%

U.S. Department

of Transportation

National Highway

Traffic Safety

Administration

DOT HS 808 061 January 1994

NHTSA Technical Report

Correlation of NCAP Performance

with Fatality Risk

in Actual Head-On Collisions

Thi6 document is available to the public from the National Technical Information Service, Springfield, Virginia 22161.
The United States Government does not endorse pnxlucts or

manufacturers. Trade or manufacturers' names appear only

because they are considered essential to the object of this report.
Technical Report Documentation Page

1. Report No.

DOT HS 808 061

2. Government Accession No. 3. Recipient's Catalog No.

4. Title end Subtitle

CORRELATION OF NCAP PERFORMANCE WITH

FATALITY RISK IN ACTUAL HEAD-ON COLLISIONS

S. Report Date

January 1994

6. Performing Orgonixotion Cod*

77, Author's)

Charles J. Kahane, Ph.D.

8. Performing Orgonixotion Report No.

9. Performing Orgonixotion Nome ond Address

Evaluation Division, Office of Strategic Planning and Evaluation

National Highway Traffic Safety Administration

Washington, D.C. 20590

10. Work Unit No. (TRAIS)

]. Contract or Gronf No.

12. Sponsoring Agency Name ond Address

Department of Transportation

National Highway Traffic Safety Administration

Washington, D.C. 20590

13. Type of Report ond Period Covered

NHTSA Technical Report

Sponsoring Agency Code

15. Supplemented Notes

16. Abstroct

The New Car Assessment Program (NCAP) hits gauged the performance of vehicles in frontal impact tests since

model year 1979. In response to Congressional direction, the National Highway Traffic Safety Administration studied the relationship

between vehicle test scores in NCAP and the fatality risk in crashes of vehicles on the road. This study is based on head-on collisions,

where the effect of crashworthiness can be separated from the effects of extraneous factors that influence fatality rates (drivers, roadways,

mileage). Collisions between two 1979-91 passenger cars in which both drivers were wearing safety belts were selected from the Fatal

Accident Reporting System. There were 396 collisions (792 cars) in which both cars were identical with or very similar to vehicles

which had been tested in NCAP. In the analyses, adjustments were made for the relative weights of the cars, and for the age and sex

of the drivers - factors which substantially affect fatality risk.

There are statistically significant correlations between NCAP scores for head injury, chest acceleration and femur

loading and the actual fatality risk of belted drivers. A composite NCAP score, based on the test results for all three body regions, has

excellent correlation with fatality risk: in a head-on collision between a car with good composite score and a car of equal weight with

poor score, the driver of the car with the better NCAP score has, on average, a 20 to 25 percent lower risk of fatal injury. Slightly

smaller, but still significant fatality reductions are obtained even when the NCAP scores for just one body region (just HIC, or chest

g's, or femur load) are used to partition the fleet into "good" and "poor" performance groups. The borderline between good and poor

NCAP scores that optimizes the differences in actual fatality risk is close to the criteria specified in Federal Motor Vehicle Safety

Standard (FMVSS) 208 for each of the three body regions.

Cars built from 1979 through 1982 had, on the average, the poorest NCAP scores. Test performance unproved

substantially in 1983 through 1986 models, and continued to improve in 1987 through 1991 cars. In parallel, fatality risk for belted

drivers in actual head-on collisions decreased by 20 to 25 percent in model years 1979-91, with the largest decreases just after 1982.

The 35 mph test speed for NCAP is 5 mph higher than the test speed for FMVSS 208. By now, most passenger cars meet the FMVSS

208 criteria at the NCAP test speed. The study shows that achievement of this enhanced level of test performance has been accompanied

by a significant reduction in actual fatality risk. However, being a statistical study, it does not address what portion of the fatality

reduction was directly "caused" by NCAP. Also, these results do not guarantee that any individual make-model with low NCAP scores

will necessarily have lower fatality risk than another make-model with higher NCAP scores.

17. K.y Words

NCAP; New Car Assessment Program;

crashworthiness; FARS; head-on collision;

accident analysis; statistical analysis;

frontal crash; fatality risk index; HIC;

chest g's; femur load; safety belts; Polk;

18. Distribution Statement

Document is available to the public through

the National Technical Information Service,

Springfield, Virginia 22161

19. Security Clossif. (of this report)

Unclassified

30. Security Clessif. (of this page)

Unclassified

21. No. of Poges

164

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

TMJLE OF OCNIENIS

Surmary v

Analysis overview vi

Correlation of NCAP scores and fatality risk ix

Improvements in actual crashworthiness and

NCAP performance during 1979-91 xiv

Principal findings, conclusions and caveats xvii

1. Introduction and analysis overview 1

1.1 NCAP performance vs. crashworthiness en the highway 2

1.2 The difficulty of isolating crashworthiness effects 3

1.3 Analysis overview 7

1.4 Sane preliminary caveats 12

2. An accident data file with NCAP information 15

2.1 Initial EARS data reduction 16

2.2 Curb weight data from Polk files 19

2.3 Adjusting Polk weights based on actual weight measurements 21

2.4 A file of NCAP test results 27

2.5 etching NCAP tests with FARS cases 29

2.6 Creation of the analysis file 32

3. Correlation of fatality risk with individual NCAP parameters . . 35

3.1 Analysis objective 35

3.2 Regression analysis procedure 36

3.3 The initial regression - including all NCAP matches ... . 39

3.4 Regressions on data sets with closer FARS-NCAP matches . . 42

3.5 Surmary 50
4. A composite NCAP scare: oarrelatiai with fatality risk 53

4.1 A composite measure of NCAP performance 53

4.2 RELEXP: actual safety performance relative to expectations 60

4.3 Correlation of DELNCAP and RELEXP 63

4.4 Fatality reduction for the car with lower NCAPINJ 64

4.5 NCAP performance of cars that did

better than expected in crashes 70

4.6 Fatality reduction for the car

with lower head or chest injury risk 74

4.7 Sensitivity test: NCAPINJ on a

different calibration data set 76

5. Collisions between a "good" and a "poor" NCAP performer 79

5.1 Cars with low NCAPINJ hit cars with high NCAPINJ 79

5.2 Cars with low NCAP chest g's hit cars with high chest g's . 86

5.3 Cars with low NCAP HIC hit cars with high HEC 89

5.4 Cars with low NCAP fenur loads

hit cars with high fenur loads 93

5.5 Partitions of the fleet based an two NCAP parameters .. . 98

5.6 Partitions of the fleet based an

all three NCAP parameters 103

5.7 Sensitivity test: collisions of two cars with similar mass 106

5.8 Sensitivity test: weighted vs. unweighted canposite score . 108

5.9 Sensitivity test: analyses on a

different calibration data set 110

5.10 Summary 115

6. Fatality risk indices for "good" and "poor" NCAP performers . . . 119

6.1 Procedure for catputing fatality risk indices 119

6.2 Risk indices for good and poor NCAP performers 123

7. Actual crashworthiness and NCAP performance during 1979-91 . . . 129

7.1 Cars with late model years

hit cars with early model years 131

7.2 Fatality risk index and average NCAPINJ by model year . . . 135

7.3 Comparison of the NCAPINJ and mode]-year effects 142

References 149

IV
SIMYBRY

The Appropriations Act for Fiscal Year 1992 directs "NHISA to provide

a study to the House and Senate Qcnttdttees on Appropriations comparing the

results of NCAP data fran previous model years to determine the validity of these

tests in predicting actual on-the-road injuries and fatalities over the lifetime

of the models." In Decentoer 1993, the agency responded with a Report to the

Congress that ccnpared NCAP results and real-world crash experience, based an

various analyses of accident data files. One set of analyses demonstrated a

statistically significant correlation between NCAP performance and the fatality

risk of belted drivers in actual Vwiri-nn collisions. This technical report

provides a more detailed exposition of the data sources, analytic approach and

statistical findings in the analysis of head-on collisions.

NHISA's goal was to see if cars witn poor NCAP scores had more belteddriver fatalities than would be expected, given the weights of the cars, and the

age and sex of the drivers involved in the crashes. Without adjustment for

vehicle weight, driver age and sex, the large diversity of fatality rates in

accident data mainly reflects the types of people who drive the cars, not the

actual crashworthiness of the cars. For example, "high-performance11 cars popular

with young male drivers have an exceptionally high frequency of fatal crashes -

because they are driven in an unsafe manner - even though they nay be just as

crashworthy as other models. NHISA's analysis objective was to isolate the

actual crashworthiness differences between cars, removing differences

attributable to the way the cars are driven, the ages of the occupants, etc., and

then to correlate NCAP performance with crashworthiness an the highway.
Arra"lysis overview

Since NCAP is a frontal irtpact test involving dummies protected by

safety belts, the agency limited the accident data to frontal crashes involving

belted occupants. However, NHISA did not consider all types of frontal crashes,

but further limited the data to head-on collisions between two passenger cars,

each with a belted driver, which resulted in a fatality to one or to both of the

drivers. A head-on collision is a special type of highway crash ideally suited

for studying frontal crashworthiness differences between two cars. Both cars are

in essentially the same frontal collision. It doesn't matter if one of than had

a "safe" driver and the other, an "unsafe" driver; at the manent they collide

head-an, how safely they were driving before the crash is nearly irrelevant to

what happens in the crash. Which driver dies and which survives depends

primarily an the intrinsic relative crashworthiness of the two cars, their

relative weights, and the age and sex (vulnerability to injury) of the two

drivers.

If car 1 and car 2 weigh exactly the same, and both drivers are the

same age and sex, the likelihood of a driver fatality in a head-on collision

would be expected to be equal in car 1 and car 2. If car 1 and car 2 have

different weights, etc., it is still possible to calibrate formulas predicting

the expected fatality risk for each driver in a head-an collision between the two

cars, as a function of each vehicle's weight and each driver's age and sex. The

formulas measure the relative vulnerability to fatal injury of the two drivers,

given that their cars had a head-on collision. The risk is greater in the

lighter car than the heavier car, and a female or older driver is more vulnerable

to injury than a male or younger driver. For example, given 100 fatal head-on

collisions between 3000-pound-cars driven by belted, 20-year-old males and 2500

vi
pound cars driven by belted, 50-year-old females, these formulas predict 10.8

tines as many deaths among the older females in the lifter cars as among the

young males in the heavier cars.

Cars with average crashworthiness capabilities will experience an

actual number of fatalities very close to what is predicted by these formulas,

which are calibrated frcm the collision experience of production vehicles. If

a group of cars, however, consistently experiences more fatalities than expected

in their head-on collisions, then the empirical evidence suggests that this group

of cars is less crashworthy than the average car of similar mass. The gist of

the analyses is to see if groups of cars with poor NCAP scores have significantly

more belted-driver fatalities per 100 actual head-on collisions than expected

(and there are several ways to define a "poor" score). The analyses measure the

reduction in fatality risk, in actual head-on collisions, for a car with good

NCAP scores relative to a car with poor NCAP scores. They measure the overall

reduction in fatality risk, for belted drivers in head-on collisions, since model

year 1979, when NCAP testing began, until 1991, the latest model year for which

substantial accident data were available as of mid 1993.

The analyses require a data file of actual head-on collisions, with

both drivers belted, resulting in a fatality to at least one of the drivers,

indicating, for both cars, the curb weight, the driver's age and sex, and the

HIC, chest g's and femur loads that were recorded for the driver dunmy when that

car was tested in NCAP. NHISA's Fatal Accident Reporting System (EBRS), complete

through mid-1992, provided the basic accident data for the study. The EBRS data

were supplemented with accurate curb weights, derived frcm R. L. Folk's files and

NHISA compliance tests. Insufficient NCAP and FKRS data were available to

vii
include light trucks, vans or sport utility vehicles in the analyses. Thus, the

study is limited to collisions between two 1979-91 passenger

NHISA staff reviewed the cars involved in head-on collisions on EARS

and identified, where possible, the NCAP test car that came closest to matching

the ERRS case. They found 396 head-on collisions, involving 792 cars, in which

both drivers were belted and both cars match up acceptably with an NCAP case:

(l) The make-models an EARS and NCAP are identical or true "corporate cousins"

(e.g., Dodge Qnni and Plymouth Horizon). (2) The model years on EARS and NCAP

are identical, or the EARS model year is later than the NCAP model year, but that

model was basically unchanged during the intervening years. The EARS cases were

supplemented with the matching NCAP test results for each car. The sample is

large enough for a statistical analysis of NCAP scores and fatality risk.

EARS data do not single out those head-on collisions that closely

resemble an NCAP test: perfectly aligned collisions of two nearly identical cars,

with minijiBl offset, a closing speed close to 70 mph, and both drivers 50thpercentile males. In addition, EARS cases may involve injury to the neck or

abdomen: the potential for injury to these body regions is not specifically

measured in NCAP. It is inappropriate to expect perfect correlation between NCAP

test results and actual fatality risk in the full range of head-on collisions

represented in the EARS sample. Mareover, if there is any significant

correlation between the two, it suggests that the NCAP scores say something about

actual crashworthiness in a range of crashes that goes far beyond the specific

type tested in NCAP.
Correlation of NCAP scor^fs and fatality risk

Ihe goal of the analysis is to test if cars with poor scores an the

NCAP test have higher fatality risk for belted drivers, in actual head-on

collisions, than cars with good or acceptable scores. There are many ways to

define "poor" and "good" scores and measure the difference in fatality risk. All

of the methods tried out by NHISA staff demonstrate a statistically significant

relationship between NCAP scores and actual fatality risk, as shown in the

accompanying table.

A straightforward way to delineate "poor" and "good" scores is to

partition the cars based on their NCAP score for a single body region - chest

g's, HIC or femur load - and to consider only a subset of the 392 head-on crashes

where one car has a score in the "poor" range and the other car has a score in

a good or acceptable range. This subset should contain approximately 120

crashes, which is equivalent to defining the worst 20 percent of cars as "poor"

performers and the remaining 80 percent as good or acceptable. Do the cars with

the poor NCAP scores have significantly more driver fatalities than expected?

When chest g' s are used to partition the cars into acceptable and poor

performance groups, the cars with high chest g's almost always have significantly

more fatalities than the cars with acceptable chest g's. For example, there are

125 actual head-cm collisions (both drivers belted) in which one of models had

more than 56 chest g's for the driver when it was tested in NCAP, and the other

had 56 g's or less. In the 125 cars with chest g's > 56, 80 drivers died,

whereas only 68.2 fatalities were expected, based on car weight, driver age and

sex. In the 125 cars with chest g's < 56, there were 74 actual and 77.6 expected

driver fatalities. That is a statistically significant fatality reduction of

ix
COLLISIONS OF CARS WITH "GOOD" NCAP SCOPES INTO CARS WITH "POOR" NCAP SCORES

(N of crashes approximately 120 in each analysis)

•Good"

Perfon

NCAP •Poor" NCAP

Perform

Performance in Actual Crashes

N of Fatality

Crashes for Good Car (%)

Chest g's < 56

HIC < 1000

L Femur < 1600 AND

R Fenur < 1600 AND

L+R Fenur < 2600

Chest g's > 56

HIC > 1200

L Fenur > 1600 OR

R Fenur > 1600 OR

L+R Fenur > 2600

125

113

132 2 0 "

HIC < 1100 AND

Chest g's < 60

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 900 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur <. 2400

HIC > 1300 OR

Chest g's > 60

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

HIC > 1300 OR

L Fenur > 1700 CR

R Fenur > 1700 OR

L+R Fenur > 2700

125 19*

134 2 2 "

121 19*

HIC < 900 AND

Chest g's < 56 AND

L Fenur <. 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

NCAPINJ < .6

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

NCAPINJ > .6

118

117

21**

2 6 "

•Statistically significant at the .05 level

••Statistically significant at the .01 level
1 - [(74/80) / (77.6/68.2)] = 19 percent

for the cars with the lower chest g's. Ihe relationship between chest g's en the

NCAP test and fatality risk over the range of head-on collisions experienced en

the highway, although statistically significant, is not perfect. Marely having

the lower NCAP score of the two cars in the collision does not guarantee

survival, even if the two cars are of the same weight and the drivers of the same

age and sex. Yet, on the average, in collisions between cars with < 56 chest g's

an NCAP and cars with > 56 chest g's, the driver of the car with the better NCAP

score had 19 percent less fatality risk than the driver of the car with the

poorer NCAP score, after controlling for weight, age and sex.

Fifty-six chest g's are just one possible boundary value between

"good" and "poor" performance. Ihe fatality reduction for "good" performers can

be magnified by using a higher boundary value or by replacing a single boundary

value with a gap, putting some distance between the "good" and the "poor" groups.

For example, in collisions of cars with chest g's < 60 into cars with chest g's

> 60 (the pass-fail criterion in FMVSS 208), the fatality reduction in the "good"

performers is 24 percent. However, there are only 92 crashes meeting those

criteria, ftfeny other boundary values between low and high chest g's will also

produce statistically significant fatality reductions for the group with low

chest g's, but the boundary value of 56 iraxiinizes the fatality reduction for an

accident sample close to 120 crashes.

Ihe Head Injury Criterion (HIC) can be used to partition the cars into

two performance groups. In 113 head-on collisions between a car with HIC < 1000

an the NCAP test and a car with HIC > 1200, the fatality risk was a statistically

significant 14 percent lower in the cars with HIC < 1000. Ihe femur loads

xi
measured on the NCAP tests can also, by themselves, differentiate safer from less

safe cars. The "good" performers are defined to be the cars with < 1600 pounds

on each leg, and. the sum of the two loads < 2600 pounds. The "poor" performers

are those with > 1600 pounds en frit^y leg, p£ a sum > 2600 pounds. In 132 headon collisions, the fatality reduction for the "good" NCAP femur load performers

was a statistically significant 20 percent.

One reason that chest g's, HEC and femur load all "work" by themselves

is that the three NCAP test measurements are not independent observations an

isolated body regions. Cars with intuitively excellent safety design tend to

have low scores an all parameters, while cars with crashworthiness problems tend

to have high scores an one or more parameters, but it is not always predictable

which one. Still, the reasons for the significant correlation between NCAP femur

load and actual fatality risk are not completely understood at this time, since

injuries to the lower extremities, by themselves, are generally not fatal.

Any two NCAP parameters, working together, can do an even more

reliable job than any single parameter. In 125 actual head-on collisions between

cars with driver HIC < 1100 and chest g's < 60 on the NCAP test and cars with

either HIC > 1300 or chest g's > 60, the fatality risk was a statistically

significant 19 percent lower in the cars with low HIC and chest g's. The

accompanying table shows how chest g' s and femur load, or HIC and femur load can

be used to partition the cars, with statistically significant 19-22 percent

fatality reductions for the "good" performers, in samples of 121-134 crashes.

NCAP scores for all three body regions, with an independent "passfail" criterion on each score, work about as well as scores for any two body

xii
regions. "Good" performance could be defined as HIC < 900 and chest g's < 56 and

fenur load <. 1400 en each leg and <. 2400, total, while HIC > 1300 or. chest g's

> 60 or fenur load > 1700 en either leg or > 2700, total defines "poor"

performance. The fatality risk in 118 actual head-on collisions between a good

and a poor NCAP performer is a statistically significant 21 percent lower for the

drivers of the cars with good NCAP scores, after controlling for vehicle weight,

driver age and sex. These criteria can be varied by a moderate amount and the

fatality reduction for the "good" performers will still be statistically

significant, as long as the HIC cutoff is reasonably close to or slightly above

the FMVSS 208 value of 1000, the chest g cutoff is not far fron the FMVSS 208

value of 60 g's, and the femur load cutoff ranges from about 1400 pounds up to

the FMVSS 208 value of 2250 pounds.

A highly efficient way to use the NCAP scores for the three body

regions, however, is to combine them into a single composite score, wherein

excellent performance an two body regions might compensate for moderately poor

performance on the third. The composite score could be same type of weighted or

unweighted average of the scores for the various body regions. For example, a

weighted average measure of NCAP performance, NCAP3NJ, was derived by a two-step

process. First, the actual NCAP results for the driver dummy were transformed

to logistic injury probabilities. HEADHiT, CHESTTNJ, LFEM1RINJ and RFEMURINJ,

each ranging from 0 to 1. The weighted average

NCAPINJ « .21 HEADINJ + 2.7 CHESTINJ + 1.5 (LFHURINJ + RFHURINJ)

has the empirically strongest relationship with fatality risk for belted drivers

in the specific data set of actual head-on collisions described above (396

collisions, 792 cars). The accident data include 117 head-on collisions of a car

with NCAPINJ < 0.6 into a car with NCAPINJ > 0.6. Fatality risk is a

xiii
statistically significant 26 percent lover in the cars with NCAPINJ <0.6. Since

ICAPINJ is a weighted sum of NCAP scores for all of the body regions, the cars

with NCAPINJ <.0.6 have, an the average, substantially lower HIC, chest g's and

femur loads than cars with NCAPINJ > 0.6.

The purpose of defining NCAPINJ was to illustrate the strength of the

overall relationship between NCAP performance and fatality risk. However,

NCAPINJ is not a "magic bullet" or "ideal" way to combine the NCAP scores,

resulting in far higher correlations than other methods. Many other weighted

averages, or even an unweighted sum of the logistic injury probabilities, work

almost as well for differentiating the safer from the less safe cars on the

principal accident data set. On a more restricted alternative accident data set

of 310 collisions and 620 cars, where the ERRS vehicles are also required to have

the same number of doors as their matching NCAP test vehicles, NCAPINJ is not the

optimum weighted average (although it comes close to the optimum), and it is only

slightly more correlated with fatality risk than an unweighted sum of the

logistic injury probabilities. Mareover, on this alternative data set, HIC and

femur load have about equally strong correlation with fatality risk.

The performance of passenger cars on the NCAP test has greatly

improved since the program was initiated in 1979. That was demonstrated in

NHTSA's 1992-93 reports to the Congress and several other studies, which cite

specific improvements in vehicle structures and occupant protection systems

resulting in better NCAP performance. Has the historical trend of better

performance on the NCAP test been matched by a reduction in the actual fatality

risk of belted drivers in head-on collisions?

xiv
In general, it is not easy to ccnpare the crashworthiness of cars of

different model years. Fatality rates per 100 million vehicle miles have been

declining for a long time. In any given year, the fatality rate per 100 million

miles or per 100 crashes is lower for new cars than for old cars. Both trends

create the impression that "cars are getting safer all the time," but, in fact,

the declines in fatality rates to a large extent reflect changes in driving

behavior, roadway environments, demographics or accident-reporting practices.

A head-on collision between cars of two different model years, however, reveals

their relative crashworthiness. Both cars are in essentially the same frontal

collision, on the same road, in the same year, on the same accident report. The

behavior of each driver, prior to the impact, has little effect an who dies

during the impact. After adjustment for differences in car weight, driver age

and sex, the model year with more survivors is more crashworthy.

There have been 241 actual head-on collisions between a model year

1979-82 car and a model year 1983-91 car, in which both drivers were belted.

These collisions allow a comparison of cars built during the first four years of

NCAP to subsequent cars, where manufacturers have had time to build in safety

improvements. In the 241 older cars, 146 drivers died, whereas only 126.6

fatalities were expected, based an car weight, driver age and sex. In the newer

cars, there were 132 actual and 147.1 expected driver fatalities. For the 1983-

91 cars, that is a statistically significant fatality reduction of

1 - [(132/146) / (147.1/126.6)] « 22 percent

A more generalized analysis, which allows a larger sample size of 1189

crashes, applies to head-on collisions in which the "case" vehicle of interest

is a 1979-91 car that matches up with an NCAP test, whose driver wore belts, but

xv
the "other" vehicle in the crash can be any 1976-91 passenger car with a belted

driver. For any subset of crashes, a fatality -Hpic p;yv»y can be computed for the

"case" vehicles, based an the ratio of actxial to expected fatalities in the case

and other vehicles. The lower the risk index, the more crashworthy the car (100

•= average). The actual fatality risk indices can be ccnpared in three model-year

groups, 1979-82, 1983-86 and 1987-91. So can the NCAP test performance, as

measured by a ccrrposite score such as NCAPINJ, or by the average values of the

actual NCAP parameters for the three body regions:

Mode l Year s

1979-82 1983-86 1987-91

Fatality risk index in

actual head-on collisions

Average value of NCAPINJ

Percent of cars with NCAPINJ > 0.6

Average HEC

Average chest g's

Average left femur load

Average right femur load

The trends in the actual fatality risk and the average value of

NCAPINJ are almost identical. The risk index decreased by a statistically

significant 20 percent frcm 1979-82 to 1983-86, and by another 4 percent from

then until 1987-91 (nonsignificant). In all, the actual fatality risk for belted

drivers in head-on collisions decreased by a statistically significant 24 percent

frcm model years 1979-82 to 1987-91. A composite NCAP score, such as NCAPINJ,

nicely portrays the improvement in NCAP performance over time. Parallel to the

reduction in the fatality risk index, NCAPINJ greatly improved frcm an average

xvi

119

.59

49

1052

54.9

928

1079

95

.40

14

915

46.8

883

784

91

.37

9

827

46.5

1002

1018
of 0.59 in model years 1979-82 to 0.40 in 1983-86, with an additional, modest

improvemsnt to 0.37 in 1987-91. If NCAPINJ = 0.6 is defined as the limit of

"acceptable" NCAP performance, the passenger car fleet has truly progressed since

the inception of NCAP: initially, 49 percent of the cars had NCAPINJ > 0.6, but

that decreased to 14 percent in 1983-86 and 9 percent in 1987-91. Average HTC

and chest g's declined substantially during the NCAP era; average femur loads

stayed about the same, but well below the 2250 pounds permitted in FMVSS 208.

cone] i ipions and cnveats

o There is a statistically significant correlation between the performance

of passenger cars an the NCAP test and the fatality risk of belted drivers

in actual head-on collisions. Since many head-on collisions differ

substantially from NCAP test conditions, this suggests NCAP scores are

correlated with actual crashworthiness in a wide range of crashes.

o In a head-on collision between a oar with "acceptable" NCAP performance

and a car of equal mass with "poor" performance, the driver of the "good"

car has, on the average, about 15-25 percent lower fatality risk.

o A highly effective way to differentiate "good" from "poor" NCAP

performance is by a single, composite NCAP score, such as a weighted

caibination of the scores for the three body regions. However, even the

NCAP score for any single body region can be used to partition the fleet

so that the cars with "good" scores .have significantly lower fatality risk

than the cars with "poor" scores. The borderline between "good" and

"poor" NCAP scores that optimizes the differences in actual fatality risk

is close to the FMVSS 208 criteria for each of the three body regions.

XVll
NCKP scores have iirproved steadily since the inception of the program in

1979, with the greatest iirprovement in the early years. By now, most

passenger cars meet the EMVSS 208 criteria in the 35 mph NCAP test. This

achievement has been paralleled by a 20-25 percent reduction of fatality

risk for belted drivers in actual head-on collisions in model years 1979-

91, with the largest decreases during the early 1980's.

This is a staHpHraT study and it is not appropriate for conclusions

about cause and effect. It shows that passenger cars became significantly

safer in head-on collisions during 1979-91, as NCW? scores improved. It

does not prove that the NCftP program was the stimulus for each of the

vehicle modifications that saved lives during 1979-91. (For example, the

automatic protection requirement of EMVSS 208 was another important

stimulus.)

The correlation between NCAP scores and actual fatality risk is

statistically significant, but it is far from perfect. On the whole, cars

with poor NCM> scores have higher-than-average fatality risk in head-on

collisions, but there is no guarantee that every specific make-model with

poor NCAP scores necessarily has higher fatality risk than the average

car. Conversely, there is no guarantee that a specific model with average

or even excellent scores necessarily has average or lower-than-average

fatality risk in head-on collisions.

The data show that cars with poor NCfcP scores (e.g., above the, EMVSS 208

criteria) have significantly elevated fatality risk in head-on collisions,

but they do not show a significant difference between the fatality risk of

cars with exceptionally good NCAP performance and those with merely

average performance.

XVlll
CHAPTER 1

INIRCDUCTICN AND ANALYSIS OVERVIEW

The Appropriations Act for Fiscal Year 1992 directs "NHTSA to provide

a study to the House and Senate Carrmittees an Appropriations comparing the

results of New Car Assessment Program (NCAP) data f ran previous model years to

determine the validity of these tests in predicting actual cn-the-road injuries

and fatalities over the lifetime of the models" [5], p. 35. In February 1992,

the agency responded to the directive with a plan to compare NCAP results and

real-world crash experience, based on various analyses of accident data files

[23]. A Report to Congress, presenting the highlights of the analyses, was

ccmpleted in December 1993 [24]. One analytic approach, described in Section 3

of the Report to Congress, addressed the correlation between NCAP performance and

the fatality risk of belted drivers in actual h<=«d-<^n collisions. This technical

report provides a more detailed exposition of the data sources, analytic approach

and statistical findings in the analysis of head-on collisions.

The New Car Assessment Program was developed in response to Title II

of The Motor Vehicle Information and Cost Savings Act of 1973 (MVICS) [20], which

authorized NHTSA to develop consumer information en the crashwort^ineps of

passenger vehicles. Since 1979, NCAP has been a program of frontal impact tests

at 35 mph into a barrier, with belted dummies at the driver and right-front seat

positions. The 35 mph impact speed is 5 mph faster than the test speed in

NHISA's Federal Motor Vehicle Safety Standards for occupant protection in frontal

crashes (FMVSS 204, 208, 212, 219 and 301), and it produces a velocity change

close to the average in actual fatal frontal impacts. Measurements en the

dummies are used to calculate the Head Injury Criterion (HIC), chest g forces (3
millisecond peak) and left and right fenur loads (peak axial loads at knee). HIC

measures the cumulative impact force an the head during the crash. An average

of 30 passenger cars and light trucks are tested each year, including make/models

that are new or significantly redesigned in that model year.

1.1 NCAP performance vs. crashworth''TvaHfji on the highway

FMVSS 208 requires all passenger cars to have HEC <. 1000, chest g's

<. 60 and femur load < 2250 pounds an a 30 mph test. NCAP is not a regulatory

program and does not set pass-fail levels of performance for its 35 mph test.

Nevertheless, it could be argued that the level of frontal occupant protection

guaranteed by the basic FMVSS at 30 mph has largely been extended to 35 mph since

NCAP started in 1979. In model year 1979, fewer than 25 percent of cars met the

FMVSS 208 criteria at 35 mph. In subsequent years, NCAP results were widely

disseminated to consumers, manufacturers and insurers. By 1986-91, over 60

percent of passenger cars met the FMVSS 208 criteria at 35 mph. While statistics

do not prove that the NCAP program was solely responsible for the improvement in

test results (e.g., automatic occupant protection installed in response to FMVSS

208 was another obvious factor), the trend is certainly in the right direction

and it appears to fulfill one promise of a consumer information program: the

manufacturers significantly enhanced safety performance as measured by the

publicized test protocol. They are now "designing their vehicles to 35 mph."

While there is overwhelming evidence that vehicle performance on the

NCAP test has improved since the inception of the program, that evidence, by

itself, does not prove that cars have become safer in actual crashes en the

highway. The ultimate goal of all safety programs, including consumer

information programs such as NCAP is the reduction of deaths and injuries an the
highway. There is a desire for evidence that cars with poor NCM> scores are less

safe in actual crashes than cars with acceptable scores, and, more generally,

that cars have becctne safer in actual crashes since the beginning of MIBP.

Researchers have eagerly explored the correlation between N3VP performance and

fatality risk in actual crashes since the initial years of IO P [4]. There are

two reasons why their efforts have had little success in past years. ICaP is a

test program involving belted dumtdes, and, until very recently, there siitply

have not been enough fatal or serious-injury accident data involving belted

occupants for a meaningful caqparisan with NCRP results. UCKP describes

differences in the crashworthiness of vehicles on identical 35 nph tests, whereas

in accident data it is quite difficult to isolate the effects of crashworthiness

(the ability of a vehicle to protect its occupants fron death or injury, given

that a crash has occurred) fron other factors that affect fatality rates of cars:

the types of people who drive the cars, and the enviraments where they are

driven.

