVES source T yp eID value ,  indicates the MOVES roadT yp eID value , and  indicates the MOVES emission processID value. Building the new SCC values in this way will allow additional source types, fuel types, road ty pes , and emission processes to be easil y added to the list of SCC values as changes are made to future versions of MOVES. Using the mapping described above, modelers can also easily identify the sourceTy

peID, fuelTy

peID, roadTy

peID , and processID of emissions reported by SCC without needing a decoding table. Refer to tab les in the MOVES User Guide for the descriptions of the sourceTy peI D, fuelTy

peI D, roadTy

peI D , and processID values currentl y used by MOVES. The explicit coding of fuel ty pe, source typ e, road ty

pe , and emission process also allow s the MOVES SCCs to indicate aggregations. For example, a zero code (00) for any of the sourceT ype ID, fuelT ypeID, roadT ype ID , and processID strings that make up the SCC indicate s that the reported emissions are an aggregation of all categories of that ty pe. The SCC values used in previous versions of MOVES do not have a one
- to - one correspondence with the new SCC values. However, MOVES2014 has the capability to report results by regulatory

class as well as by

SCC , which will aid in comparing SCC results from earlier versions of MOVES. All feasible SCC values are listed in the SCC table within the default database.11



### 2.7. **Source Bins** To est imate emissions, MOVES must know the emission
- related characteristics of the vehicle such as the type of fuel that it is designed to use and the emis sion standards it is subject to. Therefore , we group vehicles into source bins that classify a vehicle by

discriminators relevant for emissions and energy calculations , including fuel ty pe , regulatory

class, and model year group. Each sourceBinID is a uniq ue 19
- digit identifier in the following form: 1 0000000000 , where

 1 is a placeholder,  is a MOVES fuelTy peID,  is a MOVES engTech ID , b  is a MOVES regClassID,  is a MOVES shortModYrGroupID, and  10 trailing zeros for future characteristics. A mapping of model year to model year groups is stored in the PollutantProcessModelYear table. Distributions of fuel and engine technologies and regulatory class are stored by

model ye ar in th e SampleVehiclePopulation table
. The MOVES Source Bin Distribution Generator combines information from these two tables to create a detailed SourceBinDistribution. These bins may vary by pollutant and process as indicated in the SourceTy pePolProcess table
. In general, fuel type and model ye ar group are relevant for all emission calculations , but the relevance of regulatory

class and model year group depend on the pollutant and process being modeled. Since MOVES2014 can produce results by various vehicle classifications

— source ty pe, SCC, or regulatory

class

—the mapping between SourceBinDistribution and SampleVehiclePopulation differs depending on the output selected.

b In MOVES2014, engTechID 1 is used for all fuel types except electric vehicles, where engTechID 30 is used instead. Thus, in this version, engTechID is somewhat r edundant with fuel type and adds no new information when determining source bin distributions or calculating emissions.12 **Table 2**  **- 5**  **Data Tables Used to Allocate Source Type to Source**  **Bin Generator**  **Table Name Key Fields** * **Additional Fields Notes**  SourceTypePolProcess s ourceTypeID p olProcessID 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 p olProcessID modelYearID mo delYearGroupID Assigns model years to appropriate mo del year groups for each polProcessID. Sam pleVehiclePopulation s ourceTypeID modelYearID f uelTypeID e ngTechID r egClassID stmyFuelEngFraction

stmyFraction Includes fuel type and regulatory class fractions for each source type and mo del year, even for some source ty pe/fuel type combinations that do not currently have any appreciable market share (i.e. electric cars). This table provides defaults for the Alternative Vehicle Fuel & Technology (AFVT) i mp orter , and is used to determine reg class fractions when users mo dify alternative fuel vehicle fleet fractions. * In these tables, the s ourceT ypeID and modelYearID are combined into a single s ourceTypeModelYearID. While details of the SourceTy pePolProcess and PollutantProcessModelYear tables are discussed in the reports on the development of the light
- and heav y- duty emission rates, the SampleVehiclePopulation (SVP) table is a topic for this r eport and is discussed in Section 5.2. 

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

source type in the SampleVehiclePopulation table. Table 2
- 7 summarizes the allowable source ty pe - regulatory class combinations in MOVES2014. Any vehicles in regulatory

class 40 and less are considered light
- duty while any

vehicles in regulatory

class 41 and greater are considered heavy
- duty. Similarly , source types 32 and less are considered light
- duty and source types 41 and above are considered heav y - duty. Table 2
- 8 joins together the information in the two matrices about source ty pe, fuel type, and regulatory

class combinations in MOVES2014. Each source type - fuel type combination contains all regulatory

classes listed, except for gasoline transit buses, which have been called out separately
.13 **Table 2**  **- 6**  **Matrix of the allowable source type -**  **fuel type com binations in MOVES2014 (allowable**  **com binations are m**  **arked with an X)**

**Source Use Types**MotorcyclesPassengerCarsPassengerTrucksLightCommercialTrucksIntercityBusesTransitBusesSchoolBusesRefuseTrucksShort-HaulSingleUnitTrucksLong-HaulSingleUnitTrucksMotorHomesShort-HaulCombinationTrucksLong-HaulCombinationTrucks **Fuel Types** 11 21 31 32 41 42 43 51 52 53 54 61 62 Gasoline 1 X X X X X X X X X X X Diesel 2 X X X X X X X X X X X X CNG 3 X E85 5 X X X Electricity 9 X X X
**Table 2**  **- 7**  **Matrix of the allowable source type**  **- regulatory class com** **binations in MOVES2014 (allowable com** **binations are m arked with an X)** 

**Source Use Types**MotorcyclesPassengerCarsPassengerTrucksLightCommercialTrucksIntercityBusesTransitBusesSchoolBusesRefuseTrucksShort-HaulSingleUnitTrucksLong-HaulSingleUnitTrucksMotorHomesShort-HaulCombinationTrucksLong-HaulCombinationTrucks **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 X14

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

**sourceTypeID fuelTypeID**  **regClassID** 11 1 10 21 1, 2, 5, 9 20 31 1, 2, 5, 9 30, 40 32 1, 2, 5, 9 30, 40 41 2 41, 42, 46, 47 42 1 42, 46, 47

2, 3 48 43 1, 2 41, 42, 46, 47 51 1, 2 41, 42, 46, 47 52 1, 2 41, 42, 46, 47 53 1, 2 41, 42, 46, 47 54 1, 2 41, 42, 46, 47 61 1, 2 46, 47 62 2 46, 47 

### 2.9. **Default Inputs and Fleet and Activity Generators** Population and activity

data are critical inputs for calculating emission inventories from emissions processes such as running exhaust, start exhaust, and evaporative emissions
. In MOVES, m ost running emissions are distinguished by operating modes, depending on road type and vehicle speed. Start emissions are determined based on the time a vehicle has been parked prior to the engine starting ( “ soak”)
. E vaporative 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 ty

pe, regulatory

classification , and other vehicle and activity characteristics. Because of these distinctions, MOVES calculators require information on vehicle population and activity at a very fine scale. In project
- level modeling, th is 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 readil y available format . MOVES uses “generators” to create fin e - scale information from user inputs and MOVES default s . The MOVES Total Activity Generator (TAG) estimates hours of vehicle activity using vehicle miles travelled (VMT) and speed information to transform VMT into source hours operating (SHO). Other typ e s of vehicle activity are generated by

appl yi ng 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 anal ys is ye ar (1990 and 1999
- 2050)
. For national inventory runs, annual national activity is distributed temporally

and spatially using allocation factors. A s eparate version of the TAG creates inputs for the runs that onl y generate emission rates. The Source Bin Distribution Generator (SBDG) uses information on fuel ty pe fractions, regulatory class distributions, and similar information to estimate the number of vehicles15

belonging to each source bin as a function of source ty pe and model year. The SBDG maps the activity data (by source ty pes) to sourcebins which map directl y to the MOVES emission rates. There are a number of MOVES modul es that generate operating mode distributions based on vehicle activity

inputs. The Rates Operating Mode Distribution Generator and the L ink Operating Mode Distribution Generator use information on speed distributions and driving patterns (driving schedules) to develop op erating mode fractions for each source ty pe, road ty

pe, and time of day
. Similarly , the Evaporative Emissions Operating Mode Generator and the Start Operating Mode Distribution Generator use MOVES inputs to develop operating mode distributions for starts and vapor venting. The details of each these generators and other MOVES2014 algorithms are described in the MOVES2014 Module Reference
. 8 This report documents the sources and calculations used to produce the default population and activity data in the M OVES2014 database used to compute national level emissions based on defaults for individual counties, months, day types, and hours of the day . In particular, this report will describe the data used to fill the tables listed in Table 2
- 9
.16

**Table 2**  **- 9**  **MOVES Database Elements Covered in this Report Database Table Nam**  **e Content Summary**  **Report Sections** AvgSpeedDistribution Distribution of time among average speed bins Section 0 Day VMTFraction Distribution of VMT between weekday s and weekend days Section 12. DriveSchedule Average speed of each drive schedule Section 0 Dr iveScheduleAssoc Mapping of which drive schedules are used for each combination of source type and road type Section 0 DriveScheduleSecond Speed for eac h second of each drive schedule Section 0 FuelT ype Broad fuel categories that indicate the fuel vehicles are capable of using. Section 2. HotellingActivityDistribution D istribution of hotelling activity to the various operating modes Section 0 HotellingCalendarYear Rat e of hotelling hours per rural restricted access VMT Section 0 HourVMTFraction Distribution of VMT among hours of the day Section 12. HPMSVtypeYear Annual VMT by HPMS vehicle types Section 4. MonthGroupHour Coefficients to calculate air conditioning demand as a function of heat index Section 15. MonthVMTFraction Distribution of annual VMT among mo nths Section 12. PollutantProcessModelYear Assigns model years t o 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 O perating mode distributions by source type, road ty pe , and speed bin Section 0 RoadType Distinguishes roadways by population density of geographic area and by type of access, particularly the use of ramps for entrance and exit. Section 2 RoadTypeDistribution Distribution of VMT among road types Section 8. Sam pleVehicleDay Identifies vehicles in the SampleVehicleTrip table Section 12. Sam pleVehiclePopulation Fuel type and regulatory class distributions by source type and model year. Section 4. Sam pleVehicleTrip Trip start and end times used to determi ne vehicle start and soak times Section 12. SCC Source Classification Codes that identify the vehicle ty pe, fuel type, road type and emission process in MOVES output
. Section 2.
SourceBinDistribution Distribution of population among different vehicle sub
- ty pes (source bins) Section 4.17

**Table 2**  **- 9**  **MOVES Database Elements Covered in this Report Database Table Nam**  **e Content Summary**  **Report Sections** SourceTypeAge Rate of survival to subsequent age , relative mileage accumulation rates , and fraction of functional air conditioning equipment Section 7. Section 15. SourceTypeAgeDistribution Distribution of vehicle population among ages Section 7. SourceTypeHour The distribution of total daily hotelling among hours of the day Section 12. SourceTypeModelYear Prevalence of air conditioning equipment Section 15. SourceTypePolProcess Indicates which sourcebin discriminators are relevant for each source type and pollutant/process Section 4. SourceTypeYear Vehicle counts by year Section 0 SourceUseT ype Mapping from HPMS class to source type, including source type names Section 2. SourceUseT ype Phy sics Road load coefficients and vehicle masses for each s ource type used to calculate Vehicle Specific Power and Scaled Tractive Power Section 14.
Zone Allocati on of activity to zone (county) Section 0 ZoneRoadType Allocation of driving time to zone (county) and road type Section 018



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

### 3.1. **VIUS** Until 2002, the US Census Bureau conducted the Vehicle Inventory and Use Survey

(VIUS) 9 to collect data on the ph ys ical 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, m otorcycles, government
- owned vehicles, ambulances, buses, motor homes , and nonroad equipment. For MOVES, VIUS provides information to characterize trucks by source ty pe and to estimate age, fuel type , and regulatory class distributions as well as relativ e mileage accumulation rates
. MOVES2014 uses data from both the 1997 and 2002 10 survey s. 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 man y of the o lder 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 recentl y about reinitiating the survey
. 

### 3.2. **Polk**

**NVPP® and TIP®** Acquired by

I HS in July

2013 , R.L . Polk & Co. was a private compan y providing automotive information services. The company maintain ed two databases relevant for MOVES: the National Vehicle Population Profile (NVPP®) 11 and the Trucking Industry Profile (TIP®Net) Vehicles in Operation database. 12 The first focuse d on light
- duty cars and trucks, the second focuse d on medium and heav y- dut y trucks. Both compile d 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 t ype, and age for each vehicle source ty pe 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 SampleVehiclePopu lation table and the 2011 age distributions. More details on how we constructed the Sample Vehicle Counts dataset can be found in Section 5.2. 

### 3.4. **FHWA Highway Statis**  **tics** Each ye ar the US DOT Federal Highway Administration’s (FHWA) Office of Highway Policy In formation publishes Highway Statistics . This volume summarizes a vast amount of roadway and vehicle data from the Highway Performance Monitoring Sy stem, a nationa l information19

sy stem that collects data from states and other sources on many facets of the US roadway sy stem. In MOVES2014, vehicle miles traveled (VMT) and vehicle population data for the historic ye ars 1990 and 1999
- 2011 come from four tables in Highw ay Statistics : MV
- 1 13 , MV
- 10 14 , VM
- 1 15 , and VM
- 2 16 , which we will reference b y table name. For some ye ars, the VMT values were revised b y 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 Histo rical Year**  **s 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 , originall y used in MOBILE6. We have used 1999
- 2011 sales data from the 2009 and 2012 Fact Book to calculate age distributions. 

### 3.7. **MOBILE6** MOBILE6 was a precursor to MOVES used to estimate highway vehicle emissions. In some cases, we have used estimates from MOBILE6 model with only minor adaptation. In particular, we used MOBILE6 data for some relative mileage accumulation rates, air conditioning usage rates, and driving schedul es.20

The MOBILE6 data is documented in technical reports, particularl y 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 Mode**  **ling System** The Annual Energy Outlook (AEO) 21 describes Department of Energy forecasts for future energy consumption. The National Energy Modeling Sy stem (NEMS) is used to generate these projections based on economic and demographic forecasts. Vehicle sale s and miles travelled are included in the projections because they strongly influence fuel consumption. Therefore, the AEO is an important source of future projections in MOVES. For MOVES2014, we used AEO2014 to forecast VMT and vehicle populations in year s 2012
- 2050. 

### 3.9. **Transportation Energy Data Book** Each ye ar, Oak Ridge National L aboratory 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, i ncluding EPA, FHWA, Polk, and Ward’s Automotive, Inc. For MOVES we used information for estimating vehicle sales and survival fractions for historic ye ars 1990 and 1999
- 2011 from TEDB Edition 32, published in 2013
. 22 

### 3.10. **FHWA Weigh -**  **in -**  **Motion** FHWA compiles truc k weight data by axle configuration and roadway t ype 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 (VTRI S) W
- 3 Tables u sing data collected in 2011. 

### 3.11. **Motorcycle Industry Council Statistical Annual**  The Motorcycle Industry Council (MI C) collects data on sales, ownership, and activity trends each ye ar. MIC’s Statistic al Annual summarizes this data, 24 which we used in MOVES2014 , particularl y the 1999
- 2011 sales of highway motorcycle s .21



## 4. **VMT by Calendar**  **Year and Vehicle Type** For national level calculations, MOVES calculates source operating hours from national VMT by vehicle type. The default database contains national VMT estimate s for all anal ys is years, which include 1990 and 1999
- 2050. Years 1991
- 1998 are excluded because there is no regulatory

requirement to anal yz e them and including them would increase model complexity
. Calendar ye ar 1990 continues to be a base year because o f the Clean Air Act Amendments of 1990. The national VMT estimates are stored in the HPMSVTy peYear table, which includes three data fields: HPMSBaseYearVMT (discussed below), baseYearOffNetVMT, and VMTGrowthFactor. Off Network VMT refers to the portion o f activity that is not included in travel demand model networks or an y VMT that is not otherwise reflected in the other four road ty pes. The field baseYearOffNetVMT is provided in case it is useful for modeling local areas. However, the reported HPMS VMT v alues, 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 typ es. A dditionally , the VMT GrowthFactor field is not used in MOVES2014 and is set to zero for all vehicle ty pes. 

### 4.1. **Historic Vehicle Miles Traveled (1990 and 1999 -**  **2011)** The HPMSBaseYearVMT field stores the total national VMT for each HPMS vehicle ty pe for all anal ys is years. For historical years 1990 and 1999
- 2011, the VMT is deri ved from the FHWA VM
- 1 tables. In reporting ye ars 2007 and later, the VM
- 1 data use an updated methodology with different HPMS vehicle type categories. The current HPMS categories are Li ght
- Dut y (Short Wheelbase), Li ght
- Dut y (L ong Wheelbase), Motorcycles, Buses, Single Unit Trucks, and Combination Trucks. Because MOVES categorizes light
- duty source types based on vehicle typ e and not wheelbase length, the short and long wheelbase categories are combined into a single category of Li ght
- Dut y Vehicles (HPMSVTy peID 25) . Internally , the MOVES Total Activity Generator c allocates this VMT to MOVES source ty pes and ages using vehicle populations, age distributions and relative mileage accumulation rates. For ye ars prior to 2007, the VM
- 1 data with historical vehic le type groupings needed to be adjusted for consistency . In earl y 2011, the FHWA released such adjusted VMT data for ye ars 2000
- 2006 to match the new category definitions. Shortly

afterward, the agency

replaced these adjusted numbers with the unadjusted VM T data stating, “[FHWA] determined that it is more reliable to retain the original 2000
- 2006 estimates because the information available for those ye ars does not full y meet the requirements of the new methodology .” d However, lacking a better adjustment methodology , we used the retracted FHWA
- adjusted values as the VMT for 2000
- 2006.

c For more information on the MOVES Total Activity Generator, please see the MOVES2014 Module Reference, available on the MOVES website: http://www.epa.gov/otaq/models/moves/#user d This text appears in a footnote to FHWA’s Highway Statistics Table VM
- 1 for publication years 2000
- 2009.22

This left two years, 1990 and 1999, that needed to be adjusted to be consistent with the new HPMS vehicle categories. These adjustments were made using the average ratio of th e methodology

change for each vehicle category . This was found by dividing the FHWA
- adjusted VMT for each vehicle category by the original VMT for each year 2000
- 2006 and then calculating the average ratio for each category . This ratio was then applied to the corresponding VMT values reported in VM
- 1 for 1990 and 1999. Since FHWA’s adjustments conserved the original total VMT estimates, we normalized our adjusted values such that the original total VMT for the ye ars 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
- adjus ted, 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 Yea r VMT by HPMS Vehicle Class in M**  **illions of M**  **iles Y**  **ear 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 describe s historic fleet VMT. This section presents how EPA projected those values into the future. The VMT growth in years beyond 2011 is based on the VMT projections as described in AEO2014. Due to differences i n methodology , the absolute VMT values presented in AEO differ slightly from the HPMS values in VM
- 1 where the anal ys is years overlap. Therefore, the projections in AEO were not used directl y. Instead, percent changes from ye ar 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 wer e assumed to continue to grow at the average growth rate over 2031
- 2040. A mapping between the two data sources was necessary because the vehicle categories differed between AEO and HPMS. AEO’s light
- duty category was mapped to both the combined HPMS light
- duty and the motorcycle categories. Motorcycles were included here becau se they were not23

explicitly accounted for elsewhere in AEO. Since buses span a large range of heav y- duty vehicles and activity , the combination of AEO’s light medium, medium, and heavy heavy
- dut y categories was mapped to the HPMS bus category. AEO’s light medium and medium heavy
- duty categories were combined for mapping to the HPMS single unit truck category, and AEO’s heav y heav y- duty category was mapped to the HPMS combination truck category. The percent growth changes over time from the groupings described above were calculated and applied by

HPMS category

to the 2011 base year VMT from VM
- 1. The resulting values are presented in Table 4
- 2 below.24 **Table 4**  **- 2**  **VM T projections for 2012**  **- 2050 by HPMS Vehicle Class in M**  **illions of M**  **iles 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 25,425 182,803 306,72625



## 5. **Vehicle Populations by Calendar Year**  MOVES uses vehicle populations to characterize emissions activity that is not directly dependent o n 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 SourceT ypeYear table , which has three data fields: s ourceT ypePopulation, s alesGrowthFactor , and m igrationRate
. 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. Inthis 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 t o their relativel y small effects on national population estimates. Motorcycle and car data were used without adjustment, but since MOVES populations are input b y source type, allocations within the general categories of trucks and buses were necessary , as shown in Figure 5
- 1
.26

**Figure 5**  **- 1**  **Conceptual m ap of allocating FHWA MV**  **- 1 vehicle registration estim**  **ates to MOVES source types**

Trucks were separated into single unit and combination trucks using registration data in the Highway Statistics VM
- 1 Table. The remaining MV
- 1 truck registrati ons were allocated to the light
- duty trucks. Single unit and combination trucks were further allocated among their respective source typ es using the EPA sample vehicle counts data . Since we only had sample vehicle counts for calendar years 1999 and 2011, the 2000
- 2010 distributions among source ty pes within the general truck categories were linearly interpolated between 1999 and 2011 rather than using the predictions for these ye ars as in MOVES2010b. This ensured that every source type population would more or less track its general MV
- 1 population, as shown for example in for allocating between short
- haul and long
- haul combination trucks below (see Figure 5
- 2 ) . This li near interpolation method was also used for single unit (see Figure 5
- 3 ) and light
- duty truck source t ypes (see Figure 5
- 4 ). Car and motorcycle populations are reported directly in MV
- 1 and thus were not subject to linear interpolation adjustments.27 **Figure 5**  **- 2**  **Com bination truck source type populations**  **interpolated 1999 -**  **2011**

**Figure 5**  **- 3**  **Single unit truck source type populations interpolated 1999**  **- 2011**  - 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011**N a** **t i** **o n** **a l** **V e** **h i** **c l** **e R** **e g** **i s** **t r** **a t** **i o** **n s** 

**Year** Long-Haul Combination Trucks (62) Short-Haul Combination Trucks (61)

- 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 9,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011**N a** **t i** **o n** **a l** **V e** **h i** **c l** **e R** **e g** **i s** **t r** **a t** **i o** **n s** 

**Year** Motor Homes (54) Long-Haul Single Unit Trucks (53) Short-Haul Single Unit Trucks (52) Refuse Trucks (51)28 **Figure 5**  **- 4**  **L ight**  **- duty vehicle source type populations**  **; light trucks interpolated 1999 --**  **2011**

