LBNL-xxxxx

Assessment 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 by

Tom Wenzel

Energy Analysis 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 Efficiency and Renewable Energy of the U.S. Department of Energy

under Contract No. DE-AC02-05CH11231.
DISCLAIMER

This document was prepared as an account of work sponsored by the 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 California, nor any of their employees, makes any warranty, express or 

implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, 

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

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

manufacturer, or otherwise, 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 

and 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 opportunity 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 Transportation Safety Administration, U.S. 

Department of Transportation; and Phil Price, Lawrence Berkeley National Laboratory.

The report was funded by Carol 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 of this research.

This work was supported by the Assistant Secretary for Energy Efficiency and Renewable 

Energy, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
ii
iii

Executive Summary

NHTSA recently completed a logistic regression analysis updating its 2003 and 2010 studies of 

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

new study updates the previous analyses in several ways: 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 category for car-based crossover utility 

vehicles (CUVs) and minivans is created; crashes with other light-duty vehicles are divided into 

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

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

stability controls (ESC), side airbags, and methods to meet voluntary agreement to improve light 

truck compatibility with cars, are included.

Using the updated NHTSA databases, our analysis finds that reducing vehicle mass by 100 lbs 

while holding footprint fixed would increase fatality risk per VMT by 1.44% for lighter than

average cars, and 0.52% for lighter than average light-duty trucks. However, mass reduction in 

heavier than average light-duty trucks, 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 uncertainty around these point estimates, 

NHTSA found that only the effect of mass reduction on 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 significant fatality 

increase and many 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, particularly for cars. NHTSA attributes this reduction in the importance 

of mass reduction on safety to the phase-out of relatively light cars that had unusually high 

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

tendency to be involved in serious crashes, and voluntary improvements made to light trucks to 

improve their compatibility with other vehicles. The 2011 NHTSA analysis finds that reducing 

vehicle footprint by one square foot while holding mass fixed would increase fatality risk per 

VMT by 1.89% in cars and 1.73% in CUVs and minivans (the effect on risk in light trucks is 

small and not statistically significant). 

Rather than relying on the confidence intervals output by the logistic regression models, NHTSA 

estimates the uncertainty around its point estimates using a jack-knife technique that accounts for 

the sampling error in the FARS fatality and state crash data. These uncertainty estimates are 

larger than the confidence intervals output by the logistic regression models included in this 

report. As a result, in its report NHTSA finds that only the 1.44% increase in risk from mass 

reduction in lighter than average cars is statistically significant.

This report replicates the 2011 NHTSA analysis, and examines the data in slightly different ways 

to get a deeper understanding of the relationship between vehicle weight/footprint and safety. 

The results of these alternative analyses are summarized in Table ES.1; statistically significant 

results, based on the confidence intervals output by the logistic regression models, 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 2017 

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

detrimental effect of footprint reduction, on risk in cars, CUVs and minivans (Alternative 1 

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

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

crashes with stationary objects, and mass reduction reduces risk, while footprint reduction 

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

• Many of the control variables NHTSA includes in its logistic regressions are statistically 

significant, and have a large effect on fatality risk. For example, a car’s mass could be 

reduced by 800 lbs while adding ESC without increasing its fatality risk. And increasing 

the amount of vehicle travel on highways 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 100 

lbs. While the effect of mass reduction may result 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 strongly for passenger cars. NHTSA 

includes both variables in their regression models, introducing the possibility that multicollinearity may create biased results. When footprint is allowed to vary along with 

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

constant. Similarly, when mass is allowed to vary along with footprint, footprint reduction 

results in larger increases in risk (Alternative 2 in Table ES.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 similar

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

any combination of vehicle type and crash type. Mass reduction increases risk in a 

majority of footprint deciles for 13 of the 27 crash and vehicle combinations, but few of 

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

light-duty trucks in rollovers). On the other hand, mass reduction decreases risk in a 

majority of footprint deciles for 9 of the 27 crash and vehicle combinations. In some cases 

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

crashes with stationary objects; light-duty trucks in crashes with light and heavy cars; and 

CUVs and minivans in crashes with heavy cars).

• Logistic regression does not allow a statistic, such as the model R

2

in a linear regression 

model, to measure how much variability in risk by vehicle model is explained by the 

control variables included in the model. Analysis 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 many 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 risk by vehicle model and mass is 

very low. There also is no significant correlation between the residual, unexplained risk 

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

driver, and crash factors, the variance in risk by vehicle 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 slight 

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

per vehicle mile traveled, 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 type (Alternative 3 in Table ES.1). Calculating risk as total fatalities per 

induced exposure crash, rather than per vehicle mile traveled, reverses the sign of the 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 types. Footprint 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 (Alternative 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 year in which the crash occurred, to 

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

time. However, including these calendar year variables in the regression models appear to 

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

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

risk of SUVs and heavy-duty pickup trucks. Excluding these calendar year variables from 

the regression models increases the detrimental effect of mass reduction on risk in light 

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). Including 

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 fullsize 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 will have 

ESC installed by 2017, which will reduce the fraction of fatalities in rollovers and crashes 

with stationary objects, and thus will increase the detrimental overall effect of mass 

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

However, other recent trends that are likely to continue through 2017 may also affect the 

distribution of crashes in that year. For example, side airbags in cars will likely reduce 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, 
vi

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

less of a detrimental effect on risk when cars are struck in the side than when they are 

involved in frontal or rear-end crashes, so any future reduction in fatalities in car side 

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

not clear whether full adoption of side airbags or compatibility measures for light trucks 

will reduce fatality risk when light-duty trucks, CUVs or minivans are struck in the side.

• Finally, in part because of high gas prices and the poor economy, households have been 

purchasing smaller and lighter vehicles in the last decade. For example, the explosion of 

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

recent years (MY05 to MY07) SUVs and pickups. It is likely that these trends would 

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

market shifts from SUVs or pickups to cars or car-based CUVs and minivans will result in 

much larger reductions in fatality risk than the relatively small increases in risk expected 

from mass or footprint reduction. For 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 nearly 4% (Section 6.3).

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

alternative model specifications examined in this report (again, results that are statistically 

significant are shown in red in the table). The first two columns of the table indicate that 

NHTSA’s 2011 analysis 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 analysis, particularly 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). The 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). However, holding footprint constant increases the effect of mass reduction slightly 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 analyzing crossover utility vehicles 

and minivans as a separate vehicle class, rather than as light trucks, in the 2011 analysis.

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 results of the 2010 and 2011 analyses.

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

fatality risk is small. This report indicates that although the effect is sensitive to what variables 

and data are included in the regression analysis, in nearly all cases the effect is less, in some 

cases dramatically less, than reported in the 2003 NHTSA study. This report also finds that the 

effect of other control variables, such as vehicle type, specific safety technologies, and 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. Finally, this report 

shows that after accounting for the many vehicle, driver, and crash variables NHTSA used in its 
vii

regression analyses, there remains a wide variation in risk by vehicle 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 year 2000 to 2007). These 

relationships may or may not 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.
viii

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

Variable Case vehicle type N

H

T

S

A

pre

ferre

d

m

o

d

el (fatalities p

er

V

M

T)

1. Usin

g

c

urre

nt

distrib

utio

n

o

f

fatalities2. E

x

clu

din

g. m

ass

or fo

otprint v

aria

ble

3. F

atal crash

es p

er

V

M

T

4. F

atalities p

er

in

du

c

e

d

e

x

p

osure

crash5. A

c

c

o

u

ntin

g

for

v

e

hicle

m

a

n

u

fa

cturer

6. E

x

clu

din

g

C

Y

v

aria

bles

7. E

x

clu

din

g

crash

es

with

alc

o

h

ol/dru

gs

8. E

x

clu

din

g. b

a

d

driv

ers

9. In

clu

din

g sp

orts,

sq

u

a

d, A

W

D

c

ars

a

n

d

fullsiz

e

v

a

ns

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 uncertainty using a jack-knife method, only mass reduction in cars less than 3,106 lbs has a 

statistically significant effect on US fatality risk.

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

scenarios analyzed in this report

Variable Case vehicle type

NHTSA

(2003) 

excluding 

footprint

NHTSA 

(2011) 

excluding 

footprint

NHTSA 

(2010) 

including 

footprint

NHTSA 

(2011) 

including 

footprint

Range of different scenarios analyzed 

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 0.92%

Footprint

reduction

Cars 1.89% 1.52% to 2.92%

LTs -0.02% -1.28% to 0.25%

CUV/ minivan 1.73% -0.09% to 2.26%
ix

Table of Contents

Acknowledgements.......................................................................................................................... i

Executive Summary.......................................................................................................................iii

1. Introduction................................................................................................................................. 1

2. NHTSA results............................................................................................................................ 1

2.1. Data and methods................................................................................................................. 2

3. Multi-collinearity between vehicle mass and footprint ............................................................ 14

4. Fatality risk by 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. Influence 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 SUVs 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 fatality 

risk compared with different scenarios analyzed in this report ........................................... viii

Table 2.1. Control variables used in regression models, by subject vehicle type........................... 3

Table 2.2. Baseline fatal crash involvements, by case vehicle type and crash type ...................... 4

Figure 2.1. Effect of mass and size variables on risk, across all crash types and weighted average 

effect in each type of crash, by vehicle type........................................................................... 6

Figure 2.2. Effect of mass and footprint reduction on risk in cars, by type of crash...................... 7

Figure 2.3. Effect of mass and footprint reduction on risk in light trucks, by type of crash .......... 8

Figure 2.4. Effect of mass and footprint reduction on risk in CUVs/minivans, by type of crash... 9

Figure 2.5. Effect of selected control variables on risk, passenger 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 selected 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. Correlation between vehicle curb weight and footprint, by vehicle model and three 

vehicle types.......................................................................................................................... 14
x

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

vehicle types.......................................................................................................................... 15

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

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

type........................................................................................................................................ 17

Figure 3.5. Effect of reduction in light-duty truck mass or footprint on US fatality risk per VMT, 

by crash type ......................................................................................................................... 17

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

by crash type ......................................................................................................................... 18