Thanks to the steady increase in belt use after 1984, as more and more

States enacted belt use laws, enough accident data involving belted occupants had

accunulated, by 1993, for meaningful statistical analyses. But it is still

necessary to find a method which isolates the crashworthiness differences between

cars and filters out the differences attributable to the way the cars are driven.

The method used in this report is to analyze fatal h^wi-on collisions between two

papsencrer

1.2 The ^'iffic'i ty of isolating crashworth"'r|*

ags effects

Before any discussion of the unique advantages of head-on collisions

as a data source, it helps to review the foibles of conventional measures of
fatality risk, such as the occupant fatality rate per million vehicle years. It

is well known that "high-performance" cars popular with young male drivers have

a higher frequency of fatal crashes than family sedans, and it is generally

suspected that the difference is primarily due to the way the cars are driven,

not crashworthiness. But a look at seme actual fatality rates helps clarify the

extent to which differences in drivers and exposure influences the variation in

fatality rates.

For example, Table 1-1 displays the actual rate of fatalities per

million vehicle registration years for model year 1985-87 cars in calendar years

1986-88 (data compiled by the Insurance Institute for Highway Safety [28]). The

actual rates range from 60 in the Volvo 740/760 to 520 in the Corvette - almost

a 9:1 ratio. The 15th percentile of actual risk is 120 (Pcntiac Grand An) and

the 85th percentile is 310 (Dodge Daytcna) - still a 3:1 variation in risk across

the middle 70 percentiles. It is intuitively obvious that 9:1 differences

between cars are due primarily to the types of people who drive them, rather than

real variation in crashworthiness. It is most unlikely than one make-model is

intrinsically 9 times as dangerous as another. The make-models in Table 1-1 with

the lowest fatality rates are primarily luxury and family cars. The models with

the highest rates are "performance" oars and small economy cars. Even within a

specific make-model, station wagons have lower fatality rates than four-door

sedans, while two-door coupes have higher rates.

These differences are sanewhat diminished, but still persistent, even

after "adjusting" the rates for key variables such as car weight, driver age and

sex. The Insurance Institute attempted to control for at least sane of the

driver differences by computing, for each make/model, a predicted fatality rate
TBHLE 1-1

EA3MJTY RISK INDICES BASED ON FATALITIES

PER MELLICN REGISTERED VEHICLE YEARS

(tfodel year 1985-87 cars in calendar years 1986-88)

Fatality Rate

Actual

Volvo 740/760 4dr

Ford Taurus Vfagan

T-Hnnoin itwn Car

VWJetta 4dr

Chev Cavalier Wagcn

Toyota Cressida

Audi 5000

Olds dera Wagon

Caddy DeVille 2dr

Caddy DeVille 4dr

Ford Esaart Wagcn

Afalvo 240

Pent Grand An 4dr

Olds Ciera 2dr

Pent Grand Prix

Buick Century 4dr

Ifercury Gr Iferquis

Ifercury Sable

Pantiac 6000

60

70

80

110

no110

110

no

no

no120

120

120

120

120

120

120

130

130

Chev Oelefarity Wagcn 130

Olds dera 4dr

Buick Electra

Ford Taurus

Olds Calais 4dr

Honda accord 2dr

Subaru Wagon

Qiev Caprice Wagon

Ford Crown Vic

Nissan Sentra 2dr

Hcnda Prelude

Buick Somerset 2dr

Mazda 626

Honda Accord 4dr

Olds 98

Olds Delta 88

Chrys 5th Avenue

Toyota Oelica

Toyota Corolla 4dr

Mercury Topaz 4dr

Chrys New Yorker

Chev Caprice 4dr

Honda Civic 4dr

Chev Celebrity 4dr

130

130

140

140

140

140

140

140

150

150

150

150

150

150

150

150

160

160

160

160

160

170

170

Predictd

140

150

120

250

200

190

170

150

140

120

220

190

190

180

170

160

150

200

170

170

150

140

200

190

180

170

170

160

430

310

220

200

170

150

130

120

280

230

200

160

140

260

160

Fatality

C4al

Indax

43

47

67

44

55

58

65

73

79

92

55

63

63

67

71

75

80

65

76

76

87

93

70

74

78

82

82

88

35

48

68

75

88

100

115

125

57

70

80

100

n4

65

106

Fatality Rate

Actual Predictd

Ford cm-illi 4dr

Ford Tfenpo 4dr

Buick IfiSabre

Olds Calais 2dr

Ford Ttenpo 2dr

VWGaLf 4dr

Nissan Msoma

Chev Nova 4dr

Buick Regal 2dr

Subaru 4dr

Pent Grand An 2dr

Honda Civic 2dr

Fond T-Bird

Dodge Qtni 4dr

Chev Cavalier 4dr

Msrcuzy Cougar

Chev Olftoity 2dr

Toyota Corolla 2dr

Nissan 200SX

Pent Sunbird4dr

B W 300 2dr

Hyundai Exnel 4dr

Plym Reliant 4dr

Chev Cavalier 2dr

Pent Sunbdrd 2dr

Plym Harizcn 4dr

Chev Itnte Carlo

Dodge Aries 4dr

Ford ^an|> i 2dr

Dodge Dacytona

Chev Spectrum 2dr

Chev Chevette 2dr

Pcntiac Fiero

Plym TurisoD

Pcntiac Firebird

Honda CRX

Chev Sprint

Chev Chevette 4dr

Nissan 300ZX

Ford Mjstang

Dodge Charger

Chev Camaro

Chev Corvette

180

180

180

190

200

200

200

200

200

200

210

230

230

230

230

240

240

250

250

250

260

260

260

270

280

280

280

290

300

310

320

340

360

360

380

390

410

410

420

440

450

490

520

270

180

140

190

260

250

250

210

190

180

280

280

250

210

190

220

150

380

330

180

340

260

160

260

240

210

210

190

290

320

250

250

380

260

310

530

290

190

420

370

330

380

360

Fatality

P4aV KJUBIC

Index

67

100

129

100

77

80

80

95

105

ni

75

82

92

no121

109

160

66

87

139

76

100

163

104

U 7

133

133

153

103

97

128

136

95

138

123

74

141

216

100

US

136

129

144

Actual fatality rate = actual fatalities per million registration years (source:

USB [28])

Predicted fatality rate "attetrpts to take into account the age and sex of drivers

involved and the car size [28]."

Fatality risk Index = 100 * Actual/Predicted
which "takes into account the age and sex of drivers involved and the car size"

to the extent that they affect annual mileage, collision propensity and

vulnerability to fatal injury. The predicted rates are shown next to the actual

fatality rates in Table 1-1. The adjusted f^aiiirY risk index, equal to the

ratio of the adjusted to the predicted fatality rate (and multiplying by 100) was

computed for each make-itiodel and is shown in the right columns of Table 1-1.

Cars with an index below 100 have lower fatality rates than expected; indices

above 100 denote "higher than expected" fatality rates. The index ranges frcm

35 in the Nissan Sentra 2 door to 216 in the Chevrolet Chevette 4 door - almost

a 6:1 ratio. The 15th percentile of the risk index is 67 (Lincoln Town Car) and

the 85th percentile is 129 (Chevrolet Camaro) - nearly a 2:1 variation in the

risk index across its middle 70 percentiles.

This fatality risk index filters out same of the worst disparities in

the actual fatality rates, but still does not isolate crashworthiness

differences. The differences between the best and the worst cars still seem

larger than what could likely be ascribed to variations in crashworthiness. The

index shows seme large differences between cars that ought to be about equally

crashworthy. For example, the Celebrity wagon has an index of 76, the Celebrity

4-door sedan's index is 106 and the 2-door model's index is 160. it is true that

4-door cars have a safety advantage over 2-door cars in certain types of crashes,

but not that large an advantage, dearly, the types of people who drive station

wagons are much less likely to have serious accidents than the drivers of the

same age and sex who drive 2-door coupes. Similarly, Chevrolets and Fords

consistently have higher risk indices than "corporate cousin" vehicles sharing

essentially identical components. For example, the Chevrolet Mante Carlo has an

index of 133, while the Pantiac Grand Prix has an index of 71; the Ford Tempo 4-
door has an index of 100 while the Msrcury Topaz 4-door has an index of 80.

Differences liJce these are nuch too large to ascribe to crashworthiness and

probably reflect socioecananic or geographic differences of the drivers. Almost

all the iitported cars have risk indices below 100, often far below 100. The

advantage for iitported cars, however, may be due to the clientele, not the

vehicle: the Chevrolet Nova is essentially the same car as the Toyota Corolla 4-

door, but the Nova has an index of 95 while the Corolla's index is 70. In

surmary, simple fatality rates per million car years, even if they are adjusted

for driver age and sex, are poor measures of crashworthiness because 30-year-old

males who drive Volvos have completely different driving patterns and far lower

accident prcmeness than 30-year-old males who drive Corvettes.

1.3 Analysis overview

The objective of isolating the actual crashworthiness differences

between cars is better attained by studying hAafl-fln collisions between two

passenger cars, each with a belted driver, which resulted in a fatality to one

or to both of the drivers. A head-on collision is a special type of highway

crash ideally suited for studying crashworthiness differences between two cars;

it canes close to a controlled laboratory test. Both cars are in essentially the

same frontal collision. The outcome is a verdict en the intrinsic relative

crashworthiness of the two cars and the intrinsic relative vulnerability to

injury of the two drivers. Events that happened before the moment of impact -

unsafe driving acts, crash avoidance capabilities - are mostly irrelevant in

deciding which driver survives the crash and which dies. Records of every fatal

head-on collision since 1975 may be found in NHISA's Fatal Accident Reporting

System (EARS).
To set the scene, consider a head-on collision between a Volvo 740 and

a Corvette. Both cars weigh just over 3000 pounds. Both drivers, in this

collision, are 30-year-old rrales. At the manent these two cars hit head-on, it

becanes irrelevant that Corvettes have 9 times as high an overall fatality rate

per million car years as Volvos, as shown in Table 1-1. It is irrelevant that

one of the drivers (guess who) had an unblemished record and used his car only

to cccmute between his office and his home at a prudent 5 mph below the speed

limit, while the other was weaving at high speed down the wrong side of the road

in a drunk and drugged haze, and had a long record of accidents and violations.

The carmendable past history of the one driver will not protect him in the headon collision. If there are any survivors, the likelihood is that the driver of

the intrinsically more crashworthy car will be the one to survive.

The preceding example of a head-on collision was a special case in

that both cars had identical weights and both drivers were 30-year-old males.

Neither car had an inherent advantage. In the absence of specific knowledge

about the intrinsic crashworthiness of the two cars, each driver would be

expected to have the same fatality risk in the crash. In most head-on

collisions, the two cars have different weights, and their drivers are not

necessarily the same age. Still, it is possible to predict the expected fatality

risk for each driver in a head-on collision between these two cars, as a function

of the weights of the two cars and the age and sex of each driver. Weight, age

and sex are important because the lighter car experiences a greater velocity

change than the heavier car, and an older/female driver is more vulnerable to

injury than a younger/male driver. The fact that age and sex are also correlated

with crash-proneness is irrelevant here, because the attempt is to predict the

relative fatality risk of each driver, given that the two cars have already

8
collided, head-on.

Ihe expected fatality risk of each driver is calibrated fron the

accident data by a logistic regression. Regression coefficients vary slightly,

depending an the calibration data set, but the following pair of regression

fornLilas is typical for head-on collisions in which both drivers are belted. The

expected fatality risk for driver 1 is

expr.616 - 5.427(locrW, - loaW.) + .053KA,-A7) + .34(F,-F,)1

1 + exp[.616 - 5.427(logW, - logW2 ) + .0531(Ai-A2) + .34(Fi-F2)]

where Wj is the curb weight of car 1, At is the age of driver 1 and Fj is 1 if

driver l is female, 0 if male. Ihe expected fatality risk for driver 2 is

expf.616 + 5.427(locrWl - locrW?) - .0531 (A,-A?) - .34(F,-F,)1

1 + exp[.616 + 5.427(logW, - log W2) - .0531(Ai-A2) - .34(Fi-F2)]

These formulas, as stated above, measure the relative vulnerability

to fatal injury of the two drivers, given that their cars had a head-on collision

not the propensity of cars to get involved in head-on collisions. For example,

given 100 fatal head-on collisions between 3000 pound cars driven by belted, 20-

year-old males and 2500 pound cars driven by belted, 50-year-old females, the

f ormulas predict 9 deaths among the young males in the heavier cars and 97 deaths

among the older females in the lighter cars (for a total of 106 fatalities in the

100 collisions, since seme of them resulted in fatalities to both drivers).

Cars with average crashworthiness capabilities will experience an

actual number of fatalities very close to what is predicted by these forrrulas,

which are calibrated from the collision experience of production vehicles. If

a particular group of cars, however, consistently experiences more driver
fatalities than expected in their head-on collisions, then it has to be

concluded, based en the empirical evidence, that this group of cars is less

crashworthy than the average car of similar mass. In the preceding example, if

the 3000 pound cars with the young male drivers had 8, 9 or 10 deaths in the 100

crashes, they are doing about as well as expected, but if they had 30 deaths,

they are less crashworthy than the 2500 pound cars with the older female drivers.

More generally, given a set of head-on collisions between one group

of cars A and another group of cars B, it is possible to compare the

crashworthiness of the two groups. The cars in group A are less crashworthy in

head-on collisions than the cars in group B if the actual number of driver

fatalities in group A is higher than the expected number of fatalities in the

collisions, given the weight, driver ages, etc. in groups A and B. The actual

fatalities and expected probabilities of fatality are sunned for over all the

crashes for groups A and B, as follows:

Head-On Collisions between Groups A and B

Car Group A Car Group B

Actual fatalities 100 60

Expected fatalities 91.8 68.2

To the extent that the cars in group A (in this example) are, on the average,

slightly smaller than those in group B, more fatalities are expected in A than

in B. If the actual and expected fatalities had been equal, groups A and B would

have been judged equally crashworthy. m fact, group A performed slightly worse

than expected. There were more fatalities than expected in A and fewer than

expected in B. The increase in fatality risk for A relative to B is

[ (100/91.8) / (60/68.2) ] - 1 - 23.8 percent

10
Conversely, the fatality reduction for B relative to A is

1 - [ (60/68.2) / (100/91.8) ] = 19.2 percent

In the central analyses of this report, group A is a set of passenger

cars with "poor" NCAP scores and group B is a set of cars with "acceptable" NCAP

scores. In the actual head-on collisions between group A cars and group B cars,

do the cars with poor NCAP scores have significantly more driver fatalities than

would be expected? The analyses will measure the reduction in fatality risk, in

actual head-on collisions, for a car with good NCAP scores relative to a car with

poor NCAP scores. There are several methods to define "poor" and "acceptable"

NCAP performance - e.g., based on a single NCAP parameter (chest g's, KEC or

femur load), or based an a composite of these parameters. How big is the

fatality reduction, for the "acceptable" vs. the "poor" cars, by each method?

Other analyses will measure the overall reduction in fatality risk, for belted

drivers in head-on collisions, since model year 1979, when NCAP testing began,

until 1991, the latest model year for whicn substantial accident data were

available as of mid 1993.

Two studies utilized the special advantages of head-on collisjorpF for

isolating crashworthiness differences between cars. Zador, Jones and Ginsburg

analyzed the relative fatality risk of the two drivers in a fatal head-on

collision and, even with the quite limited data en belted drivers available in

the 1975-83 EARS, found some significant correlations between NCAP scores and

fatality risk [30]. NHISA's 1988 Evra"hifltion o^ QTTViyit Protection in Fiun^^i

Interior Impact also studied head-on collisions, but concentrated on the

unrestrained driver [19], pp. 111-140. After significant gains during the late

1960's, little net improvement in frontal crashworthiness was found for the

11
unrestrained driver during model years 1970-84. Since these two studies were

published, there has been a vast increase in ERRS cases involving belted drivers,

permitting detailed analyses of the crashworthiness of passenger cars in head-on

collisions for belted drivers.

1.4 Seme

While head-on collisions, as reported in the Fatal Accident Reporting

System, have many advantages for correlational analyses with NCAP results, it

must be pointed out that many of these head-on collisions do not come close to

resembling an NCAP test. EARS data can be used to distinguish head-on collisions

from other crashes, but they currently do not identify many important details

about the collisions, such as the impact speeds, the exact alignment of the

vehicles, the height and weight of the drivers, or the specific body region with

fatal lesions. All NCAP tests are 35 mph impacts straight ahead into a flat

barrier, with contact over the entire front of the car, which is regarded

equivalent to a perfectly aligned head-on collision of two identical cars, each

travelling 35 mph. The driver duttmies simulate 50th percentile males. NCAP test

results are limited to three body regions (head, chest, femur).

The EARS sample, on the other hand, includes the full range of closing

speeds that nay occur an the highway, and the cars, although hitting front-tofront, may be aligned at an angle, and with small or substantial offset. The

drivers may be any height or weight, and may have adjusted the seat forward or

backward as they wish. Ifeny fatal lesions are in the neck or abdomen: body

regions not specifically tested in NCAP. It is not possible, with EARS, to

single out those head-on collisions that come really close to an NCAP test. As

a censequence, it is inappropriate to expect perfect correlation between NCAP

12
test results and actual fatality risk in the FRRS sanple. Moreover, if there is

any significant correlation between the two, it suggests that the NCKP scores say

something about actual crashworthiness in a range of crashes that goes far beyond

the specific type tested in NC&P.

None of the analyses of this report are conducted at the make-model

level. Ihere are not nearly enough head-on crashes with belted drivers to

compute a fatality risk index by make-itodel and to compare this index with NCftP

scores, by make-model. Thus, the analyses will indicate whether, on the whole,

cars with poor NCfcP scores have higher-than-average fatality risk in head-on

collisions, but they will not identify any specific make-model (with or without

poor NCAP scores) as being significantly less safe than the average car.

Since the accident data in this study are limited to head-on

collisions between two cars with belted drivers, the correlations found here do

not necessarily extend to other types of frontal impacts, such as collisions with

fixed objects or trucks, let alone side inpacts, rear impacts, rollovers or

crashes involving drivers who do not wear safety belts.

13

CHAPTER 2

AN ACCIDENT DATA FILE WITH NCAP INFORMATION

Mast of the analyses of this report examine head-on collisions between

two passenger cars with belted drivers. Two groups of cars are selected, based

en their NCAP scores. The collisions between cars of the two groups are

examined, and the actual number of driver fatalities in each group is catpared

to the expected number, given each car's curb weight and each driver's age and

sex. Thus, the type of data needed for the analysis, ideally, would be a file

of actual head-on collisions, with both drivers belted, resulting in a fatality

to at least one of the drivers, indicating the age and sex of each driver, the

curb weight of each vehicle, and the HIC, chest g's and femur load that were

recorded for the driver dummy when that vehicle was tested in NCAP.

NHTSA's Fatal Accident Reporting System (EARS) contains a record of

every fatal crash in the United States since 1975. EARS data identify what

crashes were head-on collisions; indicate the age, sex and belt use of each

driver; and identify the vehicles by their Vehicle Identification Numbers (VIN).

However, EARS data, themselves, do not include an accurate measure of curb weight

or any information about NCAP results for the vehicles involved in the crash.

Accurate curb weights are indispensable, because the relative fatality risk for

two vehicles in a head-on collision is so sensitive to the relative weight (as

evidenced by the coefficient of 5.427 in the formulas of Section 1.3). This

chapter describes how the VIN and other vehicle codes are used to link EARS with

other data files - the R. L. Polk Ifational Vehicle Population Profile and the

NHTSA file of NCAP test results - so that accurate curb weights and NCAP scores

can be appended to the accident data.

15
2.1 initial FARS data

At the time of this study, FARS data were available through mid 1992

[10], [11], [12]. The type of crash of specific interest for this analysis is

a head-on collision between two passenger r^m resulting in a fatality to at

least one of the drivers. Although light trucks have been tested in NCM> since

model year 1983, collisions between a car and a light truck, or between two light

trucks were not included in the study, because of problems in obtaining accurate

weight information an trucks, and also because the samples of crashes involving

belted truck drivers were insufficient.

The model years included in the study should, at least, range fron

1979, the first year of NC&P testing, through 1991, the last year before air bags

became the predaninant type of occupant protection. In the initial data

reduction, cars of model years 1976-78 were also included, because the designs

frcm those model years sometimes carried over into the NCAPera. Cars of model

year 1975 and earlier were excluded because they usually had different belt

systems from later models (ignition interlock or separate lap and shoulder

belts).

Fran 1975 through mid 1992, FARS contains 1,006 records of head-on

collisions involving two passenger cars of model years 1976-91, fatal to at least

one driver, in which both drivers wore safety belts (2,012 cars). A 2-vehide

file is designed, with one record for each collision, containing information on

vehicle no. 1 and its driver and on vehicle no. 2 and its driver. A "head-on"

collision has to be a crash involving exactly two vehicles (VE_FORM3 = 2) ; both

vehicles have to be passenger cars (BCCYJKP 1-9); both have to have frontal

damage (IMPACE = 11, 12 or 1); the "most harmful event" for each vehicle has to

16
be a collision with another motor vehicle, in transport or in "other roadway"

(codes 12-13; prior to 1979, this variable was not defined an EARS, so it is not

used as a filter); for both drivers, EARS must record their age (and it most be

in the range of 14-98) and sex. A driver is "belted" if either a manual or an

automatic belt was used, according to EARS (MAN_REST = l, 2 or 8 or AUT_REST =

1 in 1975-90; RESTJJSE = 1, 2 or 8 in 1991-92). Cars with air bags are included

in the study only if drivers wore their belts.

There are questions about the accuracy and completeness of EARS beltuse data, which are mostly based an information in police reports. Officers are

usually not present at the scene at the moment of the crash and must rely an

statements by survivors and witnesses, physical evidence and judgment. Belt use

is coded "unknown" for 18 percent of the drivers and is not necessarily accurate

in the remaining cases. The greatest concern is in States with buckle-up laws,

where belt use may be overreported by survivors to escape penalties. Based an

1983-92 trends in reported belt use among FPrJZ fatalities vs. actual belt use

observed an the road, NUESA believes that the belt use of the fatally injured

occupants, at least, is quite accurately reported in EARS. In many cases, these

fatally injured occupants may not have been moved between the time of the crash

and arrival of police, allowing easy identification of belt use. While the belt

use of survivors may not be as accurately reported as for fatalities, at least

there is no reason to suspect that reported belt use is in any way confounded

with a vehicle's NCAP performance.

Before EARS data can be linked to the Polk or NCAP files by makemodel, it is obviously necessary to have accurate make-model information an EARS.

The make-model codes an the basic EARS file, which are manually entered and not

17
decoded from the VIN, are not suitable for the analysis. Jfeny cars are miscoded,

especially where model names are easily confused (e.g., Cutlass, Cutlass CLera,

Cutlass Calais, Cutlass Supreme). Also, the model is often coded "unknown" when

there is a valid, deccdable VEN. ihe basic EARS file contains a 3-digit VINA_MX>

code, which is obtained from the VIN, accurate, and suitable for linking EARS to

Polk data, which contain a similar code with the name SERS_ABR. Ihe VINA_MX>

code, however, is not well-suited for linking EARS with other data files, such

as NCAP results, which do not have a corresponding code.

A program was written to decode VINs and define make-models, using

approximately the same 4 digit numeric scheme as in the basic EARS, superseding

the values in the original EARS data. Cases in which one or both of the vehicle

records has a blank or nanvalid VIN are deleted. To prevent excessive deletions,

however, one set of "minor" errors in the VIN is permitted: if a field which must

have a numeric code has alphabetic 0 the program "corrects" it to numeric 0;

likewise I to 1, Z to 2, S to 5, G to 6 and B to 8 - and vice versa if look-alike

numeric codes appear in an alphabetic field. The model year decoded from the VIN

supersedes the model year code on EARS. Ihe exclusion of cases with unknown or

nanvalid VINs reduces the file to 934 collisions (1,868 cars).

Ihe make-model code, by itself, is not sufficient for linking EARS to

the file of NCAP test results. Ihe same make-model code may be used for two

quite different cars (e.g., 1979 and 1991 Honda Civic), sometimes even in the

same year (e.g., 1988 Buick LeSabre H-body sedan or a B-body station wagon).

Conversely, the same of quite similar cars can have different model codes (e.g.,

Dodge Colt and Plymouth Colt). As will be seen, nearly identical make-models in

the accident data will sometimes be linked to the same NCAP test. Based an the

18
VIN, passenger cars of the 1976-91 era were classified into about 300 car groups

with shared body platforms - e.g., all O4 N-body cars. A 4-digit code for the

car group supplements the make-model code. When a car gets a major redesign, a

new car group is defined - e.g. Toyota Celica in 1976, 1978 and 1987. "Shared

body platform" generally means the same wheelbase, track width and drive system

(front-wheel or rear-wheel). Not all cars in a car group are nearly identical

"corporate cousins." Sometimes, they may vary by several hundred pounds in

weight or have easily visible differences in structure or interior layout (e.g.,

1983 Cadillac Seville and Eldorado). These differences will be discussed further

in Section 2.4.

2.2 Qirfo weight data from Polk files

The single most important safety factor in a head-on collision is the

relative weight of the two cars. As stated above, a l percent weight advantage

for one of the cars translates into more than a 5 percent reduction of expected

fatality risk for that car. By the same token, a 1 percent error in the weights

of a group of cars can throw off their expected fatality risk by 5 percent.

Vehicle weights should be as accurate as possible and biases must be avoided.

The weight variable in the FARS data, vTN_WaT, is not usable for

several reasons. It lists the "shipping weight" (unoccupied car without fuel or

other fluids) of some cars in some years and, arbitrarily, the "curb weight"

(unoccupied car with fuel and other fluids) at other times, especially after

1981: about a 100 pound discrepancy [19], p. 118. It is defined at the makemodel level (which, itself, is inaccurately reported in the basic FARS) and does

not take into account the extra weight of optional engines, station wagon bodies,

etc. Curb weights from Automotive News Almanacs [2] should also be avoided in

19
this study (although, at least, they do not have severe year-to-year biases).

In general, the Almanac lists only one or two nirh weights for a specific makemodel in a given year and does not indicate exactly which engine and level of

decor (e.g., L, GL, or IX) this weight applies to.

The most accurate listings of curb weights are the official Automobile

Specifications supplied by the manufacturers through the American Automobile

Pfenufacturers Association (then called the Motor Vehicle tfenufacturers

Association, or MJMh). The books list the baseline curb weight of every makemodel and level of decor plus the incranental weight of each optional engine and

other equipment. The vast amount of data in these hard-copy files has been

encoded in the tapes of R. L. Polk's National Vehicle Population Profile [21],

which lists a curb weight for each combination of make, model year, series (model

and decor level, expressed in the 3-digit SERS_AER code), body style, engine code

and, possibly, fuel code. These Polk weights are highly accurate for a car which

contains no equipment beyond that which is standard in a particular make-model

and subseries. (The Polk file, however, does not include curb weights for light

trucks. Although curb-weight information may be available from other sources,

its utility is uncertain, because a truck's weight may be substantially augmented

by cargo.) A program was written to define variables on EARS that mimic those

an the Polk files and merge the two files, ftfeke and mode] year are already on

EARS. So is the 3 digit series code (called VTNA_MX> on EARS and, if missing,

obtained manually by analyzing the V3N). The body style, engine and fuel codes

are derived from the VTN. After the initial computer merge, and after a manual

search through the VNMk specification books in those cases where the Polk file

had missing weights, it was possible to identify a curb weight for both vehicles

in 926 head-on collisions (1852 vehicles).

20
2.3 Adjusting1 Polk weights b^ped on actual weight

Although the Polk weights are detailed and based en authoritative

sources, they are still not the weights of actual cars on the road. Before the

Polk weights are accepted at face value, it is wise to cenpare them to measured

weights of same actual cars. Moreover, it is likely that the actual cars would

be heavier, because most cars contain at least some optional equipment such as

air conditioning, radios, etc. Those items are not included in the Polk weight

unless they are standard equipment en a particular make-model and subseries.

NHISA's data bank of ccnpliance tests for new cars is a reliable

source of actual curb weights. Since 1968, NHISA has performed hundreds of

ccnpliance tests each year, under contract at test laboratories, checking

selected new vehicles or safety equipment to see if they meet certain Federal

Motor Vehicle Safety Standards (FMVSS) or other regulations. A subset of the

ccnpliance tests involve FMVSS where the measurement of curb weight is an

integral part of the test. For example, it is essential to know the curb weight

when testing Roof Crush Resistance (FMVSS 216), because 150 percent of the curb

weight (or 5000 pounds, whichever is less) has to be applied to the roof

structure. The FMVSS and other NHISA regulations whose ccnpliance tests always

include a measurement of curb weight [3] are FMVSS 105 (hydraulic brake systems),

110 (tire selection and rims), 204 (steering control rearward displacement), 208

(occupant crash protection), 212 (windshield mounting), 214 (side door strength),

FMVSS 215/Part 581 (bunpers), 216 (roof crush resistance), 219 (windshield zone

intrusion), 301 (fuel system integrity) and Part 575 (consumer information

regulations). In addition, the curb weight is sometimes measured and included

in test reports for FMVSS 124 (accelerator control systems) and 207 (seating

systems), even though it is not an essential part of the ccnpliance test. Twenty

21
ccntractors have worked an compliance tests which include weighing the cars.

NHISA does not test every make-model for every FMVSS every year, but

operates according to a sampling plan. In general, new FMVSS and new or

redesigned make-models are tested intensively, while existing FMVSS and carryover

make-models are spot-checked an a cyclical basis. On the average, 100 curb

weights are measured each year, but not for 100 different make models; 2-4 cars

of the same make-model may be tested one year, especially if this is a new or

redesigned model. Moreover, a single car may be tested for several different

FMVSS by one or more test laboratories. For example, a nondestructive test

(FMVSS 105) may be followed by a crash test which produces data an several FMVSS

(204, 212, 219 and 301). The car may be weighed several times or it may be

weighed just once and the same weight entered in more than one test report.

An iitportant feature of the compliance tests is that they are

performed en "real" cars. Contractors go to nearby retail dealerships and buy

cars off the lot, in all likelihood equipped with the types of options consumers

usually want for that make-model (automatic transmission, air conditioning,

radios, fancy decor, popular engines, etc.). Sometimes the contractor gets a car

more "loaded" than usual and sanetimes more "stripped," but it averages out to

the typical car of that type.

In all, as of July 1990, NHISA compliance test reports furnish 2006

curb weight data points for passenger cars of model years 1968-89. The curb

weight data were manually retrieved from catpliance test reports and encoded

along with the VHfe of the cars, the number of the FMVSS being tested, the name

of the contractor, etc. The VIN decode program that was developed for F7VRS data

22
(see above) was also used an the compliance test file to define the make-model

and the car group. VTNs were further decoded to make the compliance test file

compatible for merging with Folk's Ifeticnal Vehicle Population Profile - by

defining the 3-digit series code (VINA_MD), body style, engine code and,

possibly, fuel code. The merged file contains 1966 records of passenger cars

with an "actual" curb weight measured by the contractor and a "normative" or

"prescriptive" curb weight from the Polk file (40 cases in the compliance test

file were lost due to errors in the VIN or because they were rare cars for which

Polk has no corresponding record). The 1966 records comprise 1840 distinct

weight measurements (126 cases are entries of a previous weight measurement into

a 2nd or 3rd compliance test report), 1563 distinct vehicles (277 cases are 2nd

or 3rd weighings of the same vehicle) and 1192 distinct combinations of the merge

variables (make-model, Mf, series code, body style, engine - as stated above,

NHISA often tests more than one car of a particular type).

