# 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 tec hnical report does not necessarily represent final EP A 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 Elemen ts 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 Popul ations 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 Popu lations in Draft MOVES2009

............................................................. 14 Table 3-12. SalesGrowthFactor by Calendar Year an d 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 Ag e (All Use Types Except Motorcycles)

..................................... 24 Figure 6.1 1999 Age Distri butions 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 Us ed by SourceB inGenerator

............................................................................... 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 Databa se 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 Distribu tions for Buses by Fuel Type

....................................................................... . 50 Table 7-17. Fuel Fractions fo r Refuse Trucks by Model Year

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

................................................................. 52 Table 7-19. Diesel Fract ions 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 Technol ogies for 2000-and-later Model Years

. .................................... 56 Table 7.23. Fuel Fractions for 2002 and Newe r Passenger Cars and Light Duty Trucks

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

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



### iiiTable 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 Co m bination 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. SourceU seType 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 Calculati on 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 C ontinued

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

.................................................................................................................................. 82 Table 18-1. SCC Mappings fo r 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



### i v

# 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 polluta nts and selected air toxics from highway vehicles. When finalized, MOVES2009 will serve as a replacment for MOBILE6.2 The primary vehicle classification in MOVE S is “SourceType.” (Also sometimes called "SourceUseType").The MOVES Sour ceTypes are listed in Table 1- 1, along with the associated DOT Highway Performance Monitoring System (HPMS) vehicle classes. To estimate emissions accurately, we must us e 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 MO VES2009 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-schoo l, 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 curren tly developing draft technical guidance to describe these requirements.



### 1

### Table 1-2. MOVES Database Elem ents 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 rele vant to this report. The most important sources used to populate the vehi cle population and ac tivity 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 characteristic s and activity of U.S. trucks every five years. The survey is a sample of private and co mmercial trucks that were registered in the U.S. as of July of the survey year. The survey ex cludes automobiles, motorcycles, government-owed vehicles, ambulances, buses, motor homes a nd nonroad equipment. For MOVES, VIUS provides information to characterize trucks by So urceType and to estimate age distributions. Draft MOVES2009 uses data from both the 1997 and 2002 2 

### 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 de termining 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 Vehi cle Population Profile (NVPP®) 4 

### and the Trucking Industry Profile (TIP®N et) Vehicles in Operation database. 5 

### The first focuses on light-duty cars and trucks, the s econd focuses on medium and heavy-duty trucks. Both compile data from state vehicle regist ration 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) su mmarizes financial and operating data from U.S. mass transit agencies in th e National Transit Database (NTD). 1 0 

### For Draft MOVES2009, we used 1999 data from the report, “Age Distri bution 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. 1 1 

### 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.” 1 2 

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

## 2.7. Annual Energy Outlook & Nati onal Energy Modeling System The Annual Energy Outlook (AEO) 1 3, 1 4 

### 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 dem ographic projections. For Draft MOVES2009 we used AEO200 6 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 trans portation and energy data from a variety of sources. For MOVES2004, we relied on Edition 22, published in September 2002 1 5 

### and Edition 23, published in October 2003. 1 6 

### For Draft MOVES2009 we updated sales growth based on Edition 27, published in 2008. 1 7 

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



## 2.9. Oak Ridge National Laborato ry Light-duty Vehicle Database Oak Ridge National Laboratory Center for Transportation Analysis has compiled a database of light-duty vehicle information whic h combines EPA Test vehicle data and Ward's Automotive Inc. data spanning 1976 – 2001. 1 8 

### We used this database to determine weight distributions for light tr ucks by model year.

5

# 3. SourceTypeYear

The SourceTypeYear table st ores three data fields–– SourceTypePopulation, SalesGrowthFactor , and Migration Rate . Each field is describe d 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 ve hicles by SourceType for a given base year and domain. For Draft M OVES2009, 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 vehi cles that were registered on July 1 of 1999. FHWA data includes all vehicles that have been regi stered at any time throughout the year and thus may include vehicles that were re tired during the year or may double count vehicles registered in two or more states. 2. Polk and FHWA may differ in how they clas sify 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 re gistered within a state.

Also, FHWA data is available for Puerto Ri co, but Puerto Rico doe s not appear to be included in our Polk data set. MOVES will c over Puerto Rico and the Virgin Islands. In addition, Polk collects data on Gross Vehicle We ight (GVW) class 3 vehicles in both the NVPP® and TIP® databases, but the values are no t the same. Polk staff recommended using the TIP® values. 1 9 

### Finally, our 1999 Polk data set include s school buses and motor homes (which can be counted separately), but does not include “non-school buses.” Motorcycle population estimates were availa ble 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 Nationa l Household Transportation Survey (NHTS) combines the previous National Personal Transpor tation Survey and the American Travel Survey to collect data on personal travel patterns and in cludes data on motorcycle s, personal trucks and automobiles. 2 0 

### 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 P opulation 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 vehi cles 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 su b-classified into seven Source Types. 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 tota l truck population from Table 3-2 to derive population values for MOVES.

Table 3-3a. VIUS 1997 Codes Used fo r 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 fo r 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 to tal 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 Associati on “Motorcoach Census 2000”. 2 2 

### The FTA NTD provides population numbers for a variety of transit options. To determine the number of transit buses, we summed their c ounts for Articulated Motor Buses, Motor Bus Class A, B & C, and Double Decked buses.

Table 3-5. 1999 Bus P opulation Comparisons Data Source Total Buses Intercity Bu ses Transit Buses School Buses FHWA MV-1 732,189

FHWA MV-10 (excludes PR) 728,777 592,029*

FTA NTD 55,706 APTA 23 *** 75,087 Polk TIP® 460,178 School Bus Fleet Fact Book 429,086

Motorcoach Census** 44,200

* Includes some church & industrial buses. ** Includes C a nada



### . *** Includes trolleybuses.
As Table 3-5 shows, estimates of bus populat ions vary. We chose to use the FHWA value because it includes church and industrial bu ses 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 FH WA 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 substantiall y exceeds the estimate of intercity buses provided

9

### by the Motorcoach Census. We request co mment 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 estim ated 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 lo wer than the other estimates. If time and resources allow, EPA may investigate this furt her 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 th e 2001 NHTS. As for motorcycles, the FHWA registration count is noticeably lo wer than the household survey estimate. This could reflect population growth in the years be tween 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 Po pulations 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 em issions, the MOVES database includes a 1990 base year. The SourceTypePopulation inputs fo r 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 vehi cles are counted. (See previous section.) Additionally, the 1990 Polk data does not in clude 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 Statisti cal Annual published by the Motorcycle Industry Council. Valu es 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 56,023,000 3 na na 

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

### Motorcycle Industry Council 5 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 (s tate 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 i s calculated using the appendix table and the proportion of MCs from Table 20 of the 2001 NHTS Summary of T ravel Trends. 5 The Motorcycle number was obtained as a sum of on-h ighway 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 a nd 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 tota l truck population from Table 3-7 to derive population values for MOVES.

11

### Table 3-8. TIUS92 Codes Used fo r 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 to tal buses from FHWA to the three MOVES classes. Additional information on bus numbers wa s 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 PRand 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.

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



### Table 3-11 summarizes the 1990 vehicle popu lations used in Draft MOVES2009. For motor homes we used the only available data fr om NPTS. We used the TEDB data for buses. For trucks the TIUS data was used; the re maining values were based on FHWA data.

13

### Table 3-11. 1990 SourceTyp e 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 multiplica tive factor indicating changes in sales by SourceType for calendar years afte r 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" m eans a two percent decrease in sales. SalesGrowthFactor is used in the Total Activity Generator calculation of s ource type populations for calendar years after the base year-- in Draft MOVES2009, years 2000 through 2050. Note that the sales growth f actors are not used in the ca lculation 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 primar y 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 act ual sales data and update d sales forecasts, but rates for the remaining sourcetypes were not changed. Beyond 2030, the SalesGrowthFactor was set to the 2030 value. For th e final MOVES2009, we anticipate updating sales information, at least for the domina nt sourcetypes. The data sources and methodologies by s ource type are described below:

14

### • Passenger Cars and Passenger Trucks: Sa lesGrowthFactors for calendar year 2000 through 2005 were derived from total sale s 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 Motorcyc le Industry Council Statistical Annual. 2 4 

### 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 nu mbers reported in the TEDB26 Table 4.6. Factors for Calendar year 2002 through 2030 di ffer from passenger trucks. It is possible that they were mistakenly retained from an earlier version of the model. We plan to investigate this fu rther for the final MOVES2009. .

