Acurex Environmental Final Report 97-1050
UPDATE OF FLEET CHARACTERIZATION DATA FOR
USE IN MOBILES
May 16, 1997
EPA Contract No. 68-C6-0068
Work Assignment No. 0-01
Acurex Environmental Project No. 7251
Prepared For
U.S. Environmental Protection Agency
Office of Mobile Sources
2565 Plymouth Road
Ann Arbor, MI 48105
By
Acurex Environmental Corporation
555 Clyde Avenue
P.O. Box 7044
Mountain View, California 94039
Acurex
Environmental
CORPORATION
A Geraghty & Miller Company
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FOREWORD
This report and the information and data described herein have been funded by
the USEPA under Contract 68-C6-0068, Work Assignment 0-01. It is being released for
information purposes only. It may not reflect the views and positions of the USEPA on
the topics and issues discussed, and no official endorsement by USEPA of the report or
its conclusions should be inferred.
This report has not been peer or administratively reviewed. .
This report was authored by Louis Browning, Doug Coleman and Charlotte Pera
of Acurex Environmental Corporation. Dr. Louis Browning was the Acurex
Environmental Corporation Project Manager. Ms. Tracie Jackson was the USEPA Work
Assignment Manager. Mr. Terry Newell was the USEPA Project Officer.
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TABLE OF CONTENTS
SECTION 1 INTRODUCTION 1-1
SECTION 2 REVIEW OF DATA SOURCES 2-1
2.1 SECONDARY DATA SOURCES 2-1
2.2 PRIMARY DATA SOURCES 2-3
SECTION 3 DATA ANALYSIS METHODOLOGIES 3-1
3.1 VEHICLE REGISTRATIONS 3-1
3.1.1 RL. Polk & Company Database 3-1
3.1.2 Federal Transit Administration Database 3-2
3.2 ANNUAL MILEAGE ACCUMULATION 3-3
3.2.1 Truck Inventory and Use Survey 3-3
3.2.2 Nationwide Personal Transportation Survey 3-6
3.2.3 Federal Transit Administration Database 3-7
SECTION 4 RESULTS 4-1
4.1 REGISTRATIONS 4-2
4.1.1 LDVs 4-2
4.1.2 LDTs 4-3
4.1.3 Heavy-Duty Trucks and School Buses 4-3
4.1.4 Transit Buses 4-3
4.1.5 Aggregated Classes 4-3
4.2 ANNUAL MILEAGE 4-3
4.2.1 LDVs 4-3
4.2.2 LDTs 4-4
4.2.3 HDGVs 4-4
4.2.4 HDGBs 4-4
4.2.5 HDDVs 4-4
4.2.6 HDDBs 4-5
4.3 TABLES 4-5
APPENDIX A-l
in
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LIST OF ILLUSTRATIONS
Figure 4-1 Registration Comparison by Model Year LDV
Figure 4-2 Registration Comparison by Model Year LDGT1 . . .
Figure 4-3 Registration Comparison by Model Year LDGT2 . . .
Figure 4-4 Registration Comparison by Model Year LDDT1 . . .
Figure 4-5 Registration Comparison by Model Year LDDT2 . . .
Figure 4-6 Registrations by Model Year HDGV1
Figure 4-7 Registrations by Model Year HDGV2
Figure 4-8 Registrations by Model Year HDGB School
Figure 4-9 Registrations by Model Year HDGB Transit
Figure 4-10 Registrations by Model Year HDDV(2B)
Figure 4-11 Registrations by Model Year HDDV(3)
Figure 4-12 Registrations by Model Year HDDV(4-5)
Figure 4-13 Registrations by Model Year HDDV(6-7)
Figure 4-14 Registrations by Model Year HDDV(8A)
Figure 4-15 Registrations by Model Year HDDV(8B)
Figure 4-16 Registrations by Model Year HDDB School
Figure 4-17 Registrations by Model Year HDDB Transit
Figure 4-18 Registrations by Model Year Aggregated LDGT ....
Figure 4-19 Registrations by Model Year Aggregated LDDT ....
Figure 4-20 Registrations by Model Year Aggregated LOT
Figure 4-21 Registrations by Model Year Aggregated HDGV ....
Figure 4-22 Registrations by Model Year Aggregated HDGB ....
