The Effect of Decreases in Vehicle Weight

on Injury Crash Rates

January 1997
i

EXECUTIVE SUMMARY

This study was conducted as a part of the effort by the National Highway Traffic Safety

Administration (NHTSA) to study the effect of changes in vehicle size on the crashes, injuries,

and fatalities of passenger car and light truck occupants. This study focuses on the effect on fatal

and incapacitating injuries resulting from a reduction of one hundred pounds in vehicle weight.

NHTSA’s earlier work in this area, A Collection of Recent Analyses of Vehicle Weight and Safety

(DOT HS-807 677, May 1991), examined the effect of a 1,000 pound reduction in the average

weight of new passenger cars on occupant fatalities and injuries. The 1991 study estimated that

the reduction of the average weight of passenger cars from 3,700 pounds (in 1970) to 2,700

pounds (in 1982) resulted in increases of approximately 2,000 fatalities and 20,000 serious injuries

each year. The large increase in rollover crashes contributed the most to these increases.

This analysis estimates the effect of a one hundred (100) pound reduction in average vehicle

weight on the per crash rates of incapacitating injury to drivers. In the analysis, three “scenarios”

were considered: (1) the effect of the 100 pound reduction on light trucks and vans (LTVs), with

the weight of passenger cars (PCs) unchanged; (2) the effect of the 100 pound reduction on PCs,

with the weight of LTVs unchanged; and (3) the effect of the 100 pound reduction on all

passenger vehicles.

Data from Illinois (1990-1992) and Florida (1991-1993) were used. Using the KABCO injury

scale, data from these states on fatal injuries (K) plus incapacitating injuries (A) for crash involved

drivers were analyzed. K+A injuries, while including the most severe of all injuries, typically

include many injuries that could be considered minor. The analysis focused on the crash

experience of model year 1985-93 passenger vehicles. The effect of the three scenarios on K+A

injuries to drivers in crashes of passenger vehicles with fixed objects, with heavy trucks, and with

other passenger vehicles were examined. For two-vehicle crashes, drivers of both vehicles were

included in the analysis. The analysis controlled for driver age and a surrogate measure of travel

speed. 

The estimated increases in the driver incapacitating injury rates resulting from the 100 pound

reduction ranged from 1.5 percent for LTVs in crashes involving fixed objects to 5.9 percent for

LTVs in crashes with PCs with the weight unchanged. For crashes involving PCs with LTVs, the

analysis indicated the weight reduction to one type of passenger vehicle (either the PCs or the

LTVs, scenarios 1 and 2) had a small beneficial effect for the vehicles that were not reduced. In

other words, occupants of PCs fared better in crashes with LTVs when the LTVs were reduced

by 100 pounds; and conversely, occupants of LTVs fared better in crashes with PCs when the PCs

were reduced by 100 pounds. 

For the three scenarios, the following findings were noted using the estimated percent increases in
ii

driver incapacitating injury rates and extrapolated to all occupants:

C A 100-pound reduction in the average weight of LTVs (with PCs unchanged) would result

in an estimated increase of 1,795 incapacitating injuries.

C A 100-pound reduction in the average weight of PCs (with LTVs unchanged) would result

in an estimated increase of 8,804 incapacitating injuries.

C A 100-pound reduction in the average weight of all passenger vehicles (both PCs and

LTVs) would result in an estimated increase of 10,543 incapacitating injuries.

These results are considered consistent with the results from the 1991 NHTSA study. 

 
1

INTRODUCTION

In 1991, NHTSA published the results of several studies of this issue in A Collection of Recent

Analyses of Vehicle Weight and Safety (Klein, et al; DOT-HS-807 677; May 1991). The 1991

NHTSA study examined the effect of a reduction in the average weight of passenger cars by

1,000 pounds, i.e., from 3,700 pounds in 1970 to 2,700 pounds in 1982, on the frequency and

severity of crash risk. Klein, et al. found that the reduction of the average weight of passenger

cars (PCs) by 1,000 pounds was associated with an estimated increase of approximately 2,000

fatalities and 20,000 serious injuries each year. The 1991 study contained the findings from an

analysis of the impact of the weight reduction for PCs involved in four crash types: rollovers,

collisions with fixed objects, collisions with heavy trucks, and two-car collisions. A relatively

large increase in rollover fatalities was found associated with the PC weight reduction. Smaller

increases in serious injuries were found for the collisions with fixed objects and with heavy trucks. 

