LBNLAssessment of NHTSA’s Report

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

Year 2000-2007 Passenger Cars and LTVs”

Final report prepared for the Office of Energy Efficiency and Renewable Energy,

US Department of Energy

Prepared by

Tom Wenzel

Building Technology and Urban Systems Department

Environmental Energy Technologies Division

Lawrence Berkeley National Laboratory

Berkeley, CA 94720

August 2012

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. We also 

would like to thank the formal peer reviewers of the Final Draft: Charles Farmer, Insurance 

Institute for Highway Safety; David Greene, Oak Ridge National Laboratory; Kara Kockelman

and Donna Chen, University of Texas; and Michael Van Auken, Dynamic Research, Inc.

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 databases, NHTSA estimates that reducing vehicle mass by 100 lbs while 

holding footprint fixed would increase fatality risk per VMT by 1.55% for lighter-than-average 

cars, 0.51% for heavier-than-average cars, and 0.52% for lighter-than-average light-duty trucks, 

but reduce risk by 0.34% for heavier-than-average light-duty trucks and by 0.38% for 

CUVs/minivans. NHTSA found that only the estimated 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 estimated in 2012 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 2012 NHTSA analysis estimates that 

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

risk per VMT by 1.87% in cars and 1.72% in CUVs and minivans (the effect on risk in light 

trucks is small and not statistically significant). 

This report replicates the 2012 NHTSA analysis, and reproduces their main results. This report

uses the confidence intervals output by the logistic regression models, which are smaller than the 

intervals NHTSA estimated using a jack-knife technique that accounts for the sampling error in 

the FARS fatality and state crash data. As a result, in its report NHTSA finds that only the 1.55% 

estimated increase in risk from mass reduction in lighter-than-average cars is statistically 

significant. In addition to reproducing the NHTSA results, this report also examines the NHTSA

data in slightly different ways to get a deeper understanding of the relationship between vehicle 

weight, footprint, and safety. This final report incorporates revisions from the preliminary report 

released in November 2011, including revised estimates of national weights for vehicle miles 

traveled, inclusion of 2008 police-reported crash data from eight additional states, and responses 

to reviewers’ comments.
iv

The results of these alternative analyses are summarized in Tables ES.1 and ES.2; statistically 

significant estimates, based on the confidence intervals output by the logistic regression models,

are shown in red in the tables. In particular, we found that:

• NHTSA’s (reasonable) assumption that all vehicles will have ESC installed by 2017 

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

the estimated 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 appears to reduce risk, while 

footprint reduction appears to increase risk, in these types of crashes, particularly in cars 

and CUVs/minivans. A single regression model including all crash types results in slightly 

different estimates of the relationship between decreasing mass and risk, as shown in 

Alternative 2 in Table ES.1.

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

significant, and have a much larger estimated effect on fatality risk than vehicle mass. For 

example, installing torso side airbags, electronic stability control, or an automated braking 

system in a car is estimated to reduce fatality risk by about 10%; cars driven by men are 

estimated to have a 40% higher fatality risk than cars driven by women; and cars driven at 

night, on rural roads, or on roads with a speed limit higher than 55 mph are estimated to 

have a fatality risk over 100 times higher than cars driven during the daytime on low-speed 

non-rural roads. While the estimated effect of mass reduction may result in a statisticallysignificant increase in risk in certain cases, the increase is small and is overwhelmed by 

other known vehicle, driver, and crash factors.

Table ES.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)

1. Weighted by current distribution of fatalities2. Single regression model for all crash types

3. Excluding footprint

or weight

4. Fatal crashes per

VMT5. Fatalities per induced exposure crash6. Fatalities per registered vehicle-year

Mass

reduction

Cars < 3106 lbs 1.55%* 1.27% 1.26% 2.74% 1.95% -0.22% 0.93%

Cars > 3106 lbs 0.51% 0.37% 0.35% 1.95% 0.89% -1.45% 2.40%

LTs < 4594 lbs 0.52% 0.42% 0.41% 0.47% 0.54% -1.13% -0.09%

LTs > 4594 lbs -0.34% -0.36% -0.42% -0.39% -0.42% -0.76% -0.76%

CUV/ minivan -0.38% -0.70% -0.74% 0.60% -0.47% -0.84% -0.40%

Footprint

reduction

Cars 1.87% 2.16% 2.28% 2.98% 1.83% 2.28% 0.32%

LTs -0.07% 0.14% 0.22% 0.07% 0.14% -1.30% -0.08%

CUV/ minivan 1.72% 2.25% 2.26% 1.33% 1.79% 2.18% 0.03%

* 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.

Estimates that are statistically significant at the 95% level are shown in red.
v

• 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

(i.e. the regression model accounts for weight but not footprint), mass reduction results in a 

larger estimated increase in risk for cars and CUVs/minivans than when footprint is held 

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

results in a larger estimated increases in risk for cars, but a smaller estimated increase in 

risk for CUVs and minivans (Alternative 3 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. Risk increases with decreasing mass in a 

majority of footprint deciles for 12 of the 27 crash and vehicle combinations, but few of 

these increases are statistically significant. On the other hand, risk decreases with 

decreasing mass in a majority of footprint deciles for 5 of the 27 crash and vehicle 

combinations; in some cases these risk reductions are large and statistically significant.

• Logistic regression methods do not have a statistic, such as the model R2 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-R2 and R2 from a linear 

regression model suggests that much of the variation 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 estimated 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 variation in risk by vehicle model is quite large and 

unrelated to vehicle weight (addressed in more detail in Section 4). The large remaining 

unexplained variation in risk by vehicle model could be attributable to other differences in 

vehicle design, or how drivers who select certain vehicles drive them. It is possible that 

including variables that account for these factors in the regression models would change the 

estimated relationship between mass or footprint and risk.

• NHTSA’s estimates are sensitive to the definition of risk used in its regression models. 

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 estimated 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 4 in Table ES.1). Calculating risk as total fatalities per induced exposure 

crash, rather than per vehicle mile traveled, reverses the sign of the estimated effect of mass 

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

estimated reduction in risk in all vehicle types. Footprint reduction continues to result in 

large estimated increases in risk per induced exposure crash for cars and CUVs/minivans, 

but leads to a large estimated reduction in fatality risk per induced exposure crash for light 

trucks (Alternative 5 in Table ES.1). Calculating risk per registered vehicle-year, rather 

than per mile driven, substantially reduces the estimated effect of mass reduction on risk 
vi

for lighter cars and lighter light trucks, but substantially increases the estimated effect on 

risk for heaver cars (Alternative 6 in Table ES.1; further addressed in Section 5.1).

• NHTSA’s estimates are sensitive to changes in the data and variables used in its regression 

models, as shown in Table ES.2. Adding control variables for vehicle manufacturer tends 

to increase the estimated effect of mass reduction, but decrease the estimated effect of 

footprint reduction, on risk for cars and light trucks, and estimates that mass reduction

becomes substantially detrimental, and footprint reduction beneficial, for CUVs/minivans 

(Alternative 7 in Table ES.2; further addressed in Section 5.2). Adding control variables 

for five luxury brands (Cadillac, Lincoln, Acura, Infiniti and Lexus) magnifies these

changes in the estimated effects for cars (Alternative 8 in Table ES.2). 

• An alternative to control variables for vehicle manufacturers is a single continuous variable 

for the vehicle’s initial purchase price; purchase price may better account for other

differences in vehicle design that may influence traffic safety. Adding this single variable 

to NHTSA’s regression models substantially increases the estimated increase in risk from 

mass reduction of heavier-than-average cars, slightly lowers the estimated increase in risk 

in lighter cars and lighter trucks; and substantially increases the estimated beneficial effect 

from mass reduction in heavier light trucks and CUVs/minivans. Accounting for vehicle 

purchase price increases the estimated beneficial effect of footprint reduction in light 

trucks, but has little impact on the estimated detrimental effects of footprint reduction in 

cars or CUVs/minivans (Alternative 9 in Table ES.2; further addressed in Section 5.2).

• 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 estimated 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 estimated increased risk of SUVs and heavy-duty pickup trucks. Excluding these 

calendar year variables from the regression models increases the estimated detrimental 

effect of mass reduction on risk in light trucks (Alternative 10 in Table ES.2, addressed in 

Section 5.3).

• Because details on the driver’s condition or behavior are not consistently reported in the 

state crash data used to develop the induced exposure cases and VMT weights, it is not 

possible to control for individual drivers’ behavior in the regression model. However, 

excluding crashes involving alcohol or drugs, or drivers with poor driving records, 

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

estimated detrimental effect of footprint reduction on risk (Alternatives 11 and 12 in Table 

ES.2, Section 5.4). One possible surrogate for the behavior of drivers who tend to select 

certain vehicle models is driver household income. Including a measure of household 

income by vehicle model reduces the estimated detrimental effect of mass reduction on risk 

in cars, but has little effect on the association of mass with risk in trucks or CUVs/minivans

(Alternative 13 in Table ES.2). Including all-wheel-drive, sports, and police cars increases 

the estimated effect of mass reduction, but reduces the estimated effect of footprint 

reduction, on risk for cars; while including fullsize vans reduces the estimated effect of 
vii

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

trucks (Alternative 14 in Table ES.2, Section 5.5). 

• As mentioned above, for its baseline fatalities NHTSA assumes that all new 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 estimated detrimental overall 

effect of mass reduction, but decrease the estimated 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, at least in side impact crashes (Section 6.2). However, it 

appears that mass reduction has less of an estimated 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 

relationship between mass and 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.

Table ES.2. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

excluding certain data or using different control variables

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)7. Accounting for vehicle manufacturer8.Accounting for vehicle manufacturer plus five luxury brands9. Accounting for initial vehicle purchase price10. Excluding CY variables11. Excluding crashes with alcohol/drugs12. Excluding. bad drivers13. Accounting for median household income14. Including sports, squad, AWD cars and fullsize vans

Mass

reduction

Cars < 3106 lbs 1.55%* 1.90% 2.04% 1.42% 1.52% 1.88% 2.32% 1.20% 1.79%

Cars > 3106 lbs 0.51% 0.75% 1.80% 0.84% 0.43% 0.88% 1.19% 0.16% 0.49%

LTs < 4594 lbs 0.52% 0.59% 0.57% 0.45% 1.20% 0.78% 1.01% 0.68% 0.49%

LTs > 4594 lbs -0.34% -0.11% -0.11% -0.52% 0.30% -0.35% -0.11% -0.30% -0.77%

CUV/ minivan -0.38% 1.62% 1.28% -0.92% 0.03% -0.16% -0.01% -0.44% -0.38%

Footprint

reduction

Cars 1.87% 1.71% 1.20% 1.99% 2.11% 1.65% 1.32% 2.30% 1.64%

LTs -0.07% -0.29% -0.28% -0.36% -0.42% -0.26% -0.39% -0.19% -0.02%

CUV/ minivan 1.72% -0.77% -0.28% 1.57% 1.61% 1.36% 1.12% 1.82% 1.72%

* 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.

Estimates that are statistically significant at the 95% level are shown in red.

• 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 
viii

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 over 3% (Section 6.3).

Table ES.3 shows the results of additional sensitivity tests NHTSA conducted in response to 

comments from the peer reviewers of its preliminary 2012 report. Alternative 15 in Table ES.3 

indicates that using only stopped vehicles, and not all vehicles judged to be not-at-fault in twovehicle crashes, substantially reduces the estimated detrimental effect of mass reduction on risk

in all types of vehicles, while substantially increasing the estimated detrimental effect of 

footprint reduction on risk in cars. Replacing vehicle footprint with its two components, track 

width and wheelbase, similarly reduces the estimated detrimental effect of mass reduction, as 

shown in Alternative 16; an increase in risk is associated with decreasing track width in cars and 

light trucks, but with decreasing wheelbase in CUVs/minivans. Alternative 17 indicates that 

combining these two sensitivities, i.e. using stopped vehicles as the measure of exposure and 

replacing footprint with track width and wheelbase, further reduces the estimated detrimental 

effect of mass reduction, such that fatality risk is estimated to increase in light cars by a 

statistically non-significant 0.26%. Weighting the distribution of fatalities in CUVs and 

minivans by their respective shares of sales in 2010 (which reflects more CUVs and fewer 

minivans) causes the estimated effects to change signs, with decreasing mass now associated 

with an estimated increase in risk, and decreasing footprint with an estimated reduction in risk, 

Table ES.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications suggested by NHTSA peer reviewers

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)

15. Using stopped instead of non-culpable vehicles for induced exposure16. Including track width and wheelbaseinstead of footprint

17. Using stopped vehicles and track width/wheelbase18. Reweighting CUVs and minivans by 2010 sales19. Excluding nonsignificant control variables

Mass

reduction

Cars < 3106 lbs 1.55%* 0.97% 0.95% 0.26% 1.55% 1.63%

Cars > 3106 lbs 0.51% -0.63% 0.24% -0.90% 0.51% 0.69%

LTs < 4594 lbs 0.52% 0.35% -0.07% -0.10% 0.52% 0.35%

LTs > 4594 lbs -0.34% -0.80% -0.58% -0.97% -0.34% -0.54%

CUV/ minivan -0.38% -0.33% -0.25% -0.14% 0.55% -0.46%

Footprint

reduction

Cars 1.87% 3.43% — — 1.87% 1.73%

LTs -0.07% -0.03% — — -0.07% 0.11%

CUV/ minivan 1.72% 1.81% — — -0.61% 1.97%

Track 

width 

reduction

Cars — — 4.36% 6.03% — —

LTs — — 1.07% 0.90% — —

CUV/ minivan — — 0.08% -0.55% — —

Wheel 

base

reduction

Cars — — -0.09% 0.38% — —

LTs — — -0.09% -0.09% — —

CUV/ minivan — — 1.16% 1.45% — —

* 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.
ix

for CUVs and minivans (Alternative 18). Alternative 19 removes non-significant control 

variables from each of the regression models; the result is slightly larger estimated detrimental 

effects of reduced mass in cars, and slightly smaller estimated effects in light trucks.

Table ES.4 compares the results from NHTSA’s 2003, 2010, and 2012 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 2012 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 estimated increase in 

fatalities than in NHTSA’s 2003 analysis, particularly for lighter cars (a 2.74% increase rather 

than a 4.39% increase) and light trucks (a 0.47% increase rather than a 2.90% increase). The 

third and fourth columns of the table indicate a similar reduction in estimated 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 estimated 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 0.34% reduction rather than a 1.90% reduction in fatalities for the 

heavier light trucks). This small estimated 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 2012 analysis.

The last column in Table ES.4 shows that the results of the alternative model specifications 

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

and often lower than the results of the 2010 and 2012 analyses.

Table ES.4. Previous NHTSA results of the estimated effect of mass and footprint 

reduction on US fatality risk per VMT, compared with different scenarios analyzed in this 

report

Variable Case vehicle type

NHTSA 

(2003) 

excluding 

footprint

NHTSA 

(2012) 

excluding 

footprint

NHTSA 

(2010) 

including 

footprint

NHTSA 

(2012) 

including 

footprint

Range of different 

scenarios analyzed 

in this report

Mass

reduction

Cars < 3106 lbs 4.39% 2.74% 2.21% 1.55% -0.22% to 2.74%

Cars > 3106 lbs 1.98% 1.95% 0.89% 0.51% -1.45% to 2.40%

LTs < 4594 lbs 2.90% 0.47% 0.17% 0.52% -1.13% to 1.20%

LTs > 4594 lbs 0.48% -0.39% -1.90% -0.34% -0.97% to 0.30%

CUV/ minivan — 0.60% — -0.38% -0.92% to 1.62%

Footprint

reduction

Cars — — — 1.87% -0.09% to 3.43%

LTs — — — -0.07% -1.30% to 0.22%

CUV/ minivan — — — 1.72% -0.77% to 2.26%

The 2012 NHTSA study, and this report, conclude that the effect of mass reduction while 

maintaining footprint on societal US fatality risk is small, and may be non-existent. This report 

indicates that although the estimated effects are sensitive to what variables and data are included 

in the regression analysis, in nearly all cases the effects are less, in some cases dramatically less, 

than reported in the 2003 NHTSA study. This report also finds that the estimated effects 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 are much 
x

larger, in some cases two orders of magnitude larger, than the estimated 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.

Although the purpose of the NHTSA and LBNL reports is to estimate the effect of vehicle 

mass reduction on societal risk, this is not exactly what the regression models are estimating. 

Rather, they are estimating the recent historical relationship between mass and risk, after 

accounting for most measurable differences between vehicles, drivers, and crash times and 

locations. In essence, the regression models are comparing the risk of a 2600-lb Dodge Neon 

with that of a 2500-lb Honda Civic, after attempting to account for all other differences 

between the two vehicles. The models are not estimating the effect of literally removing 100 

lbs from the Neon, leaving everything else unchanged. 

In addition, the analyses 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. 