Figure 3.7. Range in curb weight for the footprint deciles, by vehicle type................................. 19

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

risk, by vehicle and crash type.............................................................................................. 20

Figure 3.8. Effect of car mass reduction on fatality risk, by footprint decile and crash type ....... 21

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

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

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

type........................................................................................................................................ 22

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

100-lb increments of curb weight, by vehicle type............................................................... 23

Figure 4.2. Relationship between US fatality risk and curb weight, with vehicles grouped 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 increments 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 weight, CUVs and minivans....................................................... 24

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

weight, by vehicle type ......................................................................................................... 25

Figure 4.6. US fatality risk per VMT and curb weight, by 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. Residual 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 footprint 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 and 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....................... 33
xi

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 type 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 mass and 

footprint, by vehicle type 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 type ........................................ 37

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

for vehicle manufacturer, by vehicle type............................................................................. 39

Figure 5.3. Effect of calendar year variables on risk, by vehicle type.......................................... 40

Figure 5.4. Effect of calendar year variables on light truck risk, by crash type ........................... 40

Figure 5.5. NHTSA 2003 effect of calendar year variables on risk, by vehicle type ................... 41

Figure 5.6. NHTSA 2003 effect of calendar year variables on light truck risk, by crash type..... 42

Figure 5.7. US fatality risk per VMT, by vehicle type and calendar year.................................... 43

Figure 5.8. Total US VMT for MY2002 vehicles, by vehicle type and calendar year................. 43

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

variables................................................................................................................................ 44

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

year 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, by 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, by vehicle type.......................... 48

Figure 6.1. Market penetration of ESC, by vehicle type and model year..................................... 49

Figure 6.2. Effect of ESC on fatality risk, by vehicle type and crash type................................... 50

Figure 6.3. Market penetration of side impact airbags, by vehicle type and model year ............. 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 type ..................................................... 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 type......................................... 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 type ..................................................... 54

Figure 6.7. Effect of mass reduction on risk to car occupants only in two-vehicle crashes, by

crash partner vehicle type and crash configuration............................................................... 55

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

Figure 6.9. Effect of mass reduction on risk to light truck occupants only in two-vehicle crashes, 

by crash partner vehicle type and crash configuration.......................................................... 56
xii

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

model year............................................................................................................................. 57

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

vehicles, by crash partner vehicle type and crash configuration .......................................... 58

Figure 6.12. US registrations of new vehicles, by vehicle type and year..................................... 59

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

fatality risk by vehicle type compared to four-door sedans.................................................. 60

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

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

Table 6.3. Average annual fatalities and VMT in model year 2000 to 2007 light-duty vehicles 

between 2004 and 2008, by vehicle type .............................................................................. 62

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

types...................................................................................................................................... 63

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

model specifications.............................................................................................................. 68

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

compared with different scenarios analyzed in this report ................................................... 68
1

1. Introduction

NHTSA recently completed a logistic regression analysis updating its 2003 and 2010 studies of 

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

new study updates the previous analyses in several ways: updated FARS data for 2002 to 2008 

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

in several additional states are added; a new vehicle category for car-based crossover utility

vehicles (CUVs) and minivans is created; crashes with other light-duty vehicles are divided into 

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

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

stability controls (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 vehicle weight, size (actually footprint, or vehicle wheelbase times track 

width), and US fatality risk per vehicle miles traveled (VMT), for model year 2000 to 2007 lightduty vehicles involved in fatal crashes between 2002 and 2008. In addition, we examine the data 

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

vehicle mass and footprint, and overall safety. 

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

includes in their preferred regression models. Section 3 examines in more detail the multicollinearity between vehicle mass and footprint, and the methods NHTSA took to address that 

multi-collinearity, In 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 vehicle model. In 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 used and different model specifications. Finally in 

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

effect of mass reduction on risk in 2017 to 2025.

2. NHTSA results

For its analysis of the effect of changes in vehicle mass on US fatality risk per VMT, NHTSA 

used information on all US traffic fatalities, from the Fatality Analysis Reporting System 

(FARS). For the measure of exposure, NHTSA used a subset of non-culpable vehicles involved 

in two-vehicle crashes from police-reported 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. NHTSA developed weighting factors to scale the induced exposure vehicles up to 

national level vehicle registrations. NHTSA then multiplied the vehicle registration-years by 

annual vehicle miles traveled (VMT) factors it developed by vehicle type and age, from 

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

to NHTSA 2011.
2

In this 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, vehicle mass and footprint. 

2.1. Data and methods

For this new analysis NHTSA used FARS data on fatal crashes, and police-reported crash data 

from 13 states, for MY00 to MY07 light-duty vehicles between 2002 and 2008. NHTSA used a 

subset of nonculpable vehicles in two-vehicle crashes as a measure of induced exposure; these 

records provide distributions of on-road vehicles by vehicle year, make, and model, driver age 

and gender, and crash time and location (day vs. night, rural vs. urban counties, and high-speed 

roads). Each induced exposure record is then 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 by 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 following vehicle attributes, by model year, make and 

model: curb weight and footprint (wheelbase times track width), as well as the presence of allwheel drive and automated braking systems. NHTSA added several new variables for new 

safety technologies and designs: electronic stability controls (ESC), four types 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 types of vehicles.

To reflect changes 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 

types, 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.). The analysis involves running a logistic regression model with total crash 

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

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

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

are weighted by the 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 regression models, for each of the case 

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

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

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

vehicle type: 3,106 lbs for cars and 4,594 lbs for light-duty trucks. Because there are fewer 

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

weight. As in the 2003 and 2010 analyses, eight variables for driver age and gender are used. In 

the 2003 analysis, NHTSA excluded the driver airbag control variables in the regressions for 

rollovers and crashes with pedestrians. In the 2011 analysis, NHTSA includes the control 
3

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 control 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

UNDRWT00 C C

OVERWT00 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 models of rollover crashes involving cars 

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

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

crashes involving cars or CUVs/minivans.
4

Rather than reporting coefficients for the variables of interest (curb weight and footprint) from a 

single regression model across all crash types, NHTSA reports a weighted average of the 

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

“baseline” distribution of fatalities across the crash types, 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 derives the baseline fatalities from MY04-09 vehicles in 

crashes between 2004 and 2008. NHTSA 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 analysis that found that ESC 

reduces fatal rollovers by 56% in cars and 74% in light trucks; fixed-object impacts by 47% in 

cars and 45% in light trucks; and other non-pedestrian crashes by 8% in both cars and light 

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

This “post-ESC” distribution of fatalities by crash type is then multiplied by the regression 

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

risk. Table 2.2 shows the baseline distribution of fatalities, by case vehicle type and crash type, 

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

regressions for each crash type.

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 1,315 829 311 -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 LOGIST procedure (with the exception of those for the mass and footprint variables 

UNDRWT00, OVERWT00, LBS100, and FOOTPRNT, which NHTSA often multiplies by -1 so 

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

throughout this report). The output from the SAS LOGIST procedure 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 ex

– 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 is small; however it substantially increases the percent change for explanatory 

variables that have a large effect on the log-odds of fatality (such as the crash location variables). 

For example, the fatality risk from a rollover crash involving a car has a 2.20 times higher logodds of fatality if it occurs in a rural county; after conversion, this crash has a 802 percent higher 

probability of fatality 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 LOGIST procedure.

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

coefficients for each of the 9 crash types are weighted by the distribution of 2016 baseline fatal 

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

are slightly different from those provided in the 2011 NHTSA report, perhaps because of 

rounding errors and our reporting of percent changes in risk as probabilities rather than as logodds.) The figure indicates that mass reduction increases societal2

fatality risk by about one

percent for cars and lighter-than-average light trucks, while mass reduction leads to a slight 

reduction in fatality risk 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 both categories of 

light-duty trucks, are statistically significant. The 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 confidence intervals in its 2011 report; rather it uses a 

jack-knife technique to estimate the range in uncertainty around the point estimates. The 

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

2011 report indicates 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 the 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 occupants, and steps manufacturers have taken to 

improve light truck compatibility on the risk light trucks impose on other vehicle occupants, in two-vehicle crashes), 

all risks in this report also are societal risk.
6

Figure 2.1. Effect of mass and size variables on risk, across all crash types and weighted 

average effect in each type of crash, by vehicle type

Figure 2.1 also shows that reduction in footprint consistently increases risk for all three types 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 risk in cars and CUVs/minivans by close to 

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

Results from a single regression analysis across all crash types are also shown in Figure 2.1 (in 

dark turquoise), as are the results of the nine regression models by crash type weighted by the

current distribution of fatalities (light turquoise), not the distribution NHTSA assumes for 2017-

2025 based on full ESC penetration. Full penetration of ESC in the on-road fleet slightly 

increases the safety penalty from mass reduction, as the NHTSA weighted values (in light blue) 

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

penetration reduces the safety penalty from a reduction in footprint, for all vehicle types.

Figures 2.2 through 2.4 show the effect of changes in mass or footprint on risk, by type of crash. 

For cars, mass reduction increases risk in all crash types except rollovers and crashes with 

stationary objects, 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 is less clear why mass reduction would reduce risk in crashes with 

stationary objects. Because NHTSA assumes that by 2017 ESC will have eliminated many of 

the fatalities in rollovers and crashes with stationary objects, and these are the only types of 

crashes 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 results in the largest risk increases in rollovers and 

1.43%

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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

US fatalities per VMT, wtd avg

US fatalities per VMT, all crashes 

US fatalities per VMT, wtd by current crashes 

100-lb reduction in weight 1-sq ft reduction in footprint
7

crashes with stationary objects (Figure 2.2), so removing fatalities in these types of crashes by 

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

columns in Figure 2.1).

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

light truck. For heavier cars, mass reduction results in generally smaller increases in risk for 

most types of crashes. A reduction in car footprint increases risk in all types of crashes, 

including rollovers and crashes with stationary objects. In fact, footprint reduction leads to the 

largest increases in risk in these two crash types (7.79% and 3.99%), followed by crashes with a 

lighter light-duty 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. In 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) increases in risk in lighter 

truck rollovers and heaver truck crashes with objects. As 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 reduction in light truck footprint tends to increase risk, although the increases are 

small and often not statistically-significant. However, contrary to cars, footprint reduction in 

light trucks significantly reduces fatality risk in crashes with pedestrians and cyclists, and with 

heavier light trucks. 

