Draft MOVES2009 Highway Vehicle 

Population and Activity Data 
Draft MOVES2009 Highway Vehicle 


Population and Activity Data 


Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

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

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

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

technical developments. 

EPA-420-P-09-001 

August 2009 
Table of Contents 

1. Introduction................................................................................................................................. 1 

2. Data Sources ............................................................................................................................... 4 

2.1. VIUS(and TIUS).................................................................................................................. 4 

2.2. Polk NVPP® and TIP® ....................................................................................................... 4 

2.3. FHWA Highway Statistics................................................................................................... 4 

2.4. FTA National Transit Database ........................................................................................... 4 

2.5. School Bus Fleet Fact Book................................................................................................. 4 

2.6. MOBILE6 ............................................................................................................................ 5 

2.7. Annual Energy Outlook & National Energy Modeling System .......................................... 5 

2.8. Transportation Energy Data Book ....................................................................................... 5 

2.9. Oak Ridge National Laboratory Light-duty Vehicle Database............................................ 5 

3. SourceTypeYear ......................................................................................................................... 6 

3.1. 1999 SourceTypePopulation................................................................................................ 6 

3.2. 1990 SourceTypePopulation.............................................................................................. 10 

3.3. SalesGrowthFactor............................................................................................................. 14 

3.3. MigrationRate .................................................................................................................... 16 

4. SourceTypeModelYear............................................................................................................. 17 

5. SourceTypeAge......................................................................................................................... 18 

5.1. SurvivalRate....................................................................................................................... 18 

5.2. Relative MAR .................................................................................................................... 20 

5.3. FunctioningACFraction ..................................................................................................... 23 

6. SourceTypeAgeDistribution ..................................................................................................... 25 

6.1. 1999 Motorcycles............................................................................................................... 25 

6.2. 1999 Passenger Cars .......................................................................................................... 25 

6.3. 1999 Trucks ....................................................................................................................... 26 

6.4. 1999 Intercity Buses .......................................................................................................... 28 

6.5. 1999 School Buses and Motor Homes............................................................................... 28 

6.6. 1999 Transit Buses............................................................................................................. 28 

6.7. 1990 Motorcycles............................................................................................................... 30 

6.8. 1990 Passenger Cars .......................................................................................................... 30 

6.9. 1990 Trucks ....................................................................................................................... 30 

6.10. 1990 Intercity Buses ........................................................................................................ 31 

6.11. 1990 School Buses and Motor Homes............................................................................. 31 

6.12. 1990 Transit Buses........................................................................................................... 31 

7. SourceBinDistribution .............................................................................................................. 32 

7.1. Motorcycles........................................................................................................................ 34 

7.2. Passenger Cars ................................................................................................................... 35 

7.3. Trucks ................................................................................................................................ 36 

7.4. Buses.................................................................................................................................. 46 

7.5. Refuse Trucks .................................................................................................................... 50 

7.6. Motor Homes ..................................................................................................................... 52 

7.7. SourceBinDistributions for 2000-and-later........................................................................ 55 

8. SourceUseType ......................................................................................................................... 62 

8.1. SourceMass........................................................................................................................ 62 

i 
8.2. Road Load Coefficients ..................................................................................................... 63 

9. RoadTypeDistribution............................................................................................................... 66 

10. Average Speed Distribution.................................................................................................... 67 

11. HPMSVTypeYear................................................................................................................... 70 

11.1. HPMSBaseYearVMT ...................................................................................................... 70 

11.2. BaseYearOffNetVMT...................................................................................................... 70 

11.3. VMTGrowthFactor .......................................................................................................... 70 

12. Temporal Distributions of VMT............................................................................................. 74 

12.1. MonthVMTFraction......................................................................................................... 74 

12.2. DayVMTFraction............................................................................................................. 75 

12.3. HourVMTFraction ........................................................................................................... 75 

13. Driving Schedule Tables......................................................................................................... 77 

14. Drive Schedule Association.................................................................................................... 79 

15. SourceTypeHour..................................................................................................................... 83 

15.1. IdleSHOFactor ................................................................................................................. 83 

16. ZoneRoadType........................................................................................................................ 85 

17. Zone ........................................................................................................................................ 86 

17.1. StartAllocFactor............................................................................................................... 86 

17.2. IdleAllocFactor ................................................................................................................ 86 

18. SCC Mappings........................................................................................................................ 88 

18.1. SCCVtypeDistribution.................................................................................................... 88 

18.2. SCCRoadTypeDistribution............................................................................................. 89 

19. MonthGroupHour ................................................................................................................... 91 

20. Sample Trip Data................................................................................................................. 92 

21. References............................................................................................................................... 94 

ii 
List of Tables and Figures 

Table 1-1. MOVES SourceTypes .................................................................................................................1

Table 1-2. MOVES Database Elements Covered in This Report .................................................................2

Table 3-1. Vehicle Population Comparisons 1999 ......................................................................................7

Table 3-2. Adjusted Vehicle Populations ....................................................................................................7

Table 3-3a. VIUS 1997 Codes Used for Distinguishing Truck SourceTypes. ............................................8

Table 3-3b. VIUS 2002 Codes Used for Distinguishing Truck SourceTypes. ............................................8

Table 3-4. 1999 Truck SourceType Distribution and Populations ...............................................................9

Table 3-5. 1999 Bus Population Comparisons.............................................................................................9

Table 3-6. 1999 SourceType Populations in Draft MOVES2009...............................................................10

Table 3-7. 1990 Vehicle Population Comparisons ....................................................................................11

Table 3-8. TIUS92 Codes Used for Distinguishing Truck SourceTypes................................................... 12

Table 3-9. 1990 Truck SourceType Distribution and Populations .............................................................12

Table 3-10. 1990 Bus Population Comparisons..........................................................................................13

Table 3-11. 1990 SourceType Populations in Draft MOVES2009.............................................................14

Table 3-12. SalesGrowthFactor by Calendar Year and Source Type ......................................................... 16

Table 4.1. AC Penetration Fractions in Draft MOVES2009 .....................................................................17

Table 5-1. SurvivalRate by Age and SourceType.......................................................................................20

Table 5.2. Equations for Calculating Annual Mileage Accumulation Rates used in MOVES..................22

Figure 5.1. Relative Mileage Accumulation Rates in Draft MOVES2009................................................23

Table 5-3. FunctioningACFraction by Age (All Use Types Except Motorcycles).....................................24

Figure 6.1 1999 Age Distributions for Passenger.......................................................................................26

Figure 6.2 1999 Age Distributions for Passenger and Light Commercial Trucks.....................................27

Table 6-1. 1999 Age Fractions for MOVES Source Types......................................................................29

Table 7-1. Data Tables Used by SourceBinGenerator...............................................................................33

Table 7-2. Motorcycle Engine Size and Average Weight Distributions for Selected Model Years..........34

Table 7-3. Mapping Polk Fuel Codes to MOVES. ....................................................................................35

Table 7-4. Mapping VIUS ENGTYP to MOVES FuelTypeID .................................................................36

Table 7-5. Diesel Fractions for Trucks .....................................................................................................38

Table 7-6. Mapping VIUS Engine Size Categories to MOVES EngSizeID...............................................39

Table 7-7. Mapping VIUS Average Weight to MOVES WeightClassID...................................................42

Table 7.8. Light Truck Class 2 Weight Distribution..................................................................................43

Table 7-9. Fraction of Light-Duty Trucks among Gasoline-Fueled Trucks..............................................44

Table 7-10. Fraction of Light-Duty Trucks among Diesel-fueled Trucks.................................................45

Table 7-11. Mapping National Transit Database Fuel Types to MOVES Fuel Types ..............................46

Table 7-12. Fuel Fractions for Transit Buses.............................................................................................47

Table 7-13. Fuel Fractions for School Buses.............................................................................................48

Table 7-14. FTA Estimate of Bus Weights.................................................................................................48

Table 7-15. California School Buses .........................................................................................................49

Table 7-16. Weight Distributions for Buses by Fuel Type ........................................................................50

Table 7-17. Fuel Fractions for Refuse Trucks by Model Year ..................................................................51

Table 7-18. Refuse Truck SizeWeight Fractions by Fuel Type.................................................................52

Table 7-19. Diesel Fractions for Motor Homes. ........................................................................................53

Table 7-20. Weight Fractions for Diesel Motor Homes by Model Year ...................................................54

Table 7-21. Weight Fractions for Gasoline Motor Homes by Model Year ...............................................55

Table 7-22. Supported Fuels and Technologies for 2000-and-later Model Years. ....................................56

Table 7.23. Fuel Fractions for 2002 and Newer Passenger Cars and Light Duty Trucks...........................58

Table 7.24. Fuel and Engine Technology Fractions for 2000-and-later Buses...........................................58

iii 
Table 7.25. Fuel and Engine Technology Fractions for 2002 and Newer Motor Homes and Single-Unit 

Short-haul and Long-haul Trucks ......................................................................................................59

Table 7.26. Fuel and Engine Technology Fractions for Refuse Trucks and Short-haul and Long-haul 

Combination Trucks...........................................................................................................................61

Table 8-1. MOVES Weight Classes ..........................................................................................................63

Table 8-2. Road Load Coefficients for Heavy-Duty Trucks, Buses, and Motor Homes ........................... 64

Table 8-3. SourceUseType Characteristics...............................................................................................65

Table 9-1. Road Type Codes in MOVES ..................................................................................................66

Table 9-2. Roadtype Distributions by Sourcetype .....................................................................................67

Table 10-2. MOVES Speed Bin Categories...............................................................................................68

Figure 10.1 Speed Distribution by Roadtype.............................................................................................69

Table 11-1. 1999 VMT by HPMS Vehicle Class ......................................................................................70

Table 11-2. VMTGrowthFactor Calculation for Passenger Cars and Light Trucks...................................72

Table 11-3. VMT Growth Factors in Draft MOVES2009.........................................................................73

Table 12-1. MonthVMTFraction ...............................................................................................................74

Table 12-2. DayVMTFractions..................................................................................................................75

Figure 12.1 Hourly VMT Fractions in Draft MOVES2009.......................................................................76

Table 13-1. Default MOVES Drive Schedules...........................................................................................78

Table 14-1. Drive Schedule Mapping.........................................................................................................79

Table 14.2 Proposed Drive Schedules for Passenger Cars, Passenger Trucks and Light Commercial 

Trucks in Final MOVES2009 ............................................................................................................81

Table 14.2 Continued..................................................................................................................................82

Table 14.2 Continued..................................................................................................................................82

Table 18-1. SCC Mappings for Selected SourceTypes...............................................................................89

Table 18-1. SCC RoadTypes ......................................................................................................................89

Table 19-1. Air Conditioning Activity Coefficients...................................................................................91

Figure 19-1: Air Conditioning Activity Demand as a Function of Heat Index..........................................91

Table 20.1. Source Data for Sample Vehicle Trip Information..................................................................92

Table 20.2. Synthesis of Sample Vehicles for Source Types Lacking Data..............................................93

Table 20.2. Starts per Day by SourceType ................................................................................................93

iv 
1. Introduction 

The Environmental Protection Agency’s MOVES (Motor Vehicle Emission Simulator) 

is a new set of modeling tools for estimating emissions produced by on-road (cars, trucks, 

motorcycles, etc.) and nonroad (backhoes, lawnmowers, etc) mobile sources. This report 

partially documents the Draft MOVES2009 version, released in April 2009. Draft MOVES2009 

estimates greenhouse gases (GHG), criteria pollutants and selected air toxics from highway 

vehicles. When finalized, MOVES2009 will serve as a replacment for MOBILE6.2 

The primary vehicle classification in MOVES is “SourceType.” (Also sometimes called 

"SourceUseType").The MOVES SourceTypes are listed in Table 1-1, along with the associated 

DOT Highway Performance Monitoring System (HPMS) vehicle classes. 

To estimate emissions accurately, we must use accurate estimates of vehicle populations 

and activity. This paper documents the sources and calculations used to produce the default 

population and activity data in the DRAFT MOVES2009 database used to compute national 

level emissions.a

 In particular, this paper will describe the data used to fill the tables and fields 

listed in Table 1-2. 

Table 1-1. MOVES SourceTypes 

SourceType ID SourceType HPMS Vehicle Class 

11 Motorcycles Motorcycles 

21 Passenger Cars Passenger Cars 

31 Passenger Trucks (primarily personal use) Other Two-Axle/Four Tire, Single Unit 

32 Light Commercial Trucks (other use) Other Two-Axle/Four Tire, Single Unit 

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

42 Transit Buses Buses 

43 School Buses Buses 

51 Refuse Trucks Single Unit 

52 Single Unit Short-haul Trucks Single Unit 

53 Single Unit Long-haul Trucks Single Unit 

54 Motor Homes Single Unit 

61 Combination Short-haul Trucks Combination 

62 Combination Long-haul Trucks Combination 

“Long-haul” trucks are defined as trucks for which most trips are 200 miles or more. 

a

 For many uses, local inputs are required. EPA is currently developing draft technical guidance to describe these 

requirements. 

1 
Table 1-2. MOVES Database Elements Covered in This Report 

Database Table Name* Fields 

SourceTypeYear sourceTypePopulation 

salesGrowthFactor 

migrationRate 

SourceTypeModelYear ACPenetrationFraction 

SourceTypeAge survivalRate 

relativeMAR 

functioningACFraction 

SourceTypeAgeDistribution ageFraction 

SourceBinDistribution* sourceBinActivityFraction 

SourceUseType rollingTerm 

rotatingTerm 

dragTerm 

sourceMass 

RoadTypeDistribution roadTypeVMTFraction 

AvgSpeedDistribution avgSpeedFraction 

HPMSVtypeYear HPMSBaseYearVMT 

baseYearOffNetVMT 

VMTGrowthFactor 

MonthVMTFraction monthVMTFraction 

DayVMTFraction dayVMTFraction 

HourVMTFraction hourVMTFraction 

DriveSchedule averageSpeed 

DriveScheduleSecond speed 

DriveScheduleAssociation sourceTypeID 

roadTypeID 

driveScheduleID 

isRamp 

SourceTypeHour idleSHOFactor 

ZoneRoadType SHOAllocFactor 

Zone idleAllocFactor 

startAllocFactor 

SHPAllocFactor 

SCCVTypeDistribution SCCVTypeFraction 

2 
MonthGroupHour AC Activity Terms (A, B & C) 

SampleVehicleDay dayID 

sourceTypeID 

SampleVehicleTrip priorTripID 

keyontime 

keyOffTime 

SampleVehiclePopulation stmyFuelEngFraction 

stmyFraction 

*See also Table 7-1, listing tables and fields used by the SourceBinGenerator. 

3 
2. 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 MOVES database are 

described here. These sources are referred to throughout this document by the abbreviated name 

given in this description, but the reference citation is only given here. 

2.1. VIUS(and TIUS) 

Until 2002, the U.S. Census Bureau conducted the Vehicle Inventory and Use Survey 

(VIUS)1

 to collect data on the physical characteristics and activity of U.S. trucks every five 

years. The survey is a sample of private and commercial trucks that were registered in the U.S. 

as of July of the survey year. The survey excludes automobiles, motorcycles, government-owed 

vehicles, ambulances, buses, motor homes and nonroad equipment. For MOVES, VIUS 

provides information to characterize trucks by SourceType and to estimate age distributions. 

Draft MOVES2009 uses data from both the 1997 and 20022

 surveys. Before 1997, VIUS was 

known as TIUS (Truck Inventory and Use Survey). To populate the 1990 base year, we used data 

from the 1992 TIUS.3. 

Note that Census Bureau has discontinued the VIUS survey. We request comments on 

alternate data sources or approaches for determining truck populations in the future. 

2.2. Polk NVPP® and TIP® 

R.L. Polk & Co. is a private company providing automotive information services. The 

company maintains two databases relevant for MOVES: the National Vehicle Population Profile 

(NVPP®)4 and the Trucking Industry Profile (TIP®Net) Vehicles in Operation database.5 The 

first focuses on light-duty cars and trucks, the second focuses on medium and heavy-duty trucks. 

Both compile data from state vehicle registration lists. For Draft MOVES2009, EPA used the 

1999 NVPP® and TIP®. 

2.3. FHWA Highway Statistics 

Each year the Federal Highway Administration’s (FHWA) Office of Highway Policy 

Information publishes Highway Statistics. This volume summarizes a vast amount of roadway 

and vehicle data from the states and other sources. For MOVES, we will use data on vehicle 

registrations and vehicle miles traveled (VMT), summarized in four tables. 6 7 8 9 . Hereafter, 

references will be to FHWA MV-1, MV-10, VM-1, and VM-2. For the 1999 base year, we used 

the 1999 statistics; for the 1990 base year, we used 1990 numbers. 

2.4. FTA National Transit Database 

The Federal Transit Administration (FTA) summarizes financial and operating data from 

U.S. mass transit agencies in the National Transit Database (NTD).10 For Draft MOVES2009, 

we used 1999 data from the report, “Age Distribution of Active Revenue Vehicle Inventory: 

Details by Transit Agency.” 

2.5. School Bus Fleet Fact Book 

The School Bus Fleet 1999 Fact Book includes estimates, by state, of number of school 

buses and total miles traveled.11 The Fact Book is published by Bobit Publications. 

4 
2.6. MOBILE6 

In some cases, we have been able to use data from MOBILE6 with only minor 

adaptation. The MOBILE6 data is documented in technical reports, particularly M6.FLT.002 

“Update of Fleet Characterization Data for Use in MOBILE6 - Final Report.”12 Additional 

MOBILE6 documentation is available on the web at http://www.epa.gov/otaq/m6.htm 

2.7. Annual Energy Outlook & National Energy Modeling System 

The Annual Energy Outlook (AEO) 13,14 describes Department of Energy forecasts for 

future energy consumption. The National Energy Modeling System (NEMS) is used to generate 

these projections based on economic and demographic projections. For Draft MOVES2009 we 

used AEO2006 to forecast VMT growth and vehicle sales growth. For the final MOVES2009, we 

propose updating these results with more recent forecasts. 

2.8. Transportation Energy Data Book 

Each year, Oak Ridge National Laboratory produces the DOE Transportation Energy 

Data Book (TEDB). This book summarizes transportation and energy data from a variety of 

sources. For MOVES2004, we relied on Edition 22, published in September 200215 and Edition 

23, published in October 2003.16 For Draft MOVES2009 we updated sales growth based on 

Edition 27, published in 2008.17 and updated 1990 values using Edition 13, published in 1993. 

2.9. Oak Ridge National Laboratory Light-duty Vehicle Database 

Oak Ridge National Laboratory Center for Transportation Analysis has compiled a 

database of light-duty vehicle information which combines EPA Test vehicle data and Ward's 

Automotive Inc. data spanning 1976 – 2001.18 We used this database to determine weight 

distributions for light trucks by model year. 

5 
3. SourceTypeYear 

The SourceTypeYear table stores three data fields––SourceTypePopulation, 

SalesGrowthFactor, and Migration Rate. Each field is described below in terms of what 

information it contains, the sources of the data used for the field, and, when applicable, certain 

data points used in determining the field parameters. 

3.1. 1999 SourceTypePopulation 

The SourceTypePopulation field stores the total population of vehicles by SourceType 

for a given base year and domain. For Draft MOVES2009, this is the entire United States in 

1999. An additional base year is 1990. Some of the values are taken directly from the indicated 

sources; other values needed to be derived from the available data. 

SourceTypePopulation provides base year populations and provides the basis for Total 

Activity Generator calculation of populations in calendar years after the base year. These 

populations are, in turn, used to generate travel fractions by age and SourceType and to allow 

allocation of VMT by age. 

The primary sources for calendar year 1999 vehicle population data are the FHWA 

Highway Statistics Tables MV-1 and MV-10 and the Polk NVPP® and TIP® databases. The 

Transportation Energy Data Book (TEDB) explains three factors that account for differences 

between the two sources: 

1. Polk data includes only vehicles that were registered on July 1 of 1999. FHWA data 

includes all vehicles that have been registered at any time throughout the year and 

thus may include vehicles that were retired during the year or may double count 

vehicles registered in two or more states. 

2. Polk and FHWA may differ in how they classify some minivans and SUVs as trucks 

or automobiles. (This difference is less important since 1990). 

3. FHWA includes all non-military Federal vehicles. Polk data includes only those 

Federal vehicles that are registered within a state. 

Also, FHWA data is available for Puerto Rico, but Puerto Rico does not appear to be 

included in our Polk data set. MOVES will cover Puerto Rico and the Virgin Islands. In 

addition, Polk collects data on Gross Vehicle Weight (GVW) class 3 vehicles in both the 

NVPP® and TIP® databases, but the values are not the same. Polk staff recommended using the 

TIP® values.19

 Finally, our 1999 Polk data set includes school buses and motor homes (which 

can be counted separately), but does not include “non-school buses.” 

Motorcycle population estimates were available from both FHWA registration data and 

from the Motorcycle Industry Council. The MIC estimate is based on 1998 sales estimates, 

adjusted to subtract noped sales (nopeds are similar to mopeds, but lack pedals) and to account 

for scrappage. 

The Department of Transportation’s National Household Transportation Survey (NHTS) 

combines the previous National Personal Transportation Survey and the American Travel Survey 

to collect data on personal travel patterns and includes data on motorcycles, personal trucks and 

automobiles.20 Data from the 2001 survey is included in Table 3-1, but is not used in MOVES 

6 
because it is two years newer than the FHWA and Polk data, and it excludes non-household 

vehicles. Values from the five data sources are compared in Table 3-1. 

Table 3-1. Vehicle Population Comparisons 1999 

Data Source Motorcycles Automobiles Trucks (total) Buses (total) Motor 

Homes 

FHWA MV-1 (w Puerto 

Rico and publically 

ownedvehicles) 

4,173,869 134,480,432 83,178,092 732,189 na 

FHWA MV-10 (w/o 

Puerto Rico and 

publically owned 

vehicles) 

131,076,551 81,060,369 

Polk NVPP® & TIP® na 126,868,744 80,323,528* na 902,949 

NHTS (2001) 4,951,747 115,914,908 80,499,939 1,446,469 

MIC (1998)21 4,605,439 

*Excluding motor homes and NVPP® GVW3 trucks. 

For automobiles and trucks, it is possible to do a direct comparison of Polk and FHWA 

data. To estimate the MOVES population, we adjust the FHWA data to account for doublecounting by multiplying the total FHWA population by the ratio of the Polk population to the 

FHWA population without public vehicles and Puerto Rican vehicles. 

Adjusted Population = FHWA w public & PR * (Polk/FHWA w/o public & PR) 

This leads to the values in Table 3-2.b 

Table 3-2. Adjusted Vehicle Populations 

Population 

(Draft MOVES2009) 

Population 

(proposed for final MOVES2009) 

Automobiles 130,163,354 130,163,354 

Trucks (total) 83,348,540 83,007,993 

For MOVES, total trucks are sub-classified into seven SourceTypes. The proportion of 

total trucks in each subtype was estimated using VIUS responses for Axle Arrangement, Primary 

Area of Operation, Body Type and Major Use as detailed in Table 3-3a and Table 3.3b. 

With these definitions and with vehicles that lack AREAOP codes assigned 

proportionally to the corresponding SourceTypes, we computed the distributions in Table 3-4. 

b

 There was an error in the calculation of the value for total trucks used in Draft MOVES2009. We plan to correct 

this error in the final version of MOVES2009 as indicated here. 

7 
These distributions were multiplied by the total truck population from Table 3-2 to derive 

population values for MOVES. 

Table 3-3a. VIUS 1997 Codes Used for Distinguishing Truck SourceTypes. 

SourceType Axle Arrangement Primary Area of 

Operation 

Body Type Major Use 

Passenger Trucks 2 axle/4 tire (AXLRE= 

1,5,6,7) 

any any personal 

transportation 

(MAJUSE=20) 

Light Commercial 

Trucks 

2 axle/4 tire (AXLRE= 

1,5,6,7) 

any any any but personal 

transportation 

Refuse Trucks Single Unit (AXLRE = 

2-4, 8-16) 

off-road, local or shortrange (AREAOP <=4) garbage hauler 

(BODTYP=30) 

Any 

Single Unit Shorthaul Trucks 

Single Unit (AXLRE = 

2-4, 8-16) 

off-road, local or shortrange (AREAOP <=4) any except 

garbage hauler 

Any 

Single Unit Longhaul Trucks 

Single Unit (AXLRE = 

2-4, 8-16) 

long-range (AREAOP 

>=5) 

any Any 

Combination Shorthaul Trucks 

Combination (AXLRE 

>=17) 

off-road, local or 

medium (AREAOP <=4) 

any Any 

Combination Longhaul Trucks 

Combination (AXLRE 

>=17) 

long-range (AREAOP 

>=5) 

any Any 

Table 3-3b. VIUS 2002 Codes Used for Distinguishing Truck SourceTypes. 