Therefore, throughout this report we use the phrase “the estimated effect of mass (or footprint) 

reduction on risk” as shorthand for “the estimated change in risk as a function of its relationship 

to mass (or footprint) for vehicle models of recent design.”
xi

Table of Contents

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

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

Table of Contents ...........................................................................................................................xi

List of Tables and Figures..............................................................................................................xi

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

1.1. Expected relationships between vehicle mass, size and fatality risk....................................1

2. NHTSA results ............................................................................................................................3

2.1. Data and methods.................................................................................................................4

3. Multi-collinearity between vehicle mass and footprint.............................................................18

4. Fatality risk by vehicle model ...................................................................................................27

5. Sensitivity of NHTSA results to data used and model specification.........................................48

5.1. Alternative measures of risk...............................................................................................48

5.2. Vehicle manufacturer .........................................................................................................50

5.3. Calendar year variables ......................................................................................................53

5.4. Effect of alcohol/drug use and driving behavior................................................................60

5.5. Effect of including sports, police, and all-wheel drive cars, and fullsize vans ..................63

6. Influence of recent trends on the expected effect of mass reduction on risk in 2017-2025 ......66

6.1 Effect of electronic stability control (ESC).........................................................................66

6.2 Effect of side airbags...........................................................................................................68

6.3 Effect of measures to increase light truck compatibility .....................................................74

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

vehicles................................................................................................................................75

7. Conclusions ...............................................................................................................................81

8. References .................................................................................................................................89

List of Tables and Figures

Table ES.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications..................................................................iv

Table ES.2. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

excluding certain data or using different control variables .................................................. vii

Table ES.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications suggested by NHTSA peer reviewers ............ viii

Table ES.4. Previous NHTSA results of the estimated effect of mass and footprint reduction on 

US fatality risk per VMT, compared with different scenarios analyzed in this report...........ix

Table 2.1. Control variables used in regression models, by subject vehicle type ...........................5

Table 2.2. Baseline fatal crash involvements, by case vehicle type and crash type.......................6

Figure 2.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, across 

all crash types and weighted average effect in each type of crash, by vehicle type................8

Figure 2.2. Estimated effect of mass or footprint reduction on risk in cars, by type of crash.........9

Figure 2.3. Estimated effect of mass or footprint reduction on risk in light trucks, by type of 

crash.......................................................................................................................................10
xii

Figure 2.4. Estimated effect of mass or footprint reduction on risk in CUVs/minivans, by type of 

crash.......................................................................................................................................11

Table 2.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, by type 

of crash ..................................................................................................................................11

Figure 2.5. Estimated effect of selected control variables on risk, passenger cars .......................13

Figure 2.6. Estimated effect of selected control variables on risk, passenger cars .......................13

Table 2.4. Estimated effect on US fatality risk per VMT, by vehicle type ...................................14

Figure 2.7. Estimated effect of selected control variables on risk, light trucks.............................16

Figure 2.8. Estimated effect of selected control variables on risk, light trucks.............................16

Figure 2.9. Estimated effect of selected control variables on risk, CUVs and minivans..............17

Figure 2.10. Estimated effect of selected control variables on risk, CUVs and minivans............17

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

vehicle types..........................................................................................................................19

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

vehicle types..........................................................................................................................19

Table 3.1. Correlation coefficients and variance inflation factors of curb weight with footprint, 

by vehicle type.......................................................................................................................20

Figure 3.3. Estimated effect of mass or footprint reduction on risk, by vehicle type: mass only, 

footprint only, and both .........................................................................................................21

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

crash type...............................................................................................................................22

Figure 3.5. Estimated effect of reduction in light-duty truck mass or footprint on US fatality risk 

per VMT, by crash type.........................................................................................................22

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

per VMT, by crash type.........................................................................................................23

Figure 3.7. Range in curb weight for the footprint deciles, by vehicle type .................................24

Table 3.2. Number of footprint deciles in which lower vehicle mass is associated with an 

increase or decrease in US fatality risk by VMT, by vehicle and crash type........................25

Figure 3.8. Estimated effect of car mass reduction on fatality risk, by footprint decile and crash 

type ........................................................................................................................................26

Figure 3.9. Estimated effect of light truck mass reduction on fatality risk, by footprint decile and 

crash type...............................................................................................................................26

Figure 3.10. Estimated effect of CUV/minivan mass reduction on fatality risk, by footprint decile 

and crash type ........................................................................................................................27

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

100-lb increments of curb weight, by vehicle type ...............................................................28

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

100-lb increments of curb weight, passenger cars.................................................................28

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

100-lb increments of curb weight, light trucks......................................................................29

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

100-lb increments of curb weight, CUVs and minivans .......................................................29

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

weight, by vehicle type..........................................................................................................30

Table 4.1. Correlation between risk and a decrease in curb weight, for vehicles grouped in 100-lb 

curb weight bins ....................................................................................................................31
xiii

Figure 4.6. US fatality risk per VMT and curb weight, by vehicle model....................................32

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

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

except mass and footprint, vs. curb weight ...........................................................................33

Figure 4.9. US fatality risk per VMT vs. curb weight, car models...............................................34

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

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

Figure 4.12. US fatality risk per VMT vs. curb weight, light truck models..................................37

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

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

Figure 4.15. US fatality risk per VMT vs. curb weight, CUV/Minivan models...........................38

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

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

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

vehicle mass, after accounting for all driver, crash, and vehicle variables except mass and 

footprint, by vehicle type and model.....................................................................................40

Table 4.3. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle mass, after accounting for all driver, crash, and vehicle variables except mass, by 

vehicle type and model..........................................................................................................41

Table 4.4. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle footprint, after accounting for all driver, crash, and vehicle variables except mass 

and footprint, by vehicle type and model ..............................................................................42

Table 4.5. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle footprint, after accounting for all driver, crash, and vehicle variables except 

footprint, by vehicle type and model.....................................................................................42

Figure 4.18. Relationship between vehicle initial purchase price and predicted US fatality risk 

per VMT, by vehicle model...................................................................................................44

Figure 4.19. Relationship between vehicle mass and initial purchase price, by vehicle model....44

Figure 4.20. Relationship between household income and predicted US fatality risk per VMT, by 

vehicle model.........................................................................................................................45

Figure 4.21. Relationship between vehicle mass and household income, by vehicle model ........45

Figure 4.22. Relationship between vehicle initial purchase price and median household income, 

by vehicle model....................................................................................................................46

Table 4.6. Relationship between predicted fatality risk, mass, and initial vehicle purchase price, 

by vehicle type and model.....................................................................................................47

Table 4.7. Relationship between predicted fatality risk, mass, and median household income, by 

vehicle type and model..........................................................................................................47
xiv

Figure 5.1. Estimated effect of mass or footprint reduction on US fatalities, using three different 

measures of exposure (VMT, induced exposure crashes, vehicle registration-years) and fatal 

crashes per VMT ...................................................................................................................49

Figure 5.2. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

controlling for vehicle manufacturer, by vehicle type...........................................................51

Figure 5.3. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

controlling for vehicle manufacturer or for initial vehicle purchase price, by vehicle type..52

Table 5.1. Correlation coefficients and variance inflation factors of curb weight with initial 

purchase price, by vehicle type .............................................................................................52

Figure 5.4. Effect of calendar year variables on risk, by vehicle type ..........................................54

Figure 5.5. Effect of calendar year variables on light truck risk, by crash type............................54

Figure 5.6. NHTSA 2003 effect of calendar year variables on risk, by vehicle type....................55

Figure 5.7. NHTSA 2003 effect of calendar year variables on light truck risk, by crash type .....56

Figure 5.8. US fatality risk per VMT, by vehicle type and calendar year.....................................57

Figure 5.9. Total US VMT for MY2002 vehicles, by vehicle type and calendar year .................57

Figure 5.10. Effect of increasing weight or size on risk, including and excluding calendar year 

variables.................................................................................................................................58

Figure 5.11. Estimated effect of selected control variables on car risk, including and excluding 

calendar year variables..........................................................................................................59

Figure 5.12. Estimated effect of selected control variables on light truck risk, including and 

excluding calendar year variables..........................................................................................59

Figure 5.13. Estimated effect of selected control variables on CUV/minivan risk, including and 

excluding calendar year variables..........................................................................................60

Figure 5.14. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

excluding case vehicles whose driver was drinking, using drugs or exhibited bad driving 

behavior, or controlling for median household income, by vehicle type ..............................61

Table 5.2. Correlation coefficients and variance inflation factors of curb weight with average 

median household income, by vehicle type...........................................................................62

Figure 5.15. Estimated effect of mass and footprint reduction on US fatality risk per VMT, after 

including sports, police, and all-wheel drive cars, and fullsize passenger and cargo vans, by 

vehicle type............................................................................................................................63

Table 5.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications suggested by NHTSA peer reviewers ..............64

Figure 5.16. Estimated effect of mass and footprint reduction on US fatality risk per VMT, after 

reweighting CUV/minivan fatalities to 2010 sales and excluding non-significant control 

variables, by vehicle type ......................................................................................................65

Figure 6.1. Market penetration of ESC, by vehicle type and model year .....................................67

Figure 6.2. Estimated effect of ESC on fatality risk, by vehicle type and crash type ...................67

Figure 6.3. Market penetration of side impact airbags, by vehicle type and model year..............68

Figure 6.4. Estimated 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.................................................69

Figure 6.5. Estimated 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 ...............................70

Figure 6.6. Estimated 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 ...................................71
xv

Figure 6.7. Estimated effect of mass reduction on risk to car occupants only in two-vehicle 

crashes, by crash partner vehicle type and crash configuration ............................................72

Figure 6.8. Estimated effect of mass reduction on risk to CUV/minivan occupants only in twovehicle crashes, by crash partner vehicle type and crash configuration................................73

Figure 6.9. Estimated effect of mass reduction on risk to light truck occupants only in twovehicle crashes, by crash partner vehicle type and crash configuration................................73

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

model year .............................................................................................................................74

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 ...........................................75

Figure 6.12. US registrations of new vehicles, by vehicle type and year......................................76

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 ..................................................77

Table 6.1. Average driver characteristics and crash times and locations, by vehicle type............78

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

...............................................................................................................................................79

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

2004 and 2008, by vehicle type.............................................................................................80

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

types.......................................................................................................................................80

Table 7.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications...........................................................................82

Table 7.2. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

excluding certain data or using different control variables ...................................................85

Table 7.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications suggested by NHTSA peer reviewers ..............86

Table 7.4. Previous NHTSA results of the estimated effect of mass and footprint reduction on 

US fatality risk per VMT, compared with different scenarios analyzed in this report..........87

Table ES.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications...........................................................................88

Table ES.2. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

excluding certain data or using different control variables ...................................................88

Table ES.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, under 

alternative regression model specifications suggested by NHTSA peer reviewers ..............88

Table ES.4. Previous NHTSA results of the estimated effect of mass and footprint reduction on 

US fatality risk per VMT, compared with different scenarios analyzed in this report..........88
xvi
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; 

Kahane 2012). 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 carbased 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. This final report incorporates revisions from the 

preliminary report released in November 2011, including revised estimates of national weights 

for vehicle miles traveled, inclusion of 2008 police-reported crash data from eight additional 

states, and responses to reviewers’ comments.

The section below summarizes the expected relationships between vehicle mass, size and fatality

risk. In Section 2 we reproduce NHTSA’s results, and analyze 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.

1.1. Expected relationships between vehicle mass, size and fatality risk

In Section 1.5 of its 2012 report, NHTSA describes the hypothetical physical factors of vehicle 

design that could explain the historical relationship between vehicle mass and societal fatality 

risk. One would expect lighter vehicles to have higher fatality rates for their own occupants, all 

else being equal, for several reasons: 

• in frontal or rear crashes, light vehicles tend to be smaller than heavy vehicles, and therefore 

do not have the crush space which protects occupants; 
2

• in two-vehicle crashes, as the mass differential between the two vehicles increases, the delta 

V (change in velocity) for the lighter vehicle, and therefore the risk to its occupants, 

increases relative to that of the heavier vehicle. 

• in crashes with a stationary object additional mass may be sufficient to knock the object, 

such as a tree or pole, down, allowing the vehicle to continue moving and reducing its delta 

V than if it was completely stopped by the object. In a previous study NHTSA estimated 

that the object is knocked down in about 25% of frontal collisions with stationary objects 

(Partyka, 1995). 

• in crashes with a medium- or heavy-duty truck, additional mass in the light-duty vehicle 

would transfer more of its momentum to the truck, reducing the delta V of, and fatality risk 

in, the light vehicle without increasing the risk in the heavier vehicle.

NHTSA notes that accounting for vehicle size in the regression analysis may reduce or eliminate 

the estimated benefit of additional vehicle mass correlated with additional crush space. And that 

accounting for societal risks, that is risk of fatality both to the occupants of the subject vehicle 

and its crash partner, may reduce or eliminate the effect of mass differential in two-vehicle 

crashes, as increased fatalities in the lighter vehicle may be offset by reduced fatalities in the 

heavier vehicle.

On the other hand, there are situations where lower mass is expected to reduce fatality risk:

• in crashes with an immovable stationary object, reducing the mass of a vehicle while 

maintaining its crush space and structural strength would lower the kinetic energy of the 

crash, reducing the amount of energy for the vehicle’s structure to absorb, and likely 

reducing occupant fatality risk;

• in rollovers, reducing mass without changing the vehicle’s roof structure would reduce the 

force applied on the roof once a vehicle turns over.

• lower-mass vehicles should respond more quickly to steering, braking, or acceleration, 

thereby reducing their crash frequency.

Changing the size of a vehicle is expected to reduce risk in several ways. Increasing wheelbase 

or track width, or better yet frontal or side overhang, can increase crush space and reduce risk in 

all types of crashes. Adding to a vehicle’s track width also increases a vehicle’s static stability, 

and reduces its propensity to rollover.

Changing other vehicle dimensions also can reduce risk. Lowering bumpers or the “average 

height of force” in larger, heavier vehicles such as pickups and SUVs can make them more 

compatible with cars, and reduce risk to occupants in crash partner vehicles. Similarly, raising 

the door sill of a car provides more structure to engage with a bumper of a taller vehicle, such as 

a pickup or SUV, striking the car in the side. And lowering the center of gravity also is 

important in increasing stability and preventing rollovers. Finally, strengthening a vehicle’s 

frontal or side structure can increase the amount of energy it can absorb in all types of crashes; 

however, increasing frontal stiffness will likely have negative impacts on the occupants of a 

crash partner in a frontal collision.
3

All of these hypothetical effects of the changes in vehicle mass, footprint, or other dimensions 

assume no other changes to the vehicle. However, this is rarely the case, as often the source of 

the additional mass is the installation of a particular safety feature (such as 4-wheel drive or 

ESC), and manufacturers often make other changes to a vehicle design at the same time they 

change its mass or footprint. In short, it is possible that other changes in vehicle design, as well 

as introduction of safety technologies, can mitigate the hypothetical increase in risk from 

reducing vehicle mass or footprint.

In Section 1.6 NHTSA discusses the issue that, despite their theoretical advantage in terms of 

handling, braking, and accelerating, small and light vehicles historically have had higher crash 

and insurance claim frequency per vehicle mile traveled. This discrepancy suggests that small 

and light vehicles have not been driven as well as larger, heavier ones. NHTSA provides two 

hypotheses for why this would be the case: that less capable drivers tend to chose smaller and 

lighter vehicles; and that drivers of more maneuverable smaller and lighter vehicles tend to drive 

them more recklessly. As an example of the latter, NHTSA cites the high crash rates in vehicles 

with large engines, which in theory should reduce crash frequency because they allow a vehicle 

to accelerate out of dangerous situations.

In summary, the complexity of the factors in vehicle design and operation makes it extremely 

difficult to isolate their effect on occupant and societal risk. As NHTSA concludes, “although 

[the 2010 NHTSA] report and this one both concentrate on the effects of mass and footprint, 

because that is their purpose, these effects are indeed small relative to design and engineering, 

which shape a vehicle’s intrinsic safety and also bear indirectly on its fatality rates by 

influencing what types of drivers choose the vehicle.”

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 Sections 2.3 through 2.6 of the 2012 NHTSA report (Kahane 2012).

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. 
4

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). As in its previous analyses, NHTSA excluded three types of cars, models used 

as sports cars, police cars, and models with all-wheel drive, as well as fullsize passenger and 

cargo vans, from its initial regression analyses; in addition, NHTSA excluded all Ford Crown 

Victorias, which tend to be high-mileage vehicles, on the basis that the sparse odometer data 

available for this large car model are not representative. We followed NHTSA’s convention of 

excluding these vehicles from our analyses; we test the sensitivity of the estimates to excluding 

these vehicles in Section 5.5.

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 
5

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

rollovers and crashes with pedestrians. In the 2012 analysis, NHTSA includes the control 

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 

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 D D D

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

C #: for some models the VIN does not indicate whether a particular vehicle is equipped with that option or not. In 

these cases the fraction of that model that is equipped with the particular feature is used. 

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.
6

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

were included in the regression models for light trucks. 

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 938 1,627 277 454 508 100 -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 1,500 1,922 534 1,399 1,791 503 -7% -7% -6%

6: w/hvy car 1,984 1,758 604 1,846 1,640 569 -7% -7% -6%

7: w/lgt LT 963 893 259 894 830 244 -7% -7% -6%

8: w/hvy LT 1,328 837 311 1,232 779 294 -7% -7% -5%

9: Other 4,375 3,621 1,459 4,082 3,388 1,380 -7% -6% -5%

Total 18,091 15,778 5,143 15,400 13,160 4,572 -15% -17% -11%

All of the regression coefficients presented in the NHTSA 2012 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 

 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.
7

converted from log-space to linear space, and from odds to probabilities. We use the conversion 

factor e

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

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

in the log-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 is 

estimated to have a 2.20 times higher log-odds of fatality if it occurs in a rural county; after 

conversion, this crash is estimated to have 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 2012 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 lower mass is associated with an increase in societal2 fatality 

risk of about one percent for cars and lighter-than-average light trucks, while lower mass is 

associated with 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 2012 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 2012 report indicates that only the estimated 1.55% increase in risk from mass 

reduction for the lighter cars is statistically significant.

Figure 2.1 also shows that lower footprint is associated with increased risk for all three types of 

vehicles, and has a larger estimated effect on risk than lower mass for cars and CUVs/minivans. 

A 1-square foot reduction in footprint is estimated to increase fatality risk in cars and 

CUVs/minivans by close to 2 percent, but has no estimated 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 estimated 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 

 2

 All of the fatality risks reported in the 2012 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.
8

ESC penetration reduces the estimated safety penalty from a reduction in footprint, for all 

vehicle types.

Figure 2.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

across all crash types and weighted average effect in each type of crash, by vehicle type

Figures 2.2 through 2.4, and Table 2.3, show the estimated effect of changes in mass or footprint 

on risk, by type of crash. For cars, mass reduction is estimated to increase risk in all crash types

except rollovers and crashes with stationary objects, as shown in Figure 2.2. As described in 

Section 1.1, 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; and mass 

reduction is expected to reduce risk in crashes with immovable 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 

is estimated to reduce 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, lower footprint is associated with the largest increases in risk in rollovers and crashes with 

stationary objects (Figure 2.2), so removing fatalities in these types of crashes by 2017 will 

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

columns in Figure 2.1).

Mass reduction in the lighter cars is associated with the biggest increase in risk (5.80%) in 

crashes with a heavy light truck. For heavier cars, mass reduction is associated with generally 

smaller increases in risk for most types of crashes. A reduction in car footprint is estimated to 

increase risk in all types of crashes, including rollovers and crashes with stationary objects. In 

fact, lower footprint is associated with the largest increases in risk in these two crash types 1.55% 0.51% 0.52% -0.34% -0.38% 1.87% -0.07% 1.72% 1.26% 0.35% 0.41% -0.42% -0.74% 2.28% 0.22% 2.26% 1.27% 0.37% 0.42% -0.36% -0.70% 2.16% 0.14% 2.25% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk, by vehicle type 

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 
9

(7.76% and 3.93%), followed by crashes with a lighter light-duty truck (3.88%) and with a 

heavy-duty truck (2.92%). 