**Figure 5**  **- 5**  **Bus source type populations in MOVES2014**

Buses were allocated in a similar fashion as trucks, but using different data sources. School bus estimates for all ye ars 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 t ype. Since school and transit bus registrations in Puerto Rico were not readil y available, we estimated them by multiplying the US transit or school bus - 50,000,000 100,000,000 150,000,000 200,000,000 250,000,000 300,000,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011**N a** **t i** **o n** **a l** **V e** **h i** **c l** **e R** **e g** **i s** **t r** **a t** **i o** **n s** 

**Year** Light Commercial Trucks (32) Passenger Trucks (31) Passenger Cars (21) Motorcycles (11)

- 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011**N a** **t i** **o n** **a l** **V e** **h i** **c l** **e R** **e g** **i s** **t r** **a t** **i o** **n s** 

**Year** School Buses (43) Transit Buses (42) Intercity Buses (41)29

registrations by

the ratio of bus registrations in Puerto Rico to the total MV
- 1 bus registrations. MOVES2014 bus populations are shown in Figure 5
- 5 . Note that the precipitous drop in bus populations from 2010 to 2011 is reflected in the MV
- 1 bus registration data published by FHWA, which has been used in MOVES2014 without adjustment.30 **Table 5**  **- 1**  **Historic Source Type Populations for Calendar Years 1990 and 1999 -**  **2011 (in thousands)** Year Motorcycle Passenger Car Passenger

Truck Light

Commercial Truck Intercity

Bus Transit

Bus School

Bus Refuse Truck Single Unit Short
haul Truck Single

Unit Long
haul 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 eventuall y a decline for Passenger Cars and Combination L ong - haul trucks as shown in Table 5
- 1 . This suggests that the decline in sales was accompanied b y a delay in the scrappage of older vehicles. The dyn amic vehicle survival rates in MOVES and their impact on age distributions are discussed in Section 7.1.2.31



### 5.2. **Projected Vehicle**  **Populations (2012 -**  **2050)** The previous section described the historic fleet as it appeared in the data. This section presents how EPA projected those vehicle populations into the future. This work is inherentl y 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 project ed VMT growth as a surrogate for vehicle population growth. In examining VMT per vehicle by HPMS class over the historic years presented above, this surrogate appears reasonable. Table 5
- 2 shows the VMT values of Table 4
- 1 divided by

the vehicle populations of Table 5
- 1 grouped b y HPMS classification. At this level of aggregation, VMT per vehicle is relativel y constant with no clear trends over time
. **Table 5**  **- 2**  **VM T per Vehicle by HPMS Classificat**  **ion Year**  **Motorcycles Light**  **- Duty**  **Vehicles Buses**  **Single Unit Trucks Combination Trucks**  1999 3,263 11,518 20,291 14,776 67,928 2000 2,787 11,497 19,740 15,271 66,876 2001 2,258 11,240 17,240 14,837 68,381 2002 2,223 11,533 17,455 15,401 69,655 2003 2,111 11,552 17,155 15,969 72,459 2004 2,576 11,575 16,946 15,501 72,037 2005 2,201 11,505 16,238 14,783 71,075 2006 2,830 11,385 16,995 15,885 68,702 2007 2,950 11,298 17,322 14,767 69,825 2008 2,645 11,007 17,480 15,291 71,058 2009 2,588 11,044 17,026 14,372 64,160 2010 2,279 11,369 16,181 13,464 68,802 2011 2,163 11,197 20,541 13,227 66,731 Therefore, the AEO growth factors used to project future VMT as described in S ection 4.2. were used to project populations. (Motorcycle growth was calculated using factors from light
- dut y vehicles.) Since these growth factors are by HPMS class , the 2011 source type populations were aggregated b y 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 S ection 7.1.2.2. for a detailed discussion on this topic. The resulting projected source type populations are tabulated in S ection 17. (Appendix A).32 **Table 5**  **- 3**  **Projected HPMS Category Populations for 2012 -**  **2050 (in thousands) Year**  **Motorcycles Light**  **- Duty**  **Vehicles Buses**  **Single Unit Trucks Combination Trucks**  2012 8,571 236,285 704 8,198 2,471 2013 8,687 239,479 734 8,637 2,566 2014 8,706 240,028 757 8,973 2,635 2015 8,747 241,178 789 9,393 2,745 2016 8,844 243,868 822 9,790 2,857 2017 8,943 246,584 844 10,056 2,935 2018 9,018 248,692 860 10,218 2,990 2019 9,098 250,904 875 10,373 3,045 2020 9,178 253,126 890 10,543 3,100 2021 9,260 255,371 906 10,733 3,159 2022 9,337 257,508 923 10,934 3,220 2023 9,416 259,695 941 11,126 3,281 2024 9,498 261,966 956 11,290 3,338 2025 9,585 264,368 974 11,463 3,400 2026 9,680 266,983 990 11,620 3,459 2027 9,781 269,767 1,004 11,747 3,510 2028 9,888 272,745 1,015 11,858 3,554 2029 9,996 275,707 1,027 11,978 3,600 2030 10,103 278,670 1,041 12,107 3,650 2031 10,215 281,752 1,053 12,234 3,698 2032 10,328 284,871 1,063 12,335 3,737 2033 10,439 287,918 1,075 12,454 3,783 2034 10,538 290,669 1,091 12,606 3,842 2035 10,633 293,277 1,106 12,745 3,901 2036 10,724 295,790 1,122 12,877 3,961 2037 10,813 298,244 1,137 13,007 4,021 2038 10,901 300,667 1,152 13,129 4,081 2039 10,983 302,932 1,166 13,238 4,136 2040 11,055 304,914 1,180 13,346 4,193 2041 11,155 307,671 1,196 13,472 4,251 2042 11,256 310,453 1,210 13,599 4,311 2043 11,357 313,260 1,226 13,731 4,371 2044 11,460 316,092 1,241 13,864 4,432 2045 11,564 318,951 1,257 13,998 4,494 2046 11,668 321,835 1,273 14,135 4,556 2047 11,774 324,745 1,289 14,273 4,620 2048 11,880 327,681 1,304 14,411 4,684 2049 11,988 330,642 1,322 14,550 4,750 2050 12,096 333,632 1,338 14,691 4,81633



## 6. **Fleet Characteristics** MOVES categorizes vehicles into thirteen source use ty pes as described in Section 2.1. , which are defined using phy sical characteristics , such as number of axles and tires, and travel behavior characteristics , such as ty pical trip lengths
. This section describes the defining characteristics of the source types in greater detail and explains how source type is related to fuel type and regulatory

class , primarily

through the SampleVehiclePopulation table. 

### 6.1. **Source Type Definitions** MOVES Source types are intended to further divide HPMS vehicle classifications into groups of vehicles with similar activity patterns For example, passenger trucks and light commercial trucks are expected to have different dail y trip patterns. VIUS was our main source of information for distinguishing the se vehicles
. Table 4
- 6 summarizes how the VIUS2002 parameters were used to delineate the light
- dut y, single unit, and combination truck source types for MOVES2014. Axle arrangement (AXLE_CONFIG) was used to define three categories: straight trucks with two axles and six tires (codes 2, 9, 10, 11), all straight trucks (codes 1
- 21), and all tractor
- trailer combinations (codes 21+). Primary distance of operation (PRIMARY_TRIP) was used to define short
- haul (codes 1
- 4) for vehicles with primary operation dist ances 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 eq ual to 10,000 lbs. as heav y- duty. An y 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 perso nal (OPCLASS 5) and non
- persona l use.34

**Table 6 -**  **1 VIUS2002 Param eters Used to Distinguish Truck Source Types in MOVES2014**  **Source Type**  **Axle**

**Arrangem ent**  **Prim ary 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 ≠2 1 Any AXLE_CONFIG <=21 TRIP_PRIMARY

in (1,2,3,4) ADM_GVW > 2 &

VIUS_GVW > 3 BODYTYPE ≠2 1 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 Com bination Short
- Haul Trucks AXLE_CONFIG

>=21 TRIP_PRIMARY

in (1,2,3,4) Any Any Any Com bination Long
- Haul Trucks AXLE_CONFIG

>=21 TRIP_PRIMARY

in (5,6) Any Any Any † In the MOVES2014 analysis, we did not constrain axle configuration of light
- duty trucks, so there are some, albeit very few, trucks that have three axles or more and/or six tires or more
. These vehicles are classified as light
- duty truck s based primarily on their weight . Only 0.27% of light
- duty trucks have such tire and/or axle parameters and they have a negligible impact on vehicle populations and emissions. * For a source type with multiple rows , the source type is applied to any vehicle with either set of parameters. Motorcycles and passenger cars in MOVES borrow vehicle definitions from the FHWA Highway Performance Monitoring Sy stem (HPMS) classifications from the Highway Statistics MV
- 1 Table
. Source t ype 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 (sourceTy peID 11) are , “ all two
- or three
- wheeled motorized vehicles, ty picall y with sa ddle seats and steered by handlebars rather than a wheel.” 25 This category usually includes any

registered motorcycles, motor scooters, mopeds, and motor
- powered bicycles
. N either the 2011 Polk dataset nor VIUS contain any information on motorcycles
. As not ed in Section 0 i nformation on motorcycle populations comes from HPMS MV
- 1 registrations
.

**6.1.2. Passenger Cars**  Passenger cars are defined as an y coupes, compacts, sedans, or station wagons with the primary purpose of carrying passengers. 25 All passenger cars (sourceTy peI D 21) are categorized in the35

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** L ight
- duty trucks include pickups, sport utility vehicles (SUVs), and vans . Depending on use and GVWR, we categorize them into two different MOVES source types: 1) passenger trucks (sourceT yp eID 31) , and 2) light commercial trucks (sourceT yp eID 32) . According to 2011 VM
- 1 vehicle classifications from FHWA, light
- duty vehicl es are those weigh ing less than 10,000 pounds (i.e. they

are in weight class 1 and 2) , except Class 2b trucks with two axles and six tires are assigned to the single unit truck category
. VIUS contains man y 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 fr equent operator classification (OPCLASS) must be personal transportation
. Inversel y, l ight commercial trucks (sourceTy peI D 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 (sourceT yp eID 41), transit (sourceTy peI D 42), and school buses (sourceTy

peI D 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 passe ngers on fixed routes and schedules. According to FHWA, school buses are defined as vehicles designed to carry more than 10 passengers, used to transport K
- 12 students between their home and school. 26 Intercity buses are , as defined b y the Bureau of Transpo rtation Statistics, “ interstate motor carrier of passengers with an average annual gross revenue of at least one million dollars , ” but MOVES also considers an y bus that cannot be categorized as either a transit or school bus to be an intercity bus. **6.1.5. Singl**  **e Unit Trucks** The single unit HPMS class in MOVES consists of refuse trucks (sourceTypeID 51), short
- haul single unit trucks (sourceTy peI D 52), long
- haul single unit trucks (sourceTy peI D 53), and motor homes (sourceTy

peID 54). With 2013 VM
- 1 updates to veh icle 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 ‘dua lie
. ’ As with light
- duty

truck source types, single unit trucks are sorted using VIUS parameters, in this case that includes axle configuration (AXLE_CONFIG) for straight trucks (codes 1
- 21) , vehicle weight (both ADM_GVW and VIUS_GVW), most common trip distance (TRIP_PRIMARY), and body ty

pe (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 sing le unit trucks with a36

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 th ese tractor - trailers into two MOVES source types: short
- haul (sourceTy peI D 61) and long
- haul combination trucks (sourceTy peID 62). Like single unit trucks, short
- haul and long
haul combination trucks are distinguished b y 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 combinatio n trucks are older than long
- haul combination trucks and these short
- haul trucks often purchased in secondary markets , such as for drayage applications , after being used primarily for long
- haul trips
. 27 

### 6.2. **Sample Vehicle Population** To match source typ es to emission rates, MOVES must associate each source type with specific fuelty pes and regulatory

classes. Muc h of default the information on fleet characteristics is stored in the SampleVehiclePopulation table, which contains two frac tions: 1) stmyFracti on, and 2) stmyFuelEngFraction. The former fraction defines the default fuel ty pe distribution, which can be modifi ed by

the user through the Alternative Fuel Vehicle and Technology (AVFT) table, and the latter fraction forms the defaul t regulatory class distribution. Both SVP fractions are computed through the EPA sample vehicle counts dataset that joins 2011 national R.L
. Polk vehicle registration data with Vehicle Inventory and Use Survey

(VIUS) classifications. **6.2.1. Fuel Type and Regula**  **tory Class Distributions** The stmyFraction is the default national fuel type and regulatory class allocation for each source ty pe and model year. Written out mathematically , we define the stmyFraction as, ( ) , , , = , , ,

, , , , , Equation 1

where the number of vehicles in a given model ye ar , regulatory class , fuel ty

pe , and source type is divided by the sum of vehicles across the set of all regulatory classes and all fuel ty pes . That is, the denominator only differs by

source t ype 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 ty pe and model year. A value of zero indicates that the MOVES default population of vehicles of that source ty pe, model ye ar, fuel ty

pe and regulatory class is negligible or d oes not exist.37

While stmy

Fraction indicates MOVES default values, the stmyFuelEngFraction allows the modeling of non
- default fuel ty pe distributions. For each allowable combination of source ty pe, 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 stm yF uelEngFraction as, ( ) , , , = , , ,

, , , , Equation 2

for number of vehicles , model year , regulatory class , fuel ty

pe , source type , and the set of all regulatory

classes
. The denominator differ s by source type, model ye ar, and fuel ty pe in this case
. For example, for model year 2010 gasoline passenger trucks, the table will list a stmyFuelEngFraction for regulatory class 30 and another for regulatory class 4 0. These fractions sum to one for each combination of source ty pe, model year and fuel ty pe . For example , while the stmyFraction indicates that the MOVES defaults assign zero fraction of model year 2010 passenger trucks to the electric ity fuel ty pe, the stmyFuelEngFraction indicates a default (hy

pothetical ) regulatory

class distribution if these vehicles existed . In this case, MOVES would model them all as belonging to regulatory class 30. The stmyFraction is particularl y important because users can edit fuel ty pe distributions using the Alte rnative 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 re quiring this input from the user. This means an allowed stm yF uelEngFraction must never be zero. As an example, Figure 6
- 1 shows the national default fuel ty pe fractions for all light
- duty vehicles among the different MOVES fuel types. As noted in Section 2.4. these fuel type frac tions indicate the fuel capability of the vehicle and not the fuel being used by the vehicle . In this report’s nomenclature , E85
- capable and flexible fuel vehicles are s ynomous
— meaning they can accept either gasoline or E85 fuel. Although these vehicle are capable of running on E85, the fuel ty pe distributions do not have any information on how often they actually use E85. Discussion on fuel usage can be found in the MOVES2014 Fuel Supply Report. 538

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

**6.2.2. Sample Vehicle Counts**  The SampleVehiclePopulation table fractions have been developed by EPA using the sample vehicle counts dat aset referenced in Section 3. , which primarily joins calendar year 2011 registration data from R.L. Polk and the Vehicle Inventory and Use Survey (VIUS ) results. The sample vehicle counts dataset was generated by multiplying the 2011 Polk vehicle populations by the source t ype allocations from VIUS.39

While VIUS provide source type classifications, we relied primarily on the 2011 Polk vehicle registration dataset to form the basis of the fuel type and regulatory class distributions in the SampleVehiclePopulation table. We purchased the Polk dataset in April 2012, so it did not have complete registration records for model ye ar 2012 vehicles and therefore model year 2012 vehicles were omitted from the SVP analy sis. The Polk data was provided with the following fields: vehicle type (cars or trucks), fuel type, gross vehicle weight rating (GVW R) for trucks , household vehicle counts, and work vehicle counts. We combine d the household and work vehicle counts . The MOVES distinction between personal and commercial travel for light
- duty trucks comes from VIUS
. The Polk records b y FHWA truck weight class were grouped into MOVES GVWR
- based regulatory classes , as shown in **Error! Reference source not found.** below. As stated above, all passenger cars were assigned to regClassID 20. The mapping of weight class to regulatory class is straightforward with one notable exception for delineating trucks weighing more or less than 8,500 lbs. **Table 6**  **- 1**  **Initial m apping from** **FHWA truck classes to MOVES regulatory classes.**

**Vehicle Category FHWA Truck Weight Class**  **Weight Range (lbs) regClassID**  Trucks 1 < 6,000 30 Trucks 2a 6,001

– 8,500 30 * Trucks 2b 8,501

– 10,000 41 * Trucks 3 10,001

– 14,000 41 Trucks 4 14,001

– 16,000 42 Trucks 5 16,001

– 19,500 42 * Trucks 6 19,501

– 26,000 46 Trucks 7 26,001

– 33,000 46 Trucks 8a 33,001

– 60,000 47 Trucks 8b > 60,001 47 Cars 20 * After the Polk data has been sorted into source types (d escribed later in this section) , some regulatory classes were merged or divided . Any regulatory class 4 1 vehicles in light
- duty truck source types have been reclassified into the new regulatory class 40 (see explanation in Section 2.3. ) , any regulatory class 30 vehicles in single unit truck source types have been reclassi fied into regulatory class 41, and any regulatory class 42 vehicles in combination truck source types have been reclassified into regulatory class 46. Since the Polk dataset did not distinguish between Class 2a (6,001
- 8,500 lbs) and Class 2b (8,501
- 10,000 lbs) trucks , but MOVES regulatory classes 30, 40, and 41 all fall somewhere in Class 2, we needed to use a secondary data source to allocate the Polk gasoline and diesel trucks between Class 2a and 2b. According an Oak Ridge National Laboratory (ORNL) paper28 , just over 25 percent of diesel Class 2 trucks are Class 2a and about 81 percent of gasoline Class 2 trucks are in Class 2b. These fractions are summarized in Table 6
- 2 . W e used these fractions to allocate the Polk Class 2 gasoline and diesel trucks into the appropriate regulatory classes (Class 2a are in regulatory

class 30, and Class 2b are in regulatory class 40/41).40 **Table 6**  **- 2**  **Fractions of Class 2a and 2b trucks fueled by gasoline and diesel**  **Fuel Type Truck Class** 

**2a 2b**  Gasoline 0.975 0.760 Diesel 0.025 0.240

1.000 1.000 Additionally , the Polk dataset includes a variet y of fuels, some that are included in MOVES and others that are not. Onl y the Polk gasoline and diesel vehicles were included in our anal ys is ; all other alternative fuel vehicles were omitted. While MOVES2014 does model light
- duty E
- 85 and electric vehicles , and compressed natural gas (CNG) transit buses , these relative penetrations of alternative fuel vehicles have been developed from secondary data sources rather than Polk. Polk excludes some government fleets and retrofit vehicles that could potentially be large contributors to these alternative fuel vehicle populations. Instead we used flexible fuel vehicle sales data reported for EPA certifi cation , and dedicated CNG bus populations from the National Transit Database
. The Table 6
- 3 illustrates how Polk fuels were mapped to MOVES fuel ty pes, and which Polk fuels were not used in MOVES. This mapping in Table 6
- 3 led us to discard 0.22 percent, roughly 530,000 vehicles (mostly dedi cated 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 distributi ons in the SampleVehiclePopulation table.41 **Table 6**  **- 3**  **A list of fuels from the Polk dataset used to develop MOVES fuel type distributions.**

**Polk Fuel Type MOVES fuelTypeID**  **MOVES fuelType** Unknown N/A Undefined N/A Both Gas and Electric 1 Gasoline Gas 1 Gasoline Gas/Elec 1 Gasoline Gasoline 1 Gasoline Diesel 2 Diesel Natural Gas N/A Com pressed Natural G as 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 transform ed the VIUS dataset into MOVES format. T he VIUS vehicle data was first assigned to MOVES source types using the constraints in **Error! Reference source not found.** and then to MOVES regulatory classes using the mapping described in **Error! Reference source not found.**  , including the allocation between Class 2a and 2b trucks from the ORNL study in Table 6
- 2
. Similar to our fuel ty pe mapping of the Polk dataset, we chose to omit alternative fuel vehicles, as summarized below in Table 6
- 4
. Table 6
- 1 VIUS2002 Parameters Used to Distinguish Truck Source Types in MOVES201442

**Source Type**  **Axle**

**Arrangem ent**  **Prim ary 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 ≠2 1 Any AXLE_CONFIG <=21 TRIP_PRIMARY

in (1,2,3,4) ADM_GVW > 2 &

VIUS_GVW > 3 BODYTYPE ≠2 1 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 Com bination Short
- Haul Trucks AXLE_CONFIG

>=21 TRIP_PRIMARY

in (1,2,3,4) Any Any Any Com bination Long
- Haul Trucks AXLE_CONFIG

>=21 TRIP_PRIMARY

in (5,6) Any Any Any † In the MOVES2014 analysis, we did not constrain axle configuration of light
- duty trucks, so there are some, albeit very few, trucks that have three axles or more and/or six tires or more
. These vehicles are classified as light
- duty truck s based primarily on their weight . Only 0.27% of light
- duty trucks have such tire and/or axle parameters and they have a negligible impact on vehicle populations and emissions. * For a source type with multiple rows , the source type is applied to any vehicle with either set of parameters.43 **Table 6**  **- 4**  **Mapping of VIUS2002 fuel types to MOVES2014 fuel types VIUS**  **Fuel Type VIUS Fuel Code**  **MOVES fuelTypeID MOVES fuelType**  Gasoline 1 1 Gasoline Diesel 2 2 Diesel Natural gas 3 N/A Propane 4 N/A Alcohol fuels 5 N/A Electricity 6 N/A Gasoline and natural gas 7 1 Gasoline Gasoline and propane 8 1 Gasoline Gasoline and alcohol fuels 9 1 Gasoline Gasoline and electricity 10 1 Gasoline Diesel and natural gas 11 2 Diesel Diesel and propane 12 2 Diesel Diesel and alchol fuels 13 2 Diesel Diesel and electricity 14 2 Diesel Not reported 15 N/A Not applicable 16 N/A This process yi elded VIUS data by MOVES source t ype, model ye ar, regulatory class, and fuel ty pe. The VIUS source ty

pe distributions were calculated in a similar fashion to the SampleVehiclePopulation fractions discussed above for each regulatory class
- fuel ty pe - model ye ar combination
. Stated formally , for an y given model year , regulatory class , and fuel type , the source type population fraction for a specified source type will be the number of VIUS trucks in that source typ e divided by the sum of VIUS trucks across the set of all source t yp es . The source ty

p e population fraction is summarized in the following formula:

( ) , , , = , , ,

, , , Equation 3 The VIUS data in our analy sis 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 schematicall y in Figure 6
- 2
. Expressed in equatio n form, the sample vehicle counts are, ( ) , , , = ( ) , , , ∙ ( ) , , , , Equation 444

where is the number of vehicles used to generated the SampleVehiclePopulation table, is the 2011 Polk vehicle populations, and is the source ty pe distributions from VIUS. **Figure 6**  **- 2**  **A schem atic overview of how the 2011 Polk dataset and VIUS 2002 were joined to create EPA’s**  **sample vehicl e counts for MOVES2014. Note that**  **data on buses, m**  **otorcycles, and motor hom es**  **was pulled from** **other sources.**

