Eiip Volume 4 Mobile Sources Preferred And Alternative Methods


             United States
             Environmental Protection
             Agency
Office of Air Quality
Planning and Standards
MD-14
EPA-454/R-97-004d
July 1997
r/EPA      EIIP Volume IV
             Mobile Sources
             Preferred and Alternative
             Methods

-------
                                   PREFACE
As a result of the more prominent role given to emission inventories in the 1990 Clean
Air Act Amendments (CAAA), inventories are receiving heightened priority and
resources from the U.S. Environmental Protection Agency (EPA), state/local agencies,
and industry.  More than accountings of emission sources, inventory data are now
providing the prime basis for operating permit fee systems, State Implementation Plan
(SIP) development (including attainment strategy demonstrations), regional air quality
dispersion modeling assessments, and control strategy development.  This new emphasis
on the use of emissions data will require significantly increased effort by state/local
agencies to provide adequate, accurate, and transferrable information to meet various
agency and regional program needs.

Existing emission inventory data collection, calculation, management, and reporting
procedures are not sufficient or of high enough quality  to meet all of these needs into
the next century. To address these concerns, the Emission Inventory Improvement
Program (EIIP) was created. The EHP is a jointly sponsored endeavor of the  State and
Territorial Air Pollution Program Administrators/Association of Local Air  Pollution
Control Officials (STAPPA/ALAPCO) and the U.S. EPA, and is an outgrowth of
recommendations put forth by the Standing Air Emissions Work Group (SAEWG) of
STAPPA/ALAPCO.  The  EIIP Steering Committee and technical committees are
composed of state/local agency, EPA, industry, consultant, and academic representatives.
In general, technical committee participation is open to anyone.

The EIIP is defined as a program to develop and use standard procedures to collect,
calculate, store, and report emissions data. Its ultimate goal is to provide cost-effective,
reliable, and consistent inventories through the achievement of the following objectives:

       •    Produce a coordinated system of data measurement/calculation methods as
            a guide for estimating current and future  source emissions;

       •    Produce consistent quality assurance/quality control (QA/QC) procedures
            applicable to all phases of all inventory programs;

       •    Improve the  EPA/state/local agency/industry system of data  collection,
            reporting, and transfer; and
       •    Produce an integrated source data reporting procedure that consolidates
            the many current reporting requirements;

-------
EIIP goals and objectives are being addressed through the production of seven guidance
and methodology volumes. These seven are:

      •      Volume I:         Introduction and Use of EIIP Guidance for Emissions
                               Inventory Development
             Volume II:         Point Sources Preferred and Alternative Methods
             Volume III:        Area Sources Preferred and Alternative Methods
             Volume IV:        Mobile Sources Preferred and Alternative Methods
             Volume V:         Biogenics Sources Preferred and Alternative Methods
             Volume VI:        Quality Assurance Procedures
             Volume VII:       Data Management Procedures

The purpose of each volume is to evaluate  the existing guidance on emissions estimation
techniques, and, where  applicable, to identify the preferred and alternative emission
estimation procedures.  Another important  objective in each volume is to identify gaps in
existing methods, and to recommend activities necessary to fill the gaps. The preferred
and alternative method findings are summarized in clear,  consistent procedures so that
both experienced and entry-level  inventory  personnel can  execute them with a reasonable
amount of time and effort. Sufficiently detailed references are provided to enable the
reader to identify any supplementary information. Users  should note that the number of
source categories or topics covered in any volume is constantly expanding as a function
of EIIP implementation and availability of new information.

It is anticipated that the EIIP materials will become the guidance standard for the
emission inventory  community. For this reason, the production of EIIP volumes will be
a dynamic, iterative process where documents are updated over time as better data and
scientific understanding support improved estimation, QA, and data management
methods. The number  of individual source categories addressed by the guidance will
grow as well over time. The EIIP welcomes input and suggestion from all groups and
individuals on how the  volumes could be improved.

-------
 VOLUME IV: CHAPTER 2
PREFERRED AND ALTERNATE
METHODS FOR GATHERING AND
LOCATING SPECIFIC EMISSION
INVENTORY DATA
June 1996
                   Prepared by:
                   EVIRON Corporation
                   Sonoma Technology, Inc.

                   Prepared for:
                   Mobile Sources Committee
                   Emission Inventory Improvement Program

-------
                                   DISCLAIMER

This document was furnished to the Emission Inventory Improvement Program and the U.S.
Environmental Protection Agency by EVIRON Corporation, Novato, California, and Sonoma
Technology, Inc., Santa Rosa, California. This report is intended to be a final document and has
been reviewed and approved for publication. The opinions, findings, and conclusions expressed
represent a consensus of the members of the Mobile Sources Committee of the Emission
Inventory Improvement Program. Any mention of company or product names does not
constitute an endorsement by the U.S. Environmental Protection Agency.

-------
                              ACKNOWLEDGEMENT

This document was prepared by EVIRON Corporation and Sonoma Technology, Inc., for the
Mobile Sources Committee, Emission Inventory Improvement Program, and for Greg Janssen of
the Office of Mobile Sources, U.S. Environmental Protection Agency. Members of the Mobile
Sources Committee contributing to the preparation of this document are:
Rob Altenburg, Co-chair, Pennsylvania Department of Environmental Protection
Greg Janssen, Co-chair, Office of Mobile Sources, U.S. Environmental Protection Agency
Kwame Agyei, Puget Sound Air Pollution Control
Lynne Hamlin, Texas Natural Resource Conservation Commission
Mark Janssen, Lake Michigan Air Directors Consortium
Tom Kearney, New York Department of Transportation
David Lax, American Petroleum Institute
Wienke Tax, Region 9, U.S. Environmental Protection Agency
Emission Inventory Improvement Program                                                   Hi

-------
CONTENTS
Section                                                               Page

1     Introduction and Summary  	  1-1

         Development of Registration Distributions and Mileage Accumulation
         Rates from I/M Program Data	  1-2
         Development of Fleet Characteristics and Activity from Remote
         Sensing Program Data	  1-4
         Development of Fuel Consumption and VMT from Tax Revenue and
            Other Data Sources  	1-5

2     Use of Local I/M Data to Develop Registration and Mileage Accumulation
      Distributions	  2-1

         Introduction	  2-1
         Literature Review	  2-2
         Methodology for Calculating Registration and Mileage Accumulation
         Distributions Using Inspection and Maintenance Program Data	2-6
         Example Application	2-16

3     Use of Remote Sensing Data to Generate Vehicle Activity Characteristics	  3-1

         Overview of Fleet Data from Remote Sensing Programs	3-2
         Literature Review	  3-5
         Methodology 	 3-10
         Example Applications  	 3-19

4     Estimation of Mobile Source Fuel Consumption and Area VMT	4-1

         Introduction	  4-1
         Literature Review	  4-1
         Methodology for Estimating VMT  	  4-5
         Example Applications  	 4-13

 5     References 	   5-1
                                              Emission Inventory Improvement Program
 iv

-------
  CONTENTS
  Section

  Appendix A:  List of Contacts in Federal and State Agencies
  Appendix B:  Estimated Vehicle Fuel Economies by Vehicle Class and Model Year
  Appendix C:  Estimated National Average VMT Mix for 1994
  Appendix D:  Multivariate Linear Regression Procedure
  Appendix E:  Technique to Extract National Average Travel Fractions and Fuel Economics
             From the MOBILES a Source Code
Emission Inventory Improvement Program

-------
FIGURES
Page

Figure 2-1:    Relationship Between Registration Status of California
             Vehicles and Period of Time with Expired Registrations	2-25

Figure 2-2:    Distributions of Mileage Accumulation by Number of Vehicles 	2-26

Figure 2-3:    Comparison of MOBILES and Arizona Registration
             Distributions for All Light Duty Vehicles  	  2-27

Figure 2-4:    Arizona I/M Mileage Distribution Across All Vehicle Ages 	2-28

Figure 2-5:    Comparison of Mileage Accumulation Distributions Under
             Different Truncation Schemes  	  2-29