The comparison of actual vs. Polk weights would be simple if only the

"actual" weights themselves were completely accurate or, at worst, imprecise only

to the extent of tolerances allowed in scales. In fact, a few of the weights are

inaccurate, as evidenced, for example, by discrepancies as high as 350 pounds in

two weighings of the same car by different contractors and 100 pounds in two

weighings of the same car by the same contractor on different EMVSS. A case-bycase review was conducted to eliminate records in which the measured weight was

suspected of inaccuracy. Ihe review took into account the HWSS being tested;

the contractor; the size of the discrepancy between the measured and the Polk

weight; and, when a car was weighed more than once, the discrepancy between the

various "actual" weights. Ihe 126 records which were merely entries of a

previous weight measurement into a 2nd or 3rd compliance test report were also

23
deleted, since they provide no new information.

Sane FMVSS were associated with fairly evident biases in the curb

weight measurements. For example, with EMVSS 124 and 207, where measurement of

curb weight is not really needed for performing the test, the weights were biased

upward and had to be discarded in most cases. In general, cases where the

measured weight was more than 10 percent above or 5 percent below the Polk weight

were discarded unless they demonstrated a problem with the Polk weight (in which

case the Polk weight was corrected, based on backup sources). For FMVSScantractor combinations where the weight seemed to be biased in a particular

direction, discrepancies of more than 8 percent above or 3 percent below Polk

weight were not tolerated. If the same car was weighed twice and there was more

than 3 percent discrepancy between the weights, the less plausible measurement

was discarded. In all, 114 records were eliminated in the case-by-case review,

leaving a file of 1726 distinct weight measurements.

The 1726 weight measurements were aggregated into 61 make-model

groups. Mast of the 61 groups had 10 or more weight measurements. The sinple

arithmetic percentage average of the excess of "actual" weight over Polk weight

was calculated for each group and shown in Table 2-1. On the average, actual

orrfr weights are 2 to 3 percent higher than those en the Polk files - i.e., 70

to 105 pounds for a 3500 pound car, which seems about right for optional

equipment included in the typical car. The average excess ranged frcm 0.4 in

Ifezdas to 5.3 percent in CM X-body cars, m general, domestic cars of the

1970's, which were usually sold with automatic transmission and air conditioning

as optional equipment, had the highest excess of actual weight over Polk weight.

24
TM3LE 2-1

EXCESS OF ACTUAL CURB WEIGHTS OVER POLK WEIGHTS BY MAKE-MCDEL GROUP

(actual weights fron NHISA ccnpliance test reports)

Make-Model Group

AMC older models

AMC newer models

Chrysler Dart/Valiant

Chrysler Belvedere/Coronet

Chrysler old fullsized

Chrysler Cordoba/Charger

Chrysler Aspen/Volare/later RWD

Chrysler Cmni/Horizan

Chrysler K car derivs.

Ford LID till 78

Ford old luxury cars

Ford Maverick

Ford Pinto

Ford Torino

Ford Mustang II

Ford Granada

Ford Fairmont

Ford Mustang 79-

Ford new fullsized 79-

Ford Escort

Ford new midsized RWD

Ford new midsized FWD

GM Corvette till 82

GM Nova RWD

GM Camaro till 81

GM low-priced fullsized till 76

GM med-priced fullsized till 76

GM luxury till 76

GM Vega

GM midsized 116" wb 73-77

GM midsized 112" wb 73-77

GM Monte Carlo 73-77

GM Monza 75-80

N of Test

Reports

23

47

22

15

47

21

46

17

46

23

24

15

17

26

5

19

23

16

17

19

17

16

8

32

18

12

15

39

14

11

22

10

22

Avg. Excess of Actual

over Polk Weight (%)

1.95

2.70

2.53

4.03

3.65

3.58

3.34

2.23

2.51

3.01

2.36

4.53

1.51

3.86

3.48

2.99

2.59

3.16

1.75

1.58

1.75

3.55

2.36

2.23

4.09

2.58

3.55

4.04

4.10

4.09

3.27

4.74

2.76

25
TWBLE 2-1 (continued)

EXCESS OF ACIUAL CURB WEIGHTS OVER POLK WEIGHTS BY MAKE-MDEL GROUP

(actual weights fron NHISA catpliance test reports)

Make-Model Group

GM Chevette

GM downsized big cars EWD 77-

GM downsized luxury RWD 77-

GM downsized intermeds RWD 78-

GM Mt Carlo/Supreme G 78-89

GM X cars

GM J cars

GM Canaro/Corvette 82-

GM midsized FWD 82-

GM big/luxury FWD 79-

VW rear engine

VW front engine

European sports cars

European luxury cars

European ecanorty cars

Nissan midsized RWD till 81

& sports cars RWD till 79

Nissan ecanoiy RWD

Nissan sports cars RWD 79-

Nissan FWD

Hondas of the 70's

Hondas of the 80's

Mazda

Subaru

Toyota Corolla RWD

Toyota Celica/RWD

Toyota FWD

Mitsubishi

All other cars 58 1.27

N o f Test

Reports

29

29

10

22

20

21

9

10

30

26

17

72

31

71

89

18

8

19

45

13

42

41

33

17

36

32

76

Avg. Excess of Actual

over Folk Weight (%)

3.22

3.35

2.23

2.66

2.90

5.33

4.36

2.38

3.25

0.85

0.93

1.17

2.22

1.61

2.54

4.09

1.72

1.80

1.54

1.88

1.60

0.37

0.83

3.24

1.40

2.31

1.58

26
Finally, each of the 1,852 Polk weights en the head-on collision file

were adjusted upwards by the percentage shown in Table 2-1, depending on the

make-model group to which the car belonged. The type of occupant protection

system at the driver's position was decoded from each car's VIN, based on

programs developed in NHISA's evaluation of occupant protection [6]; 84 percent

of the cars on the file had manual belts only, 3 percent had an air bag plus

manual belts and 16 percent had seme type of automatic belt.

2.4 A file of NCAP test results

An average of 30 passenger vehicles are tested each year. They are

crashed into a rigid barrier at a target speed of 35 mph, which is 5 mph faster

than the speed for compliance with Standards 204, 208, 212, 219 and 301. There

are correctly restrained, instrumented 50th percentile male "Part 572" dummies

at the driver and right front passenger seat locations.

NHISA maintains a data file containing information about each NCAP

test conducted since the program began with model year 1979. The information on

the data file matches the listing of test results in several NHISA publications

[13], [14], [15], [25], [26]. The variables on the file include the make and

model (written in plain English, not in a numerical code), the model year, the

vehicle's body style and type of occupant protection (depicted by 2-digit codes)

and the NCAP scores for the driver dummy: the Head Injury Criterion (HEC), chest

g's (3 millisecond peak) and left and right femur loads (peak axial loads at

knee). Same of the NCAP scores are missing in a few cases when there were

operational problems with parts of the test instrumentation. The file includes

305 NCAP tests of passenger cars of model years 1979-91.

27
The original NCAP test file was modified to facilitate linkage with

the accident data. The plain-English make and model descriptions were replaced

by the pair of 4-digit numeric codes indicating the make-model and car-group, as

defined in Section 2.1. The codes for body style and type of occupant protection

were converted to the numeric codes defined on the accident file.

Although NCAP scores most accurately characterize the performance of

the specific car that was tested, they may also apply, with seme accuracy, to

cars of the next several model years. The customary procedure in NCAP is to test

a make-model with high sales-volume in the first model year of its existence, or

as soon as possible thereafter. A given make-model is not retested until it is

significantly redesigned. For example, models are retested after a change in the

body platform or vehicle structure, a shift in the type of occupant protection

(e.g., fran manual belts only to air bags plus belts), or when manufacturers

inform KHISA that they have modified safety-related interior ccnponents in a way

that might significantly change test results. Thus, an NCAP test result may be

considered valid for subsequent model years up to the next significant redesign

[26].

NHISA staff reviewed the 1979-91 NCAP tests and determined the "end

year" for each test: the last year before the car was discontinued or redesigned.

NCAP test results are considered valid from the model year of the test vehicle

to the "end year." Results of 305 NCAP tests are listed in [22], ordered by cargroup, make-model and model year, indicating the test number, type of occupant

protection, body style, HIC, chest g's and femur load; and the "end year."

28
2.5 Matchi'ty? NC&P tests with EftRS

The ideal matching of NCftP tests with the accident data would be a

sinple merge by make-model, model-year, type of occupant protection and body

style. In other words, given a specific car involved in an actual head-on

collision, if a basically identical car was tested in N£AP, the driver's HIC,

chest g's and femur load are transcribed to the accident file. The problem is

that thousands of different cars (make-model-model year-body style combinations)

were sold during 1979-91, but only 305 cars were tested in NCM>. Many of the

cars an EftRS do not match up exactly with an NCAP case; there are only 12 head-on

collisions in which both drivers were belted and both vehicles match up exactly

with an NCAP case.

However, as noted above, NCAP test results are considered valid for

several subsequent model years, until a car is redesigned [26]. Moreover, when

two or more make-models, produced by the same manufacturer, not only share a body

platform, but also have nearly identical interior and exterior components (e.g.,

Dodge Qnai and Plymouth Horizon), a test for one of these models is considered

valid for its "corporate cousins" [25]. Ihese two extensions in the reach of

NCAP results make it possible to natch an NCAP test to a lot more accident cases.

Perhaps there are yet other situations where NCAP test results could be accepted

for somewhat dissimilar crash-involved cars, further extending the size of the

accident sample that can be matched with NCAP. For example, the results for a

2-door car might be acceptable for a 4-door car of the same make-model, and viceversa.

NHISA. staff reviewed each of the cars in the head-on collision file

and identified the NCAP-tested car, if any, which most closely resembled it,

29
based on four affinity factors. Each of the four affinity factors has several

quality levels ranging f ran best (catplete agreement of the NCAP car and the EARS

car) to worst. Ihe affinity factors and their quality levels are the following:

Body platform and

4 (best) EARS and NCAP cars have the same make-model, body and chassis.

3 EARS and NCAP cars are "true corporate cousins" (identical body and

chassis, as evidenced by equal wheelbase, weight and exterior

dimensions). Example: Dodge Aries and Plymouth Reliant. Different

nameplates suggest, at most, slight differences of interior

canpanents.

2 EARS and NCAP cars are built on the body platform, but are not true

corporate cousins. Above the chassis, the cars are not the same, as

evidenced by unequal weights, exterior dimensions, or appearance.

Example: FWD Buick LeSabre and FWD Buick Electra (or Olds 98).

1 (worst) EARS and NCAP cars are built an different chassis, as evidenced by

unequal wheelbase, but one chassis is basically a "stretch" version

of the other, and the overall designs are similar. Example: RWD Olds

98 and Cadillac Fleetwood Brougham.

General model year range

A (best) The model year of the EARS car is within the range of applicable

model years for the NCAP test - i.e., no earlier than the model year

of the NCAP test vehicle and no later than the "end year."

B Ihe model year of the EARS car pyyrgdfs the model year of the

"matching" NCAP test.

C (worst) Ihe model year of the EARS car is later than the "end year" for the

matching NCAP test.

Specific model year

Best Ihe EARS and NCAP cars are the identical model year.

Ine EARS and NCAP model years are not identical, but differ by N

years.

30
Body style

Best The ERRS and NCAP cars have the sane number of doors and exactly the

same body style (sedan/coupe, hatchback, station wagon, convertible).

The EARS and NCAP cars have the same number of doors and ?iTMpgtr the

same body style (one is a hatchback and the other is a sedan/coupe).

The EARS and NCAP cars have the same number of doors, but different

body styles (station wagon vs. sedan, convertible vs. coupe).

Worst One is a 2-dcor and the other is a 4-door.

The "best" NCAP match was identified case-by-case, based an staff

discussions, rather than by an automated procedure. When there is no perfect

match, but two or more choices among imperfect matches, the best choice depends

an the specific make-model involved. For example, if the EARS case is a 4-door

car, and the two NCAP tests for the same make-model are a 2-door car of the same

model year and a 4-door car of a different model year, the best choice depends

on whether, for this particular make-model, the difference between the 2-dcor and

the 4-door version exceed the change in the 4-door version over time. In all

cases, though, the EARS and NCAP cars had to have the same type of occupant

protection.

Reference [9] lists every car on the EARS file (model year 1976-91

cars involved in head-an collisions where both drivers were belted) and, next to

it, the NCAP test vehicle, if any, which was judged to be the best match. It

exhibits, side by side, the make-model, model year and body style of the EARS and

NCAP cars, illustrating how well (or poorly) they match. The MATCHLVL data

field, a number followed by a letter, indicates the quality of the match

according to the first two criteria: body platform/nHke-model and general model

year range. For example, the first car an the EARS file, when it is ordered by

car group and make-model, is a 1980 fMZ Spirit 2-dcor hatchback with manual

31
belts. The only NCAP test vehicle that canes close is a 1981 AMI Spirit 2-door

hatchback with manual belts. These two cars natch exactly on the car group,

make-model, body style and type of occupant protection. However, the EARS model

year (1980) is less than the NCAP model year (1981) and precedes the time span

frcm the NCAP model year to the "end year" for that NCAP test (1981-83). Thus,

the MflCHLVL is rated 4B: 4 because the nake-model and car group natch exactly,

B because the EARS model year precedes the NCAP year. It should be noted that

quite a few cars an EARS, such as the 1978 PM2 Pacer, do not closely resemble any

car tested in NCAP, and do not have an NCAP match.

MATCHLVL 3A and 4A may be considered especially important in the

analyses. Here, the EARS and NCAP vehicles are of the same make-model or true

corporate cousins, and the ERRS mode] year is within the "valid" range of the

NCAP test. As noted above, NHTSA has not asserted that NCAP test results can be

extended to cars which only natch an NCAP test at a lower level than that [25],

[26]. The head-on collisions in which both cars match an NCAP test at the 3A or

4A level and, possibly, also match an number or doors, then, would seem to be the

most natural data sets to look for correlations between NCAP scores and fatality

risk. However, lower levels of matching, such as 2A or 4B, are not excluded from

the data set at this time; in Chapter 3, these cases will be empirically tested

for correlation between NCAP and fatality risk.

2.6 Creation of the analysis file

In all, there are 739 head-on collisions, involving 1,478 model year

1976-91 passenger cars, in which both drivers were belted and both cars match up

at any level with an NCAP case. Fran the previous EARS file of 926 head-on

collisions (1,852 vehicles), about 20 percent of the cases cannot be used in most

32
of the analyses, because one or both of the crash-involved vehicles do not match

up with any NCAP case. The following variables are defined for each vehicle and

driver on the file:

o VIN

o Model year

o Car group (4 digit code derived from VIN)

o Mate-model (4 digit code derived fron VIN)

o Fatality outcane: "1" if the driver died; "0" if the driver survived

o Polk weight

o Curb weight (Polk weight escalated by correction factor)

o Driver age (has to be 14-98)

o Driver sex (has to be known)

o Body style (convertible, 2 dr coupe/sedan, 2 dr hatchback, 2 dr hardtop, 4

dr sedan, 4 dr hatchback, 4 dr hardtop, station wagon)

o Type of occupant protection (manual belts only, air bag plus manual belt,

motorized belt, automatic 3-point belt, nonnotorized 2-point belt)

o Test number of the matching NCAP test car

o Model year of the matching NCAP test car

o Car group of the matching NCAP test car

o Make-model of the matching NCAP test car

o Body style of the matching NCAP test car

o "End year" for the matching NCAP test car

o Match level for the EARS-NCAP match

o HIC for the driver in the matching NCAP test car

o Chest g's for the driver in the matching NCAP test car

o Left fenur load for the driver in the matching NCAP test car

o Right femur load for the driver in the matching NCAP test car

It should be noted that the HIC, chest g's and femur load numbers

written an this file are those recorded an the driver dummy in the NCAP test

vehicle during a 35 raph barrier crash and not those actually experienced by the

driver of the crash-involved vehicle an EARS, which are, of course, unknown.

On the analysis file, the two vehicles in the collision are referred

to as the "case" vehicle and the "other" vehicle, rather than vehicles "1" and

"2." Each record in the original EARS file is written twice onto the analysis

file: first with the original vehicle number 1 as the "case" vehicle and vehicle

number 2 as the "other" vehicle; then with the original vehicle number 2 as the

33
"case" vehicle and vehicle nurrber 1 as the "other" vehicle. The concept here is

that a head-on collision is essentially a symmetrical event; while ERRS nay call

one of the vehicles "No. 1" and the other "No. 2" (arbitrarily, or based on

pre-crash events that are no longer relevant to the analysis) it could just as

well have reversed the order. Both vehicles have participated as "case" vehicles

in a head-on collision. Thus the analysis file contains 1,852 records of head-on

collisions, comprising 1,852 different vehicles (each of which appears twice an

the file, ance as the "case" vehicle and cnce as the "other" vehicle). The

analyses will primarily deal with the subset of 1,478 collision records in which

both the "case" and the "other" vehicle match up with an NCAP test.

34
CHAPTER 3

CORRELATION OF FATALITY RISK WITH INDIVIDUAL NCAP PARAMEIERS

The initial investigation of the relationship between NCAP test

performance and fatality risk in actual head-on collisions is based on regression

analyses of fatality risk by HIC, chest g's, femur load, vehicle weight, driver

age and sex. Specifically, since there are two cars in a head-on collision, the

fatality risk for the driver of the case vehicle is modeled as a function of the

relative HIC scores for the two vehicles on the NCAP test, the relative chest

g's, etc. Does fatality risk increase significantly with higher NCAP scores?

3.1 Analysis rihriective

In Chapter 2, a file of head-on collisions was created, including

1,478 records in which both vehicles could be "matched" to an NCAP test. These

1,478 cases are the raw material for the regression analyses, because they

contain all the necessary variables (weight, *je, sex and NCAP scores for both

cars). The qualify of the matches, however, varied in the 1,478 cases.

Sometimes, the crash-involved car and the "matching" NCAP car had nothing more

in carmen than a similar body platform, while at other times they were

essentially identical vehicles.

The principal task of this chapter is to identify a subset of the

1,478 cases which best indicates the relationship between NCAP parameters and

fatality risk. That involves a trade-off between sample size and the quality of

the matches. The full data set has the largest sample size, but the poor quality

of some of the matches could obscure the relationship: the NCAP scores assigned

to some of the crash-involved vehicles may simply be inappropriate for those

35
cars. On the other hand, demanding too close a natch between the ERRS and the

NCAP cars could reduce the sanple size to the point where significant correlation

is unlikely. The approach of this chapter is to try out the regression analysis

an various subsets of the data file, defined by how closely the EARS and NCAP

cases natch, and to find a subset which yields excellent correlations and is also

intuitively reasonable. Based on NHTSA statements on the applicability of NCAP

tests, the best results might be expected when the EARS and NCAP vehicles are of

the sane make-model or true corporate cousins, the EARS model year is within the

"valid" range of the NCAP test, and, possibly, the EARS and NCAP vehicles have

the same number of doors [25], [26]. However, regressions will also be performed

an a variety of other subsets of the data file.

3.2 Regression an?lysis procedure

The method for calibrating fatality risk as a function of relative

NCAP scores, weight, age and sex is logistic regression on disaggregate cfoty*.

using maximum likelihood principles [18]. Logistic regression uses a large

number of -inrti virtual observations of success (case driver survival) or failure

(driver fatality) given different actual combinations of the independent

variables to predict the driver's pir^Rbility of f?frftlAty under any hypothetical

combination of the independent variables. Specifically, the model generates an

equation which expresses the log-odds of a fatality as a linear function of the

independent variables.

However, the scores for KLC, chest g's and femur load, as actually

measured on the NCAP tests, are not well-suited as independent variables in a

regression analysis. Their distributions are skewed in one direction - e.g.,

there are a few tests with extremely high KLC (above 2000). The extreme values

36
of HIC would have excessive weight in any regression analysis and that would

seriously distort the calibration of fatality risk as a function of HIC. For the

regression analyses to work, each actual injury criterion needs to be transformed

to variables with a normal distribution - or, at least, to a variable with a

syniretric distribution that has a wide peak and narrow tails. Several procedures

exist for normalizing variables; one of these was especially appropriate for the

present regression analyses. The actual NCAP results for the driver dunmy were

transformed to logistic iniT|TY pTrfrv^ility functions for each body region:

HEADINJ «= 1 / [1 + exp(5.02 - .00351 HIC)]

CHESTTNJ = 1 / [1 + exp(5.55 - .06930 chest g's)]

LFEMJRINJ = 1 / [1 + exp(7.59 - .00294 left femur load)]

RFEMURINJ = 1 / [1 + exp(7.59 - .00294 right femur load)]

These functions were developed by General Motors and others, based an empirical

testing with human surrogates and dummies [13], [27], [29]. They measure the

probability of life-threatening or fatal head and chest injury (4-6 on the

Abbreviated Injury Scale [1]) and severe leg injury (AIS > 3), as a function of

HIC, chest g's and femur load. The logistic injury probabilities correspond to

actual NCAP scores as follows:

Logistic Injury

Probability

.001

.01

.02

.05

.10

.20

.30

.40

.50

.60

.70

.80

.90

.95

.99

.999

37

HIC

121

321

591

804

1035

1189

1315

1430

1546

1672 •

1825

2056

2269

2739

3398

Chest

g's

24

38

48

60

68

74

80

86

92

100

112

Femur

load

232

1019

1258

1580

1834

2110

2293

2444

2582

2720

2870

3053
The original NCAP scores have been transformed into measures of relative injury

risk that can readily be used in regression analyses. The transformed variables

for the different body regions can be tested for correlation, added to one

another, and combined into weighted or unweighted averages. The logistic injury

probabilities compress high values of the original scores into a narrow band and

eliminate the skew to the high side present in the original scores - e.g., all

HIC over 2056 are compressed into a range from .90 to 1. The low values of the

original scores (e.g., HIC below 800, chest g's below 48) are also compressed

into a narrow band. The mid-ranges of the original scores, which are critical

for differentiating between acceptable and poor safety performance, occupy a wide

middle band (.1 to .9) of the logistic injury probability distribution. The

logistic transformation acts like a lens that magnifies differences in the middle

of the range, but diminishes them at the low and high ends. The resulting

distributions, as desired, have short tails and wide peaks.

As explained in Section 2.6, each record an the analysis file contains

information an the two vehicles in a head-on collision, and their drivers: the

"case" vehicle and the "other" vehicle. This information is now supplemented

with logistic injury probabilities derived from the NCAP tests. CHIC, CCG, CLFEM

and CRFEM are the logistic injury probability scores for HIC, chest g's, left and

right femur load for the driver of the case vehicle. CHIC, OCG, OLFEM and CREEM

are the corresponding scores for the driver of the other vehicle.

In the initial regression model, each of the 1,478 head-on collision

records in which both vehicles match up with NCAP tests becomes a data point.

The dependent variable is the actual outcome of the crash for the driver of the

case vehicle, equaling 1 for a fatality and 0 for a survivor. There are 6

38
independent variables W, A, S, DELHIC, DELCG and DELFEM, all of which are

calculated for the case vehicle relative to the other vehicle, as follows:

W

DELHIC

DELCG

DELEEM

is the difference of the natural logs of the curb weight of the case

vehicle and the other vehicle (NHISA' s Ev?"h»atian of Frontal Interior

Impact [19], pp. 138-140, showed exceptionally good fit when the

weight variable is expressed in this f onn).

is the sinple arithmetic ^•ffgTenoe of the ages of the two drivers,

the case driver's age minus the other driver's age (with 14 or 15

year old drivers counted as 16 year olds). Evans [8] showed

exceptionally good fit when the age variable is expressed in this

form.

is 0 if both drivers were males or both were females; -1 if the

driver of the case vehicle was male and the other, female; and +1 if

the driver of the case vehicle was female and the other, male

= one - omc

= CCG - OCG

(CLFEM + CRFEM) - (OLFEM + OREEM) if all 4 of these are known,

= 2 CLEEM - (OLFEM + ORFEM) if CRFEM is unknown, the others known,

- 2 CRFEM - (OLFEM + CRFEM) if CLFEM is unknown, the others known,

etc.

W should have negative correlation with the dependent variable (the

heavier the case vehicle the lower the fatality risk for its driver). A and S

should have positive correlation with the dependent variable (older drivers and

female drivers are more vulnerable to fatal injury). DELHIC, DELCG and DELFEM

should also have positive correlation with the dependent variable: high HIC in

the case vehicle would be associated with high fatality risk in the case vehicle.

High HIC in the other vehicle would be associated with high fatality risk in the

other vehicle and, since most head-on crashes kill only one of the drivers, low

fatality risk in the case vehicle.

3.3 Ihe regression -

Ihere are 1478 data points in the full data set of head-on collisions

39
where both drivers were belted and both cars could be matched to an NCAP test at

any level of match quality. After excluding 86 data points where DELHIC, DEDCG

or DELFEM could not be defined because of missing data in the NCAP tests, there

are 1392 cases available for the initial regression. The regression coefficients

and their associated statistical significance levels are:

Initial Regression - All NCAP ^fetches

W (car weight)

A (driver age)

S (driver sex)

DELHIC

DELCG

DELEEM

Reg.

Coeff.

.5555

-5.5296

.0532

.3400

.0225

1.8400

.3975

Chi

Square

51.45

281.72

242.35

9.06

.01

6.62

.77

Stat.

Sig.?

RR

RR

RR

RR

R

Partial

Corr.

-.384

.356

.061

.049

In the preceding table, a Chi-square (x2) statistic is calculated for

each regression coefficient, to see if the variable makes a statistically

significant contribution to fatality risk. If x2 is greater than 6.64 and the

coefficient has the "right" sign (as discussed above), the variable has a highly

significant association with fatality risk (two-sided alpha less than .01), even

after controlling for the other variables, as indicated by an "RR" in the

statistical significance column. If x2 is between 3.84 and 6.64 and the

coefficient has the "right" sign the variable has a significant association with

fatality risk (two-sided alpha between .05 and .01), as indicated by an "R." If

the regression coefficient is nonsignificant, the statistical significance column

is left blank. The partial correlation coefficient measures the direction and

relative strength of the contribution of a variable to the prediction of fatality

risk (if x2 is less than 2, this coefficient is set to zero).

40
In this initial regression, knowledge of NCAP chest g's significantly

enhanced the ability to predict whether the driver of the case vehicle was killed

in a head-on collision (x2 = 6.62, p < .05). If all other factors such as car

weight, driver age, etc. are equal, the driver of the car with the lower NCAP

chest g's has a significantly better chance of survival than the other driver.

Of course, chest g's are far less important than relative car weight and driver

age as predictors of fatality risk in head-on collisions, as demonstrated by the

much larger x2 and partial correlation coefficients for W and A. But chest g's

have almost as nuch influence an relative fatality risk as the sex of the drivers

(S). Knowledge of HIC and femur load add little more to the ability to predict

fatality risk in this initial analysis, as evidenced by x2 of 0.01 and 0.77,

although, at least, both coefficients have the right sign (positive).

A possible reason that the HIC and femur load variables do not

contribute much to the prediction of fatality risk is that both of them are

intercorrelated with chest g's, and, as a rasult, sane of the information

potentially conveyed by HIC and femur load is already contained in the chest g

variable. The logistic injury probability CHESITNJ has correlation coefficients

Of .281 with HEADINJ, .162 with RFMJRINJ and .062 with IFEMJRHU, all

statistically significant, suggestiJig at least a partial overlap in the scores.

Although, ordinarily, these are not damaging levels of collinearity for

independent variables, it will be seen in Chapter 5 that HIC and femur load, when

analyzed separately fran chest g's, have stronger relationships with fatality

risk than those revealed in the regression approach of this chapter.

This initial data set, however, obscures the relationship between any

NCAP scores and actual fatality risk primarily because of the poor quality of

41
many of the EARS-NCAP vehicle "matches." The HIC, femur load and chest g's, in

seme cases, are derived frcm NCAP tests of cars that are fairly distinct from the

ERRS vehicle and might be inaccurate for that vehicle. The regression needs to

be rerun with subsets of crashes in which both cars match up more closely with

NCAP tests. The regression coefficients can be expected to increase while the

sample sizes decrease. If the right kind of subsets are selected, the gains in

the regression coefficients will overshadow the loss of sample size, and the x2

will increase.

The closeness of the EARS-NCAP match was described in Section 2.5 by

a quality rating an each of four affinity factors. The affinity factors and

their quality levels were body platform/make-model (4 = same make-model, 3 = true

corporate cousin, 2 - same platform only, 1 = similar platform); general model

year range (A = EARS Mf within NCAP Mf-END MY range, B = EARS Mf precedes NCAP

m, C = EARS Mf after END Mf); specific model year (EARS and NCAP MJf identical,

MJf's off by 1, off by 2, ...) ; and body style (identical body style, similar body

style, identical N of doors only, different N of doors). The affinity factors

and their quality levels are a basis for defining subsets of the head-on

collision file. A minimum acceptable match-quality level is specified for each

affinity factor, and the regression is run for the subset of crashes in which

both cars meet or exceed the match-quality levels. For example, NHISA has never

claimed that NCAP tests are valid for cars that are less than true corporate

cousins or for cars outside the range of model years fran the NCAP test vehicle

year up to the "end year" [25], [26]. That is equivalent to demanding at least

levels 3 and A, and excluding levels 1, 2, B and C, although not setting any

mininum requirement on the specific model year. It would define a subset of

42
crashes in which both cars match an NCAP test at either level 3A or 4A. Surely,

that has to be one of the candidate subsets for the regression analysis. Another

important subset is the one where both ERRS cars match an NCAP test at level 3A

or 4A and on the number of doors.

Although the 3A/4A subset may seem, intuitively, the "right" data set,

it is worthwhile first to analyze sane intermediate subsets, larger than the

3A/4A group, but smaller than the full data set. The first subset to be analyzed

consists of crashes where both cars must match an NCAP test at level 3 or better

(identical make/model or true corporate cousin). No demands are made an the

general model year range, the specific model year or the body style. This subset

eliminates same of the least satisfactory E&RS-NCAP matches: the crashes where

one or both vehicles only itatched NCAP at level 2 (same chassis, different body)

or level l (similar chassis). Since there were relatively few level 1 and 2

matches (see [9]), the sample size only decreased from 1392 to 1110 cases. The

regression coefficients and their statistical significance levels are:

Level 3 and 4 etches - Identical ^feke-M3del or Corporate Cousin

W (car weight)

A (driver age)

S (driver sex)

DEEfflC

DELCE

DELFEM

Reg.

Coeff.

.6234

-5.9405

.0590

.3065

.2204

2.1339

.8514

Chi

Square

47.51

218.11

205.49

5.60

.55

7.06

2.04

Stat.

Sig.?

RR

RR

RR

R

RR

Partial

Corr.

-.378

.366

.049

.058

.005

The results are a definite improvement on the initial regression. The

coefficient for chest g's increased from 1.84 to 2.13 and the x2 increased from

43
6.62 to 7.06, despite the reduction in sample size. The contribution of chest

g's is now significant at the .01 level and its x2 is greater than the x2 for

gender. The coefficient for KCC increased from .02 to .22 and for femur load

from .40 to .85; their x2 increased substantially, although not to the level of

statistical significance.

Table 3-1 surrmarizes the main findings of the two preceding

regressions, plus the others that will be discussed in this chapter. It shows

the sample size for each subset, the regression coefficients for head, chest and

femur injury, and the x2 for those variables.

The next subset to be analyzed takes one more step upwards an the

first affinity factor. It consists of crashes where both cars must match an NCAP

test at level 4 - i.e. have the same make-model as the NGRP test. As before, no

demands are made on the general model year range, the specific model year or the

body style. The third line of Table 3-1 diows that eliminating "corporate

cousins" does not improve the results. The sample size drops severely, from 1110

to 612. Although the regression coefficient for chest g's increased fran 2.13

to 2.37, its x2 fell from 7.06 to 4.93, because of the reduced sample size,

dropping it out of the .01 significance level. The coefficient and x2 for femur

load increased, but the coefficient for HIC dropped out of the positive range to

a value close to zero.

