EPA/AA/CTAB/FE-82-6
Technical Report
Analysis of In-Use Fuel
Economy Data: Stage I
Manuscript Completed
August, 1982
NOTICE
Technical Reports do not necessarily represent final EPA decisions or
positions. They are intended to present technical analysis of issues
using data which are currently available. The purpose in the release of
such reports is to facilitate the exchange of technical information and
to inform the public of technical developments which may form the basis
for a final EPA decision, position or regulatory action.
U. S. Environmental Protection Agency
Office of Air, Noise and Radiation
Office of Mobile Sources
Emission Control Technology Division
Control Technology Assessment and Characterization Branch
2565 Plymouth Road
Ann Arbor, Michigan 48105
-------
Technical Report
Analysis of In-Use Fuel
Economy Data: Stage I
Manuscript Completed
August, 1982
NOTICE
Technical Reports do not necessarily represent final EPA decisions or
positions. They are intended to present technical analysis of issues
using data which are currently available. The purpose in the release of
such-reports is to facilitate the exchange of technical information and
to inform the public of technical developments which may form the basis
for a final EPA decision, position or regulatory action.
U. S. Environmental Protection Agency
Office of Air, Noise and Radiation
Office of Mobile Sources
Emission Control Technology Division
Control Technology Assessment and Characterization Branch
2565 Plymouth Road
Ann Arbor, Michigan 48105
-------
FOREWORD
This report summarizes the need for, and the background work done on, ad-
justing the EPA MPG values to more closely correspond to actual fuel economy
experience on the road. The majority of the report deals with the deriva-
tion of mathematical algorithms that could be used to perform the needed ad-
justment, using an extensive data base of in-use fuel economy, algorithms
are developed which depend on certain design features of the vehicles, sub-
stantial improvements in the accuracy of the Fuel Economy Labeling and Gas
Mileage Guide Programs will result when adjustments to the current values
are adopted.
Most of the data upon which this report is based comes from sources entirely
independent of the Environmental Protection Agency. They have in most cases
been screened to eliminate "bad" data before being submitted to the Agency.
In the absence of independent knowledge of what the distribution of mpg im-
portant factors should be, it is difficult to evaluate each source or to de-
tect the presence of sampling or data processing errors in the data.
Thus, it was learned after the body of this analysis was completed, that
data supplied by Ford Motor Company for model years 1979 through 1980 in-
cluded incorrectly coded city fraction values. This error casts doubt on
some details of the bi-modal analyses presented in Chapter IV, section E.
The general agreement between this and the non-modal analysis results, how-
ever, suggests that the error is not fatal.
The reader's attention is, therefore, directed to the Stage II Report of
this series for an updated analysis of an even larger in-use data base in
which the Ford data processing error has been corrected. This Stage I
Report remains useful as a complete description of the analysis methodology
and comparison of the effects of various technological factors on the in-use
fuel economy shortfall.
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CONTENTS
I. EXECUTIVE SUMMARY 1-1
II. BACKGROUND II-l
III. THE DATA III-1
A. Data Sources III-l
B. Data Preparation 111-18
C. Data Screening 111-30
IV. ANALYSIS IV-1
A. Data Source Characterization IV-1
B. Consensus Findings IV-20
C. Second Stage Screening IV-25
D. Adjustment Algorithm, Non-Modal ........ IV-34
E. Adjustment Algorithms, Bi-Modal IV-70
V. EVALUATION OF CANDIDATE LABELING APPROACHES V-l
A. Methodology V-l
B. Test Fleets V-2
C. Evaluation of Labeling Approaches. ....... V-5
D. Conclusion V-20
VI. ACKNOWLEDGMENTS VI-1
APPENDIXES
A. The Fuel Economy Influence Index
B. Source-Specific Analyses
C. Analysis of Perceived vs. Measured Fuel Economy
D. Non-Modal and Bi-Modal GPM Ratio Relation
E. Listing of Model Type Data Base
F. Histograms of Label Error
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I. EXECUTIVE SUMMARY
A. PURPOSE
This study has two purposes — to analyze in-use fuel economy data and to
develop adjustment approaches that might be used for changes to the EPA fuel
economy labeling procedure.
B. THE DATA
The data base used for this report is a sampling of the on-road fuel economy
of more than fifty thousand vehicles. Built by eighteen manufacturers in
the U.S., Japan, Germany, Sweden, France and Italy, these vehicles were
operated in all States of the U.S., plus the District of Columbia and Puerto
Rico, and were operated during all months of the year. We estimate they
accumulated nearly three hundred million vehicle miles and consumed some
eighteen million gallons of motor fuel during their survey intervals.
Data originating outside of EPA were furnished by four organizations:
Chrysler Corporation, the U.S. Department of Energy (DOE), Ford Motor
Company, and General Motors. The Chrysler and Ford data are actual
measurements gathered by company employees driving Chrysler and Ford lease
cars in ordinary consumer service. The DOE data are of three basic types:
consumer-measured MPG data, consumer-perceived MPG data, and annualized
measured MPG data from commercial fleets. The GM data come from postcard
logs gathered in nationwide samplings of private-vehicle owners. The EPA
data all come from the Emission Factors program, and include some measured
postcard data and some perceived-MPG data.
The following table lists the vital statistics of this data base.
1-1
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Summary of On-Road FE Data Base Characteristics
(all data sources)
Total No. Data Points: 52,780
By Data Source:
Ford
30267
GM
10337
Chrys
1282
DOE
9228
EPA
1646
By Model Year:
1975
2246
1976
2160
1977
3424
1978
16189
1979
13027
198O
14183
1981
1531
By Technology:
(not additive)
Rear Drive
48425
Front Drive
4355
Automatic
45126
Manua1
7654
Carbureted
50928
FI & Diesel
1852
By Manufacturer:
By Month:
By State:
(estimated)
Ford GM
33285 12560
Honda Toyta
3O7 236
Chrya
4427
Mazda
157
AMC
698
Volvo
77
VW+AP Oatsn
486 362
Peugo MerBz
58 56
(balance of 71 divided among 6 others:
BMW. Subru. Flat. Renau. Saab. Alfa)
Jan
840
Jul
12OO
plus . .
Calif
498O
FloM
66O
Feb
2355
Aug
342
"Winter
Texas
1730
Mlchl
.22160
Mar
1362
Sep
387
" : 7934
NYork
1130
NJers
S9O
Apr
1820
Oct
2552
"Summer"
Ohio
1940
Georg
49O
May
3661
Nov
2091
: 1347O
Penns
1110
NCaro
35O
Jun
4054
Dec
1047
"Annual " :
111 In
1210
Indl
670
Unk
1649
8016
Unk
7370
(balance of 929O divided among 40
other states, 1ncl. D.C and P.R.)
1-2
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C. DATA PREPARATION'
This study utilized substantial personnel and computer resources,-not
counting precursor in-house studies nor the resources expended by those who
supplied the data. Over two thirds of these in-house resources went into
the building and verification of computer tools for data management and
analysis, and pre-analysis use of these tools.
Five major operations were performed on the data to prepare it for analysis:
• Format Standardization - The data set from each data
source was furnished in a format unique to that source.
A standard format was established and all data converted
to that standard format.
• Augmentation - Given only two measures, the time (month)
of driving and the place (ZIP code or, for some data,
telephone area code), additional information on fuel
economy influences can be added to the data, to augment
it. From our own data banks, which contain climatic,
topographic, and demographic data, the reformated raw
data were augmented with such parameters as temperature,
elevation, topographic code, road condition, and other
important fuel economy influences.
• EPA MPG Assignment - Using vehicle specifications such as
car line name, engine displacement and transmission type,
the EPA City, Highway, and Combined MPG figures were
assigned to each vehicle in the data base by accessing
in-house fuel economy data files.
• Technology Coding - To permit analysis of various
combinations of vehicle technologies, the in-use data
1-3
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were coded with respect to front or rear wheel drive,
transmission type, fuel system type (carbureted, fuel
injected or Diesel), and body type, using a large array
of lookup files.
Influence Indexing - Formulae describing the relative
fuel consumption impact due to in-use influences (such as
temperature) were developed, and an overall index of
their cumulative influence was computed and assigned to
each vehicle. This cumulative influence is called the
Influence Index. This is a concept long needed in
on-road fuel economy analysis, and this study has ad-
vanced the state of the art in quantitatively accounting
for the effect of on-road influences experienced in
actual use. The influence index was used only to screen
the data for outliers. It did not figure in the adjust-
ment algorithms.
1-4
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D. ANALYSIS AND MAJOR FINDINGS
Extensive characterization analysis of each source's data was carried out
prior to either rejecting data or aggregating it and analyzing it en masse.
This was done for two reasons: first, to apply consistent analytic methods
(our own) to each of the sources.' data in order to verify findings reached
and published by them separately using only their own data and methods; and
second, to assess what consensus findings emerge from the group of surveys,
treated as independent attempts to quantify in-use fuel economy. Four
conclusions derive from that characterization analysis:
Consensus Findings: Source-Specific Data
No conclusions reached by the separate sources, as far as
they went, are opposite our own findings using their data.
There is a shortfall. The separate sources' data are
unanimous on this point. Comparing overall road MPG with
overall EPA MPG, every source's data set shows a
shortfall. Furthermore, the magnitude of the average
shortfall is amazingly consistent: all source data sets
place the average shortfall between 15% and 18%, with the
single exception of the DOE perceived-MPG data at 9%.
There are statistically significant differences between
the shortfalls of various vehicle technologies. The
sources are in majority agreement that the shortfalls of
1-5
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various technology classes fall into three statistically
*
distinguishable groups, ranked below from best to worst
(unranked within each group):
Low (Best) Shortfall; front wheel drive
manuals, rear wheel drive Diesels, and rear
wheel drive fuel injected manuals;
Average Shortfall; front wheel drive auto-
matics, rear wheel drive carbureted manuals,
and rear drive fuel injected automatics;
High (Worst) Shortfall; rear wheel drive
carbureted automatics, a class by themselves.
Note; our subsequent analysis of all of the data, aggre-
gated, confirms the above grouping except that rear wheel'
drive carbureted manuals and rear wheel drive fuel in-
jected automatics fall into the Low Shortfall group. Our
own grouping of technology-dependent shortfall is statis-
tically supported- at a confidence level exceeding 98%.
For most vehicle technologies, road shortfalls become
worse for higher EPA MPG levels. Two notable exceptions
to this finding are fuel injected automatics (both front
and rear wheel drive), which show decreasing shortfall
with increasing MPG. (Our subsequent analysis found this
to be caused by differential shortfall as a function of
engine size.)
1-6
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Prior to the development of adjustment factors, the data base was
screened to evaluate data applicability to the mid-1980's time
frame. This screening led to three major conclusions:
• Engines of 400 or more cubic inches displacement are not
appropriate in a data base to be used to develop MFC
adjustments for the future. Engines this large have not
been used in passenger cars since 1979, except for
Rolls-Royce cars. Data from vehicles with engine dis-
placements greater than or equal to 400 cubic inches were
not used.
• There is no model year trend in road fuel economy offset
which warrants deletion of any model years' data. In
fact, it is particularly important to retain data from
the 1976 to 1978 model years, to assure representation of
the 1975 test rigor relaxation that occurred then and is
occurring now with respect to manual transmission shift
schedules.
• Perceived-MPG data and fleet car data disagree with each
other and with measured-MPG consumer data. These two
classes of data are not appropriate for use in developing
MPG adjustments for consumer information. In our
opinion, perceived-MPG data has no useful purpose in
meaningful on-road fuel economy analysis of any kind.
Only consumer-driven, measured fuel economy data were
used in the development of the adjustment algorithms.
1-7
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E. MPG ADJUSTMENT METHODS
The general form of any fuel economy label adjustment is a formula,
L
- A(E)
where E is the EPA MPG number to be adjusted, A is an adjustment operation
performed on the EPA number, and L is the resulting label MPG number.
As explained in the text, the most accurate adjustment operation is division
of the EPA number by a fuel consumption ratio,
L
- E/GPMR
where GPMR is Gallons-Per-Mile Ratio, the ratio of road fuel consumption to
EPA fuel consumption; this ratio is greater than 1.0 whenever road fuel
consumption is higher than EPA fuel consumption.
The analysis determined that this GPMR is dependent upon the EPA number
itself, upon the number of engine cylinders, and upon the kinds of
technology used in the vehicle type.
Thus the generalized labeling formula is
L = E/[GPMR - f(E, Ncyl, Tech)]
where Ncyl is number of cylinders and Tech indicates the combination of
specific technology descriptors for the vehicle type. GPMR = f( )
indicates functional dependence of GPMR upon the variables within the
parentheses.
The full equation for the GPMR function is
GPMR = 1.097 -I- .0059(E) + .0085(N-8) + FWD(-.185 + .03N)
+ MAN(-.070) + INJ(-.184 + .0225N) + DSL(-.142)
1-8
-------
where E is the EPA MPG number to be adjusted, N is the number of cylinders,
FWD = 1 if the vehicle type is front wheel drive and 0 if not, MAN = 1 if
the vehicle is equipped with a manual transmission and 0 if not, INJ = 1 if
the vehicle is equipped with gasoline fuel injection and 0 if not, and DSL =
1 if the engine type is Diesel and 0 if not.
Thus, GPMR is represented as a sum of deviations from the GFMR value for
rear wheel drive, automatic transmission, carbureted gasoline engine
vehicles.
Effectiveness of the Technology-Specific Adjustment
This adjustment formula is applicable to any and all of the EPA numbers and
was compared to several alternatives. The alternatives evaluated ranged
from simplistic adjustments (LABEL = 0.90 x EPA , etc.) to
separate city and highway formulae, both with the above general form for the
GFMR function. The recommended formula was actually developed on data which
represented overall driving and is also an adjustment for a one-number fuel
economy label system.
In testing of all of the alternative adjustment methods, a systematic
procedure was used to rank the methods in terms of their effect upon the
distribution of error between calculated Label MPG values and actual road
MPG from the in-use data base. In the use of this adjustment evaluation
procedure, the technology-specific formula performed essentially as well as
the best sets of separate city and highway adjustments; moreover, it
performed better than they did in two important ways; it produced no
reversals in highway vs. city labeling (i.e. no cases with calculated
highway label MPG lower than calculated city label MPG), and it showed a
higher inclusion range (more than 70% of overall in-use fuel economy is
captured within the range bounded by this formula's adjusted city and
highway figures).
1-9
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It should be noted that evaluation of the alternative adjustments was done
with both model-type average data and raw (disaggregate) data. The use of
model type average data is valid in terms of what EPA has said the labeling
system should be; an estimate of the average road MFG for all cars within a
model type. By averaging out the wide scatter in individual cars' in-use
MPG, improvement in label accuracy for all systems can be shown. However,
use of disaggregate data in such tests is also valid, reflecting what the
driving public seems to be insisting the labeling system should provide: an
estimate of the road fuel economy of every individual vehicle. We maintain
that such an expectation provides the analyst with a most difficult task.
Nevertheless, we tested the alternatives against this expectation, defining
"success" in terms of hitting within 10% of each vehicle's road MPG: indeed
a demanding test.
The figures overleaf are examples of distributions of label error in this
test. The example shown is for the technology-specific formula and for the
current (unadjusted) labeling system. The vertical band in each figure
spans the range from -10% label error to +10% label error. That portion of
the distribution within this band consists of cars whose road MPG is within
10% of the label value. They are "correctly labeled". The left-hand tail
of each distribution contains all the individual cars for which label MPG is
less than road MPG: they are "underlabeled". The right-hand tail includes
"overlabeled" cars: the label number is too high compared to their actual
road MPG.
The technology-specific adjustment correctly labels more than half of the
city-driven cars, where the current system succeeded for only one third of
the cars. For highway-driven cars, the adjustment's improvement in label
correctness is almost exactly the same.
More dramatically, the technology-specific adjustment cuts the fraction of
city cars overlabeled from 60% to less than a third, and cuts the fraction
of highway cars overlabeled from nearly 60% to less than 20%.
1-10
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Labeling Accuracy of Formula Adjustment and Current System:
CITY-DRIVEN CARS
i-n
-------
Adjusted
HIGHWAY LABEL
Current
HIGHWAY LABEL
Labeling Accuracy of Formula Adjustment and Current System:
HIGHWAY-DRIVEN CARS
1-12
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Implementation of the Technology-Specific Adjustment
To implement the technology-specific adjustment for a small number of model
types, solution of the adjustment formula is quite easily done case by case
on a programmable pocket calculator. A pocket calculator program is listed
below.
SETUP: 1.
. 1
PROGRAM: 1
2
3
4
5
6
7
a
9
10
11
12
13
14
15
16
17
IS
19
20
21
22
23
24
097 STO 0 .01 STO 3 .0059 STO 6
O85 STO 1 .O225 STO 4
85 STO 2 . 142 STO 5
RCL 0 25 RCL 7
R/S
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To perform the adjustment on a computerized data base, a simple FORTRAN
program, listed below, can be used. It calculates adjusted label MPG values
for 1000 model types in fourteen seconds, at a cost of $1.90.
CC SPUN [OBJECT CODE] 5» (INPUT FILE] S*> [OUTPUT FILE]
REAL'S NAM1.NAM2
REAL MANF.MDLY.NOBS,IWGT
DIMENSION EPA3O ) , ALA8EL ( 3 ) . GPMR( 3 )
DATA CHF.CHM.CHI,CHD/'F'.'M'.'I'.'O'/
10 FORMAT(F3.0.F4.0.F3.0.A4,IX,2A7,F5.0,F3.0,4A1.F6.0.3F5.1,F5.0,
+ F6.3.F5.1.FS.3.FS.O.F4.0.3F6.2)
20 READ (5.10.ENO-40) SORC.MANF.MOLY.ESTO.NAM1.NAM2.ECID,ECYL.OVTYP.
+ TRTYP.CFID,BOTYP.NOBS,EPA3.CTYF,EOGR.ROAO.AOGR.IWGT.CLAS
FWO ' 0.
TRN « 0.
FINJ = 0.
DSL • 0.
IF(DVTYP.EO.CHF) FWO ° 1.
IF(TRTYP. EO.CHM) TRN » 1.
IF(CFID.EO.CHI) FINJ = 1.
IF(CFID.EO.CHO) DSL = 1.
DO 30 I«1,3
GPMR(I)» 1.097 + ,OOS9«EPA3(I) + FWO»(-.18S + 0.030'ECYL) - .070*TRN
A + FINd»(-.184 + .022S*£CYL) - .142»OSL * (ECYL - 8.)•.008S
ALABEL(I) ' EPA3(I)/GPMR(I)
30 CONTINUE
WRITE(6. 10) SORC,MANF.MOLY,ESTO,NAM1,NAM2,EC ID.ECYL.DVTYP.TRTYP.
* CFID,BOTYP.NOBS.EPA3.CTYF,EOGR,ROAO,AOGR,IWGT.CLAS.ALABEL
GO TO 20
40 STOP
ENO
1-14
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II. BACKGROUND
The Environmental Protection Agency has published fuel economy (MPG) values
of one sort or another for about ten years. For that same period, the re-
lationship between the EPA fuel economy values and those obtained on the
road has been an issue. In the earliest comparison by the .EPA, national
average MPG estimates using the EPA city values were compared to national
average MPG estimates reported by the Department of Transportation. Later,
the city test was altered to the one still in use today, the 1975 FTP, and a
non-urban or "highway" driving cycle was added. Using the composite (55/45)
fuel economy calculated from the EPA city and highway values, comparison
with DOT data indicated that the EPA composite MPG overpredicts Road MPG.
Initially, EPA interest in the relationship between the EPA and Road fuel
economy values was stimulated by skepticism about the several versions of
the Fuel Economy Labeling Program that have existed. The reaction came pri-
marily from consumers who "didn't get the EPA numbers" and to a lesser ex-
tent from those studying fuel consumption and making fuel demand projections.
More recently, work on the issue of the EPA to Road shortfall has been re-
ported from several contributors from industry and the private and public
sectors.
Working independently in some subject areas and jointly in others, analysts
in the automobile industry, the government, and government contractors have
developed and improved techniques for in-use data analysis. Important con-
tributions from the automobile industry have come from Ford Motor Company
and General Motors. EPA and the Department of Energy have provided the
relevant government inputs, and Energy and Environmental Analysis, Inc. and
Falcon Research and Development Co. have been the significant contractors in
the overall effort.
Collectively this group of organizations has developed and refined the con-
cepts of (1) expressing the measure of EPA-to-Road MPG offset as the ratio
of Road fuel consumption to EPA fuel consumption, (2) model type averaging,
II-l
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(3) differential usage among various vehicles, (4) seasonal effects, (5) the
use of city driving fraction as a key influence variable, and (6) the recog-
nition of vehicle technology differences as an important factor in Road MFC
offset behavior.
Much of the analysis in this report builds on this earlier work. The incre-
mental advances made herein are: the conceptualization of the "influence
index", a more detailed investigation of vehicle technology parameters as
shortfall-influences, and a systematic procedure for comparing competing
fuel economy labeling schemes. Only the influence index and labeling eval-
uation procedure are new. The dependence of road MFC offset upon vehicle
technology has been well known for years. The contribution made here is to
refine and quantify the technology-dependency relationship for vehicle types
that populate today's and tomorrow's fleet.
The purpose of this report is to analyze existing Road fuel economy data and
to develop fuel economy adjustments that can improve the MFC values pre-
sented to consumers by the government (Fuel Economy Labels and the Gas
Mileage Guide). First, however, some nomenclature and definitions are
necessary. The following terms are used in a special way in this report and
by others analyzing in-use fuel economy.
GPMR - The £allons P_er Mile Ratio, a ratio of the fuel consumption (gallons
per mile) at one condition to the fuel consumption at another. The fuel
consumption values of interest in this report are the Road fuel consumption
and one of the various EPA fuel consumption values.
Influence Index - The influence index is a measure of the net effect of a
combination of fuel economy-influencing variables. An index greater than
unity indicates an influencing factor combination that increases fuel con-
sumption (reduces fuel economy). An index less than unity indicates an in-
fluencing factor combination which improves fuel economy. Functionally, the
influence index is supposed to explain why a vehicle achieves the GPMR that
II-2
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it does, in terms of the influences it was subjected to. A well-formulated
influence index should approach equality to each vehicle's GPMR value.
Although the influence index has promise for several applications, it was
used here only to screen data, because it is still developmental. This
index is discussed further in Appendix A.
Technology Shorthand - Certain aspects of vehicle technology, important for
reasons of analysis and accessible in in-use data bases, are represented by
a shorthand notation, three letters and a number. The first letter is
either R or F, for Rear-wheel or Front-wheel drive; the second is A or M,
for Automatic or Manual transmission; and the third is C, I, or D, for Car-
buretion, gasoline fuel Injection, or Diesel. The number is the number of
cylinders. Counting the four most important cases (4, 5, 6, and 8), there
are 48 classes that could be populated. Because there are so few 5-cylinder
cars, they are sometimes merged with the 6-cylinder cars when describing
vehicle fleet attributes. For most of the analysis in this report, 29 clas-
ses were populated.
Diffuse and Model Type Data - These two descriptors describe the data used
in the analysis. In-use fuel economy data shows an amazing amount of scat-
ter. This reflects to some extent the difficulty of the analysis problem.
Since this "as is" data looks like a diffuse cloud when plotted, the term
diffuse was coined to distinguish it from model type data. Of the many ways
to aggregate in-use data, we have chosen an approach which averages together
the data from vehicles that are "like each other". Our definition of "like
each other" corresponds to-the model type definitions used for the current
fuel economy labeling program. The use of this model type data reduced the
data base by more than an order of magnitude and was important in obtaining
a better understanding of the pertinent influences, since individual-vehicle
scatter is reduced when numbers of vehicles are averaged together.
Non-Modal and Bi-Modal - These describe one or another of two analysis is-
sues. This report investigates the adjustments needed for a fuel economy
labeling program which uses only one MPG value and also investigates the ad-
II-3
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justments that would be needed for a fuel economy labeling program which
uses two MPG values. The analysis and discussion of the one MPG value ad-
justment is called by the names Non-Modal and overall in this report • The
two MPG value work is referenced by the terms Bi-Modal, 2-number and city
and highway.
Analysis Space - During DOE's studies it became obvious that evaluation of
the EPA versus Road MPG issue by merely looking at the Road MPG on one axis
and the EPA MPG on another suffers from technical inaccuracy. After some
rigorous investigation, a space was developed in which the GPMR, road fuel
consumption divided by EPA fuel consumption, was the dependent variable and
the appropriate EPA fuel economy the independent variable. This is called
"analysis" space in this report.
From engineering considerations it has been shown that the GPMR should be a
linear function of EPA MPG in analysis space. That is why only linear re-
gressions in analysis space are used in this report. However, a linear re-
lationship in analysis space maps into a curve in Road MPG versus EPA MPG
space.
II-4
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III. THE DATA
A. DATA SOURCES
The data base accumulated and analyzed for this report covers some 53,000
cars built by 18 manufacturers In seven model years.
Eight in-use fuel economy survey data sources make up this data base, each
source being a unique combination of surveyor, type of survey, and quality
and completeness of data. Table III-l presents vital statistics for the
overall data base.
Eighty percent of the data were furnished by three auto manufacturers:
Chrysler, Ford, and General Motors; the remaining 20% were collected by DOE
and EPA (mostly DOE). Eighty percent of the data represent three model
years, 1978 through 1980, primarily because the largest data source (Ford)
is concentrated in those three years.
The three-character vehicle technology categories shown in Table III-l are
coded as discussed earlier; "SAI" vehicles are rear-wheel drive, automatic,
fuel injected vehicles, "FMC" vehicles are front-wheel drive, manual, car-
bureted vehicles, etc. Eighty percent of the data are for RAG vehicles;
only 27 data points exist for RMD vehicles.
Manufacturer representation is dominated by Ford., due to the large sample
size of Ford's surveys of its own models.
Distribution of the data by month/season shows a slight bias toward the Sum-
mer months (44% of the data, compared to 28% Winter and 28% Spring/Fall).
III-l
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By data source:
By model year:
By technology:
By manufacturer:
By month:
By state:
(estimated)
Tab 1e 111 -1
Summary of On-fload FE Data Base Characteristics
(A)1 Data Sources)
Total No. Data Points: 52,780
Ford
30267
GM
10357
1975
2246
RAC
42110
FAC
1772
Ford
33285
Honda
307
DOE-f
8016
1976
2160
RAI
623
FAI
15*
GM
12560
Toyta
236
Chrys
1282
1977
3424
RAD
396
FAD
71
Chrys
4427
Mazda
157
EPA-ra
1011
1978
16189
RMC
5063
FMC
1983
AMC
698
Volvo
77
OOE-p
807
1979
13027
RMI
206
FMI
236
VW+AP
486
Peugo
58
EPA-p
635
1980
14V83
RMD
27
FMO
139
Datsn
362
MerBz
56
DOE
40
1981
1551
(Balance of 71 divided among 6 others:
BMW, Subru, Fiat, Renau, Saab, Alfa)
Jan
840
Jul
1200
p 1 us ...
Calif
4980
Fieri
660
Feb
2355
Aug
342
"Winter"
Texas
1730
Michi
22160
Mar
1362
Sep
387
: 793^
NYork
1130
NJers
690
Apr
1820
Oct
2552
"Summer":
Ohio
1940
Georg
490
May
3661
Nov
2091
13470
Penns
1110
NCaro
350
Jun
4054
Dec
1047
"Annual":
1 11 in
1210
1 nd i .
670
Unk
1649
8016
Unk
7370
(Balance of 3290 divided among 40
other states, incl. D.C and P.R.)
III-2
-------
This page intentionally
blank and un-numbered
-------
The geographic spread of the data is heavily biased toward Michigan, with
42% of the data. The twelve States listed in Table III-l are listed in
order of decreasing annual vehicle miles traveled; together they account for
572 of total U.S. VMT*. The general overrepresentation of Northern-tier
states (78% of the data for these 12 states) compensates for the aforemen-
tioned overrepresentation of the Summer months, however; using DOE-developed
data** on the effects of season and geographic region on in-use fuel econ-
omy, the average MPG error for our data base is estimated at less than 0.2
MPG (a 1% error), compared to the true time-space distribution of in-use
cars.
The paragraphs following describe each of the eight survey data sources.
Chrysler Corporation
In the Spring of 1981, Chrysler surveyed the in-use Fuel economy of some
1300 lease cars operated by its employees in commuter/personal service. The
cars were all 1981 model Chrysler products, and were split roughly equally
between rear-drive automatics, front-drive automatics, and front-drive man-
uals (all carbureted). Over 80% of the cars were operated in southeastern
Michigan, but there are some data from each of 27 other states.
The statistics for this data source are given in Table III-2. The data base
did not include figures on miles traveled per day or dates for the survey
interval.
* U.S. Department of Transportation, FHWA, Highway Statistics 1977, annual.
** Unpublished; draft report in preparation.
III-3
-------
Table I I 1-2
Summary of Data Source Characteristics
Data source: Chrysler Corp. (Measured MPG)
Survey method: Questionnaire on on-road fuel economy of lease cars operated for
—-—-——— personal transportation by Chrysler Corporation supervisory/manage-
ment personnel. Overall in-use MPG figures, not fuel/odometer log, but 95$
of the survey data are based on measurements rather than perceptions.
(Chrysler vehicles only.)
No. data points: 1282
By model year:
By technology:
1975
5 1976
RAC RAI
1*30 ' 3
FAC FAI
579
1977 1978 1979
RAO RMC RMI
1
FAD FMC FMI
269
1980 1981
1282
RMD
FMD
By month:
Survey ran Feb 81 thru Jun 81; April assumed.
By state:
Key parameters missing:
Michi indi Ohio Alaba NYork
106A 57 31* 28 21
(Balance of 78 divided
among 23 other states.)
Location (state,ZIP)
Time (month) *a
Odometer
Miles/day X
Ci ty dr ivi ng
Other:
estimated based on survey time interval
III-4
-------
DOE Fleet Data
This data source covers 8000 cars operated in fleet service. Five model
years are represented. This data base is unique in that one full year of
operation is represented for each vehicle. Practically all the vehicles
(99.52) are rear-drive automatic carbureted technology. Some 6000 of the
data records come from the Runzheimer Co., a commercial marketer of data on
vehicle operation and cost statistics; another 1700 of the fleet cars are
from state Departments of Transportation. Except for these State fleets,
the locations where the cars were operated are not identified in the data.
Table III-3 gives the statistics for this data base, with individual sources
of data and their model year breakout shown in Table III-4. Unfortunately,
city driving fraction and miles traveled per day are not specified, which
prohibits quantitative study of the operating characteristics for these cars.
DOE Measured-MPG Data
This source, 405 cars, is the smallest data source used in this study. The
"typical" vehicle in this data base is part of a small fleet used by an oil
company for periodic tests on emissions and octane requirements. Between
such tests, the vehicles are operated for mileage accumulation purposes by
company employees in ordinary personal transportation service. The vehicles
were usually rotated among several employee operators for an interval on the
order of one month each. In-use fuel economy is computed from odometer
mileage and fuel purchase records kept by each operator. As with the DOE
fleet data, this data base does not include data on- the time or location of
vehicle travel, nor the city fraction or daily mileage accumulation. Table
III-5 gives statistics for this data source. Six model years are covered,
and three-fourths of the cars are rear-drive automatic carbureted tech-
nology. The respective number of cars for individual contributors to the
DOE measured data are given in Table III-6.
III-5
-------
Table I I 1-3
Summary of Data Source Characteristics
Data source: Dept. of Energy (Fleet MPG)
Survey method: Fleet record-keeping system, with odometer mileage and fuel purchases
————- summed quarterly, and four such sum pairs used to compute annual MPG.
Table \\\-k lists individual sources of DOE fleet MPG data, and the respective
number of cars involved.
No. data points: 8016
By model year:
1975
1976
48
1977
2881
1978
3093
1979
1861
1980
133
1981
By technology:
RAC RAI
7977
FAC FAI
RAD
2V
FAD
RMC RMI
10
FMC FMI
RMD
FMD
5
By month:
Data represent annual average MPG.
By state:
Key parameters missing:
Maryl Wisco I 11 in Tenne Maine link
506 162 81 3 6270
Location (state,ZlP) X
Time (month) X
Odometer
Miles/day X
Ci ty dr i vi ng X
Other:
III-6
-------
Table I\\-k
Individual Sources of DOE Fleet Data
and Number of Cars by Model Year
Source 1975 1976 1977 1978 1979 1980 Total
AT&T
1 1 1 i noi s DOT
Maine DOT
Maryland DOT
Runzheimer
Tennessee DOT
Wisconsin DOT
N. L. Wuertz
40 75 17 14 3
99 63
3
8 463 523
2109 2348 1662
47 34
83 -108 185 130
2
149
162
3
994
6119
81
506
2
Total 48 2881 3093 1861 133 8016
III-7
-------
Table I I I-5
Summary of Data Source Characteristics
Data source: Dept. of Energy (Measured MPG)
Survey method: Vehicles were used for personal transportation by employees of the
—————— companies; MPG was computed from records kept by these employees.
Surveys were run over periods of 1-3 months.
Table I I 1-6 lists individual sources of DOE measured MPG data, and the respec-
tive number of cars involved.
No. data points: 405
By model year:
By technology:
1975 19.76
2 12
RAC RAI
317 2
FAC FAI
19
1977 1978 1979
121 175 ^g
RAD RMC RMI
2k 19
FAD FMC FMI
1 3
1980
46
RMD
4
FMD
16
1981
By month:
Not identified,
By state:
Not identified.
Key parameters missing:
Location (state,21P) X
Time (month) X
Odometer
Miles/day X
Ci ty dr ivi ng X
Other:
III-8
-------
Source
Tab Ie I I I-6
Individual Sources of DOE Measured Data
and Number of Cars by Model Year
1975 1976 1977 1978 1979 1980
Total
Amer ican Oi 1
Atlantic-Richf ield
DOE Diesel collection
Dupont Chemical
Libby-Owens-Ford
Mob i 1 Oil
Shell Oil
Texaco
5 27
2 1 5
3.9
3 22
58
25 31
26 11 2
12 20 6
ko
55
6
11 18 7
88
39
46
40
55
18
61
58
Total
12
121
175
405
III-9
-------
•DOE Perceived-MPG Data
This, the third data source from DOE, come-s from a questionnaire survey of
nearly 10,000 vehicle owners. The survey, conducted by the J.D. Power com-
pany under contract to DOE, was a nationwide multi-manufacturer survey cov-
ering vehicles of model years 1978 and 1979.
There are a number of problems' with this data source. The principal problem
is that it is not measured vehicle fuel economy data at all; it is data on
the owners' perceptions of their vehicles' on-road fuel economy. On-road
fuel economy, in fact, was an incidental part of the survey, whose primary
purpose was to solicit opinions on fuel economy information and fuel supply
crisis issues.
Another serious problem with this data is the lack of sufficient vehicle in-
formation to permit accurate assignment of EFA MFC values; specifically,
engine displacement was not recorded for the vehicles. The survey did col-
lect data on the number of cylinders, but in many cases this is a marginally
useful parameter. For example, many 1978 and 1979 vehicles were available
with three or four different sizes of 8-cylinder engines, ranging from
about 300 cubic inches to about 450 cubic inches.
Yet another problem with this survey is that respondents were not asked to
identify whether their vehicle had a Diesel engine. Since cars were avail-
able with gasoline and Diesel engines with the same cylinder count (most
notably, GM 8-cylinder and VW 4-cylinder), identification of "Dieselity or
non-Dieselity" for this data base is a tricky business fraught with pitfalls.
In addition to these problems, wherein critical parameters are not included
on any of the cars surveyed, there are numerous instances where respondents
did not log key variables that were sought, such as cylinder count, trans-
mission type, or car line. When all that is known about a data record is
that it was an "Other" or "Unspecified" or "2-door" vehicle from manufac-
turer X, it is a totally useless record in terms of identifying what its EPA
111-10
-------
MPG value should be. Even the questionnaire's inclusion of a blank in which
respondents could indicate what they thought the EPA MPG numbers were for
their car is not always useful: errors as high as 15 MPG were found in this
data (owner perception of EPA MPG erred as much as 15 MPG).
Because most of these problems occurred in RAG and RMC technologies, and be-
cause these technologies are already well-populated by other data sources,
we used only non-RAC and non-RMC data (less than 10% of the original data),
working with that as best we could.
Table III-7 gives the statistics for this data source.
EPA Measured-MPG Data
Beginning in 1980, a small sample of the participants in EPA's Emission
Factors Program have participated in a "Postcard Survey" in which they note
odometer readings and fuel quantities for about six successive fuel pur-
chases; at least the first and last purchases are required to be fillups.
This measured-MPG survey is a multi-model year, multi-manufacturer data
source, with vehicle technologies covered roughly in the proportions they
occur in the overall fleet. Because (to date) the Emission Factors contrac-
tors doing the surveying do not have Diesel emission test capability, no
Diesels are included in the Postcard survey. In addition, again because of
the finite number of contractors in the program, geographic coverage of in-
use vehicles is highly localized rather than nationwide.
A little over 1000 cars are included in the current data base as of the end
of October 1981. Table III-8 gives the statistics for this data source.
EPA Perceived-MPG Data
Since 1975, all of the participants in the EPA Emission Factors program have
filled out a detailed questionnaire on travel and maintenance practices for
their vehicles. One of the responses sought is the owner's estimate of in-
use MPG in city, highway, and overall driving. While we have not rigorously
III-ll
-------
Table I I1-7
Summary of Data Source Characteristics
Data source: Dept. of Energy (Perceived MPG, "J.D. Power")
Survey method: Mailed questionnaire soliciting new car buyers' opinions on the federal
fuel economy information program, and energy/fuel crisis issues. The
1978 survey sought owner estimates of city, highway, and overall on-road MPG;
the 1979 survey sought only one (overall) on-road MPG figure. A nationwide ran-
dom sample selected from vehicle registration files.
No. data points: 807
By model year:
1975
1976 1977 1978
1979
360
1980 1981
By technology:
RAC
FAC
169
RAI
J»9
FAI
.39
RAD
108
: FAD
• k
RMC
FMC
201
RMI
W
FMI
122
RMD
FMD
68
By month:
Jan Feb Mar Apr May Jun
616 121 70
By state:
Key parameters missing:
Location(state,2IP)
Time (month) »a
Calif Michi NYork Ohio Texas
112 104 95 76 57
(Balance of 358 divided
among 30 other states.)
Odometer
Ci ty dr i vi ng
Miles/day
Other: Engine CID
estimated based on survey time interval
and questionnaire return dates.
111-12
-------
Table I I I-8
Summary of Data Source Characteristics
Data source: EPA (Measured MPG)
Survey method: Postcard log of fuel purchase quantities and odometer readings. Survey
objectives and procedures for filling logs discussed personally with each
participant. Sample consisted of car owners participating in EPA Emission Factors
in-use vehicle emission surveillance program.
No. data poi nts: 1011
By model year:
By technology:
By month:
By state:
Key parameters missing:
Location(state,ZlP)
Time(month)
1975 1976 .1977
38 37 57
RAC RA 1
582 10
FAC FAI
98 23
Jan Feb
US 91
Jul Aug
no 68
Texas Cal i f
521 257
(survey 1
Odometer
Miles/day
1978 1979 1980 1981
}J,k 161 315 269
RAO RMC RMI RMD
1J»5 ]k
FAD FMC FMI FMD
112 27
Mar Apr May Jun
107 198 183 1AO
Sep Oct Nov Dec
62 1 6
Colo Wash! D.C. Misou
205 11 10 7
imi ted to 6 states .)
% Ci ty dr i vi ng ;':a
Other:
... five choices: 0, 25, 50, 75.
111-13.
-------
pursued analysis of this overall data base due to the issue of perceived MPG
data validity, some of these data were furnished to DOE approximately two
years ago. This data source is what was used by DOE; its statistics appear
in Table III-9.
Ford Motor Company
Ford has been collecting postcard-type measured data on in-use fuel economy
of its management lease car fleet since 1978. Two survey waves, Winter and
Summer, are conducted each year. Two vehicle technologies, RAG and RMC, 96%
of the data, cover practically all of Ford's model lines for 1978-1980;
RAI's and FMC's are model-specific, including only throttle-body injection
Lincolns (RAI), and Fiestas (FMC). Two-thirds of the data represent South-
eastern Michigan driving, but 37 other states are represented — and not too
badly at that, given the very large sample size of the Ford survey. An ad-
vantage of a survey such as this, with a sampling frame drawn from company
records, is that states which restrict release of their registration data by
organizations such as R.L. Polk can still be sampled (e.g., Pennsylvania).
Table 'III-IO gives statistics for the Ford survey data.
General Motors
GM is probably the originator of the postcard survey method of collecting
accurate measurements of in-use fuel economy. Their 1975 and 1976 Winter
and Summer surveys, and their 1978 Summer survey, covered privately-owned GM
cars; their 1980 Fall survey was expanded to include vehicles from twelve
other manufacturers as well. All of the twelve vehicle technologies are in-
cluded, and geographic (State) coverage is quite uniform.
Statistics for the GM survey data base appear in Table III-ll.
111-14
-------
Tab 1e I I I-9
Summary of Data Source Characteristics
'Data source: EPA (Perceived MPG)
Survey method: Questionnaire on vehicle operation and maintenance practices of parti-
————— cipants in EPA Emission Factors program. Owner estimates of city, high-
way, and overall on-road MPG.
No. data points: 635
By model year:
B,y technology:
1975'
RAC
322
FAC
11
1976 1977 1978
260 365 10
RAI RAO RMC
20 1 1 87
FAI FAD FMC
l 59
1979
RMI
25
FMI
9
1980
RMD
FMD
By month:
By state:
Not identified.
Not identified.
Key parameters missing:
Location(state,ZIP) X
Time (month) X
Odometer
Miles/day X
Ci ty driving X
Other:
111-15
-------
Table I II-10
Summary of Data Source Characteristics
Data source: Ford Motor Co. (Measured MPG)
Survey method: Postcard log of fuel purchase quantities and odometer readings. Lease
cars operated for personal transportation by Ford Motor Co. supervisory/
management personnel. Ford Motor Co. vehicles only.
No. data points: 30,26?
By model year:
1975 1976
1977 1978 1979
10808 10596
1980 1981
8863
By technology:
RAC
25W5
FAC
RAI
382
FAI
RAO
FAD
RMC RMI
3550
FMC FMI
890
RMD
FMD
By month: (1990 subset only;
1978-79 assumed similar)
Jan
77
Jul
962
Feb
2000
Aug
50
Mar
1193
Sep
1
Apr
ko
Oct
1
May Jun
3^1 3727
Nov tjDec
k 10
•
link
^57
By state: (1980 subset only;
1978-79 assumed similar)
Key parameters missing:
Michi Calif
5898 1117
Ohio Penns Texas
320 170 137
(Balance of 1221 divided
among 33 other states.)
Location (state,ZIP) »a
Time(month) Ab
Odometer
Miles/day
Ci ty dr ivi ng
Other:
*a ... location based on telephone area code.
*b ... "Winter" or "Summer" for 1978 and 1979-
111-16
-------
Table 111-11
Summary of Data Source Characteristics
Data source: General Motors (Measured MPG)
Survey method: Postcard log of fuel purchase quantities and odometer readings. Nation-
wide random sample selected from GM sales records for GM cars, registra-
tion records for other cars. Carline quota sampling. 1975i7&i78 surveys GM cars
only; Multi-manufacturer survey for model year 1980.
No. data points: 10,357
By model year:
By technology:
By month:
By state:
Key parameters missing:
Location (state,ZIP)
Time (month)
1975 1976
2206 1803
RAC RAI
7037 157
FAC FAI
891 91
Jan Feb
102 143
Jul Aug
128 224
Michi Cali
848 787
1977
RAD
239
FAD
66
Mar
62
Sep
324
f NYork
780
1978
1522
RMC
1151
FMC
449
Apr
230
Oct
2550
1 11 in
737
1979
RMI
120
FMI
78
May
3137
Nov
2087
Ohi
733
1980 1981
4826
RMO
23
FMD
55
Jun
187
Dec Unk
1031 152
o Texas Unk
677 58
(Balance of 5795 divided among
46 other states, incl. P.R.) •>
Odometer
Mi les/day
% Ci ty dr i vi
Other:
ng x (1975-78)
III-17
-------
B. DATA PREPARATION
Four categories of computer software were developed to process in-use fuel
economy data:
o Programs for augmenting data and writing it into a standard storage
format.
o Command files for cleaning and further coding of the data in stan-
dard storage format.
o Command files for quantifying the estimated fuel economy effect of
certain influencing parameters, and writing files in standard anal-
ysis formats.
o Software for collapsing data in standard analysis formats into
model type averages.
Each of these items of software is described below. They are not portable
outside of the Michigan Terminal System (MTS), but are portable between MTS
accounts.