Figure 4-23 Registrations by Model Year Aggregated HDDV(3-5)
4-6
4-7
4-8
4-9
4-10
4-11
4-12
4-13
4-14
4-15
4-16
4-17
4-18
4-19
4-20
4-21
4-22
4-23
4-24
4-25
4-26
4-27
4-28
IV
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LIST OF ILLUSTRATIONS (CONTINUED)
Figure 4-24 Registrations by Model Year Aggregated HDDV(8) 4-29
Figure 4-25 Registrations by Model Year Aggregated HDDV All 4-30
Figure 4-26 Registrations by Model Year Aggregated HDDB 4-31
Figure 4-27 Annual Miles by Age LDV 4-32
Figure 4-28 Annual Miles by Age LDGT1 4-33
Figure 4-29 Annual Miles by Age LDGT2 4-34
Figure 4-30 Annual Miles by Age LDDT1 4-35
Figure 4-31 Annual Miles by Age LDDT2 4-36
Figure 4-32 Annual Miles by Age HDGV1 4-37
Figure 4-33 Annual Miles by Age HDGV2 4-38
Figure 4-34 Annual Miles by Age HDGB School 4-39
Figure 4-35 Annual Miles by Age HDGB Transit 4-40
Figure 4-36 Annual Miles by Age HDDV(2B) 4-41
Figure 4-37 Annual Miles by Age HDDV(3) 4-42
Figure 4-38 Annual Miles by Age HDDV(4-5) 4-43
Figure 4-39 Annual Miles by Age HDDV(6-7) 4-44
Figure 4-40 Annual Miles by Age HDDV(8A) 4-45
Figure 4-41 Annual Miles by Age HDDV(8B) 4-46
Figure 4-42 Annual Miles by Age HDDB School 4-47
Figure 4-43 Annual Miles by Age HDDB Transit 4-48
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LIST OF TABLES
Table 1-1 Vehicle Types 1-2
Table 2-1 Data Sources and Base Years 2-3
Table 3-1 BUSDATA.DBF Data Structure 3-2
Table 3-2 Vehicle Type Codes Used in the Analysis 3-2
Table 3-3 TIUSDAT.DBF Data Structure 3-4
Table 3-4 Additional Fields in TIUSDAT.DBF 3-4
Table 3-5 LDVS3.DBF Data Structure 3-6
Table 4-1 Total VMT, Registrations, and Annual Mileage 4-2
Table 4-2 Vehicles in Operation as of July 1996 4-49
Table 4-3 Annual Mileage Raw Data 4-51
Table 4-4 Annual Mileage Curve Fit Data 4-53
Table 4-5 Vehicles in Operation as Percent of Class as of July 1996 4-55
Table 4-6 Vehicles in Operation, Aggregated Classes as of July 1996 4-57
Table 4-7 Vehicles in Operation, as Percent of Aggregated Classes as of July 1995 4-58
Table 4-8 Gasoline/Diesel Sales Fraction 4-59
VI
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SECTION 1
INTRODUCTION
The USEPA's highway emissions factor model, MOBILE, is the primary model used by state and
local agencies to simulate mobile source emissions generated in their areas. In order to model emissions
from motor vehicles, MOBILE must incorporate data characterizing the fleet of vehicles in use in the
United States. The vehicle population is characterized by the total number of vehicles in operation within
certain vehicle weight categories, the age distribution and fuel type (gasoline or diesel) within each
weight category, and the mileage accumulation rates specific to vehicle age, fuel type, and weight
category. The characterization for a given calendar year is based on a July 1 "snapshot".
Due to changes in economic and demographic conditions in recent years, there have been
significant changes in the age distribution, vehicle-type distribution, and mileage accumulation rates for
the current national motor vehicle fleet since the last version of the model (MOBILES a) was released in
March 1993. As such, the fleet characterization data currently used in MOBILE must be updated to
reflect these changes.
This study utilized the latest available data to update the characterization of the national fleet of
vehicles used in the MOBILESa model. The results of this study may be used in MOBILE6, currently
under development. Acurex Environmental Corporation reviewed potential sources of information and
analyzed the most relevant and useful data sources. The results of this study, as well as a description of
the data and methodologies used to obtain the results, are presented in this document.
Table 1-1 lists the individual vehicle type categories included in the study. Registrations and
average annual mileage as functions of vehicle age were developed for each of these categories.
Registrations were also summed for the entire set of vehicles and certain subsets.
With respect to registrations, Acurex attempted to use 1996 as the base year for the
characterization. Transit bus registrations are the exception, for which the base year is 1994. Similarly,
it was not possible to use 1996 as the base year for annual mileage accumulation rates, since sources of
these data were developed prior to 1996. The base year for each of these data sources will be identified
in the relevant subsections in Section 3, below.
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Table 1-1. Vehicle Types
Designation
Description
Gross Vehicle
Weight (Ib)
LDGV
LDDV
LDGT1
LDGT2
LDDT1
LDDT2
HDGV (classes 2B-3)
HDGV (classes 4-8)
HDDV (class 2B)
HDDV (class 3)
HDDV (classes 4-5)
HDDV (classes 6-7)
HDDV (class 8A)
HDDV (class 8B)
HDGB (school)
HDGB (transit)
HDDB (school)
HDDB (transit)
Light-duty gasoline vehicles
Light-duty diesel vehicles
Light-duty gasoline trucks
Light-duty gasoline trucks
Light-duty diesel trucks
Light-duty diesel trucks
Heavy-duty gasoline vehicles
Heavy-duty gasoline vehicles
Light heavy-duty diesel trucks
Light heavy-duty diesel trucks
Light heavy-duty diesel trucks
Medium heavy-duty diesel trucks
Heavy heavy-duty diesel trucks
Heavy heavy-duty diesel trucks
Heavy-duty gasoline school buses
Heavy-duty gasoline transit buses
Heavy-duty diesel school buses
Heavy-duty diesel transit buses
0 - 8,500
0 - 8,500
<6,000
6,001-8,500
<6,000
6,001-8,500
8,501-14,000
>14,000
8,501-10,000
10,001-14,000
14,001-19,500
19,501-33,000
33,001-60,000
>60,000
all
all
all
all
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SECTION 2
REVIEW OF DATA SOURCES
To develop a fleet characterization, Acurex reviewed numerous data sources for relevant and
accurate content. In this section, both the data sets analyzed, and those that were reviewed but not used
(or used minimally), are described. A description of the data sources reviewed but not used is found in
Section 2.1. A description of the data sources used for this study is found in Section 2.2. Analysis
methodologies are described in Section 3. Results of the analyses are found in Section 4.
2.1 SECONDARY DATA SOURCES
Most of the sources used minimally by Acurex were tables and charts listed in popular industry
publications. In most cases, Acurex found that either pertinent data from these sources was missing or
provided in a format that could not be translated into the weight class/age/fuel type breakdowns required
for this study. For example, often vehicles were tabulated according to location instead of weight or age.
In these cases, we used total vehicle data from these sources only to verify the final results obtained from
other data sources. These sources have been called "secondary data sources".
The Gallup Organization Final Report for the Motor Vehicle Manufacturers Association. 1993
The Gallop Organization study was conducted to determine private light-duty vehicle driving
patterns to help make estimations of emissions and fuel economy. It consisted of two samples, one taken
by telephone and the other by mail. The total number of respondents to both surveys was approximately
8,300. The data recorded includes a number of different items such as driver profile, vehicle make and
model, cargo carried, the number and purpose of trips, and the type of road utilized.