The initial data set contained a moderate number of level B and C

matches, where the ERRS model year preceded the NCBP model year or came after the

"end year" specified for the NC3\P test. These are rather questionable matches

and good candidates for deletion. The 4th line of Table 3-1 shows results for

44
TABLE 3- 1

EFFECT OF NCAP-FARS MATCH QUALITY ON LOGISTIC RBGRESSION5 FOR NCAP VARIABLES

Both Cars Match with NCAP N of

Tests at the Following Level: Cars

Betas (Regression Coefficients)

Head Chest Femur

Injury Injury Injury

Chi-Squares

Head Chest Femur

Injury Injury Injury

At any. level 1392

Level 3 or 4: identical make/model

or "corporate cousin" 1110

Level 4: identical make/model 612

Level A: NCAP MY < FARS MY < HDYR 872

3A or 4A: sam sake/nodal or corporate

& cousin, NCAP MY < FARS MY < BCTCR 740

4A: same make/model,

NCAP MY < FARS MY < QOYR 402

3A or 4A, and. (PARS MY - NCAP MY) < 2 416

3A or 4A, and. (PARS MY - NCAP MY) < 1 252

3A or 4A, and. FARS MY = NCAP MY 78

3A or 4A, and. N of doors match 588

4A, and N of doors match 306

3A or 4A, and body stvl e matches exactly 452

.02 1.84 .40 .01 6.62^ .77

22

01

07

21

14

04

60

93

42

23

07

2.13

2.37

2.26

2.70

2.43

2.70

3.98

2.92

2.52

1.89

2.72

.85

1.42

1.53

1.41

3.67

2.30

3.44

2.59

.73

2.90

.67

.55

.00

.06

.41

.09

.01

1.40

.33

1.20

.18

.03

7 -0 6

4.93^

6.35-

7.70**

3.28

3.79

3.41

.39

5.62^

1.74

4.94^

2.04

2.83

5.98

3.94

6.62

2.31

2.21

.13

.98

3.50

.44

•statistically significant, alpha < .05 ••statistically significant, alpha < .01
the subset of level A matches, where both cars in the crash must have their model

year in the range from the NCAP model year to the "end" year; no demands are made

an the other affinity factors (e.g., level 1 and 2 matches are included here).

The sample size is reduced to 872. Compared to the initial regression, the

results for chest g's and, especially, femur load are improved. Both of these

coefficients are statistically significant at the . 05 level. But the coefficient

for HIC drops out of the positive range.

Since limiting the data to levels 3 and 4 helped, and restricting to

level A also helped, good results can be expected if both limitations are applied

at the same time - i.e., both cars in the crash have to have be the same make

model or a true corporate cousin of their NCM> match, and the ERRS model years

have to be in the "valid" range for the NCftP tests. The specific model year or

the body styles are not required to match. The sample size is 740 vehicles,

which is slightly more than half the initial data set (actually, 792 vehicles,

but 52 had to be excluded from this regression because DELHIC, DELCG or DELFEM

were missing). The regression coefficients (surrnarized in the 5th, bold line of

Table 3-1) are the following:

Level 3A and 4A etches

INlliK.USt'i/

W (car weight)

A (driver age)

S (driver sex)

DELHTC

DELCG

DELFEM

Reg.

Ooeff.

.6883

-5.7355

.0579

.3665

.2116

2.7004

1.4109

Qli

Square

39.19

142.47

134.54

5.33

•41

7.70

3.94

Stat.

Sig.?

RR

RR

RR

R

RR

R

Partial

Corr.

-.374

.364

.058

.075

.044

46
They are the most satisfactory results of any of the regressions, and this subset

will be used for most of the analyses in the remainder of this report. The

coefficient for chest g's is 2.7, and its x2 is 7.70, which is significant at the

.01 level and is the highest x2 found in any of the regressions surmarized in

Table 3-1. Femur load has a coefficient of 1.41, consistent with the two

preceding regressions in Table 3-1 and significant at the .05 level. Head injury

has a positive coefficient, although not statistically significant.

A comparison of the x2 here vs. the initial regression en the full

data set shows a higher x2 for each of the NCAP variables in the 3A/4A regression

than in the full data set, despite a reduction in the sample size from 1392 to

740. That suggests there is little or no "correlation11 between NCAP and ERRS

when the EARS vehicles match NC&P at affinity levels less than 3A. The overall

correlations found in the larger subsets merely reflects the 3A/4A cases within

those subsets. Indeed, when the same regression is performed an the 652 cases

(i.e., 1392 - 740) where one or both cars do not match an NCAP test at level 3A

or 4A, the coefficients for the NC3\P variables are all nonsignificant: -0.22 for

head injury (x2 = 0.22), 1.23 for chest injury (x2 = 1.22) and -0.36 for femur

injury (x2 « 0.66).

A further subset ting of the data, from level 3A/4A to exclusively

level 4A matches does not improve the results. The sample size is reduced to 402

cases. When sample sizes drop much below 500, the set of head-on collisions

becomes too small to include a representative mix of cars, and anomalous results

can be expected when the regression model seizes on certain properties of the

vehicles in the sample and "attributes" the results to HEC, chest g's or femur

load. The regression coefficients (summarized in the 6th line of Table 3-1) are

47
the following:

Level 4A Matches

W (car weight)

A (driver age)

S (driver sex)

DELEEC

DEUCE

DELFEM

Reg.

Coeff.

.5846

-5.7218

.0630

. .4360

.1433

2.4291

3.6708

Chi

Square

15.38

77.50

72.27

4.06

.09

3.28

6.62

Stat.

Sig.?

RR

RR

RR

R

R

Partial

Corr.

-.371

.358

.061

.048

.092

The regression coefficient for chest g's drops to 2.43, which is not

statistically significant at that sample size. The coefficient for femur load

dints to an unexpectedly high 3.67, and is significant at the .05 level. The

ferrur load coefficient is higher than in any of the other regressions in Table

3-1 and seems out of line with the general trend in that table.

So far, the analyses have shown that level 3A and 4A matches between

EARS and NCAP are satisfactory, while level 1, 2, B and C matches should not be

used. None of the subsets demanded the EARS and NCAP vehicles to match on

specific model year or body style. In the remaining regressions, EARS and NCAP

will always have to match up at the 3A or 4A level, and the effect of further

restricting the data to close matches an model year or body style will be

considered.

Given that the EARS model year is within the valid range of model

years for the NCAP test (level A), there is no advantage to further limits an the

model year. If it is demanded that the EARS and NCAP model years can differ by

48
no more than 2 (7th line of Table 3-1), the sample size is reduced from 740 to

416, and all x2 drop out of the significant range. The coefficient for HIC drops

out of the positive range. Further limiting the EARS and NCAP model years to be

within one, or to be identical,, cuts sample sizes to unsatisfactory levels and

does not produce statistically significant coefficients.

When NHISA staff matched the EARS and the NCAP cases, they placed high

priority an matching the vehicles by body style. They generally preferred to

match a corporate cousin or an NCAP test several years old, but with the same

body style, than an NCAP test of the same make-model and model year, but with a

different body style. The great majority of the 740 level 3A and 4A matches also

had identical body styles or at least the same number of doors. The results were

slightly modified, but not necessarily improved by limiting the analyses to

subsets of crashes in which both, vehicles matched NCAP test vehicles an N of

doors and/or exact body style.

There were 588 crashes in which both cars had the same number of doors

as their NCAP test matches. The regression coefficients (suttmarized in the 10th

line of Table 3-1) are the following:

EARS/NCAP ffetch at Level 3A and 4A and Same N of Doors

Reg. Chi Stat. Partial

Coeff. Square Sig.? Corr.

.6600 29.17 RR

W (car weight) -5.6001 109.31 RR -.367

A (driver age) .0545 103.41 RR .356

S (driver sex) .4066 5.04 R .062

DELHIC .4218 1.20

DELCG 2.5179 5.62 R .067

DELEEM .7316 .98

49
Ihe regression coefficients show sane changes frcm the 3A/4A analysis. The headinjury coefficient increased from .21 to .42, which is the highest level it

reached in any of the analysis of Table 3-1 that are based en more than 500

cases; nevertheless, it did not reach statistical significance (x2 • 1.20). The

chest-injury coefficient stayed about the same (2.52 vs. 2.70), although its

statistical significance dropped from the .01 level to the .05 level, in part due

to the reduction in sample size. Ihe coefficient for femur load, which was

significant in the 3A/4A analysis, dropped out of the significant range here,

having a x2 slightly lower than the head-injury coefficient.

The preceding analysis suggests a possibility that the higher

correlation coefficient for head injury is a result of requiring FBSS and NCAP

to match an N of doors. If so, an even stronger requirement - viz. that EftRS and

NC&P cases have the exactly the same body style (4-door sedan, station wagon,

etc.) - could be expected to maintain or even further increase the head-injury

coefficient. There are 452 crashes where both vehicles matched an NCM> test at

levels 3A or 4A and. exact body style: only a small reduction frcm the 588 cases

in the preceding analysis. However, the last line of data in Table 3-1 shows

that, for this subset, the head-injury coefficient dropped back close to zero.

Given the sample sizes on which the various regressions are based, the subtle

variations in the regression coefficients are quite probably due to chance.

3.5 Sumnary

The rtain purpose of this chapter was to identify a large set of headon collisions in which both cars match up close enough with NCftP test vehicles

that the scores for the NCK> vehicles accurately depict the 35 mph barrier

performance of the actual crash-involved vehicles. The empirical evidence is

50
that a "level 3A or 4A" match between the EARS and NCAP vehicles is close enough

- i.e., the ERRS and NCAP vehicles are of the same make-model or true corporate

cousins, and the ERRS model year is within the "valid" range of the NCAP test.

Anything less than a level 3A/4A match is insufficient. An alternative, somewhat

smaller data set that produces good, but slightly different correlations consists

of EARS cases in which both cars match an NCAP test at level 3A or 4A and have

the same number of doors as the NCAP test vehicle. The empirical findings are

consistent with NHISA' s earlier claims that an NCAP test result can be extended

to a car's corporate cousins, and for subsequent model years until the car is

redesigned [25], [26].

While the methods of this chapter are not a preferred way to test for

correlation between NCAP performance and actual fatality risk, they still showed

that significant correlations exist. In the analyses sumnarized in Table 3-1,

NCAP chest g's had a statistically significant regression coefficient in every

regression with a sample size greater than 500, while the coefficient for femur

load reached significance in three analyses. (The methods in subsequent chapters

will confirm these correlations, and also show significant correlation with HIC

under certain conditions). At the same time, the statistics in Table 3-1

indicate that the regression coefficients can vary quite a bit in response to

moderate changes in the calibration data set. The range of Chi-Squares for chest

g's overlaps with the range for femur load, which, in turn, overlaps with the

range for HIC. In other words, while the accident data set is sufficient to

indicate an overall significant relationship between NCAP scores and actual

fatality risk, there are not enough data indicate the exact relative importance

of the three NCAP body regions in predicting fatality risk.

51

CHAPTER 4

A OCMPOSITE NCAP SCORE: CORRELftTICN WTIH FATALITY RISK

HIC, chest g's and ferrur load each provide sane information about a

vehicle's safety performance on an NCAP test. An appropriate weighted average

of the scores for the three body regions could provide more information about a

car's overall safety performance than any score for a single body region, and

have greater correlation with actual fatality risk than any single NCAP score.

The objectives of this chapter are to identify a ccnposite NCAP score, NCAPINJ,

that has maximum correlation with the fatality risk of belted drivers in the

principal calibration data set of actual head-on collisions, and to measure the

extent of that correlation. NCAPINJ is a specific weighted average of head,

chest and femur scores. However, sensitivity tests in this chapter will show

that NCAPINJ is not the only ccnposite score that has excellent correlation with

fatality risk; other weighted averages, and even an unweighted sum of logistic

injury probabilities, also correlate well with actual risk. NCAPINJ may not be

the optimum ccnposite score en another calibration data set. Thus, the purpose

of defining NCAPINJ is not to find a unique "magic bullet" that is the best and

only way to express the NCAP results, but to show that existing NCAP test scores

for the three body regions, when combined by seme reasonable scheme, have highly

significant correlation with actual fatality risk in head-on collisions.

4.1 A ccnposite measure of NCAP performance

The regression analyses of Chapter 3 supply most of the framework for

generating a composite NCAP measure that has excellent correlation with fatality

risk. Here are sane of the relevant analytic tools developed in Chapter 3. The

actual NCAP results for the driver dummy were transformed to logistic injury

53
probability functions, ranging fran 0 to 1:

HEADINJ = 1 / [1 + exp(5.02 - .00351 HIC)]

CHESTTNJ = 1 / [1 + exp(5.55 - .06930 chest g's)]

LFEMIRINJ = 1 / [l + exp(7.59 - .00294 left femur load)]

REEMJRINJ = 1 / [1 + exp(7.59 - .00294 right femur load)]

NCAP performance for the "case" vehicle relative to the "other" vehicle in a

head-on collision was defined in terms of these functions:

DEUHC =

DELCE =

DELEEM =

The most appropriate data set of head-on collisions for studying correlation with

NCftP was found to be the crashes in which both cars matched up with an NCAP test

vehicle at levels

 n3An or "4An: the model year on EARS is within the range of

model years considered valid an the NCAP test, and the make-models on EARS and

NCAP are identical or true corporate cousins. That data set includes 396 head-on

collisions (792 vehicles); however, DELEEC, DEDCG, or DELFEM are undefined, due

to missing data an NCAP tests, in 26 collisions, leaving a sample of 370

collisions (740 vehicles) for the initial regression analysis. The logistic

regression model that best predicts the fatality risk of the driver of the case

vehicle, in those 740 cases, has the following regression coefficients and

chi-squares (x2):

Regression Coefficient Chi-Square

INTERCEPT .69 39.19

W (car weight) -5.74 142.47

A (driver age) .0579 134.54

S (driver sex) .367 5.33

-5

2

1

.69

.74

.0579

.367

.21

.70

.41

DELHIC .21 .41

DEUCE 2.70 7.70

DELEEM 1.41 3.94

The initial goal is to find a single variable DEUTCAP, which would

54
replace DELHIC, DELCG and DELFEM in the preceding regression and get a high x2-

If the choice of DELNCAP is limited to linear combinations of DELHIC, DEUCE and

DELFEM, i.e.,

DELNCAP = Wl DELHIC + W2 DELOG + W3 DELEEM

then the preceding regression coefficients .21, 2.70 and 1.41, if substituted for

Wl, W2 and W3, generate the DEUO£> that maximizes x2 in those 740 cases. With

minor modifications, that vail become the composite measure of NCAP performance.

The regression in Chapter 3 was limited to the 740 cases in which HIC,

chest g's and femur load an at least one leg were known for both vehicles (i.e.,

they were successfully measured in the NCAP test that is matched with the crashinvolved vehicle). It is desired to expand the analysis to include cases where

the NCAP results are partially missing, to include as many of the 792 level 3A

and 4A matches as possible. Just as DELNCAP, as defined above, was a linear

combination of the relative scores for two vehicles, it is possible to define a

canposite logistic injury score for the driver of one vehicle if the NCAP results

are all known:

NCAPINJ «= Wl HEADINJ + W2 CHESTINJ + W3 (LFEMJRINJ + RFEMJRINJ)

where Wl, W2 and W3 are constants which remain to be determined. LFEMJRJ2U and

RFEMJRINJ have similar means and distributions, and one can be used as a

surrogate for the other, if it is unknown - e.g., if only LFEMJRINJ is unknown,

NCAPINJ = Wl HEADINJ + W2 CHESTOU + 2 W3 RFEMJRINJ

The situation is more complicated if HEADINJ or CHESTINJ are unknown or if both

LFEMURINJ and RFEMJRINJ are unknown, because these variables nave different means

and make different contributions to NCAPINJ. In these 792 cases, the average of

HEADINJ is .196, average CHESTINJ is .123, and average (LFEMJRINJ + RFEMJRINJ)

is .057. If HEADINJ is unknown, define an inflation factor

55
MISSHIC = (.196 Wl + .123 W2 + .057 W3) / (.123 W2 + .057 W3)

and inflate the NCAPINJ based on the other two body regions by this factor:

NCAPINJ = MISSHIC [W2 CHESTINJ + W3 (LFEMJRINJ + RFEMJRINJ) ]

or, if LEEMJRINJ is also unknown,

NCAPINJ = MISSHIC [W2 CHESTINJ + 2 W3 RFHVHRINJ]

Similar inflation factors are applied if just chest g's or if both femur loads

are unknown. That expands the analysis to 784 cases in which NCAP scores were

known on at least two body regions for each vehicle. Only 8 cases had to be

deleted because NCAP results were unknown on more than one body region. There

are 756 cases in which NCAP scores are known for the chest (which has the highest

of the three relative weights) and at least one other body region.

Starting with the values of .21, 2.7 and 1.41 for Wl, W2 and W3,

and defining DELNCAP = NCAPINJo^ - NCAPINJomot / regressions are run in which the

dependent variable is fatality risk in the "case" vehicle, and the independent

variables are DELNCAP plus W, A and S (as defined in Chapter 3). These

regressions are performed for the full set of 784 cases (NCAP scores known for

at least two body regions) and its subset of 756 cases where chest g's are known

for both vehicles. Two series of regressions are run with alternative values for

Wl and W3, relative to W2. m the first series, a sort of fine tuning to

maximize x2 / examines the effects of slight variations fran the starting values

of Wl, W2 and W3. The x2 for DE3JNCAP were as follows:

56
Relative Weights

Head

Injury

.21

. 1

.3

.21

.21

.21

Chest

Injury

2 .7

2.7

2 . 7

2 . 7

2.7

2 . 7

Pemrr

Injury

1.41

1.41

1.41

1.3

1.5

1.6

Chi-Squares

784

Cases

16.08

16.25

15.84

16.03

16.09

16.06

756

Cases

13.93

13.75

13.94

13.96

13.89

13.81

All of the regressions produce x2 close to the first one, indicating

a plateau rather than a peak of optirrum correlation. While none of the

regressions has the maximum x2 for both the 784 and the 756 cases,

NCAPINJ « .21 HEHD2NJ + 2.7 CHESTCNJ + 1.5 (LFBSJRINJ + RFEMDRINJ)

can be considered the best of the composite injury scores. The x2 for the 784

cases reaches a local maximum of 16.09, while the x2 of 13.89 for the 756 cases

is still close to the maximum. Although the model with Wl « . 1 for head injury

has a higher x2 for the 784 cases, the x2 for the 756 cases is the lowest of the

group.

The second series of regressions examines the vddth of the plateau of

near-optimum correlation. It compares the x2 for the NCAPINJ with the optimum

weights (.21, 2.7 and 1.5), the x2 for an unweighted injury function

INJ = HEADINJ + CHESTINJ + LFEMURINJ + RFEMJRINJ

and for three intermediate injury functions, proceeding by harmonic steps from

the optimum weights to the unweighted function:

57
Relative Weights

Bead

injury

0.21

0.31

0.46

0.68

1

Chest

Injury

2.70

2.10

1.64

1.28

1

Penur

Injury

1.50

1.36

1.22

1.11

1

CM-Squares

784

Cases

16.09

15.58

14.19

11.40

7.86

756

Cases

13.89

13.67

12.71

10.42

7.34

All x2 are statistically significant at the .01 level, and in the

second and third line the x2 are still close to the optimum values even though

the relative weights are quite different fron their optimum values. This second

series of regressions shows that the correlation of fatality risk with a

ccnposite NCAP score is relatively insensitive to the exact choice of the weight

factors and that any reasonable combination of head, chest and femur injury

scores will correlate well with fatality risk.

The optimum score,

NCAPINJ = .21 HEADINJ + 2.7 CHESTINJ + 1.5 (LFEMJRINJ + RFEMJRINJ)

can range from 0 to 5.91. It is a relative measure of overall NCAP performance

(the higher the NCAPINJ, the worse the performance), but specific values of

NCAPINJ, such as 0.5 or 1.0, do not correspond to any intuitive, absolute level

of injury.

NCAPINJ, at first glance, seems to give a very low weight to head

injury and a surprisingly high weight to femur injury, as indicated by Wl «= .21

and W3 = 1.5. However, the Wi's, by themselves, do not indicate the relative

weights of the body regions. As noted above, the average value of HEADINJ is

58
.196, average CHESTINJ is .123, and average (LE^MJRINJ + RJEMURINJ) is .057.

Thus, the relative contribution of HEADINJ to NCAPINJ is .21 x .196 = .041; the

contribution of CHESTINJ is 2.7 x .123 = .332; and the contribution of femur

injury is 1.5 x .057 •= .086. To the extent that HIC, chest g's and femur load

are intercorrelated, their "relative contributions" to the composite score need

not reflect the actual relative importance of head, chest and lower-body injuries

in crashes - i.e., CHESTHU may be making such a large contribution to NCAPINJ

because it incorporates information of the probability of head and femur injury,

in addition to chest injury. The ccnpceite score is a mathematical method of

combining NCAP information to get the best correlation with fatality risk in the

current data set of head-on collisions. (NCAPINJ is optimized for the current

data file of fatal head-on collisions, but when additional accident data become

available, the relative weights for the three body regions might change).

The complete results of the regression model, with independent

variables W, A, S and DELJNCAP = NCAPINJCASE - NCAPINJamER, where NCAPINJ = .21

HEADINJ + 2.7 CHESTTNJ + 1.5 (LFEMJRINJ + REEMJRINJ), for the 784 head-cn

collision cases in which both cars matched up with an NCAP test vehicle at levels

"3A" or "4A" and DELUCAP could be calculated, are as follows:

DEUTCAP = .21 DELHIC + 2.7 DEL/33 + 1.5 DELFEM

INIERCEPT

W (car weight)

A (driver age)

S (driver sex)

DELflCAP

Reg.

Coeff.

.6345

-5.3305

.0558

.4200

1.0665

Chi

Square

37.42

146.49

143.87

7.91

16.09

Stat.

Sig.?

RR

RR

RR

RR

RR

Partial

Corr.

-.368

.365

.075

.115

59
The x2 of 16.09 for DELJKRP indicates a strongly significant correlation (p <

.0001) between the composite NCAP score and fatality risk. It exceeds the sum

of the x2 for head, chest and femur injury when they were entered as separate

variables in the regression, because cases that had to be excluded from that

regression due to missing NCAP data can now be included. However, the x2 of the

regression coefficient for DELNCAP is just one of several ways to measure the

extent or strength of the relationship between NCAPINJ and fatality risk in

actual head-on collisions. The remainder of this chapter presents other methods

to gauge the relationship, and to measure the actual fatality reduction for a

good NCAPINJ relative to a poor score.

4.2 T?77r.'EyP; actual safety performance relative to expectations

DELNCAP = NCAPINJcasE - NCAPINJOJHER is a measure of the relative NCAP

performance of two vehicles that became involved in a head-on collision. If the

case vehicle had better performance an the NCAP test than the other vehicle,

DELNCAP is negative. Another variable, RELEXP, will now be defined for each

head-on collision, measuring the relative actual performance of the two vehicles

in that collision. RELEXP will be negative when the driver of the case vehicle

did better than expected (e.g., survived) and the driver of the other car did

worse than expected, given the weights of the two cars and the age and sex of

each driver. RELEXP can be tested for correlation with DELNCAP; more generally,

the average value of RELEXP can be computed for various groups of crashes (e.g.,

collisions of good NCAP performers with poor NCAP performers).

Ihe first step in computing RELEXP is a regression on the file of 784

head-on collision cases defined in Section 4.1 (both cars natch an NCAP test

vehicle at levels 3A or 4A, and DELNCAP could be calculated), but without any

60
Stat.

Sig.?

RR

RR

RR

R

Partial

Oorr.

-.378

.363

.056

NCAP variable. In other words, the dependent variable is the autocue for the

driver of the case vehicle (fatality = 1, survival = 0) and the independent

variables are only W, A and S - relative vehicle weight, driver age and sex:

Without NC&P Information

Reg. Chi

Goeff. Square

INIERCEPT .616 36.57

W (car weight) -5.427 154.15

A (driver age) .0531 142.35

S (driver sex) .34 5.39

The intercept and coefficients for W, A and S are similar to those obtained in

the preceding regression with DELNCAP. This logistic regression model can be

used to predict the expected fatality risk for each driver in a head-on

collision, in the absence of NCAP information. The expected fatality risk E,^

for the driver of the case vehicle is

expr.616 - 5.427(log W ^ - log W^_) + .0531 (A^ -A^ ) + .34(FCMr-Fffar)l

1 + exp[.616 - 5.427(logW_ - log W^ ) + .053KA..-A,,.) + •34(Far-F<to)]

where W,^ is the curb weight of the case vehicle, A,.c is the age of the driver

of the case vehicle and F ^ is 1 if the driver of the case vehicle is female, 0

if the driver is male. The expected fatality risk E,*,,, for the driver of the

other vehicle is

expf.616 + 5.427(lcaW.w - loa W^ ) - .OSSKA^-A^) - ,34(Fr,r-F1ltrr)1

+ exp[.616 + 5.427(logWaB - log W^ ) -

These formulas measure the relative vulnerability to fatal injury of

the two drivers, given that their cars had a head-on collision. The risk is

greater in the lighter car than the heavier car, and the older/female driver is

61
more vulnerable to injury than the younger/male driver. The forrrulas do not

address the propensity of cars to get involved in head-on collisions as a

function of driver age, sex, etc. For example, if the case vehicle is a 2500-

pound car driven by a belted, 50-year-old female and the other vehicle is a 3000-

pound car driven by belted, 20-year-old male, E,,. = .97 and E ^ = .09 (for a

total of 1.06 fatalities expected in the collision, since there is a 6 percent

chance that both drivers died).

If A ^ and A ^ are the actual outcome of the collision for the driver

of each car (fatality = 1, survival = 0),

XELEXP - (A,, - B«.) - (A ^ - E.^)

measures actual performance "relative to expectations." It can range from -2 to

+2. Ihe more negative it is, the better the actual performance of the case

vehicle relative to expectations. For example, if the 50-year-old female in the

2500-pound case car actually survived, while the 20-year-old male in the 3000-

pound other car died, RELEXP = -1.88 (much better than expectations). If she

died and he survived, RELEXP = +0.12 (about what would be expected). If both

drivers died in the crash, RELEXP - -0.88 (not a good outcome for either driver,

of course, but the case vehicle performed better than expected, relative to the

other vehicle). Note that RELEXP is measured for a two-car crash, not for a

vehicle. It does not measure the absolute safety of a vehicle, just the

performance of the case vehicle relative to the other vehicle.

Ihe population standard deviation of RELEXP was computed for the full

set of 784 crashes and for many subsets of these crashes. In every case, the

standard deviation was very close to 0.64. That makes it easy to test if the

average value of RELEXP is significantly less than zero for a specific group of

62
crashes (i.e., the case vehicles were significantly safer than the other cars),

or if the difference in average RELEXP for two groups of crashes is statistically

significant.

4.3 Correlation of D^MT^ P ?T¥?

DELNZAP, a measure of the relative N3VP performance of two vehicles

that became involved in a head-on collision, and RELEXP, a measure of their

relative actual performance are defined for each of 392 collisions an the file.

DELNCAP and RELEXP are both close enough to a normal distribution that their

correlation can be tested by the conventional Pearson method. (In the analysis

file, there are 784 collision records, but there are only 392 actual collisions,

since each crash is listed twice. Reversing the "case" and the "other" vehicle

merely changes the sign of both DELNCAP and RELEXP, so the second listing of each

crash provides no new information for the analysis, and using N = 784 would

spuriously inflate significance levels.)

DELNCAP and RELEXP have a correlation coefficient of .166, which has

strong statistical significance (p = .001, N = 392). In other words, the higher

the ccmposite NCAP score for car 1 relative to car 2, the higher the fatality

risk for driver l relative to driver 2, after adjusting for car weight, driver

age and sex.

The correlation coefficient and its significance level both say a lot

about the relationship between NCAP performance and actual fatality risk in all

types of head-on collisions an the highway. On the one hand, the correlation of

.166 is far from perfect: the driver of the car with the lower NCAP score will

not always be the survivor in any type of head-on collision with a car having a

63
higher score, even if both cars have the same weight. Furthermore, needless to

say, which driver dies in a head-on collision has a lot more to do with relative

vehicle weight and driver age than with NCAP scores. On the other hand, the

significance level of . 001 suggests beyond doubt that there is sane correlation

between NCAP and actual fatality risk in head-on collisions: on the average, cars

with acceptable NCAP scores have lower fatality risk, across the range of head-on

collisions, than cars of the same weight with high scores.

This analysis approach also makes it possible to test if NCAP

information for a single body region, in the absence of information about the

other two body regions, is correlated with fatality risk. The approach is

different frcm Chapter 3, in which information for all three body regions was

slnultaneously entered in a regression, and the relative contribution of each

NCAP score to fatality risk was estimated. DELCG, DELEEM and DELHIC are the

measures of relative NCAP performance, based en logistic injury probability

functions (see Section 3.2). DELCG has a statistically significant correlation

with RELEXP (r = .136, p = .008, N = 378). In other words, there is a

significant correlation between chest g's, by itself, and fatality risk. DELJ5EM

has a positive correlation with RELEXP, but not quite statistically significant

(r •= .094, p •= .065, N - 387); DELHIC also has a nonsignificant positive

correlation (r «= .050, p - .321, N - 389).

4.4 Fatality reduction for the car with lower NCAPINJ

The accident data file contains 784 head-on collision records for

which DELNCAP - NCAPINJO^E - NCAPINJOTSR (relative composite NCAP performance)

is known. The records can be ranked by DELNCAP and listed in order, from the

case with DELNCAP * -1.98 (largest differential in favor of the case vehicle) to

64
the case with DEUO P = +1.98. The 16 records in the middle, with DELWCAP = 0,

are deleted from the list: both cars in the collision matched up with the same

NCAP test, so NCAP gives no information to favor one car or the other. The last

384 records, with DElflCAP > 0, are also deleted: they are merely the same crashes

as the first 384 records, with the "case" and "other" vehicles reversed. That

leaves a file of 384 distinct head-on collisions, comprising 768 distinct

vehicles, in which the NCAP performance of the case vehicle is always better than

the performance of the other vehicle. DELNCAP ranges fran -1.98 for the first

record to -0.000243 for the last (a very small advantage for the case vehicle).

The objective of this analysis is to compute the reduction in fatality

risk for the 384 "better11 NCAP performers relative to the 384 "poorer" NCAP

performers, and to test if the reduction is statistically significant. Moreover,

if the analysis is limited to the first half/quarter/tenth of the file, where the

NCAP performance advantage of the case vehicle successively increases, does the

fatality reduction for the case vehicle also escalate?

As explained in Section 4.2, each collision has an outcome A,, for the

driver of the case vehicle (fatality • 1, survival • 0) and A ^ for the driver

of the other car. The expected outcomes E ^ and E ^ (expected probabilities of

fatality) are based an the relative vehicle weights and the age and sex of the

drivers. The actual and expected fatalities are summed over all the crashes

included in the analysis: sumCA^) and sum(AAlr) are the actual numbers of driver

fatalities in the case and the other vehicles; sum(Eaic) and sumCE^) are the

numbers of driver fatalities that would be expected in the case vehicles and the

other vehicles, given the relative weight, age and sex in each crash. The

fatality reduction for the case vehicles, relative to the other vehicles, is

65
Fatality Reduction = 1 - ([sum(Aaie)/sum(Aote)] /

Specifically, in the 384 collisions with DELNCAP < 0, there were 202

actual driver fatalities in the case vehicles and 246 fatalities in the other

vehicles. Since the vehicle weights, driver age and sex distributions are

similar, an the average, in the better and poorer NCAP performers, the expected

numbers of fatalities are about the same: 220.7 in the case vehicles and 227.7

in the other vehicles. That is a relative fatality reduction of

1 - [(202/246) / (220.7/227.7)] =15.3 percent

for the better NCAP performers. Conversely, the poorer performers had 18 percent

higher fatality risk.