These sample vehicle counts by source type we re then utilized to calculate the SVP fractions , stmyFraction and stm yFu elEngFraction , as defined above . Due to a small sample size of vehicles 30 year s old and older in both the Polk and VIUS datasets, MOVES2010b SVP fractions were used for MY 198 1 and earlier , which roughl y follows the same procedure outlined above but instead uses a 1999 Polk vehicl e 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 ty pe 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 t ype, as d escribed 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 ty

pes. Once the sample vehicle counts were available b y source ty pe, any light
- dut y trucks ( sourceTy

peI D 31 or 32) in the original LHD regulatory class less than 14,000 lbs (regClassID 41 ) were reclassified i n the new LHD regulatory class less than 10,000 lbs (regClassID 40 ), whereas any heavy
- duty vehicles ( sourceT yp eID 41 and above) remained in regClassID 41. Similarly , any single unit trucks ( sourceT yp eID 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 t ypes to regClassID 46 because tractor
trailers must be either Class 7 or 8 trucks. This ensures a clean break between light
- and heav y duty emission results and that the emission calculations use the appropriate fixedMassFactor45

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

vehicles. As noted above, the ini tial 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 subs ections below, we have provided more detailed descriptions by source ty pe. **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 b y gasoline. We acknowledge that some alternative fuel motorcycles have been prototy ped 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. Passen** **ger 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 gasoli ne and diesel cars in the SampleVehiclePopulation table. Flexible fuel (E85
- capable) cars were also included in the SVP fuel ty pe distributions but added after the sample vehicle counts analy sis. We assume that a flexible fuel vehicle would directly displa ce 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 (fuelTy peI D 5) vehicles and deducted them from the gasoline cars in the Polk dataset. Similarly , for model years 2012 and later, we used Annual Energy Outlook (AEO) car sales projections from AEO2014 ’s table labeled “Light
- Duty Vehicle Sales b y Technology Ty pe” to derive flexible fuel vehicle penetrations and applied them to the Sample Vehicle Population, exclusively

for regulatory

class 20. 29 All other alternative fueled cars were determined to have insignificant market shares no w 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 gas oline vehicles). Please consult the MOVES2014 documentation on greenhouse gases 30 and brake and tirewear 31 , respectively , for more information on the development of the energy and emission rates themselves. **6.2.2.3. Light** 

**- Duty**  **Trucks** Since passenger and light commercial trucks are defined as light
- duty vehicles, they are constrained to regulatory class 30 and 40. Within the sample vehicle counts, GVWR Class 1 and 2a trucks were classified as regulatory class 30 and Class 2b trucks with two axles and four tires46

were c lassified as regulatory class 40. Both light
- duty truck source types are divided between gasoline and diesel using the underl yi ng 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 ye ars (M Y 2012 and later) from AEO2014 ’s Tab le 64. The flexible fuel vehicle penetration was applied to regClassID 30 for both E
- 85 (fuelT yp eID 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 In formation Administration (EIA) assumptions, all intercity buses in MOVES are powered b y diesel fuel. 32 The following non - school bus regulatory class distribution for intercity buses was applied to all model years based on 2011 FHWA data , as shown in Table 6
- 5
. 33 **Table 6**  **- 5**  **Regulatory class fractions of school and non -**  **school buses using 2011 FHWA data Vehicle Type**  **MOVES regClassID**

41 42 46 47 Total Non
- School Buses 0.1856 0.0200 0.1214 0.6730 1 School Buses 0.0106 0.0070 0.9371 0.0453 1 The National Transit Database (NTD) Revenue Vehicle Inventory (Form 408) closel y tracks the number of motor buses (MB) b y fuel type each ye ar and those statistics are used to develop the MOVES fuel ty

pe distributions for transit buses. The mapping from NTD fuel ty pes to MOVES fuel ty pes is summarized in Table 6
- 6
. **Table 6**  **- 6**  **Mapping National Transit Database Fuel Types to MOVES Fuel Types NTD code**  **NTD description**  **fuelTypeID MOVES Fuel**  **Description** BD Bio
- diesel 2 diesel BF Bunker fuel N/A CN Com

pressed 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 Hy

brid diesel 2 diesel HG Hy

brid 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/A47

While some other MOVES fuel ty pes are included in the NTD, the transit bus fuel ty pe distributions were allocated between diesel, CNG, and gasoline only . T ogether these three fuel ty pes account for more than 99 percent of all transit buses in 2011, so no other alternative fuels are allowed within the transit bus source ty pe due to negligible market shares. Biodiesel does not appear in the SampleVehiclePopulation table

—in MOVES it is considered a fuel subty pe rather than a fuel ty

pe
— so biodiesel buses were added to the diesel buses from the NT D. L iquefied 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 readil y available emission rate data on LNG buses, we grouped all natural gas fue led transit buses together. This means we effectivel y model LNG buses as if they were powered b y 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 t o MY 1990. All other market shares of transit bus fuel ty pes are derived using the NTD, as shown in Table 6
- 7
. MOVES modelers can adjust these distributions between the fuel ty pes using the AVFT tool.48 **Table 6**  **- 7**  **Fuel type**  **ma rk** **et shares by m odel year for**  **transit buses in MOVES2014** 

**Model Year MOVES Fuel Type** 

**Gasoline Diesel**  **CNG** 19 82
- 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 separatel y from other heav y- duty vehicles, under 40 CFR 86.091
- 2. 34 For this reason, CNG and diesel transit buses are each categorized in regulatory class 48. La cking better data, we used a single regulatory class distribution from a study of diesel and CNG transit buses, highlighted in the MOVES2014 HD Emissio ns Rates Report4 , for g asoline transit buses as shown in Table 6
- 8 below
.

**Table 6**  **- 8**  **Regulatory class fractions of gasoline transit buses in MOVES2014 MOVES Source Type & Fuel Type**  **MOVES regClassID** 42 46 47 Total Gasoline Transit Buses 0.2683 0.0976 0.6341 1 The MOVES2014 school bus fuel ty pe distribution is based on MOBILE6 estimates , originall y calculated from 1996 and 1997 Polk bus registration data , for model ye ars 1982
- 1996 are summarized in Table 6
- 9
. The Union of Concerned Scientists estimates that roughl y one percent of school buses run on non
- diesel fuels, so we have assumed that one percent of school buses are49

gasoline fueled in MY 1997 and later. 35 The school bus regulatory class distribution was also derived from the 2011 FHWA data in Table 6
- 5
.

**Table 6**  **- 9**  **Fuel type m arket shares by m**  **odel 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 directl y from the EPA’s sample vehicle counts datasets, except motor homes. The single unit source ty pes are split between gasoline and diesel onl y. Single unit vehicle are distribute d among the heav y- duty regulatory

classes (regClassIDs 41, 42, 46, and 47) based on the underl yi ng 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 thro ugh supplementary data sources. The fuel type distribution for motor homes is unchanged from MOVES2010b, originally based on interpolating information from the Recreation Vehicle Industry Association (RVIA) on fuel ty pe market shares. 3650 **Table 6**  **- 10**  **Fuel type m arket shares for m**  **otor homes in MOVES2014 Model Year**  **Percent of Diesel Percent of Gasoline**  198 2
- 1993 15% 85% 1994 18% 82% 1995 21% 79% 1996 23% 77% 1997 26% 74% 1998 29% 71% 1999 32% 68% 2000 34% 66% 2001 37% 63% 2002 40% 60% 2003 41% 59% 2004 43% 57% 2005 44% 56% 2006 46% 54% 2007 47% 53% 2008 49% 51% 2009 50% 50% 2010+ 50% 50% The motor home regulatory class distribution , shown below in Table 6
- 11 , is used across all model years based on the same 2011 FHWA dataset referenced above for school and non
- school buses
. **Table 6**  **- 11**  **Regulatory class fractions of m otor 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. Com**  **bination 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), predominantl y in short
- haul. Similarly , almost all combination trucks ar e diesel fueled. MOVES does not model gasoline long
- haul combination trucks. Even for the short
- haul source ty pe, 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 currentl y this remains largel y in the planning stages. T here has not been sufficient testing of these trucks to develop MOVES emission rates. We will consider adding natural gas combination trucks as they become more prevalent and their emissions are more thoroughly

tested
.51



## 7. **Vehicle Characteristics that Vary by Age** Age is an important factor in calculating vehicle emission inventories, identifying high emitters, and characterizing travel behavior. MOVES employ s 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 heav y- duty em ission rates. 3 , 4 Inthis section, there is discussio n 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 anal ys is
. Age distributions in MOVES vary by source type and range from zero to 30+ ye ars, 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 veh icle 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 f ractions from each source ty

pe’s age distribution sum to one across a calendar ye ar. 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 ; a ll other years are available in the MOVES2014 default database SourceTy peAgeDistribution table. **7.1.1. Age Distributions**  **from Registration Data** Id eall y all historic age distributions could be derived from registration data sources for each anal ys is year available in MOVES. However, acquiring such data is prohibitively costly , so MOVES2014 only

contains registration
- based age distributions for two analy sis years: 1990 and 2011. The following sections detail how th ese data were anal yz ed and used in MOVES2014.

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

**7.1.1.2. 2011 Age Distributions**  The 2011 age distributions for cars and trucks we re derived from the sample vehicle counts dataset, as discussed earlier in Section 6.2.2. This sample vehicle data includes eight of the thirteen source typ es: 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+ ye ar age distributions in 2011 for the eight source types mentioned. For the source types that were not included in the sample vehicle data

— specificall y motorcycles, motorhomes, and buses

— we calculated the 2011 age distributions by running MOVES2010b52

wi th the latest sales data available. That is, MOVES2010b used 1999 populations, sales , and scrappage forecasts to project future populations by model year , which we then used to compute age distributions. This approach kept the MOVES2010b base populations and scrappage rates but substituted in MY 1999
- 2011 sales. We pulled sales for motorcycles (11) from the Motorcycle Industry

Council’s Statistical Annual report24 , transit buses (42) from internal EPA estimates based on manufacturer reporting , and s chool buses (43) from the School Bus Fleet Fact Book 18 . Since 2011 age distributions were calculated independentl y, intercity bus (41) and motor home (54) sales data were based on slightly different assumptions. Both of these source typ es used an avera ge of Ward ’ s Class 3
- 8 truck sales in Oak Ridge’s Transportation Energy Data Book 22 , transformed into MOVES source types using the allocation of sample vehicle counts described in Section 6.
. For more information on these data sources, revisit Section 3. Table 7
- 1 shows the fraction of vehicles b y age (0
- 30+ years) and source t ype for cal endar year 2011. These 2011 age distributions became the basis for all the forecast age distributions in Section 7.1.2.2. and all backcast age distributions in Section 7.1.2.3.53 **Figure 7**  **- 1**  **2011 Age Distributions for MOVES Source Types**54 **Table 7**  **- 1**  **2011 Age Fractions for MOVES Source Types**  **Age 11**  **21 31**  **32 41**  **42 43**  **51 52**  **53 54**  **61 62**  0 0.0585 0.042 0.0496 0.0557 0.0477 0.0628 0.0368 0.0334 0.035 0.0237 0.046 0.0219 0.0478 1 0.0565 0.0472 0.044 0.0482 0.0421 0.0385 0.0403 0.0265 0.0216 0.015 0.0406 0.0164 0.0378 2 0.0614 0.043 0.0335 0.0372 0.0353 0.0393 0.048 0.0351 0.0231 0.0176 0.034 0.0213 0.0501 3 0.1088 0.0545 0.0587 0.0668 0.0458 0.0555 0.0529 0.0273 0.0479 0.031 0.0442 0.0192 0.0392 4 0.0968 0.0597 0.0626 0.0703 0.0601 0.0539 0.0548 0.0956 0.0629 0.0544 0.0579 0.0629 0.1371 5 0.0917 0.0562 0.0644 0.0743 0.0617 0.0389 0.0644 0.0718 0.0666 0.0486 0.0594 0.0468 0.1028 6 0.0803 0.0562 0.0677 0.077 0.0638 0.0607 0.0574 0.0677 0.0577 0.045 0.0615 0.0455 0.0971 7 0.0682 0.0526 0.0686 0.0781 0.062 0.0498 0.0565 0.0407 0.0506 0.0333 0.0597 0.0288 0.0584 8 0.0583 0.0551 0.0638 0.0724 0.0574 0.0488 0.0487 0.04 0.0438 0.0284 0.0553 0.0256 0.057 9 0.0514 0.055 0.0624 0.0702 0.0538 0.0495 0.0511 0.029 0.0393 0.0238 0.0518 0.0199 0.0415 10 0.0436 0.0534 0.0562 0.0647 0.0517 0.057 0.0467 0.0357 0.0427 0.059 0.0498 0.0391 0.0482 11 0.0348 0.0575 0.0545 0.055 0.0492 0.0385 0.0508 0.0488 0.0697 0.1457 0.0474 0.0535 0.0766 12 0.0263 0.05 0.0504 0.0433 0.0478 0.0374 0.047 0.0702 0.0591 0.1267 0.0461 0.0482 0.0572 13 0.0224 0.0441 0.0424 0.0273 0.0362 0.0439 0.0371 0.0645 0.0334 0.0213 0.0271 0.049 0.0381 14 0.0215 0.042 0.0372 0.0305 0.0295 0.0401 0.0345 0.0312 0.0459 0.0175 0.0417 0.0398 0.0215 15 0.0188 0.0354 0.0284 0.0203 0.0244 0.0369 0.0298 0.0406 0.0308 0.0198 0.0258 0.0556 0.0234 16 0.0142 0.0367 0.0274 0.0219 0.0317 0.0303 0.038 0.0521 0.0423 0.0338 0.0305 0.0628 0.0209 17 0.0163 0.029 0.025 0.0137 0.0244 0.0264 0.0184 0.0367 0.0323 0.0279 0.0291 0.0524 0.0127 18 0.0133 0.0249 0.0175 0.0136 0.0201 0.0219 0.0219 0.0167 0.0225 0.0777 0.02 0.038 0.0086 19 0.0111 0.0209 0.0142 0.0073 0.0148 0.019 0.0177 0.0149 0.0179 0.0137 0.0175 0.0292 0.0052 20 0.0088 0.0178 0.012 0.007 0.0168 0.0192 0.0226 0.0233 0.0162 0.0213 0.013 0.0272 0.004 21 0.0071 0.015 0.0106 0.0075 0.0188 0.0281 0.0255 0.0166 0.022 0.0132 0.0171 0.0337 0.0031 22 0.0053 0.0124 0.0108 0.008 0.0187 0.0214 0.0145 0.0256 0.0211 0.0535 0.0221 0.0343 0.0031 23 0.0045 0.0097 0.0092 0.0073 0.0174 0.0168 0.0173 0.0147 0.0188 0.017 0.0196 0.0317 0.0019 24 0.0044 0.008 0.007 0.0057 0.018 0.0156 0.0175 0.0132 0.0171 0.0061 0.0191 0.025 0.0032 25 0.0037 0.0065 0.0071 0.0053 0.0151 0.0131 0.0153 0.0068 0.0154 0.0064 0.0141 0.0174 0.0009 26 0.0031 0.0053 0.0049 0.0037 0.0132 0.0113 0.0131 0.0068 0.0132 0.0055 0.015 0.0177 0.0009 27 0.0028 0.0042 0.004 0.0031 0.0104 0.0088 0.0101 0.0056 0.0113 0.0048 0.0152 0.0145 0.0007 28 0.002 0.0025 0.0024 0.0019 0.0041 0.0083 0.0037 0.0025 0.0067 0.0028 0.0098 0.0062 0.0003 29 0.0016 0.0017 0.0019 0.0015 0.0035 0.0045 0.0027 0.0029 0.0067 0.0028 0.0057 0.0073 0.0004 30 + 0.0025 0.0016 0.0016 0.0012 0.0047 0.0039 0.0047 0.0035 0.0066 0.0027 0.0039 0.0089 0.000455 **7.1.2. Forecast**  **ing and Backcasting Age Distributions**  Since purchasing registration data for all historic years is prohibitively costly and impossible for future ye ars, 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 dy namic survival rate. However, while sale s data for historic years are well known and projections for future ye ars are common in economic modeling, historic and projected vehicle survival are not well studied. For MOVES2014, a generic survival rate was scaled up or down for each calendar year bas ed 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 ye ar 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 re gistration data as described in a study conducted for the EPA. 37 Survival rates for passenger cars , passenger trucks and light commercial trucks came from NHTSA's survivability Table 3 and Table 4. 38 These survival rates are based on a detailed anal ys is 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 MOV ES passenger truck and light commercial truck source ty pes .  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 onl y to age 25
. Therefore , we extrapolat ed 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 accordingl y.  Because MOVES requires survival rates for ageIDs < 2, the se values were linearl y interpolated with the assumption that the survival rate prior to age ID 0 is 1. Effectivel y , this results in a near constant survival rate until ageID 3 for light
- duty vehicles and until ageID 4 for heav y - dut y vehicles
.  NHTSA defines survival rate as the ratio of the number of vehicles remaining in the fleet at a giv en ye ar as compared to a base ye ar. However, MOVES defines the survival rate as the ratio of vehicles remaining from one ye ar to the next, so we transformed the NHTSA rates accordingl y.56

Because MOVES age ID 30 is intended to represent all vehicles 30 ye ars 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 age I D 30 was set to 0.3. The actual survival rate of these age 30+ vehicles is unknown. Quantitatively , the following piecewise formulas were used to derive the MOVES survival rates. In them, represents the MOVES survival rate at age , and represents the NHTSA survival rate at age . When this generic survival rate is discussed below, the shorthand notation ⃗ will represent a one - dimensional array of values at each permissible age as described below. Age 0 : = 1
− 1
− 3 Equation 5 Age 1: = 1
− 2 ( 1
− ) 3 Equation 6 Age 2
- 29: =

... = Equation 7 Age 30: = 0 . 3 Equation 8 With limited data available on heav y- duty vehicle scrappage, survivability for all other source ty pes came from the Transportation Energy Data Book. We used the heavy
- duty vehicle survival rates for model year 198 0 ( TEDB32 , Table 3.14 ). The 1990 model year rates were not used because they

were significantly

higher than rates for the other model ye ars in the anal ys is ( i.e . 45 percent survival rate for 30 year - old trucks), and seemed unrealistically high. While limited data exists to con firm 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 ye ars 1988
- 1991 between the 1992 and 1997 survey s was 88 percent. The comparable s urvival rate for 1990 model year heav y- duty vehicles from TEDB was 96 percent, while the rate for 1980 model year trucks was 91 percent. This comparison lends credence to the decision that the 1980 model year survival rates are more in line with available data. TEDB does not have separate survival rates for medium
- duty vehicles , so it was necessary to apply the heavy
- duty rates uniforml y 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 SourceTy peAge table. Please note that since MOVES2014 does not calculate age distributions during a run, these values are not activel y used b y MOVES. However, they were used in the development of the national age57

distributions stored in the SourceTy peAgeDistribution table, and remain in the default database for reference. **Table 7**  **- 2**  **Survival Rate by Age and**  **HPMS Class Age**  **Motorcycles Light**  **- Duty**  **Vehicles Buses**  **Single Unit Trucks**  **Combination**

**Trucks Passenger**  **Cars Passenger Trucks** 

**Light Comm. Trucks** 0 1.000 0.997 0.991 1.000 1.000 1.000 1 0.979 0.997 0.991 1.000 1.000 1.000 2 0.940 0.997 0.991 1.000 1.000 1.000 3 0.940 0.993 0.986 1.000 1.000 1.000 4 0.940 0.990 0.981 0.990 0.990 0.990 5 0.940 0.986 0.976 0.980 0.980 0.980 6 0.940 0.981 0.970 0.980 0.980 0.980 7 0.940 0.976 0.964 0.970 0.970 0.970 8 0.940 0.971 0.958 0.970 0.970 0.970 9 0.940 0.965 0.952 0.970 0.970 0.970 10 0.940 0.959 0.946 0.960 0.960 0.960 11 0.940 0.953 0.940 0.960 0.960 0.960 12 0.940 0.912 0.935 0.950 0.950 0.950 13 0.940 0.854 0.929 0.950 0.950 0.950 14 0.940 0.832 0.913 0.950 0.950 0.950 15 0.940 0.813 0.908 0.940 0.940 0.940 16 0.940 0.799 0.903 0.940 0.940 0.940 17 0.940 0.787 0.898 0.930 0.930 0.930 18 0.940 0.779 0.894 0.930 0.930 0.930 19 0.940 0.772 0.891 0.920 0.920 0.920 20 0.940 0.767 0.888 0.920 0.920 0.920 21 0.940 0.763 0.885 0.920 0.920 0.920 22 0.940 0.760 0.883 0.910 0.910 0.910 23 0.940 0.757 0.880 0.910 0.910 0.910 24 0.940 0.757 0.879 0.910 0.910 0.910 25 0.940 0.754 0.877 0.900 0.900 0.900 26 0.940 0.754 0.875 0.900 0.900 0.900 27 0.940 0.567 0.875 0.900 0.900 0.900 28 0.940 0.752 0.873 0.890 0.890 0.890 29 0.940 0.752 0.872 0.890 0.890 0.890 30 0.300 0.300 0.300 0.300 0.300 0.300 **7.1.2.2. 2012** **- 2050 Age Distributions**  The 2012
- 2050 age distributions were derived from the 2011 age distribution described above using population, survival, and sales projections. Age distributions are easily calculated from population counts, if the populations are known by age:

, = Equation 9 Here, , is the age fraction to be calculated, is the population of vehicles at age , and is the total population in calendar ye ar . In this section, arrow notation will be used if the operations are to be performed at the individual age level. For example, ⃗ would be used to58

represent all age fractions in calendar ye ar . Another example is ⃗ ; it represents an array of va lues at each permissible age in calendar year . In contrast, represents the total population in year . Intuitivel y, projecting an age distribution forward one year involves removing the vehicles scrapped in the base ye ar and adding the new vehicl es sold in the next year, as shown in Equation 10 :

⃗ = ⃗

− ⃗ + ⃗ Equation 10 where ⃗ is the population (known at each age) of the next year, ⃗ is the population in the base year, ⃗ is the population of vehicles removed in the in the base ye ar, and ⃗ is new vehicles sold in the next year. Please note th at the final term only inc ludes new vehicles at age 0 ; if the equation is evaluated for any > 0 , the sales term is zero
. Equation 10 can be used algorithmicall y to forecast a k nown population distribution as follows: 1. Starting with the base population distribution ( ⃗), remove the number of vehicles that did not survive ( ⃗) at each age level. 2. In crease the population age index by one (for example, 3 year old vehicles are rec lassified as 4 year old vehicles). 3. Add new vehicle sales ( ⃗) as the age 0 cohort. 4. Combine the new age 30 and 31 vehicles into a single age 30 group. 5. This results in the next year population distribution ( ⃗). If this algorithm is to be repeated, ⃗ becomes ⃗ for the next iteration. Unfortunatel y, as described in the section above, the only survival information we have is a single snapshot. Because vehicle populations and new sales change differentially (for example, the historic populations shown in Section 5.1. level off during the recent recession; at the same time, sales of most vehicle ty pes plummeted), it is important to adjust the survival curve in response to changes in population and sales. We did so by defining a scalar adjustment factor that can be algebraicall y calculated from population and sales estimates. Its use in determining the population of vehicles removed and its relationship to the generic survival rate ⃗ is given b y Equation 11 . Note that the open circle operator ( ∘ ) represents entry wise product; that is, each element in an array is multiplied by the corresponding element in the other one, and it results in an array with the same number of elements
.
⃗ = ∙ 1
− ⃗ ∘ ⃗ Equation 11 Substituting Equation 11 into Equation 10 yi elds :

⃗ = ⃗

− ∙ ( 1
− ⃗) ∘ ⃗ + ⃗ Equation 1259

Since both the value of the scalar adjustment factor and the actual distribution of the next year’s population are unknown, Equation 12 can’t be used yet. However, by using an estimate of next ye ar’s total population, it can be transformed into:

= − 1
− ⃗ ∘ ⃗ + Equation 13 This was algebraicall y solved for and evaluated for each HPMS category e using the following information:

 Total p opulations and by HPMS category . For analy sis 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 S ection 5.2.  Survival ⃗ by

HPMS category , which is described in Section 7.1.2.1.  Population distribution ⃗ by HPMS category
. For analy sis 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 b y HPMS category. For ye ars 2012+, this comes from ⃗ of the previous ye ar.  New vehicle sales by HPMS category , which are derived from AEO2014. The projection of sales was calculated as a percentage of the total population using the vehicle category mapping discussed in Section 4.2. ; this is converted to the number of new vehicles b y multiplying by the HPMS category

population. After determini ng by

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

 Population distribution ⃗ by source type . For anal ys is 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 ye ars 2012+, this comes from ⃗ of the previous year.  The scalar adjustment factor and generic survival rate ⃗ applied by source ty pe using the HPMS to source ty

pe mapping described by Table 2
- 1 . Please note that limits we re placed on the ( 1
− ⃗) term of Equation 12 : the value of this term for each age was restricted to being between 0 and 1.

e Because vehicle survival rates use the categories of motorcycles, passenger cars, light
- duty trucks, bu ses, 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 tr

ucks, the term “HPMS category” is used here for simplicity.60

 New vehicle sales ⃗ , determined as a percentage of the total population in AEO2014 as discussed above; this is converted to the number of new vehicles b y multiplying by the total source type population. With all of this information, the population distributions were alg orithmically determined for ye ars 2012
- 2050. The resulting total source type populations ( ) are stored in the SourceT ypeYear table of the default database. The resulting age distributions are stored in the SourceT ypeAgeDistribution table.