• Buses, Single Unit Trucks & Motor Home s: 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 AEO200 6Supplemental Table 55.

• Combination Trucks, Refuse Trucks: Ca lendar years 2000-2001 were based on sales as reported in TEDB23 Table 5.3 (gross we ight 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 SalesGrowt hFactors 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 multip licative factor used to estimate how many vehicles join or leave the populat ion of a SourceType in the given domain in a given year. We expect this field may be useful when modeling emissions on relatively sma ll 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 MOVES 2009 were taken from MOBILE6. 2 5 

### Market penetration data by model year were gathered from Wa rd’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 y ears of available data are quite va riable, so values for early model years were estimated by applying the 1972 and 1975 rates for cars and trucks, respectively. Projections beyond 1995 were develope d 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 conditi oning, 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 SourceT ypeAge 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 a ll model years in a Sour ceType in all calendar years. SurvivalRates for Motorcycles were calculate d based on regression of data provided by the Motorcycle I ndustry Council (MIC). 2 6 

### Survival rates for Passenger Cars, Passenger Tr ucks and Light Commer cial Trucks came from NHTSA's survivability Table 3 and Table 4. 2 7 

### These survival rate s 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 rate s 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 fo r MOVES ages < 2, the survival rates for age 0 and age 1 were interpolated us ing 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 gi ven 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 Surviv al 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 su rvival rates can be deri ved 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 perc ent. The comparable survival rate for 1990 model year Heavy-Duty vehicles from TE DB was 96 percent, while the rate for 1980 model year trucks was 91 percent. This comp arison 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 rate s for all "age 30" sourcetypes c 

### were set to 0.3. This is assumed to be the fraction of all vehicles 30-and-olde r 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 Passeng er 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 surviv al 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 (R elative MAR) is listed for each MOVES SourceType and Age. The Relative MAR is com puted as the annual MAR divided by the highest MAR within the HPMS vehicle class. This allo ws 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 th e six HPMS Vehicle Classes. The following discussion refers to the S ource 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 smoot h 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 repor t on survivability and mileage schedules. 2 8 

### In the NHTSA analysis, annual mileage by age was dete rmined 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 cat egories, we then shifted the relative MARs such that the NHTSA "age1" ratio was used fo r 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 MO BILE6 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, whic h 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. 2 9 

### The initial MARs for the MOVES truck categories were then calculated as the product of the we ighting fractions and the MARs from MOBILE6.

5.2.3. Buses For the School Buses (category 43) the in itial 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 take n from the MOBILE6 values for diesel transit buses (HDDBT). This m ileage data was obtained from the 1994 Federal Transportation Administration survey of transit agencies. 3 0 

### For Intercity Buses (category 41), the initial MARs were taken from Motorcoach Census 2000. 3 1 

### 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 bus es, or if it indicates a problem . We welcome comments with ideas for validati ng or improving this estimate.

21

### 5.2.4. Motor Homes For motor homes (category 54), the initial MARs were taken from an independent research study 3 2 

### conducted in October 2000 among members of the Good Sam Club. The members are active recreation vehicle (RV) enth usiasts 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 th e MARs from the 25 ages in MOBILE6 to the 30 ages in MOVES, we used statis tical 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. No te, 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 A nnual Mileage Accumulation Rates used in MOVES MOVES Source Type Source Type ID Regression Equation R 2 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=0.0016x 2 -0.0762x +0.9655 0.977 Combination Long-haul Trucks 62 y=0.0021x 2 -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 Sour ceType 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 A ccumulation 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 AgeR elative M A R



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



## 5.3. FunctioningACFraction The FunctioningACFraction fiel d 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 estima tes were developed for all source types using the “unrepaired malfunction” rates used for 1992-and-later model years in MOBILE6. 3 3 

### 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 w ith sales and scrappage information to compute the age distribution in the calenda r year selected for analysis. This section first describes how the age dist ributions were determined for the primary default base year of 1999, and then for the 1990 ba se year. Age distributions for the 1999 base year are summarized in table 6-1. Age distribu tions for the 1990 base ye ar 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 motorcyc les in use by model year in 1998. We used the estimates of sales growth and survival rates to grow thes e 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 de termine age fractions for passenger cars. Our original approach (used for MOVE S2004 and MOVES Demo) began with Polk NVPP® 1999 data on car registration by model y ear. 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 D ecember 1999. Model Year 1998 cars were added to the previous estimate of “Age 1” cars and M odel 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 2 000 as "Age 0", the Age 0 age fraction is inflated. When the MOVES Total Activity Generator applies growth f actors, the number of car s 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 TEDB 3 4 

### and applied the NHTSA survival fractions, extrapolated to age 30 and shifted such that NHTSA age n = MOVES age n+1. Survival fractions for MOVE S age 0 and 1 were interpolated as described in Section 5.1. Not surprisingly, the age distri butions resulting from the three approaches are very similar, as illustrated in Figure 6.1. All show a fairly flat age dist ribution 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 we re 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 c onsidering updating these values to better account for more recent data.

25

### Figure 6.1 1999 Age Distri butions 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 AgeFraction 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 a nd records ages as 0 through 10 and 11-andgreater. Because we needed greater detail on th e older vehicles, we followed the practice used for MOBILE6 and determined the model year fo r 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 obtaine d 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, fo r each source type, we adjusted the age 11-andolder vehicle counts by dividing th e original count by model year by the fraction of the older

26

### vehicles that were coded as “obt ained new.” This created an array of adjusted vehicle counts by model year for calendar year 1997. This 1997 ar ray 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 give n 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 an d 6.11 to grow the population to the 1999 base year and then we calculated age fractions. Initially, we determined age fractions for pa ssenger 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 co mmercial light trucks and using TEDB sales data. The results of the three approa ches 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 AgeFraction 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 di p 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 brie f leveling off, another steep decline from about age 12 to 17 and a final leveling off. For th e MOVES default database, we selected the age distribution generated with the "Sales and Scra ppage" 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 ag e 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, desc ribed above. While we believe this choice is reasonable given the lack of data, we welcom e suggestions of improved data sources or algorithms to improve the intercity bus age frac tions 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 Bu s and Motor Home counts were available by model year. Unlike the Polk data for passenger cars, th ese counts reflect regi stration at the end of the calendar year and, thus, did not require ad justment. We converted model year to age and calculated age fractions. These ar e 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 pa rticular, 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 Ag e 0 vehicles at the end of 1999, it was necessary to adjust the counts fo r 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.