Figure 2-6:    Comparison of Mileage Accumulation Distributions Under
             Four Different Truncation Schemes	  2-30

Figure 3-1:    Two sites on opposing directions of Highway 50 in Sacramento showing
             weekday morning and afternoon commute patterns 	3-31

Figure 3-2a:  LDGT1 registration distribution data determined from the California
             Pilot I/M Program remote sensing data and the emission factor models,
             MOBILESa andEMFACTF	  3-32

Figure 3-2b:  LDGV registration distribution data determined from the California
             Pilot I/M Program remote sensing data and the emission factor models,
             MOBILESa and EMFAC7F	  3-33

Figure 3-3a:  Weekday diurnal travel distributions from EPS2 (EPA, 1992) and from
             the remote sensing data of the California Pilot I/M Program  	3-34

Figure 3-3b:  Saturday diurnal travel distribution calculated from the remote sensing
             data of the California Pilot I/M Program	  3-35

Figure 3-4:   24-hour weekday diurnal travel distributions from EPS2 (EPA, 1992)
             and from the remote sensing data of the California Pilot I/M
             Program 	3-36

                                                 Emission Inventory Improvement Program
vi

-------
TABLES
                                                                               Page

Table 2-1:  Summary of Registration Distribution by Age Records	  2-20

Table 2-2:  Summary of Annual Mileage Accumulation Rates Records	  2-21

Table 2-3:  MOBILES Default Mileage Accumulation Rates per Vehicle, by
           Vehicle Class, All Calendar Years	  2-22

Table 2-4:  Mileage Accumulation Rates Calculated in California using
           I/M and DMV Data	  2-23

Table 2-5:  Summary of Decision Criteria Used in Development of Mileage
           Accumulation Rates from I/M Data	  2-24

Table 3-la: Unadjusted and Adjusted LDGV Registration Distributions
           Data from the California Pilot I/M Program	  3-25

Table 3-lb: Unadjusted and Adjusted LDGT1 Registration Distribution
           Data from the California Pilot I/M Program	  3-26

Table 3-2:  Population-weighted Average Fleet Ages Based on Registration
           Distribution Data	  3-27

Table 3-3:  Population-weighted Fleet Average Ages for Twelve Randomly
           Selected Sites of the California Pilot I/M Program Database 	  3-27

Table 3-4:  24-hour Weekday Diurnal Distribution from EPS2 (EPA, 1992)	  3-28

Table 3-5:  VMT Mix Data for the Sum of all Sites and for Twelve
           Randomly Selected Sites of the California Pilot I/M
           Program Database	  3-29

Table 3-6:   MOBILESa 1994 Default VMT Mix Data and VMT Mix Data
           Calculated from the California Pilot I/M Program Database	  3-30
 Emission Inventory Improvement Program                                                  Vll

-------
TABLES
                                                                              Page

Table 4-1:   Gasoline Distributed in Selected States for 1994 (FHWA, 1994)	  4-7

Table 4-2:   On-road Special Fuels Consumption in Selected States for 1994
           (FHWA, 1994)	  4-7

Table 4-3:   Emission Factors for Gasoline Dispensing Facilities (1 lb/1000
           gallons throughput)	4-9

Table 4-4:   Loss Rates for Gasoline Dispensing Facilities (gallons lost/1000
           gallons throughput)	4-10

Table 4-5:   1994 VMT Mix by Class Predicted by MOBILESa	4-12

Table 4-6:   Sacramento County and California 1994 Relevant Statistics	4-14

Table 4-7:   Sacramento County/State Proportions for Various Factors	4-15

Table 4-8:   Maricopa County Data Obtained for 1994	4-17

Table 4-9:   Maricopa County/State Proportions for Various Factors	4-18

Table 4-10: Calculation of National Average Fleet Fuel Economics from
           MOBILESa Defaults	4-20
 vm

-------
1

INTRODUCTION AND

SUMMARY

As part of the Emission Inventory Improvement Program (EIIP), The U.S. Environmental
Protection Agency (EPA) is developing a number of guidance documents to provide information
on potential ways of improving emission inventories. Two volumes present guidance on ways to
improve the estimation of motor vehicle emissions. This volume presents guidance on the use of
three specific data sources for use in developing distributions used in the MOBILE emission
factor model. These distributions include registration, mileage accumulation, travel
distributions, fuel consumption, and vehicle miles traveled (VMT). Volume II covers the use of
transportation models to estimate variables such as VMT, reconciliation of VMT with the
highway performance monitoring system (HPMS), speeds, trip durations, and operating mode
weighting factors.

In this document, methodologies and example calculations of the estimation of local on-road
vehicle fleet characteristics and activity from inspection and maintenance (I/M) program, remote
sensing, and fuel sales data are presented. These data resources provide a relatively untapped
source for the evaluation of local fleet characteristics and activity data. Specific evaluations
include the estimation of mileage accumulation rates, registration distributions, diurnal travel
distributions and regional vehicle miles traveled (VMT). This guidance is separated into three
topics according to the type of data being evaluated:

• development of registration distributions and mileage accumulation rates from I/M
program data;

• development of registration distributions, diurnal travel distributions, VMT mix, fleet
registration information, and I/M program status from remote sensing program data; and

• development of fuel consumption and VMT from tax revenue and other data sources.

The provision of guidance on the three areas listed above is not intended to imply there are not
other data sources, nor that all areas will be able to utilize the sources discussed. For example,
some areas may not have remote sensing programs or have only small pilot programs. Others
may have I/M programs that do not collect all the data needed for developing registration or
mileage accumulation distributions. As another example, there are data sources such as the
Truck Inventory and Use Survey, which is performed by the U.S. Department of Commerce.
This survey is a source of data for registration and mileage accumulation for light, medium, and
heavy-duty trucks. However, coverage of such sources is not within the scope of this document.

Emission Inventory Improvement Program 1-1

-------
INTRODUCTION AND SUMMARY 06/96

The remainder of this section briefly summarizes the methodologies covered in this report.

DEVELOPMENT OF REGISTRATION DISTRIBUTIONS AND
MILEAGE ACCUMULATION RATES FROM I/M PROGRAM
DATA

Inspection and Maintenance (I/M) program data can be used to develop registration distributions
and mileage accumulation rates for use in the MOBILE model. I/M programs are required in
moderate and worse ozone nonattainment areas as well as in carbon monoxide nonattainment
areas. The programs generally cover light- and medium-duty vehicles and sometimes include
heavy-duty vehicles as well.

Registration distributions describe, for each vehicle class (i.e., light-duty autos), the fraction of
vehicles of various ages (i.e., percent of light-duty autos that are 10 years old). The distribution
is used in weighting the fleet emission factors. The default registration distribution in the
MOBILE model is commonly used. This distribution represents the 1990 national average,
derived from registration data maintained by R. L. Polk, and may be inappropriate for some
areas of the U.S. Many areas have unique characteristics and may have significantly more older
or newer vehicles than the national average. Since the registration distribution affects not only
the calculated emission factors but also the calculated effectiveness of mobile source control
measures, it is useful to have a locality-specific distribution. For example, a locality with many
older vehicles would have a higher benefit from a retired vehicle program than would be
estimated for this area if they used a national default registration distribution.

The vehicle mileage accumulation rates also affect calculated emission factors. Mileage
accumulation, expressed as annual miles driven is used to calculate deterioration of the emissions
as the vehicle ages. The mileage accumulation is also used in the calculation of vehicle travel
fractions. There are significant differences between urban and rural areas, as well as among
different areas of the U.S. in mileage accumulation by age. The default national mileage
accumulation rate used in MOBILE was developed using national travel survey results.

The data collected by I/M programs generally include sufficient information to develop locality-
specific distributions. As a part of registration requirements, most light-duty vehicles in
nonattainment regions are required to submit to an I/M test. These data provide a logical
opportunity for developing locality-specific registration and mileage accumulation distributions.
The data collected in I/M programs that may be used to determine mileage and registrations
distributions include:

• vehicle class (i.e., truck or auto),

1-2 Emission Inventory Improvement Program

-------
06/96 INTRODUCTION AND SUMMARY

• vehicle model year,

• license plate or Vehicle Identification Number (VIN),

• county of registration,

• date of test (useful since the data recorded on retests are often not as carefully recorded as
the initial test), and

• odometer reading.