Vehicle age and mileage were recorded, but the results were not published in a cross-correlated
fashion. In other words, it is not possible to associate the vehicle's age with the miles it drove. Further,
to define a vehicle's classification, Gallup used their own definitions that generally were not consistent
with the vehicle categories required for this study. For example, Gallup divided vehicles into either
passenger cars or vans/trucks, with no reference to weight. Finally, the resultant data was not expanded
to national levels, but instead reported in percentages of the overall sample. Given these limitations,
Acurex felt that such data were not applicable for use in the present study.
1995 Motor Vehicles Facts & Figures (American Automobile Manufacturers Association)
The 7995 Motor Vehicles Facts & Figures by the American Automobile Manufacturers
Association (AAMA) is a compendium of tables and graphs sorted into several categories, such as
production, retail sales, registrations, ownership, travel trends, and automobile related fatalities. It has
a wealth of information pertaining to the motor vehicle industry, and was used by Acurex as a point of
departure for exploring other potential sources. It was not used for raw data itself because it breaks
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vehicles out into categories that are different from those used in this study. One table does categorize
passenger cars by model year, but it does so only for LDVs, and only back to model year 1979. The data
on vehicle registrations presented in this publication was derived from data maintained by R.L Polk &
Company (which was used extensively in this study). Acurex used the AAMA publication to verify total
numbers of vehicles on the road.
Automotive Fleet 1994 Fact Book (Bobit Publication)
In this annual publication, a number of quantitative tables characterizing the various vehicle fleets
in the United States are presented. Information about truck market share, yearly model registrations,
operating costs, and fleet size is provided, but few data referring to fleet age or mileage are included.
Acurex did not use information from this source.
School Bus Fleet 1997 Fact Book Issue (Bobit Publication)
The Fact Book Issue of the magazine School Bus Fleet presents statistical data regarding the
United States school bus fleet, including total registrations, annual mileage, students transported, and
fatalities. This issue was used for one important statistic: average school bus annual mileage. Since
annual mileage data by model year were unavailable from other sources, this value (9,939 miles per year)
was used as the mileage for every model year.
1995 Highway Statistics (Federal Highway Administration)
The 7995 Highway Statistics book is a compilation of statistics in several categories, with most
data items in the areas of highway finance and road characteristics. The Federal Flighway Administration
(FFIWA) records this information from various state and local administrative agencies and publishes the
Highway Statistics annually. While the document does contain some substantial registration information
according to vehicle type, data is not specified by vehicle age. Further, the statistics contain no
information regarding fuel type. Thus, in most cases, Acurex was not able to use these data for this study.
However, information on total vehicle miles traveled in the United States was used to verify the numbers
generated using more detailed. Also, certain statistics indicated that the number of government owned
vehicles are insignificant compared to the total number of vehicles on the road. This supported the
application of private vehicle annual mileage rates (provided in the NPTS database - see Section 2.2) to
both public and private vehicles as sufficiently accurate for this study.
1995 Transit Passenger Vehicle Fleet Inventory (American Public Transit Association)
This American Public Transit Association (APTA) inventory categorizes and lists passenger
vehicles according to the fleet in which they are operated. It provides counts of vehicles in a variety of
categories, namely manufacturer, city of construction, specification, cost, ownership, and seating
capacity. The book also includes a section listing transit buses by age but not by fuel type. Since data
from the Federal Transit Administration (see Section 2.2) did contain both model year and fuel type, it
was chosen to provide data for this study over that presented in the APTA document. Acurex used this
publication mainly to verify the registration data obtained from the Federal Transit Administration.
The 100-Year Almanac and 1996 Market Data Book (The Automotive News)
The 100-Year Almanac provides a historical representation of the automotive industry since the
turn of the century by highlighting key events and following manufacturer sales on a yearly basis. The
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1996 Market Data section of the magazine compiles statistics related to production, sales, and new
registrations for the year. The data contained in this source is extensive with respect to vehicle make,
model, and fuel type, but has very limited data categorizing vehicles by weight or model year. Therefore,
Acurex was unable to incorporate this source of data into the study results.
2.2 PRIMARY DATA SOURCES
The following data sources were used extensively in this study and are referred to as "primary
data sources." The data sources found most useful were contained in electronic databases from the R.L.
Polk & Company (Polk), the 1992 Truck Inventory and Use Survey (TIUS), the 1995 Nationwide
Personal Transportation Survey (NPTS), and in databases from the Federal Transit Administration (FTA).
Primary data sources used for each vehicle type registration and annual mileage accumulation are
listed below in Table 2-1 and described below.
Table 2-1. Data Sources and Base Years
Vehicle Type
Light Duty Vehicle
Light & Heavy Duty Truck
School Buses
Transit Buses
Vehicle Registrations
Source
Polk
Polk
Polk
FTA
Base Year
1996
1996
1996
1994
Annual Mileage
Accumulation
Source
NPTS
TIUS
Bobit
FTA
Base Year
1990
1992
1996
1994
R.L. Polk & Company
The only centralized source of nationwide vehicle registration data of the type needed for this
study is assembled by R.L. Polk & Company. Polk compiles Department of Motor Vehicle registration
information from each state into their database on a quarterly basis. These data include, for each vehicle,
information describing make, model year, fuel type, and gross vehicle weight.
Data from two databases at Polk were required for this study. The first database records light-duty
vehicles and truck registrations according to make, model year, fuel type, and gross vehicle weight.
Information was available from this database as of July 1, 1996.
The second database contains heavy-duty trucks and school buses, again recording make, model
year, fuel type, and gross vehicle weight. Acurex received two sets of heavy-duty vehicle data from Polk:
Oct 1, 1996 and Jan 1, 1997. In Section 3, the methodology used to convert data from these dates to a
July 1, 1996 snapshot is described.
For consistency, Acurex defined Age 1 vehicles to be model year 1996 for all Polk registrations.
Mileage accumulation rates were not acquired from this source.
