LBNL

- xxxxx



## A ssessment of NHTSA’s Report

“Relationships Between Fatality Risk, Mass, and Footprint in Model

Year 2000

- 2007 Passenger Cars and LTVs”

Draft Final report prepared for the Office of Energy Efficiency

and Renewable Energy

, US Department of Energy

Prepared b y

Tom Wenzel

Energy Anal ys is Department

Environmental Energy

Technologies Division

Lawrence Berkeley

National Laboratory

Berkeley , CA 94720

November 2011

This work was supported by the Vehicle Technologies Program

, Office of Energy

Efficienc y and Renewable Energy of the U.S. Department of Energy

under Contract No. DE

- AC02

- 05CH11231.DISCLAIMER

This document wa s prepared as an account of w ork sponsored by

t he United States Government. While this

document is believed to contain correct information, neither the United States Government nor any agency thereof,

nor The Regents of the University of C alifornia, nor any of

t heir em ployees, ma kes any warranty, express or

im plied, or assumes any legal responsibili ty for the accuracy, completeness, or usefulness of any inform ation,

apparatus, product, or process disclosed, or represents that its use would not infringe privately ow ned rights.

Reference herein to any specific commercial product, process, or service by its trade name, trademark,

manufacturer, or otherw ise, does not necessarily constitute or imply its endorsement, recommendation, or favoring

by the United States Government or any agency thereof, or The Regents of the University of California. The views

a nd opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or

any agency thereof, or The Regents of the University of California.

Ernest Orlando Lawrence Berkeley National Laboratory is an equal oppo rtunity employer.i

**Acknowledgements**

We would like to thank those who reviewed earlier drafts of this report, and provided helpful

comments and insights: Tom White and Peter Whitman, Office of Policy, U.S. Department of

Energy ; Chi Li

, Office of Transportation and Air Quality

, U.S. Environmental Protection

Agency ; Chuck Kahane, National Highway T ransportation Safet y Administration, U.S.

Department of Transportation; and Phil Price, L awrence Berkeley National L aboratory

.

The report was funded by C arol Schutte of the Vehicle Technologies Program in the Office of

Energy Efficiency

and Renewable Energy

of the U.S. Department of Energy . We are grateful for

her support o f this research.

This work was supported by t he Assistant Secretary f or Energy

E fficiency

a nd Renewable

Energy , of the U.S. Department of Energy under Contract No. DE

- AC02

- 05CH11231.iiiii

**Executive Summary**

NHTSA recentl y completed a logistic regression anal ys is updating its 2003 and 2010 studies of

the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT). The

new study

u pdates the previous analy ses in several way s: updated FARS data from 20 02 to 20 08

for MY0 0 to MY0 7 ve hicles are used ; induced exposure data from police reported crashes in

several additional states are added ; a new vehicle category f or car

- based crossover utility

vehicles (CUVs) and minivans is created ; crashes with other light

- duty v ehicles are divided i nto

two groups based on the crash partner vehicle’s weight

, and a category f or all other fatal crashes

is added ; and new control variables for new safety t echnologies and designs

, such as electronic

stability c ontrols (ESC), side airbags, and methods to me et voluntary agreement to improve light

truck compatibility

with cars , are included

.

Using the updated NHTSA databases, our anal ysis finds that reducing vehicle mass by 1 00 lbs

while holding footprint fixed would increase fatality risk per VMT by 1 .4 4 % fo r lighter than

average cars

, and 0.52% for lighter than average light

- dut y trucks

. However, mass reduction in

heavier than average light

- duty t rucks, while holding footprint constant, would reduce risk by

0.40%. The effect of mass reduction on heavier cars and CUVs and minivans are not statistically

significant

. (Using a different method to estimate the uncertaint y around these point estimates,

NHTSA found that onl y the effect of mass reduction o n lighter than average cars is statistically

significant.) NHTSA concludes that

, when footprint is held fixed, “ no judicious combination of

mass reductions in the various classes of vehicles results in a statistically s ig nificant fatalit y

increase and man y potential combinations are safety

- neutral as point estimates ”.

The effect of mass reduction on risk that NHTSA calculated in 2011 is much smaller than in its

2003 and 2010 studies

, particularl y for cars

. N HTSA attributes this reduction in the importa nce

of mass reduction on safety t o the phase

- out of relativel y light cars that had unusually h ig h

fatality r isk, an observed improvement in how light, small cars are driven which reduces their

tendency t o be involved in serious crashes, and voluntary i mpro vements made to light trucks to

improve their compatibility w ith other vehicles

. The 2011 NHTSA anal ysis finds that reducing

vehicle footprint by

o ne square foot while holding mass fixed would increase fatality r isk per

VMT by 1

. 8 9% in cars and 1.7 3 % in C UVs and minivans (the effect on risk in light trucks is

small and not statistically significant).

Rather than rel yi ng on the confidence intervals output by the logistic regression models, NHTSA

estimates the uncertaint y around its point estimates using a jack

- knife technique that accounts for

the sampling error in the FARS fatality a nd state crash data. T hese uncertainty e stimates are larger than the confidence intervals output by t he logistic regression models included in this

report. As a result, in its report NHTSA finds that only t he 1.44% increase in risk from mass reduction in lighter than average cars is statistically significant.

This report replicates the 2011 NHTSA analy sis, and examines the data in slightly different way s

to get a deeper und erstanding of the relationship between vehicle weight/ footprint and safety

. The results of these alternative anal ys es are summarized in Table ES.1; statistically s ignificant

results

, based on the confidence intervals output by t he logistic regression mode ls, are shown in

red in the table. In particular, we found that:iv

• NHTSA’s (reasonable) assumption that all vehicles will have ESC installed by 2 017

slightly increases the detrimental effect of mass reduction, but slightly decreases the

detrimental effec t of footprint reduction, on risk in cars, CUVs and minivans ( A lternative 1

in Table ES.1; explained in more detail in Section 2.1 of this report)

. This is because

NHTSA projects ESC to substantially r educe the number of fatalities in rollovers and

crashes with stationary

o bjects, and mass reduction reduces risk, while footprint reduction

increases risk, in these t ypes of crashes, particularly in cars and CUVs/minivans.

• Man y of the control variables NHTSA includes in its logistic regressions are statis tically

significant, and have a large effect on fatalit y risk. F or example, a car’s mass could be

reduced by

8 00 lbs while adding ESC without increasing its fatality r isk. A nd increasing

the amount of vehicle travel on highway s with speed limits greater than 55 miles per hour

by 0.35% would result in the same increase in risk as reducing the mass of all cars by 1 00

lbs. While the effect of mass reduction may r esult in a statistically

- significant increase in

risk in certain cases, the increase is small and is overwhelmed by o ther known vehicle,

driver, and crash factors.

• V ehicle mass and footprint are correlated, but only s trongl y for passenger cars. NHTSA

includes both variables in their regression models, introducing the possibility that multi

colli nearity may create biased results. When footprint is allowed to vary a long with

weight, mass reduction results in a larger increase in risk than when footprint is held

constant. S imilarly

, when mass is allowed to vary along with footprint, footprint redu ction

results in larger increases in risk (Alternative 2 in Table ES.1, further addressed in S ection

3 of this report)

. T o isolate the effect of mass reduction from footprint reduction on risk,

NHTSA estimates the effect of mass reduction on risk for decil es of vehicles with similar

footprint. Mass reduction does not consistently increase risk across all footprint deciles for

any c ombination of vehicle ty pe and crash ty

pe. M ass reduction increase s risk in a majority o f footprint deciles for 13 of the 27 c rash and vehicle combinations

, but few of

these increases are statistically significant ( the increases are statistically significant only

f or light

- duty t rucks in rollovers). O n the other hand, mass reduction decreases risk in a majority of footprint deci les for 9 of the 27 crash and vehicle combinations . I n some cases

these risk reductions are large and statistically s ignificant ( such as in cars in rollovers and

crashes with stationary

o bjects; light

- dut y trucks in crashes with light and heav y cars; and

CUVs and minivans in crashes with heav y cars).

• Lo gistic regression does not allow a statistic, such as the model R 2 in a linear regression

model, to measure how much variability i n risk by

v ehicle model is explained by t he

control variables included in the model. A naly sis of pseudo

- R 2 and R 2 from a linear

regression model suggests that much of the variance in risk remains unexplained, even after

accounting for man y important vehicle, driver, and crash variables. After accounting for

all of the variables in NHTSA’s logistic regression model, except for vehicle mass and

footprint

, we find that the correlation between fatality r isk by v ehicle model and mass is

very l ow. T here also is no significant correlation betw een the residual

, unexplained risk

and vehicle weight. These results indicate that, even after accounting for many v ehicle,v

driver, and crash factors, the variance in risk by v ehicle model is quite large and unrelated

to vehicle weight (addressed in more detail in Section 4)

.

• Changes in the data and variables NHTSA used in its regression models have only s light

changes on NHTSA’s results. C alculating risk as fatal crashes, rather than total fatalities,

per vehicle mile traveled , as suggested by one o f the independent reviewers of the previous

NHTSA reports, increases the detrimental effect of mass reduction on risk in cars, but has

no effect on mass reduction in light trucks or CUVs/minivans, or on footprint reduction in

any v ehicle ty

pe (Alternative 3 in Table ES.1)

. C alculating risk as total fatalities per induced exposure crash, rather than per vehicle mile traveled, reverses the sign of th e effect

of mass reductions on risk in cars and the lighter light trucks, with mass reduction leading

to a red uction in risk in all vehicle ty pes. F ootprint reduction continues to result in large

increases in risk per induced exposure crash for cars and CUVs/minivans, but leads to a large reduction in fatality risk per induced exposure crash for light trucks (Alt ernative 4 in

Table ES.1; further addressed in Section 5.1)

.

• Adding control variables for vehicle manufacturer tends to increase the effect of mass

reduction, but decrease the effect of footprint reduction, on risk for cars and light trucks,

and makes mass reduction detrimental, and footprint reduction slightly beneficial, for

CUVs/minivans (Alternative 5 in Table ES.1).

• NHTSA included control variables for the calendar ye ar in which the crash occurred, to

reflect reducing risk from changes to vehicl es, driver behavior and driving conditions over

time. H owever, including these calendar ye ar variables in the regression models appear to

weaken the benefit of curtain side air bags i n cars, CUVs, and minivans, and compatibility

measures and ESC in light trucks. T hese variables also appear to minimize the increased

risk of SUVs and heavy

- duty p ickup trucks. Excluding these calendar year variables from

the regression models increases the detrimental effect of mass reduction on ris k in li ght

trucks (Alternative 6 in Table ES.1, addressed in Section 5.3).

• Excluding crashes involving alcohol or drugs, or drivers with poor driving records, also

increases the detrimental effect of mass reduction on risk, but reduces the detrimental effect

of footprint reduction on risk (Alternatives 7 and 8 in Table ES.1, Section 5.4)

. I ncluding

all

- wheel

- drive, sports, and police cars increases the effect of mass reduction, but reduces

the effect of footprint reduction, on risk for cars; while including f ullsize vans reduces the

effect of mass reduction, and increases the effect of footprint reduction, on risk for light

trucks (Alternative 9 in Table ES.1, Section 5.5)

.

• As mentioned above, for its baseline fatalities NHTSA assumes that all vehicles wil l have

ESC installed by 2017

, which will reduce the fraction of fatalities in rollovers and crashes

with stationary

o bjects, and thus will increase the detrimental overall effect of mass

reduction, but decrease the detrimental overall effect of footprint r eduction, on risk.

However, other recent trends that are likel y to continue through 2017 may a lso affect the

distribution of crashes in that ye ar. F or example, side airbags in cars will likely r educe the

fraction of fa talities in side

- impact crashes (Sec tion 6.1), and better alignment of light truck

bumpers with those of other vehicles appears to reduce the risk imposed on car occupants,vi

at least in side impact crashes (Section 6.2). H owever, it appears that mass reduction has

less of a detrimental effec t on risk when cars are struck in the side than when they a re

involved in frontal or rear

- end crashes, so an y future reduction in fatalities in car side

impact crashes will not necessarily influence the effect of mass reduction on risk. And it is

not clea r whether full adoption of side airbags or compatibility m easures for light trucks

will reduce fatality

risk when light

- dut y trucks, CUVs or minivans are struck in the side.

• Finally

, in part because of high gas prices and the poor econom y, households h ave been

purchasing smaller and lighter vehicles in the last decade

. F or example, the explosion of

CUVs appears to have led to a reduction in the market share of minivans, cars, and in

recent ye ars (MY05 to MY07) SUVs and pickups. It i s likely t hat these trends would

continue, even in the absence of stronger CAFE and GHG emission standards. A ny future

market shifts from SUVs or pickups to cars or car

- based CUVs and minivans will result in

much larger reductions in fatality r isk than the relatively small i ncreases in risk expected

from mass or footprint reduction

. F or example, we estimate that a large

- scale shift in the

market share of pickups and SUVs to CUVs, minivans, and cars will reduce overall

fatalities by

nearl y 4 % (Section 6.3).

Table ES.2 compares the results from NHTSA’s 2003, 2010, and 2011 anal ys es with the

alternative model specification s examined in this report (again, results that are statisticall y significant are shown in red in the table)

. The first two columns of the table indicat e that

NHTSA’s 2011 analy sis of a simultaneous reduction in mass and footprint (i.e. excluding a

control variable for footprint in the regression model) results in a smaller increase in fatalities

than in NHTSA’s 2003 anal ys is, particularl y for lighter car s (a 2.64% increase rather than a

4.39% increase) and light trucks (a 0.52% increase rather than a 2.90% increase). T he third and

fourth columns of the table indicate a similar reduction in additional fatalities for cars when

footprint is held constant (i .e. when a control variable for footprint is included in the regression

model). H owever, holding footprint constant increases the effect of mass reduction slightl y in

light trucks (a 0.52% increase rather than a 0.17% increase in fatalities for lighter li ght trucks,

and a 0.40% reduction rather than a 1.90% reduction in fatalities for the heavier light trucks).

This small increase in light truck risk may be due to NHTSA analy zing crossover utility vehicles

and minivans as a separate vehicle class, rather t han as light trucks, in the 2011 anal ys is.

The last column in Table ES.2 shows that the results of the alternative model specifications

examined in this report are, in all cases, lower than the results of the 2003 NHTSA report, and

often lower than the re sults of the 2010 and 2011 analy ses.

The 2011 NHTSA study, and this report, conclude that the effect of mass reduction on US

fatality r isk is small. T his report indicates that although the effect is sensitive to what variables

and data are included in th e regression anal ys is, in nearl y all cases the effect is less, in some

cases dramaticall y less, than reported in the 2003 NHTSA study

. T his report also finds that the

effect of other control variables, such as vehicle ty pe, specific safety

t echnologies, a nd crash

conditions such as whether the crash occurred at night, in a rural county , or on a high

- speed road,

on risk is much larger than the effect of mass or footprint reduction on risk. Finall y, this report

shows that after accounting for the man y vehic le, driver, and crash variables NHTSA used in itsvii

regression anal ys es, there remains a wide variation in risk by v ehicle make and model, and this

variation is unrelated to vehicle mass.

The results of the NHTSA study and this assessment of it are based on the relationship of vehicle

mass and footprint on risk for recent vehicle designs (model ye ar 2000 to 2007). T hese

relationships may

o r may n ot continue into the future as manufacturers utilize new vehicle

designs and incorporate new technologies, such a s more extensive use of strong lightweight

materials and specific safety technologies.viii

**Table ES.1. Effect of mass and footprint reduction on fatality risk, under alternative regression model specifications**

Variable Case vehicle t ypeNHTSApreferredmodel(fatalitiesperVMT)1.Usingcurrentdistributionoffatalities2.Excluding.massorfootprintvariable3.FatalcrashesperVMT4.Fatalitiesperinducedexposurecrash5.Accountingforvehiclemanufacturer6.ExcludingCYvariables7.Excludingcrasheswithalcohol/drugs8.Excluding.baddrivers9.Includingsports,squad,AWDcarsandfullsizevans

Mass

reduction Cars < 3106 lbs 1.43%* 1.18% 2.64% 1.84%

- 0.24% 1.81% 1.39% 1.66% 2.03% 1.64%

Cars > 3106 lbs 0.48% 0.30% 1.94% 0.86%

- 1.43% 0.67% 0.39% 0.77% 1.04% 0.73%

LTs < 4594 lbs 0.52% 0.38% 0.52% 0.54%

- 1.14% 0.60% 1.22% 0.75% 0.97% 0.34%

LTs > 4594 lbs

- 0.40%

- 0.44%

- 0.41%

- 0.48%

- 0.75%

- 0.21% 0.25%

- 0.40%

- 0.16%

- 0.95%

CUV/ minivan

- 0.47%

- 0.77% 0.52%

- 0.56%

- 0.69% 0.92%

- 0.06%

- 0.28%

- 0.20%

- 0.47%

Footprint

reduction Cars 1.89% 2.22% 2.92% 1.85% 2.20% 1.72% 2.14% 1.77% 1.52% 1.65%

LTs

- 0.02% 0.25% 0.10% 0.19%

- 1.28%

- 0.24%

- 0.38%

- 0.17%

- 0.29% 0.16%

CUV/ minivan 1.73% 2.26% 1.26% 1.82% 1.97%

- 0.09% 1.63% 1.43% 1.26% 1.73%

* Based on NHTSA’s estimation of uncertaint y using a jack

- knife method, only m ass reduction in cars less than 3,106 lbs has a statistically

significant effect on US fatalit y risk.