28Table 6-1. 1999 Age Fractions for MOVES Source Typessourcetype112131& 3242435152535461 & 4162age00.09470.06460.10110.06240.07940.04980.06220.16970.07370.08430.166810.09350.06020.09060.07710.06600.03980.05200.14190.04560.06720.133120.07550.06100.08370.07420.06470.03400.04120.11240.07390.05760.114030.06810.06240.07910.07270.05940.07670.04660.05850.04870.05060.114040.06130.06260.07200.06270.07980.09260.05590.06090.06050.06930.118650.05700.06420.07000.05760.04060.06040.05720.10170.06080.05620.080460.05200.05970.06030.05040.05110.05440.04340.07830.04410.04880.064370.04330.05620.05020.04610.04350.02430.03440.01850.04080.03790.040380.03700.05430.04290.04920.05850.06960.03510.01380.03200.04530.030490.03550.05960.04500.07590.06960.06250.04350.06860.04420.05350.0315100.03360.06080.04310.06090.04190.05140.05780.07480.06020.05600.0320110.03880.06220.04220.05060.05260.07300.05310.05170.05630.05500.0290120.04610.05490.03790.04890.05560.06100.04600.01290.05740.05970.0080130.04220.05220.03510.04340.05120.07960.05800.00310.04470.05280.0087140.03830.04190.03110.03940.04640.04420.04300.00640.05010.04870.0115150.03450.03200.02440.03200.03740.04790.02510.00670.05310.04000.0062160.03070.02260.01700.03210.01440.01450.04090.00000.03630.01670.0013170.02700.01550.01270.01810.01110.01690.02200.00320.02210.01470.0011180.02340.01290.01000.00820.01360.01560.02190.00240.01270.01330.0035190.01980.01050.01000.02310.01380.00400.02390.00000.00170.01800.0012200.01630.00800.00810.00710.01180.00430.01900.00020.01380.01120.0010210.01290.00600.00660.00320.01040.00430.02250.01010.01910.00900.0006220.00950.00450.00530.00070.01070.00000.00880.00060.02670.00990.0010230.00620.00340.00410.00130.00730.00920.01120.00110.01690.00380.0000240.00290.00260.00320.00090.00920.00270.01150.00050.00450.00480.0009250.00000.00190.00310.00090.00000.00700.01250.00000.00000.00480.0003260.00000.00140.00300.00020.00000.00010.01300.00210.00000.00400.0003270.00000.00080.00290.00040.00000.00000.02650.00000.00000.00360.0000280.00000.00060.00270.00030.00000.00000.00590.00000.00000.00260.0002290.00000.00050.00260.00010.00000.00000.00320.00000.00000.00060.0000300.00000.00000.00000.00020.00000.00000.00260.00000.00000.00000.000029

## 6.7. 1990 Motorcycles To determine age fractions for motorcycles, we began with Motorcycle Industry Council estimates of the number of motorcycles in us e, by model year, in 1990. However, data for individual model years star ting from 1978 and earlier were not available. A logarithmic regression curve ( R 2 

### value = 0.82) was fitted to availabl e 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 ca rs, 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 Decemb er 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 M odel Year 1990 and 1991 cars were adde d 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 i ndividual ages 15+ based on an exponential curve (R 2 

### value =0.67) fitted to available data.



## 6.9. 1990 Trucks To determine age fractions for passenger trucks , light commercial tr ucks, refuse trucks, short-haul and long-haul single un it trucks and short-haul and l ong-haul combination trucks, we used data from the TIUS92 (1992 Tr uck Inventory and Use Survey) database. Vehicles in the TIUS92 database were assigne d to MOVES source types as summarized in Table 3-3.
TIUS92 does not include a model year field a nd records ages as 0 through 10 and 11-andgreater. Because we needed greater detail on th e older vehicles, we followed the practice used for MOBILE6 and determined the model year fo r 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 vehi cle?” (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, fo r each source type, we adjusted the age 11-andolder vehicle counts by dividing th e original count by model year by the fraction of the older vehicles that were coded as “obt ained 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 origin al 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 c hoice 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 ag e fractions since year 1990 data on transit buses was not available from the Fede ral Transit Administration database.

31

# 7. SourceBinDistribution

The SourceBinDistribution describes the char acteristics of a Sour ceType population as a distribution among SourceBins. These SourceBins cl assify a vehicle by discriminators relevant for emissions and energy calcula tions: fuel and engine techno logy, average vehicle weight and engine displacement, model year group, and regulatory class.

While SourceBinDistributions could be i nput directly, MOVES usually generates the values in this table using values in a collecti on of other tables. The S ourceBinGenerator 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 assi gned fractions of vehicles to alternative fuels, for Draft MOVES2009, we simplified the model by pr oviding default fractions only for gasoline and diesel vehicles. We expect to retain this simplified ap proach for the final MOVES2009. Users wishing to model alternative fuels will still have the option of using the Alternative Vehicle Fuels and Technology stra tegy to input their own fuel a nd 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 determ ines which discriminators are relevant for a given pollutant/process combination and multiplies the relevant fractions from the tables listed above to determine the detailed SourceBin Distribution for each combination of Pollutant, Process, SourceType, and Model Year.
More detailed descriptions of the SourceB in 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 ch aracteristics were assigned based on information from EPA motorcycle experts and from the Motorcycle Industry Council.

7.1.1. FuelEngFraction We assume all motorcycles are power ed by conventional gasoline engines. 7.1.2. SizeWeightFraction The Motorcycle Industry C ouncil “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 certi fication data was used to establish displacement distri butions for model year 2000. We assumed that displacement distributions were the same in 1969 as in 1990, a nd interpolated between the established values to determine displacement di stributions for all model years from 1990 to 1997 and for 1999. Model year 2000 values were intend ed to be used for all 2000-and- later model years, however in Draft MOVES2009, the 1999 value was used. Fo r final MOVES2009, we intend to replace the current 2000-and-later model year values with those based on th e model year 2000 certification data. We then applied weight distributions fo r each displacement category as suggested by EPA motorcycle experts. The average weight es timate 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 DraftMOVES 2009, 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 importe d cars were consolidat ed 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 th e 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 th e MOVES fuels were summed and fractions were computed. While previous versions of MOVES included default values for alternative fueled vehicles, DraftMOVES2009 includes only ga soline and diesel vehi cles in the default database. In model years where alte rnative vehicles were present,

7.2.2. SizeWeightFraction The Polk cubic displacement values were c onverted 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 co mputed. 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 sales 3 5 

### , two-thirds of electric vehicles were assigned to weight class 35 and one third was assigned to weight cla ss 40. Also, further analysis indicated a likely error in the Polk data (a n 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 Tr ucks, 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 describe d in separate sections following.
The Vehicle Inventory and Use Survey (VIUS) conducted by the Census Bureau was the primary source for information on truck distri butions. 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 ve hicles were surveyed by VIUS, we duplicated fractions from the nearest avai lable model year. The 2002 VIUS was used 1986 and later model years and 1997 VIUS information was only used fo r the older model years not surveyed in the 2002 VIUS. In the Draft MOVES2009 release, the oldest model year obser ved diesel fractions were applied to the older model years for combin ation 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 di stinguish LPG and CNG ve hicles using VIUS. Based on historical data, we assigned the pre-1990 LPG/LNG ve hicles to LPG and the 1990-andlater vehicles to CNG. While these vehicles fo rm a very small portion of the national fleet, we would like to update this assignm ent if better information becomes available. Also, it was not possible to identify the fuel used for the VIUS cat egory “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 we re 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 assigne d to EngTechID “1" (conventional).
Table 7-5 summarizes the pre-1999 diesel fract ions for MOVES gene ral truck categories by model year. The gasoline fractions can be esti mated as one minus the diesel fractions listed here. For light trucks, fuel distributi on information is also available from Polk. While the Polk data cannot easily be mapped to the truck Source Types used in MOVES, if future resources allow, it would be instructive to compare the Po lk 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. Fo r comparison, the engine size ranges for both the VIUS and MOVES categories are listed in cubic inches displacement.

Table 7-6. Mapping VIUS Engine Si ze 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 complicat ed. The VIUS 1997 data combines information from two different surv ey 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 ch assis. 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 br oad average weight cate gory (AVGCK): 6,000 lbs or

39

### less, 6,001-10,000 lbs, 10,001-14,000lbs, etc. To determ ine a more detailed average engine size and weight distribution for thes e vehicles, we used the Oak Ri dge 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 st rata 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 heav ier (10,001-14,000 lbs, etc) VIUS 1997 broad categories were m atched one-to-one with the MOVES weight classes.

• For trucks in the lower broad categor ies (6,000 lbs or less and 6001-10,000 lbs), we used VIUS 1997 to determ ine 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 dut y 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 we ight 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 ve hicles 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 di stributed 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, t hus 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 de tailed 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 heav ier (10,001-14,000 lbs, etc) VIUS 1997 broad categories were m atched one-to-one with the MOVES weight classes.