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Truck Inventory and Use Survey
The Truck Inventory and Use Survey was conducted during the 1992-1993 timeframe by the U. S.
Bureau of the Census. The database, which was supplied to Acurex on CD-ROM, compiles a statistically
significant sample of on-road light-duty and heavy-duty trucks. Each record is the equivalent of one
vehicle. Data for each record is extensive, and includes the required attributes of age, gross vehicle
weight, and fuel type. Most importantly, the database records miles driven in calendar year 1992 by these
vehicles. This data was used to determine mileage accumulation for light-duty and heavy-duty trucks.
Model year 1992 was designated as Age 1 vehicles for the TIUS data.
Nationwide Personal Transportation Survey
Data recorded in the Nationwide Personal Transportation Survey was developed by a consortium
of Department of Transportation agencies to characterize the nature of personal travel (as opposed to
commercial and institutional travel). The 1990 survey, the fourth in the NPTS series, consists of 47,499
individual telephone interviews. The survey asked interviewees about their travel habits, including trip
length, number and purpose of trips, and time of day. Also, information related to the subjects' personal
background and vehicle characteristics was recorded during the study. Of relevance to the Acurex study,
the NPTS contains data regarding light-duty vehicle age and annual mileage. However, it does not
contain data recording fuel type. Like the TIUS data, Acurex received the NPTS data on CD-ROM.
Age 1 vehicles in the NPTS data are defined as model year 1990 vehicles. Data from a new
NPTS study conducted in 1995 will become available in August 1997. This might have an impact on the
light-duty vehicle mileage accumulation rates reported here. In addition, due to the current popularity
of using light-duty trucks (light pick-up trucks and sports utility vehicles) in passenger car applications,
mileage accumulation rates for light-duty trucks might also be affected.
Federal Transportation Administration
The Federal Transit Administration supplied transit bus inventory data to Acurex in electronic
form. The FTA helps fund transit districts across the nation; as part of this program, funded agencies are
required to submit revenue and vehicle inventory forms to the FTA on a yearly basis. Included among
the data recorded on these forms (Form 408) is the vehicle mileage traveled during the previous year and
the model year of the bus. This allowed calculation of transit bus mileage by age in addition to total
vehicle counts.
The latest available version of this data was recorded in 1994. Thus Age 1 is defined as model
year 1994 for this data set.
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SECTION 3
DATA ANALYSIS METHODOLOGIES
Acurex performed analyses of both vehicle registrations and annual mileage data sources. The
following subsections describe the methodologies used to determine the values for these tasks. The data
sources and methodologies used for registration purposes are described in Section 3.1, those used to
characterize annual mileage data are described in Section 3.2. Results of these analyses can be found in
Section 4.
3.1 VEHICLE REGISTRATIONS
As required under Work Assignment Tasks 1, 2, and 3, Acurex determined total vehicle
registrations and vehicle registrations as a function of age for all vehicle types listed in Table 1-1. The
primary source of this data was the Polk database, while information on transit bus registrations was taken
from the FTA database.
3.1.1 R.L. Polk & Company Database
Several manipulations were required to the data provided by Polk in tabular, electronic form as
an EXCEL spreadsheet.
First, Acurex divided Class 8 heavy-duty diesel vehicle registrations in the Polk database into
Class 8A and Class 8B subclasses, since these breakdowns were not available from Polk. Using
registration data from the TIUS database, the percent of Class 8 vehicles below 60,000 pounds GVW for
each model year was calculated. These percentages were then applied to the Polk data for Class 8 trucks
to estimate total registrations of Class 8A and Class 8B vehicles. For model years not included in the
TIUS data (1993 -1996), Acurex assumed 26.5% of Class 8 vehicles are Class 8 A. This value represents
the average percentage of all vehicles in model years 1983 to 1992.
After splitting the data in this fashion, Acurex translated the heavy-duty vehicle October 1, 1996,
and January 1, 1997 data to July 1, 1996. The two data sets allowed calculation of attrition rates of
registered vehicles between October 1, 1996 and January 1, 1997 specific to each model year, for all
heavy trucks combined, but not for individual weight classes. Acurex used these attrition rates to
"backcast" estimated registrations as of July 1, 1996, using linear extrapolation (Linear extrapolation of
attrition rates is a reasonable approximation considering the difficulty of obtaining and analyzing attrition
data on monthly or shorter periods, and is consistent with EPA's treatment and use of attrition
information in MOBILESa and earlier versions of the model). The same attrition rate calculated for all
heavy trucks of each model year was applied to each weight class for that model year. Note that the
number of model year 1995 and 1994 trucks registered increased in the fourth quarter of 1996. This,
according to Polk personnel, is not unusual and reflects stored inventory of one- and two-year old
vehicles continuing to be sold through 1996.
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For model year 1996 heavy-duty vehicles, Acurex performed a different procedure to include new
registrations. The values used for the July 1, 1996 snapshot for the 1996 model year are one-half of the
values provided originally from Polk as of January 1, 1997. These are the number of vehicles sold
halfway through the year, assuming an even introduction throughout the year (For the same reason as
described in the previous paragraph regarding attrition rates, use of linear extrapolation for sales data is
appropriate in the MOBILE model series).
Once the manipulations of the data were complete, the resulting tabular data was transferred to
new spreadsheets and graphs were created. Where data was available from other sources like
MOBILES a, these data were plotted along side the Polk data for comparison purposes. The results of
these comparisons are described in Section 4.
3.1.2 Federal Transit Administration Database
Transit bus registration data for urban buses was obtained from the Federal Transit
Administration as an EXCEL spreadsheet. It was saved as a dBASE file for processing. The structure
of the dBASE data file is shown in Table 3-1 below:
Table 3-1. BUSDATA.DBF Data Structure
Field Name
NUMVEH
TYPE
MY
NUMACTVEH
FUELTYPE
SEATING
ANNMILES
Description
Number of buses in fleet
Bus type code
Bus Model Year
Number of active buses in fleet
Bus fuel type
Seating capacity of the bus
Total Fleet annual mileage
Registration information for urban transit buses was determined from this data. Only vehicle type
codes listed in Table 3-2 were used in the analysis.