Figure 2.2. Effect of mass 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: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

Cars < 3,106 Cars > 3,106 All cars 
8

Figure 2.3. Effect of mass 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. The 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-duty truck, a 3.69% increase. The effect of 

reductions in footprint in CUVs and 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: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

LTs < 4,594 LTs > 4,594 All LTs 
9

Figure 2.4. Effect of mass and footprint reduction 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 terms of other car characteristics, Figure 2.5 indicates that 

two-door cars increase US fatality risk per VMT by 8%, while TORSO side airbags, automated 

braking systems (ABS), and electronic stability control (ESC), reduce risk by about 10%. The 

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

increasing US fatality risk per VMT from 5% to 8%. Car age causes a small increase in risk, 

while a brand new car increases risk by 10%, presumably because the driver is unfamiliar with a 

new car’s controls, handling, and/or braking capabilities. The calendar year variables have a 

decreasing effect on risk over time, declining from a 6% increase in risk in 2002 to a 13% 

reduction in risk in 2008. The calendar year variables are examined in more detail in Section

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: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight 1-sq ft reduction in footprint
10

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. For 

instance, a 100-lb reduction in curb weight for an underweight car is expected to increase risk by

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

UNDERWT

O

VERWT

FO

OTPRINT

TW

ODO

OR

CURTAIN

CO

MBO

TORSO

ABS

ESC

M14_30M30_50M50_70M70_96

F14_30F30_50F50_70F70_96

VEHA

GE

BRANDNEW

CY2002CY2003

CY2004

CY2005CY2007CY2008

P

ercent change in risk (fatality probability per 10

10 V

M

T)

0% 

50% 

100% 

150% 

200% 

250% 

300% 

350% 

400% 

UNDERWT OVERWT FOOTPRINT DRVMALE NITE RURAL SPDLIM55 HIFAT_ST

P

ercent change in risk (fatality probability per 10

10 V

M

T)
11

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

could be reduced by 800 lbs while adding ESC, without increasing fatality risk. 

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

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

fatality risk per VMT by over 25%, while the three control variables for crash environment, 

NITE, RURAL, and SPDLIM55, more than triple fatality risk per VMT. A crash occurring in a 

high-fatality state carries a 25% higher fatality risk per VMT than a crash in other states. Since 

driving on a roadway with 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 risk in crashes involving 

light trucks. SUVs, and to a lesser extent heavy-duty pickups, have a higher fatality risk than 

regular pickups. NHTSA includes two variables identifying approaches to comply with 

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

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

bumper. These two approaches have about the same effect on reducing risk. As with cars, risk 

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

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

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

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

in 2002 to a 16% decrease in risk in 2008. The calendar year variables are discussed in more 

depth in Section 5.3.

ESC reduces risk by 18% in light trucks (Figure 2.8) as opposed to only 11% in cars (Figure 

2.5), while male drivers increase risk in trucks only 18% but 40% in cars (Figure 2.6). All-wheel 

drive (AWD) reduces risk in pickups by nearly 15%. As in cars, driving at night, in rural areas, 

and on roadways 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 slightly lower fatality risk than CUVs. For CUVs and 

minivans, 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 why torso air bags could increase risk, this result indicates the lack of precision in the 

regression coefficients for some of the safety technologies, especially in regressions limited to 

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

CUVs and minivans, opposite to the results seen for cars in Figure 2.5. In terms of driver 

characteristics, young and elderly drivers have the highest increase in risk. The coefficients on 

the calendar year control variables are smaller than those for light trucks (with the exception 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 types, many of the control 

variables shown in Figures 2.3 through 2.8 have a much greater effect on fatality risk 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

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

UNDERWT

O

VERWT

FO

OTPRINT

SUV

HD_PKP

BLOCKER1BLOCKER2

M14_30M30_50M50_70M70_96

F14_30F30_50F50_70F70_96

VEHA

GE

BRANDNEW

CY2002CY2003

CY2004

CY2005CY2007CY2008

P

ercent change in risk (fatality probability per 10

10 V

M

T)

-25%

25% 

75% 

125% 

175% 

225% 

275% 

325% 

375% 

425% 

UNDERWT OVERWT FOOTPRINT ESC AWD DRVMALE NITE RURAL SPDLIM55 HIFAT_ST

P

ercent change in risk (fatality probability per 10

10 V

M

T)
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

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

CURBWT

FO

OTPRINT

MINIVAN

CURTAIN

CO

MBO

TORSO

ABS

ESC

M14_30M30_50M50_70M70_96

F14_30F30_50F50_70F70_96

VEHA

GE

BRANDNEW

CY2002CY2003

CY2004

CY2005CY2007CY2008

P

ercent change in risk (fatality probability per 10

10 V

M

T)

-25%

25% 

75% 

125% 

175% 

225% 

275% 

325% 

375% 

425% 

CURBWT FOOTPRINT AWD DRVMALE NITE RURAL SPDLIM55 HIFAT_ST

P

ercent change in risk (fatality probability per 10

10 V

M

T)
14

3. Multi-collinearity between vehicle mass and footprint

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

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

correlated with each other. Including two or more highly-correlated variables in the same 

regression model can lead to biased or incorrect results. Figure 3.1 shows the correlation 

between curb weight and footprint by vehicle model in the NHTSA database; only the most 

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

(106 car models, 131 light truck models, and 38 CUV/minivan models). The figure indicates 

that curb weight and footprint are more highly correlated for cars (R2of 0.87) than for light 

trucks (R2of 0.65) or CUVs/minivans (R2of 0.60). Figure 3.2 shows the same data as Figure 

2.12, but uses six vehicle types. Here 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 0.26 for minivans. The 

correlation of 0.65 for all light trucks (pickups and SUVs) combined in Figure 3.1 is improved 

when these two types of trucks are analyzed separately in Figure 3.2: 0.86 for pickups and 0.81

for SUVs in Figure 3.2. On the other hand, separating CUVs from minivans improves the 

correlation between curb weight and footprint for CUVs (0.66) but not for minivans (0.26). The 

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

R2 = 0.87

R2 = 0.65

R2 = 0.60

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 7600 

F

o

otprint (whe

elbase

x track width, sq ft)

Curb weight (lbs) 

Cars 

Light trucks 

CUVs and Minivans 
15

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 of the results from the preferred model. 

The first 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 with vehicle mass. This sensitivity increases the risk from a 100-lb mass 

reduction in cars (from 1.43% to 2.64% 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 risk in light-duty trucks. These results are quite similar to those 

reported by NHTSA in Section 3.7 of the 2011 report; the slight differences are likely due to 

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

log-odds, as described above.

R2 = 0.65

R2 = 0.89

R2 = 0.86

R2 = 0.81

R2 = 0.66

R2 = 0.26

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 7600 

F

o

otprint (whe

elbase

x track width, sq ft)

Curb weight (lbs) 

2-dr cars 

4-dr cars 

Pickups 

SUVs 

CUVs 

Minivans 
16

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

in light purple in Figure 3.3. Allowing 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. Allowing light truck mass to be reduced along with footprint does not change 

the effect of a reduction in footprint on risk in light trucks. Figure 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 crash type; in contrast to 

Figures 2.2 through 2.4, the figures indicate that including only mass or footprint in the 

regression models reduces or eliminates any reduction in risk in a particular crash type, 

particularly 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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding footprint

Excluding curb wt

100-lb reduction in weight 1-sq ft reduction in footprint
17

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: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding footprint

Excluding curb wt

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

Cars < 3,106 Cars > 3,106 All cars 

-8%

-6%

-4%

-2%

0% 

2% 

4% 

6% 

8% 

10% 

12% 

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding footprint

Excluding curb wt

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

LTs < 4,594 LTs > 4,594 All LTs 
18

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

VMT, by crash type

In its 2011 analysis NHTSA examined the relationship between curb weight and fatality risk for 

deciles of vehicles with roughly the same footprint. Figure 3.7 shows the range in curb weights 

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

a large degree of overlap in the curb weights of vehicles with roughly the same footprint; this is 

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

absolute.

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

crash and vehicle type, and footprint decile, a total of 270 regression models; the two mass 

variables, UNDERWT00 and OVERWT00, originally used for cars and light trucks were 

replaced by a single mass variable LBS100. NHTSA 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: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding footprint

Excluding curb wt

100-lb reduction in weight 1-sq ft reduction in footprint
19

Figure 3.7. Range in curb weight for the footprint deciles, by vehicle type

Table 3.1 replicates this analysis and results, and includes the number of footprint deciles in 

which the coefficient on vehicle mass is statistically significant, for each combination of vehicle 

and crash type. There are four columns for each vehicle type 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 significant. Red print indicates cases in which three or more 

footprint deciles have significant coefficients. The second two columns for each vehicle type 

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

the number that are statistically significant. For example, in car rollover crashes, mass reduction 

increases risk in only three footprint deciles, and none of those increases is statistically 

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

crashes, and four of those seven are statistically significant. Table 3.1 indicates that mass 

reduction increases risk in only a small number of car rollover crashes and crashes with a 

stationary object. On 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 cars) are statistically significant. For CUVs and minivan crashes 

with heavy cars, mass reduction increases risk in eight deciles, with three of those increases 

being statistically significant.