Ihe test for statistic i significance of the fatality reduction is

based an the variable RELEXP, which is computed for each collision record, as

explained in Section 4.2. RELEXP measures actual performance of the case vehicle

relative to expectations. Ihe average value of RELEXP is

mean RELEXP •= [(202 - 220.7) - (246 - 227.7)] / 384 = -.096

For this population of crashes, as for most others, the standard deviation of

RELEXP is very close to .64. With a sample size of 384, the t statistic for

RELEXP is 2.96, which is significant at the .01 level. In other words, the

better NCAP performers had significantly fewer fatalities than expected, relative

to the poorer performers.

Table 4-1 shows the fatality reductions and other statistics for all

crashes in which DELNCAP is less than zero or is more negative than sane

specified amount (i.e., the case vehicles did better on NCAP than the other

66
TKBLE 4- 1

EAI7VLTIY REDUCTION FOR TOE DRIVER OF TOE CAR WTIH TOE LOWER OCMPOSriE NCAP SCORE

NCAPINJ Differential

between the "Case" Car

and the "Other" Car

DELNCAP < 0

DELNCAP < -0.1 8

DELNCAP < -0.4 2

DELNCAP < -0.63 5

N of

Crashes

384

203

95

38

% Fat Red

for Better Car

15.3

15.8

24.5

40.1

Iteal-Mbrld P

Mean

Relexp

-.096

-.102

-.171

-.253

Sun

Relexp

-37.00

-20.67

-16.25

- 9.61

T-Test

for Relexp

2.96

2.46

2.84

2.82

NCAPINJ Differential

between the "Case" Car

the "Other" Car

Mean NCAP Scores and Model Year ("Case" vs. "Other" Car)

me

Case Other

Chest G's

Case Other

L Femur

Case Other

R Feraxr

Case Other

Model Year

Case Other

DELWCAP < 0

CHUNCAP < -0.18

DELNCAP < -0.42

DELNCAP < -0.635

815 1007

820 1082

835 1095

832 1072

43.9 53.5

43.0 57.7

43.5 62.7

43.7 61.8

858 1025

873 1141

891 1213

883 1738

843 1033

831 1103

854 1081

903 1225

85.6 85.0

85.8 84.6

85.7 83.3

85.6 83.0
vehicles, by sane specified amount). The first line in the upper half of Table

4-1 displays statistics for the analysis described above: the crashes with

DEUJCAP < 0. It shows the sample size, the fatality reduction for the case

vehicles, mean RELEXP, the t test and one other statistic: the sum of RELEXP.

In this case, the sum is -37 = 384 x -.096. Intuitively, this sum describes the

total amount of "information" provided by the NCAP results for identifying safer

cars; the more negative the sum, the better. The bigger the actual safety

difference between the good and poor NCAP performers, and the more cars involved

in the analysis, the more negative the sum.

The first line in the lower half of Table 4-1 compares the actual NCAP

performance of the case vehicles and the other vehicles. The case vehicles (the

better NCAP performers), appropriately, had lower HTC, chest g's and femur loads,

an the average, than the poor performers. Average HIC was 815 in the case

vehicles, 1007 in the other cars. Chest g's averaged 10 less in the case

vehicles; femur loads averaged 167 pounds less on the left side and 190 pounds

less an the right side. These are average differences; they do not mean that

HIC, chest g' s and femur loads are lower for the case vehicle in each individual

crash. Since NCAPINJ is a weighted sum of HIC, chest g's and femur load, the

case vehicle might have the higher HIC in some crashes, but DELNCAP would still

be negative if the case vehicle has much lower chest g's. The average model year

is slightly more recent in the case vehicles (85.6) than the other vehicles

(85.0).

In the preceding analysis, the case vehicle was only required to have

better NCAP performance than the other vehicle; even an infinitesimal difference

was sufficient. The fatality reduction for good NCAP performance is even greater

68
when a "gap" is placed between good and poor performance and DEUTCRP is required

to be more negative than some specified amount. The results are shown in the

2nd, 3rd and 4th lines of each section of Table 4-1. Approximately half the

crashes (203) an the original file have DEL*OP < -0.18. In that group, the

better NCAP performers have 15.8 percent lower fatality risk than the poor

performers; the reduction is statistically significant (t for RELEXP is 2.46, p

< .05). The case vehicles enjoy an even larger advantage in HIC, chest g's and

femur load than in the preceding analysis. The gap in average model year also

grows (85.8 vs. 84.6), reflecting the superior NCAP performance of more recent

cars.

The sample size is again halved when DELNCAP < -0.42. In those 95

crashes, the case vehicles had 24.5 percent lower fatality risk than the poor

NCAP performers, which is a statistically significant reduction (t for RELEXP is

2.84, p < .01). Although the fatality reduction is greater than in the preceding

analyses, the sum of RELEXP is smaller: here, NCAP identifies a small number of

cars that are quite unsafe; previously, NCAP identified a much larger number of

cars that were slightly less safe than average. The lower half of Table 4-1

shows that chest g's are much higher in the other cars (62.7) than in the case

cars (43.5). The gap in HIC and femur load, on the other hand, is about the same

as in the preceding analysis. The difference in average model year continues to

escalate.

Finally, when DELNCAP < -.635, the accident file is reduced to 38

crashes, one-tenth of the original number. In these crashes with a large

contrast in NCAP performance, the good cars had 40 percent lower fatality risk

than the poor performers. The reduction is statistically significant (t for

69
RELEXP is 2.82, p < .01). The "other" cars have much higher chest g's and femur

loads than the case vehicles. The difference in fatality risk between these best

and worst NCAP performers is almost as great as the difference between a belted

occupant and an unrestrained occupant.

4.5 NCAP performance of nar« <"^?f ffifi better thf*n expected in

How about crashes with astonishing outcomes - where an older driver

in a smaller car walks away and the younger driver in the larger car dies? Did

the car with the unanticipated good performance have lower NCAP scores? This

analysis is the converse of the preceding one, comparing the NCAP scores of two

cars in a head-on collision, when the driver of the case vehicle, in the actual

crash, did better than expected (as evidenced by negative RELEXP). The 784 headon collision records are ranked by RELEXP and listed in order, from the case with

RELEXP = -1.89 (largest differential in favor of the case vehicle) to the case

with DELNCAP = +1.89. The last 392 records, with RELEXP > 0, are deleted: they

are merely the same crashes as the first 392 records, with the "case" and "other"

vehicles reversed. That leaves a file of 392 distinct head-on collisions,

comprising 784 distinct vehicles, in which the actual outcome, relative to

expectations, is always better for the case vehicle than the other vehicle.

RELEXP ranges fron -1.89 for the first record to -0.004 for the last. The

crashes with RELEXP more negative than -1 are the ones in which the case car

driver survived even though the expected fatality risk was lower for the other

driver. A negative RELEXP that is close to zero does not signify an astonishing

outcare: it means that the driver with a heavy advantage did, in fact, survive;

or that two cars were almost evenly matched, and both drivers died.

Table 4-2 compares the NCAP performance of the "case11 and the "other"

70
13VBLE 4- 2

WZAP PERPOFMMCE OF CARS WHOSE DRIVERS FARED BETTER THAN EXPECTED IN ACTUAL HEAD-ON COLLISIONS

Mean NCAP Scores ("Case" vs. "Other" Car)

RELEXP Differential

between the "Case" Car

and the "Other" Car

RELEXP < 0

RELEXP < -0.5

RELEXP < -0.75

RELEXP < -1.0

N of

Crashes

392

152

88

49

Mean

DEUICAP

-.063

-.132

-.117

-.128

T-Test

for DELNCAP

3.17

4.08

3.17

3.11

Case

899

903

896

885

me

Other T-Test

924

942

907

895

1.24

1.05

.32

.02

Chest G's

Case Other T-Test

47.7

46.6

47.1

46.2

49.7

50.3

49.7

48.1

3.14

3.47

2.02

1.32

RELEXP Differential

between the "Case" Car

and the "Other" Car

RELEXP < 0

RELEXP < -0. 5

RELEXP < -0.75

RELEXP < -1.0

Mean NCAP Scores and Model Year ("Case" vs. "Other" Car)

L Femur R Fenur T-Test for Model Year

Case Other Case Other Equal Fenur Inj Case Other T-Test

935

879

884

847

940

953

959

1000

944

858

829

791

932

941

1007

1053

1.11

2.05

2.50

2.53

85.5

86.1

86.4

86.2

85.2

85.0

85.3

85.0

1.42

3.66

2.62

2.02
vehicles, in the crashes where RELEXP is less than zero or is more negative than

sane specified amount. The first line in the upper half of Table 4-2 analyzes

all 392 cases in which RELEXP < 0. The average value of DELNCAP is - .063, i.e.,

the cars with better actual perf onnance also had better NCAP performance, as

evidenced by NCaPINJ being .063 lower. These are average differences; they do

not mean that NZAPINJ is lower for the case vehicle in each individual crash.

However, the average difference in NCAPINJ performance is statistically

significant at the .01 level: a test of the hypothesis that DELNCAP = 0 yields

a t value of 3.17.

The case vehicles had lower average HIC (899) than the other vehicles

(924). Those are arithmetic averages of the original HIC scores. A significance

test for the difference in HIC can be based en the variable DELHIC, the

difference in the logistic injury probabilities. The t value for the hypothesis

that DELHIC = 0 is 1.24, so the difference is not significant. But the

difference in average chest g's, 47.7 vs. 49.7 is significant at the .01 level

(t for DELCG is 3.14, p < .01). The first line in the lower section of Table 4-2

shows that the case and other vehicles had about the same average femur loads and

model years.

There are 152 crashes in which the actual performance of the case

vehicle was a fair amount better than expected, as evidenced by RELEXP < -0.5.

NCAP performance of the case and the other vehicle is compared in the second line

of Table 4-2. The average difference of 1OPINJ is -.132, which is more than

double the value in the preceding analysis. The cars with better actual

performance had lower NCAPINJ, by a highly significant amount (t for DELNCAP is

4.08, p < .0001). MDreover, the cars with better actual performance had better

72
NCAP results en every body region. HIC averaged 903 for the case vehicles and

942 for the other vehicles; chest g's averaged 46.6 for the case vehicles and

50.3 for the other vehicles; femur loads averaged about 80 pounds lower an both

legs. The difference in chest g's is significant at the .01 level, and for fenur

load, at the .05 level. The cars with better actual performance had a

significantly more recent model year (86.1 vs. 85.0, t = 3.66, p < .001).

RELEXP was more negative than -0.75 in about one-fourth of the

crashes. Ihe difference in NCAPINJ (- .117) is about the same as in the preceding

analysis, and it is statistically significant (t for DEUNCAP is 3.17, p < .01).

However, the difference in HIC and chest g's decreased from the preceding cases,

while the gap in femur load increased. Finally, in the 49 crashes with really

surprising outcomes, as evidenced by RELEXP < -1.0, 1CAPINI is once again

significantly lower in the cars with better actual performance (DELNCAP - - .128,

t «= 3.11, p < .01). HIC is about the same in both cars; chest g's are lower in

the case cars, on the average, by 2. Femur loads are significantly lower in the

case cars, by an average of 150 pounds an the left side and 260 pounds an the

right side. The average difference in model year remains dose to one year.

Ihe four analyses in Table 4-2 are strong evidence that cars with

better-than-expected actual performance, as evidenced by negative RELEXP, had

significantly better NCAP performance than the cars they hit. That, by itself,

is not really a new finding, since a highly significant correlation between

RELEXP and DELNCAP was already shown in Section 4.3. This analysis, however,

reveals some traits of the relationship.

First, the average differences in NCAP performance, although

73
statistically significant, are not vast in absolute terms. Average HIC differed

by 40 or less, chest g's by 2-4 and femur load by no more than 250 pounds. Who

survives and who dies in a specific head-on collision, depends on many factors

besides vehicle performance as measured in an NCAP test; it depends a lot on the

personal vulnerability to injury of the individual occupants and the unique

circumstances that may be present in that crash. Nevertheless, en the average,

the cars with better actual performance had lower NCAP scores.

Second, the composite performance measure NCAPINJ had a stronger

relationship with actual performance than did any of the NCAP scores for

individual body regions. In all four analyses, the difference in NCAPINJ was

significant at the .01 level, with t-values always over 3. Although HIC, chest

g's and femur loads were consistently lower, on the average, in the safer case

vehicles, the differences were not always statistically significant, and only

rarely at the .01 level (chest g's in the first two analyses).

4.6 Fatality reduction for the ray with lower h^ad or chpp*" fojy'y risk

NHTSA's December 1993 Report to Congress contains an analysis similar

to the approach in Section 4.4, but with the collision records ranked by head or

chest injury risk, rather than NCAPINJ [24], p. 72. The analysis is called "Case

A" in the Report to Congress. The accident data file contains 740 head-on

collision records in which NCAP HIC and chest g's are known for both drivers.

The TMMHWIHI bead/chest Injury for a driver is the greater of the logistic injury

probability functions, HEADINJ and CHESTINJ (see Section 4.1):

MAXHCINJ « max (HEADINJ, CHESTINJ)

The performance for the case vehicle relative to the other vehicle is

DEL*«XHC

74
Ihe records were ranked by DELMRXHC, starting with the crash having the largest

differential in favor of the case vehicle. Ihe 12 records in the middle, with

DELM^XHC = 0, and the last 384 records, with DELMSXHC > 0, are deleted, as in

Section 4.4. That leaves a file of 364 distinct head-on collisions, catprising

728 distinct vehicles, in which the NCAP head/chest injury performance of the

case vehicle is always better than the performance of the other vehicle.

In the 364 collisions with DELZ>S*XHC < 0, the average NCAP scores for

the "case" and "other" vehicles, and the actual and expected fatality counts were

as follows:

Average HEC

Chest g's

Left fettur load

Right femur load

Actual fatalities

Expected fatalities

Patality reduction (%)

Mean EELEXP

T-test for RELEXP

Case Vehicle

721

45.0

1012

1002

199

207.8

8.7

- .053

1.62

Other Vehicle

1111

52.7

895

902

228

217.4

Chest g's and, especially, HIC are lower, on the average, in the

"case" vehicles. Femur loads, which are not factored into the calculation of

MRXHCINJ, are actually slightly higher in the case vehicles. The reduction in

actual fatality risk, adjusted for vehicle weight, driver age and sex, is

1 - [(199/228) / (207.8/217.4)] - 8.7 percent

in the case vehicles, and it is not statistically significant, although it comes

close to significance (t - 1.62).

75
4.7 Sensitivity test: NCAPINJ on a rH•Ff^rent calibration data set

NCAPINJ was the specific weighted average of head, chest and fenur

scores that had maximum correlation with fatality risk an the basic calibration

data set of EARS cases in which both cars natch with an NCAP test at levels 3A

or 4A. In Section 4.1, regression analyses with that data set showed excellent

correlations of NCAP scores and fatality risk even when the relative weights for

the three body regions diverged substantially from .21, 2.7 and 1.5, the weights

in NCAPINJ. As an additional sensitivity test, these regressions can also be run

on a subset of the EARS cases: where each EARS car not only iratches up with an

NGAP car at level 3A or 4A, but also nust have the same nuntoer of doors as the

NCAP car. In Section 3.4, it was shown that the regression analysis with DELHIC,

DELCG and DELFEM as separate variables assigned them regression coefficients of

.42, 2.52 and .73. Thus, the optimum composite measure of injury for this data

set would have relative weights for the three body regions close to these values

- i.e., a slightly higher weight for HIC and a lower weight for femur load than

in the full data set.

The new calibration data set contains 620 cases in which NCAP scores

are known on at least two body regions for each vehicle, and 598 cases in which

the scores are known for the chest and at least one other body region. The

second series of regressions in Section 3.1, which compared the x2 f° r the

NCAPINJ with the original optimum weights (.21, 2.7 and 1.5), for an unweighted

injury function, and for three intermediate injury functions, is rerun for the

new calibration data set. A regression is also run for a composite injury

function based an the new optimum weights (.42, 2.52 and .73):

76
Relative Heights Ch1-Squares

Bead

Injury

0.21

0.31

0.46

0.68

1

0.42

Chest

Injury

2.70

2.10

1.64

1.28

1

2.52

Femur

Injury

1.50

1.36

1.22

1.11

1

0.73

620 598

10.80

10.73

10.41

9.31

7.49

11.24

8.43

8.52

8.47

7.78

6.46

9.61

All x2 are statistically significant at the .01 level (except the

regression en 598 cases with the unweighted injury function, which is significant

at the .05 level). The last regression produces the highest x2 • Ihe original

NCAPHU is not the optimal injury function en this calibration data set, although

its x2 is just slightly less than the maximum values. The drop-off inx2 , as the

regressions proceed from the original NCBPINJ to the unweighted injury function,

are less precipitous than en the original calibration data set. Here, they drop

by about 30 percent; there, the descent was closer to 50 percent.

The sensitivity tests confirm that any. reasonable combination of head,

chest and fenur injury scores, not just NCBPINJ, will correlate well with

fatality risk. While the FKRS sanple is adequate to show that chest g's need to

be given a substantial weight in any cenposite score, the accident sanple is not

really large enough to determine exactly the relative inportance of the head and

the fenur injury scores.

77

CHAPTER 5

COLLISICNS BETWEEN A "GOCD" AND A "POCK" NCAP PERFORMER

Probably, the sinplest way to estimate the fatality reduction

associated with good NCAP scores is to partition the cars based en a specific

NCAP score - e.g., HIC, chest g's, femur load or a composite score such as

NCAPINJ - and to consider only the subset of head-on collisions in which the case

vehicle has a score in the "good" range and the other vehicle has a score in the

"poor" range. Do the cars with the poor NCAP scores have significantly more

driver fatalities than expected, after control for the curb weight, driver age

and sex?

Most of the analyses in this chapter are based an the data set of EARS

head-on collisions between two passenger cars in which both cars matched up with

an NCAP test vehicle at levels "3A" or

 n4A": the model year on EARS is within the

range of model years considered valid for the NCAP test, and the make-models on

EARS and NCAP are identical or true corporate cousins. That data set includes

396 head-on collisions (792 vehicles). As a sensitivity test, some of the

analyses are repeated, in Section 5.9, on the subset of head-on collisions in

which both vehicles not only match an NCAP test vehicle at levels 3A or 4A, but

also have the same number of doors as the NEAP test vehicle (310 collisions, 620

cars).

5.1 Cars with low NCAPINJ hit cars with high NCAPINJ

A composite measure of NCAP performance, NCAPHU was defined in

Section 4.1, as a weighted average of logistic injury probability functions for

the head, chest and femurs. The weights were chosen to maximize the correlation

79
of NZAPINJ with fatality risk in the principal calibration data set (crashes

where both vehicles natch an NCAP test at the '3A or 4A level') but are not

necessarily optimal for other accident data sets (e.g., crashes where both

vehicles natch an NCAP test at the '3A or 4A level and N of doors'). In Section

4.3, 384 head-cn collisions were identified in which the case vehicle had a lower

(better) NCAPINJ score than the other vehicle. The fatality risk was a

statistically significant 15 percent lower in the case vehicles. In that

analysis, the NCAPINJ for the case vehicle did not have to be below any specified

level, nor did the NCAPINJ for the other vehicle have to be above sane specified

level: it was only required that the case vehicle did better than the other

vehicle. Thus, the set of 384 collisions includes seme where both vehicles did

quite well (in absolute terms) and seme where both did poorly.

The approach of this chapter is generate subsets of the 384 collisions

in which all the case vehicles had "good" NCAP performance: better than sane

specified level A. All the other vehicles had ^poor" performance: higher than

another specified level B, where B > A. By eliminating the cases where both cars

did well, or both did poorly, there should be an even larger differentiation of

fatality risk between the case and the other vehicles.

Table 5-1 presents the results of nine analyses for the special case

where B = A; i.e., there is a single boundary between "good" and "poor"

performance. All cars with NCAPINJ lower than the boundary are "good" and all

above it are "poor." The nine analyses use boundary values of 0.2, 0.3, ... ,

1.0, respectively. In every analysis, the fatality risk is significantly lower

in the good NCAP performers than in the poor performers, as evidenced by t-test

results greater than 1.65 (p < .05). The fatality reduction for a good NCAPINJ

80
TABLE 5- 1

COLLISIONS OF CARS WTIH LOW NCAPINJ INTO CARS WTIH HIGH NCAPINJ:

EFFECT OF M3V3N3 THE BOUNDARY BETWEEN "LOW" AND "HIGH" NCAPINJ

CO

Definition Of

"Low" NCAPINJ

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ < 1

.2

.3

.4

.5

.6

.7

.8

.9

.0

Definition Of

"High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ > 1

.2

.3

.4

.5

.6

.7

.8

.9

.0

N of

Crashes

115

186

186

147

117

108

80

55

27

Comparison of H

% Fat Red for

Low NCAPINJ

19.1

11.6

17.0

17.3

26.4

27.2

28.7

31.9

41.2

Bad-World

Mean

Relexp

-.112

-.072

-.108

-.114

-.181

-.189

-.201

-.216

-.279

Sun

Relexp

-12.91

-13.47

-20.17

-16.83

-21.13

-20.46

-16.10

-11.88

- 7.53

T-Test

for Relexp

1.83

1.68

2.56

2.44

3.22

3.34

3.13

2.73

2.84

Mean NCAP Scores and Model Year ("Low" vs. "High" NCAPINJ Car)

1 Of

"Low" NCAPINJ

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

.2

.3

.4

.5

.6

.7

.8

.9

1.0

Definition of

"High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

.2

.3

.4

' .5

.6

.7

.8

.9

1.0

HIC

Low

647

709

801

852

898

922

942

962

1026

High

952

1064

1070

1103

1106

1103

1090

1069

908

Chest G's

Low

36.0

40.4

42.1

43.9

45.0

45.9

47.5

47.8

49.0

High

50.3

53.5

55.7

59.8

62.6

62.7

64.1

63.8

62.4

L

Low

721

825

816

908

878

866

940

947

979

Femur

High

941

1044

1121

1119

1161

1226

1179

1487

1979

R Pemxr

Low

694

749

775

901

846

865

953

951

974

High

993

1078

1136

1031

1117

1164

1058

1294

1200

Model

Low

85.9

85.9

85.8

85.8

85.8

85.5

85.0

84.9

85.0

Year

High

85.4

85.4

85.0

84.3

83.1

83.2

83.8

83.2

84.0
score, relative to a poor score, ranges from 12 percent when 0.3 is the boundary

value to 41 percent when 1.0 is the boundary value. In general, the higher the

boundary value, the greater the fatality reduction for good vs. poor performance.

However, the analyses with high boundary values have sharply reduced sample

sizes, because there are few cars an the file which had really high NCAPINJ

results. Ihese last analyses only provide information about a fraction of the

cars an the file; they don't say much about the "typical" car an the road.

The ideal analysis should combine a large fatality reduction and a

large sample size. The variable "sum RELEXP," which is the product of mean

RELEXP and sample size, intuitively describes the total "information" provided

by an analysis. Sum RELEXP reaches a maximum of 21.13 when "good" NCAP

performance is defined as NCAPINJ ^ 0.6 and "poor" NCAP performance is defined

as NCAPINJ > 0.6. There are 117 head-on collisions of a "good" performer with

a "poor" performer, in which both drivers are belted. In the 117 cars with

NCAPINJ > 0.6, 77 drivers died, whereas only 65.5 fatalities were expected, based

an car weight, driver age and sex. In the 117 cars with NCftPINJ < 0.6, there

were 62 actual and 71.6 expected driver fatalities. (The good performers weighed

almost the same as the poor performers - 2868 vs. 2869 pounds, on the average,

but the drivers of the low-NCAPINJ cars were older than the drivers of the highNCAPINJ cars - 44.7 vs. 40.5 years, an the average; thus, the expected fatalities

are slightly higher in the cars with low NC3VPINJ.) The fatality risk is

1 - [(62/77) / (71.6/65.5)] -= 26 percent

lower in the good performers than in the poor performers, after controlling for

vehicle weight, driver age and sex (t far RELEXP is 3.22, p < .001).

Of course, even in these accident samples tailored to highlight the

82
safety benefits associated with good NCAP scores, the relationship between the

NCAPINJ and fatality risk over the range of head-on collisions experienced an the

highway is not perfect. Merely having the lower catposite NCAP score of the two

cars in the collision does not guarantee survival, even if the two cars are of

the same weight and the drivers of the same age and sex. Yet, on the average,

in collisions between cars with NCAPINJ < 0.6 and cars with NCAPINJ > 0.6, the

driver of the car with the better carposite NCAP score had 26 percent less

fatality risk than the driver of the car with the poorer NCAP score, even after

controlling for weight, age and sex.

The sample size of 117 is about a third of the 384 cases considered

in Section 4.3. Although it seems small, it is close to the "ideal" sample size

for the analyses of this chapter, whose technique is to exclude the crashes

between two "good" cars or two "poor" cars, and include only the crashes between

a "good" and a "poor" car. If exactly half the cars had "poor" NCAP performance,

that would eliminate exactly half the crashes, leaving a sample of 190.

Intuitively, though, substantially less than half the cars can be called really

"poor" performers en NCAP. If the "poor" performers are the worst 20 percent or

so, and the "acceptable" performers are the best 70-80 percent (with perhaps 10

percent in a borderline area), the file of 384 crashes can be expected to contain

about 110-130 collisions between an "acceptable" and a "poor" performer. In

general, the objective in this chapter is to find boundary values between

"acceptable" and "poor" performance that frpviraize the fatality reduction for

"acceptable" relative to "poor" performance while iiH-irif3Tnina a sample size dose

to the target of

The lower half of Table 5-1 compares the average scores of "good" and

83
"poor" NCAPINJ performers on the individual NCAP body regions. Since NCAPINJ is

a weighted sum of injury probabilities for all of the body regions, the cars with

NCAPINJ <. 0.6 have, on the average, lower HIC than the cars with NCAPINJ > 0.6

(898 vs. 1106), also lower chest g's (45 vs. 62.6), and lower femur loads (878

vs. 1161 an the left; 846 vs. 1117 on the right). Similar patterns are found in

the other analyses, except when the boundary value is 1.0, where the sanple is

quite small. As the boundary values rise, so do HIC, chest g's and fenur loads,

for both the "acceptable" and the "poor" groups. As NCAPINJ rises above 0.7,

though, HIC and chest g's tend to level off, while fenur loads continue to

escalate. Reflecting the trend of iitprovament in NCAP results during 1979-91,

the average model year for the "good" performers ranges from 0.4 to 2.7 years

more recent than for the "poor" performers.

Table 5-2 shows what happens when a "gap" or borderline area is placed

between "low" and "high" NCAPINJ. Mare and more crashes drop out of the sample,

as one or both cars have NCAPINJ in the borderline region. Table 5-2 starts with

a single boundary of NCAPINJ « 0.6 (the "best" analysis in Table 5-1) and

expands, by 0.1 at a time, the distance between the lower and upper boundary

values. For example, in the second analysis of Table 5-2, the "good" cars are

the ones with NCAPINJ < 0.5 and the "poor" ones have NCAPINJ > 0.6. As the

sanple size drops frcm 117 to 22, the fatality reduction for good performance

rises frcm 26 to 57 percent. Although the analyses with the larger gaps have

impressive fatality reductions and high statistical significance (t values as

high as 4.11), they don't really mean as much as the analysis without a gap, as

evidenced by steadily declining sum RELEXP. In the lower half of Table 5-2,

average scores for the individual body regions indicate that the analyses with

big gaps compare really good all-around NCAP performers to really poor all-around

84
TABLE 5-2

COLLISIONS OF CARS WITH DOW NCAPINJ INTO CARS WITH HIGH NCAPINJ:

EFFECT OF PLACING A GAP BETWEEN "LOW" AND "HIGH" NCAPINJ

CO

01

Definition of

"Low- NCAPINJ

NCAPINJ < .6

NCAPINJ < .5

NCAPINJ < .5

NCAPINJ < .4

NCAPINJ < .4

NCAPINJ < .3

NCAPINJ < .3

Definition of

•High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

.6

.6

.7

.7

.8

.8

.9

N of

Crashes

117

102

92

71

• 45

34

22

% Fat Red for

Low NCAPINJ

26.4

24.1

26.1

29.6

38.2

42.9

57.0

Mean

Relexp

-.181

-.165

-.181

-.208

-.276

-.282

-.370

nnce

Sum

Relexp

-21.13

-16.78

-16.66

-14.78

-12.40

- 9.60

- 8.15

T-Test

for Relexp

3.22

2.99

3.28

3.28

4.11

3.44

3.72

Mean NCAP Scores and Model Year ("Low11 vs. "High" NCAPINJ Car)

Definition of

"Low- NCAPINJ

NCAPINJ £. .6

NCAPINJ < .5

NCAPINJ < .5

NCAPINJ < .4

NCAPINJ < .4

NCAPINJ < .3

NCAPINJ < .3

nn4Hv)4^4rv1 Of

"High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

.6

.6

.7

.7

.8

.8

.9

ETC

LOW

898

846

864

857

850

748

733

High

1106

1093

1093

1102

1086

1064

1207

Chest: G'a

LOW

45.0

43.6

44.1

42.3

42.8

41.8

41.6

High

62.6

62.3

62.4

62.8

64.3

63.6

62.8

L

Low

878

891

880

783

786

111

813

Feaur

High

1161

1198

1247

1195

1166

1152

1399

R Fsnur

Low

846

873

887

771

793

727

746

High

1117

1111

1117

1187

1079

1098

1327

Model

Low

85.8

85.7

85.7

85.5

85.1

85.4

85.1

Year

High

83.1

83.2

83.2

83.0

82.9

82.9

82.6
NCAP performers. Crashes of that type are rare, but the advantage is strongly

with the good NCAP performer.

5.2 Cars with low NCAP chest g's hit cars with high chest g's

The ccnposite score NCAPINJ is quite efficient for partitioning the

cars into a "safer" and a "less-safe" group, as evidenced by the 26 percent lower

fatality risk for cars with NCAPINJ <. 0.6 in 117 crashes where they hit cars with

NCAPINJ > 0.6. NCAPINJ is a weighted sum of NCAP scores for three body regions.

Do the NCAP scores for any single body region have ccnparable efficiency for

identifying differences in actual safety performance?

Chest g's, which had significant correlation with actual fatality risk

throughout Chapters 3 and 4 and are the largest ccnpanent of NCAPINJ, are a

reliable single parameter for partitioning the cars into safer and less-safe

groups. Table 5-3 describes 14 analyses, each using a different single boundary

between "good" and "poor" chest g's. The boundary ranges from 42 to 68 g's. For

exarrple, 60 chest g's, the maxiirum value allowed by Federal MDtor Vehicle Safety

Standard (FMVSS) 208, is used as the boundary in one of the analyses. There are

92 actual head-on collisions (both drivers belted) in which one of rrodels had >

60 chest g's for the driver when it was tested in NCAP, and the other had <. 60

g's. The fatality risk is a statistically significant 24 percent lower in the

cars with < 60 g's than in the cars with > 60 g's (t for RELEXP is 2.74, p <

.01). However, 60 g's, the pass-fail value in FMVSS 208 is just one possible

boundary. All of the other cutoff points from 42 to 68 g's, except 48 and 50,

produce statistically significant differences, as evidenced by t values greater

than 1.65. The boundary value that yields a sanple size closest to the target

of 120 crashes is 56 g's: the fatality reduction for the "good" cars is 19

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percent (t for RELEXP is 2.32, p < .05).