Table 3-2. Vehicle Type Codes Used in the Analysis
Code
AB
BA
BB
BC
DB
Bus Type
Articulated motor bus
Motor bus with > 35 seats
Motor bus with 25-35 seats
Motor bus with < 25 seats
Double-decker bus
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Vehicle types codes other than those listed in Table 3-2 refer to non-bus transit vehicles, and these
records were eliminated from the database. As an urban bus is defined in the Code of Federal
Regulations (§86.093-2) as "a passenger carrying vehicle powered by a heavy heavy-duty diesel engine,
or of a type normally powered by a heavy heavy-duty diesel engine, with a load capacity of fifteen or
more passengers ...", records which indicated that a bus had less than a 15 seat capacity were eliminated
from the database. Buses designated as inactive in the FTA database were, however, included in this
calculation as they still exist within the inventory. (Inactive buses accounted for approximately 4% of
the inventory.) Even though gasoline-powered buses are not technically designated urban buses, the
dataset also contained a set of gasoline-powered buses that carried 15 or more passengers. For
consistency, gasoline-powered urban bus registrations were also documented in this report. No attempt
was made to translate the 1994 FTA data to the 1996 baseline.
The program BUSES.PRG, used to calculate bus registrations and mileage accumulation, is listed
in Appendix A. Bus registrations and comparisons to other data sets are discussed in Section 4.
Discussion of the calculation of mileage accumulation rates for transit buses can be found in Section
3.2.3.
3.2 ANNUAL MILEAGE ACCUMULATION
Acurex acquired and analyzed information regarding the annual mileage accumulation of all
vehicle types by age as stated in Work Assignment Task 5. The methodologies applied to each of these
source are described in the subsections below.
3.2.1 Truck Inventory and Use Survey
To provide the best analysis of the TIUS data for the purposes needed by this work assignment,
Acurex manipulated the TIUS data on a record-by-record basis. To do this, pertinent data from the TIUS
data file TI92MDF.DAT was converted into a comma-delimited file using the C program TIUSCONV.C
listed in the appendices. The comma-delimited file was then read into a dBASE file following the
structure presented in Table 3-3.
Two additional fields were added to TIUSDAT.DBF to further help in the manipulation of the
data for this work assignment. They are listed in Table 3-4.
Of the 247,282 records that comprised the original data set and were appended from
TI92MDF.DAT to TIUSDAT.DBF, 1,612 were deleted because they had no model year designation and
3,694 records were deleted because they designated fuels other than gasoline or diesel (liquefied gas or
other). This left 241,976 records.
Each TIUS data record (representing one truck from the survey) had several different weight and
weight class fields. MAXWT represented the maximum gross weight at which the vehicle or
vehicle/trailer combination was operated. TIUGVW represented the gross vehicle weight of the vehicle
based on the average weight and is receded to TIUS specifications. While this is based on average
weight, it is probably a good indication of the gross vehicle weight (GVW) of large trucks. PKGVW
represents the GVW class based upon the vehicle's vehicle identification number (VEST) and is obtained
from the manufacturer. PKRWGT represents the gross vehicle registered weight which comes from state
registration data. The PKGVW and PKRWGT are the same values used by Polk to determine weight
classes.
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Table 3-3. TIUSDAT.DBF Data Structure
Field Name
Description
EXPANF
MDLYR
ACQMON
ACQYR
OBTAIN
DISPOZ
DISMON
DISYR
HOWRID
MAXWT
ENGTYP
ANNMIL
LTMIL
TIUGVW
PKGVW
PKRWGT
VEHSZE
Expansion Factor
Model Year
Acquired Month
Acquired Year
How was vehicle obtained?
Was the vehicle disposed of?
Month the vehicle was disposed
Year the vehicle was disposed
How was the vehicle disposed?
Maximum Gross Weight
Fuel Type
Annual Mileage during 1992
Lifetime Mileage
TIUS Gross Vehicle Weight Class
Polk Gross Vehicle Weight Class
Polk Registered Weight
Vehicle Size
Table 3-4. Additional Fields in TIUSDAT.DBF
Field Name
VEHTYPE
FUELTYPE
Description
Vehicle Class Description
Fuel Type
In reviewing the data, inconsistencies between the various weight class designations and weights
were found. For example, a single record (1 truck) could list a registered weight (PKRWGT) of 4,000
pounds GVWRbut also list a gross vehicle weight class (PKGVW) of 8, indicating a registered weight
of 33,000 pounds or greater. Discussions with Census Bureau staff provided an explanation for these
inconsistencies. The two types of weights, PKGVW and PKRWGT, are based on truck registrations, and
are often entered into databases based on the VIN. Prior to 1983, VEST coding was not uniform, and, as
a result, incorrect interpretations of gross vehicle weight may have occurred during data entry. Therefore,
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discrepancies between the PKRWGT and PKGVW data fields should be more prevalent for trucks older
than model year 1983. The recommendation of Census Bureau staff was to use the PKRWGT and
PKGVW for comparison to TUIGVW as a check for this parameter's accuracy. TIUGVW gives an
average gross weight value rather than the manufacturers gross vehicle weight, but Census Bureau staff
felt that the TIUGVW data was of high quality and, for large trucks especially, would be a good
indication of the gross vehicle weight rating. While Acurex did not find that post-1983 data was
necessarily more consistent than pre-1983 data, the methodology recommended by the Census Bureau
staff was applied to determine which records were valid for use in this study. Only data records which
met one of the following criteria were retained:
• Records where the Polk class (PKGVW) and weight (PKRWGT) were consistent
• Records where the TIUS class (TIUGVW) and the Polk Weight (PKRWGT) were consistent
• Records where the TIUS class (TIUGVW) and Polk Class (PKGVW) were consistent
This resulted in 220,544 records for the analysis, approximately 90% of the original data set.