**Table ES.2. Previous NHTSA results of the ef**  **fect of mass and footprint reduction on fatality risk compared with different**

**scenarios analyzed in this report**

Variable Case vehicle t ype NHTSA

(2003)

excluding

footprint NHTSA

(2011)

excluding

footpr int NHTSA

(2010)

including

footprint NHTSA

(2011)

including

footprint Range of different scenarios analy zed

in this report

Mass

reduction Cars < 3106 lbs 4.39% 2.64% 2.21% 1.43%

- 0.24% to 2.64%

Cars > 3106 lbs 1.98% 1.94% 0.89% 0.48%

- 1.43% to 1.94%

LTs < 4594 lbs 2.90% 0.52% 0.17% 0.52%

- 1.14% to 1.22%

LTs > 4594 lbs 0.48%

- 0.41%

- 1.90%

- 0.40%

- 0.95% to 0.25%

CUV/ minivan 0.52%

- 0.47%

- 0. 77 % to 0. 9 2%

Footprint

reduction Cars 1.89% 1.52% to 2.92%

LTs

- 0.02%

- 1.28% to 0.2 5 %

CUV/ minivan 1.73%

- 0.09% to 2.2 6 %ix **Table of Contents**

Acknowledgements

................................ ................................ ................................ .......................... i

Executive Summary

................................ ................................ ................................ ....................... iii

1. I ntroduction

................................ ................................ ................................ ................................ . 1

2. NHTSA results

................................ ................................ ................................ ............................ 1

2.1. Data and methods

................................ ................................ ................................ ................. 2

3. Multi

- collinearity

between vehicle mass and footprint

................................ ............................ 14

4. Fatalit y risk b y vehicle model

................................ ................................ ................................ ... 22

5. Sensitivity

of NHTSA results to data used and model specification

................................ ........ 37

5.1. Alternative measures of exposure

................................ ................................ ...................... 37

5.2. Vehicle manufacturer

................................ ................................ ................................ ......... 38

5.3. CY variables

................................ ................................ ................................ ....................... 39

5.4. Effect of alcohol/drug use and driving behavior

................................ ................................ 46

5.5. Effect of including sports, police, and all

- wheel drive cars, and fullsize vans

.................. 48

6 . I nfluence of recent trends on the expected effect of mass reduction on risk in 2017

- 2025

..... 49

6.1 Effect of electronic stability control (ESC)

................................ ................................ ........ 49

6.2 Effect of side airbags

................................ ................................ ................................ .......... 50

6.3 Effect of measures to increase light truck compatibility

................................ .................... 57

6.4 Effect of sales shift from SU Vs and other light trucks to CUVs and other car

- based

vehicles

................................ ................................ ................................ ............................... 58

7. Conclusions

................................ ................................ ................................ ............................... 64

**List of Tables and Figures**

Table ES.1. Effect of mass and footprint reduction on fatality risk, under alternative regression

model specifications

................................ ................................ ................................ ............. viii

Table ES.2. Previous NHTSA results of the effect of mass and footprint reduction on fatalit y risk compared with different scenarios anal yz ed in this report

................................ ........... viii

Table 2.1. Control variables used in regression models, by subject vehicle ty

pe

........................... 3

Table 2.2. Bas eline fatal crash involvements, by case vehicle t yp e and crash ty pe

...................... 4

Figure 2.1. Effect of mass and size variables on risk, across all crash t ypes and weighted average effect in each ty

pe of crash, by

vehicle ty

pe

................................ ................................ ........... 6

Figure 2.2. Effect of mass and footprint reduction on risk in cars, b y t ype of crash

...................... 7

Figure 2.3. Effect of mass and footprint reduction on risk in light trucks, by ty pe of crash

.......... 8

Figure 2.4. Effect of mass and footprint reduction on risk in CUVs/minivans, by t ype of crash

... 9

Figure 2.5. Effect of selected control variables on risk, pas senger cars

................................ ....... 10

Figure 2.6. Effect of selected control variables on risk, passenger cars

................................ ....... 10

Figure 2.7. Effect of selected control variables on risk, light trucks

................................ ............ 12

Figure 2.8. Effect of s elected control variables on risk, light trucks

................................ ............ 12

Figure 2.9. Effect of selected control variables on risk, CUVs and minivans

.............................. 13

Figure 2.10. Effect of selected control variables on risk, CUVs and minivans

............................ 13

Figure 3.1. Correlati on between vehicle curb weight and footprint, by vehicle model and three

vehicle t ypes

................................ ................................ ................................ .......................... 14x

Figure 3.2. Correlation between vehicle curb weight and footprint, by veh icle model and seven

vehicle t ypes

................................ ................................ ................................ .......................... 15

Figure 3.3. Effect of reduction in mass or footprint on US fatality risk per VMT, by

vehicle t ype: mass only , footprint onl y, and both

................................ ................................ ...................... 16

Figure 3.4. Effect of reduction in car mass or footprint on US fatality risk per VMT, by

crash

ty pe

................................ ................................ ................................ ................................ ........ 17

Figure 3.5. Effect of reduction in light

- duty truck mass or footprint on US fatality risk per VMT,

by crash t ype

................................ ................................ ................................ ......................... 17

Figure 3.6. Effect of reduction in CUV/minivan mass or footprint on US fatality risk per VMT,

by crash t ype

................................ ................................ ................................ ......................... 18

Figure 3.7. Range in curb weight for the footprint deciles, by vehicle t yp e

................................ . 19

Table 3.1. Number of footprint deciles in which mass reduction increases or decreases fatalit y risk, by vehicle and c rash ty

pe

................................ ................................ .............................. 20

Figure 3.8. Effect of car mass reduction on fatality risk, by footprint decile and crash t yp e

....... 21

Figure 3.9. Effect of light truck mass reduction on fatality risk, by footprint decile and crash t ype

................................ ................................ ................................ ................................ ............... 21

Figure 3.10. Effe ct of CUV/minivan mass reduction on fatality risk, by footprint decile and crash

ty pe

................................ ................................ ................................ ................................ ........ 22

Figure 4.1. Relationship between US fatality risk and curb weight, with vehicles grouped into

100

- lb increments of curb weight, b y vehicle t ype

................................ ............................... 23

Figure 4.2. Relationship between US fatality risk and curb weight, with vehicles groupe d into

100

- lb increments of curb weight, passenger cars

................................ ................................ 23

Figure 4.3. Relationship between US fatality risk and curb weight, with vehicles grouped into

100

- lb in crements of curb weight, light trucks

................................ ................................ ..... 24

Figure 4.4. Relationship between US fatality risk and curb weight, with vehicles grouped into

100

- lb increments of curb w eight, CUVs and minivans

................................ ....................... 24

Figure 4.5. Relationship between US fatality risk in crashes with stationary objects and curb

weight, b y vehicle t ype

................................ ................................ ................................ ......... 25

Figure 4.6. US fatality

risk per VMT and curb weight, b y vehicle model

................................ ... 27

Figure 4. 7. Predicted US fatality risk per VMT after accounting for all driver, crash, and vehicle

variables except mass and footprint, vs. curb weight

................................ ........................... 27

Figure 4.8. Resid

ual US fatality

risk after accounting for all driver, crash, and vehicle variables

except mass and footprint, vs. curb weight

................................ ................................ ........... 28

Figure 4.9. US fatality

risk per VMT vs. curb weight, car models

................................ ............... 29

Figure 4.10. Predicted US fatality risk per VMT after accounting for all driver, crash, and vehicle

variables except mass and f ootprint vs. curb weight, car models

................................ ......... 29

Figure 4.11. Residual US fatality risk per VMT after accounting for all driver, crash, and vehicle

variables except mass an d footprint vs. curb weight, car models

................................ ......... 30

Figure 4.12. US fatality

risk per VMT vs. curb weight, light truck models

................................ . 31

Figure 4.13. Predicted US fatality risk per VMT after accounting for all driver, crash, and vehicle

variables except mass and footprint vs. curb weight, light truck models

............................. 32

Figure 4.14. Residual US fatality risk per VMT after accounting for all driver, crash, and vehicle

variables except mass and footprint vs. curb weight, light truck models

............................. 32

Figure 4.15. US fatality

risk per VMT vs. curb weight, CUV/Minivan models

.......................... 33

Figure 4.16 . Predicted US fatality risk per VMT after accounting for all driver, crash and vehicle

variables except mass and footprint vs. curb weight, CUV/Minivan models

....................... 33xi

Figure 4.17. Residual US fatality risk per VMT after accounting for all driver, crash and vehicle

variables except mass and footprint vs. curb weight, CUV/Minivan models

....................... 34

Table 4.1. Relationship between actual, predicted, and residual fatality risk, and vehicle mass reduction, after accounting for all driver, crash, and vehicle variables except mass and

footprint, by

vehicle t ype and model

................................ ................................ .................... 35

Table 4.2. Relationship between actual, predicted, and residual fatality risk, and vehicle footprint

reduction, after accounting for all driver, crash, and vehicle variables except ma ss and

footprint, by

vehicle t ype and model

................................ ................................ .................... 36

Figure 5.1. Effect of mass and footprint reduction on US fatalities per VMT, fatal crashes per

VMT, and fatalities per induced exposure crash, by vehicle t ype

................................ ........ 37

Figure 5.2. Effect of mass and footprint reduction on US fatality risk per VMT, after controlling

for vehicle manuf

acturer, by

vehicle t ype

................................ ................................ ............. 39

Figure 5.3. Effect of calendar year variables on risk, by vehicle t ype

................................ .......... 40

Figure 5.4. Effect of calendar year variables on light truck risk, by crash t yp e

........................... 40

Figure 5.5. NHTSA 2003 effect of calendar year variables on risk, b y vehicle ty pe

................... 41

Figure 5.6. NHTSA 2003 effect of calendar y ear variables on light truck risk, by crash t ype

..... 42

Figure 5.7. US fatality

risk per VMT, by

vehicle type and calendar ye ar

................................ .... 43

Figure 5.8. Total U S VMT for MY2002 vehicles, by vehicle t ype and calendar year

................. 43

Figure 5.9. Effect of increasing weight or size on risk, including and excluding calendar ye ar

vari ables

................................ ................................ ................................ ................................ 44

Figure 5.10. Effect of selected control variables on car risk, including and excluding calendar

ye ar variables

................................ ................................ ................................ ........................ 45

Figure 5.11. Effect of selected control variables on light truck risk, including and excluding

calendar year variables

................................ ................................ ................................ .......... 45

Figure 5.12. Effect of selected control variables on CUV/minivan risk, including and excluding

calendar year variables

................................ ................................ ................................ .......... 46

Figure 5.13. Effect of mass and footprint reduction on US fatality risk per VMT, after excluding

case vehicles whose driver was suspected of drinking or using drugs, or exhibited bad

driving behavior, b y vehicle type

................................ ................................ .......................... 47

Figure 5.14. Effect of mass and footprint reduction on US fatality risk per VMT, after including

sports, police, and all

- wheel drive cars, and fullsize vans, b y vehicle t yp e

.......................... 48

Figure 6.1. Market penetration of ESC, by vehicle ty

pe and model y ear

................................ ..... 49

Figure 6.2. Effect of ESC on fatality ris k, by vehicle ty

pe and crash t ype

................................ ... 50

Figure 6.3. Market penetration of side impact airbags, b y vehicle t ype and model y ear

............. 51

Figure 6.4. Effect of side airbags on risk to car occupants only , when struck in the side by

another light

- duty

vehicle, by

crash partner vehicle ty

pe

................................ ..................... 52

Figure 6.5. Effect of side airbags on risk to CUV/minivan occupants only , when struck in the

side by another light

- duty

vehicle, by

crash partner vehicle t yp e

................................ ......... 52

Figure 6.6. Effect of side airbags on risk to light truck occupants only , when struck in the side by

another light

- duty

vehicle, by

crash partner vehicle ty

pe

................................ ..................... 54

Figure 6.7. Effect of mass reduction on risk to car occupants onl y in two

- vehicle crashes, by

crash partner vehicle t yp e and crash configuration

................................ ............................... 55

Figure 6.8. Effect of mass reduction on risk to CUV/minivan occupants only in two

- vehicle

crashes, b y crash partner vehicle t ype and crash configuration

................................ ............ 56

Figure 6.9. Effect of mass reduction on risk to light truck occupants onl y in two

- vehicle crashes,

by crash partner vehicle ty

pe and crash configuration

................................ .......................... 56xii

Figure 6.10. Market penetration of compatibility measures in light trucks, by light truck t ype and

model y ear

................................ ................................ ................................ ............................. 57

Figure 6.11 . Effect of compatibility measures on risk imposed by light trucks on other light

- dut y

vehicles, b y crash partner vehicle t ype and crash configuration

................................ .......... 58

Figure 6. 12. US registrations of new vehicles, by vehicle t ype and ye ar

................................ ..... 59

Figure 6.13. Effect of mass and footprint reductions across all vehicle ty pes, and a comparison of

fatality risk by vehicle t ype compared to four

- door sedans

................................ .................. 60

Table 6.1. Average driver characteristics and crash times and locations, by vehicle t ype

........... 61

Table 6 .2. Actual risk per billion VMT, and risk adjusted to the average driver and crash

time/location in a four

- door sedan, all

- wheel

- drive car, CUV, and minivan, by vehicle t ype

................................ ................................ ................................ ................................ ............... 61

Table 6.3. Average annual fatalities and VMT in model y ear 2000 to 2007 light

- duty vehicles

between 2004 and 2008, by vehicle t ype

................................ ................................ .............. 62

Table 6.4. Es timated change in annual fatalities from four scenarios of shifts among vehicle

ty pes

................................ ................................ ................................ ................................ ...... 63

Table 7.1. Effect of mass and footprint reduction on fatality risk, unde r alternative regression

model specifications

................................ ................................ ................................ .............. 68

Table 7.2. Previous NHTSA results of the effect of mass and footprint reduction on fatality risk

compared with d ifferent scenarios anal yz ed in this report

................................ ................... 681

**1. Introduction**

NHTSA recentl y completed a logistic regression anal ys is updating its 2003 and 2010 studies of

the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT). The

new study

u pdates the previous anal ys es in several way s: updated FARS data for 20 02 to 20 0 8

involving MY00 to MY07 vehicles are used; induced exposure data from police reported crashes

in several additional states are added; a new vehicle category f or car

- based crossover utility

vehicles (CUVs) and minivans is created; crashes with other light

- duty v ehicles are divided into

two groups based on the crash partner vehicle’s weight, and a category f or all other fatal crashes

is added; and new control variables for new safety t echnologies and designs, such as electronic

stabi lity c ontrols (ESC), side airbags, and methods to meet voluntary agreement to improve light

truck compatibility

with cars, are included.

This report uses the updated databases NHTSA has created to replicate their findings on the

relationship between vehic le weight, size (actually f ootprint, or vehicle wheelbase times track

width) , and US fatalit y risk per vehicle miles traveled (VMT) , for model year 2000 to 200 7 light

duty vehicles involved in fatal crashes between 2002 and 2008

. In addition, we examine t he data

in slightly

different way s

, to get a deeper understanding of the relationship between reductions in

vehicle mass and footprint, and overall safet y.

Section 2 of this report replicates NHTSA’s results, and analy zes the control variables NHTSA

includes in their preferred regression models. Section 3 examines in more detail the multi

collinearity

b etween vehicle mass and footprint, and the methods NHTSA took to address that

multi

- collinearity

, I n Section 4 we examine the relationship between vehicle mass and risk by

vehicle model, before and after accounting for differences in driver characteristics, crash

locations, and other vehicle attributes by v ehicle model. I n Section 5 we test alternative

specifications of the regression models developed by NHTSA, in order to examine the sensitivity

of their results to the assumptions they u sed and different model specifications. Finally i n

Section 6 we examine the influence of recent trends in vehicle market share on the expe cted

effect of mass reduction on risk in 2017 to 2025.

**2. NHTSA results** 

For its analy sis of the effect of changes in vehicle mass on US fatality r isk per VMT, NHTSA

used information on all US traffic fatalities, from the Fatalit y Anal ys is Reporting Sy ste m (FARS). F or the measure of exposure, NHTSA used a subset of non

- culpable vehicles involved

in two

- vehicle crashes from police

- repo rted crash data from 13 states; NHTSA refers to this

subset of vehicles as “induced exposure” cases. The induced exposure cases provide information

on driver and crash characteristics for vehicles that are not involved in fatal crashes, as in the

FARS data. N HTSA developed weighting factors to scale the induced exposure vehicles up to

national level vehicle registrations. N HTSA then multiplied the vehicle registration

- years by

annual vehicle miles traveled (VMT) factors it developed by v ehicle ty

pe and age, from

odometer data provided by RL Polk. For more details on NHTSA’s data and methodology , refer

to NHTSA 2011.2

In thi s section we replicate the logistic regression results NHTSA obtained using the database

they constructed. We also test the effect certain changes in the regression model specifications

have on the coefficients for the independent variables of interest, v ehicle mass and footprint.

**2.1. Data and methods** 

For this new anal ys is NHTSA used FARS data on fatal crashes, and police

- reported crash data

from 13 states, for MY00 to MY07 light

- duty v ehicles between 2002 and 2008. N HTSA used a

subset of nonculpable vehicles in two

- vehicle crashes as a measure of indu ced exposure; these

records provide distributions of on

- road vehicles by v ehicle year, make, and model, driver age

and gender, and crash time and location (day v s. night, rural vs. urban counties, and high

- speed

roads ). Each induced exposure record is the n given a registered vehicle weighting factor, so that

each induced exposure record represents a number of national vehicle registrations; the sum of

the weighting factors equals the number of vehicles registered in the country . Each record is also

given a VMT weighting factor, based on vehicle year, make/model, and age, using odometer data

provided b y R.L. Polk. The data can be used to estimate US fatality risk per registered vehicle or

vehicle miles traveled (VMT).

NHTSA compiled a database of the fo llowing vehicle attributes, by m odel year, make and

model: curb weight and footprint (wheelbase times track width), as well as t he presence of all

wheel drive and automated braking sy stems

. N HTSA added several new variables for new

safet y technologies and designs: electronic stability c ontrols (ESC), four ty

pes of side airbags,

and two methods to comply with the voluntary manufacturer agreement to better align light truck

bumpers to make them more compatible with other ty pes of vehicles.

To reflect change s in the vehicle mix since the 2003 study

, NHTSA added a third vehicle

category

, car

- based crossover utility vehicles (CUVs) and minivans

. It also added two new crash

ty pes, for a total of nine: crashes with other light

- duty vehicles are divided into two groups based

on the crash partner vehicle’s weight, and all other fatal crashes (involving more than two

vehicles, etc.). T he analy sis involves running a logistic regression model with total crash

fatalities as the dependent variable for each of the nine crash ty pes and the three vehicle ty pes,

for a total of 27 regressions. B ecause all fatalities in the crash are used, the risks reflect societal

risk, rather than just the risk to the occupants of the case vehicle. T he induced exposure cases

are weighted by

t he number of vehicle registrations and the annual mileage, so that the models

are estimating the effect of changes in the control variables on US fatalities per vehicle mile

traveled (VMT).

Table 2.1 shows the control variables NHTSA used in its reg ression models, for ea ch of the case

vehicle ty pes. For cars and trucks, NHTSA uses two variables (UNDRWT00, OVERWT00) for

vehicle weight, allowing the effect of weight on risk to vary f or lighter and heavier cars and

trucks. The determination of the two weight classes is based on the average weight for each

vehicle ty

pe: 3,106 lbs for cars and 4,594 lbs for light

- dut y trucks. Because there are fewer

CUVs and minivans in the database, NHTSA uses a single variable, LBS100, for CUV / minivan

weight. As in t he 2003 and 2010 analy ses, eight variables for driver age and gender are used. I n

the 2003 analy sis

, NHTSA exclude d the driver airbag control variable s in the regressions for

rollovers and crashes with pedestrians. In the 2011 analy sis, NHTSA includes th e control3

variable ROLLCURT airbags only in the regression models for rollover crashes involving cars or

CUVs/minivans; regression models of pedestrian crashes do not include any c ontrol variables for

airbags; and the control variables for CURTAIN, COMBO, and TORSO airbags are included in

regression models for all other crashes involving cars or CUVs/minivans. No airbag variables

were included in the regression models for light trucks.

**Table 2.1**  **. Control variables used in regression models, by subject vehicle type** 

Control variable Cars LTVs CUVs/minivans

UND RWT 00 C C

OVERWT 00 C C

LBS100 C

FOOTPRINT C C C

TWODOOR D

SUV D

HD_PKP D

BLOCKER1 D

BLOCKER2 D

MINIVAN D

ROLLCURT * C C

CURTAIN * C C

COMBO * C C

TORSO * C C

ABS C C

ESC C C C

AWD C C

DRVMALE C C C

M14_30 C C C

M30_50 C C C

M50_70 C C C

M70_96 C C C

F14_30 C C C

F30_50 C C C

F50_70 C C C

F70_96 C C C

NITE D D D

RURAL D D D

SPDLIM55 D D D

HIFAT_ST D D D

VEHAGE C C C

BRANDNEW D D D

CY2002 D D D

CY2003 D D D

CY2004 D D D

CY2005 D D D

CY2007 D D D

CY2008 D D D

C: continuous variable

D: dummy variable, coded as either 1 or 0

* The control variable for ROLLCURT airbags is only used in regression mo dels of rollover crashes involving cars

or CUVs/minivans; regression mo dels of pedestrian crashes do not include any control variables for airbags; the

control variables for CURTAIN, COMBO, and TORSO airbags are included in regression mo dels for all other

crashes involving cars or CUVs/minivans

.4

R ather than reporting coefficients for the variables of interest (curb weight and footprint) from a single regression model across all crash ty pes, NHTSA reports a weighted average of the

coefficients from the nine regression models run for each of the nine crash ty pes. NHTSA uses a

“baseline” distribution of fatalities across the crash ty pes, to represent the expected distribution

of fatalities in the 2017 to 2025 timeframe of the new CAFE and GHG emission standards.

Similar to the 2003 study, NHTSA deriv e s the baseline fatalities from MY04

- 09 vehicles in

crashes between 2004 and 2008

. N HTSA then adjusts this baseline distribution downward to

account for the assumption that all vehicles in the 2017

- 2025 timeframe will have ESC installed.

The assumptions used for this adjustment are taken from a NHTSA anal ys is that found that ESC

reduces fatal rollovers by 5 6% in cars and 74% in light trucks; fixed

- object impacts by 4 7% in

cars and 45% in light trucks; and other non

- pedestrian crashes by 8 % in both cars a nd light

trucks. 1 These assumptions treat crossover SUVs and minivans as light trucks rather than cars.

This “post

- ESC” distribution of fatalities by c rash ty pe is then multiplied by t he regression

coefficients for each crash ty pe to create the weighted average effect of each control variable on

risk

. Table 2. 2 shows the baseline distribution of fatalities, by case vehicle ty pe and crash ty

pe,

which are used to create the overall coefficient estimates weighted by t he results from the

regressions for each crash ty pe.