Figure 2.3 shows the estimated effect of mass and footprint reductions on risk in light trucks. In 

general, the estimated effects on risk are smaller for light trucks than for cars, and there are more 

cases in which lower mass is associated with reduced risk, although the effects are often small 

and not statistically-significant. Mass reduction is associated with 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 estimated effect of weight reduction in lighter trucks 

is in crashes with a heavier light truck, with an estimated 4.36% increase in risk. A reduction in 

light truck footprint is estimated to increase risk, although the increases are small and often not 

statistically-significant. However, contrary to cars, lower footprint in light trucks is associated 

with a statistically significant reduction in fatality risk in crashes with pedestrians and cyclists, 

and with heavier light trucks. 

Figure 2.2. Estimated effect of mass or footprint reduction on risk in cars, by type of crash -1.85% -0.46% 2.01% 2.24% 0.75% 0.48% 1.17% 5.88% 1.93% -2.93% -1.30% -0.14% 0.39% 0.26% 1.61% 0.53% 2.32% 1.16% 7.76% 3.93% 0.91% 2.92% 0.23% 0.49% 3.88% 1.75% 1.13% 

-8% 

-6% 

-4% 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

12% 

1: Rollovers 

2: w/object 

3: 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/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in cars, by crash type 

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

 Cars < 3,106 Cars > 3,106 All cars 
10

Figure 2.3. Estimated effect of mass or footprint reduction on risk in light trucks, by type 

of crash

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

minivans are shown in Figure 2.4. The estimated effects from mass reduction tend to be larger in 

CUVs and minivans than in cars or light trucks, with a greater than 7% estimated reduction in 

risk in rollovers and an estimated 3.68% reduction in risk in crashes with objects. Mass 

reduction in CUVs/minivans is estimated to have the most detrimental effect on risk in crashes 

with a light light-duty truck, a 3.75% increase. The estimated effect of reductions in footprint in 

CUVs and minivans is similar to that for cars, with a larger, statistically-significant estimated 

increase in risk in rollovers (10.94%) and crashes with objects (7.39%). 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 estimated effect of mass reduction, but a decrease in 

the estimated effect of footprint reduction, on risk in CUVs and minivans (light blue columns in 

Figure 2.1).

0.65% 

-1.40% 

1.06% 

1.61% 

-0.09% 

-0.71% 

-0.63% 

4.36% 

0.73% 

-1.29% 

0.76% 

-0.05% 

0.32% 

-0.92% 

-1.38% 

-0.97% 

0.53% 

-0.11% 

1.18% 

1.97% 

-1.25% 

0.75% 

-0.21% 

0.31% 

1.00% 

-1.70% 

-0.44% 

-8% 

-6% 

-4% 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

12% 

1: Rollovers 

2: w/object 

3: 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/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in LTs, by crash type 

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

 LTs < 4,594 LTs > 4,594 All LTs 
11

Figure 2.4. Estimated effect of mass or footprint reduction on risk in CUVs/minivans, by 

type of crash

Table 2.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, by 

type of crash

Type of crash

Mass reduction Footprint reduction

Cars < 

3106 lbs

Cars > 

3106 lbs

LTs < 

4594 lbs

LTs > 

4594 lbs

CUVs/ 

minivans Cars LTs

CUVs/ 

minivans

1: Rollovers -1.85% -2.93% 0.65% -1.29% -7.27% 7.76% 1.18% 10.94%

2: w/object -0.46% -1.30% -1.40% 0.76% -3.68% 3.93% 1.97% 7.39%

3: Ped etc. 2.01% -0.14% 1.06% -0.05% -1.58% 0.91% -1.25% 0.37%

4: w/HDT 2.24% 0.39% 1.61% 0.32% 1.92% 2.92% 0.75% 4.56%

5: w/lgt car 0.75% 0.26% -0.09% -0.92% -0.09% 0.23% -0.21% -0.79%

6: w/hvy car 0.48% 1.61% -0.71% -1.38% 1.67% 0.49% 0.31% -2.21%

7: w/lgt LT 1.17% 0.53% -0.63% -0.97% 3.75% 3.88% 1.00% -4.13%

8: w/hvy LT 5.88% 2.32% 4.36% 0.53% -0.93% 1.75% -1.70% 3.73%

9: Other 1.93% 1.16% 0.73% -0.11% -0.40% 1.13% -0.44% 2.68%

All 1.55% 0.51% 0.52% -0.34% -0.38% 1.87% -0.07% 1.72%

Estimates that are statistically significant at the 95% level are shown in red. -7.27% -3.68% -1.58% 1.92% -0.09% 1.67% 3.75% -0.93% -0.40% 10.94% 7.39% 

0.37% 

4.56% 

-0.79% 

-2.21% 

-4.13% 

3.73% 

2.68% 

-8% 

-6% 

-4% 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

12% 

1: Rollovers 

2: w/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in CUVs, by crash type 

 100-lb reduction in weight 1-sq ft reduction in footprint 
12

Figures 2.5 through 2.10, and Table 2.4, compare the estimated effect of mass and footprint 

reduction on risk with that of the other control variables, by vehicle type. In terms of other car 

characteristics, Figure 2.5 indicates that two-door cars are estimated to increase US fatality risk 

per VMT by 8%, while TORSO side airbags, automated braking systems (ABS), and electronic 

stability control (ESC), are estimated to 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 estimated increase in risk, while a brand 

new car is estimated to increase risk by 10%, presumably because the driver is unfamiliar with a 

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

estimated to have a decreasing effect on risk over time, declining from an estimated 6% increase 

in risk in 2002 to an estimated 13% reduction in risk in 2008. The calendar year variables are 

examined in more detail in Section 5.3.

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

have a much lower estimated 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 estimated to increase 

risk by 1.55%, while installing ESC would reduce risk by 11.9%; the models estimate that the 

beneficial effect of adding ESC is nearly ten times that of reducing mass by 100 lbs. 

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

footprint reduction variables, or the control variables presented in Figure 2.5. Male drivers are 

estimated to increase US fatality risk per VMT by nearly 40%, while the three control variables 

for crash environment, NITE, RURAL, and SPDLIM55, are estimated to more than triple fatality 

risk per VMT. A crash occurring in a high-fatality state carries an estimated 30% 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 is estimated to increase risk by over 400%, the regression models 

suggest that a 0.39% increase in driving on high-speed roads would result in the same increase in 

fatalities as estimated for a 100-lb reduction in mass for every car (1.6% / 414% = 0.39%).
13

Figure 2.5. Estimated effect of selected control variables on risk, passenger cars

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

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

UNDRWT00 

OVERWT00 

FOOTPRNT 

TWODOOR 

ROLLCURT 

CURTAIN 

COMBO 

TORSO 

ABS 

ESC 

M14_30 M30_50 M50_70 M70_96 

F14_30 F30_50 F50_70 F70_96 

VEHAGE 

BRANDNEW 

CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, passenger cars

0% 

50% 

100% 

150% 

200% 

250% 

300% 

350% 

400% 

UNDRWT00 

OVERWT00 

FOOTPRNT 

DRVMALE 

NITE 

RURAL 

SPDLIM55 

HIFAT_ST 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, passenger cars
14

Table 2.4. Estimated effect on US fatality risk per VMT, by vehicle type 

Type Control variable

Cars Light-duty trucks CUVs/ minivans

Estimate 95% CI Estimate 95% CI Estimate 95% CI

Vehicle

variables

UNDRWT00 1.55% 0.60% 0.52% 0.41% — —

OVERWT00 0.51% 0.72% -0.34% 0.34% — —

LBS100 — — — — -0.38% 0.87%

FOOTPRINT 1.87% 0.69% -0.07% 0.30% 1.72% 1.18%

TWODOOR 8.45% 2.50% — — — —

SUV — — 8.94% 3.94% — —

HD_PKP — — 1.73% 4.04% — —

BLOCKER1 — — -1.41% 2.11% — —

BLOCKER2 — — -2.32% 3.08% — —

MINIVAN — — — — -0.94% 7.43%

ROLLCURT -0.73% 3.38% — — -1.67% 1.16%

CURTAIN 1.00% 3.24% — — -2.85% 7.22%

COMBO -1.10% 2.95% — — -6.43% 4.94%

TORSO -8.77% 2.91% — — 0.86% 5.39%

ABS -7.87% 3.08% — — -16.5% 9.69%

ESC -11.9% 4.62% -18.8% 4.40% -3.89% 7.83%

AWD — — -14.5% 2.31% -14.0% 5.74%

VEHAGE 2.54% 0.55% 3.57% 0.62% 5.50% 1.23%

BRANDNEW 10.22% 3.12% 3.62% 3.32% 8.76% 6.03%

Driver

variables

DRVMALE 39.2% 4.74% 19.3% 4.87% 37.1% 8.06%

M14_30 4.63% 0.37% 3.54% 0.35% 3.92% 0.97%

M30_50 1.40% 0.24% 1.25% 0.19% 0.84% 0.48%

M50_70 2.20% 0.29% 1.24% 0.26% 1.82% 0.51%

M70_96 8.08% 0.44% 7.65% 0.70% 7.10% 1.03%

F14_30 2.81% 0.42% 3.64% 0.58% 4.77% 0.99%

F30_50 0.09% 0.27% 0.22% 0.32% -0.47% 0.48%

F50_70 3.21% 0.32% 3.10% 0.52% 3.22% 0.60%

F70_96 8.00% 0.57% 6.36% 1.86% 7.69% 1.61%

Crash

variables

NITE 194% 1.8% 192% 1.9% 160% 4.0%

RURAL 223% 1.8% 207% 1.9% 215% 3.7%

SPDLIM55 414% 1.8% 409% 1.9% 405% 3.7%

HIFAT_ST 29.5% 1.73% 24.6% 1.95% 33.8% 3.65%

CY2002 5.56% 3.89% 22.4% 4.09% 7.59% 8.80%

CY2003 3.60% 3.48% 18.2% 3.67% 4.97% 7.67%

CY2004 1.69% 3.21% 14.1% 3.36% -3.28% 6.97%

CY2005 -0.60% 3.03% 7.86% 3.16% 0.02% 6.22%

CY2007 -1.42% 2.83% -1.19% 3.03% -4.99% 5.64%

CY2008 -13.3% 3.00% -15.0% 3.28% -19.7% 6.08%

Estimates that are statistically significant at the 95% level are shown in red.

Figures 2.7 and 2.8 present the estimated effect of the control variables on fatality risk in crashes 

involving light-duty trucks. SUVs (8.9%), and to a lesser extent heavy-duty pickups (1.7%),

have a higher estimated 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. Each of these two approaches are estimated to 

reduce fatalities by about 1%. As with cars, risk is estimated to be higher with young male

(19%) and elderly (7%) drivers. Brand new light trucks (3.6%) have a lower estimated risk than 

brand new cars (10%), 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 
15

variables have a decreasing effect on risk over time, but the decline is much greater, from an 

estimated 22% increase in risk in 2002 to an estimated 15% decrease in risk in 2008. The 

calendar year variables are discussed in more depth in Section 5.3.

ESC is estimated to reduce risk by 19% in light trucks (Figure 2.8) as opposed to only 12% in 

cars (Figure 2.5), while male drivers are estimated to increase risk in trucks only 19% but 39% in 

cars (Figure 2.6). All-wheel drive (AWD) is estimated to reduce risk in pickups by nearly 15%. 

As in cars, driving at night, in rural areas, and on roadways with high speed limits are estimated 

to 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 are estimated to have a slightly (1%) lower fatality risk than 

CUVs. For CUVs and minivans, combination airbags are estimated to have a larger reduction in 

risk than side curtain airbags (6.4% vs. 2.9%), while torso airbags are associated with a slight, 

non-significant (0.9%) 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 is associated with a bigger reduction in risk (17%) than ESC (4%) for CUVs and 

minivans, opposite to the results seen for cars (8% and 12%, respectively) in Figure 2.5. In 

terms of driver characteristics, young and elderly drivers are associated with 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 2.5 through 2.10 and Table 2.4 have a much greater estimated effect 

on fatality risk than reductions in vehicle mass or footprint.
16

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

Figure 2.8. Estimated effect of selected control variables on risk, light trucks

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

UNDRWT00 

OVERWT00 

FOOTPRNT 

SUV 

HD_PKP 

BLOCKER1 BLOCKER2 

ESC 

AWD 

M14_30 M30_50 M50_70 M70_96 

F14_30 F30_50 F50_70 F70_96 

VEHAGE 

BRANDNEW 

CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, light trucks

0% 

50% 

100% 

150% 

200% 

250% 

300% 

350% 

400% 

UNDRWT00 

OVERWT00 

FOOTPRNT 

DRVMALE 

NITE 

RURAL 

SPDLIM55 

HIFAT_ST 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, light trucks
17

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

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

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

LBS100 

FOOTPRNT 

MINIVAN 

ROLLCURT 

CURTAIN 

COMBO 

TORSO 

ABS 

ESC 

AWD 

M14_30 M30_50 M50_70 M70_96 

F14_30 F30_50 F50_70 F70_96 

VEHAGE 

BRANDNEW 

CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, CUVs

0% 

50% 

100% 

150% 

200% 

250% 

300% 

350% 

400% 

LBS100 

FOOTPRNT 

DRVMALE 

NITE 

RURAL 

SPDLIM55 

HIFAT_ST 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of selected control variables on risk, CUVs
18

3. Multi-collinearity between vehicle mass and footprint

In its 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. Using two or more variables that are strongly correlated in the same regression 

model (referred to as multi-collinearity) can lead to biased results. The variance inflation factor, 

or VIF, is a measure of the degree of multi-collinearity in a regression model. Allison3 “begins 

to get concerned” with VIF values greater than 2.5, while Menard4 suggests that a VIF greater 

than 5 is a “cause for concern”, while a VIF greater than 10 “almost certainly indicates a serious 

collinearity problem”; however, O’Brien5 suggests that “values of VIF of 10, 20, 40 or even 

higher do not, by themselves, discount the results of regression analyses.” .

DRI showed that regression analyses that included both mass and size (i.e. wheelbase and track 

width) in the same regression model (i.e. that estimated the effect of mass while holding size 

constant, and vice versa) estimated smaller effects for changes in mass or size on US fatality risk

per VMT (Van Auken and Zellner 2002, 2003, 2004, 2005a, 2005b). In its 2010 and 2012

analyses, NHTSA included both mass and size (i.e. footprint, or wheelbase times track width) in 

the same regression model, in part because the model year 2012 to 2016 light truck standards

adopted in 2010, and the proposed 2017 to 2025 standards for all light-duty vehicles, assign a

target fuel economy/greenhouse gas emission level based on a vehicle’s footprint (Kahane 2010 

and 2012). 

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 (Pearson correlation coefficient, or r, of 0.90) than for light trucks (r of 0.75) 

or CUVs/minivans (r of 0.78). Figure 3.2 shows the same data as Figure 3.1, but uses seven 

vehicle types. Here the correlation ranges from over 0.80 for 4-door cars, SUVs, small pickups, 

and CUVs to less than 0.80 for large pickups and 2-door cars, to only 0.49 for minivans. The 

correlation of 0.75 for all light trucks (pickups and SUVs) combined in Figure 3.1 is improved 

when the types of trucks are analyzed separately in Figure 3.2: 0.90 for SUVs and 0.86 for small 

pickups, but only 0.67 for large pickups. On the other hand, separating CUVs from minivans 

improves the correlation between curb weight and footprint for CUVs (0.86) but not for 

minivans (0.49). 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 0.63.

 3 Allison, P.D.. Logistic Regression Using SAS, Theory and Application. SAS Institute Inc., Cary NC, 1999.

4 Menard, S. Applied Logistic Regression Analysis, Second Edition. Sage Publications, Thousand Oaks CA, 2002.

5 O’Brien, R.M. “A Caution Regarding Rules of Thumb for Variance Inflation Factors,” Quality and Quantitiy, (41) 

673-690, 2007.
19

Figure 3.1. Correlation between vehicle curb weight and footprint, by vehicle model and 

three vehicle types

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

seven vehicle types

R! = 0.87 

R! = 0.65 

R! = 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 

Footprint (wheelbase x track width, sq ft) 

Curb weight (lbs) 

Average footprint and curb weight, by vehicle type and model 

Cars 

Light trucks 

CUVs and Minivans 

r = 0.90 

r = 0.75 

r = 0.78 

R! = 0.65 

R! = 0.89 

R! = 0.81 

R! = 0.43 

R! = 0.81 

R! = 0.66 

R! = 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 

Footprint (wheelbase x track width, sq ft) 

Curb weight (lbs) 

Average footprint and curb weight, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Large pickups 

SUVs 

CUVs 

Minivans 

r = 0.76 

r = 0.91 

r = 0.86 

r = 0.67 

r = 0.90 

r = 0.86 

r = 0.49 
20

Table 3.1 shows the correlation coefficients of curb weight with footprint, and variance inflation 

factors, by vehicle type. The values in the table are weighted by the VMT weights for individual 

makes and models. The table indicates that curb weight is most highly correlated with footprint 

for 4-door cars and SUVs (r over 0.90), followed by small pickups and CUVs (r of 0.80) and 2-

door cars (0.76). The correlation between weight and footprint is lowest for large pickups (0.67) 

and minivans (0.49); the low correlation between weight and footprint for minivans is strongly 

influenced by one model, the Kia Sedona, which is unusually heavy for its size. Removing this 

model from the analysis increases the correlation to 0.63. Table 3.1 also indicates that six of the 

seven vehicle types (all except minivans) have a VIF associated with curb weight greater than 

2.5, the point at which multi-collinearity becomes a concern.