Software for Augmenting and Storage Formating
XFORM is a FORTRAN program that reads an as-received in-use fuel economy
data file and produces an output data file in a standard storage format.
The output file is an augmented version of the input data, incorporating
topographic, climatic, and demographic parameters in each output record.
The augmentation module of XFORM uses each input record's date and zip code
to look up these parameters.
111-18
-------
Other portions of XFORM perform functions such as Julian date conversion of
in-use FE survey dates, computation of a miles-per-day figure, and decoding
of engine size codes and car line codes.
The input file formats and coding conventions which XFORM can handle at
present are listed below. The complete format layouts for these are
available from EPA upon request.
GM 1975 - JD POWER
GM 76/78 - GM 1980
EPA Emission Factors - Chrysler 1981
FORD . - Tha SSFID format
The format for the XFORM output, "SSFID", is given in Table 111-12. This is
our standard format for storing in-use FE data.
PLUG is a FORTRAN program which assigns EPA City and Highway fuel economy
values to a data file in SSFID format. It reads "match fields" specified by
the operator, and consults fuel economy data lookup files to find or
calculate the EPA FE values appropriate to the level of aggregation associ-
ated with the match fields. Sales-weighted harmonic averaging is used.
The program has the unique capability to deal with model name spelling
problems, and in so doing it develops an ever-expanding directory of re-
spelling conventions. When given a carline name that it cannot find in the
lookup files, it offers the operator all carline names which have that
record's match field vector. Upon operator choice of a lookup file surro-
gate for the unretrievable input name, subsequent appearances of that input
name are handled internally without further operator intervention.
PLUG dumps data records to which it cannot assign EPA numbers into a reject
list in a separate file from the PLUGged data.
111-19
-------
Table I I 1-12
Standard Storage Format for !n-use Data
Variable Format Columns Field Description
I SCR
NREC
MFR
MODEL
IW
CID
TRANS
NCYL
CARB
16
\k
15
\k
A3
12
A2
1-2
3-8
9-12
14-33
34-38
39-42
44-46
48-49
51-52
KLASS
MY
STD
IZIP
IPOP
IPDEN
MONTH
PCTURB
AKPO
FECG
FEHG
FECE
FEME
FECQ
FEHQ
FEQ
MAXTMP
MINTMP
TEMP
HUMAM
HUMPM
ELEV
TOPO
STATE
STABR
CITY
EXPREG
ODOM
DRIVE
ECS
AIRCDN
FEG
FEE
FECM
FEHM
FEM
12
12
A4
13
18
16
12
13
14
F6.2'
F6.2
F6.2
F&.2
F6.2
F6.2
F6.2
14
\k
14
13
13
16
A3
14A1
A2
4A4
13
17
Al
12
A2
F6.2
F6.2
F6.2
F6.2
F6.2
54-55
57-58
60-63
65-67
68-75
76-81
83-8J.
86-88
89-92
93-98
99-104
105-110
111-116
117-122
123-128
129-134
135-138
139-142
11*3-11*6
147-149
150-152
153-158
160-162
166-179
180-181
183-198
199-206
207-209
210-216
218-218
220-221
223-221*
225-230
231-236
237-21*2
243-248
21*9-25*1
256-276
D
Fl
n
TO
Tl
7
Car i
Mode
Engii
F irs'
Popu
Popu
Monti
Perci
Aver:
City
Hiwa'
City
Hiwa'
City
Hiwa1
Comb
Aver:
Aver:
Aver;
Aver;
Aver
Aver;
Surf;
Name
Post
Name
Resei
Vehii
Odomi
Fron
Emi s:
0
AC 01
55/4!
55/4!
City
Hiwa:
Comb
Reset
Input data type (format 6 coding rules)
1-GM76/78 2-Emission Factors 3-GM75 4-Ford
5-GM80 6-Chrysler 7-JOPower 8-this format
Record number in i nput file
Manufacturer code
Model name as input
Inertia weight
Engine displacement, cu inches
1st character is A or M, usually
2nd character is no. of forward gears
3rd character is 1-no overd, 2-overdrive
Number of cylinders in engine
Carburet ion, as follows:
1 Carburetted gas, single barrel, etc.
Diesel
Fuel injected gasoline
Turbocharged gas, 4-barrel, etc.
Turbocharged Diesel
Turbocharged gas, fuel injected
Carburetted gas, unknown no. of bbls
Car class, based on interior volume
Mode I year
Engine standard: FLDV, CLOD, etc
First 3 digits of respondent ZIP code
Population count for ZIP region
Population density (pop/sq mi) for ZIP
Month of year when survey data applicable
Percent of driving done in city
Average miles per day
City MPG from certification data
Hiway MPG from cert data
City MPG from emission test
Hiway MPG from emission test
Ci ty perceived MPG
Hiway perceived MPG
Combined perceived MPG
Average max ambient temp (deg F) for month
Average min ambient temp (deg F) for month
Average ambient temperature for month
Average morning humidity (%RH) for month
Average afternoon humidity (%RH) for month
Average elevation (feet) of ZIP region
Surface landform
Name of state
Post Office abbreviation of state name
Name of largest city in ZIP region
Reserved for future use
Vehicle exposure region
• reading
tear, or It wheel drive
Emission control system
(missing data)
AC or NO air conditioning, otherwise blank
55/1*5 MPG from cert data
55A5 MPG from emission test
City MPG as measured in-use
Hiway MPG as measured in-use
Combined MPG as measured in-use
Reserved for future use
111-20
-------
MINUS.ONES is an editor command file that fills in missing values of certain
FE influence variables with "typical" numbers so that FE influence GPM
ratios (calculated later) take on a typical year-round or nationwide value
if the raw data cannot pinpoint the time or place of the FE survey. Influ-
ences handled in this manner, and their fill-in values, ares Population
density (4444), Month (13), Percent city driving (55), Miles per day (44),
Temperature (55), State (US), Exposure region (25), and Odometer (6999). To
provide an audit trail, actual occurrences of these fill-in values are first
reset, as follows: Population density of 4444 becomes 4443, Percent city
driving of 55 becomes 54, Miles per day of 44 becomes 45, Temperature of 55
becomes 56, and Odometer of 6999 becomes 7000; none of these resettings af-
fects the respective influence index by more than 0.4% of its non-reset
value. This is completely inconsequential compared to the basic accuracy of
the influences (e.g. temperature figures are 30-year averages).
PATCH is a FORTRAN program that fills in missing in-use MPG data, and writes
in wind speed values, in an SSFID-format data file.
Six types of in-use MPG data are accomodated by the SSFID format:
Perceived MPG: City
Highway
Overall
Measured MPG: City
Highway
Overall
It happens that most FE survey data files rarely have all 3 fields filled in
each of the two groups. Given any one or two of the three perceived MPG
values, PATCH estimates the missing values by taking the available MPG
values, the EPA highway-to-city MPG ratio, and the survey city fraction to
compute the missing MPG values. PATCH fills in missing measured MPG values
similarly.
111-21
-------
The wind speed function of PATCH is an XFORM-type augmentor; it uses the
"month" and "exposure region" parameters in each data record to look up a
wind speed value and write it into that record.
PATCH also calculates EPA 55/45 MPG from the EPA City and Highway figures.
PATCH is non-interactive (can be batched), and looks for its input in file
"-INPUT" and writes its output to file "-PATCHED". It also outputs, as file
"-ERR", an error-message list which indicates which lines of the data file
were not patched with MPG values due to insufficient data, and which lines
were not patched with wind speed values due to missing months or exposure
regions.
Files for Cleaning and Coding SSFID Data
FWDnn files are lists of editor commands for coding front wheel drive/rear
wheel drive vehicles on the basis of model name, or — in the case where all
of a given manufacturer's vehicles are FWD — manufacturer code number.
The files overwrite blanks and coding errors. The files are model year spe-
cific, with the "nn" part of each file's name denoting the model year, e.g.
FWD78.
To use the FWD files, all records for a given model year must be extracted
from a data file and that model year group edited. The edited files must,
of course, be reaggregated if multi-year data bases are to be kept together.
FIJnn files are lists of editor commands for coding data file records as to
carburetion, fuel injection, or Diesel. With respect to fuel injection,
they operate in exactly the same way as the FWD files. Turbochar^ing de-
scriptors are preserved. Diesel coding assumes the "standard" field (FLDD,
etc.) to be accurate. Blanks or miscodes which are carbureted (known not to
be FI or Diesel) are overwritten with a "7", which is easily recognizable as
something other than the number of barrels.
111-22
-------
Use of the FIJ files, as with FWD's, requires isolation of model year spe-
cific data, then editing, followed by reaggregation.
TRANS is a small edit command file which recedes all lockups as automatics,
and all duals, shift-automatics and creepers as manuals. It is applicable
to all model years and mixes of them.
SEDWAG codes each data record which has "WAG", "SW, or "STAW" in its model
name field as a "W", and each which does not as an "S" (sedan). Mercedes,
Volvo, and GM wagons such as 300TDs, 245s, and Safaris are also handled.
Files for Influence Indexing and Analysis Formating
LANDFORM is an edit command file that assigns GFM ratios for the effect of
landform on in-use fuel consumption, based on each record's topological
code. It overwrites in the SSFID field that identifies the fully-spelled
State name for each record, so it should only be used on a temporary copy of
the subject data file.
ST.SPD uses the editor to assign highway speed GFM ratios based on the State
code for each SSFID data record. It too, overwrites an alphabetic field,
and should be used on a temporary data file copy.
ST.RON writes State-specific GPM ratios for road condition into an SSFID
file, via the editor. Separate GPM ratios are written for urban and non-
urban driving. The pairs of ratios are overwritten in an alphabetic field.
SSF.READ is a command file that reads certain SSFID variables into MIDAS
after all of the above operations have been performed. It cannot digest a
raw SSFID file. Table 111-13 is the format layout of the as-read data.
SSF.READ looks for its input in file "-COMBYR".
111-23
-------
Tab Ie 111-13
In-use Fuel Economy Data as read by SSF.READ
Variable
1. ISCR
2. MFR
3. MY
4. STD
5- MODEL!
6. MODEL2
7- CID
8. NCYL
9. DRIVE
10. TRANS
11. CARB
12. BODY
13. MONTH
14. STABR
15. ZIP
16. FECG
17- FEHG
18. FEG
19. PCTURB
20. FECM
21. FEHM
22. FEM
23. OOOM
2k. TEMP
25- AMPO
26. IPOEN
27- TOPO
28. GRTOP
29. GRSPD
30. EXPREG
.31 . WIND
32. GRRCC
33. GRRCH
34. iw
35- KLASS
Format
As Read
F2
F4
F2
A4
A7
A7
F4
F2
Al
A1
Al
Al
F2
A2
F3
F6.2
F6.2
F6.2
F3
F6.2
F6.2
F6.2
F7
F4
1X.F3
F6
A3
F5-3
F5-3
F2
F5-1
F5-3
F5-3
F5
F2
SSFIO
Columns
1-2
9-12
57-58
60-63
14-20
21-27
39-42
48-49
218
44
52
33
83-84
180-181
65-67
93-98
99-104
225-230
86-88
237-242
243-248
249-254
210-216
143-146
90-92
76-81
160-162
166-170
183-187
208-209
199-203
189-193
194-198
34-38
54-55
Field Description
Input data type (same as SSFID fmt)
Manufacturer code
Model year
FLDV, etc.
1st 7 char's of model name
Next 7 char's of model name
Engine cu. in. displacement.
No. engine cy1i nders
Front/Rear or 4wheel (F/R/4)
Auto or manu (A/M)
Carb, Fl, or Diesel (C/I/D)
Sedan or wagon (S/W)
...or Pkup, Van or Jeep (P/V/J)
Month
2-char. state abbrev.
3-digit ZIP code
EPA City MPG
EPA Hi way MPG
EPA 55/45 MPG
Percent city driving
Measured road MPG, city
Measured road MPG, hi way
Measured road MPG, overall
Odometer miles
Avg. temp (deg f) for month
Avg. mi 1es per day
Population density of ZIP
Topographic code
Topographic GPM ratio
Speeding GPM ratio
Exposure region
Wind mph for EXPOREG & month
Road condition GPM ratio, city
Road condition GPM ratio, hiway
Inerti a wei ght
Vehicle size class code
111-24
-------
IMPUTE is a multipurpose MIDAS command file. Operating on data read in via
SSF.READ, it does the following:
a) Calculates GPM ratios for the fuel economy effects of temperature,
miles per day, population density, odometer mileage, and wind, and
aggregates these (and previously-assigned-and-read GPM ratios) into
an imputed city GFM ratio and a highway GPM ratio;
b) Imputes road city MPG and road highway MPG using the above GFM
ratios and the EPA City and Highway MPG values, and imputes an
overall road MPG using these and the city driving fraction;
c) Calculates an influence index GFM ratio (which is thus the signa-
ture of the vector of FE influences) using the result of b) and the
EPA 55/45 MPG value;
d) Calculates actual EPA-to-road GPM ratios. :
VERB.WRITE outputs a 48-variable, 236-column (verbose) file with the re-
sults of IMPUTE included. This file has all of the variables deemed usable
for data analysis. The verbose file layout is given in Table 111-14.
The written output is file"-VERB".
TERS.WRITE outputs a 22-variable, 94-column (terse) file of the key vari-
ables necessary for data analysis. It is expected that most analysis can be
done with such terse files. Table 111-15 is the terse file layout.
The written output is file "-TERS"
VERB.TERS is a utility command file which reads a verbose file and writes
out a terse version of it.
111-25
-------
VERB.TERS reads file "-VERB" and writes its output into file "-TERSED".
This file simply reads and writes data as alphanumeric fields; the data as
read are, therefore, not suitable for doing math in MIDAS•
PRE-NALY is a simple edit command file that re-codes the carburetion char-
acter in the vehicle technology field of a verbose or terse file as a "C",
and places a "1." in the "number of observations" field.
Model Type Collapse Software
The CLAP programs are FORTRAN programs which collapse individual-vehicle
records in analysis files into model type averages. They treat each vari-
able in the line set being collapsed in one of three ways:
(a) Descriptor variables - These are common to all records in the set
being collapsed, and are not mathematically manipulated, but simply
carried along. Descriptor variables include manufacturer code,
emission standard, model name, engine CID, no. cylinders, and tech-
nology code;
(b) Averageable variables - These include MPG values (averaged harmoni-
cally) and other numerics which are averaged arithmetically. The
latter group includes FE influences (city fraction, odometer, temp-
erature, miles per day, population density, and wind MPH), their
respective GPM ratios, and inertia weight;
(c) Counted variables - These are categorical variables, either alpha-
betic or numeric. The number of categorical levels in the set
being collapsed is counted.. These include data source, model year,
number of observations, month, state, ZIP, landform code, exposure
region, and vehicle size class.
.111-26
-------
Tab 1 e I I I -1 4
Verbose Analysis File Format
Variable
Format
Columns
Field Description
1.
2.
3.
4.
5.
6.
7-
8.
#9-
#10.
#11.
#12.
13-
14.
'5.
16.
17-
18.
19.
20.
21.
22.
23.
24.
25-
26.
•27.
28.
29-
30.
31-
32.
33.
3*.
35-
36-
37-
38.
39-
itO.
lil.
42.
43-
44.
45-
46.
47-
48.
(a)
\ot
(b)
(e)
vw
(d)
(e)
\c/
(f)
(g)
th)
SORC
MANF
HOLY
ESTD
NAM1
NAH2
ECID
ECYL
DVTYP
TRTYP
INTYP
80TYP
NOBS
MNTH
STAT
ZIPC
EPAC
EPAH
EPA
CTYF
E.CGR
EHGR
EOGR
ROADC
ROAOH
ROAD
ACGR
AHGR
AOGR
000
ODOG
TEM
TEMG
MPO
MPOG
POD
PODG
TPO
TPOG
SPOG
WND
WNGC
WNGH
ROGC
ROGH
EXPO
IWGT
CIAS,
in 3
i n a
E n a
in a
i n a
in a
i n a
i n a
F3-0 (a)
F4.0
F3.0 (b)
A4
IX, A?
A7
1X.F4.0
FJ.O
A1
Al
A1
Al
F6.0
F3.0 (c)
2X.A2 (d)
1X.F4.0 (e)
F5.1
F5.1
F5.1
F5.0
F6.3
F6.3
F6.3
F5.1
F5-1 •
F5.1
F6.3
F6.3
F6.3
F7-0
F6.3
F4.0
F6.3
F5-0
F6.3
F7.0
F6.3
IX, A3 (f)
F6.3
F6.3
F5.1
F6.3
F6.3
F6.3
F6.3
U.F3.0 (g)
F6.0
F4.0 (h)
co 1 1 apsed f i
col lapsed f i
co 1 1 apsed f i
col lapsed f i
co 1 1 apsed f i
coi lapsed ft
col lapsed f i
col lansed f i
1-3
4-7
8-10
n-14
15-22
23-29
30-34
35-37
38
39
40
41
42-4?
48-50
51-54
55-59
60-64
65-69
70-74
75-79
80-85
86-91 '
92-97
98-102
103-107
108-112
113-118
119-124
125-130
131-137
138-143
144-147
148-153
154-158
159-164
165-171
172-177
178-181
182-187
188-193
194-198
199-204
205-210
211-216
217-222
223-226
227-232
233-236
le« the number
le. the number
1e, the number
le, the number
le, the number
le, the number
le, the number
le. the number
Data source (20-Chrysler, 30-Ford, etc)
Manufacturer code
Model year
FLOV, etc
1st 7 char's of model name
Next 7 char's of model name
Engine cu. in. displacement
No. engine cylinders
Drive type (Front/Rear)
Transmission type (Auto/Manu)
Induction type (Carb/F l/Osl)
Body type (Sedan/Wagon)
No. of observations in record
Month
2-char. state abbrev.
3-digit ZIP code
EPA City MPG
EPA Hi way MPG
EPA 55A5 MPG
Percent city driving
1 mputed c i ty i nf 1 uence i ndex
Imputed hi way influence index
Imputed overall influence indisx
Measured road MPG, city
Measured road MPG, hi way
Measured overall road MPG
Actual city GPM ratio (EPAC/ROAD)
Actual hi way GPM ratio (EPAH/ROAO)
Actual GPM ratio (EPA/ROAD)
Odometer mileage
Odora. GPM ratio
Avg. temp (deg F} for month & ZIP
Temp. GPM ratio
Avg. mi les per day
AMPO GPM ratio
Population density of ZIP
Popdens GPM ratio
Topographic code
Topo GPM ratio
Speeding GPM ratio
W i nd mph
Wi nd GPM ratio, ci ty
Wind GPM ratio, hiway
Road condition GPM ratio, city
Road condition GPM ratio, hiway
Exposure region
Inertia weight
Vehicle size class code
of model years: format remains F3-0
of states; .... **new format** 1X.F3-0
of topo codes; **new format** 1X.F3.0
of expo regions; format remains 1X.F3.0
of classes: format remains F4.0
111-27
-------
Table I I 1-15
Terse Analysis File Format
Variable
Format
Columns
Field Description
1 . SORC
2. MANF
3. MDLY
4. ESTD
5. NAM1
6. NAM2
7. EC1D
8. ECYL
#9. OVTYP
#10. TRTYP
#11. INTYP
#12. BOTYP
13. NOBS
14. EPAC
15. EPAH
16. EPA
17- CTYF
18. EOGR
19. ROAD
20. AOGR
21. 1 WGT
22. CLAS
F3.0
F4.0
F3-0
A4
IX, A7
A7
1X.F4.0
F3.0
Al
Al
Al
Al
F6.0
F5.1
F5-1
F5-1
F5.0
F6.3
F5.1
F6.3
F6.0
FI4.0
1-3
k-1
8-10
11-U
15-22
23-29
30-3A
35-37
38
39
40
1*1
42-i»7
48-52
53-57
58-62
63-67
68-73
74-78
79-84
85-90
91-94
Data source (20»Chrysler, 30~Ford, etc)
Manufacturer code
Model year
FLDV, etc
1st 7 char's of model name
Next 7 char's of model name
Engine cu. in. displacement
No. engine cylinders
Drive type (Front/Rear)
Transmission type (Auto/Manu)
Induction type (Carb/F1/Dsl)
Body type (Sedan/Wagon)
No. of observations in record
EPA City MPG
EPA Hiway MPG
EPA 55/45 MPG
Percent city driving
Imputed overall influence index
Measured overall road MPG
Actual GPM ratio (EPA/ROAD)
Inertia weight
Vehicle size class code
111-28
-------
The general form for CLAP program names is CLAP(X); The specific CLAP pro-
grams are described below. Each can process 20,000 data records.
CLATTERS«1 collapses a terse input file into model type averages; it gener-
ates four output files. Output "-1" is a terse format file with the model
type records from each data source (e.g. Ford surveys) and each model year
disaggregated. Output file "-2" aggregates across all sources, with model
years remaining disaggregated. Output "-3" aggregates across all model
years, with sources remaining disaggregated. Output "-4" aggregates across
both model year and data source.
CLAPVERB.1 collapses a verbose input file into four verbose outputs, with
the same data source and model year aggregations as above.
CLAPTERS.2 collapses a terse input file into the same four aggregation
schemes as above, but it generates two outputs at each of the four aggrega-
tion levels: a City file and a Highway file. The inclusion ranges of city
fractions which define "city-driven" and "highway-driven" cars are specified
in the RUN command, along with the input file assignment.
CLAPVERB.2 is the verbose-format version of CLAPTERS.2.
111-29
-------
C. DATA SCREENING
A number of strategies can be used to decide whether data are "usable."
Data usefulness criteria can be and have been applied to individual vehicles
and indeed to entire data bases.
All of the data used in this investigation came to us pre-screened to some
extent. Data received from DOE was pre-screened as follows.*
o Data from dynamometer tests (rather than on-road operation) were de-
leted;
o Data from road "tests" by organizations such as Motor Trend Magazine
and Consumers Union were deleted;
o Data from consumer complaint letters were eliminated (not to dis-
count them as invalid data, but simply because they capture only one
tail of the distribution of in-use fuel economy);
o Vehicles with less than 2,000 odometer miles were eliminated;
o Vehicles from model years 1975 and 1976 were eliminated if they did
not have Diesel, fuel injected, or four-cylinder engine, manual
transmission, front wheel drive, or a combined EPA fuel economy of
25 MPG or greater;
s
o Outliers were eliminated; the outlier limits were 50% and 150% of a
vehicle's combined EPA MPG rating, i.e., vehicles were eliminated
which achieved less than half the EPA combined number or more than
one and a half times the EPA combined number.
* Development of Adjustment Factors For On-Road Fuel Economy, prepared by
Energy and Environmental Analysis, Inc. EPA 460/3-81-003, March, 1981.
111-30
-------
Data pre-screening used by GM* was as follows;
o Vehicles with initial odometer readings less than 100 miles or
greater than 20,000 were eliminated;
o Vehicles with less than 200 miles traveled between initial and
final fillups were deleted;
o Vehicles with undeterminable EPA rating (e.g., unavailable engine/
car line/transmission type) were eliminated;
o Outliers were eliminated using the same 50% and 150% outlier limits
as the DOE data screening.
Data from the other sources (Ford, Chrysler, and EPA-Emission Factors) were
pre-screened using protocols generally similar to the GM data.
Our own screening of the data as received from the various sources was done
in two stages: "data source characterization", and "adjustment factor de-
velopment". First-stage screening is discussed next; second-stage screen-
ing will be addressed after the Data Source Characterization section.
First-Stage Screening
In the first stage, only two criteria were used to eliminate data: undeter-
minable EPA rating, and outlier limits.
Vehicles with undeterminable EPA ratings generally had either unavailable
car line/drive train combinations or obvious key punch/typographical er-
rors. Where correctible cypo errors** occurred in vehicle technologies that
were relatively underpopulated, they were corrected. Most of these errors
occurred in the Rear drive-Automatic-Carbureted category, however, and with
* SAE Paper 810384 In-Use Fuel Economy of 1980 Passenger Cars by R.W.
Schneider, B.W. Lipka, and F.K. Miller, February, 1981.
** e.g., Ford 117 CID 6-cylinder engine ...must have been 171 CID.
111-31
-------
the huge preponderance of data in that category, we were quick to throw out
SAC data with even miniscule typographical errors* — it simply was not
needed, and was deemed not worth the trouble of respelling. Approximately
5% of the data (mostly SAC technology) was discarded by use of this cri-
terion.
The outlier screening procedure used the influence index concept (see Appen-
dix A) to derive minimum and maximum limits* This was done by selecting
very favorable and very unfavorable values of the influences for which GPM
algorithms had been developed, and calculating the total influence index re-
sulting from these influences occurring simultaneously. Table 111-16 give
the influence values selected, their individual GPM ratios, and the resul-
tant total influence indexes. This suggests that MFC factors of 25% and
1672 might not be impossible to encounter — unlikely, perphaps, but not im-
possible. Note that for a "typical" car with an EPA highway-to-city MPG
ratio of 1.5, achieving 25% of its combined MPG figure is equivalent to
achieving about 30% of its EPA City number; one which get 167% of its com-
bined MPG is exceeding its EPA Highway figure by about 30%'..
Naturally, it can always be argued that even worse, or better, conditions
could stack up to yield even wider outlier limits ...in the end, it still
comes down to a judgment call. Our judgment call is that "enough is enough"
at values of 25% and 167%. Hence these were the limits used to purge out-
liers. Less than 1% of the data were deleted using these limits. Some data
deleted were — in our judgment — clearly erroneous, such as a 73 MPG
Impala V-8 and a 3 MPG 4-cylinder Horizon.
e.g., Pontiac Catalena instead of Pontiac Catalina.
111-32
-------
Table 111-16
FE Influences used for Outlier Limits*
Influence
Temperature
Wind
Topography
Population Density
Road Condition
Odometer
City Fraction
Miles per Day
Total Influence
GPM Ratio Values
selected for out-
lier limits
"Worst FE" Case:
Value GPM Ratio
"Best FE" Case:
Value GPM Ratio
0°F
20 MPH
D6
33,000
"Poor"
10
100Z
1
1.282
1.030 (city)
1.560
1.024
1.210
1.500
1.148
2.083
3.837
90°F
0
"Smooth"
N/A
"Good"
25,000
0%
100
41
@100 mi /day
@41 mi /day
0.877 (No Air Cond)
1.000
1.000
1.000
1.012
0.939
0.820
0.889
1.000
0.537
0.648
4.0
0.60
% of EPA Comb. MPG
25
167
See Appendix A.
111-33
-------
IV. ANALYSIS
A. DATA SOURCE CHARACTERIZATION
A number of standard analyses were performed on each data source using can-
ned software packages. These Included:
a. Overall data source road offset
b. Overall data source road offset, by model year
c. Constant road factors, by technology
d. Constant road factors, by technology and model year
e. MPG-dependent road factors, by technology
f. MPG-dependent road factors, by technology and model year.
For those data sources with enough information to permit computation of fuel
economy influence indexes, that metric was also analyzed along with actual
road offset.
This section presents the results of analyses a, c, and e, and the influence
index analysis. The other analyses are collected in Appendix B.
Chrysler Corporation
Table IV-1 is the overall road offset analysis for the Chrysler data. It
shows an overall in-use fuel economy shortfall of 14.6%.
Table IV-2 is the constant-factor analysis of the Chrysler data by vehicle
technology. Statistical significance tests for each technology's GPM ratio
and influence index are included; these tests indicate the confidence level
a*-, which each technology's mean GPM ratio and influence index can be said to
IV-1
-------
3
Table IV-1
Overall Road MPG Offset for Chrysler - Measured MPG Data Source
Fuel Economy
EPA Road
GPM Ratio
MPG Factor
Harmonic
Standard
Veh 1 c 1 e
Techno)
R : A : C ,
R: A: I
R:M:C
F : A:C
F :M:C
Average 24.9 21.2
Devlat Ion 4.6 5 . 1
In-Use Fuel Economy
... 12
v Data Source
+ + + EPA MPG +++
of Veh Mln HMAV Max
430 19 19.9 23
3 19 19.3 19
1 24 23.5 24
579 26 27.3 29
269 29 31.6 37
Average 1.171 (From GPM Ratio)
Standard Deviation O. I7O
Table IV-2
Offset: Non-Modal Constant-Factor Analysis
Vehicle Technology Strata ...
: Chrysler (Measured MPG). 1981
++* Road MPG ++* GPMR
f*OU C If^rvf V v*£ !••
urM bignr inriu
Mln HMAV Max Ratio Olff* Index
9 16.6 28 1.205 1OO% 1.162
17 18.4 21 1.053 82% 1.178
25 25. 0 25 .941 1.138
13 23.5 36 1.166 47% 1.181
2O 27.9 38 1.1 3O 1OO% 1 . 1O2
O.854
Influ
C 1 MV*£
a Ignf
01ff«
36%
27%
(00%
100%
-------
be different from the overall data source mean GPMR and influence index, re-
spectively. For example, it is concluded at very high confidence that the
RAG stratum's GPMR of 1.205 is different from the overall source GPMR of
1.171 (from Table IV-1); the RAG stratum's influence index of 1.162 is not
statistically different from the overall source influence index of 1.158.
Table IV-3 (next page) details the MPG regression analyses for each of
Chrysler's technology strata. This table gives the MPG-dependence algorithm
and its correlation coefficient and standard error. The GPM ratio solutions
to each algorithm at the minimum and maximum EPA MPG values are also given;
these values can be used to plot the algorithms in analysis space. The ac-
tual range of GFMRs is, of course, wider than the range represented by the
calculated endpoint solutions of the algorithms.
MPG dependence is undeterminable in two of the strata, RAI and RMC; for the
former because there is only one EPA MPG value for the set, and for the lat-
ter because there is insufficient data for regression.
The algorithm solutions for the three regressible strata are interpreted in
terms of road MPG in Table IV~4;
Table IV-4
Road MPG for Algorithms in Table IV-3
Minimum EPA MPG Maximum EPA MPG
Technology EPA GPMR Road EPA GPMR Road
RAG 19/1.25 - 15.2 23/1.11 = 20.7
FAC 26/1.10 = 23.6 29/1.24 = 23.4
FMC 29/1.12 => 25.9 37/1.15 = 32.2
IV-3
-------
Table IV-3
In-Use Fuel Economy Offset: Non-Modal MPG-Dependent Analysis
... 12 Vehicle Teohnology Strata ...
Data Source : Chrysler (Measured MPQ). 1981
Solutions at Mln/Max EPA MPG
Techno 1
R:A:C
R: A: I
R:M:C
Kegression equation
E = EPA 55/45 MPG R-Value Std Err
GPMR = 1.8O8 - O3O3(E) . O67 .192
OATASET NOT REGRESSIBLE
OATASET NOT REGRESSIBLE
GPMR* » EPA =
1.25 19
19
24
GPMR= • EPA =
1.11 23
19
24
Actual urMK
Range
.719 - 2.267
.944 - 1. 145
.941 - .941
-------
For RAG vehicles, GPMR decreases with EPA MPG, implying that at EPA = 19,
road MPG = 15.2, and at EPA = 23, road MPG = 20.7.
For FAC vehicles, GPM ratio increases with EPA MPG at such a rate that cal-
culated road MPG is essentially constant over the relatively narrow EPA MPG
range. This is a good example of the marginal usefulness of MPG-dependency
regression analysis of a stratum with a narrow range of EPA MPG. For FMC
vehicles, there is no significant change in calculated GPM ratio as a func-
tion of EPA MPG.
Other Data Sources
Tables IV-5 through IV-25 are the constant-factor and MPG-dependent ana-
lyses, by vehicle techology, for the other data sources. These will not be
discussed individually.
IV-5
-------
This page intentionally
blank and un-numbered
-------
Table IV-5
Overall Road MPG Offset for DDE-Fleet MPG Data Source
Fuel Economy EPA Road GPM Ratio MPG Factor
Harmonic Average 19.O 15.7 Average 1.225 (From GPM Ratio) O.817
Standard Deviation ' 2.8 3.2 Standard Deviation O.242
Table IV-6
In-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysis
... 12 Vehicle Technology Strata . . .
Data Source : DOE (Fleet MPG). All Vears
EPA MPG +•»•+ + + * Road MPG * + + GPMH
Vehicle Number ----------------- ----------------- GPM Stgnf
Techno I of Veh Mln HMAV Max Mln HMAV Max Ratio OlffO
R:A.C 7977 12 19.0 3O 5 15.6 42 1.225 93/4
R:A:D 24 24 25.5 27 17 22.4 3O 1.142 97X
R:M:C 10 17 18.2 26 12 14.8 21 1.242 34X
F:M:D 5 44 44.5 45 31 4O.3 SO 1 . 1O6 72*
-------
Table IV-7
In-Use Fuel Economy Offset: Non-Modal MPG-Oependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : DOE (Fleet MPG). All Years
Solutions at MIn/Max EPA MPG
veri ic i e
Technol
R: A :C
R: A:O
R:M:C
F :M:D
Kegression equation
E = EPA 55/45 MPG R-Valua Std Err
GPMR = .775 + O232(E) .259 .234
GPMR = .593 + O214(E) .177 .162
GPMR = .923 + .OI70(E) .442 .144
GPMR = 2. 481 - O3O9(E) .059 .239
GPMR = » EPA= GPMR- f EPA=
1.O5 12 1.46 3O
1.11 24 1.17 27
1.2O 17 1.37 26
1.13 44 1.O9 45
GPMR
Range
.604 - 3.SI4
.871 - 1.6O6
.957 - 1.477
.896 - 1.429
-------
Table IV-8
Overall Road MPG Offset for DOE-Measured MPG Data Source
Fuel Economy
EPA
Road
GPM Ratio
MPG Factor
Harmonic Average
Standard Deviation
2O. 8
6.O
17.6
6.5
Average
Standard Deviation
1 . 181
O. 136
(From GPM Ratio)
O.847
Table IV-9
In-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysis
... 12 Vehicle Technology Strata ...
Data Source : OOE (Measured MPG). All Years
EPA MPG +++
+++ Road MPG
GPMR
veil i u i e
Techno 1
R
R
R
R
R
F
F
F
F
: A
: A
:A
:M
:M
:A
: A
:M
:M
:C
: I
:D
:C
:D
:C
:O
:C
:D
wumutjr
of Veh
317
.2
24
19
4
19
1
3
16
Mln
13
17
22
2O
27
IS
24
29
40
HMAV
19
18
24
28
27
23
24
32
44
.5
.6
.7
.4
.7
.4
.O
.2
.O
Max
30
20
27
3'9
29
28
24
39
45
Mln
11
14
16
15
24
13
22
21
31
HMAV
16.4
15
22
21
26
2O
22
29
43
.7
.5
.7
.2
.8
.0
.3
.3
Max
26
18
30
31
29
26
22
29
53
urn
Rat to
1. 193
1 . 183
1 -O98
1 .305
1 .056
1 . 125
1 .O9O
1 .274
1 O15
a lynr
Olff*
94X
4X
96X
1OOX
93X
100X
74X
10054
-------
Table IV-1O
In-Use Fuel Economy Offset: Non-Modal MPG-Dependent Analysts
... 12 Vehicle Technology Strata ...
Data Source : DOE (Measured MPG). All Years
Solutions at Mln/Max EPA MPG
ven ic le
Technol
R
R
R
R
R
F
F
F
F
: A
: A
: A
:M
:M
: A
: A
:M
:M
:C
: I
:D
:C
;D
:C
:D
:C
:D
Kegression equation
E = EPA 55/45 MPG R-Value Std Err
GPMR =
0ATASET
GPMR =
GPMR =
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
.994
NOT
. 148
1.581
.842
1 092
NOT
1 . ISO
3.883
+ OIOO(E) .251 .116
REGRESSIBLE
+ .0384(E) .271 .176
- .O094(E) .296 .158
+ .OO77(E) . IO8 .106
+ .OO14(E) .063 .068
REGRESSIBLE
+ .O029(E) . 142 . 145
- 065KE) .461 .155
GPMR = » EPA=
1.12 13
17
.99 22
1 . 39 2O
1 .05 27
1.11 15
24
1.26 29
1.29 4O
GPMR =
1.29
1 . 18
1 .22
1.O7
1 . 13
1 .29
.95
0 EPA"
3O
2O
27
39
29
28
24
39
45
ACIUBI Ut-MK
Range
.920 -
1. 120 -
-BOB -
1 . 103 -
. 98O -
1.O32 -
1.O9O -
1 . 156 -
.845 -
1.671
1.247
I.55O
1.694
1. 138
1.325
1.O9O
1 .351
1.4O8
-------
I
I—•
O
Table IV-11
Overall Road MPG Offset for DOE-Percelved MPG Data Source
Fuel Economy
EPA
Road
GPM Ratlo
MPG Factor
Harmonic
Standard
Average 26 . 1 24 .O
Deviation 7.4 B. 1
Average
Standard Oevlat
Table IV-12
In-Use Fuel Economy Offset: Non-Modal
... 12 Vehicle Technology
Vehicle
Techno 1
R: A : I
R: A :D
R:M: I
F : A :C
F : A : I
F : A :D
F:M:C
F:M: I
F:M:D
Data Source
+++ EPA MPG +++
of Veh Mln HMAV Max
49 14 16.5 21
1O8 22 24.6 28
47 14 2O. 6 23
169 II 23. 5 32
39 17 2O. 2 27
4 24, 23.9 24
2OI 23 32.3 4O
122 . 18 28.8 3O
68 40 44.2 45
1 .
Ion O.
Constant
Strata .
: DOE (Perceived MPG).
+++ Road MPG
Mln HMAV
10 14.9
12 20.9
ie 20. 7
9 21.8
1O 19.3
14 17.6
19 28.9
17 28.2
24 4O.3
* + +
Max
25
37
32
40
32
2O
45
48
55
095 (From GPM Ratio)
295
-Factor Analysis
AH Vears
GPMR
GPM Slgnf Influ
Ratio Dlffe Index
1.1O1 3O% 1.227
1 . 1BO 1OO% 1 .210
.998 10O% 1.2O8
1.O84 25% 1.283
1.O38 Bay, 1.275
1 . 357 89% 1 . 234
1.128 99% 1.2O2
1.O24 1OO% 1.225
1.O96 17% 1.2O2
O.913
Influ
Slgnf
Dlffff
9%
72%
55%
99%
57%
4%
9O%
17%
71%
-------
Table IV-13
In Use Fuel Economy Offset: Non-Modal MPG-Dependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : DOE (Perceived MPG). All Years
Solutions at Mln/Max EPA MPG
venicie
Techno 1
R: A: I
R:A:D
R:M: I
F : A:C
F:A: I
F : A;D
F :M:C
F :M:.I
F :M:D
Kegression t qua i ion
E = EPA 55/45 MPG
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
GPMH =
1 .456
.055
1 . 029
.967
1 .325
NOT
.417
.947
4.321
- O2I3(E)
+ .O456(E)
- OO15(E)
+ .0047(E)
- .O138(E)
REGRESSIBLE
+ .02I7(E)
+ .0027(E)
- .0729(E)
R-Value
.217
.279
.016
.092
.246
.435
O19
.332
Std Err
. 188 •
.231
. 144
.232
. 195
. 173
. 2O4
.204
GPMR= 0 EPA =
1. 16
1 .06
1.01
1 .02
1 .09
.93
.99
1 .42
14
22
14
11
17
24
23
18
4O
GPMR= 4
1 .OO
t .31
.99
1 . 12
.96
t .28
1 .03
1 .06
t EPA =
21
28
23
32
27
24
4O
30
45
ACIUa 1 Uf HK
Range
.625
.674
.671
.668
.672
t. 196
.785
.608
.820
- 1.488
- 2.238
- 1.245
- 2.970
- 1.676
- 1 . 7O9
- 1.863
- 1.646
- 1.838
-------
Table IV-14
Overall Road MPG Offset for EPA Measured MPG Data Source
Fuel Economy
EPA
Road
GPM Ratio
Harmonic Average
Standard Deviation
22.2
6. 1
ta.2
6.5
Average
Standard Deviation
MPG Factor
1 .211
O. 185
(From GPM Ratio)
O.826
I
H-*
NJ
Table IV-15
In-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysis
... 12 Vehicle Technology Strata ...
Data Source : EPA (Measured MPG). All Years
Vehicle
Techno 1
R: A:C
R: A: I
R : M : C
R:M: I
F : A:C
F : A: I
F :M:C
F ' M * I
Number
of Veh
582
10
145
14
98
23
1 12
27
444
Min
12
18
18
19
13
16
24
27
EPA »
MM/
19
21
26
22
25
19
32
29
7 15
13 18
14 23
17 21
11 21
11 15
18 28
19 28
KPG
W
.6
2
.9
O
3
a
9
9
444
Max
31
23
38
31
31
26
50
38
/*ou
uPM
Ratio
1.251
1. 159
1 . 187
1 . 092
1 . 184
1. 192
1.121
1 .022
GPMR
C 4 nnf
5 tgnr
Dlf f*
100%
74%
89%
98%
83%
44%
100%
IOO%
¥ nf 1 • •
inr lu
Index
1. 105
1. 107
1 .095
.991
1. 139
1 . 154
1.093
1.O18
Inf lu
C 4 r***f
a ignr
Olff«
53%
8%
29%
100%
94%
78%
35%
99%
-------
Table IV-16
In-Use Fuel Economy Offset: Non-Modal HPQ-Oependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : EPA (Measured MPG). All Years
Solutions at MIn/Max EPA MPG
venicie
Techno 1
R: A:C
R: A: I
R:M:C
R:M: I
H
1 F : A:C
C F:A:I
F :M:C
F :M: I
Regression equation
E = EPA 55/45 MPG
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
1 .207 +
1.274 -
1 . 157 +
.841 *
1 . 161 *
1 .444 -
.728 +
1 . 127 -
.OO22(E)
.OO54(E)
. OOIO(E)
.oioa(E)
.0009(E)
OI2BIE)
OI2O(E)
.0036(E)
R-Value
.042
.117
. O3O
.276
.013
.373
.341
O46
Std Err
. 182
. 142
. 173
. 161
. 186
. 139
. ISO
. 146
GPMR- 0 EPA =
1.23
1. 18
1. 18
1.04
1. 17
1.24
1.02
I.O3
12
18
18
19
13
16
24
27
GPMR= f EPA=
1 .28
1 . 12
1 .20
1. 19
1 . 19
1 .09
1 .23
1 .01
32
28
41
32
32
27
42
33
•ClUBI UfMK
Range
.BOS -
1.OOO -
.693 -
.868 -
.B7O -
1.OO9 -
.645 -
. 809 -
1.972
1.463
1.68O
1.311
2.OSO
1.436
1.604
1.474
-------
Table IV-17
Overall Road MPG Offset for EPA-PecceIved MPG Data Source
Fuel Economy
EPA
Road
GPM Ratio
Harmonic Average
Standard Deviation
21.9
6.4
18.6
5.9
Average
Standard Deviation
MPG Factor
1.191
O.273
(From GPM Ratio)
O.B4O
Table IV-18
In-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysts
... 12 Vehicle Technology Strata ...
Data Source : EPA (Perceived MPG). All Years
Vehicle
Technol
R:
R:
R:
R:
R:
F :
F:
F :
F :
A
A
A
M
M
A
A
M
M
:C
: I
;D
:C
: I
:C
: I
:C
: I
Number
of Veh
322
2O
1
187
25
1 1
1
59
9
+++ EPA MPG +*+ +++
Mln
13
16
24
20
2O
14
28
24
28
HMAV
18
16
24.
27
21
16
28
33
28
8
8
3
7
3
.4
1
.2
6
Max
29
17
24
42
26
28
28
45
30
Mln
a
12
28
1 1
16
12
29
12
22
Road
MPG
HMAV
16. 1
14
27
22
2O
15
29
27
26
.8
.7
.5
.2
.6
.3
.4
.7
+ + +
Max
31
2O
28
35
27
24
29
38
32
GPM
Rat lo
1 . 175
1 . 134
.877
1 .248
1 .061
1 .053
.958
1 .222
1 .071
GPMR
Slgnf
Dlf f<»
83%
94%
96%
100%
98%
54%
95%
-------
Table IV-19
In-Use Fuel Economy Offset :• Non-Modal MPG-Dependent Analysts
... 12 Vehicle Technology Strata ...