**Table 2.**  **2. Baseline fatal crash involvements, by case vehicle type and crash type**

Crash type Baseline fatal crash

involvements:

MY04

- 07 vehicles in CY04

- 08 Adjusted for full penetration

of ESC Percent difference

Cars LTVs CUVs/

minivans Cars LTVs CUVs/

minivans Cars LTVs CUVs/

minivans

1: Rollovers 937 1,626 277 453 507 100

- 52%

- 69%

- 64%

2: w /object 3,496 2,090 571 2,076 1,253 373

- 41%

- 40%

- 35%

3: Ped etc. 2,259 2,192 812 2,259 2,192 812 0% 0% 0%

4: w /HDT 1,248 838 316 1,158 779 297

- 7%

- 7%

- 6%

5: w /lgt car 1,474 1,890 525 1,375 1,761 494

- 7%

- 7%

- 6%

6: w /hvy car 1,441 1,364 477 1,341 1,272 449

- 7%

- 7%

- 6%

7: w /lgt LT 1,385 1,220 366 1,286 1,135 345

- 7%

- 7%

- 6%

8: w /hvy LT 1,418 890 330 1,31 5 829 311

- 7%

- 7%

- 6%

9: Other 4,431 3,663 1,469 4,134 3,427 1,389

- 7%

- 6%

- 5%

Total 18,089 15,773 5,143 15, 397 13,155 4,570

- 15%

- 17%

- 11%

All of the regression coefficients presented in the NHTSA 2011 report are the direct output from

the SAS LO GI ST procedure ( with the exception of those for the mass and fo otprint variables

UNDRWT00, OVERWT 00, LBS100, and FOOTPRNT , which NHTSA often multiplies by

- 1 so

that they r eflect the effect of a decrease in vehicle mass or footprint ; we use the same convention

throughout this report)

. The output from the SAS LO GI ST p rocedure reflect the percent change

in the log

- odds of fatality per billion VMT for a one

- unit increase in the explanatory variable. In

order to obtain the percent change in the probability of fatality

, the SAS outputs need to be

converted from log

- space to linear space

, and from odds to probabilities. We use the equation e x

– 1, where x is the logistic regression coefficient from the SAS output, to make this conversion.

This conversion has no effect on the output regression coefficients when the change in the log

-

1 Sivinski R. (2011). Update of NHTSA’s 2007 Evaluation of the Effectiveness of Light Vehicle Electronic Stability

Control (ESC) in Crash Prevention

, NHTSA Technical Report No. DOT HS 811 486. Washington, DC: National

Highway Traffic Safety Administration. http://www

- nrd.nhtsa.dot.gov/Pubs/811486.pdf

.5

odds of fatality

i s small ; however it substantiall y increases the percent change for explanatory

variables that have a large effect on the log

- odds of fatalit y (such as the crash location variables).

For example, the fatality risk from a rollov er crash involving a car has a 2.20 times higher log

odds of fatality

if it occurs in a rural county ; after conversion, this crash has a 802 percent higher

probability of fatalit y if it occurs in a rural county ( EXP(2.20)

- 1 = 8.02). Unless noted

otherwise, the 95% confidence intervals shown in this report are calculated the same way

, using

the standard error of the log

- odds output by the SAS L OGI ST procedure.

Figure 2.1 presents the regression coefficients from the NHTSA report (in light blue); the

coefficients for each of the 9 crash ty pes are weighted by

t he distribution of 2016 baseline fatal

crash involvements, after adjustment for full ESC penetration, from Table 2.2. (The coefficients

are slightly

d ifferent from those provide d in the 2011 NHTSA report, perhaps because of

rounding errors and our reporting of percent changes in risk as probabilities rather than as log

odds. ) The figure indicates that mass reduction increases societal 2 fatality risk by about one

percent for cars and lighter

- than

- average light trucks, while mass reduction leads to a slight

reduction in fatality

r isk for the heavier light trucks and CUV/minivans

. The 95% confidence

intervals in the figure indicate that the changes in risk for lighter cars

, and bot h categories of

light

- duty t rucks

, are statistically

s ignificant. T he confidence intervals shown in the figure, and

all figures in this report, represent the weighted average standard error from the SAS output,

times 1.96. NHTSA does not report these con fidence intervals in its 2011 report; rather it uses a jack

- knife technique to estimate the range in uncertaint y around the point estimates. T he

resulting confidence intervals are larger than those shown in this report. As a result, NHTSA’s

2011 report i ndicates that only the 1.43% increase in risk from mass reduction for the lighter cars

is statistically

significant.

2 All of the fatality risks reported in th e 2011 NHTSA report are societal risk, that is fatalities to all vehicle

occupants and non

- occupants involved in the crash are included. Unless specified otherwise (i.e. in Section 6, when

we examine the effect of side impact airbags on risk to car occupa nts, and steps manufacturers have taken to

im prove light truck compatibility o n the risk light trucks impose on other vehicle occupants, in tw o

- vehicle crashes),

all risks in this report also are societal risk.6

**Figure 2.**  **1 . Effect of**  **ma ss**  **and size variables on risk**  **, across all crash types and weighted**

**average effect in each type of crash**

**, by vehicl**  **e type**

Figure 2.1 also shows that reduction in footprint consistently i ncreases risk for all three ty pes of

vehicles, and has a larger effect on risk than reduction in mass for cars and CUVs/minivans. A

1

- square foot reduction in footprint increases fatality r isk in cars and CUVs/minivans by close to

2 percent, but has no effect on risk in light trucks.

Results from a single regression analy sis across all crash ty pes are also shown in Figure 2.1 (in

dark turquoise )

, as are the results of the nine regression models by c rash ty pe weighted by

t he

current distribution of fatalities (light turquoise)

, not the distribution NHTSA assumes for 2017

-

2025 based on full ESC penet ration. F ull penetration of ESC in the on

- road fleet slightl y

increases the safet y penalty f rom mass reduction, as the NHTSA weighted values (in light blue)

are all higher than the unweighted values (in light turquoise ). O n the other hand, full ESC

penet ration reduces the safet y penalty from a reduction in footprint, for all vehicle t ypes.

Figures 2. 2 through 2. 4 show the effect of changes in mass or footprint on risk, by ty pe of crash.

For cars, mass reduction increases risk in all crash ty pes except r ollovers and crashes with

stationary o bjects

, as shown in Figure 2. 2. A possible explanation for why mass reduction

reduces risk in rollovers is that once a vehicle rolls over, a lighter vehicle applies less force on its

roof than a heavier vehicle. It i s less clear wh y mass reduction would reduce risk in crashes with

stationary o bjects. Because NHTSA assumes that by 2 017 ESC will have eliminated man y of

the fatalities in rollovers and crashes with stationary o bjects, and these are the onl y ty pes of

cras hes in which mass reduction reduces risk, NHTSA’s weighted regression results for 2017

-

2025 show a larger increase in overall risk than the results based on recent crashes (in Figure

2.1). On the other hand, footprint reduction result s in the largest risk increases in rollovers and1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.14%0.31%0.41%-0.48%-0.84%2.30%0.27%2.29%1.18%0.30%0.38%-0.44%-0.77%2.22%0.25%2.26%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT) U S f a t a l i t i e s p e r VMT

, w t d a vg

U S f a t a l i t i e s p e r VMT

, a l l cra sh e s

U S f a t a l i t i e s p e r VMT

, w t d b y cu rre n t cra sh e s

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f o o t p ri n t7

crashes with stationary

o bjects (Figure 2.2), so removing fatalities in these ty pes of crashes by

2017 will reduce the detrimental effects of footprint reduction (as shown in the light blue

columns in Figure 2.1).

Mass reductio n in the lighter cars has the biggest increase in risk (5.80%) in crashes with a heavy

light truck. F or heavier cars, mass reduction results in generall y smaller increases in risk for

most ty pes of crashes. A r eduction in car footprint increases risk in all ty pes of crashes,

including rollovers and crashes with stationary o bjects. I n fact, footprint reduction leads to the

largest increases in risk in these two crash ty pes (7.79% and 3.99%), followed by c rashes with a ligh t er light

- dut y truck (3.88%) and with a heavy

- duty truck (2.96%).

Figure 2.3 shows the effect of mass and footprint reductions on risk in light trucks. I n general,

the effects on risk are smaller for light trucks than for cars, and there are more cases in which

mass reduction reduces risk, although the effects are often small and not statistically

- significant.

Mass reduction leads to a statistically

- significant reduction in risk in lighter truck crashes with

objects, and heavier truck rollovers; but (statistically

- insignificant) incre ases in risk in lighter

truck rollovers and heaver truck crashes with objects. A s with light cars, the biggest effect of

weight reduction in lighter trucks is in crashes with a heavier light truck, with a 4.37% increase

in risk. A r eduction in light truc k footprint tends to increase risk, although the increases are

small and often not statistically

- significant. H owever, contrary t o cars, footprint reduction in

light trucks significantl y reduces fatalit y risk in crashes with pedestrians and cy clists, and with

heavier light trucks.

**Figure 2.**  **2 . Effect of**  **ma ss**  **and footprint reduction**  **on risk in cars, by type of crash** -2.18%-0.68%1.93%2.12%0.68%0.37%1.09%5.80%1.83%-2.93%-1.38%-0.13%0.33%0.22%1.55%0.52%2.29%1.13%7.79%3.99%0.91%2.96%0.23%0.52%3.88%1.76%1.15%

-8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

C a rs < 3

, 1 0 6 C a rs > 3

, 1 0 6 Al l ca rs8

**Figure 2.**  **3 . Effect of**  **ma ss**  **and footprint**  **reduction on risk**  **in light trucks**  **, by type of crash**

The effect of reductions in mass and footprint on risk in crashes involving CUVs and minivans

are shown in Figure 2.4. T he effects from mass reduction tend to be larger in CUVs and

minivans than in cars or light trucks, with a greater than 7% reduction in risk in rollovers and a

3.77% reduction in risk in crashes with objects. Mass reduction in CUVs/minivans has the most

detrimental effect on risk in crashes with a light light

- dut y truck , a 3.69 % increase. The effect of

reductions in footprint in CUVs an d minivans is similar to that for cars, with a larger,

statistically

- significant increase in risk in rollovers (10.48%) and crashes with objects (7.34%).

As with cars, NHTSA’s assumption of fewer fatalities in rollovers and crashes with stationary

objects due to full adoption of ESC by 2017 results in an increase in the effect of mass reduction,

but a decrease in the effect of footprint reduction, on risk in CUVs and minivans (light blue

columns in Figure 2.1).0.78%-1.34%1.05%1.66%-0.11%-0.72%-0.62%4.37%0.69%-1.37%0.69%-0.11%0.23%-0.96%-1.44%-1.03%0.48%-0.16%1.18%2.03%-1.20%0.79%-0.17%0.35%1.05%-1.64%-0.38%

-8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

L T s < 4

, 5 9 4 L T s > 4

, 5 9 4 Al l L T s9

**Figure 2.**  **4 . Effect of**  **ma ss**  **and footprint**  **red uction**  **on risk in**  **CUVs/minivans , by type of** 

**crash**

Figures 2. 5 and 2. 6 compare the effect of mass and footprint reduction on risk with that of the

other control variables, for cars. In t erms of other car characteristics, Figure 2. 5 indicates that

two

- door cars increase US fatality r isk per VMT by 8 %

, while TORSO side airbags, automated

braking sy stems (ABS), an d electronic stability c ontrol (ESC), reduce risk by about 10%

. T he

driver age variables tend to increase risk, with young male and elderly d rivers (male and female)

increasing US fatality

r isk per VMT from 5% to 8%. C ar age causes a small increase in risk,

while a brand new car increases ris k by 1 0%, presumably because the driver is unfamiliar with a

new car’s controls, hand ling, and/or braking capabilities

. T he calendar ye ar variables have a decreasing effect on risk over time, declining from a 6% increase in risk in 2002 to a 1 3 %

reduction in risk in 2008

. The calendar ye ar variables are examined in more detail in S ection

5.3

.-7.17%-3.77%-1.65%1.79%-0.17%1.56%3.69%-1.04%-0.50%10.48%7.34%0.41%4.43%-0.74%-2.17%-4.13%3.66%2.75%

-8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t10

**Figure 2.**  **5 . Effect of**  **selected control variables on risk**  **, passenger cars**

**Figure 2.**  **6 . Effect of**  **selected control variables on risk**  **, passenger cars**

Note that the three vehicle variables of interest, UNDRWT, OVERWT, and FOOTPRINT, all

have a much lower effect on risk than almost all of the control variables in Figure 2.5. F or

instance, a 100

- lb reduction in curb weight for an underweight car is expect ed to increase risk by -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%UNDERWTOVERWTFOOTPRINTTWODOORCURTAINCOMBOTORSOABSESCM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percentchangeinrisk(fatalityprobabilityper1010VMT)

0% 50% 100% 150% 200% 250% 300% 350% 400%

U N D ER W T O VER W T F O O T PR I N T D R VMAL E N I T E R U R AL SPD L I M5 5 H I F A T

_ STPercentchangeinrisk(fatalityprobabilityper1010VMT)11

1.4%, while installing ESC would reduce risk by 11.4%. T herefore, the mass of a lighter car

could be reduced b y 800 lbs while adding ESC, without increasing fatalit y risk.

The control variables in Figure 2.6 have a much bigger ef fect on risk than the mass or footprint

reduction variables, or the control variables presented in Figure 2.5. M ale drivers increase US

fatality r isk per VMT by

o ver 25%, while the three control variables for crash environment,

NI TE, RURAL, and SPDLIM55, more than triple fatality r isk per VMT. A c rash occurring in a high

- fatality

s tate carries a 25% higher fatality r isk per VMT than a crash in other states. S ince

driving on a roadway

w ith a posted speed limit greater than 55 miles per hour increases risk by

over 400%, a 0.35% increase in driving on high

- speed roads would result in the same increase in

fatalities as a 100

- lb mass reduction in all cars (1.4% / 400% = 0.35%).

Figures 2. 7 and 2. 8 present the effect of the control variables on fatality r isk in crashes involving

light trucks. S UVs

, and to a lesser extent heavy

- duty p ickups, have a higher fatality r isk than

regular pickups. NHTSA includes two variables identify ing approaches to comply w ith

voluntary m easures to reduce light truck aggressivity towards cars : BLOCKER1, vertical

alignment of bumpers, and BLOCKER2, employment of an additional blocker beam behind the

bumper. T hese two approaches have about the same effect on reducing risk. A s with cars, risk

is higher with young males and elderly drivers. B rand new light trucks have a lower risk than

brand new cars, which is surprising as one would think unfamiliarity w ith the handling of a light

truck would increase the chance of it rolling over. As with cars, the calendar ye ar variables have

a decreasing effect on risk over time, but the decline is much greater, from 22% increase in risk

in 2002 to a 1 6 % decrease in risk in 2008. The calendar ye ar variables are discussed in more depth in Section 5.3.

ESC reduces risk by 18 % in light trucks (Fi gure 2. 8) as opposed t o only 1 1 % in cars (Figure

2. 5), while male drivers increase risk in trucks only 1 8% but 40 % in cars (Figure 2. 6). All

- wheel

drive (AWD) redu ces risk in pickups by n earl y 15 %. A s in cars, driving at night, in rural areas,

and on roa dway s with high speed limits more than triple the risk in trucks, while driving in high

fatality states has a similar increase in risk as in cars (25%).

Figure 2. 9 indicates that minivans have a slightl y lower fatality r isk than CUVs. F or CUVs and

miniv ans, combination airbags result in a larger reduction in risk than side curtain airbags, while

torso airbags result in a slight , non

- significant increase in risk. As there is no logical explanation

of wh y torso air bags could increase risk, this result in dicates the lack of precision in the

regression coefficients for some of the safet y technologies, especially i n regressions limited to

relativel y small subsets of the data. ABS has a bigger reduction in risk (15%) than ESC (4%) for

CUVs and minivans, oppo site to the results seen for cars in Figure 2. 5. I n terms of driver

characteristics, young and elderly d rivers have the highest increase in ri sk. The coefficients on

the c alendar ye ar control variables are smaller than those for light trucks (with the exc eption of

CY2008); these are discussed in more detail in Section 5.3.

The control variables for CUVs and minivans presented in Figure 2. 10 are similar to those for

cars (Figure 2. 6) and light trucks (Figure 2. 8). For all vehicle ty pes, many

o f the contro l

variables shown in Figures 2.3 through 2.8 have a much greater effect on fatality r isk than

reductions in vehicle mass or footprint.12

**Figure 2.**  **7 . Effect of**  **selected control variables on risk**  **, light trucks**

**Figure 2.**  **8 . Effect of**  **selected control variables on risk**  **, light trucks** -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%UNDERWTOVERWTFOOTPRINTSUVHD_PKPBLOCKER1BLOCKER2M14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percentchangeinrisk(fatalityprobabilityper1010VMT)

-2 5 % 25% 75% 125% 175% 225% 275% 325% 375% 425%

U N D ER W T O VER W T F O O T PR I N T ESC AW D D R VMAL E N I T E R U R AL SPD L I M5 5 H I F A T

_ STPercentchangeinrisk(fatalityprobabilityper1010VMT)13

**Figure 2.**  **9 . Effect of**  **selected control variables on risk**  **, CUVs and minivans**

**Figure 2.**  **10 . Effect of**  **selected control variables on risk**  **, CUVs and minivans** -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%CURBWTFOOTPRINTMINIVANCURTAINCOMBOTORSOABSESCM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percentchangeinrisk(fatalityprobabilityper1010VMT)

-2 5 % 25% 75% 125% 175% 225% 275% 325% 375% 425%

C U R BW T F O O T PR I N T AW D D R VMAL E N I T E R U R AL SPD L I M5 5 H I F A T

_ STPercentchangeinrisk(fatalityprobabilityper1010VMT)14

**3. Multi** 

**- collinearity between vehicle**  **mass and footprint** 

In the 2003 anal ys is NHTSA resisted including vehicle mass and size (in that case, wheelbase

and track width) in the same regression model, because the two variables were strongl y

correlated with each other. Including two or more highl y

- correlated variables i n the same regression model can lead to biased or incorrect results. F igure 3.1 shows the correlation

between curb weight and footprint by v ehicl e model in the NHTSA database; onl y the most

popular 275 models, with at least 10 billion VMT or 100 fatalities, are included in the figure

(10 6 car models, 131 light truck models, and 3 8 CUV/minivan models)

. The figure indicates

that curb weight and footprint are more highly c orrelated for cars (R 2 of 0.87) than for light

trucks (R 2 of 0 .65) or CUVs / minivans (R 2 of 0 .60)

. Figure 3.2 shows the same data as Figure

2. 12, but uses six vehicle ty pes. H ere the correlation ranges from over 0. 80 for 4

- door cars, pickups, and SUVs, to less than 0.70 for CUVs and 2

- door cars, to only 0 .2 6 for minivans. The

correlation of 0.6 5 for all light trucks (pickups and SUVs) combined in Figure 3.1 is improved

when these two ty

pes of trucks are anal yz ed separatel y in Figure 3.2 : 0. 86 for pickups and 0.8 1

for SUVs in Figure 3.2

. On the other hand, separating CUVs from minivans improves the

correlation between curb weight and footprint for CUVs (0.6 6 ) but not for minivans (0.2 6 ). T he

correlation is so poor for minivans in part because of the Kia Sedona, which has a much higher

weight (4,730 lbs) for its footprint (51.3 sq ft) than other minivans; removing this model

improves the correlation for minivans to 0. 78

.

**Figure 3.1** 

**. Correlation between vehicle curb weight and footprint, by vehicle model and** 

**three vehicle types**

R 2 = 0

. 8 7 R 2 = 0

. 6 5

R 2 = 0

. 6 0

34 38 42 46 50 54 58 62 66 70 74 78

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600Footprint(wheelbasextrackwidth,sqft)

C u rb w e i g h t (l b s) C a rs

L i g h t t ru cks

C U Vs a n d Mi n i va n s15

**Figure 3.2** 

**. Correlation between vehicle curb**  **weight and footprint, by vehicle model and**

**seven vehicle types**

Figure 3.3 compares NHTSA’s preferred model, in light blue from Figure 2. 1

, with two

alternative model specifications to test the sensitivity o f the results from the preferred model.

