LBNL



## An Analysis of the Relationship between Casualty Risk Per Crash and

Vehicle Mass and Footprint for Model Year 2000

- 2007 Light

- Duty

Vehicles

Draft final report prepared for the Office of Energy Efficiency and Renewable Energy, US Department of Energy

Prepared by

Tom Wenzel Energy Analysis Department Environmental Energy Technologies Division Lawrence Berkeley National Laboratory Berkeley, CA 94720

December 2011

This work was supported by the Vehicle Technologies Program

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

under Contract No. DE

- AC02

- 05CH11231.DISCLAIMER

This document was prepared as an account of work sponsored by the United States Government. While this

document is bel ieved 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 tr ade 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 opin ions 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. Environmen tal Protection Agency; Chuck Kahane, National Highway Transportation Safety Administration, U.S. Department of Transportation; and Phil Price, Lawrence Berkeley National Laboratory.

The report was funded by Carol Schutte of the Vehicle Technologies Progra m 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. Depar tment of Energy under Contract No. DE

- AC02

- 05CH11231.iiiii Executive Summary

NHTSA recently completed a logistic regression analysis ( Kahane , 2011) updating its 2003 and 2010 studies of the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT). The new study updates the previous analyses in several ways: updated FARS data for 2002 to 2008 involving MY00 to MY07 vehicles are used; induced exposure data from police reported crashes in several additional states are added; a ne w 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.

In a companion report ( Wen zel , 2011b), we use 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), fo r model year 2000 to 2007 light

- duty vehicles involved in fatal crashes between 2002 and 2008. The data are examined in slightly different ways, to get a deeper understanding of the relationship between reductions in vehicle mass and footprint, and overal l safety.

This report compares the logistic regression results of the NHTSA analysis of US fatality risk per VMT with an analysis of 13

- state fatality risk and casualty risk per crash. This analysis differs from the NHTSA analysis in two respects: firs t, it analyzes risk per crash, using data on all police

- reported crashes from thirteen states, rather than risk per estimated VMT; and second, it analyzes casualty (fatality plus serious injury) risk, as opposed to just fatality risk. There are several go od reasons to investigate the effect of mass and footprint reduction on casualty risk per

crash. First, risk per VMT, accounts for two effects that influence whether a person is killed or seriously injured in a crash: how well a vehicle can be , or actuall y is, driven (based on its handling, acceleration, and braking capabilities) to avoid being involved in a serious crash (crash avoidance), and, once a serious crash has occurred, how well a vehicle protects its occupants from fatality or serious injury (cr ashworthiness). By encompassing both of these aspects of vehicle design, risk per VMT gives a complete picture of how vehicle design can promote, or reduce, road user safety. On the other hand, risk per crash isolates the second of these two safety

effec ts, crashworthiness, by examining the effect of mass and footprint reduction on how well a vehicle protects its occupants once a crash occurs.

Second, estimating risk on a per crash basis only requires using data on police

- reported crashes

from states , a nd does not require combining them with data from other sources, such as vehicle registration data and VMT information , as in NHTSA 2011 . Because only sixteen states currently record the vehicle identification number of vehicles involved in police

- reported crashes, which is necessary to determine vehicle characteristics, and only thirteen states also report the posted speed limit of the roadway on which the crash occurred, extending the analysis to casualties (fatalities plus serious/incapacitating injuries ; i.e. level “K” and “A” injuries in police reports ) reduces the statistical uncertainty of analyzing just fatalities per crash. Finally, a serious incapacitating injury can be just as traumatic to the victim and her family, and costlyiv from an economic pe rspective, as a fatality. Limiting the analysis to the risk of fatality, which is an extremely rare event, ignores the effect vehicle design may have on reducing the large number of incapacitating injuries that occur each year on the nation’s roadways. A ll risks in this report are societal risk, including fatalities and serious injuries in the case vehicle and any crash partners, and include not only driver casualties but passenger and non

- occupant casualties as

well.

However, the frequency of police

- rep orted crashes per V MT and of casualties per police

reported crash can both be influenced, in opposite directions, by the probability that a collision

event b ecomes a police

- reported crash. If collisions of certain vehicles are slightly less likely to

be report ed , because these vehicles are either somewhat less damage

- prone or are uninsured , this would tend to increase the observed detrimental effect of mass reduction on reported crashes per VMT and conversely decrease its detrimental effect on casualties per reported crash. By contrast, fatalities or casualties per VMT would be not be affected by crash

- reporting rates,

because the crash

- reporting rate is not part of the formula for calculating ri sk. The extent to

which any reporting bias of non

- injury c rashes exists, t he observed effects for police

- reported crashes per VMT might not correspond exactly to the “effect of mass reduction on crash avoidance” and the observed effects for casualties per police

- reported crash might not correspond exactly to the “effect of mass reduction on crashworthiness.” We suspect that large pickups are less likely to suffer damage in non

- injury crashes than other vehicle types; and that older, less expensive, or uninsured vehicles are less likely to report crash damage to p olice. In addition, one vehicle crashes are more likely to suffer from this reporting bias, as there is no crash partner who may file an insurance claim.

Table ES.1 summarizes the results of our analysis of the effect of vehicle mass or footprint

reduct ion on the two components of risk per VMT, crash frequency (number of crashes per VMT) and crashworthiness (risk per crash), for both fatality and casualty risk, using data from

13 states. We convert the percent change in the log

- odds of casualty or fatality per crash output from the SAS LOGIST procedure to the percent change in the probability of casualty or fatality per crash. This conversion has no effect on the output regression coefficients when the change in the log

- odds of casualty is small , b ut substantially increases the percent change for explanatory variables that have a large effect on the log

- odds of casualty

. Effects that are statistically significant are shown in red in the table; significance is based on the 95% confidence interval

de rived from the standard error of the output of the SAS LOGIST procedure , converted to a percent probability interval.

Table ES.1 indicates that f or cars and light trucks, the effects from the two components , crash frequency and crashworthiness, roughly a dd together to result in the overall effect on fatality risk per VMT. For example, a 100

- lb reduction in the mass of lighter cars leads to a 1.84% increase

in crash frequency (columns B) , while mass reduction leads to a 0.7 6 % decrease in the number

of fat alities per crash (column C) ; the net effect is only a 1.08% increase in the risk of fatality per

VMT (column D) . For CUVs/minivans, the relationship is different; mass reduction leads to a

0.71% increase in crash frequency, as well as a 0.87% increase in the number of fatalities per crash; however, the net result, a 1.29% increase in the number of fatalities per VMT, is less than the sum of the two components.v Table ES.1 indicates that mass reduction increases crash frequ ency (columns B and E) in all five vehicle types, with larger increases in lighter

- than

- average cars and light

- duty trucks. As a result, mass reduction has a more beneficial effect on crashworthiness, or casualty risk per crash (column F), than on casualt y risk per VMT (column G), and on fatality risk per crash (column C) than on fatality risk per VMT (column D). Mass reduction actually decreases casualty risk per crash (column F) in all vehicles except light cars, and in three of the four cases these redu ctions are statistically significant, albeit small. Footprint reduction increases crash frequency (columns B and E) in cars and light trucks, but slightly reduces crash frequency in CUVs/minivans; footprint reduction does not have a statistically

- signific ant effect on casualty risk per crash (column F), and only for fatality risk per crash (column C) for light trucks. In general mass or footprint reduction has a smaller effect on casualty risk per crash (column F) than on fatality risk per crash (column C ).

The effect of mass reduction on 13

- state casualty risk per VMT (column G) is quite consistent

with that NHTSA estimated for US fatality risk per VMT (column A), although the effect is estimated to be slightly larger for lighter cars and lighter light t rucks, and slightly smaller for

heavier cars , heavier light trucks, and CUVs/minivans.

In contrast with NHTSA’s findings on US fatality risk per VMT (column A), mass reduction

reduces casualty risk per crash (column F) for four of the five vehicle types, and three of these four reductions are statistically significant. Mass reduction results in a small but insignificant increase in casualty risk per crash for lighter cars. And footprint reduction results in much

smaller , and not statistically significant

, effects on casualty risk per crash (column F) than on US fatality risk per VMT (column A)

.

Many of the control variables included in the logistic regressions are statistically significant, and have a large effect on fatality or casualty risk per crash, although not as large an effect as on fatality risk per VMT. For example, a car’s mass could be reduced by 2,300 lbs while adding ABS without increasing its casualty risk per crash. While the effect of mass reduction may result in a statistically

- signifi cant increase in risk in certain cases, the increase is small and is overwhelmed by other known vehicle, driver, and crash factors.

Control variables for ABS and ESC in cars, AWD in light trucks and CUVs/minivans, and drivers aged 14 to 30 have a larger effect on crash frequency than fatality risk per crash. On the other hand, whether a vehicle is a two

- door car, SUV, or heavy

- duty pi ckup, side airbag variables in cars, and middle

- aged drivers have a larger effect on fatality risk per crash than on crash frequency. Whether a driver is male has essentially no effect on crash frequency, but surprisingly causes a statistically significan t increase in fatality risk once a crash occurs. Driving at night on high

- speed rural roads, also has a surprisingly much lower effect on crash frequency than on fatality risk given a crash.

In contrast with NHTSA’s results for US fatalities per VMT, al lowing footprint to vary along with weight results in little change in the effect of mass reduction on casualty risk per crash than when footprint is held constant ; however, the detrimental effect of mass reduction on casualty risk per crash in lighter car s is increased just enough to make it statistically significant . When mass is allowed to vary along with footprint, footprint reduction slightly reduces the alreadyvi small detrimental effect on casualty risk per crash (Alternative 1 in Table ES.2; further addressed in Section 3 of this report). As with NHTSA’s analysis of fatality risk per VMT, mass reduction does not consistently increase casualty risk per crash across all footprint deciles for any combination of vehicle type and crash type. Mass reducti on increases casualty risk per crash in a majority of footprint deciles for 10 of the 27 crash and vehicle combinations, but few of these increases are statistically significant. On the other hand, mass reduction decreases risk in a majority of footprint deciles for 14 of the 27 crash and vehicle combinations.

Similar to our findings on US fatality risk per VMT, after accounting for all of the control variables in the logistic regression model, except for vehicle mass and footprint, we find that the

corr elation between mass and the casualty risk per crash by vehicle model is very low. There also is no significant correlation between the residual, unexplained risk and vehicle weight. These results corroborate our earlier finding that, even after accounti ng for many vehicle, driver, and crash factors, the variance in risk by vehicle model is quite large and unrelated to vehicle weight (addressed in more detail in Section 4).

Other changes in the data and variables used in the regression models result in only slight changes in the effect of mass or footprint reductions on casualty risk per crash, as summarized in Table ES.2. For example:

• Regression analyses using police

- reported crash data from states must use control variables

to account for differen ces in definitions of “serious” or “incapacitating” injuries, and reporting requirements, across states. Removing the 12 state control variables results in a large reduction in casualty risk per crash from mass reduction in all five vehicle types, a large increase in risk from footprint reduction in cars and CUVs/minivans, and a small reduction in risk from footprint reduction in light trucks (Alternative 2 in Table ES.2). Including only two variables to control for states with high and low casualty risk per crash substantially reduces the effect of mass and footprint reduction on casualty risk (Alternative 3 in Table ES.2), while including the 12 control variables for individual states

reduces the effect a little bit more. These results indicate that exc luding control variables for the state in which a crash occurred from a regression model using state police

- reported crash data can give inaccurate estimates of the effect of mass or footprint reduction on casualty risk per crash (addressed in Section 5.1)

.

• Calculating risk as casualty crashes, rather than total casualties, per crash results in little

change in the effect of mass reduction on risk, but does increase the small detrimental effect of footprint reduction in cars on risk , and makes it statist ically significant (Alternative 4 in Table ES.2; addressed in Section 5.2).

• Adding control variables for vehicle manufact urer results in mass reduction increasing

casualty risk per crash in cars , but increasing the small beneficial effect of mass reduction in light trucks and CUVs/minivans. Accounting for vehicle manufacturer causes a reduction in footprint to reduce casualty risk per crash in cars, but increase casualty risk per crash in light trucks and CUVs/minivans (Alternative 5 in Table ES.2; addressed in Section 5.3).vii • As we found in our assessment of NHTSA’s analysis of US fatality risk per VMT,

including calendar year variables in the regression models appears to weaken the benefit of side air bags in cars and CUVs/miniva ns, and compatibility measures and ESC in light trucks. These variables also appear to minimize the increased risk of SUVs and heavy

- duty pickup trucks. Excluding the calendar year variables from the regression models slightly

reduces the beneficial effe ct of mass reduction on casualty risk per crash in light trucks and CUVs/minivans, and slightly increases the detrimental effect of footprint reduction on casualty risk per crash in cars (Alternative 6 in Table ES.2; addressed in Section 5.4).