Table 3.1. Correlation coefficients and variance inflation factors of curb weight with 

footprint, by vehicle type

Vehicle type

Correlation 

coefficient 

(r)

Variance inflation factor (VIF)

Accounting for vehicle and 

type variables

Accounting for all 

variables

CURBWT FOOTPRNT CURBWT FOOTPRNT

Cars 0.896 6.2 5.6 7.3 6.0

Light trucks 0.748 6.5 8.6 8.1 9.8

CUVs/minivans 0.781 3.8 6.7 4.7 8.7

2-dr cars 0.758 3.3 2.7 3.8 3.0

4-dr cars 0.910 6.8 6.2 8.6 6.8

Sm pickups 0.862 4.1 4.0 6.5 4.8

Lg pickups 0.673 2.0 1.9 3.6 2.3

SUVs 0.904 6.0 5.8 7.8 1.0

CUVs 0.863 5.1 4.4 7.9 6.4

Minivans 0.488 1.5 1.4 2.1 1.8

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 estimated 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 an estimated 1.55% to 2.74% for lighter cars, and from an estimated 

0.51% to 1.95% for heavier cars) and CUVs/minivans (from an estimated 0.38% decrease in risk

to an estimated 0.60% 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 2012

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.
21

Figure 3.3. Estimated effect of mass or footprint reduction on risk, 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 along with footprint increases 

the estimated effect of a reduction in footprint on car risk, from an estimated 1.87% increase to 

an estimated 2.98% increase, but decreases the effect of footprint reduction on CUV/minivan 

risk, from an estimated 1.72% increase to an estimated 1.33% increase. Allowing light truck 

mass to be reduced along with footprint does not change the estimated 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 estimated 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 estimated 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. 1.55% 0.51% 0.52% 

-0.34% 

-0.38% 

1.87% 

-0.07% 

1.72% 

2.74% 

1.95% 

0.47% 

-0.39% 

0.60% 

2.98% 

0.07% 

1.33% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk, by vehicle type

NHTSA preferred model 

Excluding footprint 

Excluding curb wt 

 100-lb reduction in weight 1-sq ft reduction in footprint 
22

Figure 3.4. Estimated effect of reduction in car mass or footprint on US fatality risk per 

VMT, by crash type

Figure 3.5. Estimated 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/object 

3: 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/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in cars, by crash type

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/object 

3: 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/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in LTs, by crash type 

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 
23

Figure 3.6. Estimated effect of reduction in CUV/minivan mass or footprint on US fatality 

risk per VMT, by crash type

In its 2012 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 lower mass reduction is associated with an increase in fatality risk. 

-8% 

-6% 

-4% 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

12% 

1: Rollovers 

2: w/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in CUVs, by crash type 

NHTSA preferred model 

Excluding footprint 

Excluding curb wt 

 100-lb reduction in weight 1-sq ft reduction in footprint 
24

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

Table 3.2 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 3.2; the first two indicate 

the number of footprint deciles in which lower vehicle mass is associated with increased 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 lower vehicle mass is associated with reduced

risk, and the number that are statistically significant. For example, in car rollover crashes, lower 

mass is associated with increased risk in five footprint deciles; however, none of those increases 

is statistically significant. On the other hand, lower mass is associated with decreased risk in five 

footprint deciles in car rollover crashes, and four of those five are statistically significant. Table 

3.2 indicates that lower car mass is associated with increased risk in a majority of footprint 

deciles in only four crash types: with a pedestrian/cyclist, a heavy-duty truck, a heavy light-duty 

truck, or other crashes; however, very few of these relationships are statistically significant. On 

the other hand, lower car mass is associated with reduced risk in rollover crashes for five of the 

footprint deciles, and four of these reductions. Lower light truck mass is associated with 

increased risk for six deciles in rollover crashes; three of these increases are statistically 

significant. Lower light truck mass is associated with decreased risk for seven deciles in crashes 

with light or heavy cars, and several of these decreases are statistically significant. For CUVs 

and minivan crashes with heavy cars, lower mass is associated with increased risk in eight 

deciles, with three of those increases being statistically significant.

1600 2600 3600 4600 5600 6600 7600 

Footprint decile 

Curb weight (lbs) 

Range in curb weight for footprint deciles, by vehicle type 

Cars 

Light trucks 

CUVs/minivans 
25

Table 3.2. Number of footprint deciles in which lower vehicle mass is associated with an

increase or decrease in US fatality risk by VMT, by vehicle and crash type 

Crash type

Cars Light trucks CUVs/Minivans

Number of deciles withincreasing riskNumber of deciles with estimates that are statistically significantNumber of deciles withdecreasing riskNumber of deciles with estimates that are statistically significantNumber of deciles withincreasing riskNumber of deciles with estimates that are statistically significantNumber of deciles withdecreasing riskNumber of deciles with estimates that are statistically significantNumber of deciles withincreasing riskNumber of deciles with estimates that are statisticallysignificantNumber of deciles withdecreasing riskNumber of deciles with estimates that are statistically significant

1: Rollovers 5 0 5 4 6 3 4 2 4 0 6 2

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

3: w/ped etc. 7 2 3 2 5 1 5 1 3 0 7 0

4: w/HDT 7 0 3 0 7 2 3 0 6 1 4 0

5: w/lgt car 5 0 5 0 3 1 7 4 5 1 5 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 2 3 0 9 2 1 0 5 0 5 2

9: Other 6 1 4 1 5 1 5 1 6 1 4 1

The data in Table 3.2 give no information on the size of the estimated effect of mass reduction 

on risk in the footprint deciles. Figures 3.8 through 3.10 show the estimated 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 

lower mass is associated with reduced risk in car rollover crashes (shown as blue diamonds) in 

five deciles, by over 10% in footprint deciles six and seven, and by 20% in footprint decile eight. 

Figure 3.10 indicates that mass reduction in CUVs/minivans is estimated to reduce 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 estimated effect of mass reduction on risk when vehicles are grouped by footprint 

decile. 

 
26

Figure 3.8. Estimated effect of car mass reduction on fatality risk, by footprint decile and 

crash type

Figure 3.9. Estimated 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 

Percent change in risk from mass reduction 

Footprint decile (lowest to highest) 

Effect of mass reduction on car risk, by footprint decile and crash type 

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 

Percent change in risk from mass reduction 

Footprint decile (lowest to highest) 

Effect of mass reduction on LT risk, by footprint decile and crash type 

Rollovers w/object 

w/lgt car w/hvy car 

w/lgt LT w/hvy LT 
27

Figure 3.10. Estimated 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 variation 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 for cars and 

CUVs/minivans, there remains a fair degree of variability, as indicated by the Pearson 

correlation coefficient (r) values below 0.70. On the other hand, societal risk increases slightly 

as light truck mass increases, and the correlation is quite low (r of -0.13). 

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 

Percent change in risk from mass reduction 

Footprint decile (lowest to highest) 

Effect of mass reduction on CUV risk, by footprint decile and crash type 

Rollovers w/object 

w/lgt car w/hvy car 

w/lgt LT w/hvy LT 
28

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

R! = 0.36 R! = 0.02 

R! = 0.43 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

US fatality risk (total fatalities per 10 billion VMT) 

Curb weight (lbs/100) 

Fatality risk per curb weight, by vehicle type 

Cars 

Light trucks 

CUVs and Minivans 

r = 0.60 

r = -0.13 

r = 0.66 

R! = 0.50 R! = 0.02 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

US fataltiy risk (total fatalities per 10 billion VMT) 

Curb weight (lbs/100) 

Fatality risk per curb weight, passenger cars 

2-dr cars 

4-dr cars 

r = 0.71 

r = 0.13 
29

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

R! = 0.38 

R! = 0.26 R! = 0.45 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

US fataltiy risk (total fatalities per 10 billion VMT) 

Curb weight (lbs/100) 

Fatality risk per curb weight, light trucks 

Small pickups 

Large pickups 

Truck-based SUVs 

r = 0.62 

r = -0.51 

r = 0.67 

R! = 0.56 

R! = 0.89 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

US fataltiy risk (total fatalities per 10 billion VMT) 

Curb weight (lbs/100) 

Fatality risk per curb weight, CUVs and minivans 

Crossover SUVs 

Minivans 

r = 0.75 

r = 0.94 
30

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. The relationship between light truck mass and risk in 

crashes with stationary objects improves dramatically over that for all crashes (0.55 vs. -0.13); 

however, the relationship for cars and CUVs/minivans is slightly lower in crashes with stationary 

objects than in all types of crashes. 

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 roll over, 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

Table 4.1 summarizes the correlations between risk and a decrease in curb weight by 100-lb 

weight bins, by vehicle type, presented in Figures 4.1 through 4.5. 

R! = 0.32 

R! = 0.31 

R! = 0.41 

0 

10 

20 

30 

40 

50 

60 

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

US fatality risk (total fatalities per 10 billion VMT) 

Curb weight (lbs/100) 

Fatality risk in crashes with objects per curb weight, by vehicle type 

Cars 

Light trucks 

CUVs and Minivans 

r = 0.57 

r = 0.55 

r = 0.64 
31

Table 4.1. Correlation between risk and a decrease in curb weight, for vehicles grouped in 

100-lb curb weight bins

Vehicle type

US fatality risk, all crashes

US fatality risk, crashes with 

stationary objects

Estimate r R2 Estimate r R2

Cars 2.4% * 0.60 0.36 0.60% * 0.57 0.32

Light trucks -0.4% -0.13 0.02 0.38% * 0.55 0.31

CUVs/minivans 2.5% * 0.66 0.43 0.45% * 0.64 0.41

2-dr cars 6.9% * 0.71 0.50 1.07% 0.36 0.13

4-dr cars 0.5% 0.13 0.02 -0.70% -0.27 0.07

Sm pickups 1.8% * 0.62 0.38 0.66% * 0.62 0.39

Lg pickups -4.3% * -0.51 0.26 -0.37% -0.24 0.06

SUVs 4.0% * 0.67 0.45 0.92% * 0.80 0.64

CUVs 3.5% * 0.75 0.56 0.71% * 0.77 0.60

Minivans 5.8% * 0.94 0.89 1.25% * 0.89 0.80

Figures 4.1 through 4.5 and Table 4.1 show that grouping vehicles into 100-lb mass increments 

suggests that fatality risk decreases as mass increases, for most vehicle types (the exception is

large pickups). 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 R2 (0.17) indicates that this is not a very 

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 2,881 lbs, has a fatality risk of 

278 per 10 billion VMT, while model B, which weighs essentially the same (2,871 lbs) has a 

fatality risk of only 82.

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.6 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 

 6

 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.
32

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.

Figure 4.6. US fatality risk per VMT and curb weight, by vehicle model

R! = 0.17 

R! = 0.01 

R! = 0.12 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

US fatality risk (fatalities per 10 billion VMT) 

Curb weight (lbs) 

Unadjusted US fatality risk per VMT and curb weight, by vehicle type 

Cars 

Light trucks 

CUVs and Minivans 

A 

B 
33

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

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

variables except mass and footprint, vs. curb weight

R! = 0.09 

R! = 0.07 

R! = 0.13 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

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

Predicted US fatality risk (fatalities per 10 billion VMT) 

Curb weight (lbs) 

US fatality risk per VMT vs. curb weight, after accounting for 

all driver, crash, and vehicle variables except weight and footprint 

Cars 

Light trucks 

CUVs and Minivans 

A 

B 

R! = 0.07 

R! = 0.03 

R! = 0.00 

-150 

-100 

-50 

0 

50 

100 

150 

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

Residual US fatality risk (fatalities per 10 billion VMT) 

Curb weight (lbs) 

Residual US fatality risk per VMT vs. curb weight, after accounting for 

all driver, crash, and vehicle variables except weight and footprint 

Cars 

Light trucks 

CUVs and Minivans 
34

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

separately. The figures indicate 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 228 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 207 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, 21 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 (208 vs. 91 fatalities per ten billion VMT). Similarly, model F 

(3,107 lbs, 43.9 sq ft, 164 fatalities per VMT) has a predicted fatality risk nearly twice that of 

model G (3,111 lbs, 41.4 sq ft, 95 fatalities per VMT), while model H (2,647 lbs, 42.3 sq ft, 170 

fatalities per VMT) has a predicted risk 50% higher than that that of model I (2,501 lbs 41.8 sq 

ft, 117 fatalities per VMT). Models E, G, and I all have risk similar to or lower than that of 

Figure 4.9. US fatality risk per VMT vs. curb weight, car models

R! = 0.18 

R! = 0.13 

0 

50 

100 

150 

200 

250 

300 

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

US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

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

2-door 

4-door 

B 

A 

C 
35

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

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

R! = 0.12 

R! = 0.04 

0 

50 

100 

150 

200 

250 

300 

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

Predicted US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

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

2-door 

4-door 

F 

D 

K 

I 

H 

G E 

J 

C 

A 

B 

R! = 0.06 

R! = 0.10 

-150 

-100 

-50 

0 

50 

100 

150 

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

Residual US fatality risk, after accounting for all driver, 

crash, and vehicle variables except mass and footprint 

Curb weight (lbs) 

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

2-door 

4-door 

C 
36

models J and K, which are both substantially larger and heavier (J: 3,908 lbs, 52.5 sq ft, 110 

fatalities per ten billion VMT; K: 3,990 lbs, 48.7 sq ft, 109 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 2012 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; and Figure 4.11 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); 

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.
37

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

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

R! = 0.02 

R! = 0.63 

R! = 0.08 

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 

US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

US fatality risk per VMT and curb weight, by vehicle type 

Small pickups 

Large pickups 

SUVs 

R! = 0.00 

R! = 0.45 

R! = 0.05 

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 US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

US fatality risk per VMT and curb weight, by vehicle type 

Small pickups 

Large pickups 

SUVs 
38

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

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

R! = 0.04 

R! = 0.11 

R! = 0.04 

-150 

-110 

-70 

-30 

10 

50 

90 

130 

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

Residual US fatality risk, after accounting for all driver, 

crash and vehicle variables except mass and footprint 

Curb weight (lbs) 

US fatality risk per VMT and curb weight, by vehicle type 

Small pickups 

Large pickups 

SUVs 

R! = 0.36 

R! = 0.00 

0 

40 

80 

120 

160 

200 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

Adjusted US fatality risk per VMT and curb weight, after accounting for 

all driver, crash, and vehicle variables except weight 

CUVs 

Minivans 
39

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

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

R! = 0.32 

R! = 0.07 

0 

40 

80 

120 

160 

200 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

Predicted US fatality risk (crash fatalities per 1010 VMT) 

Curb weight (lbs) 

Adjusted US fatality risk per VMT and curb weight, after accounting for 

all driver, crash, and vehicle variables except weight 

CUVs 

Minivans 

R! = 0.02 

R! = 0.12 

-50 

-40 

-30 

-20 

-10 

0 

10 

20 

30 

40 

50 

2800 3000 3200 3400 3600 3800 4000 4200 4400 4600 4800 

Residual US fatality risk, after accounting for all driver, 

crash and vehicle variables except mass and footprint 

Curb weight (lbs) 

Adjusted US fatality risk per VMT and curb weight, after 

CUVs 

Minivans 
40

Table 4.2 summarizes the relationships between predicted and residual fatality risk and vehicle 

curb weight that are presented in Figures 4.6 through 4.17. The table shows the estimated 

percent change in actual, predicted, and residual fatality risk per 100-pound decrease in mass, as 

well as the correlation between risks and curb weight. 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 estimated relationship between fatality risk and 

decreasing vehicle weight, i.e. where risk is estimated to decrease as weight decreases, are shown 

in red in the table, and cases where the correlation between risk and decreasing weight by vehicle 

model exceeds 0.30 are shown in blue. Table 4.2 indicates that fatality risk increases as weight 

decreases for cars, on average; however, while the increases are statistically significant for both 

two-door and four-door cars in terms of actual risk, the increases are smaller, and not statistically 

significant, after accounting for all variables included in NHTSA’s regression models except

vehicle mass and footprint. In addition, there is a wide range in risk for cars of similar mass, as 

evidenced by the rather low R2 values. On the other hand, fatality risk decreases as weight 

decreases for heavy-duty pickups, and that the estimated effect is large, 11% for actual fatality 

risk, and 7% for predicted risk after accounting for all variables except vehicle mass and 

footprint. In these two cases the correlation between risk and weight is also rather high (R2 of 

0.62 and 0.43). For CUVs, the correlation between actual and predicted fatality risk and mass is 

moderate, with an R2 of over 0.30, with estimated risk increasing about 3% for every 100-lb 

reduction in mass. 

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

vehicle mass, 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

Estimate R2 Estimate R2 Estimate R2

Cars 4.0% * 0.17 2.4% * 0.09 1.6% * 0.07

Light trucks -0.6% 0.01 -1.2% * 0.07 0.6% 0.03

CUVs/minivans 1.7% * 0.12 1.7% * 0.13 0.0% 0.00

2-dr cars 6.1% * 0.18 3.3% 0.12 2.8% 0.06

4-dr cars 2.9% * 0.13 1.3% 0.04 1.6% * 0.10

Small pickups 0.5% 0.02 -0.2% 0.00 0.7% 0.04

Heavy-duty pickups -9.2% * 0.63 -5.9% * 0.45 -3.3% 0.11

SUVs 1.8% * 0.08 0.9% 0.05 0.9% 0.04

CUVs 2.9% * 0.36 2.5% * 0.32 0.4% 0.02

Minivans 0.0% 0.00 2.3% 0.07 -2.3% 0.12

Cars < 3106 3.3% 0.03 0.8% 0.00 2.5% 0.04

Cars > 3106 -0.5% 0.00 0.3% 0.00 -0.8% 0.01

LTs < 4594 2.1% 0.04 1.2% 0.03 0.9% 0.02

LTs > 4594 -5.2% * 0.28 -4.1% * 0.20 -1.0% 0.03

CUVs/ minivans 1.7% * 0.12 1.7% * 0.13 0.0% 0.00

* statistically significant at the 95% confidence level
41

In his peer review of the LBNL draft Phase 1 report, Mike Van Auken (DRI) commented that the 

predicted risk should be estimated after accounting for all variables NHTSA used in its 

regression models except vehicle weight; that is, after also accounting for vehicle footprint (SRA 

2012). Table 4.3 shows this analysis, the predicted (and residual) risks after accounting for all 

variables in the regression models except vehicle weight. Table 4.3 indicates that, after also 

accounting for footprint, the estimated effect of mass reduction on predicted risk is generally 

more detrimental for six of the seven vehicle types (in the middle section of the table), 

particularly for cars, and slightly reduces the estimated detrimental effect of mass reduction in 

heavy-duty pickups. The correlation between predicted risk and mass also is higher in Table 4.3

than in Table 4.2, again particularly for cars (from 0.09 to 0.27). However, the correlations are 

still rather weak, with none greater than 0.40 except CUVs. And the estimated residual risks 

after accounting for footprint in Table 4.3 are of smaller magnitude, and even less correlated 

with mass, than those shown in Table 4.2.