Some areas have already used I/M data to develop locality-specific mileage accumulation rate.
California is an example of one state that has recently developed I/M-based rates. In California,
the Bureau of Automotive Repair and the Air Resources Board (ARB) have developed
distributions for various purposes, including use with California's emission factor model EMFAC
to estimate mobile source emissions. A study was also performed for the EPA in 1985 that
developed locality-specific distributions in Arizona, Connecticut, and Washington. Section 2 of
this document reviews relevant literature on the use of I/M data for this purpose.

Section 2 also presents a detailed methodology for estimating the registration distributions and
mileage accumulation rates, including data collection and initial analysis. This methodology is
briefly summarized below. It is relatively straightforward to develop the distributions from the
I/M data. For registration distributions, vehicle data from a given time period of an I/M program
(i.e. a quarter or a year) are analyzed. Examining either license plates or VIN numbers, vehicles
with more than one record in the data (due to retests, for example) are removed. Then, vehicles
are grouped together by vehicle class. For each vehicle class, the number of vehicles of that
class of a given age is divided by the total number of vehicles of that class to derive the fraction
of the vehicles that are of a given age. The set of fractions is the registration distribution for a
given vehicle class.

For mileage accumulation distributions similar steps are followed, except that two time periods
of I/M data are needed. For example, in a biennial program, at least two years would be needed
(for example, 1992 and 1994). After removing retests from each individual year, the number of
miles accumulated on the odometer between the two years is divided by the number of days
between tests and multiplied by 365 to estimate annual miles driven. Then vehicles of the same
age are grouped together. For each group, the annual miles for each vehicle are summed
together and this sum divided by the number of vehicles in the group to calculate the average
annual miles for the age group.
Emission Inventory Improvement Program 1-3

-------
INTRODUCTION AND SUMMARY                                                   06/96
When calculating annual mileage accumulation rates, there are several ways to handle unusual
odometer readings. Many researchers remove odometer readings that indicate very low or very
high accumulated mileage in a given time period. There is great variation in what is considered
very low or high. It is recommended that the accumulated mileage be graphed before making
these decisions to see whether there are obvious break points in the distribution. Odometer
rollover may be indicated if the odometer reading from a later test is lower than for an earlier
test. If 100,000 miles are added to the later reading and the estimated annual miles accumulated
is within the limits decided upon, then rollover can be assumed.   More detailed discussion of
this issue is provided in Section 2.
DEVELOPMENT OF FLEET CHARACTERISTICS AND
ACTIVITY FROM REMOTE SENSING PROGRAM DATA

Data from large-scale remote sensing programs offer a valuable source of information that can be
used to generate regional fleet characteristics data for input into the MOBILE emission factor
model and mobile source emission inventory development. Vehicle license plates are typically
recorded using video cameras as part of remote sensing programs. The license plates can be
cross checked with state motor vehicle records providing valuable information on the
characteristics of the local fleet. The potential areas of evaluation include vehicle registration
distributions, fleet VMT mix, diurnal travel distributions, diesel sales fractions, evaluation of
unregistered vehicles, and evaluation of vehicle county/state of origin to determine vehicle I/M
status. Combining the license plate data with site location data allows for the evaluation of fleet
data by region, subregion, or by facility class (i.e., roadway classification). Although there are
physical limitations  in the remote sensing apparatus that restrict its use to certain roadway
facility and vehicle classes, the data can accurately evaluate the light-duty vehicle classes in
several types of on-road locations.

The following data are typically collected in a remote sensing program and can be used to
evaluate activity and characteristics of the vehicle fleet:

    •  site description and location,
    •  time of day,
    •  date of measurement,
    •  vehicle license plate,
    •  vehicle identification number (VIN),
    •  vehicle model year,
    •  vehicle class,
    •  vehicle fuel type,
    •  county and/or state of registration, and

 1-4                                                  Emission Inventory Improvement Program

-------
06/96 INTRODUCTION AND SUMMARY
• registration expiration date.

Of these, the last five items generally need to be determined from state registration records
and/or through decoding the vehicle identification number (VIN). Complications may arise due
to variation of state maintained data or due to VIN decoding limitations; these are discussed
further in Section 3.

Section 3 of this document presents the methodologies and example calculations of the
evaluation of fleet activity and characteristics data from remote sensing programs. In addition,
this section also includes background information on related previous work in the form of a
literature review. The methodology included in Section 3 provides step-by-step instructions for
completing the evaluations and handling potential biases and limitations of the remote sensing
database. The example calculations are based on remote sensing data recently collected as part
of the California Pilot I/M Program. These data are from a large-scale remote sensing program
completed in the city of Sacramento from July to September 1994. Over three hundred sites
were evaluated and approximately two million valid remote sensing readings were obtained.
Combining the remote sensing data with state records produced 1,329,694 remote sensing
records with matched department of motor vehicle records; 47 percent coverage of the eligible
Sacramento vehicle population was achieved.
DEVELOPMENT OF FUEL CONSUMPTION AND VMT FROM
TAX REVENUE AND OTHER DATA SOURCES

Section 4 describes procedures to estimate VMT based on fuel consumption and fuel economy.
VMT estimates are an integral part of any mobile source emission inventory. Unfortunately,
VMT estimates are also subject to substantial uncertainty, and can vary depending upon the tools
used to develop current estimates and forecast future travel. Therefore, emission modelers may
wish to use a simple screening tool in order to independently verify their VMT estimates. The
fuels-based screening tool described in Section 4 can help analysts get a "ballpark" estimate for
VMT based on how much fuel is consumed in a given area, and the expected fuel economy (in
miles-per-gallon) of the vehicle fleet. This estimate can then be compared to VMT estimates
produced through transportation demand modeling, the Highway Performance Monitoring
System (HPMS), or other methods used locally. Major discrepancies between the ballpark
estimate and traditionally generated VMT statistics should signal a need for further review and
analysis. It should be noted that this screening method serves as a double-check, not as a
replacement, for other estimation techniques.

The methodology to estimate VMT includes five steps:
Emission Inventory Improvement Program 1-5

-------
INTRODUCTION AND SUMMARY	06/96


1.   Estimate on and off-road fuels use using Federal Highway Administration (FHWA) data.
    Fuel use is distinguished between gasoline and other fuels such as diesel.
2.   Disaggregate state data for use at the county level using economic and population statistics.

3.   Adjust fuel consumption figures to account for refueling losses using EPA or ARB data to
    make these adjustments. (Since the modifications made by this step are usually small, this
    step can be eliminated in most cases; the methodology includes this to be complete.)

4.   Calculate fleet fuel economy using EPA data unless local data are available.

5.   Estimate VMT based on the fuel  consumption and fuel economy information developed in
    steps 1 through 4.

To better describe the methodology, Section 4 includes sample calculations for two metropolitan
areas: Sacramento County, California and Maricopa County, Arizona.
 1-6                                                   Emission Inventory Improvement Program

-------
2

USE OF LOCAL  I/M DATA TO


DEVELOP  REGISTRATION AND


MILEAGE  ACCUMULATION


DISTRIBUTIONS	

Two key inputs to the MOBILE model are the mileage accumulation rates and registration
distributions. These distributions can significantly affect the calculated emission factors. Many
nonattainment areas use the default registration and mileage accumulation distributions in the
MOBILE model when developing emission factors.  These default distributions are national and
are not necessarily accurate for a given locality.  The use of locality-specific registration and
mileage accumulation distributions can increase the accuracy of the emission factors calculated
by MOBILE.  Inspection and Maintenance (I/M) program data can be used to develop area-
specific registration distributions and mileage accumulation rates for use as MOBILE  inputs.
I/M program data bases normally include odometer readings, vehicle age, vehicle class, and data
necessary to estimate miles accumulated between annual or biennial I/M tests. This section
presents a literature review, describes the methodology for using I/M data to develop locality-
specific registration and mileage accumulation distributions, and provides example calculations
using a subset of a state I/M data base. While other data sources such as remote sensing data or
surveys may also be used, they are not the subject of this chapter.