The fi rst sensitivity

, in dark purple, includes the weight variables in the regression model but

excludes the footprint variable ; this model tests the effect of mass reduction while allowing

footprint to vary

w ith vehicle mass

. T his sensitivity increases the ri sk from a 100

- lb mass

reduction in cars (from 1. 43 % to 2. 6 4 % for lighter cars, and from 0. 48 % to 1.94 % for heavier

cars) and CUVs/minivans ( from a 0. 47 % decrease in risk to a 0. 52 % increase in risk ; however,

there is no change in fatality r isk in light

- dut y trucks. These results are quite similar to those

reported by

N HTSA in Section 3.7 of the 2011 report ; the slight differences are likely d ue to

rounding errors, as well as our reporting of percent changes in risk as probabilities rather than as log

- odds, as described above

. R 2 = 0

. 6 5

R 2 = 0

. 8 9

R 2 = 0

. 8 6

R 2 = 0

. 8 1

R 2 = 0

. 6 6

R 2 = 0

. 2 6

34 38 42 46 50 54 58 62 66 70 74 78

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600Footprint(wheelbasextrackwidth,sqft)

C u rb w e i g h t (l b s) 2 -d r ca rs

4 -d r ca rs

Pi cku p s

SU Vs

C U Vs

Mi n i va n s16

**Figure 3.3**  **. Effect of reduction in**  **mass or**  **footprint on**  **US fatality risk**  **per VMT, by vehicle** 

**type: mass**  **only, footprint only, and both**

The second sensitivity

k eeps footprint in the regression model, but removes mass, and is shown

in light purple in Figure 3.3. A llowing vehicle mass to be reduced with footprint increases the

effect of a reduction in footprint on car risk, from a 1.89% increase to a 2.92% increase, but

decreases the effect of footprint reduction on CUV/minivan risk, from a 1.73% increase to a

1.26% increase. A llowing light truck mass to be reduced along with footprint does not change

the effect of a reduction in footprint on risk in light trucks. F ig ure 3.3 suggests that including

both mass and footprint reductions in the same regression model somewhat reduces the effect of

both variables in cars and CUVs/minivans, but has no effect on the variables for light trucks.

Figures 3.4 through 3.6 show the effect of these two sensitivities by c rash ty pe; in contrast to

Fi gures 2.2 through 2.4, the figures indicate that including onl y mass or footprint in the

regression models reduces or eliminates an y reduction in risk in a particular crash ty pe, particularl y for mass reductions in car rollover and object crashes, and for mass or footprint

reductions in CUV/minivan crashes.1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%2.64%1.94%0.52%-0.41%0.52%2.92%0.10%1.26%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g f o o t p ri n t

Excl u d i n g cu rb w t

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t17

**Figure 3.4**  **. Effect of reduction in car**  **mass or**  **footprint on**  **US fatality risk**  **per VMT**

**, by** 

**crash type** 

**Figure 3.**  **5 . Effect of**  **reduction in light**

**- duty truck**  **mass or**  **footprint on**  **US fatality risk**  **per**

**VMT**

**, by**  **crash type** -8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g f o o t p ri n t

Excl u d i n g cu rb w t

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

C a rs < 3

, 1 0 6 C a rs > 3

, 1 0 6 Al l ca rs

-8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f

e rre d mo d e l

Excl u d i n g f

o o t p ri n t

Excl u d i n g cu rb w t

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

L T s < 4

, 5 9 4 L T s > 4

, 5 9 4 Al l L T s18

**Figure 3.**  **6 . Effect of**  **reduction in CUV/minivan mass**  **or footprint**  **on US fatality**  **risk per** 

**VMT, by crash type**

In its 2011 analy sis NHTSA examined the relationship between curb weight and fatality r isk for

deciles of vehicles wi th roughl y the same footprint. Figure 3. 7 shows the range in curb weights

for the footprint deciles NHTSA used for the three vehicle ty pes. The figure shows that there is

a large degree of overlap in the curb weights of vehicles with roughl y the same foo tprint; this is

an indication that the correlation between curb weight and footprint may b e strong but is not

absolute.

NHTSA ran a new regression model with all of the control variables except footprint, for each

crash and vehicle ty

pe, and footprint dec ile, a total of 270 regression models; the two mass

variables, UNDERWT00 and OVERWT00, originall y used for cars and light trucks were replaced by

a s ingle mass variable LBS100. N HTSA listed the number of the regression models

for the ten footprint deciles in which the regression coefficient on vehicle mass was positive; that

is, where a mass reduction would be harmful and increase fatality risk. -8 % -6 % -4 % -2 % 0% 2% 4% 6% 8% 10% 12%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g f

o o t p ri n t

Excl u d i n g cu rb w t

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t19

**Figure 3.**  **7**

**. Range in curb weight for the footprint deciles, by vehicle type** 

Table 3.1 replicates this anal ys is and results, and includes the number of footprint deciles in

which the coefficient on vehicle mass is statistically s ignificant

, for each combination of vehicle

and crash ty

pe

. T here are four columns for each vehicle ty p e in Table 3.1; the first two indicate

the number of footprint deciles in which a reduction in vehicle mass increases risk, and the

number that are statistically s ig nificant. Red print indicates cases in which three or more footprint deciles have signific ant coefficients. The second two columns for each vehicle ty pe

indicate the number of footprint deciles in which a reduction in vehicle mass reduces risk, and

the number that are statistically sig nificant. For example, in car rollover crashes, mass reduc tion

increases risk in only

t hree footprint deciles, and none of those increases is statistically

significant. H owever, mass reduction reduces risk in seven footprint deciles in car rollover

crashes, and four of those seven are statistically s ig nificant. Table 3.1 indicates that mass

reduction increases risk in only a s mall number of car rollover crashes and crashes with a stationary o bject. O n the other hand, mass reduction reduces risk in light truck crashes with cars

for seven of the footprint deciles

, and several of these reductions (four in crashes with light cars,

three in crashes with heavy c ars) are statistically

s ig nificant. F or CUVs and minivan crashes

with heavy

c ars, mass reduction increases risk in eight deciles, with three of those increase s being statisticall y significant.

1600 2600 3600 4600 5600 6600 7600Footprintdecile

C u rb w e i g h t (l b s) C a rs

L i g h t t ru cks

C U Vs/ mi n i va n s20

**Table 3.1. Number of footprint deciles in which mass reduction increases or decreases**

**fatality risk, by vehicle and crash type**

Crash type Cars Light trucks CUVs/MinivansNumberofdecileswithincreasingriskNumberofdecileswithestimatesthatarestatisticallysignificantNumberofdecileswithdecreasingriskNumberofdecileswithestimatesthatarestatisticallysignificantNumberofdecileswithincreasingriskNumberofdecileswithestimatesthatarestatisticallysignificantNumberofdecileswithdecreasingriskNumberofdecileswithestimatesthatarestatisticallysignificantNumberofdecileswithincreasingriskNumberofdecileswithestimatesthatarestatisticallysignificantNumberofdecileswithdecreasingriskNumberofdecileswithestimatesthatarestatisticallysignificant

1: Rollovers 3 0 7 4 6 3 4 2 4 0 6 2

2: w /object 4 0 6 3 5 0 5 0 6 0 4 1

3: w /ped etc. 6 2 4 2 5 1 5 1 4 0 6 0

4: w /HDT 7 0 3 0 6 2 4 0 6 2 4 0

5: w /lgt car 4 0 6 0 3 1 7 4 4 1 6 0

6: w /hvy car 5 1 5 0 3 0 7 3 8 3 2 1

7: w /lgt LT 5 1 5 1 6 0 4 0 7 0 3 0

8: w /hvy LT 7 1 3 0 9 2 1 0 4 0 6 2

9: Other 7 1 3 1 5 1 5 1 6 1 4 1

The data in Table 3.1 gi ve no information on the size of the effec t of mass reduction on risk in

the footprint deciles. F igures 3. 8 through 3. 10 show the percent change in risk from mass

reduction for each footprint decile, by vehicle ty

pe , for six of the nine crash ty pes (rollovers , and

crashes with stationary

o bjects, cars, and light trucks)

. Figure 3. 8 indicates that mass reduction

reduces risk in car rollover crashes (shown as blue diamonds) in seven deciles, by o ver 10% in

footprint deciles six and seven, and by o ver 20% in footprint decile eight. F ig ure 3. 10 indi cates

that mass reduction in CUVs/minivans reduces risk in crashes with a heav y light

- duty t ruck

(shown as purple circles) in five of the footprint deciles, and that these reductions are quite large

(over 45%), and statistically s ig nificant, for the larges t two deciles of CUVs/minivans. F igures

3. 8 through 3. 10 suggest that there are no consistent trends in the effect of mass reduction on risk

when vehicles are grouped by footprint decile.21

**Figure 3.**  **8 . Effect of**  **car mass reduction on**  **fatality risk**  **, by footprint decile and crash type** 

**Figure 3.**  **9 . Effect of**  **light truck mass reduction on**  **fatality risk**  **, by footprint decile and**

**crash type** -2 5 % -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15%

0 1 2 3 4 5 6 7 8 9 10Percentchangeinriskfrommassreduction

F o o t p ri n t d e ci l e (l o w e st t o h i g h e st )

R o l l o ve rs w / o b

j e ct

w / l g t ca r w / h vy ca r

w/lgt L T w / h vy L T

-1 0 % -5 % 0% 5% 10% 15%

0 1 2 3 4 5 6 7 8 9 10Percentchangeinriskfrommassreduction

F o o t p ri n t d e ci l e (l o w e st t o h i g h e st ) R o l l o ve rs w / o b

j e ct

w / l g t ca r w / h vy ca r

w/lgt L T w / h vy L T22

**Figure 3.**  **10 . Effect of**  **CUV/minivan mass reduction on**  **fatality risk**  **, by footprint decile and**

**crash type**

**4. Fatality risk**  **by vehicle model**

Unless noted otherwise, a ll fatality r isks in this report are societal risk, including fatalities in the

case vehicle and an y crash partners, and include not only d river fatalities but passenger fatalities

as well. I n this section we examine the variance in societal fatalit y risk by v ehicle model, both

before and after accounting for the vehicle, driver and crash variables NHTSA includes in its

regression models

. Figure 4.1 plots unadjusted US fatality r isk per VMT against a verage curb

weight, with vehicles grouped into 100

- lb increments of vehicle curb weight. F ig ure 4.1

indicates that, although risk does tend to decrease linearly a s curb weight increases, there

remains a fair degree of variability

, particularl y for trucks and CUVs/minivans, as indicated by

the R 2 values below 0.50

. N ote that the relationship between mass and fatalit y risk for light

trucks changes above 5,900 lbs, where risk begins to increase as mass increases; we plot these

heavier trucks, which are predominatel y 3⁄4

- and 1

- ton pickups, separatel y in Figure 4.1

.

Figures 4.2 through 4.4 show the relationship between unadjusted risk and mass by more detailed

vehicle ty pe. F ig ure 4.2 indicates that the relationship between curb weight and fatality r isk is

weaker for 4

- door cars than for 2

- door cars. F igure 4.3 suggests that for large pickups risk

increases as curb weight increases. A nd Figure 4.4 indicates that the relationship between risk

and curb we ight is strongest for minivans. -6 5 % -4 5 % -2 5 % -5 % 15% 35%

0 1 2 3 4 5 6 7 8 9 10Percentchangeinriskfrommassreduction

F o o t p ri n t d e ci l e (l o w e st t o h i g h e st )

R o l l o ve rs w / o b

j e ct

w / l g t ca r w / h vy ca r

w/lgt L T w / h vy L T23

**Figure 4.1** 

**. Relationship between US fatality**  **risk and curb weight**  **, with vehicles grouped**

**into 100**

**- lb increments of curb weight** 

**, by vehicle type** 

**Figure 4.2** 

**. Relationship between US fatality**  **risk and curb weight**  **, with vehic les grouped** 

**into 100**

**- lb increments of curb weight** 

**, passenger cars** 

R 2 = 0

. 5 8 R 2 = 0

. 1 6 R 2 = 0

. 2 1

R 2 = 0

. 2 9

0 40 80 120 160 200 240 280 320 360 400 440 480

20 24 28 32 36 40 44 48 52 56 60 64 68 72USfatalityrisk(totalfatalitiesper10billionVMT)

C u rb w e i g h t (l b s/ 1 0 0 ) C a rs

L i g h t t ru cks (<5 9 0 0 l b s)

L i g h t t ru cks (>5 9 0 0 l b s)

C U Vs a n d Mi n i va n s

R 2 = 0

. 4 8

R 2 = 0

. 2 2

0 40 80 120 160 200 240 280 320 360 400 440 480

20 24 28 32 36 40 44 48 52 56 60 64 68 72USfataltiyrisk(totalfatalitiesper10billionVMT)

C u rb w e i g h t (l b s/ 1 0 0 )

2 -d r ca rs

4 -d r ca rs24

**Figure 4.3** 

**. Relationship between US fatality**  **risk and curb weight**  **, with vehicles grouped**

**into 100**

**- lb increments of curb weight** 

**, light truck**  **s**

**Figure 4.4** 

**. Relationship between US fatality**  **risk and curb weight**  **, with vehicles grouped**

**into 100**

**- lb increments of curb weight** 

**, CUV**  **s and**  **minivans** R 2 = 0

. 0 9

R 2 = 0

. 5 6

R 2 = 0

. 2 8

0 40 80 120 160 200 240 280 320 360 400 440 480

20 24 28 32 36 40 44 48 52 56 60 64 68 72USfataltiyrisk(totalfatalitiesper10billionVMT)

C u rb w e i g h t (l b s/ 1 0 0 )

Sma l l p i cku p s

L a rg e p i cku p s

T ru ck-b a se d SU Vs

R 2 = 0

. 4 4 R 2 = 0

. 7 6

0 40 80 120 160 200 240 280 320 360 400 440 480

20 24 28 32 36 40 44 48 52 56 60 64 68 72USfataltiyrisk(totalfatalitiesper10billionVMT)

C u rb w e i g h t (l b s/ 1 0 0 )

C ro sso ve r SU Vs

Mi n i va n s25

It i s possible that the relationship between vehicle mass and fatalit y risk is greater in certain

ty pes of crashes. F ig ure 4.5 presents the relationship by v ehicle ty

pe for fatalit y risk in one

vehicle crashes with a stationary o bject, the ty

pe of crash in which vehicle mass is thought to

provide occupants the most protection. A lthough the relationship between vehicle mass and risk

is quite consistent am ong vehicle ty pes, the strength of this relationship is no grea ter in crashes

with stationary

objects than in all t ypes of crashes (Figure 4.1 ), and in the case of light trucks, the

relationship is w eaker in crashes with stationary objects.

Note that, for a given vehicle weight, light trucks have a higher fatality r isk in crashes with

stationary objects than cars, and an even higher rate than CUVs and minivans. Since there are no

crash partner fatalities in crashes with stationary o bjects, we suspect th at light trucks have a higher risk than cars in Figure 4.5 because of their tendency t o rollover, their increased use on more dangerous rural roads, and perhaps more passenger fatalities in light trucks than in cars.

**Figure 4.5** 

**. Relationship between US fa**  **tality risk**  **in crashes with stationary objects and** 

**curb weight**

**, by vehicle type** 

Figures 4.1 through 4.5 show that grouping vehicles into 100

- lb mass increments suggests that

fatality r isk decreases as mass increases, for most vehicle ty pes (the exception s are large pickups

and fullsize vans). Figure 4.6 shows the relationship between vehicle mass and unadjusted

fatality r isk by v ehicle model

. Only 275 models with at least 10 billion VMT, or at least 100

fatalities, are included ( 106 car models, 131 ligh t truck models, and 3 8 CUV/minivan models) ; these models represent about 90% of all fatalities, vehicle registration

- years, and VMT

. Here we

see that fatality

r isk declines with increasing mass for cars

, and at a smaller rate for CUVs and

minivans, while risk increases as mass increases for light trucks. H owever, although risk

declines with increasing car weight, the relativel y low R 2 (0.18) indicates that this is not a very R 2 = 0

. 4 8 R 2 = 0

. 0 5

R 2 = 0

. 3 0

0 10 20 30 40 50 60 70

20 24 28 32 36 40 44 48 52 56 60 64 68 72USfatalityrisk(totalfatalitiesper10billionVMT)

C u rb w e i g h t (l b s/ 1 0 0 ) C a rs

L i g h t t ru cks

C U Vs a n d Mi n i va n s26

strong relationship; there is a large range in risk for individual vehicle models at a given weight.

For example, the model labeled as A in the figure

, which weighs 2,879 lbs, has a fatality r isk of

331 per 10 billion VMT, while model B

, which weighs essentially t he same (2,870 lbs) has a fatality risk of only

96.

Of course, differences in vehicles (footprint, two

- vs. four

- doors, and presence of side impact air

bags, automated braking sy stems, or electronic stability c ontrols), drivers (age and gender)

, and

crash characteristics (at night, on high

- speed roads, or in rural vs. urban areas or high

- fatalit y states) by v ehicle model may

e xplain some of the large range in risk by vehicle weight. T o

account for these various variables, we reran NHTSA’s logistic regression models including all

of the driver, crash, and vehicle contr ol variables except vehicle mass and footprint

, across all

ty pes of crashes for each of the three vehicle ty pes

. We then calculated the predicted risk for

each induced exposure vehicle from the 13 state crash databases. 3 We first multiplied the

logistic regression coefficients for all driver, crash, and vehicle variables except mass and

footprint by

t he characteristics of each vehicle

, to obtain the predicted log odds of fatality p er

vehicle. W e then multiplied these odds by t he VMT weighting each induce d exposure vehicle

represents, to obtain the number of predicted fatalities in each induced exposure vehicle, and

summed across vehicle make and model. F inally w e divided the total number of predicted

fatalities in each make and model by t heir total VMT, to obtain predicted risk, the number of

predicted fatalities per ten billion VMT

. We exclude footprint as well as mass in the predicted

risks we calculate from the NHTSA regressions, as the two vehicle attributes are moderatel y

correlated.

Figure 4.7 sh ows the risks predicted by the regression model coefficients for all control variables

except vehicle mass and footprint

. Figure 4.7 indicates that

, even after controlling for the all of

the driver, crash, and vehicle variables NHTSA used in their logistic regression model, except

vehicle mass and footprint, there still is a large range in fatality r isk across vehicle models of

similar weight, for all three vehicle ty pes

.

Figures 4. 8 show s the remaining residual

, or unexplained, risks after accounting for all variables

except vehicle mass and footprint

. I n essence Figure 4. 8 shows that there is no relationship

between vehicle mass and risk, after accounting for driver, crash and all other vehicle attributes.

3 Because all of the induced exposure vehicl es are the non

- culpable vehicle in a two

- vehicle

crash, we could not account for ty pe of crash in the three new logistic regression models we ran

for the three vehicle t yp es.27

**Figure 4.6** 

**. US fatality risk per VMT and curb**  **weight, by vehicle model**

**Figure 4.7** 

**. Predicted**  **US fatality risk per VMT after accounting**  **for all driver**  **, crash, and**

**vehicle variables**  **except mass**  **and footprint**

**, vs. curb weight**  R 2 = 0

. 1 8

R 2 = 0

. 0 1

R 2 = 0

. 1 1

0 40 80 120 160 200 240 280 320 360 400

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200USfatalityrisk(fatalitiesper10billionVMT)

C u rb w e i g h t (l b s) C a rs

L i g h t t ru cks

C U Vs a n d Mi n i va n s A

B

R 2 = 0

. 0 9

R 2 = 0

. 0 6

R 2 = 0

. 1 3

0 40 80 120 160 200 240 280 320 360 400

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200PredictedUSfatalityrisk(fatalitiesper10billionVMT)

C u rb w e i g h t (l b s) C a rs

L i g h t t ru cks

C U Vs a n d Mi n i va n s A

B28

**Figure 4.**  **8**

**. Residual US fatality risk**  **after accounting for all driver, c**  **rash , and vehicle** 

**variables except mass**  **and footprint , vs. curb weight** 

Figures 4.9 through 4.1 1 show similar plots for 106 car models, with 2

- and 4

- door cars shown

separately

. The figures indicated that t wo

- door car models tend to have higher risk than 4

- door

models. The model labeled C provides an example of the values shown in Figures 4.9 through

4.11. T his model has an actual risk of 272 fatalities per ten billion VMT, while the NHTSA

regression model predicts that a vehicle with the same driver, crash location, and vehicle

attributes (except mass and footprint) would have a risk of onl y 248 fatalities per ten billion

VMT. In other words, after accounting for all of the variables except vehicle mass an d footprint,

th is vehicle model has a higher actual risk than predicted by the NHTSA regression model. This

remaining residual risk, 24 fatalities per ten billion VMT, can be attributed to the model’s mass

and footprint relative to other car models

, as we ll as other, unexplained differences among

vehicles.