• Including all

- wheel

- drive, sports, and police cars, and fullsize vans results in virtually no

change in the effect of mass or footprint reduction on casualty risk per crash for cars or light trucks (Alternative 7 in Table ES.2; addressed in Section 5.5).

• Adding data from three additional states, including vehicles with unreported model year,

correcting VIN transcription errors, and expanding the analysis to calendar years 2000, 2001, and 2008, increases the number of state crash records by about 40 percent. Inc luding these data in the regression analyses results in the effect of mass reduction in lighter cars being slightly more detrimental, and the effect of mass reduction in heavier cars, all light

trucks

, and CUVs/minivans being slightly less beneficial. Inc luding these data slightly reduces the small detrimental effect of footprint reduct ion on casualty risk per crash for

car s , increases the beneficial effect for light trucks, and increases the detrimental effect for

CUVs/minivans . However, increasing the s ample size included in the regression analysis by 40% does not noticeably reduce the confidence intervals around the point estimates (Alternative 8 in Table ES.2; addressed in Section 5.6).

In conclusion, casualty risk per crash is not necessarily a bette r metric than fatality risk per VMT for evaluating the effect of mass or footprint reduction on risk; rather, it provides a different perspective in assessing the benefits or drawbacks of mass and footprint reduction on safety in vehicles. However, it doe s allow the risk per VMT to be separated into its two components, crash frequency and risk per crash. Our analysis indicates that much of the detrimental effect of mass or footprint reduction on risk can be attributed to the tendency for mas s or footprint reduction to increase crash frequency, rather than to reduce vehicle crashworthiness (risk once a crash has occurred).

As with our analysis of US fatalities per VMT, this report concludes that the effect of mass reduction on casualty risk p er crash is small, and tend s to be overwhelmed by 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. This report

indica tes that the effects are only slightly sensitive to what variables and data are included in the regression analysis. Finally, as in our analysis of US fatality risk per VMT, this report shows that after accounting for many vehicle, driver, and crash variab les there remains a wide variation in casualty risk per crash by vehicle make and model, and this variation is unrelated to vehicle

mass.

It is important to recognize that the results of this study are based on the relationship of vehicle mass and footpri nt on risk for recent vehicle designs (model year 2000 to 2007). Theseviii 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

ma terials and specific safety technologies.ix Table ES.1. Effect of mass and footprint reduction on two components of fatality and casualty risk per VMT, crash frequency (crashes per VMT) and crashworthiness (risk per crash) ; effects in red are statistically significant (see text)

Variable Case vehicle type A. NHTSA

US fatalities

per VMT B. 13

- state
crashes per VMT C. 13

- state
fatalities per crash D. 13

- state
fatalities per VMT E. 13

- state
crashes per VMT F. 13

- state
casualties per crash G. 13

- state
casualties per VMT

Mass

reduction Cars < 3106 lbs 1.43%* 1.84%

- 0.76% 1.08% 1.84% 0.15% 1.76%

Cars > 3106 lbs 0.48% 1.23%

- 2.44%

- 1.42% 1.23%

- 0.91% 0.32%

LTs < 4594 lbs 0.52% 1.42%

- 1.98%

- 0.43% 1.42%

- 0.37% 1.41%

LTs > 4594 lbs

- 0.40% 0.93%

- 1.22%

- 0.46% 0.93%

- 0.69%

- 0.22%

CUV/ minivan

- 0.47% 0.71% 0.87% 1.29% 0.71%

- 0.13%

- 0.02% Footprint

reduction Cars 1.89% 1.05% 1.24% 2.80% 1.05% 0.32% 2.00%

LTs

- 0.02% 1.15% 0.73% 1.92% 1.15%

- 0.04% 1.13%

CUV/ minivan 1.73%

- 0.37%

- 2.25%

- 1.54%

- 0.37% 0.33% 1.52% * Based on NHTSA’s estimation of uncertainty using a jack

- knife method, only mass reduction in cars less than 3,106 lbs has a statistically significant effect on US fatality risk.

Table ES.2. Effect of mass and footprint reduction on 13

- state casualty risk, under alternative regression model specifications and using additional data ; effects in red are statistically significant (see text)

Variable Case vehicle type13-state casualtiesper police-reportedcrash1. Excluding. massor footprint variable2. Excludingstatecontrol variables3.Including onlytwo state controlvariables4. Casualty crashesper crash5.Including vehiclemake variables6.Excluding CYvariables7. Includingsports,squad, AWD carsand fullsize vans8. Includingalladditional data

Mass

reduction Cars < 3106 lbs 0.15% 0.36%

- 0.78%

- 0.06% 0.24% 1.13% 0.12% 0.16% 0.30%

Cars > 3106 lbs

- 0.91%

- 0.67%

- 1.91%

- 1.32%

- 0.85% 0.55%

- 1.07%

- 0.88%

- 0.60%

LTs < 4594 lbs

- 0.37%

- 0.41%

- 0.46%

- 0.44%

- 0.32%

- 0.54% 0.07%

- 0.50%

- 0.06%

LTs > 4594 lbs

- 0.69%

- 0.72%

- 1.85%

- 0.79%

- 0.67%

- 0.76%

- 0.34%

- 0.93%

- 0.60%

CUV/ minivan

- 0.13% 0.02%

- 1.95%

- 0.43%

- 0.25%

- 0.58% 0.29%

- 0.13%

- 0.14% Footprint

reduction Cars 0.32%

- 0.05% 1.78% 0.55% 0.55%

- 0.37% 0.68% 0.32% 0.19%

LTs

- 0.04%

- 0.44%

- 0.84%

- 0.09% 0.19% 0.22%

- 0.24% 0.10%

- 0.21%

CUV/ minivan 0.33% 0.20% 3.58% 0.46% 0.74% 1.35% 0.26% 0.33% 0.40%x Table of Contents

Acknowledgements

................................ ................................ ................................ .......................... i Executive Summary

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

1. Introduction

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

2. 13

- state fatality and casualty risk per crash

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

2.1. Data and methods

................................ ................................ ................................ ................. 3 2.2. Accounting for the state in which the crash occurred

................................ ........................ 10 2.3. Effect of mass and footprint reduction on fatality and casualty risk per crash

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

3. Multi

- collinearity between vehicle mass and footprint

................................ ............................. 32 4. Casualty risk by vehicle model

................................ ................................ ................................ .. 38 5. Sensitivity of 13

- state casualty risk per crash results to data used and model specification

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

5.1. State control variables

................................ ................................ ................................ ........ 47 5.2. Alternative measure of exposure

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

5.3. Vehicle manufacturer

................................ ................................ ................................ ......... 48 5.4. Calendar year variables

................................ ................................ ................................ ...... 49 5.5. Effect of including sports, police, and all

- wheel drive cars, and fullsize vans

.................. 55 5.6. Effect of adding additional data

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

6. Conclusions

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

7. References

................................ ................................ ................................ ................................ . 65

List of Tables and Figures

Table ES.1. Effect of mass and footprint reduction on two components of fatality and casualty risk per VMT, crash frequency (crashes per VMT) and crashworthiness (risk per crash); effects in red are statistically significant (see text)

................................ ................................ . ix Table ES.2. Effect of mass and footprint reduction on 13

- state casualty risk, under alternative regression model specifications and using additional data; effects in red are statistically significant (see text)

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

........................... 5 Figure 2.1. Distribution of crash

- involved vehicles, by vehicle and crash type

.............................. 8 Figure 2.2. Driver fatality risk per 100,000 crashes in 16 states

................................ ................... 10 Figure 2.3. Driver casualty risk per 10,000 crashes in 16 states

................................ ................... 11 Figure 2.4. Percent of police

- reported crashes that are non

- injury crashes, in 16 states

............... 12 Figur e 2.5. Effect of mass and footprint reduction on three types of risk, by vehicle type

........... 15 Figure 2.6. Effect of mass and footprint reduction on three types of risk per VMT, by vehicle

type

................................ ................................ ................................ ................................ ........ 15 Figure 2.7. Effect of mass and footprint reduction on crashes per VMT (vehicle crash frequency), fatalities per crash (vehicle crashw orthiness), and fatalities per VMT, by vehicle type

....... 16 Figure 2.8. Effect of mass and footprint reduction on crashes per VMT (vehicle crash frequency),

fatalities per crash (vehicle crashworthiness), and fatalities per VMT, by vehicle type

....... 17xi Figure 2.9. One

- vehicle, non

- injury crashes and all crashes per 10,000 vehicle re gistration

- years, by vehicle type and state

................................ ................................ ................................ ........ 18 Figure 2.10. Effect of mass and footprint reduction on three types of risk, by vehicle type

......... 19 Figure 2.11. Effect of mass and size reduction on 13

- state casualty risk per crash by vehicle type, across all crash types and weighted average effect in each type of crash

............................. 20 Figure 2.12. Effect of mass and footprint reduction on 13

- state casualty risk per crash in cars, by type of crash

................................ ................................ ................................ .......................... 21 Figure 2.13. Effect of mass and footprint reduction on 13

- state casualty risk per crash in light trucks, by type of crash

................................ ................................ ................................ .......... 22 Figure 2.14. Effect of mass and footprint reduction on 13

- state casualty risk per crash in CUVs/minivans, by type of crash

................................ ................................ .......................... 22 Figure 2.15. Effect of mass and footprint reduction, and selected control variables, on 13

- state casualty risk per crash, passenger cars

................................ ................................ .................. 23 Figure 2.16. Effect of mass and footprint reduction, and selected control variables, on 13

- state casualty risk per crash, passenger cars

................................ ................................ .................. 24 Figure 2.17. Effect of mass and footprint reduction, and selected control variables, on 13

- state casualty risk per crash, light trucks

................................ ................................ ....................... 25 Figure 2.18. Effect of mass and footp rint reduction, and selected control variables, on 13

- state casualty risk per crash, light trucks

................................ ................................ ....................... 26 Figure 2.19. Effect of mass and footprint reduction, and sele cted control variables, on 13

- state casualty risk per crash, CUVs and minivans

................................ ................................ ......... 26 Figure 2.20. Effect of mass and footprint reduction, and selected control variabl es, on 13

- state casualty risk per crash, CUVs and minivans

................................ ................................ ......... 27 Figure 2.21. Effect of state in which the crash occurred on 13

- state casualty risk per crash, by

vehi cle type

................................ ................................ ................................ ............................ 28 Figure 2.22. Effect of mass and footprint reduction, and selected control variables, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars

........... 29 Figure 2.23. Effect of mass and footprint reduction, and selected control variables, on 13

- state crashes per VMT, fatality risk p er crash, and fatality risk per VMT, light trucks

................ 30 Figure 2.24. Effect of mass and footprint reduction, and selected control variables, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, CUVs/minivans

......... 30 Figure 2.25. Effect of mass and footprint reduction, and driver gender and crash time/loc ation control variables, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars

................................ ................................ ................................ ............. 31 Figure 2.26. Effect of mass and footpri nt reduction, and state control variables, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars

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

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

................................ ................................ ................................ .......................... 33 Figure 3.3. Effect of reduction in mass or footprint on 13

- state casualty risk per crash, by vehicle type: holding footprint constant, allowing footprint to vary with mass, and allowing mass to vary with footprint

................................ ................................ ................................ ................. 34 Figure 3.4. Range in curb weight for the footprint deciles, by vehicle type

................................ . 35 Table 3.1. Number of footprint deciles in which mass reduction increases or decreases 13

- state casualty risk per crash, by vehicle and crash type

................................ ................................ . 36xii Figure 3.5. Effect of car mass reduction on 13

- state casualty risk per crash, by footprint decile and crash type

................................ ................................ ................................ ........................ 37 Figure 3.6. Effect of light truck mass reduction on 13

- state casualty risk per crash, by footprint decile and crash type

................................ ................................ ................................ ............. 37 Figure 3.7. Effect of CUV/minivan mass reduction on 13