INTRODUCTION

Mileage accumulation rates describe the average number of miles driven per year for a given
vehicle class and age. Typically, vehicles are driven more when they are newer and less
frequently as they age. The mileage accumulation rate is the main factor affecting the emission
deterioration rate; the more miles on a vehicle the higher the predicted MOBILES a emission rate.

Registration distributions describe the fraction of vehicles on the road by vehicle class and  age.
Registration distributions are combined with mileage accumulation rates to determine the
weighting factors for each model year required for estimating total emissions by vehicle class.
These weighting factors, also called travel fractions, describe the fraction of total vehicle miles
traveled (VMT) for each model year and vehicle class. The distribution significantly  affects
emission rates; the age distribution of a given vehicle class determines the average emission rate
from that vehicle class.

Emission Inventory Improvement Program                                           2-1

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
AND MILEA GE ACCUMULA TION DISTRIBUTIONS 06/96
I/M data can be used to develop locality-specific registration and mileage accumulation
distributions. I/M data from either centralized or decentralized areas can be used. Both maintain
I/M data at a central location, and the I/M data collection methods are relatively standard. In this
section we first review existing documents that describe such calculations or provide related
guidance. We then describe in detail the methods of analysis that can be used to develop the
registration and mileage accumulation distributions from local I/M data.
LITERATURE REVIEW

The literature review covers areas that have used I/M data in developing registration data and
mileage accumulation rates and other relevant literature. The first three references are
summarized very briefly as they do not discuss specifically how to use I/M data in the
development of registration distributions and mileage accumulations. However, they do provide
a context for this type of analysis.

The following references were reviewed:

1. Software Requirement Specification for TAS260 Vehicle Miles Traveled (BAR, 1994)

2. Analysis of Data from the California Enhanced I/M Program (EPA, 1995),

3. On-Road Motor Vehicle Activity Data Volume II: Vehicle Age Distribution and Mileage
Accumulation Rate by County (CARB, 1994),

4. Methodology for Estimating Emissions from On-Road Motor Vehicles Volume II: Weight
(E7FWT) by California Air Resources Board (CARB, 1993); and

5. Estimation of Mileage Accumulation Rates and I/M Failure Rates from I/M Program Data
(EEA, 1985)

6. Volume IV: Mobile Sources, Procedures for Emission Inventory Preparation (EPA, 1992),

7. MOBILESa User's Guide (EPA, 1994),

8. Federal Test Procedure Review Project Preliminary Technical Report (EPA, 1993),

A review of these sources follows.
2-2 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
06/96 AND MILE A GE A CCUMULA TION DISTRIBUTIONS
SOFTWARE REQUIREMENT SPECIFICATION FOR TAS260 VEHICLE MILES TRAVELED
(BAR, 1994)

This report discusses how the California Bureau of Automotive Repair (BAR) developed mileage
accumulation estimates and registration distributions using I/M data. They use these
distributions primarily for developing reports on vehicle miles traveled that summarize the
number of vehicles, average miles traveled, standard deviation and error, average current and
previous HC and CO readings, and the percent failing. These summaries are provided for five
model year groupings for light-duty autos (1955-1971,1972 -1974,1975-1979,1980-1989,
1990 and newer). Totals are listed for light-duty trucks, medium-duty trucks, heavy-duty trucks.

BAR's methodology was to first match vehicles by license plate number using the earliest and
the latest test dates when a license plate had more than one test in a given year. They then
calculated the number of days between the tests and eliminated tests less than 300 days apart or
more than 1100 days apart. If any of the following conditions occurred the data were also
discarded:

• The vehicle was less than two years old and odometer showed more than 60,000 miles
traveled in a year.

• The vehicle was two years old and odometer showed more than 50,000 miles traveled in a
year.

• The vehicle was more than two years old and odometer showed more than 40,000 miles
in a year.

The document does not discuss how this procedure was derived. Presumably it is intended to
account for the fact that newer vehicles are driven more than older vehicles and therefore should
have higher cut-offs. No tests were conducted for unusually low mileage. However, if the
odometer showed fewer miles in a later test, than an earlier test, then the following adjustments
were made:

1. 100,000 miles were added to the odometer reading in the current year.

2. The previous year odometer reading was subtracted from the current year reading.

3. If the result in Step 2 was greater than 100,000; 100,000 was subtracted from the
result.
Emission Inventory Improvement Program 2-3

-------
USE OF LOCAL l/M DA TA TO DEVELOP HEG/STftA TION
AND M1LEAGE ACCUMULA TION DISTRIBUTIONS 06/96
In order to calculate the number of miles traveled in a year, the odometer reading (plain or
adjusted according to the above three steps) was divided by the number of days between the
earliest and latest tests and multiplied by 365 to determine the vehicle miles traveled per year.

ESTIMATION OF MILEAGE ACCUMULATION RATES AND I/M FAILURE RATES FROM I/M
DATA (EEA, 1985).

The primary objective of this study was to derive annual mileage accumulation rates of light-duty
autos and trucks as a function of age using I/M data. The report notes that the default mileage
accumulation rates (in MOBILE2) were derived from a 1979 voluntary survey of families living
in single unit households. The study utilized I/M data from Arizona, Washington, and
Connecticut, which tested between 1.2 and 4.2 million vehicles per year. Energy and
Environmental Analysis (EEA) used data from initial tests (as opposed to retests). Inspections
were matched using the Vehicle Identification Number (VIN), vehicle make, license plate, and
model year. In most cases the VIN number alone generated matches. In cases where VIN
matching failed, the license plate, make, and model were matched. All three of these variables
had to match in order for a match to be considered successful. The distinction between light-
duty trucks and light-duty autos was made using VIN decoder software developed by the
Highway Loss Data Institute called VINDICATOR. In cases where the VIN decoding software
could only determine the model year within a range of years, the model year recorded by the I/M
program was used to verify the results.

Data editing procedures focused on anomalous odometer readings. It was found that Arizona and
Connecticut recorded only three-digit odometer readings in thousands of miles. The third digit
was recorded only if the motorist told the inspector that the odometer had rolled over. In
addition, it was found that in Seattle a two-digit odometer reading was recorded and listed as 99
if the motorist told the inspector that the odometer had rolled over. Therefore EEA deleted all
odometer readings of 99. They also deleted odometer readings greater than 200,000 because
they were concerned that higher readings indicated that the reading had potentially been shifted
accidentally (as in 700,000 being recorded for 70,000).

In analyzing the data, EEA found high growth in the number of vehicles with low odometer
readings as vehicles aged. This was a result of owners not reporting rollover to the inspectors.
They also noted that there was a chance that an owner could report an odometer rollover in one
year but not the next, potentially leading to an erroneous deduction of a second rollover.

EEA assumed that vehicles driving less than 500 miles per year were suspect, and eliminated
vehicles which had over 60,000 miles per year.
2-4 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
06/96 AND MILEAGE ACCUMULATION DISTRIBUTIONS
They present an example calculation of mileage accumulation, which has the following steps:

1. Subtract latest odometer reading from previous reading.

2. If the difference is negative, add 100.

3. Determine the interval between test dates (in days).

4. Multiply the odometer reading by the ratio of 365 to the number of days calculated in
step 3 to calculate the accumulation rate.

EEA found significant differences between states in mileage accumulation rates. For example,
new Arizona light-duty autos traveled over 12,000 miles per year; the same vehicle classes in
Seattle and Connecticut traveled over 14,000 per year (14,319 and 14,738, respectively). Cars
more than twelve years old traveled 6,798 miles per year in Connecticut, 7,981 in Seattle, and
7,428 in Arizona.

ON-ROAD MOTOR VEHICLE ACTIVITY DATA VOLUME II: VEHICLE AGE DISTRIBUTION
AND MILEAGE ACCUMULATION RATE BY COUNTY (ARB, 1994).

This study was performed by Valley Research Corporation (VRC) for the California Air
Resources Board. The objectives of the study were to develop county level registration
distributions and mileage accumulation rates based on California's I/M data. VRC used
California Department of Motor Vehicle (DMV) registration records, BAR's I/M data, and the
DMV's VIN decoding software in this effort.