1600 2600 3600 4600 5600 6600 7600 

F

o

otprint decile

Curb weight (lbs) 

Cars 

Light trucks 

CUVs/minivans 
20

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/Minivans

N

u

m

ber of deciles with

increasin

g risk

N

u

m

ber of deciles with

estimates that are statisticallysig

nificant

N

u

m

ber of deciles with

decreasin

g risk

N

u

m

ber of deciles with

estimates that are statisticallysig

nificant

N

u

m

ber of deciles with

increasin

g risk

N

u

m

ber of deciles with

estimates that are statisticallysig

nificant

N

u

m

ber of deciles with

decreasin

g risk

N

u

m

ber of deciles with

estimates that are statisticallysig

nificant

N

u

m

ber of deciles with

increasin

g risk

N

u

m

ber of deciles with

estimates that are statisticallysig

nificant

N

u

m

ber of deciles with

decreasin

g risk

N

u

m

ber of deciles with

estimatesthat are statistically sig

nificant

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 give no information on the size of the effect of mass reduction on risk in 

the footprint deciles. Figures 3.8 through 3.10 show the percent change in risk from mass 

reduction for each footprint decile, by vehicle type, for six of the nine crash types (rollovers, and

crashes with stationary objects, 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 over 10% in 

footprint deciles six and seven, and by over 20% in footprint decile eight. Figure 3.10 indicates 

that mass reduction in CUVs/minivans reduces risk in crashes with a heavy light-duty truck

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

(over 45%), and statistically significant, for the largest two deciles of CUVs/minivans. Figures 

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

-25%

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

0 1 2 3 4 5 6 7 8 9 10 

P

ercent change in risk fro

m

m

ass reduction

Footprint decile (lowest to highest) 

Rollovers w/object

w/lgt car w/hvy car 

w/lgt LT w/hvy LT

-10%

-5%

0% 

5% 

10% 

15% 

0 1 2 3 4 5 6 7 8 9 10 

P

ercent change in risk fro

m

m

ass reduction

Footprint decile (lowest to highest) 

Rollovers w/object

w/lgt car w/hvy car 

w/lgt LT w/hvy LT
22

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, all fatality risks in this report are societal risk, including fatalities in the 

case vehicle and any crash partners, and include not only driver fatalities but passenger fatalities 

as well. In this section we examine the variance in societal fatality risk by vehicle 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 risk per VMT against average curb 

weight, with vehicles grouped into 100-lb increments of vehicle curb weight. Figure 4.1

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

remains a fair degree of variability, particularly for trucks and CUVs/minivans, as indicated by

the R

2

values below 0.50. Note that the relationship between mass and fatality risk for light 

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

heavier trucks, which are predominately 3⁄4- and 1-ton pickups, separately in Figure 4.1. 

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

vehicle type. Figure 4.2 indicates that the relationship between curb weight and fatality risk is 

weaker for 4-door cars than for 2-door cars. Figure 4.3 suggests that for large pickups risk 

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

and curb weight is strongest for minivans.

-65%

-45%

-25%

-5%

15% 

35% 

0 1 2 3 4 5 6 7 8 9 10 

P

ercent change in risk fro

m

m

ass reduction

Footprint decile (lowest to highest) 

Rollovers w/object

w/lgt car w/hvy car 

w/lgt LT w/hvy LT
23

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 vehicles grouped 

into 100-lb increments of curb weight, passenger cars

R2 = 0.58

R2 = 0.16

R2 = 0.21

R2 = 0.29

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 72 

U

S fatality risk (total fatalities per 10 billion V

M

T)

Curb weight (lbs/100) 

Cars 

Light trucks (<5900 lbs) 

Light trucks (>5900 lbs) 

CUVs and Minivans 

R2 = 0.48

R2 = 0.22

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 72 

U

S fataltiy risk (total fatalities per 10 billion V

M

T)

Curb weight (lbs/100) 

2-dr cars 

4-dr cars 
24

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

into 100-lb increments of curb weight, light trucks

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

into 100-lb increments of curb weight, CUVs and minivans

R2 = 0.09

R2 = 0.56

R2 = 0.28

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 72 

U

S fataltiy risk (total fatalities per 10 billion V

M

T)

Curb weight (lbs/100) 

Small pickups 

Large pickups 

Truck-based SUVs 

R2 = 0.44

R2 = 0.76

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 72 

U

S fataltiy risk (total fatalities per 10 billion V

M

T)

Curb weight (lbs/100) 

Crossover SUVs 

Minivans 
25

It is possible that the relationship between vehicle mass and fatality risk is greater in certain 

types of crashes. Figure 4.5 presents the relationship by vehicle type for fatality risk in onevehicle crashes with a stationary object, the type of crash in which vehicle mass is thought to 

provide occupants the most protection. Although the relationship between vehicle mass and risk 

is quite consistent among vehicle types, the strength of this relationship is no greater in crashes 

with stationary objects than in all types of crashes (Figure 4.1), and in the case of light trucks, the 

relationship is weaker in crashes with stationary objects. 

Note that, for a given vehicle weight, light trucks have a higher fatality risk 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 objects, we suspect that light trucks have a 

higher risk than cars in Figure 4.5 because of their tendency to 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 fatality 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 risk decreases as mass increases, for most vehicle types (the exceptions are large pickups 

and fullsize vans). Figure 4.6 shows the relationship between vehicle mass and unadjusted 

fatality risk by vehicle model. Only 275 models with at least 10 billion VMT, or at least 100 

fatalities, are included (106 car models, 131 light truck models, and 38 CUV/minivan models); 

these models represent about 90% of all fatalities, vehicle registration-years, and VMT. Here we 

see that fatality risk declines with increasing mass for cars, and at a smaller rate for CUVs and 

minivans, while risk increases as mass increases for light trucks. However, although risk 

declines with increasing car weight, the relatively low R

2

(0.18) indicates that this is not a very

R2 = 0.48

R2 = 0.05

R2 = 0.30

0 

10 

20 

30 

40 

50 

60 

70 

20 24 28 32 36 40 44 48 52 56 60 64 68 72 

U

S fatality risk (total fatalities per 10 billion V

M

T)

Curb weight (lbs/100) 

Cars 

Light trucks 

CUVs and Minivans 
26

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 risk of 

331 per 10 billion VMT, while model B, which weighs essentially the 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 systems, or electronic stability controls), drivers (age and gender), and 

crash characteristics (at night, on high-speed roads, or in rural vs. urban areas or high-fatality 

states) by vehicle model may explain some of the large range in risk by vehicle weight. To 

account for these various variables, we reran NHTSA’s logistic regression models including all 

of the driver, crash, and vehicle control variables except vehicle mass and footprint, across all 

types of crashes for each of the three vehicle types. 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 the characteristics of each vehicle, to obtain the predicted log odds of fatality per 

vehicle. We then multiplied these odds by the VMT weighting each induced exposure vehicle 

represents, to obtain the number of predicted fatalities in each induced exposure vehicle, and 

summed across vehicle make and model. Finally we divided the total number of predicted 

fatalities in each make and model by their 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 moderately 

correlated. 

Figure 4.7 shows 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 risk across vehicle models of 

similar weight, for all three vehicle types.

Figures 4.8 shows the remaining residual, or unexplained, risks after accounting for all variables 

except vehicle mass and footprint. In 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 vehicles are the non-culpable vehicle in a two-vehicle 

crash, we could not account for type of crash in the three new logistic regression models we ran 

for the three vehicle types.
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

R2 = 0.18

R2 = 0.01

R2 = 0.11

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 

U

S fatality risk (fatalities per 10 billion V

M

T)

Curb weight (lbs) 

Cars 

Light trucks 

CUVs and Minivans 

A 

B 

R2 = 0.09

R2 = 0.06

R2 = 0.13

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 

Predicted U

S fatality risk (fatalities per 10 billion V

M

T)

Curb weight (lbs) 

Cars 

Light trucks 

CUVs and Minivans 

A 

B 
28

Figure 4.8. Residual US fatality risk after accounting for all driver, crash, and vehicle 

variables except mass and footprint, vs. curb weight

Figures 4.9 through 4.11 show similar plots for 106 car models, with 2- and 4-door cars shown 

separately. The figures indicated that two-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. This 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 only 248 fatalities per ten billion 

VMT. In other words, after accounting for all of the variables except vehicle mass and footprint, 

this 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 well as other, unexplained differences among 

vehicles. 

Figure 4.10 indicates that some vehicles on the road today have the same, or lower, risk than 

models that weigh substantially more, and are substantially larger in terms of footprint. For 

example, after accounting for differences in driver age and gender, safety features 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 risk over twice that of model E, which weighs 3,346 lbs and has a 

footprint of 43.8 square feet (248 vs. 106 fatalities per ten billion VMT). Similarly, model F 

(3,107 lbs, 43.9 sq ft, 196 fatalities per VMT) has a predicted fatality risk nearly twice that of 

model G (3,111 lbs, 41.4 sq ft, 111 fatalities 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 

R2 = 0.06

R2 = 0.03

R2 = 0.00

-150

-100

-50

0 

50 

100 

150 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 

R

esidual U

S fatality risk (fatalities per 10 billion V

M

T)

Curb weight (lbs) 

Cars 

Light trucks 

CUVs and Minivans 
29

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

R2 = 0.19

R2 = 0.13

0 

50 

100 

150 

200 

250 

300 

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 

U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

2-door 

4-door 

B 

A 

C 

R2 = 0.12

R2 = 0.04

0 

50 

100 

150 

200 

250 

300 

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 

Predicted U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

2-door 

4-door 

F 

D 

K I 

H 

G 

E 

J 

C 

A 

B 
30

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 larger and heavier (J: 3,909 lbs, 52.5 sq ft, 126 

fatalities per ten billion VMT; K: 3,990 lbs, 48.7 sq ft, 135 fatalities per ten billion VMT). 

Clearly differences in vehicle design can, and already do, mitigate any theoretical safety penalty 

from reduced mass. The fact that NHTSA attributes the change in its regression results between 

the 2003 study and the 2011 study in part to the redesign or removal of certain smaller and 

lighter models of poor design confirms that vehicle design can overcome any perceived safety 

penalty in lightweight or small vehicles. Figure 4.10 suggests that manufacturers can continue to 

design vehicles that overcome any theoretical safety penalty from reducing mass in order to 

improve fuel economy and reduce greenhouse gas emissions.