Finally, for two-car collisions, a significant increase in serious injuries was found.

It was considered timely to reexamine this issue for several reasons. The 1991 NHTSA study

focused on the safety impact of weight reductions for PCs. With the increasing numbers of light

trucks and vans (LTVs) already in and continuing to enter the passenger vehicle fleet, an

assessment of the safety impact of a weight reduction for LTVs is of great interest. The earlier

study found the largest increases in rollover crashes occurred with the reduction in the average

passenger car weight. The use of safety belts is known to be particularly effective in preventing

fatalities in rollover crashes. With belt use continuing to increase, it is important to determine if

the large increases in fatal rollover crashes will continue to occur as the average weight of

passenger vehicles is reduced.

This study examines the safety impact of a weight reduction on incapacitating injuries for the new

PC fleet using the latest available State data and extends the analysis to include estimates for the

safety impact associated with a weight reduction for the new LTV fleet. The analysis and

findings described in this study are one portion of NHTSA’s analytical efforts to assess the safety

impact of a weight reduction. In addition to the findings reported in this study, NHTSA plans to

publish the results of similar studies of the safety impact of a weight reduction on fatalities and

AIS > 2 injuries.
 For more information, refer to NHTSA’s brochure titled State Data Program, National 1

Center for Statistics and Analysis, NRD-31, 400 7th Street, S. W., Washington DC 20590. 

 Determining vehicle weights and accurately classifying vehicles as PC or LTV is not a 2

trivial exercise. Dr. Charles J. Kahane of NHTSA’s Plans and Policy developed programming

code to accomplish this task using VINs. 

2

DATA

Data from NHTSA’s State Data Program for selected States were used to analyze the changes in 1

the rate of driver incapacitating injury that would be associated with a reduction in vehicle weight. 

The states used in the analysis were Illinois (data for the calendar years 1990-1992) and Florida

(data for the calendar years 1991-1993). The three most recent years of data available for each of

the two states were used. Florida and Illinois were selected for use in the analysis, based on two

“criteria”: both states experience a considerable number of crashes each year, thereby yielding

large sample sizes and both states collect and record vehicle identification numbers (VINs) for

crash involved vehicles. This second criterion was particularly important, as the VINs were

essential to obtaining the weight of each vehicle used in the analysis. Finally, the crash data used 2

was restricted to those crashes involving vehicles of model years 1985-93.

The measure of safety that was used for this analysis was the per crash rate of driver

incapacitating (K+A) injury using the KABCO injury coding scheme in which K=fatal injury,

A=incapacitating injury,

B=non-incapacitating injury and C=possible injury. K+A injuries, while including the most severe

of all injuries, typically include many injuries that could be considered minor.

Three analytical “scenarios” for crashes with driver incapacitating injury were considered: (1) the

effect of the 100 pound reduction on LTVs, with the weight of PCs unchanged; (2) the effect of

the 100 pound reduction on PCs, with the weight of the LTVs unchanged; and (3) the effect of

the 100 pound reduction on all passenger vehicles, i. e., both PCs and LTVs.

For each of the analytical scenarios described above, changes in the rate of driver incapacitating

injury were analyzed for four crash types. The crash types studied were:

C crashes between the vehicle and a fixed object;

C crashes between the vehicle and a heavy truck;

C crashes between the vehicle and a PC; and

C crashes between the vehicle and an LTV.
 Hertz, Ellen, Ph. D., J. Hilton, and D. M. Johnson. An Analysis of the Crash Experience 3

of Light Trucks Equipped with Antilock Braking Systems (DOT HS-808 278, May 1995), and An

Analysis of the Crash Experience of Passenger Cars Equipped with Antilock Braking Systems

(DOT HS-808 279, May 1995), U. S. Department of Transportation.

 Klein, Terry M. A Statistical Analysis of Vehicle Rollover Propensity and Vehicle 4

Stability, SAE Technical Paper Series 920584, The Society for Automotive Engineers, [1992].

 Hosmer, David W. and S. Lemeshow. Applied Logistic Regression. John Wiley & Sons. 5

[1989]

3

The analysis focused on these four crash types as these crashes are those for which changes in the

vehicle’s weight could be expected to be related to changes in the risk of driver incapacitating

injury. 