Figure 4.10 indicates that some vehicles on the road today h ave the same, or lower, risk than

models that weigh substantially m ore, and are substantially l arger in terms of footprint. F or example, af

ter accounting for differences in driver age and gender, safety f eatures installed, and

crash times and locations, model D, which weighs 3,367 lbs and has a footprint of 46.6 square

feet, has an expected fatality r isk over twice that of model E, which weig hs 3,346 lbs and has a footprint of 43.8 square feet (248 vs. 106 fatalities per ten billion VMT). S imilarly

, model F

(3,107 lbs, 43.9 sq ft, 196 fatalities per VMT) has a predicted fatality r isk nearl y twice that of

model G (3,111 lbs, 41.4 sq ft, 111 fa talities per VMT), while model H (2,599 lbs, 42.3 sq ft, 219

fatalities per VMT) has a predicted risk 50% higher than that that of model I (2,500 lbs 41.8 sq ft, 141 fatalities per VMT). Models E, G, and I all have risk similar to or lower than that of R 2 = 0

. 0 6

R 2 = 0

. 0 3

R 2 = 0

. 0 0

-1 5 0 -1 0 0 -5 0 0 50 100 150

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200ResidualUSfatalityrisk(fatalitiesper10billionVMT)

C u rb w e i g h t (l b s) C a rs

L i g h t t ru cks

C U Vs a n d Mi n i va n s29

**Figure 4.9** 

**. US fatality risk per VMT vs. curb weight, car models** 

**Figure 4.10** 

**. Predicted US fatality risk per VMT after accounting for all driver, crash, and** 

**vehicle variables except mass and footprint vs. curb weight, car models** R 2 = 0

. 1 9

R 2 = 0

. 1 3

0 50 100 150 200 250 300

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200USfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s) 2 -d o o r

4 -d o o r

B A

C

R 2 = 0

. 1 2

R 2 = 0

. 0 4

0 50 100 150 200 250 300

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200PredictedUSfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s) 2 -d o o r

4 -d o o r

F D

K I H

G E J C A

B30

**Figure 4.11** 

**. Residual**  **US fatality risk per VMT after accounting for all driver, crash, and**

**vehicle variables except mass and footprint vs. curb weight, car models**

models J and K, which are both substantially l arger and heavier (J: 3,909 lbs, 52.5 sq ft, 126

fatalities per te n billion VMT; K: 3,990 lbs, 48.7 sq ft, 135 fatalities per ten billion VMT).

Clearly d ifferences in vehicle design can, and alread y do, mitigate an y theoretical safet y penalt y

from reduced mass. T he fact that NHTSA attributes the change in its regressio n results between

the 2003 study

a nd the 2011 study

i n part to the redesign or removal of certain smaller and

lighter models of poor design confirms that vehicle design can overcome any p erceived safet y penalty in lightweight or small vehicles. Figure 4.1 0 suggests that manufacturers can continue to

design vehicles that overcome an y theoretical safet y penalt y from reducing mass in order to

improve fuel economy

and reduce greenhouse gas emissions.

Figure 11 indicates that, after accounting for all variable s except mass and footprint, there is no correlation between the remaining residual risk and curb weight.

Figures 4.12 and 4.13 show the actual and predicted risks vs. curb weight for pickup trucks and

truck

- based SUV models. R isk declines with increas ing curb weight for SUVs, but increases

with increasing weight for pickups. H owever, the correlations between fatality r isk and curb

weight are weak, even after accounting for all of the driver, crash, and other vehicle variables in

the NHTSA logistic reg ression model. F igure 4.14 indicates that there is no correlation between

the residual risk and weight, after accounting for all variables except for mass and footprint.

Risk, predicted risk, and residual risk for CUV and minivan models are shown in Figu res 4.15

through 4.17. T here is a reasonable correlation between risk and mass for CUVs (Figure 4.15); R 2 = 0

. 0 6

R 2 = 0

. 0 9

-1 5 0 -1 0 0 -5 0 0 50 100 150

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200ResidualUSfatalityrisk,afteraccountingforalldriver,crash,andvehiclevariablesexceptmassandfootprint

C u rb w e i g h t (l b s) R e si d u a l U S f a t a l i t y ri sk p e r VMT vs. cu rb w e i g h t

, ca r mo d e l s

2 -d o o r

4 -d o o r

C31

however, the correlation decreases after accounting for all variables except mass and footprint

(Figure 4.16). A fter accounting for all variables excep t vehicle mass and footprint, there is no

correlation between residual risk and CUV curb weight, while residual risk increases with

increasing minivan weight, as shown in Figure 4.17.

It is possible that other differences in vehicle models, particularly o ther aspects of vehicle design

or subtle differences in driver behavior, explain some of the remaining variation in risk for

vehicles of similar weight.

**Figure 4.1**  **2**

**. US fatality risk per VMT**  **vs. curb weight, light truck models** 

R 2 = 0

. 0 8

R 2 = 0

. 1 5

0 40 80 120 160 200 240 280 320 360

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600USfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s) SU Vs

Pi cku p s32

**Figure 4.1**  **3**

**. Predicted US fatality risk per VMT after accounting for all driver, crash, and** 

**vehicle variables except mass**  **and footprint vs. curb weight,**  **light truck models**

**Figure 4.1**  **4**

**. Residual US fatality risk per VMT after accounting for all driver, crash, and** 

**veh icle variables except**  **mass and footprint**  **vs. curb weight, light truck models** R 2 = 0

. 0 5

R 2 = 0

. 2 8

0 40 80 120 160 200 240 280 320 360

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600PredictedUSfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s)

a l l d ri ve r

, cra sh

, a n d ve h i cl e va ri a b l e s e xce p t w e i g h t

SU Vs

Pi cku p s

R 2 = 0

. 0 4

R 2 = 0

. 0 0

-1 5 0 -1 0 0 -5 0 0 50 100 150

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600ResidualUSfatalityrisk,afteraccountingforalldriver,crashandvehiclevariablesexceptmassandfootprint

C u rb w e i g h t (l b s) SU Vs

Pi cku p s33

**Figure 4.1**  **5**

**. US fatality risk per VMT**  **vs. curb weight, CUV**  **/Minivan models** 

**Figure 4.1**  **6**

**. Predicted US fatality risk per VMT after accounting for all driver, crash and** 

**vehicle variables except mass**  **and footprint vs. curb weight, CUV/Minivan models**  R 2 = 0

. 3 7

R 2 = 0

. 0 0

0 40 80 120 160 200

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800USfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s)

a l l d ri ve r

, cra sh

, a n d ve h i cl e va ri a b l e s e xce p t w e i g h t

C U Vs

Mi n i va n s

R 2 = 0

. 3 3

R 2 = 0

. 0 9

0 40 80 120 160 200

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800PredictedUSfatalityrisk(crashfatalitiesper1010VMT)

C u rb w e i g h t (l b s)

a l l d ri ve r

, cra sh

, a n d ve h i cl e va ri a b l e s e xce p t w e i g h t

C U Vs

Mi n i va n s34

**Figure 4.1**  **7**

**. Residual**  **US fatality risk per VMT after accounting for all driver, crash and** 

**vehic le variables except**  **mass and footprint**  **vs. curb weight, CUV/Minivan models**

Table 4.1 summarizes the relationships between predicted and residual fatality r isk and vehicle

curb weight that are presented in Figures 4.6 through 4.17. In addition to the co rrelation between

fatality r isk and curb weight, the table shows the slope of the linear regression line drawn

through the risks by

v ehicle model

, which represents the percent change in fatality r isk per 100

- pound reduction in mass or 1

- square foot reducti on in footprint

. T he relationship for the three

vehicle ty pes is shown at the top of the table, followed by t hose for the seven detailed vehicle

subty pes (with small , i.e. compact and 1⁄2

- ton, pickups shown separatel y from heav y

- dut y , i.e. 3⁄4

- and 1

- ton, pic kups ), and finally t he five vehicle ty

pe and weight groups NHTSA used in its

regression anal ys es

. C ases where there is a positive relationship between fatality r isk and

vehicle weight, i.e. where risk decreases as weight decreases , are shown in red in the table. Cases

where the correlation between risk and weight by v ehicle model exceeds 0.30 are shown in blue.

Table 4.1 indicates that fatality r isk consistently increases as weight increases for heav y

- dut y pickups, and that in some cases the correlation b etween risk and weight is rather high ( R 2 of 0.62

for actual fatality

risk, and 0.43 for predicted risk after accounting for all variables except vehicle

mass and footprint). I n these two cases the slope of the regression line is also high, between 7%

and 11%. F or CUVs, the correlation between actual and predicted fatality r isk and mass is

moderate, with an R 2 of over 0.30, with risk increasing about 3% for every 1 00

- lb reduction in

mass. R 2 = 0

. 0 1

R 2 = 0

. 1 3

-5 0 -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 50

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800ResidualUSfatalityrisk,afteraccountingforalldriver,crashandvehiclevariablesexceptmassandfootprint

C u rb w e i g h t (l b s) C U Vs

Mi n i va n s35

**Table 4.1**  **. Rel ationship between actual, predicted, and residu**  **al fatality risk, and vehicle**

**mass reduction** 

**, after accounting for all driver, crash, and vehicle variables except mass** 

**and footprint, by vehicle type and model** 

Vehicle t ype Actual US fatalit y risk Predicted risk Residual risk

R 2 Slope R 2 Slope R 2 Slope

Cars 0.16 4.7% 0.09 2.9% 0.05 1.7%

Li ght trucks 0.01

- 0.5% 0.06

- 1.4% 0.03 0.7%

CUVs/minivans 0.11 2.0% 0.13 2.1% 0.00

- 0.1%

2

- dr cars 0.16 6.5% 0.12 3.9% 0.03 2.4%

4

- dr cars 0.12 3.4% 0.04 1.6% 0.07 1.7%

Small pickups 0.03 1.1% 0.00

- 0.2% 0.03 0.8%

Heav y

- dut y pickups 0.62

- 10.9% 0.43

- 6.7% 0.12

- 4.2%

SUVs 0.08 2.1% 0.05 1.1% 0.04 1.0%

CUVs 0.37 3.5% 0.33 3.1% 0.01 0.4%

Minivans 0.00 0.1% 0.09 2.9% 0.13

- 2.8%

Cars < 3106 0.05 4.4% 0.02 2.3% 0.02 2.1%

Cars > 3106 0.16 4.7% 0.09 2.9% 0.05 1.7%

LTs < 4594 0.01

- 0.5% 0.06

- 1.4% 0.03 0.7%

L Ts > 4594 0.11 2.0% 0.13 2.1% 0.00

- 0.1%

CUVs/ minivans 0.16 6.5% 0.12 3.9% 0.03 2.4%

Table 4 .2 presents the same information as Table 4.1, but for the relationship between risks and

vehicle footprint. A s in Table 4.1, fatalit y risks for heav y

- dut y pickups consistently i ncrease as footprint increases, between 5% and 10%. H owever, the correlation between actual risk and

footprint by

heavy

- dut y pickup model (0.43) is not as high as with mass (0.62 in Table 4.1). And

the correlations between actual and predicted risk and CUV/minivan footprint (0.08 and 0.06,

respectivel y) are much lower than the co rrelations with mass (0.37 and 0.33 in Table 4.1). T his

is somewhat surprising, as Figure 3.3 indicates that footprint reduction has a relativel y large

(detrimental) effect on CUV and minivan fatality r isk, while mass reduction has a smaller,

beneficial e ffect. H owever, the correlation between footprint and risk for CUVs and minivans

shown in Table 4.2 is quite low.36

**Table 4.2**  **. Rel ationship between actual, predicted, and residual fatality risk, and vehicle** 

**footprint reduction,**  **after accounting for all dri ver, crash, and vehicle variables except** 

**mass and footprint, by vehicle type and model** 

Vehicle t ype Actual US fatalit y risk Predicted risk Residual risk

R 2 Slope R 2 Slope R 2 Slope

Cars 0.11 5.2% 0.04 2.6% 0.07 2.6%

Li ght trucks 0.08

- 1.7% 0.24

- 2.7% 0.05 0.9%

CUV/minivans 0.00 0.1% 0.00 0.0% 0.00 0.1%

2

- door cars 0.12 8.5% 0.04 3.1% 0.07 5.3%

4

- door cars 0.06 3.2% 0.01 0.8% 0.08 2.4%

Small pickups 0.02 0.8% 0.00

- 0.3% 0.07 1.1%

Heav y

- dut y pickups 0.43

- 10.2% 0.19

- 5.0% 0.16

- 5.2%

SUVs 0.05 2.1% 0.00 0.3% 0.07 1.7%

CUVs 0.08 2.2% 0.06 1.9% 0.01 0.4%

Minivans 0.17 4.3% 0.06 3.1% 0.02 1.2%

Cars < 3106 0.05 5.8% 0.01 1.8% 0.05 4.0%

Cars > 3106 0.02

- 2.5% 0.02

- 2.4% 0.00

- 0.1%

LTs < 4594 0.04

- 1.8% 0.22

- 3.0% 0.04 1.3%

L Ts > 4594 0.40

- 5.6% 0.53

- 6.0% 0.00 0.4%

C UVs/ minivans 0.00 0.1% 0.00 0.0% 0.00 0.1%37

**5. Sensitivity of NHTSA results**  **to data used and model specification**

In t his section we examine the sensitivity o f the NHTSA results on the effect of mass and

footprint on US fatality

r isk per VMT. W e ex amine the effect of calculating risk of a fatal crash

(as opposed to risk of fatality), and risk of fatality per non

- culpable vehicle ; and how the effect

of mass or footprint reduction changes after accounting for vehicle manufacturer, after excluding

the calendar ye ar control variables, and after excluding crashes involving alcohol or drug use, or otherwise bad driving behavior

.

**5.1. Alternative m easure**  **s of exposure** 

Figure 5.1 compares the results for US fatality r isk per VMT using NHTSA’s preferred

regression model specification (in light blue) with two o ther measures of US fatality r isk. T he

first measure is the risk of a fatal crash, rather than the risk of all fatalities that occurred in the

crash. In o ther words, the fatal crash cases are not weighted by t he total number of fatalities,

either in the c ase vehicle or in its crash partner, as they a re in NHTSA’s preferred model. In his

review of the previous NHTSA studies, Paul Green suggested that analy zing risk at the crash,

rather than person, level might be a better approach; each fatal case would be a single

independent observation, and may s erve to increase an y under

- estimation of the uncertainty

around the parameter estimates. As shown in Figure 5.1, this alternative measure of risk , the risk

of a fatal crash per ten billion VMT (shown in dark ora nge) increases the detrimental effect of

mass reduction on risk in cars, from 1.43% to 1.84% for lighter than average cars, and from

0.48% to 0.86% for heavier cars. On the other hand, anal yz ing risk of fatal crash per VMT has

essentially

no impact on the effect of mass reduction in light trucks or CUVs/minivans.

**Figure 5.1**  **. Effect of ma**  **ss and**  **footp r**  **int reduction**  **on US fatalit**  **ies per VMT**  **, fatal crashes**

**per VMT, and fatalities per induced exposure crash** 

**, by vehicle type** 1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.84%0.86%0.54%-0.48%-0.56%1.85%0.19%1.82%-0.24%-1.43%-1.14%-0.75%-0.69%2.20%-1.28%1.97%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT) N H T SA p re f e rre d mo d e l

F a t a l cra sh e s p e r VMT

F a t a l i t i e s p e r i n d e xp cra sh

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t38

The statistical uncertainties around the point estimates of the effect of mass reduction on risk per

fatal crash in Figure 5.1 are onl y slightly hig her than the uncertainties around the risk per fat ality

point estimates, in part because on average there are only 1 .16 fatalities per fatal crash (123,324

fatalities in 106,613 fatal crashes).

We also investigate the effect NHTSA’s weighting of the induced exposure crashes has on its

regression results. N HTSA uses the non

- culpable vehicle in two

- vehicle crashes from the 13

states as its measure of induced exposure. It then creates weights so t hat the crashes from the 13

states can first be scaled up to represent national vehicle registration

- ye ars, and then multiplied

by a verage annual VMT by v ehicle age and ty

pe to arrive at national VMT. I n the light orange

columns in Figure 5.1 we exclude t hese two calculations, and examine US fatality r isk per

induced exposure crash from the 13 states (rather than VMT)

. U sing induced exposure crashes

as the measure of exposure changes the sign of the effect of car mass reduction, and light truck

mass and f

ootprint reduction, on risk; for all vehicle ty pes, mass reduction actually reduces

fatality r isk given that a crash occurs. F ootprint reduction in light trucks similarly r educes

fatality risk per crash, while it slightly increases risk per crash in cars and CUVs/minivans.

The effect of anal yz ing fatality r isk per crash shown in Figure 5.1 is approximate, as total U.S.

fatalities are combined with induced exposure crashes for onl y 13 states. A m ore exact anal ys is

would utilize both fatalities and crash es from the same states. W e will perform just such an

anal ys is in the near future, using fatalit y, serious injury , and crash data from the same source, the

police

- reported crashes from 13 states.

**5.2. Vehicle manufacturer** 

The anal ys is by

v ehicle model i n Section 4 indicates that the variables included in the NHTSA

preferred model only

account for a fraction of the variability in risk. We suspect that other, more

subtle differences in vehicle models, or driver behavior, may e xplain the large remaining

va riability

i n risk. W e tested that assumption by a dding 18 dummy

v ariables based on the

vehicle nameplate manufacturer. GM brands (Buick, Cadillac, Chevrolet, GMC, Oldsmobile,

Pontiac, and Saturn) are treated as the default value, since combined they r epre sent the most

vehicles by

m anufacturer, both in fatalities and VMT. T he six Chry sler brands (Jeep, Chry sler,

Dodge, Ply

mouth, AM General and Sprinter) and the three Ford brands (Ford, L incoln, Mercury )

were combined in a single Chry sler and Ford category

. T he luxury b rands of Toy ota, Honda,

and Nissan (Lexus, Acura, and Infiniti, respectively ) were treated as separate manufacturers. A

small number of low

- volume manufacturers were grouped into a separate Other manufacturer

category

.

Figure 5.2 compares the effect of adding variables for each of the 18 manufacturers (shown in

red) in NHTSA’s preferred regression model specification (shown in light blue)

. F or cars and

light trucks, accounting for vehicle manufacturer increases the effect of mass reduction

, but

decreases the effect of footprint reduction, on risk

. Accounting for vehicle manufacturer in the

CUV/minivan regression model s makes mass reduction detrimental, and footprint reduction

slightly beneficial

.39

**Figure 5.2**  **. Effect of ma**  **ss and**  **footprint r**  **eduction on**  **US fatality risk**  **per VMT , after** 

**controlling for vehicle**  **manufacturer**

**, by vehicle type** 

**5.3. CY variables** 

One interesting effect is the reduction in risk over time, as indicated in the calendar ye ar control

variables. This is consistent for each vehicle ty pe, but largest for light trucks, as shown in Figure

5. 3

. The calendar ye ar variables account for changes in both case vehicles and their crash

partners

, as well as the crash environment, over time, changes that are not explicitly i ncluded as

other control variables in the regression models. NHTSA interprets the trend of reduced risk

over time as a reflection of general improvements in vehicle and roadway safet y, increase in curb

weight of crash partners, an d

, in particular, improvement in light truck design to reduce their

tendency to rollover.