Annual mileage accumulations were calculated using a pair of odometer readings from the same
vehicle that were at least six months apart. The statewide average mileage accumulation rate was
11,061 miles per year and tended to be higher in urban than in rural areas.

VRC used the VIN number not only to match vehicles but also, whenever possible, to determine
vehicle class, weight, and model year. The BAR data do include indicators for model year,
whether auto or truck, fuel type, and gross vehicle weight; however, VRC believed the data were
not necessarily correct although they do not indicate the reason for this belief. In order to
determine the model year, VRC used the VIN number; when the VIN result produced an illogical
result, the DMV data on the first year sold was used. For some reason, roughly only half the
vehicles in the BAR database were matched and had more than one odometer reading (out of 24
million records, only 13 million were matched).
Emission Inventory Improvement Program 2-5

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
AND MILEAGE ACCUMULA TION DISTRIBUTIONS 06/96
VOLUME IV: MOBILE SOURCES, PROCEDURES FOR EMISSION INVENTORY
PREPARATION (EPA, 1992).

This document discusses procedures for generating emissions for mobile sources, including on-
road vehicles, aircraft, and locomotives. It also provides guidance on selecting MOBILE inputs
such as registration distributions and mileage accumulation. Locality-specific mileage
accumulation rates by age require 200 input values: for each of the 25 model years for each of
the eight vehicle classes, the estimated annual mileage accumulation must be supplied. The same
number of inputs is required for locality-specific registration distributions: for each vehicle type,
a set of 25 values would be used to represent the fraction of all vehicles of that type that are a
given age.

This volume cautions that local mileage accumulation data sources can be subject to sampling
bias or data entry errors and recommends that local annual mileage accumulation rates should not
change from one evaluation year to the next. It does not discuss recommended sample sizes.

MOBILE5A USER'S GUIDE

The MOBILE users guide provides a detailed summary of the record formats for mileage
accumulations rates and the registration distribution records. Once locality-specific mileage
accumulation rates and registration distributions are developed, an agency may refer to these
tables and the text from these sections of the users guide) particularly 2.2.3 in order to
incorporate the distributions into the MOBILE model.

FEDERAL TEST PROCEDURE REVIEW PROJECT PRELIMINARY TECHNICAL REPORT
(EPA, 1993).

This report discusses the Federal Test Procedure, in-use driving emissions, driving survey
methods, and test cycle development methods. It was suggested for review but does not contain
information useful for this work effort.
METHODOLOGY FOR CALCULATING REGISTRATION AND
MILEAGE ACCUMULATION DISTRIBUTIONS USING
INSPECTION AND MAINTENANCE PROGRAM DATA

This section presents methodologies for using Inspection and Maintenance (I/M) data to develop
locality-specific registration distributions and mileage accumulation rates by vehicle class. The
use of I/M data, which is usually available for virtually all registered vehicles in an area, can

2-6 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
06/96 AND MILEAGE ACCUMULA TION DISTRIBUTIONS
provide a significant improvement in accuracy over the use of default values or of distributions
developed using sampling methods such as traffic counts. These improvements can result in
more accurate emission inventories and provide insight as to the most appropriate control
measures. For example, if the locality-specific registration distribution shows a higher fraction
of older light-duty vehicles than the defaults, the effect of a vehicle scrappage program will be
shown to be higher. These methods are straightforward although there are some variations in the
manner in which anomalies in the data can be identified and addressed.

In this section, data collection methods and issues are first described, including issues that should
be considered when obtaining the data, data costs, formats, and initial processing. The initial
processing is needed both for the development of mileage accumulation rates and registration
distributions. Next, the methodology for developing registration distributions, including data
processing procedures and the handling of issues such as unregistered or out-of-state vehicles is
discussed. Finally, the methodology for developing mileage accumulation distributions,
including data processing procedures, and how to identify and address anomalous odometer
readings is described. Included in the methodology for calculating mileage accumulation
distributions are suggested procedures that may be used to address anomalous odometer
readings. A more qualitative evaluation of the odometer data can be substituted for these
procedures to obtain reasonable results that are significant improvements over the use of default
distributions.

DATA COLLECTION

Obtaining I/M data will usually take at least one month after formally ordering the data from the
appropriate agency. In most cases it is best to allow for approximately two to three months to
fulfill the data request. It will also take time to identify the correct personnel and procedures for
ordering.

There is great variation in the electronic media, format, costs, and staff availability to fill data
requests. As these issues can become time consuming to the point that they over-ride technical
considerations in actually developing the distributions, they are discussed in some detail below.

Ordering the Data

Start by identifying the agency responsible for the I/M program in the state of interest. Often the
best way to proceed will be to contact personnel with responsibility for mobile source issues at
the state air agency. In some states the air agency itself will collect the data; in others the state
Department of Motor Vehicles; in others, a private contractor responsible for the program. If a
private contractor is responsible it will be helpful to alert the state air agency to your efforts to
collect the data. This will give the agency the opportunity to monitor the contractor's procedures
for transferring data as well as information on who in the state is developing local-specific data.

Emission Inventory Improvement Program 2-7

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
AND MILEA GE ACCUMULA TION DISTRIBUTIONS 06/96
It is important to identify the appropriate office from which to order the data. For example, in
New York state, the DMV Freedom of Information Office is responsible for I/M data.

It is also important to find out exactly what vehicles are tested and what data fields are available.
Although I/M programs are legally required only for light-duty vehicles, some states also test
medium and heavy duty vehicles. When this is the case, additional information regarding the
extent and type of the program, the representation of diesel vehicles, and representation of
vehicles registered out-of-state (if any) should be discussed.

Data Costs

There will often be a charge for the data, even if the data is requested by other agencies. In
addition, the I/M program may not always collect the information necessary to differentiate
between autos and trucks. This issue should also be discussed before ordering the data. Some
states charge a flat fee for each month or quarter. For example, Arizona currently charges $185
per month of data. Others charge a nominal flat fee, often less than $10 plus any time used by a
programmer to fulfill the request. Other states do not have established procedures and treat each
request on a case-by-case basis.

Data Formats

Initially, consideration should be given to the quality of the data. Some areas have long periods
for which the tape media upon which the data are recorded has been corrupted and cannot be
used at all. Data needed for developing a registration distribution by county include vehicle age,
vehicle class (LDA or LDT), and county of residence. Many I/M programs record these items
directly. It is critical to be able to differentiate between autos and trucks, and the manner in
which this is done should be discussed. If I/M data for vehicle class are missing or suspect,
information from the VIN can be used. In 1980, an internationally used VIN number system was
adopted by most auto manufacturers and can be used for 1981 and later vehicles. This system
readily identifies a vehicle's model year, vehicle class, and gross vehicle weight. For vehicles
manufactured prior to 1981, VIN decoding software such as the California Bureau of Auto
Repair's VINI program or the Highway Data Loss Institute's VINDICATOR software can be
used to determine the model year and vehicle class. However, VIN decoding software can be
difficult to acquire and VRC rated its results as unreliable.

Many agencies routinely analyze the I/M data in various ways and therefore have the ability to
develop subsets of the data. This can be helpful, as most raw I/M data records have close to one
hundred fields and can be very awkward to manipulate. It is not uncommon to test a million cars
a month; the volume of data is quite substantial. Given this, it can be very helpful to order only
the data needed for the analysis (if the agency has the ability to provide it in this way).
2-8 Emission Inventory Improvement Program

-------
                                       USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
06/96                                       AND MILEAGE ACCUMULA TION DISTRIBUTIONS
The media upon which the data are supplied needs to be discussed with the providing agency. It
is critical to know the precise characteristics of the data and media your agency or company can
handle and be sure they are compatible with those of the agency providing the data.  It is difficult
to provide the data on floppy disks; even when compressed, millions of records can require
numerous floppies.  Common media are direct file transfer through modem or the Internet, 4 mm
DAT tapes, 8 mm exobyte tapes, or 9 track tapes. Electronic mail should not be used to transfer
large data sets because most electronic mail software cannot conveniently handle very large file
sizes. Unless it is certain that your operating system and software are compatible with that being
used by the transmitting agency, data are most easily transferred when written in ASCII format.