The lower half of Table 5-3 ccnpares the average NCAP performance of

the cars with "good" and "poor" chest g's. Needless to say, there is a large

difference in the average chest g's. The difference is about 16 g's in the first

four analyses, and gradually gets larger as the boundary value increases,

eventually reaching 22 g's. However, the "good" cars are also better, in many

cases, on the other body regions. As noted in Section 3.3, chest g's have a

strong correlation coefficient of .281 with HIC, and weaker correlations of .162

with right femur load and .062 with left femur load. Thus, the cars with the

higher chest g's also tend to have higher HIC, by about 200-250, on the average,

in all of the analyses with boundary values up to 62 g's. The fenur loads are

about the same, or slightly higher in the high-chest g cars, up to the analysis

with a boundary of 56 chest g's. But, above those boundary values, the pattern

reverses. The small groups of cars with very high chest g's tend to compensate

for it with lower HIC and femur loads than their counterparts with low chest g' s.

Reflecting the trend of improvement in NCAP results during 1979-91, the average

model year for the "good" performers ranges from l.i to 2.3 years more recent

than for the "poor" performers.

On the whole, chest g's are not as efficient as NCAPINJ for

discriminating safer and less-safe cars, as evidenced by a comparison of Tables

5-1 and 5-3. For accident samples of comparable size, the fatality reduction for

low NCAPINJ is consistently greater than the reduction for low chest g's - e.g.,

26 vs. 19 percent at the target sample size of 120 crashes. Sum RELEXP exceeds

20 three times in Table 5-1 and never in Table 5-3. The t value is always

significant and goes above 3 in three NCAPINJ analyses; but in the chest g

88
analyses it is usually close to 2 and is nonsignificant in two cases. In almost

every analysis of Table 5-1, the cars with higher N3\PINJ had, en the average,

higher NCAP scores an all three body regions; in lable 5-3, high chest g's were

not always accompanied by high HIC and often coincided with low fenur loads.

Table 5-4 shows that the fatality reduction for good vs. poor chest

g's can be rragnified by placing a gap between the "good" and the "poor" groups.

The results parallel earlier findings for NCAPD4J (Table 5-2). When the gap is

8 g's or more, the fatality reductions for the "good" cars approach 40 percent,

with high statistical significance (t values of 4 or more). However, the samples

of crashes are quite limited; these analyses really don't say much about the

relationship between chest g's and fatality risk in the overall vehicle fleet.

5.3 Or s with low NCAP HIC hit cars with high HIC

Table 5-5 presents 30 analyses that partition cars into "good" and

"poor" groups based on the Head Injury Criterion (HIC). Sane of them use a

single boundary between "good" and "poor" HIC, while others have two boundary

values and gap. A HIC of 1000 is the maximum amount permitted by EMVSS 208;

boundary values ranging fron 800 to 1600 are considered in Table 5-5. The

analyses are ordered by the lower boundary value and by the size of the gap.

Table 5-5 shows that HIC is moderately reliable, by itself, for

identifying differences in actual safety performance. In 29 of the 30 analyses,

the cars with low HIC have lower fatality risk than the high-HIC cars they

collided with, and the reduction is statistically significant in 5 of the

analyses (t > 1.65, p < .05). The comparison that maximizes fatality reduction

with a sample size close to 120 defines HIC <. 1000 as a "good" car and HIC > 1200

89
TABLE 5-4

COLLISIONS OF CARS WITH "GOOD" NCAP CHEST G SCORES INTO CARS WTIH "POOR" CHEST G's

EFFECT OF PLACING A GAP BETWEEN "GOOD" AND "POOR" NCAP CHEST G'S

vo

o

Definition of

a "Good" Car

Chest g's < 60

Chest g's < 58

Chest g's < 58

Chest g's < 56

Chest g's < 56

Chest g's < 54

Chest g's < 54

Definition of

a "Poor" Oar

Chest g's > 60

Chest g's > 60

Chest g's > 62

Chest g's > 62

Chest g's > 64

Chest g's > 64

Chest g's > 66

Ocnparison of Real-World Performance

Sun

Relexp

-15.28

-13.92

-11.03

-13.58

-11.88

-12.67

-12.67

N Of

Crashes

92

89

60

56

40

39

39

% Fat Red

for Good Car

24.2

22.8

26.3

32.9

37.9

40.7

40.7

Mean

Relexp

-.166

-.156

-.184

-.242

-.297

-.325

-.325

T-Test

for Relexp

2.74

2.51

.50

.70

4.11

4.75

4.75

2.

3.

Mean NCAP Scores and Model Year ("Good" vs. "Poor" Car)

a "Good" Car

Chest g's <. 60

Chest g's < 58

Chest g's < 58

Chest g's < 56

Chest g's <. 56

Chest g's < 54

Chest g's < 54

of

a "Poor" Car

Chest g's :

Chest g's :

Chest g's :

Chest g's :

Chest g's :

Chest g's :

Chest g's :

> 60

> 60

> 62

> 62

> 64

> 64

> 66

HIC

Good

896

895

913

905

979

982

982

Poor

1145

1147

1088

1108

907

914

914

Chest G's

Good

46.0

45.6

46.1

45.3

45.7

45.4

45.4

Poor

66.1

66.2

68.4

68.3

70.2

70.2

70.2

L Fewr

Good

970

977

1016

994

967

970

970

Poor

864

882

839

856

801

822

822

R Femur

Good

953

967

1009

981

950

944

944

Poor

839

834

631

621

579

594

594

Model

Good

85.3

83.4

85.4

85.6

85.6

85.6

85.6

Year

Poor

83.2

83.3

83.7

83.6

82.8

82.8

82.8
T3VBLE 5-5

COLLISIONS OF CARS WTTH "GOOD" NCAP HIC SCORES INTO CARS WTIH "POOR" HIC

Conpariaon of Rsal-Wbrld Performance

Definition of Definition of N of % Pat Red Mean Sun T-Tast

a "Good" Car a "Poor" Car Crashes for Good Car Relexp Relexp far Relexp

HIC < 800 HIC > 800 182 1.3 -.006 - 1.14 .13

HIC < 800 HIC > 900 133 4.2 -.024 - 3.26 .44

HIC < 800 HIC > 1000 85 2.6 -.016 - 1.33 .22

HIC < 800 HIC > 1100 73 ncne +.007 + .52 n.a.

HIC < 800 HIC > 1200 59 14.1 -.090 -5.29 1.21

HIC < 900 HIC > 900 190 10.7 -.066 -12.53 1.39

HIC< 900 HIC > 1000 127 10.7 -.067 -8.47 1.23

HIC < 900 HIC > 1100 109 8.0 -.049 -5.34 .86

HIC < 900 HIC > 1200 92 19.0 -.126 -11.55 2.13

HIC < 900 HIC > 1300 68 26.5 -.181 -12.31 2.61

HIC < 1000 HIC > 1000 155 10.4 -.064 -9.99 1.30

JS HIC < 1000 HIC > 1100 133 7.8 -.048 -6.36 .91

HIC < 1000 HIC > 1200 113 14.2 -.090 -10.22 1.68

HIC < 1000 HIC > 1300 81 20.2 -.130 -10.56 2.03

HIC £ 1000 HIC > 1400 58 15.8 -.102 -5.91 1.25

HIC < 1100 HIC > 1100 139 6.2 -.037 -5.15 .72

HIC < 1100 HIC > 1200 118 13.1 -.082 -9.69 1.54

HIC < 1100 HIC > 1300 85 17.1 -.107 -9.13 1.70

HIC < 1100 HIC > 1400 62 11.6 -.072 -4.49 .92

HIC < 1100 HIC > 1500 48 13.2 -.084 -4.01 .92

HIC < 1200 HIC > 1200 120 10.8 -.067 -8.03 1.25

HIC < 1200 HIC > 1300 87 ' 14.0 -.086 -7.48 1.35

HIC < 1200 HIC > 1400 64 7.3 -.044 -2.83 .56

HIC < 1200 HIC > 1500 50 7.7 -.047 -2.35 .52

HIC < 1300 HIC > 1300 91 12.0 -.071 -6.50 1.12

HIC < 1300 HIC > 1400 66 8.5 -.051 -3.34 .66

HIC < 1300 HIC > 1500 52 9.1 -.055 -2.87 .63

HIC £ 1300 HIC > 1600 44 9.1 -.056 -2.48 .64

HIC < 1400 HIC > 1400 68 8.5 -.051 -3.44 .68

m e < 1500 fflC > 1500 54 9.1 -.055 -2.96 .65
TABLE 5-5 (Continued)

COLLISIONS OF CARS WITH "GOOD" NCAP HIC SCORES INTO CARS WITH "POOR" HIC

Mean NCAP Scares and Model Year ("Good" vs. "Poor" Car)

vo

to

Definition of

a "Good" Car

HIC < 800

HIC < 800

HIC < 800

HIC < 800

HIC < 800

HIC < 900

HIC < 900

HIC < 900

HIC < 900

HIC < 900

HIC < 1000

HIC < 1000

me < IOOO

HIC < 1000

HIC < 1000

HIC < 1100

HIC < 1100

HIC < 1100

HIC < 1100

HIC < 1100

HIC < 1200

HIC < 1200

HIC < 1200

HIC < 1200

HIC < 1300

HIC < 1300

HIC < 1300

HIC < 1300

HIC < 1400

HIC < 1500

Definition of

a "Poor" Car

HIC > 800

HIC > 900

HIC > 1000

HIC > 1100

HIC > 1200

HIC > 900

HIC > 1000

HIC > 1100

HIC > 1200

HIC > 1300

HIC > 1000

HIC > 1100

m e > 1200

HIC > 1300

HIC > 1400

HIC > 1100

HIC > 1200

HIC > 1300

HIC > 1400

HIC > 1500

HIC > 1200

HIC > 1300

HIC > 1400

HIC > 1500

HIC > 1300

HIC > 1400

HIC > 1500

HIC > 1600

1400

HIC > 1500

Good

620

618

605

599

590

691

690

686

688

703

738

735

739

746

747

749

752

761

767

781

759

769

778

795

790

792

812

811

808

832

mePoor

1159

1272

1449

1512

1598

1299

1468

1537

1608

1741

1476

1546

1616

1767

1936

1556

1629

1782

1946

2084

1634

1786

1947

2080

1777

1943

2069

2164

1938

2058

Chest

Good

44.8

45.7

44.8

44.4

44.2

46.5

45.8

45.7

45.8

46.3

47.2

47.0

47.3

47.4

48.1

47.5

47.8

48.2

49.1

49.3

47.6

48.0

48.9

49.0

48.4

49.1

49.3

49.6

49.6

49.9

G'S

Poor

51.4

52.8

52.6

51.5

54.3

53.3

53.5

52.7

54.7

54.5

53.5

52.9

54.8

54.2

51.2

53.0

54.9

54.4

51.7

50.4

54.7

54.3

51.5

50.3

54.3

51.6

50.4

50.2

51.5

50.3

I*

Good

997

946

971

993

988

921

919

942

932

926

939

958

964

958

1000

965

970

963

1005

989

967

959

998

981

945

985

966

959

983

963

Femur

Poor

899

901

837

838

862

918

880

871

895

914

852

840

861

899

978

829

851

881

953

927

853

883

953

928

872

936

907

856

940

913

R Fenur

Gcod

974

934

992

1016

1009

915

939

963

956

946

968

988

995

983

1035

986

993

986

1035

1011

980

969

1010

980

971

1000

969

972

1008

979

Poor

885

888

737

739

794

894

787

772

812

762

789

765

807

761

678

761

804

762

684

695

800

757

680

690

769

681

691

695

679

687

Model

Good

86.4

86.5

86.6

86.6

86.4

86.1

86.1

86.1

86.0

85.9

85.5

85.5

85.3

85.3

85.5

85.4

85.2

85.2

85.3

85.0

85.2

85.2

85.3

84.9

85.1

85.2

84.9

84.6

85.1

84.8

Year

Poor

84.4

84.1

84.7

84.9

84.8

84.1

84.5

84.6

84.4

84.6

84.5

84.5

84.3

84.8

85.4

84.5

84.3

84.7

85.3

85.0

84.2

84.7

85.2

84.9

84.7

85.3

85.1

85.0

85.3

85.1
as a "poor" car: the fatality reduction for the good cars is a statistically

significant 14 percent (t for RELEXP is 1.68, p < .05). The other analyses that

show statistically significant differences, with snaller sanples, also have

boundary values for HIC close to 1000 or just above it, and they have a modest

gap: 900/1200, 900/1300, 1000/1300 and 1100/1300. Boundary values above 1300 did

not produce large fatality reductions (unlike the situation with NCAPINJ and

chest g's, where high boundary values produced large fatality reductions,

although with snail sanple sizes.)

Ihe second page of Table 5-5, which describes the average NCAP scores

for the low-HIC and high-HIC groups, explains sane of the trends in the fatality

reductions. HIC is significantly correlated with chest g's. In every case, the

low-HIC group has average chest g's under 50 and the high-HIC group has over 50

g's. However, the divergence between the low-HIC and the high-HIC group varies

from 0.4 to 8.9. The average chest g's for the high-HIC group varies from 50.2

to 54.9. The analyses with a statistically significant or near-significant

fatality reduction almost all have average chest g's over 54 in the high-HIC

group, and vice-versa. These high-HIC groups contain a rich selection of highchest g cases, and have elevated fatality risk. When the boundary value for HIC

goes above 1300, the divergence in chest g's is diminished, and so is the

difference in fatality risk. Table 5-5 also shows that the driver dummies with

high HIC had, en the average, slightly or even appreciably lower femur loads than

the durrmies with low HIC.

5.4 CflT"|B with low NCAP fen*iT* i°f*f^i'ia ^"it cars with high femiT* i6

In the NCAP test, femur loads are measured separately on the dummy's

left and right legs (2250 pounds on either leg is the critical value en FMVSS

93
208). The definition of a "car" with high [low] femur load has to take into

account the results for both legs. The approach used here is to say a car is a

"good" performer if the left femur load < A and. the right-side measurement is

also <. A and the sum of the two measurements <. another number B < 2A. A car is

a "poor" performer if the left femur load > C gr the right femur load > C gr the

sum of the loads > D, where A <. C and B <. D <. 2C. Table 5-6 presents six

analyses in which there is no gap between the lower and upper boundary values (A

= C and B = D) and the critical value for the sum of the loads is 1000 above the

load an either leg. For example, in the first analysis of Table 5-6, "good" cars

must have femur loads <. 1300 pounds en each leg and <. 2300, total; performance

is "poor" if femur load exceeds 1300 pounds on either leg or 2300 total.

Table 5-6 shows that femur load is rather reliable for differentiating

safer fron less safe cars. Over a range of boundary values from 1300 to 1800

pounds on one leg (and 2300 to 2800 pounds, total), the "good" performers

consistently have a fatality reduction that is statistically significant at the

.05 level. The significance never reaches the .01 level, as with chest g's and

NCAPINJ, but it never falls below the .05 level, as with HIC. The comparison

that maximizes sum RELEXP with a sample size close to 120 defines femur load <.

1600 en each leg (and 2600 total) as a "good" car and femur load > 1600 on either

leg (or 2600 total) as a "poor" car: the fatality reduction for the good cars is

a statistically significant 20 percent (t for RELEXP is 2.36, p < .05). The

fatality reduction remains close to 20 percent for boundary values in the 1400-

1800 pound range (for one leg; 2400-2800 pounds for both legs). Analyses were

also tried with various gaps between "good" and "poor" performance; the gaps

merely reduced sample size without appreciably escalating the fatality reduction

for "good" performers. The second page of Table 5-6 shows that cars with low

94
TKELE 5-6

CDLLISIONS OF CARS WTIH "GOOD" NCAP FEMUR LOAD SCORES INTO CARS WTIH "POOR" FEMUR LOADS:

EFFECT OF MJVING TOE BOUNDARY BETWEEN "GOOD" AND "POOR" NCAP FEMJR LOADS

Definition of

a "Good" Car

Definition of

a "Poor" Car

Comparison of Real-Mbrld Performance

N of % Fat Red Mean Sun T-Test

Crashes for Good Car Relexp Relexp for Relexp

L Fenur < 1300 AND

R Femur < 1300 AND

L+R Fenur < 2300

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Penur < 2400

L Penur < 1500 AND

R Penur < 1500 AND

L+R Penur < 2500

L Penur < 1600 AND

R Fesur < 1600 AND

L+R Fesur <, 2600

L Penur < 1700 AND

R Penur < 1700 AND

L+R Penur < 2700

L Penur > 1300 OR

R Fenur > 1300 OR

L+R Penur > 2300 164

L Penur > 1400 OR

R Fenur > 1400 OR

L+R Penur > 2400 157

L Penur > 1500 OR

R Penur > 1500 OR

L+R Fenur > 2500 1<2

L Fenur > 1600 OR

R Femur > 1600 OR

L+R Fenur > 2600 132

L Fenur > 1700 OR

R Penur > 1700 OR

L+R Penur > 2700 128

13.5

17.7

18.0

20.1

20.2

-.084

-.113

-.116

-.131

-.132

-13.81

-17.74

-16.53

-17.30

-16.85

1.73

2.24

2.18

2.36

2.30

L Fenur < 1800 AND

R Penur < 1800 AND

L+R Penur < 2800

L Penur > 1800 OR

R Fenur > 1800 OR

L+R Fenur > 2800 123 19.0 -.123 -15.07 2.08
77VBLE 5-6 (Continued)

COLLISIONS OF CARS WITH "GOOD" NCAP FEMUR LORD SCORES INTO CARS WITH "POOR" FEMJR LOADS:

EFFECT OF NDVING TOE BOUNDARY BETWEEN "GOOD11 AND "POOR" NCAP FEMUR LOADS

Definition of

a "Good" Car

Definition of

a "Poor" Car

Mean NCAP Scores and Model Year ("Good" vs. "Poor" Car)

me

Good Poor

Chest G's

Good Poor

L Fenur

Good Poor

R Femur

Good Poor

Model Year

Good Poor

ID

LFem < 1300 AND

RFem <. 1300 AND

L+R Fern < 2300

LFem < 1400 AND

RFem < 1400 AND

L+R Fern < 2400

LFem < 1500 AND

RFem < 1500 AND

L+R Pem < 2500

LFem < 1600 JWD

RFem < 1600 AND

L+R Fan < 2600

LFem < 1700 AND

RFem < 1700 AND

L+R Fern < 2700

LFem < 1800 AND

RFem < 1800 AND

L+R Fern < 2800

LFem > 1300 OR

RFem > 1300 OR

L+R Fern > 2300

LFem > 1400 OR

RFem > 1400 OR

L+R Fern > 2400

LFem > 1500 OR

RFem > 1500 OR

L+R Fem > 2500

LFem > 1600 OR

RFem > 1600 OR

L+R Fern > 2600

LFem > 1700 OR

RFem > 1700 OR

L+R Fem > 2700

LFem > 1800 OR

RFem > 1800 OR

L+R Fem > 2800

942 877 48.9 50.0 713 1442 709 1486 85.1 85.6

913 881 48.6 49.8 746 1480 738 1545 85.4 86.0

906 889 48.3 50.7 763 1505 747 1619 85.4 85.7

913 898 48.4 51.0 759 1531 734 1659 85.5 85.6

920 896 48.3 51.1 763 1542 740 1665 85.5 85.6

918 880 48.6 51.4 777 1540 752 1703 85.4 85.5
femur loads also have, an the average, slightly lower chest g's than the cars

with high femur loads; the difference in chest g's ranges from 1.1 to 2.8. There

is little difference in HTC. Ihe modest reduction in chest g's that accompanies

low femur load may be a contributing factor in the fatality reduction, but

probably not an important one.

In the preceding analyses, chest g's were usually more efficient than

femur load for discriminating the actual safety performance of cars; femur load,

in turn, was more usually reliable than HIC, although there was seme overlapping

in the results. Ihe findings, which are consistent with the correlation analyses

of Chapters 3 and 4, raise two interesting, related questions. Why are chest g's

especially efficient? Given that femur injuries are rarely fatal, why does femur

load correlate at all with fatality risk in actual crashes? The answer appears

to be that the three NCAP test measurements are not independent observations an

isolated body regions. There is not just a statistical correlation but,

probably, also an intuitive overlap between the scores. Cars with intuitively

excellent safety design tend to have low scores an all parameters. Cars with

crashworthiness problems tend to have high scores an ane or more parameters, but

it is not always predictable which one. Thus, high femur load could reflect a

more general problem affecting injury risk to other body regions in sane crashes.

Chest g's have two special advantages. Since the chest is the body region "in

the middle," chest g's are correlated with both HIC and femur load; a poor score

an chest g's often reflects poor scares an the other parameters. The measurement

of chest g's tends to be less sensitive than the other parameters to moderate

changes in the test conditions. That will make chest g's work especially well

with the accident data used here, which, of necessity, include vehicles that do

not exactly match the lOiP test vehicle, occupants of various heights and

97
weights, and all types of head-on collisions, not just those that closely

reseirble an NCAP test.

Any single NCAP parameter, as shown above, can do an adequate job of

partitioning the cars into a safer and a less safe group. Two parameters for two

separate body regions, working together, can often do an even more reliable job.

Table 5-7 examines the relative fatality risk in cars with good HIC and chest g

scores when they hit cars with poor HIC or chest g scores. The approach used

here is to say a car is a "good" performer if HIC <. A and chest g's < B. A car

is a "poor" performer if HIC > C or. chest g's > D, where A <. C and B <. D. In

every analysis of Table 5-7, the fatality risk is lower for the good performers.

The reduction is statistically significant in every analysis which uses boundary

values for HIC close to 1000, with a modest gap, and boundary values for chest

g's close to 60, without a gap or with a modest gap. Some of the analyses show

fatality reductions well over 20 percent, with samples of 80-90 crashes.

However, in the comparison that maximizes fatality reduction with a sample size

close to 120, the "good" performers are defined as the cars with driver HIC <

1100 and. chest g's <. 60, and the "poor" performers as the ones with either HIC

> 1300 or chest g's > 60. The fatality risk is a statistically significant 19

percent lower for the drivers of the cars with the better NCAP scores (t for

RELEXP is 2.31, p < .05) .

The second page of Table 5-7 shows, not surprisingly that the average

HIC and chest g's of the good performers are substantially lower than for the

poor performers. The femur loads, however, tend to be somewhat higher for the

good HlC-chest g performers. This page of Table 5-7 shows quite similar trends

98
TABLE 5-7

COLLISIONS OF CARS WTIH "GOOD" NCM> HIC AND CHEST G SCORES INTO CARS WITH "POOR" HEC OR CHEST G'S

Definition of

a "Good" Car

m e < 800 AND

Chest g's < 48

Definition of

a "Poor" Car

me > 8oo OR

Chest g's > 48

N of

Crashes

165

uonpariBon or

% Fat Bed

far Good Car

3.3

Keaj.-nor.La per

Mean

Relexp

-.020

romance

Sun

Relexp

- 3.23

T-TBSt

for Relexp

.39

me < 8oo AND me > noo OR

Chest g's < 48 Chest g's > 64 63 8.2 -.047 -2.9 7 .58

me < 900 AND me > 1250 OR

Chest g's < 55 Chest g's > 60 92 23.0 -.155 -14.23 2.64

me < 900 AND me > 1250 OR

^ Chest g's < 55 Chest g's > 62 86 26.7 -.181 -15.57 3.07

me < 900 AND me > 1250 OR

Chest g's < 55 Chest g's > 65 81 26.7 -.184 -14.91 3.02

me < 900 AND me > 1300 OR

Chest g's < 56 Chest g's > 60 93 24.7 -.167 -15.58 2.83

me < IOOO AND me > 1000 OR

Chest g's < 60 Chest g's > 60 170 13.5 -.084 -14.36 1.82

me < iooo AND me > 1200 OR

Chest g's < 60 Chest g's > 70 104 19.1 -.121 -12.58 2.16

me < 1100 AND me > 1100 OR

Chest g's < 64 Chest g's > 64 155 11.2 -.070 -10.79 1.43

me < 1100 AND me > 1300 OR

Cheat g's < 60 Chest g's > 60 125 18.9 -.122 -15.32 2.31

me < 1200 AND me > 1200 OR

Chest g's < 70 Chest g's > 70 120 12.3 -.076 -9.12 1.39
o

o

TABLE 5-7 (Continued)

COLLISIONS OF CARS WITH "GOOD" NCAP HIC AND CHEST G SCORES INTO CARS WITH "POOR" HIC OR CHEST G'S

Maan NCAP Scares and Modal Year ("Good" vs. "Poor" Car)

Definition of Definition of HIC Chest O's h Femur R Femur Model Year

a "Good" Car a "Poor" Car Good Poor Good Poor Good Poor Good Poor Good Poor

HIC < 800 M B HIC > 800 OR

Chest g'S < 48 Chest g'B > 48 613 1056 39.8 52.8 1106 890 1026 897 86.6 84.5

HIC < 800 M B HIC > 1100 OR

Chest g's < 48 Chest g's > 64 600 1387 39.9 55.3 1075 840 1010 801 87.0 85.1

HIC < 900 M B HIC > 1250 OR

Chest g's < 55 Chest g's > 60 711 1432 43.3 59.0 980 918 946 793 86.2 84.2

HIC < 900 M B HIC > 1250 OR

Chest g's < 55 Chest g's > 62 712 1465 43.3 58.8 968 855 936 785 86.1 84.3

HIC < 900 M B HIC > 1250 OR

Chest g'S < 55 Chest g's > 65 713 1503 43.3 58.5 972 863 944 760 86.2 84.4

HIC < 900 M O HIC > 1300 OR

Chest g's < 56 Chest g's > 60 709 1426 43.4 59.0 990 923 946 801 86.2 84.3

HIC < 1000 M B HIC > 1000 OR

Chest g'B < 60 Chest g'S > 60 748 1339 45.6 55.9 1001 847 1003 801 85.5 84.6

HIC < 1000 M B HIC > 1200 OR

Chest g'S < 60 Chest g'S > 70 742 1608 45.3 55.0 984 871 995 835 85.4 84.4

HIC < 1100 M B HIC > 1100 OR

Chest g'S < 64 Chest g'S > 64 760 1395 46.2 56.0 994 809 1018 745 85.5 84.5

me < lioo MB me > 1300 OR

Chest g's < 60 Chest g's > 60 769 1414 46.0 59.3 1009 846 1016 779 85.3 84.2

HIC < 1200 M B HIC > 1200 OR

Chest g's < 70 Chest g's > 70 757 1609 47.4 55.4 967 869 982 809 85.2 84.2
to the second halves of Tables 5-3 (partition by chest g's) and 5-5 (partition

by HIC). To the extent that HIC and chest g's are fairly correlated, they are

somewhat redundant measures in a statistical sense. They act as a check on one

another, and using both of them together enhances the reliability of their

information. Cars with low HIC tend to have low chest g's, and vice-versa, but

the easiest way to find cars with low HIC and chest g's is to look at both of the

variables. Neither variable, however, conveys the information that is contained

in the femur load variable.

Since femur load is rather orthogonal (statistically uncorrelated)

with HIC and chest g's, it might be expected that the combination of femur load

with one of the other two variables is exceptionally useful for partitioning the

fleet. Table 5-8 confirms that chest g's-and-femur load, or HIC-and-femur load

can be used to differentiate the safer and the less-safe cars. If "good"

performance is defined as chest g's < 56 and femur load <. 1400 on each leg and

<. 2400, total, while chest g's > 60 or femur load > 1700 en either leg or > 2700,

total delineates "poor" performance, the fatality risk in 134 collisions between

good and poor performers is a statistically significant 22 percent lower for the

drivers of the cars with good NdWP scores (t for RELEXP is 2.93, p < .01). The

lower half of Table 5-8 shows that the "good" performers, in this analysis, have

lower average scores en all three body regions, not just the chest and femurs.

Msst interestingly, the second analysis in Table 5-8 shows that HIC

and femur load, without chest g's. can be used to partition the safer from the

less safe cars. When the criterion for "good" performance is HIC < 900 and femur

load <. 1400 an each leg and <. 2400, total, and the criterion for "poor"

performance is HIC > 1300 or femur load > 1700 on either leg or > 2700, total,

101
TABLE 5-8

COLLISIONS OF CARS WITH "GOCD" NCAP SCORES FOR TWO BODY REGIONS

INTO CARS WITH "POOR" NCAP SCORES FOR AT LEAST ONE OF THDSE BODY REGIONS

Comparison of Real-World Performance

o

to

Definition of

a "Good" Car

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

Definition of

a "Poor" Car

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

N of

Crashes

134

% Fat Red Mean

for Good Car Relexp

HIC < 900 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC > 1300 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

22.1 -.147

Sun

Relexp

-19.66

121 19.4 -.128 -15.44

T-Test

for Relexp

2.93

2.30

Definition of

a "Good" Car

Chest g < 56 AMD

LFem < 1400 AND

RFem < 1400 AND

L+R Fern < 2400

HIC < 900 AND

LFem < 1400 AND

RFem < 1400 AND

L+R Fern < 2400

Definition of

a "Poor" Car

Chest g > 60 OR

LFem > 1700 OR

RFem > 1700 OR

IrlR Fent > 2700

HIC > 1300 OR

LFem > 1700 OR

RFem > 1700 OR

L+R Fern > 2700

Mean NCAP Scores and Model Year ("Good" vs. "Poor" Car)

HIC Chest G's L Fenur R Feaur Model Year

Good Poor Good Poor Good Poor Good Poor Good Poor

890 983 43.9 55.6 754 1311 742 1336 85.5 84.8

698 1194 46.8 51.5 738 1311 766 1294 85.7 85.1
the fatality risk in 121 head-on collisions between good and poor performers is

a statistically significant 19 percent lower for the drivers of the cars with low

HIC and femur load (t for RELEXP is 2.30, p < .05). The lower section of Table

5-8 shows that the cars with good HIC and femur load have substantially lower

chest g's (46.8 vs. 51.5) than the cars with poor HIC or femur load. That HIC

with femur load works about as well as HIC with chest g's or fenur load with

chest g's illustrates the extent to which the three NCAP scores contain both

overlapping and complementary information.

5.6 pg-rtitions of the fleet l*>qped on fOl three NCAP p

A reliable differentiation of safer and less-safe cars may be obtained

by using NCAP scores for all three body regions, with separate boundary values

("pass-fail" criteria) for each body region. This method is perhaps not quite

as efficient as a composite variable such as NCAPINJ, but is just as reliable,

or more so, than the analyses based an one or two body regions. Table 5-9

illustrates five analyses using various boundary values for HIC, chest g's, and

femur load. An accident sample close to the target of 120 crashes is obtained by

defining "good" performance as HIC <. 900 and chest g' s <. 56 and femur load < 1400

an each leg and < 2400, total. HIC > 1300 or. chest g's > 60 gr_ femur load > 1700

on either leg or > 2700, total defines "poor" performance. The fatality risk in

118 actual head-on collisions between a good and a poor performer is a

statistically significant 21 percent lower for the drivers of the cars with good

NCAP scores (t for RELEXP is 2.68, p < .01).