SourceType Axle Arrangement 

Primary Area 

of Operation Body Type 

Operator 

Classification 

Passenger 

Trucks 

axle_config in (1,6,7,8) any any opclass=5 

Light 

Commerical 

Trucks 

axle_config in (1,6,7,8) any any opclass<>5 

Refuse 

Trucks 

axle_config in 

(2,3,4,5,9,10,11,12,13,14,15,16,17,18,19,20) 

trip_primary in 

(1,2,3,4) 

bodytype=21 any 

Single Unit 

Short-Haul 

Trucks 

axle_config in 

(2,3,4,5,9,10,11,12,13,14,15,16,17,18,19,20) 

trip_primary in 

(1,2,3,4) 

Any except 

bodytype=21 

any 

Single Unit 

Long-Haul 

Trucks 

axle_config in 

(2,3,4,5,9,10,11,12,13,14,15,16,17,18,19,20) 

trip_primary in 

(5,6) Long 

Range 

any any 

Combination 

Short-Haul 

Trucks 

axle_config>=21 trip_primary in 

(1,2,3,4) 

sample_strata=5 

Combination 

Trucks 

any 

Combination 

Long_Haul 

Trucks 

axle_config>=21 trip_primary in 

(5,6) Long 

Range 

sample_strata=5 

Combination 

Trucks 

any 

8 
Table 3-4. 1999 Truck SourceType Distribution and Populations 

SourceType Percent Population 

(Draft MOVES2009) 

Population 

(final 

MOVES2009) 

Passenger Trucks 68.90% 57,424,819 57,190,192 

Light Commercial Trucks 23.02% 19,184,642 19,106,257 

Refuse Trucks 0.11% 88,970 88,607 

Single Unit Short-haul Trucks 5.39% 4,489,140 4,470,798 

Single Unit Long-haul Trucks 0.32% 265,520 264,435 

Combination Short-haul 

Trucks 

1.31% 1,088,815 1,084,366 

Combination Long-haul Trucks .97% 806,633 803,337 

Total 100.00% 83,348,540 83,007,993 

For buses, we needed to distribute the total buses from FHWA to the three MOVES 

classes. Additional information on bus numbers was available from the FTA NTD, Polk TIP®, 

and the School Bus Fleet Fact Book, and the American Bus Association “Motorcoach Census 

2000”.22 The FTA NTD provides population numbers for a variety of transit options. To 

determine the number of transit buses, we summed their counts for Articulated Motor Buses, 

Motor Bus Class A, B & C, and Double Decked buses. 

Table 3-5. 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* 

FTA NTD 55,706 

APTA23 *** 75,087 

Polk TIP® 460,178 

School Bus Fleet Fact 

Book 

429,086 

Motorcoach 

Census** 

 44,200 

* Includes some church & industrial buses. 

** Includes Canada. 

*** Includes trolleybuses. 

As Table 3-5 shows, estimates of bus populations vary. We chose to use the FHWA 

value because it includes church and industrial buses that we believe have activity patterns more 

similar to school buses than to intercity buses. To calculate the number of buses for the 

categories needed for MOVES, we used the FHWA school bus value and the FTA transit bus 

value. We assigned the remaining total FHWA buses (732,189-592,029-55,706 = 84,454) to the 

intercity category. Note this value substantially exceeds the estimate of intercity buses provided 

9 
by the Motorcoach Census. We request comment on ways to improve our national bus 

population estimates. 

For motorcycles we used the 1999 FHWA value from table MV-1. For comparison, 

Table 3-1 also shows the 1998 population as estimated by the Motorcycle Industry Council based 

on sales and estimated scrappage rates, and the 2001 population estimated by the 2001 NHTS. 

The FHWA population estimates are noticeably lower than the other estimates. If time and 

resources allow, EPA may investigate this further for future versions of the MOVES model. 

For motor homes we used the population from the Polk TIP® database. In Table 3-1, this 

value is compared to the estimate from the 2001 NHTS. As for motorcycles, the FHWA 

registration count is noticeably lower than the household survey estimate. This could reflect 

population growth in the years between the estimates, or it may reflect difference in the way 

motor homes are defined in the two studies, or be an artifact of the method used to extrapolate 

from the NHTS sample to the national population estimate. If time and resources allow, EPA 

may investigate this further for future versions of the MOVES model. 

Table 3-6 summarizes the 1999 vehicle populations used in Draft MOVES2009. 

Table 3-6. 1999 SourceType Populations in Draft MOVES2009 

SourceType ID SourceType 1999 Population 

11 Motorcycles 4,173,869 

21 Passenger Cars 130,163,354 

31 Passenger Trucks 57,424,800 

32 Light Commercial Trucks 19,184,600 

41 Intercity Buses 84,454 

42 Transit Buses 55,706 

43 School Buses 592,029 

51 Refuse Trucks 88,607 

52 Single Unit Short-haul Trucks 4,489,140 

53 Single Unit Long-haul Trucks 265,520 

54 Motor Homes 902,949 

61 Combination Short-haul Trucks 1,088,820 

62 Combination Long-haul Trucks 806,633 

3.2. 1990 SourceTypePopulation 

Because SIPs require estimates of 1990 emissions, the MOVES database includes a 1990 

base year. The SourceTypePopulation inputs for 1990 were developed using methods and data 

similar to those used for 1999. 

The primary sources for calendar year 1990 vehicle population data are the FHWA 

Highway Statistics Tables MV-200, VM- 201A, MV-10 and the Polk NVPP® databases. As 

in 1999, the FHWA and Polk data differ in how vehicles are counted. (See previous section.) 

Additionally, the 1990 Polk data does not include buses and motor homes. The National 

Personal Transportation Survey includes data on personal trucks, automobiles and motorcycles. 

10 
Data on motorcycles were also obtained from the Motorcycle Statistical Annual published by the 

Motorcycle Industry Council. Values from all four sources are compared in Table 3-1. 

Registration data on vehicles registered in Puerto Rico for year 1990 was obtained from 

FHWA’s Highway Statistics 1990. 

Table 3-7. 1990 Vehicle Population Comparisons 

Data Source Motorcycles Automobiles Trucks (total) Buses (total) Motor 

Homes 

FHWA(w/ Puerto 

Rico and Publicly 

owned vehicles)1 

4,278,286 135,022,124 54,673,458 629,943 na 

FHWA (w/o Puerto 

Rico and w/ Publicly 

owned vehicles)

2 

4,259,461 133,700,497 54,470,430 626,987 na 

Polk NVPP® na 123,276,600 56,023,0003

 na na 

NPTS (1990)4 2,089,523 120,712,000 37,110,000 na 821,000 

Motorcycle Industry 

Council5 4,310,000 na na na na 

1 Data on Puerto Rico was obtained from Highway Statistics 1990, published by the FHWA. 

2 For these numbers, used data from FHWA Highway Statistics TableVM-201A, April 1997 and Table MV-200

(state motor vehicle registrations, by years 1990-1995). 3

 As published in TEDB edition 23. Does not include Puerto Rico and publicly –owned vehicles. 

4 1990 NPTS special report on travel modes- Chapter3, the demography of the US Vehicle Fleet. The motorcycle

number is calculated using the appendix table and the proportion of MCs from Table 20 of the 2001 NHTS

Summary of Travel Trends. 5 The Motorcycle number was obtained as a sum of on-highway and dual motorcycles for year 

1990 as published in the 1999 Motorcycle Statistical Annual. 

For MOVES, total trucks are sub-classified into seven SourceTypes. The proportion of 

total trucks in each subtype was estimated using TIUS92 responses for Axle Arrangement, 

Primary Area of Operation, Body Type and Major Use as detailed in Table 3-8. 

With these definitions and with vehicles that lack AREAOP codes assigned 

proportionally to the corresponding SourceTypes, we computed the distributions in Table 3-9. 

These distributions were multiplied by the total truck population from Table 3-7 to derive 

population values for MOVES. 

11 
Table 3-8. TIUS92 Codes Used for Distinguishing Truck SourceTypes. 

SourceType Axle Arrangement Primary Area of 

Operation 

Body Type Major Use 

Passenger Trucks 2 axle/4 tire (AXLRE= 

1,5,6,7) 

any Any personal 

transportation 

(MAJUSE=20) 

Light Commercial 

Trucks 

2 axle/4 tire (AXLRE= 

1,5,6,7) 

any Any any but personal 

transportation 

Refuse Trucks Single Unit (AXLRE = 

2-4, 8-16) 

off-road, local or shortrange (AREAOP <=4) garbage hauler 

(BODTYP=30) 

any 

Single Unit Shorthaul Trucks 

Single Unit (AXLRE = 

2-4, 8-16) 

off-road, local or shortrange (AREAOP <=4) any except 

garbage hauler 

any 

Single Unit Longhaul Trucks 

Single Unit (AXLRE = 

2-4, 8-16) 

long-range (AREAOP 

>=5) 

any any 

Combination Shorthaul Trucks 

Combination (AXLRE 

>=17) 

off-road, local or 

medium (AREAOP <=4) 

Any any 

Combination Longhaul Trucks 

Combination (AXLRE 

>=17) 

long-range (AREAOP 

>=5) 

Any any 

Table 3-9. 1990 Truck SourceType Distribution and Populations 

SourceType Percent Population 

Passenger Trucks 67.32% 37,713,840 

Light Commercial Trucks 24.07% 13,483,198 

Refuse Trucks 0.11% 59,037 

Single Unit Short-haul Trucks 6.12% 3,426,459 

Single Unit Long-haul Trucks 0.23% 128,776 

Combination Short-haul Trucks 1.35% 758,091 

Combination Long-haul Trucks 0.81% 453,599 

Total 100.00% 56,023,000 

For buses, we needed to distribute the total buses from FHWA to the three MOVES 

classes. Additional information on bus numbers was available from the American Public Transit 

Association (APTA) Fact Book, the School Bus Fleet Fact Book, and the Transportation Energy 

Data Book. 

12 
Table 3-10. 1990 Bus Population Comparisons 

Data Source Total Buses Intercity Buses Transit Buses School Buses 

FHWA 

(w/o PR and with 

Publicly-owned 

Vehicles)* 626,9871 20,6802 545,7223 

FHWA (w/o PR 

and w/o Publiclyowned Vehicles) 275,4931 

APTA 1991 

Transit Fact Book 60,585 

TEDB** 58,141 59,753 508,261 

School Bus Fleet 

Fact Book*** 391,714 

*

 FHWA Highway Statistics, Summary to 1995, Table MV-200

** Transportation Energy Data Book : Edition 13, March 1993, Table 3.29. 1990 buses. "Intercity Buses" is sum

of "Intercity Bus" and "Other;" "School Buses" includes other non-revenue buses. 

*** Average of school years 1989-90 and 1990 -91, School Bus Fleet Fact Books 1990 and 1991.

Table 3-11 summarizes the 1990 vehicle populations used in Draft MOVES2009. For 

motor homes we used the only available data from NPTS. We used the TEDB data for buses. 

For trucks the TIUS data was used; the remaining values were based on FHWA data. 

13 
 Table 3-11. 1990 SourceType Populations in Draft MOVES2009 

SourceType ID SourceType 1990 

Population 

11 Motorcycles 4,278,286 

21 Passenger Cars 135,022,124 

31 Passenger Trucks 37,713,840 

32 Light Commercial Trucks 13,483,198 

41 Intercity Buses 58,141 

42 Transit Buses 59,753 

43 School Buses 508,261 

51 Refuse Trucks 59,037 

52 Single Unit Short-haul Trucks 3,426,459 

53 Single Unit Long-haul Trucks 128,776 

54 Motor Homes 821,000 

61 Combination Short-haul Trucks 758,091 

62 Combination Long-haul Trucks 453,599 

3.3. SalesGrowthFactor 

The SalesGrowthFactor field stores a multiplicative factor indicating changes in sales by 

SourceType for calendar years after the base year. It determines the number of new vehicles 

added to the vehicle population each year, and is expressed relative to the previous year’s sales. 

For example, "1" means no change from previous year sales levels, "1.02" means a two percent 

increase in sales, and "0.98" means a two percent decrease in sales. SalesGrowthFactor is used 

in the Total Activity Generator calculation of source type populations for calendar years after the 

base year-- in Draft MOVES2009, years 2000 through 2050. 

Note that the sales growth factors are not used in the calculation of county-level or 

project level emissions, where users must input local vehicle populations for each year that is 

modeled. For MOVES2004, SalesGrowthFactor estimates were derived from actual sales data 

from TEDB23 (2003), whose primary source is Ward’s Motor Vehicle Facts and Figures, and 

from sales projections from AEO2004. For Draft MOVES2009, the sales data for passenger cars 

and light trucks were updated to account for actual sales data and updated sales forecasts, but 

rates for the remaining sourcetypes were not changed. Beyond 2030, the SalesGrowthFactor was 

set to the 2030 value. For the final MOVES2009, we anticipate updating sales information, at 

least for the dominant sourcetypes. 

The data sources and methodologies by source type are described below: 

14
• Passenger Cars and Passenger Trucks: SalesGrowthFactors for calendar year 2000 

through 2005 were derived from total sales numbers reported in the TEDB26 Table 

4.5. Factors for calendar years 2006 through 2030 were derived from new car sales 

estimates presented in AEO2006 Supplemental Table 45, generated by NEMS. 

• Motorcycles: SalesGrowthFactors for calendar year 2000 and 2001 were computed 

from sales values in the Motorcycle Industry Council Statistical Annual.24 

SalesGrowthFactors for years 2006 through 2030 were set equal to passenger car 

growth factors. 

• Commercial Trucks: SalesGrowthFactors for calendar year 2000 through 2005 were 

derived from total light truck sales numbers reported in the TEDB26 Table 4.6. 

Factors for Calendar year 2002 through 2030 differ from passenger trucks. It is 

possible that they were mistakenly retained from an earlier version of the model. We 

plan to investigate this further for the final MOVES2009. . 

• Buses, Single Unit Trucks & Motor Homes: Calendar years 2000-2001 were based on 

sales as reported in TEDB23 Table 5.3 (gross weight range 10,000-33,000 lbs). 

Years 2004 through 2030 were calculated from medium-duty truck sales projections 

from AEO2006Supplemental Table 55. 

• Combination Trucks, Refuse Trucks: Calendar years 2000-2001 were based on sales 

as reported in TEDB23 Table 5.3 (gross weight range 33,001 and greater pounds). 

Years 2004 through 2030 were calculated from heavy-duty truck sales projections 

found in AEO2006 Supplemental Table 55. 

The resulting SalesGrowthFactors by source type are shown in Table 3-12: 

15 
Table 3-12. SalesGrowthFactor by Calendar Year and Source Type 

Calendar 

Year 

Motorcycles Passenger 

Cars 

Passenger 

Trucks 

Light 

Comm. 

Trucks 

Buses, 

Single Unit 

Trucks & 

Motor 

Homes 

Combination 

Trucks 

2000 1.017 1.017 1.039 1.039 0.963 0.809 

2001 0.952 0.952 1.037 1.037 0.850 0.660 

2002 0.970 0.962 1.001 1.001 0.882 0.923 

2003 1.015 0.939 1.026 1.026 1.067 1.042 

2004 1.013 0.986 1.047 1.047 1.170 1.310 

2005 1.039 1.021 0.991 0.991 1.082 1.130 

2006 1.059 1.059 0.905 0.998 1.001 1.010 

2007 0.997 0.997 1.059 1.047 1.001 0.940 

2008 0.987 0.987 1.031 1.007 1.003 0.990 

2009 0.985 0.985 1.043 1.039 1.026 1.000 

2010 0.980 0.980 1.042 0.994 0.992 1.000 

2011 1.005 1.005 1.016 1.015 0.997 0.990 

2012 0.996 0.996 1.017 0.983 0.986 1.000 

2013 0.991 0.991 1.011 0.996 1.000 1.010 

2014 0.989 0.989 1.015 1.011 1.029 1.020 

2015 0.994 0.994 1.008 1.019 1.035 1.020 

2016 1.001 1.001 1.012 1.022 1.025 1.020 

2017 1.002 1.002 1.017 1.016 1.015 1.020 

2018 1.005 1.005 1.019 1.015 1.010 1.000 

2019 1.004 1.004 1.015 1.009 0.995 0.980 

2020 1.007 1.007 1.013 1.011 0.997 0.980 

2021 1.007 1.007 1.018 1.012 1.006 1.000 

2022 1.009 1.009 1.019 1.015 1.012 1.010 

2023 1.009 1.009 1.021 1.015 1.015 1.010 

2024 1.009 1.009 1.021 1.016 1.018 1.020 

2025 1.008 1.008 1.020 1.015 1.018 1.020 

2026 1.010 1.010 1.021 1.016 1.016 1.020 

2027 1.008 1.008 1.020 1.015 1.012 1.010 

2028 1.007 1.007 1.016 1.012 1.006 1.000 

2029 1.008 1.008 1.018 1.014 1.010 1.010 

2030+ 1.008 1.008 1.017 1.013 1.013 1.010 

3.3. MigrationRate 

The MigrationRate field stores a yearly multiplicative factor used to estimate how many 

vehicles join or leave the population of a SourceType in the given domain in a given year. We 

expect this field may be useful when modeling emissions on relatively small geographic scale. 

For the default MOVES database, the domain is the entire U.S. and we are using a 

simplifying assumption of no migration: that is, a migration rate of 1. 

16 
4. SourceTypeModelYear 

SourceTypeModelYear stores the field ACPenetrationFraction, which is the fraction of 

vehicles equipped with air conditioning, by source type and model year. ACPenetrationRate is 

used in the calculation of the A/C adjustment. 

Default values in Draft MOVES2009 were taken from MOBILE6. 25 Market penetration 

data by model year were gathered from Ward’s Automotive Handbook for light-duty vehicles 

and light-duty trucks for model years 1972 through the 1995 for cars and 1975-1995 for light 

trucks. Rates in the first few years of available data are quite variable, so values for early model 

years were estimated by applying the 1972 and 1975 rates for cars and trucks, respectively. 

Projections beyond 1995 were developed by calculating the average yearly rate of increase in the 

last five years of data and applying this rate until a predetermined cap was reached. A cap of 

98% was placed on cars and 95% on trucks under the assumption that there will always be 

vehicles sold without air conditioning, more likely on trucks than cars. For MOVES, the lightduty vehicle rates were applied to passenger cars, and the light-duty truck rates were applied to 

all other sourcetypes (except motorcycles, for which AC penetration is assumed to be zero). 

Table 4.1. AC Penetration Fractions in Draft MOVES2009 

Motorcycles Passenger Cars All Trucks and Buses 

1972-and-earlier 0 0.592 0.287 

1973 0 0.726 0.287 

1974 0 0.616 0.287 

1975 0 0.631 0.287 

1976 0 0.671 0.311 

1977 0 0.720 0.351 

1978 0 0.719 0.385 

1979 0 0.694 0.366 

1980 0 0.624 0.348 

1981 0 0.667 0.390 

1982 0 0.699 0.449 

1983 0 0.737 0.464 

1984 0 0.776 0.521 

1985 0 0.796 0.532 

1986 0 0.800 0.544 

1987 0 0.755 0.588 

1988 0 0.793 0.640 

1989 0 0.762 0.719 

1990 0 0.862 0.764 

1991 0 0.869 0.771 

1992 0 0.882 0.811 

1993 0 0.897 0.837 

1994 0 0.922 0.848 

1995 0 0.934 0.882 

1996 0 0.948 0.906 

1997 0 0.963 0.929 

1998 0 0.977 0.950 

1999+ 0 0.980 0.950 

17 
5. SourceTypeAge 

Three fields comprise SourceTypeAge in Draft MOVES2009: SurvivalRate, Relative 

MAR, and FunctioningACFraction. Each one is described below, including data sources and 

some relevant data points used in the model. 

5.1. SurvivalRate 

The SurvivalRate field describes the fraction of vehicles of a given SourceType and Age) 

that remain on the road one year to the next. SurvivalRate is used in the Total Activity 

Generator in the calculation of source type populations by age in calendar years after the base 

year. In MOVES, a separate SurvivalRate is applied to each age in each SourceType fleet. 

These SurvivalRates in MOVES are used for all model years in a SourceType in all calendar 

years. 

SurvivalRates for Motorcycles were calculated based on regression of data provided by 

the Motorcycle Industry Council (MIC).26 

Survival rates for Passenger Cars, Passenger Trucks and Light Commercial Trucks came 

from NHTSA's survivability Table 3 and Table 4.27 These survival rates are based on a detailed 

analysis of Polk vehicle registration data from 1977 to 2002. We modified these rates to fit 

them into the MOVES format: 

• NHTSA rates for Light Trucks were used for both MOVES Passenger Trucks 

and MOVES Light Commercial Trucks. 

• MOVES calculates emissions to age 30 for both cars and trucks, but NHSTA car 

rates were available only to age 25, so we extrapolated car rates to age 30 using 

the estimated survival rate equation in section 3.1 of the NHTSA report. 

• According to the NHTSA methodology, NHTSA "age= 1" corresponds to 

MOVES "ageid =2," so the survival fractions were shifted accordingly. 

• Because MOVES requires survival rates for MOVES ages < 2, the survival rates 

for age 0 and age 1 were interpolated using a linear interpolation and assuming 

that the survival rate prior to age 0 is 1. 

• NHTSA defines survival rate as the ratio of the number of vehicles remaining in 

the fleet at a given year as compared to a base-line year. MOVES calculations 

require a value that is the ratio of a given year to the previous year, so we 

transformed the NHTSA rates to MOVES rates using this ratio. 

• Because MOVES ageid= 30 is intended to represent all ages 30-and-greater, the 

survival rate for ageid=30 was set to 0.3. 

• Quantitatively the formula used to derive the MOVES Survival rates was: 

MOVES Survival Rate (ageid =0) = 1 - (1-NHTSA Survival Rate (age =2)/3) 

MOVES Survival Rate (ageid =1) = 1 - (1- 2* NHTSA Survival Rate (age =2)/3) 

18 
MOVES Survival Rate (age = 2 through 29) = 

 NHTSA Survival Rate (age = n-1)/ NHTSA Survival Rate (age = n-2) 

MOVES Survival Rate (age = 30) = 0.3 

The data for all other SourceTypes came from the Transportation Energy Data Book 

(TEDB22, unchanged for version 23). We used the Heavy-Duty rates for the 1980 model year 

(TEDB22, Table 6.11, same as TEDB26 Table 3.10). The 1990 model year rates were not used 

because they were significantly higher than the other model years in the analysis (e.g. 45 percent 

survival rate for 30 year-old trucks), and seemed unrealistically high. While limited data exists to 

confirm this judgment, a snapshot of 5-year survival rates can be derived from VIUS 1992 and 

1997 results for comparison. According to VIUS, the average survival rate for model years 

1988-1991 between the 1992 and 1997 surveys was 88 percent. The comparable survival rate for 

1990 model year Heavy-Duty vehicles from TEDB was 96 percent, while the rate for 1980 

model year trucks was 91 percent. This comparison lends credence to the decision that the 1980 

model year survival rates are more in line with available data. 

TEDB22 does not include scrappage rates for GVWR 10,000-26,000 vehicles, so it was 

necessary to apply the Heavy-Duty rates to predominantly Medium-Duty use types. 

The TEDB survival rates were transformed into MOVES format in the same way as the 

NHTSA rates. Survival rates for all "age 30" sourcetypesc

 were set to 0.3. This is assumed to be 

the fraction of all vehicles 30-and-older that survive an additional year. 

SurvivalRates used in Draft MOVES2009 are shown in Table 5-1. 

c

Except motorcycles. See note below Table 5-1. 