- state casualty risk per crash, by footprint decile and crash type

................................ ................................ .............................. 38 Figure 4.1. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight, by vehicle type

................................ .......... 39 Figure 4.2. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight, passenger cars

................................ ........... 39 Figure 4.3. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight, light trucks

................................ ................ 40 Figure 4.4. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight, CUVs and minivans

................................ .. 40 Figure 4.5. Relationshi p between 13

- state casualty risk per crash and curb weight for minivans, by average risk for 100

- lb increments of curb weight and

individual models

................................ ................................ ................................ ................... 41 Figure 4.6. Relationship between 13

- state casualty risk per crash in crashes with stationary objects and curb weight, by vehicle type

................................ ................................ ............... 42 Figure 4.7. 13

- state casualty risk per crash and curb weight, by vehicle model

........................... 43 Figure 4.8. Predicted 13

- state casualty r isk per crash after accounting for all driver, crash, and vehicle variables except mass and footprint, vs. curb weight

................................ ............... 44 Figure 4.9. Residual 13

- state casualty ri sk per crash after accounting for all driver, crash, and vehicle variables except mass and footprint, vs. curb weight

................................ ............... 44 Table 4.1. Relationship between actual, predicted, and residual 13

- state casualty risk per crash,

and vehicle mass reduction, after accounting for all driver, crash, and vehicle variables except mass and footprint, by vehicle type and model

................................ .......................... 45 Table 4.2. Relationship between actual, predicted, and residual 13

- state casualty risk per crash,

and vehicle footprint reduction, after accounting for all driver, crash, and vehicle variables except mass and footprint, by vehicle type and model

................................ .......................... 46 Figure 5.1. Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, under three methods of controlling for the state in which the crash occurred

.............. 47 Figure 5.2. Effect of mass and footprint reduction on 13

- state casu alties and casualty crashes per crash, by vehicle type

................................ ................................ ................................ ............ 48 Figure 5.3. Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle
type, a fter controlling for vehicle manufacturer

................................ ................................ .... 49 Figure 5.4. Effect of calendar year variables on 13

- state casualty risk per crash, by vehicle type

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

............................ 51 Figure 5.6. Effect of calendar year variables on crashes with heavier light

- duty trucks, by case vehicle type

................................ ................................ ................................ ............................ 52 Figure 5.7. Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, including and excluding calendar year variables

................................ .......................... 53 Figure 5.8. Effect of selected control variables on car risk, including and excluding calendar year

variables

................................ ................................ ................................ ................................ . 54xiii Figure 5.9. Effect of selected control vari ables on light truck risk, including and excluding calendar year variables

................................ ................................ ................................ .......... 54 Figure 5.10. Effect of selected control variables on CUV/minivan risk, including and excluding calendar year variables

................................ ................................ ................................ .......... 55 Figure 5.11. Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, after including spor ts, police, and all

- wheel drive cars, and fullsize vans

................... 56 Figure 5.12. Number of vehicles involved in casualty crashes by vehicle type, using additional

state crash data

................................ ................................ ................................ ...................... 57 Figure 5.13. Effect of mass and footprint reduc tion on casualty risk per crash by vehicle type, using additional state crash data

................................ ................................ ............................ 58 Figure 5.14. Effect of mass and footprint reduction on casualty risk per cras h by vehicle type, using additional state crash data, after accounting for full adoption of ESC

........................ 58 Table 6.1. Effect of mass and footprint reduction on two compo nents of fatality and casualty risk per VMT, crash frequency (crashes per VMT) and crashworthiness (risk per crash); effects in red are statistically significant (see text)

................................ ................................ ........... 64 Table 6.2. Effect of mass and footprint reduction on 13

- state casualty risk, under alternative regression model specifications and using additional data; effects in red are statistically significant (see text)

................................ ................................ ................................ .............. 641 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, 2011 ). 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 crash es 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.

In a companion report ( Wenzel , 2011 b )

, we use 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 fat ality risk per vehicle miles traveled (VMT), for model year 2000 to 2007 light

- duty vehicles involved in fatal crashes between 2002 and 2008. The data are examined in slightly different ways, to get a deeper understanding of the relationship between reduc tions in vehicle mass and footprint, and overall safety.

This report compares the logistic regression results of the NHTSA analysis of US fatality risk per VMT with an analysis of 13

- state casualty risk per crash. This analysis differs from the NHTSA

a nalysis in two respects: first, it analyzes risk per crash, using data on all police

- reported crashes from thirteen states, rather than risk per estimated VMT; and second, it analyzes casualty (fatality plus serious injury) risk, as opposed to fatality ris k. There are several good reasons to investigate the effect of mass and footprint reduction on casualty risk per crash.

First, risk per VMT , which NHTSA has studied extensively, accounts for two effects that influence whether a person is killed or seri ously injured in a crash: how well a vehicle can be driven ( based on its handling, acceleration, and braking capabilities) to avoid being involved in a serious crash (crash avoidance), and, once a serious crash has occurred, how well a vehicle protects its occupants from fatality or serious injury (crashworthiness). By encompassing both of these aspects of vehicle design, r isk per VMT gives a complete picture of how vehicle design can promote, or reduce, road user safety. On the other hand, risk per crash isolates the second of these two safety effects, crashworthiness, by examining the effect of mass and footprint reduction on how well a vehicle protects its occupants once a crash occurs. In general, NHTSA safety regulations focus on crashworthiness (e.g . crash test requirements and NCAP star ratings, seatbelt and airbag requirements, and roof crush standards), although some standards require the installation of technologies, such as automated braking systems (ABS) and electronic stability control (ESC), that improve a vehicle’s crash avoidance.

Second, e stimating risk on a per crash basis requires using data on police

- reported crashes from

states . Although NHTSA generates a national sample of police

- reported crashes, the N ational Automotive Sampling Sys tem, General Estimates System (NAS S GES ) , that can be used to estimate per crash risks on a national basis, the database is relatively small and may be biased

towards crashes that occur in relatively urban areas . Only sixteen states currently record the2 v ehicle identification number of all vehicles involved in police

- reported crashes, which is necessary to determine the model year, make

, and model of e ach vehicle, in order to assign its correct curb weight, footprint, type, and installed safety features (s uch as side airbags, ABS, ESC, and all

- wheel drive). The sixteen states that report VIN information represent about one

third of the country, so estimating fatality risk per crash from these sixteen states increases the statistical uncertainty of the analysis, relative to that from estimating fatality risk per VMT using

all US fatalities. 1 Extending the analysis to casualties (fatalities plus serious/incapacitating injuries) reduces the statistical uncertainty of analyzing just fatalities per crash. In addition, a serious incapacitating injury can be just as traumatic to the victim and her family, and costly from an economic perspective, as a fatality. Limiting the analysis to the risk of fatality, which is an extremely rare event, ignores the effect vehicle design may have on reducing the large number of incapacitating injuries that occur each year on the nation’s roadways

.

In an earlier report LBNL compared fatality risk per vehicle registration

- year and casualty risk per crash, using the same data base of all police

- reported crashes in five states ( Wenzel , 2011a). For the most part, the trend in casualty risk by vehicle type is quite similar to that of fatality risk, when vehicle registration

- years are used as the measure of exposure, although casu alty risks are substantially lower than fatality risks for sports cars and for pickups. The trend in casualty risk by vehicle type is similar regardless of whether vehicle registration years or police

- reported crashes are used as the measure of exposure. Casualty risks for subcompact and compact cars are relatively lower per crash than per vehicle, while casualty risks for large and import luxury cars, minivans, large SUVs, and pickups are relatively higher per crash than per vehicle. We

accounted for mi les driven by vehicle make and model using odometer readings from vehicle emission inspection and maintenance programs in four of the five states. For most vehicle types, adjusting casualty risk per vehicle registration

- year for miles driven has little to no effect (although the adjustment does substantially increase casualty risk for sports cars, which are driven many fewer miles than other vehicles, by 30%, and slightly reduces casualty risk for

fullsize vans and !

- ton pickups, which are driven more mile s than the average vehicle).

In summary, casualty risk per crash is not necessarily a better metric than fatality risk per VMT; rather, it provides a different perspective in assessing the benefits or drawbacks of mass and footprint reduction on safety in vehicles. Unless noted otherwise, all casualty risks in this report are societal risk, including fatalities and serious injuries in the case vehicle and any crash partners, and include not only driver casualties but passenger casualties as well.

Secti on 2 of this report replicates the logistic regression models NHTSA used in its analysis of US fatality risk per VMT, and compares the effect of mass and footprint reduction on US fatality risk per VMT, 13

- state fatality risk per crash, and 13

- state casual ty risk per crash . Section 3

examines in more detail the multi

- collinearity between vehicle mass and footprint, and methods

to address that multi

- collinearity when assessing their effect on casualty risk per crash

. In

Section 4 we examine the relationshi p between vehicle mass and casualty risk per crash by

1 This report further limits the analysis to the thirteen states that provide the posted speed limit of the roadway on which the crash occurred, an important variable NHTSA uses in its regression models that approximates the travel speed of the vehicles involved in the crash. In Section 5 we run a sensitivity analysis using data from the three additional states that report VIN but not posted speed limit, using a technique developed by NHTSA to impute the posted speed limit based on the type of roadway on which the cras h occurred (NHTSA, 2003).3 vehicle model, before and after accounting for differences in driver characteristics, crash locations, and other vehicle attributes. In Section 5 we test alternative specifications of the regression mo dels, in order to examine the sensitivity of our results to different model specifications , and using additional data

.

2. 13

- state fatality and casualty risk per crash

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

- culpabl e vehicles involved

in two

- vehicle crashes from police

- reported crash data from thirteen states; NHTSA refers to this subset of vehicles as “induced exposure” cases. The induced exposure cases provide information

on driver and crash characteristics for ve hicles 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 vehicl e 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 Kahane, 2011.

In this section we use basically the same logistic regression models NHT SA developed for their analysis of US fatality risk per VMT to assess the effect of mass and footprint reduction on 13

- state fatality and casualty risk per crash, using data from all police

- reported crashes in thirteen

states. We also examine in detail th e effect mass and footprint reduction have on 13

- state casualty risk per crash in each type of crash, as well as the effects the various other vehicle, driver, and crash condition variables have on casualty risk per crash.

2.1. Data and methods

For its analysis NHTSA used FARS data on fatal crashes, and police

- reported crash data from 13 states, for MY00 to MY07 light

- duty vehicles between calendar years 2002 and 2008. NHTSA used a subset of nonculpable vehicles in two

- vehicle crashes as a measure of in duced 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 t hen 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 give n 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 all

wheel drive and automated braking systems. NHTSA added several new variables for new safety technologies a nd 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.4 To reflect chan ges 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 tw o 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 nin e 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 weight ed by the number of vehicle registrations and the annual mileage, so that the models are estimating the effect of changes in the control variables on US fatalities per vehicle mile traveled (VMT).

Table 2.1 shows the control variables NHTSA used in its r egression 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 t wo weight classes is based on the average weight for each vehicle type: 3,106 lbs for cars and 4,594 lbs for light

- duty trucks. Because there are fewer CUVs and minivans in the database, NHTSA uses a single variable, LBS100, for CUV/minivan weight. As in the 2003 and 2010 analyses, eight variables for driver age and gender are used. In the 2003 analysis, NHTSA excluded the driver airbag control variables in the regressions for rollovers and crashes with pedestrians. In the 2011 analysis, NHTSA includes the control variable ROLLCURT airbags only in the regression models for rollover crashes involving cars or CUVs/minivans; regression models of pedestrian crashes do not include any control variables for airbags; and the control variables for CURTAIN, COMBO , and TORSO airbags are included in regression models for all other crashes involving cars or CUVs/minivans. No airbag variables were included in the regression models for light trucks.5 Table 2.1 . Control variables used in regression models, by subject vehicle type

Control variable Cars LTVs CUVs/minivans

UND RWT 00 C C

OVERWT 00 C C

LBS100 C

FOOTPRINT C C C

TWODOOR D

SUV D HD_PKP D

BLOCKER1 D

BLOCKER2 D

MINIVAN D

ROLLCURT * C C

CURTAIN * C C

COMBO * C C

TORSO * C C

ABS C C

ESC C C C

AWD C C

DRVMALE C C C M14_30 C C C M30_50 C C C M50_70 C C C M70_96 C C C F14_30 C C C F30_50 C C C F50_70 C C C F70_96 C C C

NITE D D D

RURAL D D D

SPDLIM55 D D D HIFAT_ST D D D

VEHAGE C C C

BRANDNEW D D D

CY2002 D D D

CY2003 D D D

CY2004 D D D

CY2005 D D D

CY2007 D D D

CY2008 D D D

C: continuous variable D: dummy variable, coded as either 1 or 0 * The control variable for ROLLCURT airbags is only used in regression 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.