Data Source : EPA (Perceived HPG). All Years
Solutions at Mln/Max EPA MPG
venic le
Techno)
3
tn
R
R
R
R
R
F :
F
F
f
: A:C
: A: I
: A :D
:M:C
:M: I
: A:C
: A : I
• M • C*
• M • I
Kegression t qua t ion
E = EPA 55/45 MPG
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
1 .017
.747
NOT
. 127
.835
9O6
NOT
.569
4 .489
+ . O08 1 ( E
R-Value
) . 168
+ 023KE) .113
REGRESSIBLE
+ .0397(E
* .O1O5(E
+ .0087(E
) .403
) .294
) .205
REGRESSIBLE
+ .0194(E
- .1I96(E
) .239
) . 28O
Std Err GPMR= » EPA =
.186 1.12 13
.127 1.11 16
24
. 35O .91 2O
. O9 1 1 . O4 2O
.173 1.O2 14
28
.319 1.O4 24
. 158 1 . 12 28
GPMR= 9 EPA =
1.25 29
1.14 17
24
1.79 42
1.11 26
1.15 28
28
1.43 45
.96 3O
ACIUBI (jfMK
Range
.717 -
.881 -
.877 -
.781 -
.919 -
.881 -
.958 -
.768 -
.915 -
1
1
3
1
1
2
1
.869
.355
.877
.575
. 3O3
.467
.958
.829
.325
-------
Table IV-2O
Overall Road MPG Offset for Ford-Measured MPG Data Source
Fuel Economy
EPA Road
GPM Ratio
MPG Factor
Harmonic Average
Standard Deviation
18.9
4.1
15.6
4.4
Average
Standard Deviation
1.214
O.2I9
(From GPM Ratio)
O.824
Table IV-21
In-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysis
... 12 Vehicle Technology Strata ...
Data Source : Ford (Measured MPG). All Vears
Wc3t-t 1 r* 1 A
veil I c I e
Techno 1
R: A:C
R: A: I
R:M:C
F :M:C
NunbGP
of Veh
25445
382
355O
89O
+ * +
Mln
14
19
18
29
EPA MPG
HMAV
18. 1
19.3
24 .O
32.5
+ •» +
Max
26
19
29
38
+ + +
Mln
5
a
8
it
Road MPG
HMAV
14..9
15.7
2O. 1
29.7
+ + .+
Max
37
27
34
56
r*Du
ur*M
Ratio
1.220
1 .227
1 . 197
1. 103
GPMR
C t f*r\t
^ ignr
DlffO
10O%
89%
I OO%
1OO%
i f\f 1 1 •
inr lu
Index
1. 191
1 . 188
1 . 183
1 . 157
Inf lu
C 1 fvnf
9 ignr
DlffO
67%
15%
91%
100%
-------
Vehicle
Techno1
Table IV-22
In-Use Fuel Economy Offset: Non-Modal HPG-Oependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : Ford (Measured MPG). All Years
Regression Equation
E = EPA 55/45 MPG
R-Value Std Err
Solutions at Mln/Max EPA MPG
GPMR= » EPA = GPMR = O EPA-
Actual GPMR
Range
R:A:C GPMR = 1.069 +• .OOSI(E) . 1O7
.217
I . 18
1.28 26
.602 - 3.781
R:A:I GPMR = .266 + .O499(E) .O32
.2IO
1 .20
19
1 .23
19
.699 - 2.331
R:M:C GPMR = .621 + ,O237(E) .3O2
.210
I .06
18
1 .31
29
.693 - 3.550
I
M
^J
F:M:C GPMR = .389 + .O217(E) .377
19O
1.O2 29
1 .22
38
.679 - 3.O27
-------
M
00
Table IV-23
Overall Road MPG Offset for GM-Measured MPG Data Source
Fuel Economy
EPA
Road
GPM Ratlo
MPG Factor
Harmonic
Standard
Vehicle
Techno!
R: A:C
R: A: I
R: A :D
R:M:C
R:M: I
R : M . D
F : A:C
F : A : I
F : A :D
F :M:C
F :M: I
F:M:D
Average
Oev tat Ion
In-Use
+ + +
Number
of Veh Mln
7O37 1 1
157 15
239 22
1151 14
12O 19
23 3O
891 1O
91 17
66 25
449 24
78 21
55 32
19.3
5.6
16.3
5.9
Fuel Economy
EPA MPG
HMAV
17.8
18. 1
25.8
25.4
26. 1
3O.8
19.4
18 8
25.2
30. 1
28.6
4O.6
Average
Standard Dev la t
Table
IV-24
Offset: Non-Modal
Ion
1. 187
O.2O1
Constant -Factor
+ + + +++ Road MPG •«• + *
Max
32
29
3O
42
32
31
32
28
26
41
32
47
Min
7
8
13
9
14
24
6
9
16
16
17
24
HMAV
14 .9
14.7
23.2
22.3
23.9
29.6
16.3
15.9
20. a
28.4
27.4
38. 1
Max
38
31
4O
59
49
35
37
3O
32
46
37
49
R
1
1
1
1
1
1
1
1
1
1
1
1
GPM
atlo
. 2O6
.223
. 112
. 146
.094
.043
. 199
. 170
.210
.064
.044
.067
(From GPM
Analysis
GPMR
Slgnf
OlffO
100%
93%
,00%
100%
100%
100%
82%
63%
66%
1OO%
1OO%
100%
Ratio)
Inf lu
Index
1. 165
1. 180
1 .093
1 . 171
1. 116
1 . 133
1.227
1 . 2O8
1 . 181
1. 17O
1 . 174
1.OB6
O.842
Inf lu
Slgnf
Dtffff
97%
53%
100%
13%
1OO%
77%
100%
SO%
35%
3%
15%
100%
-------
Table IV-25
In-llse Fuel Economy Offset: Non-Modal MPG-Dependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : GM (Measured MPG). All Vears
Solutions at Mln/Max EPA MPG
venicie
Techno 1
R: A:C
R: A: I
R: A:D
R:M:C
H R:M: I
1
H4 R:M:D
VO
F : A:C
F:A: I
F: A:0
F :M:C
F :M: I
F :M:D
Kegression equation
E = EPA 55/45 MPG
GPMR =
GPMR =
GPMR =
GPMR »
GPMR =
GPMR =
GPMR -
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
1 .O1O +
1.565 -
t . 198 -
.942 +
.930 +
.465 +
1 . 10O +
1.426 -
t . 4O8 -
.777 +
.683 +
I.O24 +
.OI06(E)
.0185
-------
B. CONSENSUS FINDINGS
1. There is a road MPG shortfall. All data sources agree that there is on
the average, a road MPG shortfall relative to the EPA Combined figure.
Furthermore, all sources are in general agreement on the magnitude of
the shortfall except for the DOE-perceived MFC data source. (In this
DOE data the shortfall is optimistic by a factor of nearly two; this
may be due to the absence of high-shortfall RAG technology vehicles
from this data set.) See Table IV-26.
2. Certain technologies have statistically different road MPG offsets com-
pared to overall survey average offsets. Table IV-27 shows, for each
technology, the number of data sources which yield road offsets statis-
tically different for that technology from the overall source average
offset.
The sources are in general agreement that the RAG technology has
uniquely high (worse) road offset, and the following technologies have
uniquely low (better) road offsets:
RAD, RMI, RMD,
FMC, FMI, FMD
Hence, as far as non-modal constant offset factors are concerned, one
fleetwide (all-technologies) offset factor is statistically invalid;
technology specificity must be considered.
3. For most vehicle technologies, road MPG offsets become worse for higher
EPA MPG levels. MPG. dependence of the technologies' GPM ratios is sum-
marized in Table IV-28. For eight technologies, an increasing GPM
ratio is evident in a majority of the sources' data.
For two Diesel groups, FAD and FMD, negative slopes in the MPG depen-
dance are found only in sources for which the EPA MPG range is narrow*,
IV-20
-------
i.e., the MPG dependence regressions are not useful. The one FMD
source with a reasonably wide MPG range, 32 to 45 MPG, does in fact
show a small positive slope.
For two technologies, RAI and FAI, negative MPG dependence slopes are
indicated. Np_ data source shows positive slope for FAI vehicles; both
sources which do give positive slopes for RAI vehicles have very narrow
MPG ranges, leaving sources with negative slopes as the most reliable.
The explanation for this behavior by these two technologies involves
differentiation by engine size, and appears later.
For FAD, 24 to 26 MPG; for FMD, 40 to 45 MPG.
IV-21
-------
Table IV-26
Overall Road MPG Offset for the
Eight Data Sources
Survey Overall MPG Slip No. Vehicles
Chrysler, Measured MPG -14.6% 1,282
DOE, Fleet -18.3% 8,016
DOE, Measured MPG -15.3% 405
DOE, Perceived MPG - 8.7% 807
EPA, Measured MPG -17.4% 1,011
EPA, Perceived MPG -16.0% 635
Ford, Measured MPG -17.6% 30,267
GM, Measured MPG -15.8% 10,357
All Sources -17.1% 52,780
(Sample-weighted)
IV-22
-------
Table IV-27
Technologies* whose MPG offset is Significantly**
Different.from Overall Source Average Offset
Number of Offset Higher (worse) or
Technology Occurences Lower (better) than average
SAC 5 of 7 High all 5
RAI 2 of 5 One high, one low
RAD 4 of 4 3 low, 1 high
RMC 4 of 6 2 low, 2 high
RMI 4 of. 4 Low all 4
RMD 1 of 1 Low
FAC 2 of 6 Both low
FAI None signif.
FAB None signif.
FMC 5 of 6 4 low, 1 high
FMI 3 of 3 All low
FMD 2 of 3 Both low
* Number of vehicles greater than or equal to 10
** Significance greater than or equal to 90%
IV-23
-------
Table IV-28
MPG Dependency of Road Offset, by Technology
Unweighted
Number of Occurrences*: Average Slope,
Technology Increasing ** Decreasing GPMR per MPG
1 0.005
3 0.006
1 0.026
1 0.014
1 0.007
0 0.019
0 0.012
3 -0.013
1 -0.008
0 0.015
1 0.004
2 -0.046
Number of vehicles greater than or equal to 10.
** "Increasing" means road offset worsens for higher MPG ratings;
"decreasing" means road offset lessens for higher MPG ratings.
RAG
RAI
RAD
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
6
2
3
5
3
1
6
0
0
6
2
1
IV-24
-------
C. SECOND STAGE SCSEENING
Close scrutiny of the data base is necessary prior to use of the data to
develop adjustment factors for future vehicles. The central question is
"what past and present vehicles should be allowed to represent future
vehicles?"
Vehicle/Engine Technology
A strict approach to this question might consider throwing out cars on the
basis of the demise or expected demise of all sorts of technical parameters,
such as non-electronic feedback control, longitudinally mounted front-drive
engines, etc* We have chosen a liberal approach, retaining all technologies
in the data base except for engines with displacements of 400 cubic inches
or more. Engines that large have not been used in new passenger cars since
model year 1979, with the single exception of Rolls-Royce cars*, which con-
tinue to be available.
Emission Control
The on-road FE data have not been coded as to emission control system type,
so a direct measure of the dependence of road FE offset upon emission system
is not yet possible. However, data from dynamometer tests conducted in the
FY1980 Emission Factors Program** has been reviewed for possible insight in-
to emission system effects. Table IV-29 summarizes the relationships be-
tween City dyno test MFC and EPA Guide (City) MPG, expressed as MFG ratios,
as a function of catalyst type and Carbon Monoxide emission standard.
This table suggests that tighter emissions standards or higher technology
emission controls might correspond to .greater MPG slip, as measured by dyna-
mometer tests. However, the vehicles were not stratified by odometer mile-
age or AMPD, so it cannot be said if the apparent trend is a real one.
* There are no Rolls-Royces in the in-use FE data base.
** Kearis, Final Monthly Report on In-Use Vehicle Test Programs Conducted
During the 1980 EFP, Memo to Director, Emission Control Technology Division,
December 2, 1981.
17-25
-------
Table IV-29
Dyno Test MPG Offset, by Catalyst type
and Stringency of Emission Standard
(Low Altitude Test Sites)
Catalyst
Type
None
Ox(^)
3 way(e)
Ox + 3 way
CO = 15
Number
9
36
-
-
gm/miCa)
FTP /Guide
0.95
0.94
-
-
CO = 7-9
Number
11
119
136
99
gm/miCb)
FTP /Guide
0.97
0.93
0.95
0.91
CO = 3.4
Number
-
27
10
47
FTP /Guide
-
0.88
0.89
0.89
(a)49-states, 1978-1979
(b) 1980 49-states (7); 1981 waivered 49-states (7);
1980 Calif (9); 1981 Calif (7)
(c) 1981 49-states
(d) Ox = Oxidation
(e) 3 way = Oxidation and Reduction
IV-26
-------
No vehicles have been eliminated from the on-road fuel economy data base as
a result of this inquiry.
Model Year Differences
The question of on-road MPG offset as a function of model year is impor-
tant. If there is a trend in the offset with model year, that trend cer-
tainly would have to be accounted for * in developing adjustment factors for
future model years. If vehicles of certain model years perform in a manner
uncharacteristic of what is expected of future vehicles, it can be argued
that those unrepresentative vehicles should be excluded from adjustment
factor development.
Model year effects were examined using model-type average ("collapsed")
data, to improve the distribution among manufacturers and technologies in-
herent in the raw, diffuse data. The three distinct data types: consumer-
measured MFC, consumer-perceived MPG, and fleet data, were kept separate so
that their respective trends could be determined.
Table IV-30 shows the model year trend in average fuel consumption ratio,
EPA MPG, Road MPG, and in-use MPG shortfall for the three data types. With
the exception of model year 1977**, the average EPA MPG for the consumer-
measured data set is quite close to published fleet average EPA fuel economy
figures. This suggests that the consumer-measured data is a representative
sample of the entire in-use fleet, at least as far as its EPA MPG character-
istic. The time series of fuel economy offsets for the consumer-measured
data is disjoint, showing a rise-fall-rise pattern.
The perceived-MPG data obviously reflect a biased sample of higher-MPG cars;
this was expected, due to retention of only higher-technology cars from the
DOE perceived data source. The FE offset time series here is again dis-
joint, but does not parallel that of the consumer-measured data.
* e.g., by extrapolating the trend.
** A relatively small sample, only 37 model types.
IV-27
-------
Table IV-30
Model Year Trend in In-Use MPG and EPA MPG
Model Year:
1975 1976 1977 1978 1979 1980 1981
Avg. EPA MPG* 15.8 17.5 18.3 19.9 20.1 22.4 25.0
Consumer Data, Measured MPG (43,322 cars)
GPM Ratio 1.13
Avg. EPA MPG 15.8
Avg. Road MPG 13.9
MPG Shortfall 12%
Consumer Data, Perceived
GPM Katio
Avg. EPA MPG
Avg. Road MPG
MPG Shortfall
Fleet Data (8016 cars)
GPM Ratio
Avg. EPA MPG
Avg. Road MPG
MPG Shortfall
* From SAE Paper 8103
through 1981 by J.A. Fost<
1.21
17.7
14.6
18%
1.25
19.8
15.9
20%
1.22
19.5
16.0 .
18%
1.16 1.18
20.2 22.8
17.4 19.2
14% 16%
1.20
24.8
20.6
17%
MPG (1442 cars')
1.18
24.9
21.2
15%
1.95
25.8
13.7
47%
86, Light
— f
1.20
20.2
17.1
15%
1.23
18.2
14.9
18%
'. 4
Duty
sr, J.D. Murrell,
1.06
26.4
25.2
4%
1.26
20.1
16.0
21%
Automotive
1.13
24.9
21.9
12%
1.15 1.04
18.5 21.6
16.1 20.7
13% 4%
Fuel Economy. .
—
_m
-
—
—
.Trends
and S.L. Loos. Fphmai-v lost
IV-2 8
-------
The fleet data for 1976 are atypical (on the high-MPG side) due to the pre-
screening of the DOE data. The 1977-1980 fleet cars, however, do have aver-
age EPA values that agree reasonably well with average fleet EPA fuel econ-
omy figures for those model years. For the fleet data, a trend of general
improvement is seen in the road offset.
There is no consensus between the three data types as to a model year trend
in the road MPG offset. Stated differently, the perceived-MPG data and the
fleet data fail to replicate the model year trend of the consumer-measured
MPG data.
When the consumer-measured MPG model year trend is plotted in analysis
space, its disjoint behavior immediately becomes understandable. Figure
IV-1 shows that the consumer data fall into two distinct model year groups:
model years 1975 and 1979-81 belong to one population*, and model years
1976-78 form a different population.
There is a fundamental difference between these two model-year groups which"
makes this finding no surprise at all — the only surprise is that it shows
up so clearly: certain aspects of the 1975 EPA test procedure were allowed
to relax significantly in 1976-78, most notably test weight classification
and shift schedules for manual transmissions. In these three model years,
manufacturers were allowed a certain latitude to "optimize testing proce-
dures for favorable fuel economy test results'*. In 1979, a return to the
rigor of the 1975 procedures was enforced in recognition of the "optimiza-
tion" that was occurring. The higher in-use MPG offsets of the "optimiza-
tion" years show that MPG gains achieved in the EPA tests during those years
were paper gains - they did not materialize on the road.
Figure IV-2 indicates that the fleet and perceived-MPG data show trends with
time that disagree with the measured data and with each other.
The "natural MPG dependence" on Figure IV-1 is explained
in the next Section.
IV-29
-------
FUEL CONSUMPTION
RATIO
1.30
1.25
Model
Teir
1975
1976
1977
1978
1979
1980
1981
1.20
i i.is
•i-
E
a.
CJ
1-10
o
Q=
No. of
Models
70
78
37
138
105
452
99
1.05
1.00
Road Mpg < EPA Mpg
Road Mpg>£PA Mpg
0.5
Figure
IV-1
10 15 20 25
EPA Composite (55/45) MPG
30 35
EPA
FUEL ECONOMY
Model Year Trend in Fuel Economy Offset:
Consumer Measured Mpg Data
IV-30
-------
FUEL CONSUMPTION
RATIO
1.30
1.25
1.20
I 1.15
E
o.
CT3
O
1.10
1.05
1.00
Road Mpg < EPA Mpg
Road Mpg>EPA Mpg
I
Q(
(GPMR - 1.95)
Fleet Mpg
Perceived Mpg
I
Figure
IV-2
15 20 25 30
EPA Composite (55/45) MPG
Model Year Trends in Fuel Economy Offset:
Perceived-Mpg and Fleet Data
IV-31
35
EPA
FUEL ECONOMY
-------
At this point, two things are clear:
o the decision to retain data from model years 1976 through 1978 de-
pends entirely on the question of the degree to which the rigor of
the 1975 test is going to be maintained into the mid-80's; and
o the random and inconsistent behavior of perceived-MFG and fleet data
with respect to the model year trend raises a question as to the ad-
visability of using these two data types to develop in-use adjustment
factors appliable to consumer driving.
Future Relaxation or enforcement of 1975 Test Rigor - Future relaxation of
test stringency can be estimated roughly by considering the history of excep-
tions to the 1975 manual transmission shift specifications, shown in Table
IV-31. (This is only one of several test items whose stringency has a quanti-
fiable time trend; it is shown here as an illustration of a typical time
trend.)
Table IV-31
Trend in Usage of Alternate Shift Schedules
(Percent of all Manual Trans. Tests)
1976 1977 1978 1979 1980 1981 1982
Domestic 43 49 62 45 59 71 - 75
European 6 28 44 53 49 66 88
s
Japanese 27 54 88 66 33 57 46
Total 28 44 64 54 48 65 68
' ^
This table shows that the fractional usage of alternate shift schedules grew
significantly through 1978, dropped slightly in 1979 and 1980, but has re-
sumed its increase in the past two model years. The quantity of alternate
shift usage is only part of the story, however; the relative MPG gains
IV-32
-------
achieved by alternate shifting schedules are believed to be less significant
in the more recent model years. It is to be noted, though, that all alternate
shift schedules are used for fuel economy benefit: we know of no instance in
which a manufacturer has requested alternate shifting in order to decrease
test MFG from what it would have been using the 1975 specifications.
It is also worth noting that alternate shifting is the only aspect of testing
in which the manufacturers have requested that test procedures be modified to
reflect real-world conditions. So far there have been no requests for better
test simulation of real world temperatures, road surfaces, accessories opera-
tion, tire pressures, or other conditions not favorable to fuel economy.
Conclusion on model year data usage - It is abundantly clear from the above
discussion that data from model years 1976 through 1978, which reflect a de-
gree of relaxation from 1975 test rigor, are appropriate for use in adjustment
factor development.
Our decision, then, is to keep all model year data, 1975-1981. This data re-
tention decision is tantamount to assuming that relaxation of test rigor will
be equivalent to having some 28% of the mid 1980's fleet be represented by ex-
cess road fuel consumption similar to the 1976-1978 cars.
Perceived-MPG Data and Fleet Data
These two types of data were compared in detail against measured-MPG data for
consumer vehicle operation. Appendix C contains the complete analysis.
It is concluded from that analysis that perceived-MPG data and fleet data dis-
agree with measured MFC data more often than they agree. It is also concluded
that inclusion of perceived-MPG or fleet data which do agree with the measured
data would not change the conclusions reached by using measured consumer data
alone, and therefore only measured-MPG consumer data are appropriate for in-
use adjustment factor development.
IV-33
-------
D. ADJUSTMENT ALGORITHM DEVELOPMENT...NON-MODAL
Model Type Averaging
The EPA has never claimed that its label values equal the fuel economies of
individual consumer vehicles; the label values have always been recognized
as some sort of "model type" averages*. This in itself is reason enough to
consider using model type averaged data to develop road adjustment factors
for use in labeling. Other benefits do accrue from the use of model type
data, however: the relative apportionment of model type data among manufac-
turers and among technologies improves, and of course the sheer size of the
data base shrinks to a much more manageable level.
The consumer-driven, measured-MPG data were collapsed into model type aver-
ages as follows.
1. Discrepancies in car line naming conventions among the various data
sources were eliminated.
2. All data records for a given model year and model type (MYMT) were col-
lapsed into one record for each data source, with each data field con-
taining the average value for all samples within that source.
3. The individual data source collapsed records for each MYMT were then ag-
gregated into single master MYMT records with all data fields containing
sample-weighted average values. When thus aggregating across data
sources, records with sample sizes below a value of 30 were weighted ac-
cording to their actual size; sources with larger sample sizes were
weighted at the value of 30. This was done to prevent source records
representing, say, 20 or 25 cars from being completely swamped by
sources which happened to survey hundreds of that same model.
* The 1974 Gas Mileage Guide included individual vehicles' test results, to
one decimal, but since 1975 the Guide has been based on model type averaging
quite intentionally. The 1975 Guide even averaged across transmission
types, a practice which was discontinued in 1976.
IV-34
-------
The above process collapsed 43,322 data records down to 1,483 MYMT records.
Even after aggregating across data sources, however, there were still 413
MYMTs represented by only one vehicle. These were not used. An additional
91 MYMTs, involving engines of 400 CID or. greater, were also not used.
Thus, there were 979 data records left.
Table IT-32 compares the model year, manufacturer, and vehicle technology
distributions of the diffuse data and the model type data.
All of the model type data are listed in Appendix E.
Analysis
The constant-factor analysis for the consumer-measured MFC model type data
appears in Table IV-33. Relative to this data set's weighted average GPMR
of 1.182,
o RAG vehicles have statistically worse road FE offset (higher GPMR);
o All front drive automatics (FAC, FAI, FAD) are indistinguishable; and
o All other technologies have statistically better road FE offsets
(lower GPMR).
A number of superficial conclusions could be drawn from the constant-factor
analysis. Ignoring the statistically significant technology differences in
FE offset, and taking only the inverse of the data set average GPMR, an MPG
adjustment factor of 1/1.182 « 0.846 would be indicated. Ignoring the sam-
ple weighting, the simple average of the twelve technologies' GPMRs is
1.122, and the inverse of this would suggest an MPG adjustment factor of
- +
Q.891. Using our projected mid-80's sales weighting for the 12 technolo-
gies, a weighted average GPMR of 1.149 would be calculated, giving an MPG
adjustment factor of 0.871. For any of these jumps to simple conclusions,
there are individual technologies with average GPMR in error by more than 9
percent.
IV-35
-------
Table IV-32
Comparison between Diffuse Data and
Model Type Data
Distribution by
Model Year: 1975, 1979-81 1976-78
Diffuse Data 66.1% 33-9*
Model Type Data 7^.2% 25-8%
Distribution by
Manufacturer: GM Ford Chrys AMC Europ Japan
Diffuse ig.6% 72.5% 5.0% 0.2* 1.7% 1.0%
Model Type M».8% 26.3% 12.0% 1.0% 10.1% 5.8%
Distribution by
Technology: RAC RAI RAD RMC RMI RMD
Diffuse 78.0% 1.3% 0.6% H.2% 0.3% 0.1%
Model Type 58.5% 2.0% 2.8% 18.1% 1 .J% 0-5%
FAC FAI FAD FMC FMI FMD
Di ff use
Model Type
3
6
• 7%
.5%
0.3%
1.1%
0
0
.2%
.5%
J»
6
.0%
.0%
0.2%
l.U
0
0
.2%
.7%
IV-36
-------
Table IV-33
in-Use Fuel Economy Offset: Non-Modal Constant-Factor Analysis
... 12 Vehicle Technology Strata ...
Data Source : Model Type Consumer Data
Veh i c 1 e
Techno 1
R:A:C
R:A: 1
R:A:0
R:M:C
R:M: 1
R:M:D
F:A:C
F:A: 1
F:A:0
F:M:C
F:M: 1
F:M:0
11, tfBtfc* A w
NUmDer
of Mdl
573
20
27
177
17
5
61*
11
5
59
11*
7
•H-f
Mi n
13
15
22
11*
19
27
18
16
25
21*
21
32
EPA MPG
HMAV
19.1*
20.6
25-9
23-9
23.6
29.6
25.2
19.0
21*. 9
30.7
28.7
1*1.8
•H-4-
Max
32
29
30
1*2
32
31
32
26
26
1*2
33
U5
***
Min
11
12
18
13
15 ;
25'
13
13
20
18
20
30
Road MPC
HMAV
16.0
18.1*
23.1
21.0
22.2
28.0
21.3
16.1
20,9
27-9
27.6
39-6
; -I-H-
Max
32
28
29
36
31
30
29
25
22
35
3<<
1*7
Ratio
1.216
1.111*
1.120
1.11*6
1.073
1.057
1.180
1.172
1.190
1.106
1.039
1.053
GPMR
S; _ _.r
i gnf
Diff@
100%
98%
100%
100%
100%
100%
1%
21%
^5%
100%
100%
99%
IV-3 7
-------
This page intentionally
blank and un-numbered
-------
Such simple multiplicative constant adjustment factors have a certain con-
ceptual appeal. For example, one could find comfort in a system where the
Label value is simply 0.83 times the EPA-55/45 fuel economy value. In anal-
ysis space this is the case when the GPMR is not a function of the EPA MPG,
but has a constant value of 1.20.
To illustrate how simple constant adjustment factors can lead to inaccurate
— or even erroneous — results, consider the following example.
The equations for GPMR are of the simple form EPA/ROAD = a + b EPA. If b is
zero, Road =" EPA/a as illustrated in Figure IV-3 for the two cases: al =•
1.20 and a2 = 1.08. In MPG space ROAD1 = 0.83 EPA and ROAD2 = 0.93 EPA as
shown in Figure IV-4. Since there is a greater shortfall (larger GPMR) for
vehicle type 1, it falls below vehicle type 2 in MPG space and for any given
EPA 55/45 fuel economy, vehicle type 2 is presumed to be "better" (higher
label MPG value) than vehicle 1.
In Case B, shown in Figure IV-5, the GPMR appears to be MPG-dependent. For
case Bl, the GPMR is
EPA/ROAD =• 0.90 + 0.01 EPA.
For case B2, it is
EPA/ROAD - 1.00 + 0.01 EPA.
The MPG space plots for these two vehicle types are shown in Figure IV-6.
Now vehicle type 1 is seen to be "better" than vehicle type 2.
Cases A and B are, however, just two different ways to look at the same
data. In Case A, the MPG-dependence of the data is ignored in favor of a
simple MPG-independent "correction factor" in MPG space. This is tantamount
to condensing all the data in analysis space to its centroid and using that
average value as the GPMR. In this example centroids are shown as the
points 1* and 2* in Figure IV-5.
IV-38
-------
FUEL
CONSUMPTION RATIO
1.50
1.40
1 1.10
1.00
0.90
—CASE A—
MPG-INDEPENDENT GPM RATIO
s.1.30
e:
o
o.
0 , „„ Vehicle Type 1, GPMR - 1.20
2: 1-20
LU
•I-
E
o.
CD
Vehicle Type 2, GPMR = 1.08
I I
0 10 20 30 40 50
EPA Composite (55/45) Mpg EPA
FUEL ECONOMY
FIGURE
IV-3 Assumed Constant GPM Ratios
for Two Vehicle Types
IV-3 9
-------
LABEL
FUEL ECONOMY
50
40
00
Q.
S? 30
f 20
CD
J3
TO
10
/
-CASE A-
MPG-INDEPENDENT GPM RATIO
10
20 30
EPA Composite (55/45) Mpg
40 50
EPA
FUEL ECONOMY
FIGURE
IV-4
Label Mpg Implications of Assumption of
Constant Gpm Ratios for Two Vehicle Types
IV-40
-------
FUEL
CONSUMPTION RATIO
1.50
1.40
1.1.30
o
oj
^
o
o.
E
o
21.20
E
Q.
§ 1.10
0=
1.00
0.90
-CASE B-
MPG-OEPENDENT GPM RATIO
10
20 30
EPA Composite (55/45) Mpg
40 50
EPA
FUEL ECONOMY
FIGURE
IV-5
Mpg-Dependent Gpm Ratios
for Two Vehicle Types
IV-41
-------
LABEL
FUEL ECONOMY
50
40
-£ 30
2
as
o
^ 20
CO
10
/
/
/
/
/
-CASE B-
MPG-OEPENDENT GPM RATIO
/
10
20 30
EPA Composite (55/45) Mpg
40 50
EPA
FUEL ECONOMY
FIGURE
IV-6
Label Mpg Implications of Mpg-Dependent
Gpm Ratios for Two Vehicle Types
IV-42
-------
The reason for the reversal between Cases A and B is obvious. Vehicle type
1 has a higher average GPMR than vehicle type 2, but its average EPA MPG is
also higher.
Occasionally this sort of reversal actually occurred during the source-
specific analyses to generate simple MFG-independent factors. It occured
when vehicle type 1 was BMC and vehicle type 2 was RAG. Since the bulk of
the 43,322 vehicle data base is made up of RAG and RMC data, we are very
confident in saying that their GPMRs are MPG-dependent and that carbureted
rear wheel manual transmission vehicles average better Road and EPA MPG than
their automatic transmission counterparts. Hence, simple assumptions of
MPG-independence of GPMR can be misleading.
Not all MPG-dependent analyses are inherently good or useful ones, however.
Figure IV-7 is an analysis-space plot of the data from table IV-33. It
suggests a decrease (improvement) in road fuel economy offset for higher EPA
MPG levels, due to the higher MPG levels and lower GPMRs for the "higher
technologies". The line of unweighted regression in the figure has, as in—
use fuel economy data goes, a rather high correlation (R-value) of 0.64. If
this regression equation were used for MPG adjustment, the adjustment algo-
rithm would be
Label MPG ^ ******
1.282 - 0.0061 (EPA MPG)
which would give MPG adjustment factors of 0.819 at 10 MPG, and 1.024 at 50
MPG. Errors of 7% in average GPM ratio still exist for certain individual
technologies.
This is a particularly dangerous adjustment equation. Although increasing
EPA MPG via advanced technology usually gives less road shortfall (lower
GPMR), EPA MPG improvements do not necessarily give less shortfall. EPA MPG
can be increased, of course, without any technological improvement at all.
It can be increased by beating the test. In such an occurrence, a vehicle
gains double advantage with this MPG-dependent type of adjustment both by
gaining paper EPA MPG and by an additional reward of less shortfall. To
IV-43
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
E 1.30
-------
illustrate, using this adjustment formula a 25 EPA MPG vehicle would be
labeled at 22 MPG (GPMR = 1.130). If a way were found to beat the test and
get a 30 MPG EPA rating, the calculated label would be 27 MPG and the new
GPMR would be 1.099. With this adjustment, a 5 MPG paper gain is rewarded
with an additional 3.1% GPMR bonus.
If on the other hand the test somehow beats the vehicle, reducing its EPA
MPG to 20 MPG, it would be doubly penalized: this adjustment assigns a GPMR
of 1.160. The vehicle has been dealt a 5 MPG paper loss compounded by an
additional 3.0% GPMR penalty.
MPG dependance; Analysis by technology - MPG dependence algorithms for the
individual technologies are given in Table IV-34. Neglecting for the moment
the generally low R-values, the twelve technologies would seem to fall into
three groups of MPG-dependency behavior:
o Offset worsens with increasing MPG: RAC, RMC, RMI, FAD, FMC,; FMI
o Too close to call: RMD, FAC, FMD
o Offset improves with increasing MPG: RAI, RAD, FAI.
Figures IV-8, IV-9, and IV-10 illustrate these MPG dependencies in analysis
space. The blocks plotted for each technology correspond to the regression
lines plus or minus one standard error, and contain some two thirds of the
model type data points for that technology. Conclusions as to whether there
is a "real" MPG dependence for individual technologies depend upon the
criteria chosen as sufficient proof of the dependence. If it takes a sample
size of 20 and an R-value of 0.20 to "prove" MPG dependence, four tech-
nologies have significant MPG dependence: RAC, RMC, and FMC with positive
(worsening) slopes in the 6.006 to 0.01 range, and RAI with a negative
(improving) slope of -0.007. However, if it takes a sample size of 50 and
an R-value of 0.50 to prove MPG dependence, none of the technologies are
significantly MPG-dependent.
IV-45
-------
Table IV-34
In-Use Fuel Economy Offset: Non-Modal MPG-Dependent Analysis
... 12 Vehicle Technology Strata ...
Data Source : Model Type Consumer Data
Solutions at Mln/Max EPA MPG
vemc le
Techno 1
R: A:C
R:A: I
R: A :D
R:M:C
R:M: I
R:M:0
F : A:C
F : A : I
F :A:0
F :M:C
F:M: I
F :M:D
Regression equation
E = EPA 55/45 MPG
GPMR =
GPMR =
GPMR =
GPMR -
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
1 . O5 1 +
1.272 -
1 . 290 -
.999 *
.849 +
1.055 +
1 . 178 +
1.584 -
.925 +
.798 +
.928 +
I.O89 -
.0083(E)
.0074(E)
.OOGS(E)
.0059(E)
0092(6)
. 000 1 ( E )
. 000 1 ( E )
.021 HE)
.01O7(E)
0099(E)
OO38(E)
.0009(E)
R-Value
.277
. 26O
. 157
.294
.390
.OOS
.OO2
.774
.205
.440
. 165
.048
Std Err
. O94
. IO9
. O8I
. IO3
. 1O2
.039
.096
.067
.039
. O94
.O7O
. O9O
GPMR= t
1. 16
1. 16
1. 15
1.O8
1.O2
1.O6
1. 18
1.25
1. 19
I.O4
1.01
1 .06
> EPA"
13
15
22
14
19
27
18
16
25
24
21
32
GPMR = 0 EPA=
1.32 32
1 .06
1 . tO
1 .25
1 . 15
1 .06
1.18
1 .03
1 . 2O
1.21
1 .05
1 .05
29
3O
42
32
31
32
26
26
42
33
45
Mutual urMK
Range
.838 -
.916 -
I.OOB -
.884 -
.913 -
1.O3O -
.944 -
i.oia -
1. 145 -
.871 -
.962 -
.961 -
1.531
1 .328
1 .344
1 .578
1.3O7
1.114
1.4O8
1.319
I.24O
1 .551
1 .221
1.2O1
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
1.30
1.20
o
o
0.
E
o
C_3
e
0.
1 1.10
0=
1.00
0.90
10
20 30 40
EPA Composite (55/45) Mpg
50
60
EPA
FUEL ECONOMY
FIGURE
IV-8
Technology Specific Gpm Ratio:
GASOLINE CARBURETED
IV-47
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
I 1.30
CJJ
o
Q.
E
o
1.20
•I-
E
Q.
CJ
1 1.10
cc.
1.00
0.90
RAI
FAI
10
20 30 40
EPA Composite (55/45) Mpg
50
60
EPA
FUEL ECONOMY
FIGURE
IV-9
Technology Specific Gpm Ratio:
GASOLINE FUEL INJECTED
IV-48
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
I 1.30
CJJ
—
7i
o
a.
E
o
O
1.20
•I-
£
a.
cj
1 1.10
az
1.00
0.90
10
RMD
1
20 30 40
EPA Composite (55/45) Mpg
50
60
EPA
FUEL ECONOMY
FIGURE
IV-10
Technology Specific Gpm Ratio:
DIESEL
IV-49
-------
This page intentionally
blank and un-numbered
-------
As a second illustration of the importance of MPG dependence, consider the
comparison detailed in Table IV-35. In this example, two types of vehicles
are to be labeled: RAC vehicles and FMC vehicles. The MPG-dependent GPMR
algorithms (from Table IV-34) for these technologies are shown, along with
their constant, non-MPG-dependent GPMR factors from Table 17-33. Also shown
is an assumed universal constant factor intermediate between the two tech-
nologies' constant factors.
The label values computed from these three approaches are given for three
different EPA-MPG versions of each of the two technologies. If the tech-
nology-specific MPG-dependent adjustment represents "truth", it is seen that
large label errors can result from use of the oversimplified universal-
constant and technology-constant approaches.
Thus the question of technology specificity and MPG dependence is not a
trivial one. It is too important to leave it on a note of uncertainty, or,
alternatively, to force it to a conclusion by arbitrary selection of proof
criteria. Additional inquiry is warranted.
IV-50
-------
Table IV-35
Illustration of Consequences of
Three Adjustment Approaches
GPM Ratio;
RAG Vehicle
FMC Vehicle
MPG-dep. technology factor*
Technology constant factor**
Universal constant factor***
Adjusted (Label) MFC:
MPG-dep. technology factor
Technology constant factor
Universal constant factor
Label Error Relative to
MPG-dep. Technology Factor:
Technology constant factor
Universal constant factor
Unwarranted Disadvantage
Imposed on FMC vehicle;
Technology constant factor
Universal constant factor
1.051 + .0083E
1.216
1.15
E-20 E-30 E*40
16.4 23.1 28.9
16.4 24.7 32.9
17.4 26.1 34.8
E"20 E=-30 E-40
0 +2 +4
+1 +3 +6
.798 + .0099E
1.106
1.15
E-20 E=30
20.1 27.4
18.1 27.1 -
17.4 26.1
E-20 E-30
-2 0
-3 -1
E=20 E^3Q
2MPG 2MPG
4MPG 4MPG
E«40
33.5
36.2
34,8
E=40
-t-2
+1
E=.4Q
2MPG
5MPG
* From Table.IV-34.
** From Table IV-33.
*** An assumed value between 1.216 and 1.106.
IV-51
-------
Discovered; an Explanatory Variable
Recognizing that the independent variable, EPA MPG, is probably a surrogate
for some vehicle technical parameter which is the true causative agent,
vehicle parameters which are collinear with MPG level were reviewed. The
most obvious of these are vehicle weight and engine size.
Weight was not investigated because the actual road weights of the vehicles
in the data base are unknown*.
Analyses performed using engine displacement and cylinder count as explana-
tory variables produced results similar to each other, with cylinder count
yielding the clearer pattern of behavior. Therefore, only the cylinder
count analysis is discussed here.
Figures IV-11 through IV-22 characterize the road offset data in analysis
space for vehicles with 4, 5, 6, and 8-cylinder engines. Each block en-
closes two thirds of the data for its respective technology and cylinder
count, and was based on a scatter plot of the data, rather than on computed
standard deviations, although the two produce very similar results. The
centroids of each cylinder count data block are shown as small circles en-
closing the cylinder count.
For individual technology/cylinder count (Tech/Ncyl) strata, there are only
a few hints of MPG dependence. Of the 29 such strata, two (FAC4 and FMC4)
show significant MPG dependence, both having sample sizes exceeding 30 and
R-values exceeding 0.50. Their MPG-dependent slopes are 0.020 and 0.014
respectively.
To interpret these figures in terms of what they tell us about the MPG
dependence of road offset for the twelve technologies, sample sizes of the
cylinder count subsets must be considered. If there is some natural MPG
dependence, it is to be found in the RAG and RMC data, which together
account for 77% of all of the model type data (59% and 18% respectively).
For some of the vehicles, EPA certification inertia weights have been
estimated, but even for these vehicles there is no way of knowing their
weight on the road.
IV-52
-------
FUEL CONSUMPTION
RATIO
E
Q.
.40
1.30
I 1-20
•I- 1.10
E
O.
CD
TD
(X3
O
1.00
0.90
m
TECHNOLOGY:
-RAC-
10 20 30
EPA Composite (55/45) Mpg
40
50
1 40
E
a.
CD
O.
o
CJ
1.30
1.20
I 10
E
a.
CD
1.00
0.90
I
TECHNOLOGY:
-FAC-
10 20 . 30 40
EPA Composite (55/45) Mpg
50
OJ
1.40
Jl.30
o>
o
E 1.20
o
o
.,. 1.10
E
o.
CD
I 1.00
0.90
10
n
TECHNOLOGY:
20 30
EPA Composite (55/45) Mpg
40
50
140
|l30
E 1.20
3
1.10
E
o.
1.00
o
ct:
0.90
TECHNOLOGY:
-FMC-
10 20 30
EPA Composite (55/45) Mpg
40
50
EPA
FUEL ECONOMY
IV-11, 12, 13, 14,
Technology-Specific Gpm Ratio, by Cylinder Count
-------
FUEL CONSUMPTION
RATIO
1.40
ii l i
10 20 30 40 5
EPA Composite (55/45) Mpg
1.40
S'1.30
-------
FUEL CONSUMPTION
RATIO
E
o.
o>
tjn
O
Q.
E
o
CJ
111. 20
-------
Figure IV-23 illustrates that when the regression line for all RAG data is
compared with the cylinder-specific average "spots", its slope appears high,
especially as far as predicting the FE offset of the 4-cylinder group. This
happens because of the sample distribution of the RAG data among the three
cylinder subsets; 52% 8-cylinder, 322 6-cylinder, and only 16% 4-cylinder.
The RMC all-cylinder regression line, however, is a good fit to the cylin-
der-specific data (the RMC data are distributed 10% 8-cylinder, 29% 6-cylin-
der, and 61% 4-cylinder).
Interestingly, a line constructed through the RAG cylinder-specific data
points themselves runs exactly parallel (slope = 0.0059) to the RMC slope.
Thus an MPG-dependent slope of 0.0059 actually turns out to be the best-fit
slope for both RAC and RMC data, when the overrepresentation of 8-cylinder
RACs in this particular sample is corrected for.
This model of the behavior of these two strata implies that there is a
constant difference in GPMR of about 0.10 due to the use of manual trans-
missions, compared to automatics, and this "technology effect" is not
dependent upon cylinder count.
For other technologies, a model of cylinder-independent effect on GPM ratio
does not usually hold. In fact, what we observe is that the various alter-
nate technologies usually affect the GPM ratio in a highly cylinder-
dependent way. This is obvious from figures IV-24 through IV-26, which are
cylinder-specific replots of the Tech/Ncyl centroids from the earlier
figures. •"
The method used to estimate the cylinder-specific GPM ratio effect of the
various technologies is illustrated using the following example.
Consider four Tech/Ncyl cases:
RAC4, GPMR = 1.246 at 25.23 MPG
RMC4, GPMR = 1.159 at 28.15 MPG
FAC4, GPMR = 1.165 at 27.30 MPG
FMC4, GPMR = 1.102 at 31.93 MPG
IV-5 6
-------
FUEL CONSUMPTION
RATIO
1.30
1.25
E
Q.
O
_CO
1 1.20
Q.
E
o
CJ
11.15
o
-o
CO
o
1.10
1.05
10
All Data
Regressions
I
15 20 25
EPA Composite (55/45) Mpg
30 35
EPA
FUEL ECONOMY
Figure
IV-23
MPG Dependence for RAC and RMC
Vehicles, by Cylinder Count
IV-57
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
E 1-30
Q.
43
O)
"7l
o
Q.
O
O
Q_
43
1.20
1.10
1.00
0.90
•RAC
FAC*
•RMC
RAI« «FMC
•RAD
FAI«.
RMIC m<
•FMD
10
20
30
40
50
60
EPA Composite (55/45) Fuel Economy
EPA
FUEL ECONOMY
Figure
IV-24
Technology Specific Average GPM Ratio,
Vehicles with 4—Cylinder Engines
IV-58
-------
FUEL CONSUMPTION
RATIO
l.DU
1 40
l.*rU
E 1.30
0.
*>
o
(^1
o
2 1.20
UJ
•1-
Q_
C3
•o
* 1.10
1 00
1 ,UU
Onn
.yu
0
Figure
IV-25
i i ii
A -
• =
-
RAC*
FAC0
RMC« *FMC
RMI« ARAO
FAIA AFMD
RAD* AFMD
FMlA
~" ~*
RAI
1 1 1 1
10 . 20 30 40
EPA Composite (55/45) Fuel Economy
• ^
Jechnolpgy^ Specific Average
i
Cylinder
Cylinder
—
1
50 60
EPA
FUEL ECONOMY
GPM Ratios-Vehicles with 5- and 6-Cylinder Engines
IV-59
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
1.30
1.20
o
o
Q_
LU
•I-
•s.