19 
Table 5-1. SurvivalRate by Age and SourceType 

Age Motorcycles Passenger Cars Passenger Trucks 

Light Comm. Trucks 

All Other 

SourceTypes 

0 0.990 0.997 0.991 1.000 

1 0.990 0.997 0.991 1.000 

2 0.980 0.997 0.991 1.000 

3 0.970 0.993 0.986 1.000 

4 0.960 0.990 0.981 0.990 

5 0.960 0.986 0.976 0.980 

6 0.950 0.981 0.970 0.980 

7 0.940 0.976 0.964 0.970 

8 0.930 0.971 0.958 0.970 

9 0.920 0.965 0.952 0.970 

10 0.920 0.959 0.946 0.960 

11 0.910 0.953 0.940 0.960 

12 0.900 0.912 0.935 0.950 

13 0.890 0.854 0.929 0.950 

14 0.890 0.832 0.913 0.950 

15 0.880 0.813 0.908 0.940 

16 0.870 0.799 0.903 0.940 

17 0.860 0.787 0.898 0.930 

18 0.850 0.779 0.894 0.930 

19 0.850 0.772 0.891 0.920 

20 0.840 0.767 0.888 0.920 

21 0.830 0.763 0.885 0.920 

22 0.820 0.760 0.883 0.910 

23 0.820 0.757 0.880 0.910 

24 0.810 0.757 0.879 0.910 

25 0.800 0.754 0.877 0.900 

26 0.790 0.754 0.875 0.900 

27 0.780 0.567 0.875 0.900 

28 0.780 0.752 0.873 0.890 

29 0.770 0.752 0.872 0.890 

30 0.760* 0.300 0.300 0.300 

* In draft MOVES2009, we neglected to set the age 30 motorcycle survival rate to 0.30. We 

plan to fix this in the final MOVES2009. 

We request comment on the survival rates used in MOVES and the possibility of 

updating them. 

5.2. Relative MAR 

The Relative Mileage Accumulation Rate (Relative MAR) is listed for each MOVES 

SourceType and Age. The Relative MAR is computed as the annual MAR divided by the highest 

MAR within the HPMS vehicle class. This allows MOVES to maintain a constant MAR ratio 

between ages and between the sourcetypes that make up each HPMS vehicle type even as 

vehicle populations and the total VMT for an HPMS vehicle class changes over time. Table 1-2 

(previous) lists the groupings of the MOVES SourceTypes within the six HPMS Vehicle Classes. 

The following discussion refers to the Source Type ID numbers found in this table. 

For many SourceTypes, the annual MARs were derived from the MARs developed for 

MOBILE6. These were mapped from the MOBILE6 Vehicle Classes to the MOVES 

20 
 

SourceTypes. We then used regression to smooth these initial MARs and to extend the MARs 

from 25 to 30 ages. 

5.2.1. Motorcycles 

The MARs for motorcycles (category 11) were set to equal those in MOBILE6. 

5.2.2. Passenger Cars, Passenger Trucks and Light Commerical Trucks 

The MARs for passenger cars, passenger trucks and light commercial trucks (categories 

21, 31 & 32) were taken from the NHTSA report on survivability and mileage schedules.28 In 

the NHTSA analysis, annual mileage by age was determined for cars and for trucks using data 

from the National Household Travel Survey. In this NHTSA analysis, vehicles that were less 

than one year old at the time of the survey were classified as "age 1", etc. NHTSA used cubic 

regression to smooth the VMT by age estimates. 

We used NHTSA's regression coefficients to extrapolate mileage to ages not covered by 

the report. We divided each age's mileage by the NHTSA "age 1" mileage to determine a 

relative MAR. For consistency with MOVES age categories, we then shifted the relative MARs 

such that the NHTSA "age1" ratio was used for MOVES age 0, etc. We used NHTSA's light 

truck VMT to determine relative MARS for both passenger trucks and light commercial trucks. 

5.2.3. Heavy Trucks 

The initial MARs for truck categories 51, 52, 53, 61, and 62 in MOVES were calculated 

based on weighting fractions assigned to the MOBILE6 truck classes. We used VIUS 1997 

values for Gross Vehicle Weight (PKGVW) to determine weighting fractions by model year. 

To separate Light-Duty Trucks 1 and Light-Duty Trucks 2, which are distinguished by Loaded 

Vehicle Weights, we used information from the Oak Ridge National Lab Light Duty Vehicle 

database. To separate Class 2a and 2b trucks, we used information from the Oak Ridge National 

Laboratory Report by Davis and Truitt.29

 The initial MARs for the MOVES truck categories 

were then calculated as the product of the weighting fractions and the MARs from MOBILE6. 

5.2.3. Buses 

For the School Buses (category 43) the initial MARs were taken from the MOBILE6 

value for diesel school buses (HDDBS). As in MOBILE6, the same annual MAR was used for 

each age. The MOBILE6 value of 9,939 miles per year came from the 1997 School Bus Fleet 

Fact Book. 

For Transit Buses (category 42), the initial MARs were 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. 30 

For Intercity Buses (category 41), the initial MARs were taken from Motorcoach Census 

2000.31 The data did not distinguish vehicle age, so the same MAR was used for each age. This 

MAR is high compared to transit and school buses. We are not sure if this simply reflects the 

very different type of driving done by these buses, or if it indicates a problem . We welcome 

comments with ideas for validating or improving this estimate. 

21 
5.2.4. Motor Homes 

For motor homes (category 54), the initial MARs were taken from an independent 

research study32 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. 

5.2.5. Calculating Relative MARs for motorcycles, trucks and buses. 

In order to smooth the data and to extend the MARs from the 25 ages in MOBILE6 to the 

30 ages in MOVES, we used statistical regression to determine the curves that best fit the data 

for years starting in 1997 and going back to 1973 (ages 1 to 25). Table 5-2 presents the resulting 

regression equations for each MOVES category. Note, as in MOBILE6, the motorcycle values 

were estimated as a linear function to age 12. Ages 13 through 30 are then estimated as a 

constant. 

Table 5.2. Equations for Calculating Annual Mileage Accumulation Rates used in 

MOVES 

MOVES Source Type Source 

Type ID 

Regression Equation R2 from 

Regression 

Motorcycles 11 na na* 

Refuse Trucks 51 y=0.8674e-0.1148x 0.904 

Single Unit Short-haul Trucks 52 y=0.4289e-0.0990x 0.990 

Single Unit Long-haul Trucks 53 y=0.3339e-0.0762x 0.864 

Motor Homes 54 y=0.0457 na 

Intercity Buses 41 y=0.6000 na 

Transit Buses 42 y=0.46659e-0.0324x na* 

School Buses 43 y=0.0994 na 

Combination Short-haul Trucks 61 y=0.0016x2

-0.0762x +0.9655 0.977 

Combination Long-haul Trucks 62 y=0.0021x2

-0.0887x+1.0496 0.879 

* For Motorcycles and Transit Buses, the equations from MOBILE6 were used 

The values calculated from the equations were then used to calculate the relative MARs 

by computing the ratio of the value for each SourceType and age to the highest value within the 

HPMS class. For example, all of the bus values are relative to each other. The relative MARs 

for all sourcetypes are illustrated in Figure 5.1 

22 
Figure 5.1. Relative Mileage Accumulation Rates in Draft MOVES2009 

R elative Mileage Accum ulation R ates 

0 

0.2 

0.4 

0.6 

0.8 

1 

1.2 

0 5 10 15 20 25 30 35 

Age 

R elative M A R 

11 21 31 32 41 42 43 51 52 53 

54 61 62 

5.3. FunctioningACFraction 

The FunctioningACFraction field indicates the fraction of the air-conditioning equipped 

fleet with fully functional A/C systems, by source type and vehicle age. A value of 1 means all 

systems are functional. This is used in the calculation of total energy to account for vehicles 

without functioning A/C systems. Default estimates were developed for all source types using 

the “unrepaired malfunction” rates used for 1992-and-later model years in MOBILE6.33 These 

were applied to all source types except motorcycles, which were assigned a value of zero for all 

years. 

23 
Table 5-3. FunctioningACFraction by Age (All Use Types Except Motorcycles) 

Age FunctioningAC 

Fraction 

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 

24 
 

6. SourceTypeAgeDistribution 

The age distribution of for each sourcetype is stored in the SoruceTypeAgeDistribution 

table. Because sales are not constant, these distributions vary by calendar year. MOVES uses 

age distributions for the base year combined with sales and scrappage information to compute 

the age distribution in the calendar year selected for analysis. 

This section first describes how the age distributions were determined for the primary 

default base year of 1999, and then for the 1990 base year. Age distributions for the 1999 base 

year are summarized in table 6-1. Age distributions for the 1990 base year are available in the 

SourceTypeAgeDistribution table. 

6.1. 1999 Motorcycles 

To determine age fractions for motorcycles, we began with Motorcycle Industry Council 

estimates of the number of motorcycles in use by model year in 1998. We used the estimates of 

sales growth and survival rates to grow these population estimates to 1999, then computed age 

fractions. These fractions are summarized in Table 6.1. 

6.2. 1999 Passenger Cars 

We considered three approaches to determine age fractions for passenger cars. 

Our original approach (used for MOVES2004 and MOVES Demo) began with Polk 

NVPP® 1999 data on car registration by model year. This data presents a snapshot of 

registrations on July 1, 1999, and we needed age fractions as of December 31, 1999. 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 1999. Model Year 1998 cars were added to 

the previous estimate of “Age 1” cars and Model Year 1999 and 2000 cars were added to the 

“Age 0” cars. We then computed fractions by age. However, because this method counts both 

Model Year 1999 and Model Year 2000 as "Age 0", the Age 0 age fraction is inflated. When the 

MOVES Total Activity Generator applies growth factors, the number of cars in future years is 

inflated, and the fraction of passenger cars compared to other source types is skewed. Thus, we 

rejected this approach. 

A second approach was similar to the first, but with only Model Year 1999 vehicles 

counted as "Age 0" in 1999. 

Our third approach used passenger car sales data from Table 4.5 of the TEDB34

 and 

applied the NHTSA survival fractions, extrapolated to age 30 and shifted such that NHTSA age 

n = MOVES age n+1. Survival fractions for MOVES age 0 and 1 were interpolated as described 

in Section 5.1. 

Not surprisingly, the age distributions resulting from the three approaches are very 

similar, as illustrated in Figure 6.1. All show a fairly flat age distribution in the first eleven years 

followed by a steep decline and a leveling off. The third approach provides a slightly more 

generic age distribution than the second approach because the direct Polk data approach is for a 

single year and the NHTSA survival fractions were derived by regression through many years of 

data. For the Draft MOVES2009 default database, we selected the age distributions generated 

with the third approach. For future versions of MOVES, we are considering updating these 

values to better account for more recent data. 

25 
Figure 6.1 1999 Age Distributions for Passenger 

Passenger Car Age Distribution 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0.09 

0 5 10 15 20 25 30 

Age 

Fraction of SourceType

Original Polk 

Polk 2 

Cars--Sales & Scrappage

 The passenger car age fractions used in MOVES are summarized in Table 6,1 at the 

end of this section. 

6.3. 1999 Trucks 

To determine age fractions for refuse trucks, short-haul and long-haul single unit trucks 

and short-haul and long-haul combination trucks, we used data from the VIUS database. 

Vehicles in the VIUS database were assigned to MOVES source types as summarized in Table 

3-3a and Table 3.3b. 

VIUS does not include a model year field and records ages as 0 through 10 and 11-andgreater. Because we needed greater detail on the older vehicles, we followed the practice used 

for MOBILE6 and determined the model year for some of the older vehicles by using the 

responses to the VIUS 1997 questions “How did you obtain this vehicle?” (VIUS field 

“OBTAIN” in VIUS 1997 or "ACQUIREHOW" in VIUS 2002) and “When did you obtain this 

vehicle?” (VIUS field “ACQYR” in VIUS 1997 or "ACQUIREYEAR" in VIUS 2002) to derive 

the model year of the vehicles that were obtained new. These derived model years also were 

used for much of the source bin distribution work described later in this report. 

To calculate age fractions, it was important to account for the inconsistent methodologies 

used for the older and newer vehicles. Thus, for each source type, we adjusted the age 11-andolder vehicle counts by dividing the original count by model year by the fraction of the older 

26
vehicles that were coded as “obtained new.” This created an array of adjusted vehicle counts by 

model year for calendar year 1997. This 1997 array may overestimate the fraction of mid-aged 

vehicles since the fraction of vehicles purchased new likely declines with time; however, we 

believe the procedure is reasonable given the limited data available. 

We then used the sales growth for 1997 and 1998 from TEDB22 Tables 7.6 and 8.3 and 

the scrappage rates from TEDB22 Tables 6.10 and 6.11 to grow the population to the 1999 base 

year and then we calculated age fractions. 

Initially, we determined age fractions for passenger trucks and commercial trucks in the 

same way as other trucks. However, when the new NHTSA survival rates for light duty trucks 

became available, we reexamined this approach. We compared (1) our original approach with 

VIUS data for 1997 and the TEDB scrappage rates, (2) a similar approach using VIUS data and 

NHTSA survival rates, and (3) a "sales and scrappage" approach similar to that used for 

passenger cars, combining passenger trucks and commercial light trucks and using TEDB sales 

data. The results of the three approaches are illustrated in Figure 6.2. 

Figure 6.2 1999 Age Distributions for Passenger and Light Commercial Trucks 

Passenger and Commercial Light Truck Age Distributiions 

0 

0.02 

0.04 

0.06 

0.08 

0.1 

0.12 

0 5 10 15 20 25 30 

Age 

Fraction of SourceType

31 --Original VIUS 

32--Original VIUS 

31--VIUS & NHTSA 

32-- VIUS & NHTSA 

Trucks--Sales & Scrappage 

Use of the original VIUS data leads to a dip in 1996 and 1997 passenger trucks that is not 

reflected by vehicle sales data. The other approaches all create similar trends of fairly steep 

declines in age fractions until about age 7, a brief leveling off, another steep decline from about 

age 12 to 17 and a final leveling off. For the MOVES default database, we selected the age 

distribution generated with the "Sales and Scrappage" approach, which will be applied to both 

passenger trucks and light commercial trucks. These rates are summarized in Table 6-1. 

27
6.4. 1999 Intercity Buses 

We were not able to identify a data source for estimating age distributions of intercity 

buses. Because the purchase and retirement of these buses is likely to be driven by general 

economic forces rather than trends in government spending, we will use the age distribution that 

was derived for short-haul combination trucks, described above. While we believe this choice is 

reasonable given the lack of data, we welcome suggestions of improved data sources or 

algorithms to improve the intercity bus age fractions used in future versions of the MOVES 

database. 

6.5. 1999 School Buses and Motor Homes 

To determine the age fractions of School Buses and Motor Homes, we used information 

from the Polk TIP® 1999 database. School Bus and Motor Home counts were available by 

model year. Unlike the Polk data for passenger cars, these counts reflect registration at the end 

of the calendar year and, thus, did not require adjustment. We converted model year to age and 

calculated age fractions. These are summarized in Table 6-1. 

6.6. 1999 Transit Buses 

To determine the age fractions for Transit Buses, we used data from the Federal Transit 

Administration database. In particular, we used responses to 1999 Form 408, which included 

counts of in-use vehicles by year of manufacture. 

To properly account for the fraction of Age 0 vehicles at the end of 1999, it was 

necessary to adjust the counts for model-year-1999 vehicles to account for the different reporting 

periods of the various transit organizations. The counts were adjusted proportionally depending 

on the month in which the fiscal year ended. The adjusted counts were used to calculate the age 

fractions. 

28 
Table 6-1. 1999 Age Fractions for MOVES Source Types 

source type 11 21 31& 32 42 43 51 52 53 54 61 

age 0 0.0947 0.0646 0.1011 0.0624 0.0794 0.0498 0.0622 0.1697 0.0737 0.0

1 0.0935 0.0602 0.0906 0.0771 0.0660 0.0398 0.0520 0.1419 0.0456 0.0

2 0.0755 0.0610 0.0837 0.0742 0.0647 0.0340 0.0412 0.1124 0.0739 0.0

3 0.0681 0.0624 0.0791 0.0727 0.0594 0.0767 0.0466 0.0585 0.0487 0.0

4 0.0613 0.0626 0.0720 0.0627 0.0798 0.0926 0.0559 0.0609 0.0605 0.0

5 0.0570 0.0642 0.0700 0.0576 0.0406 0.0604 0.0572 0.1017 0.0608 0.0

6 0.0520 0.0597 0.0603 0.0504 0.0511 0.0544 0.0434 0.0783 0.0441 0.0

7 0.0433 0.0562 0.0502 0.0461 0.0435 0.0243 0.0344 0.0185 0.0408 0.0

8 0.0370 0.0543 0.0429 0.0492 0.0585 0.0696 0.0351 0.0138 0.0320 0.0

9 0.0355 0.0596 0.0450 0.0759 0.0696 0.0625 0.0435 0.0686 0.0442 0.0

10 0.0336 0.0608 0.0431 0.0609 0.0419 0.0514 0.0578 0.0748 0.0602 0.0

11 0.0388 0.0622 0.0422 0.0506 0.0526 0.0730 0.0531 0.0517 0.0563 0.0

12 0.0461 0.0549 0.0379 0.0489 0.0556 0.0610 0.0460 0.0129 0.0574 0.0

13 0.0422 0.0522 0.0351 0.0434 0.0512 0.0796 0.0580 0.0031 0.0447 0.0

14 0.0383 0.0419 0.0311 0.0394 0.0464 0.0442 0.0430 0.0064 0.0501 0.0

15 0.0345 0.0320 0.0244 0.0320 0.0374 0.0479 0.0251 0.0067 0.0531 0.0

16 0.0307 0.0226 0.0170 0.0321 0.0144 0.0145 0.0409 0.0000 0.0363 0.0

17 0.0270 0.0155 0.0127 0.0181 0.0111 0.0169 0.0220 0.0032 0.0221 0.0

18 0.0234 0.0129 0.0100 0.0082 0.0136 0.0156 0.0219 0.0024 0.0127 0.0

19 0.0198 0.0105 0.0100 0.0231 0.0138 0.0040 0.0239 0.0000 0.0017 0.0

20 0.0163 0.0080 0.0081 0.0071 0.0118 0.0043 0.0190 0.0002 0.0138 0.0

21 0.0129 0.0060 0.0066 0.0032 0.0104 0.0043 0.0225 0.0101 0.0191 0.0

22 0.0095 0.0045 0.0053 0.0007 0.0107 0.0000 0.0088 0.0006 0.0267 0.0

23 0.0062 0.0034 0.0041 0.0013 0.0073 0.0092 0.0112 0.0011 0.0169 0.0

24 0.0029 0.0026 0.0032 0.0009 0.0092 0.0027 0.0115 0.0005 0.0045 0.0

25 0.0000 0.0019 0.0031 0.0009 0.0000 0.0070 0.0125 0.0000 0.0000 0.0

26 0.0000 0.0014 0.0030 0.0002 0.0000 0.0001 0.0130 0.0021 0.0000 0.0

27 0.0000 0.0008 0.0029 0.0004 0.0000 0.0000 0.0265 0.0000 0.0000 0.0

28 0.0000 0.0006 0.0027 0.0003 0.0000 0.0000 0.0059 0.0000 0.0000 0.0

29 0.0000 0.0005 0.0026 0.0001 0.0000 0.0000 0.0032 0.0000 0.0000 0.0

30 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000 0.0026 0.0000 0.0000 0.0

29 
6.7. 1990 Motorcycles 

To determine age fractions for motorcycles, we began with Motorcycle Industry Council 

estimates of the number of motorcycles in use, by model year, in 1990. However, data for 

individual model years starting from 1978 and earlier were not available. A logarithmic 

regression curve ( R2 value = 0.82) was fitted to available data, which was then used to 

extrapolate age fractions for earlier years beginning in 1978. 

6.8. 1990 Passenger Cars 

To determine age fractions for passenger cars, we began with Polk NVPP® 1990 data on 

car registration by model year. However, this data presents a snapshot of registrations on July 1, 

1990, and we needed age fractions as of December 31, 1990. To adjust the values, we used 

monthly data from the Polk new car database to estimate the number of new cars registered in 

the months July through December 1990. Model Year 1989 cars were added to the previous 

estimate of “Age 1” cars and Model Year 1990 and 1991 cars were added to the “Age 0” cars. 

Also the data obtained was lumped together 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. 

6.9. 1990 Trucks 

To determine 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 3-3. 

TIUS92 does not include a model year field and records ages as 0 through 10 and 11-andgreater. Because we needed greater detail on the older vehicles, we followed the practice used 

for MOBILE6 and determined the model year for some of the older vehicles by using the 

responses to the TIUS92 questions “How was the vehicle obtained?” (TIUS field “OBTAIN”) 

and “When did you obtain this vehicle?” (TIUS field “ACQYR”) to derive the model year of the 

vehicles that were obtained new. 

To calculate age fractions, it was important to account for the inconsistent methodologies 

used for the older and newer vehicles. Thus, for each source type, we adjusted the age 11-andolder vehicle counts by dividing the original count by model year by the fraction of the older 

vehicles that were coded as “obtained new.” This created an array of adjusted vehicle counts by 

model year for calendar year 1992. This 1992 array probably overestimates the fraction of midaged vehicles since the fraction of vehicles owned by their original owner clearly declines with 

age; however, we believe the procedure is reasonable given the limited data available. 

30
6.10. 1990 Intercity Buses 

As was true for the 1999 base year, we were not able to identify a data source for 

estimating age distributions of intercity buses. Because the purchase and retirement of these 

buses is likely to be driven by general economic forces rather than trends in government 

spending, we will use the age distribution that was derived for short-haul combination trucks, 

described previously. While we believe this choice is reasonable given the lack of data, we 

welcome suggestions of improved data sources or algorithms to improve the intercity bus age 

fractions used in future versions of the MOVES database. 

6.11. 1990 School Buses and Motor Homes 

Since we were unable to obtain the Polk TIP 1990 database, we used the 1999 age 

fractions for School Buses and Motor Homes. 

6.12. 1990 Transit Buses 

For Transit Buses we used the MOBILE 6 age fractions since year 1990 data on transit 

buses was not available from the Federal Transit Administration database. 

31
7. SourceBinDistribution 

The SourceBinDistribution describes the characteristics of a SourceType population as a 

distribution among SourceBins. These SourceBins classify a vehicle by discriminators relevant 

for emissions and energy calculations: fuel and engine technology, average vehicle weight and 

engine displacement, model year group, and regulatory class. 

While SourceBinDistributions could be input directly, MOVES usually generates the 

values in this table using values in a collection of other tables. The SourceBinGenerator input 

tables are described in Table 7-1. 

This section describes how national default information was determined for MOVES. 

Note that while previous versions of MOVES assigned fractions of vehicles to alternative fuels, 

for Draft MOVES2009, we simplified the model by providing default fractions only for gasoline 

and diesel vehicles. We expect to retain this simplified approach for the final MOVES2009. 

Users wishing to model alternative fuels will still have the option of using the Alternative 

Vehicle Fuels and Technology strategy to input their own fuel and engine technology fractions. 

32
Table 7-1. Data Tables Used by SourceBinGenerator 

Generator Table Name Key Fields Additional Fields Notes 

SourceTypePolProcess SourceTypeID 

PolProcessID 

isSizeWeightReqd 

isRegClassReqd 

isMYGroupReqd 

Indicates which pollutant-processes the 

source bin distributions may be applied 

to and indicates which discriminators 

are relevant for each sourceType and 

polProcess (pollutant/process 

combination) 

FuelEngFraction SourceTypeID 

ModelYearID 

FuelTypeID 

EngTechID 

fuelEngFraction Joint distribution of vehicles with a 

given fuel type and engine technology. 

Sums to one for each sourceType & 

modelYear 

SizeWeightFraction SourceTypeID 

ModelYearID 

FuelTypeID 

EngTechID 

WeightClassID 

EngSizeID 

sizeWeightFraction Joint distribution of engine size and 

weight. Sums to one for each 

sourceType, modelYear and 

fuel/engtech combination. 

RegClassFraction SourceTypeID 

ModelYearID 

FuelTypeID 

EngTechID 

RegClassID 

regClassFraction Fraction of vehicles in a given 

“Regulatory Class.” Sums to one for 

each sourceType, modelYear and 

fuel/engtech combination. 

PollutantProcessModelYear PolProcessID 

ModelYearID 

modelYearGroupID Assigns model years to appropriate 

model year groups. 

SampleVehiclePopulation SourceTypeModelYearID 

FuelTypeID 

EngTechID 

RegClassID 

WeightClassID 

EngSizeID 

SCCVTypeID 

stmyFuelEngFraction 

stmyFraction 

Includes the fractions found in the 

FuelEngFraction, RegClassFraction, 

SizeWeightFraction and 

SCCVTypeDistribution tables, but also 

for combinations that do not exist in the 

existing fleet. This table is only used 

with the Alternative Vehicle Fuel & 

Technology Strategy inputs to generate 

alternate future vehicle fleet source 

bins. 