Table 4.3. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle mass, after accounting for all driver, crash, and vehicle variables except mass, by 

vehicle type and model

Vehicle type

Actual US fatality risk Predicted risk Residual risk

Estimate R2 Estimate R2 Estimate R2

Cars 4.0% * 0.17 4.3% * 0.27 -0.3% 0.00

Light trucks -0.6% 0.01 -0.9% * 0.04 0.3% 0.01

CUVs/minivans 1.7% * 0.12 2.2% * 0.20 -0.5% 0.02

2-dr cars 6.1% * 0.18 5.3% * 0.30 0.9% 0.01

4-dr cars 2.9% * 0.13 3.4% * 0.22 -0.5% 0.01

Small pickups 0.5% 0.02 0.4% 0.01 0.1% 0.00

Heavy-duty pickups -9.2% * 0.63 -5.6% * 0.42 -3.6% 0.13

SUVs 1.8% * 0.08 1.3% * 0.09 0.5% 0.01

CUVs 2.9% * 0.36 3.1% * 0.48 -0.2% 0.00

Minivans 0.0% 0.00 3.0% 0.13 -3.0% 0.21

Cars < 3106 3.3% 0.03 3.4% * 0.08 -0.1% 0.00

Cars > 3106 -0.5% 0.00 2.2% 0.03 -2.7% * 0.10

LTs < 4594 2.1% 0.04 1.7% 0.05 0.4% 0.00

LTs > 4594 -5.2% * 0.28 -4.1% * 0.20 -1.1% 0.03

CUVs/ minivans 1.7% * 0.12 2.2% * 0.20 -0.5% 0.02

* statistically significant at the 95% confidence level

Table 4.4 presents the same information as Table 4.2, but for the relationship between risks and 

vehicle footprint. As in Table 4.2, fatality risks for heavy-duty pickups consistently decrease as 

footprint decreases, between 4% and 9%. However, the correlation between actual risk and 

footprint by heavy-duty pickup model (0.46) is not as high as with mass (0.63 in Table 4.2). And 

the correlations between actual and predicted risk and CUV footprint (0.07 and 0.05, 

respectively) are much lower than the correlations with CUV mass (0.36 and 0.32 in Table 4.2). 

This is somewhat surprising, as Figure 2.1 indicates that footprint reduction has a relatively large 

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

smaller, estimated beneficial effect. However, there is essentially no correlation between 

decreasing footprint and risk for CUVs and minivans combined, as shown in Table 4.4.
42

Table 4.4. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle footprint, 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

Estimate R2 Estimate R2 Estimate R2

Cars 4.5% * 0.13 2.2% * 0.04 2.2% * 0.08

Light trucks -1.5% * 0.09 -2.2% * 0.26 0.7% * 0.04

CUVs/minivans 0.1% 0.00 0.0% 0.00 0.1% 0.00

2-dr cars 6.8% 0.11 2.5% 0.04 4.2% 0.07

4-dr cars 3.0% * 0.08 0.7% 0.01 2.3% * 0.12

Small pickups 0.6% 0.03 -0.1% 0.00 0.7% 0.05

Heavy-duty pickups -8.8% * 0.46 -3.9% * 0.16 -4.9% * 0.19

SUVs 1.7% 0.05 0.2% 0.00 1.5% * 0.08

CUVs 1.8% 0.07 1.4% 0.05 0.4% 0.01

Minivans 3.5% 0.16 2.1% 0.04 1.4% 0.03

Cars < 3106 5.6% 0.07 1.3% 0.01 4.3% * 0.09

Cars > 3106 -2.3% 0.03 -2.1% 0.02 -0.2% 0.00

LTs < 4594 -1.0% 0.02 -1.8% * 0.12 0.8% 0.03

LTs > 4594 -5.2% * 0.47 -5.2% * 0.53 0.0% 0.00

CUVs/ minivans 0.1% 0.00 0.0% 0.00 0.1% 0.00

* statistically significant at the 95% confidence level

Table 4.5. Relationship between actual, predicted, and residual fatality risk, and decreasing 

vehicle footprint, after accounting for all driver, crash, and vehicle variables except 

footprint, by vehicle type and model

Vehicle type

Actual US fatality risk Predicted risk Residual risk

Estimate R2 Estimate R2 Estimate R2

Cars 4.5% * 0.13 4.5% * 0.18 0.0% 0.00

Light trucks -1.5% * 0.09 -1.7% * 0.16 0.1% 0.00

CUVs/minivans 0.1% 0.00 0.1% 0.00 0.0% 0.00

2-dr cars 6.8% 0.11 5.3% * 0.15 1.5% 0.01

4-dr cars 3.0% * 0.08 3.2% * 0.12 -0.2% 0.00

Small pickups 0.6% 0.03 0.6% 0.03 0.0% 0.00

Heavy-duty pickups -8.8% * 0.46 -3.7% 0.13 -5.1% * 0.19

SUVs 1.7% 0.05 0.7% 0.02 1.0% 0.03

CUVs 1.8% 0.07 1.7% 0.09 0.1% 0.00

Minivans 3.5% 0.16 2.7% 0.06 0.8% 0.01

Cars < 3106 5.6% 0.07 4.0% * 0.08 1.6% 0.01

Cars > 3106 -2.3% 0.03 -1.2% 0.01 -1.1% 0.02

LTs < 4594 -1.0% 0.02 -1.5% * 0.08 0.5% 0.01

LTs > 4594 -5.2% * 0.47 -4.7% * 0.49 -0.4% 0.01

CUVs/ minivans 0.1% 0.00 0.1% 0.00 0.0% 0.00

* statistically significant at the 95% confidence level

Table 4.5 shows the relationships between risks and decreasing vehicle footprint, after 

accounting for all other vehicle attributes as well as mass. Accounting for vehicle mass in the 
43

estimates of predicted risk has little effect on the relationship between predicted risk and 

decreasing footprint.

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. We examined the relationship between fatality risk, vehicle mass, 

and two other variables: the initial vehicle purchase price and the driver’s income. The initial 

purchase price of a vehicle may be a proxy for the quality of design of a particular vehicle 

model. We obtained initial purchase price based on the estimates provided by R.L. Polk’s VIN 

decoding software. Some researchers have speculated that low-income drivers tend to drive 

poorly, or in environments that are more dangerous than higher income drivers. Neither FARS 

nor the state crash databases report driver income (FARS reports the zip code on the driver’s 

license, but the states do not). We used a database of California vehicle registrations from 2010 

to estimate the average income of the household owning the vehicle, based on the zip code of its 

registered owner. We used the median household income for each zip code in California from 

the 2000 US Census. Although this income variable likely does not reflect the actual income of 

the households included in the FARS or state crash databases, it does capture the range in the 

average income of the drivers of different vehicle models. 

Figure 4.18 plots predicted US fatality risk per VMT by vehicle initial purchase price, by vehicle 

type and model, while Figure 4.19 plots vehicle purchase price by curb weight. Figure 4.18 

indicates that fatality risk tends to decrease as vehicle purchase price increases, although the 

correlation between fatality risk and vehicle price is fairly weak for all vehicle types except twodoor cars (R2 of 0.40) and CUVs (R2 of 0.53). However, Figure 4.19 indicates that the 

correlation between vehicle weight and purchase price is strong for all vehicle types except 

minivans, with price increasing as weight increases.7

Figures 4.20 and 4.21 show the relationships between average median household income and 

predicted US fatality risk (Figure 4.20) and vehicle curb weight (Figure 4.21). Predicted fatality 

risk is estimated using the procedure used in Figures 4.7, 4.10, 4.13, 4.16 and the middle column 

of Table 4.2; that is, after accounting for all driver and crash characteristics, and all vehicle 

variables except mass and footprint. Figure 4.20 indicates that fatality risk decreases as 

household income increases, and that the correlation between fatality risk and household income 

is fairly strong for all vehicle types except full-size pickups, with R2 ranging from 0.42 for small 

pickups to 0.65 for CUVs, and essentially zero for fullsize pickups. However, Figure 4.21 

indicates that, while vehicle weight tends to increase as household income increases, there is 

relatively low correlation between household income and vehicle weight; the trend of increasing 

fatality risk as income declines does not appear to be explained by low income households 

driving lighter vehicles. 

 7 The extremely low correlation between minivan weight and price is caused by a single model, the Kia Sedona, 

with a high weight relative to its size; removing the Kia Sedona improves the correlation between minivan weight 

and price to 0.39. Similarly the correlation between two-door car weight and price improves to 0.68 after removing 

the outlier Mercedes S. 
44

Figure 4.18. Relationship between vehicle initial purchase price and predicted US fatality 

risk per VMT, by vehicle model 

Figure 4.19. Relationship between vehicle mass and initial purchase price, by vehicle model

R! = 0.40 

R! = 0.19 

R! = 0.00 

R! = 0.10 

R! = 0.18 

R! = 0.53 

R! = 0.02 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

$0 $10 $20 $30 $40 $50 $60 $70 $80 

Predicted US fatality risk (crash fatalities per 1010 VMT) 

Average initial purchase price ($000s) 

Predicted risk and initial purchase price, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 

R! = 0.60 

R! = 0.64 

R! = 0.77 

R! = 0.51 

R! = 0.77 

R! = 0.74 

R! = 0.00 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

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

Average initial purchase price 

Curb weight (lbs) 

Curb weight and initial purchase price, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 

minivan outlier is Sedona (R2=0.39 without it); 

4-dr car outlier is Mercedes S (R2=0.68 without it) 
45

Figure 4.20. Relationship between household income and predicted US fatality risk per 

VMT, by vehicle model 

Figure 4.21. Relationship between vehicle mass and household income, by vehicle model

R! = 0.60 

R! = 0.61 

R! = 0.42 

R! = 0.03 

R! = 0.52 

R! = 0.65 

R! = 0.46 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

$40 $45 $50 $55 $60 $65 $70 

Predicted US fatality risk (crash fdatalities per 1010 VMT) 

Average median household income of zip code where vehicle registered ($000s) 

Predicted risk and household income, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 

R! = 0.04 

R! = 0.16 

R! = 0.00 

R! = 0.03 

R! = 0.12 

R! = 0.19 

R! = 0.03 

$40 

$45 

$50 

$55 

$60 

$65 

$70 

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

Median household income of registration zip code ($000s) 

Curb weight (lbs) 

Curb weight and initial purchase price, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 
46

Figure 4.22 indicates that initial vehicle purchase price is correlated with median household 

income for CUVs and cars, and to a lesser extent for SUVs, while pickup and minivan price is 

not correlated with household income.

Figure 4.22. Relationship between vehicle initial purchase price and median household 

income, by vehicle model

Tables 4.6 and 4.7 show the relationships between predicted fatality risk, mass, initial vehicle 

purchase price, and household income, for the seven vehicle types in Figures 4.18 through 4.22, 

as well as for the three general vehicle types (cars, light-duty trucks, CUVs/minivans) and the 

five vehicle types and mass groups NHTSA used in their regression models. 

How a particular individual drives their vehicle, how closely they obey traffic regulations and 

how quickly or well they adapt to dangerous situations, could account for much of the remaining 

risk unexplained by our regression models. As described above, while this information is 

available in FARS, it is not consistently recorded in state crash data. However, it may be 

possible to more directly estimate the effect of driver behavior in particular states that record this 

information. 

We examine the estimated effect of mass or footprint reductions on US fatality risk after 

accounting for initial vehicle purchase price and average median household income in Sections 

5.2 and 5.4.

R! = 0.42 

R! = 0.46 

R! = 0.01 

R! = 0.21 

R! = 0.25 

R! = 0.61 

R! = 0.09 

$40 

$45 

$50 

$55 

$60 

$65 

$70 

$0 $10 $20 $30 $40 $50 $60 $70 $80 

Median household income of registration zip code ($000s) 

Initial purchase price ($000s) 

Curb weight and initial purchase price, by vehicle type and model 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 

minivan outlier is Sedona (R2=0.12 without it); 

4-dr car outlier is Mercedes S (R2=0.42 without it) 
47

Table 4.6. Relationship between predicted fatality risk, mass, and initial vehicle purchase 

price, by vehicle type and model

Vehicle type

Predicted risk and 

initial purchase price 

(Figure 4.18)

Initial purchase price 

and mass 

(Figure 4.19)

Estimate R2 Estimate R2

Cars -1.8% * 0.22 1.7% * 0.64

Light trucks -1.3% * 0.05 0.6% * 0.58

CUVs/minivans -2.0% * 0.26 0.8% * 0.47

2-dr cars -3.8% * 0.40 1.2% * 0.60

4-dr cars -1.4% * 0.19 1.8% * 0.64

Small pickups 0.0% 0.00 0.7% * 0.77

Heavy-duty pickups 3.5% 0.10 0.6% * 0.51

SUVs -1.6% * 0.18 0.9% * 0.77

CUVs -2.5% * 0.53 1.1% * 0.74

Minivans 0.9% 0.02 0.0% 0.00

Cars < 3106 -1.4% 0.02 1.1% * 0.62

Cars > 3106 -1.6% * 0.19 2.8% * 0.51

LTs < 4594 -2.6% * 0.16 0.8% * 0.57

LTs > 4594 -4.2% * 0.27 0.3% * 0.07

CUVs/ minivans -2.0% * 0.26 0.8% * 0.47

* statistically significant at the 95% confidence level

Table 4.7. Relationship between predicted fatality risk, mass, and median household 

income, by vehicle type and model

Vehicle type

Predicted risk and 

household income 

(Figure 4.20)

Household income 

and mass 

(Figure 4.21)

Household income 

and initial purchase 

price (Figure 4.22)

Estimate R2 Estimate R2 Estimate R2

Cars -5.4% * 0.48 0.4% * 0.14 0.3% * 0.44

Light trucks -9.3% * 0.39 0.1% * 0.03 0.2% * 0.24

CUVs/minivans -3.2% * 0.59 0.2% 0.03 0.6% * 0.40

2-dr cars -6.6% * 0.60 0.2% 0.04 0.5% * 0.42

4-dr cars -5.1% * 0.61 0.4% * 0.16 0.3% * 0.46

Small pickups -10.2% * 0.42 0.0% 0.00 0.0% 0.01

Heavy-duty pickups -5.2% 0.03 0.1% 0.03 0.2% * 0.21

SUVs -7.0% * 0.52 0.1% * 0.12 0.2% * 0.25

CUVs -3.4% * 0.65 0.4% * 0.19 0.6% * 0.61

Minivans -3.5% * 0.46 0.3% 0.03 0.4% 0.09

Cars < 3106 -4.0% * 0.20 0.4% * 0.07 0.6% * 0.35

Cars > 3106 -5.5% * 0.58 0.5% 0.05 0.4% * 0.46

LTs < 4594 -8.4% * 0.47 0.1% 0.04 0.3% * 0.22

LTs > 4594 -11.0% * 0.43 0.0% 0.01 0.3% * 0.41

CUVs/ minivans -3.2% * 0.59 0.2% 0.03 0.6% * 0.40

* statistically significant at the 95% confidence level
48

5. Sensitivity of NHTSA results to data used and model specification

In this section we examine the sensitivity of the NHTSA results on the estimated effect of mass 

or footprint reduction 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 estimated 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 risk

Figure 5.1 compares the estimates 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 (Green et al 2011). 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 estimated detrimental effect of mass reduction on risk in cars, from 1.55% to 1.95% for 

lighter than average cars, and from 0.51% to 0.89% for heavier cars. On the other hand, 

analyzing risk of fatal crash per VMT has essentially no impact on the estimated effect of mass 

reduction in light trucks or CUVs/minivans. 

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 estimates. 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 estimated effect of car mass reduction, and 

light truck mass and footprint reduction, on risk; for all vehicle types, mass reduction is now 

associated with a reduction in fatality risk given that a crash occurs. Footprint reduction in light 

trucks is similarly associated with a reduction in fatality risk per crash, while it is associated with 

increased risk per crash in cars and CUVs/minivans. 
49

Figure 5.1. Estimated effect of mass or footprint reduction on US fatalities, using three 

different measures of exposure (VMT, induced exposure crashes, vehicle registrationyears) and fatal crashes per VMT

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.

In his review of the draft LBNL Phase 1 report, Mike Van Auken (DRI) suggested using vehicle 

registration-years, rather than vehicle miles traveled, as the measure of exposure. VMT is 

preferable to registration-years as the measure of exposure, as a vehicle that is not driven has 

zero risk (SRA 2012). Registration years have been used as the measure of exposure when 

accurate estimates of annual vehicle miles traveled have not been available by vehicle model and 

year. The estimates NHTSA obtained from Polk appear to be accurate, although they do not 

reflect the decline in driving in 2008 when gas prices rose and the economy stumbled.

Nonetheless, LBNL conducted a sensitivity using vehicle registration years rather than VMT as 

the measure of exposure. This alternative estimates lower effects of mass reduction on risk in 

lighter cars and light trucks, no change in CUVs/minivans, but a substantially higher effect of 

mass reduction in heavier cars (from an estimated 0.51 % increase in risk to an estimated 2.40% 

increase in risk), as shown in Figure 5.1. 1.55% 0.51% 0.52% 

-0.34% 

-0.38% 

1.87% 

-0.07% 

1.72% 

1.95% 

0.89% 

0.54% 

-0.42% 

-0.47% 

1.83% 

0.14% 

1.79% 

-0.22% 

-1.45% 

-1.13% 

-0.76% 

-0.84% 

2.28% 

-1.30% 

2.18% 

0.93% 

2.40% 

-0.09% 

-0.76% 

-0.40% 

0.32% 

-0.08% 

0.03% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk, by vehicle type

NHTSA preferred model 

Fatal crashes per VMT 

Fatalities per ind exp crash 

Fatalities per veh regn-years 

 100-lb reduction in weight 1-sq ft reduction in footprint 
50

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 14 dummy variables based on the 

vehicle nameplate manufacturer.8 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 five Chrysler brands (Jeep, Chrysler, 

Dodge, Plymouth, and Sprinter) were combined in a single Chrysler category, while the three 

Ford brands (Ford, Lincoln, Mercury) were combined in a single Ford category. Ten lowvolume manufacturers were grouped into a separate Other manufacturer category.9

Figure 5.2 compares the estimated effect of adding variables for each of the 14 manufacturers 

(shown in red) to NHTSA’s preferred regression model specification (shown in light blue). For 

cars and light trucks, accounting for vehicle manufacturer increases the estimated effect of mass 

reduction, but decreases the estimated effect of footprint reduction, on risk. Accounting for 

vehicle manufacturer in the CUV/minivan regression models makes the estimated effect of mass 

reduction detrimental and statistically-significant, while the estimated effect of footprint 

reduction becomes beneficial but no longer statistically-significant.