This analytical effort focused on vehicle crashworthiness, i.e., injury rates conditioned on the

occurrence of a crash. Vehicle crash avoidance was not addressed in this study due to the greater

inherent difficulty of the problem but also because reducing the average weight of the passenger

vehicle fleet might well result in a change in driving patterns that may be associated with smaller

cars. Also, no attempt was made to address possible environmental, economic, or other questions

and issues that may be part of the picture involving a reduction in average vehicle weight. 

Standard errors are calculated for these estimates of per crash driver injury rates and, in general,

they suggest that the estimates are very close.

Driver incapacitating injury, rather than injury to any occupant, was used as the measure of safety

impact as state police accident reports (PARs) often do not list uninjured occupants. If one

assumes that the effect of change in vehicle weight is similar on all occupants, estimates based on

analyzing the safety impact of reductions in vehicle weight on driver injury can be extended to

estimating the safety impact for all occupant injuries.

METHODOLOGY

Logistic regression was chosen as the analytical method for measuring the effect of changes in

vehicle weight on the odds of driver incapacitating injury. Logistic regression has been used

successfully in other NHTSA studies, including NHTSA’s 1991 study on vehicle weight and

safety . , Logistic regression is a statistical methodology that is used in analytical situations in 3 4

which the outcome is dichotomous.5

Using data from Florida and Illinois, a logistic regression model was estimated for each of the

four crash types and three scenarios. Each model was estimated using observations

corresponding to a crash-involved vehicle with a K or A injury to the driver as the positive
4

response. For the crashes involving a PC and a fixed object, an LTV and a fixed object, a PC and

a heavy truck and an LTV and a heavy truck , a logistic regression was modeled of the form:

Logit (p) = CURBWT RURAL DRVAGE; (1)

where p is the probability of a positive response, CURBWT is the vehicle weight, RURAL is an

indicator of whether the crash occurred in a rural vs. an urban area, and DRVAGE is an indicator

variable representing the age of the crash-involved driver.

For the crashes involving a PC or an LTV with another PC or an LTV, two logistic regression

models were estimated so that all possibilities would be accounted for. These models were of the

form:

 Logit (p) = CURBWT OTHERWT RURAL DRVAGE (2a)

and

Logit (p) = CURBWT DIFFWT RURAL DRVAGE (2b)

where

 DIFFWT = CURBWT - OTHERWT.

In these logistic regression models, the coefficient of an independent variable represents the effect

of a unit change in that variable on the log odds of a positive response, when the other

independent variables in the model are held constant. As such, the coefficient of CURBWT in

(2a) measures the effect on the PC (or LTV) driver who hits an LTV (or PC) when only PCs (or

LTVs) are reduced but not the other vehicle. CURBWT in (2a), therefore, measures the safety

impact on driver injury rates for the drivers of the vehicles with reduced weight when involved in

a crash with a vehicle without the weight reduction. The coefficient of CURBWT in (2b)

measures the effect on either the PC or LTV involved in a crash with a PC or LTV and the weight

of both vehicles are reduced. The coefficient of OTHERWT in (2a) measures the effect on the

case vehicle when only the other vehicle is reduced, that is, the effect on the injury rates of PC (or

LTV) drivers who hit LTVs (or PCs) under the scenario that only LTVs (or PCs) are reduced. 

OTHERWT, therefore, measures the safety impact on driver injury rates for the drivers of the

vehicle with the weight unchanged when in a crash with a vehicle with the weight reduction. 

The variables RURAL, a surrogate for the travel speed at the time of the crash, and DRVAGE

were included in the models to account for the possibility that each may have an effect on injury
 For a thorough treatment on combining log odds ratios, consult Fleiss, Joseph L. 6

Statistical Methods for Rates and Proportions, John Wiley & Sons, [1981]. 

5

probabilities and also could be associated with vehicle size, and therefore the vehicle’s weight. 

Initially, the models were also estimated including a variable representing the sex of the driver. 

The final models were reestimated without including the sex of the driver, as the coefficient was

not found to be significant in any of the models.

Ideally, it would have been desirable to include a variable representing seat belt use in the final

models. The percentage of surviving crash victims reporting belt use in crashes, however, is

typically greater than the percentage belt use observed for the general public (Hunter et al, 1988). 