The MOVES Source Bin Generator code determines which discriminators are relevant 

for a given pollutant/process combination and multiplies the relevant fractions from the tables 

listed above to determine the detailed SourceBinDistribution for each combination of Pollutant, 

Process, SourceType, and Model Year. 

More detailed descriptions of the SourceBin Distribution inputs for each SourceType 

follow. The Inputs for 2000-and-later vehicles of all SourceTypes are described in Section 7.7. 

33
7.1. Motorcycles 

For 1999-and-earlier motorcycle characteristics were assigned based on information from 

EPA motorcycle experts and from the Motorcycle Industry Council. 

7.1.1. FuelEngFraction 

We assume all motorcycles are powered by conventional gasoline engines. 

7.1.2. SizeWeightFraction 

The Motorcycle Industry Council “Statistical Annual” provides information on 

displacement distributions for highway motorcycles for model years 1990 and 1998. These were 

mapped to MOVES engine displacement categories. Additional EPA certification data was used 

to establish displacement distributions for model year 2000. We assumed that displacement 

distributions were the same in 1969 as in 1990, and interpolated between the established values 

to determine displacement distributions for all model years from 1990 to 1997 and for 1999. 

Model year 2000 values were intended to be used for all 2000-and-later model years, however in 

Draft MOVES2009, the 1999 value was used. For final MOVES2009, we intend to replace the 

current 2000-and-later model year values with those based on the model year 2000 certification 

data. 

We then applied weight distributions for each displacement category as suggested by 

EPA motorcycle experts. The average weight estimate includes fuel and rider. The weight 

distributions depended on engine displacement but were otherwise independent of model year. 

This information is summarized in Table 7-2. 

Table 7-2. Motorcycle Engine Size and Average Weight Distributions for 

Selected Model Years 

Displacement 

Category 

1969 MY 

distribution 

(assumed) 

1990 MY 

distribution 

(MIC) 

1998 MY 

distribution 

(MIC) 

2000 MY 

distribution 

(certification 

data)* 

Weight distribution 

(EPA staff) 

0-169 cc (1) 0.118 0.118 0.042 0.029 100%: <= 500 lbs 

170-279 cc (2) 0.09 0.09 0.05 0.043 50%: <= 500 lbs 

50%: 500lbs -700lbs 

280+ cc (9) 0.792 0.792 0.908 0.928 30%: 500 lbs-700 lbs 

70%: > 700lbs 

*Not entered in DraftMOVES2009, but planned for final. 

7.1.3. RegClassFraction 

All Motorcycles are assigned to the “Motorcycle” (MC) regulatory class. 

34
7.2. Passenger Cars 

For base year 1999, passenger car distributions were derived from the 1999 Polk 

NVPP®. The national files for domestic and imported cars were consolidated into a single file. 

7.2.1. FuelEngFraction 

The FuelEngFraction table assigns a fraction of each source type and model year to all 

relevant combinations of fuel type bin and engine technology bin. 

The Polk fuel code was converted to the MOVES FuelTypeID using the mapping in 

Table 6-3. . 

Table 7-3. Mapping Polk Fuel Codes to MOVES. 

Polk MOVES 

FUEL_CD FUEL_NAME FuelTypeID Fuel Description 

C DSL TURBO 2 Diesel 

D DIESEL 2 Diesel 

E ELECTRIC 9 Electric 

F GAS TURBO 1 Gasoline 

G GAS 1 Gasoline 

N NATURAL GAS 3 CNG 

P PROPANE 4 LPG 

R METHANOL 6 Methanol 

V CONVERTIBLE 1 Gasoline 

X FLEXIBLE 1 Gasoline 

For each model year, the car counts for the MOVES fuels were summed and fractions 

were computed. While previous versions of MOVES included default values for alternative 

fueled vehicles, DraftMOVES2009 includes only gasoline and diesel vehicles in the default 

database. In model years where alternative vehicles were present, 

7.2.2. SizeWeightFraction 

The Polk cubic displacement values were converted to liters and assigned to the MOVES 

engine size bins. The weight ID was assigned by adding 300 lbs to the Polk curb weight and 

grouping into MOVES weight bins. For each fuel type, model year, engine size, and weight bin, 

the number of cars was summed and fractions were computed. In general, entries for which data 

was missing were omitted from the calculations. However, because no curb weight data was 

available from Polk for electric cars, additional analysis was performed. Based on data from the 

Electric Drive Association on electric vehicle sales35

, two-thirds of electric vehicles were 

assigned to weight class 35 and one third was assigned to weight class 40. Also, further analysis 

indicated a likely error in the Polk data (an entry for 1997 gasoline-powered Bentleys with 

35
engine size 5099 and weight class 20). This fraction was removed and the 1997 values were 

renormalized. 

7.2.3. RegClassFraction 

All Passenger Cars were assigned to the “Light-Duty Vehicle” (LDV) regulatory class. 

7.3. Trucks 

This section describes how default Source Bin information was compiled for Passenger 

Trucks, Light Commercial Trucks, Single-Unit Short-haul and Long-haul Trucks, and 

Combination Short-haul and Long-haul Trucks. Source Bin information for Buses, Refuse 

Trucks, and Motor Homes are described in separate sections following. 

The Vehicle Inventory and Use Survey (VIUS) conducted by the Census Bureau was the 

primary source for information on truck distributions. Information from the 1997 and 2002 

VIUS was supplemented with information from MOBILE6 and from the Oak Ridge National 

Laboratory Light Duty Vehicle database. 

VIUS records were assigned to SourceTypes as described above in Table 3-3. Not all 

SourceTypes had data for all model years, and no data was available beyond model year 2002. 

For years where no vehicles or only a few vehicles were surveyed by VIUS, we duplicated 

fractions from the nearest available model year. The 2002 VIUS was used 1986 and later model 

years and 1997 VIUS information was only used for the older model years not surveyed in the 

2002 VIUS. In the Draft MOVES2009 release, the oldest model year observed diesel fractions 

were applied to the older model years for combination trucks only. These older model years for 

the other truck categories were assumed to have no diesel trucks. 

7.3.1. FuelEngFraction 

The VIUS ENGTYP field was converted to the MOVES FuelTypeID using the mapping 

in Table 7-4. Note, it was not possible to distinguish LPG and CNG vehicles using VIUS. 

Based on historical data, we assigned the pre-1990 LPG/LNG vehicles to LPG and the 1990-andlater vehicles to CNG. While these vehicles form a very small portion of the national fleet, we 

would like to update this assignment if better information becomes available. Also, it was not 

possible to identify the fuel used for the VIUS category “Other.” Vehicles in this category were 

omitted from the analysis and model year results were renormalized. For the Draft MOVES2009 

release, all non-gasoline trucks were set to be diesel fuel, so that the default fleet contains only 

gasoline and diesel fuel trucks. 

Table 7-4. Mapping VIUS ENGTYP to MOVES FuelTypeID 

VIUS MOVES 

1 Leaded gasoline 1 Gasoline 

2 Unleaded gasoline 1 Gasoline 

3 Diesel 2 Diesel 

4 Liquefied gas (petroleum 

(LPG) or natural (LNG)) 

3 or 4 CNG or 

LPG 

5 Other None 

36
All 1999-and-earlier trucks were assigned to EngTechID “1" (conventional). 

Table 7-5 summarizes the pre-1999 diesel fractions for MOVES general truck categories 

by model year. The gasoline fractions can be estimated as one minus the diesel fractions listed 

here. 

For light trucks, fuel distribution information is also available from Polk. While the Polk 

data cannot easily be mapped to the truck SourceTypes used in MOVES, if future resources 

allow, it would be instructive to compare the Polk distributions to the combined passenger truck 

and light commercial truck distributions. This could help estimate the uncertainty in the fuel 

fraction estimates for these vehicles. The Census Bureau has discontinued the VIUS project, so 

it will be necessary to use Polk data or other sources for this type of information for future 

updates of these factors. 

37
Table 7-5. Diesel Fractions for Trucks 

Source 

Type 

Passenger 

Trucks 

31 

Light 

Commerical 

Trucks 

32 

Single-Unit 

Short-haul 

Trucks 

52 

Single-Unit 

Long-haul 

Trucks 

53 

Combination 

Short-haul 

Trucks 

61 

Combination 

Long-haul 

Trucks 

62 

Model 

Year 

1969 0.00000 0.00000 0.00000 0.00000 0.73282 1.00000 

1970 0.00000 0.00000 0.00000 0.00000 0.73282 1.00000 

1971 0.00000 0.00000 0.06238 0.00000 0.73282 1.00000 

1972 0.00000 0.00000 0.01695 0.00000 0.73282 1.00000 

1973 0.00000 0.00906 0.04465 0.00000 0.73282 1.00000 

1974 0.00000 0.08203 0.02377 0.00000 0.73282 1.00000 

1975 0.00000 0.02876 0.02130 0.47356 0.73282 1.00000 

1976 0.00000 0.00000 0.06518 1.00000 0.73282 1.00000 

1977 0.00000 0.00000 0.32805 1.00000 0.73282 1.00000 

1978 0.00000 0.00000 0.01731 0.06120 0.73282 1.00000 

1979 0.01392 0.04185 0.11083 1.00000 0.73282 1.00000 

1980 0.00000 0.05726 0.15791 1.00000 0.73282 1.00000 

1981 0.03557 0.03149 0.16825 0.20453 0.96590 1.00000 

1982 0.00000 0.29896 0.19327 0.87629 0.94257 1.00000 

1983 0.04182 0.15086 0.67378 1.00000 0.92500 1.00000 

1984 0.00000 0.21648 0.57100 1.00000 0.91464 1.00000 

1985 0.01633 0.17784 0.52692 0.99148 0.89852 1.00000 

1986 0.03626 0.07360 0.28809 0.31785 0.96279 0.99427 

1987 0.00000 0.04131 0.50033 0.82097 0.99402 1.00000 

1988 0.00562 0.11345 0.48870 0.89909 0.98549 1.00000 

1989 0.00833 0.04988 0.51855 0.40003 1.00000 1.00000 

1990 0.00826 0.05767 0.60288 0.82450 1.00000 1.00000 

1991 0.02875 0.08897 0.66240 0.91614 1.00000 1.00000 

1992 0.01429 0.13401 0.57597 1.00000 1.00000 1.00000 

1993 0.02557 0.04579 0.62871 0.41192 1.00000 1.00000 

1994 0.01917 0.06397 0.62889 0.89764 1.00000 1.00000 

1995 0.00792 0.09397 0.65834 0.45123 1.00000 1.00000 

1996 0.02474 0.06139 0.64296 0.88378 1.00000 1.00000 

1997 0.02167 0.12999 0.68158 0.56891 1.00000 1.00000 

1998 0.00654 0.04804 0.61441 0.61159 1.00000 1.00000 

1999 0.03755 0.11866 0.73754 0.67638 1.00000 1.00000 

38
7.3.2. SizeWeightFraction 

Engine size distributions for trucks were determined using the VIUS 2002 database. The 

VIUS database categorizes engine size by fuel type and the categories do not exactly match the 

MOVES categories. We mapped from the VIUS engine size categories to the MOVES engine 

size categories as described in Table 7-6. For comparison, the engine size ranges for both the 

VIUS and MOVES categories are listed in cubic inches displacement. 

Table 7-6. Mapping VIUS Engine Size Categories to MOVES EngSizeID 

Fuel Type VIUS 

Fuel_CID 

code 

VIUS CID 

Range 

MOVES 

EngSizeID 

Code 

MOVES CID 

Range 

Gasoline 1,2 1-129 20 1-122 

Gasoline 3,4 130-149 2025 122-153 

Gasoline 5,6 150-179 2530 153-183 

Gasoline 7,8 180-209 3035 183-214 

Gasoline 9,10 210-239 3540 214-244 

Gasoline 11,12 240-299 4050 244-305 

Gasoline 13-18 300 & Up 5099 305 & Up 

Diesel 20 1-249 3540 214-244 

Diesel 21 250-299 4050 244-305 

Diesel 22-36 300 & Up 5099 305 & Up 

Propane 38-41 All 5099 305 & Up 

Alcohol 43 1-229 3035 183-214 

Alcohol 44 230-269 3540 214-244 

Alcohol 45 270-339 4050 244-305 

Alcohol 46 340 & Up 5099 305 & Up 

Other 48 1-99 20 1-122 

Other 49 100-149 2025 122-153 

Other 50 150-199 2530 153-183 

Other 51 200-249 3540 214-244 

Other 52 250-299 4050 244-305 

Other 53-56 300 & Up 5099 305 & Up 

Fuel Not 

Reported 

58-61 All 5099 305 & Up 

Vehicle Not 

In Use 

63-66 All 5099 305 & Up 

All 19,37,42,47,5 

7,62,67 

Unknown 0 Unknown 

Determining weight categories for light trucks was fairly complicated. The VIUS 1997 

data combines information from two different survey forms. The first form was administered 

for VIUS “strata” 1 and 2 trucks: pickup trucks, panel trucks, vans (including mini-vans), utility 

type vehicles (including jeeps) and station wagons on truck chassis. The second form was 

administered for all other trucks. While both surveys requested information on engine size, only 

the second form requested detailed information on vehicle weight. Thus for strata 1 and 2 

trucks, VIUS classifies the trucks only by broad average weight category (AVGCK): 6,000 lbs or 

39
less, 6,001-10,000 lbs, 10,001-14,000lbs, etc. To determine a more detailed average engine size 

and weight distribution for these vehicles, we used the Oak Ridge National Laboratory (ORNL) 

light-duty vehicle database to correlate engine size with vehicle weight distributions by model 

year. 

In particular, for Source Types 31 and 32 (Passenger Trucks and Light Commercial 

Trucks): 

• VIUS 1997 trucks of the SourceType 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 SourceType in strata 1 and 2 were identified by 

enginesizeID and broad average weight category. 

• Strata 1 and 2 trucks in the heavier (10,001-14,000 lbs, etc) VIUS 1997 broad 

categories were matched one-to-one with the MOVES weight classes. 

• For trucks in the lower broad categories (6,000 lbs or less and 6001-10,000 lbs), we 

used VIUS 1997 to determine the fraction of trucks by model year and fuel type that 

fell into each engine size/broad weight class combination (the “VIUS fraction”) 

• We assigned trucks in the ORNL light duty vehicle database to a weightclassID by 

adding 300lbs to the recorded curb weight and determining the appropriate MOVES 

weight class. 

• For the trucks with a VIUS 1997 average weight of 6,000 lbs or less, we multiplied 

the VIUS 1997 fraction by the fraction of trucks with a given weightclassID among 

the trucks in the ORNL database that had the given engine size and an average weight 

of 6,000 lbs or less. Note, the ORNL database did not provide information on fuel 

type, so the same distributions were used for all fuels. 

• Because the ORNL database included only vehicles with a GVW up to 8500 lbs, we 

did not use it to distribute the trucks with a VIUS 1997 average weight of 6,001­

10,000 lbs. Instead these were distributed equally among the MOVES 

WeightClassIDs 70, 80, 90 and 100. 

Source Types 52 and 53 (Long- and Short-haul Single Unit Trucks) also included some 

trucks in VIUS 1997 strata 1 and 2, thus a similar algorithm was applied. 

• VIUS 1997 trucks of the Source Type in strata 3, 4, and 5 were assigned to the 

appropriate MOVES weight class based on VIUS 1997 detailed average weight 

information. 

• VIUS 1997 trucks of the Source Type in strata 1 and 2 were identified by 

enginesizeID and broad average weight category. 

• Strata 1 and 2 trucks in the heavier (10,001-14,000 lbs, etc) VIUS 1997 broad 

categories were matched one-to-one with the MOVES weight classes. 

• For trucks in the lower broad categories (6,000 lbs-or-less and 6001-10,000 lbs), we 

used VIUS 1997 to determine the fraction of trucks by model year and fuel type that 

fell into each engine size/broad weight class combination (the “VIUS fraction”) 

40
• We did not believe the ORNL light duty vehicle database adequately represented 

single unit trucks. Thus, the trucks with a VIUS 1997 average weight of 6,000 lbs or 

less and an engine size less than 5 liters were distributed equally among the MOVES 

weight classes 20, 25, 30, 35, 40, 45, 50, and 60. Because no evidence existed of 

very light trucks among the vehicles with larger engines (5 liter or larger), these were 

equally distributed among MOVES weight classes 40, 45, 50 and 60. 

• The trucks with a VIUS 1997 average weight of 6,001-10,000 lbs were distributed 

equally among the MOVES weight classes 70, 80, 90 and 100. 

SourceTypes 61 and 62 (Long- and Short-haul combination trucks) did not include any 

vehicles of VIUS 1997 strata 1 or 2. Thus we used the detailed VIUS 1997 average weight 

information and engine size information to assign engine size and weight classes for all of these 

trucks. 

The VIUS 2002 contains an estimate of the average weight (vehicle weight plus cargo 

weight) of 1998-2002 model year vehicle or vehicle/trailer combination as it was most often 

operated when carrying a typical payload during 2002. These estimates were used to determine 

the MOVES weightClassID categories for these trucks. Table 7.7 shows the weight ranges used 

for each weightClassID. Any vehicles without a non-zero value for the average weight and 

without a weight classification in the WeightAvgCK field were excluded from the analysis for 

determining the average weight distributions. 

Since there is a smaller number of gasoline trucks among the single unit and refuse 

trucks, all model years (1998-2002) were combined to determine a single weight distribution to 

use for these model years. 

The average weight distributions for light duty trucks (sourceTypeID = 31, 32) and none 

of the average weight distributions for any trucks for model years before 1998 were updated and 

the VIUS 1997 estimates were retained. 

In cases where distributions were missing (no survey information), distributions from a 

nearby model year with the same source type was used. Weight distributions for all 2003 and 

newer model years were set to be the same as for the 2002 model year for each source type. 

41
Table 7-7. Mapping VIUS Average Weight to MOVES WeightClassID 

Where WeightAvg is not zero: 

weightClassID WeightAvg Range 

20 1-2000 

25 2000-2499 

30 2500-2999 

35 3000-3499 

40 3500-3999 

45 4000-4499 

50 4500-4999 

60 5000-5999 

70 6000-6999 

80 7000-7999 

90 8000-8999 

100 9000-9999 

140 10000-13999 

160 14000-15999 

195 16000-19499 

260 19500-25999 

330 26000-32999 

400 33000-39999 

500 40000-49999 

600 50000-59999 

800 60000-79999 

1000 80000-99999 

1300 100000-129999 

9999 130000 & Up 

Where WeightAvg is zero: 

weightClassID WeightAvgCK 

140 4 (10000-14000) 

160 5 (14000-16000) 

195 6 (16000-19500) 

7.3.3. RegClassFraction 

Trucks were split between the regulatory classes “Light-Duty Trucks” (LDT) and 

“Heavy-Duty Trucks” (HDT) based on gross vehicle weight (GVW) (the maximum weight that a 

truck is designed to carry.) 

In particular, we used the VIUS response “PKGVW” in VIUS 1997 and ADM_GVW in 

VIUS 2002 and the Davis & Truit report on Class 2b Trucks36 to determine GVW fractions by 

fuel type. The VIUS fields are intended to identify the Polk weight class. Work for MOBILE6 

using the VIUS precursor, TIUS 1992 indicated that the PKGVW measure in VIUS is 

problematic. TIUS PKGVW is taken from the truck VIN, but is not always consistent with the 

indicated average and maximum weight. (For example, the reported “maximum weight” often 

exceeded the PKGVW.) These problems were also seen in VIUS. However, “maximum 

weight” was not available for smaller trucks, and the other measures of weight reported in VIUS 

were not consistent with the need for an indicator of the relevant emission standards. When the 

42
PKGVW led to unusual results, for example, particularly high fraction of LDT among 

combination trucks, we checked additional VIUS fields to determine if the PKGVW was 

mistaken. In some cases, the PKGVW was manually revised to a higher value and fractions 

were recomputed. In other cases, the PKGVW was consistent with the other fields, and the 

difference reflected the fact that our SourceType categories are based on axle counts and trailer 

configurations rather than weight. For example, a 6-tire (“dually”) pickup that regularly pulls a 

trailer is classified as a “Combination Truck,” although it is in the LDT regulatory class. Some 

model years had relatively high fractions of such trucks. It is likely these high values indicate a 

problem with small sample size for the model year, but they were left unchanged for now. 

Also, because the split between the LDT and HDT regulatory class is at 8500 lbs, it was 

necessary to split the Polk GVW Class 2 into class 2a (6001-8500 lbs) and class 2b (8501-10,000 

lbs). Davis & Truitt37

 report that, on average, 23.3 percent of Class 2 trucks are in Class 2b; 

97.4 percent of Class 2a trucks are powered by gasoline, and 76 percent of Class 2b trucks are 

powered by gasoline. From this information, we estimate that 19.2 percent of gasoline-powered 

Class 2 trucks are Class 2b and that 73.7 percent of diesel-powered class 2 trucks are Class 2b. 

Table 7.8. Light Truck Class 2 Weight Distribution 

Class 2a Class 2b 

Fuel Type 6001-8500 lbs. 

GVWR 

8501-10000 lbs. GVWR Class 2b Fraction 

Gasoline 74.7% 17.7% 19.2% 

Diesel 2.0% 5.6% 73.7% 

Any 76.7% 23.3% 

The regulatory class fractions for trucks are listed below in Table 7-9 and Table 7-10. 

Fractions of LDT for gasoline- and diesel-fueled vehicles are provided separately. The 

remaining trucks are classified as HDT. Entries of “#N/A” indicate that no vehicles of that 

SourceType and FuelType were surveyed in that model year. Values for alternative-fuel vehicles 

are available in the MOVES database. All 1986 and newer model year data was obtained from 

VIUS 2002. The pre-1986 model year values are from VIUS 1997. 