**7.1.2.3. 1999** **- 2010**  **Age Distributions** The method used to backcast the 1999
- 2010 age distributions from the 2011 distribution is very similar to the forecasting method described above
. For backcasting an age distribution one year, Equation 10 of the previous section can be rewritten as:

⃗ = ⃗

− ⃗ + ⃗ Equation 14 Essentially , this can be thought of as taking the base year’s population distribution, removing the vehicles sold (or added to the population) in that year, and then adding the vehicles that were removed in the ye ar before. This can be represented alg orithmically as follows: 1. Starting with the base population distribution ( ⃗), remove the age 0 vehicles ( ⃗). 2. Decrease the population age index by one (for example, 3 year old vehicles are reclassified as 2 year old vehicles). 3. Add the vehicles that were removed in the previous year ( ⃗). 4. This results in the previous year population distribution ( ⃗). If this algorithm is to be repeated, ⃗ becomes ⃗ for the next iteration. The equation governing vehicle removal discussed the previous section is also applicable here. Taking careful note of the subscripts, Equation 11 and Equation 14 can be combined:

⃗ = ⃗

− ⃗ + ∙ 1
− ⃗ ∘ ⃗ Equation 15 As in the forecasting situation, the value of the scalar adjustment factor and the actual distribution of the previous year’s population are unknown. With a similar strategy of using the previous year’s known total population, Equation 15 can be transformed into:

= − + 1
− ⃗ ∘ ⃗ Equation 16 However, this still leaves a ⃗ term, which is unavoidable because the total number of vehicles removed is dependent on the age distribution of those vehicles. To properl y solve Equation 16 for and ⃗ , a numerical method of approximation could be employed. However, due to61

lack of resources, ⃗ was used as a simple approximation of ⃗ on the left hand side of Equation 16
. T he following sources were used to determine by HPMS category :

 Total p opulations and by HPMS category . For all historic analy sis years , this information is described source type in Section 5.1. and simply needs to be summed by HPMS category

across all ages for use here.  Survival ⃗ by

HPMS category , which is described in Section 7.1.2.1.  Population distribution ⃗ by HPMS category
. For analy sis 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 ye ars, this comes from ⃗ of the previous iteration.  New vehicle sales data, which was collected b y s ource type from a variety of sources. Each of these was summed by HPMS category . Motorcycles sales comes from the Mo torcycle Industry

Council; sales data for passenger cars, passenger trucks, light commercial trucks, refuse trucks, short
- haul and long
- haul single unit trucks, and short
haul and long
- haul combination trucks comes from TEDB and VIUS; transit buses product ion 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 ye ar 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 estimate d by averaging the sales of refuse, short
- haul, and long
- haul single unit trucks as a fraction of their total population. After determining by HPMS category , Equation 15 was used with the following information to compute the previous ye ar’s age distribution by source typ e :

 Population distribution ⃗ by source type . For anal ys is 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 ye ars, this comes from ⃗ of the previous iteration.  The scalar adjustment factor and generic survival rate ⃗ applied by source ty pe using the HPMS to source ty pe mapping described by Table 2
- 1 . As with before, limits were placed on the ( 1
− ⃗) te rm, such that the value of this term for each age was restricted to being between 0 and 1. Also, the ⃗ term used when calculating the number of vehicles removed was approximated by ⃗

.  New vehicle sales ⃗ , from the sources listed above and applied by source typ e. With all of this information, the population distributions were algorithmically determined for ye ars 1999
- 2010. The resulting age distributions are stored in the SourceTypeAgeDistribution table.62



### 7.2. **Relative Mileage Accumulation Rate** MOVES uses a relative mileage accumulation rate (RMAR) in combination with source type populations (see Section 0 and age distributions described earlier in thi s section to distribute the total annual miles driven by each HPMS vehicle type (see Section 4. ) to each source typ e 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 b y user input, by the f ive Highway

Performance Monitoring Sy stem (HPMS) vehicle classifications. These classifications are further broken down into the groupings of the MOVES source use ty pes, 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 typ es 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, n ew (age 0) passenger trucks and light commercial trucks have a RMAR of one (1.0)
. f Based on the data, new passenger cars have a RMAR of 0.885. This means that when the VMT assigned to the HPMS vehicle classification (25) is allocated to passenger cars, passenger trucks and light commercial trucks, a passenger car of age 0 will be assigned onl y 88.5% of the VMT assigned to a passenger truck or light commercial truck of age 0. The RMAR values for MOVES2014 for the source typ es 11 (motorcycles), 41 (intercity buses), 42 (transit buses), 43 (school buses) and 54 (motorhomes) were not changed from the values used in MOVES2010b. Passenger car and light
- duty truck RMAR values were recalculated to reflect the change in the HPM S vehicle classifications used for VMT input and the remaining heav y- 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 fo r motorcycles (category 11) based on the model years and odometer readings listed in motorcycle advertisements. A stratified sample of about 1,500 ads were examined. A modified Weibull curve was fit to the data to develop the relative mileage accumulation rates used in MOVES
. 37

f Within each HPMS vehicle class, an RMAR value of one is assigned to the source type and age with the highest annual VMT accumulation . Because we use the same mileage accumulation data for pass enger trucks and light commercial trucks, they both have a value of one.63 **7.2.2. Passenger Cars, Passenger**  **Trucks and Light Commercial Trucks** In MOVES2010b, passenger cars were not included in the same HPMS vehicle classification as for passenger trucks and light commercial trucks. For MOVES2014, the MOVES2010b passenger car RMAR values were adjusted to reflec t the relative difference in annual mile accumulation between passenger cars and the light trucks. Anal ys is of the data determined that new passenger cars (age 0) accumulate only 88.5% of the annual miles accumulated by new light trucks. All of the RMAR values for passenger cars were adjusted to be 88.5% of their previous values. The MOVES2010b RMAR values for passenger cars, passenger trucks and light commercial trucks (categories 21, 31 & 32) were taken from the NHTSA report on survivability and mileag e schedules. 38 Inthe NHTSA anal ys is, annual mileage b y age was determined for cars and for trucks using data from the 2001 National Household Travel Survey . In this NHTSA anal ys is, vehicles that were less than one ye ar old at the time of the survey were classified as " age 1", etc. NHTSA used cubic regression to smooth the VMT by age estimates. We used NHTSA's regression coefficients to extrapolate mileage to ages 26 through 30 not covered b y the report. Since passenger trucks had the highest MAR in what was then the L ight
Duty Truck HPMS category , each source typ e’s mileage b y 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 w as 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 S urvey was available, we conducted a preliminary

anal ys i s of the impact of updating the MARs based on the 2009 National Household Travel Survey . This resulted in changes in MOVES’ allocation of VMT by one percent or less for each of the vehicle categories covered b y the survey . As such, we feel that the MARs de veloped from the 2001 survey are still reasonable for use in MOVES2014. **7.2.3. Buses**  The RMAR values for all bus categories in MOVES2014 were not changed from MOVES2010b estimates. The Intercity

Bus (category

41) annual mileage accumulation rate is taken from Mo torcoach Census 2000. 39 The data did not distinguish vehicle age, so the same MAR (59,873 miles per year) was used for each age. The School Bus (category 43) annual mileage accumulation rate (9,939 miles per year) is taken from the 1997 School Bus Fleet Fac t Book. The MOVES model assumes the same annual mileage accumulation rate for each age. The Transit Bus (category

42) annual mileage accumulation rate are taken from the MOBILE6 values for diesel transit buses (HDDBT). This mileage data was obtained from the 1994 Federal Transportation Administration survey of transit agencies. 40 The MOBILE6 equation was extended to calculate values for ages 26 through 30.64 **7.2.4. Other**  **Heavy -**  **Duty Vehicles**  The RMAR values for source types 51 (refuse trucks), 52 (short haul single unit trucks), 53 (long haul single unit trucks), 61 (short haul combination trucks) and 62 ( long haul combination trucks) were updated using the data from the 2002 Vehicle Inventory and Use Survey

(VIUS). The total reported annual miles traveled b y truck s in each source type was divided by the vehicle population to determine the average annual miles traveled per truck b y source type. **Table 7**  **- 3**  **2002 VIUS Annual Mileage Accum ulation Rates**  **Age Model** 

**Year Single Unit Trucks**  **Combination Trucks**

**Refuse (51)**  **Short -**  **haul**

**(52) Long**  **- haul (53)**  **Short -**  **haul (61)**  **Long -**  **haul (62)** 0 2002 26,703 21,926 40,538 119,867 109,418 1 2001 32,391 22,755 28,168 114,983 128,287 2 2000 31,210 24,446 30,139 110,099 117,945 3 1999 31,444 23,874 49,428 105,215 110,713 4 1998 31,815 21,074 33,266 100,331 99,925 5 1997 28,450 21,444 23,784 95,447 94,326 6 1996 25,462 16,901 21,238 90,563 85,225 7 1995 30,182 15,453 27,562 85,679 85,406 8 1994 20,722 13,930 21,052 80,795 71,834 9 1993 25,199 13,303 11,273 75,911 71,160 10 1992 23,366 11,749 18,599 71,026 67,760 11 1991 18,818 13,675 15,140 66,142 80,207 12 1990 12,533 11,332 13,311 61,258 48,562 13 1989 15,891 9,795 9,796 56,374 64,473 14 1988 19,618 9,309 12,067 51,490 48,242 15 1987 12,480 9,379 16,606 46,606 58,951 16 1986 12,577 4,830 8,941 41,722 35,897 **0 -**  **3 1999**  **- 2002** 

**Average 30,437**  **23,250 37,069**  **61,240 116,591**  For each source type, in the first few years, the data showed onl y small differences in the annual miles per vehicle and no trend
. After that, the average annual miles per vehicle decline d in a fairl y linear manner, at least until the vehicles are at age 16 (the limit of the data). MOVES, however, requires mileage accumulation rate s for all ages to age 3 0. For MOVES2014, we assumed that the mileage accumulation rate at age 30 would be the same as used for MOVES2010b. M ileage 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 ty

pe. 2) Ages 4 through 16 use mileage accumulation rates calculated using a linear regression of the VIUS data for ages three through 16,65

3) Ages 17 through 29 use values from interpolation between the values in age 16 and age 30. 4) Age 30 uses the MOVES2010b mileage accumulation rate for age 30. These rates were allocated to MOVES source ty pes from MOBILE6 mileage accumulation rates, whic h were derived from the 1992 TIUS as documented in the Arcadis report. 41 **Table 7**  **- 4**  **Regression Statistics for Heavy**  **- Duty**  **Truck Annual Mileage Accumulation Rates Ages 4**  **- 16**  **Me asurem**  **ent Refuse** 

**Truck (51) Single**  **Unit**

**Short -**  **haul (52) Single Unit** 

**Long -**  **haul (53) Combination** 

**Short -**  **haul (61) Combination** 

**Long -**  **haul (62)** Average* 30,437 23,250 37,069 61,240 116,591 Intercept 36,315 25,442 36,305 65,773 119,867 Slope
- 1,510
- 1,209
- 1,794
- 3,447
- 4,884 Age 30 0.0320 0.0518 0.1025 0.0320 0.0571 * Average sample annual miles traveled for ages 0 through 3. The resulting relative mileage accumulation rates are shown in Table 7
- 5 below. Note that the first four values are identical and then decline linearly to age 16 and then linearl y 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 ( sourceTy peI D 54), the initial MARs were taken from an independent research study42 conducted in October 2000 among members of the Good Sam Club. The members are active recreation vehicle (RV) enthusiasts who own motor homes, trailers and trucks. The average annual mileage was estimated to be 4,566 miles. The data did not distinguish vehicle age, so the same MAR was used for each age.66 **Table 7**  **- 5**  **Relative Mileage Accumulation Rates for Heavy**  **- Duty**  **Trucks ageID**  **Refuse (51) Short Haul**  **Single Unit (52)**  **Long Haul**

**Single Unit (53)**  **Motor H**  **om e** 

**(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 0.057167



## 8. **VMT Distribution of Source Type**  **b y**  **Road T**  **yp e**  For each source type, the RoadTy peVMTFraction field in the RoadTy peDistribution table stores the fraction of total VMT for each vehicle class that is traveled on each of the five roadway ty pes. Users may

suppl y the distribution VMT to vehicle classes for each road ty pe for individual counties when us ing 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 Nati onal Emission Inventory (NE I) Version 1 43 (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) reports 44 , when state supplied estim ates are not available. The 2011 NEI v1 data45 is grouped b y the Source Classification Code (SCC) used at that time and these older classifications do not cleanly map to the source types used by MOVES. The VMT was mapped to the source types used b y M OVES by calculating the fraction VMT for each source type found in each SCC classification result in a national MOVES2010b run for calendar ye ar 2011. The factors calculated from the MOVES201b run are shown in Section 20. (Appendix D : Driving Schedules and SCC Mappings )
. The first seven digits of the 10 digit SCC (SCC7) indicate the vehicle classification. The SCC road ty

pes map cleanl y to the MOVES road types. The eighth and ninth digits of the 10
- digit SCC (SCC89) indicate the road ty pe , as shown below in Table 8
- 1
. **Table 8**  **- 1**  **Mapping of SCC Road Types to MOVES Road Types SCC Road Type**  **Code (SCC89) SCC Road Type**  **MOVES Road Type ID**  **MOVES Road Type** 11 Rural Interstate 2 Rural Restricted Access 13 Rural Other Principal Arterial 3 Rural Unrestricted Access 15 Rural Minor Arterial 3 Rural Unrestricted Access 17 Rural Major Collector 3 Rural Unrestricted Access 19 Rural Minor Collector 3 Rural Unrestricted Access 21 Rural Local 3 Rural Unrestricted Access 23 Urban Interstate 4 Urban Restricted Access 25 Urban Other Freeways & Expressways 4 Urban Restricted Access 27 Urban Other Principal Arterial 5 Urban Unrestricted Access 29 Urban Minor Arterial 5 Urban Unrestricted Access 31 Urban Collector 5 Urban Unrestricted Access 33 Urban Local 5 Urban Unrestricted Access Once the SCC VMT values have been mapped to MOVES source types and road types, the national distribution of road ty pe VMT by

source ty pe can be calculated from the NE I VMT estimates , summarized in Table 8
- 2
. The off network road type (roadT yp eID=1) is not used and is allocated none of the VMT.68 **Table 8**  **- 2**  **MOVES2014 Road Type Distribution by Source Type Source**  **Type Description**  **Road Types Off**  **Network Rural** 

**Restricted Rural** 

**Unrestricted Urban** 

**Restricted Urban** 

**Unrestricted 1**  **2 3**  **4 5**  **All** 11 Motorcycle 0 0.0804768 0.3019230 0.1913280 0.4262730 1.000 21 Passenger Car 0 0.0847394 0.2344520 0.2374280 0.4433810 1.000 31 Passenger Truck 0 0.0859437 0.2753580 0.2178360 0.4208630 1.000 32 Light Commercial Truck 0 0.0866643 0.2755600 0.2180390 0.4197360 1.000 41 Intercity Bus 0 0.1409270 0.2811960 0.2195920 0.3582850 1.000 42 Transit Bus 0 0.1384440 0.2813130 0.2196020 0.3606420 1.000 43 School Bus 0 0.1383910 0.2813150 0.2196020 0.3606920 1.000 51 Refuse Truck 0 0.2396390 0.2717580 0.2524620 0.2361420 1.000 52 Single Unit Short
haul Truck 0 0.1635030 0.2869150 0.2345890 0.3149930 1.000 53 Single Unit Long
haul Truck 0 0.1638220 0.2869700 0.2346570 0.3145510 1.000 54 Motor Home 0 0.1233290 0.2876100 0.2255300 0.3635310 1.000 61 Com

bination Short
haul Truck 0 0.2366730 0.2744240 0.2516600 0.2372430 1.000 62 Com

bination Long
haul Truck 0 0.2476010 0.2705480 0.2543110 0.2275400 1.00069



## 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. Itis 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 , MOVES 2014 uses a distribution of average speeds by bin. T he Av g SpeedDistribution table lists the default fraction of driving time for each source ty pe , road type, d ay , and h our i n each average speed bin. The fractions sum to one for each combination of source type, road type, day, and hour. The MOVES average speed bin s are defined in Table 9
- 1
. **Table 9**  **- 1**  **MOVES Speed Bin Categories Bin**  **Average Speed (m ph)**  **Average Speed Range (m ph)**  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 MOVES 2014 , the light
- duty average speed distributions are based on in
- vehicle global position system ( GPS ) data . The data was obtained through a contract with Eastern Research Group (ERG), who subcontracted with TomTom to provide summarized vehicle GPS data
. g TomTom makes in
- vehicle GPS navigation devices as well as supports cell
- p hone navigation applications. ERG provided the US EPA with updated values for the AvgSpeedDistribution calculated from the TomTom delivered data based on their consumers, which “virtually all” use them in light
- duty

cars, trucks and vans
.

g Much of the following text and tables are excerpted from the ERG Work Plan (EPA
- 121019), submitted to US EPA on January 11, 2012.70

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

 Data is self

- selective. Data is only

recorded from users of TomTom GPS units and an iPhone application. Additionally , TomTom data is only collected when the units are on. This creates bias no t only for users, but also for types of driving. Anecdotally , drivers that own GPS units are less likely to use them when they

drive in familiar areas in comparison with unfamiliar areas. Compared to the default VMT by road ty pe information in MOVES, TomTo m o ver represents behavior on rural restricted access roads , which suggests the higher usage of GPS on vacations and business trips.  No information on vehicle ty pe 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 ty pe. However, due to a lack of information on other source ty pes, the average speed distribution derived from the TomTom light
- duty GPS data is applied to all source ty

pes, a lthough the combination long
- haul trucks distribution was adjusted as described at the end of this section
. Other heav y- duty source types such as single unit long
- haul trucks were not adjusted. We recognize this as a shortcoming of MOVES, and look to incor porate source - ty

pe specific average speed information in the future.  The MOVES average speed distributions are based on the average speed in each roadway segment, not second
- by
- second speed measurements.  Only data that is associated with the vehicle networ k is included in the average speed delivery . As part of the quality control methods, TomTom excludes data that does not “ snap to the roadway

grid ” to remove points caused by loss of satellite signal and errors while the TomTom unit is trying to acquire the satellite signal. TomTom uses data quality control techniques to minimize data arising from non
- light
- duty- vehicle use, such as from pedestrians, bicycles, and airplanes Under direction of ERG, TomTom queried its database of historic traffic probes to produce a table of total distance and total time as a function of road type, weekday /weekend, hour of the day , and average speed bin for the calendar year 2011 for the 50 US states and the District of Columbia. TomTom delivere d a table identifying the total distance and total time of vehicles travelling at an average speed interval for all combinations of: 1. Id entifier for Average Speed Bin (20 levels): Average speeds wer e 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. Id entifier for Month of the Year (12 levels). 3. Id entifier for Day

of the Week (2 levels): The period for weekday is Monday , 00:00:00 to Friday , 23:59:59, and the period for weekend is Saturday , 00:00:00 to Sunday , 23:59:59. 4. Id entifier for Time of Day (24 levels): Times are binned in one hour increments, starting at midnight: 00:00:00 to 00 :59:59; 01:00:00 to 01:59:59, ... , 23:00:00 to 23:59:59.71

5. Id entifier for Road Ty

pe (4 levels): TomTom used the information in Table 9
- 2 to classify between the TomTom Functional Classes and the MOVES road ty pe description. TomTom also categorized the road ty pes a s rural or urban, according to the census definitions used in MOVES h

. **Table 9**  **- 2**  **Correspondence between**  **Tom Tom Functional Class, Census Inform**  **ation, and MOVES Road Types.**  **MOVES Road Type Description**  **Census Information for the**  **TomTom Roadway Segm ent**  **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 b y loss of satellite signal and errors while the TomTom unit is trying to acquire the satellite signal. Therefore, a difficult anal ys is would be required to separate real off
- network data from GPS error data, and even if the anal ys is could be done, the reliability of the results would probabl y be unknown . As such, only data that was associated with the roadway grid was used in the anal ys is. Table 9
- 3 shows the method for using the internal TomTom data (Columns E through I) to produce the desired output, which ERG use d to produce the MOVES2014 tables. The example in the table uses 16 obser

vations 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
- 2 1 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 b y the average speed bin, which is given in Column J. The total distance traveled and the total time spent in each combination of road ty pe, month, weekday /weekend, hour of the day , and average speed bin are given in Columns K and L
. TomTom provide d Columns A, B, C, D, J, K, and Lto ERG. T he data in those columns was purchased b y 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 w ere not provided to ERG and the US EPA
.

h http://www.census.gov/geo/www/ua/2010urbanruralcl ass.html72

**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 typ e, day , and hour of the day using the average speed bin (Column J) and the t otal of segment times (Column L) i

. ERG calculated the average speed distribution according to the 16 speed bins used in MOVES. Figure 9
- 1 plots the average speed distribution for one hour (5 pm) stored in the averageSpeedDistribution t able in MOVES, which contains average speed distributions for each hour of the day (24 hours). We are using the TomTom data to represent national default average speed distribution in MOVES
.

i MOVES uses time
- based speed because the emission rates are time
- based (e.g. gram/hour).73 **Figure 9**  **- 1**  **Average speed distribution for hour = 17 (5 pm) for source types**  **(11 through 54) stored in the**  **A vgSpeedDistribution table in MOVES** 



### 9.2. **Heavy -**  **Duty Average Speed Distributions**  It has been shown that combination trucks travel at approximately 92% of the speed of light
- duty vehicles on restricted access roads 46 . 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 typ es.74

The average speed for each roadway ty pe, day typ e, and hour can be calculated by multiplying the average speed of each bin by the corresponding distribution of time as shown in Equation 29
. Here, ̅ is the average speed of the distribution, is the avera ge speed of bin , and is the proportion of time spent in bin
.