As a result, a variable reflecting reported belt use in a crash would not be considered reliable. To

minimize injuries resulting from lack of restraint use, ejected drivers were eliminated from the

Florida data. Unfortunately, it was not possible to identify ejected persons in the Illinois data.

While data from two states were used in this analysis, it would be reasonable to expect that the

effects of a weight reduction for PCs or LTVs should not differ dramatically from state to state. 

The results for the various models did not appear to contradict this expectation. Therefore, the

coefficients of the variables of interest, CURBWT, OTHERWT and DIFFWT, were combined

over the two states, using the method described in Fleiss (1981) . This method also produces 6

standard errors of the coefficients as well as their point estimates. The results are displayed in

Table 1. WEIGHT1 is CURBWT from Models (1) and (2a). WEIGHT2 is CURBWT from

Model (2b).

The values shown in Table 1 represent the change in the log of the odds of a positive response

that would be caused by a unit gain in the specific independent variable, while holding all other

independent variables constant. Vehicle weights were expressed in one hundred pound units. 

That means, for example, that the coefficient of CURBWT in Models 1 and 2 represents

ln{[p1 10 0 /(1-p )]/[p /(1-p )]} where p 0 was the previous probability of incapacitating injury to the

driver and p is the new probability after the crash vehicle gains 100 pounds and nothing else is 1 

changed. If both values of p are small, then the ratio of the two values of 1-p is near 1 so that

the relative odds is approximately equal to the relative risk, that is, p /p . 1 0
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TABLE 1

The Effects of Vehicle Weight * per Hundred Pounds 

On the Log Odds of Driver K+A Injury

[Coefficients and Standard Errors]

Crash WEIGHT1 Std. Error WEIGHT2 Std. Error OTHERWT Std. Error

Type

PCs & FOs -0.037550 0.0031536 N/A N/A

PCs & -0.034556 0.0041539 N/A N/A

HvyTrks

LTVs & -0.015463 0.0045746 N/A N/A

FOs

LTVs & -0.035766 0.010052 N/A N/A

HvyTrks

PCs & PCs -0.046725 0.0025908 -0.030389 0.0034553 0.016346 0.0023191

PCs & -0.046926 0.0041734 -0.032713 0.0052250 0.014209 0.0031120

LTVs

LTVs & -0.057388 0.0052408 -0.044581 0.0075698 0.012684 0.0054412

PCs

LTVs & -0.052550 0.0076503 -0.031879 0.0099916 0.020946 0.0066007

LTVs

FOs = Fixed Objects

HvyTrks = Heavy Trucks

* WEIGHT1 is CURBWT from Models (1) and (2a); WEIGHT2 is CURBWT from Model (2b).
7

The coefficients in Table 1 can be used to develop estimates of the impact on the annual numbers

of occupant K+A injuries that would result from each scenario; since a small minority of crashes

in each state data base involves K+A injuries, the relative risk for a 100 pound reduction is

approximately exp(-coefficient) and the percentage change is 100{exp(-coefficient)-1}. The

target populations of K+A injuries to which these estimated changes were applied are from the

NHTSA General Estimates System (GES), 1993. In these calculations, the estimated change in

injury probability for the driver is assumed to be the same for all occupants of the passenger

vehicle.

Approximate confidence bounds for the expected injury changes can be derived using linear

approximation: As stated, the expected change, Eprop, in the probability of getting injured in a

given scenario is very nearly exp(-c)-1, where c is the relevant coefficient. Then Var(Eprop) is

approximately exp(-2c)*std(c)*std(c). Also, if Noinjs is the target population, then the expected

change in the number of injuries is Eprop*Noinjs. The linear approximation for its variance is

 Var(Expected Change) =~ Var(Eprop)*Noinjs + Var(Noinjs)*Eprop 2 2

The standard error of c is obtained from the logistic regression. The variance of Noinjs is

obtained from the formula described in the GES. The variances for the estimates under each

scenario are summed to obtain variances for the total changes. 

These results are displayed in Tables 2a-2c.
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TABLE 2a

Impact of Reducing LTVs by 100 Pounds [With PCs Unchanged]

On the Incidence of Occupant Incapacitating (K+A) Injury

K+A Injuries Percent Increase Net Change

(GES, 1993).