R ather 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 deriv es the baseline fatalities from MY04

- 09 vehicles in crashes between 2004 and 2008. NHTSA then adjusts this baseline distribution downward to6 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 an d light

trucks. 2 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 a verage 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 c rash type.

For our analysis of fatality and casualty risk per crash, w e divide d all crashes in the 13

- state
databases into the nine crash categories, and three vehicle types, used by NHTSA in its 2011 study , for the most part usin g the same criteria. One important difference is that NHTSA considered only “first

- events” in classifying one

- vehicle crashes as rollovers; vehicles that struck an object ( or another vehicle ) prior to rolling over are not included in NHTSA’s “rollover”

category. However, since a ll thirteen states do not consistently code “first” vs. “most harmful” events in the same manner, as is done in FARS, we included all vehicles involved in single

vehicle rollover crashes in our “rollover” category, regardless of whether they struck an obje ct prior to rolling over.

Table 2.2 and Figure 2 .1 compare the distribution of light

- duty vehicle crashes in the U.S. from the NHTSA 2011 report with those from the 13 states. The US fatal crash involvements in Table

2.2 include those from 2008, while the fatal and casualty crash involvements from the thirteen states do not, as 2008 data were available from only five of the states. 3 Note that there are higher

fractions of “other” crashes (that is, crashes involving more than two vehicles, or for which not all information was available) in the 13

- state data; for example, 22% of U.S. fatal car crash

involvement s in FARS are in the “ other ” category, while 29% of fatality crash involvement s, and 34% of casualty crash involvem ents, in the thirteen states are in the “other” category. The

distributions of vehicles involved in crashes in Figure 2 .1 exclude the “other” crash category , so that the fractions of all other types of crashes equal 100% for each vehicle type

.

2 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 Tra ffic Safety Administration. http://www

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

. 3 We reran the regressions on US fatalities excluding calendar year 2008, to determine what effect excluding these data h as on the results from our casualty regressions using the 13

- state data. The results of this analysis are summarized in Appendix A.7 Table 2.2 . Fatal and casualty crash involvements for model year 2000 to 2007 passenger cars in 2002 to 2008

Crash type Fatal crash involvements (FARS) Fatal crash involvements (13 states) Casualty crash involvements (13 states)

Cars LTVs CUVs/

minivans Cars LTVs CUVs/

minivans Cars LTVs CUVs/

minivans

1: Rollovers 2,709 6,269 660 489 1,083 122 2,851 4,632 533 2: w/object 9,373 7,272 1,319 1,527 1,089 187 9,372 6,812 1,300

3: Ped etc. 6,514 6,493 1,904 1,676 1,380 458 9,168 6,002 2,442

4: w/HDT 3,346 2,371 680 811 500 190 4,064 2,469 868 5: w/lgt car 4,258 5,667 1,245 1,058 1,149 317 14,719 10,330 3,503 6: w/hvy car 4,746 4,928 1,211 874 859 263 11,664 7,882 2,727 7: w/lgt LT 3,149 2,670 62 2 963 728 219 11,446 7,381 2,521 8: w/hvy LT 3,622 2,196 685 842 426 161 7,267 4,228 1,652

9: Other 10,487 9,407 2,810 3, 296 2,728 824 35,959 25,298 8,668

Total 48,204 47,273 11,136 11,536 9,942 2,741 106,510 75,034 24,214

1: Rollovers 5.6% 13.3% 5.9% 4.2% 10.9% 4.5% 2.7% 6.2% 2.2% 2: w/object 19.4% 15.4% 11.8% 13.2% 11.0% 6.8% 8.8% 9.1% 5.4%

3: Ped etc. 13.5% 13.7% 17.1% 14.5% 13.9% 16.7% 8.6% 8.0% 10.1%

4: w/HDT 6.9% 5.0% 6.1% 7.0% 5.0% 6.9% 3.8% 3.3% 3.6% 5: w/lgt car 8.8% 12.0% 11.2% 9.2% 11.6% 11.6% 13.8% 13.8% 14.5% 6: w/hvy car 9.8% 10.4% 10.9% 7.6% 8.6% 9.6% 11.0% 10.5% 11.3% 7: w/lgt LT 6.5% 5.6% 5.6 % 8.3% 7.3% 8.0% 10.7% 9.8% 10.4% 8: w/hvy LT 7.5% 4.6% 6.2% 7.3% 4.3% 5.9% 6.8% 5.6% 6.8%

9: Other 21.8% 19.9% 25.2% 28.6% 27.4% 30.1% 33.8% 33.7% 35.8%

Total 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 %

Figure 2 .1 indicates that t here is a smaller portion of fatal car crashes with stationary objects, heavy cars, and rollover crashes in the thirteen states (18.5%, 10.6%, and 5.9%, respectively) than in the entire US (24.9%, 12.6%, and 7.2%, respectively). On the other hand, there are more

fa tal car crashes in crashes with pedestrians/pedalcycles, light cars, light and heavy light

- duty trucks, and heavy

- duty trucks in the thirteen states (20.3%, 12.8%, 11.7%, 10.2%, and 9.8%, respectively) than in the US (17.3%, 11.3%, 8.3%, 9.6%, and 8.9%, re spectively). These trends hold for light trucks and CUVs/minivans as well.

In the thirteen states, there are higher fractions of crashes involving rollovers, stationary objects, pedestrians/pedalcycles, and heavy trucks, and fewer crashes with other ligh t

- duty vehicles, when the crashes result in a fatality rather than a casualty. This suggests that rollovers and crashes with stationary objects, pedestrians/pedalcycles, and heavy

- duty trucks are more likely to result in fatalities, as opposed to incapaci tating injuries, than crashes with another light

- duty

vehicle.

The distributions of fatal and casualty crashes involving cars in the thirteen states are quite similar to those involving CUVs and minivans; however, CUVs and minivans tend to be

involved in fewer crashes with stationary objects ( 9.8 % of all fatal crashes) than cars ( 1 8.5 % of

all fatal crashes ). Light trucks tend to have more fatal/casualty crashes in rollovers and crashes with lighter cars (15.0% and 15.9 % of all fatal crashes, respectively ) than cars do ( 5.9 %, and 12.8% of all fatal crashes, respectively ) , but relatively fewer crashes with stationary objects (1 5.1 %, vs. 1 8.5 % for cars) and heavier light trucks (6.9%, vs. 9.8% for cars)

.8 Figure 2 .1 . D istribution of crash

- involved vehicles , by vehicle and crash type

Note in Table 2.2 that there are many fewer fatal crash involvements in the thirteen states (e.g., 11,536 cars) than in the US FARS (48,204 cars). Extending the analysis to include incapacitating injuries substantially incre ases the number of casualty crash involvements in the thirteen states (to 106,510 cars). The focus of this report is on the effect of mass and footprint reduction on casualty risk using data from the thirteen states

, although we do compare the effect

on f atality risk in the next section. And in Sectio n 5 w e analyze the effect of extending the analysis to sixteen states, and adding other crashes to the analysis.

To the extent possible, we used the same assumptions as in the NHTSA analysis, in many cases using the same SAS programs. For example, we used the VIN decoder programs developed by NHTSA to determine model year, make, and model of each vehicle in the state crash data, and added detailed vehicle characteristics such as body style, curb weight, foo tprint, ABS, AWD, passive restraint systems, etc. And we used the NHTSA definitions to classify vehicles into five types (light cars, heavy cars, light light

- duty trucks, heavy light

- duty trucks, and CUVs/minivans), as well as the nine types of crashes de scribed above. This was done in order to allow for a more direct comparison of the results from the two studies, as well as with other studies using very similar databases and approaches.

However, it was necessary to diverge from the NHTSA analysis in several respects. First,

NHTSA used a subset of non

- culpable vehicles in two

- vehicle crashes from police

- reported

crash data from t he t hirteen states to assign driver and environment control variables to national vehicle registration years (from Polk). N HTSA selected non

- culpable vehicles in two

- vehicle crashes to determine induced exposure crash involvements. Each of these vehicles w as assigned a weight representing the national registration

- years for each particular year, make and model. 24.9%

18.5%

13.3% 19.2%

15.1%

13.7% 15.8%

9.8%

8.4% 17.3%

20.3%

13.0% 17.1%

19.1%

12.1% 22.9%

23.9%

15.7% 8.9%

9.8%

5.8% 6.3%
6.9%

5.0% 8.2%

9.9%

5.6% 11.3% 12.8%

20.9% 15.0% 15.9%

20.8% 15.0%

16.5%

22.5% 12.6% 10.6%

16.5% 13.0% 11.9%

15.8% 14.5%

13.7%

17.5% 8.3% 11.7% 16.2% 7.1% 10.1%

14.8% 7.5% 11.4%

16.2% 9.6% 10.2% 10.3% 5.8% 5.9% 8.5% 8.2% 8.4% 10.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Fatality (FARS) Fatality (13 states) Casualty (13 states) Fatality (FARS) Fatality (13 states) Casualty (13 states) Fatality (FARS) Fatality (13 states) Casualty (13 states)Distribution of crash-involved vehicles Distribution of crash-involved vehicles, by crash type

9: Other

8: w/hvy LT

7: w/lgt LT

6: w/hvy car

5: w/lgt car

4: w/HDT

3: Ped etc.

2: w/object

1: Rollovers

Cars Light Trucks CUVs/Minivans9 NHTSA developed other weights for total VMT based on a database of vehicle odometer readings by vehicle year, make and model, also obtained from Polk. For our analysis of risk per crash, we use all vehicles in the state databases, including those involved in one

- vehicle crashes and the vehicle NHTSA determined to be responsible for the crash in two

- vehicle crashes. Therefore both the number of fatalities or casualties, and the number of crashes, come from the same datasets. For our analysis of risk per V MT, we again use the number of fatalities or crashes from the thirteen state databases, coupled with the VMT weights that NHTSA derived from the induced exposure crash involvements, national vehicle registrations, and average vehicle odometer readings. Be cause NHTSA apparently included all induced exposure involvements in creating their VMT weights , and did not exclude those that resulted in a fatality, we are able to

use NHTSA’s VMT weights in our regression models of 13

- state risk per VMT . However,

NHTSA used national Polk registration data to scale the induced exposure crashes from the

thirteen states to the national level. Since we only include casualties occurring in the thirteen

state s , this scaling is not necessary for our analysis . In the future w e hope to obtain VMT weights adjust ed to total registration

- years in the thirteen states

, rather than in the entire US, for our analyses of risk per VMT

.

Second, a s noted above, NHTSA included fatal crash involvements from FARS for 2008, even though 200 8 data were available from only six of the thirteen states NHTSA used to develop the induced exposure records. For the remaining seven states, NHTSA assumed the distribution of induced exposure crashes in 2008 were identical to those in 2007. 4 Rather tha n extending this assumption to the fatality and casualty crashes from the seven states without 2008 data, we limited our initial analysis to crash data through 2007.

To make our results most comparable to NHTSA’s results for US fatalities per VMT, we also excluded the following records from our initial analysis:

• sport s cars , police cars, and all

- wheel drive cars ; • vehicles whose reported model year did not match the model year decoded from the

VIN; • vehicles whose model year was not reported in the st ate crash data (with the exception

of all crash records from Washington, which NHTSA included in their analysis of induced exposure crashes ); • vehicles involved in police

- reported crashes in 2008 ( in six states ) ; • vehicles that had apparent VIN transcri ption errors that we corrected so that the

reported model year matched the VIN model year. 5

4 NHTSA made similar assumptions for years of state crash data that were not available, such as 2002 data from Pennsylvania and 2002 and 2003 data from Michigan.

5 The apparent transcription errors are based on comparing the reported model year with the

cha racters in the 9 th and 10 th VIN position. If the reported model year matched a common transcription error (such as a “5” entered as an “S”) then we translated the character in the 10 th
VIN position to match the reported model year. If the reported model year matched the character entered in position 9, we shifted the character in position 9, and all subsequent characters, to the right one position. These VIN adjustments are described in more detail in Appendix B.10 I ncluding these records , as well as police

- reported crash data from three states that NHTSA did not include in its analysis of induced exposure crashes, increases the number of vehicles analyzed by about 40% of mass and footprint reduction on risk . We test how including all of these records in the regression analyses would change the effect of mass and footprint reduction on casualty risk per crash in Se ction 5

.