Figure 11 indicates that, after accounting for all variables 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. Risk declines with increasing curb weight for SUVs, but increases

with increasing weight for pickups. However, the correlations between fatality risk and curb 

weight are weak, even after accounting for all of the driver, crash, and other vehicle variables in 

the NHTSA logistic regression model. Figure 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 Figures 4.15 

through 4.17. There is a reasonable correlation between risk and mass for CUVs (Figure 4.15); 

R2 = 0.06

R2 = 0.09

-150

-100

-50

0 

50 

100 

150 

2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 

R

esidual U

S fatality risk, after accounting for all driv

er,

crash, and v

ehicle

v

ariables e

xcept m

ass and fo

otprint

Curb weight (lbs) 

Residual US fatality risk per VMT vs. curb weight, car models 

2-door 

4-door 

C 
31

however, the correlation decreases after accounting for all variables except mass and footprint 

(Figure 4.16). After accounting for all variables except 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 other aspects of vehicle design 

or subtle differences in driver behavior, explain some of the remaining variation in risk for 

vehicles of similar weight.

Figure 4.12. US fatality risk per VMT vs. curb weight, light truck models

R2 = 0.08

R2 = 0.15

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 

U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

SUVs 

Pickups 
32

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

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

R2 = 0.05

R2 = 0.28

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 

Predicted U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

all driver, crash, and vehicle variables except weight

SUVs 

Pickups 

R2 = 0.04

R2 = 0.00

-150

-100

-50

0 

50 

100 

150 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 

R

esidual U

S fatality risk, after accounting for all driv

er,

crash and v

ehicle

v

ariables e

xcept m

ass and fo

otprint

Curb weight (lbs) 

SUVs 

Pickups 
33

Figure 4.15. US fatality risk per VMT vs. curb weight, CUV/Minivan models

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

R2 = 0.37

R2 = 0.00

0 

40 

80 

120 

160 

200 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

all driver, crash, and vehicle variables except weight

CUVs 

Minivans 

R2 = 0.33

R2 = 0.09

0 

40 

80 

120 

160 

200 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

Predicted U

S fatality risk (crash fatalities per 10

10 V

M

T)

Curb weight (lbs) 

all driver, crash, and vehicle variables except weight

CUVs 

Minivans 
34

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

Table 4.1 summarizes the relationships between predicted and residual fatality risk and vehicle 

curb weight that are presented in Figures 4.6 through 4.17. In addition to the correlation between 

fatality risk and curb weight, the table shows the slope of the linear regression line drawn 

through the risks by vehicle model, which represents the percent change in fatality risk per 100-

pound reduction in mass or 1-square foot reduction in footprint. The relationship for the three 

vehicle types is shown at the top of the table, followed by those for the seven detailed vehicle 

subtypes (with small, i.e. compact and 1⁄2-ton, pickups shown separately from heavy-duty, i.e. 3⁄4-

and 1-ton, pickups), and finally the five vehicle type and weight groups NHTSA used in its 

regression analyses. Cases where there is a positive relationship between fatality risk 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 vehicle model exceeds 0.30 are shown in blue. 

Table 4.1 indicates that fatality risk consistently increases as weight increases for heavy-duty

pickups, and that in some cases the correlation between 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). In these two cases the slope of the regression line is also high, between 7%

and 11%. For CUVs, the correlation between actual and predicted fatality risk and mass is 

moderate, with an R

2

of over 0.30, with risk increasing about 3% for every 100-lb reduction in 

mass. 

R2 = 0.01

R2 = 0.13

-50

-40

-30

-20

-10

0 

10 

20 

30 

40 

50 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

R

esidual U

S fatality risk, after accounting for all driv

er,

crash and v

ehicle

v

ariables e

xcept m

ass and fo

otprint

Curb weight (lbs) 

CUVs 

Minivans 
35

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 type and model

Vehicle type

Actual US fatality 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%

Light 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%

Heavy-duty 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%

LTs > 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. As in Table 4.1, fatality risks for heavy-duty pickups consistently increase as 

footprint increases, between 5% and 10%. However, the correlation between actual risk and 

footprint by heavy-duty 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, 

respectively) are much lower than the correlations with mass (0.37 and 0.33 in Table 4.1). This 

is somewhat surprising, as Figure 3.3 indicates that footprint reduction has a relatively large 

(detrimental) effect on CUV and minivan fatality risk, while mass reduction has a smaller, 

beneficial effect. However, the correlation between footprint and risk for CUVs and minivans 

shown in Table 4.2 is quite low.
36

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 

mass and footprint, by vehicle type and model

Vehicle type

Actual US fatality 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%

Light 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%

Heavy-duty 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%

LTs > 4594 0.40 -5.6% 0.53 -6.0% 0.00 0.4%

CUVs/ 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 this section we examine the sensitivity of the NHTSA results on the effect of mass and 

footprint on US fatality risk per VMT. We examine 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 year control variables, and after excluding crashes involving alcohol or drug use, or 

otherwise bad driving behavior.

5.1. Alternative measures of exposure

Figure 5.1 compares the results for US fatality risk per VMT using NHTSA’s preferred 

regression model specification (in light blue) with two other measures of US fatality risk. The 

first measure is the risk of a fatal crash, rather than the risk of all fatalities that occurred in the 

crash. In other words, the fatal crash cases are not weighted by the total number of fatalities, 

either in the case vehicle or in its crash partner, as they are in NHTSA’s preferred model. In his 

review of the previous NHTSA studies, Paul Green suggested that analyzing risk at the crash, 

rather than person, level might be a better approach; each fatal case would be a single 

independent observation, and may serve to increase any 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 orange) 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, analyzing 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 mass and footprint reduction on US fatalities 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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Fatal crashes per VMT

Fatalities per ind exp crash

100-lb reduction in weight 1-sq ft reduction in footprint
38

The statistical uncertainties around the point estimates of the effect of mass reduction on risk per 

fatal crash in Figure 5.1 are only slightly higher than the uncertainties around the risk per fatality

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. NHTSA uses the non-culpable vehicle in two-vehicle crashes from the 13 

states as its measure of induced exposure. It then creates weights so that the crashes from the 13 

states can first be scaled up to represent national vehicle registration-years, and then multiplied 

by average annual VMT by vehicle age and type to arrive at national VMT. In the light orange 

columns in Figure 5.1 we exclude these two calculations, and examine US fatality risk per 

induced exposure crash from the 13 states (rather than VMT). Using induced exposure crashes 

as the measure of exposure changes the sign of the effect of car mass reduction, and light truck 

mass and footprint reduction, on risk; for all vehicle types, mass reduction actually reduces

fatality risk given that a crash occurs. Footprint reduction in light trucks similarly reduces 

fatality risk per crash, while it slightly increases risk per crash in cars and CUVs/minivans. 

The effect of analyzing fatality risk per crash shown in Figure 5.1 is approximate, as total U.S.

fatalities are combined with induced exposure crashes for only 13 states. A more exact analysis 

would utilize both fatalities and crashes from the same states. We will perform just such an 

analysis in the near future, using fatality, serious injury, and crash data from the same source, the 

police-reported crashes from 13 states.

5.2. Vehicle manufacturer

The analysis by vehicle model in 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 explain the large remaining 

variability in risk. We tested that assumption by adding 18 dummy variables 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 represent the most 

vehicles by manufacturer, both in fatalities and VMT. The six Chrysler brands (Jeep, Chrysler, 

Dodge, Plymouth, AM General and Sprinter) and the three Ford brands (Ford, Lincoln, Mercury) 

were combined in a single Chrysler and Ford category. The luxury brands of Toyota, 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). For 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 models makes mass reduction detrimental, and footprint reduction 

slightly beneficial.
39

Figure 5.2. Effect of mass and footprint reduction 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 year control 

variables. This is consistent for each vehicle type, but largest for light trucks, as shown in Figure 

5.3. The calendar year 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 included 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 safety, increase in curb 

weight of crash partners, and, in particular, improvement in light truck design to reduce their 

tendency to rollover. 

Figure 5.4 indicates that the effect of the calendar year variables on light truck risk is strongest 

for crashes with light cars and lighter light-duty trucks. NHTSA believes that this may be the 

result of the removal over time of very light and unsafe cars and light trucks as potential crash 

partners for light trucks. However, there also are consistent (although not as large) decreases 

over time in light truck risk in rollovers, crashes with heavy-duty trucks and heavy cars, and 

other (mostly multi-vehicle) crashes. NHTSA believes that the decline in light truck rollover 

risk over time may be the result of manufacturers increasing static stability factor or other 

aspects of light truck design to reduce their likelihood to rollover. However, cars and 

CUVs/minivans show a similar trend in reduced rollover risk over time (cars and 

CUVs/minivans also show similarly large reductions in risk over time in crashes with light cars 

and light LTVs). NHTSA suspects that the risk associated with light trucks involved in crashes 

with heavy-duty trucks decreases over time because heavy-duty truck activity decreases as the 

1.43%

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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Including vehicle make

100-lb reduction in weight 1-sq ft reduction in footprint
40

economy falters. The economic recession in 2008 may have reduced the number of heavy-duty 

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

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

CY2002CY2003

CY2004

CY2005CY2007CY2008CY2002CY2003

CY2004

CY2005CY2007CY2008CY2002CY2003

CY2004

CY2005CY2007CY2008

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Cars Light Trucks CUVs/Minivans 

-35%

-25%

-15%

-5%

5% 

15% 

25% 

35% 

45% 

55% 

65% 

CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008CY2002CY2003CY2004

CY2005

CY2007CY2008

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Rollover w/obj w/ped w/HDT w/lgt car w/hvy car w/lgt LT w/hvy LT Other 
41

In its 2003 report, NHTSA included calendar year 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 

analyze CUVs/minivans as a separate vehicle class in the 2003 study). Figure 5.5 shows the 

weighted average coefficients on the calendar year variables from the 2003 analysis (taken from 

the tables in Section 4.3 of that report). Note that the effect of the calendar year variables on risk 

is much smaller than in the 2011 analysis, and there is not the consistent decrease of the effect of 

calendar year on risk in later years as in the 2011 analysis. 

Figure 5.5. NHTSA 2003 effect of calendar year variables on risk, by vehicle type

The calendar year effect for light trucks is strongest on crashes with cars and other light trucks in 

the 2003 NHTSA analysis, as shown in Figure 5.6. However, calendar year increases the risk in 

crashes with cars, but decreases the risk in crashes with another light truck. In addition, there is 

no consistent trend in the variables over time.