• For trucks in the lower broad categorie s (6,000 lbs-or-less and 6001-10,000 lbs), we used VIUS 1997 to determ ine 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 ve hicle 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 larg er 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 we ight 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 cl asses 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 vehicl e/trailer combination as it was most often operated when carrying a typical payload during 2002. These estimates were used to determine the MOVES weightClassID categorie s 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 gaso line trucks among the si ngle 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 (sour ceTypeID = 31, 32) and none of the average weight distributions for any tr ucks 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 wa s 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 vehicl e weight (GVW) (the ma ximum 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 Trucks 3 6 

### to determine GVW fractions by fuel type. The VIUS fields are intended to id entify the Polk weight cl ass. Work for MOBILE6 using the VIUS precursor, TIUS 1992 indicate d that the PKGVW measure in VIUS is problematic. TIUS PKGVW is taken from the truc k 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 VI US fields to determine if the PKGVW was mistaken. In some cases, the PKGVW was manu ally revised to a higher value and fractions were recomputed. In other cases, the PKGVW wa s consistent with the other fields, and the difference reflected the fact th at 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 fracti ons of such trucks. It is li kely these high values indicate a problem with small sample size for the model y ear, but they were left unchanged for now. Also, because the split between the LDT and HD T 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 & Truitt 3 7 

### 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 ga soline, 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-power ed 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-fue led 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 survey ed 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 y ear 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-Dut y 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 Admini stration (EIA) in a ssigning all intercity buses to conventional diesel engines ( AEO2006, Supplemental Table 34 ).
The National Transit Database (NTD) re sponses to form 408 (Revenue Vehicle Information Form) included information classifyi ng 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 mode l year are summarized in Table 7-10.

Table 7-11. Mapping National Transi t Database Fuel Types to MOVES Fuel Ty pes 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 el ectric buses were assigned to E ngTechID “30" (electric only). All other 1999-and-earlier buses were assigne d to EngTechID “1 " (conventional).

The available Polk data excluded fuel inform ation on school buses and we were unable to locate any other source for bus fuel fractions. (The Union of Concerned Scie ntists estimates that about one percent of school buses are fueled by either CNG or propane , but does not provide estimates by model year. 3 8 

### ) Thus we used the diesel fr actions from MOBILE6, which were derived from Polk 1996 and 1997 data. We assi gned 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 type s 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 Congress 3 9 

### that specified the number of buses in various weight categories. This information is summarized in below in Table 7-14. Note the FT A uses the term “over-the-road bus” to refer to the class of buses roughly equivalent to the M OVES “intercity bus” category. The FTA weight categories were mapped to the equi valent 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 Ta ble3-1), we were able to estimate the fraction of all buses that were in tercity 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

Estim ated number of in tercity 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 we ights from Figure II-6 of the FTA Report to Congress 4 0 

### and added additional weight to account for passengers and alterna tive 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 inform ation 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 nati onal average distribution, it was the best data source available. The California data 4 1 

### 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 we ights 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. Similarl y, we assumed the Medium Heavy-Duty (MHDV) school buses were ev enly distributed among wei ghtClassIDs 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 di stributions for buses are summarized in Table 7-16.

7.4.3. RegClassFraction All buses were assigned to the H eavy-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 E ngTyp field to estimate FuelType and Engine Technology Fractions. The Refuse Trucks classifi ed 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 categorie s 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 Moto r 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 ma de, and how data points were determined.

52

### 7.6.1. FuelEngFraction
Detailed information on motor home fuel dist ribution was not available. Staff of the Recreational Vehicle Industry A ssociation (RVIA) told us that the fraction of diesel motor homes had been relatively constant at 10 to 20 percent for many years. 4 2 

### 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 Fract ions 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 averag e engine size and wei ght distributions for motor homes. We assumed all motor home engine s 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 weig ht 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 dist ributions 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 1 9 8 0 0

. 4 6 2 9 1 6 0

. 5 3 7 0 8 4 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 1 9 7 5 1 0 0 0 0 0 1 9 7 6 1 0 0 0 0 0 1 9 7 7 1 0 0 0 0 0 1 9 7 8 1 0 0 0 0 0 1 9 7 9 1 0 0 0 0 0 1 9 8 0 1 0 0 0 0 0 1 9 8 1 0

. 7 4 7 7 2 3 0

. 2 5 2 2 7 7 0 0 0 0 1 9 8 2 0

. 7 3 2 2 3 5 0

. 2 6 7 7 6 5 0 0 0 0 1 9 8 3 0

. 7 1 4 5 5 2 0

. 2 8 5 4 4 8 0 0 0 0 1 9 8 4 0

. 6 4 1 5 7 7 0

. 3 5 8 4 2 3 0 0 0 0 1 9 8 5 0

. 6 9 2 3 1 4 0

. 3 0 7 6 8 6 0 0 0 0 1 9 8 6 0

. 7 2 0 2 4 8 0

. 2 7 9 7 5 2 0 0 0 0 1 9 8 7 0

. 6 0 6 6 3 5 0

. 3 9 3 3 6 5 0 0 0 0 1 9 8 8 0

. 4 5 9 4 2 9 0

. 5 4 0 5 7 1 0 0 0 0 1 9 8 9 0

. 5 5 1 6 0 1 0

. 4 4 8 3 9 9 0 0 0 0 1 9 9 0 0

. 5 4 3 3 5 4 0

. 4 5 6 6 4 6 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 H eavy-Duty Truck regulatory class.



## 7.7. SourceBinDistributions for 2000-and-later MOVES was designed to support a wide variety of future fuel s and engine technologies, including compressed natural gas (CNG), liquifi ed petroleum gas (LPG), and conventional internal combustion (CIC) and advanced internal combustion (AIC) engines. In particular, emission rates were developed to support the combinati ons of fuel and engine technology listed by SourceType in Table 7-22. Note that some fuel types that were supporte d in earlier versions of MOVES (methanol and hydrogen) ar e not available in DraftMOVES2009.
The various hybrid types were split into "m ild" and "full" categor ies because there are types of hybrids which get less of an efficiency in crease from hybrid design due to larger engines and smaller electrical component s. The less efficient designs we called "mild" hybrids (like the

55

### early Honda hybrids) to distinguis h them from the more efficien t, full hybrid designs (like the Toyota Prius). Both of these categories have si gnificantly different ener gy rates and potentially different market shares. Conve ntional categories are split from advanced categories for a different reason. There have been significant im provements in internal combustion engines over time. The conventional versus advanced split is a crude accounting of th ese improvements. All of these technologies are furt her defined in the report, "Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles in the Physical Emission Rate Estimator (PERE)." 4 3



### Table 7-22. Supported Fuels and Techno logies 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 SourceB inDistributions for model years 2000-andlater were generally based on fuel and engine technology projections fr om AEO2004 and on the 1999 calendar year regulatory class, size a nd weight distributions used in MOVES.

7.7.1. Motorcycles

We assumed that all 2000-and-later motorc ycles were fueled by conventional gasoline engines, with the same size and weight distribu tions 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 fu els and future engine technologies for passenger cars and light trucks.
The FuelEngFractions for these vehicles we re determined from AEO2004. Supplemental Table 45 of the AEO2004 lists projected sales by technology type for li ght duty vehicles. Supplemental Table 56 lists projec ted technology penetrations fo r light duty vehicles. These values were mapped to the MOVES fuels and tec hnologies to project fractions for model years 2001 through 2025. Fractions from 2001 were applie d to model year 2000. Fractions from 2025 were applied to model years 2026 through 2050.
We analyzed passenger cars and light trucks se parately. All vehicl es 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 fuelEngFract ions 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 distri bution 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 conv entional 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 newe r model year diesel vehicles of all technologies use the regulatory class distribution for diesel co nventional 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 ga soline 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 tr ansit buses have used a wide range of alternative fuels, while intercity buses have been powered almost ex clusively by conventional diesel engines. For MOVES we anticipate this trend will conti nue. 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 dist ributions were also carried forward from 1999. All buses were assigned to the Heavy-Duty Truck regulatory class.

Table 7.24. Fuel and Engine Technol ogy 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 power ed 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 truc ks 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 fl eet 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 conven tional internal combustion vehicles.