LTV Occupants in 26,544 1.6 414

FO Crashes 

LTV Occupants in 3,966 3.6 144

HvyTrk Crashes

LTV Occupants in 25,642 5.9 1,515

Crashes w/ PCs

PC Occupants in 48,546 -1.4 -685

Crashes w/LTVs

LTV Occupants in 12,550 3.2 407

Crashes w/LTVs

Total 117,248 1.5 1,795

Confidence bounds

based on + 2F

1,131 - 2,459

 
9

TABLE 2b

Impact of Reducing the PCs by 100 Pounds [With LTVs Unchanged]

On the Incidence of Occupant K+A Injury

K+A Injuries Percent Increase Net Change

(GES, 1993).

PC Occupants in 68,703 3.8 2,629

Crashes w/FOs

PC Occupants in 14,009 3.5 493

Crashes w/HvyTrks

PC Occupants in 48,546 4.8 2,332

Crashes w/LTVs

LTV Occupants in 25,642 -1.3 - 323

Crashes w/PCs 

PC Occupants in 119,039 3.1 3,673

Crashes w/PCs 

Total 275,939 3.2 8,804

Confidence bounds 

based on + 2F

7,438 - 10,170

 

 
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TABLE 2c

Impact of Reducing All Passenger Vehicles [Both PCs and LTVs] by 100 Pounds

On the Incidence of Occupant K+A Injury

K+A Injuries Percent Increase Net Change

(GES, 1993).

PC Occupants in 68,703 3.8 2,629

Crashes w/ FOs

PC Occupants in 14,009 3.5 493

Crashes w/HvyTrks

LTV Occupants in 26,544 1.6 414

Crashes w/FOs

LTV Occupants in 3,966 3.6 144

Crashes w/HvyTrks

LTV Occupants in 12,550 3.2 407

Crashes w/LTVs

PC Occupants in 119,039 3.1 3,673

Crashes w/PCs

PC Occupants in 48,546 3.3 1,614

Crashes w/LTVs

LTV Occupants in 25,642 4.6 1,169

Crashes w/PCs

Total 318,999 3.3 10,543

Confidence bounds 

based on + 2F

9,071 - 12,015

 
11

DISCUSSION

The above tables present estimates of the safety impact on driver incapacitating injury rates, and,

by extension, occupant incapacitating injury rates, for each of the three scenarios involving the

reduction of average vehicle weight. Comparing the point estimates of the coefficients with their

standard errors in Table 1 indicates that, within the assumptions of the model, the estimates of the

percent changes in injury rates are very accurate. The “target” populations, that is, the class of

injured occupants that could be affected under each scenario, are from NHTSA’s General

Estimates System. The estimates of net change in Tables 2a-2c are, therefore, reliable, subject to

the assumptions under which they were derived.

Two caveats are worth noting. Interestingly, they appear to point in opposite directions in terms

of estimating the injuries that could occur. First, the most obvious limitation is that the vehicles

considered were, necessarily, vehicles that have already been designed and manufactured. The

implicit assumption becomes that a typical 2,500 pound vehicle becomes similar to a typical 2,400

pound vehicle in the same data base. However, engineering advances might very well develop

lighter vehicles with some of the advantages of present heavy vehicles. Second, in this analysis,

primary rollovers were excluded. Obviously, when a rollover takes place it is always very

dangerous. Any discussion of the relative chances of being injured in a rollover as a function of

vehicle weight would be remiss if the discussion did not include any treatment of the chances of

becoming involved in a rollover crash, as a function of vehicle weight. This issue was not

addressed.

These two caveats aside, the results from this analysis corroborates earlier NHTSA research,

while illuminating the potential crashworthiness impact of reductions in vehicle weight.

 

 
12

REFERENCES

Fleiss, Joseph L., Statistical Methods for Rates and Proportions, John Wiley & Sons, [1981].

Hosmer, David W. and Stanley Lemeshow, Applied Logistic Regression, John Wiley & Sons,

[1989].

Hunter, W., Reinfurt, D. W., and Hirsch, M. G., Analysis of Occupant Restraint Issues from

State Accident Data, University of North Carolina, HSRC-MP 40, [September 1988].]

Klein, Terry M., E. Hertz, and S. Borener, A Collection of Recent Analyses of Vehicle Weight

and Safety, U. S. Department of Transportation, DOT HS 807 677, [May 1991].

National Highway Transportation Safety Administration, General Estimates System, [1993].