̅ = ∙

= 2 . 5 ∙ + 5 ∙ + ⋯ + 70 ∙ + 75 ∙ Equation 17 To adjust the average speed for heav y - duty combination trucks, we redistributed the proportion of time spent in each speed bin such that its contribution to the average speed was 92 % of the light
- duty speed , as shown in Equation 18. This redistributed proportion of time in each speed bin is given b y .

̅combination = (
. 92 ) ̅ light
- duty
= ∙ Equation 18 To perform this redistribution, we define d two new variables, and , where is the fraction of that is shifted down one speed bin, and is the fraction of shifted down two speed bins. The new distribution at speed bin (given by ) starts with the original distribution ( ), gains the proportions moved down from the higher speed bins ( ∙ and ∙ ), and loses the proportion that i s moved to a lower speed bin ( ∙ and ∙ ). This is shown in Equation 31 :

= + ( ∙ ) + ( ∙ )
− ( ∙ )
− ( ∙ ) Equation 19 For speed bins with an average speed of less than or equal to 60 mph , we only needed to sh ift distributions using a fraction of one speed bin (or 5 mph) . Thus we onl y calculated and set = 0
. Mathematicall y, reducing a bin’s average speed by a certain fraction ( ) can be expressed with **Error! Reference source not found.** : ( 1
− ) ∙ = ∙ (
− 5 ) + ( 1
− ) ∙ Equation 20 Essentially , the fraction that is moved to the next slower bin ( ) is multiplied by the slower speed (note that each of the speed bins are 5 mph apart, so this is
− 5 ), and the fraction that remains ( 1
− ) is multiplied by the original speed . Since the average speed of the combination trucks is 92% slower, ( 1
− ) = 92% and = 0 . 08
. By rearranging terms from equation 20, and solving for we obtain:

= ∙ 5 Equation 21 However, f

or speed bins ≥ 6 5 mph , **Error! Reference source not found.**  yi elds greater than 1
. Since that logically

can’t happen, some of the distribution needed to be moved to the second next75

slower speed bin to full y account for the 8% speed reduction . This is mathematically shown in **Error! Reference source not found.** , which is the logical extension of Equation 20 :

( 1
− ) ∙ = ∙ (
− 10 ) + ∙ (
− 5 ) + ( 1
− − ) ∙ Equation 22 The difference between **Error! Reference source not found.** and **Error! Reference source not found.**  is that an additional fraction ( ) is removed from the original speed bin and is g iven the speed of two speed bins slower (or 10 mph slower). With this additional factor, there is an infinite combination of solutions that could satisfy Equation 22
. We solved this problem with a linear equation solver by

setting Equation 22 to a constraint (see **Error! Reference source not found.**  ), adding the constraint that + are less than or equal to 1 ( Equation 24 ) , and cho osing the solution that minimized
.

∙ (
− 5 ) + ∙ (
− 10 ) + ∙ (
− − ) = 0 Equation 23

+ ≤ 1 Equation 24 This linear program was used to solve for and f or each speed bin between 65 and 75 mph. With and known for each bin, the new distributions were calculated. **Figure 9**  **- 2**  **An illustration of**  **adjustments ma**  **de to the average speed bin 55 mph. Here, the original speed**  **distribution is shown in light gray. The darker gray is the proportion of speed bin 55 that is m oved**  **out to the**  **slower speed bin 50 mph, and the black areas are**  **the distributions from speed bin 60 and 65 that are m oved**  **in to speed bin 55 mph.**76

An additional adjustment was made for the highest speed bins because w e assumed that the maximum speed bin ha d a triangular distribution with an average speed of 75 mph (see Figure 9
- 2 )
. In the new distribution, all of the maximum speed bin fraction was redistributed to the 65 and 70 mph bins. Therefore, the new maximum speed bin (70 mph) was also assumed to have a triangular distribution. Geometricall y, 1/9 th of a triangular distribution averaging 70 mph is faster than 72.5 mph . Since the 75 mph speed bin is defined as an y speed ≥ 72.5 mph , 1/ 9 th of the new 70 mph fraction ( ) was reclassified as the new fraction for the 75 mph bin
. T his process was repeated for both short
- and long
- haul combination trucks on restricted
- access road ty pes for every

hour and day

type combination. See Figure 9
- 3 f or an example illustration of the results of this analysis.77 **Figure 9**  **- 3**  **Average weekday speed distribution for hour = 17 (5 pm )**  **by source typ e stored in the avgSpeedDistribution table in MOVES**  **.**78



## 10. **Driving Schedules and Ramps** Drive schedule refers to a second
- by
- second vehicle speed trajectory . A drive schedule t ypicall y includes all vehicle operation from the time the engine starts until th e engine is keyed off, both driving (travel) and idling time. Drive schedules are used in MOVES to determine the operating mode distribution for most MOVES running processes for calculation of emissions and energy consumption. In brief, there is an emission rate (in grams per hour of vehicle operation) for each operating mode of vehicle operation. Each second of vehicle operation is assigned to an operating mode as a function of vehicle velocity in each second and the specific power (VSP), or scaled tractive power (STP) for heavy
- duty vehicles, is calculated from the driving schedules. The average speed distribution is used to weight the operating mode distributions determined from driving schedules with different average speeds into a composite operating mode distribution that represents overall travel by vehicles. The distribution of operating modes is used by MOVES to weight the emission rates to account for the vehicle operation. 

### 10.1. **Driving Schedules** A key feature of MOVES is the capability to accommodate a number of drive schedules to represent driving patterns across source type, roadway ty pe and average speed. For the national default case, MOVES2014 employ s 49 drive schedules with various average speeds, mapped to specific s ource types and roadway types. MOVES stores all of the drive schedule information in four database tables. DriveSchedule provides the drive schedule name, identification number, and the average speed of the drive schedule. DriveScheduleSecond contains the second
- by
- second vehicle trajectories for each schedule. In some cases the vehicle trajectories are not contiguous; as detailed below, they may be formed from several unconnected microtrips that overall represent driving behavior. DriveSchedu leAssoc d efines the set of schedules which are available for each combination of source use typ e and road ty pe. Ramps use operating mode distributions directly and do not use drive schedules to calculate operating modes. The RoadOpModeDistribution table l i sts operating mode distributions used for ramps for each source use type, road type and speed bin. The tables below list the driving schedules used in MOVES2014. Some driving schedules are used for both restricted access (freeway) and unrestricted acce ss (non
- freeway) driving. In most cases, these represent atypical conditions, such as extreme congestion or unrealistic free flow speeds. In these conditions, we assume that the road ty pe itself has little impact on the expected driving behavior (driving schedule). Normally , these conditions represent only a small portion of overall driving. Similarly , some driving schedules are used for multiple source types where vehicle specific information was not available. In the past, if there was no appropriate driving schedule to use for modeling an average speed bin, MOVES would use the nearest schedule. MOVES2014 now requires driving schedules that can be used as the upper bound and the lower bound for all average speed bins. New default79

driving schedules h ave been added to assure that all average speed bins have appropriate driving schedules for all the MOVES average speed bins. **Table 10**  **- 1**  **Driving Cycles for Motorcycles, Cars, Passenger Cars and Light Commerc ial Trucks**  **(11,21,31,32) ID**  **Cycle Nam e**  **Average Speed**  **Unrestricted Access Restricted access** 

**Rural Urban**  **Rural Urban**  101 LD Low Speed 1 2.5 X X X X 1033 Final FC14LOSF 8.7 X X 1043 Final FC19LOSAC 15.7 X X 1041 Final FC17LOSD 18.6 X X 1021 Final FC11LOSF 20.6 X X 1030 Final FC14LOSC 25.4 X X 153 LD LOS E Freeway 30.5 X X 1029 Final FC14LOSB 31.0 X X 1026 Final FC12LOSE 43.3 X 1020 Final FC11LOSE 46.1 X X 1011 Final FC02LOSDF 49.1 X 1025 Final FC12LOSD 52.8 X 1019 Final FC11LOSD 58.8 X X 1024 Final FC12LOSC 63.7 X X 1018 Final FC11LOSC 64.4 X X 1017 Final FC11LOSB 66.4 X X 1009 Final FC01LOSAF 73.8 X X X X 158 LD High Speed Freeway 3 76.0 X X X X

**Table 10**  **- 2**  **Driving Cycles for Intercity Buses (41)**  **ID Cycle Nam**  **e 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 5m

ph 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 X80 **Table 10**  **- 3**  **Driving Cycles for Transit and School Buses (42,43)**  **ID Cycle Nam**  **e Average**  **Speed Unrestricted access**  **Restricted access**

**Rural Urban**  **Rural Urban**  398 CRC E55 HHDDT Creep 1.8 X X X X 201 MD 5m

ph 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 mp h Flow* 30 X X 206 MD 30mph Non
- Freeway 31.5 X X 251 MD 30mph Freeway 34.4 X X 252 MD 40mph Freeway 44.5 X X 403 Bus 45 mph Flow* 45 X X 253 MD 50mph Freeway 55.4 X X X X 254 MD 60mph Freeway 60.4 X X X X 255 MD High Speed Freeway 72.8 X X X X 397 MD High Speed Freeway Plus 5 mph 77.8 X X X X * To be consistent with the speed distributions described in Section 9. , this speed represents the average for the traffic the bus is traveling in, not the average speed of the bus, which is lower due to stops.
**Table 10**  **- 4**  **Driving Cycles for Refuse Trucks**  **(51) ID**  **Cycle Nam e**  **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 5m

ph 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 X81 **Table 10**  **- 5**  **Driv ing Cycles for**  **Single Unit Trucks and Motor Hom**  **es (52,53,54)**  **ID Cycle Nam**  **e Average**  **Speed Unrestricted access**  **Restricted access**

**Rural Urban**  **Rural Urban**  398 CRC E55 HHDDT Creep 1.8 X X X X 201 MD 5m

ph Non
- Freeway 4.6 X X X X 202 MD 10mph Non
- Freeway 10.7 X X X X 203 MD 15mph Non
- Freeway 15.6 X X X X 204 MD 20mph Non
- Freeway 20.8 X X X X 205 MD 25mph Non
- Freeway 24.5 X X X X 206 MD 30mph Non
- Freeway 31.5 X X X X 251 MD 30mph Freeway 34.4 X X X X 252 MD 40mph Freeway 44.5 X X X X 253 MD 50mph Freeway 55.4 X X X X 254 MD 60mph Freeway 60.4 X X X X 255 MD High Speed Freeway 72.8 X X X X 397 MD High Speed Freeway Plus 5 mph 77.8 X X X X
**Table 10**  **- 6**  **Driving Cycles for Com bination Trucks**  **(61,62) ID**  **Cycle Nam e**  **Average Speed**  **Unrestricted access Restricted access** 

**Rural Urban**  **Rural Urban**  398 CRC E55 HHDDT Creep 1.8 X X X X 301 HD 5m

ph 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 20m

ph Non
- Freeway 19.4 X X X X 305 HD 25mph Non
- Freeway 25.6 X X X X 306 HD 30mph Non
- Freeway 32.5 X X X X 351 HD 30mph Freeway 34.3 X X X X 352 HD 40mph Freeway 47.1 X X X X 353 HD 50mph Freeway 54.2 X X X X 354 HD 60mph Freeway 59.4 X X X X 355 HD High Speed Freeway 71.7 X X X X 396 HD High Speed Freeway Plus 5 mph 77.8 X X X X The default drive schedules for light
- duty vehicles listed in the tables above were developed from several sources. “LD LOS E Freeway” and “HD High Speed Freeway” were retained from MOBILE6 and are documented in report M6.SPD.001. 47 “LD Low Speed 1” is a historic cycle used in the development of speed corrections for MOBILE5 and is meant to represent extreme stop - and
- go “creep” driving. “ LD High Speed Freeway 3 ” was developed for MOVES to represent very

high speed restricted access driving. It is a 580
- second segment of restricted access driving from an in
- use vehicle instrumented as part of EPA’s On
- Board Emission Measurement “Shootout” program, 48 with an average speed of 76 mph and a maximum speed of 90 mph. Fifteen new light
- duty “Final” cycles were developed b y a contractor for MOVES based on urban and rural data collected in California in 2000 and 2004
. 37 The new cycles were selected to best cover the range of road types and average sp eeds modeled in MOVES.82

Most of the driving schedules used for buses are borrowed directl y from driving schedules used for single unit trucks (described below). The “New York City Bus ” 49 and “ WMATA Transit Bus ” 50 drive schedules are included for urban drivi ng that includes transit ty pe bus driving behavior. The “ CRC E55 HHDDT Creep ” 51 cycle was included to cover extremel y low speeds. The “ Bus 30 mph Flow ” and “Bus 45 mph Flow ” cycles used for transit and school buses were developed by

EPA based on Ann Arbor Transit Authority buses instrumented in Ann Arbor , Michigan
. 52 The bus “flow” cycles were developed using selected n on - contiguous snippets of driving from one stop to the next stop, including idle, to create cycles with the desired average driving sp eeds
. The bus “flow” cycles have a nominal speed used for selecting the driving cycles that does not include the idle time and only considers the free
- flow speed between stops. The actual average speed of the cycle (including stops) are shown in Section 20. (Appendix D : Driving Schedules and SCC Mappings ). Note that the “ Bus Lo w Speed Urban” bus cycle is the last 4 50 seconds of the standard New York City Bus c ycl e. The “ Refuse Truck Urban” cycle represents refuse truck driving with many stops and a maximum speed of 20 mph , but an average speed of 2.2 mph. This cycle was developed by West Virginia University for the State of New York. The CRC EFF HHDDT Creep cycle was used instead for restricted access driving of refuse trucks at extremely low speeds. All of the other driving cycles used for refuse trucks were borrowed from driving cycles developed for heav y- duty combination trucks, described below. Single unit and combination trucks use driving cycles developed specifically for MOVES, based on work performed for EPA by Eastern Research Group (ERG), Inc. and documented in the report “Roadway
- Specific Driving Schedules for Heavy
- Duty Vehicles.” 53 ERG anal yz ed data from 150 medium and heavy
- duty vehicles instrumented to gather instantaneous speed and GPS measurements. ERG se gregated the driving into restricted access and unrestricted access driving for medium and heav y- duty vehicles, and then further stratified vehicles trips according the pre
defined ranges of average speed covering the range of vehicle operation. The medium duty cycles are used with single unit trucks and heav y- duty cycles are used with combination trucks. The schedules developed by ERG are not contiguous schedules which could be run on a chassis dy namometer, but are made up of non
- contiguous “snippets” of driving (microtrips) meant to represent target distributions. For use with MOVES, we modifi ed 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 (inste ad of one) when each new microtrip begins. This two
- second increment signifies that MOVES should not regard the microtrips as contiguous operation when calculating accelerations. Both single unit and combination trucks use the CRC EFF HHDDT Creep cycle f or all driving at extremely low speeds. At the other end of the distribution, none of the existing driving cycles for heav y- dut y 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 analy sis and added 5 mph to each point, effectivel y increasing the average speed of the driving cycle without increasing the acceleration rate at any point. We have checked the feasibility of these new driving cycles (396 and 397) using simulations with the EPA’s Greenhouse Gas Emissions Model (GEM) 54 for medium
- and heav y- duty vehicle83

compliance. GEM is a forward
- looking full vehicle simulation tool that calculates fuel economy and GHG emi ssions 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 w ere 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 ef fect of ramp operation is added separatel y 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. Itincludes all of the activity between operat ion on the unrestricted road and operation on the restricted road. None of the driving schedules used to represent restricted access (freeway) driving contain vehicle operation on entrance or exit ramps. The effect of ramp operation is calculated separat ely . 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 avera ge speed for each of the source ty

pes. The operating mode distributions used for ramps in MOVES2014 were estimated to represent the driving connecting to and from a freew ay with the given average speed
. These operating mode distributions (i.e. the fractio ns of time spent in each of the operating modes for each source type on each road type at each average speed) can be found in the in the default MOVES2014 database (RoadOpModeDistribution table). Each set of ramp operating modes is associated with a corresponding average speed that does not include ramp operation. Since operating modes for ramp emissions are affected b y the distribution of the average speed bins on the surrounding roads, the determination of average speeds for restricted access roa ds (both urban and rural) should not include the time or distance of vehicles on ramps. However, the VMT on ramps should be included with restricted access VMT. The emission impact of ramp activity is combined with the other driving activity found in the restricted access (freeway) driving cycles using a ramp fraction. This fraction defines the fraction of all time spent on a road that occurs on entrance and exit ramps. The fraction used (8%) in MOVES2014 is derived from the ramp fraction value developed originall y for the MOBILE6 model
. 5584



## 11. **Hotelling Activity** MOVES2014 defines "hotelling" as an y long period of time that drivers spend at their vehicles during mandated down times during long distance deliveries by tractor/trailer combination heav y- duty tr

ucks. 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 orde r to account for use of the truck engine (referred to as “extended idling”) to power air conditioning, heat and other accessories and account for the use of auxiliary power units (APU), which are small on
board power generators. In MOVES2014, only

the lon g haul combination truck source use type (sourceT yp eID=62) is assumed to have an y 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 fraction s set to zero. 

### 11.1. **National Default Hotelling Rate** Federal law limits long haul truck drivers to 10 hours driving followed b y a mandatory 8 hour rest period. These regulations are described in the Federal Register. 56 Inlong
- 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 drivi ng divided b y 10 gives the number of 8
- hour rest periods needed and thus the national total hotelling hours. A method is needed to allocate these total hotelling hours to locations. For MOVES2014, we decided to determine a “hotelling rate” (hours of ho telling per mile of travel) that could be used, in combination with VMT information to allocate the hotelling hours. We calculate a hotelling rate as the national total hours of hotelling divided by the national total miles driven by long haul trucks on rural restricted access (freeway s) roads. Driving time on all roads contributes to the total hotelling hours calculation. However, most locations used for hotelling are located near the roadway s (restricted access) most travelled by long haul trucks. In order to prevent large amounts of hotelling to be allocated to congested urban areas, we decided to only use the VMT on rural restricted roads as the surrogate for allocating the total hotelling hours. The hotelling rate (hotelling hours per mile of rural restricted access travel by long haul combination trucks) is applied to the estimate of rural restricted access VMT by long haul combination trucks to estimate the default hotelling hours for any location, month or day. The allocation of Hotelling to speci fic 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 cale ndar ye ar 2011 shown in Table 11
- 1
.85 = h Equation 25

= 10 h Equation 26

= ∗ 8 h Equation 27

= Equation 28
Where:  Total Hours is the calculated time long haul combination trucks spend driving.  Total Vehicle Miles Traveled is the total miles traveled b y diesel long haul combination trucks in the nation in calendar ye ar 2011 on all road ty pes taken from MOVES defaults.  Average Speed is an estimate of the average s peed (distance divided b y time) for diesel long haul combination trucks on all road types while operating.  Total Trips is the calculated number of trips b y long haul combination trucks.  Hotelling Hours is the calculated amount of rest time for long haul co mbination trucks.  Rural Restricted Miles is the total miles traveled by diesel long haul combination trucks on only

rural restricted access roads (freeway s) in calendar year 2011 using MOVES defaults.
**Table 11**  **- 1**  **Calculation of Hotelling Hours from Long Haul Combination Truck VMT**  **Description Annual Value**  **units** Rural Restricted 31392300000 miles Rural Unrestricted 34301700000 miles Urban Restricted 32243100000 miles Urban Unrestricted 28848900000 miles Total annual VMT 126786000000 miles Hours (58.3 mph) 2174716981 hours Trips (10 hrs per trip) 217471698 trips Hotelling hours (8 hrs per trip) 1739773585 hours Hotelling hours per mile for rural restricted roads 0.055414 rate86

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 Distributio n**  Hotelling differs from simple parking. In MOVES, hotelling hours are divided into operating modes which define the emissions associated with the ty pe of hotelling activity . Long haul trucks are often equipped with sleeping berths and other amenities to m ake the drive rest periods more comfortable. These amenities require power for operation. This power can be obtained b y running the main truck engine (extended idle) or by use of smaller on
- board power generators (auxiliary power units, APU). Some truck stop locations include power hookups (truck stop electrification) to allow use of amenities without running either the truck engines or APUs. Some of rest time may

occur without use of amenities at all. Table 11
- 2 shows the hotelling operating modes used in MOVES. **Table 11**  **- 2**  **Hotelling Activity Operating Modes OpModeID**  **Description** 200 Extended Idling of Main Engine 201 Hotelling Diesel Auxiliary Power Unit (APU) 203 Hotelling Battery or AC (plug in) 204 Hotelling All Engines and Accessories Off The HotellingActivityDistribution table contains the MOVES default values for the distribution of hotelling activity

to the operating modes. **Table 11**  **- 3**  **Default Hotelling Activity Distributions beginModelYearID**  **endModelYearID opModeID**  **opModeFraction** 1960 2009 200 1 1960 2009 201 0 1960 2009 203 0 1960 2009 204 0 2010 2050 200 0.7 2010 2050 201 0.3 2010 2050 203 0 2010 2050 204 0 All of the hotelling hours for long haul trucks of model years before 2010 are assumed to use extended idle to power accessories. Starting with the 2010 model year, the trucks are assumed to87

use extended idle 70 percent of the time and use APUs 30% of the time based on EPA’s assessment of technologies used b y tractor manufacturers to comply with the Heav y- Dut y Greenhouse Gas standards.88