Table 7.25. Fuel and Engine Technolog y 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 Co mbination Trucks, MOVES uses the AEO2004 projections for heavy-duty frei ght trucks. AEO Table 55 lists sa les projections for heavy-duty freight trucks powered by diesel, gasoline, liquified petroleum gas a nd compressed natural gas. All non-gasoline trucks, other than diesel, were assigned to the MOVE S gasoline conventional combustion category. All diesel trucks with we re assigned to the MOVE S diesel conventional internal combustion category.
Furthermore, AEO Table 146 lists technology pe netrations for Class 7-8 freight trucks with “higher cylinder pressure”, “improved injection & combusti on” and “waste heat/thermal management”. All trucks were assigned to the MOVES the conventiona l internal combustion categories. The resulting distri butions 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 th e Heavy-Duty Truck regulatory class. Combination Trucks were distributed among th e Light Duty Truck (LDT) and Heavy Duty Truck (HDT) regulatory classes. Wh ere a future fuel or technol ogy 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 Technolog y 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 ve hicle mass and three average road load coefficients for each SourceType. The mass is liste d 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 • sin θ 

## ) • v

. M M M

where A, B, and C are the road load coefficien ts in units of (kiloWatt second)/(meter tonne), (kiloWatt second 2 

### )/(meter 2 

### tonne), and (kiloWatt second 3 

### )/(meter 3 

### tonne), respectively. M is the mass of the vehicle in kilograms
, g is the acceleration due to gravity ( 9.8 meter/ second 2 

### ), v is the vehicle speed in meter/second
, a is the vehicle acceleration in meter/second 2 

### , and sin K is the (fractional) road grade. The values in the SourceUseType table were av eraged 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, th e 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 vehicl e mid-point mass or one of the three different road load coefficients determined from the ro ad load–vehicle mass rela tions described below: α j 

### were the sourceBinActivityFractions in the MOVES database and β i 

### 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 w ith vehicles in the MSOD were us ed 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. MO VES 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 co efficients varied by regulatory class. Motorcycle road load coefficients are typically parameterized 4 4 

### 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 coeffi cients were derived from the track road load horspower (TRLHP @50mph) 

### recorded in the MSOD. 4 5 

### The calculations applied the following empirical equations: 4 6



### 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 paramete rs were available in the MSOD. Instead EPA used the relationships of road load co efficent to vehicle mass from a study done by V.A. Petrushov, 4 7 

### 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 fo r 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*s 2 /m 2 )/

M(tonne) 0 0 0 0

C(kW*s 3 /m 3 )

/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 we re computed for each SourceBin-associated vehicle in the MSOD and a weighted average wa s computed as described above. The final SourceMass and road load coefficients fo r all SourceTypes are listed in Table 8-3.

64

### Table 8-3. SourceU seType Characteristics
Source TypeID HPMS Vtype ID SourceType Name Rolling TermA (kW-s/m) Rotating TermB (kW-s 2 /m 2 ) Drag TermC (kW-s 3 /m 3 ) Source Mass (metric tons) 11 10 Motorcycle 0.0251 0 0.000315 0.285 21 20 Passenger Car 0.156461 0 .002002 0.00 0493 1.478803 31 30 Passenger Truck 0.22112 0.002838 0.00 0698 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 denom inator in the Vehicle Specific Power (VSP) equation, which generates a relationship between pow er 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 Manual 4 8 

### ) 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 va rying the sourceMass, the fi xedMassFactor 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 ar e listed in Table 9-1. These road types are aggregations of the HPMS f unctional facility types that ar e 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 anal ysis, 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 fr actions for each HPMS Vehicle Type. We then aggregated the values to th e 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) se lf-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 assu me that all VMT reported by HPMS is accumulated on one of the 12 HPMS roadway type s 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 Di stributions 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 di stributions 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 ge nerally would have a greater fraction of travel on rural restricted access roadways than the short-ha ul 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 we re adapted to MOVES by converting the fraction of miles travelled to the fracti on of time used, and by mapping from the MOBILE6 road types to the MOVESroad t ypes, with the MOBILE6 "freeway" values mapped to the MOVES "urban restricted" roadty pe and the MOBILE6 "arterial" values mapped to the MOVES "urban unrestricted" roadtype. Th e 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 driv ing data collected in California under studies performed for the California Department of Tr ansportation (Caltrans). Under these Caltrans driving studies, instrumented “chase cars” were equipped with laser rangefinders mounted behind the front grill of each chas e car. The studies were perfor med in the Sacramento area, the San Francisco Bay area and the San Joaquin Valley. Another dr iving 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. 4 9 

### The datasets contained driving in both ur ban and rural areas. In the post-processing that was performed under each of these studies, th e type of roadway the vehicle was traveling on

67

### during each second was also recorded in the outpu t 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 ar ea travel in these datasets wa s discarded for this analysis.) The average speed was calculated over each li nk traverse for the individual links in each data set. A link traverse is defined as a one-w ay driving traverse of the entire extent of a roadway link. A review of the li nks 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 lim ited access freeways and highways or remote county roads. In rural areas, the difference in av erage speeds calculated over conve ntionally defined links versus longer link sections as identified in the route-based driving studie s 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 Sp eed 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 contra ctor-provided values of “Time-weighted Distributions (% of time) of California Rural Chase Car Driving Data by Average Link Speed for each HPMS Functional Class.” 5 0 

### 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 fi ve 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 di stribution (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 aver age speed and re-noma lizing. We then computed a sum-product

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### of the speed bin fractions and the road class dist ributions 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 distri butions were used for all hours of the day. And, while the California chase car data also only included li ght-duty vehicles, the resulting speed distributions are also used for heavy-duty ve hicles. Also the existing data from the studies used in this analysis were collected entirely in Ca lifornia. Thus, use of th ese California results to represent national rural speed di stributions must include the cr itical assumption that average speeds within each HPMS functional class do no t significantly vary across the U.S on rural roadways. National default speed distributions are av ailable in the default database for each roadtype, sourcetype and hourday, a nd are not provided here. However, for illustration, Figure 14.1, shows the speed distributions on different ro adtypes 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 3 0 40 50 60 70
Avg. S pe e d of S pe e d BinFraction

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 calculati ng fractions, we calcula ted VMT sums by HPMS Vehicle Class. The resulting VMT for 1999 and 1990 by HPMS Ve hicle 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 activ ity that is not include d 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 m odeling local areas. However, the reported HPMS VMT values, used to calculate the national averag es 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 multiplica tive factor indicating changes in total vehicle miles for calendar years af ter the base year. Total VMT data are reported according the HPMS vehicle classes discussed pr eviously, i.e. passenger car, ot her 2-axle / 4-tire vehicle, single-unit truck, combination truck, bus and motorcycle. VMTGrowthFactor is expressed relative to the previous year’s VMT; for exampl e, 1 means no change from previous year VMT, 1.02 means a two percent increase in VMT, and 0.98 means a two percent d ecrease in VMT. VMTGrowthFactor is used in the Total Activity Generator calculation of VMT for calendar years after the base year, meani ng calendar years 2000 through 2050 in Draft MOVES2009. It is important to note that VM TGrowthFactor 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, su rvival rates, etc. are only used to allocate total VMT. Default estimates for VMTGrowthF actor were taken from FHWA Highway Statistics for 2000 through 2004, and from AEO2006 for years 2005-a nd-later. For passenger cars and lightduty trucks, additional calculatio ns 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 disc ussed previously, i.e. passenger car, other 2­ axle / 4-tire vehicle, single-unit truck, combin ation 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 AEO200 6. In the AEO analysis, VMT projections are provided for total Light-Duty ( AEO2006Supplemental 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 gr owth 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 truc ks; as such they do not reflect the higher growth rate of light trucks relative to passenger cars brought on by steadily increasing sa les of light duty trucks. Separate VMTGrowthFactors for the Passenger Ca r and Other 2-axle/4-wheel Vehicle classes were therefore developed based on estimates of car and light tr uck populations from AEO2006

. Using the AEO2006 estimates of total light-duty VMT and vehicle population (i.e., stock) growth rates listed in AEO Supplement al 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 ca r 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.