It is also important that data formats  are understood.  For example, some agencies record the
odometer reading in thousands rather than the entire reading. A model year may be recorded as a
four or a two-digit number. Similarly, missing data codes should be understood, listed in
writing, and checked after receipt of the data. It is not uncommon for a missing data code to be
useable as actual data (i.e. an odometer reading of "99").

Specific data needed for developing  a registration distribution for a given time period (i.e. a year
or a quarter) are:

   •   vehicle class (whether passenger car or truck, and the weight class for trucks),

   •   model year,

   •   test date,

   •   license plate and/or VIN code, and

   •   county of registration.

It is recommended that only the counties within the nonattainment area of interest be used (as
opposed to the entire state).  If there  is a mix of both rural and urban counties in the
nonattainment area it may even be desirable to develop two registration distributions, one for the
urban and one for the rural counties.

Conceptually, to develop a registration distribution all that is needed are the vehicle class and
model.  However, vehicles will be tested more than once in a given time period if they fail a test
or are sold.  Inclusion of all records would thus bias the distributions. Data from the first test
date should be used as records for the first test may often be more carefully completed than for a
re-test.  The license plate and/or VIN code can be used to identify vehicles that have been tested
more than once.


Emission Inventory Improvement Program                                                     2-9

-------
USE OF LOCAL f/M DA TA TO DEVELOP REGISTRA TION
AND MILEAGE ACCUMULATION DISTRIBUTIONS 06/96
A reasonable registration distribution can often be developed using less than one full year of
data. Exceptions are areas where large numbers of vehicles of a certain type are registered at a
particular time. For example, trucks in Arizona must be registered by the end of the year so that
in November and December there is a disproportionate number of trucks being registered.

To develop a mileage accumulation distribution for two full time periods (i.e., two consecutive
years for an annual program or four years for a biennial program), data needs are the same as
listed above, with the addition of the odometer reading. For mileage accumulation, the test dates
are critical since they establish the number of days between tests and therefore the information
needed to calculate the number of miles driven per year. The license plates or VIN numbers are
needed to match vehicles at two points in time.

Initial Processing

It is recommended that after obtaining the data set, some basic checks be made to verify the
written description of the data format, to verify missing data codes, and to check for biases that
may be introduced due to entry practices. For example, it can be very useful to print out data
records for retests to see whether vehicle data on retests is entered as completely as for initial
tests. It would not be unusual to find the same odometer reading on a retest as on the initial test,
even for tests several days or weeks apart. Simply to print out several records of the fields that
are most critical for developing the distributions can be very helpful to look at questions such as:
How are the odometer readings listed? How are the test dates shown? How do the listed vehicle
class and weight classes correspond? Do the model years match the year first sold field?

METHODOLOGY FOR REMOVING VEHICLES TESTED MORE THAN ONCE IN A TEST
CYCLE FROM THE DATA SET

Repeated records for the same vehicle in a given test cycle (one year for annual program, two for
a biannual program) need to be removed. For the registration distribution, vehicles that have
been retested need to be removed because otherwise they will be counted twice in the registration
distribution. Since most retests involve a failure of the emission test, not removing retests from
the data will over-represent those vehicles which fail the first test and therefore bias the
distributions. It is likely that many of these vehicles will be older vehicles. For the mileage
accumulation, it is necessary to have a long period between retests in order to evaluate the
average miles accumulated over a period of time. Most retests are performed within a month of
the first test, which is not long enough to adequately represent annual miles accumulated since
there is often several variation in travel. Some retests (i.e., for vehicles that have been sold in
between cycles) may occur over six months apart, which may used to calculate a reasonable
representation of annual miles, but attempting to retain these data is likely to entail more effort
than justified by the benefit of including this small number of vehicles.
2-10 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
06/96 AND Ml LEA GE A CCUMULA TION DIS TRIBUTIONS
In order to determine whether more than one record for the same vehicle is present, the license
plate number, the VIN number, or both can be compared. If the license plate alone is compared
there is a smaller likelihood of data entry error than for the VIN number, which is much longer.
If both the license plate and the VIN number are used to match, the likelihood of one of those
being misrecorded is roughly the square of the likelihood that one would be in error.

If the license plate alone is compared, vehicles that have new license plates or license plates that
have been entered in error will be counted twice. If there are license plate entries that
erroneously duplicate the license plate of another vehicle, one will be mistakenly removed from
the data set. If the VIN number alone is compared, vehicles will be counted twice if one of the
test records is erroneous. If there are VIN number entries that are erroneously duplicated, the
VIN number of another vehicle will be mistakenly removed from the data set. This latter case is
less likely to occur (because of the size of the VIN number) than for license plates. If the
combination of VIN and license plate numbers is used to indicate unique vehicles, it is very
unlikely that any vehicles would be counted twice. However, a large number of vehicles may be
removed from the data set due to data entry errors for either the VIN or the license plate. Since
the number of data entry errors is highest when matching on both the VIN and license plate
number, and higher when matching on the VIN than on the license plate, it is recommended that
license plate matching alone be performed. This will minimize the amount of data thrown out
when a valid match could have been made.'

In the absence of data base software that can generate unique record identifiers, the simplest
approach for removing retested vehicles is to write a brief computer program that stores the data
for each record. The license plate or VIN number (or both) on each new record is compared to
the stored records to check whether that license plate or VIN number has been recorded before.
If it has, and the record is for a later test that record would not be used. If the license plate or
VIN number has been stored already but the test date is earlier, then the record with the newer
test date should be removed from the data set.

METHODOLOGY FOR DEVELOPING THE REGISTRATION DISTRIBUTION

The registration distribution is a series of percentages of vehicles by age and class. For example,
the portion of the registration distribution for light-duty passenger autos will list, for each vehicle
age, the percent of light-duty autos that are of that age. Perhaps six percent would be one year
old vehicles, eight percent would be two year old vehicles, and so forth; the percentages by age
sum up to 100 percent. The methodology for developing the registration distribution is relatively
straightforward. The steps to be followed are described here.
'Another factor to consider is the availability of "vanity" plates. In states where vanity plates are available, VIN
matching may be preferable if vanity plates are popular (because converting from a regular plate to a vanity plate will create a
mismatch).

Emission Inventory Improvement Program 2-11

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
AND MILEAGE ACCUMULA TION DISTRIBUTIONS                                        06/96
Evaluate the Overall Characteristics of the Data

Calculate the total number of light-duty autos and light-duty trucks in the data set. If there are
other vehicle classes represented, total these also. From these values, calculate the fraction of
light-duty autos, trucks and any vehicle classes that are present. Ensure that these are reasonable
before proceeding further. A typical light-duty vehicle class distribution is 75 percent autos and
25 percent trucks although some areas may have higher percentages of trucks, such as Montana
or Wyoming.  If the distributions seem unreasonable, the vehicle class information in the data
may not be accurate. In this case the agency providing the data should be contacted and the data
reevaluated in light of the new information obtained.

Calculate the number of vehicles of each age and vehicle class and then divide the total by the
number in that vehicle class to obtain the registration distribution.  This is shown by the
following equation.



                               MYDist    J   J = -^	
                                      i, unadjusted    \  p(~)p
where:
       MYDist is the model year registration distribution for a given vehicle class for model year
       I, and

       POP is the vehicle population for that vehicle class for model year I.

For light-duty vehicles, MOBILE requires a combined registration distribution for diesel and
gasoline vehicles. MOBILE has diesel and gasoline sales fractions it will use to adjust these
vehicle classes internally to the model. Any vehicles greater than 25 years old should be counted
as 25 years old.  Also note that MOBILE does not distinguish emission factors for motorcycles
more than twelve years old. Therefore, any motorcycles that are more than twelve years old
should be included in the count for twelve-year old motorcycles.