Figure 5.2 also shows a second case in which five additional control variables are included for 

five luxury brands (Cadillac, Lincoln, Acura, Infiniti, and Lexus). The effect of including the 

five luxury brands in the regression models is that the estimated detrimental effect of mass 

reduction on risk in the heavier cars is much higher, comparable to that of the lighter cars, and is 

statistically significant. It also substantially reduces the estimated detrimental effect of footprint 

reduction on cars. Including the five luxury models somewhat mitigates the large change in the 

estimated effect of mass or footprint reduction in CUVs/minivans when accounting for vehicle 

manufacturer. 

 8 The 14 manufacturers are: Chrysler, Ford, BMW, Honda, Hyundai, Kia, Mazda, Mercedes-Benz, Mitsubishi, 

Nissan, Subaru, Toyota, Volkswagen, and Volvo. 9 The manufacturers included in the Other category are: AM General, Audi, Daewoo, Isuzu, Jaguar, Land Rover, 

Mini, Porsche, Saab, and Suzuki.
51

Figure 5.2. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

controlling for vehicle manufacturer, by vehicle type

Initial vehicle purchase price, rather than manufacturer nameplate, is another proxy for the 

general quality of vehicle design. We obtained the initial purchase price from the Polk VIN 

decoder, using 2010 California registration data from the state Department of Motor Vehicles. 

Every $1,000 increase in initial purchase price is estimated to increase risk in cars by 0.21% (+/-

0.12%), but decrease risk in light trucks by 0.56% (+/- 0.11%) in light trucks and by 0.80% (+/-

0.27%) in CUVs/minivans. Figure 5.3 shows how accounting for vehicle purchase price changes 

the estimated effect of mass or footprint reduction on risk, compared to the other measures of 

quality of vehicle design. Including initial purchase price in the regression models substantially 

increases the estimated effect of mass reduction in heavier-than-average cars (from an estimated 

0.51% to 0.84% increase in risk), and substantially increases the estimated beneficial effect of 

mass reduction in heavier-than-average light-duty trucks and CUVs/minivans. Accounting for 

initial vehicle purchase price increases the estimated beneficial effect of footprint reduction in 

light trucks, but results in little change in the estimated effect of footprint reduction on risk for 

cars and CUVs/minivans. Accounting for vehicle purchase price has a smaller effect on the 

estimated effect of mass reduction on risk than accounting for vehicle manufacturer, except for 

heavier-than-average light trucks; accounting for price has a larger effect on the estimated effect 

of footprint reduction on risk than accounting for manufacturer. 1.55% 0.51% 0.52% -0.34% -0.38% 

1.87% 

-0.07% 

1.72% 

1.90% 

0.75% 

0.59% 

-0.11% 

1.62% 

1.71% 

-0.29% 

-0.77% 

2.04% 

1.80% 

0.57% 

-0.11% 

1.28% 

1.20% 

-0.28% 

-0.28% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of increase in weight or size on risk, by vehicle type

NHTSA preferred model 

Including 14 vehicle makes 

Plus 5 luxury brands 

 100-lb reduction in weight 1-sq ft reduction in footprint 
52

Figure 5.3. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

controlling for vehicle manufacturer or for initial vehicle purchase price, by vehicle type

Table 5.1 shows the correlation and VIF between curb weight and initial purchase price, by 

vehicle type. The table indicates that vehicle mass is correlated fairly strongly with initial 

purchase price for all vehicle types, with r greater than 0.65 and VIF greater than 2.5 for all 

vehicle types except minivans (0.273, 1.3) and large pickups (0.561, 1.6). 

Table 5.1. Correlation coefficients and variance inflation factors of curb weight with initial 

purchase price, by vehicle type

Vehicle type

Correlation 

coefficient (r)

Variance inflation factor (VIF) 

accounting for vehicle variables

CURBWT PRICE000

Cars 0.760 7.8 3.6

Light trucks 0.677 4.4 2.5

CUVs/minivans 0.663 4.8 2.4

2-dr cars 0.714 4.7 3.2

4-dr cars 0.778 8.3 3.8

Sm pickups 0.785 4.4 3.2

Lg pickups 0.561 2.1 1.6

SUVs 0.761 9.1 2.8

CUVs 0.833 8.8 4.1

Minivans 0.273 1.5 1.3 1.55% 0.51% 0.52% -0.34% -0.38% 1.87% -0.07% 

1.72% 

1.90% 

0.75% 

0.59% 

-0.11% 

1.62% 

1.71% 

-0.29% 

-0.77% 

2.04% 

1.80% 

0.57% 

-0.11% 

1.28% 

1.20% 

-0.28% 

-0.28% 

1.42% 

0.84% 

0.45% 

-0.52% 

-0.92% 

1.99% 

-0.36% 

1.57% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of increase in weight or size on risk, by vehicle type

NHTSA preferred model 

Including 14 vehicle makes 

Plus 5 luxury brands 

Initial purchase price 

 100-lb reduction in weight 1-sq ft reduction in footprint 
53

The control variables for vehicle manufacturer and initial purchase price attempt to account for 

differences in vehicle models not controlled for in the NHTSA regression models. Other vehicle 

attributes which could explain the remaining unexplained risk include:

• relatively low bumper height, which increases the extent to which a vehicle’s front bumper 

overlaps the bumper or door sill of a crash partner, may reduce risk in two-vehicle crashes;

• lower center of gravity, or static stability factor, may reduce the tendency of a vehicle to 

roll over; 

• high engine power-to-weight ratio may increase crash frequency, and

• measures of braking distance and handling capabilities which may affect the ability of 

vehicles to avoid crashes;

LBNL may estimate the effect of accounting for these vehicle attributes in future analyses.

5.3. Calendar year 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 

5.4. 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 5.5 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 

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. 
54

Figure 5.4. Effect of calendar year variables on risk, by vehicle type

Figure 5.5. Effect of calendar year variables on light truck risk, by crash type

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 CY2002 CY2003 

CY2004 

CY2005 CY2007 CY2008 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of calendar year variables on risk, by veh type

 Cars Light Trucks CUVs/Minivans 

-35% 

-25% 

-15% 

-5% 

5% 

15% 

25% 

35% 

45% 

55% 

65% 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of control variables on risk, light trucks 

Rollover w/obj w/ped w/HDT w/lgt car w/hvy car w/lgt LT w/hvy LT Other 
55

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 5.6 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 2012 analysis, and there is not the consistent decrease of the effect of 

calendar year on risk in later years as in the 2012 analysis. 

Figure 5.6. 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 5.7. 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% 

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

CY1995 CY1996 CY1997 CY1998 CY2000 CY1995 CY1996 CY1997 CY1998 CY2000 CY1995 CY1996 CY1997 CY1998 CY2000 

10 VMT) 

Effect of control variables on risk, CUVs and minivans 

Cars Light Trucks CUVs/Minivans 

NA !"########!"#########!"########!"##############################################################################################################!"########!"########!"########!"########!"
56

Figure 5.7. 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 5.8 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 5.9 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 5.8

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 5.10 through 5.13 show the effect of removing the calendar year variables from 

NHTSA’s preferred regression model (shown in light blue). Figure 5.10 indicates that excluding 

the calendar year variables has little effect on the estimated coefficients for mass or footprint 

reduction in cars, or for footprint reduction in CUVs/minivans. However, removing the calendar 

year variables increases the estimated effect of mass reduction in trucks and CUVs/minivans; 

reducing by 100 lbs the weight of lighter trucks now is estimated to increase risk by 1.20% (as 

opposed to 0.52%), is associated with an estimated 0.30% increase rather than an estimated

0.34% decrease in risk in the heavier trucks, and lowers the estimated decrease in risk in 

CUVs/minivans from a 0.38% decrease to essentially a slight estimated increase in risk.

-35% 

-25% 

-15% 

-5% 

5% 

15% 

25% 

35% 

45% 

55% 

CY1995 

CY1996 CY1997 CY1998 CY2000 

CY1995 

CY1996 CY1997 CY1998 CY2000 

CY1995 

CY1996 CY1997 CY1998 CY2000 

CY1995 

CY1996 CY1997 CY1998 CY2000 

CY1995 

CY1996 CY1997 CY1998 CY2000 

CY1995 

CY1996 CY1997 CY1998 CY2000 

Percent change in risk (log-odds of fatality per 1010 VMT) 

Effect of control variables on risk, light trucks 

 Rollover w/obj w/ped w/HDT w/car w/LT 
57

Figure 5.8. US fatality risk per VMT, by vehicle type and calendar year

Figure 5.9. Total US VMT for MY2002 vehicles, by vehicle type and calendar year

0 

50 

100 

150 

200 

250 

300 

350 

2001 2002 2003 2004 2005 2006 2007 2008 2009 

US fatality risk (fatalities per 10 billion VMT) 

Year 

US fatality risk per VMT, by vehicle type and year 

2-dr cars 4-dr cars 

Pickups HD pickups 

SUVs CUVs 

Minivans 

0.0E+00 

2.0E+10 

4.0E+10 

6.0E+10 

8.0E+10 

1.0E+11 

1.2E+11 

0.0E+00 

5.0E+09 

1.0E+10 

1.5E+10 

2.0E+10 

2.5E+10 

3.0E+10 

3.5E+10 

4.0E+10 

2001 2002 2003 2004 2005 2006 2007 2008 2009 

US VMT (10 billion) 

Year 

US VMT for MY00 vehicles, by vehicle type and year 

2-dr cars 

Pickups 

HD pickups 

SUVs 

CUVs 

Minivans 

4-dr cars (right) 
58

Figure 5.10. 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 5.11 through 5.13 show the effect on the vehicle 

control variables; there is little to no effect on the driver or crash control variables (not shown). 

Figures 5.11 through 5.13 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 estimated effect of vehicle age on risk in all three vehicle types. 

Figures 5.10 through 5.13 suggest that NHTSA’s inclusion of the calendar year variables in their 

preferred model dilutes the estimated effect of airbag technologies in cars and CUVs and 

minivans, the estimated added risk in SUVs and heavy-duty pickups, and the estimated 

beneficial effect of ESC in light trucks in general, while over-stating the estimated effect of 

vehicle age in all three vehicle types. 1.55% 0.51% 0.52% 

-0.34% 

-0.38% 

1.87% 

-0.07% 

1.72% 

1.52% 

0.43% 

1.20% 

0.30% 

0.03% 

2.11% 

-0.42% 

1.61% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of increase in weight or size on risk, by vehicle type

NHTSA preferred model 

Excluding CY variables 

 100-lb reduction in weight 1-sq ft reduction in footprint 
59

Figure 5.11. Estimated effect of selected control variables on car risk, including and 

excluding calendar year variables

Figure 5.12. Estimated effect of selected control variables on light truck risk, including and 

excluding calendar year variables

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

TWODOOR 

ROLLCURT 

CURTAIN 

COMBO 

TORSO 

ABS 

ESC 

VEHAGE 

BRANDNEW 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of control variables on risk, passenger cars 

NHTSA preferred model 

Excluding CY variables 

-30% 

-25% 

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

SUV 

HD_PKP 

BLOCKER1 BLOCKER2 

ESC 

AWD 

VEHAGE 

BRANDNEW 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of control variables on risk, light trucks 

NHTSA preferred model 

Excluding CY variables 
60

Figure 5.13. Estimated 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 10% of car and light truck drivers, and 6% 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; we also excluded these cases when calculating the weighted 

average effect across all crash types after full penetration of ESC by 2017. 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.10 The dark green columns in Figure 5.14 indicate that removing from the analysis the 

FARS cases where alcohol or drug used was involved slightly increases the estimated effect of 

mass reduction on risk, but slightly reduces the estimated effect of footprint reduction on risk, as 

compared with NHTSA’s preferred regression model. 

In its 2003 report NHTSA created a “bad driver rating” variable based on whether 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 11% of car and light truck drivers, and another 8% 

of CUV/minivan drivers, in the FARS cases. The light green columns in Figure 5.14 indicate 

that also excluding these bad drivers from the analysis further increases the estimated effect of 

mass reduction on risk. For example, excluding all bad drivers increases the estimated increase 

 10 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. 

-20% 

-15% 

-10% 

-5% 

0% 

5% 

10% 

15% 

20% 

MINIVAN 

ROLLCURT 

CURTAIN 

COMBO 

TORSO 

ABS 

ESC 

AWD 

VEHAGE 

BRANDNEW 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of control variables on risk, CUVs and minivans 

NHTSA preferred model 

Excluding CY variables 
61

in risk from mass reduction from 1.55% to 2.32% in lighter cars, from 0.51% to 1.19% in heavier 

cars, and from 0.52% to 1.01% in lighter trucks. On the other hand, excluding all bad drivers 

from the analysis further reduces the estimated 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 above), removing crashes involving these drivers from the analysis makes overall mass 

reduction more harmful, and footprint reduction less harmful. 

The pale blue column in Figure 5.14 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 estimated 

effect on risk than excluding cases where alcohol or drug use was reported, for cars, but a similar 

estimated 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 estimated effects shown in green in Figure 5.14.

Figure 5.14. Estimated effect of mass or footprint reduction on US fatalities per VMT, after 

excluding case vehicles whose driver was drinking, using drugs or exhibited bad driving 

behavior, or controlling for median household income, by vehicle type

Household income can also act as a proxy for driver behavior; as shown in Figure 4.18 above, 

there is a fairly strong correlation between household income and predicted fatality risk, with 

risk decreasing as income increases. And Figure 4.20 above indicates that crash frequency 

increases as household income increases, particularly for cars. Every $1,000 increase in 1.55% 0.51% 0.52% -0.34% -0.38% 1.87% -0.07% 1.72% 1.88% 0.88% 0.78% -0.35% -0.16% 1.65% -0.26% 1.36% 2.32% 1.19% 1.01% -0.11% -0.01% 1.32% -0.39% 1.12% 2.08% 0.96% 0.77% -0.55% -0.33% 1.20% 0.16% 0.68% -0.30% -0.44% 2.30% -0.19% 1.82% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk, by vehicle type

NHTSA preferred model 

Excluding drinking/drug use 

Excluding all bad drivers 

NHTSA excluding BAC > 0.08 

Including household income 

 100-lb reduction in weight 1-sq ft reduction in footprint 
62

household income is estimated to reduce US fatality risk per VMT 0.72% (+/- 0.26%) for cars, 

and 0.24% (+/- 0.16%) for light trucks, while increasing risk 0.04% (+/- 0.24%) for 

CUVs/minivans. The last columns in Figure 5.14 (shown in violet) show the estimated effect of 

mass or footprint reduction on risk after accounting for household income. Accounting for 

household income has a bigger influence on the estimated effect of mass or footprint reduction in 

cars than in light trucks or CUVs/minivans: accounting for household income substantially 

reduces the estimated effect of mass reduction in cars, and substantially increases the estimated 

effect of footprint reduction in cars. This is in contrast to excluding the alcohol/drug use and bad 

driving behavior cases, which substantially increased the estimated effect of mass reduction in 

cars on risk (and reduced the estimated effect of footprint reduction). 

Table 5.2 shows the Pearson correlation coefficient r and VIF between curb weight and average 

median household income, by vehicle type. Table 5.2 indicates that vehicle mass is not 

correlated with initial purchase price, with a correlation coefficient greater than 0.50 only for 

CUVs (0.539) and VIF less than 2.5 for all vehicle types (the high VIFs for CURBWT in Table 

5.2 are the results of the correlation between curb weight and footprint, as shown in Table 3.1 

above). 

Table 5.2. Correlation coefficients and variance inflation factors of curb weight with 

average median household income, by vehicle type

Vehicle type

Correlation 

coefficient (r)

Variance inflation factor (VIF) 

accounting for vehicle variables

CURBWT INC000

Cars 0.278 6.5 1.7

Light trucks 0.132 3.0 1.5

CUVs/minivans 0.264 4.0 1.7

2-dr cars 0.343 3.7 2.1

4-dr cars 0.286 7.2 1.6

Sm pickups 0.123 4.2 1.1

Lg pickups 0.228 2.0 1.2

SUVs 0.214 6.8 1.5

CUVs 0.539 6.9 2.1

Minivans 0.418 1.6 1.4

LBNL believes that the information in FARS on driver behavior in the current crash, as well as 

their recent driving history, is the best available to account for how a particular individual drives 

their vehicle, how closely they obey traffic regulations and how quickly or well they adapt to 

dangerous situations. While this information is not consistently recorded in state crash data, it 

may be possible to more accurately control for the effect of driver behavior in the relationship 

between mass or footprint and fatality risk, using data from particular states that record this 

information. 
63

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

As mentioned above, NHTSA excluded three types of cars, models used as sports cars, police 

cars, and models with all-wheel drive, all Ford Crown Victorias, and fullsize passenger and 

cargo vans, from its preferred regression model. Including these vehicles in the analysis, and 

adding five control variables for the additional vehicle types, increases the estimated effect of 

mass reduction in lighter-than-average cars, has little change in heavier cars and lighter light 

trucks, and increases the estimated beneficial effect of mass reduction in heavier light trucks, as 

shown in Figure 5.15. Including these vehicles has little effect on the estimated effect of 

footprint reduction on risk in light trucks, but reduces the estimated increase in risk from 

footprint reduction in cars.

Figure 5.15. Estimated effect of mass and footprint reduction on US fatality risk per VMT, 

after including sports, police, and all-wheel drive cars, and fullsize passenger and cargo 

vans, by vehicle type

5.6. Effect of changes suggested by NHTSA peer reviewers

In its review of the preliminary NHTSA 2012 study, DRI commented that some drivers may be 

better able to avoid a crash, due to skill, level of alertness, or reaction time, than others, even in 

crashes in which they were not determined to be at fault (Van Auken and Zellner 2012a and

2012b). Using all vehicles deemed not-at-fault in two-vehicle crashes, rather than only those that 

were stopped at the time of the crash, as the measure of exposure might over-represent the effect 

of poor driving behavior in the regression results. DRI suggested that NHTSA use only stopped 

vehicles, rather than all non-culpable vehicles, in developing the weights for vehicle registrationyears and miles-driven to be used as the measure of crash exposure. In addition, DRI suggested 

that NHTSA account for the two components of vehicle footprint, wheelbase and track width, 1.55% 0.51% 0.52% -0.34% -0.38% 1.87% -0.07% 1.72% 1.79% 0.49% 0.49% -0.77% -0.38% 1.64% -0.02% 1.72% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of increase in weight or size on risk, by vehicle type

NHTSA preferred model 

Including all cars and fullsize vans 

 100-lb reduction in weight 1-sq ft reduction in footprint 
64

separately in the regression models. DRI has found that these two changes to the regression 

models tended to reduce the estimated detrimental effect of mass reduction on risk (Van Auken 

and Zellner 2005b and 2012b).