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### 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 estim ates need to be allocated to months, days, and hours. A 1996 report from the Office of Highway Information Management (OHIM) 5 1 

### describes analysis of a sample of 5,000 continuous traffi c counters distributed thro ugh 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, w ith different values for leap year and non-leap year. We computed the fractions using the report va lues and the number of days in each month.

Table 12-1. M onthVMTFraction

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 va lues for each day and hour of a typical week for urban and rural roadway types for va rious regions of the United States for both 1992 and 1995. The data obtained from the OHIM report is not disaggreg ated 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 M OVES2009, 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 classifi es days into two type s 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 rura l 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 ne twork" 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 da ta as for DayVMTFraction. We converted the OHIM report data to percent of da y by dividing by the DayVMTFraction. There are separate sets of HourVMTFractions for "Urban" and "Rur al" 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.

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### Figure 12.1 Hourly VMT Fr actions 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 1 0 2 0 3 0 Hour of Da yFraction 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 distingu ishes hourly VMT by FHWA vehicle category. Analysis of this data can pr ovide HourVMTfractions differ by sourcetype. We request comment on the relative priority of incorpor ating this data into MOVES defaults.

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# 13. Driving Schedule Tables

DriveSchedule refers to a second-by-second vehicle speed trajectory. Drive schedules are used in MOVES to determ ine the operating mode distri bution 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 pa tterns across source type, roadway type and average speed. For the national default case, Dr aft 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 road type 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 spee ds 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 di stribution of any roadtype’s speed distribution is determined from the available driving schedule s. For more details, see the Operating Mode Distribution Generator secti ons in the MOVES Software and Design Reference Manual. 5 2 

### For brevity, the entire body of drive schedul e information is not presented in this document. The reader is referred to the MOVE S 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-se cond 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 lis t 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.

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### 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 traveli ng 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 ar e 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 R oadtypes 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 Medium Heavy-Duty Non-Freeway Single Unit Short Haul 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 Heavy Heavy-Duty Non-Freeway Combination Long Haul



### 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. 5 3 

### 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, includi ng local roadways. The lightduty schedules not included in the MOBILE6 work are Low Sp eed, New York City, High Speed 2 and High Speed 3. Low Speed is a historic cycl e 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 sc hedule representing congested urba n travel with lots of stop-andgo. It is used in EPA’s running loss certification test procedure. 5 4



### High Speed 2 and 3 were developed specifica lly 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 wa s 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 n eed 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 cycl e, 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, 5 5 

### 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 ar e only applied in Draft MOVES2009 if AverageSpeedDistri bution contains operation in the highest speed bins; i.e. 70 mph and greater.
Medium-Duty and Heavy-Duty schedules we re 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.” 5 6 

### ERG analyzed data from 150 medium and heavy- duty vehicles instrumented to gather instantaneous speed and GPS measurements. ERG segregated the driv ing into freeway and non-freeway driving for medium and heavy-duty vehicles, and then furthe r stratified vehicles trips according the predefined ranges of average speed covering the range of vehicl e 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 sche dules 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 ope ration on freeway ramps. The segments of freeway microtrips identified by ERG as taki ng 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 driv ing do not contain ramp operati on. Another minor modification to the schedules for use in MOVES was made to th e time field in order to signify, within a drive schedule, when one microtrip ended and one be gan. The time field increments two seconds instead of one when each new micr otrip 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 ar e intended to cover mo st of the driving on these respective roadtypes. However, some sp eed distributions for non-freeway roadtypes will include average speeds faster than the fastest non-f reeway cycles. The reverse will be true for some freeway speed distributions. In these ca ses, the model will use appropriate average speed drive schedules from a different roadtype. Th is 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, wh en the average speed is very low or very high, the roadtype has little imp act on the driving pattern.
For Final MOVES2009 we plan to incorpor ate additional driving schedules and to replace many of the older driving schedules, whic h we do not think adequately represent today’s vehicles and driving behavior. A contractor has developed 45 driving schedules for light

- duty vehicles. 5 7 

### These are based on urban and rural da ta collected in California in 2000 and 2004. The proposed mapping of driving cycles to road types for Final MOVES2009 is summarized in Table 14-2, below. This mappi ng would apply to passenger cars, passenger trucks and light commercial trucks. We also hope to have additio nal 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 ch aracterized as LOSB, but now considered AB.



### 82

# 15. SourceTypeHour

The SourceTypeHour table pr ovides one data field: IdleSHOFactor.



## 15.1. IdleSHOFactor
The IdleSHOFactor field is the number us ed to determine the number of hours of extended idling for each Source Type by day of th e week and hour of the day. Extended idling, also referred to as "hoteling," is defined as a ny long period of discretiona ry 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 exte nded 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., 5 8 

### 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 desc ribed in the Federal Register. 5 9 

### 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 av erage 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 Dr iving Schedules for H eavy-Duty Vehicles," 6 0 

### combines data from several instrumented truck studies. The data contai ns detailed information about truck driver behavior; howev er, none of the trucks in any of the studies was involved in long haul, interstate activ ity. We assumed that all long haul truck trips have the same hourly truck trip distribution as the heavy heavy-duty trucks in the instru mented 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 durati ons from the Lutsey report was applied to these trip-end distributions. From these calculations, we es timated the number of hours of truck operation and hours of truck hoteling. For MOVES, we then cal culated the ratio of hoteling hours to truck operation hours for each hour of the day. W eekday data was used for both weekday and weekend fractions.

83E 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
HourIdleSHOFactor



### Note that the Draft MOVES2009 defaults assu me no anti-idling measures or truck-stopelectrification efforts. In futu re versions of MOVES, we intend to make it easier for users to modify the inputs of extended idling behavior to account for new or lo cally 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, fo r national runs using Draft MOVES2009 this table is used for all calendar ye ars. 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 en tered 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. 6 1 

### These estimates are based on the Highway Performance Monitoring System (HPM S) data collected by the Federal Highway Administration 6 2 

### for use in transportation planning and vehicle type breakdowns from the EPA MOBILE6 Emission Factor model. 6 3 

### 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)(( C ountyVMT(i,j,k,m)/Average Speed(k,m))) / (Sum(ij)((CountyVMT(i,j,k,m)/AverageSpeed(k,m)))
The county allocation values for each roadwa y type sum to one for the nation. Although the data is from 1999 calendar year estimates, th e same allocations are used for all calendar years.

85

# 17. Zone

In Draft MOVES2009, activity data and meteorol ogical data are assigne d 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 va lues for four fields: CountyID
, StartAllocFactor
, IdleAllocFactor, and SHPAllocFactor. CountyID is the identifier fo r the county in which the zone is located. StartAlloc Factor geographically allocates domain-wide start activity. IdleAllocFacor allocates extended idle activ ity, and SHPAllocFactor allocates time parking (important for evaporative emissions). While geographic allocations clearly change over time, fo r national runs using Draft MOVES2009 this table is used for all calendar ye ars. 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 en tered 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 nati onal 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. 6 4 

### The NEI estimates are based on the Highway Performance Monitoring System (HPMS) data collected by the Federal Highway Administration 6 5 

### for use in transportation planning and vehicle type breakdowns from the EPA MOBILE6 Emission Factor model. 6 6 

### The NEI VMT estimates have been incorporated in to 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 al location calculated using the fo llowing formula, where “i” represents each individual county.
CountyAllocation(i) = ( CountyV MT(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 determ ine the hours of extended idling in each zone in each calendar year. No sources exist that directly measure exte nded 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 represen t long-haul trips. Extended idling mainly occurs among the largest (Class 8) trucks, which are now almost ex clusively diesel. Since we have estimates for

86

### the amount of rural and urban interstate VMT by Cl ass 8 heavy-duty diesel trucks in each county of the nation, we can use this estimate to creat e a national allocation fa ctor for extended idling hours. The actual total demand for overnight parki ng by trucks has been estimated by the Federal Highway Administrati on on a state by state basis. 6 7 