Q_
C3
-o
s
°= 1.10
1.00
0.90
Figure
IV-26
•FAC
• FAI
RACWRAI
•FAD
•RAO
RMC*
I
10 20 30 40
EPA Composite (55/45) Fuel Economy
Technology Specific GPM Ratio-
Vehicles with 8-Cyiinder Engines
50
60
EPA
FUEL ECONOMY
IV-60
-------
There are two ways of estimating the effect of manual transmission on the
GPMR of 4-cylinder engine vehicles: by comparing RMC4 to RAC4, and by
comparing FMC4 to FAC4. Since the Tech/Ncyl points occur at different MPG
values, however, MPG adjustment is required, using the natural MPG
dependence slope of 0.0059.
Thus, one estimate of the effect of manual transmission is:
= GPMRRMC4 ' GPMRBAC4
where GPMRRAC4 ]Mp(, means "the GPMR
RMC4
of RAC4 evaluated at the MPG of the RMC4
to which it is being compared".
At 28.15 MPG, the RAC4 GPMR value is 1.263, so
AGPMRMAN4 = 1.159 - 1.263 = - 0.104.
The other direct estimate of AGPMR....,, is:
AGPMRMAN4 ' GPMRTMC4 ' GPMRFAC4
= 1.102 -
= 1.102 - 1.192 = - O.Q90.
Similarly, the effect of front wheel drive can be estimated by comparing
FAC4 to RAC4 and FMC4 to RMC4, with the results -0.093 and -0.085,
respectively. -
IV-61
-------
But these are not the only possible estimates of the effects of manual
transmission and front wheel drive. They are only the primary estimates,
derived by comparing technology pairs that are different by only one of the
three technology descriptors. Secondary estimates can also be made. The
effect of manual transmission can be additionally estimated by comparing the
FMC and RAG GFM ratios and "backing out" the front wheel drive portion of
that two-technology difference. Similarly, a secondary estimate of the
front wheel drive effect can be developed from the same FMC vs RAG
comparison by backing out the manual transmission contribution.
To illustrate, the combined effect of manual transmission and front wheel
drive is:
GPMRFMC ' GPM*RAC
1.102 - 1.258 = - 0.156.
Now, the average effect of front wheel drive (FWD) from the two primary
estimates is -0.089 (the average of -0.093 and -0.085), so a secondary
estimate of the MAN effect is:
-0.156 - (-0.089) = -0.067.
Likewise, the average primary MAN effect is -0.097 (the average of -0.104
and -0.090), giving a secondary FWD effect estimate of:
-0.156 - (-0.097) = -0.059.
Thus we have three estimates of the MAN effect:
-0.104, -0.090 and -0.067
and three estimates of the FWD effect:
-0.093, -0.085 and -0.059.
IV-62
-------
By including secondary (and in a few instances, tertiary) estimates of
individual technologies' effects on GPMR, we account at least partially for
possible synergisms or antagonisms arising from combinations of the
technologies.
This method was applied using all of the 29 Tech/Ncyl centroid values to
compute every possible estimate of cylinder-specific GPMR sensitivity to
technology. Figure IV-27 is a four-graph illustration of the results. For
front wheel drive and fuel injection, there is quite a dramatic dependence
of GPM ratio effect upon cylinder count. For 4-cylinder cars, fuel
injection typically improves GPMR by 9 percentage points, and front wheel
drive improves GPMR by 7 points; for 8-cylinder cars, however, fuel
injection shows no benefit at all and front wheel drive worsens GPMR by some
6 points. For manual transmission and Diesel technologies, the GPMR effect
is not significantly cylinder dependent. Regressions of manual and Diesel
GPMR against cylinder count show slopes an order of magnitude lower than
those of front drive and fuel injection, and R-values below 0.20.
Non-modal Adjustment System
From the above analysis, we may write a general equation for the GPM ratio
of RAC vehicles,
GPMRRAC =• 1.097 + 0.0059 (E)
and difference equations for other technologies' GPMR departure from the RAC
equation:
= -0.185 + 0.0300 (Ncyl)
AGPMRMAN = -°-°7°
AGPMR_NJ = -0.184 + 0.0225 (Ncyl)
AGPMR^ = -0.142
IV-63
-------
CHANGE IN
GPM RATIO
(AGPMR)
as
Q_
| -0.10
v
• \
1 1 1 1
2468
No. Cylinders (N)
DIESEL
a
„ 8
Ji •
AGPMR= -.142—1
1 1 1 1
2468
No. Cylinders (N) No. Cylinders (N)
Figure
IV-27 Cylinder-Dependent Effects of Technology
on the GPM Ratio
IV-64
-------
Thus a general equation for GPMR is:
GPMR = f(BAC) + Af(Tech, Ncyl)
- 1.097 + 0.0059(E) + Af(Tech, Ncyl)
or
GPMR = 1.097 + 0.0059(E) + FWD(-.185 + .0300N) + MAN(-.070)
+ INJ(-.184 -I- .0225N) + DSL(-.142)
where E - EPA composite Fuel Economy
FWD • 1 if front drive, zero if rear drive
MAN » 1 if manual, zero if automatic
INJ * 1 if gasoline FT, zero if carb. or Diesel
DSL » 1 if Diesel, zero if carb. or gasoline FI
N • cylinder count
For example, a. 25 MPG 6-cylinder RMI vehicle has a GPMR of i
GPMR,nuT - 1.097 + 0.0059(25) + 0[-.185 +.0300(6)]
oRMI
+ 1[-.184 + .0225(6)] 4- 1[-.070] + 0[-.142]
= 1.126
GPM ratios for the 29 Tech/Ncyl sets, calculated using this general
equation, are compared to the actual GPM ratios in Table IV-36. The calcu-
lated GPMR is within five percentage points of actual GPMR for all cases
except 6-cylinder RAJ and RAD vehicles, whose calculated GPMRs are 0.171 and
0.065 high, respectively.
IV-65
-------
Table IV-36
Comparison of Road GPH Ratio and Calculated GPM Ratio.
Non-Modal Adjustment System
Vehicle
Technology
RAC
RAI
RAD
RMC
RMI
H
"f RMD
CT>
FAC
FAI
FAD
FMC
FMI
FMD
EP
MP
25
24
28
28
25
29
27
25
-
31
30
43
A
G
. 2
.4
.6
. 1
.5
.7
.3
.8
-
.9
. 1
.9
•* vj y • • ( i\-i^
Actu
GPMR
1 . 246
1 . 1O4
1 .090
1 . 159
t ,O48
1 .057
1 . 165
1 .069
1 . 102
1 .04 2
1 O5I
Calc EPA Actu Calc
GPMR MPG GPMR GPMR
1.246 --
1. 147
1 . 124 26. 1 1 . 123 1 . 109
1 . 193 --
1.O83 --
1 . 06O - - -
1 . 193 --
1.090 21.1 1.O78 1.115
1.1 SO --
1.O46 25. O 1.O3O 1.O69
1.079 32.4 1.O70 1.O41
EPA Actu Calc EPA Actu Calc
MPG GPMR GPMR MPG GPMR GPMR
20.5 1.215 1.218 17.8 1 . 2O9 1 . 2O2
20.7 O.999 1.170 18. O 1 . 2O9 1.199
29. 0 1.061 1.126 25. 0 1.135 1 . 1O3
21.4 1.I4O 1.153 17.3 1.O95 1.129
22.2 1.118 1 . 1O9 —
._
22.8 1.194 1.227 18. S 1.284 1.261
16.8 1.253 1.247
24.9 1.19O 1.157
24.6 1.15O 1.167 —
-_
—
-------
To illustrate the effectiveness of this general GPMR equation in terms of
adjusted MPG values, the labeling equation below is used;
L - E/GPMR where L - Label MPG
E - EPA MPG
and GPMR - GPM ratio from
the general equation.
The adjusted MPG values calculated for the 29 Tech/Ncyl sets using their
average EPA MPG figures are compared with average actual road MPG in Table
IV-37. There are average errors of -0.7 and -0.9 MPG, respectively, for
4-cylinder and 6-cylinder vehicles. (Note that negative MPG errors are
conservative, erring on the side of the consumer).
The adjusted MPG values in Table IV-37 compare much more favorably with the
road values than do the unadjusted values. In 27 of the 29 cases the
adjusted values are closer to the road MPG than are the unadjusted EPA
numbers. The weighted average difference changes from being 2.9 MPG high
for the unadjusted values, to being within 0.6 MPG for the adjusted values.
These weighted average differences are based on the projected 1985 mix of
Tech/Ncyl combinations.
Upon further examination of Table IV-37, we note that the adjustment is too
stringent for 4-cylinder non-RAC vehicles, producing average label values
that are 1 MPG too low, with some consistency. These 4-cylinder non-RAC
vehicles are projected to make up more than half of the 1985 fleet. The
6-cylinder non-RAC vehicles (projected to make up nearly 20% of the 1985
fleet) are also somewhat underlabeled by the general equation although not
quite as consistently as the 4-cylinder non-RACs.
In terms of GPM ratio, the average GPMR error for 4-cylinder non-RACs,
weighted in 1985 proportions, is + 0.035; for 6-cylinder non-RACs, it is
+0.016; for 8-cylinder engines it is negligible. Thus a slight correction
to the general GPMR equation is warranted: a correction which is a function
of cylinder count.
IV-67
-------
Table IV-37
Comparison of Road MPG and Adjusted EPA MPG.
Non-Modal Adjustment System
00
Vehicle
Technology
RAC
RAI
RAO
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
EPA
MPG
25.2
24
28
28
25
29
27
25
-
31
3O
43
.4
.6
. 1
.5
.7
.3
.8
-
.9
. 1
.9
** vj y • i i nj«.
Actu
MPG
2O. 4
22
26
24
24
28
23
24
-
29
28
42
. 1
.2
.4
.2
. 1
.5
.2
-
.O
9
,O
Calc EPA Actu Calc EPA Actu Calc
MPG MPG MPG MPG MPG MPG MPG
20.2 -- -- -- 2O. 5 16.9 16.8
21
25
23
23
28
22
23
-
27
28
4O
.3 -- -- -- 2O. 7 2O. 7 17.7
.4 26.1 23.2 23.5 29. O 27.3 25.8
.6 -- -- -- 21.4 18.9 18.6
.5 -- -- -- 21.4 20.0 2O. O
.0
.9 -- -- -- 22.8 19.2 18.6
.7 21.1 19.5 18.9
-
.7 -- -- -- 24.6 21.6 21.1
.8 25. O 24.4 23.4
.7 32.4 3O.3 31.1
EPA Actu Calc
MPG MPG MPG
17.8 14.8 14.8
IB. O 15. O 15.0
25. O 22.2 22.7
17.3 15.8 15.3
__
__
18. 5 14.4 14.7
16.8 13.4 13.5
24.9 2O. 9 21.5
-_
--
—
-------
The expression
SGPMR = 0.0085 (Ncyl - 8)
provides such a correction, changing the GPMR by -0.034 for- Ncyl
-0.017 for Ncyl = 6; it has no effect for Ncyl - 8.
4 and by
Incorporating this correction in the general equation, we have the
Non-Modal Master Equation;
where
GPMR = 1.097 + .0059(E) + .0085(N - 8.) + FWD(-.185 + .03N)
+ MAN(-.070) + INJ(-.184 + .0225N) + DSL(-.142)
E = EPA 55/45 MPG
N = cylinder count
FWD » 1 if front drive, zero if rear drive
MAN » 1 if manual trans, zero if automatic
INJ » 1 if gasoline FI, zero if carb. or Diesel
DSL = 1 if Diesel, zero if carb. or gas FI
This Master Equation produces an average MPG error of less than 0.1 MPG.
Table IV-38 compares, by cylinder count, the average MPG errors for no
adjustment, for the original general equation, and for the Master Equation
given above.
Vehicle
Type
4-cylinder
6-cylinder
8-cylinder
Overall
Table IV-38
Average MPG Error, Non-modal
Unadjusted
MPG Error
+3.1
+2.7
"+2.8
+2.9
1st General Eqn.
MPG Error
-0.8
.-0.5
+0.04
-Q.6
Master Equation
MPG Error
-0.02
-0.17
+0.04
-0.06
IV-69
-------
E. ADJUSTMENT ALGORITHM DEVELOPMENTS...BI-MODAL
Analysis
Bi-modal analysis differs from non-modal analysis in four ways;
1) Some method must be used to define a "city-driven" car and a "high-
way-driven" car; this forced dichotomization permits comparison of
the on-road fuel economy experience of the "city" cars against the
EPA City MPG, and likewise with the "highway" cars.
2) Whichever metric in the data which categorizes "cityness" of
driving is not usually present in all of the data records
(example-some entire data sources lack estimates of city driving
fraction), so those records without that metric must be discarded.
3) Even data records which do have the desired metric will not all
have values of the metric satisfactory to a clearly "city" or
"highway" categorization (e.g. 50% city driving fraction); either
such records must also be discarded, leaving only the "purely-
modally-extreme" cars, or some method must be employed to keep and
use more of the data records.
4) Model type data cannot be used, because model type collapsing
smears all of the categorization metrics together, destroying their
power to segregate data records into the designated modal bins;
It has become more or less customary in bi-modal analysis to make use of
estimated city driving fraction as a single cityness categorization metric,
albeit frequently accompanied by some degree of discomfort about reliance on
such a perceived quantity. An excellent report* on the subject of cityness
categorization makes a persuasive case for consideration of other quanti-
tites (e.g. average miles per day (AMPD), population density (POPDENS)) as
complements to — or even replacements for — city fraction.
*Urban/Highway Split; A Gray Area, by H.T. McAdams, Falcon Research and
Development Co., Working Paper No. 3 of Argonne Program, November, 1981.
IV-70
-------
In the analysis herein, we considered AMPD and POPDENS in this context.
Upon closer examination, problems with the accuracy of the POPDENS data were
found, leading to rejection of it as a cityness categorization aid.
Taking a closer look at the EPA "City" and "Highway" values, the dichotomy
which they represent (and the dichotomy to which we are forced to relate)
embodies more than just "cityness" as reflected by things like average
speed, stops per mile, etc. The most obvious of these is state of warmup of
the vehicle. Indeed, the EPA City/Highway dichotomy is more properly viewed
as a low MPG/high MPG dichotomy.
AMPD is of definite relevance to low MPG/high MPG categorization, and exists
in a, reasonable fraction of the data. In fact, slightly more of the data
have AMPD values than have city fraction. It complements city fraction in
that it tells us something of the vehicle's state of warmup, which city
fraction does not even hint at. For example, a vehicle whose owner
perceives to have a "low city fraction" could quickly be assigned to the
"high MPG car" bin; but if it is driven an average of only two miles per
day, such a categorization may be questionable.
It was therefore decided to use city fraction and AMPD for the low MPG/high
MPG categorization. Data records with both city fraction and AMPD (some
13,000) were extracted for use as the low MPG/high MPG data set. After
considering several options for use of the two categorization metrics, we
chose the (admittedly simple) distributional intersection method for
categorizing low MPG/high MPG vehicle subpopulations. The distribution of
city fraction has tercile cuts at 39% and 74% *; the AMPD distribution has
tercile cuts at 26 and 43 miles per day. The union of the high city
fraction and low AMPD terciles was defined as encompassing "low MPG" cars,
and the union of the low city fraction and high AMPD terciles was defined as
encompassing "high MPG" cars. Recognizing that low MPG cars are to be
* One-third of the city fraction values are below 39%, 1/3 are
between 39% and 74%, and 1/3 are above 74%.
IV-71
-------
related to the EPA low-MPG (City) number and the high MPG cars to the EPA
high MPG (Highway) number, our nomenclature at this point bows to the
pressure of convention and returns to labels of "city cars" and "highway
cars". The city cars (those to be analyzed against EPA City MPG) are cars
with high city fractions and low AMPD. Highway cars (those to be analyzed
against EPA Highway MPG) are cars with low city fraction and high AMPD).
To drive home point (3) above about bi-modal categorization decimating the
data base, observe that the above scheme produced 1420 bona fide city cars
and 1353 highway cars, leaving about 10,000 cars uncategorizable. No more
need be said on the necessity of employing a data retention technique.
The city car set had a city fraction average of 88%, and an AMPD average of
17 miles per day. The highway car set had a city fraction average of 16%
and an AMPD average of 96 miles per day.
A fine method of using all the data in a bi-modal analysis is discussed in
the EEA report.* This method is not appropriate in the current context,
however, because it involves only city fraction and not AMPD. The method
used herein first imputed a road city MPG number and a road highway MPG
number using the single overall road MPG, the city fraction and the EPA
highway-to-city MPG ratio. For cars with 100% city fraction, the achieved
road MPG value was assigned as road city MPG; for cars with 0% city
fraction, the acheived road MPG value was assigned as road highway MPG; for
cars with intermediate city fractions, the EPA H/C ratio was used with the
standard harmonic weighting formula to impute both road city and road
highway MPG values keyed off the achieved road MPG number.
Derived from
__ T CFRAC/100 , l-CFRAC/100 "1 -1
R-L—i— —i
and
assumed R,/R = E,/E = H/C,
n c n c
* Op. Cit. Development of Adjustment Factors .
IV-7 2
-------
The imputed road city and highway MPG values R and R, are:
R = R|-
c L
CFRAC + l-CFRAC/100"
RC x H/C
Using assumed numbers E - 20 MPG, E, = 28 MPG, H/C then = 1.40,
EQ = 23 MPG, R = 19 MPG, CFRAC = 60%:
R = 16.8 MPG
c
23.5 MPG
Since the city fraction analysis drives the overall road value to either a
city value (CF = 100%) or a highway value (CF = 0%) continued inclusion of
CFRAC in the following analysis is appropriate.
Next, city and highway GPM ratios were calculated from the EPA City and
Highway MPG values and these imputed road city and highway MPG numbers.
These GPM ratios were multiple-regressed against city fraction and AMPD and
the appropriate EPA MPG value. Taking no chances with aggregation, these
multiple regressions were performed on each separate Tech/Ncyl stratum. The
resulting family of multiple regression equations permits us then to develop
arrays of either constant city and highway GPM ratios or MPG-dependent city
and highway GPMR algorithms (or both), representing the entire 13,000-car-
multi-modal source dataset — not merely the 2800 cars in the "truly-
extreme" subsets.
IV-73
-------
Surely an illustration is in order. For 2610 6-cylinder RAG vehicles in the
overall dataset, the two multiple regression equations are:
CGPMR - 0.867 - .00173(CFRAC) - .00084(AMPD) + .0266(Ec)
and
HGPMR - 0.774 - .00171(CFRAC) - .00086(AMPD) + .0230(Eh)
From these, we can have MPG-dependent algorithms;
CGPMR = 0.701 + .0266(Ec) by solving with
CFRAC = 88, AMPD = 17
and
HGPMR = 0.664 -I- .0230(Eh) by solving with
CFRAC = 16, AMPD = 96
or, if preferred, we can have constant factors:
CGPMR = 1.208 by solving with
EC - 19.1, the mean EC
of the 6RACs
and
HGPMR = 1.264 by solving with
Eh » 26.1, the mean Eh
of the 6RACs.
IV-7 4
-------
Bi-Modal Analysis using the Non-Modal Method - Using Tech/Ncyl average city
and highway GPMRs from the multiple regression solutions, a bi-modal analy-
sis exactly identical to the non-modal analysis method was performed.
As with the non-modal analysis, the first step was to arrive at the natural
MPG-dependence GPMR equations for RAG vehicles. This is shown in figure
IV-28. The RAG city GPMR shows an MPG dependence slope of 0.0133 (compare
to the earlier non-modal slope of 0.0059). The RAG highway GPMR has no
statistically significant MPG dependence.
The Tech/NCyl city GPMR centroids are listed in Table 17-39, and in figures
IV-29 through IV-31 for 4-cylinder, 5- and 6-cylinder, and 8-cylinder
groups. The highway GPMR counterparts to these are listed in Table IV-40
and plotted in figures IV-32 through 17-34. These figures show dramatic
differences in road fuel economy offset among the Tech/Ncyl groups, as was
found in the non-modal analysis. Highway GPMRs are generally higher than
city GPMRs, the only exceptions being RAD4, FAI4, FMC6 and RA18.
Proceeding through the Non-modal method's point-to-point analysis of the
Ncyl-dependent effect of technology upon GPM ratio, the following GPMR
general equations result;
CGPRM - 0.970 + .0133(E ) + FWD(-.268 + .0370N) + MAN(-.IOO)
-I- INJ(-.124 + .0125N) + DSL(-.198)
and
HGPMR = 1.282 + FWD(-.095 + .0121N) + MAN(-.OOS)
+ INJ(-.226 -I- .0205N) + DSL(-.324 + .0429N)
As in the non-modal analysis, the average adjusted MPG values were compared
to actual road MPG values for the Tech/Ncyl groups. Also similar to the
non-modal analysis were the findings that these general equations reduce
average label error to less than one MPG, and that vernier corrections to
the general equations could further improve their performance.
IV-7 5
-------
FUEL CONSUMPTION
RATIO
1.35
1.30
o
1.25
o
en
op
be
o
>,
O
o
cc
1.20
1.15
1.10
15
Figure
IV-28
6 Cyl
Cyl
Cyl
A
6 Cyl
Highway
HGPMR = 1.282
(no mpg dependence)
20 25 30
EPA City or Highway Fuel Economy
(City GPM Ratio Plotted vs. EPA City Mpg;
Hwy GPM Ratio Plotted vs. EPA Hwy Mpg)
"Natural Mpg Dependence" for
RAC Vehicles, City and Highway Modes
35
EPA
FUEL ECONOMY
IV-7 6
-------
Table IV-39
Average EPA City HPG and Road City GPM Ratio.
by Technology and Cyltnder Count
4-CylInder
S-CylInder
6-CylInder
8-CylInder
Vehicle
Technology
RAC
RAI
RAD
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
EPA
MPG
22.
21 .
27.
23.
24.
28.
23.
22.
--
26.
25.
39.
I
6
4
4
O
6
3
6
7
O
4
Actu EPA Actu
GPMR HPG GPMR
1.266 --
1 . 117
I.IOI 23.5 I.O67
1.I7O --
I.O9O —
I.O39 --
1.133 --
1 . IO9 17.4 1.O23
--
I.O32 --
O.995 21.9 O 938
1.054 --
EPA Actu EPA Actu
MPG GPMR HPG GPMR
19.1 1.208 16.4 1.199
18. S 1.O7O 16.6 1.244
21.6 1.O92
2O. 3 1.12O --
19.6 1.O73 --
--
19.9 1.197 IS. 7 1.248
14. O 1.162
21.4 1.119
2O. 1 1 . 114 --
--
— —
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
1.30
E
Q.
Q_
LU
.,. 1.20
E
Q.
CJ
•a
1.10
1.00
0.90
•RAC
•RMC
•FAC
RMI«
•RAD
RMD*
FMC«
FMI
FMD«
I
I
I
10
20 30 40
EPA City Fuel Economy (MPG)
50
60
EPA
FUEL ECONOMY
Figure
IV-29
Technology Specific Average City
GPM Ratios-Vehicles with 4-Cylinder Engines
IV-7 8
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
E 1.30
Q.
CD
a.
o
ce.
1.20
1.10'
1.00
0.90
I
= 5-Cylinder
= 6-Cjflinder
FAC
AFAI
AFMI
I
_L
I
10
20 30 40
hPA City Fuel Economy (MPG)
50
60
EPA
FUEL ECONOMY
Figure
IV-30 Technology Specific Average City
GPM Ratios—Vehicles with 5— or 6-Cylinder Engines
IV-7 9
-------
FUEL CONSUMPTION
RATIO
1.3U I
1.40
I 1.30
0
=»
a.
LU
g 1.20
a.
o
°* 1.10
1.00
OOA
.yu
0
Figure
IV-31
i i i i i
8-Cylinder.
- -
- -
FAC%RA,
RAC*
FAI«
FAD«
RAD«
! 1 1 1 1
10 20 30 . 40 50 60
EPA City Fuel Economy (MPG) £pfl
FUEL ECONOMY
Technology Specific Average City
GPM Ratios-Vehicles with 8-Cylinder Engines
IV-80
-------
Table IV-4O
Average EPA Highway HPG and Road Highway GPM Ratio.
by Technology and CylInder Count
4-CylInder
5-CylInder
6-CylInder
8-CylInder
I
oo
Vehicle
Technology
RAC
RAI
RAO
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
EP
MP
3O
27
3O
36
34
34
33
31
-
38
38
51
A
G
.7
.6
.8
.2
.9
.2
. 1
.8
-
. 1
.7
.7
ActU EPA Actu
GPMR HPG GPMR
1.29O --
1 . 125
1 O9I 29.7 1 . 187
1.336 —
1.096 --
I.O76 --
1.241 --
0.976 25.4, 1.118
--
1 . 147 --
1 . 138 33 fl 1 .055
1 . 1O6 --
EPA Actu EPA ActU
MPG GPMR MPG GPMR
26.1 1.264 25.2 1.291
23.7 1.O78 24. O 1.231
32.7 1.225
29. 1 1 .241 --
29.6 1 . 147 --
--
29.8 1.238 23.1 1.264
22.2 1.245
32.2 1.359
33.9 1.O47
__
— —
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
S 1-30
•a:
o_
E 1.20
a.
re
o
1.10
i.OO
0.90
Figure
IV-32
•RMC
RAC«
FAC*
FMC*
FMI
RAD«
•FMO
RMO
FAI«
I
10 20 30 40
EPA Highway fuel Economy (MPG)
50
60
EPA
FUEL ECONOMY
Technology Specific Highway GPM Ratios-
Vehicles with 4-Cylinder Engines
IV-82
-------
FUEL CONSUMPTION
RATIO
1.3U 1
1.40
E 1.30
0
<£
Q_
7 1.20
E
o.
CJ5
5"
"0
J 1.10
1.00
0.90
0
i i i i i
^\ ^^^
• =
RAC«
AC
RAOA
RMI«
FAIA
—
•RAI
AFMI
FMC»
5-Cylinder
Cylinder
_
1 1 1 1 1
10 20 30 ' 40
EPA Highway Fuel Economy (MPG)
. i
Rgure
50 60
EPA
FUEL ECONOMY
IV-33 Technology Specific Highway GPM Ratios-
Vehicles with 5— or 6-Cylinder Engines
IV-8 3
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
E 1.30
Q.
CD
•a:
Q_
•'• 1.20
E
CL
CJ3
1.10
1.00
0.90
Figure
IV-34
10
FAD*
•RAC
•FAC
FAI«
RAI
RAD*
20 30 40
EPA Highway Fuel Economy (MPG)
50
60
EPA
FUEL ECONOMY
Technology Specific Highway
GPM Ratios-Vehicles with 8-Cylinder Engines
IV-84
-------
The vernier correction for the CGPMR equation is the expression
(0.0175)(Ncyl - 6), and the vernier correction for the HGPMR is the quantity
-0.025, an Ncyl-independent correction. Thus the Bi-Modal Master Equations
(Non-modal Method) are:
CGPMR = 0.970 + .0133(E ) + ,0175(N - 6) + FWD(-.268 +• .0370N)
+ MAN(-.IOO) + IHJ(-.124 + .0125N) + DSL(-.198)
and
HGPMR = 1.257 + FWD(-.095 + .0121N) -I- MAN(-.OOS)
+ INJ(-.226 + .0205N) + DSL(-.324 + .0429N)
Table IV-41 shows the EPA and Road City MPG figures and those calculated by
the above CGPMR equation. While the current EPA City figure is erroneously
high by an average of 2.3 MPG (using projected 1985 technology proportions),
the average error from the final Master City Equation is virtually zero.
Table IV-42 is the same kind of table as IV-41. It shows how an average
overlabeling figure of 5.1 MPG has been eliminated by the Master Highway
Equation.
A Second Method of Bi-Modal Analysis - The 13,000-car data set used for
development of the above bi-modal GPMR. equations is less than one-third of
the total 43,000-car data base used for the non-modal analysis. Since the
13,000-car subset is not necessarily a perfect microcosm of the entire data
base, there are sometimes inconsistencies between the GPMR values from the
Non-Modal Master Equation and those from the Bi-Modal Master Equations
above*. None of these inconsistencies are serious, but they do violate the
mathematical necessity — proven in Appendix D — that for any given vehicle
type the non-modal GPMR must be in the range between the two bi-modal GPMR
figures.
* For example, the Non-Modal GPMR for 6-cylinder RAI vehicles is 1.170 (at
20.5 EPA MPG), while the Bi-Modal GPMR values are 1.070 City (at 19.1 EPA
City MPG) and 1.07.8 Highway (at 23.7 EPA Highway MPG).
17-85
-------
Table IV-41
Comparison of EPA Ctty MPG. Road City MPG, and Adjusted EPA City MPG
using Bi-Modal Master CGPMR Equation *1 (Non-Modal Method)
00
Vehicle
Techno! ogy
RAC
RAI
RAO
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
EPA
MPG
22. 1
21
27
23
24
28
23
22
-
26
25
39
.6
.4
4 —
.0
.6
.3
.6
-
.7
.O
.4
Actu
MPG
17.4
19
24
2O
21
27
2O
2O
-
25
25
37
.3
.9
.O
. 1
.5
.5
. 3
-
.9
. 1
.4
Calc EPA Actu Calc
MPG MPG MPG MPG
18. O
18
24
20
22
28
20
21
-
25
25
37
.8 --
.9 23.5 22. 1 22. 1
.4
.2
. 1
.7
.7 17.4 17. 0 16.8
-
.O
.7 21.9 23.3 22. O
a
EPA Actu Calc EPA Actu Calc
MPG MPG MPG MPG MPG MPG
19. t 15.8 15.6 16.4 13.7 13.2
18.5 17.3 15.8 16.6 13.3 13.8
21.6 19.8 19.7
2O. 3 18. 1 17.8
19.6 18.3 18.1
__
19.9 16.6 16.8 IS. 7 12.6 12.7
14. O 12. O 11.7
21.4 19. 1 19. 1
2O. 1 IB .O 18.4
--
— • — — — '
Label Error
(No Adjustmt)
+ 2.2 MPG
+ O.4 MPG
+2.7 MPG
+2.6 MPG
Label Error
(1st Adjustmt)
-O.5 MPG
-O.7 MPG
-O.1 MPG
+O.5 MPG
Label Error
(Add Vernier)
+O.O3 MPG
-O.2 MPG
-O.1 MPG
-O.1 MPG
-------
Table IV-42
Comparison of EPA Highway MPG. Road Highway MPG. and Adjusted EPA Highway MPG
using BI-Modat Master IIGPMR Equation *l (Non-Hodal Method)
Vehicle
Technology
RAC
RAI
RAO
RMC
RMI
4 RMD
OO
^ FAC
FAI
FAD
FMC
FMI
FMD
EPA
MPG
30.7
27.
30.
36.!
34.
34.
33.
31 .
--
38.
38.
51.
6
8
2
a
2
i
8
1
7
7
•» v* y i 1 1 IUG
ACtU
MPG
23.3
24
28
27
31
31
26
32
-
33
34
46
.5
.2
. 1
.7
.8
.6
.5
-
.2
.0
.8
Calc EPA Actu Calc EPA Actu Calc
MPG MPG MPG MPG MPG MPG MPG
24.4 -- -- -- 26.1 20. 6 20. 7
24
27
29
31
31
27
29
-
31
36
46
.8 -- -- -- 23.7 21.9 2O. 5
.8 29.7 25. O 25.9
.O -- -- -- 29.1 23.5 23.3
.6 -- -- -- 29.6 25.8 25.8
.2
.3 -- -- -- 29.8 24. O 24.1
.8 25.4 22.7 23.2
-
.7 -- -- -- 33.9 32.4 27.7
.6 33.8 32. O 31. O
.8
EPA Actu Calc
MPG MPG MPG
25.2 19.5 2O. O
24. O 19.5 ' 2O. 1
32.7 26.7 25.6
__
--
23.1 18.3 18.3
22.2 17.9 18.6
32.2 23.7 25.2
—
-_
. •
Label Error
(No Adjustrot)
.3 MPG
+3.2 MPG
+4.7 MPG
+5.4 MPG
Label Error
(1st Adjustmt)
-O 7 MPG
-O.2 MPG
-O.6 MPG
-O.1 MPG
Label Error
(Add Vernier)
-O. I MPG
+O.4 MPG
-O.7 MPG
+O.2 MPG
-------
Thus an alternate approach to bi-modal analysis is to take each Tech/Ncyl
group's non-modal GPMR value as a better truth (since it comes from the
whole 43,000 car data base), and calculate City and Highway GPMR values
referenced to these non-modal figures.
This was done by first finding the difference between each Tech/Ncyl group's
actual (not calculated) HGPMR and CGPMR. Table IV-43 lists these dif-
ferences.
Table IV-43
Difference between Actual Highway GPMR
and City GPMR, by Technology and Cylinder Count
(CGPMR subtracted from HGPMR)
Technology 4-cylinder 5-cylinder 6-cylinder 8-cylinder
- .056 .092
- .008 -.013
.120 - .133
- .121
- .074
- .041 .016
.095 - .083
.240
- -.067
.117
* HGPMR =• 1.290; CGPMR = 1.266; difference = 0.024, etc.
RAC
RAI
RAD
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
.024*
.008
-.010
.166
.006
.037
.108
-.133
-
.115
.143
.052
IV-88
-------
This AGPMR is a metric which can be analyzed for Tech/Ncyi dependency in
exactly the same fashion as GPM ratios themselves were analyzed previously.
Doing this, expressions for this AGPMR result as follows, referenced as
usual to RAC technology as the base case:
» 0.013
• 0.219 - .0347N
=• -0.044
=• -0.251 + .0468N
The FWD and INJ expressions are independent of cylinder count: both have
slopes of about 0.007 and regression R-values of about 0.13.
Now, since these expressions relate the AGPMR difference for non-RAC
vehicles to that of .RAC vehicles, we can develop algorithms for CGPMR and
HGPMR once two things are established: CGPMR and HGPMR equations for RACs,
and the connection between these and the non-RACAGPMR equations.
Reviewing the previously established general GPMR equations,
GPMRRAC = 1.097 + .0059(E) Non-modal
CGPMRRAC = 0.970 + .0133(E) Bi-Modal #1
HGPMR_ = 1.282 Bi-Modal //I
Since the RAG bi-modal equations came from the 13,000-car subset, they are
not reconciled with the RAC non-modal equation. There are an infinite
number of ways to calculate reconciled bi-modal equations using the above,
but we chose to retain the relation HGPMR = 1.282 and algebraically derive a
CGPMR expression.*
* Vernier correction at the end of the process will take care of
any error due to this choice.
IV—89
-------
The solution CGPMR expression, which does produce CGPMR values consistent
with the non-modal equation and with HGPMR = 1.282, is:
CGP
MRRAC
Since this is specifically applicable to RAG vehicles, whose H/C values
average 1.43 overall (1.39 for 4-cyl, 1.37 for 6-cyl, 1.53 for 8-cyl), and
the • term in brackets is not very sensitive to H/C variation*, we solve at
H/C = 1.43 to obtain
CGPMRRAC = 0.991 + .0107(Ec)
Now the connection between an HGPMR-CGPMR difference and a non-modal GPMR
(referring again to Appendix D) is approximately
HGPMR - GPMR + 2/3 (HGPMR - CGPMR)
and
CGPMR = GPMR - I/ 3 (HGPMR-CGPMR)
Folding all of this together, the CGPMR expression using this method is
developed from;
CGPMR = .991 + .0107(EC) + AG(Tech,Ncyl)n(m_modal
-1/3AGHC (Tech,
where AG(Tech, Ncyl) , , represents
non-modal
the non-RAC terms in the non-modal
GPMR equation and
AGHC(Tech, Ncyl) , , , represents
' this method
the- non-RAC HGPMR-CGPMR difference
terms developed in this method.
* for H/C =1.37, the bracket term is 0.986; for H/C = 1.53, it is 0.998.
IV-90
-------
TheAG and AGHC terms are:
AGHC
FWD -.185 + .0300N -1/3(.013)
MAN -.070 -1/3(.219 + .0347N)
INJ -.184 + .0225N -l/3(-.044)
DSL . -.142 -l/3(-.251 + .0468N)
The CGFMR general equation is then:
CGPMR = .991 + .0107(E ) + FWD(-.189 + .030N)
c
+ MAN(-.143 + .0102N) + INJ(-.169 + .0225N)
-I- DSLC-.058 - .0156N)
Similarly, the HGPMR expression comes from:
HGPMR =» 1.282+AG(Tech,
+ 2/3AGHC(tech,
whose AG and AGHC terms are:
AG AGHC
FWD -.185 + .0300N- + 2/3(.013)
MAN - .070 -I- 2/3(.219 + .0347N
INJ -.184 + .0225N + 2/3(-.044) .
DSL - .142 + 2/3(-.251 + .0468N)
which produces the general HGPMR equation:
HGPMR = 1.282 + FWD(-.176 + .0300N) + MAN(.076 -.0231N)
+ INJ (-.213 + .0225N) + DSL(-.309 + 0.312N)
IV-91
-------
The performance of these equations is compared to the average Tech/Ncyl MPG
values from the 13,000 car subset is tables IV-44 (city) and IV-45
(highway). The calculated MPG figures in these tables correspond to the
above general equations with vernier correction terms incorporated, as
follows:
CGPMR - [above] + 0.016(N - 6.5)
HGPMR = [above] - 0.024
Comparing Table IV-44 with the earlier Table IV-41, the adjusted city MPG
values for the two bi-modal methods are quite similar. Comparing Table
IV-45 with the earlier Table IV-42, similarity of the two methods' adjusted
highway MPG values is also obvious.
IV-92
-------
Table IV-44
Comparison of EPA City MPG. Road City MPG. and Adjusted EPA City MPG
using Bl-Modal Master CGPMR Equation HI (Oelta-HC Method)
Vehicle
Technology
RAC
RAI
RAD
RMC
RMI
RMD
M
1 FAC
vo
OJ
FAI
FAD
FMC
FMI
FMD
EPA
MPG
22. 1
21
27
23
24
28
23
22
-
26
25
39
.6
.4
.4
.O
.6
.3
.6
-
.7
.O
.4
Actu
MPG
17.4
19.
24 .
2O.
21 .
27 .
2O.
20.
--
25.
25.
37 .
3
9
O
1
5
5
3
9
1
4
Calc EPA Actu Calc
MPG MPG MPG MPG
18. O
18.9
25. 0 23.5 22. 1 22.2
2O. 5
22.3
28.2
2O. 8 •--
21.8 17.4 17. O 16.9
-_ •
25.1
25.8 21.9 23.3 22. 1
38. 0
EPA Actu Calc EPA Actu Calc
MPG MPG MPG MPG MPG MPG
19.1 15.8 15.7 16.4 13.7 13.5
IB. 5 17.3 IS. 9 16.6 13.3 13.9
21.6 19.8 19.9
2O. 3 18.1 17.9
19.6 IB. 3 18.3
-_
19.9 16.6 16.9 19.7 12.6 12.8
14. O 12. O 11.8
21.4 19.3 19. 1
2O. 1 18. 0 18.5
--
.
Label Error
(No Adjustmt)
+2.2 MPG
+O.4 MPG
+2.7 MPG
+2.6 MPG
Label Error
(AdJ + Vernier)
+O.16 MPG
-O.22 MPG
-O.O4 MPG
+O.12 MPG
-------
table IV-45
Comparison of EPA Highway MPG, Road Highway MPG. and Adjusted EPA Highway MPG
using BI-Modal Master HGPMR Equation Ml (Delta-HC Method)
Vehicle EPA
Technology MPG
RAC
RAI
RAD
RMC
RMI
RMD
FAC
FAI
FAD
FMC
FMI
FMD
3O.
27.
3O.
36.
34.
34.
33.
31 .
--
38.
38.
51 .
7
6
a
2
9
2
,1
a
i
7
7
Actu
MPG
23.3
24
23
27
31
31
26
32
-
33
34
46
,5
.2
. 1
.7
.8
.6
.5
-
.2
.0
.6
Calc EPA Actu Calc
MPG MPG MPG MPG
24.4
24
28
29
31
32
27
29
-
32
36
51
.3
.6 29.7 25. O 26.8
. 1
.2
3
5
.4 25.4 22.7 22.5
-
.1
.4 33.8 32.0 31 .O
.6
EPA Actu Calc EPA Actu Calc
MPG MPG MPG MPG MPG MPG
26.1 2O. 6 20.7 25. 2 19.5 2O.O
23.7 21.9 20. O 24. O 19.5 19.6
32.7 26.7 .27.3
29. 1 23.5 24.4
29.6 25.8 26.4
--
29.8 24. O 23.6 23.1 18.3 17.5
22.2 17.9 17.3
32.2 23.7 25.5
33.9 32.4 28.3
--
__
Label Error
(No Adjustmt)
+5.3 MPG
+3.2 MPG
+4.7 MPG
+5.4 MPG
Label Error
(Adj •» Vernier)
+O.IO MPG
+O.33 MPG
-O.63 MPG
+O.23 MPG
-------
This page intentionally
blank and un-numbered
-------
V. EVALUATION OF CANDIDATE LABELING APPROACHES
A. METHODOLOGY
Each adjustment algorithm is an equation of the form:
L - f(E),
where L is the label fuel economy, and E is the corresponding EPA fuel
economy. Each algorithm was evaluated by first constructing a test fleet
which is representative of the fleet for which the adjustments will be used,
and for which the on-road fuel economy, R, is known.
Next the quantity D = L/R was constructed for the test fleet and displayed
as a histogram. A perfect adjustment system would have D = L/R = 1.0 for
each vehicle. The better the adjustment system, the closer the histogram is
to a sharp peak, at unity. Figures of merit are defined as follows:
PW10 = The percent of the vehicles for which the label MPG is within
+_ 10 percent of the Road MPG. The bigger PW10 is, the
better.
CN10, CP10 = The centroids of the distribution on the Negative and Positive
sides respectively, omitting the central +10 percent slice.
The closer to unity CN10 and CP10 are, the better, assuming
that the distribution extends more than 10 percent on either
side of unity.
PG10 = The percent of the distribution which is greater than the plus
10 percent cutpoint. The smaller PG10 is, the better.
PL10 = The percent of the distribution which is less than the minus
10 percent cutpoint. The smaller PL10 is, the better.
RC10 = The value of the quantity [(CP10-1)(PG10)]/[(1-CN10)(PL10)].
It corresponds to a weighted estimate of the "evenhandedness"
of the distribution. The closer RC10 is to 1.0, the better.
V-l
-------
PW10/PG10 = The ratio of the percent of the vehicles within the + 10%
cutpoints to the percent of vehicles labeled too high. The
bigger this ratio is, the better.
1-PG10 » This statistic measures "percent not overlabeled".
Some mention should be made of the use of the ratio, L/R, as the appropriate
statistic. The ratio is used because the fleet overall MFC is expected to
increase in the future. Indeed, our future fleets are typically 7 to 8 MPG
higher in average MPG than is today's fleet. A statistic using MPG
differences becomes a more stringent test as average MPG goes up, implying
that an improvement in GPMR prediction accuracy is needed as MPG increases.
Actually, since the adjustments were developed from GPMR, the difference in
MPG (at constant GPMR accuracy) should increase as MPG goes up. The use of
the ratio, L/R, eliminates any problems that might be caused by the
difference approach and a growing fleet average MPG.
B. TEST FLEETS
The test fleet should be one that is representative of the fleet of -vehicles
to which the adjustments are to be applied. It appears that the earliest
fleet to which these adjustments could apply would be the 1984 model year
fleet. Assuming that the adjustments will be used for a period of time
before they are changed, a "mid-1980s" fleet is the fleet of interest.
Obviously, this requires some extrapolation. In order to con- struct the
fleet, we used a report* called the Delphi Forecast in this section.
From the cylinder-count number projections in the Delphi Forecast it can be
seen that the bulk of the market is projected to be 4s and 6s. The Delphi
Forecast projected about 55% 4-cylinder, 35% 6-cylinder, and 10% 8-cylinder
* U.S. Automotive Industry in the 1980's; A Domestic and Worldwide
Perspective; The Second Delphi Forecast - July 1981, by Arthur Anderson &
Company, The Michigan Manufacturers Association, and the University of
Michigan.
V-2
-------
for U.S. Produced cars. The fleet is also projected to be 15% Diesel. 70%
of the fleet is projected to be transverse front wheel drive, with the
automatic transmission/manual transmission split at 70/30.