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000CY1995CY1996CY1997CY1998CY2000

P

ercent change in risk (log-odds of fatality per 10

10 V

M

T)

Cars Light Trucks CUVs/Minivans 

NA NA NA NA NA       NA NA  NA NA NA
42

Figure 5.6. NHTSA 2003 effect of calendar year variables on light truck risk, by crash type

In its current analysis, NHTSA attributes the large reduction in risk in 2008 to the sharp drop in 

fatalities in that year; however, it is not clear the extent to which fatalities declined in 2008 

because of real safety changes, or because of reduced driving because of high gas prices and the 

economic recession. Figure 5.7 shows that, for most vehicle types, fatality risk per VMT was 

fairly constant between 2002 and 2007, with a consistent decline in 2008; pickups were the only 

vehicle type that showed reduction in risk in any years other than 2008. Figure 5.8 shows that 

the NHTSA assumptions regarding vehicle miles traveled do not account for any additional 

reduction in VMT for model year 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 types, and is plotted along the 

right vertical axis.) So 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 the 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 year variables from NHTSA’s 

preferred regression model (shown in light blue). Figure 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. However, removing the calendar year variables 

increases the effect of mass reduction in trucks and CUVs/minivans; reducing by 100 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 decrease in 

risk in CUVs/minivans from a 0.47% decrease to only a 0.06% decrease.

-35%

-25%

-15%

-5%

5% 

15% 

25% 

35% 

45% 

55% 

CY1995

CY1996CY1997CY1998CY2000

CY1995

CY1996CY1997CY1998CY2000

CY1995

CY1996CY1997CY1998CY2000

CY1995

CY1996CY1997CY1998CY2000

CY1995

CY1996CY1997CY1998CY2000

CY1995

CY1996CY1997CY1998CY2000

Percent change in risk (log-odds of fatality per 10

10 V

M

T)

Rollover w/obj w/ped w/HDT w/car w/LT
43

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 2009 

U

S fatality risk (fatalities per 10 billion V

M

T)

Year 

2-dr cars 4-dr cars 

Pickups HD pickups 

SUVs CUVs 

Minivans 

0.E+00

1.E+10

2.E+10

3.E+10

4.E+10

5.E+10

6.E+10

7.E+10

8.E+10

9.E+10

0.E+00

5.E+09

1.E+10

2.E+10

2.E+10

3.E+10

3.E+10

4.E+10

4.E+10

2001 2002 2003 2004 2005 2006 2007 2008 2009 

U

S

V

M

T (10 billion)

Year 

2-dr cars 

Pickups 

HD pickups 

SUVs 

CUVs 

Minivans 

4-dr cars (right) 
44

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

year variables

We next examined what effect removing the calendar year variables had on the control variables 

NHTSA used in their preferred model. Figures 5.10 through 5.12 show the 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 year 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. In addition, the figures indicate that removing the calendar 

year variables lowers the effect of vehicle age on risk in all three vehicle types. Figures 5.9 

through 5.12 suggest that NHTSA’s inclusion of the calendar year variables in their preferred 

model dilutes the effect of airbag technologies in cars and CUVs and minivans, the added risk in 

SUVs and heavy-duty pickups, and the beneficial effect of ESC in light trucks in general, while 

over-stating the effect of vehicle age in all three vehicle types. 

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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding CY variables 

100-lb reduction in weight 1-sq ft reduction in footprint
45

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

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

CURTAIN

CO

MBO

TORSO

ABS

ESC

VEHA

GE

BRANDNEW

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding CY variables 

-40%

-30%

-20%

-10%

0% 

10% 

20% 

SUV

HD_PKP

BLOCKER1BLOCKER2

ESC

AWD

VEHA

GE

BRANDNEW

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding CY variables 
46

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. We 

examined the effect of excluding case vehicles where the driver was reported to have been 

drinking or using drugs from our regression analysis. Although we excluded fatal crashes 

involving case vehicles whose drivers were reported to have been drinking or using drugs, we 

did not make any adjustments to the induced exposure cases from the 13 states. Most 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 Figure 5.13 indicate that removing such cases from the 

analysis slightly 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 years. These 

additional “bad” drivers account for another 14% of car and light truck drivers, and another 9% 

of CUV/minivan drivers, in the FARS cases. The light green columns in Figure 5.13 indicate 

that excluding these bad drivers from the analysis further increases the effect of mass reduction 

on risk. For 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 analysis 

-20%

-15%

-10%

-5%

0% 

5% 

10% 

15% 

20% 

MINIVAN

CURTAIN

CO

MBO

TORSO

ABS

ESC

AWD

VEHA

GE

BRANDNEW

P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding CY variables 
47

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 crashes than in 

all other crash types. Because mass reduction is most beneficial, and footprint reduction most 

harmful, in these two types of crashes (as shown in Figures 2.2 through 2.4), removing crashes 

involving these drivers from the analysis makes overall 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) 

level exceeded 0.08, rather than whether alcohol or drug use was reported. NHTSA’s analysis 

indicates that excluding 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 reported, for cars, but a 

similar effect on risk for trucks and CUVs/minivans. This is likely because NHTSA excludes 

drivers who were imputed to have been drinking, based on other characteristics of the driver and 

crash. Excluding 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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Excluding drinking/drug use

Excluding all bad drivers 

NHTSA excluding BAC > 0.08

100-lb reduction in weight 1-sq ft reduction in footprint
48

5.5. Effect of including sports, police, and all-wheel drive cars, and fullsize vans

NHTSA excluded three types 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. Including these 

vehicles in the analysis increases the effect of mass reduction, but reduces the effect of footprint 

reduction, on risk for cars, as shown in Figure 5.14. Including fullsize vans has the opposite 

effect on the results for light trucks: the effect of mass reduction is reduced, while the effect of 

footprint reduction is increased.

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% 

Cars < 3106 Cars > 3106 LTs < 4594 LTs > 4594 CUVs/

minivans 

Cars LTs CUVs/

minivans P

ercent change in risk (fatality probability per 10

10 V

M

T)

NHTSA preferred model

Including all cars and fullsize vans 

100-lb reduction in weight 1-sq ft reduction in footprint
49

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 affect baseline fatalities in 

2017 through 2025. Side airbags are becoming standard equipment on most vehicles, and 

manufacturers are taking measures to improve light truck compatibility with other vehicles in 

frontal crashes. And in recent years there has been a market shift from truck-based SUVs to carbased CUVs. In this section we analyze what influence these trends, if they continue, may have 

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. As 

NHTSA has required ESC on all light-duty vehicles by MY12, there likely has 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 estimates that ESC reduces fatal rollovers by 56% in cars and 74% in 

light trucks; fixed-object impacts by 47% in cars and 45% in light trucks; and other non0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

2001 2002 2003 2004 2005 2006 2007 

P

ercent of ne

w

v

ehicle fle

et

Model year 

ESC, car 

ESC, LT

ESC, CUV 
50

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 analysis, by vehicle and crash type. The figure indicates that the current analysis 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 current analysis 

estimates a lower ESC effectiveness for car crashes with objects, and CUV/minivan rollovers, 

than the Sivinski study. On the other hand, the current study finds substantially higher ESC 

effectiveness in reducing risk in crashes with other vehicles.

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. The data in Figure 6.2 are only for vehicles coded in the NHTSA database as having 

zero or 100% of a particular airbag technology; because side airbag technologies are optional 

equipment on many models, particularly light trucks, NHTSA used the fraction of vehicles of 

these models that had the technology installed. The 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 year. Because many 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%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0% 

-120%

-100%

-80%

-60%

-40%

-20%

0% 

20% 

40% 

60% 

80% 

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

1: Rollovers

2: w/object3: Ped etc.

4: w/HDT

5: w/lgt car

6: w/hvy car

7: w/lgt LT

8: w/hvy LT

9: Other

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Cars Light trucks CUVs/Minivans 

NHTSA (Sivinski):
51

airbags, the data for each vehicle type are not additive; by MY07, 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, particularly for light 

trucks; by MY07, 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 fatality risk in crashes when another vehicle strikes the case vehicle 

in the side (although curtain airbags are designed to also deploy in rollovers and severe frontal 

crashes). Figures 6.4 through 6.6 show the effect the three side airbag technologies have on the 

risk of fatality to the occupants in the case vehicle only, by case vehicle type. The effect of side 

airbags is shown for crashes where the case vehicle is struck in its side (either driver or 

passenger side) by its crash partner; the vehicle type of the crash partner is shown along the xaxis (light and heavy cars, light and heavy light-duty trucks). Because the NHTSA regressions do 

not include the side airbag variables for light trucks, the data are only shown for cars and 

CUVs/minivans.

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

2001 2002 2003 2004 2005 2006 2007 

P

ercent of ne

w

v

ehicle fle

et

Model year 

Curtain, car Curtain, LT Curtain, CUV 

Torso, car Torso, LT Torso, CUV 

Combo, car Combo, LT Combo, CUV 
52

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 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%

-100%

-80%

-60%

-40%

-20%

0% 

20% 

40% 

60% 

80% 

100% 

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Curtain side airbag Combo side airbag Torso side airbag Any side airbag

-69%

77%

-63%

-60%

-47%

38%

-29%

-9%

-39%

23%

85%

-16%

-44%

4%

-4%

-11%

-100%

-80%

-60%

-40%

-20%

0% 

20% 

40% 

60% 

80% 

100% 

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Curtain side airbag Combo side airbag Torso side airbag Any side airbag
53

Figure 6.4 indicates that, for example, combo side airbags in a car struck in the side by a light car 

reduce the fatality risk in the case car by 53%. The figure suggests that combo and torso side 

airbags 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 type. Curiously, side curtain airbags have a much smaller reduction in fatality risk than 

side torso or side combo airbags, and actually increase fatality risk in cars struck by another 

lighter car (however, none of the effects of side curtain airbags are statistically significant). The 

rightmost panel in Figure 6.4 (darker blue) shows the result of a separate regression model that 

combines the side airbag variables into a single SIDEAB variable, as many vehicles have both 

the curtain and the torso side airbag. The inclusion of any type of side airbag results in slightly 

lower benefits than estimated for torso side airbags only, and reduces the sampling error 

somewhat. Note that any side airbag technology is slightly more beneficial to car occupants 

when struck by another car than when struck by a light truck. The benefits do not appear to be 

affected by the 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 struck in the side by a light truck, the protective effect of side airbags is 

unaffected by 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 another vehicle; side airbags appear to increase fatality risk in cars struck in the side by

certain vehicle types, although none of the effects of the three individual airbag technologies are 

statistically significant in any of the side impact crashes with CUVs/minivans (except side torso 

airbags in CUVs/minivans struck in the side by a light light-duty truck, which has a statistically 

significant 85% increase in fatality risk). Modeling the effect of any type of side airbag 

(rightmost panel, in darker blue) results in a large, but statistically insignificant, reduction in risk 

in CUVs and minivans struck in the side by a light car; the results for the other striking vehicle 

types are smaller and also not statistically significant.