## 12. **Temporal Distributions** MOVES is designed to estimate emissions for every hour of every

day typ e 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, day s and hours using allocation factors (distributions), either default values or values provided by users. Default values for most temporal VMT allocations are derived from a 1996 report from the Office of Highway

Information Management (OHIM) 57 . The report describes anal ys is of a sample of 5 ,000 continuous traffic counters distributed through the United States. EPA obtained the data used in the report and used it to generate the VMT temporal distribution inputs in the form needed for MOVES2014. The OHIM report does not specify VMT b y vehicle ty

pe, 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. Th e temporal distribution for engine start and corresponding engine soak (parked) distributions are calculated from vehicle activity data stored in the SampleVehicle and SampleVehicleTrip tables of the MOVES database. These tables contain a set of vehicle trip activity information from over 37,000 trips, taken from a sample of vehicles intended to be representative of activity for each source type. Evaporative emissions are also affected b y the time of day and the duration of parking. Some of the vehicles i n the samples take no trips. **Table 12**  **- 1**  **Sam ple Vehicle Day Table Sample Sizes**  **Source Type Vehicle Sample**  **sourceTypeID Description**  **Weekday (dayID=5) Weekend (dayID=2)** 

11 Motorcycle 2214 983 21 Passenger Car 821 347 31 Passenger Truck 834 371 32 Light Commercial Truck 773 345 41 Intercity Bus 190 73 42 Transit Bus 110 14 43 School Bus 136 59 51 Refuse Truck 205 65 52 Single Unit Short
- haul Truck 112 58 53 Single Unit Long
- haul Truck 123 50 54 Motor Home 5431 2170 61 Com

bination Short
- haul Truck 130 52 62 Com

bination Long
- haul Truck 122 4989



### 12.1. **VMT Distribution by Month of the Year** In MOVES, VMT is entered as an annual value and allocated to month us ing 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 day s in each month. The calculations assume a non - leap ye ar (365 days). **Table 12**  **- 2**  **MonthVMTFraction Month**  **Norm alized** 

**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 i n man y regions of the United States, motorcycles are driven much less frequently in the winter months. For MOVES 2014 an allocation for motorcycles was derived using monthly national counts of fatal motorcycle crashes from the National Highway Traffic Safety Administration Fatality

Analy sis S y stem for 2010 58

. This allocation increases motorcycle activity (and emissions ) in the summer months and decreases them in the winter compared to the other source typ es . These default values for motorcycles are onl y a national average and do not reflect the strong regional differences that would be expected due to climate
.90 **Table 12**  **- 3**  **MonthVMTFraction for Motorcycles Month**  **M onth 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 Day VMTFraction distribution divides the weekly VMT into two day

ty pes. 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 Unit ed States. Since the day

– of
- the
- week data obtained from the OHIM report is not disaggregated by month or source ty

pe, the same values were used for every

month and source typ e. 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) day s. The OHIM reported percentages for each day of the week were summed in their respective categories an d converted to fractions. The OHIM report explains that data for “3am” refers to data collected from 3am to 4am. Thus data labeled “midnight” belongs to and was summed with the upcoming day
. **Table 12**  **- 4**  **DayVM TFractions**  **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 ty pes and the “Urban” fractions to the urban road ty pes. The fraction of weekl y VMT reported for a single weekday in MOVES will be one
fifth of the weekday

fraction and the fraction of weekl y VMT for a single weekend day will be one - half the weekend fraction.91



### 12.3. **VMT Distribution by Hour of the Day** HourVMTFraction uses the same data as for DayVMTFraction. We converted the OHIM report’s VMT data b y hour of the day in each day ty pe to percent of day

by dividing by

the total VMT for each day

typ e, as described for the DayVMTFraction. The OHIM report explains that data for “3am” refers to data collected from 3am to 4am. Thus data labeled “midnight” belongs to and was included with the upcoming day
. There are separate sets of HourVMTFractions for " urban" and " r ural" road ty

pes, but unrestricted and unrestricted roads use the same HourVMTFraction distributions. All source types use the same HourVMTFraction distributions. **Table 12**  **- 5**  **Distribution of VMT by Hour of the Day h**  **ourID Description**  **Urban Rural**  **Weekday Weekend**  **Weekday Weekend**  1 Hour beginning at 12:00 midnight 0.0098621 0.0214739 0.0107741 0.0164213 2 Hour beginning at 1:00 AM 0.00627248 0.0144428 0.0076437 0.0111921 3 Hour beginning at 2:00 AM 0.00505767 0.0109684 0.0065464 0.0085415 4 Hour beginning at 3:00 AM 0.00466686 0.0074945 0.0066348 0.00679328 5 Hour beginning at 4:00 AM 0.00699469 0.0068385 0.0095399 0.00721894 6 Hour beginning at 5:00 AM 0.018494 0.0103588 0.0200551 0.0107619 7 Hour beginning at 6:00 AM 0.0459565 0.0184303 0.0410295 0.01768008 8 Hour beginning at 7:00 AM 0.0696444 0.0268117 0.0579722 0.0268751 9 Hour beginning at 8:00 AM 0.0608279 0.0363852 0.0534711 0.0386587 10 Hour beginning at 9:00 AM 0.0502862 0.0475407 0.0525478 0.0522389 11 Hour beginning at 10:00 AM 0.0499351 0.0574664 0.0550607 0.0631739 12 Hour beginning at 11:00 AM 0.0543654 0.0650786 0.0576741 0.0699435 13 Hour beginning at 12:00 Noon 0.0576462 0.0713228 0.0591429 0.0729332 14 Hour beginning at 1:00 PM 0.0580319 0.0714917 0.0608019 0.0731218 15 Hour beginning at 2:00 PM 0.0622554 0.0717226 0.0652985 0.0736159 16 Hour beginning at 3:00 PM 0.0710049 0.0720061 0.0726082 0.0744608 17 Hour beginning at 4:00 PM 0.0769725 0.0711487 0.0773817 0.0742165 18 Hour beginning at 5:00 PM 0.077432 0.0678874 0.0754816 0.0700091 19 Hour beginning at 6:00 PM 0.059783 0.0617718 0.0587059 0.0614038 20 Hour beginning at 7:00 PM 0.0443923 0.0516882 0.0439864 0.0505043 21 Hour beginning at 8:00 PM 0.0354458 0.0428658 0.0357309 0.0412072 22 Hour beginning at 9:00 PM 0.031824 0.0380302 0.0307428 0.0336373 23 Hour beginning at 10:00 PM 0.0249419 0.0322072 0.0238521 0.0262243 24 Hour beginning at 11:00 PM 0.0179068 0.0245677 0.0173177 0.0191666

**Sum of All Fractions 1.000**  **1.000 1.000**  **1.000**92 **Figure 12**  **- 1**  **Hourly VMT Fractions by Day Type and Road**  **Type**



### 12.4. **Engine Starts and Parking** To properl y 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 ty pical patterns of trip starts and ends, MOVES uses information from instrumented vehicles. This data is stored in two table s in the MOVES default database. We have made onl y minor changes for MOVE2014. The first table, SampleVehicleDay , lists a sample population of vehicles, each with an identifier (vehID), an indication of vehicle type (sourceT yp eID), 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 Georgi a . 59 This change is described in greater detail in the report describing evaporative emissions in MOVES2014
. 60 The second table, SampleVehicleTrip, lists the trips in a day made b y 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), a nd the times at which the engine was turned on and off for the trip. The keyOnTime and keyOffTime are recorded in minutes since **0 0.01**  **0.02 0.03**  **0.04 0.05**  **0.06 0.07**  **0.08 0.09** 

**1 2**  **3 4**  **5 6**  **7 8**  **9 10**  **11 12**  **13 14**  **15 16**  **17 18**  **19 20**  **21 22**  **23 24** **F r** **a c** **t i** **o n** **o f** **D a** **i l** **y V** **e h** **i c** **l e** **M i** **l e** **s T** **r a** **v e** **l e** **d**

**Hour of the Da y**  **Rural Weekend Urban Weekend**  **Rural Weekday Urban Weekday** 93

midnight of the day

of the trip. 439 trips (about 1.1%) were added to this table to assure that at least on trip is done by

a v ehicle from each source ty pe 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 typ es (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 vehicl es that have no trips in the SampleVehicleTrip table to account for vehicles that sit for one or more day s without driving at all. The data and processing algorithms used to populate these tables are detailed in two contractor reports. 61 , 62 The data comes from a variety of instrumented vehicle studies, summarized in Table 12
- 6 . This data was cleaned, adjusted, sampled and weighted to develop a distribution intended to represent average urban vehicle activity
. **Table 12**  **- 6**  **Source Data for Sample Vehicle Trip Information Study**  **Study Area Study**  **Years Vehicle Types**  **Vehicle Count**  3
- City FTP Study Atlanta, GA; B altimore, 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 D iesel dump trucks 4 For vehicle classes that were not represented in the available data, the contractor s ynthesized 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 a pplication of s ynthetic trips is summarized in Table 12
- 7
.94 **Table 12**  **- 7**  **Synthesis of Sample Vehicles for Source Types Lacking Data**  **Source Type**  **Based on Direct Data?**  **Synthesized From** Motorcycles No Passenger Cars Passenger Cars Yes n/a Passenger Trucks Yes n/a Light Commercial Trucks No Passenger Trucks Intercity Buses No Com bination long
- haul trucks Transit Buses No Single unit short
- haul trucks School Buses No Single unit short
- haul trucks Refuse Trucks No Com bination short
- haul trucks Single unit short
- haul trucks Yes n/a Single unit long
- haul trucks No Com bination long
- haul trucks Motor homes No Passenger Cars Com bination short
- haul trucks Yes n/a Com bination 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 an y calendar year. **Table 12**  **- 8**  **Starts per Day by Source Type**  **Source Ty pe**  **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 Com bination short
- haul trucks 5.93 1.16 Com bination 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 t ype and hour of the day, as well as by source type and vehicle age. These user inputs override the default values provided b y 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 emissio n 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 adjust ed based on soak time bins (operating modes). 3 , 4 The distribution of operating modes in each hour of the day is part of the calculation used to determine the engine start emissions for that hour of the day
.95

A more complete discussion of the relationship between engine soak time and emission s will be found in the MOVES report covering engine start emission rates used in MOVES
. 3 

### 12.5. **Hourly Hotelling Activity** The hotelling hours in each day should not track the miles traveled in each hour, since hotelling occurs onl y when drivers are not driving. Instead, the fraction o f hours spent hotelling by time of day can be derived from other sources . In particular, the report, Roadway

- Specific Driving Schedules for Heavy

-Duty Vehicles , 53 combines data from several instrumented truck studies and contains detailed information about truck driver behavior . While none of the trucks w ere involved in long haul interstate activity , for lack of better data, we have assumed that lo ng 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 e arlier. 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 h**  **ourID Hour of the**  **Day Trip S**  **tarts T**  **rip E**  **nds** 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 21196

An estimate of the distribution of truck hotelling duration times is derived from a 2004 CRC paper 63 based on a survey of 365 truck drivers at 6 different locations. Table 12
- 10 lists the fraction of trucks in each duration bin. Some trucks are hotelling for more than the required eight hours, but some are hotelling for less than eight hours. **Table 12**  **- 10**  **Distribution of Truck Hotelling Activity Duration Duration (hours)**  **Fraction of Trucks** 2 0.227 4 0.135 6 0.199 8 0.191 10 0.156 12 0.057 14 0.014 16 0.021 **Total 1.000**  We assume that all hotelling activity begins at the trip ends shown in Table 12
- 9 . The distribution of hotelling durations from Table 12
- 10 is applied to these trip ends. These calculations are shown in Table 12 - 11 . The column “2 hours” adds the number of trip ends in the previous 2 hours, assuming all trips will have 2 hours of hotelling after their trip ends, the column “4 hours” adds the trips ends in the previous 4 hours, etc. The first hour of hotelling in each category is reduced by 60% to account for the fact that not all of the trips actually ended at the beginning of the hour. The total hotelling hours is the product of the total hours in each column multiplied by the fraction of trucks with each duration from Table 12
- 10
.97 **Table 12**  **- 11**  **Calculation of Hourly Distributions of Hotelling Activity hourID**  **Trip Starts Trip Ends** * **2 hours 4 hours**  **6 hours 8 hours**  **10 hours 12 hours**  **14 hours 16 hours**  **Total Hours Distribution**  1 78 171 255.4 679 1204.8 1736 2120.4 2343.6 2495.4 2638.2 1276 0.0628 2 76 167 235.4 629.4 1100 1643.6 2118.6 2408.8 2593 2739.2 1234 0.0611 3 65 144 210.8 566.4 990 1515.8 2047 2431.4 2654.6 2806.4 1166 0.0577 4 94 98 155.6 477.4 871.4 1342 1885.6 2360.6 2650.8 2835 1056 0.0526 5 107 71 110.2 379.8 735.4 1159 1684.8 2216 2600.4 2823.6 930 0.0458 6 131 73 101.4 299.6 621.4 1015.4 1486 2029.6 2504.6 2794.8 823 0.0407 7 194 71 100.2 254.2 523.8 879.4 1303 1828.8 2360 2744.4 728 0.0357 8 230 52 80.4 224.4 422.6 744.4 1138.4 1609 2152.6 2627.6 630 0.0306 9 279 85 105.8 237.2 391.2 660.8 1016.4 1440 1965.8 2497 581 0.0289 10 267 48 82 213.4 357.4 555.6 877.4 1271.4 1742 2285.6 507 0.0255 11 275 78 97.2 231.8 363.2 517.2 786.8 1142.4 1566 2091.8 479 0.0238 12 240 76 107.2 236 367.4 511.4 709.6 1031.4 1425.4 1896 457 0.0221 13 201 65 95.4 238.2 372.8 504.2 658.2 927.8 1283.4 1707 434 0.0221 14 211 94 120 266.2 395 526.4 670.4 868.6 1190.4 1584.4 447 0.0221 15 171 107 144.6 296.4 439.2 573.8 705.2 859.2 1128.8 1484.4 476 0.0238 16 167 131 173.8 358 504.2 633 764.4 908.4 1106.6 1428.4 526 0.0255 17 144 194 246.4 469.6 621.4 764.2 898.8 1030.2 1184.2 1453.8 635 0.0323 18 98 230 307.6 597.8 782 928.2 1057 1188.4 1332.4 1530.6 767 0.0374 19 71 279 371 755.4 978.6 1130.4 1273.2 1407.8 1539.2 1693.2 933 0.0458 20 73 267 378.6 853.6 1143.8 1328 1474.2 1603 1734.4 1878.4 1068 0.0526 21 71 275 381.8 913 1297.4 1520.6 1672.4 1815.2 1949.8 2081.2 1194 0.0594 22 52 240 350 893.6 1368.6 1658.8 1843 1989.2 2118 2249.4 1268 0.0628 23 85 201 297 822.8 1354 1738.4 1961.6 2113.4 2256.2 2390.8 1289 0.0645 24 48 211 291.4 762 1305.6 1780.6 2070.8 2255 2401.2 2530 1308 0.0645 **Totals 3428**  **3428 4799**  **11655 18511**  **25367 32223**  **39079 45935**  **52791 20213**  **1.0000** * Assumes all trip ends occur 10 hours after trips start and all trips are 10 hours long. The first hour of hotelling in each hour bin column sum is reduced by 60% to account for trip ends in a column that are not a full hour.98

The distribution calculated using this method is similar to the behavior observed in a dissertation 64 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 mid
December 2003 and August 2004. Rather than use a study at a specific location, MOVES2014 uses the more generic simulated values to determine the diurnal distribution of hotelling behavior. The distribution of total hotelling hours to hours of the day is calculated from the total hotelling hours and store d in the SourceTy peHour table of the default MOVES2014 database. MOVES2014 uses this same default hourly distribution from Table 12 - 11 for all day s and locations, as shown below in Figure 12
- 2
. Note, this distribution o f hotelling b y hour of the day is similar to the inverse of the VMT distribution used for these trucks by hour of the day. **Figure 12**  **- 2**  **Truck hotelling distribution by hour of the day in MOVES2014**



### 12.6. **Single and Multiday Diurnals**  The evaporative vapor losses from gasoline vehicle fuel tanks are affected by man y 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 sy stems 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 limi ted and multiple day s of parking (diurnals) will overload the storage capacity of these sy stems, resulting in larger losses of evaporative vapors in subsequent day s. The soak time calculations are discussed in Section 12.4. The detailed description of the calculation for the number of vehicles that have been soaking for more than a day and the amount of time that the vehicles have been soaking can be f ound in the MOVES technical report on evaporative emissions. 62

0% 1% 2% 3% 4% 5% 6% 7%

12:00 AM 12:00 PM 12:00 AM**P e** **r c** **e n** **t o** **f F** **l e** **e t** **H o** **t e** **l l** **i n** **g**

**Hours of Day**99



## 13. **Geographical Allocation of Activity** MOVES is designed to model activity at a “domain” level and then to allocate that activity to “zones.” The MOVES2014 default database is populated for a domain of the entire United States (including Puerto Rico and the Virgin Islands), and the default zones correspond to individual counties. The MOVES design onl y allows for one set of geographic allocations to be stored in the default database. While geographic allocations clearl y change over time, the MOVES defaults were developed using the data from calendar year 2011, and are used for all calendar ye ars. For this reason, the MOVES default allocation of activity is rarel y used for an y official purpose b y either EPA or local areas. National
- level emissions can be generated with calendar ye ar specific geographical information by running each ye ar separatel y, with different user
- input allocations for each run. County
- and Project
- level calculations do not u se the default geographical allocation factors at all. Instead, County and Project scales require that the user input local total activity

for each individual ye ar being modeled. The MOVES geographic allocation factors are stored in two tables, Zone and Z oneRoadTy pe. 

### 13.1. **Source Hours Operating Allocation to Zones** Most of the emission rate calculations in MOVES2014 are based on emission rates b y time units (hour). Using time units for emissions is the most flexible approach, since the a ctivity for some processes (like leaks) and some source types (like nonroad generators) are more naturall y 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 roadty pe is allocated to county using the SHOAllocFactor field in the ZoneRoadTy pe table. The all ocation factors are derived using 2011 VMT and MOVES default VMT. In particular, the MOVES2014 default estimates for the VMT b y county come from Version 1 of the 2011 National Emission Inventory (NE I) anal ysis
. 43 These estimates are based on the Highway Performance Monitoring Sy stem (HPMS) state level data collected by the Federal Highway Administration 65 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 anal ys is. 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 NEI 66 for each county by road ty pe. Vehicle miles traveled can be converted to hours of travel using average speeds. The average speed estimates were taken directl y from the AvgSpeedD istribution table of the MOVES default database. The default average speed distributions do not vary by county or source type, but do vary by road typ e, day

type (weekday and weekend day) and hour of the day. The 2011 NEI VMT was aggregated into the four MOVES road ty pes 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 typ e and hour100

distributions from the MOVES default database tables, DayMVTFraction and HourVMTFraction. Using the nomina l 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 ty

pe for each day

ty pe 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 multipl ied each value of each distribution (in terms of time) by the corresponding nomi nal average speed value for that average speed bin to calculate distance (hours * miles/hour). Then we divide d each distance value in the distribution by

the sum of all distance values in that distribution to calculate the average speed distribution in te rms of distance. Finally , we multiplied the total VMT corresponding to each average speed distance distribution (by road type, b y day

ty pe , by hour of the day) b y each of the values in the distribution to calculate the VMT corresponding to each average sp eed bin. We then then calculate d operating hours by dividing the VMT in each average speed bin by the corresponding nominal average speed value. SHO = VMT ( miles ) / Speed ( miles per hour ) Equation 29 Once the hours of operation have been calculated, the hours in each county were summed by road ty pe. The allocation factor for each county was calculated b y dividing the county hours for each road type b y the national total hours of operation for each roa d type.

SHOAllocFactor = County SHO / National SHO Equation 30 The county allocation values for each roadway typ e 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 i n the Zone table. In the default database for MOVES2014, the domain is the nation and the zones are countie s. 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 analy sis for calendar ye ar 2011
. 66 VMT estimates for each county in each state and the allocation is calculated using the following formula, where represents each individual county and is the set of all US counties
.101 CountyAllocation = CountyVMT CountyVMT Equation 31 The county allocation values sum to one (1.0) for the nation. The same StartAllocFactor set is the default for all calendar years at the National scale. County
- and Project
- level calculations do not use the default StartAllocFactor allocations at all. Instead, County and Project scales require that the user input all local activity
. 

### 13.3. **Park**

**ing Hours Allocation to Zones** The allocation of the domain
- wide hours of parking (engine off) to zones is stored in the SHPAllocFactor in the Zone table. In the default database for MOVES2014, the domain is the nation and the zones are the counties. There is no national source for hours of parking by county, so for MOVES2014, we have used the same VMT
- based allocation as used for the allocation of starts in the StartAllocFactor (see above). The county allocation values for parking hours sum to one (1.0) for the nation. The same SHPAllocFactor set is the default for all calendar years at the National scale. County
- and Project
- level calculations do not use the default SHPAllocFactor allocations at all. Instead, County and Project scales require that the user input all local activity
. In MOVES2014, hotelling hours (including extended idling and auxiliary power unit usage) are calculated from long haul combination truck VMT in each location and does have its own allocation factors.102



## 14. **Vehicle Mass and Road Load Coefficients** The MOVES model calculates emissions using a weighted average of emisson rates b y operating mode. This level of detail is required for microscale modeling, which in MOVES is called project level anal ys is. For running exhaust emissions, the operating modes are defined b y 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
- dut y vehicles (source ty pes 11
- 32) and STP is used for heav y duty vehicles (source typ es 41
- 62). The SourceUseTy

pePhy sics 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 ty pe according the equations:

= ∙ + ∙ + ∙ + ( + ∙ sin ) ∙ Equation 32

= + + + ∙ ∙ ( + ∙ ) Equation 33 where , , and are the road load coefficients in units of kW
- s m ⁄ , kW
- s m

⁄ , and kW
- s m ⁄ respectivel y. is associated with tire rolling resistence, with mechanical rotating friction as well as higher order rolling resistance losses, and with aerody namic drag. is the source mass for the source ty pe in metric tons , is the acceleration due to gravity 9 . 8 m s ⁄ , v is the instantaneous v ehicle speed in m s ⁄ , is the instantaneous vehicle acceleration in m s ⁄ , sin is the (fractional) road grade , and is a scaling factor. When mapping a ctual emissions data to VSP bins with **Error! Reference source not found.** , the vehicle’s measure d weight is used as the source mass factor. In contrast, when calculating average VSP distributions for an entire source ty pe with MOVES , the average source type mass is used instead
. STP is calculated with **Error! Reference source not found.** , which is very similar to the VSP equation except the denominators are different. In the case of VSP, the power is normalized by

the mass of the vehicle ( = ). For heavy
- duty vehicles using STP, depends on their regulatory class and is used to bring the numerical range of tractive power into the same numerical range as the VSP values when assigning operating modes. Class 40 trucks us e = 2.06, which is equal to the mass of source typ e 32 in metric tons . This is because operating modes for passenger trucks and light
- commercial trucks are assigned operating modes using VSP, and using a fixed mass factor of 2.06 essentially ca lculates VSP
- based emission rates. Running operating modes for all the heav y- duty source types (buses, single unit, and combination trucks) are assigned using STP with = 17.1, which is roughl y equivalent to the average running weight in metric t ons of all heavy
- duty vehicles. Additional discussion regarding VSP and STP are provided in the MOVES light
- duty3 and heav y - duty 4 emission rate rep orts, respectivel y.103

In both cases, operating mode distributions are derived from combining second
- by
- second speed and acceleration data from a specific drive schedule with the proper coefficients for a specific source type . More information about drive s chedules can be found in Section 10.1. The following sections detail the derivation of values used in **Error! Reference source not found.** and **Error! Reference source not found.**
. 