By adding to the Delphi Forecast figures our own figures on today's import
fleet, an overall fleet weighted 79% U.S.-built and 21% imported was
constructed. Its statistics are; 65% front wheel drive; 62% automatic
transmission; 67% carbureted, 18% fuel injected, 15% Diesel; and 67%
4-cylinder, 25% 5- and 6-cylinder, and 8% 8-cylinder.
An overall test fleet was constructed meeting these proportions. Compared
to today's fleet it has more 4-cylinder engine equipped cars, more manual
transmission equipped cars, and more front wheel drive cars. Also, more
cars have Diesel engines.
Test fleets representing "city-driven" and "highway driven" vehicles were
similarly constructed.
Each of the three initial (as-is) test fleets had a distribution of
technologies different from the future fleet projections. Each fleet was
adjusted in technology mix to be more representative of the mid-1980s fleet,
thus creating three additional fleets.
The "as is" and "technology-mixed" fleets are described in the 'following
table. It can be seen that the fleets with adjusted technology mix are
consistent with the mid-1980s projections.
V-3
-------
Table V-l
Test Fleet Descriptions
Fleet
Overall-
As Is
Overall-
Technology
Mix Adjusted
City-Driven
As Is
City-Driven
Technology
Mix Adjusted
Highway-Driven
As Is
Highway-Driven
Number of
Vehicles
41,906
10,347
12,664
5,084
12,666
5,139
FWD
8
65
15
64
15
63
reiceiiL. uj. rj.e
Auto
Tran Garb FI Dsl
84 97 2 1
62 67 18 15
77 92 6 2
62 70 17 13
77 92 6 2
62 69 17 14
eu— — — — -
4
Cyl
18
67
31
67
31
67
5&6
Cyl
27
25
27
25
27
26
8
Cyl
55
8
42
8
42
8
Technology
Mix Adjusted
V-4
-------
All three technology-adjusted fleets have properties which make them usable
as test fleets for evaluating the different adjustment algorithms. They are
all closer to a mid-1980s fleet than is today's fleet. The fleets are not
to be taken as predictions of the future, merely tools to evaluate various
labeling approaches. They are more appropriate than test fleets with
today's mix. The city and the highway fleets consists of vehicles that are
primarily city-driven and highway-driven, respectively. The fact that the
fleets are not made up simply of vehicles with extreme values of city
fraction (100 and zero) is also appropriate. The reason for this, explained
in Section IV, is that both city fraction (CF) and average miles per day
(AMPD) were used as joint indicators of city-driven or highway-driven cars.
The analysis showed that a fleet with an average CF of 88 percent and an
average AMPD of 17 was characteristic of city-driven cars, while an average
CF of 16 percent and an average AMPD of 96 characterized highway-driven
cars.
C. EVALUATION OF LABELING APPROACHES
The histogram-derived figures of merit can be used to compare and rank the
various labeling approaches. For example, one can first sort on the figure
of merit considered most Important, and discard those labeling approaches
that fall below an acceptable figure of merit. This sorting process can
then be repeated with the next most important figure of merit, and so on,
until the labeling approach which is the best is identified.
The histograms generated in this evaluation are shown in Appendix F; the
evaluations of the adjustment algorithms are discussed below.
The Non-Modal Master equation is of the form
Eo/Ro » OM (Eo, tech, cyl)
This can be rewritten as a labeling algorithm by solving for Ro; ideally,
the label value, Lo, should be equal to the road value.
V-5
-------
Eo
= OM (Eo, tech, cyl)
Since we have dual equations for the city and highway adjustments, four
bi-modal labeling algorithms result;
,. _ Ec , , _ Ec
CM1 (Ec, tech, cyl) CM2 (Ec, tech, cyl)
Eh, .. Eh
' Lh «
HM1 (Eh, tech, cyl) HM2 (Eh, tech, cyl)
The statistic D » L/R can be generated several ways, depending on the
candidate adjustment approach. (For today's unadjusted case, Li • Ei ).
There are historical values to reconsider, for example, the October 1980 EPA
report* had simple MPG - independent adjustment factors. Consider the
following values based on Table II p. 21 of that report.
0.90 Ec (city), 0.79 Eh (hwy) from the "All-3" Data Base
0.98 Ec (overall), 0.86 Eo (overall) from the "All-3" Data Base
MFG-independent values similar to these historical values have been
generated. For Non-Modal analysis, MPG factors from 0.85 to 0.89 can be
calculated (p. IV-35). From the "All-3" data above, the value of 0.86 was
previously derived. These can all be represented by a test of a factor of
0.87.
In a similar manner, values of 0.90 and 0.84 were developed as
MPG-independent adjustments to the city MPG and highway MPG value,
respectively. These are comparable to the 0.90 and 0.79 values above.
The following tables give some combinations that were evaluated as candidate
labeling approaches.
* Technical Support for Regulatory Action - Light duty Vehicle Fuel Economy
Labeling, EPA/AA/CTAB/FE-81-6
V-6
-------
Table V-2*
Non-Modal Adjustments
Case Statistic
01 Lo(Eo)/Ro
Lo(Eo)
Eo
OM (Eo, tech, cyl)
Test
Fleet
a. Overall-
As Is
b. Overall-
Technology
Adjusted
Comment
Non-Modal
Master
Equation.
02- Lo(Eo)/Ro
Lo(Eo) • Eo
a. Overall-
As Is
b. Overall-
Technology
Adjusted
Non-Modal
Today's
Composite
Case.
03 Lo(Eo)/Ro
Lo(Eo) « EC
a. Overall-
As Is
b. Overall-
Technology
Adjusted
Non-Modal,
What EPA says is
"estimated MPG".
04
Lo(Eo)/Ro
Lo(Eo)
0.87 Eo
a. Overall-
As Is
b. Overall-
Technology
Adjusted
Current estimate
of non—modal
simple "factor".
* Histograms of the label error for each case appear in Appendix F,
V-7
-------
Case
Cl
Table V-3*
Bi-Modal Adjustments — City Cases
Statistic
Lc(Ec)/Rc
Lc(Ec)
EC
CM1 (Ec, tech, cyl)
Test
Fleet
a. City-As Is
b. City-
Technology
Adjusted
Comment
Bi-Modal Master
City equation #
C2
Lc(Ec)/Rc
Lc(Ec)
EC
a. City-As Is
b. City-
Technology
Adjusted
Today's unadjusted
city number tested
on City cars.
C3
Lc(Ec)/Rc
Lc -
EC
CM2 (Ec, tech, cyl)
a. City-As Is
b. City-
Technology
Adjusted
;Bi-Modal Master
City equation #2,
C4
Lc(Ec)/Rc
Lc(Ec)
Ec
OM (Ec, tech, cyl)
a. City-As Is
b. City-
Technology
Adjusted
Non-Modal
Master Equation
used to adjust Ec.
C5
Lc(Ec)/Rc
Lc(Ec)
0.90 Ec
a. City-As Is
b. City-
Technology
Adjusted
Current estimate
of simple City
"factor", applied
to City cars.
Histograms of the label error for each case appear in Appendix F.
V-8
-------
Case
HI
Table V-4*
Bi-Modal Adjustments—Highway Cases
Statistic
Lh(Eh)/Rh
Lh(Eh)
Eh
HM1 (Eh, tech, cyl)
Test
Fleet
a. Highway-
As Is
b. Highway-
Technology
Adjusted
Comment
Bi-Modal Master
equation #1.
H2
Lh(Eh)/Sh
Lh(Eh)
Eh
a. Highway-
As Is
b. Highway-
Technology
Adjusted
Unadjusted highway
number tested on
Highway cars;
what advertising
H3 Lh(Eh)/Rh
a. Highway-
As Is
b. Highway-
Technology
Adjusted
Bi-Modal Master
Highway equation
I 2.
Lh(Eh)
Eh
HM2 (Eh, tech, cyl)
H4
Lh(Eh)/Rh
Lh(Eh)
Eh
OM (Eh, tech, cyl)
Highway-
As Is
Highway-
Technology
Adjusted
Non-Modal Master
Equation used to
adjust Eh.
H5
Lh(Eh)/Rh
Lh(Eh)
0.84 Eh
a. Highway-
As Is
b. Highway-
Technology
Adjusted
Current estimate
of simple Highway
"factor", applied
to Highway cars.
* Histograms of the label error for each case appear in Appendix F.
V-9
-------
The four candidate adjustment cases for the Non-Modal adjustment were ex-
amined first. The statistic PW10, the percent correctly labeled, is shown
below;
Table V-5
PW10 Statistics-Kon-Modal Fleets
Overall Overall
Case As Is Technology-Adjusted
Fleet Fleet
PW10 Rank PW10 Rank
Ola/Olb 48% 1 56% 1
02a/02b 29% 4 39% 4
03a/03b 46% 3 51% 3
04a/04b 47% 2 52% 2
As indicated in Table IV-5, the current system (case 02) is clearly last.
The other adjustments are better, with a slight edge for the overall master
equation. It should be noted that as the technology mix approaches that of
the mid-1980s projections, (the change from the as-is fleet to the adjusted
fleet) the percent correctly labeled improves. For this case there is no
rank reversal due to fleet differences.
V-10
-------
For the four cases the following table of RC10, the weighted evenhandness
statistic, was then evaluated.
Case
Ola/Olb
Table V-6
RC10 Statistics-Non-Modal Fleets
Overall
As Is
Fleet
RC10
1.39
Rank
1
Overall
Technology-Ad justed
Fleet
RC10
1.26
Rank
3
02a/02b
34.60
15.70
03a/03b
2.18
0.94
04a/04b
2.47
1.01
Table IV-6 shows that the RC10 statistic clearly identifies the current
system (case 02) as the last in rank. Adjusting the technology mix results
in some rank change along with an improvement in RC10 for all cases
studied.
V-ll
-------
Another statistic of interest is the ratio PW10/PG10 where PG10 is the
percent overlabeled. This statistic combines the good and bad of a labeling
scheme, and also accentuates the difference between some labeling schemes.
The bigger PW10/PG10, is, the better.
Case
Ola/Olb
Table V-7
PW10/PG10 Statistics-Non-Modal Fleets
Overall
As Is
Fleet
PW10/PG10
1.80
Rank
1
Overall
Technology-Adjusted
Fleet
PW10/PG10
2.58
Sank
1
02a/02b
0.44
0.72
03a/03b
1.42
2.48
04a/04b
1.40
2.50
The current system overlabels more cars than it labels correctly. All the
other adjustments label correctly about 2.5 times as many cars as they
overlabel. The ranks here did not change with the fleet mix.
V-12
-------
The alternative statistic parallel to PW10 in meaning is the value 1-PG10.
This is the percent not overlabeled ("correctly labeled" if the absence of
overlabeling is defined as "correct").
Case
Ola/Olb
Table V-8
1-PG10 Statistics-Non-Modal Fleets
Overall
As Is
Fleet
1-PG10
74%
Rank
1
Overall
Technology-Adjusted
Fleet
1-PG10
79%
Rank
IT
02a/02b
33%
45%
03a/03b
67%
2T
79%
IT
04a/04b
67%
2T
79%
IT
Again the current system is last in rank with the 3 other cases being close
to equal and all significantly better than the current system.
V-13
-------
The city and highway labeling algorithms were evaluated similar to the
foregoing. The results are shown in the following tables.
Case
Cla/Clb
Table V-9
PW10 Statistics-City Fleets
City
As Is
Fleet
PW10
52%
Rank
1
City
Technology-Adjusted
Fleet
PW10
54%
Rank
IT
C2a/C2b
22%
35%
C3a/C3b
50%
53%
C4a/C4b
51%
54%
IT
C5a/C5b
41%
51%
The trend seen before in the overall adjustment cases repeats itself.
Labeling can be improved substantially over the current system by more than
one approach. The- rest of the statistics for the city and highway labeling
./
approaches follow.
V-14
-------
Table V-10
RC10 Statistics-City Fleets
Case
Cla/Clb
C2a/C2b
C3a/C3b
C4a/C4b
C5a/C5b
City
As Is
Fleet
RC10
2.26
57.50
3.75
3.22
7.87
Rank
1
5
3
2
4
City
Technology-Adjusted
Fleet
RC10
2.75
23.29
3.13
2.75
2.71
Rank
2T
5
4
2T
1
Table V-ll
PWlO/PGlO-Statistics-City Fleets
Case
Cla/Clb
C2a/C2b
C3a/C3b
C4a/C4b
C5a/C5b
City
As Is
Fleet
PW10/PG10
1.73
0.29
1.39
1.50
0.84
City
Technology-Adjusted
Fleet
Rank
1
5
3
2
4
PW10/PG10
1.74
0.57
1.67
1.73
1.59
Rank
IT
5
3
IT
4
V-15
-------
Table V-12
1-PG10 Statistics-City Fleets
Case
Cla/Clb
C2a/C2b
C3a/C3b
C4a/C4b
C5a/C5b
City
As Is
Fleet
1-PG10
70%
25%
64%
64%
51%
Rank
1
5
2T
2T
4
City
Technology- Ad justed
Fleet
1-PG10
69%
39%
68%
69Z
68%
Rank
IT
5
3T
IT
. 3T
Table V-13
PW10 Statistics-Highway Fleets
Case
Hla/Hlb
H2a/H2b
H3a/H3b
H4a/H4b
H5a/H5b
Highway
As Is
Fleet
PW10
51%
24%
51%
52%
51%
Rank
2T
5
2T
1
2T
Highway
Technology-Adjusted
Fleet
PW10
50%
34%
50%
52%
48%
Rank
2T
5
2T
1
4
V-16
-------
Table V-14
RC10 Statistics-Highway Fleets
Case
•
Hla/Hlb
H2a/H2b
H3a/H3b
H4a/H4b
H5a/H5b
Highway
As Is
Fleet
RC10
0.72
43.90
0.74
0.99
1.68
Rank
3
5
2
1
4
Highway
Technology- Ad justed
Fleet
RC10
0.48
10.05
0.55
0.66
0.53
Rank
4
5
2
1
3
Table V-15
PW10/PG10 Statistics-Highway Fleets
Case
Hla/Hlb
H2a/H2b
H3a/H3b
H4a/H4b
H5a/H5b
Highway
As Is
Fleet
PW10/PG10
1.62
0.34,
2.69
2.41
1.83
Rank
4
5
1
2
3
Highway
Technology-Ad jus t ed
Fleet
PW10/PG10
3.33
0.59
. 3.13
2.98
2.90
Rank
1
. 5
2
3
4
V-17
-------
Case
Hla/Hlb
Table V-16
1-PG10 Statistics-Highway Fleets
Highway
As Is
Fleet
1-PG10
69%
Rank
4
Highway
Technology-Ad jus ted
Fleet
1-PG10
85%
Rank
1
H2a/H2b
27%
. 42%
H3a/H3b
81%
84%
2T
H4a/H4b
79%
82%
H5a/H5b
72%
84%
2T
The current system is clearly non-competitive with any of the adjustment
systems: The current system is so deficient, any improvement seems at-
tractive.
Further analysis, an unweighted rank analysis, was performed on the results
in Tables V-5 through V-16.
For Non-Modal adjustment, the Overall Master equation was clearly the best.
The results for the city and highway labels were not as clear cut: for the
city case the City Master equation #1 and the Overall Master equations were
the better candidates, while for the highway case the Highway Master
equations #1 and #2 and the Overall Master equation were all essentially
tied.
V-18
-------
A weighted rank analysis was performed next, using the following
weightings:
Most Important: Percent Correctly Labeled (PW10)
Second in Importance: Percent Not OverLabeled (1-PG10)
More Correct Than Over- (PW10/PG10)
Labeled
Third in Importance: Evenhandedness (RC10)
This weighted analysis yielded a slight edge to the Overall Master Equation
for City adjustment and did not resolve the issue among the three contending
Highway algorithms.
Evaluation of the three contending highway adjustments on collapsed data
also did not produce a clear choice, so other attributes of the Bi-Modal
adjustments were considered. For each set of city and highway labels, a
range inclusion statistic can be evaluated. This is the percent of vehicles
whose overall Road MPG value falls between the City label value and the
Highway label value. This is relevant in a bi-modal labeling scheme since
drivers who mix city and highway driving could expect their Road MPG to fall
between the two label values. When this analysis was performed the fol-
lowing results were obtained.
Table V-17
Range Inclusion Results
Adjustment System Range Inclusion
Lc_ Lli Percent
OM(Ec) OM(Eh) 71
CMl(Ec) HMl(Eh) 68
CM2(Ec) HM2(Eh) 69
V-19
-------
The Non-Modal Master equation is seen to have an edge here, indicating that
it deserves some careful consideration. (The current system has a 56% value
for this metric, as a point of interest.)
Another concern in Bi-Modal labeling is the general palatability of having
Highway Label MPG values be numerically greater than City Label MPG values.
This is the second attribute examined for the contending Bi-Modal ad-
justments.
The test fleet chosen for this was the Overall Technology-Mix Adjusted fleet
from Table V-l. For each of the three competing systems in Table V-17 the
ratio of Highway Label MPG to City Label MPG was computed for each car. For
the system using the Non-Modal Master equation, not once in 10,347 times did
the ratio equal 1.0. This means the adjusted Highway Label MPG was always
greater than the adjusted City Label MPG. In fact, the lowest ratio of
Label Highway MPG to Label City MPG was 1.075. In contrast, the other two
Bi-Modal adjustment Systems produced several instances of crossover (label
Highway MPG lower than label City MPG).
D. CONCLUSION
On balance then, the Non-Modal Master equation is the Fuel Economy Label
algorithm of choice for Bi-Modal labeling. It is superior to the current
system in every metric examined, and performs better, all in all, than the
other candidate Bi-Modal algorithms. Interestingly, this adjustment
algorithm has the same functional form whether a one-number Label or a two-
number Label is to be considered. For one-number labeling, this master
equation is used with the EPA Composite, or "55/45" MPG figures. For two-
number labeling, it is used once to adjust EPA City MPG values and once
again to adjust EPA Highway MPG values.
V-20
-------
This page intentionally
blank and un-numbered
-------
VI. ACKNOWLEDGMENTS
This report was prepared by a team of analysts, writers, programmers, and
typists. Their names are listed below. They must be credited with a sig-
nificant addition to the understanding of EPA-to-Road MPG differences.
J.P. Cheng R.M. Heavenrich, Jr. E.I. LeBaron
D. Curtis K.H. Hellman S.L. Loos
J.A. Foster S.L. Jarvis J.D. Murrell
VI-1
-------
Annotated List of Appendixes
Appendix A (5 pages) ; this explains and illustrates the computation of the
Influence Index, a metric which quantifies the fuel consumption effect of a
combination of real-world influences. The Influence Index can normalize
actual GPM Ratios in on-road data to null out effects of differential usage
and exposure between vehicle types* In this report, the Influence Index was
not used as such a normalizer but only as a tool for screening data for
outliers. It needs further refinement before it should be considered for
functional usage.
Appendix B (72 pages) ; this is a compilation of analyses of the separate
data sources. In addition to the first-level (all model years) analyses
that appeared in Section IV, this appendix gives separate analyses for each
model year within each data source; it also gives Influence Index data for
those sources with enough parameters covered (e.g., temperature, AMPD) to
permit Influence Index computation.
Appendix C (34 pages) : this analysis compares perceived-MFG data and fleet
data against measured-MPG consumer data. It was part of the basis for
deciding which data types to use in development of fuel economy adjustment
systems.
Appendix D (4 pages) ; this short appendix is a mathematical proof of a
relationship between non-modal and bi-modal GPM Ratios. It is related to
one of the bi-modal adjustment schemes in Section IV.
Appendix E (37 pages) ; this is a listing of the model type data base into
which Che entire 52,780-car data base was collapsed. There are three tables
following the format for the listing: Table E-l, -2, and -3 list the data
in three groups: consumer-measured MFC, fleet-measured annualized MPG, and
consumer-perceived MPG. Each line in a given data table is the aggregation
of all individual car data for a specific model year /model type. The
"number of observations" column indicates how many vehicle data points the
line represents, except when that number is 30 or more the number of cars
represented could be much greater than 30. (see text, page IV-1.)
Appendix F (32 pages) ; this is the set of histograms of the ratio of label
MPG to road MPG, for the adjustment schemes evaluated in Section V. The
adjustment being tested and the test fleet it was tested on are identified
for each histogram.
-------
This page intentionally
blank and un-tnunbered
-------
APPENDIX A
FUEL ECONOMY INFLUENCE INDEX
-------
APPENDIX A
The Influence Index
The concept of the influence index arose during the preparation of the EPA
404 report*. In that report, evaluations were made of the .individual
shortfall influencing variables, e.g., the in-use temperature is not
generally the same as EPA test temperature. Any two in-use vehicles are
likely to experience conditions that differ both from the EPA conditions and
also from each other's. What was needed was a simple numerical metric that
measures for each vehicle the overall departure from a set of baseline
conditions. This metric is the influence index.
For each in-use parameter that is important for determining EPA-to-Road MPG
influences, there is a sensitivity coefficient.
(MPGi - MPGo) / (MPGi + MPGo)
* (Fi - Fo) / (Fi + Fo)
where MPGi - MPGo is the departure in MPG for the baseline case MPGo caused
by factor i, Fi is the value of the factor causing the departure, Fo is the
value of the factor at the baseline condition, and SFi is the percent change
in MPG due to a percent change in factor i.
According to these definitions,
MPGi = MPGo [1 + SFi(Fi - Fo)/(Fi + Fo)]/[l - SFi(Fi - Fo)/(Fi + Fo)]
This is the usual definition of sensitivity. For calculations of the
influence index, the fuel consumption ratio was considered to be an
appropriate index. That is,
(1/MPGi)/(I/MPGo) = MPGo/MPGi
This ratio is called GPMRi. Thus,
GPMRi = [1 - SFi(Fi - Fo)/(Fi + Fo)]/[l + SFi(Fi - Fo)/(Fi + Fo)]
*Passenger Car Fuel Economy; EPA and Road, EPA 460/3-80-010, September, 1980.
-------
One of the original influence index concepts was for a GPMR which involved
more than one influence factor. If the GPMR for a large number of .changes
were, known it might be possible to learn how to combine individual GPMRi
effects. Also, using an in-use data base, the influence index could be used
to derive sensitivity coefficients. These then could be compared to
existing values of sensitivity coefficient derived from controlled
engineering tests. This work is planned for the future.
When this report was prepared, only the engineering test results were
known. Another use of the influence index concept however, is possible.
We have not herein involved the influence index in any adjustment
methodologies, such as normalizing data to a common influence index.
Specifically, we recommend against any such practice with the influence
index, in its present form, at this time. But the question of differential
influences between different data subsets will remain an item of concern
until some degree of improved influence indexing becomes practicable.
In this report, the influence index was used as a tool to screen data.
Calculations were made of the MPG ratio effects of individual factors.
These were then converted, based on engineering test results, to a series of
GPMRi equations. Next, a linear combination of the effects of the
influencing factors was assumed, and the largest (4.0) and smallest (0.6)
reasonable influence indexes computed. The data-base was then screened by
deleting data that had an overall Road GPMR (EPA composite MPG/Road MPG)
outside these bounds.
s
The individual GPMR equations used in this report are shown below in Table
A-l. Figures A-l and A-2 outline the process described above and give a
numerical example.
A-2
-------
Table A-l
Adjustments Used for Influence Index Calculation
Parameter
Temperature
Average Miles
Per Day
Population
Density
Wind (City)
Wind (Hwy)
Odometer
Topography
GPMR Equation
1/[1 - 0.004(55 - T)]
1/[0.48 + 0.14 In(AMPD)]
I/[1.099 - 0.118 ln(Pop. Dens.)]
1/[1 + ((0.0084/Ec2/3 - 0.00227))W]
1/[1 + ((0.0498/Eh2/3 - 0.0107))W]
1/[0.551 + (0.0507 In(Odo)]
Look-up table
Road Condition State-by-state look-up table
Highway Speed State-by-state look-up table
Reference
Docket A-80-32
2, p. 104
2, p. 151
2, p. 243
2, p. 116
2, p. 116
6
2, p. 119
2, p. 122, plus
DOT FHWA Data
2, p. 127 plus
DOT FHWA data
Notes:
1) The adjustments in Table A-l were referenced to the average in use
conditions for all influences except for wind and road condition. These
were referenced to the EPA test conditions.
2) The algorithm for an overall GPMR comprised of n individual influences is
given by 1.0 + (the sum of the signed differences between each individual
factor and 1.0).
3) The combined GPMRs were applied to the EPAc and EPAh fuel economy
values. The resulting city and highway road fuel economy values were
combined with the city driving fraction to calculate a composite
city-highway weighted adjusted road fuel economy. This was divided into the
EPA (55/45) composite fuel economy. The result is the influence index used
in this report. In essence, differences from 1.0 in the influence index
measure how severely the vehicle is being stressed compared to the base
condition. Values greater than 1.0 imply worse fuel economy conditions,
while values less than one imply better fuel economy conditions.
A-3
-------
TOTAL
CITY
AGPMR
Due to
Population
Density
Due to
City Road
Condition
4
Due to
City
Wind
Due to
Miles
Per Day
Due to
Odometer
Due to
Topography
Due to
Temperature
AGPMR for
CITY INFLUENCES
AGPMR (or
HIWAY INFLUENCES
Figure
A-l
Development of Influence Index
-------
1.045
Pop. Dens.
(.045)
1.186
City Road
(.186)
4
1.020
City Wind
(.020)
.949
Miles/Day
(-.051)
4
0.975
Odometer
(-.025)
«
1.033
Topography
(.033)
1.086
Temperature
(.086)
GPM RATIO
FOR INFLUENCE GIVEN
(AGPMR)
Total City AGPMR= 086+.033-.025-.051+.020+.186+.045 = 0.294
So City GPMR= 1.294
City Fraction=0.72
Imputed City
Road Mpg=
20/1.294
= 15.
Imputed Hiway
Road Mpg=
21.2
/"EPA
/ Combined Mpg =
\^
Similar Buildup of Iliway GPMR. Say ... 1.320
Example of Influence Index Calculation
-------
APPENDIX B
ANALYSES OF SEPARATE DATA SOURCES
-------
TABLE B-l.1
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : CHRYSLER (MEASURED MPG). 1981
W CH I /"" 1 C
v t M 1 v.L t
TECHNOL
R: A :C
R: A: I
R:M:C
F : A:C
F :M:C
Kll IMC) C D
INlJMD t K
OF VEII
43O
3
1
579
269
444
MIN
19
19
24
26
29
EPA MPG
HMAV
19.9
19. 3
23.5
27 . 3
31.6
444
MAX
23
19
24
29
37
444
MIN
9
17
25
13
20
ROAD MPG
HMAV.
16.6
18.4
25.0
23.5
27.9
444
MAX
28
21
25
36
38
RATIO
1 .205
1 O53
.941
1 . 166
1. 13O
GPMR
C I f*MC
5 1 uNr
DIFF*
100%
82%
47%
1OO%
INFLU
INDEX
1 . 162
1 . 178
1 . 138
1.181
t . 102
INFLU
SIGNF
DIFFO
36%
27%
,00%
100%
B- 1
-------
VEHICLE
TECHNOL
TABLE B-l .2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENOENT ANALVSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : CHRYSLER (MEASURED MPG). 1981
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR = » EPA- GPMR = » EPA =
ACTUAL GPMR
RANGE
R:A:C GPMR = 1.8O8 - .O3O3(E) .067
192
1 .25
19
1 . II
23
,719 - 2.267
R:A:I DATASET NOT REGRESSIBLE
19
19
.944 - 1.145
R:M:C DATASET MOT REGRESSIBLE
24
24
.941 - .941
F:A:C GPMR = -.24 1"+ .O5I5(E) .375
151
1 . 1O 26
1 .24
29
.776 - 2.ISO
F:M:C GPMR = .99O + .OO44(E)
.05)
132
1 . 12
29 1.15
37
.82O - 1.636
B-2
-------
TABLE B-1.3
DATA BASE CHARACTER IZATION: FUEL ECONOMY INFLUENCES
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : CHRYSLER (MEASURED MPG). 1981
FUEL ECON
INFLUENCE
ODOMETER
MILEPDAY
PCT.CITY
POPUDENS
TEMPTURE
WIND MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
CITYWIND
HWA Y W I ND
TPOGRAFY
HWY SPEED
CITYROAD
IIWAYROAD
TOTINFLU
VEHICLE
R: A:C
6321
44
64
681O
49
1 1
R: A :C
1 O1
.99
1 .03
1 .OO
1 .02
1 .0.1
1 .06
1 .00
1 .01
1 .06
1 07
1 . 16
TECHNOLOGY:
R: A: I R: A:D
1 1O33
44
70
87O6
48
11
R: A: I R: A:D
.98
.99
1 .05
1 .01
1 .03
1 .01
1 O6
1 .OO
1 .01
1 .07
1 .08
1.18
(3 CARS)
R:M:C R:M:I
420O
44
SO
6768
48
1 1
R:M:C R:M:I
1 .03
.99
.98
1 .OO
1 .03
1 Ol
1 .06
1 .OO
1 .01
1 O7
1 .08
1.14
( t CAR)
R:M:D F:A:C F : A: I
5499
44
63
69OO
49
1 I
R:M;D F:A:C F:A:I
1.O3
.99
1 . 03
1 . OO
1.O3
1 .01
1 . 06
1 .OO
1 . 02
1 . 06
1 . 07
1 . 18
F:A:D F:M:C F:M:I F:M:D
9889
44
52
7137
49
11
F:A:0 F:M:C F:M:I F:M:D
.99
.99
.99
1 .OO
1.03
1.O1
1 . 06
1 . OO
1 .01
1 . 06
1 . O7
1 . 10
B-3
-------
TABLE B-2.I
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : OOE (FLEET MPG). ALL YEARS
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFFe
'R:A:C 7977 12 19.O 3O
R: A:D 24 24 25.5 27
R:M:C 1O 17 18.2 26
F:M:0 5 44 44.5 45
15.6 42
17 22.4 3O
12 14.8 21
31 4O.3 5O
t .225
t . 142
1 .242
1 . 106
93%
97%
34%
72%
B-4
-------
TABLE B-2.2
IN:US£ FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (FLEET MPG). ALL YEARS '
SOLUTIONS AT MIN/MAX EPA MPG
VtlllCLt
TECHNOL
R: A :C
R: A:D
R:M:C
KtljKLii lUN CUUAI1UN
E = EPA 55/45 MPG
GPMR
GPMR
GPMR
.775 + .
.593 + .
.923 + .
0232(E)
0214(E)
OI7CHE)
R-VALUE
.259
. 177
.442
STD ERR
.234
. 162
. 144
GPMR =
1.O5
1.11
1 .20
0 EPA =
12
24
17
GPMR = 4
1.46
1. 17
1 .37
?> EPA =
3O
27
26
ML. 1 UAL U
RANGE
.604 - 3.
.871 - 1.
.957 - 1.
rMK
S14
6O6
477
F:M:D GPMR = 2.481 - ,O3O9(E) .059
239
1.13
44
1.O9
45
.896 - 1.429
-------
TABLE B-2.3
IN-USE FUEL ECONOMV OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (FLEET MPG). 1976
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFFff
R:A:C
41
26 28.4 29
8 13 8 27
2.O71
8654
R:M:C
17 16.6 17
12 13.4
IS
1.233 100%
B-6
-------
TABLE B-2.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (FLEET MPG). 1976
SOLUTIONS AT MIN/MAX EPA MPG
VEHICLE REGRESSION EQUATION ACTUAL GPMR
TECHNOL E = EPA 55/45 MPG R-VALUE STO ERR GPMR = » EPA = GPMR* f EPA= RANGE
R:A:C GPMR =-2.872 + .1736(E) .276 .5OI 1.57 26 2.11 29 .952 - 3 SOS
R:M:C DATASET NOT REGRESSIBLE 17 17 1.12O - 1.4O4
B-7
-------
TABLE B-2.5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (FLEET MPG). 1977
VEHICLE
TECHNOL
NUMBER
OF VEH
MIN
EPA MPG +++
HMAV MAX
ROAD MPG
MIN
HMAV MAX
GPMR
GPM SIGNF
RATIO DIFF*
R:A:C 288O 12 18.2 3O 6 14.9 31
F:M:O 1 44 43.9 44 39 38.7 39
1 .228
1 136
28%
B-8
-------
TABLE B-2.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ....
DATA SOURCE : DOE (FLEET MPG). 1977
SOLUTIONS AT MIN/MAX EPA MPG
VEHICLE REGRESSION EQUATION ACTUAL GPHR
TECHNOL E » EPA 55/45 MPG R-VALUE STD ERR GPMR= * EPA= GPMR= » EPA= RANGE
R:A:C GPMR = .496 + O397 (E ) .421 .213 .97 12 1.67 3O .669 - 2.878
F:M:O DATASET NOT REGRESSIBLE 44 44 1.136 - 1.136
B-9
-------
TABLE B-2.7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (FLEET MPG). 1978
VEHICLE
TECHNOL
NUMBER
OF VEH
+++ EPA MPG
MIN HMAV MAX
+++ ROAD MPG *++
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFF*
R: A:C
3O8O
14 2O. 1 28
16.O 42
I .262
35*
R:A:0
24 24.3 24
19 2O.8 25
1 . 165
R:M:C
2O 23.6 26
18 19.2 21
1 .263
IX
F :M:D
45 45.O 45
41 4O.5 41
1 . 1 1O
B- 10
-------
VEHICLE
TECHNOL
TABLE B-2.B
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (FLEET MPG). 1978
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR= 9 EPA = GPMR= « EPA=
ACTUAL GPMR
RANGE
R:A:C GPMR = 1.172 + .OO44(E) .048
.243
1 . 23
14
1.3O 28
.604 - 3.SOB
R:A:D DATASET NOT REGRESSIBLE
24
.971 - 1.2S8
R:M:C GPMR = -.486 + .O729(E) .975
.086
.96 2O
1 .42
26
.957 - 1.477
F:M:D DATASET NOT REGRESSIBLE
45
45
1 . 110 - t. I tO
B-1 1
-------
TABLE B-2.9
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (FLEET MPG). 1979
EPA MPG +++
VEHICLE NUMBER .
TECHNOL OF VEH MIN HMAV MAX
+++ ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFFe
R: A:C
1846
15 18.5
27
16. I
27
1 . 152
28%
R. A:D
15
24 26.4
27
17 23.5
3O
1 . 128
34%
B-12
-------
TABLE B-2.1O
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENOENT ANALYSIS
. . 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (FLEET MPG). 1979
SOLUTIONS AT HIN/MAX EPA MPG
VEHICLE REGRESSION EQUATION ACTUAL GPMH
TECHNQL E = EPA 55/45 MPG R-VALUE STD ERR GPMR= » EPA- GPMR= » EPA= RANGE
R:A:C GPMR = .989 + OOB8(E) .087 .(89 1.12 15 1.22 27 .661-3.514
R:A:0 GPMR =-1.O87 + O839(E) .460 .177 .93 24 1.18 27 .871 - J.6O6
B-13
-------
TABLE B-2.11
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (FLEET MPG). I9SO
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAO MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO OIFFe
R:A:C
13O
20 21.3 27
14 2O.5
31
I O4O
354
F:M:D
45 44.6
45
31 4O.8
SO
1 .094
22%
B- 14
-------
TABLE B-2.12
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (FLEET MPG). I98O
SOLUTIONS AT MIN/MAX EPA MPG
VEHICLE REGRESSION EQUATION ACTUAL GPMR
TECHNOL E = EPA 55/45 MPG R-VALUE STD ERR GPMR= * EPA= GPMR= «• EPA= RANGE
R:A:C GPMR = 1.O24 + .OOO7(E) .Oil .122 I . O4 2O 1 .04 27 .777 - 1.42O
F:M:D DATASET NOT REGRESSIBLE 45 45 .896 - 1.429
B-15
-------
TABLE B-3.1
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (MEASURED MPG). ALL YEARS
VEHICLE
TECHNOL
R
R
R
R
R
F
F
F
F
: A :C
:A: I
:A :D
:M:C
:M:D
: A:C
: A:O
:M.C
:M:D
NUMBER
OF VEH
317
2
24
19
4
19
1
3
16
•f**
MIN
13
17
22
20
27
15
24
29
4O
EPA MPG +++ +++
HMAV
19
ta
24
28
27
23
24
32
44
.5
.6
.7
.4
.7
.4
.O
.2
.0
MAX
3O
2O
27
39
29
28
24
39
45
MIN
It
14
16
15
24
13
22
21
31
ROAD
MPG +++
HMAV
16.4
15
22
21
26
2O
22
25
43
.7
.5
7
.2
.a
.O
3
3
MAX
26
18
3O
31
29
26
22
29
53
GPM
RATIO
1 . 193
t . 183
1 O98
1 .305
1 . 056
1 . 125
1 . 09O
1 .274
1 .015
GPMR
SIGNF
OIFFP
94%
4%
96%
,00%
93%
IOO%
74%
10O%
B- 16
-------
TABLE B-3.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (MEASURED MPG). ALL YEARS
SOLUTIONS AT HIN/MAX EPA MPG
vtmci.t
TECHNOL
R
R:
R:
R:
R:
F :
F :
F
F;
: A
: A :
: A:
M
M
: A:
: A
:M
M
:C
: I
D
;C
:D
:C
:D
:C
:D
HtliKtbblUN tUUAIlUN
E = EPA 55/45 MPG
GPMR =
DATASET
GPMR =
GPMR *
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
J.
.994
NOT
. 148
1 .581
.842
1 .092
NOT
1 . 1BO
3.883
+ .0100(E
R-VALUE
) .251
REGRESSIBLE
i- .O384(E
) .27 1
- .OO94(E) .296
* .OO77(E
+ .0014(E
) . IO8
) .063
REGRESSIBLE
+ .OO29(E
- .065I(E
) . 142
) .461
STD ERR GPMR = 9 EPA =
.116 1.12 13
17
. 176 .99 22
.158 1.39 2O
. 1O6 1 .OS 27
.068 1.11 15
24
. 145 1 . 26 29
. 155 1 .29 4O
GPMR= » EPA =
1.29 3O
20
1 . 18 27
1 . 22 39
1.O7 29
1 . 13 28
24
1.29 39
.95 45
AL.IUAL. UrMIt
RANGE
.920 -
1 . 12O -
. aoa -
1 . 1O3 -
.9BO -
1 .032 -
1 . 09O -
1 . 156 -
.845 -
1.671
1.247
1.SSO
1 .694
1 . 138
1 .325
1 .O9O
1.351
1 . 4O8
B- 17
-------
TABLE B-3.3
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (MEASURED MPG). 1977
VEHICLE
TECHNOL
R
R
R •
R
R
F :
f ;
:A:C
: A: f
;A:D
:M:C
:M:D
: A:C
:M:O
+ + +
NUMBER •
OF VEH MIN
1O9 15
1 20
1 25
6 27
1 28
1 15
2 44
EPA MPG
HMAV
19. 0
20.3
25.0
28.8
28.4
15. 1
43.9
•* + +
MAX
30
20
25
3O
28
15
44
+ + +
MIN
II
18
23
18
29
13
35
ROAD
MPG
HMAV
15.7
18
23
21 .
29
13.
36
. 1
4
9
0
2
6
+ + -f
MAX
26
18
23
26
29
13
38
GPM
RATIO
1 . 215
1 . 12O
1 .O7O
1 .313
98O
1 . 148
1 .201
GPMR
SIGNF
OIFFO
32%
---
93%
23%
B-18
-------
VEHICLE
TECHNOL
TABLE B-3.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (MEASURED MPG). 1977
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE
STO ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR= » EPA= GPMR= «• EPA =
ACTUAL GPMR
RANGE
R:A:C GPMR = .954 + .O135(E) .389
R:A:I DATASET NOT REGRESSIBLE
R:A:0 DATASET NOT REGRESSIBLE
R:M:C GPMR = 2.292 - .O339(E) .6O6
R:M:D OATASET NOT REGRESSIBLE
F:A:C DATASET NOT REGRESSIBLE
F.M:D DATASET NOT REGRESSIBLE
IO6
1 . 15
.087
1 .39
15
2O
25
27
28
15
44
1 .35
1 .27
3O
2O
25
SO
28
15
44
.929 -
1 . 1 2O -
1.O7O -
1 . 177 -
. 98O -
1 . 148 -
1 . 153 -
1 .564
1 . 12O
1 .O7O
1.455
.980
I. 148
1 . 25O
B- 19
-------
TABLE B-3.5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA .
DATA SOURCE : DOE (MEASURED MPG). 1978
VEHICLE
TECHNOL
R: A :C
R: A :D
R:M:C
R:M:0
F:M:C
f :M:D
+++ EPA MPG +++ ++*
NUMBER
OF VEH MIN HMAV MAX MIN
151 13 19.4 28 12
7 22 23.9 24 16
1O 2O 28.7 39 16
1 29 29. 1 29 26
2 31 34.3 39 27
4 45 45. O 45 43
ROAD
HMJ
16
20
23
25
27.