43
Table 7-9. Fraction of Light-Duty Trucks among Gasoline-Fueled Trucks 

Model 

Year 

SourceType 

Passenger 

Trucks 

31 

Light 

Commercial 

Trucks 

32 

Single-Unit 

Short-haul 

Trucks 

52 

Single-Unit 

Long-haul 

Trucks 

53 

Combination 

Short-haul 

Trucks 

61 

Combination 

Long-haul 

Trucks 

62 

1967 0.902303 #N/A #N/A #N/A #N/A #N/A 

1968 0.879238 #N/A #N/A #N/A #N/A #N/A 

1969 1 #N/A 0.109337 #N/A #N/A #N/A 

1970 0.983681 #N/A 0.046808 #N/A #N/A #N/A 

1971 0.956315 #N/A 0.38324 #N/A #N/A #N/A 

1972 0.957791 0.74768 0.683527 #N/A #N/A #N/A 

1973 0.953535 0.59472 0.300171 0 #N/A #N/A 

1974 0.946371 0.65248 0.132987 0 #N/A #N/A 

1975 0.966522 0.724827 0.134558 0 #N/A #N/A 

1976 0.951185 0.883189 0.125404 #N/A #N/A #N/A 

1977 0.887739 0.793622 0.061817 #N/A #N/A #N/A 

1978 0.847443 0.809907 0.45065 0.62437 #N/A #N/A 

1979 0.863942 0.776929 0.255077 #N/A #N/A #N/A 

1980 0.897151 0.74161 0.171485 #N/A 0 #N/A 

1981 0.959489 0.893686 0.304625 0.643456 0 #N/A 

1982 0.939455 0.719863 0.544875 0 0 #N/A 

1983 0.95116 0.903414 0.494159 #N/A 0 #N/A 

1984 0.937822 0.86782 0.332359 #N/A 0 #N/A 

1985 0.933322 0.869615 0.253229 0.808384 0 #N/A 

1986 0.926321 0.818333 0.317167 0.429721 0 #N/A 

1987 0.951630 0.897109 0.458448 0 0 #N/A 

1988 0.949331 0.890861 0.421998 0 0 #N/A 

1989 0.951473 0.891322 0.525825 0 0 #N/A 

1990 0.950769 0.911313 0.508253 0 0 #N/A 

1991 0.958130 0.887311 0.405240 0 #N/A #N/A 

1992 0.953552 0.905625 0.453636 0.624370 0 #N/A 

1993 0.953891 0.908697 0.672601 0 #N/A #N/A 

1994 0.950555 0.872257 0.510745 0 0 #N/A 

1995 0.945395 0.877733 0.453314 0 0 #N/A 

1996 0.948863 0.861956 0.515149 0 0 #N/A 

1997 0.950000 0.877692 0.447634 0 #N/A #N/A 

1998 0.947357 0.891901 0.412569 0 0 #N/A 

1999 0.930476 0.870745 0.366611 0 0.082522 #N/A 

2000 0.937397 0.884837 0.615046 0 #N/A #N/A 

2001 0.935546 0.880982 0.537060 0.429721 0 #N/A 

2002 0.945155 0.897487 0.587987 0 #N/A #N/A 

44
Table 7-10. Fraction of Light-Duty Trucks among Diesel-fueled Trucks 

Model 

Year 

SourceType 

Passenger 

Trucks 

31 

Light 

Commercial 

Trucks 

32 

Single-Unit 

Short-haul 

Trucks 

52 

Single-Unit 

Long-haul 

Trucks 

53 

Combination 

Short-haul 

Trucks 

61 

Combination 

Long-haul 

Trucks 

62 

1967 #N/A #N/A #N/A #N/A #N/A #N/A 

1968 #N/A #N/A #N/A #N/A #N/A #N/A 

1969 #N/A #N/A #N/A #N/A #N/A #N/A 

1970 #N/A #N/A #N/A #N/A #N/A #N/A 

1971 #N/A #N/A 0 #N/A #N/A #N/A 

1972 #N/A #N/A 0 #N/A #N/A #N/A 

1973 #N/A 0 0 #N/A #N/A #N/A 

1974 #N/A 0 0 #N/A #N/A #N/A 

1975 #N/A 0 0 0 #N/A #N/A 

1976 #N/A #N/A 0 0 #N/A #N/A 

1977 #N/A 0 0 0 #N/A #N/A 

1978 #N/A 0 0 0 #N/A #N/A 

1979 0 0.072135 0 0 #N/A #N/A 

1980 #N/A 0.397873 0 0 0.009394 #N/A 

1981 0.892664 0.118825 0 0 0 #N/A 

1982 #N/A 0.271488 0.047107 0 0 #N/A 

1983 0.54614 0.232866 0.219283 #N/A 0 #N/A 

1984 0.262872 0.243221 0.019513 0 0 0 

1985 0.259661 0.231416 0.041111 0 0.006796 0 

1986 0.456608 0.351492 0.021218 0.028255 0 0 

1987 0.951630 0.088341 0.129185 0 0 0 

1988 0.254950 0.210368 0.054122 0.068212 0 0 

1989 0.260932 0.144417 0.031919 0 0 0 

1990 0.260713 0.062091 0 0 0 0 

1991 0.261741 0.176872 0.111821 0.184952 0 0 

1992 0.262386 0.222906 0.042603 0.029801 0 0 

1993 0.262899 0.149897 0.156027 0.538647 0 0 

1994 0.298405 0.159601 0.073051 0.042628 0 0 

1995 0.308964 0.200670 0.117612 0 0 0 

1996 0.289104 0.211153 0.113798 0.084009 0 0 

1997 0.261310 0.356162 0.120503 0 0 0 

1998 0.263000 0.142366 0.017443 0 0 0 

1999 0.260865 0.214650 0.155014 0 0 0 

2000 0.358104 0.216855 0.171699 0.298503 0 0 

2001 0.234050 0.342721 0.120036 0.188003 0 0 

2002 0.282868 0.262352 0.085967 0 0 0 

45
7.4. Buses 

Because buses are not included in VIUS and because the Polk data we had for school 

buses was incomplete, the source bin fractions for buses is based on a variety of data sources and 

assumptions. Values for transit buses, school buses, and intercity buses were calculated 

separately. 

7.4.1. FuelEngFraction 

We followed the Energy Information Administration (EIA) in assigning all intercity 

buses to conventional diesel engines (AEO2006, Supplemental Table 34). 

The National Transit Database (NTD) responses to form 408 (Revenue Vehicle 

Information Form) included information classifying transit buses to a variety of fuel types by 

model year. The mapping from NTD fuel types to MOVES fuel types is summarized in Table 

7-11. The resulting fractions by model year are summarized in Table 7-10. 

Table 7-11. Mapping National Transit Database Fuel Types to MOVES Fuel 

Types 

NTD code NTD description MOVES 

Fuel ID 

MOVES Fuel 

Description 

BF Bunker fuel na 

CN Compressed natural gas 3 CNG 

DF Diesel fuel 2 diesel 

DU Dual fuel 2 diesel 

EB Electric battery 9 electric 

EP Electric propulsion 9 electric 

ET Ethanol 5 ethanol 

GA Gasoline 1 gasoline 

GR Grain additive na 

KE Kerosene na 

LN Liquefied natural gas 3 CNG 

LP Liquefied petroleum gas 4 LPG 

MT Methanol 6 methanol 

OR Other na 

46
Table 7-12. Fuel Fractions for Transit Buses 

Model 

Year 

Gasoline Diesel CNG LPG Ethanol Methanol Electric 

1969 0 1 0 0 0 0 0 

1970 0 1 0 0 0 0 0 

1971 0 1 0 0 0 0 0 

1972 0 1 0 0 0 0 0 

1973 0 1 0 0 0 0 0 

1974 0 1 0 0 0 0 0 

1975 0 1 0 0 0 0 0 

1976 0 1 0 0 0 0 0 

1977 0 1 0 0 0 0 0 

1978 0 1 0 0 0 0 0 

1979 0.033981 0.966019 0 0 0 0 0 

1980 0 1 0 0 0 0 0 

1981 0.002088 0.997912 0 0 0 0 0 

1982 0.001894 0.992424 0 0 0 0 0.005682 

1983 0 1 0 0 0 0 0 

1984 0.001603 0.998397 0 0 0 0 0 

1985 0 0.999565 0.000435 0 0 0 0 

1986 0.00079 0.996447 0.002764 0 0 0 0 

1987 0.001402 0.998598 0 0 0 0 0 

1988 0.002377 0.997623 0 0 0 0 0 

1989 0.00113 0.998306 0 0 0.000565 0 0 

1990 0.002941 0.990271 0.006787 0 0 0 0 

1991 0.003134 0.978064 0.018106 0 0 0 0.000696 

1992 0.010769 0.933903 0.046417 0.000743 0 0.005941 0.002228 

1993 0.003061 0.918707 0.07551 0.00068 0.001361 0 0.00068 

1994 0.010711 0.900625 0.084796 0.000893 0 0 0.002975 

1995 0.009555 0.835108 0.153153 0 0 0 0.002184 

1996 0.017963 0.881825 0.097613 0.000709 0 0 0.001891 

1997 0.012702 0.810162 0.174365 0.000462 0 0 0.002309 

1998 0.012003 0.838409 0.1487 0 0 0 0.000889 

1999 0.005998 0.878041 0.113296 0 0 0 0.002666 

All 1999-and-earlier electric buses were assigned to EngTechID “30" (electric only). All 

other 1999-and-earlier buses were assigned to EngTechID “1" (conventional). 

The available Polk data excluded fuel information on school buses and we were unable to 

locate any other source for bus fuel fractions. (The Union of Concerned Scientists estimates that 

about one percent of school buses are fueled by either CNG or propane, but does not provide 

estimates by model year.38

) Thus we used the diesel fractions from MOBILE6, which were 

derived from Polk 1996 and 1997 data. We assigned non-diesel buses to gasoline. These 

fractions are summarized in Table 7-13. In the future it would be desirable to obtain up-to-date, 

detailed fuel information for school buses from Polk or some other source. 

47
Table 7-13. Fuel Fractions for School Buses 

Model Year Gasoline Diesel 

1972 1 0 

1973 1 0 

1974 1 0 

1975 0.991272 0.008728 

1976 0.99145 0.00855 

1977 0.976028 0.023972 

1978 0.970936 0.029064 

1979 0.95401 0.04599 

1980 0.94061 0.05939 

1981 0.736056 0.263944 

1982 0.674035 0.325965 

1983 0.676196 0.323804 

1984 0.615484 0.384516 

1985 0.484507 0.515493 

1986 0.326706 0.673294 

1987 0.265547 0.734453 

1988 0.249771 0.750229 

1989 0.229041 0.770959 

1990 0.124036 0.875964 

1991 0.089541 0.910459 

1992 0.010041 0.989959 

1993 0.120539 0.879461 

1994 0.147479 0.852521 

1995 0.114279 0.885721 

1996 0.041539 0.958461 

7.4.2. SizeWeightFraction 

While the vast majority of buses of all types have engine displacement larger than five 

liters (EngSizeID=5099), it was difficult to find detailed information on average bus weight. 

For intercity buses, we used information from Table II-7 of the FTA 2003 Report to 

Congress39 that specified the number of buses in various weight categories. This information is 

summarized in below in Table 7-14. Note the FTA uses the term “over-the-road bus” to refer to 

the class of buses roughly equivalent to the MOVES “intercity bus” category. The FTA weight 

categories were mapped to the equivalent MOVES weight classes. 

Table 7-14. FTA Estimate of Bus Weights 

Weight (lbs) 

MOVES 

Weight 

ClassID 

MOVES 

Weight Range 

(lbs) 

Number 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 

48
Using our 1999 bus population estimates (in Table3-1), we were able to estimate the 

fraction of all buses that were intercity buses and then to estimate the fraction of intercity buses 

in each weight bin. In particular: 

 Estimated number of intercity buses in 2000: 

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

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

87,028 - 67,905 = 19,123 

 Estimated intercity bus weight distribution: 

Class 400 = 19,123/87,028 = 22% 

Class 500 = 67,905/87,028 = 78% 

This distribution was used for all model years. 

For transit buses, we took average curb weights from Figure II-6 of the FTA Report to 

Congress40

and added additional weight to account for passengers and alternative fuels. The 

resulting in-use weights were all in the range from 33,850 to 40,850. Thus all transit buses were 

assigned to the weight class “400” (33,000 - 40,000 lbs) for all model years. This estimate could 

be improved if more detailed weight information for transit buses becomes available. 

For school buses, we used information from a survey of California school buses. While 

this data may not be representative of the national average distribution, it was the best data 

source available. The California data41 provided information on number of vehicles by gross 

vehicle weight class and fuel as detailed in Table 7-15. 

Table 7-15. California School Buses 

Gas Diesel Other Total 

LHDV 2740 4567 8 7315 

MHDV 467 2065 2 2534 

HHDV 892 11639 147 12678 

Total 4099 18271 157 

To estimate the distribution of average weights among the MOVES weight classes, we 

assumed that the Light Heavy-Duty (LHDV) school buses were evenly distributed among 

weightClassIDs 70, 80, 90, 100, and 140. Similarly, we assumed the Medium Heavy-Duty 

(MHDV) school buses were evenly distributed among weightClassIDs 140, 160, 195, 260, and 

330 and the Heavy Heavy-Duty (HHDV) school buses were evenly distributed among 

weightClassIDs 195, 260, 330, and 440. 

The final default weight distributions for buses are summarized in Table 7-16. 

7.4.3. RegClassFraction 

All buses were assigned to the Heavy-Duty Truck regulatory class. 

49
Table 7-16. Weight Distributions for Buses by Fuel Type 

Intercity 

Buses 

(41) 

Transit Buses 

(42) 

School Buses (43) 

Weight Class Diesel Diesel & Gas Diesel Gas 

70 0.0500 0.1337 

80 0.0500 0.1337 

90 0.0500 0.1337 

100 0.0500 0.1337 

140 0.0726 0.1565 

160 0.0226 0.0228 

195 0.1819 0.0772 

260 0.1819 0.0772 

330 0.1819 0.0772 

400 0.2197 1.0000 0.1593 0.0544 

500 0.7800 

7.5. Refuse Trucks 

Values for Refuse Trucks (Source Type 51) were computed from information in VIUS. 

7.5.1. FuelEngFraction 

As for other trucks, we used the VIUS EngTyp field to estimate FuelType and Engine 

Technology Fractions. The Refuse Trucks classified in VIUS as “CNG or LPG” are assigned to 

diesel. All Refuse Trucks were assumed to have conventional internal combustion engines. 

7.5.2. SizeWeightFraction 

Because the sample of Refuse Trucks in VIUS was small, the same SizeWeight 

distributions were used for model year groups. As for other trucks, the EngineSize group was 

determined from the VIUS engine size categories and the WeightClass was determined from the 

VIUS reported average weight. 

50
Table 7-17. Fuel Fractions for Refuse Trucks by Model Year 

Model 

Year 

Gasoline Diesel 

1983 0.0155 0.9845 

1984 0 1.0000 

1985 0.2206 0.7794 

1986 0.2132 0.7868 

1987 0.1687 0.8313 

1988 0 1.0000 

1989 0.0231 0.9769 

1990 0.1109 0.8891 

1991 0 1.0000 

1992 0.1120 0.8880 

1993 0.0292 0.9708 

1994 0.0415 0.9585 

1995 0.0119 0.9881 

1996 0 1.0000 

1997 0.0201 0.9799 

1998 0 1.0000 

1999 0.0349 0.9651 

2000 0.0184 0.9816 

2001 0 1.0000 

2002 0 1.0000 

51
Table 7-18. 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 

7.5.3. RegClassFraction 

Using the VIUS data on gross vehicle weight, all Refuse Trucks were classified as 

Heavy-Duty Trucks. 

7.6. Motor Homes 

Determining source bin distribution for Motor Homes required a number of assumptions 

and interpolation due to the lack of detailed information. For each field, the following describes 

the information available, assumptions made, and how data points were determined. 

52
7.6.1. FuelEngFraction 

Detailed information on motor home fuel distribution was not available. Staff of the 

Recreational Vehicle Industry Association (RVIA) told us that the fraction of diesel motor 

homes had been relatively constant at 10 to 20 percent for many years.42 This fraction began to 

increase steadily in the mid-1990s and is now 40%. Based on this information, we used linear 

interpolation to estimate the diesel fractions in Table 7-19. The remaining 1999-and-earlier 

motor homes are assumed to be gasoline-fueled. We assumed all 1999-and-earlier motor homes 

have conventional internal combustion engines. 

Table 7-19. Diesel Fractions for Motor Homes. 

Model Year Fraction Diesel 

1993-and-earlier 0.150000 

1994 0.177778 

1995 0.205556 

1996 0.233333 

1997 0.261111 

1998 0.288889 

1999 0.316667 

7.6.2. SizeWeightFraction 

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 and should be updated if better information 

becomes available. The Polk TIP® information did not specify fuel type, so we assumed that the 

heaviest vehicles in the Polk database were diesel-powered and the remainder are powered by 

gasoline. This led to the weight distributions in Table 7-20 and Table 7-21. 

53
Table 7-20. Weight Fractions for Diesel Motor Homes by Model Year 

Polk GVW 

bin 

3 4 5 6 7 8 

MOVES 

weight class 

140 160 195 260 330 400 

Model Year Diesel 

1975 0.171431 0.792112 0.029828 0 0.006629 0 

1976 0.637989 0.340639 0.018755 0.000436 0.002181 0 

1977 0.68944 0.292308 0.012168 0.000277 0.005531 0.000277 

1978 0.423524 0.574539 0 0.000387 0.00155 0 

1979 0.096922 0.899344 0 0.001067 0.002667 0 

1980 0.462916 0.537084 0 0 0 0 

1981 0 0.941973 0 0.030174 0 0.027853 

1982 0 0.868333 0 0.049 0.03 0.052667 

1983 0 0.912762 0.000203 0.014845 0.030096 0.042094 

1984 0 0.932659 0.000835 0.009183 0.036732 0.020592 

1985 0 0.881042 0.001474 0.010761 0.083285 0.023438 

1986 0 0.855457 0.013381 0.022962 0.089534 0.018667 

1987 0 0.791731 0.085493 0.022498 0.087164 0.013113 

1988 0 0.72799 0.148917 0.015469 0.093335 0.014289 

1989 0 0.73298 0.128665 0.043052 0.082792 0.012511 

1990 0 0.173248 0.614798 0.043628 0.149939 0.018387 

1991 0 0 0.619344 0.063712 0.296399 0.020545 

1992 0 0 0.551548 0.01901 0.385085 0.044356 

1993 0 0 0.345775 0.471873 0.144844 0.037509 

1994 0 0 0.45546 0.354386 0.159622 0.030531 

1995 0 0 0.635861 0.163195 0.17468 0.026264 

1996 0 0 0.553807 0.229529 0.184208 0.032456 

1997 0 0 0.666905 0.193167 0.111299 0.028628 

1998 0 0 0.267 0.335069 0.357508 0.040423 

1999 0 0 0 0.736656 0.233886 0.029458 

54
Table 7-21. Weight Fractions for Gasoline Motor Homes by Model Year 

Polk GVW 

bin 

3 4 5 6 7 8 

MOVES 

weight class 

140 160 195 260 330 400 

Model Year Gasoline 

1975 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 

7.6.3. RegClassFraction 

We assigned all motor homes to the Heavy-Duty Truck regulatory class. 

7.7. SourceBinDistributions for 2000-and-later 

MOVES was designed to support a wide variety of future fuels and engine technologies, 

including compressed natural gas (CNG), liquified petroleum gas (LPG), and conventional 

internal combustion (CIC) and advanced internal combustion (AIC) engines. In particular, 

emission rates were developed to support the combinations of fuel and engine technology listed 

by SourceType in Table 7-22. Note that some fuel types that were supported in earlier versions 

of MOVES (methanol and hydrogen) are not available in DraftMOVES2009. 

The various hybrid types were split into "mild" and "full" categories because there are 

types of hybrids which get less of an efficiency increase from hybrid design due to larger engines 

and smaller electrical components. The less efficient designs we called "mild" hybrids (like the 

55
early Honda hybrids) to distinguish them from the more efficient, full hybrid designs (like the 

Toyota Prius). Both of these categories have significantly different energy rates and potentially 

different market shares. Conventional categories are split from advanced categories for a 

different reason. There have been significant improvements in internal combustion engines over 

time. The conventional versus advanced split is a crude accounting of these improvements. All 

of these technologies are further defined in the report, "Fuel Consumption Modeling of 

Conventional and Advanced Technology Vehicles in the Physical Emission Rate Estimator 

(PERE)."43 

Table 7-22. Supported Fuels and Technologies for 2000-and-later Model Years. 

Fuel Engine 

Technology 

Motorcycles Passenger 

Cars, 

Light 

Passenger 

& 

Commerci 

al Trucks 

Transit & 

School 

Buses; 

SingleUnit Short 

Haul 

Trucks & 

Motor 

Homes 

Intercity 

Buses 

Refuse 

Trucks 

Single 

Unit Long 

Haul 

Trucks 

Combination 

Short & 

Long 

Haul 

Trucks 

Gasoline Conventional 

IC 

X X X X X X 

Gasoline Advanced IC X X X X 

Gasoline CIC Hybrid 

Mild

 X X 

Gasoline CIC Hybrid 

Full 

X X 

Gasoline AIC Hybrid 

Mild

 X X 

Gasoline AIC Hybrid 

Full 

X X 

Diesel Conventional 

IC 

X X X X X 

Diesel Advanced IC X X X X X 

Diesel CIC Hybrid 

Mild

 X X 

Diesel CIC Hybrid 

Full 

X X 

Diesel AIC Hybrid 

Mild

 X X 

Diesel Diesel AIC 

Hybrid Full 

X X X 

CNG Conventional 

IC 

X X X X 

LPG Conventional 

IC 

X X X X 

Ethanol Conventional 

IC 

X X X X 

Electricity Electric only X X X 

56
The inputs for determining default SourceBinDistributions for model years 2000-andlater were generally based on fuel and engine technology projections from AEO2004 and on the 

1999 calendar year regulatory class, size and weight distributions used in MOVES. 

7.7.1. Motorcycles 

We assumed that all 2000-and-later motorcycles were fueled by conventional gasoline 

engines, with the same size and weight distributions as in 1999. All motorcycles are in the 

“Motorcycle” regulatory class. 

7.7.2. Passenger Cars, Light Passenger Trucks and Light Commercial Trucks 

Draft MOVES2009 supports a wide range of fuels and future engine technologies for 

passenger cars and light trucks. 

The FuelEngFractions for these vehicles were determined from AEO2004. Supplemental 

Table 45 of the AEO2004 lists projected sales by technology type for light duty vehicles. 

Supplemental Table 56 lists projected technology penetrations for light duty vehicles. These 

values were mapped to the MOVES fuels and technologies to project fractions for model years 

2001 through 2025. Fractions from 2001 were applied to model year 2000. Fractions from 2025 

were applied to model years 2026 through 2050. 

We analyzed passenger cars and light trucks separately. All vehicles were assigned to 

either the gasoline or diesel fuel conventional engine technology category for all future years. 

MOVES contains no projections for the use of hybrid or advanced engine technology or the use 

of alternative fuels in future calendar years. The resulting fuelEngFractions for conventional 

gasoline and diesel fueled vehicles are listed in Table 7.23. 

We used the size and weight distributions from the 2002 model year for all 2003 and 

newer model years. The size and weight distribution for 2002 model year gasoline conventional 

internal combustion engines were used for all 2003 and newer model year technologies and fuel 

types, other than diesel. The 2003 and newer model year diesel vehicles of all technologies use 

the size and weight distribution for diesel conventional internal combustion engines of the 2002 

model year. 

All Passenger Cars were assigned to the Light Duty Vehicle (LDV) regulatory class. 

Light Trucks were distributed among the Light Duty Truck (LDT) and Heavy Duty Truck (HDT) 

regulatory classes. We used the 2002 model year regulatory class distribution for gasoline 

conventional internal combustion vehicles for all 2003 and newer model year technologies and 

fuel types, other than diesel.. The 2003 and newer model year diesel vehicles of all technologies 

use the regulatory class distribution for diesel conventional internal combustion vehicles of the 

2002 model year. 

57
 

Table 7.23. Fuel Fractions for 2002 and Newer Passenger Cars and Light Duty 

Trucks 

Passenger Cars Passenger Trucks Commerical Light Trucks 

Model Year gasoline diesel gasoline diesel gasoline diesel 

2002 0.9900 0.0100 0.9870 0.0130 0.9870 0.0130 

2003 0.9900 0.0100 0.9870 0.0130 0.9870 0.0130 

2004 0.9900 0.0100 0.9870 0.0130 0.9870 0.0130 

2005 0.9900 0.0100 0.8597 0.0123 0.8597 0.0121 

2006 0.9900 0.0100 0.5942 0.0109 0.5823 0.0101 

2007 0.9900 0.0100 0.4264 0.0100 0.4234 0.0089 

2008 0.9900 0.0100 0.2171 0.0089 0.2122 0.0074 

2009 0.9900 0.0100 0.1994 0.0088 0.1935 0.0072 

2010 0.9900 0.0100 0.1747 0.0087 0.1727 0.0071 

2011 

and newer 

0.9900 0.0100 0.1658 0.0087 0.1500 0.0070 

7.7.3 Buses 

Historically, school buses and transit buses have used a wide range of alternative fuels, 

while intercity buses have been powered almost exclusively by conventional diesel engines. For 

MOVES we anticipate this trend will continue. Fuel and technology projections were not 

available from AEO. The MOVES estimates for 1999 distributions of transit and school buses 

are carried forward to 2050. These distributions are summarized in Table 7.24. Engine size 

and vehicle weight distributions were also carried forward from 1999. All buses were assigned 

to the Heavy-Duty Truck regulatory class. 

Table 7.24. Fuel and Engine Technology Fractions for 2000-and-later Buses 

Diesel CIC Gasoline CIC 

Intercity Buses 1.00000 0 

Transit Buses 0.99399 0.00601 

School Buses 0.95846 0.04154 

7.7.4. Motor Homes and Single Unit Short-haul and Long-haul Trucks 

For Motor Homes and Single Unit Short-haul and Long-haul Trucks, MOVES uses the 

AEO2004 projections for medium duty vehicles. AEO Table 55 lists sales projections for 

medium-duty freight trucks powered by diesel, gasoline, liquified petroleum gas and compressed 

natural gas. Furthermore, AEO Table 146 lists technology penetrations for Class 4-6 freight 

vehicles. All non-gasoline trucks, other than diesel, were assigned to the MOVES gasoline 

58
conventional combustion category. All diesel trucks with were assigned to the MOVES diesel 

conventional internal combustion category. The resulting distributions are summarized in Table 

7.25. 