### These estimates were used to determine the allocation to each State(i) using the following formula:
StateAllocation(i) = StateParkingDema nd(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., Hawa ii, Virgin Islands, or Puerto Rico, which make sense, since none of these areas have VMT associated with rural or ur ban interstates. The estimate for the VMT from Class 8 heavy-duty diesel trucks by county comes from the 1999 National Emission Inventory (NEI) analysis. 6 8 

### The NEI estimates are based on the Highway Performance Monitoring System (HPM S) data collected by the Federal Highway Administration 6 9 

### for use in transportation planning and vehicle type breakdowns from the EPA MOBILE6 Emission Factor model. 7 0 

### The NEI VMT estimates have been incorporated into the National Mobile Inventory Model (NMIM) county database. The VMT estimates were obtained from the NM IM database. VMT estimates for Class 8 heavy-duty diesel trucks on rural and urban inte rstates were determined for each county in each state and the allocation calculated using the follo wing formula where “j” refe rs 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 stat e 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 Gaso line 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 aggregat e emissions to the user’s choice of SourceType or SCC using the SCCVTypeDistribution table. For each combination of SourceType, Model Year and FuelType, the S CCVTypeDistribution table lists IDs for the possible SCC and the fraction of vehicles assigned to each SCC. The full SCC also includes a suffix that indi cates 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 S CCRoadTypeDistribution ta ble described below.



## 18.1. SCCVtypeDistribution Because the existing SCCs only identify gaso line 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 tr ucks 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 inform ation 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. 7 1 

### The resulting truck mappings are too complex to summarize here, but are available in the MOVES database.

88

### Table 18-1. SCC Mappings fo r 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 calcula tions are done for MOVES roadtypes, the SCCRoadTypeFraction provides an allocation of emissi ons 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 geogr aphically, the mapping of MOVES roadTypes to SCCRoadTypes varies by zone (in this ca se, 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 (NE I) VMT by the 1999 NEI average speed. Both measures were documented by Pechan & Associates. 7 2 

### The NEI VMT estimates are based on the Highway Performance Monitoring System (HPMS) data collected by the Federal Highway

89

### Administration 7 3 

### for use in transportation planning and vehicle type breakdowns from the EPA MOBILE6 Emission Factor model. 7 4 

### 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 us ing 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 SCCRoadT ype 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 qua dratic equation that calculates air conditioning activity demand as a function of the heat index. These term s are applied in the calculation of the A/C adjustment in the ener gy consumption calculator. The methodology and the terms themselves were originally derive d for MOBILE6 and are documented in the report “Air Conditioning Activit y Effects in MOBILE6.” 7 5 

### 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 differe nt A/C activity demand functions at a given heat index by season and time of day (this accounts for differences in so lar 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 coeffici ents 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 Conditi oning 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 be tween vehicle trips. (T his between-trip duration is often called “soak time.” To determine typical patterns of trip starts and ends, MOVES uses information from instrumented vehicles. This da ta is stored in two tables: SampleVehicleDay and SampleVehicleTrip. The first table, SampleVehicleDay, lists a “s ample population” of vehicles, each with an identifier (vehID), an indicati on of vehicle type (sourceTypeID), and a “dayID” that indicates whether the vehicle is part of the weekend or wee kday 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 tr ip (hourID), the trip number of the prior trip (priorTripID), and the times at wh ich the engine was turned on and off for the trip (keyOnTime and keyOffTime, each recorded in minut es since midnight of the day of the trip). To account for overnight soaks, many first trip s 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 SampleVehicleD ay 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. 7 6, 7 7 

### 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 av erage urban activity across the U.S. For vehicle classes that were not represented in the available data, th e 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 tr ips is summarized in Table 20.2. The resulting trip per day estimates are summ arized and compared to MOBILE6 in Table 20.3.

Table 20.1. Source Data for Samp le 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

# 21. References

1 

### U.S. Census Bureau, 1997 Vehicle Inventory and Use Survey, CD-EC97-VIUS. January 2000. Online at www.census.gov/prod/www/abs/vius-pdf.html

2 2 

### U.S. Census Bureau, 2002 Vehicle Inventory and Use Survey. Online at www.census.gov/svsd/www/vius/2002.html

3 

### U.S. Census Bureau, 1992 Truck Inve ntory and Use Survey. Online at www.census.gov/svsd/www/92vehinv.html

4 

### R.L. Polk & Co., National Vehicle Populati on Profile.® Southfield, MI. 1999. Information online at http://usa.polk.com/Products/1_nvpp.htm.

5 

### R.L. Polk & Co, Trucking Industry Profile TIP® Vehicles in Operati on. Southfield, MI. 1999. Information online at http://usa.polk.com/Products/14_tipnet.htm

.

6 

### U.S. Federal Highway Administration. Highway Statistics, 1999 . Table MV-1, “State Motor Vehicle Registrations,” October 2000. Online at www.fhwa.dot.gov/ohim/hs99/index.htm

7 

### U.S. Federal Highway Administration. Highway Statistics, 1999 . Table MV-10, “Bus Registrations,”October 2000. Online at www.fhwa.dot.gov/ohim/hs99/index.htm

8 

### U.S. Federal Highway Administration. Highway Statistics, 1999 . Table VM-1, “Annual Vehicle Distance Travelled in Miles and Related Data by Highway Vehicle Category and Vehicle Type,” October 2000. Online at www.fhwa.dot.gov/ohim/hs99/index.htm

9 

### U.S. Federal Highway Administration. Highway Statistics, 1999 . Table VM-2, “Functional System Travel,” January 2002. Online at www.fhwa.dot.gov/ohim/hs99/index.htm

10 

### U.S. Federal Transit Administration. Na tional Transit Database 1999, Table 29. “Age Distribution of Active Revenue Vehicle Inventory: Details by Transit Agency.” Online at www.ntdprogram.com

11 

### Bobit Publications, School Bus Fleet Fact Book. Torrance. CA, 1999. www.schoolbusfleet.com

12 

### Browning, Louis, Michael Chan, Doug Coleman, and Charlotte Pera. ARCADIS Geraghty & Miller Inc. “Update of Fleet Characterization Data for Use in MOBILE6 - Final Report.” M6.FLT.002, EPA420-P-98-016, June 1998. Online at www.epa.gov/otaq/models/mobile6/m6flt002.pdf

9413 

### Energy Information Adminstration. Annual Energy Outlook 2003 ( AEO2003 ), Report #: DOE/EIA-0383 (2003), released January 9, 2003. Online at www.eia.doe.gov/oiaf/archive/aeo03/index.html

14 

### Energy Information Administration, Supplem ental Tables to the Annual Energy Outlook 2006, Transportation Demand Sector, February 2006. Online at www.eia.doe.gov/oiaf/archive/aeo06/supplement/index.html

15 

### Davis, Stacy C. and Susan W. Diegel, Tran sportation Energy Data Book, Edition 22. Center for Transportation Analysis, Oak Ridge Na tional Laboratory. ORNL-6967. September 2002.

16 

### Davis, Stacy C. and Susan W. Diegel, Tran sportation Energy Data Book, Edition 23. Center for Transportation Analysis, Oak Ridge Nati onal Laboratory. ORNL- 6967. October 2003.

17 

### Davis, Stacy C., Susan W. Diegel and R obert G. Boundy, Transportation Energy Data Book, Edition 27. Center for Transportation Anal ysis, Oak Ridge National Laboratory. ORNL6991. 2008. Online www-cta.ornl.gov/data/

18 

### Ward's Automotive Inc. www.wardsauto.com/

19 

### Hart, Larry. R.L. Polk & Compa ny. Personal communication, June 16, 2003.

20 

### National Household Transportation Survey (NHTS). 2001 Online at http://nhts.ornl.gov/download.shtml

21 

### Motorcycle Industry Council, 1998 Population. Available in EPA Docket A-2000-01, II B-22

22 

### American Bus Association. “Motorcoach Census 2000,” conducted by R. L. Banks and Associates, Inc. July 2000. www.buses.org/indus try/ABA-RLBanksReport.pdf

23 

### American Public Transportation Association, 20 07 Public Transportation Fact Book as cited in TEDB 27, Table 5.13 (page 5-20).