Adjust the Distribution to July 1

The distribution needs to be adjusted to a July 1 distribution to reflect vehicle sales in the months
prior to the model year and  fleet turnover during the year.  Specifics of the calculations needed
for this adjustment are provided in Section 3. Write out the fractions in the format specified for
MOBILE  in Table 2-1. Note that there will be 200 values entered: fractions for each of the 25
model years for all vehicle classes except motorcycles. For motorcycles, write ".000" for

2-72                                                    Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
06/96 AND MILEAGE ACCUMULA TION DISTRIBUTIONS
motorcycles aged 13 to 25. If I/M data are not available for a particular vehicle class, write out
the MOBILE defaults for that class unless a registration distribution for that class has been
developed through an alternate method. Compare the calculated fractions to the default values in
MOBILE or to previously developed distributions to ensure that they appear reasonable.

Unregistered Vehicles

I/M data will of course not be available for non-registered vehicles, out-of-state vehicles, and
out-of-country vehicles. Such vehicles could contribute disproportionately to the actual
registration distribution and hence to estimated emissions but are difficult to account for.

A recent survey in California showed that roughly nine percent of the vehicles on the road are
unregistered (Sierra, 1992). However, this does not mean that all of these vehicles will never be
registered. As shown in Figure 2-1, many unregistered vehicles are registered within a few
months of expiration and nearly all unregistered vehicles are registered within two years of
expiration. This figure does not account for out-of-state vehicles. In areas located along the
Mexican border, it may be especially useful to account for out-of-state vehicles.

The only accurate way to account for unregistered and out-of-state vehicles is to perform a
survey. The Sierra (1992) survey for California's Air Resources Board looked at the registration
status of approximately 30,000 vehicles in parking lots in the Los Angeles area. While this
survey focused on registration tags, it would be straightforward to modify a similar survey to
include out-of-state vehicle counts. No such studies were identified in the course of preparing
this document.

METHODOLOGY FOR DEVELOPING THE MILEAGE ACCUMULATION DISTRIBUTION

Development of the mileage accumulation distribution is more complex than development of the
registration distribution. Data required include vehicle age, vehicle class, and two or more
odometer readings from each vehicle. This methodology focuses on mileage accumulation by
vehicle age (i.e., a ten-year-old vehicle is assumed to drive the same number of miles in the year
2000 as a ten-year-old vehicle in 1995), as is currently required in MOBILESa. In California,
county-specific mileage accumulation rates that are also model-year specific were developed for
use in the EMFAC7F model (which calculates California-specific motor vehicle emission
factors). Development of model-year specific mileage accumulation distributions can capture
important variability in specific years as well as any trends in driving patterns (for example
average driving distances to work becoming longer). However, this approach would necessitate
updating mileage accumulation distributions annually. Further, model-year specific mileage
accumulation rates cannot be readily incorporated into the current version of the MOBILE
model.
Emission Inventory Improvement Program 2-13

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
AND MILEAGE ACCUMULA TION DISTRIBUTIONS                                       06/96
Briefly, the methodology entails averaging annual miles accumulated by vehicles of a given age
and listing out the average for each vehicle age in the format specified in Table 2-2. The basic
procedure is described below. Procedures for addressing anomalous odometer readings are then
described. Note that the steps described should not be applied until anomalous odometer
readings have been either adjusted or removed from the data set.

Developing Data Sets of Unique Vehicles and Initial Processing

Develop, for each year of data, a data set containing only unique vehicles using one of the
approaches described above. It is recommended that a count of vehicles by model year and
vehicle class also be made at this time to evaluate whether the number of vehicles of a given age
is large enough to yield a reliable estimate of average annual miles driven by a vehicle of that
age: the more vehicles in a given age group, the more reliable the average annual miles will be.
Typically the oldest age groups will have the smallest fraction of vehicles; it is recommended
that the data base be large enough to have at least hundred vehicles in each of the age groups.2

Additional data summaries should also be examined, including:

        •      the range of raw odometer readings,
        •      a frequency distribution of raw odometer readings, and
        •      average odometer readings by vehicle age

This summary information can be valuable in determining whether the data set will be adequate
to develop the distribution, and whether there are particular vehicle ages in the data set that have
a high frequency of unusual odometer readings or significantly higher variation in the odometer
readings than other vehicle ages. Note that the variation in odometer readings is likely to be
larger for older vehicles.

Calculate the Annual Miles Driven by Each Vehicle

Subtract the earliest odometer reading from the later reading and divide by the number of days
between the two readings. If the difference between the odometer readings is negative, add
100,000 to account for odometer rollover. The value of the average daily miles is equal to the
number of miles driven between retests divided by the number of days between tests.

Subtracting the earliest odometer reading from the latest odometer reading and subtracting the
corresponding test dates (counting January one of the first test year as day one and January 1 of
        Based on our analysis of the example Arizona I/M data base, approximately 200 vehicles would be required in each
 age group in order to estimate the average annual mileage for each group within an uncertainty range of plus or minus 1000
 miles.

 2-14                                                    Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
06/96 AND MILEAGE ACCUMULATION DISTRIBUTIONS
the second test year as day 366 as long as the first test year was not a leap year), then multiplying
by 365 will yield the annual miles for a vehicle. If annual-miles are less than a specified
minimum or maximum (see section on anomalous odometer readings) the record can be removed
from the data.

To calculate average annual miles across vehicles within an age group, first group the vehicles by
age. It is recommended that vehicles older than 25 years be included in the set of 25-year old
vehicles.

Next, sum the annual miles for all vehicles in a group (i.e., sum annual miles for all 10-year-old
vehicles). Lastly, divide the sum of the annual miles by the number of vehicles in the age group.

Write out mileage accumulation to input into MOBILE according to the format specified in
Table 2-2. Compare them to the default mileage accumulation rates currently used in MOBILES
are listed in Table 2-3. For comparison, mileage accumulation rates calculated using California
I/M data are presented in Table 2-4. Note: mileage accumulation rates for one-year old vehicles
cannot be calculated. However, they may be estimated by fitting an exponential curve to the data
for the 2 year and older vehicles.

ANOMALOUS ODOMETER READINGS

When developing the mileage accumulation rates it is likely that some of the odometer readings
will not make sense. Some readings will imply unusually high or low miles per year; some are
recorded incorrectly; some individuals tamper with the odometers; and some odometers only
have five digits and do not register rollover after 100,000 miles. Table 2-5 presents some
examples of how researchers have addressed these issues. It is recommended that three general
factors be considered when addressing anomalous odometer readings:

1. The annual mileage accumulation will be an average for a large number of
vehicles and should not be unduly influenced by a few outliers that are incorrectly
handled due to the procedures used to address outliers. Any reasonable procedure
for identifying unusual odometer readings will be able to correct most of them.
No procedure will be able to identify and correct for all the anomalous readings.

2. The specific procedure chosen should be guided by the data itself. It is useful to
graph (at a minimum for all vehicle ages combined) annual mileage accumulation
at different ranges (for example in 2,000 mile increments) and the percentage
(and/or absolute number) of vehicles driving at each range. In most cases it will
be obvious what the outliers are; the graph will tail off smoothly at the higher
accumulations and suddenly there will be a jump. When there is an obvious point
at which the distribution seems to "jump", which will generally be in the

Emission Inventory Improvement Program 2-15

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA TION
AND MILEAGE ACCUMULA TION DISTRIBUTIONS 06/96
neighborhood of 50,000 miles per year, that can be used as the maximum annual
mileage allowed in the data set.

3. In a few cases, more detailed analysis may be helpful. If the distribution does not
break at any particular point and there are many vehicles which drive unusually
high numbers of miles, one can fit a distribution to the data and use the fitted
distribution to approximate at least the upper tail. An example of data where there
is no obvious place to "break" the distribution is presented in Figure 2-2.

Later in this chapter an example mileage accumulation calculation is presented using each of the
four sets of decision criteria listed in Table 2-5. It is noted there that using a mileage cut-off of
100,000 miles resulted in a distribution significantly higher from all the others. In addition, the
distribution implied that as vehicles aged, they drove more miles per year than newer vehicles.
The use of such a high cut-off can introduce bias since it is so much higher than the average
miles driven (approximately 12,000 miles per year). The other three sets of decision criteria
resulted in distributions that were similar in shape to each other and to the MOBILE default.
Therefore it is recommended that one of these be used.