46
MPG
\\J
.6
.7
.2
9
9
a
+ *+
MAX
26
26
31
26
29
S3
GPMR
/*OU C I f*MC
GrM SIGNr
RATIO DIFFO
1.171 10%
1 . 16O 1O%
1.237 82%
1.125
1.235 44%
.961 97%
B-2O
-------
TABLE B-3.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (MEASURED MPG). 1978
SOLUTIONS AT MIN/MAX EPA MPG
VEHICLE
TECHNOL
R
R
O ,
Q ,
F
F
: A :C
: A :D
:M:C
M:O
:M:C
;M:U
REGRESSION EQUATION
E = EPA 55/45 MPG R-VALUE
GPMR =
GPMR =
GPMR =
DATASET
OATASET
DATASET
1 .055
O6B
1.311
NOT
NOT
NOT
+ OO58(E) . 148
+ O456(E) .207
- .OO25(E) .117
REGRESSIBLE
REGRESSIBLE
REGRESSIBLE
STD ERR GPMR = 0 EPA =
. IO4 1.13 13
.214 I.O7 22
.151 1.26 2O
29
31
45
GPMR= » EPA=
1.22 28
1.18 24
1.22 39
29
39
45
ACTUAL GPMR
RANGE
.920 -
.919 -
1 . 1O3 -
1 . 125 -
1 . 156 -
.845 -
1.415
1 . 55O
1.522
1. 125
1 .315
1 .056
B-21
-------
TABLE B-3.7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE. TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (MEASURED MPG). 1979
\lf i-l I f* 1 C
vtrll CLfc
TECHNOL
R: A:C
R : A :D
R:M:C
F : A :'c
F : A:D
F :M:D
1 II ItlQ C Q
NUMfctt K
OF VEH
25
II
1
3
1
a
+++ EPA MPG +++
MIN HMAV MAX
14 19.6 27
24 24.8 27
25 24.6 25
18 23.3 27
24 24. O 24
4O 43.4 44
+ + +
MIN
II
17
15
17
22
31
ROAD
HM/
16
22
14
22
22
42
MPG
W
.6
.8
.5
.O
.O
.7
* + +
MAX
21
3O
15
26
22
SO
f DU
uPM
RATIO
1 . 188
1 .O9I
1.694
1 .052
1 . 09O
1 .012
GPMR
C t f*MC
bluNr
OIFFe
79%
56%
95%
87%
B-22
-------
TABLE B-3.8
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
. . 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (MEASURED MPG). 1979
SOLUTIONS AT MIN/MAX EPA MPG
VtlllUI t
TECHNOL
R
R
R:
F :
c ,
F ;
: A:C
: A:D
• M • f
:A:C
: A:D
:M:D
KtljKt iS IUPJ CUUAIIUN
E = EPA 55/45 MPG
GPMR =
GPMR =
DATASET
GPMR =
DATASET
GPMR =
1 .OI7
- .987
NOT
1 .216
NOT
4.201
+ .OO86(E
+ .O837(E
R-VALUE
) . 124
) .582
REGRESSIBLE
V
- .006B(E
) 1 . OOO
REGRESSIBLE
- .0733(E
) .511
STD ERR GPMR = « EPA =
. 189 1.14 14
. 173 1 .02 24
25
.OOO 1.O9 18
24
. 193 1 .28 40
GPMR =
-------
TABLE B-3.9
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (MEASURED MPG). 198O
+++ EPA MPG +++ +++ ROAD MPG +++
VEHICLE NUMBER y
TECHNOL OF VEH MIN HMAV MAX MIN HMAV MAX
R:A:C 23 19 21 5 27 13 18.1 23
R:A:D 4 26 26.1 26 22 24.6 27
R:M:C 2 28 28. O 28 19 19.6 2O
F:A:C 14 24 24. O 26 2O 21.4 25
F:M:C I 29 28.8 29 21 21.3 21
F:M:D 2 45 44.6 45 46 47.1 48
GPMR
GPM SIGNF
RATIO DIFF0
1.192 76%
1.O63 88%
1 . 428 90%
1 . 125 99%
1.351
.948 95%
B-24
-------
VEHICLE
TECHNOL
TABLE B-3.tO
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (MEASURED MPG). 198O
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMH= 9 EPA = GPMR= » EPA =
ACTUAL GPMR
RANGE
R.A:C GPMR = 1.O99 + .O043(E) O9O
R:A:D DATASET NOT REGRESSIBLE
129 1.18 19 1.21 27 1.065 - 1.671
26 26 .969 - 1.189
R:M:C DATASET NOT REGRESSIBLE
28
28
1 .385 - 1.472
F:A:C GPMR = 1. 1OI + OOIO(E) .O2B
041
I . 12
24
1 . 13
26
1.O5O - 1.212
F:M:C DATASET NOT REGRESSIBLE
F:M:D DATASET NOT REGRESSIBLE
29
45
29 1.351 - 1.351
45 .93O - .966
B-25
-------
TABLE B-4.I
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (PERCEIVED MPG) . ALL VEARS
l/C l-l I f 1 C
VCM I L.L t
TECIINOL
R: A ; I
R: A:D
R:M: I
F : A :C
F : A : I
F : A :D
F :M:C
F :M: I
F :M:D
|.|| IILJQ C D
NUMHtn
OF VEH
49
IOS
47
169
39
4
2O 1
122
68
+ + +
MIN
14
22
14
1 1
17
24
23
ta
4O
EPA MPG
HMAV
16.5
24 .6
20.6
23.5
2O. 2
23.9
32.3
28.8
44.2
+ + <-
MAX
21
28
23
32
27
24
40
3O
45
+ +*
MIN
IO
12
16
9
to
14
19
17
24
ROAD MPG
HMAV
14.9
2O. 9
20.7
21.8
19.3
17.6
28.9
28.2
40.3
4 + +
MAX
25
37
32
40
32
2O
45
48
55
/* DU
OHM
RATIO
1 . 1O1
i . tao
.998
1 .084
1 O38
1 .357
1. 128
1 .024
1 . 096
GPMR
C ¥ f KIC
b t uNr
OIFF«»
30%
100%
1OO%
25%
89%
89%
99%
100%
17%
¥ titC 1 II
INr LU
INDEX
1 .227
1 .210
1 . 2O8
1 .283
1 .275
1 .234
1 .202
1 .225
1 .202
INFLU
c Y r*ucr
b luNr
DIFFO
9%
72*
55%
99%
57X
4%
9O%
17X
71*
B-26
-------
TABLE B-4.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (PERCEIVED MPG). ALL YEARS
SOLUTIONS AT MIN/HAX EPA MPG
VtHH-Lt
TECHNOL
R:A: I
R: A:D
R:M: I
F : A ;C
F :A: I
F : A :0
F :M:C
F :M: I
F :M:D
KtUKt
E =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
GPMR =
331UN CUUAI1UN
EPA 55/45 MPG
1 .456
.055
1 -O29
.967
1 .325
NOT
.417
.947
4.321
- .O213(E)
+ .O456(E)
- .OO15(E)
+ .0047(E)
- OI38(E)
REGRESSIBLE
+ .O217(E)
+ .OO27(E)
- .0729(E)
R-VALUE
.217
.279
.016
. O92
. 246
.435
.019
.332
STD ERR
. tea
. 231
. 144
.232
. 195
. 173
.204
.204
GPMH = » EPA"
1 . 16
1 .06
1 .01
t .02
1 .09
.93
.99
1 .42
14
22
14
1 1
17
24
23
18
4O
GPMR= 9 EPA=
1.00 21
1.31
.99
t . 12
.96
1 .28
1 .03
t .06
28
23
32
27
24
4O
30
45
MlilUML. Ur*HK
RANGE
.625 -
.674 -
.671 -
.668 -
.672 -
1. 196 -
.785 -
.608 -
.820 -
1 .488
2.238
1 .245
2.97O
1 .676
t . 7O9
1 .863
1 .646
t .838
B-27
-------
TABLE B-4.3
DATA BASE CHARACTERIZATION: FUEL ECONOMY INFLUENCES
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (PERCEIVED MPG). ALL VEARS
FUEL ECON
INFLUENCE
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
WIND.MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
CITYW1ND
HWAYWIND
TPOGRAFY
HWYSPEED
CITYROAD
HWAYROAD
VEHICLE TECHNOLOGY:
R:A:C R : A : I R:A:D R:M:C
7515
36
52
479O
43
1 1
R: A:C R: A: I
1 .01
1 .04
.99
1 .OO
1 .05
1 .01
1 .07
1 .03
1 .02
1 . 05
1 .06
6648
38
41
4794
36
12
R: A:O R:M;C
1 .02
1 .04
.96
.99
1 .08
1 .01
1 .07
1 .01
1 .02
1 .OS
1 .06
R:M:I R:M:O
8543
36
52
3496
42
12
R:M:I R:M:O
1 .OO
1 . 04
.99
.99
1 . 06
1 .01
1 .07
1 .03
1 .02
1 .05
1 .06
F :A:C
6596
35
48
451 1
34
12
F:A:C
1 .02
1 .07
.aa
.99
1 . 1O
1 .Ol
1 .07
1 .01
1 .02
1 .05
t .06
F:A: I
7353
33
47
4076
39
12
F:A: I
1 .02
1 . t 1
.97
.99
1 .07
1 Ol
1 .07
1 .01
1 .02
I.O5
1 .06
F : A:D
7OOO
46
48
424O
24
14
F:A:D
t .Ol
.99
' .98
1 .00
t . 14
1 02
t .08
1 .00
t .02
1 .06
t .07
F:M:C
8169
4O
44
51 1O
36
12
F :H:C
1 .OO
1 .03
.96
1 .OO
t .OS
1 Ol
1 .07
1 .02
1 .02
t .OS
1 .06
F:M:I
7725
38
47
4479
36
12
F:M:I
t .Ot
t .OS
.97
t .OO
t .09
t .Ol
1.07
I. O2
1.O2
1.05
1.06
F:M:D
7912
40
38
6O17
36
12
F:M:D
1 .01
1 .03
.95
1 .OO
1 .09
t .Ol
1.O7
1 .Ol
1 .02
1.05
1 .06
TOTINFLU
1 .23
1.21
1.21
1 .28
1 .28
I .23
1 .20
1 .22
1.20
8-28
-------
TABLE B-4.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (PERCEIVED MPG). 1978
VEHI CLE
TECHNOL
R: A: I
R: A :O
R:M: I
F : A . C
F : A : I
F :M:C
F :M: I
F :M:D
NUMBER
OF VEH
32
3O
4O
89
24
12O
71
41
+ + +
MIN
14
22
14
1 1
18
23
18
4O
EPA MPG
HMAV
17 .O
24 .4
2O. 6
22 .9
22 . 1
34 . 2
28 .4
44 . 4
+ + +
MAX
21
28
23
32
27
40
29
45
+ + +
MIN
10
15
16
1 1
17
2O
17
26
ROAD MPC
HMAV
15.7
23.6
2O. 6
22.3
22.6
29.6
29.2
43.2
> + + +
MA.X
25
37
32
34
32
45
48
55
RATIO
1 O79
1 031
.997
1 .047
.984
1. 167
.974
1 -O25
GPMR
C 1 ^*hlC
a I GNr
DIFF*
45%
59%
99%
48%
98%
100%
1OO%
91%
INF LU
INDEX
1 . 2O6
1 . 182
1 .205
1 .219
1 . 2O6
t . tat
1 . 2O4
1 . 178
INFLU
C V f*I^IC
9 1 GNr
DIFFe
14%
4 IX
12X
57X
12%
64X
MX
43%
B-29
-------
TABLE B-4.5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENOENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : OOE (PERCEIVED MPG). 1978
SOLUTIONS AT MIN/MAX EPA MPG
VCM1L.LC
TECHNOL
R: A: I
R; A :D
R:M: I
F : A:C
F: A: I
F :M:C
F : M . I
F :M:D
HCl
E
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
iKtaalura EUUBIIUIM
= EPA 55/45 MPG
= 1.486 -
= 1.466 -
= 1.O4B -
=; .774 +
.770 +
.370 +
= 1 . 155 -
= 5.274 -
.O235(E)
.O177(E)
.O025(E)
.01 1 HE)
.O095(E)
.023O(E)
OO63(E )
.0956(E)
R-VALUE
.275
. 142
.029
.318
. 19O
.447
.054
.794
STD ERR
. 173
. 187
. 139
. 173
. 152
. 178
.209
.079
GPMR = 0 EPA =
1. 16
1 .08
1 .01
.90
.94
.91
1 .04
1 .47
14
22
14
II
18
23
18
4O
GPMR= 9 EPA*
.98 21
.98
.99
1 . 13
1 .02
1 .28
.97
1 .00
28
23
32
27
4O
29
45
MV.IUML. UrWM
RANGE
.770 -
.674 -
.671 -
.685 -
.672 -
.786 -
-6O8 -
.820 -
1 .488
1 . 63O
1 .214
t .639
1.3O3
1.863
1.646
1.552
B-30
-------
TABLE B-4.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : DOE (PERCEIVED MPG). (979
VEHICLE
TECHNOL
R: A
R: A
R:M
F : A
F : A
F : A
F :M
F :M
F :M;
: I
:D
: I
:C
: I
:O
:C
: I
D
NUMBER
OF VEH
17
78
7
ao
15
4
81
51
27
++* EPA MPG +++ + + +
MIN
14
23
21
18
17
24
29
23
4O
HMAV
15
24
21
24
17
23
29
29
43
5
7
. 1
1
a
9
8
4
9
MAX
19
27
21
27
23
24
3O
SO
44
MIN
to
12
17
9
1O
14
19
18
24
ROAD
MPG
HMAV
13.6
2O
21
21
15
17
27.
26
36.
1
.0
3
7
6
9
.9
6
+ + +
MAX
25
34
26
40
25
2O
38
40
51
GPM
RATIO
1. 142
1
t
t
1
1
t
1
t
.237
.OO4
. 125
. 124
.357
. O69
. O94
. 2O3
GPMR
SIGNF
OIFF?
3%
1OO%
90%
37%
. 20%
83%
1OO%
93%
76%
INFLU
INDEX
1 .265
1 .220
1 .226
1 .354
1 .386
1 .234
1 .234
1 .253
1 .237
INFLU
SIGNF
OIFF»
7%
97%
45%
100%
61%
30%
81%
32%
67%
B-31
-------
TABLE B-4.7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : DOE (PERCEIVED MPG). 1979
SOLUTIONS AT MIN/MAX EPA MPG
VtlllLLC
TECIINOL
R: A: I
R: A:D
R : M : I
F :A:C
F : A: I
F : A:D
F :M:C
F:M: I
F :M:D
KCVjKCiil UN CUUAI1UFM
E = EPA 55/45 MPG
GPMR -
GPMR =
GPMR =-
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
GPMR =-
.547
+ .O383(E)
- .310 + .O624(E)
2.895
1 .387
1 .755
NOT
5.767
3. 167
2. 175
+ . I848(E )
- .O1O6(E)
- .0349(E)
REGRESSI8LE
- .1577(E)
- .07O5(E)
+ . O768(E)
R-VALUE
. 164
.388
.267
. I4O
.349
. 1 12
.207
.212
STD ERR
.218
.217
. 193
.272
.231
. 165
. 174
.271
GPMR- «
1 .09
1 . 13
.89
1 .20
1 . 17
1 . 16
1 . 19
.91
f EPA =
14
23
21
18
17
24
29
28
4O
GPMR= <
1 .26
1 .37
1 .02
1 . 1O
.94
1 .03
1 .08
1.21
» EPA =
19
27
21
27
23
24
3O
30
44
»U 1 UAL UfMK
RANGE
.625
.704
.814
.668
.838
1 . 196
.789
.740
.865
- 1.434
- 2.238
- 1.245
- 2.97O
- 1.676
- 1 . 7O9
- 1 . S7O
- 1.646
- 1.838
B-32
-------
TABLE B-5.I
IN-USE FUEL ECONOMY. OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (MEASURED MPG). ALL YEARS
VEHICLE
TECHNIOL
R : A:C
R: A: I
R:M:C
R M: I
F : A:C
F : A: I
F :M:C
F . M : I '
NUMBER
OF VEH
582
to
145
14
98
23
t 12
27
+ + +
MIN
12
18
18
19
13
16
24
27
EPA MPG
HMAV
19.5
21.2
28 .4
22 7
25. 2
19. 0
32. 1
29.5
+++ +++ ROAD MPG +++
MAX
32
28
41
32
32
27
42
33
MIN
7
13
14
17
1 1
1 1
18
19
HMAV
15.6
18.2
23.9
21 .O
21.3
15.8
28.9
28.9
MAX
31
23
' 38
31
31
26
50
38
GPM
RATIO
1 .251
1 . 159
1 . 187
t .092
1 . 184
1 . 192
1.121
1 .022
GPMR
SIGNF
DIFF*
1OO%
74%
89%
98%
83%
44%
100%
1OO%
INFLU
INDEX
1 . 105
1 . 107
1 .095
.991
1 . 139
1 . 154
1 .093
1 .018
INFLU
SIGNF
OIFFff
53%
8%
29%
100%
94%
78%
35%
99%
B-33
-------
TABLE B-5.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). ALL YEARS
SOLUTIONS AT MIN/MAX EPA MPG
Vtllll.l-t
TECHNOL
R: A :C
R: A: I
R:M:C
. R:M: I
F : A:C
F.A.I
F :M:C
F :M: I
KCl
E
GPMR
GPMR.
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
IKC^OIUN tlJU
= EPA 55/45
= 1 . 2O7 + .
= 1.274 - .
= 1 . 157 + .
.841 + .
= 1 . 161 * .
= 1 . 444 - .
.728 + .
= 1 . 127 - .
A 1 1UN
MPG
O022(E)
OO54(E )
0010(E)
OtOB(E)
0009(E)
O128(E)
OI20IE)
0036(E)
R-VALUE
.042
. 1 17
.O3O
.276
013
.373
.341
.046
STD ERR
. 182
. 142
. 173
. 161
. 186
. 139
. ISO
. 146
GPMR =
1 .23
1 . 18
1 . 18
t .04
1 . 17
t .24
1 .02
1 .03
0 EPA-
12
18
18
19
13
16
24
27
GPMH =
t .28
1 . 12
1 .20
1 . 19
1 . 19
1 .09
1 .23
t .Ot
0 EPA =
32
28
4 1
32
32
27
42
33
HUIUHL UrMN
RANGE
.BOB
1 -OOO
.693
.868
87O
1 . OO9
.645
. 8O9
- 1.972
- 1.463
- 1 . 68O
- 1.311
- 2.050
- 1.436
- 1 . 604
- 1.474
B-34
-------
TABLE B-5.3
DATA BASE CHARACTERIZATION: FUEL ECONOMY INFLUENCES
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). ALL YEARS
FUEL tCON
INFLUENCE
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
WIND.MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
CI TYWIND
HWAYWIND
TPOGRAFY
HWYSPEED
CITYROAD
HWAYROAD
VEHICLE
R: A :C
»979O
36
66
41 13
67
9
R: A:C
.97
1 .06
1 .04
1 .OO
96
1 .01
1 .05
1 .00
1 .04
1 .04
1 .07
TECHNOLOGY :
R: A: I R:A :D
1014O
48
58
4528
68
9
R: A: I R: A :0
.99
1.05
1.01
1 .OO
.95
1 .01
1 .05
1 .00
1 O4
1 .04
1 .06
R:M:C
20177
37
64
4O37
65
1O
R:M:C
.97
1 .04
1 .03
1 .OO
.96
1 Ol
1 .06
1 .OO
1 .03
1 .05
1 .07
R :M: I R:M:D
22594
46
55
3749
71
9
R:M: I R:M:D
.96
1 .OO
1 .OO
1.OO .
.95
1 .Ol
1 .05
1 .OO
1 .04
1 .05
1 .07
F:A:C
1O921
34
62
4248
65
1O
F :A:C
.99
1 .06
1 .02
1 .OO
.96
1 .Ot
1 .06
1 .OO
1 .04
1 .04
1 .07
F :A: I F : A:O
13686
32
68
•48O4
66
9
F:A:I F :A:O
.99
1.O7
1 .04
1 .OO
.96
1 .01
1 .05
1 .OO
1 .04
1 .04
1 .06
F :M:C
14875
39
58
4167
63
IO
F :M:C
.98
1 .03
1 .Ol
1 .OO
.97
1 .01
1 .06
1 .OO
1 .03
1 .05
1 .07
F:M:I F:M:D
14269
44
48
4731
67 :
9
F:M:I F:M:D
.98
1.O2
.98
1.00
.96
1.01
I .OS
1.OO
1.O3
(.04
1.07
TOTINFLU
1.11
1.11
1 .09
.99
1 . 14
1 . 15
1 .09
1.O2
8-35
-------
TABLE B-5.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1977
VEHICLE
TECHNOL
R: A :C
R: A : I
R:M:C
R:M: I
F : A: I
F :M:C
F :M: I
+ + *
NUMBER
OF VEH MIN
43 13
1 19
6 2O
2 21
1 27
3 33
1 29
EPA »
MM;
17
ia
28
20
27.
38
28.
4PG
XV
. 2
9
2
9
4
5
5
+ + +
MAX
22
19
35
21
27
42
29
+ + +
MIN
9
15
16
17
26 •
26
3O
ROAD
MM
13
15
19
18
26
28
29
MP(
AV
.4
.4
.5
. 1
. 1
.4
.9
MAX
22
15
25
2O
26
32
3O
GPMR
GPM SIGNF
RATIO DIFFe
1.291 21%
1.227
1.454 96%
1.157 56%
1.O50
1 . 384 33%
.954
INFLU
INDEX
1.072
1 .494
1 . OB8
1.O37
.866
.957
I.OI2
INFLU
C ¥ ftIC
SIGNF
DIFFO
6%
25%
445C
75%
B-36
-------
VEHICLE
TECHNQL
TABLE B-5.5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1977
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR= * EPA= GPMR= » EPA=
ACTUAL GPMR
RANGE
R:A:C GPMR = 9O9 + .O2I8(E) .19O
R:A:I OATASET NOT REGRESSIBLE
R:M:C GPMR = 1.259 * .OO67(E) .275
R:M:l DATASET NOT REGRESSIBLE
F:A:1 OATASET NOT REGRESSIBLE
F:M.C GPMR = - 857 + .O575(E) .99O
F:M:I OATASET NOT REGRESSIBLE
.233
144
1 . 19
1 .39
.058
1 .05
13
19
2O
21
27
33
29
1 .38
1 .50
1 .55
22
19
35
21
27
42
29
.946 -
1 .227 -
1 .295 -
1 .O4O -
1 .050 -
1.05J -
.954 -
1
1
1
1
1
1
.972
.227
.627
.274
.050
.592
.954
8-37
-------
TABLE B-5.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (MEASURED MPG). 1978
VEHICLE
TECHNOL
R: A:C
R:M:C
R :M: I
F : A:C
F : A : I
F :M:C
F :M: I
NUMBER
OF VEH
92
24
1
2
3
1 1
1
+ + +
MIN
13
20
19
3O
23
3O
3O
EPA MPG
HMAV
19.4
'28.8
18.5
30. O
25. 0
34.4
29 5
+ * +
MAX
28
39
19
30
26
39
30
+ + +
MIN
9
14
19
23
22
24
28
ROAD MPC
HMAV
15.4
23.8
18.8
23.6
23.4
29.3
28.3
> + + +
MAX
25
36
19
24
24
35
28
RATIO
1 .259
1.215
.984
1 .274
1 .O7O
1 . 183
1 .046
GPMR
C ¥ /"MC
a IGNr
OIFF*
75%
42%
63%
99%
71%
f IkIC III
INr LU
INDEX
1 . IO4
1.O74
.872
.983
1 . 049
1 .049
.963
INFLU
C V f*MC
a IGNr
DIFFO
59%
37X
64X
45%
86%
B.-38
-------
VEHICLE
TECMNOL
TABLE B-5.7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (MEASURED MPG). 1978
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STO ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR = * EPA= GPMR- 9 EPA=
ACTUAL GPHR
RANGE
R:A:C GPMR = 1.22O * .OO2O(E)
.041
159
I .25
13
I .28
28
.862 - 1.677
R:M:C GPMR = 1.O24 + .OO64(E) .178
R:M:I DATASET NOT REGRESSIBLE
F:A:C DATASET NOT REGRESSIBLE
F:A:I GPMR = .648 + .O168(E) . 9O 1
F:M:C GPMR = .59O + .O17O(E) .438
F.M:I DATASET NOT REGRESSIBLE
.215
I . 15
.018
16O
I .04
I .09
20
48
30
23
30
3O
1.27 39
19
30
1.O9 26
1 26 39
30
.883 -
.984 -
1.252 -
1.037 -
.847 -
1 -O46 -
1
I
1
1
1
. 68O
.984
.297
.093
.434
.046
B-39
-------
TABLE B-5.8
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1979
» 1C 11 I C" t f
V t M I UL t
TECHNOL
R: A:C
R: A: I
R :M:C
R:M: I
F : A :C
F.A.I
F :M:C
F :M: I
Ml IUQ C D
NUMUcK
OF VEH
too
1
28
3
7
4
11
7
+ 4 +
MIN
13
19
20
21
18
16
27
28
EPA MPG
HMAV
19. 1
18.9
25.4
21.9
24.4
17.9
3O.5
29.2
+ + f
MAX
28
19
40
23
27
26
37
31
+ + +
MIN
7
16
16
17
15
1 1
23
22
ROAD MPC
HMAV
15.2
15.6
22.4
19.3
23.3
15.4
28.9
29.3
5 + + +
MAX
27
16
33
25
26
24
35
34
RATIO
1 .252
1 .217
1 . 136
t . 139
1 . 046
1 . 153
1 .O6O
.998
GPMR
C I f*klC
b 1 uNr
DIFF«>
100%
—
99%
25%
99%
29%
99%
99%
f hi C 1 II
INF LU
INDEX
t . 1O8
-BO1
1 .053
1 .006
.979
1 .094
1 . 105
1 . OO8
INFLU
C 1 fMC
3 luNr
DIFF»
93%
62%
57%
99%
14%
49%
60%
B-4O
-------
TABLE B-5.9
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
.. 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1979
SOLUTIONS AT MIN/MAX EPA MPG
vtl-MCUt
TECHNOL
R : A:C
R: A: I
R " M • C
R:M: I
F : A :C
F : A: I
F :M:C
F :M. I
KLL.KC
f. =
GPMR =
DATASET
GPMR =
GPMR =-
GPMR =
GPMR =
GPMR =
GPMR =
aalUN tUUAIIUPJ
EPA 55/45 MPG R-VALUE
1 .234
NOT
t . 118
2.620
1 . 238
1 .306
.679
1 .737
+ .O009(E) .012
REGRESSIBLE
+ .OOO7(E) .034
+ . 17 I7(E ) .625
- .OO77(E) .278
- .OO8KE) .229
+ .O124(E) .241
- .0253(E) .213
STD ERR GPMR = o EPA =
.199 , 1.25
. 1O5 1.13
.262 1 .OS
.095 1 . IO
.2 16 1 . 18
. 128 t .01
.161 1 .04
13
19
2O
21
IB
16
27
28
GPMR= 0 EPA=
1.26 28
19
1 . 14 4O
1.31 23
1.O3 27
1.O9 26
1.13 37
.94 31
nv*IUAL lar*MK
RANGE
.883 -
1 .217 -
.936 -
.868 -
.914 -
1 . O09 -
.841 -
.863 -
1 .921
1.217
1 .337
1.311
1. 181
1.418
1.281
1 . 28O
B-41
-------
TABLE B-5.1O
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (MEASURED MPG). 198O
VEHICLE
TECHNOL
R: A :C
R: A: I
R:M:C
R :M: I
F :A:C
F : A: I
F :M:C
F :M: I
NUMBER
OF VEH
169
5
41
3
37
13
33
14
MIN
16
2O
18
22
21
17
24
27
EPA MPG
HMAV
2O. 5
21.9
28.4
23.4
25.0
18.2
31 .O
28.9
MAX
3O
23
35
25
32
26
41
31
+++ ROAD
MIN HM/
11 16
13 18
16 24
19 22
12 21
12 14
22 29
.19 27
MPC
W
.6
.6
.O
.2
.O
.8
.3
.7
MAX
3O
23
35
26
28
22
50
38
RATIO
1 .235
t . 17O
1 . 181
1 .059
1 . 19O
1 .224
1 .057
t .044
GPMR
C 1 /"*MC
3 1 uNr
DIFFO
1OO%
19%
soy.
64%
0%
say.
100%
99%
INF LU
INDEX
1 . 13O
1 . 101
1 . 14O
.943
1 . 139
1 .207
t .091
1 .045
INFLU
C T /^MCT
SI UNr
OIFF*
0%
3O%
28%
93%
20%
75%
83%
95%
B-42
-------
TABLE B-5. 1 I
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1980
SOLUTIONS AT MIN/MAX EPA MPG
v t ru u L t
TECHNOL
R : A :C
R: A : I
R:M:C
R :M: I
F : A:C
F : A : I
F :M:C
F :M: I
Ktl
E
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
GPMR
iKCiSIUM ClJUAIIUrj
= EPA 55/45 MPG
= 1 . 151 +
= 3.920 -
= 1.352 -
. 337 +
= 1.558 -
= 1 .313 -
.959 +
.762 *
.0040(E)
. 1252(E)
.OO59(E)
.0307(E)
.0146
-------
TABLE B-5.12
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1981
iy c tj i f i c
V t M 1 C I. fc
TECHNOL
R: A :C
R: A : I
R:M:C
R:M: I
F :A:C
F : A: I
F :M:C
F :M: I
Ml IUQ H D
NUMUt K
OF VEH
1 18
3
37
4
50
2
51
4
+ + +
MIN
IB
ta
24
21
23
IB
25
33
EPA MPG
HMAV
22.5
21.7
31 O
26.7
25.8
17 .5
32.6
32 .9
+ + +
MAX
32
28
4 1
32
31
IB
42
33
+ + f
MIN
13
17
16
2O
16
13
18
31
ROAD MP(
HMAV
17 .8
2O. O
25.9
24. 0
21 .6
13.3
28.7
33. 0
3 + + +
MAX
31
23
38
31
31
14
42
37
f*QU
CiPM
RATIO
1 . 26O
I.O98
1 . 194
1 . 122
1 . 194
1.319
1 . 141
.997
GPMR
S IGNF
OIFF0
100%
82%
41%
71%
48%
67%
1OO%
99%
INFLU
INDEX
1 . 1O2
1 . 09O
1 .097
1 .024
1 . 173
1 .224
1.113
.955
INFLU
SIGNF
OIFF0
4O%
11%
36%
98%
96%
SOX
10%
92%
B-44
-------
TABLE B-5. 13
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (MEASURED MPG). 1981
SOLUTIONS AT MIN/MAX EPA MPG
vtmuLt
TECHNOL
R
R:
R:
R
F
F
f
F
: A:C
'. A i I
:M:C
M.I
: A :C
: A : I
:M:C
:M: I
KtUKt
E =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
DATASET
GPMH =
DATASET
331UN CUUAIIUN
EPA 55/45 MPG R-VALUE
1 .301
.687
1 .436
.902
.913
NOT
.889
NOT
- .OOIfl(E) .034
• + OI83(E) .994
- .OO77(E ) .181
* . OOBO(E) .317
•* .OIOfl(E) .113
REGRESSIBLE
+ .0076(E) .237
REGRESSIBLE
STD ERR GPMR = 9 EPA=
.165 1.27 18
.014 1.O1 18
. 178 1 .26 24
.159 1.O7 21
.182 1 . 17 23
18
. 145 1 .OB 25
33
GPMR= * EPA=
1 .24
1 .20
1 . 13
1 . 16
1.25
1.21
32
28
41
32
31
18
42
33
Ml*IUMU ur"MK
RANGE
.909 -
1 .020 -
.693 -
.981 -
.870 -
1.257 -
.888 -
.895 -
1.7O9
1.2O4
1.633
1 .248
1.637
1.381
1 . 6O4
1 .076
B-45
-------
TABLE B-6. I
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (PERCEIVED MPG). ALL YEARS
VEHICLE
TECHNOL
R
R
R:
R i
R:
F ;
f •
F •
f :
:A:C
:A: I
:A:D
:M:C
:M: I
: A:C
All
:M:C
M: I
NUMBER
OF VEH
322
20
1
187
25
1 1
1
59
9
+++ EPA MPG +++ +++
MIN
13
16
24
20
2O
14
28
24
28
HMAV
18
16
24
27
21
16
28
33
28
.8
.8
.3
.7
.3
4
. 1
2
6
MAX
29
17
24
42
26
28
28
45
3O
MIN
8
12
28
11
16
12
29
12
22
ROAD
MPG + + +
HMAV
16. 1
14
27
22
20
IS
29
27
26
.8
.7
.5
.2
.6
.3
4
7
MAX
31
2O
28
35
27
24
29
38
32
GPM
RATIO
1. 175
1 . 134
.877
1 .248
1 .O6I
1 .053
.958
1 .222
1 07 1
GPMR
SIGNF
OIFF»
83%
94%
96%
100%
98%
54%
95%
B-46
-------
TABLE B-6.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (PERCEIVED MPG). ALL YEARS
SOLUTIONS AT MIN/MAX EPA MPG
vemi^Lt
TECHNOL
R: A:C
R: A; I
R: A:D
R:M:C
R:M: I
F:A:C
F : A : I
F :M:C
F :M: I
KtllKt
E =
GPMR =
GPMR =
DATASET
GPMR =
GPMR =
GPMR =
OATASET
GPMR =
GPMR =
331UN tUUAIlUM
EPA 55/45 MPG R-VALUE
1 .017
.747
NOT
. 127
.835
9O6
NOT
. 569
4 .489
+ .O08KE ) . 168
+ .O23KE) .113
REGRESSIBLE
+ .0397(E) .403
+ .O1O5(E) .294
+ .OO87(E) . 2O5
REGRESSIBLE
+ .O194(E) .239
- . 1 196(E) .280
STD ERR GPMR= * EPA=
. 186 1.12 13
.127 1.11 16
24
.350 .91 2O
. O9 1 1 . O4 2O
. 173 1 .02 14
28
.319 1.O4 24
. 158 1.12 28
GPMR = 1
1 .25
1. 14
1 .79
1.11
1 . 15
1 .43
.96
> EPA =
29
17
24
42
26
28
28
45
30
MI.IUAI. Ur*MK
RANGE
.717
.881
.877
.781
.919
.881
.958
.768
.915
- 1.869
- 1 . 355
- .877
- 3.575
- 1 . 3O3
- 1.467
- .958
- 2.829
- 1.325
B-47
-------
TABLE B-6.3
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (PERCEIVED MPG). 1976
VEHICLE NUMBER
TECHNQL OF VEH
+++ EPA MPG +++
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFF9
R: A:C
R:A: I
67 19 23.5 29
14 17 17.2 17
14 19.9 29
14 15.0 2O
1 . 188
I . 143
27%
7554
R:M:C
R:M: I
1O9 2O 26.9 33
24 2O 21.3 26
12 22.1 35
16 2O.2 27
1.224
I .058
86%
1OO%
F :A:C
F:A:I
5 14 15.5 28
1 28 28.1 28
12 IS.O 24
29 29.3 29
1 .043
.958
92%
F :M:C
4O
24 32. 1
36
15 27.3
38
1 . 176
B-48
-------
TABLE B-6.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (PERCEIVED MPG). 1976
SOLUTIONS AT MIN/MAX EPA MPG
VCIIl CUE
TECHNOI.
R
R
R
R:
F
C ,
: A :C
: A: I
:M:C
:M: I
: A:C
:A: I
KtljKtaa IUN CUUAIlUrj
E = EPA 55/45 MPG
GPMR -
DATASET
GPMR =
GPMR =
GPMR =
DATASET
.754
NOT
.627
.834
.aoo
NOT
+ .O182(E
R-VALUE
) .257
REGRESSIBLE
+ .0219(E
+ .0104(E
* .0085(E
) . 193
) .294
) .412
REGRESSIBLE
STD ERR GPMR= O EPA =
. 194 1.11 19
17
. 305 1 . 06 2O
.092 1.04 2O
.136 1 .01 14
28
GPMR=
-------
TABLE B-6.S
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : EPA (PERCEIVED MPG). 1977
VEHICLE
TECIINOL
R: A :C
R: A: I
R:M:C
R:M: I
F : A:C
f :M:C
F:M:I
NUMBER
OF VEH
248
6
77
1
19
a
MIN
EPA MPG
HMAV
17 17.2
MAX
13 17.9 29
16 15.9 16
2O 28.8 42
22 21.9 22
17
33 35.9 45
28 28.5 29
+ + +
MIN
a
12
11
2O
12
12
22
ROAD
MPG
HMAV
15.3
14
23
19
16
27
26
.2
.O
.6
.2
.4
. 1
+ + +
MAX
31
17
35
2O
2O
38
3O
GPM
RATIO
1
1
1
1
1
1
1
. 173
. 1 13
.278
. 12O
. O63
.319
.091
GPMR
SIGNF
DIFF9
98%
77%
86%
84%
76%
92%
B-50
-------
TABLE B-6.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : EPA (PERCEIVED MPG). 1977
SOLUTIONS AT MIN/HAX EPA MPG
vtni cut
TECHNOL
R
R
R
R
F
F
F
: A:C
: A: I
:M:C
:M: I
: A :C
:M:C
:M: I
KtlJKt iS 1UN CUUAIILIN
E = EPA 55/45 MPG R-VALUE
GPMR =
DATASET
GPMR -
DATASET
DATASET
GPMR =
GPMR =-
1 .01 1
NOT
- .277
NOT
NOT
.741
12 .77
+ .OO88(E) . 158
REGRESSIBLE
+ 052B(E) .519
REGRESSIBLE
REGRESSIBLE
+ .O159(E ) . 177
+ .487 I(E) 34O
STO ERR GPMR = p EPA-
. 182 1 . 12 13
16
.402 .76 2O
22
17
.436 1.26 33
.154 .87 28
GPMR= 4» EPA-
1.26 29
16
1.94 42
22
17
1.45 45
1.36 29
A<_.IUAL UrMK
RANGE
.739
.957
.871
1 . 12O
.881
.871
. 94O
- 1
- 1
- 3
- 1
- 1
- a
- i
.869
.355
.875
. I2O
.467
.829
.325
B-51
-------
TABLE B-7.1
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORD (MEASURED MPG). ALL YEARS
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFF4»
INFLU
INFLU SIGNF
INDEX OIFF»
R:A:C 25445
R: A: I
R :M:C
F :M:C
382
355O
890
14 18.t 26
19 19.3 19
18 24.O 29
29 32.5 38
14.9 37
8 15.7 27
8 2O.1 34
II 29.7 56
« .220
1.227
t . 197
1 . 1O3
1OO%
89%
iooy.
100%
t . 191
I . 188
1 . 183
1 . 157
6754
91%
100%
B-52
-------
VEHICLE
TECHNOL
TABLE B-7.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENOENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : FORD (MEASURED MPG). ALL YEARS
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR = » EPA= GPMR= «• EPA =
ACTUAL GPMR
RANGE
R:A.C GPMR = 1 . O69 + .OOS1(E) .1O7
.217
I . 18
14
1 .28
26
.6O2 - 3.781
R:A:I GPMR = .266 + .O499(E) .032
.210
1 .20
19
1 .23
19
.699 - 2.331
R:M:C GPMR = .621 + .O237(E)
. 3O2
.210
1 .06
18
1.31
29
.693 - 3.55O
F:M:C GPMR = .389 + .O217(E ) .377
19O
1.O2 29
1 .22
38
.679 - 3.O27
B-53
-------
TABLE B-7.3
IN-USE FUEL ECONOMY OFFSET.: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORO (MEASURED MPG). 1978
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN IIMAV MAX
ROAD MPG
MIN IIMAV MAX
GPMR
GPM SIGNF
RATIO OIFFO
INFLU
INFLU SIGNF
INDEX DIFFO
R:A:C 9654
14 17.4 26
13.6 34
1.262 65%
I.196 97%
R:M:C
BBS
19 23.9 29
8 18.9 32
t.267 89%
1 . 19O
F :M:C
269
38 38.I 38
13 31.O 56
1 231 1OO%
1.142 1OO%
B-54
-------
VEHICLE
TECHNOL
TABLE B-7.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENOENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : FORD (MEASURED MPG). 1978
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR= » EPA = GPMR* » EPA=
ACTUAL GPMR
RANGE
R:A:C GPMR = 1.125 + .OOBfl(E) .115
.224
1 .24
14
I .35
26
.6O9 - 3.262
R:M.C GPMR = .931 + .OI38(E) .174
. 241
t . 19
19
1.33 29
.8O1 - 3.S5O
F:M:C DATASET NOT REGRESSIBLE
38
38
.679 - 3.O27
B-55
-------
TABLE B-7.5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORO (MEASURED MPG). 1979
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
M1N HMAV MAX
ROAD MPG **+
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFFf
INFLU
INFLU *SIGNF
INDEX OIFFff
R:A:C 9 122
14 17.4 24
15.2 37
(.145 99% 1.174 92%
R:M:C
127O
18 22. 1 25
9 2O.3 34
1.094 100% 1.153 99%
F :M:C
204
32 32.1 32
23 31.3 43
1.O27 1OO%
1.093 100%
B-56
-------
VEHICLE
TECHNOL
TABLE B-7.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENOENT ANALYSIS
... 13 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORD (MEASURED MPG). 1379
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR = 9 EPA- GPMR=
-------
TABLE B-7.7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : FORD (MEASURED MPG). 198O
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO OIFF0
INFLU
INFLU SIGNF
INDEX OIFFP
R: A:C
R: A: I
R:M:C
F :M:C
6668
382
1394
417
17 2O.6 25
19 19.3 19
22 26.O 29
29 29.8 3O
16.7 32
8 15.7 27
IO 2O.9 34
It 28.3 43
1.234
1 .227
1.247
1 .057
89%
20%
1OO%
1OO%
I .209
1 . 188
1 . 2O6
1 . 198
28%
96%
44*
725C
B-5S
-------
VEHICLE
TECIINOL
TABLE B-7.8
IN USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORD (MEASURED MPG). 198O
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR= » EPA° GPMR= f EPA =
ACTUAL GPMR
RANGE
R:A:C GPMR = .836 + .O192(E)
187
. 2O8
1 . 16
17
1.32 25
.644 - 3. IIS
R:A:I GPMR = .266 + .O499(E) .032
.210
1 .20
19
1 .23
19
.699 - 2.331
R:M:C GPMR = .72 I + .O2O1(E)
iaa
198
1.16 22
1.3O 29
.734 - 2.745
F:M:C GPMR = 3.23O - .O728(E) .133
193
1 . 12
29
1 .05
30
.696 - 2.732
B-59
-------
TABLE B-7.9
DATA BASE CHARACTERIZATION: FUEL ECONOMV INFLUENCES
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : FORD (MEASURED MPG). I9BO
FUEL ECON
INFLUENCE
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
WIND.MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT .CITY
POPUDENS
TEMPTURE
CITYWIND
HWAYWIND
TPOGRAFY
HWYSPEED
CITYROAD
HWAYROAD
VEHICLE
R: A:C
J.
3386
52
54
8519
56
1O
R: A:C
1 .08
1 .02
1 .OO
C 01
1 .00
1 .Ol
1 .06
1 .Ol
1 .02
1 .06
1 .07
TECHNOLOGY :
R: A: I R: A:D
4252
53
53
8907
52
II
R: A: I R: A:D
1 .06
1 .Ol
.99
1 .01
1 .02
1.01
1 .06
1 .OO
1 .02
1 .06
1 .07
R:M:C R:M: I
3752
47
St
85O6
55
10
R:M:C R:M: I
1 .07
1 .02
.99
1 .Ol
1 .Ol
1 .01
1 .06
1 .02
1 .02
1 .06
1 .07
R:M:D F:A:C F:A:I F:A:D F:M:C F:M:I F:M:D
2855
45
5O
8374
57
1O
R:M:D F:A:C F:A:I F:A:0 F:M:C F:M:I F:M:D
1 . IO .
1 .Ol
.98
I.OI
1 .OO
I.OI
1 . 06
I.OI
1 . 02
1 . 06
1.O7
TOTINFLU
I .21
1 19
1.21
1 .20
B-6O
-------
TABLE B-8.1
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG), ALL YEARS
TECHNOL
R: A :C
R : A : I
R:A:D
R:M:C
R:M: I
R:M:D
F : A:C
F :A: I
F : A :D
F:M:C
F : M : I
F :M:D
k || IILJD C O
IMUMO t K
OF VEH
7O37
157
239
1 151
I2O
23
891
91
66
449
78
55
+ + +
MIN
t 1
15
22
14
19
3O
1O
17
25
24
21
32
EPA MPG
HMAV
17 8
18 . 1
25.8
25.4
26. 1
3O.8
19.4
18.8
25.2
3O. 1
28 .6
4O.6
-» -f +
MAX
32
29
30
42
32
31
32
28
26
41
32
47
+ + +
MIN
7
a
13
9
14
24
6
9
16
16
17
24
ROAD MP(
HMAV
14.9
14.7
23.2
22.3
23.9
29.6
16.3
15.9
20. a
28.4
27.4
38. 1
MAX
38
31
4O
59
49
35
37
3O
32
46
37
49
RATIO
1 . 2O6
1.223
1.112
1 . 146
1 .094
1 -O43
t . 199
1 . 17O
1 .210
1 .064
1 .044
t .06 7
GPMR
c i r*KiP
3 1 uNr
OIFKs*
100%
93%
1OO%
1OO%
100%
100%
82%
63%
66%
100%
100%
100%
| |L |C | ||
1 Nr t_U
INDEX
1 . 165
1 . ISO
1 . 093
1 . 171
1 . 1 16
1 . 133
t .227
t . 2OB
1 . tat
1 . 17O
1 . 174
1 . OB6
INFLU
C ¥ /"MF
bluNr
OIFF0
97%
53%
100%
13%
100%
77%
100%
9O%
35%
3%
15%
1OO%
B-61
-------
TABLE B-8.2
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). ALL VEARS
SOLUTIONS AT MIN/MAX EPA MPG
VCIIII.LC
TECHNOL
R: A :C
R . A : I
R: A :D
R:M:C
R:M: I
R:M:D
F : A:C
F :A: I
F : A :D
F :M:C
F :M: I
F :M:D
KCUKC
E =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
iSlUI-J CUUAIIUN
EPA 55/45 MPG
1 .O1O +
1.565 -
1 . 198 -
.942 +
.930 +
.465 +
1 . 1OO +
1.426 -
1.40B -
.777 *
.683 *
1.024 *
.0106(E)
.OIB5(E)
.OO33(E)
.0077(E)
. 006 1 ( E )
.0187(E)
.0046(E)
.O133(E)
. OO78(E )
.0094(E)
.OI26(E )
.OOII(E)
R-VALUE
. 186
.250
. O38
.217
. 167
. 118
. 134
.202
.035
.270
.238
O4B
STD ERR
.2OO
221
. 152
. 186
. 16O
.111
. 194
.210
. 169
. 146
. 1O8
. 1 15
GPMR = 0 EPA =
1 . 13
1 . 29
1 . 12
1 .OS
1.05
1 .02
1. 15
1 .20
1.21
1 .00
.95
1 .06
11
15
22
14
19
3O
to
17
25
24
21
32
GPMR- 4
1 .35
t .03
1 . 1O
1 .27
1 . 13
t .OS
1 .25
1 .05
1 .20
1 . 16
1 .08
1 .07
» EPA =
32
29
3O
42
32
31
32
28
26
4 1
32
47
M^IUAL. UrMK
RANGE
.641
.714
.731
.713
.667
.885
.678
. BO1
.828
.672
.815
.835
- 2.4O9
- 2.OB9
- 1.828
- 2.627
- 1.622
- 1.332
- 2.O21
- t . 96O
- 1.586
- 1.815
- 1.297
- 1.357
B-62
-------
TABLE 8-83
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). 1975
VEHICLE
TECHNOL
NUMBER
OF VEH
EPA MPG
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO OIFF*
R: A:C
1883
II 15.O 24
13.1 31
1 152 99%
R :M:C
172
14 20.8 24
1O 19.6 32
1.064 100%
F : A:C
151
IO 12.5 13
11.2 16
1.116 9O%
B-63
-------
VEHICLE
TECHNO I.