2.2. Accounting for the state in which the crash occurred

In its regression models of US fatality risk per VMT , NHTSA included the control variable HIFAT_ST, which identifies states with high fatality rates per million vehi cle

- years. We investigated the effect of replacing this single control variable with two variables, for states with high and with low fatality risks per crash, as well as with 12 control variables for each state used in the analysis except Florida.

Figur es 2.2 and 2.3 show the unadjusted fatality and casualty risk per crash in 16 states. Figure

2.2 indicates that fatality risk per crash is the highest in Florida, Pennsylvania, and Wyoming, and the lowest in Michigan, Illinois, and New Jersey. Figure 2.3 suggests that casualty risk per crash is the highest in Alabama, Florida, Maryland, and Wyoming, and the lowest in Georgia, Michigan, New Jersey, and Washington. Note that driver casualty risks per crash have been fairly constant over time in most states , with the exception of Maryland and New Mexico, which exhibit fairly large, consistent reductions in casualty risk each year. We have no explanations for

this trend in these two states.

Figure 2.2 . Driver fatality risk per 100,000 crashes in 16 states

0 100 200 300 400 500 600 700

1994 1996 1998 2000 2002 2004 2006 2008Driver fatality risk per 100,000 crashes

Year Driver fatality risk per 100,000 crashes

FL

IL

MD

MI

MO

NE

NJ

NM

PA

WA

WI

WY

AL

GA

KS

KY11 Figure 2.3 . Driver casualty risk per 10,000 crashes in 16 states

The relatively high or low risks shown for some states in Figures 2.2 and 2.3 do not necessarily reflect more dangerous driving conditions in those states; rather, they reflect either dif ferent definitions of “incapacitating”, “serious”, or “major” injuries, or different reporting requirements or reporting bias in those states. For example, Pennsylvania is unique in that it reports “moderate” injuries in addition to “major” and “minor” in jury; as a result, there are relatively few “major” injuries reported in Pennsylvania, which increases its casualty risk per crash relative to other states. In Florida, there is no property damage threshold over which a crash is required

to be included in the state crash database; in most other states crashes resulting in property damage in excess of $500 must be reported. In Figure 2.4 , only about 60% of all crashes in the

Florida database are non

- injury crashes, whereas 80% to 90% of the crashes in most other states

are non

- injury crashes. As a result, risks per crash are higher in Florida than in almost any other

state. 0 100 200 300 400 500 600 700

1994 1996 1998 2000 2002 2004 2006 2008Driver casualty risk per 10,000 crashes

Year Driver casualty risk per 10,000 crashes

FL

IL

MD

MI

MO

NE

NM

PA

WA

WI

WY

NJ

AL

GA

KS

KY12 Figure 2.4 . Percent of police

- reported crashes that are non

- injury crashes, in 16 states

Based on Figures 2.2 through 2.4 , we re placed the HIFAT_ST variable NHTSA used for analysis

of US fatalities per VMT with 12 variables identifying each state except Florida for our analysis of fatality and casualty risks per crash

. We examine how replacing the 12 state control variables

with o nly two variables for high

- and low

- injury states would change the effect of mass and footprint reduction on risk in Sectio n 5

.

2.3. Effect of mass and footprint reduction on fatality and casualty risk per crash

All of the regression coefficients presented in the NHTSA 2011 report are the direct output from the SAS LOGIST procedure ( with the exception of those for the mass and footprint variables UNDRWT00, OVERWT 00, LBS100, and FOOTPRNT , which NHTSA often multiplies by

- 1 so that they reflect the e ffect of a reduction in vehicle mass or footprint ; we use the same convention throughout this report)

. The output from the SAS LOGIST procedure reflect s the percent change in the log

- odds of casualty (or fatality) per crash for a one

- unit increase in the explanatory variable. In order to obtain the percent change in the probability of casualty (or fatality) per crash , the SAS outputs need to be converted from log

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

– 1, where x i s 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 casualty is small ; however it substantially increases the percent ch ange for explanatory variables that have a large effect on the log

- odds of casualty (such as the crash location variables). For example, the casualty risk per crash from a lighter than average car involved in a rollover crash has a 1.64 times higher log

o dds of casualty if it occurs in a rural county; after conversion, this crash has a 416 percent higher probability of casualty if it occurs in a rural county (EXP( 1.64 )

- 1 = 4.16 ). Unless noted 60% 65% 70% 75% 80% 85% 90%

1994 1996 1998 2000 2002 2004 2006 2008Percent of all police-reported crashes with no injuries

Year Percent of police-reported crashes that are non-injury crashes

FL

IL

MD

MI

MO

NE

NM

PA

WA

WI

WY

NJ

AL

GA

KS

KY13 otherwise, the confidence intervals shown in this report are calculated the same way ; that is, using the standard error of the log

- odds output of the SAS LOGIST procedure to derive the 95% confidence interval of the log

- odds coefficient, and to display it as a percent probability interval

.

Figure 2.5 presents the regression coefficients of the effect of reductions in mass and footprint on US fatality risk per ten billion VMT, from the NHTSA 2011 analysis (in light blue) . T he

coefficients for each of the nine crash types are weighted by the distribution of NHTSA’s

estimated 2016 baseline fatal crash involvements, after adju stment for full ESC penetration . The figure indicates that mass reduction increases societal 6 fatality risk by about one percent for cars and lighter

- than

- average light tru cks, while mass reduction leads to a slight reduction in fatality risk for the heavier light trucks and CUV/minivans. The 95% confidence intervals in the figure indicate that the changes in risk for lighter cars, and both categories of light

- duty trucks, are statistically significant. The confidence intervals shown in the figure, and all figures in this report, represent the weighted average standard error from the SAS output, times 1.96. NHTSA does not report these confidence intervals in its 2011 repor t; 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.

F igure 2.5 compares the effect of mass and footprint reduction on U S fatalit ies per billion VMT (from NHTSA 2011, in blue) with that on 13

- state f atality risk (in red)

, and 13

- state casualty risk (in green) , per police

- reported crash

. The effect for each of the nine crash types is weighted by the expected distribution of fatal or casualty crashes in 2016, after full adoption of ESC, just as in the NHTSA 2011 report. Mass reductions in cars and light trucks , while holding footprint

constant, results in a consistent reduction in state fatality risk per crash; the reduction s are statistically significant for all but the lighter cars. Mass reduction in CUVs and minivans leads

to a statistically insignificant increase in state fatality risk per crash. Footprint reductions in cars and light trucks result in statistically sign ificant increases in state fatality risk per crash, while footprint reduction in CUVs and minivans results in a relatively large but insignificant reduction

in risk.

The results for 13

- state fatality risk per crash (shown in red) in Figure 2.5 are quite different

from the results NHTSA obtained for US fatality risk per VMT (shown in blue), which found

that mass reduction , while holding footprint constant, increased risk for cars and lighter light trucks, and slightly reduced risks for the heavier light trucks and CUVs/minivans. The different

results for fatality risk per VMT versus per crash could be attributed to at least three factors

.

• First, as discussed ab ove, the effect of mass reduction on fatality risk per VMT is the

combined effect of a vehicle’s crash avoidance and its crashworthiness ; the ability of a vehicle to avoid a crash altogether, and the extent to which a vehicle protects its
occupants once a serious crash occurs. The net detrimental effect of mass reduction on fatality risk per VMT for cars and lighter light

- duty trucks may be the result of a large

6 All of the fatality risks reported in the 2011 NHTSA report are societal risk, that is fatalities to all vehicle occupants and non

- occupants involved in the crash are included. Unless specified otherwise (i.e. in Section 6, when we examine the effect of side impact airbags on risk to car occu pants, and steps manufacturers have taken to improve light truck compatibility o n the risk light trucks impose on other vehicle occupants, in two

- vehicle crashes), all risks in this report also are societal risk.14 detrimental effect of mass reduction on these vehicles’ crash avoidance, combined with a

smalle r, beneficial effect of mass reduction on crashworthiness. We address this possibility below.

• Second, the differences between the effect of mass reduction on US fatality risk per VMT

versus 13

- state fatality risk per crash could be the result of differe nces in the mass/footprint relationship with risk in the thirteen states vs. in the country as a whole.

• Finally, the differences between fatality risk per VMT and casualty risk per crash could

indicate that casualties are much less sensitive to mass r eductions than fatalities, and that

vehicle mass reduction somehow reduces casualties but not fatalities.

Figure 2.5 indicates that mass reductions generally result in smaller effects on casualty risk per crash than on fatality risk per crash. Mass reductions result in small statistically significant reductions in casualty risk per crash for heavier cars and all light trucks, a small but not statistically significant reduction in risk for CUVs/mi nivans, and a small but not statistically significant increase in risk for lighter cars. The effect of footprint reduction on casualty risk per crash also is much smaller than its effect on fatality risk per crash, with small and not statistically signific ant increases in casualty risk per crash for cars and CUVs/minivans.

Figure 2. 6 shows the same data as Figure 2.5, but for 13

- state fatality and casualty risk per VMT

, not per crash, using the VMT weights developed by NHTSA using national registration da ta and induced exposure crashes in the thirteen states. Comparing Figure 2. 6 with Figure 2.5, one sees that the effects of mass and footprint reduction on risk per VMT are quite a bit larger than the effects on risk per crash. The difference is so large that a reduction in risk per crash becomes an increase in risk per VMT, for mass reduction in lighter cars in terms of fatality risk (shown in red), and for mass reduction in heavier cars and lighter light

- duty trucks in terms of casualty risk (shown in gr een).

Figure 2. 6 also indicates that the effect on fatality risk per VMT from the 13

- state data (shown in red) are comparable to the effect on national fatality risk per VMT (shown in blue), for mass

reduction in only lighter cars and heavier light

- duty trucks. The mass and size effects on casualty risk per VMT from the thirteen states (shown in green) are comparable to the effects on US fatality risk per VMT (shown in blue) for mass reduction in lighter and heavier ca rs, and heavier light

- duty trucks, and for footprint reduction in cars and CUVs/minivans.

The improved similarity in the US and 13

- state risks expressed in terms of VMT exposure in Figure 2. 6 suggests that the large differences between US fatality risk pe r VMT and 13

- state fatality risk per crash in Figure 2.5 are the result of changing the measure of exposure from per VMT to per crash, and not because the relationships between mass and footprint reductions and

risk in the thirteen states are different fro m those relationships in the entire US (the second possible explanation for the differences in fatality risk per VMT vs. per crash, summarized above). Figure 2. 7 compares the effect of mass and footprint reduction on the two components of risk, the number of crashes per VMT ( crash frequency, the inverse of crash avoidance, shown in orange) and the fatality risk per crash (crashworthiness, shown in light red), with their effect on fatality risk per VMT (shown in dark red), from the 13

- state crash data

.15 Figure 2.5

. Effect of mass and footprint reduction on three types of risk, by vehicle type

Figure 2.6

. Effect of mass and footprint reduction on three types of risk per VMT, by vehicle type1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%-0.76%-2.44%-1.98%-1.22%0.87%1.24%0.73%-2.25%0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%

-4.0% -3.0% -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/

minivansPercent change in risk (probability) 

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

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash

100-lb reduction in weight 1-sq ft reduction in footprint1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.08%-1.42%-0.43%-0.46%1.29%2.80%1.92%-1.54%1.76%0.32%1.41%-0.22%-0.02%2.00%1.13%1.52%

-4.0% -3.0% -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/

minivansPercent change in risk (probability) 

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

US fatalities per VMT

13-state fatalities per VMT

13-state casualties per VMT

100-lb reduction in weight 1-sq ft reduction in footprint16 The results in Figure 2.7 for crash frequency and crashworth iness were obtained using the same regression models, with the dependent variable changed from fatalities per VMT to crashes per VMT (for crash frequency) or fatalities per crash (for crashworthiness) . For cars and light trucks, the effects from the two components roughly add together to result in the overall effect on fatality risk per VMT. For example, a 100

- lb reduction in the mass of lighter cars leads to a

1.84% increase in crash frequency ( the number of crashes per VMT), while mass reduction leads to a 0.78% decrease in the number of fatalities per crash (or a 0.76% increase in crashworthiness); the net effect is only a 1.08% increase in the risk of fatality per VMT. For CUVs/minivans, the rela tionship is different; mass reduction leads to a 0.71% increase in crash frequency , as well as a 0.87% increase in the number of fatalities per crash (or decrease in crash worthiness); however, the net result, a 1.29% increase in the number of fatalities p er VMT, is less than the sum of the two components.