Figure 6.6 shows the effect of the three side airbag technologies on risk to light-duty truck 

occupants; as with CUVs/minivans, there is no consistent or statistically-significant protective 

effect of side airbags in light-duty trucks. (Recall that NHTSA did not include the side airbag 

variables in its regression equations for light-duty trucks; Figure 6.6 represents a new regression 

model that does include these variables). The inclusion of any type of side airbag improves the 

relationship somewhat, with reduced risk when struck by three of the four vehicle types, 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-duty 

vehicles, by crash partner vehicle type, for three crash configurations: front-to-front crashes (FF), crashes where the case vehicle is struck in the driver or passenger side (S-F), and all other 

crash configurations (mostly rear 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 one case: when a underweight car is struck by a light light-duty truck. In 

all other cases, the detrimental effect of mass reduction on risk is greater in either frontal crashes 

or other crashes with another light-duty 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 type of side impact crash; in crashes with other crash partners, the reduction 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 another car is slightly higher than in other crash configurations when the striking 

vehicle is a heavy car or a light light-duty truck. The effect of mass reduction is much larger 

than in other crash configurations when the striking vehicle is a heavy light-duty truck. 

Therefore it appears that a reduction in the number fatalities in crashes where a CUV/minivan is 

struck in the side would slightly reduce the detrimental effect of mass reduction in CUVs and 

minivans. However, as shown in Figure 6.5 above, it is not clear the extent to which side 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%

-100%

-80%

-60%

-40%

-20%

0% 

20% 

40% 

60% 

80% 

100% 

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

lgt car: S-F

hvy car: S-F

lgt LT: S-F

hvy LT: S-F

P

ercent change in risk (fatality probability per 10

10 V

M

T)

Curtain side airbag Combo side airbag Torso side airbag Any side airbag
55

Interestingly, Figure 6.9 indicates that the detrimental effect of mass reduction on risk to lightduty 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. In other words, increased mass appears to have a larger beneficial 

safety effect for light trucks hit in the side than for cars, CUVs, or minivans hit in the side. This 

is likely because the relatively high 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 the 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 relatively 

low, 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 type. Although 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 side. 

Overall, it appears that full adoption of side airbags will reduce fatality risk in cars struck in the 

side by another light-duty vehicle; however, if anything, reduction of this type of fatality will 

increase the detrimental effect of mass reduction in cars. It 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 

crash 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%

-18%

-14%

-10%

-6%

-2%

2% 

6% 

10% 

14% 

18% 

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

Cars < 3,106 Cars > 3,106 All cars 
56

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 light 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%

-18%

-14%

-10%

-6%

-2%

2% 

6% 

10% 

14% 

18% 

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight 1-sq ft reduction in footprint

5.35%

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%

-18%

-14%

-10%

-6%

-2%

2% 

6% 

10% 

14% 

18% 

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

lgt car: F-F

lgt car: S-F

lgt car: others

hvy car: F-F

hvy car: S-F

hvy car: others

lgt LT: F-F

lgt LT: S-F

lgt LT: others

hvy LT: F-F

hvy LT: S-F

hvy LT: others

P

ercent change in risk (fatality probability per 10

10 V

M

T)

100-lb reduction in weight, 100-lb reduction in weight, 1-sq ft reduction in footprint

LTs < 4,594 LTs > 4,594 All LTs 
57

6.3 Effect of measures to increase light truck compatibility

In 2003 manufacturers made a voluntary commitment to reduce the aggressivity of light trucks in 

crashes with other vehicles, by incorporating one of two designs in their new vehicles by MY10. 

The first is improving the overlap of light truck bumpers with those of other vehicles. The 

second is adding a secondary energy-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 met the increased 

bumper overlap guidelines; this percent has held fairly constant, but increased substantially in 

MY07. Fewer small pickups and SUVs used the blocker beam technology than bumper overlap 

in MY01, but use of the blocker beam has increased in the last few years, particularly in smaller 

pickup trucks.

Figure 6.11 shows the effect of the two measures to improve light truck compatibility on risk to 

occupants of the crash partner vehicle, by crash partner vehicle type and crash configuration. 

The figure indicates that greater bumper overlap reduces fatality risk in the crash partner vehicle 

by 1% to 12% in frontal crashes, depending on the crash partner vehicle type; however, none of 

these reductions are statistically significant. On the other hand, greater bumper overlap reduces 

fatalities in cars struck in the side by a light truck, by a statistically-significant 15% (and has only 

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 truck 

type and model year

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

2001 2002 2003 2004 2005 2006 2007 

P

ercent of ne

w

v

ehicle fle

et

Model year 

>50% bumper overlap, Sm PU Blocker beam, Sm PU

>50% bumper overlap, Lg PU Blocker beam, Lg PU

>50% bumper overlap, SUV Blocker beam, SUV 
58

Figure 6.11. Effect of compatibility measures on risk imposed by light trucks on other lightduty vehicles, by crash partner vehicle type and crash configuration

blocker beam technology tends to reduce fatality 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 light trucks is not consistently higher than in 

other types of two vehicle crashes, as shown in Figure 6.7 above. Therefore, by reducing the 

number of fatalities in crashes where light trucks strike cars in the side, full penetration of 

measures to improve light truck compatibility will likely have little impact on the effect of mass 

reduction on risk.

6.4 Effect of sales shift from SUVs and other light trucks to CUVs and other car-based 

vehicles

Figure 6.12 shows the rapid growth in market share of CUVs, from nearly zero in MY00 to over 

15% of all new MY07 vehicle registrations (from Table 1-3 in the NHTSA 2011 report). At the 

same time the share 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 is likely that 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%

-40%

-30%

-20%

-10%

0% 

10% 

20% 

lgt car: F-F

lgt car: F-S

hvy car: F-F

hvy car: F-S

lgt LT: F-F

lgt LT: F-S

hvy LT: F-F

hvy LT: F-S

lgt car: F-F

lgt car: F-S

hvy car: F-F

hvy car: F-S

lgt LT: F-F

lgt LT: F-S

hvy LT: F-F

hvy LT: F-S

P

ercent change in risk (fatality probability per 10

10 V

M

T)

>50% bumper overlap Blocker beam 
59

Figure 6.12. US registrations of new vehicles, by vehicle type and year

The effect of long-term shifts in the market shares among vehicle types can be estimated by

examining the effect of the vehicle type control variables on fatality risk. Alternative regression 

models were run for the nine crash types, where all vehicle types, including sports, police, and 

all-wheel drive cars, and fullsize vans, were included in the same regression model for each 

crash type. The single variable LBS100 was used to account for vehicle curb weight, while all 

vehicle type variables and the four side airbag variables were included. To avoid doublecounting, 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 fatalities in a 

given crash are evenly allocated to each of the case vehicles involved in the crash. The 

regression coefficients on mass, footprint, and vehicle type were then reweighted by the expected 

distribution of fatalities in 2017-2025, to reflect full adoption of ESC. Using the results by crash 

type over all vehicle types, a 100-lb reduction in vehicle mass results in a statistically significant 

0.27% increase in fatality risk, while a 1-square foot reduction in footprint results in an 

insignificant 0.09% increase in risk. Figure 6.13 compares these effects of mass and footprint 

reduction on risk with those by vehicle type 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 age and gender, and for 

equal values on the other control variables such as urbanization and time of day, sports cars 

(52%), police cars (44%), SUVs (40%), fullsize vans (37%), heavy-duty pickups (36%), and 

other pickup trucks (22%) have much higher risk than four-door sedans (shown in light blue in 

Figure 6.13). Two-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 

differences 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 2008 

Distribution of ne

w

v

ehicle registrations

Year 

2-dr cars 

4-dr cars 

Pickups 

SUVs 

CUVs 

Minivans 
60

Figure 6.13. 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 risk in pickups relative to four-door sedans is due to who drives 

these vehicles and where they are driven. Table 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 states) 

characteristics by vehicle type. Pickups have the highest fractions of male drivers, and are 

driven the most on rural, high-speed roads in high-fatality states. Therefore fatality risks by 

vehicle type need to account for driver and crash variables before estimating the effect of shifting 

the distribution from light trucks to other vehicle types. Figure 6.13 also shows the risk by

vehicle type relative to that of a four-door sedan, after accounting for the driver and crash 

variables (in purple). Accounting for how and where vehicles are driven reduces the risk of light 

trucks relative to that of four-door sedans, so that the heavy-duty pickups have essentially the 

same risk as four-door sedans.