TABLE 8-8.4
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENOENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : GM (MEASURED MPG). 1975
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR = • EPA* GPMR = e EPA =
ACTUAL GPMR
RANGE
R:A:C GPMR = 1.OB9 + .O041(E) .O56
192
I . 13
t. ta
24
.641 - 2.4O9
R:M:C GPMR = .965 + .OO46(E) .087
. 173
1 .03
14
1.O8 24
.723 - 1.663
F:A:C GPMR = .4O9 + .O563(E) . 1 IO
181
.96
IO
1 . 14
13
.754 - 1.717
B-64
-------
TABLE B 8 5
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : GM (MEASURED MPG). 1976
EPA MPG
VEHICLE NUMBER
TECHNOL OF VEH MIN HMAV MAX
+++ ROAD MPG +++
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO DIFFe
R: A:C
R: A: I
R:M:C
F : A:C
131 1
73
319
1OO
12 17.O 29
15 16.4 17
14 24.6 33
12 13.8 14
13.9 3O
8 12.6 17
9 21.4 33
B 11.5 16
t .224
1.311
t . 158
1 . 194
99%
10O%
10O%
64V.
B-65
-------
VEHICLE
TECHNO I.
TABLE B-B.6
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-QEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). 1976
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STO ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR* «• EPA- GPMR = $> EPA-
ACTUAL GPMR
RANGE
R:A:C GPMR = I.O89 + .OO77(E) .128
210
1.18 12
I .31
29
.74O - 2.248
R:A:I GPMR = .473 + O5O8(E) .182
.225
1.23 15
I .33
17
.932 - 2.O89
R:M:C GPMR = .943 + .OO84(E)
.244
171
1.O6
14
1.22 33
.744 - I.818
F:A:C GPMR = . 64O + .O4OKE) .152
168
1.11
12
1 .21
14
.889 - 1.729
B-66
-------
TABLE B-8 7
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : GM (MEASURED MPG). 1978
EPA MPG
VEHICLE
TECHNOL
NUMBER
OF VEH
MIN HMAV MAX
ROAD MPG
MIN HMAV MAX
GPMR
GPM SIGNF
RATIO OIFF»
R: A:C
1433
13 19.2 27
8 15.8 3O
1.218 38%
R: A:D
38
22 24.1 24
18 21.2 26
1 . 134 100%
R:M:C
51
17 3O.B 34
13 24 . 1 36
1.296 96%
B-67
-------
VEHICLE
TECHNOL
TABLE B-8.8
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-DEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). 1978
REGRESSION EQUATION
E = EPA 55/45 MPG
R-VALUE STD ERR
SOLUTIONS AT MIN/MAX EPA MPG
GPMR- O EPA' GPMR = V EPA=
ACTUAL GPMR
RANGE
R:A:C GPMR = 1.O2O + .OIOI(E) .137
. 182
1 . 15
13
1.2,9
27
.8O4 - 2.361
R:A:D GPMR = 1.817 - O2B3(E) .173
. 102
1 . 19 22
t . 13
24
.947 - 1.341
R:M:C GPMR = BO4 + .OI55(E) .283
.254
1 .07
17
1.33 34
.845 - 2.627
B-68
-------
TABLE B-8.9
DATA BASE CHARACTERIZATION: FUEL ECONOMY INFLUENCES
. . 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : GM (MEASURED MPG). 1978
FUEL ECON
INFLUENCE
ODOMETER
MILEPDAY
PCT .CITY( » )
POPUDENS
TEMPTURE
WIND.MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT .CITY( • )
POPUDENS
TEMPTURE
CITYWIND
HWAYWIND
TPOGRAFY
HWYSPEED
CITYROAD
HWAYROAD
TOTINFLU
VEHICLE
R: A:C
6426
48
55
4679
64
a
R:A:C
1 .Ot
1 .03
1 :OO
.99
.97
1 .01
1 .OS
1 .01
1 .02
1 .05
1 .06
1 . 10
TECHNOLOGY:
R: A: I R: A:O
1 1309
66
55
5844
71
8
R: A:D
.99
96
1 .00
.99
.94
1 .Ol
1 .05
1 .03
1 .02
1 .04
1 .06
.99
R : M : C R : M . I R : M : D F : A : C F : A : I F : A : D F : M : C F : M : I F : M : D
683O
41
55
4564
62
9
R:M:C
1 .Ol
1 .03
1 .00
.99
.97
1 Ol
1 .05
1 .03
1 .Ol
1 .05
1 .06
1 . 12
55% CITY ASSUMED; NOT IN DATA BASE.
B-69
-------
TABLE B-8 IO
IN-USE FUEL ECONOMY OFFSET: NON-MODAL CONSTANT-FACTOR ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). 198O
VEHI CLE
TECHNOL
R: A :C
R:A: I
R: A:D
R:M:C
R . M : I
R:M:D
F:A:C
F :A: I
F:A:D
F :M:C
F :M: I
F :M:D
NUMBER
OF VEH
24 1O
84
20 1
6O9
120
23
64O
91
66
449
78
55
+ + +
MIN
15
18
25
16
19
3O
18
17
25
24
21
32
EPA MPG
HMAV
20.5
19.9
26.2
27. 1
26. 1
3O.8
24. 1
18.8
25.2
30. 1
28.6
4O.6
+ + *
MAX
32
29
3O
42
32
31
32
28
26
41
32
47
+ + +
MIN
8
11
13
12
14
24
IO
9
16
16
17
24
ROAO MP(
HMAV
16.7
17.3
23.7
23.6
23.9
29.6
19 8
15.9
2O. 8
28.4
27.4
38. 1
3 +++
MAX
38
31
4O
59
49
35
37
3O
32
46
37
49
RATIO
1 .232
1 . 147
1 . 1O7
1 . ISO
t .094
1 . 043
1 .219
1 . I7O
t .210
t . O64
t .044
1 . 067
GPMR
C I f*KIC
b luNr
OlFfV
1OO%
93%
100%
1OO%
10O%
10O%
100%
55%
73%
100%
10O%
100%
I MF 1 II
INr LU
INDEX
1 . 199
1 . 173
1.111
1 . 176
1 116
1 . 133
1 .249
1 . 2O8
1 . 181
1 . 17O
1 . 174
t . O86
INFLU
C Y /"*MC
b luNr
DIFFO
89%
61%
100%
93%
1OO%
94%
1OO%
51%
32%
98%
60%
100%
B-7O
-------
TABLE B-8 1 I
IN-USE FUEL ECONOMY OFFSET: NON-MODAL MPG-OEPENDENT ANALYSIS
... 12 VEHICLE TECHNOLOGY STRATA . . .
DATA SOURCE : GM (MEASURED MPG). 19BO
SOLUTIONS AT MIN/MAX EPA MPG
VtmiLt
TECHNOL
R: A :C
R: A : I
R . A : 0
R:M:C
R:M: I
R:M:0
F :A :C
F :A : I
F :A :D
F :M:C
F :M: I
F :M:D
REGRESSION EQUATION
GPMR =
GPMR =
GPMR »
GPMR =
GPMR =
GPMR =
GPMR =
GPMR =
GPMR -
GPMR =
GPMR =
GPMR =
.972 +
1 .271 -
1.112 -
1 .063 +•
.930 +
.465 *
1 .329 -
1 .426 -
1.4O8 -
.777 +
.683 *
1 .024 +
O125(E )
. OO6 1 ( E )
. OOO2 ( E )
.O03HE)
.OO6I(E)
.O187(E)
.0045(E)
.0133(E)
.0078(E)
. 0094 ( E )
.OI26(E )
.001 HE)
R-VALUE
. 162
. O9S
.OO2
O83
. 167
118
. O65
.202
.035
.270
.238
.048
STO ERR
2O9
.200
. 159
. 184
. 16O
.111
. 198
.2 1O
. 169
. 146
. 1O8
. 1 15
GPMR =
1 . 162
1 . 161
1 . 108
1 . 1 14
1 .045
1 .022
1.250
1 .203
1.215
1 .OO5
.948
1 . 058
«PA =
15
18
25
16
19
3O
18
17
25
24
21
32
GPMR =
1 .369
1 .093
1 . 107
1 . 195
1 . 128
1 . 054
1 . 183
1 .054
1 . 2O3
1 . 161
1 .084
1 .074
AljIUMU urMN
-------
TABLE B-8. 12
DA1A BASE CHARACTERIZATION: FUEL ECONOMY INFLUENCES
... 12 VEHICLE TECHNOLOGY STRATA ...
DATA SOURCE : GM (MEASURED MPG). I98O
FUEL ECON
INFLUENCE
ODOMETER
MILEPDAY
PCT CITY
POPUDENS
TEMPTURE
WIND MPH
INFLUENCE
INDEXES
ODOMETER
MILEPDAY
PCT. CITY
POPUDENS
TEMPTURE
CITYWIND
IIUAYWIND
TPOGRAFY
HWYSPEED
CITYROAD
HWAYROAO
VEHICLE
R: A:C
3525
44
51
4282
54
t t
R: A :C
1 .05
1 .06
.99
.99
1 .OO
1 .01
1 . 06
1 .01
t .02
1 .05
1 .06
TECHNOLOGY
R: A: I R:
4885 4
43
54
46O6 4
56
to
R:A: I R:
1 .03 1
1 .03 t
t .OO
.99
1 .OO
IOI 1
1 .06 1
1 .02 t
1 .02 1
t .04 1
1 .06 1
A:D
7 18
58
45
176
57
IO
A:0
.03
.00
.97
.99
.99
.01
.06
.03
.02
04
.06
R:M:C
4O82
48
52
4O8O
53
t 1
R:M:C
1 .04
1 .03
.99
.99
1 .01
1 .Ol
1 .06
t .02
1 .02
1 .OS
1 .06
R:M: 1
5427
44
48
4453
57
to
R:M: I
1 .02
1.01
.98
1 .OO
1 .OO
1 .01
1 .06
1 .02
1 .03
1 .04
1 .06
R:M:D
6037
52
45
5177
52
to
R:M.O
1 .02
t .OO
.97
1 .OO
1 .02
1 .01
1 .06
1 .02
t .03
1 .04
t .05
F:A:C
297O
38
56
4822
53
1 t
F:A:C
1 .06
1 .07
1 .OO
.99
1 .01
1 .01
1 .06
1 .02
1 .02
t .OS
1 .06
F:A:I
36 IO
38
55
5338
54
IO
F:A: I
t .05
1 .OS
1 .OO
t .OO
1.01
1 .Ol
t .06
t .02
1 .02
1 .04
1 .06
F : A:D
3485
42
54
4862
57
to
F: A:D
1 .05
1 .03
1 .OO
.99
1 .OO
1 .Ol
1 O6
t O2
1 .02
1 .05
1 .06
F:M:C
4 184
49
51
4388
52
1 t
F :M:C
1 .04
1 .02
.99
.99
1 .Ol
t .01
1 .06
1 .03
1 .02
1 .OS
1 .06
F:M:I
5082
40
57
4OI6
S3
to
F:M: I
t .03
1.O3
1.01
.99
1.01
1.01
1 .06
t .02
t .02
1 .OS
1 .06
F:M:D
6814
58
1 55
3588
59
IO
F:M:D
1 .Ol
.98
1 .OO
.99
.99
1.01
1 .06
1 .02
1 .02
1 .OS
1 .06
TOFINFLU
.20
1 . 17
1.11
1 . 18
1 . 12
t . 13
1 .25
1.21
I . 18
1 . 17
1 . 17
1 .09
B-72
-------
This page intentionally
blank and un-numbered
-------
APPENDIX C '
ANALYSIS OF PERCEIVED VS. MEASURED FUEL ECONOMY
-------
This page intentionally
blank and un-numbered
-------
APPENDIX C
Analysis of Perceived vs. Measured Fuel Economy
Two kinds of analysis were performed to investigate the detailed relationship
between perceived fuel economy and measured fuel economy. The motivation
behind this investigation was to assess whether perceived fuel economy data is
appropriate for development of valid in-use MFG adjustment factors.
The Emission Factors Postcard Survey was used in the first analysis; it is the
only data source which contains both perceived and measured fuel economies on
the same cars. In the second analysis, the in-use fuel economy offsets of
those data sources including only perceived data were compared to the offsets
of the purely measured sources.
A. ANALYSIS OF EMISSION FACTORS POSTCARD SURVEY
The Emission Factors Postcard Survey used in this analysis contains data as of
October 28, 1981 from the Fiscal Year 1979 and 1980 Emission Factors (EF)
program. This data set contains data from 1011 vehicles from 14 manufacturers,
seven different sites, and seven model years (1975-1981).
Measured/Perceived Fuel Economy Ratios
Measured/Perceived fuel economy ratios for each vehicle were calculated for
both city and highway perceived MPG figures. These ratios are defined below:
M Measured MPG
PC Perceived City MPG
M_ _ Measured MPG
Ph Perceived Highway MPG
Four different analyses were used to determine two-mode relationships between
measured 'and perceived fuel economy from the EF Postcard data. A brief
description of each analysis follows:
C-l
-------
0 Measured/Perceived Two-Mode Fuel Economy Ratios, regression approach -
This analysis derived average Measured/Perceived fuel economy ratios, as
defined above, for those vehicle technology strata in the EF Postcard
data base. City M/P ratios and highway M/P ratios were extracted from
the data by regressing against urban fraction.
0 Measured/Perceived Two-mode Fuel Economy Ratios, mode-specific approach
This analysis derived average Measured/Perceived FE ratios for those
vehicles that were "city-driven" (75-100% urban fraction) and those that
were "highway-driven" (0-25% urban fraction); this technique, as
contrasted with the first analysis, does not use all of the data, but it
does derive two-mode M/P factors directly rather than by regression
against urban fraction.
0 Histograms were run to illustrate distributions of AMPG (Perceived MPG -
Measured MFC) and Measured/Perceived Ratios for both city and highway
driving modes.
0 MPG Dependence of City and Highway M/P Ratios - M/P ratios were derived
for Low-MPG and High-MPG subsets of the "City" and "Highway" cars from
the previous analysis, to determine whether the measured-to-perceived
relationship exhibits a dependence on MPG level.
1. Regression Approach
Measured/Perceived ratios were regressed against urban fraction. The
regression equations are listed in Table C-l for four technology strata.
Regressions for the individual fuel injection strata (RAI, RMI, FAI, FMI)
are not presented since the sample sizes were too small. Instead,
regression equations at the less specific "fuel injection" level are
presented. The, city and highway M/P ratios were determined by solving the
regression equations as noted in the footnote in Table C-l.
C-2
-------
Table C-l
Regressions of M/P Ratios Against Urban Fraction
(X = urban fraction)
City
Overall
RAG
RMC
FAC
FMC
Fuel
Injection
Highway
Overall
RAG
RMC
FAC
FMC
Fuel
Injection
Regression Equation
1.0813 - .0907X
1.0438 - .0546X
1.2152 - .2760X
0.9823 - .0137X
1.2186 - .2152X
0.9075 + .2009X
0.9470 - .0853X
0.9405 - .1044X
(Average of M/P Ratios)
0.8713 - .0492X
0.7355 + 1.088X
1.068 + .5832X
Solution* F-STAT
1.00 1.6939
.99 0.3004
.99 2.7081
.97 0.0101
1.04 1.9352
1.07 .2974
.94 0.2890
.93 0.57031
** .96
.87 0.0079
.94 1.2383
.96 2.4674
SIGNIF
.1940
.5843
.1075
.9205
.1751
.5910
.5923
.4540
.9307
.2895
.1673
* Solutions based on the weighted average of the "0" and ".25" urban
fraction levels for "highway" and the ".75" and "1" level for city.
** Not regressible since the independent variable, X, for all highway RMC's
were equal.
C-3
-------
2. Mode-Specific Approach
The M/P ratios determined by the second method (mode-specific vehicles) are
given in Table C-2. Table C-2 contains M/P ratios that are mode-specific.
That is, the average city M/P ratios are based on perceived fuel economy for
cars with urban fractions between 0.75 and 1.00, while M/P highway ratios
are based on perceived fuel economy for cars with urban fractions between 0
and 0.25. The city M/P ratios calculated using the urban specific approach
shows, as did the first analysis, that perceived city fuel economy is not
appreciably different from measured fuel economy for city driving, in most
cases. The attained SIGNIF values are high, indicating these ratios are not
significantly different from 1.00 (M/P = 1).
The highway M/P ratios still indicate overperception of in-use FE when
compared against measured FE, in most instances. However, the sample sizes
preclude definitive conclusions at high confidence about highway M/P ratios.
3. Histograms of AMPG and Measured/Perceived Fuel Economy Ratios
Histograms of M/P ratios and AMPG (Perceived MPG-Measured MPG) are shown as
figures C-l through C-12. This is important, since an underlying assumption
used throughout these analyses was normality. The M/P histograms for both
city and highway perceived MPG indicate the assumption of normality was not
justified. The histograms for both city and highway M/P ratios indicate
either positive kurtosis of the data — a violation of the normality
assumption — or too few points for conclusive determination. The
histograms of AMPG show the same types of patterns as those for M/P ratio.
C-4
-------
Table C-2
Measured/Perceived Ratios - Mode Specific
M/P Ratio
City (.75-1.00 urban fraction)
Overall
1.00
Sample
Size
(332)
SIGNIF
.6105
RAG
RMC
FAC
FMC
Fuel Injection
.99
.99
.99
1.03
1.07
(196)
(43)
(36)
(33)
(24)
.8046
.5662
.7275
.0863
.0611
Highway (0-.25 urban fraction)
Overall
.93
(91)
.0000
RAG
RMC
FAC
FMC
Fuel Injection
92
96
36
99
98
(48)
(8)
(14)
(13)
(8)
.0000
.6177
.0013
.8424
.6598
C-5
-------
n
city
X
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X X
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Rx
I I I I I I I I
1.3 1.4 1.5 1.6 .5 .6 .7 .8
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^
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t
tx
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X XI X XI
x H x H M
M X| Xi Xj M M W
1 1 1 1 1 1 1
.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
MPG Ratio
FIGURE
C-2
Histograms off Measured/Perceived Ratios: RAC
-------
C»ty
n
oo
I I I I I I I I I I I
.6 .7 .8 .;9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
Highway
I
.5
.6
I I I I I I I I I I
.7 .8 .9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
FIGURE
C-3
Histograms of Measured/Perceived Ratios: RMC
-------
City
o
VO
.5
I r
.6 .7
.8 .9
r
1.0
I
1 T I I
Highway
1.1 1.2 1.3 1.4 1.5 1.6
.5
I
.6
I
.7
I
.8
MPG Ratio
I I I I I I I I
.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
FIGURE
C-4
Histograms of Measured/Perceived Ratios: FAC
-------
i
M
O
City
J , , , II II I I I I
.5 .6 .7 .8 .9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
Highway
I I i I i I I i i i r
.5 .6 .7 .8 .9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
FIGURE
C-5
Histograms of Measured/Perceived Ratios: FMC
-------
City
MM
Highway
o
i
I I I I I I I I I I I I
.5 .6 .7 .8 .9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
I I I I I I I I I I I I
.5 .6 .7 .8 .9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
MPG Ratio
FIGURE
C-6
Histograms of Measured/Perceived Ratios:
FUEL INJECTION
-------
City
xs X >
X
X
X
X
V^> ^J
xZ
. .A. X X
C X X X X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
|xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx>
s*,
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X .
X
X
X X
X X
X X <->.
X X X X X
X X X X X -.'
o
I
I I I I I I I I I I I I
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
Highway
X
X X
*» ?%
X X X X
x ^v x >c ii£ !K.
x x x x X X
X X X X X X
1 I I I
-10 -8-6-4
I
0
I
2
I
20 2 4
MPG Difference
I
6
8 10
1
12
FIGURE
C-7
Histograms of MPG Difference (Perceived MPG-Measured MPG):
ALL TECHNOLOGIES
-------
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X X
XXX
MPG Difference
I I I I I I I I I I I I
-10 -8 -6-4-202 4 6 8 10 12
MPG Difference
FIGURE
C-8
Histograms of MPG Difference (Perceived MPG-Measured MPG): RAC
-------
City
O
I I i I I I I I I I I I
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
Highway
i i i i i i i i i i i r
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
FIGURE
C-9
Histograms of MPG Difference (Perceived MPG-Measured MPG): RMC
-------
City
o
I
I I I I I I I I I
-10 -8-6-4-20 2 4 6
MPG Difference
Highway
I I
8 10 12
1^I I I I I I I
-10 -8 -6-4-20 2 4
MPG Difference
I
8
I I
10 12
FIGURE
C-10 Histograms of MPG Difference (Perceived MPG-Measured MPG): FAC
-------
City
I
M
O\
I i i i i i r iirir
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
Highway
M
M
i i i i i i i i i i r r
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
FIGURE
C-11
Histograms of MPG Difference (Perceived MPG-Measured MPG): FMC
-------
City
r-f *->« ir*
w H H H H
w M M M M
N
o
I I I I I I I 1 I I I I
-10 -8-6-4-20 2 4 6 8 10 12
MPG Difference
Highway
I I I I I I
-10 -8 -6 -4 -2 0
I I I
246
MPG Difference
I I I
8 10 12
FIGURE
C-12
Histograms of MPG Difference (Perceived MPG-Measured MPG):
FUEL INJECTION
-------
4. MPG Dependence
Table C-3 presents mode-specific M/P ratios split over low and high EPA MPG
ranges to determine if MPG dependence exists. These M/P ratios were
compared against the average urban specific M/P ratios from Table C-2 to
determine if a significant difference was evident. The attained
significances indicate no difference at a high confidence level, implying
little if any MPG dependence for both city and highway M/P ratios. The
large attained SIGNIF value could also be attributed to the small sample
size for some strata levels.
C-18
-------
Table C-3
MPG Dependence For Measured/Perceived Ratios
Cicy Low
Overall
RAG
SMC
FAC '
FMC
Fuel Injection
Range
8-17
10-17
15-24
11-21
20-27
9-17
N
177
107
18
15
13
11
M/P Ratio
.99
1.00
.94
.96
1.02
1.07
SIGNIF*
.5029
.8078
.1024
.1228
.5416
.9313
High Range
18-35
18-30
25-37
22-31
28-39
18-27
N
155
89
26
21
20
13
M/P Ratio
1.01
.99
1.03
1.02
1.04
1.06
SIGNIF*
.2531
.7658
.1197
.3217
.7760
.8876
gjgnway
Overall
: 13-27
50
.91
.1565
28-50
40
.96
.2871
RAG
RMC
FAC
FMC
Fuel Injection
14-23
25-35
16-29
30-39
17-30
16
3
3
8
3
.90
1.08
.80
1.05
1.04
.3517
.6101
.3580
.6705
.7107
24-38
36-49
30-38
40-51
31-43
32
5
11
5
5
.93
.90
.38
.88
.94
. .5547
.0131
.6858
.1191
.1888
* SIGNIF values presented are the attained SIGNIF for a two-sided t-cest using che urban
specific M/P ratios in Taaie c-2 as che null hypothesis (e.g., for overall city Ho: M/P = 1.00),
•C-19
-------
B. GENERAL ANALYSIS
Since the EF Postcard data base includes both perceived and measured MPG on
the same cars*, it provided a basis for accurate comparison of the two
figures. Unfortunately, the small size of that data base makes its
statistical significance marginal, as was pointed out above. High
technologies are particularly sparsely populated: there are less than 200
non-RAC cars with perceived MPG figures. Less than 30 of these are fuel
injected, and there are no Diesels at all.
The other two sources of perceived MPG data (earlier Emission Factors and
DOE/J.D. Power) lack the feature of including measured MPG along with the
perceived figures, so no direct car-by-car comparison is possible. However,
there are over 1100 non-RAC cars between these two sources; more than 300 of
these are fuel injected, and there are 131 Diesels.
Hence it is important to look for perceived-to-measured relationships using
data from these two sources. The method of inquiry necessarily involves
comparing their results with the results of all of the measured-MPG sources.
Figures C-13 through C-23 are analysis-space plots of the perceived-MPG data
averages, by vehicle technology class. Neither the perceived data nor the
measured data are collapsed into single points for the comparison. The
measured data are illustrated by shaded areas which enclose the average
model type values for each separate data source and model year; two separate
shaded areas are shown for each of the two groups of model years discussed
earlier (1975 plus 1979-81, and 1976-78). Similarly, the two perceived data
sources' averages are shown individually, with the same model year
bifurcation as used for the measured data (the earlier Emission Factors
perceived data are all from the 1976-78 group, and the DOE/Power data cover
1978 and 1979: one entry for each of the two model year groups.) This
permits evaluation of each of the perceived source/year group cells against
comparable measured source/year group results.
*Perceived MPG figures are not available for every car in the EF Postcard
data base; less than 60% of the respondents gave perceived MPG figures.
C-20
-------
FUEL CONSUMPTION
RATIO
1.50
Q.
o
93
Q.
UJ
•I-
E
ra
o
as
1.40
1.30
1.20
1.10
1.00
0.90
1
TECHNOLOGY
-RAC-
(N 5elO CARS)
' 1
I
1 1
i\A
i i i
Legend
(f Th = Consumer-Measured Mpt,
M. JJX U7S-77-78 Models
^sPHi^ = Consumer-Measured flpt,
NSiaiSX 1975 & 1979^1 Models
E = Perceived Mpf
(EPA). 1976-77 Models
0 — Percieved Mpt
(DOE), 1978 Models
1 = Perceived MPI
(OOE). 1979 Models
.A, A = Fleet Mpf
(model year labeled)
E,D,A should be inside dj
1, A should be inside <£|
ID
—
1
10
20 30 40
EPA Composite (55/45) Mpg
50 60
EPA
FUEL ECONOMY
Figure
C-13
Perceived Mpg Data, Fleet Mpg Data, and
Consumer-Measured Mpg Data for RAC Vehicles
C-21
-------
FUEL CONSUMPTION
RATIO
1.3U
1.40
E 1.30
a.
01
—
60
(55/45) Mpg EPA
FUEL ECONOMY
Figure
C-14 Perceived Mpg Data, Fleet Mpg Data, and
Consumer-Measured Mpg Data for RMC Vehicles
C-22
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
1.30
§.
a
o
•I-
E
a.
1.20
1.10
1.00
0.90
TECHNOLOGY
-FAC-
(N * 10 CARS)
Legend
O'
= Consumer-Measured Mpf.
1976-77-78 MoUls
Consumer-Measured Mp|.
19751 197WlMocen
E = Perceived ttpt
'EPA. 1976-77 MOOCH
0 = Percieved Mot
•OOE, l978Moceis
I = Perceived Mot
OOE.' 1979 Models
A,_ = Fleet Mot
•noae* vear
E,D
should be inside
srioutd be inside
I
I
10
20 30 40
EPA Composite \55 45^ Mug
50
60
EPA
FUEL ECONOMY
Figure
C-15
Perceived Mpg Data, and Consumer-Measured
Mpg Data for FAC Vehicles
C-23
-------
FUEL CONSUMPTION
RATIO
i.OU
1.40
E 1.30
o.
93
O
d.
0
2 1-20
Q_
LU
•1-
0.
CJ5
T3
O
08 1.10
1.00
0.90
c
1 1
TECHNOLOGY
-FMC-
(N a 10 CARS)
Legend
(\ "X = Consumer-Measured Mpt
M, . S 1976-77-78 Models
O = Consumer-Measured MPI,
1975 & 1979-81 Models
E = Perceived MPI
IEPA). 1976-77 Models
D = Percieved Mp(
(DOE). 1978 Models
1 = Perceived MPI
_ (OOE). 1979 Models
. A, A = Fleet MPI
(model year laMled)
E,D should be inside d_| [j)
1 should be inside ^jjjjj}
_
_
U
C III
) 10 20 30 40 50 60
EPA Composite (55/45) Mpg EPA
FUEL ECONOMY
Figure
C-16
Perceived Mpg Data, and Consumer-Measured
Mpg Data for FMC vehicles
C-24
-------
FUEL CONSUMPTION
RATIO
1.3U
1.40
E 1.30
O.
CD
!
g
1976-77-78 Models
(J-^SsSSjl^ = Consunnr-tleasured Mpf,
y*iiiiiii>/ 1975 & 1979^1 Models
E = Perceived KPI ""
IEPAI, 1978-77 Models
0 = PercievedMpf
(OOei. 1978 Models
1 = Perceived MPI
(OOE). 1979 Models
A, A = Fleet Mp| —
(model yeir laoeied)
E,D should be inside (JJTJJ)
• ^t^^^^X
1 should be inside \£^£y
\ -1
\
|
/
J
1 I 1 1 1
0 10
Figure
20 30 40 50 60
EPA Composite (55/45) Mpg EPP
FUEL ECONOMY
C-17 Perceived Mpg Data, and Consumer-Measured
Mpg Data for RAI Vehicles
C-25
-------
FUEL CONSUMPTION
RATIO
1.50
E
Q.
CJ3
o
Q.
O
•I-
E
Q.
CJ
oo
O
Q=
1.40
1.30
1.20
1.10
1.00
0.90
1 1 i
TECHNOLOGY
-RMI-
(N alO CARS)
—
f\ •
1 1 1
Legend
fT\\ 1 [Th = Consumer-Measured Mp(,
VLI | | W 1976-77-78 Models
O= Consumer-Masured Mot,
197S 1 197941 Models
E = Perceived Mpf ~
(EPAI. 1976-77 Models
0 = Percteved Mp|
(OOE). 1978 Models
1 = Perceived Mpt
(OOE). 1979 Models
A, A = Fleet Mp{ —
Imodei year laoeieoi
E,D should be inside (TjjQ)
1 should be inside f^i|i|^)
' —
1
10
20 30 40
EPA Composite (55/45) Mpg
50
60
EPA
FUEL ECONOMY
Figure
C-18
Perceived Mpg Data, and Consumer-Measured
Mpg Data for RMI Vehicles
C-26
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
CL
O
o>
1.30
1.20
•I-
E
C3
-o
§
Q£
1.10
1.00
0.90
1 1
TECHNOLOGY
-FAI-
(N a 10 CARS)
—
A
- o
/IT
0
1 1
1
i
E (1 Car)
1 1
Legend
( \= Consumer-Measured Mpf ,
V . ,J> 1976-77-78 Models
O= ConsunMr-Measured Mpf ,
19754 197941 Models
E = Perceived ipt ~
(EPA). 1976-77 Models
D = Percieved Mpi
(DOE). 1978 Models
1 = Perceived Mp|
lOOE). 1979 Models
A, A = Fleet Blpf —
(model year labeled)
E,D should be inside dJJ[fr
I should be inside QJjjj}
—
\
10
20 30 40
EPA Composite (55/45) Mpg
50 60
EPA
FUEL ECONOMY
Figure
C-19
Perceived Mpg Data, and Consumer-Measured
Mpg Data for FAI Vehicles
027
-------
FUEL CONSUMPTION
RATIO
l.OU
1.40
E 1.30
Q.
C3
OJ
o
Q.
E
o
C_3
-------
FUEL CONSUMPTION
RATIO
l.OU
1.40
E 1.30
a.
OJ
O
a.
o
f i
-------
FUEL CONSUMPTION
RATIO
1.50
1.40
1.30
o
03
!75
o
Q.
O
O
Q_
UJ
•I-
E:
o.-
fa
o
oe.
1.20
1.10
1.00
0.90
1 1 — 1 1
TECHNOLOGY
-FAD-
(N »10 CARS)
L ' 1 (4 Cars)
I 'n
1 ^
1 1 1 1 — 1
1
Legend 1
(T \ = Consimw-Jlleisuted Mpj, 1
N. <" 137S-77.78MM.U 1
O= Conanwt-ieasured Mp|, 1
197S & 197W1 Models I
E = Perceived Mp| ""•
(EPA), 1976-77 Models I
0 = Percieved Mpf 1
(DOE). 1978 Models I
1 = Perceived MQI 1
(OOE). 1979 Models I
. A, A = Fleet Mp( — 1
{model year laOeledl •
1, A should be inside
-------
FUEL CONSUMPTION
RATIO
1.50
o
o.
E
o
CJ
E
Q.
O
OS
1.40
1.30
1.20
1.10
1.00
0.90
1 ! 1
TECHNOLOGY
-FMD-
~ (N a 10 CARS)
—
P
1
till
Lege
(J
i
-V *
m
id
)= Consumer-Measured Mpt,
1976-77-78 Models
•'•• -^ = Consumer-Measured Mpg,
_>^ 1975 & 1979-81 Models
E = Perceived MPI
lEPAl. 1976-77 .Models •
0 = Percieved Mp( I
lOOE). 1978 Models •
1 = Perceived Mpt 1
(OOE). 1979 Models •
A, A = Fleet Mot — l|
imodel year laOeledi B
D, A should be inside (TJJJJ) 1
A should be inside <&j^ 1
A^ 1977-78 (2 Cars)
4 1980 (3 Cars) -
y/
i
%
10
20 30 40
EPA Composite (55/45) Mpg
50 60
EPA
FUEL ECONOMY
Figure
C-23
Perceived Mpg Data, Fleet Mpg Data, and
Consumer-Measured Mpg Data for FMD Vehicles
C-31
-------
Fleet Data - figures C-13, C-14, C-21 and C-23 also show fleet data average
results, by model year, for those technologies with fleet representation.
Results
Table C-4 summarizes the comparison of perceived MPG data and fleet data
against measured data in this general analysis. For carbureted, manual
transmission cars (both front and rear drive), all indications are that both
perceived and fleet data agree reasonably well with measured consumer MPG
data. For rear drive carbureted automatics, perceived data is marginally
representative, and fleet data either is or isn't, depending on model year.
For front drive manual Diesels, perceived data is acceptable; fleet data is
not. For rear drive automatic Diesels, fleet data is acceptable, while
perceived data is not. The testimonial to perceived and fleet data ends
there. For other technology groups, use of these types of data to draw
conclusions about the in-use MPG offset of consumers' cars is likely to lead
to serious errors.
C-32
-------
Table C-4
Summary Findings on Representativeness* of
Perceived MPG data and Fleet MPG data,
by Vehicle Technology Class
Perceived Data:
Technology
RMC
FMC
RAG
Direct Comparison
(EF Postcard)
representative
representative
representative
(4% optimistic)
Indirect
Comparison
representative
representative
representative?
(6% optimistic)
Fleet Data;
representative
(10 cars)
mixed
FAC
RAI, RMI, FAI
FMI
FMD
RAD
FAD
RMD
optimistic
(not analyzed
separately)
(not analyzed
separately)
optimistic
optimistic
pessimistic
representative
not representative
pessimistic
pessimistic
(5 cars)
representative
*Judged to be "representative" if within 5% of comparable consumer -
measured MPG; "optimistic" means perceived or fleet MPG is higher than
measured MPG; "pessimistic" means perceived or fleet MPG is lower than
measured MPG; "—" means no data.
C-33
-------
Conclusions
The only vehicle technologies for which both perceived ?IPG data and Fleet
MPG data are accurate, and therefore usable, are technologies that are
well-populated with measured-MFC consumer data already.
Fleet data, but not perceived data, may be acceptable for RAD vehicles,;
perceived data, but not fleet data, may be acceptable for FMD vehicles.
If consumer/measured MPG data is assumed to be the accurate measure of what
consumer-driven cars are actually doing on the road (and we do assume it to
be), it must follow logically that:
1. Other kinds of data are not acceptable for adjustment factor
development unless they agree with consumer/measured data;
2. For other kinds of data which do agree with consumer/measured
data, merging them with consumer/measured data will not produce
conclusions different •from the conclusions reached by using
consumer/measured data alone;
3. Therefore, nothing can be gained by using perceived or fleet
data, even that which agrees with consumer/measured data.
This closes the issue of perceived and fleet data. Only consumer/measured
MPG data is appropriate for road MPG adjustment factor development.
C-34
-------
APPENDIX D
NON-MODAL AND BI-MODAL GPM RATIO
-------
This page intentionally
blank and un-numbered
-------
APPENDIX D
Proof that Non-modal GPMR Must Have
a Value Between Those of Bi-modal GPMRs
Defined: EC = EPA City- MPG ; E, = EPA Highway MPG
RC » Road City MPG ; R. » Road Highway MPG
CGPMR » E /R =» Road city fuel consumption ratio
c c
HGPMR » E. /R. • Road highway fuel consumption ratio
EQ = EPA Combined MPG, 55/45 City/Highway
R 3 Road MPG, non-specific
R » a specific road MPG case, 55/45 city/highway
GPMR = E /R = Road 55/45 fuel consumption ratio
0 O 0
True: CGPMR - E /R R - E /CGPMR (1)
c c c c
HGPMR - \/\ \ " E^HGPMR (2)
Now, at 55% city driving, the overall road MPG is:
.55CGPMR .45HGPMR"! -1
T r» I \J/
But E = R GPMR , and E = \ ^ + ^-, ,.. ...
o.o o o L EC E^J (4), (5)
So: -
which can be rewritten as:
45 1 T --__.„ , .45HGPMR"!
+ H/C J
wherein H/C is shorthand for the EPA
Highway-to-City MPG ratio
D-l
-------
Now, suppose that GPMR is greater than both CGPMR and HGPMR; we may then
write GPMR = CGPMR + e., and GPMR = HGPMR + e2 , where e. and e. are
both positive increments.
Then, CGPMR = GPMR - e, and HGPMR = GPMR - e_.
ol o 2
Substituting these in equation (7) ,
GPMT -o - *1> + il7§ (G?MRo
where N <1 means "a number less than one" and
N-<1 means "another number less than one".
Rearranging ,
.55 [l- (N^l)] = $ [(N2<1) -l]
• or
-------
Now suppose that GPMR is between CGPMR and HGPMR. Consider three possi-
bilities:
(a) CGPMR > HGPMR , for which CGPMR > GPMRQ > HGPMR ;
(b) CGPMR < HGPMR , for which CGPMR < GPMR < HGPMR ; and
(c) CGPMR = HGPMR , for which CGPMR = GPMR = HGPMR .
o
For case (a), let CGPMR - GPMR + BI and HGPMR » GPMRQ - e2 , where '-oth
e. and e~ are positive increments.
Then equation (7) leads to:
which solves as :
Similarly, case (b) leads to'
•55(Npos>
and case (c) gives:
Equations (13), (14), and (15) are all reasonable, and therefore so is the
supposition that GPMR is between CGPMR and HGPMR.
The reader is encouraged to verify that
CGPMR = GPMRQ > HGPMR leads to ^y| = jjy| GPMR = HGPMR leads to .55 = .55(N>1) ,
o
CGPMR = GPMRQ < HGPMR leads to = 7^N>1) > and
CGPMR < GPMR = HGPMR ' leads to .55 = .55(N<1) ,
o
D-3
-------
all of which are absurd. Thus GPMR must be between CGPMR and HGPMR, and
can only equal one of them when it equals both of them in the t rivial case
GPMR = CGPMR = HGPMR.
o
Note further that, for any H/C value greater than 0.82, the GPMRQ value
will be closer to CGPMR than to HGPMR, i.e., the absolute value of e.. will
be smaller than that of e~. The general relation between e.. and a*, which
derives from equation (6), is:
For a typical H/C of 1.5, e., = .55e» , so GPMR is about half as far from
CGPMR as it is from HGPMR. H/C values less than 1.0 or greater than 2.0
are extremely rare.
D-4
-------
APPENDIX E
LISTING OF MODEL TYPE DATA BASE
-------
Record Layout for Appendix E Tables
Variable
Format Columns
Field Description
1. SORC
F3.0
1-3
2.
3.
4.
5.
6.
7.
8.
9.
10.
1 1 .
12.
13.
14.
15..
16.
17.
18.
19.
20.
21 .
22.
MANF
MDLY
ESTD
NAM1
NAM2
ECID
ECYL
DVTYP
TRTYP
INTYP
BOTYP
NOBS
EPAC
EPAH
EPA
CTYF
EOGR
ROAD
AOGR
IWGT
CLAS
F4
F3
A4
1X,A7
A7
1X,F4
F3
A1
A1
A1
A1
F6
F5
F5
F5
F5
F6
F5
F6
F6
F4
.0
.0
.0
.0
.0
. 1
. 1
. 1
.0
.3
. 1
.3
.0
.0
4-7
8-10
1 1-14
15-22
23-29
30-34
35-37
38
39
40
41
42-47
48-52
53-57
58-62
63-67
68-73
74-78
79-84
85-90
91-94
Data type
1= Consumer
80= Fleet
90= Perceived
Manufacturer code
Model year
FLDV, etc
1st 7 char's of model name
Next 7 char's of model name
Engine cu. in. displacement
No. engine cylinders
Drive type (Front/Rear)
Transmission type (Auto/Manu)
Induction type (Carb/FI/Dsl)
Body type (Sedan/Wagon)
No. of observations in record
EPA City MPG
EPA Hiway MPG
EPA 55/45 MPG
Percent city driving
Imputed overall influence index
Measured overall road MPG
Actual GPM ratio (EPA/ROAD)
Inertia weight
Vehicle size class code
E-1
-------
TABLE E-1
LISTING OF MODEL TYPE DATA FOR
.CONSUMER DRIVING - MEASURED MPG
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
10.80.CU3V SPIRIT
10.3O.CLDV CONCORD
10.80.CLDV SPIRIT
1Q.SO.PLDV CONCORD
10.80.FLOV SPIRIT
10.80.FLDV SPIRIT
10.78.FLDV CONCORD
10.79.FLOV CONCORD
10.8O.FLDV CONCORD
10.7S.FLDV PACER
10.7S.FLDV PACER
10.77.FLDV PACER
10.80.FLDV SPIRIT
10.31 .FLDV SPIRIT
10.30.FLDV CONCORD
10. 77. FLDV GREMLIN
10. 80. FLDV SPIRIT
20.80.CLDV CHAMP
20.80.CLOV COLT
20.30.CLDV CHAMP
20.80.CLDV COLT
20.31 .CLDV COLT
20.80.CLDV ARROW
20. 30. CLDV ARROW
20. 79. CLDV HORIZON
20.80.CLOV HORIZON
20.79.CLOV OMNI
20. 80. CLDV OMNI
20. 30. CLDV HORIZON
20. 80. CLDV OMNI
20.31 .CLDV ARIES
20.81 .CLDV OMNI
20.31 .CLDV ARIES
20.30.CLOV COLT
20. 80. CLDV COLT W
20. 80. CLDV CHALLENGER
20. 80. CLDV SAPPORO
20. 30. CLDV CHALLENGER
20. 30. CLDV SAPPORO
20. 80. CLDV ASPEN
20.31 .CLDV CORDOBA
20. 80. CLDV DIPLOMAT
20.3O.CLOV LEBARON
20. 79. CLDV VOLARE
20. 80. CLDV VOLARE
20. 78. CLDV ASPEN
20. 80. CLDV CORDOBA
20. 80. CLDV LEBARON
20. 80. CLDV MIRAOA
20. 30. CLDV NEW YORKER
151.
258.
258.
151.
151 .
151.
258.
258.
258.
258.
258.
258.
258.
258.
258.
258.
258.
98.
98.
98.
98.
98.
98.
98.
105.
105.
105.
105.
105.
105.
135.
135.
156.
156.
156.
156.
156.
156.
156.
225.
225.
225.
225.
225.
225.
318.
318.
318.
318.
318.
4.RACS
6.RACS
6 . RACS
4.RACS
4. RACS
4 . RMCS
6. RACS
6. RACS
6. RACS
6. RACS
6. RACS
6. RACS
6. RACS
6. RACS
6 . RMCS
6 . RMCS
6 . RMCS
4.FACS
4.FACS
4 . FMCS
4 . FMCS
4 . FMCS
4. RACS
4 . RMCS
4.FACS
4.FACS
4.FACS
4.FACS
4 . FMCS
4 . FMCS
4.FACS
4.FACS
4.FACS
4. FMCS
4 . FMCW
4. RACS
4. RACS
4 .RMCS
4 .RMCS
6. RACS
6. RACS
S.RACS
6. RACS
S.RACS
6. RACS
S.RACS
3. RACS
S.RACS
S.RACS
S.RACS
1 .
3.
1 .
3.
a.
15.
1 .
2.
32.
1 .
1 .
2.
20.
1 .
4.
1 .
4.
3.
3.
9.
3.
1 .
2.
2.
1 .
€.
1 .
3.
1 .
5.
1 .
1 .
2.
1 .
1 .
2.
1 .
10.
3.
1 .
1 .
3.
3.
1 .
4 .
1 .
7 .
1 .
2.
4 .
20.
16.
18.
20.
20.
22.
16.
17.
18.
17.
17.
17.
18.
20.
17.
17.
18.
29.
29.
31.
31 .
31 .
27.
27.
2t.
23.
21.
23.
23.
23.
23.
23.
21 .
21 .
21 .
20.
20.
21 .
21 .
16.
17.
16.
16.
16 .
16.
14 .
16.
16.
16.
16.