Figure 2.7

. Effect of mass and footprint reduction on crashes per VMT (vehicle crash frequency ), fatalities per crash (vehicle crashworthiness), and fatalities per VMT, by vehicle type

Figure 2.7 in dicates that a reduction in car and light truck footprint increases both the number of crashes per VMT and the number of fatalities per crash (or decreases both the crash avoidance and crashworthiness), with the overall effect on fatalities per VMT roughly the sum of the two components. Footprint reduction in CUVs/minivans leads to a decrease in both the number of crashes per VMT and the number of fatalities per crash, with the net reduction in fatalities per VMT less than the sum of the two components.1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.84%1.23%1.42%0.93%0.71%1.05%1.15%-0.37%-0.76%-2.44%-1.98%-1.22%0.87%1.24%0.73%-2.25%1.08%-1.42%-0.43%-0.46%1.29%2.80%1.92%-1.54%

-4.0% -3.0% -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/

minivansPercent change 

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

US fatalities per VMT
13-state crashes per VMT
13-state fatalities per crash
13-state fatalities per VMT

100-lb reduction in weight 1-sq ft reduction in footprint17 C omparing the effect of mass and footprint reduction on crashes per VMT, from Figure 2.7, with the casualty risks per crash, from Figure 2.6, obtains similar results for casualty risks per VMT in Figure 2.6: the net effect of mass or footprint reductions on casualty risk per VMT is roughly the sum of the effect of mass or footprint reduction on crash avoidance and on crashworthiness, at least for cars and light trucks , as shown in Figure 2.8

.

Figure 2.8

. Effect of mass and footprint reduction on crashes per VMT (vehicle crash frequency ), fatalities per crash (vehicle crashworthiness), and fatalities per VMT, by vehicle type

There is a possibility that reporting bias in the state police

- reported crash data may influence the results of crash frequency and casualty risk per crash. Non

- injury crashes may be under

- reported for certain vehicle and crash types, such as large pickups that are less likely to suffer damage, and that older, less expensive, uninsured vehicles that are less likely to report crash dam age to police, or one vehicle crashes in which there is no crash partner that requires a police

- report in

order to file an insurance claim. If collisions of certain vehicles or crashes are slightly less likely

to be report ed , this would tend to increase th e observed detrimental effect of mass reduction on reported crashes per VMT and conversely decrease its detrimental effect on casualties per reported crash. ( By contrast, fatalities or casualties per VMT would be not be affected by crash

reporting rates, because the crash

- reporting rate is not part of the formula for calculating ri sk.) The extent to which any reporting bias of non

- injury crashes exists, the observed effects for police

- reported crashes per VMT might not correspond exactly to the “effect of mass reduction on crash avoidance” and the observed effects for casualties per police

- reported crash might not correspond exactly to the “effect of mass reduction on crashworthiness.”1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%1.84%1.23%1.42%0.93%0.71%1.05%1.15%-0.37%0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%1.76%0.32%1.41%-0.22%-0.02%2.00%1.13%1.52%

-4.0% -3.0% -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/

minivansPercent change 

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

US fatalities per VMT
13-state crashes per VMT
13-state casualties per crash
13-state casualties per VMT

100-lb reduction in weight 1-sq ft reduction in footprint18 We suspect that one

- vehicle, non

- rollover, non

- injury crashes by pic kup trucks are under

reported in the state crash data . Two

- vehicle crashes are more likely to be reported, because two parties are involved , while r ollover and injury crashes are more likely to be reported because they tend to be more severe. If pickup t ruck owners were not reporting one

- vehicle non

- injury crashes, we would expect the crash rate per registered vehicle for pickup trucks in one

- vehicle

non

- injury crashes to be lower relative to that of other vehicles than in all crashes. Figure 2.9

compare s the number of one

- vehicle, non

- injury crashes per 10,000 vehicle registration years

, and the number of all crashes per 1,000 registration years , by vehicle type ; crash rates for each type of crash are indexed to that for midsize cars. The data are from an earlier analysis, using police

- reported crash data from only five states (Wenzel, 2011a). P ickup trucks have relatively higher crash rates in one

- vehicle non

- injury crashes (in green) than in all crashes combined (in blue), relative to the crash rates of other types of vehicles. This suggests that pickup truck owners

are not under

- reporting the type of crash least likely to be reported, one

- vehicle, non

- injury

crashes

, in the state crash databases.

We plan to expand this analysis to the crash data fro m the 13 states, and investigate further the extent to which crashes involving certain types of vehicles, or certain types of crashes, are under

reported in the state crash data.

Figure 2.9 . One

- vehicle, non

- injury crashes and all crashes per 10,000 vehicle registration

years, by vehicle type and state

Recall that the risks and crashes per VMT in Figures 2. 6 through 2. 8 use the VMT weights developed by NHTSA using national registration data and induced exposure crashes in the

thirteen states. To m ore accurately calculate fatality risk per VMT for the thirteen states, we need to obtain vehicle registration data, by calendar year, and vehicle model year and model, for 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6SportsSubcompactCompactMidsizeLargeImport LuxuryMinivanFullsize VanSmall SUVMidsize SUVLarge SUVSmall CSUVMidsize CSUVCompact PU1/2-ton PU3/4-ton PU1-ton PUCrashes per vehicle registration years(indexed to crash rate for midsize cars) Crashes per 1,000 vehicle registration-years, by state and vehicle type

All crashes

1-vehicle non-injury crashes19 the thirteen states, and develop new VMT weights to represent total VMT in the thir teen states, as opposed to the national VMT weights NHTSA used in their analysis and here.

Figure 2.5 shows the effect of mass and footprint reduction on risk per police

- reported crash; Figure 2. 10 shows their effect on fatality risk per serious crash (shown in brown), with “ serious

crashes” defined as a ny police

- reported crash that resulted in a casualty to a vehicle occupant or

non

- occupant. The figure suggests that using serious, or casualty

, cra shes, rather than all police

reported crashes, as the measure of exposure has only a slight change in the effect of mass and footprint reduction on risk.

Figure 2. 10

. Effect of mass and footprint reduction on three types of risk, by vehicle type

Figu re 2. 1 1 compares the effect of mass and footprint reduction on 13

- state casualty risk per crash, after accounting for full adoption of ESC by 2017 ( in green, from Figure 2.5) with the results from the nine regression models by crash type weighted by the current distribution of

crashes (light orange). Full penetration of ESC in the on

- road fleet (going from the light orange to green columns in the figure) results in little change in the effect of mass reduction on casualty risk per crash for cars and ligh t trucks, but slightly reduces the detrimental effect of mass

reduction on risk for CUVs and minivans (from a reduction in risk of 0.26% to a reduction of 0.13%) . On the other hand, accounting for the change in the distribution of crashes after full ESC

pe netration slightly reduces the penalty from a reduction in footprint for cars (from a 0.48% increase in risk to a 0.32% increase in risk) and CUVs/minivans (from a 0.60% increase in risk to a 0.32% increase in risk)

. U sing the anticipated distribution of crashes once all vehicles are equipped with ESC results in little change in the effect of mass and footprint reduction on casualty risk per crash.1.43%0.48%0.52%-0.40%-0.47%1.89%-0.02%1.73%-0.76%-2.44%-1.98%-1.22%0.87%1.24%0.73%-2.25%-1.02%-1.51%-1.75%-0.56%1.27%0.51%0.46%-3.77% -4.0% -3.0% -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/

minivansPercent change in risk (probability) 

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

US fatalities per VMT
13-state fatalities per crash
13-state fatalities per serious crash

100-lb reduction in weight 1-sq ft reduction in footprint20 Results from a single regression analysis across all nine crash types are also shown in Figure

2. 1 1 (in dark orange). The effects of mass and footprint re duc tion on casualty risk per crash using a single regression model are quite similar to those when the effects by crash type are weighted by the current distribution of crashes ( shown in light orange in the figure).

Figure 2

. 1 1 . Effect of mass and size reduction on 13

- state casualty risk per crash by vehicle type, across all crash types and weighted average effect in each type of crash

Figures 2

. 1 2 through 2. 1 4 show the effect of changes in mass or footprint on casualty risk per crash , by type of crash. Figure 2. 1 2 indicates that m ass reduction results in a large (4.52%) reduction in risk only in crashes with objects for heavier cars ; this large reduction in risk is somewhat offset by increases in risk in car rollover crashes (by 2.04% for lighter cars, and by 0.74% for heavier cars) . On the other hand, footprint reductions in cars result in a large increase in risk in rollovers (3.99%) and crashes with objec ts (3.56%). Because full ESC adoption is expected to substantially reduce the number of rollover and crashes with objects , and footprint reduction substantially increases casualty risk in these types of crashes, removing many of these types of crashes by 2017 wil l reduce the overall detrimental effect of footprint reduction in cars ( from 0.48% to 0.32% as shown in Figure 2. 1 1 ).

Figure 2.1 2 shows the effect of mass and footprint reductions on risk in light trucks, by type of crash. In general, although relatively small, more of the effects of mass or footprint reduction on risk tend to be statistically significant for light trucks than for cars. Mass reduction results in a relatively large (2.81%) reduction in casualty risk in rollovers for heavier ligh t

- duty trucks. 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. We see the same

beneficial effect of mass reduction in casu alty rollover risk in CUVs/minivans, in Figure 2. 130.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.05%0.32%0.17%-0.98%-0.32%-0.73%-0.26%0.48%0.08%0.60%0.14%-0.99%-0.34%-0.79%-0.33%0.55%0.10%0.55%

-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/

minivansPercent change in risk (casualty probability per crash) 

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

Wtd by 13-state post-ESC crash distribution

Wtd by 13-state current crash distribution

Single regression across all crash types

100-lb reduction in weight 1-sq ft reduction in footprint21 (7.20%). As with cars, footprint reduction increases risk in rollovers and crashes with objects (by 2.14% and 1.11%, respectively) in light

- duty trucks, and to an even greater extent (by 12.6%

and 5.6 1%, respectively) in CUVs/minivans. The large, detrimental effects of footprint reduction on casualty risk in rollovers and crashes with objects account for the decrease in the effect of footprint reduction on risk after removing many of these types of cr ashes in Figure 2. 1 1 (from a 0.08% increase in risk to a 0.05% reduction in risk for light trucks, and from a 0.60% to a 0.32% reduction in risk for CUVs/minivans).

Figure 2. 1 2 . Effect of mass and footprint reduction on 13

- state casualty risk per crash in cars, by type of crash

Light truck mass reduction leads to statistically significant reductions in casualty risks in crashes with pedestrians/cyclists, lighter light

- duty trucks, and, for the heavier light

- duty trucks, heavier cars and lig hter trucks. For light trucks, as well as CUVs/minivans, mass reduction results in statistically significant increases, while footprint reduction leads to decreases, in casualty risk in crashes with heavy

- duty trucks.