Table 5-9 shows that the boundary values can be varied by a moderate

amount and the fatality reduction for the "good" performers will still be

statistically significant, often at the .01 level. Such reductions are found in

103
TABLE 5-9

COLLISIONS OF CARS WITH "GOOD" NCAP SCORES FOR ALL THREE BODY REGIONS

INTO CARS WTTH "POOR" NCAP SCORES FOR AT LEAST ONE BODY REGION

Definition of

a "Good" Car

EEC < 900 AND

Chest g's < 56 AMD

L Fenur < 1400 AND

R Fenur «j. 1400 AND

L+R Fenur < 2400

HIC < 900 AND

Chest g's < 60 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 1100 AND

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 1100 AND

Chest g's < 60 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 1100 AND

Chest g's < 60 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

Definition of

a "Poor" Car

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Four > 1700 OR

L+R Fenur > 2700

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1600 OR

R Fenur > 1600 OR

L+R Fenur > 2600

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1600 OR

R Fenur > 1600 OR

L+R Fenur > 2600

> 1300 OR

Chest g's > 60 OR

L Fenur > 1600 OR

R Fenur > 1600 OR

L+R Fenur > 2600

> 1300 OR

Chest g's > 60 OR

L Fenur > 1800 OR

R Fenur > 1800 OR

L+R Fenur > 2800

N of

Crashes

Ocnparison of Real-World Performance

% Fat Red Mean Sun

for Good Car Relexp Relexp

T-Test

for Relexp

118

128

140

153

148

21.2

20.7

19.6

18.3

19.0

-.139 -16.44

-.137 -17.54

-.128 -17.91

-.118 -18.05

-.123 -18.24

2.68

2.65

2.72

2.46

2.50
TABLE 5-9 (Continued)

COLLISIONS OF CARS WITH "GOOD" NCAP SCORES FOR ALL THREE BODY REGIONS

INTO CARS WITH "POOR" NCAP SCORES FOR AT LEAST ONE BODY REGION

o

Definition of

a •Good" Car

HIC < 900 AND

Chest g £ 56 AND

L Fern < 1400 AND

R Fern < 1400 AND

LfR Fern < 2400

HIC < 900 AND

Chest g < 60 AND

L Fern < 1400 AND

R Fern < 1400 AND

L+R Fern < 2400

HIC < 1100 AND

Chest g < 56 AND

L Fern < 1400 AND

R Fan < 1400 AND

L+R Fern < 2400

HIC < 1100 AND

Chest g < 60 AND

L Fem < 1400 AND

R Fern < 1400 AND

L+R Fem < 2400

HIC < 1100 AND

Chest g < 60 AND

L Fem < 1400 AND

R Fem < 1400 AND

L+R Fem < 2400

Definition of

a "Poor" Car

HIC > 1300 CR

Chest g > 60 OR

L Fem > 1700 OR

R Fem > 1700 OR

L+R Fem > 2700

HIC > 1300 OR

Chest g > 60 OR

L Fem > 1600 OR

R Fem > 1600 OR

L+R Fem > 2600

HIC > 1300 OR

Chest g > 60 OR

L Fem > 1600 OR

R Fem > 1600 OR

L+R Fem > 2600

HIC > 1300 OR

Chest g > 60 OR

L Fem > 1600 OR

R Fem > 1600 OR

L+R Fem > 2600

HIC > 1300 CR

Chest g > 60 OR

L Fem > 1800 CR

R Fem > 1800 OR

L+R Fem > 2800

Mean NCAP Scores and Model Year ("Good" vs. "Poor" Oar)

HIC Chest G's L Femur R Femur Model Year

Good Poor Good Poor Good Poor Good Poor Good Poor

704 1154 43.1 53.6 778 1230 775 1200 85.9 84.8

703 1156 43.9 53.3 767 1229 776 1208 85.9 84.8

739 1161 43.8 53.7 771 1201 775 1192 85.6 85.0

744 1161 45.0 53.8 755 1185 763 1198 85.5 84.9

745 1173 45.1 54.0 756 1180 770 1194 85.4 84.8
analyses with E C boundary values reasonably close to or slightly above the FMVSS

208 value of 1000, with a modest gap; chest g boundary values dose to the FMVSS

208 value of 60 g's; and femur load boundaries in the 1400-1800 pound range. The

analyses show fatality reductions of 18-21 percent in accident samples ranging

up to 153 crashes, and sum RELEXP values up to 18.24, approaching the sum RELEXP

values of 20-21 found in the analyses based en NCftPHU (see Table 5-1). The

second page of Table 5-9 shows that the "good" performers had, on the average,

substantially lower KLC, chest g's and femur loads than the poor performers.

5.7 Sensitivity test: collisions of two cars with similar mass

None of the analyses, so far, placed any limits an the relative masses

of the two cars in the head-on collisions. The data included seme crashes in

which the two cars had a severe weight mismatch - e.g., 1800 and 3800 pounds.

A question could be raised if the cases with severe mismatch are "driving" the

results. In those crashes, where the driver of the lighter car is almost certain

to be a fatality, the difference between actual and "expected" performance may

not be as meaningful as in crashes where both drivers have a good chance of

survival. Would the results be different if the sample were limited to

collisions of cars with similar weights?

Table 5-10 limits two of the earlier analyses to the subset of head-on

collisions in which the weights of the two cars differ by no more than 1000

pounds. In the subsample of 86 collisions of a car with NCaPINJ <0.6 into a car

with NCAPINJ > 0.6, both cars having n»ti weights within 1000 pounds of one

another, the fatality reduction for the good N3£> performers is 23.9 percent,

which is about the same as the 26.4 percent fatality reduction in the

unrestricted sample of 117 collisions (see Table 5-1). In the subsample of 94

106
TABLE 5-10

COLLISIONS OF CARS WITH "GOOD" NCAP SOXES INTO CARS WITH "POOR" SCORES

WHERE THE "GOOD" AND "POOR" CARS HAVE SIMILAR MASS

Conparison of Real-World Performance

Definition of

a "Good" Car

Definition of

a "Poor" Oar

N of

Crashes

% Fat Red

for Good Car

MBan

Relexp

Sun

Relexp

T-Test

for Relexp

NCAPINJ < .6 NCAPINJ > .6

and weight difference <. 1000 pounds

HIC < 900 At©

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

and weight difference < 1000 pounds

86

94

23.9

20.7

-.164 -14.14

-.135 -12.72

2.33

2.17

Definition of

a "Good" Car

Definition of

a "Poor" Car

NCAPINJ < .6 NCAPINJ > .6

and weight difference < 1000 pounds

HIC < 900 AND

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700

and weight difference < 1000 pounds

Car Weight, Driver Age, Sex and Model Year ("Good" vs. "Poor" Car)

Avg Weight

Good Poor

2872 2929

2840 2842

Avg Age

Good Poor

42.8 41.8

42.3 43.5

% Female

Good Poor

49 48

50 40

Model Year

Good Poor

86.0 83.2

86.0 84.9
collisions of a car with low HIC, chest g's and femur load into a car with high

HIC, chest g's or fenur load, the fatality reduction for the good performers is

20.7 percent, which is almost identical to the 20.8 percent reduction in the

unrestricted sample of 118 cases (see Table 5-9). The lower half of Table 5-10

shows that, in both analyses, the "good" and "poor" NCAP performers have nearly

the same average curb weights and driver ages.

5.8 Sensitivity test; weighted vs. unweighted conposite score

The ccttposite measure of NCAP performance,

NCAPINJ = .21 HEADINJ + 2.7 CHESTTNJ + 1.5 (LFEM3RINJ + REEMURINJ)

was calibrated in Section 4.1 as the weighted combination of logistic injury

probability functions for the head, chest and femurs that has maximum correlation

with fatality risk in the set of head-on collisions where both cars match an NCAP

test at the '3A or 4A level.' However, it was also shown in Section 4.1 that

this particular weighted sum did not "magically" enhance correlation; other

weighted sums had almost equally high correlations, and even an unweighted sum

of the injury probabilities had significant correlation with fatality risk.

Similarly, the approach of this chapter showed that NCAPINJ was

efficient for separating the safer cars from the less safe cars, maximizing the

difference in relative fatality risk when a "good" NCAP performer hits a "poor"

performer head-on. Here too, however, NCAPINJ is merely "first among equals" for

the purpose of identifying safer and less safe cars, lable 5-11 shows that even

an unweighted sum of the logistic injury probabilities,

INT = HERD3NJ + CHESTTNJ + IFEMJRJNJ + REH4JRINJ

accomplishes the same purpose with just slightly less efficiency. The top half

of Table 5-11 presents six analyses of head-on collisions between cars with

108
TABLE 5-11

COLLISIONS OF CARS WTIH A LOW NCAP COMPOSITE SCORE INTO CARS WTIH A HIGH NCAP OCMPOSnE SODRE:

VS. UNWEIQnED CCMPOSITE SCORE

WKldflMJ: NCAPINJ = .2 1 HEftDINJ + 2. 7 CHESTINJ + 1. 5 (LFBBRINJ + RFEMDRINJ)

o

ID

Definition Of

"Low- NCAPINJ

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

NCAPINJ <

.4

.5

.6

.7

.8

.9

Definition of

"High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

.4

.5

.6

.7

.8

.9

N of

Crashes

186

147

117

108

80

55

% Eat Red for

Low NCAPINJ

17.0

17.3

26.4

27.2

28.7

31.9

Mean

Relexp

-.108

-.114

-.181

-.189

-.201

-.216

Sum

Relexp

-20.17

-16.83

-21.13

-20.46

-16.10

-11.88

T-Test

for Relexp

2.56

2.44

3.22

3.34

3.13

2.73

INJ

•Low"

INJ <

INJ <

INJ <

INJ <

INJ <

INJ <

INJ

.3

.4

.5

.6

.7

.8

of

"High" INJ

INJ >

INJ >

INJ >

INJ >

INJ >

INJ >

.3

.4

.5

.6

.7

.8

HEADINJ +

N Of

Crashes

191

149

121

97

78

57

(JUsbTlNJ + LFEMG

% Fat Red for

Low INT

14.6

17.7

21.6

18.1

21.2

16.7

RINJ + F

Mean

Relexp

-.092

-.115

-.145

-.117

-.139

-.115

Sum

Relexp

-17.60

-17.13

-17.54

-11.33

-10.86

- 6.55

T-Test

for Relexp

2.16

2.36

2.77

2.00

2.08

1.56
"good" and "poor" NCAPINJ scores, recapitulating material fron Table 5-1. The

lower half of Table 5-11 presents results of six similar analyses based on the

unweighted composite score DU . In the NCAPINJ analyses, the fatality risk is

17-32 percent lower for the driver of the "good" car than for the driver of the

"poor" car; the reductions in the first two analyses are significant at the .05

level, and in the last four analyses they are significant at the .01 level. In

the INJ analyses, fatality reductions range fran 15 to 22 percent; the first five

analyses show statistically significant reductions, with the third analysis

significant at the .01 level. In general, INJ works about as well as NCAPINJ

when the boundary between "good" and "poor" performance is set at a fairly low

level - i.e., the first two or three analyses. Only when the boundary is set at

a high level does NCAPINJ becane visibly more efficient than INJ.

In the third analysis with the unweighted score INJ, the fatality

reduction is 21.6 percent with a sample of 121 crashes. - Thus, at the target

sample size, INJ is slightly more efficient than any single NCAP parameter, and

works about as well as combinations of two or three NCAP parameters.

5.9 Sensitivity test: analiVSfg 0° 9 djjf'fe^ent ? ^ £ y

The preceding analyses were conducted with the principal data set of

head-on collisions in which both cars matched an NCAP test vehicle at levels "3A"

or "4A": the model year an EARS is within the range of mode] years considered

valid for the NCAP test, and the make-models en FRRS and NCAP are identical or

true corporate cousins. In Section 3.4, an alternative data set was defined in

which both vehicles not only match an NCAP test vehicle at levels 3A or 4A, but

also have the same number of doors as the NCAP test vehicle (310 collisions, 620

cars). In the multiple regression approach of Section 3.4, the alternative data

110
set produced higher regression coefficients for HIC, and lower coefficients for

femur load than the principal data set. In Section 4.7 it was shown that NCAPINJ

did not have optimal correlation vdth fatality risk an the alternative data set

(although it was close to the optimum), and that the unweighted score INJ was

just slightly less correlated with fatality risk than NCAPINJ.

When the methods of this chapter are applied to the alternative data

set, the results closely parallel the earlier findings, Table 5-12 considers

head-on collisions between a "good" and a "poor" NCAP performer, based an the

values of a single NCAP parameter. The left three columns of numbers are the

sample size, fatality reduction for the good car and t-test result for RELEXP in

the principal accident data set (level 3A/4A matches). The right three columns

are the corresponding analysis results an the alternative data set (level 3A/4A.

and N of doors matching). Since the principal data set contains the alternative

set, the N's on the right are always smaller; when N is smaller, the same

percentage of fatality reduction will produce a weaker t-test result.

The first section of lable 5-12 presents six analyses of crashes of

cars with low NCAP chest g's into cars with high chest g's. Ihe fatality

reductions are virtually identical in the two data sets, ranging from 11 to 30

percent in the principal data set and 10 to 28 percent in the alternative set.

In four of the six analyses, the reductions are within 2 percent on the two data

sets; in the first analysis, the alternative data set produces a slightly higher

reduction, while in the fifth analysis, the principal data set produces a greater

effect. Ihe findings are consistent with Section 3.4, where chest g's had nearly

the same regression coefficient in the two data sets.

Ill
TABLE 5-12

COLLISIONS OF CARS WITH "GOOD" NCAP SCORES INTO CARS WITH "POOR" NCAP SCORES

COMPARISON OF TWO ACCIDENT DATA SETS

PARS Matches NCAP

a t Leve l 3A o r 4A

K)

Definition of

a "Good" Car

Chest g's < 44

Chest g's < 48

Chest g's < 52

Chest g's < 56

Chest g's <. 60

Chest g's < 64

HIC < 800

HIC < 900

me < 900

me < IOOO

me < noo

me < 1200

L Fem < 1500 AND

R Fern < 1500 AND

L+R Fern < 2500

L Fern < 1600 AND

R Fem < 1600 AND

L+R Fem < 2600

L Fem < 1700 AMD

R Fem < 1700 AND

L+R Fem < 2700

Definition of

a "Poor" Car

Chest g's > 44

Chest g's > 48

Chest g's > 52

Chest g's > 56

Chest g's > 60

Chest g's > 64

m e > 900

m e > iooo

m e > 1200

m e > 1200

m e > 1200

m e > 1300

L Fem > 1500 OR

R Fem > 1500 OR

L+R Fem > 2500

L Fem > 1600 OR

R Fem > 1600 OR

L+R Fem > 2600

L Fem > 1700 OR

R Fem > 1700 OR

L+R Fem > 2700

PARS Matches NCAP at

Level 3A/4A and N of Doors

N

172

182

145

125

92

49

133

127

92

113

118

87

% Fat Red

16.2

10.9

15.0

18.7

24.2

29.7

4.2

10.7

19.0

14.2

13.1

14.0

T-Tes

2.26

1.56

2.02

2.32

2.74

2.60

.44

1.23

2.13

1.68

1.54

1.35

142

132

128

18.0

20.1

20.2

2.18

2.36

2.30

N

139

145

113

99

71

38

108

96

68

82

86

64

% Pat Red

18.9

9.9

14.0

18.2

20.4

28.3

12.1

20.5

26.0

16.9

17.1

15.4

T-Tes

2.36

1.21

1.59

1.88

1.91

1.94

1.23

2.25

2.73

1.73

1.80

1.30

121

112

108

12.9

15.8

15.7

1.40

1.64

1.58
The middle section of Table 5-12 contains six analyses based an HIC,

each with a small gap between "good" and "poor" performers. Here, the fatality

reduction is greater for the alternative data set, especially in the first three

analyses, where the boundary values of HIC are relatively low. In the third

analysis, the fatality reduction is statistically significant at the .01 level

in the alternative data set. That level of significance was never achieved in

the principal data set with HIC, despite larger sample sizes. In the last three

HIC analyses, where the cars are partitioned at higher levels of HIC, the results

for the two data sets more or less converge, and the reductions drop out of the

statistically significant range, even in the alternative data set.

Conversely, the lower section of Table 5-12 shows that femur load does

not work as well an the alternative data set as an the principal data set.

Fatality reductions are 4-6 percent lower an the smaller data set, and they are

not statistically significant. Whereas femur load seems to be more efficient

than HIC an the main data set, HIC works sligliuxy better on the alternative set,

consistent with the regression coefficients in Section 3.4.

Table 5-13 compares the effects of the weighted composite score

NCAPINJ and the unweighted sum of logistic injury probabilities, INJ for the

principal and alternative data sets. The first half of the table presents six

analyses for NCAPINJ. In the principal data set, the reductions are always

statistically significant, and in the last four analyses the reductions range

from 26 to 32 percent and are significant at the .01 level. NCAPINJ does not

work so efficiently for the alternative data set, although it still produces

fatality reductions up to 24 percent. Three of the six analyses produce

statistically significant reductions at the .05 level.

113
TABLE 5-13

COLLISIONS OF CARS WITH A LOW NCAP COMPOSITE SCORE INTO CARS WITH A HIGH NCAP COMPOSITE SCORE:

(XHPARISGN OF T*TO ACCIDENT DATA SETS: WEIGHTED VS. UNWEIGHTED COMPOSITE SCORE

WEIGHTED: NCAPINJ = .2 1 HERDDJJ + 2. 7 CHESTINJ + 1. 5 (LFEMDRIHJ + RFEMJRINJ)

Definition of

"Low" NCAPINJ

NCAPINJ < .4

NCAPINJ < .5

NCAPINJ < .6

NCAPINJ < .7

NCAPINJ < .8

NCAPINJ < .9

Definition Of

"High" NCAPINJ

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

NCAPINJ >

.4

.5

.6

.7

.8

.9

PARS Matches NCAP

at Level 3A or 4A

N

186

147

117

108

80

55

% Fat Red

17.0

17.3

26.4

27.2

28.7

31.9

T-Tes

2.56

2.44

3.22

3.34

3.13

2.73

PARS Matches NCAP at

Level 3A/4A and N of Doors

N

146

113

88

82

61

41

% Fat Red

15.4

14.4

18.8

21.2

23.9

21.8

T-Tes

2.06

1.75

1.88

2.06

2.18

1.53

INJ • HEADINJ + CHESTINJ + LFEMJRINJ + RFEMDRDU

Definition of

"Low"

INJ <

INJ <

INJ <

INJ <

INJ <

INJ

.3

.4

.5

.6

.7

"High" INJ

INJ >

INJ >

INJ >

INJ >

INJ >

.3

.4

.5

.6

.7

N

191

149

121

97

78

PARS Matches NCAP

at Level 3A or 4A

% Fat Red T-Test

14.6

17.7

21.6

18.1

21.2

2.16

2.36

2.77

2.00

2.08

FARS Matches NCAP at

Level 3A/4A and N of Doors

N

149

115

97

76

61

% Fat Red

18.5

20.0

18.5

15.7

17.0

T-Tes

2.46

2.35

2.02

1.54

1.50
The lower half of Table 5-13 presents five analyses with the

unweighted composite score. It works about equally well an the principal and

alternative data sets: with low boundary values, the fatality reduction is

slightly greater an the alternative data set, but in the last three analyses, the

results are more favorable an the principal data set. It is especially

interesting to compare the upper and lower half of the table. With the principal

data set, NCAPUtT does a visibly better job than INJ when the cars are

partitioned at a relatively high score: NCAPIMJ pushes the fatality reduction up

to the 30 percent range, but INJ does not. On the alternative data set, NCAPINJ

does not do as well as INJ an the first two analyses, and only slightly better

an the subsequent ones.

These sensitivity tests with an alternative accident data set

illustrate two points rather clearly: (l) The EARS data show significant

relationships between each of the three NCAP parameters and fatality risk, but

they are not really sufficient to rank-order the strength of the three

relationships; small changes in the accident data set can change the rank order.

(2) The EARS data show that a composite score based on all three parameters, such

as NCfcPINJ, has excellent correlation with fatality risk, but they are not

sufficient to establish "ideal" relative weights for the three parameters; small

changes in the data set will change the optimum relative weights.

5.10 Surmary

Table 5-14 extracts from Tables 5-1 through 5-9 the analyses that

maximized fatality reduction and sum RELEXP, in the preceding tables, with sample

sizes close to the target of 120 crashes. They are the "best in their class"

analyses, based an various ways of partitioning "good" and "poor" NCM>

115
TABLE 5-14

SUMMARY: COLLISIONS OF CARS WTIH "GOOD" NCAP SCORES INTO CARS WITH "POOR" NCAP SCORES

(N of crashes appraxinately 120 in each ccnpariscn)

Comparison of Seal-World Performance

Definition of

a "Good" Gar

NCAPINJ < .6

Chest g's < 56

HIC < 1000

L Fenur < 1600 AND

R Fenur < 1600 AND

L+R Fenur < 2600

Definition of

a "Poor" Car

NCAPINJ > .6

Chest g's > 56

HIC > 1200

L Fenur > 1600 OR

R Fenur > 1600 OR

L+R Femur > 2600

N of

Crashes

117

125

113

132

HIC < 1100 AND

Chest g's < 60

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 900 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC < 900 AND

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

HIC > 1300 OR

Chest g's > 60 125

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700 134

HIC > 1300 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700 121

HIC > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

L+R Fenur > 2700 118

% Fat Red Mean

for Good Car Relexp

26.4

18.7

14.2

20.1

18.9

22.1

19.4

-.181

-.123

-.090

-.131

-.122

-.147

-.128

Sun

Relexp

-21.13

-15.42

-10.22

-17.30

-15.32

-19.66

-15.44

T-Test

for Releoqp

3.22

2.32

1.68

2.36

2.31

2.93

2.30

21.2 -.139 -16.44 2.68
TPSSLE 5-14 (Continued)

SUMMARY: COLLISIONS OF CARS WITO "GOOD" NCAP SCORES INTO CARS WTIH "POOR" NCAP SCORES

(N of crashe s approximately 120 i n each ccnparisan)

Definition of

a "Good" Car

NCAPINJ < .6

Chest g < 56

me < IOOO

LFem < 1600 AND

RPem < 1600 AND

L+R Pern < 2600

HIC < 1100 AND

Chest g < 60

Chest g < 56 AND

LPem < 1400 AND

RPem < 1400 AND

L+R Fan < 2400

HIC < 900 AND

LPem < 1400 AND

RPem < 1400 AND

L+R Pern < 2400

HEC < 900 AND

Chest g < 56 AND

LPem < 1400 AND

RPem < 1400 AND

L+R Pern < 2400

Definition of

a "Poor" Car

NCAPINJ > .6

Chest g > 56

m e > 1200

LPem > 1600 OR

RPem > 1600 OR

L+R Pern > 2600

me > 1300 OR

Chest g > 60

Chest g > 60 OR

LPem > 1700 OR

RFem > 1700 OR

L+R Fern > 2700

m e > 1300 OR

LPem > 1700 OR

RFem > 1700 OR

L+R Fern > 2700

m e > 1300 OR

Chest g > 60 OR

LFem > 1700 OR

RFem > 1700 OR

L+R Pern > 2700

Mean NCAP Scores and Model Year ("Good" vs. "Poor" Car)

HIC Chest G's L Femur R Femur Model Year

Good Poor Good Poor Good Poor Good Poor Good Poor

898 1106 45.0 62.6

914 1088 44.6 63.4

739 1616 47.3 54.8

913 898 48.4 51.0

769 1414 46.0 59.3

890 983

698 1194

43.9 55.6

46.8 51.5

878 1161 846 1117

949 964 945 930

964 861 995 807

759 1531 734 1659

1009 846 1016 779

754 1311

738 1311

742 1336

766 1294

85.8 83.1

85.8 83.5

85.3 84.3

85.5 85.6

85.3 84.2

85.5 84.8

85.7 85.1

704 1154 43.1 53.6 778 1230 775 1200 85.9 84.8
performers. Table 5-14 shews that NCAPINJ does a slightly better job than any

of the original NCAP scores, singly or in carbinaticns, in separating the safer

cars from the less safe cars, at a target sanple size of 120 crashes (with the

caveats, as noted in Sections 5.8 and 5.9: an unweighted carbinaticn of the

injury scores did nearly as good a job as NCAPINJ, especially on an alternative

data set). The fatality reduction of 26 percent is higher than any of the

others, which range fron 14 to 22 percent. Sum RELEXP and the t-test values are

also higher. A carrposite score such as NCAPINJ is more efficient than the other

methods because it allows excellent perf ozmance on two body regions to carpensate

for moderately poor performance an the third. Intuitively, a car with HIC = 999,

chest g' s = 59 and fertur loads = 1500 each did not perform as well an NCAP as a

car with HIC = 1001, chest g's « 40 and fenur loads •= 500 each. NCAPINJ (or

other cenposite scores) will put the first car in the "good" group and the second

car in the "poor" group, consistent with intuition, while the other methods, if

they had a boundary value of 1000 for HIC, would do the reverse.

The majority of cars, however, do not have unusual NCAP scores like

the two exanples above. They tend to be really good NCAP performers, or quite

poor. All of the methods developed in this chapter will assign them to the

correct group. The most inportant finding conveyed by Table 5-14 is that any

reasonable partitioning of the fleet, based an HIC, chest g's and/or fenur load

will work. In every case, there are significantly fewer fatalities in the "good"

cars than in the "poor" cars, when they collide head-on.

118
CHAPTER 6

FATMJTCY RISK INDICES FOR "QOCD" AND "POCR" NCAP PERPCRMERS

All of the analyses so far esandned head-on collisions in which both

cars matched up with an NCAP test. When one of the cars in the collision was a

good NCAP performer and the other had poor NCAP scores, there was a significant

safety advantage for the car that performed well in NCAP. Ideally, though, a car

with good NCAP performance should be safer-than-average for belted drivers over

the full range of head-on collisions - regardless of whether the NCAP performance

of the other car in the crash was poor, good, or unknown. This chapter presents

a more generalized analysis, based en a larger sample of head-on collisions. The

"case" vehicle in these collisions has to match up with an NCAP test, but the

"other" vehicle in the crash can be any 1976-91 passenger car with a belted

driver, not necessarily matching with any NCAP test, fatality

calculated separately for the case vehicles that are good NCAP performers and for

the case vehicles that are poor performers; the difference in the indices

represents the safety benefit of good NCAP performance over the full range of

head-on collisions.

6.1 Proceed "*? for computing fatality risk Vi?^???

Chapter 2 defined a file of 926 head-on collisions, comprising 1,852

distinct vehicles. Both vehicles in a collision had to be 1976-91 passenger

cars, with belted drivers; at least one of the drivers was a fatality. Seme of

the vehicles match up only weakly or not at all with an NCAP case; Section 2.5

presents criteria for assessing the quality of the match and Section 3.4

demonstrates that EARS and NCAP cases should match at least at the "3A or 4A"

level: the FARS and NCAP vehicles should be of the same make-model or true

119
corporate cousins, and the ERRS model year should be within the "valid" range of

the NC&P test. NCAPINJ, a composite measure of NCAP performance, was defined in

Section 4.1. There are 392 head-on collisions, comprising 784 distinct vehicles,

in which both cars match up with an NCftP test at the 3A or 4A level and NCftPINJ

can be calculated for that test (i.e., missing NCftP data for no more than one

body region). Those 392 collisions were the basis for the analyses in Chapters

4 and 5. However, there are an additional 405 head-on collisions in which only

one vehicle matches an NCAP test at the 3A or 4A level with known NCftPINJ, and

the other vehicle is a 1976-91 car with a belted driver.

A more generalized analysis, which allows a much larger sample size

of 1189 crashes, applies to head-on collisions in which the "case" vehicle of

interest is a 1979-91 car that matches up with an NCAP test, whose driver wore

belts, but the "other" vehicle in the crash can be any 1976-91 passenger car with

a belted driver, not necessarily matching closely with an NCAP test. Thus, there

are a total of 1189 individual vehicles (784 + 405) that have level 3A or 4A NC&P

matches with known NCAPINJ. They were involved in 797 distinct head-on

collisions (392 + 405).

The accident analysis file, comprising 1189 head-on collision records,

is created as follows. If both cars in a collision were level 3A/4A matches with

known NCAPINJ, that collision contributes two records to the analysis file: one

record with car 1 as the "case" vehicle and car 2 as the "other" vehicle, and the

other record, vice-versa. If only one car in a collision was a 3A/4A match with

known NCAPINJ, the analysis file contains one record, with that car as the "case"

vehicle. Each record on the analysis file contains NCAP scores for the "case"

vehicle and the curb weight, driver age and sex for both vehicles.

120
Ihe first step in the analysis is a regression an these 1189 head-an

collision cases whose dependent variable is the outcome for the driver of the

case vehicle (fatality = 1, survival « 0) and whose independent variables are W,

A and S - relative vehicle weight, driver age and sex:

Case Vehicle Is 3A/4A frfetch; Other Vehicle Is Any 1976-91 Car

Reg. Chi Stat. Partial

Coeff. Square Sig.? Corr.

INTERCEPT .525 39.44 RR

W (car weight) -5.214 219.92 RR -.379

A (driver age) .0516 195.72 RR .358

S (driver sex) .38 9.80 RR .072

The intercept and coefficients for W, A and S are similar to those obtained in

Section 4.2, but the chi-squares are larger because there are more accident

cases. The model is used to predict the expected fatality risk for each driver

in the collision, in the absence of NCAP information. The expected fatality risk

E,^ for the driver of the case vehicle is

expT.525 - 5.214 (log W f - 1OOW % ) + .0516 (A,-A^) + .38(F^r-Frh-)1

1 + exp[.525 - 5.214(log W ^ - log W^ ) + .0516(A,.-A^) + .38(F(«-Fota)]

where W,,^ is the curb weight of the case vehicle, A,^ is the age of the driver

of the case vehicle and F,. is 1 if the driver of the case vehicle is female, 0

if the driver is male. The expected fatality risk E ^ for the driver of the

other vehicle is

expT.525 + 5.214(loqW, - logW^ ) - . 0516 (A^-A^) - .38 (F^-F^) 1

1 + exp[.525 + 5.214(logW,« - log W^ ) -

A fatality risk index can be computed for any subset of r^«f> vehicles

(e.g., the case vehicles with poor NCAP scores), as follows. Each collision has

121
an actual outcare A,^ for the driver of the case vehicle (fatality = 1, survival

= 0) and A ^ for the driver of the other car. The actual and expected

fatalities are surttned over all the crashes included in the subset: sum(A(m) and

sumCAjfc,) are the actual nurrbers of driver fatalities in the case and the other

vehicles; sum(E(11D) and sum(EAr) are the numbers of driver fatalities that would

be expected in the case vehicles and the other vehicles, given the relative

weight, age and sex in each crash. The fatality risk index for that subset of

case vehicles is

Index . 100 [sum(A(W)/mBi(ECM)] / [man(A<te)/sun(E(ltar)]

The risk index for any subset of case vehicles measures the fatality risk for

this subset of case vehicles relative to the "average car an the road." The

critical assumption here is that the "other" vehicles in these crashes are an

essentially randan sample of 1976-91 passenger cars with belted drivers: a

representative cross-section of the "average car on the road." The assumption

will be tested later in the chapter. If this particular group of case vehicles

is as crashworthy as the average car on the road, surrUA,.e) will appraxditately

equal surrUE^) and sumCA^) will approximately equal sum(Eote): the risk index

will be close to 100. The lower the risk index, the more crashworthy the subset

of case vehicles (for belted drivers in actual head-on collisions).

The fatality reduction for one group of cars as compared to another,

taking into account the full range of head-on collisions that can occur an the

highway, is measured by the relative difference in the risk indices. For

example, a risk index is computed for a subset of case vehicles with "good" NCKP

scores and also for a subset with "poor" NCAP scores. The fatality reduction for

good NC3\P scores relative to poor 1KZAP scores is

Fatality Reduction «= 1 - (Jja3ex.poi/1n3ex.im)

122
REUEXP -

is defined for each individual collision, and it measures the actual safety

performance .of the vehicles "relative to expectations," as in Section 4.2. In

the 1189 crashes, and in most subgroups of the crashes, RELEXP has a population

standard deviation of 0.64. That makes it easy to test if the average value of

RELEXP is significantly less than zero for a specific group of crashes (i.e., the

case vehicles were significantly safer than the other cars), or if the difference

in average RELEXP for two groups of crashes is statistically significant. A

significance test for the difference in the risk indices for case vehicles with

good NCAP scores and for case vehicles with poor NCAP scores is based an

Z = [avg RELEXP^ - avg RELEXP^] / [.64*(1/^ + 5

As in Chapter 5, the case vehicles are partitioned into "good" and

"poor" NCAP performance groups (with possibly an in-between "borderline" group)

by the carposite score NCAPINJ, or by the actual NCAP test results for a single

body region, two body regions, or all 3 body regions.