0 23.0
0 24.0
0 26.0
0 25.0
0.25.0
0 30.0
0 21.0
0 23.0
0 25.0
0 25.0
0 22.0
0 23.0
0 26.0
0 26.0
0 25.0
0 26.0
0 27.0
0 35.0
0 35.0
0 41 .0
0 41 .0
041.0
0 35.0
0 4Q.O
0 29.0
0 32.0
0 29.0
0 32.0
0 37.0
4 37.0
0 33.0
0 34.0
0 28.0
0 30.0
0 30.0
0 25.0
0 25.0
0 30.0
0 30.0
0 21 .0
0 22.0
021.0
0 21 .0
021.0
0 21 .0
0 22.0
0 24.0
0 24.0
0 24.0
0 24.0
22.0
18.8
20.9
22.0
22.0
25.0
17.9
19.3
20.6
19.9
18.9
19.3
20.9
22.3
20. 1
20. 1
21 .2
31.4
31 .4
34.8
34.8
34.8
30. 1
31.6
24.0
26.3
24.0
26.3
27.7
28.0
26.6
26.9
23.7
24.3
24.3
22.0
22.0
24.3
24.3
17.9
18.9
17.9
17.9
17.9
17.9
16.7
18.8
18. 8
18. 8
18.3
50.
73.
75.
54.
42.
41 .'
50.
25.
44.
75.
100.
55.
52.
25.
75.
50.
34.
72.
37.
51 .
48.
50.
21 .
53.
50.
58.
50.
48.
25.
54.
50.
100.
50.
50.
0.
3O.
14 .
52.
65.
0.
50.
32.
34.
50.
56.
25.
55.
100.
39.
36.
1.271
1. 192
1.220
1. 151
1 . 170
1.099
1 .022
0.877
1 . 166
1 . 100
1 . 1 19
1 .076
1 . 186
0.820
1 .349
0.959
1 . 179
1 .201
1 . 193
1 .248
1 . 129
1 .047
0.928
1 .322
0.978
1 . 176
0.990
1 .396
1 .081
1 . 155
0.938
1 .618
0.995
0.976
0.956
1 .092
1.111
1 .237
0.976
1 .016
1 . 107
1 .'184-
1 . 128
1 . 145
1 . 143
1 .028
1 .085
1 . 156
0.956
0.982
20.9
16. 1
18.7
20.4
20. 1
23. 1
18.4
19.7
17.9
16.0
13.7
16.3
17.7
20.5
18.8
15.5
18.9
26.2
27.0
32.7
30.7
29.3
32. 1
30.2
25.3
21 .2
26.2
22.4
28.9
24.0
21 .2
16.6
25.0
24.6
27.8
20.8
16.6
22. 1
22. 1
16.7
14.5
15.9
15.5
18.8
16.4
15.6
15.5
18.5
17 .5
16.6
1.049
1 . 173
1.118
1 .074
1.094
1.081
0.971
0.976
1 . 148
1 .238
1 .382
1 . 182
1 . 184
1.088
1 .070
1 .303
1. 123
1.200
1. 162
1 .065
1 . 134
1 . 188
0.937
1 .047
0.947
1 .239
0.914
1. 177
0.958
1 . 169
1 .256
1.618
0.944
0.988
0.874
1 .054
1 .325
1 .098
1 .098
1 .072
1 .305
1 . 126
1 . 154
0.954
1 .089
1 .072
1.214
1 .019
1 .072
1 . 137
-0.
3900.
-0.
-0.
-O.
-0.
3500.
3500.
3000.
3500.
3500.
3500.
-0.
-0.
-0.
3000.
-0.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
2500.
-0.
-0.
-0.
2500.
-0.
-0.
-0.
-0.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-0.
35OO.
3500.
-0.
4000.
-0.
4000.
-0.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
4 .
-0.
-O.
4 .
-0.
-0.
-0.
-0.
-O.
-O.
-O.
-O.
-0.
-0.
-O.
-0.
-0.
-0.
-O.
-0.
-0.
-O.
-O.
-O.
-0.
-0.
-0.
-0.
-O.
-0.
-O.
-0.
-O.
-O.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-c.
S-2
-------
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
4,
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
1- .
1 .
1 .
1 .
1 .
•
1 .
1 .
1 .
1
1 .
1 .
1 .
•1 .
1 .
1 .
1 .
1 .
1 .
1 .
1 .
20.30.CLDV NEWPORT
20.30.CLDV ST REGIS
20.30.CLDV VOLARE
20.31 .CLDV IMPERIAL
20.79. FLDV CHAMP
20.81 .FLDV COLT
20.80.FLOV CHAMP
20. 80. FLDV COLT
20. 81. FLDV COLT
20. 80. FLDV CHAMP
20. 80. FLDV COLT
20. 81. FLDV COLT
20. 80. FLDV ARROW
20. 80. FLDV ARROW
20. 79. FLDV HORIZON
20.30. FLDV HORIZON
20.81 .FLDV HORIZON
20. 79. FLDV OMNI
20. 80. FLDV DMNI
20.81 .FLDV OMNI
20. 78. FLDV HORIZON
20. 79. FLDV HORIZON
20. 80. FLDV HORIZON
20.81 .FLDV HORIZON
20. 78. FLDV OMNI
20. 79. FLDV OMNI
20. 80. FLDV OMNI
20.81 .FLDV OMNI
20. 81-. FLDV ARIES
20.31 .FLDV HORIZON
20.81 .FLDV OMNI
20.81 .FLDV RELIANT
20.81 .FLOV ARIES W .
20. 81. FLDV RELIANT W
20.81 .FLDV ARIES
20.31 .FLDV HORIZON
20.81 .FLDV OMNI
20.81 .FLDV RELIANT
20.81 .FLDV ARIES W
20.31 .FLDV RELIANT W
20.81 .FLDV ARIES
20. SO. FLDV COLT
20.31 .FLDV RELIANT
20.81 .FLDV ARIES W
20. SO. FLDV COLT W
20.81 . FLDV RELIANT W
20. 80. FLDV COLT W
20. 30. FLDV CHALLENGER
20. 30. FLDV SAPPORO
20.31 .FLDV SAPPORO
20. 30. FLDV ARROW
20. 80. FLDV CHALLENGER
20. 80. FLDV SAPPORO
20.31 . FLDV SAPPORO
20. 78. FLOV ASPEN
20. 79. FLDV ASPEN
20. 80. FLDV ASPEN
20. 80. FLDV CORDOBA
20.81 .FLDV CORDOBA
20. 75. FLDV DART
318.
318.
318.
318.
36.
86.
98.
98.
98.
98.
98.
98.
98.
98.
105.
105.
105.
105.
105.
105.
105.
105.
105.
105.
105.
105.
105.
105.
135.
135.
135.
135.
135.
135.
135.
• 135.
135.
135.
135.
135.
156.
156.
156.
156.
156.
156.
156.
156.
156.
156.
156.
156.
156.
156.
225.
225.
225.
225.
225.
225.
3
3
3
8
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
6
S
6
€
6
6
.RACS
.RACS
.RACS
.RAIS
.FMCS
.FMCS
.FACS
.FACS
.FACS
.FMCS
.FMCS
.FMCS
.RACS
.RMCS
.FACS
.FACS
.FACS
.FACS
.FACS
.FACS
.FMCS
.FMCS
.FMCS
.FMCS
.FMCS
.FMCS
.FMCS
.FMCS
.FACS
.FACS
.FACS
.FACS
.FACW
.FACW
.FMCS
.FMCS
.FMCS
.FMCS
.FMCW
.FMCW
. FACS
.FACS
.FACS
. FACW
.FACW
. FACW
.FMCW
.RACS
.RACS
.RACS
.RMCS
.RMCS
.RMCS
.RMCS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
2..
1 .
1 .
1 .
1 .
1 .
13.
4 .
1 .
22.
20.
1 .
e.
19.
2.
29.
30.
4.
30.
30.
3.
2.
24.
32.
1 .
3.
30.
32.
16.
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BONNEVILLE W
CUST CRUISER
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40.81 . FLDV CATALINA
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40. 77. FLDV DELTA 38
40. 78. FLDV DELTA 38
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40. 80. FLDV DELTA 88
40. 77. FLDV FIREBIRD
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40. 78. FLDV GRAND PRIX
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40. 30. FLDV GRAND PRIX
40. 77. FLDV LESA8RE
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40. 30. FLDV LESA8RE
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40. 77. FLDV OMEGA
40.75.FLOV REGAL
40. 76. FLDV REGAL
40. 78. FLDV REGAL
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40. 75. FLDV SKYHAWK
40. 76. FLDV SKYHAWK
40. 77. FLDV SKYHAWK
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40. 77. FLDV VENTURA
40.78. FLDV CENTURY
40. 30. FLDV CENTURY W
40. 78. FLDV CUTLASS W
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40.75.FtDV FIREBIRD
40.75.FLDV NOVA
40.80.FUDV ELDORADO
40.81 .FUDV ELDORADO
40.80.FLOV SEVILLE
40.30.FLDV OEVILLE
40.80.FLDV FLEETWOOD
40.7S.FLDV SONNEVILLE
40.75.FLDV CATALINA
40.75.FLDV DELTA 88
40.7S.FLOV FIREBIRD
40.76.FLOV FIREBIRD
40.75.FLOV GRAND PRIX
40.76.FLDV GRAND PRIX
40.77.FLDV GRAND PRIX
40.75.FLOV IMPALA
40.7S.FLDV IMPALA
40.75.FLDV LE MANS
40.7S.FLDV LE MANS
40.75.FLDv MONTE CARLO
40.7S.FLDV MONTE CARLO
40.75.FLDV CATALINA W
40.75.FLDV IMFALA
40.76.FLDV IMPALA
40.7S.FLDV IMPALA W
40.76.FLDV IMPALA W
40.75.FLDV LE MANS W
40.76.FLDV LE MANS W
40.7S.FLDV FIREBIRD
40.76.FLDV FIREBIRD
40.77.FLDV TORONADO
40.78.FLDV DELTA 38
40.77.FLDV ELECTRA
40.78.FLDV ELECTRA
40.78.FLDV LESA8RE
40.78.FLDV RIVIERA
40.77.FLDV 98
40.78.FLDV 98
40.78.FLDV CATALINA W
40.78.FLDV CUST CRUISER
40.78.FLDV ESTATE W
40.77.FLDV OEVILLE
40.78.FLDV DEVILLE
40.78.FLDV FLEETWOOD
40.79.FLDV FLEETV/OOD
40.75.FLDV IMPALA
40.76.FLDV IMPALA
40.75.FLDV IMPALA
40.76.FLOV IMPALA
40.75.FLDV TORONAOO
40.76.FLDV TORONADO
40.75.FLDV CATALINA
40.76.FLDV CATALINA
40.75.FLDV CUTLASS
40.75.FLDV DELTA 38
40.76.FLDV DELTA 38
40.75.FLDV ELECTRA
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1 . 40. 75. FLDV LESABRE
1 . 40.75.FLDV RIVIERA
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.082
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. 133
.074
.027
. 159
.963
.074
.077
.306
.088
.366
.290
. 112
. 196
.070
.093
, 182
.012
, 163
. 149
.325
.897
.931
. 184
, 190
.213
. 196
, 168
.897
. 143
.545
.262
.992
. 145
,051
,057
.076
. 187
.994
.265
.428
.023
.051
087
,972
.301
.094
,070
,955
. 163
. SOS
.095
40.6
30.3
23.4
26.7
30.9
26. 1
28.3
21 . 1
22.4
13.3
25.0
26. 1
23.4
22.5
22.8
28.3
21 .7
28. 1
29.9
27.0
28.5
32. 1
27. 1
33.9
20.8
18.6
27. 1
20.2
20.7
20.2
21 .9
19.9
19.5
16.2
26. 1
28.7
24. 1
18. 1
20.4
20.8
16.0
15. S
22.2
20.9
22.5
22.7
15.6
17.7
14.9
27.4
34.3
18.5
29. S
1.007
1 .070
1 .093
1 . 156
0.968
1 .221
1 .046
1 .325
1 .037
1.335
1.012
1.030
1.125
1. 125
1 .306
0.962
1 .280
1 .069
0.954
1 .079
1 .044
O.S63
1 .003
0.971
1 . 102
1 .073
0.965
1 .045
1.077
1 .071
0.968
1.050
0.916
1 .209
1 . 1 10
1 .012
1.204
1 . 120
1\095
1 . 07.4
1 .348
1 .388
0.957
1 . 105
0.948
0.956
1 .217
1 .014
1 .307
1 . 183
1 .053
1 .316
1 .0-19
-0.
-0.
2250.
2500.
2250.
-0.
2250.
2250.
2500.
-0.
-0.
2230.
-O.
-O.
2230.
-0.
25OO.
-0.
2250.
2250.
-0.
2500.
-0.
-0.
-0.
-0.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
3000.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
3500.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-0.
3.
-0.
-0.
-0.
3.
-O.
-O.
-0.
-0.
-0.
-0.
-O.
-0.
-0.
-O.
-0.
-0.
-0.
-0.
-O.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-O.
-O.
-0.
-0.
-0.
4 .
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-0.
-O.
-0.
-0.
-O.
-0.
-0.
£-26
-------
TABLE E-2
LISTING OF MODEL TYPE DATA FOR
FLEET DRIVING - MEASURED ANNDALIZED MPG
80.
80.
80.
80.
80.
80.
80.
SO.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
30.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
30.
80.
80.
SO.
80.
80.
80.
80.
30.
80.
80.
80.
80.
80.
80.
80.
30.
30.
10.79.
10.78.
10.80.
10,77.
10.79.
10.77.
10.76.
10.79.
10.77.
10.79.
10.73.
10.78.
20.79.
20 . 77 .
20.78.
20.79.
20.73.
20.77.
20.78.
20.79.
20 . 30 .
20.77.
20 . 77 .
20.77.
20.79.
20.77 .
20.78.
20.77.
20.78.
20.79.
20.78.
20.78.
20.77.
30.78.
30.76.
30.78.
30.79
30.30.
30.78.
30.78.
30.78.
30.77
30.77
30.78
30.79
30.77
30.77
30,78
30.79
30.78
FLDV
FLOV
FLDV
FLDV
FLOV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
CONCORD
GREMLIN
CONCORD
HORNET
CONCORD
MATADOR
MATADOR
CONCORD
MATADOR
PACER
MATADOR
MATADOR W
ST REGIS
ASPEN
ASPEN
ASPEN
FURY
VOLARE
VOLARE
VOLARE
VOLARE
ASPEN
FURY
GRAN FURY
LEBARON
MONACO
MONACO
VOLARE
VOLARE
VOLARE
FURY
MONACO
MONACO
FAIRMONT
PINTO
PINTO
PINTO
PINTO
PINTO W
PINTO
FAIRMONT
MAVERICK
GRANADA
GRANADA
GRANADA
MAVERICK
MONARCH
MONARCH
MONARCH
FAIRMONT
12V.
121.
131.
232.
258.
258.
258.
304.
304.
304.
3SO.
360.
122.
225.
225.
225.
225.
225.
225.
225.
225.
318.
318.
318.
318.
318.
318.
318.
318.
31S.
360.
36O.
400.
140.
140.
140.
14O.
140.
14Q.
171 .
200.
200.
250.
250.
250.
250.
250.
250.
250.
302.
4
4
4
6
6
6
6
8
8
8
8
8
4
6
6
6
6
€
6
€
e
8
a
8
a
3
8
a
a
8
3
a
8
4
4
4
4
4
4
6
6
€
€
6
6
€
S
6
6
3
.SACS
. RMCS
.SACS
.RACS
. RACS
.RACS
.RMCS
.RACS
.RACS
.RACS
.RACS
. RACW
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACW
.RACW
.RACW
.RACS
.RACS
.RACS
.RACS
.RACS
.RACW
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
30.
2.
30.
30.
20.
1 .
7.
4.
30.
5.
30.
4 .
17.
30.
30.
30.
30.
30.
30.
17.
30.
30.
30.
9.
30.
30.
1 1 .
19.
a.
12.
30.
1 .
18.
30.
3.
3.
4 .
5.
30.
10.
2.
1 1 .
30.
16.
12.
1 1 .
7.
30.
6.
22.
20.0
22.0
20.0
18.0
17.0
15.0
15.0
15.0
14.0
14 .0
12.0
12.0
20.0
18.0
20.0
18.0
17.0
18.0
20.0
18.0
17.0
15.0
13.0
13.0
16.0
13.0.
14 .0
15.0
15.0
16.0
13.0
13.0
10.0
22.0
22.0
21 .0
21 .0
22. C
22.0
18.0
19.0
18.0
18.0
18.0
17.0
17.0
18.0
18.0
17.0
16.0
27.0
34.0
25.0
23.0
23.0
21.0
19.0
21.0
17.0
20.0
17.0
17.0
27.0
24.0
27.0
24.0
22.0
24.0
27.0
24.0
27.0
20.0
18.0
18.0
23.0
18.0
21 .0
20.0
22.0
23.0
20.0
20.0
16.0
33.0
32.0
29.0
28.0
31.0
31 .0
22.0
26.0
24.0
23.0
26.0
23.0
22.0
23.0
26.0
23.0
23.0
22.6
26.1
22.0
19.9
19.3
17.2
16.6
17.2
15.2
16.2
13.8
13.8
22.6
20.3'
22.6
20.3
18.9
20.3
22.6
20.3
20.4
16.9
14.9
14.9
18.5
14.9
16.5
16.9
17.5
18.5
15.4
15.4
12.0
25.9
25.6
24.0
23.7
25.3
25. 3
19.6
21 .6
20.3
19.9
20.9
19.3
18.9
19.9
20.9
19. 3
18.5
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
1.075
1.073
1 .077
1.023
1 .075
1.075
1 .076
1.075
1 .077
1 .074
1 . 129
1 .227
1 .075
1 .067
1 .075
1 .076
1 .076
1 .072
1 .071
1 .076
1 .073
1 .058
1 .088
1 .025
1 .075
1 .077
1 .064
1 .076
1 .074
1 .075
..114
.078
.077
.074
. 123
1 .075
1 .076
1 .075
1 .075
1 .077
1 . 198
1 .018
1 .076
1 .074
1 .075
1 . 108
1 .076
1 .074
1 .075
1 .075
19.6
18.5
22. 1
17.0
15.6
16. 1
13.4
16.3
14.6
16.0
12.5
12.3
21 .8
16.7
16.6
19.8
13.8
17.0
18.3
18.3
19.4
14.7
12.7
11 .5
15.8
16.8
15.6
14.0
15.0
18.6
14.6
15. 1
11.4
16.7
16.3
20.7
20.6
21 .4
19.2
20.9
8.0
18.3
16.8
17.6
17.6
12.3
18. 1
17.3
19.3
16.6
1 . 156
1.416
0.995
1. 172
1.233
1.069
1.233
1.058
1 .038
1.011
. 104
. 122
.040
.215
.363
1 .024
1 .370
1 . 193
1.235
1 . 107
1 .052
1 . 149
1 . 172
1 .294
1 . 170
0.386
1 .054
1 .204
1 . 164
0.9S8
1 .060
1 .022
.057
.549
.573
. 158
. 146
1 . 185
1 .320
0.339
2.689
1 . 107
1 . 189
1 . 186
1 .093
1 .543
1 . 102
1 .205
0.974
1.119
3000.
300O.
3250.
3500.
3000.
4000.
4000.
3000.
4000.
4000.
4500.
4500.
3000.
3500.
3500.
3500.
4000.
3500.
3500.
3500.
3500.
4000.
4000.
4500.
5000.
4500.
4000.
4000.
4000.
4000.
5000.
4500.
5000.
3500.
3000.
3000.
3000.
3000.
3000 . .
3000.
3500.
3000.
3500.
3500.
3500.
3500.
3500.
3500.
3500.
4000.
4
3
4.
3.
4
6.
6.
4
6.
4 .
6.
9.
3.
4 .
u .
5.
5.
4 .
4 .
c
5.
4 .
5.
6.
5.
6.
5.
4 .
4.
5.
8.
8.
a.
5.
2.
2.
2.
2.
7 .
2.
5.
2.
4 .
4 .
4 .
2.
4 .
4 .
4 .
5.
E-27
-------
80.
80.
80.
80.
80.
80.
80.
30.
30.
80.
30.
80.
30.
SO.
80.
80.
SO.
30.
80.
SO.
80.
80.
80.
80.
80.
80.
80.
SO.
80.
80.
80.
80.
80.
80.
80.
SO.
SO.
80.
80.
80.
80.
SO.
30.
80.
80.
80.
80.
80.
80.
80.
80.
80.
80.
SO.
80.
80.
8O.
80.
80.
80.
30.77.
30.78.
30.79.
30.77.
30.78.
30.77.
30.78.
30.79.
30 . 77 .
30.78.
30.77.
30.78.
30 . 78 .
3O . 77 .
30.78.
30.79.
30.77.
30.77.
30 . 77 .
30.77.
40.78.
40.79.
40.79.
40.76.
40 . 77 .
40.78.
40.79.
40 . 80 .
40.79.
40 . 80 .
40.78.
40.79.
40.78.
40.79.
40.79.
40.77.
40.78.
40.77.
40.77 .
40.78.
40.79.
40.78.
40.78.
40.78.
40.77
40.77
40.78
40.77
40.78
40.78
40.77
40.77
40.78
40.77
40.78
40.77
40.77
40.77
40. 77
40.78
FLOV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLOV
FLDV
FLDV
FLOV
FLOV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDD
FLOO
FLOD
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLOV
FLOV
FLDV
FLOV
FLOV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
GRANADA
GRANADA
GRANADA
LTD
LTD
LTD II
LTD II
LTD II
MONARCH
MONARCH
THUNDERS I RD
THUNDERS I RD
LTD
LTD II
LTD II
LTD W
TORINO
LTD
LTD II
LTD W
CUTLASS
CUTLASS
SEVILLE
CHEVETTE
CHEVETTE
CHEVETTE
CHEVETTE
CHEVETTE
MONZA
MONZA
CENTURY
CENTURY
MALIBU
MALIBU
MONTE CARLO
LE MANS
LE MANS
CENTURY
CUTLASS
CUTLASS
CUTLASS
DELTA 38
GRAND PRIX
LESABRE
OMEGA
SKYLARK
SKYLARK
VENTURA
SKYHAWK
IMPALA
MALIBU
NOVA
NOVA
CUTLASS
CUTLASS
OMEGA
LE MANS
LESA8RE
SKYLARK
LE MANS W
302.
302.
302.
302.
302.
302.
302.
302.
302.
302.
302.
302.
3S1.
3S1.
351.
351.
351 .
400.
400.
400.
350.
350.
350.
98.
98.
98.
98.
.. 98.
151 .
151 .
196.
196.
200.
200.
200.
229.
229.
231 .
231 .
231 .
231 .
231 .
231 .
231 .
231 .
231 .
231 .
231 .
231 .
250.
250.
250.
250.
260.
260.
260.
301 .
301 .
301 .
301 .
8
a
a
8
8
3
8
3
8
8
8
8
8
8
8
8
3
3
8
8
8
8
3
4
4
4
4
4
4
4
6
6
6
6
6
6
6
6
6
6
6
S
6
6
6
6
6
6
6
6
S
6
e
a
8
a
a
8
a
a
-RACS
. RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACW
.RACW
.RACS
.RACS
.RACW
. RAOS
. RAOS
.RAOS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RMCS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACW
30.
4 .
30.
2.
30.
30.
30.
30.
1 .
1 .
1 1 .
3.
1 .
24.
30.
1 .
30.
1 .
1 .
1 .
9.
1 1 .
1 .
30.
30.
'1 1 .
1 .
4.
5.
3.
30.
30.
30.
30.
24.
13.
13.
13.
30.
30.
30.
1 .
19.
5.
22.
30.
1 .
30.
1 .
14 .
1 .
30.
15.
15.
2:
5.
2.
1 .
27.
1 .
16.0
16.0
16.0
15.0
15.0
15.0
15.0
14.0
16.0
16.0
15.0
15.0
14.0
14.0
14.0
13.0
13.0
13.0
13.0
13.0
21 .0
24.0
21.0
26.0
26.0
25.0
25.0
25.0
22.0
24.0
18.0
20.0
19.0
18.0
18.0
17.0
19.0
17.0
17.0
19.0
19.0
17.0
19.0
17.0
19.0
18.0
18.0
18.0
16.0
17.0
1.7 .0
18.0
18.0
16.0
19.0
17.0
16.0
1.7.0
17.0
15.0
22.0
23.0
22.0
19.0
22.0
19.0
22.0
20.0
22.0
23.0
19.0
22.0
21.0
20.0
21.0
20.0
19.0
18.0
18.0
18.0
30.0
32.0
29.0
33.0
36.0
33.0
30.0
30.0
30.0
32.0
26.0
27.0
25.0
24.0
24.0
25.0
27.0
25.0
25.0
27.0
25.0
25.0
27.0
25.0
26.0
25.0
26.0
26.0
28.0
24.0
22.0
23.0
24.0
21 .0
27.0
23.0
23.0
23.0
23.0
21 .0
18.2
18.5
18.2
16. S
17.5
16. S
17.5
16.2
18.2
18.5
16.6
17.5
16.5
16.2
16.5
15.4
15. 1
14.9
14.9
14.9
24.3
27.0
24.0
28.7
29.7
28. 1
27.0
27.0
25.0
27.0
20.9
22. S
21.3
20.3
20.3
19.9
21 .9
19.9
19.9
21 .9
21.3
19.9
21 .9
19.9
"21 .6
20.6
20.9
20.9
19.8
19. S
18.9
19.9
20.3
17.9
21 .9
19.3
18.5
19.3
19.3
17.2
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55:
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
35.
55.
55.
55.
55.
55.
55.
1 .075
1 .075
1 .075
1.110
1 .074
1 .076
1 .074
1 .074
.075
.075
.076
.074
.074
.074
.080
.074
.056
1 .075
1 .075
1 .075
1 .075
1 .076
1 .075
1 . 147
1 . 140
1 . 100
1 .077
1 .077
1 .075
1 .076
1 .074
1 .075
1 .076
1 .076
1 .076
1 . 107
1 .075
1 .074
1 .074
1 .075
1 .076
1 .074
1 .075
1 .074
1 .075
1 .075
1 .074
1 .074
1 .072
1 .083
1 .076
1 .070
1 .085
1 .076
1 .075
1 .075
1 .075
1 .075
1 .075
1 .075
14.7
16.3
15.5
10.5
13.0
13.3
12.3
15.3
11.6
15.9
13.5
16.5
14.9
15.4
12.0
14.0
12.5
12. 1
12.5
11.1
20.8
22.9
23.0
13.6
15.0
18. 1
24. 1
30.9
22.8
22.7
17.6
18.3
17.4
18.0
17.7
11.8
16.3
15.2
14.8
17.6
13.3
15. 1
19. 1
15.3
16.8
17.9
19.3
16.6
20.7
16.4
17. S
16.0
17. S
15.4
17.0
15.2
18.0
15.0
14 .6
14. 3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
.240
. 141
. 180
.578
.345
.245
.365
.058
.572
. 166
1231
.064
. 105
.053
.371
. 104
.217
.228
. 139
.339
. 165
. 183
.043
.111
.986
.549
. 1 19
.373
.097
. 193
. 189
.235
.227
. 126
. 144
.688
.348
.302
. 344
.246
. 162
.315
. 146
.302
.286
. 149
.082
.256
.957
. 194
.076
.251
. 154
. 162
.292
.271
.030
.284
.323
.203
4000.
4000.
4000.
4500.
4497 .
4500.
4500.
45OO.
3500.
3500.
4500.
4 SCO.
4888.
4SOO.
4500.
5000.
5000.
5000.
4500.
5000.
3750.
3750.
4500.
2250.
2250.
2250.
25OO.
25OO.
3000.
3000.
4000.
4000.
4000.
4000.
3500.
4000.
4000.
4000.
4000.
4000.
40OO.
5000.
3500.
4500.
3500.
3500.
3500.
3500.
3500.
4000.
4000.
3500.
3500.
4000.
4000.
4000.
4000.
4500.
4000.
4500.
4
4
4
6.
S.
5.
5.
5.
4.
4.
4.
5.
6.
5.
5.
9.
7 .
6.
5.
8.
5.
5.
5.
2.
2.
3.
3.
3.
3.
3.
5.
5.
5.
5.
5.
5.
5.
5.
5.
5.
5.
6.
5.
6.
4 .
3.
4 .
4 .
3.
€.
5.
3.
4 .
S.
5.
4 .
5.
6.
3.
9.
E-28
-------
80.
SO.
80.
80.
80.
80.
80.
80.
80.
30.
SO.
SO.
SO.
80.
80.
30.
80.
80.
80.
80.
SO.
80.
30.
80.
80.
80.
40.
40.
40.
40.
40.
40.
40.
4O.
40.
40.
40.
40.
40.
40.
40.
40.
40.
40.
40.
40.
40.
40.
200.
591 .
591 .
591 .
79,
77,
78.
79.
77.
78.
77.
77.
78.
79.
78.
78,
77,
77
78
77
77
77
77
77
77
78
79
77
78
80
, FLDV
, FLOV
FLDV
FLDV
FLDV
, FLDV
FLDV
FLDV
.FLDV
FLOV
.FLDV
, FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDV
.FLDD
.FLDD
.FLOD
.FLDD
CUTLASS
IMPALA
IMPALA
IMPALA
MALIBU
MALIBU
MONTE CARLO
NOVA
IMPALA
IMPALA
MALIBU
CATALINA
CENTURY
CUTLASS
DELTA 38
GRAND PRIX
IMPALA
MALIBU
MONTE CARLO
IMPALA
MALIBU
CATALINA
300D
RABBIT
RABBIT
RABBIT
305.
305.
305.
305.
305.
305.
305.
305.
305.
303.
305.
350.
350.
350.
350.
350.
350.
350.
350.
350.
350.
400.
183.
90.
90.
90.
8
3
a
3
8
a
8
8
a
8
a
8
8
8
8
a
3
3
8
8
a
8
5
4
4
4
.RACS
.RAGS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
. RACW
. RACW
.RACW
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACS
.RACW
.RACW
.RACS
.RAOS
.FMOS
.FMOS
.FMDS
12.
30.
30.
30.
30.
30.
2.
10."
5.
17.
30.
1 .
14.
18.
3.
25.
1 .
21.
7.
2.
a.
30.
3.
1 .
1 .
3.
17.
16.
16.
16.
16.
17.
16.
16.
14.
14.
16.
15.
15.
16.
16.
14 .
15.
14.
14 .
14.
13.
14 .
23.
39.
40.
40.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
21
22
21
21
25
20
21
20
19
22
22
20
21
23
21
20
19
19
19
17
19
28
52
53
52
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
19
17
18
17
17
19
17
17
16
15
18
17
16
17
18
16
16
15
15
15
14
15
25
43
45
44
.3
.9
.2
.9
.9
.9
.6
.9
.2
.9
.2
.5
.9
.9
.5
.5
.9
.9
.9
.9
.5
.9
.0
.9
.0
.6
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.075
.076
.075
.076
.024
.074
.076
.076
.074
.075
.075
.074
.076
.076
.075
.074
.076
.075
.075
.075
.075
.079
.077
.076
.076
.076
17.
14.
14 .
14 .
14 .
14 .
14 .
15.
14.
15.
15.
13.
14.
15.
15.
14 .
13.
14 .
14 .
14 .
15.
10.
26.
38.
40.
40.
3 1
2 1
0 1
6 1
4 1
8 1
4 1
3 1
6 1
0 1
1 1
7 1
4 1
2 1
0 1
5 1
6 1
a 1
7 1
7 1
2 0
3 1
3 0
7 1
5 1
3 1
.077
.264
.301
.232
.240
.338
.217
. 169
. 105
.056
.210
.278
. 178
.180
.239
. 138
.243
.072
.080
.081
.959
.541
.952
. 136
. 110
.094
4000.
4000.
4000.
4000.
4500.
4500.
4500.
3500.
4500.
4500.
4500.
4500.
4500.
4500.
5000.
4500.
4500.
4000.
4500.
4500.
4500.
5000.
4500.
2500.
2500.
2500.
5
6
6
6
5
c
3
3
9
9
a
6
5
5
6
4
6
5
3
3
3
6
4
3
3
3
E-29
-------
TABLE E-3
LISTING OF MODEL TYPE DATA FOR
CONSUMER DRIVING - PERCEIVED MPG
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
9O.
90.
.90.
90.
90.
90.
90.
90.
10.76.
10.76.
10.77.
20.78.
20 . 78 .
20 . 78 .
20 . 76 .
20.76.
20 . 77 .
20.78.
20.79.
20.78.
20.79.
20.78.
20.79.
20.78.
20.79.
20.77.
20 . 77 .
20 . 77 .
20 . 77 .
20.76.
20.76.
20.76.
20.77.
20.77.
30.76.
30.76.
30.77.
30.76.
30.77.
30.76.
30.76.
30 . 77 .
30.76.
30.76.
30 . 77 .
30.76.
30.77.
30.76.
30.76.
30.77.
30.76.
30.76.
30.77.
30.76.
30.77.
30.77.
30.77.
30.77.
FLOV
FLDV
FLDV
CLDV
CLDV
CLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
HORNET
PACER
PACER
HORIZON
OMNI
OMNI
COLT
COLT
COLT
HORIZON
HORIZON
OMNI
OMNI
HORIZON
HORIZON
OMNI
OMNI
COLT
ASPEN
VOL A RE
ASPEN
DART
VALIANT
VOLARE
MONACO
FURY
CAPRI
MUSTANG
MUSTANG
PINTO
PINTO
PINTO W
308CAT
BOBCAT
CAPRI
MUSTANG
MUSTANG
PINTO
PINTO
PINTO W
MUSTANG
MUSTANG
PINTO
MUSTANG
MAVERICK
GRANADA
MONARCH
COUGAR
GRANADA
MONARCH
232.
232.
258.
105.
105.
1O5.
96.
98.
98.
105.
105.
105.
105.
105.
105.
105.
105.
122..
225.
225.
225.
225.
225.
225.
318.
360.
14Q.
140.
140. .
140.
140.
140.
140.
140.
140.
14O.
140.
140.
140.
140.
-- 171 .
171 .
'171 .
171 .
200.
200.
250.
302.
302.
302.
6 . RMCS
6 . RMCS
6 . RACS
4.FACS
4.FACS
4 . FMCS
4. RACS
4 . RMCS
.4. RMCS
4.FACS
4.FACS
4.FACS
4.FACS
4 . FMCS
4 . FMCS
4 . FMCS
4 . FMCS
4 . RMCS
6. RACS
6 . RACS
6 . RMCS
6. RMCS
6 . RMCS
6 . RMCU
8. RACS
8. RACS
4. RACS
4. RACS
4. RACS
4. RACS
4. RACS
4 . RACW
4 . RMCS
4 . RMCS
4 . RMCS
4 . RMCS
4 . RMCS
4 . RMCS
4 .RMCS
4 . RMCW
S.RACS
6. RACS
S.RACS
S.RMCS
6. RACS
S.RMCS
S.RMCS
8. RACS
S.RACS
8. RACS
1 .
1 .
1 .
2.
2.
1 .
1 .
9.
1 .
30.
30.
16.
29.
8.
30.
9.
28.
1 .
1 .
16.
1 .
1 .
1 .
2.
1 .
1 .
1 .
7.
2.
1 1 .
15.
1 .
2.
2.
1 .
4 .
3.
22.
13.
2.
9.
1 .
5.
1 .
1 .
1 .
1 .
2.
15.
- 9.
17.0
17.0
17.0
21.5
21.5
24. 1
24.0
24.0
29.0
23.5
23.7
23.5
23.7
24.3
25.4
24.8
25.4
20.0
16.0
16.2
17.0
19.0
19.0
18.0
13.0
1 1 .0
22.0
22.0
21 .0
22.0
23.0
22.0
24.0
26.0
18.0
24.0
23.0
24.0
26.0
24 .0
17.0
17.0
18.0
17.0
18.0
22.0
21.0
15.0
16.0
16.0
25.0
25.0
23.0
27.8
27.8
35.3
30.0
37.0
45.0
30.5
31 .6
30.5
31 .6
38.3
37.8
38.3
37.8
33.0
21.0
21.3
24.0
26.0
26.0
30.0
18.0
16.0
31.0
31.0
29.0
32.0
32.0
31 .0
34.0
37.0
27.0
34 .0
33.0
35.0
37.0
34.0
23.0
23.0
25.0
25.0
24.0
30.0
28.0
19.0
22.0
22.0
19.9
19.9
19.3
23.9
23.9
28.1
26.4
28.5
34.5
26.2
26.7
26.2
26.7
29.4
29.8
29.4
29.8
24.3
17.9
18.2
19.6
21 .6
21 .6
21 .9
14.9
12.8
25.3
25.3
24.0
25.6
26.3
25.3
27.7
30.0
21.2
27.7
26.6
27.9
30.0
27.7
19.3
19.3
20.5
19.9
20.3
25.0
23.7
16. S
18.2
18.2
55.
55.
55.
45.
80.
25.
55.
55.
55.
47.
46.
48.
57.
65.
37.
34.
46.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
55.
35.
55.
1 .074
1 .074
1.075
1.172
1.360
1 .044
1.076
1 .074
1 .073
1 .242
1 .394
1 .302
1 .350
1 .284
1 .234
1 .064
1.233
1.073
1 .076
1.076
1 .075
1 .075
1 .075
1 .072
1 .075
1 .074
1 .075
1 .075
1.075
1 .074
1 .075
1.075
1 .075
1 .075
1 .074
1 .075
1.075
1 .074
1 .075
1 .075
1 .075
1 .075
1 .075
1 .074
1 .076
1 .075
1 .076
1 .076
1 .075
1 .075
17.6
19.5
17.6
22.9
21 . 1
28.0
25.4
25.4
35.2
24.6
23.4
26.0
25. 1
27.9
28.0
29.9
28.5
23.5
15.6
16.7
22.5
16.6
16.6
18.5
17.6
13.7
21 .5
19.2
20.5
20. 3
20. 1
17.6
20.5
24.4
17.6
22.7
22.8
23.2
23. 1
2219
17 .4
17. S
17.6
25.4
14. S
17 .3
16.6
13.4
14 . 7
15.7
1 . 128
1.016
1.096
1.047
1 . 136
1.004
1 .040
1. 122
0.980
1.063
1.141
1 .006
1 .063
1.055
1.067
0.983
1 .046
1.034
1, 147
1.092
0.871
1 .302
1.302
1 . 182
0.845
0.936
1 . 177
1 .319
1 . 172
1 .260
1 .308
1 .439
1.351
1.229
1.203
1 .220
1 . 168
1 .206
1 .298
1.205
1 . 108
1 .096
1.171
0.781
1 .385
1 .422
1 .425
1 .238
1 .240
1 . 160
3500.
3500.
3500.
-0.
-O.
-0.
2500.
2500.
2500.
-0.
-O.
-0.
-0.
-0.
-0.
-0.
-0.
2750.
35OO.
3500.
35OO.
3500.
3500.
4000.
4000.
4500.
3000.
3000.
3000.
3000.
30OO.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3000.
3500.
3500.
4500.
4000.
3500.
3.
4.
4.
-0.
-O.
-O.
2.
2.
2.
-0.
-0.
-o.
-0.
-0.
-0.
-o.
-o.
2.
4.
4.
4 .
4.
4 .
8.
5.
5.
2.
2.
2.
2.
2.
7.
2.
2.
2.
2.
2.
2.
2.
7.
2.
2.
2.
2.
2.
4 .
4 .
5.
4 .
4 .
E-30
-------
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
9O.
90.
90.
90.
90.
90.
90.
90.
90.
90.
.90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
90.
9O.
90.
90.
90.
90.
90.
90.
90.
90.
90.
9O.
9O.
90.
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90.
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90.380.
90.380.
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90.380.
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90 . 380 .
90 . 380 .
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90.380.
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90.410.
90.430.
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90.470.
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90 . 570 .
90.570.
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90.591 .
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77.
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FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
CLOD
FLDD
CLDV
FLDV
CLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLDV
FLOV
FLDV
FLDV
FLDV
FLDV
CLOD
CLOD
CLDV
CLDV
CLDV
CLDV
CLDV
CLDV
CLOV
CLDV
CLDV
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FLDD
FLDD
FLDD
FLDD
FLDV
FLDV
FLOV
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FLDV
FLDV
FLDV
FLDV
FLDV
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F10
B210
B210
B210
B210
B210
610
710
710
710
810
810
280Z
280ZX
280Z
280ZX
280ZX
504 0
504 D
LE CAR
LE CAR
99
99
COROLLA
COROLLA
COROLLA
COROLLA
COROLLA
CELICA
CORONA
CORONA
CELJCA
CELICA
CORONA
DASHER
RABBIT
RABBIT
RABBIT
RABBIT
SCIROCCD
FOX
DASHER
FOX
924
924
5000
DASHER
DASHER
RABBIT
RABBIT
RABBIT
RABBIT
RABBIT
SCIROCCO
RABBIT
DASHER
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FOX
FOX
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35.
85.
85.
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4 . FMCS
4.RACS
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4.RMCS
4. RMCS
4.RACS
4.RACS
4 . RMCS
4 . RMCS
6.RAIS
6.RMIS
6.RAIS
6.RAIS
6.RMIS
6.RMIS
6.RMIS
4.RADS
4.RAOS
4 . FMCS
4 . FMCS
4.FMIS
4.FMIS
4 . RMCS
4.RACS
4 . RMCS
4 . RMCS
4 . RMCW
4.RACS
4.RACS
4 .RACW
4 . RMCS
4 . RMCS
4 . RMCS
4 .FMOS
4 . FMDS
4.FAIS
4.FMIS
4.FMIS
4.FMIS
4.FAIS
4.FMIS
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5.FAIS
4 . FMOS
4 . FMOS
4 . FMDS
4 . FMOS
4.FAIS
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4.FMIS
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4 .FACS
4.FAIS
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0.982
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90.591 .
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. FLOV
. FLDV
. FLDV
.FLDV
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.FLDV
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.FLDV
.CLDV
.CLDV
.FLDV
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SCIROCCO
DASHER
FOX
FOX
FOX
RABBIT
SCIROCCO
SCIROCCO
BEETLE
924
924
5000
5000
5000
928
240
240 W
240 W
260
260
SUBARU
SUBARU W
SUBARU
SUBARU W
SUBARU
SUBARU
SUBARU W
SUBARU W
97.
97.
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4
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.FMCS
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.FMIS
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2.
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31
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25
.0 39
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E-34
-------
This page intentionally
blank and un-numbered
-------
APPENDIX F
HISTOGRAMS OF LABEL ERROR
-------
X X
xx. x
< < <*
xx X
< < X
xx X X
XX XX
x x X * X
< X X X * X
< X X X « <*
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< X < X X X
x X X X X X
xxxx
XXXXXXXXXXXKXXX
XX XX x x
-------
X X
X X
x x
x »
x x <
XXX
x xxx
x .<•<•<
< < x x x
x < x < x
X X X X X
X X X X X
X X X X X X
•< x x x x x
X X X X X X
XXXXXXXX
X X X X < < XX
xxxxxxxx
xxxxxxxx
xxxxxxxx
xxxxxx xx
XXXXXXXX
xx^xx x x x x x-
«. x xxx xxxxx
xx xxx xx<-«x
xXXXXXXXXXi
-------
X X
X X
X X
< X
xx x
xxx xx
X X X X X X
X X X X X X
X X X X X X
x x < x x x
X X X X X X X
X X X X X X X
X X < X X X X
xxxxxxxx
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X xx x x x x tx-xxxxxxxxx*
X-
0
4
\
70
xxxx
1
0^80
[
0.90
i
1.00
I
1.10
-------
X
X X
X < X
" x x < x
XXX X
X X X X X
x x x x x
X X X X X
xxxxxxxx
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nexxxxxxxx
(HXXXXXXXX
X>x x x x x x x x
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KXXXKXXXX
X *f« X X X X X X X
.. >* X--X: x x x x x, x x x
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«« X. X X X X < X X X X < >C
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0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50
Distribution of Label/Road MPG Ratio
Test Fleet: Driving Overall
Tech/Ncyl Mix .... 1985
Data Model Type
Vehicles 1,032
Adjustment: City Number
Algorithm: Lo = Ec
-------
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0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40
Distribution of Label/Road MPG Ratio
1.50
Test Fleet: Driving ................ Overall
Tech/Ncyl Mix .... . 1985
Data ................... Diffuse
Vehicles .............. 10,347
Adjustment:
City Number
Algorithm:
Lo=Ec
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50
Distribution of Label/Road WPG Ratio
Test Fleet: Driving City
Tech/Ncyl Mix .... As-ls
Data Diffuse
Vehicles 12,664
Adjustment: Current System
Algorithm: Lc=Ec
-------
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0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40
Distribution of Label/Road MPG Ratio
1.50
Test Fleet: Driving ................ City
Tech/N'cyl Mix .... As-ls
Data ................... Diffuse
Vehicles .............. 12,664
Adjustment: .............. Non-Modal Formula
Algorithm: ................... Lc=OM (Ec)
-------
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0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50
Distribution of Label/Road MPG Ratio
Test Fleet: Driving Highway
Tech/Ncyl Mix .... As-ls
Data Diffuse
Vehicles 12,666
Adjustment: Highway Formula 1
Algorithm: LH-HMl(EH)
-------
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