Figure 5. 4 indicates that the effect of the calendar ye ar variables on light truck risk is strongest

for crashes with light cars and light er light

- dut y trucks. N HT SA believes that this may b e the

result of the removal over time of very l ight and unsafe cars and light trucks as potential crash

partners for light trucks. H owever, there also are consistent (although not as large) decreases

over time in light truck ris k in rollovers, crashes with heav y

- duty t rucks and heavy

c ars, and

other (mostly

m ulti

- vehicle) crashes

. NHTSA believes that the decline in light truck rollover

risk over time may

b e the result of manufacturers increasing static stability f actor or other

aspects of light truck design to reduce their likelihood to rollover. H owever, cars and

CUVs/minivans show a similar trend in reduced rollover risk over time (cars and

CUVs/minivans also show similarly l arge reductions in risk over time in crashes with li ght cars

and light LTVs). NHTSA suspects that the risk associated with light trucks involved in crashes

with heavy

- duty

t rucks decrease s over time because heav y

- duty t ruck activity

d ecreases as the1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.81%0.67%0.60%-0.21%0.92%1.72%-0.24%-0.09%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

I n cl u d i n g ve h i cl e ma ke

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f o o t p ri n t40

econom y falters. T he economic recession in 2008 may h ave reduced the number of heavy

- dut y trucks traveling roadways, and thus available as potential crash partners with light

- duty vehicles

.

**Figure 5.**  **3 . Effect of**  **calendar year variables on risk**  **, by vehicle type**

**Figure 5.**  **4 . Effect of**  **calendar year variables**  **on light truck**  **risk , by crash type**  -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008Percentchangeinrisk(fatalityprobabilityper1010VMT)

C a rs L i g h t T ru cks C U Vs/ Mi n i va n s

-3 5 % -2 5 % -1 5 % -5 % 5% 15% 25% 35% 45% 55% 65%CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008Percentchangeinrisk(fatalityprobabilityper1010VMT)

R o l l o ve r w / o b j w / p e d w / H D T w / l g t ca r w / h vy ca r w / l g t L T w / h vy L T O t h e r41

In its 2003 report, NHTSA included calendar ye ar variables for light trucks, but not for cars,

because “light trucks grew in weight throughout the 1990’s but cars did not” (NHTSA did not

anal yz e CUVs/minivans as a separate vehicle class in the 2003 study ). Figure 5. 5 shows the

weighted average coefficients on the calendar ye ar variables from the 2003 anal ys is (taken from

the tables in Section 4.3 of that report). Note that the effect of the calendar y ear variables on risk

is much smaller than in the 2011 anal ys is, and there is not the consistent decrease of the effect of

calendar year on risk in later ye ars as in the 2011 anal ys is.

**Figure 5.**  **5**

**. NHTSA 2003 e**  **ffect of calendar year**  **variables on risk , by vehicle type** 

The calendar year effect for light tr ucks is strongest on crashes with cars and other light trucks in

the 2003 NHTSA anal ys is, as shown in Figure 5. 6

. H owever, calendar year increases the risk in

crashes with cars, but decreases the risk in crashes with another light truck. I n addition, the re is

no consistent trend in the variables over time. -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000Percentchangeinrisk(log-oddsoffatalityper1010VMT)

C a rs L i g h t T ru cks C U Vs/ Mi n i va n s

N A N A N A N A N A N A N A N A N A N A42

**Figure 5.**  **6**

**. NHTSA 2003 e**  **ffect of calendar year**  **variables on light truck**  **risk , by crash type** 

In its current analy sis, NHTSA attributes the large reduction in risk in 2008 to the sharp drop in

fatalit ies in that ye ar; however, it is not clear the extent to which fatalities declined in 2008

because of real safet y changes, or because of reduced driving because of high gas prices and the

economic recession. Figure 5. 7 shows that, for most vehicle ty pes, fatality

r isk per VMT was

fairl y constant between 2002 and 2007, with a consistent decline in 2008; pickups were the onl y

vehicle ty pe that showed reduction in risk in any y ears other than 2008. F igure 5. 8 shows that

the NHTSA assumptions regarding vehicl e miles traveled do not account for an y additional

reduction in VMT for model ye ar 2002 vehicles in 2008, in response to higher gas prices or the

economic recession, other than the linear reduction in annual VMT as vehicles age. ( Note that

the US VMT for 4

- dr cars is much higher than for all other vehicle ty pes, and is plotted along the

right vertical axis.) S o it is possible that the calendar year trends in risk shown in Figure 5. 7

explain the large negative coefficient on the CY2008 variable, but not th e decreasing reduction

in risk for the other calendar year variables (particularly for cars and CUVs/minivans).

Figures 5. 9 through 5. 12 show the effect of removing the calendar ye ar variables from NHTSA’s

preferred regression model (shown in light blue)

. F ig ure 5. 9 indicates that excluding the

calendar year variables has little effect on the coefficients for mass or footprint reduction in cars,

or for footprint reduction in CUVs/minivans. H owever, removing the calendar ye ar variables

increases the effect of mass reduction in trucks and CUVs/minivans; reducing by 1 00 lbs the

weight of lighter trucks now increases risk 1.22% (as opposed to 0.52%), results in a 0.25%

increase rather than a 0.40% decrease in risk in the heavier trucks, and lowers the de crease in

risk in CUVs/minivans from a 0.47% decrease to only a 0.06% decrease. -3 5 % -2 5 % -1 5 % -5 % 5% 15% 25% 35% 45% 55%CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000Percentchangeinrisk(log-oddsoffatalityper1010VMT)

R o l l o ve r w / o b j w / p e d w / H D T w / ca r w

/ L T43

**Figure 5.**  **7**

**. US fatality risk per VMT, by vehicle type and calendar year** 

**Figure 5.**  **8**

**. Total US VMT for MY2002 vehicles, by vehicle type and calendar year** 

0 50 100 150 200 250 300 350 400

2001 2002 2003 2004 2005 2006 2007 2008 2009USfatalityrisk(fatalitiesper10billionVMT)

Y e a r 2 -d r ca rs 4 -d r ca rs

Pi cku p s H D p i cku p s

SU Vs C U Vs

Mi n i va n s

0

. E+0 0 1

. E+1 0 2

. E+1 0 3

. E+1 0 4

. E+1 0 5

. E+1 0 6

. E+1 0 7

. E+1 0 8

. E+1 0 9

. E+1 0

0

. E+0 0 5

. E+0 9 1

. E+1 0 2

. E+1 0 2

. E+1 0 3

. E+1 0 3

. E+1 0 4

. E+1 0 4

. E+1 0

2001 2002 2003 2004 2005 2006 2007 2008 2009USVMT(10billion)

Y e a r 2 -d r ca rs

Pi cku p s

H D p i cku p s

SU Vs

C U Vs

Mi n i va n s

4 -d r ca rs (ri g h t )44

**Figure 5.**  **9 . Effect**  **of increasing weight or size on risk, including and excluding calendar** 

**year variables** 

We next ex amined what effect removing the calendar year variables had on the control variables

NHTSA used in their preferred model. F ig ures 5. 10 through 5. 12 show th e effect on the vehicle

control variables; there is little to no effect on the driver or crash control variables (not shown).

Figures 5. 9 through 5. 12 indicate that removing the calendar ye ar variables has a large effect on

the curtain airbag variable in cars and CUVs/minivans, and the SUV, HD pickup, BLOCKER2,

and ESC variables in light trucks. I n addition, the figures indicate that removing the calendar

ye ar variables lowers the effect of vehicle age on risk in all three vehicle ty pes. F ig ures 5. 9

thro ugh 5. 12 suggest that NHTSA’s inclusion of the calendar ye ar variables in their preferred

model dilutes the effect of airbag technologies in cars and CUVs and minivans, the added risk in

SUVs and heav y

- duty

p ickups, and the beneficial effect of ESC in ligh t trucks in general, while

over

- stating the effect of vehicle age in all three vehicle t ypes.1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.39%0.39%1.22%0.25%-0.06%2.14%-0.38%1.63%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g C Y va ri a b l e s

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f o o t p ri n t45

**Figure 5.**  **10 . Effect of**  **selected control variables on**  **car risk**  **, including and excluding**

**calendar year variables**

**Figure 5.**  **11 . Effect of**  **selected control variables on**  **light truck risk**  **, including and excluding**

**calendar year variables** -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%CURTAINCOMBOTORSOABSESCVEHAGEBRANDNEWPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g C Y va ri a b l e s

-4 0 % -3 0 % -2 0 % -1 0 % 0% 10% 20%SUVHD_PKPBLOCKER1BLOCKER2ESCAWDVEHAGEBRANDNEWPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g C Y va ri a b l e s46

**Figure 5.**  **12 . Effect of**  **selected control variables on**  **CUV/minivan risk**  **, including and**

**excluding calendar year variables**

**5.4. Effect of alcohol/drug use**  **and driving behavior**

FARS indicates about 12 % of car drivers

, 14% of light truck drivers, and 7% of CUV/minivan

drivers in fatal crashes were reported to have been drinking or engaged in drug use. W e

examined the effect of excluding case vehicles where the driver was reporte d to have been

drinking or using drugs from our regression analy sis. A lthough we excluded fatal crashes

involving case vehicles whose drivers were reported to have been drinking or using drugs, we

did not make an y adjustments to the induced exposure cases from the 13 states. M ost states

report suspected driver alcohol or drug use, so we could exclude these induced exposure cases

and recalculate the vehicle registration annual VMT weights used in calculating vehicle

exposure. The dark green columns in F ig ure 5. 13 indicate that removing such cases from the

anal ys is slightl y increases the effect of mass reduction on risk, but slightly reduces the effect of

footprint reduction on risk , as compared with NHTSA’s preferred regression model

.

In the 2003 report NHTSA created a “bad driver rating” variable based on whether the alcohol or

drugs were involved in the current crash, as well as driving without a valid license or reckless

driving in the current crash, and the driver’s driving record in the last three y ears. T hese

additional “bad” drivers account for another 14% of car and light truck drivers, and another 9%

of CUV/minivan drivers, in the FARS cases. T he light green columns in Figure 5. 13 indicate

that excluding these bad drivers from the anal ys is furt her increases the effect of mass reduction

on risk. F or example, excluding all bad drivers increases the increase in risk from mass

reduction from 1.43% to 2.03% in smaller cars, from 0.48% to 1.04% in larger cars, and from

0.52% to 0.97% in smaller trucks. On the other hand, excluding all bad drivers from the analy sis -2 0 % -1 5 % -1 0 % -5 % 0% 5% 10% 15% 20%MINIVANCURTAINCOMBOTORSOABSESCAWDVEHAGEBRANDNEWPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

Excl u d i n g C Y va ri a b l e s47

further reduces the effect of footprint reduction on risk. The fraction of drivers who are drunk,

drugged, or bad drivers is two to three times higher in rollovers and fixed object cr ashes than in

all other crash ty

pes. B ecause mass reduction is most beneficial, and footprint reduction most

harmful, in these two ty

pes of crashes (as shown in Figures 2.2 through 2.4), removing crashes

involving these drivers from the anal ys is makes ove rall mass reduction more harmful, and

footprint reduction less harmful.

The last column in Figure 5. 13 (in pale blue) shows the effect calculated by NHTSA from

excluding drivers of case vehicles whose measured or imputed blood alcohol content (BAC)

leve l exceeded 0.08

, rather than whether alcohol or drug use was reported. NHTSA’s anal ys is

indicates that ex cluding cases where drivers had high measured or imputed BAC levels has a larger effect on risk than excluding cases where alcohol or drug use was repo rted, for cars, but a

similar effect on risk for trucks and CUVs/minivans. This is likely b ecause NHTSA excludes

drivers who were imputed to have been drinking, based on other characteristics of the driver and

crash. E xcluding the additional drivers who were imputed to have been drinking, but were not

reported as such, would likely increase the effects shown in green in Figure 5.13.

**Figure 5.**  **13 . Effect of**  **mass and footprint reduction**  **on US fatality**  **risk per VMT,**  **after**

**excluding case vehicles whose driver was suspected of drinking or using drugs** 

**, or exhibited** 

**bad driving behavior, by vehicle type** 1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.66%0.77%0.75%-0.40%-0.28%1.77%-0.17%1.43%2.03%1.04%0.97%-0.16%-0.20%1.52%-0.29%1.26%2.08%0.96%0.77%-0.55%-0.33%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT) N H T SA p re f e rre d mo d e l

Excl u d i n g d ri n ki n g / d ru g u se

Excl u d i n g a l l b a d d ri ve rs

N H T SA e xcl u d i n g BAC > 0

. 0 8

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t48

**5.5. Effect of**  **including sports, police, and all**

**- wheel drive cars, and fullsize vans** 

NHTSA excluded three ty pes of cars, models used as sports cars, police cars, and models with

all

- wheel drive, as well as fullsize vans, from its preferred regression model

. I ncluding these

vehicles in the anal ys is increases the effect of mass reduction, b ut reduces the effect of footprint

reduction, on risk for cars

, as shown in Figure 5. 14

. I ncluding fullsize vans has the opposite

effect on the results for light trucks: the effect of mass reduction is reduced

, while th e effect of

footprint reduction is i ncreased

.

**Figure 5.**  **14 . Effect of**  **mass and footprint reduction on**  **US fatality risk**  **per VMT, after**

**including sports, police, and all**

**- wheel drive cars, and fullsize vans, by vehicle type** 1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.64%0.73%0.34%-0.95%-0.47%1.65%0.16%1.73%

-2

. 0 % -1

. 0 % 0.0% 1.0% 2.0% 3.0% 4.0%

C a rs < 3 1 0 6 C a rs > 3 1 0 6 L T s < 4 5 9 4 L

T s > 4 5 9 4 C U Vs/

mi n i va n s C a rs L T s C U Vs/

mi n i va n sPercentchangeinrisk(fatalityprobabilityper1010VMT)

N H T SA p re f e rre d mo d e l

I n cl u d i n g a l l ca rs a n d f u l l si ze va n s

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f o o t p ri n t49

**6. Influence of recent trends on the expected effect of mass reduction on risk in 2017** 

**- 2025** 

As discussed in Section 2, NHTSA estimated the change in fatalities in 2017

- 2025 after

assuming full market penetration of electronic stability control (ESC) in new vehicles. There are

other trends in vehicle technologies, in addition to ESC, that may a ffect baseline fatalities in

2017 through 2025. S ide airbags are becoming standard equipment on most vehicles, and

manufacturers are taking measures to improve light truck compatibility w ith other vehicles in

frontal crashes. And in recent years there has been a market shift from truck

- based SUVs to car

based CUVs. I n this section we analy ze what influence these trends, if they c ontinue, may

h ave

on the effect of mass reduction on risk in the 2017

- 2025 timeframe.

**6.1 Effect of**  **electronic stability control ( ESC**  **)**

Figure 6.1 indicates that manufacturers began installing ESC as a standard feature in MY05, with

about half of MY07 CUVs and minivans, and a third of MY07 light trucks, having ESC. A s

NHTSA has required ESC on all l ight

- dut y vehicles by M Y12, there likely

h as been a quick

increase in the market penetration of ESC in new vehicles, including cars, between MY07 and

MY12.

**Figure 6.1.**  **Market penetration of ESC, by vehicle type and model year**

A recent NHTSA study

e st imates that ESC reduces fatal rollovers by 5 6% in cars and 74% in

light trucks; fixed

- object impacts by 4 7% in cars and 45% in light trucks; and other non

0% 10% 20% 30% 40% 50% 60% 70% 80%

2001 2002 2003 2004 2005 2006 2007Percentofnewvehiclefleet

Mo d e l ye a r

ESC

, ca r

ESC

, L T

ESC

, C U V50

pedestrian crashes by

8 % in both cars and light trucks. 4 (These findings treat crossover SUVs

and minivans as light trucks rather than cars.) Figure 6.2 compares these recent results, shown in

red, with the effect of ESC installation on fatality risk from NHTSA’s regression models in the

current analy sis, by vehicle and crash ty pe. T he figure indicates that the current analy sis gives

comparable results for ESC effectiveness for cars in rollovers, light trucks in rollovers and

crashes with objects, and CUVs/minivans in crashes with objects. However, the curre nt anal ys is

estimates a lower ESC effectiveness for car crashes with objects, and CUV/minivan rollovers,

than the Sivinski study

. O n the other hand, the current study f inds substantially

h igher ESC

effectiveness in reducing risk in crashes with other vehi cles.

**Figure 6.**  **2**

**. Effect of ESC on fatality risk, by vehicle type and crash type** 

**6. 2**  **Effect of side airbags**

Figure 6. 3 shows the recent trend in the market penetration of side airbag technologies in new

vehicles. T he data in Figure 6.2 are onl y for v ehicles coded in the NHTSA database as having

zero or 100% of a particular airbag technology ; because side airbag technologies are optional

equipment on man y models, particularl y light trucks, NHTSA used the fraction of vehicles of

these models that had th e technology i nstalled. T he data in Figure 6.2 account for 80% to 90%

of all cars, 60% to 100% of all light trucks, and 77% to 86% of all CUVs/minivans, depending

on the model ye ar. B ecause man y models are coded as having both side curtain and side torso

4 Sivinski R. (2011). Update of NHTSA’s 2007 Evaluation of the Effectiveness of Light Vehicle Electronic Stability

Control (ESC) in Crash Prevention

, NHTSA Technical Report No. DOT HS 811 486. Washington, DC: National

Highway Traffic Safety Administration. http://www

- nrd.nhtsa .dot.gov/Pubs/811486.pdf

.-46%-16%-4%-30%3%-3%-23%-27%-2%-76%-49%5%-34%-21%-22%-6%-24%6%-47%-36%15%-5%1%-32%9%23%5%-56%-47%-8%-8%-8%-8%-8%-8%-74%-45%-8%-8%-8%-8%-8%-8%-74%-45%-8%-8%-8%-8%-8%-8%

-8 0 %

-7 0 % -6 0 % -5 0 % -4 0 % -3 0 % -2 0 % -1 0 % 0%

-1 2 0 % -1 0 0 % -8 0 % -6 0 % -4 0 % -2 0 % 0% 20% 40% 60% 80%1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:Other1:Rollovers2:w/object3:Pedetc.4:w/HDT5:w/lgtcar6:w/hvycar7:w/lgtLT8:w/hvyLT9:OtherPercentchangeinrisk(fatalityprobabilityper1010VMT)

C a rs L i g h t t ru cks C U Vs/ Mi n i va n s

N H T SA (Si vi n ski ):51

airbags, the data for each vehicle ty pe are not additive; by

M Y07, 63% of cars, 16% of light

trucks, and 76% of CUVs/minivans had both side curtain and side torso airbags installed.

Figure 6. 3 indicates that side airbags have been available for a longer period than ESC; in MY01

23% of cars, 10% of light trucks, and 13% of CUVs and minivans came with side torso airbags.

Side curtain airbags have become more prevalent than side torso airbags, pa rticularl y for light

trucks; by M Y07, 72% of cars, 80% of CUVs/minivans, and 47% of light trucks came with side

curtain airbags.

