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

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                          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

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                                  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

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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

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  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

-------
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

-------
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

-------
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

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This page intentionally
 blank and un-numbered

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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

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•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

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                                  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

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                                  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

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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

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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

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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

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           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

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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

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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

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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

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                        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.

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This page intentionally
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         APPENDIX A
FUEL ECONOMY INFLUENCE INDEX

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                                 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.

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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

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                                   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

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This page intentionally
 blank and un-numbered

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                  APPENDIX C '
ANALYSIS OF PERCEIVED VS. MEASURED FUEL ECONOMY

-------
This page intentionally
 blank and un-numbered

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                                   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

-------
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                                             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

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                               Highway
 I
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.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
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.6  .7
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       1   T   I   I
                                                   Highway
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                        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
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 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
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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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                    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 .
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40.77.FLOV 98
40.78.FLDV 98
40.30.FLOV 98
40.78.FLOV CATALINA W
40.75.FLOV CENTURY
40.7S.FLDV CENTURY
40.75.FLDV CENTURY W
40.77.FLOV CUST CRUISER
40.78.FLDV CUST CRUISER
40.75.FLDV CUTLASS
4Q.76.FLDV CUTLASS
40.7S.FLDV CUTLASS W
40.78.FLDV ESTATE W
40.80.FLDV ESTATE W
40.78.FLDV IMPALA
40.78.FLQV IMPALA W
40.75.FLDV L£ MANS W
40.77.FLDV VISTACRUISER
40.76.FLDV .SEVILLE
40.75.FLDV CAMARO
4O.76.FLOV CAMARO
40.aO.FLDV CAMARO
40.76.FLDV CORVETTE
350.
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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
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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
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.FMIS
.FAIS
.FAIS
.FMIS
.FMIS
.RAIS
.RAIS
.RMIS
.RMIW
.RAIS-
. RMIW
.RADS
.PADS
.RADW
.RAIS
.RAIS
.RAIS
.RAIW
.RAIW
.RMIS
.RMIS
.RMIS
.RMIW
.RAIS
.RAIS
.RMIS
.RMCS
.RMCS
.SACS
.RMCS
6.
13.
1 .
1 .
5.
2.
1 .
1 .
1 .
1 .
4.
1 .
7.
1 .
1 .
4.
1 .
7.
1 .
17.
19.
1 .
1 .
3.
2.
21 .
15.
2.
5.
1 .
6.
1 .
2.
1 .
2.
3.
1 .
1 .
4 .
2.
1 .
1 .
10.
2.
2.
2.
1 .
4 .
2.
1 .
1 .
2 .
2.
36.0
27.0
22.0
26.0
25.2
27.0
25. 0
24.0
20.0
22.0
22.0
24.0
23.0
22.0
23.0
23.0
23.0
25.0
24.0
24.2
24.8
23.0
23.0
28.0
20.0
17. 1
22.0
17.0
20.0
19.0
18.0
18.0
16.0
16.0
27.0
27.0
27.0
18.0
20.0
20.0
19.0
19.0
18.0
19.4
18.0
18.0
17.0
16. 0
16.0
28.0
32.0
21 .0
26.4
49.0
43.0
32.0
40.0
38.7
41.0
38.0
35.0
29.0
31.0
31.0
33.0
32.0
31.0
39.0
35.0
37.0
40.0
37.0
38.5
39.6
37.0
35. 0
42.0
28.0
25.0
34.0
30.0
26.0
26.0
27.2
26.0
21 .0
27.0
32.0
32.0
32.0
24.0
26.0
26.0
26.0
26.0
27.4
29.4
27.5
29.0
22.0
21.0
27.0
40.0
43.0
30.0
36.2
40.9
32.4
25.6
30.9
29.9
31.9
29.5
27.9
23.3
25.3
25.3
27.4
26.3
25.3
29.8
27.2
27.7
30. 1
28.5
29. 1
29.3
27.7
27.2
32.9
22.9
20.0
26. 1
21 . 1
22.3
21 .6
21.2
20.9
17.9
19.6
29.0
29.0
29.0
20.3
22.3
22.3
21 .6
21 .6
21 .2
22.9
21.3
21 .7
18.9
17.9
19.5
32.4
36.2
24.3
30. 1
41 .
61 .
75.
55.
55.
58.
55.
55.
50.
54.
43.
50.
65.
46.
100.
56.
75.
56.
50.
58.
55.
25.
16.
25.
57.
56.
55.
57.
52.
25.
44 .
95.
35.
43.
23.
34.
17.
55.
55.
43.
30.
91 .
39.
50.
47.
20.
25.
42.
31 .
50.
50.
100.
50.
1
1
1
1
«
1
0
1
1
1
1
0
1
1
1
1 ,
1 .
1 .
1 .
1
1
0.
0,
0,
1 .
1
1 ,
1 ,
1 .
0,
1
1 ,
1 .
0.
1 ,
1 ,
1 ,
1 .
1 ,
0,
1 ,
1 ,
1
1 .
1 .
0.
0,
1 .
1 .
0.
1 .
1 .
1 ,
.082
.077
. 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.
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This page intentionally
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        APPENDIX F





HISTOGRAMS OF LABEL ERROR

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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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