Table 5.3 shows the results of the additional sensitivity tests NHTSA conducted in response to

the DRI comments. Table 5.3 indicates that using only stopped vehicles, and not all vehicles 

judged to be not-at-fault, in two-vehicle crashes substantially reduces the estimated detrimental 

effect of mass reduction on risk in cars and trucks, while substantially increasing the estimated 

detrimental effect of footprint reduction on risk in cars. Replacing vehicle footprint with its two 

components, track width and wheelbase, similarly reduces the estimated detrimental effect of 

mass reduction; an increase in risk is associated with decreasing track width in cars and light 

trucks, but with decreasing wheelbase in CUVs/minivans. The last column of Table 5.3

indicates that combining these two sensitivities, i.e. using stopped vehicles as the measure of 

exposure and replacing footprint with track width and wheelbase, further reduces the estimated 

detrimental effect of mass reduction, such that fatality risk is estimated to increase in light cars 

by a statistically non-significant 0.26%.

Table 5.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications suggested by NHTSA peer reviewers

Variable

Case vehicle 

type

NHTSA 

preferred model 

(fatalities per 

VMT)

Using stopped 

vehicles

Replace 

footprint with 

track width and 

wheelbase

Using stopped 

vehicles and 

track width/ 

wheelbase

Mass

reduction

Cars < 3106 lbs 1.55% 0.97% 0.95% 0.26%

Cars > 3106 lbs 0.51% -0.63% 0.24% -0.90%

LTs < 4594 lbs 0.52% 0.35% -0.07% -0.10%

LTs > 4594 lbs -0.34% -0.80% -0.58% -0.97%

CUV/ minivan -0.38% -0.33% -0.25% -0.14%

Footprint

reduction

Cars 1.87% 3.43% — —

LTs -0.07% -0.03% — —

CUV/ minivan 1.72% 1.81% — —

Track 

width 

reduction

Cars — — 4.36% 6.03%

LTs — — 1.07% 0.90%

CUV/ minivan — — 0.08% -0.55%

Wheel 

base 

reduction

Cars — — -0.09% 0.38%

LTs — — -0.09% -0.09%

CUV/ minivan — — 1.16% 1.45%

Estimates that are statistically significant at the 95% level are shown in red.

Other reviewers suggested that NHTSA conduct two additional sensitivities: reweighting the 

fatalities of CUVs and minivans by their market shares in 2010 (Paul Green); and removing the 

non-significant control variables from the 27 regression models for the three vehicle types and 

nine crash types (Farmer). Figure 5.16 shows the sensitivity of NHTSA’s main results to these 

changes. Weighting the distribution of fatalities in CUVs and minivans by their respective 

shares of sales in 2010 (which reflects more CUVs and fewer minivans) causes the estimated 

effects to change signs, with decreasing mass now associated with an estimated increase in risk, 
65

and decreasing footprint with an estimated reduction in risk, for CUVs and minivans (shown in 

orange). Removing non-significant control variables from each of the regression models results 

in slightly larger estimated detrimental effects of reduced mass in cars, and slightly smaller 

estimated effects in light trucks.

Figure 5.16. Estimated effect of mass and footprint reduction on US fatality risk per VMT, 

after reweighting CUV/minivan fatalities to 2010 sales and excluding non-significant 

control variables, by vehicle type 1.55% 0.51% 0.52% 

-0.34% 

-0.38% 

1.87% 

-0.07% 

1.72% 

1.55% 

0.51% 

0.52% 

-0.34% 

0.55% 

1.87% 

-0.07% 

-0.61% 

1.63% 

0.69% 

0.35% 

-0.54% 

-0.46% 

1.73% 

0.11% 

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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk, by vehicle type

NHTSA preferred model 

Reweighting CUV/minivans to 2010 sales 

Excluding non-significant control variables 

 100-lb reduction in weight 1-sq ft reduction in footprint 
66

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. Note that new safety 

technologies, such as ESC and side airbags, often are first introduced in more expensive 

vehicles, and bundled with other features that enhance occupant safety; as a result, the initially 

observed safety benefit on a per vehicle basis may be reduced as the technology is more widely 

adopted. And while a vehicle with ESC or side airbags will most likely have a lower risk than a 

vehicle without these technologies, the effectiveness of the technologies could well vary across 

vehicle models.

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. 

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 nonpedestrian crashes by 8% in both cars and light trucks.11 (These estimates treat crossover SUVs 

and minivans as light trucks rather than cars.) Figure 6.2 compares these recent results, shown in 

red, with the estimated 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 estimates 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 estimates 

substantially higher ESC effectiveness in reducing risk in crashes with other vehicles in many 

cases.

 11 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.
67

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

Figure 6.2. Estimated effect of ESC on fatality risk, by vehicle type and crash type

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

2001 2002 2003 2004 2005 2006 2007 

Percent of new vehicle fleet 

Model year 

Market penetration of ESC, by vehicle type and model year 

ESC, car 

ESC, LT 

ESC, CUV 

-46% 

-16% 

-5% 

-30% 

3% 

-3% 

-23% 

-27% 

-3% 

-76% 

-49% 

5% 

-35% 

-21% 

-23% 

-7% 

-24% 

6% 

-46% 

-34% 

16% 

-5% 

1% 

-31% 

9% 

21% 

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% 

-120% 

-100% 

-80% 

-60% 

-40% 

-20% 

0% 

20% 

40% 

60% 

80% 

1: Rollovers 

2: w/object 

3: 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/object 

3: 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/object 

3: Ped etc. 

4: w/HDT 

5: w/lgt car 

6: w/hvy car 

7: w/lgt LT 

8: w/hvy LT 

9: Other 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of ESC on risk in cars, by vehicle and crash type 

 Cars Light trucks CUVs/Minivans 

NHTSA (Sivinski): 
68

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 6.3 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 6.3 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 

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 (shown in 

Figure 6.1 above); 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 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

2001 2002 2003 2004 2005 2006 2007 

Percent of new vehicle fleet 

Model year 

Market penetration of side airbags, by vehicle type and model year 

Curtain, car Curtain, LT Curtain, CUV 

Torso, car Torso, LT Torso, CUV 

Combo, car Combo, LT Combo, CUV 
69

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). 

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 29% 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. 

Figure 6.4. Estimated 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 13% -10% -7% -21% -53% -26% -31% -17% -42% -38% -26% -29% 

-40% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of side airbags on risk in cars, by crash type 

Curtain side airbag Combo side airbag Torso side airbag Any side airbag 
70

Figure 6.5. Estimated 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

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 86% 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. -69% 74% -63% -59% -46% 39% -28% 

-8% 

-38% 

24% 

86% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of side airbags on risk in CUVs, by crash type 

Curtain side airbag Combo side airbag Torso side airbag Any side airbag 
71

Figure 6.6. Estimated 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% 32% -10% 52% -55% -69% 34% 25% 0% -19% 

-18% 

12% 

-17% 

-9% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of side airbags on risk in LTs, by crash type 

Curtain side airbag Combo side airbag Torso side airbag Any side airbag 
72

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. Estimated effect of mass reduction on risk to car occupants only in two-vehicle 

crashes, by crash partner vehicle type and crash configuration 7.26% 5.40% 7.54% 8.52% 1.44% 6.39% 2.02% 3.23% -2.52% 7.74% 6.84% 5.07% 4.79% -2.33% 8.96% 3.53% 5.75% 7.68% 2.96% -0.30% -0.68% 1.57% 4.66% 6.68% 1.86% 

6.91% 

-9.14% 

-1.32% 

3.48% 

-2.45% 

6.57% 

4.56% 

6.53% 

0.55% 

1.09% 

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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in cars, by crash type 

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

 Cars < 3,106 Cars > 3,106 All cars 
73

Figure 6.8. Estimated 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. Estimated effect of mass reduction on risk to light truck occupants only in twovehicle crashes, by crash partner vehicle type and crash configuration 3.51% 2.21% 7.98% 11.06% 11.51% 9.44% 10.00% 12.11% 10.68% 0.18% 3.96% -3.20% 4.39% 5.97% -8.00% -9.49% -5.89% -1.87% -13.13% -7.92% -9.09% 3.63% 1.32% 3.62% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in CUVs, by crash type 

 100-lb reduction in weight 1-sq ft reduction in footprint 5.29% 8.83% 0.29% 2.21% 6.98% -2.45% 3.65% 7.93% -1.25% 6.45% 6.74% 1.25% -3.41% 2.30% 0.97% 0.62% 0.58% 0.27% 3.48% 6.88% 8.09% 5.75% 7.75% -3.82% 2.72% -1.84% 1.90% 3.05% 0.51% 3.83% 2.65% -0.67% 

1.96% 

-2.66% 

-1.75% 

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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of reduction in weight or size on risk in LTs, by crash type 

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

 LTs < 4,594 LTs > 4,594 All LTs 
74

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 estimated 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 11% 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 

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 

Percent of new vehicle fleet 

Model year 

Market penetration of SEAS, by vehicle type and 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 
75

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

the side). The 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 2012 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. -6% 

-15% 

-1% 

-16% 

-1% 

-3% 

-11% 

-3% 

-10% 

-5% 

-3% 

-3% 

8% 

2% 

10% 

-5% 

-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 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of SEAS on risk imposed by light trucks, by crash type 

 >50% bumper overlap Blocker beam 
76

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 is 

associated with a statistically significant 0.31% increase in fatality risk, while a 1-square foot 

reduction in footprint is associated with a non-significant 0.05% increase in risk. Figure 6.13

compares these estimated 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 (51%), police cars (43%), SUVs 

(39%), fullsize vans (36%), 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 (11%) 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 new vehicle registrations 

Year 

US registrations for new vehicles, by vehicle type and year (NHTSA) 

2-dr cars 

4-dr cars 

Pickups 

SUVs 

CUVs 

Minivans 
77

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, 

under age 30, and over age 70) and crash (at night, on rural roads, on high-speed roads, and in 

high fatality states) characteristics by vehicle type, for the induced-exposure cases weighted by 

national vehicle registration-year and miles traveled weights. 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 166 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 161 fatalities per ten 0.31% 0.05% 16% 51% 43% -2% 22% 35% 39% 11% -2% 36% 17% 48% 34% -21% 7% 3% 15% -3% -9% 21% 

-40% 

-30% 

-20% 

-10% 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

Curb wt 

Footprint 

2-door car 

Sports car 

Police car 

4-wd car 

Small pickup 

HD pickup 

SUV 

CUV 

Minivan 

Fullsize van 

Percent change in risk (fatality probability per 1010 VMT) 

Effect of vehicle type on US fatality risk, after accounting for different sets of vehicle, 

driver, and crash variables 

All variables 

Vehicle type, and driver/crash variables only 
78

billion VMT (much higher than that of the typical car driver, 96). 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 5 fatalities per ten billion VMT (166 - 161), and not the difference 

between the risk of the average driver of a heavy-duty pickup and that of the average driver of a 

four-door sedan (70 fatalities per ten billion VMT, or 166 - 96). 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 DRVMALE AGE14_30 AGE70_96 NITE RURAL SPDLIM55

HIFAT_ST

2-dr cars 41% 55% 1.9% 22% 18% 15% 38%

4-dr sedans 42% 34% 7.2% 18% 19% 15% 39%

Sports cars 57% 46% 1.4% 23% 19% 16% 49%

Police cars 90% 31% 0.3% 38% 21% 13% 41%

AWD cars 48% 25% 4.6% 16% 14% 16% 20%

Small pickups 83% 26% 3.7% 16% 31% 20% 51%

Heavy-duty pickups 91% 20% 1.9% 13% 39% 26% 49%

SUVs 47% 26% 1.8% 18% 21% 17% 43%

CUVs 37% 21% 3.2% 16% 16% 17% 35%

Minivans 39% 13% 5.8% 14% 22% 16% 34%

Fullsize vans 85% 21% 2.0% 11% 16% 19% 35%

All 51% 30% 4.7% 17% 21% 17% 41%

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. 
79

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 117 101 79 97 91

4-dr sedans 96 96 76 93 87

Sports cars 168 114 90 110 103

Police cars 174 129 102 125 117

AWD cars 62 78 62 76 71

Small pickups 142 133 105 129 121

Heavy-duty pickups 166 161 127 156 146

SUVs 109 95 75 92 86

CUVs 72 74 59 72 67

Minivans 79 87 69 84 79

Fullsize vans 108 89 70 86 81

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 

109 to 92 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 2,411, resulting in a 3.3% reduction in fatalities.

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 2012 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.
80

Table 6.3. Total fatalities and VMT in model year 2000 to 2007

light-duty vehicles between 2004 and 2008, by vehicle type

Vehicle type Fatalities VMT (billion)

2-dr cars 5,195 446

4-dr sedans 26,001 2,749

Sports cars 1,528 93

Police cars 644 39

AWD cars 737 120

Small pickups 12,326 897

Heavy-duty pickups 4,344 276

SUVs 12,439 1,159

CUVs 4,111 578

Minivans 3,583 462

Fullsize vans 1,421 141

Total 72,329 6,961

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 -75 -0.1%

3. Replace 10% of large pickups with minivans -32 0.0%

4. Replace 80% of SUVs and small pickups, and 

50% of heavy-duty pickups -2,411 -3.3%
81

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 databases, NHTSA estimates that reducing vehicle mass by 100 lbs while 

holding footprint fixed would increase fatality risk per VMT by 1.55% for lighter-than-average 

cars, 0.51% for heavier-than-average cars, and 0.52% for lighter-than-average light-duty trucks, 

but reduce risk by 0.34% for heavier-than-average light-duty trucks and by 0.38% for 

CUVs/minivans. NHTSA found that only the estimated 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 estimated in 2012 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 2012 NHTSA analysis estimates that 

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

risk per VMT by 1.87% in cars and 1.72% in CUVs and minivans (the effect on risk in light 

trucks is small and not statistically significant). 

This report replicates the 2012 NHTSA analysis, and reproduces their main results. This report 

uses the confidence intervals output by the logistic regression models, which are smaller than the 

intervals NHTSA estimated using a jack-knife technique that accounts for the sampling error in 

the FARS fatality and state crash data. As a result, in its report NHTSA finds that only the 1.55% 

estimated increase in risk from mass reduction in lighter-than-average cars is statistically 

significant. In addition to reproducing the NHTSA results, this report also examines the NHTSA 

data in slightly different ways to get a deeper understanding of the relationship between vehicle 

weight, footprint, and safety. This final report incorporates revisions from the preliminary report 

released in November 2011, including revised estimates of national weights for vehicle miles 

traveled, inclusion of 2008 police-reported crash data from eight additional states, and responses 

to reviewers’ comments.
82

The results of these alternative analyses are summarized in Tables 7.1 and 7.2; statistically 

significant estimates, based on the confidence intervals output by the logistic regression models, 

are shown in red in the tables. In particular, we found that:

• NHTSA’s (reasonable) assumption that all vehicles will have ESC installed by 2017 

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

the estimated 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). This is 

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

and crashes with stationary objects, and mass reduction appears to reduce risk, while 

footprint reduction appears to increase risk, in these types of crashes, particularly in cars 

and CUVs/minivans. A single regression model including all crash types results in slightly 

different estimates of the relationship between decreasing mass and risk, as shown in 

Alternative 2 in Table 7.1.

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

significant, and have a much larger estimated effect on fatality risk than vehicle mass. For 

example, installing torso side airbags, electronic stability control, or an automated braking 

system in a car is estimated to reduce fatality risk by about 10%; cars driven by men are 

estimated to have a 40% higher fatality risk than cars driven by women; and cars driven at 

night, on rural roads, or on roads with a speed limit higher than 55 mph are estimated to 

have a fatality risk over 100 times higher than cars driven during the daytime on low-speed 

non-rural roads. While the estimated effect of mass reduction may result in a statisticallysignificant increase in risk in certain cases, the increase is small and is overwhelmed by 

other known vehicle, driver, and crash factors.

Table 7.1. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)

1. Weighted by current distribution of fatalities2. Single regression model for all crash types

3. Excluding footprint

or weight

4. Fatal crashes per

VMT5. Fatalities per induced exposure crash6. Fatalities per registered vehicle-year

Mass

reduction

Cars < 3106 lbs 1.55%* 1.27% 1.26% 2.74% 1.95% -0.22% 0.93%

Cars > 3106 lbs 0.51% 0.37% 0.35% 1.95% 0.89% -1.45% 2.40%

LTs < 4594 lbs 0.52% 0.42% 0.41% 0.47% 0.54% -1.13% -0.09%

LTs > 4594 lbs -0.34% -0.36% -0.42% -0.39% -0.42% -0.76% -0.76%

CUV/ minivan -0.38% -0.70% -0.74% 0.60% -0.47% -0.84% -0.40%

Footprint

reduction

Cars 1.87% 2.16% 2.28% 2.98% 1.83% 2.28% 0.32%

LTs -0.07% 0.14% 0.22% 0.07% 0.14% -1.30% -0.08%

CUV/ minivan 1.72% 2.25% 2.26% 1.33% 1.79% 2.18% 0.03%

* 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.

Estimates that are statistically significant at the 95% level are shown in red.
83

• 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 

(i.e. the regression model accounts for weight but not footprint), mass reduction results in a 

larger estimated increase in risk for cars and CUVs/minivans than when footprint is held 

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

results in a larger estimated increases in risk for cars, but a smaller estimated increase in 

risk for CUVs and minivans (Alternative 3 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. Risk increases with decreasing mass in a 

majority of footprint deciles for 12 of the 27 crash and vehicle combinations, but few of 

these increases are statistically significant. On the other hand, risk decreases with 

decreasing mass in a majority of footprint deciles for 5 of the 27 crash and vehicle 

combinations; in some cases these risk reductions are large and statistically significant. 

• Logistic regression methods do not have a statistic, such as the model R2 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-R2 and R2 from a linear 

regression model suggests that much of the variation 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 estimated 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 variation in risk by vehicle model is quite large and 

unrelated to vehicle weight (addressed in more detail in Section 4). The large remaining 

unexplained variation in risk by vehicle model could be attributable to other differences in 

vehicle design, or how drivers who select certain vehicles drive them. It is possible that 

including variables that account for these factors in the regression models would change the 

estimated relationship between mass or footprint and risk. 

• NHTSA’s estimates are sensitive to the definition of risk used in its regression models. 