These records were used to characterize average annual mileage accumulation as a function of
vehicle age and weight class. The TIUS data was not used to characterize vehicle registrations; instead,
Polk data was used as was described in Section 3.1. TIUS data could not be used to characterize
registrations because records were for survey responses only. The database does incorporate an
"expansion factor" (EXPANF) which is used as a multiplier on the survey response records to expand
the data to be representative of the entire national truck fleet. However, the expansion factors are specific
only to each of five vehicle strata within each state: pickups, vans, single-unit light (26,000 pounds GVW
or less), single unit heavy, and truck tractor. Because the expansion factors are not intended to, and the
trucks surveyed were not selected to, correctly represent a model year- and weight class-specific
breakdown of the national truck fleet, the TIUS data could not be used to characterize national truck
registrations by model year and weight class. Census Bureau staff agreed that it would be inappropriate
to use the expansion factors in this way.
Acurex calculated average annual mileage accumulations by averaging the ANNMIL data for
each weight class and model year. (The expansion factors were not applied to the data records in the
calculation of average annual mileage accumulation, since this calculation is also specific by model year
and weight class.)
To compensate for trucks used less than 12 months in the year due to acquisition or disposal
during 1992, months of operation for each record were determined. It was assumed that the acquisition
or disposal happened mid month. Thus a truck purchased in June of 1992 (ACQYR = 92 and ACQMON
= 6) was assumed to operate for 6.5 months in 1992, while a truck disposed of in June of 1992 (DISYR
= 92 and DISMON = 6) was assumed to have operated for 5.5 months prior to disposal. Months of
operation were then averaged for each model year and class and the annual mileage accumulation for that
case was then adjusted to 12 months of operation.
The TIUS data model year designations are only given for vehicles with model years of 1983 or
newer. Vehicles 11 years of age and older are categorized together as model year 11. To characterize
mileage accumulation for vehicles 11 years of age and older, records with model year 11 and OBTAIN
= 1 (purchased new) were placed in a separate database (TIUS 11.DBF) with the same structure as
TIUSDAT.DBF. In those cases, model year was assumed to be the same as acquired year (ACQYR) and
3-5
-------
average mileage was determined for each of the classes for all acquired years 1982 and earlier. Data
records for trucks of model year 11 purchased as used vehicles could not be incorporated into the analysis
because there was no data to indicate the specific model year of the truck.
Curve fits were made through the first set of data (model years 1983 through 1992). Only points
in the second set (model years earlier than 1983) that did not change the shape of the curve generated by
the first set of data more than 5% were used to produce the curve fits. Two different curve fits were
applied, exponential and 2nd order polynomial. The one that produced the best fit was chosen.
No adjustments were made to translate the 1992 TIUS data to the 1996 baseline year for this
study. This is because total VMT calculated from the 1992 TIUS mileage estimates and the Polk 1996
registrations closely approximated other data sources (see Section 3.1).
The program PKTI.PRG, used to calculate mileage accumulation, is listed in the appendices.
Results of these analyses are given in Section 4.
3.2.2 Nationwide Personal Transportation Survey
The data on CD-ROM from the Nationwide Personal Transportation Survey was converted to a
dBASE file using the Statistical Export and Tabulation System (SETS), a software program developed
by the National Center for Health Statistics and provided with the NPTS database. The program allows
data querying and tabulating, as well as exporting to dBASE files. The dBASE file structure for the data
file is shown in Table 3-5.
Table 3-5. LDVS3.DBF Data Structure
Field Name
Description
ANNMILES
MILELIMIT
VEH12MNT
VEHMILES
VEHMONTH
VEHTYPE
VEHYEAR
WTHHFIN
Annualized Miles
ANNMILES capped at 115,000
Vehicle was received in last 12 months
Vehicle mileage reported in last 12 months
Months owned if less than 12
Type of Vehicle
Model Year of vehicle
Registrations per record
In discussions with NPTS, it was decided that the mileage (VEHMILES) should be averaged
using the expansion factor (WTHHFIN) for each vehicle class (VEHTYPE) and model year
(VEHYEAR). The expansion factor, which is specific to each record, is a multiplier applied to expand
the survey responses to represent the national fleet. The expansion factors are based on several attributes
of the survey respondent and help eliminate geographic bias in the data (since much more data comes
from some states than others). NPTS staff strongly recommended that the expansion factors be applied
3-6
-------
as part of the calculations. Annual miles for vehicles that were operated less than 12 months were
adjusted to 12 months by multiplying VEHMILES by 12 and dividing by months owned (VEHMONTH).
Since this data did not differentiate between gasoline and diesel, the same mileage accumulation curve
was generated for both1. The data was then curve fit using the best fit between a 2nd order
polynomial and an exponential curve. This data was only used for determining mileage
accumulation for light-duty automobiles because the TIUS data, which includes light-duty trucks,
was 2 years more recent than the NPTS data and provided information of the type of fuel used by
each vehicle.
The program LDVS.PRG to calculate mileage accumulation is listed in Appendix A. Results
of these analyses are given in Section 4.
3.2.3 Federal Transit Administration Database
Mileage accumulation data for urban buses was calculated using the program BUSES.PRG
as discussed in Section 3.1.2. For each fuel type and model year, annual fleet mileage was summed
and divided by the total number of buses. Buses designated as inactive in the FT A database were
included in the denominator for this calculation. This results in a lower average annual miles per bus
than if only active buses were counted, but because inactive buses are part of the inventory, this
method represents the average mileage of the urban bus fleet in existence in the United States.
The program BUSES.PRG to calculate mileage accumulation is listed in Appendix A.
Results from these calculations are given in Section 4.
1 According to EPA, light-duty vehicles, whether gas or diesel, are generally used as personal transportation and so are
typically driven in patterns (annual mileage, trip frequencies, etc.) that are determined by parameters other than fuel
type.