We used the engine size and vehicle weight distributions from 2002 for future years. 

Where a future fuel was not part of the fleet in 2002, we used the 2002 size and weight 

distribution for gasoline conventional internal combustion vehicles. Where a future diesel 

engine technology was not part of the source type fleet in 2002, we used the 2002 size and 

weight distribution for diesel conventional internal combustion vehicles. 

Table 7.25. Fuel and Engine Technology Fractions for 2002 and Newer Motor 

Homes and Single-Unit Short-haul and Long-haul Trucks 

Single Unit Short Haul Single Unit Long Haul Motor Home 

Model Year gasoline diesel gasoline diesel gasoline diesel 

2002 0.2631 0.7369 0.0627 0.9373 0.2237 0.7763 

2003 0.2924 0.7076 0.2924 0.7076 0.2924 0.7076 

2004 0.2869 0.7131 0.2869 0.7131 0.2869 0.7131 

2005 0.2809 0.7191 0.2809 0.7191 0.2809 0.7191 

2006 0.2758 0.7242 0.2758 0.7242 0.2758 0.7242 

2007 0.2710 0.7290 0.2710 0.7290 0.2710 0.7290 

2008 0.2674 0.7326 0.2674 0.7326 0.2674 0.7326 

2009 0.2642 0.7358 0.2642 0.7358 0.2642 0.7358 

2010 0.2620 0.7380 0.2620 0.7380 0.2620 0.7380 

2011 0.2602 0.7399 0.2602 0.7399 0.2602 0.7399 

2012 0.2589 0.7411 0.2589 0.7411 0.2589 0.7411 

2013 0.2579 0.7421 0.2579 0.7421 0.2579 0.7421 

2014 0.2572 0.7428 0.2572 0.7428 0.2572 0.7428 

2015 0.2566 0.7434 0.2566 0.7434 0.2566 0.7434 

2016 0.2562 0.7438 0.2562 0.7438 0.2562 0.7438 

2017 0.2560 0.7440 0.2560 0.7440 0.2560 0.7440 

2018 0.2560 0.7440 0.2560 0.7440 0.2560 0.7440 

2019 0.2561 0.7439 0.2561 0.7439 0.2561 0.7439 

2020 0.2563 0.7437 0.2563 0.7437 0.2563 0.7437 

2021 0.2565 0.7435 0.2565 0.7435 0.2565 0.7435 

2022 0.2569 0.7431 0.2569 0.7431 0.2569 0.7431 

2023 0.2573 0.7427 0.2573 0.7427 0.2573 0.7427 

2024 0.2578 0.7422 0.2578 0.7422 0.2578 0.7422 

2025 0.2586 0.7414 0.2586 0.7414 0.2586 0.7414 

2026 0.2591 0.7409 0.2591 0.7409 0.2591 0.7409 

2027 0.2594 0.7406 0.2594 0.7406 0.2594 0.7406 

2028 0.2602 0.7398 0.2602 0.7398 0.2602 0.7398 

2029 0.2608 0.7392 0.2608 0.7392 0.2608 0.7392 

2030 0.2613 0.7387 0.2613 0.7387 0.2613 0.7387 

2031 and 

Newer 

0.1532 0.8468 0.1532 0.8468 0.1532 0.8468 

59
7.7.5. Refuse and Combination Trucks 

For Refuse, Short-haul and Long-haul Combination Trucks, MOVES uses the AEO2004 

projections for heavy-duty freight trucks. AEO Table 55 lists sales projections for heavy-duty 

freight trucks powered by diesel, gasoline, liquified petroleum gas and compressed natural gas. 

All non-gasoline trucks, other than diesel, were assigned to the MOVES gasoline conventional 

combustion category. All diesel trucks with were assigned to the MOVES diesel conventional 

internal combustion category. 

Furthermore, AEO Table 146 lists technology penetrations for Class 7-8 freight trucks 

with “higher cylinder pressure”, “improved injection & combustion” and “waste heat/thermal 

management”. All trucks were assigned to the MOVES the conventional internal combustion 

categories. The resulting distributions are summarized in Table 7.26. 

We used the engine size and vehicle weight distributions from 2002 for future years. 

Where a future fuel or engine technology was not part of the source type fleet in 2002, we used 

the 2002 size and weight distribution for diesel conventional internal combustion vehicles. 

All Refuse Trucks were assigned to the Heavy-Duty Truck regulatory class. 

Combination Trucks were distributed among the Light Duty Truck (LDT) and Heavy Duty 

Truck (HDT) regulatory classes. Where a future fuel or technology was not part of the source 

type fleet in 2002, we used the regulatory class distribution for diesel conventional internal 

combustion vehicles. 

60
Table 7.26. Fuel and Engine Technology Fractions for Refuse Trucks and Shorthaul and Long-haul Combination Trucks 

Refuse Trucks Combination Short Haul Combination Long Haul 

Model Year gasoline diesel gasoline diesel gasoline diesel 

2002 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 

2003 0.0332 0.9668 0.0330 0.9670 0.0330 0.9670 

2004 0.0330 0.9670 0.0328 0.9672 0.0328 0.9672 

2005 0.0328 0.9672 0.0327 0.9673 0.0327 0.9673 

2006 0.0328 0.9672 0.0327 0.9673 0.0327 0.9673 

2007 0.0330 0.9670 0.0329 0.9671 0.0329 0.9671 

2008 0.0333 0.9667 0.0331 0.9669 0.0331 0.9669 

2009 0.0336 0.9664 0.0335 0.9665 0.0335 0.9665 

2010 0.0340 0.9660 0.0338 0.9662 0.0338 0.9662 

2011 0.0344 0.9656 0.0342 0.9658 0.0342 0.9658 

2012 0.0348 0.9652 0.0347 0.9653 0.0347 0.9653 

2013 0.0353 0.9647 0.0351 0.9649 0.0351 0.9649 

2014 0.0357 0.9643 0.0356 0.9644 0.0356 0.9644 

2015 0.0362 0.9638 0.0361 0.9639 0.0361 0.9639 

2016 0.0367 0.9633 0.0365 0.9635 0.0365 0.9635 

2017 0.0371 0.9629 0.0370 0.9630 0.0370 0.9630 

2018 0.0376 0.9624 0.0374 0.9626 0.0374 0.9626 

2019 0.0380 0.9620 0.0379 0.9621 0.0379 0.9621 

2020 0.0384 0.9616 0.0383 0.9617 0.0383 0.9617 

2021 0.0388 0.9612 0.0387 0.9613 0.0387 0.9613 

2022 0.0392 0.9608 0.0391 0.9609 0.0391 0.9609 

2023 0.0396 0.9604 0.0395 0.9605 0.0395 0.9605 

2024 0.0400 0.9600 0.0399 0.9601 0.0399 0.9601 

2025 0.0404 0.9596 0.0403 0.9597 0.0403 0.9597 

2026 0.0407 0.9593 0.0406 0.9594 0.0406 0.9594 

2027 0.0411 0.9589 0.0409 0.9591 0.0409 0.9591 

2028 0.0414 0.9586 0.0413 0.9587 0.0413 0.9587 

2029 0.0417 0.9583 0.0416 0.9584 0.0416 0.9584 

2030 0.0420 0.9580 0.0419 0.9581 0.0419 0.9581 

2031 and 

Newer 

0.0164 0.9836 0.0164 0.9836 0.0164 0.9836 

61
 

8. SourceUseType 

The SourceUseType table lists average vehicle mass and three average road load 

coefficients for each SourceType. The mass is listed in metric tons. The road load coefficients 

are a rolling term “A,” a rotatating term “B,” and a drag term “C.” 

MOVES uses these coefficients to calculate vehicle specific power for each source type 

according to the equation: 

2 3 VSP = (A )• v + (B )• v + (C )• v + (a + g • sinθ )• v . M M M 

where A, B, and C are the road load coefficients in units of (kiloWatt second)/(meter tonne), 

(kiloWatt second2)/(meter2 tonne), and (kiloWatt second3)/(meter3 tonne), respectively. M is the 

mass of the vehicle in kilograms, g is the acceleration due to gravity (9.8 meter/ second2

), v is the 

vehicle speed in meter/second, a is the vehicle acceleration in meter/second2

, and sinK is the 

(fractional) road grade. 

The values in the SourceUseType table were averaged from values in the Mobile Source 

Observation Database (MSOD). The values were weighted using the age and sourcebin 

distributions described elsewhere in this report. In particular, the average values were computed 

using the equation: 

⎧ ⎛

⎜ ∑α j ⋅ unweightedvalue ⎞⎟

⎫ ⎪ j=1,total # of sourcebins ⎪

i=1, total 

∑

# of ages 

⎨

⎪

β i ⋅⎜

⎜ ∑α j 

⎟

⎟

⎬

⎪

weightedvalue = ⎩ ⎝ j=1, total # of sourcebins ⎠⎭

∑β i 

i=1,total # of ages 

where the “unweighted value” was either the vehicle mid-point mass or one of the three different 

road load coefficients determined from the road load–vehicle mass relations described below: αj 

were the sourceBinActivityFractions in the MOVES database and βi were the ageFractions in the 

MOVES database. Age fractions were matched to model years for calendar year 1999 (i.e., 

Model Year 1999 corresponds to vehicle ageID of 0; Model Year1969 corresponds to ageID of 

30.) Only sourcebins and ages with vehicles in the MSOD were used in these weightings. Thus, 

the “total number of sourcebins” in the MSOD and “total number of ages” in the MSOD were 

used to normalize the results. 

8.1. SourceMass 

The SourceMass was computed as the weighted average of the “mid-point” mass for the 

Weight Class associated with each sourcebin. Sourcebins not represented in the MSOD were 

excluded. 

62
Table 8-1. MOVES Weight Classes 

Weight 

ClassID 

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 

8.2. Road Load Coefficients 

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

Motorcycle road load coefficients are typically parameterized 44

 with mass dependent A 

and C terms which take into account rolling resistance and aerodynamic drag. Parameters 

adopted here are from the UN report: 

A = 0.088M and C= 0.26 + 1.94x10-4M 

where M is the inertial mass of the motorcycle and driver and has units of metric tonnes. 

For vehicles with a weight of 8500 lbs or less, the road load coefficients were derived 

from the track road load horspower (TRLHP@50mph) recorded in the MSOD.45 The calculations 

applied the following empirical equations:46 

A = 0.7457*(0.35/50*0.447) * TRLHP@50mph 

B = 0.7457*(0.10/(50*0.447)2

) * TRLHP@50mph 

63
C = 0.7457*(0.55/(50*0.447)3

) * TRLHP@50mph 

The rolling resistance was multiplied by a factor of 5. 

For the heavier vehicles, no road load parameters were available in the MSOD. Instead 

EPA used the relationships of road load coefficent to vehicle mass from a study done by V.A. 

Petrushov,47

 as shown in Table 8-2. The mid-point mass for the sourcebin was used as the 

vehicle mass. 

Table 8-2. Road Load Coefficients for Heavy-Duty Trucks, Buses, and Motor 

Homes 

8500 to 14000 lbs 

(3.855 to 6.350 

tonne) 

14000 to 33000 lbs 

(6.350 to 14.968 

tonne) 

>33000 lbs 

(>14.968 tonne) 

Buses and 

Motor Homes 

A(kW*s/m)/ 

M(tonne) 0.0996 0.0875 0.0661 0.0643 

B(kW*s2

/m2)/ 

M(tonne) 0 0 0 0 

C(kW*s3/m3) 

/M(tonne) 

3.40 x 10-4 

(mass is the average 

mass of the weight 

category) 

1.97 x 10-4 

(mass is the average 

mass of the weight 

category) 

1.79 x 10-4 

(mass is the 

average mass of the 

weight category) 5

105.06 

)( 

3.22 − ×+

mass kg 

5

105.22 

)( 

1.47 − ×+

mass kg 

5

5.90 10 

)( 

1.93 − ×+

masskg 

5 

104.21 

)( 

2.89 − ×+

mass kg 

In both cases, values of A, B, and C were computed for each SourceBin-associated 

vehicle in the MSOD and a weighted average was computed as described above. The final 

SourceMass and road load coefficients for all SourceTypes are listed in Table 8-3. 

64
Table 8-3. SourceUseType Characteristics 

Source 

TypeID 

HPMS 

Vtype ID 

SourceType 

Name 

Rolling 

TermA 

(kW-s/m) 

Rotating 

TermB 

(kW-s2

/m2) 

Drag 

TermC 

(kW-s3

/m3) 

Source 

Mass (metric 

tons) 

11 10 Motorcycle 0.0251 0 0.000315 0.285 

21 20 Passenger Car 0.156461 0.002002 0.000493 1.478803 

31 30 Passenger Truck 0.22112 0.002838 0.000698 1.866865 

32 30 Light Commercial 

Truck 0.235008 0.003039 0.000748 2.059793 

41 40 Interstate Bus 1.295151 0 0.003715 19.59371 

42 40 Urban Bus 1.0944 0 0.003587 16.55604 

43 40 School Bus 0.746718 0 0.002176 9.069885 

51 50 Refuse Truck 1.417049 0 0.003572 20.68453 

52 50 Single-Unit 

Commercial Truck 0.561933 0 0.001603 7.641593 

53 50 Single-Unit 

Delivery Truck 0.498699 0 0.001474 6.250466 

54 50 Motor Home 0.617371 0 0.002105 6.734834 

61 60 Combination 

Commercial Truck 1.963537 0 0.004031 29.32749 

62 60 Combination 

Delivery Truck 2.081264 0 0.004188 31.40378 

For Final MOVES2009, we will add a new field to the SourceUseType table, 

“fixedMassFactor,” that will serve as the denominator in the Vehicle Specific Power (VSP) 

equation, which generates a relationship between power and emissions that varies with the fixed 

mass. (For more on VSP, see the Operating Mode Distribution Generator descriptions in the 

Software Design and Reference Manual48

) The fixed mass is fundamental to the calculation of 

the emission rates in the MOVES emission rate tables. In Final MOVES2009, if a user wishes 

to do “what if” calculations varying the sourceMass, the fixedMassFactor should remain 

constant. Such ‘what if” calculations are not possible in Draft MOVES2009, because increasing 

the source mass would increase both the numerator and the denominator in the VSP equation, 

leading to an incorrect decrease in emissions and energy consumption. 

65
9. RoadTypeDistribution 

MOVES will calculate emissions separately for each road type and for “off-network” 

activity. The road type codes used in MOVES are listed in Table 9-1. These road types are 

aggregations of the HPMS functional facility types that are also used for SCC reporting. 

Table 9-1. Road Type Codes in MOVES 

RoadTypeID Description HPMS functional Types SCCRoadTypeID 

1 Off Network Off Network 1 

2 Rural Restricted 

Access 

Rural Interstate 11 

3 Rural Unrestricted 

Access 

Rural Principal Arterial, Minor 

Arterial, Major Collector, Minor 

Collector & Local 

13, 15, 17, 19, 21 

4 Urban Restricted 

Access 

Urban Interstate & Urban 

Freeway/Expressway 

23, 25 

5 Urban Unrestricted 

Access 

Urban Principal Arterial, Minor 

Arterial, Collector & Local 

27, 29, 31, 33 

For each SourceType, the RoadTypeVMTFraction field stores the fraction of total 

VMT that is traveled on each of the 5 roadway types. 

For MOVES2009, we used data from 1999 FHWA Highway Statistics, Tables VM-1 and 

VM-2. VM-1 provides detail on VMT by vehicle type; VM-2 provides detail by HPMS 

functional type. At the time of this analysis, VM-1 (October 2000) had not been updated, but 

VM-2 was updated in January 2002. We used the total values from the more recent VM-2 to 

distribute VMT by facility type and allocated them to vehicle class in proportion to the values in 

VM-1. We then calculated facility type VMT fractions for each HPMS Vehicle Type. We then 

aggregated the values to the five MOVES road types. 

The FHWA Highway Statistics is currently considered the best available source for 

national information regarding vehicle miles traveled. However, there are problems and 

constraints associated with using the (mostly) self-reported data in Highway Statistics. In many 

cases, locally derived VMT data may be more accurate when modeling local areas. 

The VMT distributions in Table 9-2 assume that all VMT reported by HPMS is 

accumulated on one of the 12 HPMS roadway types and thus one of the four "on-network" 

MOVES roadtypes.. No VMT is currently assigned to the "off-network" category in the national 

defaults. See the discussion of BaseYearOffNetVMT in Section 11.2. 

66
Table 9-2. Roadtype Distributions by Sourcetype 

RoadType ID Description Motorcycles Passenger 

Cars 

Other 2axle 

 - 4tire 

vehicles 

Buses Single 

unit 

trucks 

Combination 

trucks 

1 Off Network 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 

2 Rural Restricted 

Access 0.1040 0.0834 0.0846 0.1268 0.1149 0.3247 

3 Rural Unrestricted 

Access 0.3161 0.2891 0.3055 0.4821 0.3972 0.2941 

4 Urban Restricted 

Access 0.2177 0.2097 0.2031 0.1385 0.1715 0.2075 

5 Urban 

Unrestricted 

Access 0.3623 0.4178 0.4068 0.2526 0.3165 0.1737 

Total 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

We are currently assuming identical VMT distributions for all SourceTypes within an 

HPMS Vehicle Type. However the MOVES model is designed to allow roadway type allocation 

by SourceType and one would expect the different SourceTypes to have different roadway type 

allocations. For example, the long-haul trucks generally would have a greater fraction of travel 

on rural restricted access roadways than the short-haul trucks. If such data becomes available we 

would like to update the database. 

10. Average Speed Distribution 

The AvgSpeedDistribution table provides the fraction of driving time for each 

SourceType, Road Type, Day, Hour, and Speed Bin in a field called AvgSpeedFraction. The 

values sum to one for each combination of SourceType, Road Type, Day, and Hour. 

For Draft MOVES2009, the urban driving values were derived from the default speed 

distributions (SVMT) in MOBILE6. The MOBILE6 speed fractions were adapted to MOVES 

by converting the fraction of miles travelled to the fraction of time used, and by mapping from 

the MOBILE6 road types to the MOVESroad types, with the MOBILE6 "freeway" values 

mapped to the MOVES "urban restricted" roadtype and the MOBILE6 "arterial" values mapped 

to the MOVES "urban unrestricted" roadtype. The time fractions were normalized to sum to one 

for each hour of the day over all 14 speed bins. The values for the off-network roadway type 

were set to null. The detailed distributions are available in the MOVES default database. Only 

urban roadways obtain their values from the default MOBILE6 speed distributions. Average 

speed used for rural driving relied on recent driving data collected in California under studies 

performed for the California Department of Transportation (Caltrans). Under these Caltrans 

driving studies, instrumented “chase cars” were equipped with laser rangefinders mounted 

behind the front grill of each chase car. The studies were performed in the Sacramento area, the 

San Francisco Bay area and the San Joaquin Valley. Another driving study was also conducted 

in the South Coast (i.e., Los Angeles Basin), but was conducted entirely in urbanized areas. 

Thus, this data was not used for the rural area analysis. 

A contractor report describes the analysis done to develop speed distributions from these 

datasets.49

 The datasets contained driving in both urban and rural areas. In the post-processing 

that was performed under each of these studies, the type of roadway the vehicle was traveling on 

67
during each second was also recorded in the output dataset. Since the datasets contained the 

Highway Performance Monitoring System (HPMS) Functional Class designation, it was easy to 

divide the driving data from these studies into rural functional class groups for creating average 

speed distributions. (The urban area travel in these datasets was discarded for this analysis.) 

The average speed was calculated over each link traverse for the individual links in each 

data set. A link traverse is defined as a one-way driving traverse of the entire extent of a 

roadway link. A review of the links identified in the data showed that although distances of most 

links ranged between 0.5 to 5 miles, a few of them were ten miles or longer. These longer links 

were generally restricted to limited access freeways and highways or remote county roads. In 

rural areas, the difference in average speeds calculated over conventionally defined links versus 

longer link sections as identified in the route-based driving studies is not likely to be significant 

because of the general lack of traffic congestion on these rural roads. 

Once the average speed was calculated for each link traverse, it was allocated into one of 

sixteen speed bins defined by EPA for the purpose of calculating speed distributions for use in 

MOVES. The MOVES speed bins are shown in Table 10-2. 

Table 10-2. MOVES Speed Bin Categories. 

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

1 2.5 speed < 2.5 mph 

2 5 2.5 mph <= speed < 7.5 mph 

3 10 7.5 mph <= speed < 12.5 mph 

4 15 12.5 mph <= speed < 17.5 mph 

5 20 17.5 mph <= speed < 22.5 mph 

6 25 22.5 mph <= speed < 27.5 mph 

7 30 27.5 mph <= speed < 32.5 mph 

8 35 32.5 mph <= speed < 37.5 mph 

9 40 37.5 mph <= speed < 42.5 mph 

10 45 42.5 mph <= speed < 47.5 mph 

11 50 47.5 mph <= speed < 52.5 mph 

12 55 52.5 mph <= speed < 57.5 mph 

13 60 57.5 mph <= speed < 62.5 mph 

14 65 62.5 mph <= speed < 67.5 mph 

15 70 67.5 mph <= speed < 72.5 mph 

16 75 72.5 mph <= speed 

To import this information into MOVES, we started with the contractor-provided values 

of “Time-weighted Distributions (% of time) of California Rural Chase Car Driving Data by 

Average Link Speed for each HPMS Functional Class.”50 These values were used directly for 

the rural restricted access roadtype (2). For the MOVES rural unrestricted access roadtype, the 

calculation required consolidating values on the five HPMS functional road classes to the single 

MOVES roadtype. This was done separately for each HPMS Vehicle Class. For each vehicle 

class, we used the roadtype distribution (see preceding section) to calculate the fraction of VMT 

on each road class. We then changed to a time-basis by calculating the average speed on each 

road class, dividing by the average speed and re-nomalizing. We then computed a sum-product 

68
of the speed bin fractions and the road class distributions to calculate the weighted-average speed 

bin distribution for each vehicle class and assigned this distribution to each sourcetype in the 

HPMS vehicle class. 

Our use of the California rural data required a number of assumptions and extrapolations. 

For Draft MOVES2009, the same rural speed distributions were used for all hours of the day. 

And, while the California chase car data also only included light-duty vehicles, the resulting 

speed distributions are also used for heavy-duty vehicles. Also the existing data from the studies 

used in this analysis were collected entirely in California. Thus, use of these California results to 

represent national rural speed distributions must include the critical assumption that average 

speeds within each HPMS functional class do not significantly vary across the U.S on rural 

roadways. 

National default speed distributions are available in the default database for each 

roadtype, sourcetype and hourday, and are not provided here. However, for illustration, Figure 

14.1, shows the speed distributions on different roadtypes for passenger cars for the time period 

11 am. to noon on weekdays. 

Figure 10.1 Speed Distribution by Roadtype 

S peed D istributions by R oadtype 

P assenger C ars, 11am -N oon W eekdays 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

2.5 

10 

20 

30 

40 

50 

60 

70 

Avg. S pe e d of S pe e d Bin 

Fraction 

rural restricted rural unrestricted urban restricted urban unrestricted 

69
11. HPMSVTypeYear 

Three fields comprise HPMSVTypeYear in Draft MOVES2009: HPMSBaseYearVMT, 

BaseYearOffNetVMT, and VMTGrowthFactor. 

11.1. HPMSBaseYearVMT 

The HPMSBaseYearVMT field stores the base year VMT for each HPMS Vehicle Type. 

This VMT was calculated from the FHWA VM-1 and VM-2 tables as for 

RoadTypeDistribution, but instead of calculating fractions, we calculated VMT sums by HPMS 

Vehicle Class. 

The resulting VMT for 1999 and 1990 by HPMS Vehicle Class is listed in Table 11-1. 

Table 11-1. 1999 VMT by HPMS Vehicle Class 

HPMS Vehicle Class 1990 VMT 1999 VMT 

Motorcycles 9,557,000,000 10,579,600,000 

Passenger Cars 1,408,270,000,000 1,568,640,000,000 

Other 2 axle - 4 tire vehicles 574,571,000,000 900,735,000,000 

Buses 5,726,000,000 7,657,000,000 

Single unit trucks 51,901,000,000 70,273,700,000 

Combination trucks 94,341,000,000 132,358,000,000 

11.2. BaseYearOffNetVMT 

Off Network VMT refers to the portion of activity that is not included in travel demand 

model networks or any VMT that is not otherwise reflected in the other twelve categories. This 

field is provided in case it is useful for modeling local areas. However, the reported HPMS 

VMT values, used to calculate the national averages discussed here, are intended to include all 

VMT. Thus, for Draft MOVES2009 national defaults, the BaseYearOffNetVMT will be zero for 

all vehicle types. 