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 estimated 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 4 in Table 7.1). Calculating risk as total fatalities per induced exposure crash, 

rather than per vehicle mile traveled, reverses the sign of the estimated effect of mass 

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

estimated reduction in risk in all vehicle types. Footprint reduction continues to result in 

large estimated increases in risk per induced exposure crash for cars and CUVs/minivans, 

but leads to a large estimated reduction in fatality risk per induced exposure crash for light 

trucks (Alternative 5 in Table 7.1). Calculating risk per registered vehicle-year, rather than 

per mile driven, substantially reduces the estimated effect of mass reduction on risk for 
84

lighter cars and lighter light trucks, but substantially increases the estimated effect on risk 

for heaver cars (Alternative 6 in Table 7.1; further addressed in Section 5.1).

• NHTSA’s estimates are sensitive to changes in the data and variables used in its regression 

models, as shown in Table 7.2. Adding control variables for vehicle manufacturer tends to 

increase the estimated effect of mass reduction, but decrease the estimated effect of 

footprint reduction, on risk for cars and light trucks, and estimates that mass reduction 

becomes substantially detrimental, and footprint reduction beneficial, for CUVs/minivans 

(Alternative 7 in Table 7.2; further addressed in Section 5.2). Adding control variables for 

five luxury brands (Cadillac, Lincoln, Acura, Infiniti and Lexus) magnifies these changes 

in the estimated effects for cars (Alternative 8 in Table 7.2). 

• An alternative to control variables for vehicle manufacturers is a single continuous variable 

for the vehicle’s initial purchase price; purchase price may better account for other 

differences in vehicle design that may influence traffic safety. Adding this single variable 

to NHTSA’s regression models substantially increases the estimated increase in risk from 

mass reduction of heavier-than-average cars, slightly lowers the estimated increase in risk 

in lighter cars and lighter trucks; and substantially increases the estimated beneficial effect 

from mass reduction in heavier light trucks and CUVs/minivans. Accounting for vehicle 

purchase price increases the estimated beneficial effect of footprint reduction in light 

trucks, but has little impact on the estimated detrimental effects of footprint reduction in 

cars or CUVs/minivans (Alternative 9 in Table 7.2; further addressed in Section 5.2).

• 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 estimated 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 estimated increased risk of SUVs and heavy-duty pickup trucks. Excluding these 

calendar year variables from the regression models increases the estimated detrimental 

effect of mass reduction on risk in light trucks (Alternative 10 in Table 7.2, addressed in 

Section 5.3).

• Because details on the driver’s condition or behavior are not consistently reported in the 

state crash data used to develop the induced exposure cases and VMT weights, it is not 

possible to control for individual drivers’ behavior in the regression model. However, 

excluding crashes involving alcohol or drugs, or drivers with poor driving records, 

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

estimated detrimental effect of footprint reduction on risk (Alternatives 11 and 12 in Table 

7.2, Section 5.4). One possible surrogate for the behavior of drivers who tend to select 

certain vehicle models is driver household income. Including a measure of household 

income by vehicle model reduces the estimated detrimental effect of mass reduction on risk 

in cars, but has little effect on the association of mass with risk in trucks or CUVs/minivans 

(Alternative 13 in Table 7.2). Including all-wheel-drive, sports, and police cars increases 

the estimated effect of mass reduction, but reduces the estimated effect of footprint 

reduction, on risk for cars; while including fullsize vans reduces the estimated effect of 
85

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

trucks (Alternative 14 in Table 7.2, Section 5.5). 

• As mentioned above, for its baseline fatalities NHTSA assumes that all new 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 estimated detrimental overall 

effect of mass reduction, but decrease the estimated 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, at least in side impact crashes (Section 6.2). However, it 

appears that mass reduction has less of an estimated 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 

relationship between mass and 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. 

Table 7.2. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

excluding certain data or using different control variables

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)7. Accounting for vehicle manufacturer8.Accounting for vehicle manufacturer plus five luxury brands9. Accounting for initial vehicle purchase price10. Excluding CY variables11. Excluding crashes with alcohol/drugs12. Excluding. bad drivers13. Accounting for median household income14. Including sports, squad, AWD cars and fullsize vans

Mass

reduction

Cars < 3106 lbs 1.55%* 1.90% 2.04% 1.42% 1.52% 1.88% 2.32% 1.20% 1.79%

Cars > 3106 lbs 0.51% 0.75% 1.80% 0.84% 0.43% 0.88% 1.19% 0.16% 0.49%

LTs < 4594 lbs 0.52% 0.59% 0.57% 0.45% 1.20% 0.78% 1.01% 0.68% 0.49%

LTs > 4594 lbs -0.34% -0.11% -0.11% -0.52% 0.30% -0.35% -0.11% -0.30% -0.77%

CUV/ minivan -0.38% 1.62% 1.28% -0.92% 0.03% -0.16% -0.01% -0.44% -0.38%

Footprint

reduction

Cars 1.87% 1.71% 1.20% 1.99% 2.11% 1.65% 1.32% 2.30% 1.64%

LTs -0.07% -0.29% -0.28% -0.36% -0.42% -0.26% -0.39% -0.19% -0.02%

CUV/ minivan 1.72% -0.77% -0.28% 1.57% 1.61% 1.36% 1.12% 1.82% 1.72%

* 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.

Estimates that are statistically significant at the 95% level are shown in red.

• 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 
86

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 over 3% (Section 6.3).

Table 7.3 shows the results of additional sensitivity tests NHTSA conducted in response to 

comments from the peer reviewers of its preliminary 2012 report. Alternative 15 in Table 7.3 

indicates that using only stopped vehicles, and not all vehicles judged to be not-at-fault in twovehicle crashes, substantially reduces the estimated detrimental effect of mass reduction on risk 

in all types of vehicles, while substantially increasing the estimated detrimental effect of 

footprint reduction on risk in cars. Replacing vehicle footprint with its two components, track 

width and wheelbase, similarly reduces the estimated detrimental effect of mass reduction, as 

shown in Alternative 16; an increase in risk is associated with decreasing track width in cars and 

light trucks, but with decreasing wheelbase in CUVs/minivans. Alternative 17 indicates that 

combining these two sensitivities, i.e. using stopped vehicles as the measure of exposure and 

replacing footprint with track width and wheelbase, further reduces the estimated detrimental 

effect of mass reduction, such that fatality risk is estimated to increase in light cars by a 

statistically non-significant 0.26%. Weighting the distribution of fatalities in CUVs and 

minivans by their respective shares of sales in 2010 (which reflects more CUVs and fewer 

minivans) causes the estimated effects to change signs, with decreasing mass now associated 

with an estimated increase in risk, and decreasing footprint with an estimated reduction in risk, 

Table 7.3. Estimated effect of mass or footprint reduction on US fatality risk per VMT, 

under alternative regression model specifications suggested by NHTSA peer reviewers

Variable

Case vehicle 

type

NHTSA preferred model (fatalities per VMT)

15. Using stopped instead of non-culpable vehicles for induced exposure16. Including track width and wheelbase instead of footprint

17. Using stopped vehicles and track width/wheelbase18. Reweighting CUVs and minivans by 2010 sales19. Excluding nonsignificant control variables

Mass

reduction

Cars < 3106 lbs 1.55%* 0.97% 0.95% 0.26% 1.55% 1.63%

Cars > 3106 lbs 0.51% -0.63% 0.24% -0.90% 0.51% 0.69%

LTs < 4594 lbs 0.52% 0.35% -0.07% -0.10% 0.52% 0.35%

LTs > 4594 lbs -0.34% -0.80% -0.58% -0.97% -0.34% -0.54%

CUV/ minivan -0.38% -0.33% -0.25% -0.14% 0.55% -0.46%

Footprint

reduction

Cars 1.87% 3.43% — — 1.87% 1.73%

LTs -0.07% -0.03% — — -0.07% 0.11%

CUV/ minivan 1.72% 1.81% — — -0.61% 1.97%

Track 

width 

reduction

Cars — — 4.36% 6.03% — —

LTs — — 1.07% 0.90% — —

CUV/ minivan — — 0.08% -0.55% — —

Wheel 

base 

reduction

Cars — — -0.09% 0.38% — —

LTs — — -0.09% -0.09% — —

CUV/ minivan — — 1.16% 1.45% — —

* 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.
87

for CUVs and minivans (Alternative 18). Alternative 19 removes non-significant control 

variables from each of the regression models; the result is slightly larger estimated detrimental 

effects of reduced mass in cars, and slightly smaller estimated effects in light trucks.

Table 7.4 compares the results from NHTSA’s 2003, 2010, and 2012 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 2012 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 estimated increase in 

fatalities than in NHTSA’s 2003 analysis, particularly for lighter cars (a 2.74% increase rather 

than a 4.39% increase) and light trucks (a 0.47% increase rather than a 2.90% increase). The 

third and fourth columns of the table indicate a similar reduction in estimated 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 estimated 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 0.34% reduction rather than a 1.90% reduction in fatalities for the 

heavier light trucks). This small estimated 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 2012 analysis.

The last column in Table 7.4 shows that the results of the alternative model specifications 

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

and often lower than the results of the 2010 and 2012 analyses.

Table 7.4. Previous NHTSA results of the estimated effect of mass and footprint reduction 

on US fatality risk per VMT, compared with different scenarios analyzed in this report

Variable Case vehicle type

NHTSA 

(2003) 

excluding 

footprint

NHTSA 

(2012) 

excluding 

footprint

NHTSA 

(2010) 

including 

footprint

NHTSA 

(2012) 

including 

footprint

Range of different 

scenarios analyzed 

in this report

Mass

reduction

Cars < 3106 lbs 4.39% 2.74% 2.21% 1.55% -0.22% to 2.74%

Cars > 3106 lbs 1.98% 1.95% 0.89% 0.51% -1.45% to 2.40%

LTs < 4594 lbs 2.90% 0.47% 0.17% 0.52% -1.13% to 1.20%

LTs > 4594 lbs 0.48% -0.39% -1.90% -0.34% -0.97% to 0.30%

CUV/ minivan — 0.60% — -0.38% -0.92% to 1.62%

Footprint

reduction

Cars — — — 1.87% -0.09% to 3.43%

LTs — — — -0.07% -1.30% to 0.22%

CUV/ minivan — — — 1.72% -0.77% to 2.26%

The 2012 NHTSA study, and this report, conclude that the effect of mass reduction while 

maintaining footprint on societal US fatality risk is small, and may be non-existent. This report 

indicates that although the estimated effects are sensitive to what variables and data are included 

in the regression analysis, in nearly all cases the effects are less, in some cases dramatically less, 

than reported in the 2003 NHTSA study. This report also finds that the estimated effects 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 are much 

larger, in some cases two orders of magnitude larger, than the estimated effect of mass or 
88

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.

Although the purpose of the NHTSA and LBNL reports is to estimate the effect of vehicle 

mass reduction on societal risk, this is not exactly what the regression models are estimating. 

Rather, they are estimating the recent historical relationship between mass and risk, after 

accounting for most measurable differences between vehicles, drivers, and crash times and 

locations. In essence, the regression models are comparing the risk of a 2600-lb Dodge Neon 

with that of a 2500-lb Honda Civic, after attempting to account for all other differences 

between the two vehicles. The models are not estimating the effect of literally removing 100 

lbs from the Neon, leaving everything else unchanged. 

In addition, the analyses 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. 

Therefore, throughout this report we use the phrase “the estimated effect of mass (or footprint) 

reduction on risk” as shorthand for “the estimated change in risk as a function of its relationship 

to mass (or footprint) for vehicle models of recent design.”

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 databases, NHTSA estimates that reducing vehicle mass by 100 lbs while 

holding footprint fixed would increase fatality risk per VMT by 1.55% for lighter-than-average 

cars, 0.51% for heavier-than-average cars, and 0.52% for lighter-than-average light-duty trucks, 

but reduce risk by 0.34% for heavier-than-average light-duty trucks and by 0.38% for 

CUVs/minivans. NHTSA found that only the estimated 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 estimated in 2012 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 2012 NHTSA analysis estimates that 

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

risk per VMT by 1.87% in cars and 1.72% in CUVs and minivans (the effect on risk in light 

trucks is small and not statistically significant). 

This report replicates the 2012 NHTSA analysis, and reproduces their main results. This report 
89

8. References

Allison, P.D., 1999. Logistic Regression Using the SAS System. Cary, NC: SAS Institute Inc., 

pp. 48-51. 

Farmer, Charles M. 2012. Review of “Relationships Between Fatality Risk, Mass, and Footprint 

in Model Year 2000-2007 Passenger Cars and LTVs” by Charles J. Kahane, National Highway 

Traffic Safety Administration. Insurance Institute for Highway Safety. January 6.

Green, Paul E. 2012. Review of “Relationships Between Fatality Risk, Mass, and Footprint in 

Model Year 2000-2007 Passenger Cars and LTVs” by Charles J. Kahane, National Highway 

Traffic Safety Administration. University of Michigan Transportation Research Institute. 

February 15.

Green, Paul E., Lidia Kostyniuk, Tim Gordon, Matt Reed. 2011. Independent Review: 

Statistical Analyses of Relationship between Vehicle Curb Weight, Track Width, Wheelbase and 

Fatality Rates. University of Michigan Transportation Research Institute. March. UMTRI2011-12.

Kahane, C.J., 1997. Relationships between Vehicle Size and Fatality Risk in Model Year 1985-

93 Passenger Cars and Light Trucks. NHTSA DOT HS 808570. U.S. Department of 

Transportation, National Highway Traffic Safety Administration, Washington, D.C.

Kahane, C.J., 2003. Vehicle Weight, Fatality Risk and Crash Compatibility of Model Year 1991-

99 Passenger Cars and Light Trucks. NHTSA DOT HS 809 662. U.S. Department of 

Transportation, National Highway Traffic Safety Administration, Washington, D.C.

Kahane, C.J., 2004. Response to Docket Comments on NHTSA Technical Report “Vehicle 

Weight, Fatality Risk and Crash Compatibility of Model Year 1991-99 Passenger Cars and Light 

Truck”. Submission to docket no. NHTSA-2003-16318. U.S. Department of Transportation, 

National Highway Traffic Safety Administration, Washington, D.C.

Kahane, C.J. 2011. Relationships Between Fatality Risk, Mass, and Footprint in Model Year 

2000-2007 Passenger Cars and LTVs. Preliminary report prepared for the National Center for 

Statistics and Analysis, National Highway Traffic Safety Administration, Washington, D.C. July.

Kahane, C.J., 2012. Relationships between Fatality Risk, Mass, and Footprint in Model Year 

2000-2007 Passenger Cars and LTVs. NHTSA Docket Number NHTSA-2010-0152-0023. U.S. 

Department of Transportation, National Highway Traffic Safety Administration, Washington, 

D.C. August.

Menard, S. 2002. Applied Logistic Regression Analysis, Second Edition. Sage Publications, 

Thousand Oaks CA.

O’Brien, R.M. 2007. “A Caution Regarding Rules of Thumb for Variance Inflation Factors,” 

Quality and Quantity, (41) 673-690.
90

Partyka, S.C., 1995. Impacts with Yielding Fixed Objects by Vehicle Weight. NHTSA Technical 

Report. DOT HS 808 574. U.S. Department of Transportation, National Highway Traffic Safety 

Administration, Washington, D.C.

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. National Highway Traffic Safety Administration, Washington, DC. 

Systems Research and Application Corporation. 2012. Peer Review of LBNL Statistical 

Analysis of the Effect of Vehicle Mass & Footprint Reduction on Safety (LBNL Phase 1 and 2 

Reports). Prepared for Office of Transportation and Air Quality, US Environmental Protection 

Agency, EPA contract number EP-C-11-007. February.

Van Auken, R.M., Zellner, J.W., 2002. An Assessment of the Effects of Vehicle Weight on 

Fatality Risk in Model Year 1985-98 Passenger Cars and 1985-97 Light Trucks. DRI-TR02-02. 

Dynamic Research, Inc., Torrance, California.

Van Auken, R.M., Zellner, J.W., Boughton, J.P., Brubacher, J.M., 2003. A Further Assessment 

of the Effects of Vehicle Weight and Size Parameters on Fatality Risk in Model Year 1985-98 

Passenger Cars and 1985-97 Light Trucks. DRI-TR03-01. Dynamic Research, Inc., Torrance, 

California

Van Auken, R.M., Zellner, J.W., 2004. A Review of the Results in the 1997 Kahane, 2002 DRI, 

2003 DRI, and 2003 Kahane Reports on the Effects of Passenger Car and Light Truck Weight 

and Size on Fatality Risk. DRI-TR-04-02. Dynamic Research, Inc., Torrance, California.

Van Auken, R.M., Zellner, J.W., 2005a. An Assessment of the Effects of Vehicle Weight and Size 

on Fatality Risk in 1985 to 1998 Model Year Passenger Cars and 1985 to 1997 Model Year 

Light Trucks and Vans. SAE Technical Paper Series, 2005-01-1354. Society of Automotive 

Engineers, Warrendale, PA.

Van Auken, R.M., Zellner, J.W., 2005b. Supplemental Results on the Independent Effects of 

Curb Weight, Wheelbase, and Track on Fatality Risk in 1985-1998 Model Year Passenger Cars 

and 1986-1997 Model Year LTVs. DRI-TR05-01. Dynamic Research, Inc., Torrance, California.

Van Auken, R.M., and Zellner, J. W. 2012a. Updated Analysis of the Effects of Passenger 

Vehicle Size and Weight on Safety, Phase II; Preliminary Analysis Based on 2002 to 2008 

Calendar Year Data for 2000 to 2007 Model Year Light Passenger Vehicles to InducedExposure and Vehicle Size Variables. Report No. DRI-TR-12-01. Torrance, CA: Dynamic 

Research, Inc. January.

Van Auken, R.M., and Zellner, J. W. 2012b. Updated Analysis of the Effects of Passenger 

Vehicle Size and Weight on Safety; Sensitivity of the Estimates for 2002 to 2008 Calendar Year 

Data for 2000 to 2007 Model Year Light Passenger Vehicles to Induced-Exposure and Vehicle 

Size Variables. Report No. DRI-TR-12-03. Torrance, CA: Dynamic Research, Inc. February.
91

Wenzel, Tom P. 2011. Assessment of NHTSA’s Report “Relationships Between Fatality Risk, 

Mass, and Footprint in Model Year 2000-2007 Passenger Cars and LTVs”. Preliminary report 

prepared for the Office of Energy Efficiency and Renewable Energy, US Department of Energy, 

Berkeley, California. September. LBNL-.