**Figure 6.**  **3**

**. Market penetration of side impact airbags, by vehicle type and model year** 

Side airbags should reduce fatalit y risk in crashes when another vehicle strikes the case vehicle

in the side ( although curtain airbags are designed to also deploy i n rollovers and severe frontal

crashes)

. F ig ures 6. 4 through 6. 6 show the effect the three side airbag technologies have on th e

risk of fatalit y to the occupants in the case vehicle only

, by c ase vehicle ty

pe . T he effect of side

airbags is shown for crashes where the case vehicle is struck in its side (either driver or

passenger side) by

i ts crash partner; the vehicle ty pe of th e crash partner is shown along the x

axis (light and heav y cars, light and heav y light

- duty trucks). Because the NHTSA regressions do

not include the side airbag variables for light trucks, the data are onl y shown for cars and

CUVs/minivans. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

2001 2002 2003 2004 2005 2006 2007Percentofnewvehiclefleet

Mo d e l ye a r

C u rt a i n

, ca r C u rt a i n

, L T C u rt a i n

, C U V

T o rso

, ca r T

o rso

, L T T

o rso

, C U V

C o mb o

, ca r C o mb o

, L T C o mb o

, C U V52

**Figure 6.**  **4**

**. E**  **ffect of side airbags on risk to car occupants only**  **, when struck in the side by**

**another light**

**- duty vehicle, by crash partner vehicle type** 

**Figure 6.**  **5**

**. Effect of side airbags on risk to**  **CUV/minivan occupants only , when struck in** 

**the side by another light** 

**- duty vehicle, by crash partner vehicle type** 14%-9%-6%-20%-53%-26%-31%-17%-42%-38%-27%-29%-39%-35%-24%-21%

-1 0 0 % -8 0 % -6 0 % -4 0 % -2 0 % 0% 20% 40% 60% 80% 100%lgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-FPercentchangeinrisk(fatalityprobabilityper1010VMT)

C u rt a i n si d e a i rb a g C o mb o si d e a i rb a g T o rso si d e a i rb a g An y si d e a i rb a g-69%77%-63%-60%-47%38%-29%-9%-39%23%85%-16%-44%4%-4%-11%

-1 0 0 % -8 0 % -6 0 % -4 0 % -2 0 % 0% 20% 40% 60% 80% 100%lgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-FPercentchangeinrisk(fatalityprobabilityper1010VMT)

C u rt a i n si d e a i rb a g C o mb o si d e a i rb a g T o rso si d e a i rb a g An y si d e a i rb a g53

Figure 6.4 indicates that, for example, combo side airbags in a car struck in the side by a light car

reduce the fatality

r isk in the case car by

5 3%. T he figure suggests that combo and torso side

airbag s have a large, and for the most part statistically

- significant, reduction on fatality risk: 17%

to 53% for side combo airbags and 27% to 42% for side torso airbags, depending on the striking

vehicle ty pe. C uriousl y, s ide curtain airbags have a much small er reduction in fatality r isk than

side torso or side combo airbags, and actuall y increase fatality r isk in cars struck by

a nother

lighter car (however, none of the effects of side curtain airbags are statistically s ignificant). The

rightmost panel in Fig ure 6.4 (darker blue) shows the result of a separate regression model that

combines the side airbag variables into a single SI DEAB variable

, as many v ehicles have both

the curtain and the torso side airbag

. T he inclusion of an y ty pe of side airbag results in slightly

lower benefits than estimated for torso side airbags only

, and reduces the sampling error

somewhat. Note that any s ide airbag technology

i s slightl y more beneficial to car occupants

when struck by

a nother car than when struck by a l ight truck

. T he benefits do not appear to be

affected by

t he mass of the striking vehicle ; in other words, when a car is struck in the side by

another car, the protective effect of side airbags is unaffected by the mass of the striking car.

And when a car is struc k in the side by a light truck, the protective effect of side airbags is

unaffected b y the mass of the striking light truck

.

Figure 6.5 indicates that the relationship is less clear for CUVs and minivans struck in the side

by a nother vehicle; side airbags appear to increase fatalit y risk in cars struck in the side by

certain vehicle ty

pes, although none of the effects of the three individual airbag technologies are statistically

s ig nificant in any

o f the side impact crashes with CUVs/minivans (except side torso

airbags in CUVs/minivans struck in the side by a l ight light

- dut y truck, which has a statistically

significant 85% increase in fatality r isk). Modeling the effect of any t ype of side airbag

(rightmost panel, in darker blue) results in a large, but s tatistically insignificant, reduction in risk

in CUVs and minivans struck in the side by a l ight car; the results for the other striking vehicle

ty pes are smaller and also not statistically significant.

Figure 6.6 shows the effect of the three side airbag technologie s on risk to light

- dut y truck

occupants; as with CUVs/minivans, there is no consistent or statistically

- significant protective

effect of side airbags in light

- duty t rucks. ( Recall that NHTSA did not include the side airbag

variables in its reg ression equations for light

- duty t rucks; Figure 6.6 represents a new regression

model that does include these variables). The inclusion of an y ty pe of side airbag improves the

relationship somewhat, with reduced risk when struck by t hree of the four vehic le ty pes, but the

effect is statistically

insignificant in all cases.54

**Figure 6.**  **6**

**. Effect of side airbags on risk to**  **light truck occupants only , when struck in the** 

**side by another light**

**- duty vehicle, by crash partner vehicle type** 

Figure 6. 7 through 6. 9 show the effect of mass reduction in crashes between two light

- dut y

vehicles

, by

c rash partner vehicle ty

pe, for three crash configurations: front

- to

- front crashes (F

F), crashes where the case vehicle is struck in the driver or passenger side (S

- F), and all other

crash configurations (mostly r ear impact crashes). Figure 6. 7 indicates that mass reduction has a

larger effect on risk in side impact crashes (shown in dark blue) than in other crash

configurations in only

o ne case: when a underweight car is st ruck by a l ight light

- duty

t ruck. I n all other cases, the detrimental effect of mass reduction on risk is greater in either frontal crashes

or other crashes with another light

- dut y vehicle, than when the case vehicle is struck in the side.

Full adoption of side airbags, which Figure 6. 4 above suggests will reduce the number of

fatalities in cars struck in the side, will only reduce the influence of mass reduction on risk in this

one ty pe of side impact crash; in crashes with other crash partners, the redu ction of side impact

fatalities will tend to increase the overall effect of mass reduction on risk in cars.

Figure 6. 8 shows that the effect of mass reduction on risk in CUVs and minivans when struck in

the side by

a nother car is slightly

h igher than in o ther crash configurations when the striking

vehicle is a heavy

c ar or a light light

- duty t ruck. T he effect of mass reduction is much larger

than in other crash configurations when the striking vehicle is a heav y light

- dut y truck.

Therefore it appears tha t a reduction in the number fatalities in crashes where a CUV/minivan is

struck in the side would slightly r educe the detrimental effect of mass reduction in CUVs and

minivans. Howev er, as shown in Figure 6.5 above , it is not clear the extent to which sid e airbags

will reduce fatalities when CUVs/minivans are struck in the side.34%8%33%-10%51%-56%-69%33%25%1%-19%-18%12%-17%-8%-18%

-1 0 0 % -8 0 % -6 0 % -4 0 % -2 0 % 0% 20% 40% 60% 80% 100%lgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-Flgtcar:S-Fhvycar:S-FlgtLT:S-FhvyLT:S-FPercentchangeinrisk(fatalityprobabilityper1010VMT)

C u rt a i n si d e a i rb a g C o mb o si d e a i rb a g T o rso si d e a i rb a g An y si d e a i rb a g55

Interestingly

, Figure 6. 9 indicates that the detrimental effect of mass reduction on risk to light

duty truck occupants is for the most part greater when a light truck is struck in the side, than in a

frontal or rear impact crash. I n other words, increased mass appears to have a larger beneficial

safet y effect for light trucks hit in the side than for cars, CUVs, or minivans hit in the side. This

is likely b ecause the relatively

h ig h door sills of light trucks provide some protection from

intrusion of the target vehicle into the occupant compartment; casualties in side impact crashes in

light trucks are therefore mostly t he effect of changes in momentum, which can be mitigated by

increasing the mass of the struck light truck. Door sills of cars and CUVs/minivans are relativel y

lo w, and do not provide as much protection from intrusion of the striking vehicle; therefore,

increasing the mass of cars and CUVs/minivans has little safety benefit in crashes where they are struck in the side, regardless of the striking vehicle ty pe. Alth ough mass reduction is detrimental

to light trucks when they are struck in the side

, recall that Figure 6. 6 above suggests that there is

no statistically

significant benefit of side airbags in preventing fatalities in light

- duty trucks when

struck in the s ide.

Overall, it appears that full adoption of side airbags will reduce fatality r isk in cars struck in the

side by a nother light

- duty

v ehicle; however, if any thing, reduction of this ty pe of fatalit y will

increase the detrimental effect of mass reductio n in cars. I t is not clear whether full adoption of

side airbags will reduce fatality risk when light

- duty

trucks, CUVs and minivans are struck in the

side.

**Figure 6.**  **7**

**. Effect of mass reduction on risk to**  **car occupants only in two** 

**- vehicle crashes, by** 

**cra sh partner vehicle type and crash configuration** 7.34%5.48%7.40%8.52%1.43%6.32%1.99%3.23%-2.58%7.68%6.79%5.01%4.88%-2.29%8.87%3.55%5.71%7.69%3.06%-0.30%-0.72%1.58%4.62%6.71%1.73%6.85%-9.00%-1.40%3.48%-2.51%6.46%4.56%6.56%0.54%1.11%4.82%

-1 8 % -1 4 % -1 0 % -6 % -2 % 2% 6% 10% 14% 18%lgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:otherslgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:otherslgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:othersPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

C a rs < 3

, 1 0 6 C a rs > 3

, 1 0 6 Al l ca rs56

**Figure 6.**  **8**

**. Effect of mass reduction on risk to**  **CUV/minivan occupants only in two** 

**- vehicle** 

**crashes, by crash partner vehicle type and crash configuration**

**Figure 6.**  **9**

**. Effect of mass reduction on risk to**  **li ght truck occupants only**  **in two**

**- vehicle** 

**crashes, by crash partner vehicle type and crash configuration**3.47%2.14%7.77%10.99%11.31%9.64%9.93%12.12%10.52%0.07%3.93%-3.66%4.44%6.03%-8.09%-9.45%-5.61%-2.26%-13.10%-8.02%-9.01%3.50%1.27%4.62%

-1 8 % -1 4 % -1 0 % -6 % -2 % 2% 6% 10% 14% 18%lgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:otherslgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:othersPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t 1 -sq f t re d u ct i o n i n f

o o t p ri n t5.35%8.86%0.29%2.28%7.02%-2.41%3.71%8.05%-1.25%6.47%6.88%1.28%-3.47%2.26%0.95%0.54%0.55%0.25%3.41%6.85%8.06%5.68%7.70%-3.83%2.75%-1.80%1.92%3.07%0.52%3.83%2.67%-0.69%1.98%-2.60%-1.77%2.83%

-1 8 % -1 4 % -1 0 % -6 % -2 % 2% 6% 10% 14% 18%lgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:otherslgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:otherslgtcar:F-Flgtcar:S-Flgtcar:othershvycar:F-Fhvycar:S-Fhvycar:otherslgtLT:F-FlgtLT:S-FlgtLT:othershvyLT:F-FhvyLT:S-FhvyLT:othersPercentchangeinrisk(fatalityprobabilityper1010VMT)

1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 0 0 -l b re d u ct i o n i n w e i g h t

, 1 -sq f t re d u ct i o n i n f o o t p ri n t

L T s < 4

, 5 9 4 L T s > 4

, 5 9 4 Al l L T s57

**6. 3**  **Effect of measures to increase light truck compatibility**

In 2003 manufacturers made a voluntary commitment to reduce the aggressivity of light tr

ucks in

crashes with other vehicles, by incorporating one of two designs in their new vehicles by M Y10.

The first is improving the overlap of light truck bumpers with those of other vehicles. T he

second is adding a secondary e nergy

- absorbing structure (known as a “blocker beam”) behind

and below the bumper, so that it engages the bumper of the other vehicle.

Figure 6. 10 indicates that about 30% to 50% of MY01 light trucks already m et the increased

bumper overlap guidelines; this percent has held fairl y constant, but increased substantiall y in

MY07. F ewer small pickups and SUVs used the blocker beam technology t han bumper overlap

in MY01, but use of the blocker beam has increased in the last few years, particularl y in smaller

pickup trucks.

Figure 6.1 1 shows the effect of the two measures to improve light truck compatibility o n risk to

occupants of the crash partner vehicle, by c rash partner vehicle ty pe and crash configuration.

The figure indicates that greater bumper overlap reduces fatalit y risk in the crash partner vehicle

by 1 % to 12% in frontal crashes, depending on the crash partner vehicle ty pe; however, none of

these reductions are statistically s ig nificant. O n the other hand, greater bumper overlap reduces

fatalities in cars struck in the sid e by a light truck, by

a statistically

- significant 15% (and has onl y a small reduction in fatalities when a light truck strikes another light truck in the side). The

**Figure 6.**  **10**

**. Market penetration of compatibility measures in light trucks, by light tru**  **ck**

**type and model year**

0% 10% 20% 30% 40% 50% 60% 70% 80%

2001 2002 2003 2004 2005 2006 2007Percentofnewvehiclefleet

Mo d e l ye a r

>5 0 % b u mp e r o ve rl a p

, Sm PU Bl o cke r b e a m, Sm PU

>5 0 % b u mp e r o ve rl a p

, L g PU Bl o cke r b e a m, L g PU

>5 0 % b u mp e r o ve rl a p

, SU V Bl o cke r b e a m, SU V58

**Figure 6.1**  **1**

**. Effect of compatibility measures on risk imposed by light trucks on other light** 

**** **duty vehicles, by crash partner vehicle type and crash configuration**

blocker beam technology tends to reduce fatalit y risk in cars struck in either the front or the side

by a light truck, but none of the reductions are statistically significant.

Although better alignment of light truck bumpers with those of other vehicles appears to result in

a statistically

significant reduction in risk imposed on car occupants, the effect of mass reduction

on risk to car occupants when struck in the side by l ig ht trucks is not consistently h igher than in

other ty pes of two vehicle crashes, as shown in Figure 6. 7 above. T herefore, by r educing the

nu mber of fatalities in crashes where light trucks strike cars in the side, full penetration of

measures to improve light truck compatibility w ill likely

h ave little impact on the effect of mass

reduction on risk.

**6. 4**  **Effect of sales shift from SUVs and oth er light trucks to CUVs and other car** 

**- based** 

**vehicles**

Figure 6.12 shows the rapid growth in market share of CUVs, from nearly z ero in MY00 to over

15 % of all new MY0 7 vehicle registrations (from Table 1

- 3 in the NHTSA 2011 report)

. At the

same time the sh are of minivans decreased from 9% to 5%, and 2

- door cars from 11% to 5%.

Between MY01 and MY04 the advent of CUVs appears to have taken market share from 4

- door

cars (from 45% to 39%), while after MY04 the market share of SUVs and pickups declined

( from a combined 36 % to 26 %). It i s likely t hat the share of SUVs, and perhaps pickups, will

decline further if gas prices remain high, as consumers continue to switch to more fuel

- efficient

CUVs and cars

.-7%-15%-1%-16%-2%-3%-12%-3%-11%-5%-4%-4%7%1%9%-6%

-4 0 % -3 0 % -2 0 % -1 0 % 0% 10% 20%lgtcar:F-Flgtcar:F-Shvycar:F-Fhvycar:F-SlgtLT:F-FlgtLT:F-ShvyLT:F-FhvyLT:F-Slgtcar:F-Flgtcar:F-Shvycar:F-Fhvycar:F-SlgtLT:F-FlgtLT:F-ShvyLT:F-FhvyLT:F-SPercentchangeinrisk(fatalityprobabilityper1010VMT)

>5 0 % b u mp e r o ve rl a p Bl o cke r b e a m59

**Figure 6.12.**  **US registrations of new vehicles, by vehic le type and year** 

The effect of long

- term shifts in the market shares among vehicle ty pes can be estimated by

examining the effect of the vehicle ty pe control variables on fatality r isk. Alternative regression

model s were run for the nine crash ty pes

, where all vehicle ty pes

, including sports, police, and

all

- wheel drive cars, and fullsize vans

, were included in the same regression model for each

crash ty pe

. T he single variable LBS100 was used to account for vehicle curb weight, while all

vehicle ty

pe variables and the four side airbag variables were included

. To avoid double

counting, each fatal crash is weighted by the number of total fatalities in each crash divided by

the number of case vehicles involved in each crash, so that the total number of f atalities in a given crash are evenl y allocated to each of the case vehicles involved in the crash. The

regression coefficients on mass

, footprint , and vehicle ty pe were then reweighted by the expected

distribution of fatalities in 2017

- 2025, to reflect f ull adoption of ESC

. U sing the results by c rash

ty pe over all vehicle ty

pes

, a 100

- lb reduction in vehicle mass results in a statistically s ig nificant

0. 27 % increase in fatality r isk, while a 1

- square foot reduction in footprint results in a n insignifican t 0. 0 9 % increase in risk. F ig ure 6.1 3 compares the se effect s of mass and footprint

reduction on risk with those by vehicle t ype relative to risk in a four

- door sedan. Compared to

four

- door sedans of the same mass and footprint, for a driver of the same ag e and gender, and for

equal values on the other control variables such as urbanization and time of day

, s ports cars (52%), police cars (44%), SUVs ( 40 %), fullsize vans (37%), heav y

- duty p ickups ( 3 6 %), and

other pickup trucks ( 2 2 %) have much higher risk than four

- door sedans (shown in light blue in

Figure 6.13). T wo

- door sedans (16%) and CUVs ( 12 %) have higher risk

, while all

- wheel drive

cars and minivans have about the same risk as four

- door sedans, after accounting for all

differe nces in vehicles, drivers, and crash times and locations

. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Distributionofnewvehicleregistrations

Y e a r 2 -d r ca rs

4 -d r ca rs

Pi cku p s

SU Vs

C U Vs

Mi n i va n s60

**Figure 6.1**  **3**

**. Effect of mass and footprint reductions across all vehicle types, and a** 

**comparison of fatality risk by vehicle type compared to four**

**- door sedans** 

However, much of the additional ris k in pickups relative to four

- door seda ns is due to who drives

these vehicles and where they a re driven

. T able 6.1 shows the average driver (percent male and

average age) and crash (at night, on rural roads, on high

- speed roads, and in high fatality s tate s)

characteristics by

v ehicle ty

pe. P ickups have the highest fractions of male drivers, and are

driven the most on rural, high

- speed roads in high

- fatality s tates. Therefore fatalit y risks by

vehicle t ype need to account for driver and crash variables be fore estimating the effect of shifting

the distribution from light trucks to other vehicle ty pes. F ig ure 6.13 also shows the risk by

vehicle ty

pe relative to that of a four

- door sedan, after accounting for the driver and crash

variables (in purple). Acco unting for how and where vehicles are driven reduces the risk of light

trucks relative to that of four

- door sedans, so that the heav y

- dut y pickups have essentially t he

same risk as four

- door sedans.

To simulate the effect of drivers switching from a relat ivel y high

- risk vehicle such as a light

truck, to a lower risk vehicle such as a four

- door sedan, we need to estimate the risk per ten

billion VMT of light truck drivers in a four

- door sedan. Table 6.2 shows estimated risks for each

vehicle ty

pe, after ac counting for all vehicle, driver, and crash time/location control variables

used in NHTSA’s preferred regression model (first column), and after moving the average driver

of the case vehicle into one of four ty pes of safer vehicles (last four columns)

. For example,

heav y

- dut y pickups have a risk of 198 fatalities per ten billion VMT; if all the heavy

- dut y pickup

drivers, who are overwhelmingl y male and mostly d rive on high

- speed roads in rural counties in

high

- risk states, were to instead drive four

- door sedans, their risk would be 192 fatalities per ten

billion VMT (much higher than that of the ty pical car driver, 114). T he incremental effect of

heav y

- dut y pickup owners driving at the same times in the same locations, but in a car instead of

a heav y

- dut y pickup, is only 6 f atalities per ten billion VMT (198

– 192), and not the difference0.27%0.09%16%52%44%0%22%36%40%12%-2%37%16%49%35%-20%6%3%16%-3%-9%21%

-4 0 % -3 0 % -2 0 % -1 0 % 0% 10% 20% 30% 40% 50% 60%CurbwtFootprint2-doorcarSportscarPolicecar4-wdcarSmallpickupHDpickupSUVCUVMinivanFullsizevanPercentchangeinrisk(fatalityprobabilityper1010VMT) d ri ve r

, a n d cra sh va ri a b l e s

Al l va ri a b l e s

V e h i cl e t yp e

, a n d d ri ve r/ cra sh va ri a b l e s o n l y61

between the risk of the average driver of a heavy

- duty p ickup and that of the average driver of a

four

- door sedan (88 fatalities per ten billion VMT, or 198

– 114). The initial risks of light truck

drivers are shown in green in Table 6.2; the risks if those drivers instead drove a four

- door sedan,

all

- wheel drive car, CUV, or minivan are shown in red in the table. W e use the difference

between the risks in green and the risks in red in Table 6.2 to simulate the effect of a fraction of

drivers replacing their light trucks with safer cars, CUVs, and minivans.