11.3. VMTGrowthFactor 

The VMTGrowthFactor field stores a multiplicative factor indicating changes in total 

vehicle miles for calendar years after the base year. Total VMT data are reported according the 

HPMS vehicle classes discussed previously, i.e. passenger car, other 2-axle / 4-tire vehicle, 

single-unit truck, combination truck, bus and motorcycle. VMTGrowthFactor is expressed 

relative to the previous year’s VMT; for example, 1 means no change from previous year VMT, 

1.02 means a two percent increase in VMT, and 0.98 means a two percent decrease in VMT. 

VMTGrowthFactor is used in the Total Activity Generator calculation of VMT for 

calendar years after the base year, meaning calendar years 2000 through 2050 in Draft 

MOVES2009. It is important to note that VMTGrowthFactor is a key component for estimates 

of future activity in MOVES, because the level of total activity in future years for many emission 

70
processes is derived from projections of total VMT. For these processes, projections of future 

populations based on sales growth, survival rates, etc. are only used to allocate total VMT. 

Default estimates for VMTGrowthFactor were taken from FHWA Highway Statistics for 

2000 through 2004, and from AEO2006 for years 2005-and-later. For passenger cars and lightduty trucks, additional calculations were needed to allocate the more aggregate AEO estimates 

for light-duty vehicles and trucks to the MOVES Source Types. 

Calendar year 2000 through 2004 growth factors were derived from estimates of total 

VMT data as reported by FHWA’s Highway Statistics, Table VM-1. Total VMT data are 

reported according the HPMS vehicle classes discussed previously, i.e. passenger car, other 2­

axle / 4-tire vehicle, single-unit truck, combination truck, and bus. For these years the growth 

factors are simply total VMT for the calendar year divided by total VMT from the previous year. 

Growth factors for calendar years 2005 through 2030 were calculated in the same manner 

using NEMS projections of total VMT as reported in AEO2006. In the AEO analysis, VMT 

projections are provided for total Light-Duty (AEO2006 Supplemental Table 48), total MediumDuty, and total Heavy-Duty (AEO2004 Supplemental Table 55). The growth factors derived 

from the AEO2006 Medium-Duty VMT estimates were applied to the single-unit truck and bus 

HPMS vehicle classes. The growth factors derived from the AEO2006 Heavy-Duty VMT 

estimates were applied to the combination truck vehicle class. 

Light-Duty VMT as reported in AEO2006 Supplemental Table 48 applies to total lightduty growth from both cars and trucks; as such they do not reflect the higher growth rate of light 

trucks relative to passenger cars brought on by steadily increasing sales of light duty trucks. 

Separate VMTGrowthFactors for the Passenger Car and Other 2-axle/4-wheel Vehicle classes 

were therefore developed based on estimates of car and light truck populations from AEO2006. 

Using the AEO2006 estimates of total light-duty VMT and vehicle population (i.e., stock) growth 

rates listed in AEO Supplemental Table 46, we calculated the “per-vehicle” VMT implied from 

these estimates (total VMT divided by population). Assuming that per-vehicle VMT growth is 

the same for cars and light trucks, we multiplied the total light-duty per-vehicle VMT by the car 

and light truck populations to project separate car and light truck VMT for future years and then 

computed annual growth rates. Table 11-3 illustrates these calculation steps. 

For final MOVES2009, we plan to update these growth factors using updated VMT 

information and projections. 

71
Table 11-2. VMTGrowthFactor Calculation for Passenger Cars and Light Trucks 

Calendar 

Vehicle Stock (million) 

(From AEO2006) 

VMT 

(billion) 

(From 

AEO 

2006) 

PerVehicle 

VMT 

VMT by Type (pop * per-vehicle vmt) 

Year 

LD 

Total LDV LDT 

LD 

Total 

LD 

Total 

LDV 

Total Growth 

LDT 

Total Growth 

2004 211.553 130.782 80.771 2632.078 12.442 1627.147 1004.931 

2005 216.805 131.992 84.813 2619.176 12.081 1594.573 0.980 1024.603 1.020 

2006 221.645 133.602 88.043 2644.429 11.931 1593.996 1.000 1050.433 1.025 

2007 226.700 135.178 91.522 2693.347 11.881 1606.008 1.008 1087.339 1.035 

2008 231.613 136.561 95.052 2751.712 11.881 1622.438 1.010 1129.274 1.039 

2009 236.476 137.746 98.730 2818.227 11.918 1641.603 1.012 1176.623 1.042 

2010 241.264 138.705 102.559 2889.563 11.977 1661.235 1.012 1228.328 1.044 

2011 246.004 139.664 106.340 2946.387 11.977 1672.749 1.007 1273.638 1.037 

2012 250.608 140.540 110.068 3000.774 11.974 1682.825 1.006 1317.948 1.035 

2013 254.971 141.283 113.688 3055.248 11.983 1692.960 1.006 1362.288 1.034 

2014 259.118 141.888 117.231 3113.610 12.016 1704.944 1.007 1408.665 1.034 

2015 263.028 142.395 120.633 3171.164 12.056 1716.763 1.007 1454.400 1.032 

2016 266.797 142.866 123.932 3227.686 12.098 1728.375 1.007 1499.312 1.031 

2017 270.473 143.308 127.165 3288.719 12.159 1742.502 1.008 1546.217 1.031 

2018 274.100 143.740 130.360 3351.878 12.229 1757.751 1.009 1594.126 1.031 

2019 277.634 144.156 133.478 3414.157 12.297 1772.740 1.009 1641.418 1.030 

2020 281.091 144.585 136.506 3474.341 12.360 1787.100 1.008 1687.241 1.028 

2021 284.535 145.036 139.499 3535.598 12.426 1802.203 1.008 1733.395 1.027 

2022 287.982 145.515 142.467 3597.454 12.492 1817.763 1.009 1779.690 1.027 

2023 291.469 146.029 145.439 3660.468 12.559 1833.938 1.009 1826.530 1.026 

2024 295.016 146.582 148.435 3725.200 12.627 1850.899 1.009 1874.301 1.026 

2025 298.626 147.172 151.454 3791.240 12.696 1868.435 1.009 1922.804 1.026 

2026 302.333 147.816 154.517 3858.390 12.762 1886.440 1.010 1971.950 1.026 

2027 306.124 148.503 157.621 3927.069 12.828 1905.045 1.010 2022.024 1.025 

2028 309.952 149.223 160.729 3995.345 12.890 1923.515 1.010 2071.829 1.025 

2029 313.854 149.988 163.866 4064.186 12.949 1942.235 1.010 2121.952 1.024 

2030 317.825 150.802 167.023 4132.401 13.002 1960.744 1.010 2171.658 1.023 

72
Table 11-3. VMT Growth Factors in Draft MOVES2009 

Year Motorcycles Passenger 

Cars 

Passenger 

and Light 

Comm. 

Trucks 

Buses 

Single 

Unit 

Trucks 

Combination 

Trucks 

2000 0.990 1.021 1.026 0.992 1.004 1.021 

2001 0.910 1.012 1.016 0.920 1.025 1.003 

2002 0.991 1.019 1.024 0.968 1.048 1.015 

2003 1.003 1.008 1.019 0.991 1.025 1.010 

2004 1.049 1.020 1.031 0.979 1.043 1.037 

2005 0.980 0.980 1.020 0.998 0.998 1.022 

2006 1.000 1.000 1.025 1.007 1.007 1.034 

2007 1.008 1.008 1.034 1.016 1.016 1.033 

2008 1.010 1.010 1.037 1.013 1.013 1.025 

2009 1.012 1.012 1.040 1.018 1.018 1.025 

2010 1.012 1.012 1.042 1.021 1.021 1.026 

2011 1.007 1.007 1.036 1.025 1.025 1.025 

2012 1.006 1.006 1.034 1.023 1.023 1.023 

2013 1.006 1.006 1.033 1.022 1.022 1.022 

2014 1.007 1.007 1.033 1.023 1.023 1.022 

2015 1.007 1.007 1.032 1.024 1.024 1.023 

2016 1.007 1.007 1.030 1.025 1.025 1.023 

2017 1.008 1.008 1.031 1.026 1.026 1.025 

2018 1.009 1.009 1.030 1.026 1.026 1.025 

2019 1.009 1.009 1.029 1.025 1.025 1.021 

2020 1.008 1.008 1.027 1.025 1.025 1.020 

2021 1.008 1.008 1.027 1.026 1.026 1.020 

2022 1.009 1.009 1.026 1.027 1.027 1.021 

2023 1.009 1.009 1.026 1.027 1.027 1.021 

2024 1.009 1.009 1.026 1.027 1.027 1.022 

2025 1.009 1.009 1.026 1.027 1.027 1.023 

2026 1.010 1.010 1.025 1.028 1.028 1.024 

2027 1.010 1.010 1.025 1.027 1.027 1.024 

2028 1.010 1.010 1.024 1.027 1.027 1.023 

2029 1.010 1.010 1.024 1.027 1.027 1.023 

2030 1.010 1.010 1.023 1.026 1.026 1.023 

2031­

2050 

1.010 1.010 1.023 1.026 1.026 1.023 

73
12. Temporal Distributions of VMT 

MOVES can estimate emissions for every hour of every day of the year. For this reason, for 

national scale runs (“macroscale”) annual VMT estimates need to be allocated to months, days, 

and hours. 

A 1996 report from the Office of Highway Information Management (OHIM)51 describes 

analysis of a sample of 5,000 continuous traffic counters distributed through the United States. 

EPA obtained the data used in the report and used it to generate inputs in the form needed for 

Draft MOVES2009. 

The report does not specify VMT by SourceType or Vehicle Type. Thus, we currently 

use the same value for all SourceTypes. For Final MOVES2009, we plan to update the MOVES 

pre-processor tools to aid local areas entering VMT with accurate local temporal distributions. 

12.1. MonthVMTFraction 

For MonthVMTFraction, we use the data from the OHIM report’s Figure 2.2.1 “Travel 

by Month, 1970-1995,” but modified to fit MOVES specifications. 

The figure shows VMT/day, normalized to January=1. For MOVES, we need the 

fraction of total VMT per month, with different values for leap year and non-leap year. We 

computed the fractions using the report values and the number of days in each month. 

Table 12-1. MonthVMTFraction 

Month 

Normalized 

VMT/day 

MOVES 

not Leap 

Year 

MOVES 

Leap Year 

January 1.0000 0.0731 0.0729 

February 1.0560 0.0697 0.0720 

March 1.1183 0.0817 0.0815 

April 1.1636 0.0823 0.0821 

May 1.1973 0.0875 0.0873 

June 1.2480 0.0883 0.0881 

July 1.2632 0.0923 0.0921 

August 1.2784 0.0934 0.0932 

September 1.1973 0.0847 0.0845 

October 1.1838 0.0865 0.0863 

November 1.1343 0.0802 0.0800 

December 1.0975 0.0802 0.0800 

74
12.2. DayVMTFraction 

The OHIM report provides VMT percentage values for each day and hour of a typical 

week for urban and rural roadway types for various regions of the United States for both 1992 

and 1995. The data obtained from the OHIM report is not disaggregated by month or 

SourceType. The same values will be used for every month and SourceType. We used 1995 

data (which is very similar to 1992) as it is displayed in Figure 2.3.2 of the OHIM report. 

For the DayVMTFraction needed for MOVES2009, we first summed the reported 

percentages for each day of the week and converted to fractions. Note, the report explains that 

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

to the upcoming day. Because MOVES2009 classifies days into two types of days, "weekdays" 

and "weekend," we then summed the daily fractions to compute fractions for each type of day. 

Table 12-2. DayVMTFractions 

Rural Urban 

Weekday 0.2788 0.2376 

Weekend 0.7212 07624 

We assigned the “Rural” fractions to the rural Roadtypes and the “Urban” fractions to the 

urban Roadtypes. The correct distribution for “Off network” VMT is unknown. Since the 

majority of U.S. travel is urban, the default DayVMTFraction for "Off network" will be assigned 

the urban fractions. Note the MOVES2009 default VMT on “Off-network” roadtypes is zero. 

. 

12.3. HourVMTFraction 

For HourVMTFraction we used the same data as for DayVMTFraction. We converted 

the OHIM report data to percent of day by dividing by the DayVMTFraction. 

There are separate sets of HourVMTFractions for "Urban" and "Rural" roadway types. 

Roadway types were assigned as for DayVMTFraction. All SourceTypes use the same 

HourVMTFraction distributions. The Off-Network roadtype was assigned the “Urban” fractions. 

Figure 12.1 graphs the hourly VMT fractions. 

75
Figure 12.1 Hourly VMT Fractions in Draft MOVES2009 

H ourly V M T 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0.09 

0 10 20 30 

Hour of Da y 

Fraction of Daily V M T 

O ff-Network and 

Urban Roadtypes--

W eek end 

O ff-Network and 

Urban Roadtypes--

W eek day 

Rural Roadtypes--

W eek end 

Rural Roadtypes--

W eek day 

There is hourly VMT data available from Vehicle Travel Information System(VTRIS) 

database maintained by FHWA that distinguishes hourly VMT by FHWA vehicle category. 

Analysis of this data can provide HourVMTfractions differ by sourcetype. We request 

comment on the relative priority of incorporating this data into MOVES defaults. 

76
13. Driving Schedule Tables 

DriveSchedule refers to a second-by-second vehicle speed trajectory. Drive schedules 

are used in MOVES to determine the operating mode distribution for most MOVES running 

process emissions and energy consumption. 

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

schedules to represent driving patterns across source type, roadway type and average speed. For 

the national default case, Draft MOVES2009 employs 40 drive schedules, mapped to specific 

source types and roadway types. The average speed of a driving schedule is used to determine 

the weighting of that schedule for a given roadtype and sourcetype, based on the average speed 

distribution. Briefly, for each speed bin in the speed distribution, the MOVES model selects the 

two associated driving cycles with average speeds that bracket the speed bin’s average speed. 

The Vehicle Specific Power (VSP) distributions determined for each bracketing driving schedule 

are averaged together, weighted by the proximity of the speed bin average speed to the driving 

schedule average speeds. In this way, the VSP distribution of any roadtype’s speed distribution 

is determined from the available driving schedules. For more details, see the Operating Mode 

Distribution Generator sections in the MOVES Software and Design Reference Manual.52 

For brevity, the entire body of drive schedule information is not presented in this 

document. The reader is referred to the MOVES database, where three MOVES database tables 

encompass drive schedule information. DriveSchedule provides the drive schedule name, 

identification number, and the average speed of the drive schedule. DriveScheduleAssoc 

defines the set of schedules which are available for each combination of source use type and road 

type. This table also indicates which driving schedules describe freeway ramp type driving. 

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

some cases the vehicle trajectories are not contiguous; that is, they represent several unconnected 

microtrips. 

Table 13-1 shows a complete list of the driving schedules used in the default case and 

their associated average speed. Note that the speed given in the drive schedule name is just a 

nominal speed and not used in the MOVES calculations. 

77
Table 13-1. Default MOVES Drive Schedules 

Drive Schedule Set DriveScheduleName(ID) AverageSpeed (mph) 

Light-Duty Non-Freeway Low Speed 1 (101) 2.5 

New York City (102) 7.1 

Non-Freeway LOS EF (103) 11.6 

Non-Freeway LOS CD (104) 19.2 

Non-Freeway LOS AB (105) 24.8 

Light-Duty Freeway Freeway LOS G (151) 13.1 

Freeway LOS F (152) 18.6 

Freeway LOS E (153) 30.5 

Freeway LOS D (154) 52.9 

Freeway LOS AC (155) 59.7 

Freeway High Speed 1 (156) 63.2 

Freeway High Speed 2 (157) 68.2 

Freeway High Speed 3 (158) 76 

Freeway Ramp (199) 34.6 

Medium Heavy-Duty Non-Freeway 5 mph (201) 4.6 

10 mph (202) 10.7 

15 mph (203) 15.6 

20 mph (204) 20.8 

25 mph (205) 24.5 

30 mph (206) 31.5 

Medium Heavy-Duty Freeway 30 mph (251) 34.4 

40 mph (252) 44.5 

50 mph (253) 55.4 

60 mph (254) 60.4 

Ramp (299) 31 

Heavy Heavy-Duty Non-Freeway 5 mph (301) 5.8 

10 mph (302) 11.2 

15 mph (303) 15.6 

20 mph (304) 19.4 

25 mph (305) 25.6 

30 mph (306) 32.5 

Heavy Heavy-Duty Freeway 30 mph (351) 34.3 

40 mph (352) 47.1 

50 mph (353) 54.2 

60 mph (354) 59.4 

Ramp (399) 25.3 

Bus Non-Freeway Low Speed Urban (401) 15* 

30 mph flow (402) 30* 

45 mph flow (403) 45* 

Refuse Truck Refuse Truck Urban (501) 2.2 

* Speed represents average of traffic the bus is traveling in, not the average speed of the bus, which is lower 

due to stops. 

78
14. Drive Schedule Association 

The DriveSchedules listed in Table 13-1 are associated with specific SourceTypes and 

RoadTypes as summarized in Table 14-1. This table is an aggregated representation of the 

information in DriveScheduleAssociation, which contains a mapping of every schedule to each 

SourceType across each of the 12 HPMS roadway types. 

Table 14-1. Drive Schedule Mapping 

Source Use Type Restricted Access Roadtypes Unrestricted Access Roadtypes 

Motorcycle 

Light-Duty Freeway Schedules 

Light-Duty Low Speed 1 

New York City 

Non-Freeway LOS EF 

Light-Duty Non-Freeway Schedules 

Freeway LOS E 

Freeway LOS D 

Freeway LOS AC 

Freeway High Speed 1 

Freeway High Speed 2 

Freeway High Speed 3 

Freeway Ramp 

Passenger Car 

Passenger Truck 

Commercial Truck 

Intercity bus Medium Heavy-Duty Freeway 

Medium Heavy-Duty Non-Freeway 

Medium Heavy-Duty Freeway 

Single Unit Short Haul Medium Heavy-Duty Non-Freeway 

Single Unit Long Haul 

Motor Home 

Transit bus Medium Heavy-Duty Freeway 

Medium Heavy-Duty Non-Freeway 

Bus Non-Freeway 

Medium Heavy-Duty 50mph Freeway 

Medium Heavy-Duty 60mph Freeway 

MD Freeway Ramp 

School Bus 

Refuse Truck Heavy Heavy-Duty Freeway 

Heavy Heavy-Duty Non-Freeway 

Refuse Truck Urban 

Combination Short Haul Heavy Heavy-Duty Freeway 

Heavy Heavy-Duty Non-Freeway 

Heavy Heavy-Duty Freeway 

Combination Long Haul Heavy Heavy-Duty Non-Freeway 

The default drive schedules listed in Tables 13-1 and 14-1 were developed from several 

sources. The majority of the light-duty cycles are identical to those developed for MOBILE6 

and documented in report M6.SPD.001.53 What we now refer to as “non-freeway” schedules are 

the same as the “arterial” cycles used in MOBILE6; the name change was made to reflect the 

application of these schedules to all non-freeway operation, including local roadways. The lightduty schedules not included in the MOBILE6 work are Low Speed, New York City, High Speed 

2 and High Speed 3. Low Speed is a historic cycle used in the development of speed corrections 

for MOBILE5 and is meant to represent extreme stop-and-go “creep” driving. The New York 

City Cycle is a historic test schedule representing congested urban travel with lots of stop-andgo. It is used in EPA’s running loss certification test procedure.54 

High Speed 2 and 3 were developed specifically for MOVES. High Speed 1 was the 

highest speed schedule in MOBILE6, with an average speed of 63 mph. EPA received many 

comments with respect to MOBILE6 that this was not sufficient to capture the range of high 

speed freeway driving in-use. The increase in speed limits as well as improvements in vehicle 

performance over the past decades dictate the need to represent more extreme driving; High 

Speed 2 and 3 were developed to represent these conditions. High Speed 2 is a 240-second 

segment of the US06 certification compliance cycle, with an average speed of 68 mph and a 

maximum of 80 mph. High Speed 3 is 580-second segment of freeway driving from an in-use 

79
vehicle instrumented as part of EPA’s On-Board Emission Measurement “Shootout” program,55 

with an average speed of 76 mph and a maximum of 90 mph. The addition of these schedules 

will serve to increase the capacity of MOVES to reflect the higher speed freeway operation seen 

on the road today. It should be noted, however, that these schedules are only applied in Draft 

MOVES2009 if AverageSpeedDistribution contains operation in the highest speed bins; i.e. 70 

mph and greater. 

Medium-Duty and Heavy-Duty schedules were developed specifically for MOVES, 

based on work performed for EPA by Eastern Research Group (ERG), Inc. and documented in 

the report “Roadway-Specific Driving Schedules for Heavy-Duty Vehicles.”56 ERG analyzed 

data from 150 medium and heavy-duty vehicles instrumented to gather instantaneous speed and 

GPS measurements. ERG segregated the driving into freeway and non-freeway driving for 

medium and heavy-duty vehicles, and then further stratified vehicles trips according the predefined ranges of average speed covering the range of vehicle operation. ERG characterized 

representative driving within each speed range, using distributions of vehicle specific power 

(VSP), speed and acceleration. Driving schedules were then developed for each speed bin by 

creating combinations of idle-to-idle “microtrips” until the representative target metrics were 

achieved. The schedules developed by ERG are, thus, not contiguous schedules which would be 

run on a chassis dynamometer, but are made up of non-continguous “snippets” of driving meant 

to represent target distributions. For use in MOVES, the highway heavy-duty schedules 

developed by ERG were modified to isolate operation on freeway ramps. The segments of 

freeway microtrips identified by ERG as taking place on on-and off-ramps were extracted and 

used to create medium-duty and heavy-duty ramp schedules (299 and 399). Thus, the schedules 

which represent on-freeway driving do not contain ramp operation. Another minor modification 

to the schedules for use in MOVES was made to the time field in order to signify, within a drive 

schedule, when one microtrip ended and one began. The time field increments two seconds 

instead of one when each new microtrip begins. This two second increment signifies that these 

should not be regarded by the model as contiguous operation. 

The freeway and non-freeway driving cycles are intended to cover most of the driving on 

these respective roadtypes. However, some speed distributions for non-freeway roadtypes will 

include average speeds faster than the fastest non-freeway cycles. The reverse will be true for 

some freeway speed distributions. In these cases, the model will use appropriate average speed 

drive schedules from a different roadtype. This mapping is summarized in table 14-1, which 

illustrates, for example, that low-speed freeway driving is modeled using non-freeway driving 

schedules. This mapping is appropriate since, when the average speed is very low or very high, 

the roadtype has little impact on the driving pattern. 

For Final MOVES2009 we plan to incorporate additional driving schedules and to 

replace many of the older driving schedules, which we do not think adequately represent today’s 

vehicles and driving behavior. A contractor has developed 45 driving schedules for light-duty 

vehicles.57

 These are based on urban and rural data collected in California in 2000 and 2004. 

The proposed mapping of driving cycles to roadtypes for Final MOVES2009 is summarized in 

Table 14-2, below. This mapping would apply to passenger cars, passenger trucks and light 

commercial trucks. We also hope to have additional driving schedules for motorcycles, but they 

80
are not available at this time. Other sourcetypes would use the driving schedules currently used 

in Draft MOVES2009. 