6.2 Risk ir^'ices for good and poor NCAP pg*Tf

Table 6-1 presents the results of nine analyses comparing the risk

indices of good NCAP performers and poor NCAP performers. In the first of those

analyses, the performance criterion is the composite score NCAPINJ. As discussed

in Sections 4.1 and 5.8, NCAPINJ is the weighted sum of logistic injury

probabilities for the head, chest and femurs that has maximum correlation with

fatality risk in the set of head-on collisions where both cars match an NCAP test

at the '3A or 4A level.' However, other weighted (or unweighted) sums had almost

equally high correlations on this data set, and, in some cases, higher

correlations on other data sets.

123
TABLE 6-1

FATALITY RISK INDICES FOR CARS WTIH "GOCD" NCAP SCORES VS. CARS WITH "POOR" NCAP SCORES

Crashes with "Good" Case Vehicles Crashes with "Poor' Case Vehicles

to

Definition

NCRPINJ < .6

Chest g's < 5S

HIC < 900

L Fsnur < 1400 AND

R Fenur < 1400 AND

LfR Fenur < 2400

HIC < 900 AND

Chest g's < 55

Chest g'B < 56 AND

L Femur < 1400 AND

R Femur < 1400 AND

LtR Fenur < 2400

N of

Crashes

951

912

737

858

654

676

Fatality

Risk Index

93.90

94.73

95.75

93.43

94.49

91.85

HIC < 900 AND

L Femur < 1400 AND

R Fenur < 1400 AND

LfR Fenur < 2400

HIC < 900 AND

Chest g's < 56 AND

L Fenur < 1400 AND

R Fenur < 1400 AND

L+R Fenur < 2400

526

463

< 1200 AND

Chest g's < 56 AND

L Fenur < 2000 AND

R Fenur < 2000 AND

LfR Fanur < 3000

93.76

92.62

Definition

NCAP1NJ >

Chest g's

m e > 1300

L Fenur >

R Fenur >

LfR Fanur

m e > 1250

Chest g's

.6

> 56

1400 OR

1400 OR

> 2400

OR

> 65

N of

Crashes

238

259

146

325

234

Fatality

Risk Index

119.35

111.89

110.37

113.63

111.37

694 94.37

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

LfR Femur > 2700 363

m e > 1300 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

LfR Femur > 2700 349

m e > 1300 OR

Chest g's > 60 OR

L Fenur > 1700 OR

R Fenur > 1700 OR

LfR Femur > 2700 446

mC > 1400 OR

Chest g's > 62 OR

L Fenur > 2250 OR

R Femur > 2250 OR

LfR Fenur > 3200 301

113.07

113.49

109.58

Risk

% Pat. Red. Z Test for

far "Good" Car Equal Relexp

21.3 3.01

15.3 2.13

13.2 1.32

17.8

15.2

18.8

17.4

15.5

2.62

1.95

2.83

2.34

2.21

112.10 15.8 2.24
Intuitively, the boundary between "poor" and "acceptable/good"

performance should be set so that approximately 20-25 percent of cars will be in

the poor performance group: about 200-300 cars, given that the data file contains

1189 case vehicles. A single boundary value of NCAPIMJ =0.6, the same as used

in Section 5.1, puts 238 cars into the "poor" performance group, leaving 951 cars

in the "acceptable" group.

In the 951 crashes where the case vehicle had NC3VPINJ ^ 0.6, and the

other vehicle could be any 1976-91 car with a belted driver, there were 572

driver fatalities in the case vehicles, but 590.1 were expected. There were 511

fatalities in the other vehicles, but 495.0 were expected. (The "other" cars

average 200 pounds heavier than the case vehicles, so they have fewer expected

fatalities.) The fatality risk index for the case vehicles is

Index,^ = 100 (572/590.1) / (511/495.0) =93.9

In the 238 crashes where the case vehicle had NQ4PINJ > 0.6, there were 150

actual and 133.2 expected fatalities in the pcxu/NCAP performers. There were 132

actual and 139.9 expected fatalities in the "other" vehicles. The index is

Index^ = 100 (150/133.2) / (132/139.9) - 119.4

Over these 1189 collisions, the fatality reduction for good NCftPINJ scores

relative to poor NGAP scores is

Fatality Reduction - 1 - (93.9 /119.4) = 21 percent

The average value of RELEXP is -.0359 in the 951 crashes involving "good" case

vehicles and +.1038 in the 238 crashes involving "poor" case vehicles.

Z = [.1038 - (-.0359)] / [.64*(1/238 + 1/951)"5] - 3.01

so the fatality reduction is statistically significant at the .01 level.

The other analyses of Table 6-1 partition the case vehicles into

125
"good" and "poor" NCAP performance groups by the same criteria as in Chapter 5:

first by a single NCAP parameter, then by a pair of NCAP body regions and,

finally, by independent pass-fail criteria an all three body regions. In every

analysis, the group of case vehicles with good NCAP performance has a lower

fatality risk index than the poor performers. Mast of the fatality reductions

are statistically significant at the .05 level. Cars with chest g's <. 56 have

a risk index 15 percent lower than cars with chest g's > 56 (Z for equal RELEXP

is 2.13, p < .05). The fatality reduction for HIC < 900, relative to KEC > 1300

is 13 percent, which canes close to statistical significance (Z for equal RELEXP

is 1.32). Cars with low femur loads are 18 percent safer than cars with high

femur loads (Z for equal RELEXP is 2.62, p < .01).

When the cars are partitioned according to NCAP scores for any two of

the three body regions, or for all three body regions, the "good" cars always

have significantly lower risk indices than the "poor" cars. The fatality

reductions range frcm 15 to 19 percent; the sample of case vehicles with "poor"

performance ranges frcm 234 to 446.

The results in Table 6-1 can be compared to those in Table 5-14, which

summarized the analyses of those limited subsets of crashes where a "good"

performer hit a "poor" performer. The reductions in the fatality risk indices,

in the broad-based analyses of Table 6-1, range from 1 to 6 percent lower than

the effects in the specialized, high-contrast analyses of Table 5-14, as follows:

126
NPAP TUftifin

Criterion

NCAPHU

Chest g's

me

Femur load

me, chest

Chest g's,

me, femur

me, chest

g 's

femur load

load

g's, femur load

Fatality Reduction

in Table 5-14 (%)

26.4

18.7

14.2

20.1

18.9

22.1

19.4

21.2

Fatality FfriiirHcn

in Table 6-1 (%)

21.3

15.3

13.2

17.8

15.2

18.8

17.4

15.5

It is unknown why the analysis methods of Chapters 5 and 6 do not

produce identical results. The small differences of 1-6 percent could easily be

due to chance alone. Even though the effectiveness is always higher in the

Chapter 5 analysis (and, at first glance, that resembles flipping a coin and

getting "heads" 8 times in a row), the various analyses are hardly independent.

They all use the same data set; the groups of cars with "good" performance

largely overlap, and so do the groups of cars with poor performance. The 1-6

percent difference is well within the range of sampling error on a single

analysis.

If the discrepancies are not due to chance alone, one possibility is

that the "other" vehicles in the Chapter 6 analyses are not, as had been assumed

above, essentially randan samples of 1976-91 passenger cars with belted drivers.

The basic assumption of this chapter was that the "good" cars and the "poor" cars

are both hitting "average" cars, so their fatality risk indices are directly

comparable. If, in fact, the "good" cars tend to hit other "good" cars and the

"poor" cars tend to hit other "poor" cars, the difference in the risk indices

would understate the fatality reduction for good NCAP scores. Detailed

statistics, however, support the basic assumption. For example, in the first

analysis of Table 6-1, the case vehicles with NCAPINJ <0.6 have average NCAPINJ

127
= 0.31 and the case vehicles with NCAPINJ > 0.6 have average NCAPINJ = 0.90. But

the "other" cars that hit the "good11 cars have nearly the same average NCAPINJ

as the "other" cars that hit the "poor" cars: 0.453 vs. 0.466. They also have

nearly the sane average weight (2971 vs. 3010 pounds), driver age (42.7 vs. 43.2

years) and driver gender distributions (45 percent female vs. 44 percent female).

The "other" cars, in both cases, are basically identical.

Another possibility is that the specialized analyses of Chapter 5

somehow intensify the fatality reductions associated with good NCAP scores. In

the full range of head-on collisions, "good" scores reduce risk by X and "poor"

scores increase it by Y, but when a "good" car specifically hits a "poor" car,

the difference in risk may be even greater than X + Y. If so, the results of

this chapter provide a more conservative assessment of the overall reduction of

fatality risk for cars with good NCAP scores.

128
CHAPIER 7

ACTUAL CRASHWOKIHINESS AND NZAP PERFORMANCE EURINS 1979-91

It is well known that the performance of passenger cars en the

test has greatly improved since the program was initiated in 1979. Substantial

reductions in HIC and chest g's have been documented in NHISA's 1992-93 Reports

to the Congress [17], [23], [24] and in NHISA presentations at ESV conferences

during 1983-92 [7], [13], [14], [15], [16]. The studies cited specific changes

in vehicle structures and occupant protection systems that inproved NCAP

performance. The first six chapters of this report demonstrated statistically

significant associations between NCAP performance and the fatality risk of belted

drivers in head-on collisions. Given that NCAP performance improved during 1979-

91, and that good NCAP performers have lower fatality risk in actual crashes, it

is logical to expect that cars became safer in actual crashes during 1979-91.

This last chapter estimates the payoff: the reduction in the actual fatality risk

of belted drivers in head-on collisions since 1979. (Of course, this report is

a statistical study and it does not pin down cause and effect. Although it shows

that cars became safer as NCAP scores improved, it does not prove that the NCAP

program was a stimulus for each of the vehicle changes that saved lives during

1979-91. For example, Federal Motor Vehicle Safety Standard 208 has been an

important stimulus for safety improvements during the NCAP era.)

In general, the fatality rates of cars of different model years are

not directly comparable. There are two patterns in fatality rates that create

the delusion that "cars are getting safer all the time." The overall fatality

rate per 100 million vehicle miles has been declining for a long time - e.g.,

from 5 in 1969 to 3 in 1981 to 2 in 1992. But that improvement may primarily

129
reflect long-term changes in driving behavior, roadway environments and

demographics, not crashworthiness. For exanple, pedestrian fatalities, which are

unaf fected by improvements in vehicle interior crashworthiness, have declined as

rapidly as occupant fatalities. The bias fran long-term population trends could

be avoided by using accident data from a single calendar year and cortparing the

fatality rates of cars of two different model years. However, in any single

calendar year, the cars of earlier mode] years are older than the late-model

cars. Because of their drivers' demographics and behavior, older cars typically

accunulate fewer miles, but have more severe crashes; low-severity crashes of old

cars are often unreported. Ihus, the fatality rate per 100 million miles or per

100 reported crashes is lower for new cars than for old cars, even if both are

equally crashworthy.

A head-on collision between cars of two different model years,

however, reveals their relative crashworthiness. Both cars are in essentially

the same frontal collision, on the same road, in the same year, an the same

accident report. The behavior of each driver, prior to the inpact, has little

effect an who dies during the inpact. After adjustment for differences in car

weight, driver age and sex (vulnerability to injury), the model year with more

survivors is more crashworthy. The methods of the preceding chapters, used there

to caipare good and poor NCAP performers, will now be used to catpare cars of

different model years. NHISA' s 1988 Evaiw^tion of OfT'7li

Trant Protection in Fr^ii^i

Interior Inpact has already used this method to carpare the fatality risk of cars

of different model years for wpffsf??.1?^ drivers [19], pp. 111-140. It found

that cars of model years 1970 through 1984 were about equally crashworthy for

unrestrained drivers in head-on collisions. The remainder of this chapter

studies the trend in fatality risk for belted drivers.

130
7.1 C^TS with late np'i^i VQ^T S hit cars with

A straightforward way to estimate the improvement in crashworthiness

over time is to study only those collisions where a late-model car hits an earlymodel car. Table 7-1 presents seven analyses of the same accident file that was

vised in Chapter 5: collisions of two 1979-91 cars, both of which natch up with

an NCAP test at level 3A or 4A - i.e., the EARS and NCAP vehicles are of the same

make-model or true corporate cousins, and the ERRS model year is within the

"valid" range of the NCAP test (see Sections 2.5 and 3.4). m the first

analysis, the "early" cars are M£ 79-81 and the "late-model" cars are M5f 82-91.

The boundary between "early" and "late" is pushed forward, one year at a time,

in the subsequent analyses.

lable 7-1 demonstrates statistically significant improvements in

crashworthiness for belted drivers in head-on collisions. For example, the

second analysis in the table is based an 121 actual head-on collisions between

a model year 1979-82 car and a model year 19oJ~91 car. This analysis allows a

comparison of cars built during the first four years of NCAP to subsequent cars,

where manufacturers have had time to build in safety improvements. In the 121

older cars, 80 drivers died, whereas only 69.2 fatalities were expected, based

an car weight, driver age and sex. In the newer cars, there were 61 actual and

71.2 expected driver fatalities. That is a fatality reduction of

1 - [(61/80) / (71.2/69.2)] = 26 percent

for the 1983-91 cars, and it is statistically significant (t for RELEXP is 3.09,

p < .001). Fatality reductions greater than 20 percent were also found in the

first analysis (79-81 vs. 82-91) and the third analysis (79-83 vs. 84-91). When

the boundary between "early" and "late" is pushed beyond 1983, the fatality

reduction is diluted, because vehicles with safety improvements are taken out of

131
T3VBLE 7-1

COLLISIONS OF CARS WTIH "LA.TE" MODEL YEARS INTO CARS WITH "EARLY" MODEL YEARS:

EFFECT OF NDV3N3 THE BOUNDARY BETWEEN "EARLY" AND "LATE" NDDEL YEARS

(MY 1979-91 cars; bot h cars natc h a n NCAP tes t a t leve l 3A o r 4A)

Definition of

"Late" Iff Car

MY 82-91

MY 83-91

My 84-91

MY 85-91

My 86-91

My 87-91

MY 88-91

Definition of

"Early" MY Car

MY 79-81

MY 79-82

MY 79-83

MY 79-84

MY 79-85

MY 79-86

MY 79-87

N of

Crashes

98

121

146

177

183

183

143

* r —— — — -•

% Fat Red for

Late MY Car

29.9

25.9

21.3

14.7

9.0

18.8

14.8

Mean

Relexp

-.205

-.174

-.138

-.092

-.055

-.120

-.090

rfoxmanoe

Sum

Relexp

-20.06

-21.02

-20.11

-16.34

-10.15

-21.97

-12.92

T-Test

for Relexp

3,

3.

2,

1.

1,

66

09

69

93

15

2.53

1.69

Mean NCAP Scores and Model Year ("Late" vs. "Early" Car)

Definition of

"Late" MY Car

MY 82-91

MY 83-91

MY 84-91

My 85-91

MY 86-91

My 87-91

MY 88-91

Definition of

"Early" MY Car

My 79-81

My 79-82

MY 79-83

My 79-84

My 79-85

MY 79-86

My 79-87

HIC

Late

858

821

853

872

883

864

808

Early

1029

1021

1000

959

943

942

963

Chest G's

Late

47.4

46.4

46.2

46.5

46.7

47.0

47.1

Early

55.9

54.9

53.6

50.8

50.4

49.5

49.1

L Femur

Late

859

905

902

956

975

1022

1014

Early

942

976

1004

944

926

948

885

R Femur

Late

904

915

891

945

955

1022

1045

Early

999

1048

1040

983

939

911

873

Model

Late 1

86.1

86.8

87.0

87.4

87.8

88.3

89.0

Yea:

Sari}

80.0

80.6

81.1

82.0

82.6

83.2

83.7
the "late" group and placed in the "early" group.

Each of the cars analyzed in Table 7-1 matched up with an NCAP case.

Based on these match-ups, the lower section of Table 7-1 compares the average

NCAP performance of the early and late-model cars. (NCAP scores are averaged

over the various cars an the accident file - i.e., each NCAP-tested mode] is, so

to speak, weighted by the number of fatal crashes involving that model.) HEC has

improved from an average of 1029 in 1979-81 cars to 808 in 1988-91 cars. HEC was

reduced from 1030 to the mid-800's in 1982-83, stayed close to that level for the

next 5 years, and dropped below 800 after 1988. Chest g's were reduced from 56

in 1979-81 to 47 in 1982-91. MDSt of the reduction was achieved in the first 4

or 5 years of NCAP; chest g's have been close to 47 since 1982. Average femur

loads dropped fron about 1000 to 900 in the mid 1980's, but crept back to 1000

in the late 1980's. However, in the second analysis of Table 7-1, the 121 1983-

91 cars performed substantially better than the 121 1979-82 cars an every NCAP

parameter. Average HIC declined from 1021 to 821, chest g's from 54.9 to 46.4,

left femur load frcm 976 to 905 and right femur load from 1048 to 915. The

ccnposite NCAP score, NCAPH4J (defined in Section 4.1), declined by a

statistically significant 0.206 (t - 5.85, p < .0001).

The preceding analyses were based an cars that matched up with NCAP

tests: the same data base as in Chapter 5. However, if the objective is merely

to compare the crashworthiness of early vs. late-model cars, without regard to

their NCAP performance, it is not necessary to limit the data to cars with

matching NCAP information. In Table 7-2, the analysis has been extended to

include any head-an collision between two 1979-91 cars, with both drivers belted.

That is a set of 723 collisions (1,446 distinct vehicles) - nearly double the

133
u>

"BffiLE 7-2

CDLLISICMS OF CARS WTIH "LATE" MDEL YEARS INTO CARS WTIH "EARLY" NEDEL YEARS:

EFFECT OF M3VING TOE BOUNDARY BETWEEN "EARLY" AND "LATE" MODEL YEARS

(all My 1979-91 cars - not necessarily natching an NCAP test)

Caxparison of Real-World Performance

"Late" MY

My 82-91

MY 83-91

My 84-91

MY 85-91

MY 86-91

My 87-91

My 88-91

1 Of

Car

Definition of

"Early" MY Car

My 79-81

MY 79-82

MY 79-83

My 79-84

My 79-85

MY 79-86

My 79-87

N of

Crashes

196

241

274

321

321

311

248

% Fat Red for

Late MY Car

23.0

22.2

20.5

15.6

11.8

13.5

8.9

Mean

Relexp

-.147

-.143

-.130

-.096

-.071

-.083

-.053

Sun

Relexp

-28.80

-34.54

-35.64

-30.94

-22.91

-25.68

-13.12

T-Test

for Relexp

3.33

3.43

3.30

2.62

1.88

2.14

1.24
sample available for Table 7-1. Of course, Table 7-2 does not have a "lower

section" like Table 7-1, since NCAP information is unavailable for many of the

vehicles.

The pattern of fatality reductions in Table 7-2 is similar to Table

7-1, with the oscillations smoothed by the larger sample size. In the second

analysis, the fatality reduction for 1983-91 cars, relative to 1979-82 cars is

22 percent, and it is statistically significant (t for KELEXP is 3.43, p < .001).

The fatality reductions in the first three analyses are over 20 percent, as in

Table 7-1, and here they are all significant at the .001 level.

A more generalized analysis of crashworthiness trends over time is

achieved by computing fatality risk indices for cars of different model year

groups. The procedure for estimating risk indices was developed in Section 6.1,

and it was applied in Section 6.2 to compare the index for cars with good NCM>

scores vs. cars with poor scores. However, a risk index can be calculated for

any group of cars, such as all cars of a specific model year, or a group of model

years. As in Chapter 6, the data base comprises all head-on collisions in which

the "case11 vehicle of interest is a 1979-91 car that matches up with an NCK>

test, whose driver wore belts, but the "other" vehicle in the crash can be any

1976-91 passenger car with a belted driver, not necessarily matching with an NCAP

test (1189 accident records). The actual and expected fatalities are tallied in

the "case" and "other" vehicles; the fatality risk index is

100 [(actual^/actual„,„,) / (expected^/expected^)]

One advantage of this approach, unlike the method in Section 7.1, is

135
that 1979-91 cars can be partitioned into more than two model-year groups.

Specifically, case vehicles are assigned to three model-year groups: 1979-82,

1983-86 and 1987-91. Ihe initial years of NCftP were 1979-82. By 1983-86,

manufacturers had leadtime to address major deficiencies in the initial NCAP test

results. Ihe 1987-91 cars were often equipped with air bags or other automatic

protection.

In the 280 crashes with a 1979-82 case vehicle, there were 181 driver

fatalities in the case vehicles, but 166.2 were expected. There were 141

fatalities in the other vehicles (any 1976-91 car with a belted driver), but

154.0 were expected. The fatality risk index for 1979-82 cars is

Index-Rc = 100 (181/166.2) / (141/154.0) =119.0

Ihe fatality risk index for the 452 1983-86 cars is just 95.0, and the risk index

for the 457 1987-91 cars drops to 90.9 (a risk index of 100 corresponds to the

"average" 1976-91 car an the road with a belted driver). The fatality reduction

frcm 1979-82 to 1983-86 is

1 - (95.0/119.0) = 20 percent

and it is statistically significant (Z for equal RELEXP is 2.60, p < .01). The

additional fatality reduction from 1983-86 to 1987-91 is 4 percent, which is not

statistically significant. The net fatality reduction from 1979-82 to 1987-91

is a statistically significant 24 percent (Z for equal RELEXP is 3.18, p < .01).

It is especially interesting to compare the trend in the actual

fatality risk index with the trend in N3VP performance. Each of the case

vehicles in the preceding analysis matched up closely with an NCAP test and has

a composite NCM> score, NCBPINJ. The composite scores are averaged for each of

the three model-year groups on the accident file (i.e., each N3iP-tested model

136
is, so to speak, weighted fcy the number of fatal crashes involving that model).

The risk indices and average NCAP performance for each model-year group are as

follows:

M o

J79-82

119

.59

49

1052

54.9

928

1079

d e l Year s

1983-86

95

.40

14

915

46.8

883

784

1987-91

91

.37

9

827

46.5

1002

1018

Fatality risk index in

actual head-on collisions

Average value of NCAPINJ

Percent of cars with NCAPINJ > 0.6

Average HIC

Average chest g's

Average left femur load

Average right femur load

The trends in the actual fatality risk and the average value of NCAPINJ are

almost identical. The risk index decreased from 119 in model years 1979-82 to

95 in 1983-86, to 91 in 1987-91, a large reduction followed by a much smaller

reduction. In parallel, NCAPINJ greatly improved from an average of .59 in model

years 1979-82 to .40 in 1983-86, with an additional, modest improvement to .37

in 1987-91. The percentage of cars with poor NCAP performance (NCAPINJ > 0.6,

a yardstick established in Chapters 5 and 6) also took a big drop, from 49

percent in 1979-82 to 14 percent in 1983-86, followed by a small drop to 9

percent in 1987-91. As discussed in Sections 4.1 and 5.8, NCAPINJ is the

weighted sum of logistic injury probabilities for the head, chest and femurs that

has maximum correlation with fatality risk in the set of head-on collisions where

both cars match an NCAP test at the '3A or 4A level.' However, other weighted

(or unweighted) sums had almost equally high correlations an this data set, and,

in same cases, higher correlations on other data sets. While NCAPINJ nicely

137
portrays the trend of inproved NCAP performance, other weighted (or unweighted)

sums will show quite similar trends. Average HIC was substantially reduced fron

1979-82 to 1983-86, and again from 1983-86 to 1987-91. Chest g's were greatly

reduced frcm 1979-82 to 1983-86, but stayed about the same after that. The

average femur loads did not change much during 1979-91.

Figure 7-1 graphs the actual fatality risk index, by model year, from

1979 to 1991 (data grouped into two-model-year cohorts, to smooth the results).

Figure 7-2 graphs the average value of NCAPINJ, and Figure 7-3, the percentage

of cars with NCAPINJ > 0.6, by model year frcm 1979 to 1991. The three figures

have nearly identical patterns: little, if any, improvement frcm 1979 to 1981;

impressive reductions from 1982 to 1984; leveling off after 1984, with a possible

trend of further improvements after 1988.

As in the preceding section, the computation of risk indices does not

have to be limited to case vehicles which closely match an NCAP test, but can be

extended to all 1979-91 cars that collided head-on with a 1976-91 car, with both

drivers belted. That has the advantage of extending the sample size from 1189

to 1632 vehicles, although, without the NCAP matches, the trend in risk indices

cannot be compared with the trend in NCAPINJ. The risk indices in this extended

sample are about the same as in the preceding analysis:

Mf 1979-82

Mf 1983-86

Mf 1987-91

3A/4A Matches

N Risk Index

280 119.0

452 95.0

457 90.9

Extended Sanple

N Risk Index

425 117.5

610 96.5

597 92.6

138



The fatality reduction from 1979-82 to 1983-86 is a statistically significant 18

percent (Z for equal RELEXP is 2.69, p < .01). Ihe additional fatality reduction

from 1983-86 to 1987-91 is a nonsignificant 4 percent. Ihe net fatality

reduction frcm 1979-82 to 1987-91 is a statistically significant 21 percent (Z

for equal RELEXP is 3.29, p < .001), slightly less than the 24 percent in the

preceding analysis.

7.3 Comparison of the NCAPINJ and mo^i -ypRT effects

The principal finding in Chapters 4-6 is that cars with good NCAP

performance are about 20 percent safer in head-on collisions than cars with poor

NCAP performance. The principal finding here is that late-model cars are

likewise about 20 percent safer than early-model cars. These two findings don't

quite "add up." Although late-model cars have, on the average, substantially

better NCAP performance than earlier models, the late models are not all "good"

and the early models are certainly not all "poor." Thus, the 20 percent fatality

reduction for late models cannot be fully explained by the 20 percent fatality

reduction for good vs. poor NCAP performance. There has been some "residual"

improvement, during 1979-91, which is not "explained" by a composite score such

as NCAPINJ, or by other variables derived from NCAP scores. The three remaining

analyses of this chapter compare the fatality reductions associated with NCAPINJ

and the "residual" model-year effect.

An important reminder: the analyses that follow describe statistical

associations, not cause-and-effect relationships. The portion of the 1979-91

fatality reduction "attributable to the reduction of NCAPINJ" is not necessarily

"caused by NCAP." The "residual" reduction is not necessarily "caused by factors

other than NCAP." Just because a vehicle change reduced NCAP scores does not

142
prove that it was implemented purely in response to NCAP. Conversely, if a

vehicle change improves actual safety without having nuch of an effect on NCAP

scores, that does not prove that it was unrelated to NCAP: manufacturers usually

don't know, in advance, exactly how a vehicle change will affect scores; this

change might have been motivated, in part, by a hope that NCAP scares would

improve. No claim is made here to include or. exclude any portion of the actual

fatality reduction as "lives saved by the NCAP program."

Chapter 5 demonstrated that when a car with "good" NCAP scores hits

a car with "poor" scores, the "good" cars have a significant safety advantage.

However, most of the comparisons in Chapter 5 showed that the "good" NCAP

performers also had a later model year, an the average, than the "poor"

performers. For example, in Table 5-1, the average model year of cars with

NCAPINJ < 0.6 was 85.8, and the average model year of cars with NCAPINJ > 0.6 was

83.1. The first analysis asks whether the fatality reduction for good NCAP

scores exists independently of model year, or whether it is merely an artifact

of the better NCAP performers being more recent cars.

The comparisons in Chapter 5 did not place any limits on the relative

model years of the two cars in the head-an collisions. The data included sane

crashes in which one car might have been 10 or even 12 years older than the

other. Table 7-3 limits two of the Chapter 5 comparisons to subsets of head-an

collisions in which the model years of the two cars are close to one another.

In the subsample of 61 collisions of a car with. NCAPINJ <. 0.6 into a oar with

NCAPINJ > 0.6 in which -5 < Mfax» - Maroon < 3, the fatality reduction for the

good NCAP performers is 32 percent, which is statistically significant (t for

RELEXP is 3.03, p < .01) and, in fact, slightly higher than the 26 percent

143
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144
fatality reduction in the unrestricted sanple of 117 collisions (see Table 5-1).

The range of allowable model years, -5 < M5fax» - Maroon <. 3, serves to equalize

the average model year of the good and poor NCAP performers at 84.6. In the

subsample of 93 collisions of a car with low HIC, chest g' s and femur load into

a car with high HIC, chest g's or femur load in which -5 < Mfoooo " M^TOOR < 5,

the fatality reduction for the good perf onners is a statistically significant 23

percent (t for RELEXP is 2.07, p < .05), which is almost identical to the 21

percent reduction in the unrestricted sanple of 118 cases (see Table 5-9). The

last line of Table 7-3 shows that the "good" and "poor" NCAP performers have

nearly identical average model years. Thus, the strong association between NCAP

performance and actual fatality risk exists independently of the model year.

Conversely, the second analysis searches for a model year effect

independent of NCAPINJ. In Section 6.2, fatality risk indices were computed for

case vehicles with NCAPINJ <_ 0.6 and NCAPINJ > 0.6; in Section 7.2, for latemodel and early-model cars. But a risk index can be calculated for any group of

case vehicles, including groups defined by their NCAP performance and model

years:

NCAPINJ

< 0.6

> 0.6

Model

Years

1979-82

1983-91

1979-82

1983-91

N of

144

807

136

102

Risk

Index

111.2

91.1

128.0

109.2

The effect of NCAPINJ and the "residual" effect of model year are both strong and

145
nearly independent in these risk indices. Controlling for NCAPINJ < 0.6, the

late-model cars are almost 20 percent safer than the early-model cars.

Controlling for NCAPINJ > 0.6, the late-model cars again have close to 20 percent

lower risk indices than the early model cars. In other words, there is a

consistent "residual" model year effect, after controlling for NCAPINJ. But

these risk indices also show a consistent effect dose to 20 percent for NCAPINJ

within model year groups: e.g., for 1979-82 cars, the good NCAP performers had

a risk index of 111, and the poor performers, 128. The "N of Cases" column shows

a dramatic shift from poor to good NCAP performance in the late-model cars.

Thus, the net reduction in the risk index for late-model cars is associated with

shift from poor to good NCAP performance plus a "residual" mode]-year effect.

The third analysis compares, in statistical terms, the relative

"strength" of the NCAPINJ effect and the residual model-year effect in the file

of 392 head-on collisions where both cars match up with an NCAP test at level 3A

or 4A. In Section 4.3, it was shown that DELNCAP = NCAPINJ,,,,, - NCAPINJota has

a strongly significant correlation with RELEXP, actual safety performance

relative to expectations; the Pearson correlation coefficient was .166 (p = .001,

N = 392). However, if another variable, DEIMf - m^ - MJf^ is defined on that

file, it also has a significant correlation of - .133 with RELEXP (p = .008). m

other words the nej; correlation of model year with actual fatality risk is

significant. But if DELNCAP and DELMJf are grimi]%$neously entered as independent

variables in a linear regression, with RELEXP as the dependent variable, the

regression equation is

RELEXP «= .036 + .23 DELNCAP - .0148 DELMJT

The coefficient for DELNCAP is statistically significant at the .01 level (t =

2.78), whereas the coefficient for DELMJf is barely significant at the .05 level

146
(t = 1.96). That suggests the association of NCAPINJ vdth fatality risk is

strong, while the residual association of model year vdth fatality risk, after

controlling for NCAPINJ, is not quite as strong.

147

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