To simulate the effect of drivers switching from a relatively 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 type, after accounting 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 types of safer vehicles (last four columns). For example, 

heavy-duty pickups have a risk of 198 fatalities per ten billion VMT; if all the heavy-duty pickup 

drivers, who are overwhelmingly male and mostly drive 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 typical car driver, 114). The incremental effect of 

heavy-duty pickup owners driving at the same times in the same locations, but in a car instead of 

a heavy-duty pickup, is only 6 fatalities per ten billion VMT (198 – 192), and not the difference 

0.27%

0.09%

16%

52%

44%

0%

22%

36%

40%

12%

-2%

37%

16%

49%

35%

-20%

6%

3%

16%

-3%

-9%

21%

-40%

-30%

-20%

-10%

0% 

10% 

20% 

30% 

40% 

50% 

60% 

Curb wt

F

o

otprint

2-do

or car

Sports car

P

olice car

4-wd car

S

m

all pickup

H

D pickup

S

U

V

C

U

V

Miniv

an

Fullsize

v

an

P

ercent change in risk (fatality probability per 1010

V

M

T)

driver, and crash variables 

All variables 

Vehicle type, and driver/crash variables only 
61

between the risk of the average driver of a heavy-duty pickup 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. We 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 type

Driver 

characteristics Crash times and locations

D

R

V

M

A

L

E

D

R

V

A

G

E

NIT

E

RU

RA

L

S

P

D

LIM

5

5

HIF

A

T

_

S

T

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%

Small pickups 85% 40 41% 64% 61% 65%

Heavy-duty 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 vehicle type

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

Small pickups 170 159 127 155 144

Heavy-duty 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 96
62

In its 2003 report, NHTSA estimated the effect of a change in the mix of vehicle types on the 

number of fatalities (Section 5.7, page 220). We conduct a similar estimate here, based on the 

updated data on US fatalities and estimated vehicle miles traveled. Table 6.3 shows the number 

of fatalities in crashes involving case light-duty vehicles from model years 2000 through 2007, 

for the last five years of data available (between 2004 and 2008); the number of fatalities has 

been adjusted to account for full use of ESC by 2017. To avoid double-counting, the number of 

total fatalities in each crash is divided by the number of case vehicles involved in each crash, so 

that the total number of fatalities in a given crash are evenly allocated to each of the case 

vehicles involved in the crash. 

Tables 6.2 and 6.3 can be used to estimate how the number of fatalities involving model year 

2000 to 2007 vehicles would be changed by shifting VMT among the vehicle types, while 

maintaining the average driver and crash time/location characteristics of the original vehicle 

types. For example, moving SUV drivers into CUVs, while maintaining the driver 

characteristics and driving times and locations of the SUVs, would reduce the fatality risk from 

130 to 109 fatalities per ten billion VMT. Table 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. The last scenario shown in Table 6.4

simulates an aggressive shift 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 CUVs and 

25% with minivans). This aggressive shift in market share would reduce fatalities of model year 

2000 to 2007 vehicles by 2,500, resulting in a 3.5% reduction in fatalities.

Table 6.3. Average annual fatalities and VMT in model year 2000 to 2007 light-duty 

vehicles between 2004 and 2008, by vehicle type

Vehicle type 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

Heavy-duty pickups 4,443

SUVs 12,414

CUVs 4,089

Minivans 3,558

Fullsize vans 1,481

Total 72,316
63

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, and 

50% of heavy-duty 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 all 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 and heavier cars, would reduce fatalities by only 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 fatalities expected from even 

substantial reductions in vehicle mass.
64

7. Conclusions

NHTSA recently completed a logistic regression analysis updating its 2003 and 2010 studies of 

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

new study updates the previous analyses in several ways: 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 category for car-based crossover utility 

vehicles (CUVs) and minivans is created; crashes with other light-duty vehicles are divided into 

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

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

stability controls (ESC), side airbags, and methods to meet voluntary agreement to improve light 

truck compatibility with cars, are included.

Using the updated NHTSA databases, our analysis finds that reducing vehicle mass by 100 lbs 

while holding footprint fixed would increase fatality risk per VMT by 1.44% for lighter than 

average cars, and 0.52% for lighter than average light-duty trucks. However, mass reduction in 

heavier than average light-duty trucks, 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 uncertainty around these point estimates, 

NHTSA found that only the effect of mass reduction on 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 significant fatality 

increase and many 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, particularly for cars. NHTSA attributes this reduction in the importance 

of mass reduction on safety to the phase-out of relatively light cars that had unusually high 

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

tendency to be involved in serious crashes, and voluntary improvements made to light trucks to 

improve their compatibility with other vehicles. The 2011 NHTSA analysis finds that reducing 

vehicle footprint by one square foot while holding mass fixed would increase fatality risk per 

VMT by 1.89% in cars and 1.73% in CUVs and minivans (the effect on risk in light trucks is 

small and not statistically significant). 

Rather than relying on the confidence intervals output by the logistic regression models, NHTSA 

estimates the uncertainty around its point estimates using a jack-knife technique that accounts for 

the sampling error in the FARS fatality and state crash data. These uncertainty estimates are 

larger than the confidence intervals output by the logistic regression models included in this 

report. As a result, in its report NHTSA finds that only the 1.44% increase in risk from mass 

reduction in lighter than average cars is statistically significant.

This report replicates the 2011 NHTSA analysis, and examines the data in slightly different ways 

to get a deeper understanding of the relationship between vehicle weight/footprint and safety. 

The results of these alternative analyses are summarized in Table 7.1; statistically significant 

results, based on the confidence intervals output by the logistic regression models, 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 2017 

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

detrimental effect 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). 

• Many of the control variables NHTSA includes in its logistic regressions are statistically 

significant, and have a large effect on fatality risk. For example, a car’s mass could be 

reduced by 800 lbs while adding ESC without increasing its fatality risk. And increasing 

the amount of vehicle travel on highways 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 100 

lbs. While the effect of mass reduction may result 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 strongly for passenger cars. NHTSA 

includes both variables in their regression models, introducing the possibility that multicollinearity may create biased results. When footprint is allowed to vary along with 

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

constant. Similarly, when mass is allowed to vary along with footprint, footprint reduction 

results in larger 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 similar 

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

any combination of vehicle type and crash type. Mass reduction increases risk in a 

majority of footprint deciles for 13 of the 27 crash and vehicle combinations, but few of 

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

light-duty trucks in rollovers). On the other hand, mass reduction decreases risk in a 

majority of footprint deciles for 9 of the 27 crash and vehicle combinations. In some cases 

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

crashes with stationary objects; light-duty trucks in crashes with light and heavy cars; and 

CUVs and minivans in crashes with heavy 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 vehicle 

model and mass is very low. There also is no significant correlation between the residual, 

unexplained risk and vehicle weight. These results indicate that, even after accounting for 

many vehicle, driver, and crash factors, the variance in risk by vehicle 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 slight 

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

per vehicle mile traveled, 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 type (Alternative 3 in Table 7.1). Calculating risk as total fatalities per induced 
66

exposure crash, rather than per vehicle mile traveled, reverses the sign of the 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 types. Footprint 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 (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 year in which the crash occurred, to 

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

time. However, including these calendar year variables in the regression models appear to 

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

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

risk of SUVs and heavy-duty pickup trucks. Excluding 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 effect 

of footprint reduction on risk (Alternatives 7 and 8 in Table 7.1, Section 5.4). Including 

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 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). 

• Recent trends that are likely to continue through 2017 may affect the distribution of crashes 

in that year. For example, side airbags in cars will likely reduce 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). However, it appears that mass reduction has less of a detrimental 

effect on risk when cars are struck in the side than when they are involved in frontal or 

rear-end crashes, so any future reduction in fatalities in car side impact crashes will not 

necessarily influence the effect of mass reduction on risk. And it is not clear whether full 

adoption of side airbags or compatibility measures for light trucks will reduce fatality risk 

when light-duty trucks, CUVs or minivans are struck in the side. 

• Finally, in part because of high gas prices and the poor economy, households have been 

purchasing smaller and lighter vehicles in the last decade. For example, the explosion of 

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

recent years (MY05 to MY07) SUVs and pickups. It is likely that these trends would 

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

market shifts from SUVs or pickups to cars or car-based CUVs and minivans will result in 
67

much larger reductions in fatality risk than the relatively small increases in risk expected 

from mass or footprint reduction. For 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 nearly 4% (Section 6.3).

Table 7.2 compares the results from NHTSA’s 2003, 2010, and 2011 analyses with the 

alternative model specifications examined in this report (again, results that are statistically 

significant are shown in red in the table). The first two columns of the table indicate that 

NHTSA’s 2011 analysis 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 analysis, particularly 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). The 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). However, holding footprint constant increases the effect of mass reduction slightly 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 analyzing crossover utility vehicles 

and minivans as a separate vehicle class, rather than as light trucks, in the 2011 analysis.

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 analyses.

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

fatality risk is small. This report indicates that although the effect is sensitive to what variables 

and data are included in the regression analysis, in nearly all cases the effect is less, in some 

cases dramatically less, than reported in the 2003 NHTSA study. This report also finds that the 

effect of other control variables, such as vehicle type, specific safety technologies, and 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. Finally, this report 

shows that after accounting for the many vehicle, driver, and crash variables NHTSA used in its 

regression analyses, there remains a wide variation in risk by vehicle 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 year 2000 to 2007). These 

relationships may or may not 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 type N

H

T

S

A

pre

ferre

d

m

o

d

el (fatalities p

er

V

M

T)

1. Usin

g

c

urre

nt

distrib

utio

n

o

f

fatalities2. E

x

clu

din

g. m

ass

or fo

otprint v

aria

ble

3. F

atal crash

es p

er

V

M

T

4. F

atalities p

er

in

du

c

e

d

e

x

p

osure

crash5. A

c

c

o

u

ntin

g

for

v

e

hicle

m

a

n

u

fa

cturer

6. E

x

clu

din

g

C

Y

v

aria

bles

7. E

x

clu

din

g

crash

es

with

alc

o

h

ol/dru

gs

8. E

x

clu

din

g. b

a

d

driv

ers

9. In

clu

din

g sp

orts,

sq

u

a

d, A

W

D

c

ars

a

n

d

fullsiz

e

v

a

ns

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 uncertainty using a jack-knife method, only mass reduction in cars less than 3,106 lbs has a 

statistically significant effect on US fatality risk.

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

scenarios analyzed in this report

Variable Case vehicle type

NHTSA

(2003) 

excluding 

footprint

NHTSA 

(2011) 

excluding 

footprint

NHTSA 

(2010) 

including 

footprint

NHTSA 

(2011) 

including 

footprint

Range of different scenarios analyzed 

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 0.92%

Footprint

reduction

Cars 1.89% 1.52% to 2.92%

LTs -0.02% -1.28% to 0.25%

CUV/ minivan 1.73% -0.09% to 2.26%