Table 14.2 Proposed Drive Schedules for Passenger Cars, Passenger Trucks 

and Light Commercial Trucks in Final MOVES2009 

Rural Restricted Access Roadtype (2) 

ID Avgspeed Road Classification DriveScheduleDesc 

158 76.0 LD High Speed Freeway 3 

1009 73.8 Rural Interstate Final FC01LOSAF Cycle (C10R04-00854) 

1010 55.3 Rural Principal Arterial Final FC02LOSAC Cycle (C15R02-00646) 

1011 49.1 Rural Principal Arterial Final FC02LOSDF Cycle (C10R05-00513) 

1012 44.4 Rural Major Arterial Final FC06LOSAF Cycle (C15R01-00276) 

1013 42.5 Rural Minor Arterial Final FC07LOSAF Cycle (C10R07-00913) 

1014 38.6 Rural Collector Final FC08LOSAF Cycle (C10R05-00330) 

1015 30.0 Rural Local Final FC09LOSAF Cycle (C15R06-00563) 

1030 25.4 Urban Principal Arterial Final FC14LOSC Cycle (C10R04-00104) 

1031 21.7 Urban Principal Arterial Final FC14LOSD Cycle (C15R01-00836) 

1032 17.2 Urban Principal Arterial Final FC14LOSE Cycle (C15R03-00606) 

1033 8.7 Urban Principal Arterial Final FC14LOSF Cycle (C15R05-00424) 

102 7.1 LD New York City 

101 2.5 LD Low Speed 1 

Urban Restricted Access Roadtype (4) 

ID Avgspeed Road Classification DriveScheduleDesc 

158 76.0 LD High Speed Freeway 3 

1009 73.8 Rural Interstate Final FC01LOSAF Cycle (C10R04-00854) 

1023 66.4 Urban Freeway Final FC12LOSB Cycle (C15R08-00003) 

1024 63.7 Urban Freeway Final FC12LOSC Cycle (C15R04-00582) 

1025 52.8 Urban Freeway Final FC12LOSD Cycle (C15R09-00037) 

1026 43.3 Urban Freeway Final FC12LOSE Cycle (C15R10-00782) 

1014 38.6 Rural Collector Final FC08LOSAF Cycle (C10R05-00330) 

1029 31.0 Urban Principal Arterial Final FC14LOSB Cycle (C15R07-00177) 

1035 29.5 Urban Minor Arterial Final FC16LOSB Cycle (C15R03-00219) 

1034 26.6 Urban Minor Arterial Final FC16LOSA Cycle (C15R05-00755) 

1028 25.5 Urban Principal Arterial Final FC14LOSA Cycle (C15R03-00651) 

1030 25.4 Urban Principal Arterial Final FC14LOSC Cycle (C10R04-00104) 

1036 23.3 Urban Minor Arterial Final FC16LOSC Cycle (C15R05-00252) 

1037 21.9 Urban Minor Arterial Final FC16LOSD Cycle (C15R02-00561) 

1040 21.8 Urban Collector Final FC17LOSAC Cycle (C15R01-00333) 

1031 21.7 Urban Principal Arterial Final FC14LOSD Cycle (C15R01-00836) 

1041 18.6 Urban Collector Final FC17LOSD Cycle (C15R05-00480) 

1032 17.2 Urban Principal Arterial Final FC14LOSE Cycle (C15R03-00606) 

1043 15.7 Urban Local Final FC19LOSAC Cycle (C15R08-00267) 

1038 14.6 Urban Minor Arterial Final FC16LOSE Cycle (C15R05-00799) 

1044 12.0 Urban Local Final FC19LOSDF Cycle (C15R03-00074) 

1042 11.2 Urban Collector Final FC17LOSEF Cycle (C15R02-00734) 

1039 10.5 Urban Minor Arterial Final FC16LOSF Cycle (C10R02-00249) 

1033 8.7 Urban Principal Arterial Final FC14LOSF Cycle (C15R05-00424) 

102 7.1 LD New York City 

81
Table 14.2 Continued 

Rural and Urban Unrestricted Access Roadtypes (3 & 5) 

ID Avgspeed Road Classification DriveScheduleDesc 

158 76.0 LD High Speed Freeway 3 

1009 73.8 Rural Interstate Final FC01LOSAF Cycle (C10R04-00854) 

1017 66.4 Urban Interstate Final FC11LOSB Cycle (C10R02-00546)* 

1018 64.4 Urban Interstate Final FC11LOSC Cycle (C15R09-00849) 

1024 63.7 Urban Freeway Final FC12LOSC Cycle (C15R04-00582) 

1019 58.8 Urban Interstate Final FC11LOSD Cycle (C15R10-00068) 

1022 53.9 Urban Freeway Final FC12LOSA Cycle (C15R02-00501) 

1025 52.8 Urban Freeway Final FC12LOSD Cycle (C15R09-00037) 

1020 46.1 Urban Interstate Final FC11LOSE Cycle (C15R11-00851) 

1026 43.3 Urban Freeway Final FC12LOSE Cycle (C15R10-00782) 

1014 38.6 Rural Collector Final FC08LOSAF Cycle (C10R05-00330) 

1029 31.0 Urban Principal Arterial Final FC14LOSB Cycle (C15R07-00177) 

1030 25.4 Urban Principal Arterial Final FC14LOSC Cycle (C10R04-00104) 

1021 20.6 Urban Interstate Final FC11LOSF Cycle (C15R01-00876) 

1027 19.0 Urban Freeway Final FC12LOSF Cycle (C15R08-00294) 

1032 17.2 Urban Principal Arterial Final FC14LOSE Cycle (C15R03-00606) 

1033 8.7 Urban Principal Arterial Final FC14LOSF Cycle (C15R05-00424) 

102 7.1 LD New York City 

101 2.5 LD Low Speed 1 

*1009 was originally characterized as LOSB, but now considered AB. 

82
15. SourceTypeHour 

The SourceTypeHour table provides one data field: IdleSHOFactor. 

15.1. IdleSHOFactor 

The IdleSHOFactor field is the number used to determine the number of hours of 

extended idling for each Source Type by day of the week and hour of the day. Extended idling, 

also referred to as "hoteling," is defined as any long period of discretionary idling that occurs 

during long distance deliveries by heavy-duty trucks. In Draft MOVES2009, only the long haul 

combination truck sourcetype is assumed to have hoteling activity. All other source use types 

have hoteling activity fractions set to zero. 

No sources exist that directly measure extended idling in order to determine the total 

hours of extended idling estimated for heavy-duty trucks. However, hoteling mainly occurs 

among the largest (Class 8) trucks, which are now almost exclusively diesel. A 2004 paper by 

Lutsey, et al., 58

 submitted to the Transportation Research Board, provides some insights on how 

truck hoteling relates to overall truck activity. 

Federal law limits the number of hours which long haul truck drivers can operate each 

day. These regulations are described in the Federal Register.59 Using the distribution of truck 

hoteling duration times (shown in Figure 1 of the Lutsey, et al. paper) and assuming that long 

haul truck drivers travel an average of 10 hours a day when engaged in hoteling behavior, we can 

estimate the average duration of hoteling as 5.9 hours for every 10 hours of long-haul truck 

driving. 

However, for MOVES we need to know the fraction of hours spent hoteling versus hours 

of vehicle operation by time of day. This value can be derived from the known truck activity. In 

particular, the report, "Roadway-Specific Driving Schedules for Heavy-Duty Vehicles,"60 

combines data from several instrumented truck studies. The data contains detailed information 

about truck driver behavior; however, none of the trucks in any of the studies was involved in 

long haul, interstate activity. We assumed that all long haul truck trips have the same hourly 

truck trip distribution as the heavy heavy-duty trucks in the instrumented studies and that all long 

haul trips are 10 hours long, and thus deduced an hourly distribution of long haul trip ends. The 

distribution of hoteling durations from the Lutsey report was applied to these trip-end 

distributions. From these calculations, we estimated the number of hours of truck operation and 

hours of truck hoteling. For MOVES, we then calculated the ratio of hoteling hours to truck 

operation hours for each hour of the day. Weekday data was used for both weekday and 

weekend fractions. 

83
E x tended Idle A c tivity 

Ratio of E xtended Idle Tim e to Driving Tim e by Hour 

0 

0.005 

0.01 

0.015 

0.02 

0.025 

0.03 

0.035 

0.04 

0 4 8 1 2 1 6 2 0 2 4 

Hour 

IdleSHOFactor 

Note that the Draft MOVES2009 defaults assume no anti-idling measures or truck-stopelectrification efforts. In future versions of MOVES, we intend to make it easier for users to 

modify the inputs of extended idling behavior to account for new or locally available data on 

such activity. 

84
16. ZoneRoadType 

The SHOAllocFactor field is used to determine the hours of vehicle operation in each 

zone on each of the MOVES roadway types. 

While geographic allocations clearly change over time, for national runs using Draft 

MOVES2009 this table is used for all calendar years. Note that the allocation factors are not 

used when a user selects the “County” scale. At the “National” scale, users may choose to do 

multiple runs, with year-specific factors entered for each specific calendar year run. 

The spatial allocation of source hours operating distributes the domain-wide estimates of 

hours of operation to the zones. In draft MOVES2009, the default domain is the nation and the 

zones are counties. The national source hours of operation (SHO) are calculated from estimates 

of VMT and speed. 

The estimate for the VMT by county comes from the 1999 National Emission Inventory 

(NEI) analysis documented by Pechan & Associates.61 These estimates are based on the 

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

Administration62

 for use in transportation planning and vehicle type breakdowns from the EPA 

MOBILE6 Emission Factor model.63 The NEI VMT estimates were incorporated into the 

National Mobile Inventory Model (NMIM) county database. 

To calculate default inputs for Draft MOVES2009, the 1999 NEI VMT estimates were 

obtained from the NMIM database for each county and HPMS facility type. The average speed 

estimates were taken directly from Table 8 of the NEI documentation. VMT estimates for each 

MOVES road type(i) were determined for each county(j) in the nation and the allocation was 

calculated using the following formula, where k refers to the HPMS facility types within a 

MOVES road type, and m refers to the VMT for each source type. 

CountyAllocation(i,j) = (Sum(j)(( CountyVMT(i,j,k,m)/Average Speed(k,m))) / 

(Sum(ij)((CountyVMT(i,j,k,m)/AverageSpeed(k,m))) 

The county allocation values for each roadway type sum to one for the nation. Although 

the data is from 1999 calendar year estimates, the same allocations are used for all calendar 

years. 

85
17. Zone 

In Draft MOVES2009, activity data and meteorological data are assigned to zones rather 

than counties. By creating and populating their own zones, users may customize geographical 

boundaries to better represent non-attainment areas and climate differences that do not 

necessarily follow county boundaries. However, for the national default database, zones and 

counties are equivalent. 

The Zone table provides values for four fields: CountyID, StartAllocFactor, 

IdleAllocFactor, and SHPAllocFactor. CountyID is the identifier for the county in which the 

zone is located. StartAllocFactor geographically allocates domain-wide start activity. 

IdleAllocFacor allocates extended idle activity, and SHPAllocFactor allocates time parking 

(important for evaporative emissions). 

While geographic allocations clearly change over time, for national runs using Draft 

MOVES2009 this table is used for all calendar years. Note that the allocation factors are not 

used when a user selects the “County” scale. At the “National” scale, users may choose to do 

multiple runs, with year-specific factors entered for each specific calendar year run. 

17.1. StartAllocFactor 

The StartAllocFactor distributes the domain-wide estimates of the number of trip starts to 

the zones. In the default database for Draft MOVES2009, the domain is the nation and the zones 

are counties. There is no national data on the number of trip starts by county, so for Draft 

MOVES2009, we have used VMT will to determine this allocation. 

The estimate for the VMT by county comes from the 1999 National Emission Inventory 

(NEI) analysis.64 The NEI estimates are based on the Highway Performance Monitoring System 

(HPMS) data collected by the Federal Highway Administration65 for use in transportation 

planning and vehicle type breakdowns from the EPA MOBILE6 Emission Factor model.66 The 

NEI VMT estimates have been incorporated into the National Mobile Inventory Model county 

database. 

The VMT estimates were obtained from the NMIM database. VMT estimates for each 

county in each state and the allocation calculated using the following formula, where “i” 

represents each individual county. 

CountyAllocation(i) = ( CountyVMT(i) / Sum(CountyVMT(i) ) 

The county allocation values sum to one for the nation. Although the data is from 1999 

estimates, the same allocations will be used for all calendar years. 

17.2. IdleAllocFactor 

The IdleAllocFactor field stores the factor used to determine the hours of extended idling 

in each zone in each calendar year. 

No sources exist that directly measure extended idling in order to allocate the hours of 

extended idling estimated for heavy-duty trucks. However, extended idling (or hoteling) occurs 

primarily on long-haul trips across multiple states, which suggests that travel on rural and urban 

interstates would best represent long-haul trips. Extended idling mainly occurs among the 

largest (Class 8) trucks, which are now almost exclusively diesel. Since we have estimates for 

86
the amount of rural and urban interstate VMT by Class 8 heavy-duty diesel trucks in each county 

of the nation, we can use this estimate to create a national allocation factor for extended idling 

hours. 

The actual total demand for overnight parking by trucks has been estimated by the 

Federal Highway Administration on a state by state basis.67 These estimates were used to 

determine the allocation to each State(i) using the following formula: 

StateAllocation(i) = StateParkingDemand(i) / Sum( StateParkingDemand(i) ) 

The State allocation values will sum to one for the entire country. This method results in 

no idling in Washington, D.C., Hawaii, Virgin Islands, or Puerto Rico, which make sense, since 

none of these areas have VMT associated with rural or urban interstates. 

The estimate for the VMT from Class 8 heavy-duty diesel trucks by county comes from 

the 1999 National Emission Inventory (NEI) analysis.68

 The NEI estimates are based on the 

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

Administration69

 for use in transportation planning and vehicle type breakdowns from the EPA 

MOBILE6 Emission Factor model.70 The NEI VMT estimates have been incorporated into the 

National Mobile Inventory Model (NMIM) county database. 

The VMT estimates were obtained from the NMIM database. VMT estimates for Class 

8 heavy-duty diesel trucks on rural and urban interstates were determined for each county in each 

state and the allocation calculated using the following formula where “j” refers to the counties in 

each particular state. 

IdleAllocFactor(i) = StateAllocation(i) * (CountyVMT(j) / Sum(CountyVMT(j)) 

The county allocation values will sum to one for the entire country. The sum of the 

county allocations for a given state will equal the state allocation for that state, as determined 

earlier. 

87
18. SCC Mappings 

For some uses, particularly the preparation of national inventories, modelers will need to 

produce output aggregated by EPA’s Source Category Codes (SCC). The EPA’s highway 

vehicle SCC were derived from MOBILE5 and MOBILE6 and do not directly correspond to the 

MOVES SourceTypes. For example, depending on its fuel and Gross Vehicle Weight (GVW) 

limits, a vehicle in the MOVES Passenger Truck category may be coded with one of eight 

SCCs—including the SCC for a Light-Duty Gasoline Truck 1, a Light-Duty Gasoline Truck 2, a 

Heavy-Duty Gasoline Truck, a Light-Duty Diesel Truck, or one of the four codes for HeavyDuty Diesel Vehicle. 

The MOVES model is designed to aggregate emissions to the user’s choice of 

SourceType or SCC using the SCCVTypeDistribution table. For each combination of 

SourceType, Model Year and FuelType, the SCCVTypeDistribution table lists IDs for the 

possible SCC and the fraction of vehicles assigned to each SCC. 

The full SCC also includes a suffix that indicates roadway type. This is a mapping from 

the MOVES roadtype on which the emissions occur to the HPMS Facility Type used in the SCC 

codes. This mapping is captured in the SCCRoadTypeDistribution table described below. 

18.1. SCCVtypeDistribution 

Because the existing SCCs only identify gasoline and diesel-fueled vehicles, it was 

necessary to map alternatively-fueled vehicles to one of these categories. All alternative-fuel 

vehicles were mapped to the diesel SCC. In the future, SCCs may be revised to explicitly handle 

alternative fuels. 

For most SourceTypes, the mapping to SCCVtype was straightforward. These mappings 

are summarized in Table 18-1. However, the trucks span a wide range of GVWs and, thus, a 

wide range of SCCs. We used VIUS values for GVW to determine the truck SCC fractions by 

model year. To separate Light-Duty Trucks 1 and Light-Duty Trucks 2, which are distinguished 

by Loaded Vehicle Weights, we used information from the Oak Ridge National Laboratory 

Light-Duty Vehicle database. And to separate Class 2a and 2b trucks, we used information from 

Davis and Truitt.71 The resulting truck mappings are too complex to summarize here, but are 

available in the MOVES database. 

88
Table 18-1. SCC Mappings for Selected SourceTypes 

Source 

Type ID 

SourceType Fuel Type SCC-ID SCC 

prefix 

Abbreviated 

Description 

11 Motorcycle gasoline 5 2201080 Motorcycles 

21 Passenger Car gasoline 1 2201001 LDGV 

21 Passenger Car other 6 2230001 LDDV 

41 Intercity Bus gasoline 4 2201070 HDGV&B 

41 Intercity Bus other 12 2230075 HDDB 

42 Transit Bus gasoline 4 2201070 HDGV&B 

42 Transit Bus other 12 2230075 HDDB 

43 School Bus gasoline 4 2201070 HDGV&B 

43 School Bus other 12 2230075 HDDB 

54 Motor Home gasoline 4 2201070 HDGV&B 

54 Motor Home other 10 2230073 M-HDDV 

18.2. SCCRoadTypeDistribution 

Each SCC includes a suffix that indicates the HPMS Facility Class on which the 

emissions occur. Because MOVES calculations are done for MOVES roadtypes, the 

SCCRoadTypeFraction provides an allocation of emissions on each MOVES roadtype to the 

appropriate SCCRoadTypes. 

Table 18-1. SCC RoadTypes 

SCCRoadTypeID SCCRoadTypeDesc 

11 Rural Interstate 

13 Rural Principal Arterial 

15 Rural Minor Arterial 

17 Rural Major Collector 

19 Rural Minor Collector 

21 Rural Local 

23 Urban Interstate 

25 Urban Freeway/Expressway 

27 Urban Principal Arterial 

29 Urban Minor Arterial 

31 Urban Collector 

33 Urban Local 

1 Off-Network 

Because roadtype distributions vary geographically, the mapping of MOVES roadTypes 

to SCCRoadTypes varies by zone (in this case, county). For SCCRoadTypeDistribution we 

determined the proportion of hours of operation on a given MOVES roadtype within a county 

that occurred on each SCCRoadType. Hours of operation were estimated by dividing the 1999 

National Emission Inventory (NEI) VMT by the 1999 NEI average speed. Both measures were 

documented by Pechan & Associates.72

 The NEI VMT estimates are based on the Highway 

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

89
Administration73

 for use in transportation planning and vehicle type breakdowns from the EPA 

MOBILE6 Emission Factor model.74

 The VMT estimates were obtained from the NMIM 

database for each county and HPMS facility type. The average speed estimates are taken from 

Table 8 of the NEI documentation. 

The SCCRoadType fractions were calculated using the following formula, where i refers 

to the county, j refers to the MOVES roadtype, k refers to the SCCRoadType within a MOVES 

road type, and m refers to the VMT for each source type. 

SCCRoadTypeFraction(i,j,k) = Sum(j,j,k)( VMT(k,m)/Average Speed(k,m)) / 

Sum(i,j)((VMT(k,m)/AverageSpeed(k,m)) 

In cases where a county had no VMT for a given roadtype, the average values were used. 

The SCCRoadTypeFraction for OffNetwork travel was set to 1 (mapping all “off-network” 

emissions to this new roadtype. The SCCRoadType fractions for each roadway type will sum to 

one for each county. Although the data is from 1999 calendar year estimates, the same 

allocations will be used for all calendar years. 

90
19. MonthGroupHour 

ACActivityTerms A, B and C are coefficients for a quadratic equation that calculates air 

conditioning activity demand as a function of the heat index. These terms are applied in the 

calculation of the A/C adjustment in the energy consumption calculator. The methodology and 

the terms themselves were originally derived for MOBILE6 and are documented in the report 

“Air Conditioning Activity Effects in MOBILE6.”75 They are based on analysis of air 

conditioning usage data collected in Phoenix, Arizona, in 1994. 

In MOVES, ACActivityTerms are allowed to vary by monthGroup and Hour, in order to 

provide the possibility of different A/C activity demand functions at a given heat index by season 

and time of day (this accounts for differences in solar loading observed in the original data). 

However, for Draft MOVES2009, 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 19.1. 

Table 19-1. Air Conditioning Activity Coefficients 

A B C 

-3.63154 0.072465 -0.000276 

The A/C activity demand function that results from these coefficients is shown in Figure 19-1. A 

value of 1 means the A/C compressor is engaged 100 percent of the time; a value of 0 means no 

A/C compressor engagement. 

Figure 19-1: Air Conditioning Activity Demand as a Function of Heat Index 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

70 75 80 85 90 95 100 105 110 

Heat Index (F) 

A/C Activity Demand Fraction 

91
20. Sample Trip Data 

To estimate start and evaporative emissions, it is important to estimate the number of 

starts by time of day, and the duration of time between vehicle trips. (This between-trip duration 

is often called “soak time.” To determine typical patterns of trip starts and ends, MOVES uses 

information from instrumented vehicles. This data is stored in two tables: SampleVehicleDay 

and SampleVehicleTrip. 

The first table, SampleVehicleDay, lists a “sample population” of vehicles, each with an 

identifier (vehID), an indication of vehicle type (sourceTypeID), and a “dayID” that indicates 

whether the vehicle is part of the weekend or weekday vehicle population. 

The second table, SampleVehicleTrip, lists the trips made by each of these vehicles. It 

records the vehID, dayID, a trip number (tripID), the hour of the trip (hourID), the trip number of 

the prior trip (priorTripID), and the times at which the engine was turned on and off for the trip 

(keyOnTime and keyOffTime, each recorded in minutes since midnight of the day of the trip). 

To account for overnight soaks, many first trips reference a prior trip with a null value for 

keyOnTime and a negative value for keyOffTime. And, to account for vehicles that sit for one 

or more days without driving, the SampleVehicleDay table includes some vehicles that have no 

trips in the SampleVehicleTrip table. 

The data and processing algorithms used to populate these tables are detailed in two 

contractor reports.76,77

 The data comes from a variety of instrumented vehicle studies, 

summarized in Table 20.1. This data was cleaned, adjusted, sampled and weighted to develop a 

distribution intended to represent average urban activity across the U.S. For vehicle classes that 

were not represented in the available data, the contractor synthesized trips using trip-peroperating hour information from MOBILE6 and soak time and time-of-day information from 

sourcetypes that did have data. The application of synthetic trips is summarized in Table 20.2. 

The resulting trip per day estimates are summarized and compared to MOBILE6 in Table 20.3. 

Table 20.1. Source Data for Sample Vehicle Trip Information 

Study Study Area Study Years Vehicle Types Number of 

Vehicles 

3-City Atlanta, GA; 

Baltimore, MD; 

Spokane, WA 

1992 Passenger cars & 

trucks 

321 

Minneapolis Minneapolis/St. 

Paul, MN 

2004-2005 Passenger cars & 

trucks 

133 

Knoxville Knoxville, TN 2000-2001 Passenger cars & 

trucks 

377 

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

trucks 

350 

Battelle California, 

statewide 

1997-1998 Heavy duty trucks 120 

TxDOT Houston, TX 2002 Heavy, heavy duty 

diesel dump trucks 

4 

92
Table 20.2. Synthesis of Sample Vehicles for Source Types Lacking Data 

SourceType Based on Direct 

Data? 

Synthesized From 

Motorcycles No Passenger Cars 

Passenger Cars Yes n/a 

Passenger Trucks Yes n/a 

Light Commercial Trucks No Passenger Trucks 

Intercity Buses No Combination long-haul trucks 

Transit Buses No Single-unit short-haul trucks 

School Buses No Single-unit short-haul trucks 

Refuse Trucks No Combination short-haul trucks 

Single-unit short-haul trucks Yes n/a 

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

Motor homes No Passenger Cars 

Combination short-haul trucks Yes n/a 

Combination long-haul trucks Yes n/a 

Table 20.2. Starts per Day by SourceType 

SourceType Draft MOVES2009 

Weekday 

Draft MOVES2009 

Weekend 

MOBILE6* 

Motorcycles 0.78 0.79 1.35 

Passenger Cars 5.89 5.30 6.75 

Passenger Trucks 5.80 5.06 7.38 

Light Commercial Trucks 6.05 5.47 7.38 

Intercity Buses 2.77 0.88 6.88 

Transit Buses 4.58 3.46 6.88 

School Buses 5.75 1.26 6.88 

Refuse Trucks 3.75 0.92 6.88 

Single-unit short-haul trucks 6.99 1.28 6.88 

Single-unit long-haul trucks 4.29 1.29 6.88 

Motor homes 0.57 0.57 6.88 

Combination short-haul trucks 5.93 1.16 6.88 

Combination long-haul trucks 4.29 1.29 6.88 

* Note, MOBILE6 distinguished “starts” and “trips.” MOVES does not, but MOVES does 

include some very short “trips.” 

93
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99