Odometer Rollover

Odometer rollover is likely when a later odometer reading is lower than the earlier reading. It is
also possible that a lower odometer reading indicates odometer tampering, or there could be a
data entry error. If after adding 100,000 to the difference in odometer readings the annual miles
calculated falls within the limits decided upon in the steps above, it is recommended that the
reading be assumed reliable.
EXAMPLE APPLICATION

This subsection presents examples of calculating registration and mileage accumulation
distributions from I/M data using data from Arizona's I/M program. Several states and both
centralized and decentralized programs were considered for use in the example. The decision to
use Arizona data for the example was based on the fact that it was the first state for which two
usable years of I/M data were obtained and not because of any lack of data quality in the other
states considered.
2-16 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
06/96 AND MILEAGE ACCUMULATION DISTRIBUTIONS
DESCRIPTION OF THE ARIZONA I/M DATA

Arizona has maintained an annual basic I/M program for many years. Arizona tests medium and
heavy duty vehicles in addition to light duty autos and trucks. In 1995 they began a biennial
enhanced I/M program for 1981 and newer vehicles, run by a contractor. Electronic files are
maintained by the contractor for 12 months after collection and are then transferred to the
Arizona Department of Environmental Quality (ADEQ) for storage in archives.

Data were obtained for March, 1994 and 1995 for the example. If the data were intended for use
in actual inventory development it would be advisable to use at least three consecutive months in
each of two years to increase the number of vehicles in the database. This will increase the
sample size by including I/M tests made in the month before or after the month for which they
are scheduled. In this example this is particularly important because of the 1995 implementation
of the biennial enhanced program, which tested 50 percent of 1981 and newer vehicles in 1995.
The effect was to lessen the sample size of 1981 and newer vehicles. In states where a biennial
program has been in place for a number of years, it would be preferable to obtain three years
worth of data; in Arizona two consecutive years were useable since the program had just begun
and since 1981 and older vehicles were still subject to annual tests.

Data used in this example included the test date, vehicle identification number, license plate,
vehicle make, vehicle model year, vehicle style, vehicle fuel code, indicator for initial or retest,
weight class, number of cylinders, and odometer reading. It was initially expected that these data
would be sufficient to differentiate between light duty autos and light duty trucks but this was not
actually the case. In a real-world application, VIN decoding software or access to DMV data
would need to be utilized in order to differentiate between autos and trucks as there may be
significant differences in mileage accumulation and often in the registration distributions as well.
For the purpose of this example calculation this step was not taken.

There were 196,132 records in the March, 1994 data and 159,028 in the March, 1995 data. After
deleting retested vehicles there were roughly 164,000 records for 1994 and 131,000 for 1995.
62,007 vehicles, or approximately 47 percent, were matched based on the VIN number. This
match rate would be increased if more than a single month were used3. The match rate was also
lowered due to the implementation of the biennial program so that only half the 1981 and newer
vehicles were tested in 1995, whereas all had been required to be tested in 1994. Of these 62,000
For example, an analysis of two full years of British Columbia Inspection and Maintenance Program data conducted
for this document showed that roughly 50 percent of vehicles are matched when one month is used and 75 percent when
neighboring months are used. For example, a 75 percent match rate was observed when vehicles from February-April were
matched against vehicles tested in March of a different year. This is the result of individuals having their vehicles tested earlier
or later than the deadline.

Emission Inventory Improvement Program 2-17

-------
USE OF LOCAL I/M DA TA TO DEVELOP REGISTRA T10N
AND MILEAGE ACCUMULA TION DISTRIBUTIONS 06/96
matches, 4,311, or seven percent, had invalid odometer readings (defined as zero or blank entries
in the odometer field).

EXAMPLE APPLICATION FOR REGISTRATION DISTRIBUTION

A registration distribution for all light duty vehicles was estimated for 1993 and older vehicles
using the 1994 data. The 1995 data was not used for developing a registration distribution as
only 50 percent of the 1981 and newer vehicles were present in the data. In a real-world
application involving a partially annual and partially biennial program it would be necessary to
calculate distributions separately for vehicles subject to the annual program and to the biennial
program and then normalize them in order to combine them into one distribution.

Before developing the registration distribution, obvious anomalies in the data were identified and
deleted. For example, there were two vehicles with obviously invalid model years of 1995 and
1999 in the March, 1994 data. Repeated records for the same vehicle, such as retests were also
removed.

As discussed previously, the Arizona I/M program data does not explicitly differentiate between
autos and trucks. This is quite common; an informal survey revealed that most states rely on
VIN decoding software to distinguish between autos and trucks although some are planning to
change the I/M program to require that a distinction be made on the I/M record. It is reportedly
not always possible to make this distinction through visual inspection alone.

MOBILE calculates emission factors for vehicles up to 25 years old. Vehicles older than 25
years are placed in the 25 year old category. In keeping with this, all vehicles older than 25 years
were counted as 25 year old vehicles.

Figure 2-3 presents a comparison of the MOBILES registration distribution for all light duty
vehicles with the registration distribution calculated using the Arizona data. The MOBILE light
duty autos and trucks were combined together by weighting them according to the default light
duty auto and light duty truck travel fractions assumed in MOBILE. In this example, the
differences are significant in the older vehicles which also have the highest prevalence of high
emitting vehicles. There are striking differences in the later model years, with the Arizona
registration distribution being either much higher or much lower than the default MOBILE
distribution for corresponding years.

Note that the distribution does not include 1993. In Arizona, as well as many other states,
vehicles are not required to undergo an I/M test in their first year unless they are resold.
2-18 Emission Inventory Improvement Program

-------
USE OF LOCAL I/M DATA TO DEVELOP REGISTRATION
06/96 AND MILEAGE ACCUMULA TION DISTRIBUTIONS
EXAMPLE APPLICATION FOR MILEAGE ACCUMULATION

The data were first examined for odometer changes that were clearly anomalous. A simple
anomaly was when the difference in odometer readings between the two years was negative or
zero. Fully seventeen percent of the records had such differences. A visual inspection showed
that some were clearly rollovers while others were apparently errors or the result of tampering.

The odometer readings were then processed so that any mileage difference that was negative was
assumed to be due to odometer rollover and 100,000 miles were added to the reading. After
doing this, 1.1 percent were still negative and 3.7 percent were still greater than or equal to
100,000 miles.

The annual miles calculated after adding 100,000 miles to negative mileages are presented in
Figure 2-4. Most of the vehicles are seen to drive near the national average annual miles driven
although the distribution has a long tail showing very high annual miles.

Mileage accumulation distributions were then calculated using each of the sets of decision
criteria listed in Table 2-5. These are graphed in Figures 2-5 and 2-6. Figure 2-5 shows all four
distributions along with the MOBILE default distribution. Each distribution shows higher miles
per year than the MOBILE default. The VRC truncation approach, which accepts as valid
vehicles listed as driving up to 100,000 miles per year, is significantly higher that all the other
distributions. The average miles driven per year with the VRC approach is 20,000 miles. The
average of the next highest approach, that of BAR, is 11,300. The high number of older vehicles
with calculated high annual miles is thought to be due to a higher incidence of tampering and
errors in entering the odometer reading. Since odometers are more likely to be tampered in older
vehicles, the fraction of high calculated mileages (i.e., between 40,000 and 100,000 miles) for
older vehicles would also increase; this is what was observed in the Arizona data.

Figure 2-6 presents the EEA, BAR, and CARB distributions graphed on a scale that allows the
differences between them to be more clearly seen. The MOBILE default distribution forms a
smooth curve downward, starting at approximately 14,000 miles per year. The CARB
distribution is lower than the MOBILE default for newer vehicles up to five years old and is
higher for all later model vehicles. The EEA and BAR distributions are higher than the
MOBILE distribution for all years. The BAR distribution is slightly higher in the first year than
the EEA distribution because the BAR distribution allows for higher mileage the first year than
the EEA distribution does.
Emission Inventory Improvement Program 2-19