3-7
-------
SECTION 4
RESULTS
Work Assignment Task 6 specifies that the results of this study be compared to the previous data
derived during development of MOBILES a. The results of this study are presented herein as graphs and
tables. In all cases, data from Polk and FTA are utilized for registration counts, and TIUS, NPTS, FTA
and Bobit data are used for mileage accumulation by age. Table 2-1 in Section 2.2 indicates which data
source is used for each vehicle type. On some of the graphs at the end of this section, additional data
series from other sources are presented for comparative purposes only.
The Polk registration data presented in the following graphs does not perfectly coincide with the
previous characterization performed during the development of MOBILESa. Absolute values for vehicle
counts almost always differ by some discrete amount, as is to be expected when comparing data sources
developed with different methodologies. In most cases, it is extremely difficult to find explanations that
would account for the differences. In light of this, the following discussion attempts to point out where
the two (or more) data series either coincide or diverge in terms of relative trends (peaks and valleys),
rather than absolute difference. An attempt was made, however, to point out where absolute values
significantly differ and the possible reasons for these discrepancies.
The same attempt has been made with the annual mileage accumulation graphs. Here, however,
a curve fit was placed through the related data set. This has been done to smooth the results generated
from data sets that, especially within certain weight classes, do not contain enough data points to avoid
some dramatic model year variation in the averaged results. Acurex believes that while the graphs
sometimes indicate highly erratic mileage differences from one model year to the next (for the raw data),
the curve fits are generally of the appropriate magnitude. This is confirmed by two other sources. First,
Acurex multiplied the vehicle registrations from each weight class and model year with the respective
curve fit mileage, and then summed the products to obtain the total vehicle miles traveled (VMT) for the
entire U.S. fleet in 1996. This VMT figure (2.369 trillion miles) was cross-checked with an independent
number generated by the 7995 Highway Statistics (see Section 2). It was found that less than a 2%
difference existed between the two values. This comparison indicates that the mileages estimated in this
study adequately represent the activity of the national vehicle fleet. Table 4-1 below shows the
comparison of the Highway Statistics and the results from this study. The second confirmation of this
estimate is input provided to the USEPA by State air agencies and other air planning organizations that
mileage rates in MOBILES a were generally too low. The smoothed results presented here, which show
higher mileage accumulation rates, are consistent with this input.
Of particular note is the significantly higher number of heavy-duty registrations found under this
study as compared to the 7995 Highway Statistics, but the lower annual mileage recorded for these
vehicles (see Table 4-1). Conversely, the Highway Statistics show slightly higher number of registrations
and slightly lower annual mileage for light-duty vehicles. Note that Highway Statistics vehicle categories
do not perfectly coincide with the weight classes used in this study. For instance, the 7995 Highway
4-1
-------
Statistics define light-duty trucks to include GVWs up to 10,000 Ibs. while this study defines light-duty
trucks to include GVWs only up to 8,500 Ibs. Furthermore, the bus category in the 7995 Highway
Statistics probably includes buses in addition to school and transit buses. Buses other than school or
transit buses were treated as heavy-duty trucks in this study. These and other inconsistencies may
account for the higher heavy-duty registrations and lower light-duty and bus registrations. Also, Polk
school bus registration data tracked quite well with other sources, such as the 7997 School Bus Fleet Fact
Book and transit bus registrations from FTA tracked very closely with information from the 7995 Transit
Passenger Vehicle Fleet Inventory.
Table 4-1. Total VMT, Registrations, and Annual Mileage
LDVs and LDTs
HDVs
Buses
Total
LDVs and LDTs
HDVs
Buses
Total
LDVs and LDTs
HDVs
Buses
Average
TOTAL VMT
US Highway Statistics 95
2,228,435,000,000
178,160,000,000
6,383,000,000
2,412,978,000,000
REGISTRATIONS
US Highway Statistics 95
193,967,443
6,881,074
685,504
201,534,021
ANNUAL MILEAGE
US Highway Statistics 95
11,489
25,891
9,311
11,973
Current Study
2,162,120,290,487
201,702,481,456
5,914,610,445
2,369,737,382,388
Current Study
182,520,247
8,905,444
469,689
191,895,379
Current Study
11,846
22,649
12,593
12,349
% Difference
-2.98%
13.21%
-7.34%
-1.79%
% Difference
-5.90%
29.42%
-31 .48%
-4.78%
% Difference
3.11%
-12.52%
35.24%
3.14%
The differences between data obtained under this study and other sources for each weight class
are discussed below for both registrations and annual mileage accumulations.
4.1 REGISTRATIONS
4.1.1 LDVs
Light-duty registration data obtained from Polk (Figure 4-1) appear to follow the same trend as
the other sources of available registration data. In general, NPTS counts are higher than the Polk data,
which is attributable to scrapping of these vehicles between 1990 and 1996. MOBILESa data indicates
slightly higher counts than Polk in the late 1980's model years, but fewer vehicles in the early 1980's
model years. Previous to 1979, all three data sets closely track together, with Polk generally being the
lower than the other two.
4-2
-------
4.1.2 LDTs
Light-duty gasoline trucks (Figures 4-2 and 4-3) also appear to follow relatively similar patterns.
The major difference lies in model years 1976-1982, where peaks in the MOBILESa data either do not
appear or do not coincide with the Polk data. Since LDDTs were not separated into classes 1 and 2
during development of MOBILESa, discussion of this vehicle type is described below under Aggregated
Classes in subsection 4.1.5.
4.1.3 Heavy-Duty Trucks and School Buses
Comparison data could not be found in other sources for these vehicle classes (Figures 4-4 to 4-
16). In the heavy duty classes, TIUS data was found to be inconclusive for purposes of registration
counts, as was discussed above. Also, MOBILES a contains no easily reproduced block data for the
heavy-duty truck fleet. Thus, only the Polk data is provided in the charts for these vehicles types.