### 14.1. **Source Mass**  **and Fixed Mass Factor**  The two mass factors stored in the SourceUseTy pePhys ics table are the source mass and fixed m ass f actor. The source mass represents the average weight of a given source t ype, which includes the weight of the vehicle, occupants, fuel, and payload ( in the equations above), and the fixed mass factor repres ents the STP scaling factor ( in the equations above)
. While the source masses for light
- dut y were unchanged from MOVES2010b, all of the heav y duty source masses were updated with newer data. Please see Section 21. ( Appendix E : MOVES2010b Source Masses ) for a discussion of the MOVES2010b source masses. The heavy
duty source masses for 2014+ model year vehicles heavy
- duty vehicles were first updated to account for the 2014 Medium and Heav y- Duty Greenhouse Gase Rule as discussed in Section 14.2. Then the heav y - duty source masses were updated with 2011 Weigh
- in
- Motion (WIM) data made available through FHWA’s Vehicle Travel Information Sy stem (VTRIS). These data are available from FHWA by state, road t ype, and HPMS truck ty pe (si ngle unit or combination). The average national mass by truck typ e was calculated by weighting the masses with VMT by state and road typ e using FHWA’s Highway Statistics VM
- 2 table. These average values then needed to be allocated from the HPMS truck class ification to source types. This allocation was performed using the percent difference between the average WIM HPMS mass and the average MOVES2010b HPMS mass j . The MOVES2010b average masses were calculated by weighting the source type masses with the update d 2011 VMT. The percentage difference between the average single unit truck mass in MOVES2010b and the WIM data was then applied to the source masses of Single Unit Short
- haul Trucks, Single Unit L ong - haul Trucks, Refuse Trucks, and Motor Homes. Likewise, the percentage difference between the average combination truck mass in MOVES2010b and the WIM data was applied to the source masses of Combination Short
- haul Trucks and Combination L ong - haul Trucks, including the 2014+ model year groups. These differences are shown in **Error! Reference s ource not found.**  , and the resulting source ty pe masses are presented in **Error! Reference source not found.**
.

**Table 14**  **- 1**  **Weigh -**  **in -**  **Motion (WIM) Masses Weighted by VMT HPMS Category**  **Average Weight (lbs) % Change from**  **MOVES2010b** Single Unit Trucks 20,107 11.7% Com bination Trucks 52,907 - 21.7%

j For the WIM analysis, we only compared to the MOVES2010b masses because the 2014 Medium and Heavy
- Duty Rule impact is not assumed to begin phase
- in until 2014.104



### 14.2. **Road Load Coefficients**  The information available on road load coefficients varied b y regulatory class. Motorcycle road load coefficients were parameterized, in accordance with standard practice, using the following emperical equations 67 , 68 :

= 0 . 088 ∙ Equation 34

= 0 Equation 35

= 0 . 00026 + 0 . 000194 ∙ Equation 36 For light
- duty vehicles, the road load coefficients were calculated according to the following empirical equations: 69

= 0 . 7457 50 ∙ 0 . 447 ∙ 0 . 35 ∙ @ Equation 37

= 0 . 7457 ( 50 ∙ 0 . 447 ) ∙ 0 . 10 ∙ @ Equation 38

= 0 . 7457 ( 50 ∙ 0 . 447 ) ∙ 0 . 55 ∙ @ Equation 39 In each of the above equations, the first factor is the appropriate unit conversion to allow , , and to be used in **Error! Reference source not found.** and **Error! Reference source not found.**  , the second factor is the power distribution into each of the three load categories, and the third is the tractive road load horsepower rating (TRLHP). Average values for , , and for source types 21, 31, and 32 were derived from applying TRLHP values recorded in the Mobile Source Observation Database (MSOD) 70 to **Error! Reference source not found.**
- **Error! Reference source not found.** 
. While we expect light
- duty road load coefficients to improve over time due to the Li ght
- Duty Greenhouse Gas Rule , the impact of these changes have been directly incorporated into the emission and energy rates. Therefore, these coefficients remain constant over time. For the heavier vehicles, no road load parameters were available in the MSOD. For these source ty pes, relationships of road load coefficent to vehicle mass came from a study done b y V.A. Petrushov, 71 as shown in **Error! Reference source not found.** . These relationships are grouped by regulatory

class; source type values were determined by weighting the combination of MOVES2010b weight categories that comprise the individual source types. The final SourceMass, FixedMassFactor and road load coefficients for all source typ es are listed in **Error! R**  **eference source not found.**
.105

**Table 14**  **- 2**  **Road Load Coefficients for Heavy -**  **Duty Trucks, Buses, and Motor Hom es**  **for 1960 -**  **2013 Model Year Vehicles**  **Coefficient 8500 to 14000 l**  **bs**

**(3.855 to 6.350 metric ton)**  **14000 to 33000 lbs**

**(6.350 to 14.968 metric ton)**  **>33000 lbs**

**(>14.968 m etric ton)**  **Buses and Motor Hom**  **es**

- 0 . 0996 ∙ 0 . 0875 ∙ 0 . 0661 ∙ 0 . 0643 ∙

- 0 0 0 0

- 0 . 00289 +

5
. 22 × 10 ̇ ∙ 0 . 00193 +

5
. 90 × 10 ∙ 0 . 00289 +

4 . 21 × 10 ∙ 0 . 0032 +

5
. 06 × 10 ∙ In MOVES2014 , the vehicle mass and road load coefficient were updated for 2014 and later model year heavy
- dut y vehicles to account for the 2014 Medium and Heavy
- Duty Greenhouse Gase Rule
. 72 **Error! Reference source not found.** contains the combination long
- haul tractor and vocational vehicle tire rolling resistance, coefficient of drag, and weight reductions expected from the tec hnologies which could be used to meet the standards. The value in the table reflects a 400 pound mass reduction. As discussed in the rulemaking, EPA used a sales mix of 10 percent Class 7 low roof, 10 percent Class 7 high roof, 45 percent Class 8 low roo f, 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 aerodyn amic or weight improvement. The weight reduction applied to short
- haul tractors is 321 p ounds which is calculated from the 400 pound weight reduction assumed for non
- vo cational tractors, reduced b y 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 b y EPA
. Comparatively tire rolling resistance is reduced b y 9.6 percent for long
- h aul tractors and 7 percent for short
- haul tractors while aerodynamic drag is reduced 12.1 percent for long
- haul tractors and 5.9 percent for short
- haul tractors in model year 2014 and later. For further details on these assumptions about reductions in source mass and road load coefficients , please see the rulemaking documents. Discussion of incorporating the rule’s energy reductions from engine technology improvements into MOVES can be found in the MOVES2014 Heav y - Dut y Emission Rate Report. 4106

**Table 14**  **- 3**  **Estim ated Reductions in Rolling Resistance and Aerodynamic Drag Coefficients from** **Reference Case for Alternative 3 (Model Years 2014 and Later)**  **Truck Type Reduction In Tire Rolling** 

**Resistance Coefficient From**

**Baseline Reduction In**  **Aerodynam ic Drag**  **Coeffic ient From** **Baseline Weight**  **Reduction (lbs)**  Com bination long
- haul 9.6% 12.1% 400 Com bination short
- haul 7.0% 5.9% 321 Vocational vehicles (Single
unit trucks, refuse trucks, mo tor 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 typ es 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 SourceUseT yp ePhy sics table. **Table 14**  **- 4**  **SourceUseType Physics**  **Table sourceTypeID**  **Begin**

**Model Year**  **End**

**Model Year**  **Rolling**

**Term A (kW**  **- s/m)**  **Rotating**

**Term B (kW**  **- s**  **2 /m**  **2 )**  **Drag**

**Term C (kW**  **- s**  **3 /m**  **3 )**  **Source Mass**

**(m etric tons)**  **Fixed Mass**

**Factor (m etric**  **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 17.1107



## 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 o f these vehicles in which the air conditioning sy s tem is working correctly
. The ACActivityTerms relate air conditioning use to local heat and humidity
. More information on air co nditioning effects is provided in the MOVES technical report on adjustment factors. 73 

### 15.1. **ACPenetrationFraction** The ACPenetrationFraction is a field in the SourceTy peModelYear table. Default values , by source type and model ye ar were taken from MOBILE6. 74 Market penetration data b y model ye ar were gathered from Ward’s Automotive Handbook for light
- duty vehicles and light
- duty trucks for model ye ars 1972 through the 1995 for cars and 1975
- 1995 for light trucks. Rates in the first few ye ars o f available data are quite variable, so values for earl y m odel 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 ye ars 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 alway s be vehicles sold without air conditioning, more likel y trucks than cars. No data was available on heav y- duty trucks. While VIUS asks if trucks are equipped with A/C , “no response” was coded the same as “no,” making the data unusable for this purpose
. For MOVES, the light
- duty vehicle rates were applied to passenger cars, and the light
- duty truck rates were applied to all other source types (except motorcycles, for which A/ C penetration is assumed to be zero).108 **Table 15**  **- 1**  **AC Penetration Fractions in MOVES2014 Motorcycles**  **Pas senger Cars**  **All Trucks and Buses** 1972
- and
- earlier 0 0.592 0.287 1973 0 0.726 0.287 1974 0 0.616 0.287 1975 0 0.631 0.287 1976 0 0.671 0.311 1977 0 0.720 0.351 1978 0 0.719 0.385 1979 0 0.694 0.366 1980 0 0.624 0.348 1981 0 0.667 0.390 1982 0 0.699 0.449 1983 0 0.737 0.464 1984 0 0.776 0.521 1985 0 0.796 0.532 1986 0 0.800 0.544 1987 0 0.755 0.588 1988 0 0.793 0.640 1989 0 0.762 0.719 1990 0 0.862 0.764 1991 0 0.869 0.771 1992 0 0.882 0.811 1993 0 0.897 0.837 1994 0 0.922 0.848 1995 0 0.934 0.882 1996 0 0.948 0.906 1997 0 0.963 0.929 1998 0 0.977 0.950 1999+ 0 0.980 0.950 

### 15.2. **FunctioningACFraction** The FunctioningACFraction field in the SourceTypeAge table indicates the fraction of the air
conditioning equipped fleet with fully functional A/C sy stems, by source ty pe and vehicle age. A value of 1 means all sy stems are functional. This is used in the calculation of total energy to account for vehicles without functioning A/C s ys tems. Default estimates were developed for all source ty pes 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 sy stem failure by age reported in a consumer study and assumptions about repair frequency during and aft er the warranty period
. The MOBILE6 rates were applied to all source types except motorcycles, which were assigned a value of zero for all years.109 **Table 15**  **- 2**  **FunctioningACFraction by Age (All Source**  **Types e**  **xcept Motorcycles)**

**ageID f** 

**unctioningACFraction** 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. The se terms are applied in the calculation of the A/C adjustment in the energy consumption calculator. The methodology

and the terms themselves were originall y derived for MOBILE6 an d are documented in the report, Air Conditioning Activity Effects in MOBILE6
. 74 They are based on anal ys is 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 MOVES 2014 , the default data uses one set of coefficients for all MonthGroups and Hours. These default coefficients represent an average A/C activity demand function over the course of a full day
. The coefficients are listed in Table 15
- 3
.110 **Table 15**  **- 3**  **Air Conditioning Activity Coefficients A**  **B C** 

- 3.63154 0.072465
- 0.000276 The A/C activity

demand function that result s 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 engag ement.

**Figure 15**  **- 1**  **Air Conditioning Activity Dem and 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

7 0 7 5 8 0 8 5 9 0 9 5 1 0 0 1 0 5 1 1 0 **H e**  **a t**  **I n**  **d e**  **x (**  **F )** 111



## 16. **Conclusion and Areas for Future Research**  Properly characterizing emissions from vehicles requires a detailed understanding of the cars and trucks that make up the vehicle fleet, and their patterns of operation. The national default information in MOVES2014 provide a reliable basis for estimating national emissions. The most important of these inputs are well
- established: base ye ar VMT and population estimates come from long
- term, s ys tematic national measurements by US D epartment of Transportation . The emission characteristics for the most prevalent vehicle classes are well
- known; base year age distributions are well
- measured, and driving activity has been the subject of much study in recent ye ars. Still, the fleet and activity inputs do have significant limitations, and the uncertainties and variability

in this data can contribute significant uncertaint y in resulting emission estimates. Thus it is often appropriate to replace man y of the MOVES fleet and act ivity defaults with local data as explained in EPA’s Technical Guidance
. 75 The fleet and activity

defaults also are limited by the necessity

of forecasting future emi ssions. EPA utilizes annual US Department of Energy forecasts of vehicle sales and activi ty , but the inputs for MOVES2014 were developed for a 2011 base year, and much of the source data is from 2011
- and
- earlier. This information needs to be updated periodicall y to assure that the model defaults reflect the latest available data and projections on the US fleet. Updating the vehicle fleet data will be complicated by the fact that one of the primary data sources for this document, the Census Bureau’s Vehicle Inventory and Use Survey , has been discontinued. EPA is currently working with DOT and other federal agencies to revive this survey . Doing so becomes more and more important a s 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, especiall y when the data is intended to accuratel y represent the entire United States. Even when EPA does not generate data directl y (for example, compilations of state vehicle registration data) obtaining the information needed for MOVES can be costly and, thus, dependent on large
- scale budget choices. In addition to these general limitations, there are also specific MOVES data elements that could be improved with additional re search , including,

 Real
- world highway

driving cycles and operating mode distributions,  Off
- network behavior including vehicle starts and soaks,  Truck hotelling, particularly extended engine idling and APU use,  VSP/STP adjustments for speed, road grade, and loading,  Age effects such as mileage accumulation and scrappage,  Summaries of real
- time GPS and on
- board diagnostic device data streams,  Vehicle identification and sorting b y size, sector, and vocation.  Activity

weighting of source mass averages.  Air Conditioning sy stem penetration and failure rates.112

 V ehicle ty

pe distinctions in temporal activity ,  H eav y truck and bus daily trip activity patterns (particularl y at night)
. Future updates to vehicle population and activity defaults will need to continue focus on the most critical elements required for national fleet
- wide estimates, namely gasoline light
- duty cars and trucks , and diesel heavy
- duty trucks. Information collection on motorcycles, refuse trucks, motor homes, diesel light
- duty vehicles and gasoline heavy
- duty vehicles will be a lower priority. In addition to updating the model defaults, we will need to consider whether the current MOVES design continues to meet our modeling needs. Simplifications to the model to remove categories , such as source ty

pes or road ty

pes , might make noticeable improvements in run time without affecting the validity

of fleet
- wide emission estimates
. At the same time, the fundamental MOVES assumption that vehicle activity varies b y source ty pe and not by

fuel ty pe or other source bin characteristic may be challenged by

the growing market share of electric vehicles and other vehicles that may have distinct activity

patterns. As we progress w ith MOVES, the development of vehicle population and activity inputs wil l continue to be an essential area of research.113



## 17. **Appendix A: Projected Source Type Populations by Year Table**  **17 -**  **1 : Source type populations (in thousands), as**  **derived from HPMS populations in §**  **5.2. and the age distribution algorithm** **in § 7.1.2.2.**  **Year Motorcycle**  **Passenger Car**  **Passenger**

**Truck Light** 

**Comm .**  **Truck Intercity** 

**Bus Transit** 

**Bus School** 

**Bus Refuse**  **Truck Single** 

**Unit Short**  **** **haul S**  **ingle**

**Unit Long**  **** **haul Motor** 

**Hom e**  **Combination**

**Short -**  **haul Combination** 

**Long -**  **haul** 2012 8571 128033 86859 21393 18 69 617 185 6194 260 1559 1191 1280 2013 8687 129764 87924 21791 19 72 643 195 6525 274 1643 1234 1332 2014 8706 130054 88014 21960 20 74 663 203 6777 285 1708 1258 1377 2015 8747 130666 88345 22167 21 77 691 213 7093 299 1788 1306 1439 2016 8844 132117 89259 22492 22 80 720 223 7392 312 1863 1354 1503 2017 8943 133583 90198 22803 22 82 740 230 7589 322 1915 1380 1555 2018 9018 134715 90934 23043 23 84 753 235 7709 328 1946 1390 1600 2019 9098 135907 91718 23279 23 86 766 239 7824 333 1977 1400 1645 2020 9178 137105 92513 23508 23 87 780 243 7953 335 2012 1410 1690 2021 9260 138317 93324 23730 24 88 794 247 8093 340 2053 1422 1737 2022 9337 139471 94098 23939 24 90 809 252 8242 345 2095 1437 1783 2023 9416 140653 94892 24150 25 92 824 256 8385 351 2134 1453 1828 2024 9498 141880 95725 24361 25 93 838 260 8510 352 2168 1466 1872 2025 9585 143179 96598 24591 26 95 853 264 8638 357 2204 1482 1918 2026 9680 144593 97557 24833 26 97 867 267 8752 362 2239 1495 1964 2027 9781 146100 98575 25092 27 98 879 269 8846 366 2266 1505 2005 2028 9888 147713 99664 25368 27 99 889 272 8927 371 2288 1514 2040 2029 9996 149317 100741 25649 27 100 900 274 9017 375 2312 1527 2073 2030 10103 150922 101823 25925 28 101 912 277 9114 376 2340 1546 2104 2031 10215 152591 102952 26209 28 103 922 280 9209 377 2368 1567 2131 2032 10328 154280 104098 26493 28 104 931 283 9286 381 2385 1585 2152 2033 10439 155930 105216 26772 28 105 942 286 9378 385 2405 1609 2174 2034 10538 157420 106225 27024 29 106 956 290 9493 391 2432 1639 2203 2035 10633 158833 107181 27263 29 108 969 293 9599 396 2457 1669 2232114

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 2733115



## 18. **Appendix B : Fuel Type and Regulatory Class Fractions**  **for 1960**  **- 198**  **1** As noted in the text, all the fuel type and regulatory class distributions in the SampleVehiclePopulation table for model ye ar 198 1 and earlier have not changed from MOVES2010b. Th ose fuel ty pe distributions between 1960 and 198 1 for each source typ e have been summariz ed in Table 18
- 1 and Table 18
- 2
. Many of the data sources for the fuel t ype fractions are the same in M OVES2010b and MOVES2014. Truck diesel fractions in Table 18
- 1 are derived using a MOVES2010b sample vehicle counts dataset

— similar to the MOVES2014 one
— but with 1999 Polk vehicle registrations and the 1997 VIUS, except for refuse trucks and motor homes. We assumed 96 percent of refuse trucks were manufactured to run on diesel fuel in 1980 and earlier according to the average diesel fraction from VIUS across all model years. We also assumed that 15 percent of motor homes are diesel powered based on information from the Recreation Vehicle Industry Association (RVIA), as noted above in Section 6.2.2.5. **Table 18**  **- 1**  **Diesel Fractions for Trucks** *****

**Source Type Model**  **Year Passenger** 

**Trucks (**  **31 )**  **Light Commer**  **ci al**  **Trucks (**  **32 )**  **Refuse**

**Trucks (**  **5 1)**  **Single U**  **nit**

**Trucks (52 &**  **53 )**  **Motor Hom**  **es**

**(54) Short**  **- haul** 

**Combination Trucks**  **( 61**  **) Long**  **- haul** 

**Combination Trucks**  **( 62**  **)** 19 60
- 19 79 0.0139 0.0419 0.96 0.2655 0.15 0.9146 1.0000 1980 0.0124 0.1069 0.96 0.2950 0.15 0.9146 1.0000 1981 0.0178 0.0706 0.96 0.3245 0.15 0.9146 1.0000 * All other trucks are assumed to be gasoline powered Like in MOVES2010b, lacking both emission rate and population data we assume in MOVES2014 that all motorcycles will be gasoline powered , all intercity buses will be diesel powered over all model year s, and all transit buses will be run on diesel from 1960 to 198 1
. School bus fuel ty

pe fractions are reused from MOBILE6, originally based on 1996 and 1997 Polk data. Passenger cars are split between gasoline and diesel for 1960
- 198 1 using the MOVES2010b sample vehicle counts dataset. **Table 18**  **- 2**  **Diesel Fractions for Non**  **- truck Source Types** * **Source Type Model**  **Year Motorcycles** 

**(1 1**  **) Passenger** 

**Cars (21)**  **Intercity Buses**

**(41) Transit Buses** 

**(42) School Buses** 

**(43)** 19 60
- 19 7 4 0 0.0 069 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 0.0 764 1.000 1.000 0.2639 * All other vehicles are assumed to be gasoline powered116

The 1960
- 198 1 regulatory class distributions are difficult to represent succinctly , but they have been derived from the MOVES2010b sample vehicle counts dataset as well, with a few exceptions. Motorcycles (sourceT yp eID 11 and regClassID 10) and passenger cars (sourceT yp eID 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 tr ucks (sourceTy

peI D 31) and light commercial trucks (sourceTy peID 32) are split between fuel ty pe 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 comm**  **ercial 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% 7 6% 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% 2 5% 7% 93% 1973 95% 5% 38% 62% 59% 41 % 7% 93% 1974 95% 5% 38% 62% 65% 35 % 7% 93% 1975 97% 3% 38% 62% 72% 28 % 7% 93% 1976 95% 5% 38% 62% 88% 12 % 7% 93% 1977 89% 11% 38% 62% 79% 21 % 7% 93% 1978 85% 15% 38% 62% 81% 19 % 7% 93% 1979 87% 13% 38% 62% 78% 22 % 7% 93% 1980 90% 10% 38% 62% 74% 26 % 40% 60 % 1981 96% 4% 38% 62% 89% 11 % 12% 88 % The bus and motor home source types each have a single regulatory class distribution for all model years, as described in Section 6.2.2. The 1960
- 198 1 regulatory c lass distributions for diesel
- fueled single unit and combination trucks have been summarized in Table 18
- 4 below. All 1960
- 198 1 gasoline
- fueled single unit and combination trucks fall into the medium heavy- dut y (MHD) regulatory

class (regClassID 46).117

**Table 18**  **- 4**  **Percentage of MHD**  **Trucks (regClassID 46)**  **am ong Diesel**  **- fueled Single**  **Unit and Combination Trucks** * **Source Type Model**  **Y ear**  **Refuse Trucks (**  **51 )**  **Single Unit**

**Trucks (**  **52&53 )**  **Short -**  **haul**

**Comb .**  **Trucks (**  **61 )**  **Long -**  **haul Comb .** 

**Trucks (**  **62 )**  1960
- 1972 100% 0% 0% 0% 1973 100% 3% 8% 0% 1974 0% 6% 30% 0% 1975 0% 14% 3% 0% 1976 0% 44% 13% 0% 1977 0% 43% 31% 0% 1978 0% 36% 18% 0% 1979 0% 34% 16% 0% 1980 0% 58% 29% 5% 1981 0% 47% 31% 6% * For these source types, all remaining trucks are in the HHD regulatory class (regClassID 47)
.118



## 19. **Appendix C**  **: 1990 Age Distributions 19.1.**  **Motorcycles** The motorcycle age distributions are based on Motorcycle Industry Council estimates of the number of motorcycles in use, by model year, in 1990. However, data for individu al 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 ye ars beginning in 1978. 