Curiously, mass reduction in CUVs/mi nivans leads to a large increase in casualty risk (2.03%) in crashes with lighter light

- duty trucks, but a large decrease in risk (4.34%) in crashes with heavier light

- duty trucks, as shown in Figure 2. 1 4 . Footprint reduction in CUVs/minivans leads to a large increase in casualty risk (5.54%) in crashes with heavier light

- duty trucks.2.04%-0.10%-0.10%0.86%0.13%-0.69%-0.15%-0.06%0.49%0.74%-4.52%-1.31%-1.41%-0.61%-1.38%-0.88%1.01%-0.76%3.99%3.56%-0.47%0.09%0.60%0.61%0.59%-0.15%-0.22%

-8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12%1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: Other1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: Other1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: OtherPercent change in risk (casualty probability per crash) 

### 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 cars22 Figure 2. 1 3 . Effect of mass and footprint reduction on 13

- state casualty risk per crash in light trucks, by type of crash

Figure 2. 1 4 . Effect of mass and footprint reduction on 13

- state casualty risk per crash in CUVs/minivans, by type of crash0.88%-0.61%-1.40%1.70%-0.15%-0.36%-1.67%0.26%-0.24%-2.81%0.09%-1.29%1.63%-0.92%-1.19%-1.44%0.64%-0.54%2.14%1.11%0.22%-1.43%-0.07%-0.19%0.92%-0.66%-0.34%

-8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12%1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: Other1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: Other1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: OtherPercent change in risk (casualty probability per crash) 

### 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-7.23%-2.02%-1.18%4.73%-0.61%-0.28%2.03%-4.34%0.27%5.61%0.09%-2.66%1.15%-0.27%-0.90%5.54%-0.80%

-8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12%1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: Other1: Rollovers2: w/object3: Ped etc.4: w/HDT5: w/lgt car6: w/hvy car7: w/lgt LT8: w/hvy LT9: OtherPercent change in risk (casualty probability per crash) 

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

100-lb reduction in weight 1-sq ft reduction in footprint23 Figures 2. 1 5 through 2. 20 compare the effect of changes in the other vehicle, driver, and crash control variables on US fatality risk per VMT , from the NTHSA 2011 analysis (in light blue)

, with their effects on 1 3

- state fatality (light red) and casualty risk (in light green) per crash. Figure 2. 1 5 indicates that w hile 2

- door cars continue to have a much higher fatality risk per crash

than 4

- door sedans, this eff ect essentially disappears in terms of casualty risk per crash. Curtain and combo side airbags have a much larger positive safety benefit in terms of fatality risks per crash than in terms of fatality risk per VMT; the beneficial effects of torso side air bags, ABS, and ESC are similar among the three types of risk, with the exception of ABS in fatality risk per

crash. The driver age and gender variables have a smaller effect on fatality risk per crash, and a much smaller effect on casualty risk per crash, than on fatality risk per VMT; this suggest that much of the driver effect on risk contributes to the occurrence of a serious crash, and not the crashworthiness of the vehicle once a crash has occurred. Nevertheless, risk increases for the

oldest drivers , even in terms of casualty risk per crash. The effects of vehicle age and brand new vehicles on the risks per crash are much smaller than on fatality risk per VMT. Likewise the effect of the calendar year variables also are smaller on risks per crash, a nd the consistent reduction in risk over time observed in fatality risk per VMT is not as strong in terms of fatality

risk per crash. The calendar year variables are examined in more detail in Section 5.3.

Figure 2. 1 5 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, passenger cars

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

have a much lower effect on risk than almost all of the control variables in Figure 2. 1 5 . For instance, a 100

- lb reduction in curb weight for an underweight car is expected to increase casualty risk per crash by only 0.15%, while installing ABS would reduce risk by 3.5%. -20% -15% -10%

-5% 0% 5% 10% 15% 20%UNDERWTOVERWTFOOTPRINTTWODOORCURTAINCOMBOTORSOABSESCM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (probability) 

### Effect of control variables on risk, passenger cars

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash24 Therefore, the regression result s suggest that, in theory, the mass of a lighter car could be reduced by as much as 2,300 lbs while adding ABS, without increasing casualty risk per crash.

The control variables in Figure 2. 1 6 have a much bigger effect on risk than the mass or footpr int reduction variables, or the control variables presented in Figure 2. 1 5 . Male drivers and driving at nighttime, on rural roads, and on roads with posted speed limits above 55 mph have a smaller effect on fatality risk per crash, and a much smaller ef fect on casualty risk per crash, than on fatality risk per VMT; in fact, once a crash occurs, male drivers have essentially the same risk of casualty as female drivers. This suggests that the detrimental effect of male drivers has to do with their higher tendency of getting into a serious crash rather than their sensitivity to injury once a serious crash has occurred. Similarly, driving at night, on rural and high

- speed roads, has a bigger effect on whether a vehicle is involved in a crash, than on how we ll a vehicle protects its occupants once a crash has occurred. Even so, these three variables do substantially increase the likelihood of casualty once a crash occurs.

As discussed above, the NHTSA regression of US fatality risk per VMT included a variab le for high

- fatality states; our regression models for fatality and casualty risk per crash include twelve variables for each state in the database. We analyze the effect of each state on fatality and casualty risk per crash in Section 5.1

Figure 2. 1 6

. Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, passenger cars

Figures 2. 1 7 and 2. 1 8 present the effect of the control variables on the three types of risk in crashes involving light trucks. SUVs have a much lower, and heavy

- duty pickups a much higher, fatality risk per crash than regular pickups, which is opposite of the effects for fata lities per VMT and casualties per crash. And while AWD provides a large safety benefit in terms of -80% -40%

0% 40% 80% 120% 160% 200% 240% 280% 320% 360% 400%UNDERWTOVERWTFOOTPRINTDRVMALENITERURALSPDLIM55HIFAT_STPercent change in risk (probability) 

### Effect of control variables on risk, passenger cars

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash25 fatalities per VMT, it results in increased risk in terms of fatalities or casualties per crash. The other variables in Figure 2. 1 7 have similar effects for the three types of risk, with the exception of three of the four female driver age variables, that show lower fatality risk per crash than for a

50

- year old female.

Figure 2. 1 9 indicates that minivans have a slightly lower fatality risk per VMT, a slightly higher fatality risk per crash, and a substantially higher casualty risk per crash, than CUVs. The three side airbag variables show inconsistent effects across the three types of risk for CUVs and

minivans

. ABS and ESC tend to provide a higher safety benefit in terms of fatality risk per crash, and a lower benefit in terms of casualty risk per crash. AWD provides a large safety benefit in terms of fatality risk per VMT, and a smaller benefit in terms of casualty risk per crash, but increases fa tality risk per crash. As with cars and light trucks, the increased risk for elderly drivers in CUVs/minivans is smaller in terms of casualty risk per crash than in terms of fatality risk per VMT or per crash, while the effects of vehicle age and brand ne w vehicles on casualty risk per crash is negligible.

Figure 2. 1 7 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, light trucks

-20% -15% -10%

-5% 0% 5% 10% 15% 20%UNDERWTOVERWTFOOTPRINTSUVHD_PKPBLOCKER1BLOCKER2ESCAWDM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (probability) 

### Effect of control variables on risk, light trucks

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash26 Figure 2. 1 8 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, light trucks

Figure 2. 1 9 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, CUVs and minivans -80% -40%

0% 40% 80% 120% 160% 200% 240% 280% 320% 360% 400%UNDERWTOVERWTFOOTPRINTDRVMALENITERURALSPDLIM55HIFAT_STPercent change in risk (probability) 

### Effect of control variables on risk, light trucks

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash

-20% -15% -10%

-5% 0% 5% 10% 15% 20%CURBWTFOOTPRINTMINIVANCURTAINCOMBOTORSOABSESCAWDM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (probability) 

### Effect of control variables on risk, CUVs and minivans

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash27 Figure 2. 20 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state casualty risk per crash, CUVs and minivans

The effects of male drivers and crash characteristics on risks per crash for light trucks (Figure

2. 1 8 ) and CUVs/minivans (Figure 2. 20 ) are quite similar to those for cars (Figure 2. 1 6 ). For all vehicle types, most of the vehicle, driver, and crash location variables shown in Figures 2. 15 through 2. 20 have a much greater effect on the three types of risk than reductions in vehicle mass or footprint.

Figure 2. 2 1 shows the effect of each of the state control variables on casualty risk per crash, relative to the risks in Florida, by vehicle type. Note that in Figure 2. 2 1 the model predicts a 10% to 25% lower casualty risk in Al abama than in Florida, while Figure 2.3 above indicates that Alabama has a roughly 25% higher casualty risk per crash than Florida. This discrepancy may be explained by the regression model also accounting for where crashes occurred in each state:

over ha lf of all police

- reported crashes in Alabama occurred on roads in rural areas, which tend to have higher risks than crashes in urban areas, whereas only 15% of all crashes in Florida were in rural areas. After accounting for the greater amount of driving in dangerous rural areas in Alabama, the regression model indicates that driving in Alabama is actually 10% to 25% safer, depending on the type of vehicle, than driving in Florida. Similarly, the regression model predicts that a vehicle has a 75% lower cas ualty risk per crash in Wyoming than in Florida, while Figure 2.3 above indicates that the casualty risk per crash in Wyoming is only about 40% lower than that in Florida. All driving in Wyoming is in rural areas. -80% -40%

0% 40% 80% 120% 160% 200% 240% 280% 320% 360% 400%CURBWTFOOTPRINTDRVMALENITERURALSPDLIM55HIFAT_STPercent change in risk (probability) 

### Effect of control variables on risk, CUVs and minivans

US fatalities per VMT

13-state fatalities per crash

13-state casualties per crash28 Figure 2. 2 1

. Effect of state in which the crash occurred on 13

- state casualty risk per crash, by vehicle type

Figures 2. 2 2 through 2. 2 4 show the effect of the vehicle, driver, and calendar year control variables on the two components of fatality risk per VMT ( shown in dark red) , crash frequency (shown in orange) and fatality risk per crash (shown in light red). ABS and ESC in cars, AWD in light trucks and CUVs/minivans, and drivers aged 14 to 30 have a larger effect on crash frequency than risk per crash, whil e t wo

- door cars, SUVs, and heavy

- duty pickups, the side airbag variables in cars, and middle

- aged drivers have a smaller effect on crash frequency than risk given a crash. Surprisingly, for CUVs/minivans, the side airbag variables reduce crash frequency b ut have little effect on fatality risk per crash, ABS has the same benefit in reducing crash frequency as fatality risk, and ESC has essentially no effect on crash frequency but a large benefit in reducing fatality risk. For light trucks, ESC has about th e same benefit in reducing crash frequency as in reducing fatality risk given a crash. The control variables for CY2002 and CY2003 show a large reduction in crash frequency, but a large increase in risk given a crash, for all three types of vehicles.

F igures 2. 2 5 and 2. 2 6 show the effect of driver gender and the crash time and location, and the state control variables, on the two components of risk, crash frequency and fatality risk given a crash. The data are shown for passenger cars only, but t he results are very similar for all three vehicle types. Male drivers have essentially no effect on crash frequency, but cause a statistically significant increase in fatality risk once a crash occurs, which is surprising. The age/gender variables in Fig ures 2. 22 through 2. 2 4 probably account for the risky behavior typical of male drivers; however, one would expect that, given a crash, males would have a lower fatality risk than females, as females are thought to be more susceptible to fatality or serious injury than males. The crash and location variables, driving at night on high

- speed rural roads, have a much lower effect on crash frequency than on fatality risk given a crash. This also is somewhat surprising, as crash risk is thought to be grea ter under these three driving conditions, -100%

-90% -80% -70% -60% -50% -40% -30% -20% -10%

0%

AL GA IL KS KY MD MI MO NE NJ NM PA WA WI WYPercent change in risk (probability of casualty per crash) 

### Effect of state control variables on risk, relative to casualty risk per crash in Florida, by vehicle type

Cars

Light trucks

CUVs/minivans29 but apparently the severity of the crashes that occur under these conditions is greater than their effect on crash frequency.