24 

### Motorcycle Industry Council. Motorcycle Statistical Annual Ma gazine. 2000 and 2002. 2 Jenner St., Suite 150, Irvin, CA 92718. Phone: 714 727-4211, Fax: 714 727-4217. www.mic.org/

25 

### John Koupal. M6.ACE.001 “Air Conditioning Activity Effects in MOBILE6,” EPA420-R-01­ 054, November 2001. www.epa.gov/otaq/models/mobile6/r01054.pdf

26 

### Motorcycle Industry Counc il, “On-Highway Motorcycle s 1998 Population Estimate.” November 21, 1999. Available in the U.S. EPA docket: A-2000-01, II B-22.

27 

### NHTSA. "Vehicle Survivability and Travel Mileage Schedules," DOT HS 809 952, January 2006. www-nrd.nhtsa.dot.gov/Pubs/809952.PDF

9528 

### NHTSA, 2006.

29 

### Davis, Stacy C. and Lorena F. Truitt. “Invest igation of Class 2b Trucks (Vehicles of 8,500 to 10,000 lbs GVWR),” Oak Ridge National La boratory. ORNL/TM-2002.49, March 2002.

30 

### U.S. Federal Transit Administration (FTA). “S tudy & Report to Congress: Applicability of Maximum Axle Weight Limitations to Ov er-the-Road and Public Transit Buses,” December 2003.

31 

### American Bus Association, July 2000.

32 

### Good Sam Club, "Highways Member Study 2000." TL Enterprises, Inc., Ventura, California. (805) 667-4100.

33 

### Koupal, November 2001.

34 

### Davis and Diegel, 2007

35 

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36 

### Davis and Truitt, March 2002.

37 

### Davis and Truitt, March 2002.

38 

### Union of Concerned Scientists. www.ucsusa.org/

39 

### U.S. Federal Transit Administration, December 2003.

40 

### U.S. Federal Transit Administration, December 2003.

41 

### Yuji Horie, Craig Tranby and Steven Sidawi , Valley Research Corporation. “On-Road Motor Vehicle Activity Data: Volume I – Bus Popul ation and Activity Pattern, Final Report.” Tables 3-9 & 2-2. Contract A132-182. Prep ared for California Air Resources Board, September 1994.

42 

### Brian, Mac. Recreational Vehicle Industr y Association. Phone conversation, October 29, 2003.

43 

### Nam, Edward and Robert Giannelli, “Fue l Consumption Modeling of Conventional and Advanced Technology Vehicles in the Phys ical Emission Rate Estimator (PERE),” EPA420-P-05-001, February 2005. Available online at www.epa.gov/otaq/models/ngm/420p05001.pdf

9644 

### USEPA Code of Federal Regualtions. (CFR ) 40 section 86.529-78 and United Nations (UN) “Worldwide Harmonised Motorcycle Emissi ons Certification Procedure”, Informal document No. 15, 46th GRPE, 19-23 May 2003, agenda item number 3. www.epa.gov/epahome/cfr40.htm

45 

### USEPA. “IM240 and Evap Technical Guidance,” EPA420-R-00-007, April 2000. Online at www.epa.gov/otaq/regs/im/r00007.pdf

46 

### Warila,J. “Derivation of Mean Energy Consumption Rates within the MOVES Modal Framework”, 14th Coordinating Research C ouncil On-Road Vehicle Emissions Workshop Poster Session, San Diego, California, March 29-31, 2004.

47 

### Petrushov, V.A., “Coast Down Method in Ti me-Distance Variables,” SAE 970408, February 24, 1997. www.sae.org/

48 

### U.S. Environmental Protection Agency, “Dra ft Motor Vehicle Emission Simulator (MOVES) 2009 Software Design and Reference Manua l,” EPA420-b-09-007, March 2009. Online at www.epa.gov/otaq/models/moves/420b09007.pdf

49 

### Sierra Research, Inc. Memo from Tom Carls on to John Koupal, "Analysis of Rural Average Speed Distributions for MOVES," Purchase Order EP05B00129, December 1, 2004.

50 

### Sierra Research, Inc. Memo from Tom Carls on to John Koupal, "Analysis of Rural Average Speed Distributions for MOVES," Purchase Order EP05B00129, December 1, 2004

51 

### Festin, Scott. “Summary of National and Regional Travel Trends: 1970-1995,” Office of Highway Information Management, Dept. of Transportation, May 1996. Online at www.fhwa.dot.gov/ohim/bluebook.pdf

52 

### U.S. EPA, March 2009.

53 

### Sierra Research, Inc. M6.SPD.001 “Developmen t of Speed Correction Cycles.” EPA Contract No. 68-C4-0056, Work Assignment 2-01, June 26, 1997. Online at www.epa.gov/otaq/models/mobile6/m6spd001.pdf

54 

### USEPA Combined Federal Regi ster. CFR 40, 86, Appendix I. www.epa.gov/epahome/cfr40.htm

55 

### Hart, Constance. “EPA's Onboard Analysis Shootout: Overview and Results.” EPA420-R-02­ 026, October 2002. Online at www.epa.gov/otaq/ngm.htm
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97 5657 

### Sierra Research, “Development of Generi c Link-Level Driving Cycles.” SR2009-05-02 EPA Contract EP-C-05-037, Work Assignment 3-02, May 5, 2009.

58 

### Lutsey, Nicholas, Christie-Joy Brodrick, Dani el Sperling, and Carollyn Oglesby. "Heavy-Duty Truck Idling Characteristics - Results from a Nationwide Truck Survey." Annual Meeting of the Transportation Research Board, January 2004.

59 

### USEPA Combined Federal Register Vol. 65, No. 85, Tuesday, May 2, 2000. Proposed Rules, 49 CFR Parts 350, 390, 394, 395 and 398. Online at www.fmcsa.dot.gov/Pdfs/050200p.pdf

60 

### Eastern Research Group, August 2003.

61 

### Pechan, E.H. & Associates, Inc. "Docum entation for the Onroad National Emissions Inventory (NEI) For Base Years 1970-2002," prepared for EPA Office of Air Quality Planning and Standards, January 2004. Online at ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/mobile/onroad_nei_base1970 _2002.pdf.

62 

### U.S. Federal Highway Administration (FHA). Highway Performance Monitoring System Field Manual . OMB No. 21250028, December, 2000.

63 

### Jackson, September 2001.

64 

### .Pechan & Associates, Inc. October 2002.

65 

### U.S. Federal Highway Administration, December 2000.

66 

### Jackson, September 2001.

67 

### Fleger, Stephen A., Robert P. Haas, Jeffrey W. Trombly, Rice H. Cross III, Juan E. Noltenius, Kelley K. Pécheux, and Kathryn J. Chen. "Study of Adequacy of Commercial Truck Parking Facilities." Table 7. U.S. Fede ral Highway Administration, FHWA-RD-01-158, March 2002. Online at www.tfhrc.gov/safety/pubs/01158/

68 

### Pechan & Associates, Inc. October 2002.

69 

### U.S. Federal Highway Administration, December 2000.

70 

### Jackson, September 2001.

71 

### Davis and Truitt, March 2002.

72 

### Pechan, E.H. & Associates, Inc., January 2004.

9873 

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74 

### Jackson, September 2001.

75 

### Koupal, November 2001.

76 

### Sierra Research, “Development of Trip a nd Soak Activity Defaults for Passenger Cars andTrucks in MOVES2006,” SR2006-03-04, EPA Contract EP-C-05-037, Work Assignment No. 0-01, March 27, 2006.

77 

### Sierra Research, “Development of Trip and Soak Activity Defaults for Passenger Cars and Trucks in MOVES,” SR2007-06-01, EPA Cont ract EP-C-05-037, Work Assignment No. 1­ 01, June 29, 2007.

99