### 19.2. **Passenger Cars** To determi ne the 1990 age fractions for passenger cars, we began with Polk NVPP® 1990 data on car registration b y 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 w ere 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. **Truck s**  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 . L ike 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 b y dividing the original count by model year b y the fraction of the olde r vehicles that were coded as “obtained new.”119 **Table 19**  **- 1**  **VIUS 1997 Codes Used for Distinguishing Truck Source Types Source Type**  **Axle Arrangement Prim**  **ary 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 Short
haul Trucks Single Unit

(AXLRE=2
- 4, 8
- 16) Off
- road, local or short
- range (AREAOP<=4) Any except garbage

hauler Any Single Unit Long
haul Trucks Single Unit

(AXLRE=2
- 4, 8
- 16) Long
- range

(AREAO P>=5) Any Any Com bination Short
haul Trucks Com bination (AXLRE>=17) Off
- road, local or medium (AREAOP<=4) Any Any Com bination Long
haul Trucks Com bination (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 w ere 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 avai lable 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 ye ar to age and calculated age fractions. Because we did not have acce ss to 1990 data, these fractions were used for 1990. 

### 19.6. **Transit Buses** For 1990 Transit Bus age distributions, we used the MOBILE 6 age fractions since 1990 data on transit buses was not available from the Federal Transit Administration database. MOBILE 6 age fractions were based on fitting curves through a snapshot of vehicle registration data as of July 1, 1996, which was purchased from R.L. Polk Company . To develop a general curve, the 1996 model year vehicle populations were removed from the s ample because it did not represent a full ye ar, and a best fit analy sis was performed on the remaining population data. The best fit anal ys es resulted in age distribution estimates for vehicles ages 1 through 25+. However, since the vehicle sales ye ar begi ns in October, the estimated age 1 population was multiplied by 0.75 to account for the fact that approximately 75% of the year’s sales will have occurred by July 1 st of a given calendar year.120

Both Weibull curve fitting and exponential curve fitting were u sed to create the age distributions. The nature of the Weibull curve fitting formula is to produce an “S” shaped curve, which is relativel y flat for the first third of the data, decreases rapidl y 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 unrealisticall y 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 pres ents the equations used to create the age distribution and the years in which the equations were used.

**Table 19**  **- 2**  **Curve Fit Equations for Registration Distribution by Age Vehicle**  **Age Equation**  1
- 17 = 3462 ∗ e . . 18
- 25+ 24987 . 0776 ∗ e . ∗121



## 20. **Appendix D : Driving Schedules and SCC Mappings**  **20.1. SCC Mappings**  The Source Classification Code (SCC) used before MOVES2014 do not cleanl y map to the source types used b y M OVES. 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 b y MOVES by

calculating the fraction VMT for each source type found in each SCC classification result in a national MOVES2010b run for calendar ye ar 2011. The factors calculated from the MOVES201b run are shown in
. **Table 20**  **- 1**  **Mapping of Previous**  **SCC Vehicle Classifications to MOVES Source Types for Calculation of Road Type Distributions**  **SCC (7**  **digits) Description**  **Source Type**  **Description 2011**  **Fractions** 2201001 Gasoline Light
- Duty Vehicles (Passenger Cars) 21 Passenger Car 1.000000 2201020 Gasoline Light
- Duty Trucks (0
- 6,000 lbs. GVWR) 31 Passenger Truck 0.779270 2201020 Gasoline Light
- Duty Trucks (0
- 6,000 lbs. GVWR) 32 Light Commercial Truck 0.220730 2201040 Gasoline Light
- Duty Trucks (6,001
- 8,500 lbs. GVWR) 31 Passenger Truck 0.779269 2201040 Gasoline Light
- Duty Trucks (6,001
- 8,500 lbs. GVWR) 32 Light Commercial Truck 0.220731 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 31 Passenger Truck 0.450274 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 32 Light Commercial Truck 0.267803 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 42 Transit Bus 0.000664 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 43 School Bus 0.002476 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 51 Refuse Truck 0.000509 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 52 Single Unit Short
haul Truck 0.221958 2201070 Gasoline Heavy
- Duty Gasoline Vehicles (8501 lbs. and

greater GVWR) 53 Single Unit Long
haul 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 Com bination Short
haul 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 0.305092122

**SCC (7**  **digits) Description**  **Source Type**  **Description 2011**  **Fractions** 2230072 Diesel Class 3, 4 & 5 Heavy
- Duty Vehicles (10,001
-

19,500 lbs. GVWR) 32 Light Commercial Truck 0.694908 2230073 Diesel Class 6 & 7 Heavy
- Duty Vehicles (19,501
-

33,000 lbs. GVWR) 51 Refuse Truck 0.001726 2230073 Diesel Class 6 & 7 Heavy
- Duty Vehicles (19,501
-

33,000 lbs. GVWR) 52 Single Unit Short
haul Truck 0.623978 2230073 Diesel Class 6 & 7 Heavy
- Duty Vehicles (19,501
-

33,000 lbs. GVWR) 53 Single Unit Long
haul 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 Com bination Short
haul Truck 0.194650 2230073 Diesel Class 6 & 7 Heavy
- Duty Vehicles (19,501
-

33,000 lbs. GVWR) 62 Com bination Long
haul 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 Short
haul Truck 0.100296 2230074 Diesel Class 8a & 8b Heavy
- Duty Vehicles (33,001 lbs.

and greater GVWR) 53 Single Unit Long
haul 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 Com bination Short
haul Truck 0.323425 2230074 Diesel Class 8a & 8b Heavy
- Duty Vehicles (33,001 lbs.

and greater GVWR) 62 Com bination Long
haul 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


### 20.2. **Driving Schedules** A key feature of MOVES is the capability to accommodate a number of drive schedules to represent driving patterns across source type, roadway ty pe and average speed. For the national default case, MOVES2014 employ s 49 drive schedules with various average s peeds, mapped to specific source types and roadway types. T below list s 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 ty pes or multiple road ty

pes where vehicle specific information was not available.123 **T able**  **20 -**  **2 MOVES2014 Default Driving Schedule Statistics**  **drive schedule id**  **drive schedule name avg**  **speed ma**  **x speed**  **idle**

**tim e**  **(sec) percent of** 

**tim e idling**  **mi les**  **tim e (sec)**  **mi nutes**  **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 5m

ph 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 1.328124 **T able**  **20 -**  **2 MOVES2014 Default Driving Schedule Statistics**  **drive schedule id**  **drive schedule name avg**  **speed ma**  **x speed**  **idle**

**tim e**  **(sec) percent of** 

**tim e idling**  **mi les**  **tim e (sec)**  **mi nutes**  **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 0.242125



## 21. **Appendix E : MOVES2010b Source Masses**  Li ght
- duty source masses were unchanged from MOVES2010b. In addition, the heav y- duty source masses originally

come from MOVES2010b, although they have been updated as described in Section 14.1. In MOVES2010 b, weight data (among other kinds of information) were used to allocate source ty pes to source bins using a field called weightClassID. Each source ty pe’s source mass was calculated using an activity
- weighted average of their associated source bins’ midpoint weights:

= ∑ ∙ ∑ ∙

∑ ∑ Equation 40 where is the source mass factor for the source typ e, is the age fraction at age , is the source bin activity

fraction for source bin , and is the vehicle midpoint mass. **Error! Reference source not found.**  lists the vehicle midpoint mass for each weightClassID. The source bin activity

fraction in MOVES2010b is a calculated value of activity based on fuel type, engine technology , regulatory class, model year, engine size, and weight class. This calculation is outs ide the scope of this document, but more information can be found in the MOVES2010b SDRM.126 **Table 21**  **- 1**  **MOVES Weight Classes WeightClassID**  **Weight Class Name Midpoint Weight** 

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

### 21.1. **Motorcycles** The Motorcycle Industry Council “Statistical Annual” provides information on displacement distributions for highway motorcycles for model years 1990 and 1998. These were mapped to MOVES engine displacement categories. Additional EPA certification data was used to establish displacement distributions for model year 2000. We assumed that displacement distributions were the same in 1969 as in 1990, and interpolated between the establ ished 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 cate gory as suggested b y EPA motorcycle experts. The average weight estimate includes fuel and rider. The weight distributions depended on engine displacement but were otherwise independent of model year. This information is summarized in **Error! Reference source not found.**
.127 **Table 21**  **- 2**  **Mo torcycle Engine Size and Average Weight Distributions for Selected Model Years**  **Displacement Category**  **1969 MY**

**distribution (assumed)**  **1990 MY**

**distribution (M**  **IC) 1998 MY** 

**distribution (M**  **IC) 2000 MY** 

**distribution (certification**  **data) Weight distribution (EPA** 

**staff)** 0
- 169 cc (1) 0.118 0.118 0.042 0.029 100%: <= 500 lbs 170
- 279 cc (2) 0.09 0.09 0.05 0.043 50%: < = 500 lbs 50%: 500lbs
- 700lbs 280+ cc (9) 0.792 0.792 0.908 0.928 30%: 500 lbs
- 700 lbs 70%: > 700lbs 

### 21.2. **Passenger Cars** Passenger car weights come from Polk. The weightClassID was assigned by adding 300 lbs to the Polk curb weight and grouping into MOVES weight bins. For each fuel ty pe, 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, analy sis indicated a likel y error in the Polk data (an entry for 1997 gasoline
- powered Bentley s with engine size 5099 and weight class 20). This fraction was removed and the 1997 values were renormalized. 1999 model year values were used for all 2000
- and
- later model years. 

### 21.3. **General Trucks**  **21.3.1. Light**  **- Duty**  **Trucks** Determining weight categories for light trucks was fairly complicated. The VIUS 1997 data combines information from two different survey forms. The first form was administered for VIUS “Strata” 1 and 2 trucks: pickup trucks, pane l trucks, vans (including mini
- vans), utility ty pe vehicles (including jeeps) and station wagons on truck chassis. The second form was administered for all other trucks. While both survey s requested information on engine size, only the second form request ed detailed information on vehicle weight. Thus for Strata 1 and 2 trucks, VIUS classifies the trucks onl y b y 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 si ze 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 Autom o tive Inc. 76 data, to correlate engine size with vehicle weight distributions by model year. In particular, for source ty pes 31 and 32 (Passenger Trucks and Light Commercial Trucks):  VIUS 1997 trucks of the source type in Strata 3, 4, and 5 were assigned to the appropriate MOVES weight class based on VIUS detailed average weight information.  VIUS 1997 trucks of the source type in Strata 1 and 2 were identified b y engine size and broad average weight category
.  Strata 1 and 2 trucks in the heavier (10,001
- 14,000 lbs, etc) VIUS 1997 broad categories were matched one
- to - one with the M OVES weight classes.  For trucks in the lower broad categories (6,000 lbs or less and 6001
- 10,000 lbs), we used VIUS 1997 to determine the fraction of trucks b y model year and fuel type that fell into each engine size/broad weight class combination (the “VIUS fraction”)128

 We assigned trucks in the ORNL light
- duty vehicle database to a weightClassID by adding 300lbs to the recorded curb weight and determining the appropriate MOVES weight class.  For the trucks with a VIUS 1997 average weight of 6,000 lbs or less , we multiplied the VIUS 1997 fraction by

the fraction of trucks with a given weightClassID among the trucks in the ORNL database that had the given engine size and an average weight of 6,000 lbs or less. Note, the ORNL database did not provide informatio n on fuel ty pe, so the same distributions were used for all fuels.  Because the ORNL database included onl y vehicles with a GVW up to 8500 lbs, we did not use it to distribute the trucks with a VIUS 1997 average weight of 6,001
- 10,000 lbs. Instead these wer e distributed equall y among the MOVES weightClassID 70, 80, 90 and 100. **21.3.2. Single Unit Trucks**  Source ty pes 52 and 53 (Long
- and Short
- haul Single Unit Trucks) also included some trucks in VIUS 1997 Strata 1 and 2, thus a similar algorithm was applied.

 VIUS 1997 trucks of the source type in Strata 3, 4, and 5 were assigned to the appropriate MOVES weight class based on VIUS 1997 detailed average weight information.  VIUS 1997 trucks of the source type in Strata 1 and 2 were identified b y engine size and broad average weight category
.  Strata 1 and 2 trucks in the heavier (10,001
- 14,000 lbs, etc) VIUS 1997 broad categories were matched one
- to - one with the MOVES weight classes.  For trucks in the lower broad categories (6,000 lbs
- or - less and 6001
- 10,000 lbs), we u sed VIUS 1997 to determine the fraction of trucks b y 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 tr ucks. Thus, the trucks with a VIUS 1997 average weight of 6,000 lbs or less and an engine size less than 5 liters were distributed equally among the MOVES weight classes 20, 25, 30, 35, 40, 45, 50, and 60. Because no evidence existed of very light trucks among the vehicles with larger engines (5 liter or larger), these were equally distributed among MOVES weight classes 40, 45, 50 and 60.  The trucks with a VIUS 1997 average weight of 6,001
- 10,000 lbs were distributed equally among the MOVES weight classes 7 0, 80, 90 and 100. **21.3.3. Combination Trucks**  Long
- and short
- haul combination trucks (source ty pes 61 and 62) did not include an y vehicles of VIUS 1997 Strata 1 or 2. Thus we used the detailed VIUS 1997 average weight information and engine size information to assign engine size and weight classes for all of these trucks. When VIUS2002 became available, we updated values that had been based on VIUS1997. The VIUS 2002 contains an estimate of the average weight (vehicle w eight plus cargo weight) of 1998
- 2002 model year vehicle or vehicle/trailer combination as it was most often operated when129

carryi ng a ty

pical payload during 2002 . These estimates were used to determine the MOVES weightClassID categories for these trucks. Table 4
- 5 shows the weight ranges used for each weightClassID. An y 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 smalle r 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 ty pes 31 and 32) and for all model years prior to 1998. In cases where distributions were missing (no survey information), distributions from a nearb y model year with the same source typ e was used. Weight distributions for all 2003 and newer model years were set to be the same as for the 2002 model year for each source type. 

### 21.4. **Buses** For intercity

buses, we used information from Table II
- 7 of the FTA 2003 Report to Congress 40 that specified the number of buses in various weight categories. This information is summarized in below in **Error! Reference source not found.**  . Note the FTA uses the term “over
- the
- road bus” to refer to the class of buses roughl y equivalent to the MOVES intercity bus category . The FTA weight categories were mapped to the equivalent MOVES weight classes. **Table 21**  **- 3**  **FTA Estimate of Bus Weights**  **Weight (lbs) MOVES Weight** 

**ClassID MOVES Weight** 

**Range (lbs) Number**  **of**

**buses (2000) Bus type**  0
- 20,000 173,536 school & transit 20,000
- 30,000 392,345 school & transit 30,000
- 40,000 400 33,000
- 40,000 120,721 school & transit & intercity 40,000
- 50,000 500 40,000
- 50,000 67,905 intercity total 754,509

**Table 21**  **- 4**  **1999 Bus Population Comparisons Data Source**  **Total Buses Intercity Buses**  **Transit Buses School Buses**  FHWA MV
- 1 732,189 FHWA MV
- 10 (excludes PR) 728,777 592,029* FHWA adjusted for PR 594,800 FTA NTD 55,706 APTA 77 *** 75,087 Polk TIP® 460,178 School Bus Fleet Fact Book 429,086 Motorcoach Census 39 ** 44,200 * Includes some church & industrial buses. ** Includes Canada.130

*** Includes trolleybuses. Using the 1999 bus population estimates in **Error! Reference source not found.** , we were able to estimate the fraction of all buses that were intercity buses and then to estimate the fraction of intercity buses in each weight bin. In particular: Estimated number of intercity buses in 2000: 754,509 * (84,454/(84,454+55,706+592,029)) = 87,028 Estimated number of intercity buses 30,000
- 40,000 lbs: 87,028
- 67,905 = 19,123 Estimated intercity

bus weight distribution: Class 400 = 1 9,123/87,028 = 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 Congress 40 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. Whi le this data is older and may

not be representative of the national average distribution, it was the best data source available. The California data 78 provided information on number of vehicles by gross vehicle weight class and fuel as detailed in **Error! Reference source not found.**
. **Table 21**  **- 5**  **California School Buses Gas**  **Diesel Other**  **Total** LHDV 2740 4567 8 7315 MHDV 467 2065 2 2534 HHDV 892 11639 147 12678 Total 4099 18271 157 To estimate the distribution of average weights among the MOVES weight classes, we assumed that the L ight Heavy
- Duty (LHDV) school buses were evenl y distributed among weightClassIDs 70, 80, 90, 100, and 140. Similarly , we assumed the Medium Heavy
- Dut y (MHD V) school buses were evenl y distributed among weightClassIDs 140, 160, 195, 260, and 330 and the Heav y Heav y- Duty

(HHDV) school buses were evenl y distributed among weightClassIDs 195, 260, 330, and 440. The final default weight distributions for buses are summarized in **Error! Reference source not found.** 
.131 **Table 21**  **- 6**  **Weight Distributions for Buses by Fuel Type Intercity Buses (41)**  **Transit Buse s**  **(42) School Buses (43)**  **Weight Class Diesel**  **Diesel & Gas Diesel**  **Gas** 70 0.0500 0.1337 80 0.0500 0.1337 90 0.0500 0.1337 100 0.0500 0.1337 140 0.0726 0.1565 160 0.0226 0.0228 195 0.1819 0.0772 260 0.1819 0.0772 330 0.1819 0.0772 400 0.2197 1.0000 0.1593 0.0544 500 0.7800 

### 21.5. **Refuse Trucks** Because the sample of Refuse Trucks in VIUS was small, the weight distributions were calculated for model ye ar groups rather than individual model years. As for other trucks, the WeightClass was determined from the VIUS reported average weight.132 **Table 21**  **- 7**  **Refuse Truck SizeWeight Fractions by Fuel Type Gasoline**  **Engine Size Weight (lbs.)**  **Pre -**  **1997 1997 and**  **Newer** 3
- 3.5L 5000
- 6000 0.009074 0 >5L 7000
- 8000 0.148826 0 >5L 9000
- 10000 0.070720 0 >5L 10000
- 14000 0.135759 0.324438 >5L 14000
- 16000 0.199961 0.593328 >5L 16000
- 19500 0.055085 0 >5L 19500
- 26000 0.205341 0 >5L 26000
- 33000 0.022105 0 >5L 33000
- 40000 0.153129 0 >5L 50000
- 60000 0 0.082234 **Sum 1.000000**  **1.000000 Diesel**  **Engine Size Weight (lbs.)**  **Pre -**  **1998 1998**  **1999 2000**  **2001 2002 and**  **Newer** 3.5
- 4L 10000
- 14000 0.007758 0 0 0 0 0 4
- 5L 10000
- 14000 0 0 0 0 0 0.006614 4
- 5L 14000
- 16000 0 0 0 0.015505 0 0 4
- 5L 16000
- 19500 0 0 0 0 0.011670 0 >5L 9000
- 10000 0.006867 0.009593 0 0 0 0 >5L 10000
- 14000 0.011727 0 0 0 0.019438 0 >5L 14000
- 16000 0.022960 0 0 0 0 0 >5L 16000
- 19500 0.063128 0 0.011367 0.047200 0 0 >5L 19500
- 26000 0.099782 0.035378 0.026212 0.052132 0.018329 0.026079 > 5L 26000
- 33000 0.102077 0.019625 0.067419 0.072106 0.043877 0 >5L 33000
- 40000 0.237485 0.103922 0.088975 0.085991 0.042678 0.046966 >5L 40000
- 50000 0 0.283642 0.275467 0.165624 0.266357 0.194716 >5L 50000
- 60000 0.336484 0.338511 0.326902 0.384612 0.315133 0.474469 >5L 60000
- 80000 0.111730 0.196424 0.193238 0.176831 0.282517 0.224995 >5L 80000
- 100000 0 0 0.010420 0 0 0.013081 >5L 100000
- 130000 0 0.012904 0 0 0 0.013081 **Sum 1.000000**  **1.000000 1.000000**  **1.000000 1.000000**  **1.000000** 

### 21.6. **Motor Homes** No detailed information was available on average engine size and weight distributions for motor homes. We assumed all motor home engines were 5 L or larger. As a surrogate for average weight, we used information on gross vehicle weight provided in the Polk TIP® 1999 database by model year and mapped the Polk GVW Class to the MOVES weight bins. These values are likely to overestimate average weight. The Polk TIP® information did not specify fuel type, so we assumed that the heaviest vehicles in the Polk dat abase were diesel
- powered and the remainder were powered by gasoline. This led to the weight distributions in **Error! Reference source not found.**  and **Error! Reference source not found.**
.133 **Table 21**  **- 8**  **Weight Fractions for Diesel Motor Hom es 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 0.029458134 **Table 21**  **- 9**  **Weight Fractions for Gasoline Motor Hom es 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 

## References

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