**Table 6.1. Average driver characteristics and crash times and locations, by vehicle type**

Vehicle t ype Driver

characteristics Crash times and locationsDRVMALEDRVAGENITERURALSPDLIM55HIFAT_ST

2

- dr cars 61% 32 52% 48% 52% 51%

4

- dr sedans 57% 42 40% 49% 52% 53%

Sports cars 76% 31 59% 47% 48% 59%

Police cars 87% 41 59% 44% 41% 59%

AWD cars 69% 40 42% 42% 45% 31%

Sm all pickups 85% 40 41% 64% 61% 65%

Heav y

- dut y pickups 91% 41 35% 71% 65% 62%

SUVs 59% 39 39% 53% 58% 58%

CUVs 51% 44 35% 47% 53% 48%

Minivans 55% 48 30% 54% 58% 49%

Fullsize vans 85% 42 26% 46% 54% 47%

All 66% 41 40% 53% 56% 56%

**Table 6.2. Actual risk per billion VMT, and risk adjusted to the average driver and crash**

**time/location in a four** 

**- door sedan, all** 

**- wheel** 

**- drive car, CUV, and minivan, by vehicle type** 

Case v ehicle t ype Actual risk

(per 10

billion VMT) Adjusted risk after moving the average driver and

crash location/time of the case vehicle into a :

4

- door

sedan AWD car CUV Minivan

2

- dr cars 140 121 96 117 109

4

- dr sedans 114 114 91 111 103

Sports cars 201 134 107 131 122

Police cars 206 153 121 148 138

AWD cars 73 92 73 89 83

Sm all pickups 170 159 127 155 144

Heav y

- dut y pickups 198 192 152 186 173

SUVs 130 112 89 109 102

CUVs 85 87 70 85 79

Minivans 94 104 82 101 94

Fullsize vans 129 106 84 103 9662

In i ts 2003 report, NHTSA estimated the effect of a change in the mix of vehicle ty pes on the

number of fatalities (Section 5.7, page 220). W e conduct a similar estimate here, based on the

updated data on US fatalities and estimated vehicle miles traveled. T able 6. 3 shows the number

of fatalities in crashes involving case light

- dut y vehicles from model years 200 0 through 200 7

, for the last five years of data avai lable (between 2004 and 2008 ) ; the number of fatalities has

been adjusted to account for full use of ESC by 2 017

. T o a void double

- counting, the number of

total fatalities in each crash is divided by t he number of case vehicles involved in each crash, so

that the total number of fatalities in a given crash are evenl y allocated to each of the case

vehicles involved i n the crash.

Table s 6. 2 and 6.3 can be used to estimate how the number of fatalities involving model ye ar

200 0 to 2007 vehicles would be changed by s hifting VMT among the vehicle ty pes

, while

maintaining the average driver and crash time/location charact eristics of the original vehicle

ty pes

. F or example, moving SUV driver s into CUVs

, while maintaining the driver

characteristics and driving times and locations of the SUVs, would reduce the fatalit y risk from

130 to 109 fatalities per ten billion VMT. Tab le 6. 4 shows the results from three scenarios of

shifting market share: replacing 10% of SUVs with CUVs, replacing 10% of small pickups with

CUVs, and replacing 10% of large SUVs with SUVs. T he last scenario shown in Table 6. 4

simulates an aggressive shif t in vehicle market share: replacing 80% of SUVs (50% with CUVs,

20% with minivans, and 10% with AWD cars); replacing 80% of small pickups (60% with

CUVs, and 20% with four

- door cars); and replacing 50% of large pickups (25% with C UVs and

25% with mini vans ). T his aggressive shift in market share would reduce fatalities of model ye ar

200 0 to 2007 vehicles by 2,5 00 , resulting in a 3.5 % reduction in fatalities.

**Table 6. 3** 

**. Average annual f**  **atalities and VMT**  **in model year 2000**  **to 200 7**  **light**

**- duty** 

**vehicles between**  **200 4**  **and 2008, by vehicle type** 

Vehicle t ype Fatalities

2

- dr cars 5,188

4

- dr sedans 26,015

Sports cars 1,522

Police cars 631

AWD cars 561

Small pickups 12,414

Heav y

- dut y pickups 4,443

SUVs 12,414

CUVs 4,089

Minivans 3,558

Fullsize vans 1,481

Total 72,31663

**Table 6. 4**  **. Estimated change in annual**  **fatalities from four scenarios of shifts among vehicle**

**types**

Scenario Decrease in

fatalities Percent change

in fatalities

1. Replace 10% of SUVs with CUVs

- 183

- 0.3%

2. Replace 10% of small pickups with CUVs

- 79

- 0.1%

3. Replace 10% of large pickups with minivans

- 43

- 0.1%

4. Replace 80% of SUVs and small pickups, an d

50% of heav y

- dut y pickups

- 2,500

- 3.5%

Although the reductions in fatalities from shifting light truck drivers into safer, car

- based

vehicles are small in percentage terms, they are larger than the mass reduction scenarios NHTSA

simulated in its 2011 report (Section 3.6, Table 3

- 8). The four mass reduction scenarios NHTSA

simulated, including the effect of reducing mass of a ll vehicles by 100 lbs, all result in less than a

0.5% change in fatalities. Even an aggressive mass reduction scenario, in which the mass of

lighter and heavier light trucks are reduced 1,351 and 1,872 lbs, respectively , to the average mass

of lighter an d heavier cars, would reduce fatalities by o nl y 0.5%.

. Therefore, shifts in market

share from more dangerous vehicles such as light trucks to safer car

- based vehicles would result

in much larger reductions in fatalities than the small changes in fatalitie s expected from even

substantial reductions in vehicle mass.64

**7. Conclusions**

NHTSA recentl y completed a logistic regression anal ys is updating its 2003 and 2010 studies of

the relationship between vehicle mass and US fatality risk per vehicle mile travel ed (VMT). The

new study

u pdates the previous analy ses in several way s: updated FARS data from 2002 to 2008

for MY00 to MY07 vehicles are used; induced exposure data from police reported crashes in

several additional states are added; a new vehicle categor y for car

- based crossover utility

vehicles (CUVs) and minivans is created; crashes with other light

- duty v ehicles are divided into

two groups based on the crash partner vehicle’s weight, and a category f or all other fatal crashes

is added; and new control variables for new safety t echnologies and designs, such as electronic

stability c ontrols (ESC), side airbags, and methods to meet voluntary agreement to improve light

truck compatibility

with cars, are included.

Using the updated NHTSA databases, our anal ysis finds that reducing vehicle mass by 1 00 lbs

while holding footprint fixed would increase fatality r isk per VMT by

1 .44% for lighter than

average cars, and 0.52% for lighter than average light

- dut y trucks. H owever, mass reduction in

heavier than avera ge light

- duty t rucks, while holding footprint constant, would reduce risk by

0.40%. The effect of mass reduction on heavier cars and CUVs and minivans are not statistically

significant. ( Using a different method to estimate the uncertaint y around these point estimates,

NHTSA found that onl y the effect of mass reduction o n lighter than average cars is statistically

significant.) N HTSA concludes that, when footprint is held fixed, “no judicious combination of

mass reductions in the various classes of vehicles results in a statistically s ig nificant fatalit y

increase and man y potential combinations are safety

- neutral as point estimates”.

The effect of mass reduction on risk that NHTSA calculated in 2011 is much smaller than in its

2003 and 2010 studies, particularl y for cars. N HTSA attributes this reduction in the importa nce

of mass reduction on safety t o the phase

- out of relativel y light cars that had unusually h ig h

fatality r isk, an observed improvement in how light, small cars are driven which reduces their

tendency t o be involved in serious crashes, and voluntary i mpro vements made to light trucks to

improve their compatibility w ith other vehicles. T he 2011 NHTSA anal ysis finds that reducing

vehicle footprint by

o ne square foot while holding mass fixed would increase fatality r isk per

VMT by 1 .89% in cars and 1.73% in C UVs and minivans (the effect on risk in light trucks is

small and not statistically significant).

Rather than rel yi ng on the confidence intervals output by the logistic regression models, NHTSA

estimates the uncertaint y around its point estimates using a jack

- knife technique that accounts for

the sampling error in the FARS fatality a nd state crash data. T hese uncertainty e stimates are larger than the confidence intervals output by t he logistic regression models included in this

report. A s a result, in its report NHTSA finds that only t he 1.44% increase in risk from mass reduction in lighter than average cars is statistically significant.

This report replicates the 2011 NHTSA analy sis, and examines the data in slightly different way s

to get a deeper understanding of the relationship between vehicle weight/footprint and safety

. The results of these alternative an al ys es are summarized in Table 7 .1; statistically s ig nificant

results, based on the confidence intervals output by t he logistic regression mo dels, are shown in

red in the table. In particular, we found that:65

• NHTSA’s (reasonable) assumption that all vehicles will have ESC installed by 2 017

slightly increases the detrimental effect of mass reduction, but slightly decreases the

detrimental eff

ect of footprint reduction, on risk in cars, CUVs and minivans (Alternative 1

in Table 7 .1; explained in more detail in Section 2.1 of this report).

• Man y of the control variables NHTSA includes in its logistic regressions are statistically

significant, and have a large effect on fatalit y risk. F or example, a car’s mass could be

reduced by

8 00 lbs while adding ESC without increasing its fatality r isk. A nd increasing

the amount of vehicle travel on highway s with speed limits greater than 55 miles per ho ur by 0 .35% would result in the same increase in risk as reducing the mass of all cars by 1 00

lbs. While the effect of mass reduction may r esult in a statistically

- significant increase in

risk in certain cases, the increase is small and is overwhelmed by other known vehicle,

driver, and crash factors.

• Vehicle mass and footprint are correlated, but only s trongl y for passenger cars. NHTSA

includes both variables in their regression models, introducing the possibility t hat multi

collinearity

m ay c reate bia sed results. W hen footprint is allowed to vary a long with

weight, mass reduction results in a larger increase in risk than when footprint is held

constant. S imilarly

, when mass is allowed to vary along with footprint, footprint reduction

results in large r increases in risk (Alternative 2 in Table 7 .1, further addressed in Section 3

of this report). To isolate the effect of mass reduction from footprint reduction on risk,

NHTSA estimates the effect of mass reduction on risk for deciles of vehicles with sim ilar

footprint. Mass reduction does not consistently increase risk across all footprint deciles for

any c ombination of vehicle ty pe and crash ty

pe. Mass reduction increases risk in a majority o f footprint deciles for 13 of the 27 crash and vehicle combin ations, but few of

these increases are statistically significant (the increases are statistically significant only

f or light

- duty t rucks in rollovers). O n the other hand, mass reduction decreases risk in a majority of footprint deciles for 9 of the 27 cra sh and vehicle combinations. In some cases

these risk reductions are large and statistically s ignificant (such as in cars in rollovers and

crashes with stationary

o bjects; light

- dut y trucks in crashes with light and heav y cars; and

CUVs and minivans in cr ashes with heav y cars).

• After accounting for all of the variables in NHTSA’s logistic regression model, except for

vehicle mass and footprint, we find that the correlation between fatality risk by v ehicle

model and mass is very

l ow. T here also is no s ignificant correlation between the residual,

unexplained risk and vehicle weight. These results indicate that, even after accounting for

many v ehicle, driver, and crash factors, the variance in risk by v ehicle model is quite large

and unrelated to vehicle weight (addressed in more detail in Section 4).

• Changes in the data and variables NHTSA used in its regression models have only s light

changes on NHTSA’s results. C alculating risk as fatal crashes, rather than total fatalities,

per vehicle mile trav eled, as suggested by one of the independent reviewers of the previous

NHTSA reports, increases the detrimental effect of mass reduction on risk in cars, but has

no effect on mass reduction in light trucks or CUVs/minivans, or on footprint reduction in

any vehicle t ype (Alternative 3 in Table 7. 1). Calculating risk as total fatalities per induced66

exposure crash, rather than per vehicle mile traveled, reverses the sign of th e effect of mass

reductions on risk in cars and the lighter light trucks, with mass reduction leading to a

reduction in risk in all vehicle ty pes. F ootprint reduction continues to result in large

increases in risk per induced exposure crash for cars and CUVs/minivans, but leads to a large reduction in fatality risk per induced exposure c rash for light trucks (Alternative 4 in

Table 7. 1; further addressed in Section 5.1).

• Adding control variables for vehicle manufacturer tends to increase the effect of mass

reduction, but decrease the effect of footprint reduction, on risk for cars and light trucks,

and makes mass reduction detrimental, and footprint reduction slightly beneficial, for

CUVs/minivans (Alternative 5 in Table 7. 1).

• NHTSA included control variables for the calendar ye ar in which the crash occurred, to

reflect reducing ris k from changes to vehicles, driver behavior and driving conditions over

time. H owever, including these calendar ye ar variables in the regression models appear to

weaken the benefit of curtain side air bags in cars, CUVs, and minivans, and compatibility

me asures and ESC in light trucks. T hese variables also appear to minimize the increased

risk of SUVs and heavy

- duty p ickup trucks. E xcluding these calendar year variables from

the regression models increases the detrimental effect of mass reduction on risk in light

trucks (Alternative 6 in Table 7. 1, addressed in Section 5.3).

• Excluding crashes involving alcohol or drugs, or drivers with poor driving records, also

increases the detrimental effect of mass reduction on risk, but reduces the detrimental eff ect

of footprint reduction on risk (Alternatives 7 and 8 in Table 7. 1, Section 5.4). I ncluding

all

- wheel

- drive, sports, and police cars increases the effect of mass reduction, but reduces

the effect of footprint reduction, on risk for cars; while includin g fullsize vans reduces the

effect of mass reduction, and increases the effect of footprint reduction, on risk for light

trucks (Alternative 9 in Table 7. 1, Section 5.5).

• R ecent trends that are likely to continue through 2017 may a ffect the distributio n of crashes

in that year. F or example, side airbags in cars will likely r educe the fraction of fatalities in

side

- impact crashes (Section 6.1), and better alignment of light truck bumpers with those of

other vehicles appears to reduce the risk imposed on car occupants, at least in side impact

crashes (Section 6.2). H owever, it appears that mass reduction has less of a detrimental

effect on risk when cars are struck in the side than when they a re involved in frontal or

rear

- end crashes, so any future redu ction in fatalities in car side impact crashes will not

necessarily

i nfluence the effect of mass reduction on risk. A nd it is not clear whether full

adoption of side airbags or compatibility m easures for light trucks will reduce fatality r isk

when light

- d

ut y trucks, CUVs or minivans are struck in the side.

• Finally

, in part because of high gas prices and the poor econom y, households have been

purchasing smaller and lighter vehicles in the last decade. F or example, the explosion of

CUVs appears to have led to a reduction in the market share of minivans, cars, and in

recent ye ars (MY05 to MY07) SUVs and pickups. I t is likely t hat these trends would

continue, even in the absence of stronger CAFE and GHG emission standards. An y future

market shifts from SU Vs or pickups to cars or car

- based CUVs and minivans will result in67

much larger reductions in fatality r isk than the relatively

s mall increases in risk expected

from mass or footprint reduction. F or example, we estimate that a large

- scale shift in the

mar ket share of pickups and SUVs to CUVs, minivans, and cars will reduce overall

fatalities by

nearl y 4% (Section 6.3).

Table 7. 2 compares the results from NHTSA’s 2003, 2010, and 2011 analy ses with the

alternative model specifications examined in this repor t (again, results that are statisticall y significant are shown in red in the table). The first two columns of the table indicate that

NHTSA’s 2011 analy sis of a simultaneous reduction in mass and footprint (i.e. excluding a

control variable for footprint i n the regression model) results in a smaller increase in fatalities

than in NHTSA’s 2003 anal ys is, particularl y for lighter cars (a 2.64% increase rather than a

4.39% increase) and light trucks (a 0.52% increase rather than a 2.90% increase). T he third an d

fourth columns of the table indicate a similar reduction in additional fatalities for cars when

footprint is held constant (i.e. when a control variable for footprint is included in the regression

model). H owever, holding footprint constant increases th e effect of mass reduction slightl y in

light trucks (a 0.52% increase rather than a 0.17% increase in fatalities for lighter light trucks,

and a 0.40% reduction rather than a 1.90% reduction in fatalities for the heavier light trucks).

This small increase in light truck risk may be due to NHTSA analy zing crossover utility vehicles

and minivans as a separate vehicle class, rather than as light trucks, in the 2011 anal ys is.

The last column in Table 7. 2 shows that the results of the alternative model specifications

examined in this report are, in all cases, lower than the results of the 2003 NHTSA report, and

often lower than the results of the 2010 and 2011 analy ses.

The 2011 NHTSA study, and this report, conclude that the effect of mass reduction on US

fatality r isk is small. T his report indicates that although the effect is sensitive to what variables

and data are included in the regression anal ys is, in nearl y all cases the effect is less, in some

cases dramaticall y less, than reported in the 2003 NHTSA study

. T his report also finds that the

effect of other control variables, such as vehicle ty pe, specific safety

t echnologies, and crash

conditions such as whether the crash occurred at night, in a rural county , or on a high

- speed road,

on risk is mu ch larger than the effect of mass or footprint reduction on risk. F inall y, this report

shows that after accounting for the man y vehicle, driver, and crash variables NHTSA used in its

regression anal ys es, there remains a wide variation in risk by v ehicle m ake and model, and this

variation is unrelated to vehicle mass.

The results of the NHTSA study and this assessment of it are based on the relationship of vehicle

mass and footprint on risk for recent vehicle designs (model ye ar 2000 to 2007). T hese

relat ionships may

o r may n ot continue into the future as manufacturers utilize new vehicle

designs and incorporate new technologies, such as more extensive use of strong lightweight

materials and specific safety technologies.68

**Table 7.**  **1. Effect of mass and footprint reduction on fatality risk, under alternative regression model specifications** 

Variable Case vehicle t ypeNHTSApreferredmodel(fatalitiesperVMT)1.Usingcurrentdistributionoffatalities2.Excluding.massorfootprintvariable3.FatalcrashesperVMT4.Fatalitiesperinducedexposurecrash5.Accountingforvehiclemanufacturer6.ExcludingCYvariables7.Excludingcrasheswithalcohol/drugs8.Excluding.baddrivers9.Includingsports,squad,AWDcarsandfullsizevans

Mass

reduction Cars < 3106 lbs 1.43%* 1.18% 2.64% 1.84%

- 0.24% 1.81% 1.39% 1.66% 2.03% 1.64%

Cars > 3106 lbs 0.48% 0.30% 1.94% 0.86%

- 1.43% 0.67% 0.39% 0.77% 1.04% 0.73%

LTs < 4594 lbs 0.52% 0.38% 0.52% 0.54%

- 1.14% 0.60% 1.22% 0.75% 0.97% 0.34%

LTs > 4594 lbs

- 0.40%

- 0.44%

- 0.41%

- 0.48%

- 0.75%

- 0.21% 0.25%

- 0.40%

- 0.16%

- 0.95%

CUV/ minivan

- 0.47%

- 0.77% 0.52%

- 0.56%

- 0.69% 0.92%

- 0.06%

- 0.28%

- 0.20%

- 0.47%

Footprint

reduction Cars 1.89% 2.22% 2.92% 1.85% 2.20% 1.72% 2.14% 1.77% 1.52% 1.65%

LTs

- 0.02% 0.25% 0.10% 0.19%

- 1.28%

- 0.24%

- 0.38%

- 0.17%

- 0.29% 0.16%

CUV/ minivan 1.73% 2.26% 1.26% 1.82% 1.97%

- 0.09% 1.63% 1.43% 1.26% 1.73%

* Based on NHTSA’s estimation of uncertaint y using a jack

- knife method, only m ass reduction in cars less than 3,106 lbs has a statistically

significant effect on US fatalit y risk.

**Table 7.**  **2. Previous NHTSA results of the ef**  **fect of mass and footprint reduction on fatality risk compared with different**

**scenarios analyzed in this report**

Variable Case vehicle t ype NHTSA

(2003)

excluding

footprint NHTSA

(2011)

excluding

footprint NHTSA

(2010)

including

footprint NHTSA

(2011)

including

footprint Range of different scenarios analy zed

in this report

Mass

reduction Cars < 3106 lbs 4.39% 2.64% 2.21% 1.43%

- 0.24% to 2.64%

Cars > 3106 lbs 1.98% 1.94% 0.89% 0.48%

- 1.43% to 1.94%

LTs < 4594 lbs 2.90% 0.52% 0.17% 0.52%

- 1.14% to 1.22%

LTs > 4594 lbs 0.48%

- 0.41%

- 1.90%

- 0.40%

- 0.95% to 0.25%

CUV/ minivan 0.52%

- 0.47%

- 0. 77 % to 0. 9 2%

Footprint

reduction Cars 1.89% 1.52% to 2.92%

LTs

- 0.02%

- 1.28% to 0.2 5 %

CUV/ minivan 1.73%

- 0.09% to 2.2 6 %