4.1.4 Transit Buses
The data provided by the FTA (Figure 4-17) very closely follows the comparison APTA data.
This further supports the suggestion mentioned above that the apparent large discrepancy between bus
registrations in Table 4-1 is questionable.
4.1.5 Aggregated Classes
Certain aggregations of vehicle classes have been included here upon request of the USEPA.
Aggregated classes are: LDGTs (Figure 4-18) which include LDGT1 andLDGT2, LDDTs (Figure 4-19)
which include LDDT1 and LDDT2, LDTs (Figure 4-20) which include LDGT1, LDGT2, LDDT1 and
LDDT2, HDGVs (Figure 4-21) which include HDGV1 and HDGV2, HDGB (Figure 4-22) which
includes gasoline transit and school buses, FIDDVs(3-5) (Figure 4-23) which includes FIDDV(3) and
HDDVs(4-5), HDDVs(8) (Figure 4-24) which includes HDDV(8A) and HDDV(8B), HDDVs (Figure
4-25) which includes HDDV(2B), HDDV(3), HDDV(4-5), HDDV(6-7), HDDV(8A) and HDDV(8B),
and FIDDBs (Figure 4-26) which include diesel transit and school buses. The registration data presented
in these charts is simply the sum of the registrations in the classes indicated in the chart's title. Of
particular note is the comparison between data sources of LDDTs (Figure 4-19). As mentioned above,
MOBILES a did not discriminate between LDDTls and LDDT2s. For all LDDTs, MOBILES a shows a
peak in model years 1981, with registrations tapering off thereafter. The Polk data shows significantly
more LDDTs in the 1980s and 1990s, as well as a smaller peak in 1974. The reason for the discrepancy
between these two sources is unknown.
4.2 ANNUAL MILEAGE
4.2.1 LDVs
As shown in Figure 4-27, the annual mileage accumulation data provided from the NPTS shows
perhaps the most consistent data series of all the weight classes. Deviation of the data points from the
curve fit is generally small. The graph shows that mileage accumulation rates developed in this study
from 1990 NPTS data are higher than the rates incorporated into MOBILESa, which was based on the
1983 NPTS study. This is consistent with recent trends indicating that people drive more miles each year
than they used to.
4-3
-------
4.2.2 LDTs
Light-duty truck mileage provided by the TIUS fairly closely tracks MOBILES a data (Figures
4-28 to 4-31). TIUS data for LDGTs indicates somewhat higher accumulation rates for the newer
vehicles and lower rates for older vehicles. For LDDTs, the data indicates that mileage accumulation is
somewhat higher than previously modeled for all vehicle ages, especially in the younger vehicles.
4.2.3 HDGVs
Heavy-duty gasoline trucks follow the same pattern as LDTs. However, the TIUS data indicates
that the older, heavier HDGV2s acquire fewer annual miles than MOBILESa indicates (Figures 4-32 and
4-33)
4.2.4 HDGBs
Acurex could not obtain suitable annual mileage rates for the gasoline transit buses on an age
basis. The 7997 School Bus Fleet Fact Book Issue provides information placing average school bus
accumulation rates at 9,939 miles per year (for both gasoline and diesel school buses).
4.2.5 HDDVs
For the lighter heavy-duty diesel classes (2B and 3), the TIUS data indicates the same trends as
both LDTs and HDGVs (Figures 4-36 and 4-37) However, the relative difference compared to
MOBILESa is greater for these vehicle classes than the others, up to as much as 50% greater than the
previously used value.
Classes 4 and 5 (Figure 4-38) show an altogether different pattern. Here, TIUS annual
accumulation rates are significantly greater than previous MOBILE values for all vehicle ages. This is
especially true at about Age 10, where there is about a 100% increase in mileage. One possible reason
may partly explain this occurrence. Note that the MOBILES a curve is the same for both Figures 4-37
and 4-38. This is because, in MOBILESa, mileage rates were accumulated for Classes 3 through 5 as an
aggregate sum. Since there are nearly twice as many vehicles in Class 3 as there are in both 4 and 5
combined, the average mileage accumulation rate would tend to be lower, closer to the individual Class
3 average. Thus the MOBILES a curve plotted here for Classes 4 and 5 is probably too low to properly
characterize these weight classes.
Classes 6 and 7 (Figure 4-39) indicate the opposite; TIUS mileage values are slightly less than
older MOBILESa values.
Classes 8A and 8B (Figures 4-40 and 4-41) mileage rates developed in this study are slightly
lower and higher, respectively, than MOBILESa. This is due partly to the same reason as explained for
Classes 4 and 5. MOBILESa only tracked Class 8 mileage accumulation rates. That curve, which
appears on both these figures, lies in between the two TIUS data sets, since it is the average accumulation
rate for all the vehicles in both sets (although weighted about 3 to 1 toward the 8B class, since there are
about 3 times as many 8B vehicles as there are 8A trucks.)
4.2.6 HDDBs
4-4
-------
Like gasoline school buses, diesel school buses have an average mileage accumulation for all
vehicles of 9,939 miles per year. Heavy-duty diesel transit bus mileages have been derived from an
analysis of the FTA Form 408 data set (Figure 4-42). Comparison data from other sources is not
available for this class.
4.3 TABLES
Additional information is provided in Tables 4-2 to 4-8 at the end of this report. Each is
categorized by weight class, fuel type and model year. Table 4-2 shows the total registrations received
from Polk and modified by Acurex as described above. Table 4-3 shows the raw annual mileage data
available from the various sources listed above, while Table 4-4 is the annual mileage curve fit for each
weight class. In Table 4-5, the percentage of vehicles in each model year as a fraction of the total
vehicles in the weight class is shown. Both total vehicles and percentages are also displayed for
aggregated weight classes in Tables 4-6 and 4-7. Finally, Table 4-8 is the gasoline/diesel sales fraction
of light-duty vehicles and trucks from 1985 to 1996, as requested in Work Assignment Task 4.
4-5
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