Figure 2. 2 2 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars

-30% -25% -20% -15% -10%

-5% 0% 5% 10% 15% 20%UNDERWTOVERWTFOOTPRINTTWODOORCURTAINCOMBOTORSOABSESCM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change 

### Effect of control variables on risk, passenger cars

13-state crashes per VMT
13-state fatalities per crash

13-state fatalities per VMT30 Figure 2. 2 3 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state crashes per VMT, fatality risk per crash, and fatality ris k per VMT, light trucks

Figure 2. 2 4 . Effect of mass and footprint reduction, and selected control variables

, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, CUVs/minivans -40% -30% -20% -10%

0% 10% 20% 30% 40% 50%UNDERWTOVERWTFOOTPRINTSUVHD_PKPBLOCKER1BLOCKER2ESCAWDM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change 

### Effect of control variables on risk, light trucks

13-state crashes per VMT
13-state fatalities per crash

13-state fatalities per VMT

-35% -25% -15%

-5% 5% 15% 25%CURBWTFOOTPRINTMINIVANCURTAINCOMBOTORSOABSESCAWDM14_30M30_50M50_70M70_96F14_30F30_50F50_70F70_96VEHAGEBRANDNEWCY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (probability) 

### Effect of control variables on risk, CUVs and minivans

13-state crashes per VMT
13-state fatalities per crash

13-state fatalities per VMT31 Figure 2. 2 5 . Effect of mass and footprint reduction, and driver gender and crash

time/location control variables

, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars

Figure 2. 2 6 . Effect of mass and footprint reduction, and state control variables

, on 13

- state crashes per VMT, fatality risk per crash, and fatality risk per VMT, passenger cars -80% -40%

0% 40% 80% 120% 160% 200% 240% 280% 320% 360% 400%UNDERWTOVERWTFOOTPRINTDRVMALENITERURALSPDLIM55HIFAT_STPercent change 

### Effect of control variables on risk, passenger cars

13-state crashes per VMT
13-state fatalities per crash

13-state fatalities per VMT

-100%

-60% -20% 20% 60% 100% 140% 180% 220%ALKSKYMDMIMONENJPAWAWIWYPercent change 

### Effect of control variables on risk, passenger cars

13-state crashes per VMT
13-state fatalities per crash

13-state fatalities per VMT32 3. Multi

- collinearity between vehicle mass and footprint

In its 2003 and 2010 analysis NHTSA resisted including vehicle mass an d size (in that case, wheelbase and track width) in the same regression model, because the two variables were strongly correlated with each other. Including two or more highly

- correlated variables in the same regression model can lead to biased or incorrec t results. However, in its 2011 analysis, NHTSA does include vehicle mass and footprint in the same regression models. Figure 3.1 shows the correlation between curb weight and footprint by vehicle model; only the most popular 275 models, with at least 10 billion VMT or 100 fatalities in the US fatality database NHTSA used for its 2011 analysis , 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 highl y correlated for cars (R 2 of 0.87) than for light trucks (R 2 of 0.65) or CUVs/minivans (R 2 of 0.60). Figure 3.2 shows the same data as Figure 3.1 , but uses six vehicle types. Here the correlation ranges from over 0.80 for 4

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

- door cars, to only 0.26 for minivans. The correlation of 0.65 for all light trucks (pickups and SUVs)

combined i n Figure 3.1 is improved when these two types of trucks are analyzed separately in

Figure 3.1

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

Figure 3.2: 0.86 for pickups and 0.81 for SUVs in Figure 3.2. On the other hand, separating CUVs from minivans improves the correlation between curb weight and footprint for CUVs (0.66) but not for minivans (0.26). The correlation is so poor for minivans in part because of the Kia Sedona, which has a much higher weight (4,730 lbs) for its footprint (51.3 sq ft) than other minivans; removing this model improves the correlation for minivans to 0.78. 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 7600Footprint (wheelbase x track width, sq ft)

Curb weight (lbs) Average footprint and curb weight, by vehicle type and model

Cars

Light trucks

CUVs and Minivans33 Figure 3.2

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

Figu re 3.3 compares the results from the reg ression models for 13

- state casualty risk per crash , in light green from Figure 2. 5 , with two alternative model specifications to test the sensitivity of the results. The first sensitivity, in dark purple, includes the weight variables in the regression model but excludes the footprint variable; this model tests the effect of mass reduction while allowing footprint to vary with vehicle mass. This sensitivity slightly increases the risk from a 100

- lb

mass reduction i n lighter cars (from 0.15 % to 0.36 % ) and CUVs/minivans (from a 0.1 3 %

decrease in risk to a 0.0 2 % increase in risk )

, and reduces the beneficial effect of mass reduction in heavier cars ( from a 0. 91 % to a 0.67 % reduction in risk ); however, there is essential ly no change in casualty risk per crash in light

- duty trucks. The effects of mass reduction when footprint is allowed to vary with mass for casualty risk per crash are quite different from the

effects for fatality risk per VMT. In an earlier analysis (Fi gure 3.3, LBNL, 2011a) , we showed

that mass reduction result s in much larger increases in fatality risk per VMT when footprint is allowed to vary with mass, than when footprint is held constant, at least for cars and CUVs/minivans. Here, with risk measure d as casualty risk per crash, mass reductions result in only slightly larger increases in risk when footprint is allowed to vary with mass (shown in dark purple), than when footprint is held constant (shown in green). R ! = 0.65

R ! = 0.89

R ! = 0.86

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 7600Footprint (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

Pickups

SUVs

CUVs

Minivans34 Figure 3.3 . Effect of reduction in ma ss or footprint on 13

- state casualty risk per crash

, by vehicle type: holding footprint constant, allowing footprint to vary with mass, and allowing mass to vary with footprint

The second sensitivity keeps footprint in the regression model, but removes mass, and is shown in light purple in Figure 3.3. Allowing vehicle mass to be reduced with footprint reduces the effect of footprint reduction on casualty risk per crash in all three vehicle types: from a 0.32%

increase to a 0.05% decrease in risk for car s, from a 0.04% decrease to a 0.44% decrease in risk for light trucks, and from a 0.33% increase to a 0.20% increase for CUVs/minivans. Figure 3.3 suggests that including both mass and footprint in the same regression model results in only slight changes i n the effect of mass and footprint reductions on casualty risk per crash.

In its 2011 analysis NHTSA examined the relationship between curb weight and fatality risk for deciles of vehicles with roughly the same footprint. Figure 3. 4 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 cur b 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 posit ive; that is, where a mass reduction would be harmful and increase fatality risk.0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%0.36%-0.67%-0.41%-0.72%0.02%-0.05%-0.44%0.20%

-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/

minivansPercent change in risk (casualty probability per crash) 

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

13-state casualties per crash
Effect of mass reduction, allowing footprint to vary with mass
Effect of footprint reduction, allowing mass to vary with footprint

100-lb reduction in weight 1-sq ft reduction in footprint35 Figure 3. 4

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

We replicate this analysis for 13

- state casualty risk in Table 3.1 , which includes the numb er of footprint deciles in which the coefficient on vehicle mass is statistically significant. There are four columns for each vehicle type in Table 3.1; the first two indicate the number of footprint

deciles in which a reduction in vehicle mass increases casualty risk per crash , 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 d eciles in which a reduction in vehicle mass reduces casualty risk per crash

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

reduction increases casualty risk in six footprint deciles, but only one of those in creases is statistically significant. On the other hand

, light truck mass reduction reduces casualty risk in

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

3.1 indicates that mass reduction does not consistently result in increased casualty risk for vehicles with similar footprint. Mass reduction increases casualty risk in a majority of footprint deciles for 10 of the 27 crash and vehicle combinations, but few of these increases are

statist ically significant. On the other hand, mass reduction reduces risk in a majority of footprint deciles for 14 of the 27 crash and vehicle combinations, although few of these reductions are statistically significant. 1600 2600 3600 4600 5600 6600 7600Footprint decile

Curb weight (lbs) Range in curb weight for footprint deciles, by vehicle type

Cars

Light trucks

CUVs/minivans36 Table 3.1. Number of footprint decile s in which mass reduction increases or decreases 13

- state casualty risk per crash , by vehicle and crash type

Crash type Cars Light trucks CUVs/MinivansNumber of deciles withincreasingriskNumber of decileswithestimates that are statisticallysignificantNumber of deciles withdecreasingriskNumber of deciles withestimates that are statisticallysignificantNumber of deciles withincreasingriskNumber of deciles withestimates that are statisticallysignificantNumber of deciles withdecreasingriskNumber of deciles withestimates that are statisticallysignificantNumber of deciles withincreasingriskNumber of deciles withestimates that are statisticallysignificantNumber of deciles withdecreasingriskNumber of deciles withestimatesthat are statistically significant

1: Rollovers 6 1 4 1 5 4 5 2 4 0 6 2 2: w/object 2 1 8 1 6 2 4 0 5 0 5 0 3: w/ped etc. 3 0 7 1 2 0 8 1 4 1 6 2

4: w/HDT 3 2 7 1 6 2 4 0 6 1 4 0 5: w/lgt car 7 1 3 2 6 3 4 2 5 0 5 1 6: w/hvy car 4 1 6 3 4 1 6 4 7 1 3 0 7: w/lgt LT 4 2 6 2 1 0 9 2 7 0 3 0 8: w/hvy LT 4 2 6 2 8 2 2 1 3 2 7 2

9: Other 2 1 8 4 3 2 7 2 6 3 4 2

The data in Table 3.1 give no information on the size of the effect of mass reduction on casualty

risk per crash in th e footprint deciles. Figures 3. 5 through 3. 7 show the percent change in casualty 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. 5

indicates that mass reduction increases casualty risk in car rollover crashes by about 15% in deciles nine and ten, but reduces casualty risk by nearly 35% in decile eight . Figure 3. 7 indicates

that mass reduction in CUVs/minivan s reduces casualty risk in rollovers in six of the footprint deciles, and that these reductions are quite large (over 4 0 %), and statistically significant, for footprint deciles four, nine and ten. Figures 3. 5 through 3. 7 suggest that there are no cons istent

trends in the effect of mass reduction on risk when vehicles ar e grouped by footprint decile.37 Figure 3. 5 . Effect of car mass reduction on 13

- state casualty risk per crash , by footprint decile and crash type

Figure 3. 6 . Effect of light truck ma ss reduction on 13

- state casualty risk per crash , by footprint decile and crash type -35% -25% -15%

-5% 5% 15%

0 1 2 3 4 5 6 7 8 9 10Percent change in casualty risk per crashfrom 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 10Percent change in casualty risk per crashfrom 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 LT38 Figure 3. 7 . Effect of CUV/minivan mass reduction on 13

- state casualty risk per crash , by footprint decile and crash type

4. Casualty risk by vehicle model

In this section we examine the variance in societal casualty risk by average vehicle mass and by vehicle model, both before and after accounting for the vehicle, driver and crash variables NHTSA includes in its regression models of US fatality risk per VMT . Figure 4.1 plots unadjusted 13

- state casualty risk per crash against average curb weight, with vehicles grouped

into 100

- lb increments of vehicle curb wei ght. Figure 4.1 indicates that casualty risk per crash

decrease s slightly as curb weight increases for light trucks less than 5,900 lbs

, and increases with increasing mass for light trucks greater than 5,900 lbs; there is no trend between car mass and casualty risk. T here also is a large degree of variability for cars, the lighter light trucks

, and CUVs/minivans, as indicated by the R 2 values below 0. 2 0.

Figures 4.2 through 4.4 show the relationship between unadjusted casualty risk per crash and mass by more detailed vehicle type. Figure 4.2 indicates that the relationship between curb weight and casualty risk per crash is weaker for 4

- door cars than for 2

- door cars, with risk actually increasing with increasing mass for 4

- door cars between 3,600 and 4,200 lbs . Figure 4.3 suggests that for large pickups (3/4

- and 1

- ton pickups) casualty risk increases as curb weight increases. And Figure 4.4 indicates that the relationship between casualty risk and curb weight is
strong

. with a dramatic reduction in cas ualty risk as mass increases and a relatively high degree

of correlation , for minivans, and CUVs when they are divided into those below and above 3,600

lbs

. -65% -45% -25%

-5% 15% 35%

0 1 2 3 4 5 6 7 8 9 10Percent change in casualty risk per crashfrom 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 LT39 Figure 4.1

. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight

, by vehicle type

Figure 4.2

. Relationship bet ween 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight

, passenger cars R ! = 0.00

R ! = 0.09

R ! = 0.51

R ! = 0.14

0 20 40 60 80 100 120

20 24 28 32 36 40 44 48 52 56 60 64 68 7213-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs/100) Casualty risk per crash, by curb weight andvehicle type

Cars

Light trucks (<5900 lbs)

Light trucks (>5900 lbs)

CUVs and Minivans

R ! = 0.34

R ! = 0.00

0 20 40 60 80 100 120

20 24 28 32 36 40 44 48 52 56 60 64 68 7213-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs/100) Fatality risk per curb weight, passenger cars

2-dr cars

4-dr cars40 Figure 4.3

. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increm ents of curb weight

, light trucks

Figure 4.4

. Relationship between 13

- state casualty risk per crash and curb weight, with vehicles grouped into 100

- lb increments of curb weight

, CUVs and minivans R ! = 0.04

R ! = 0.01

R ! = 0.13

0 20 40 60 80 100 120

20 24 28 32 36 40 44 48 52 56 60 64 68 7213-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs/100) Fatality risk per curb weight, light trucks

Small pickups

Large pickups

Truck-based SUVs

R ! = 0.89

R ! = 0.48

R ! = 0.95
0 20 40 60 80 100 120

20 24 28 32 36 40 44 48 52 56 60 64 68 7213-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs/100) Fatality risk per curb weight, CUVs and minivans

Crossover SUVs (<3600 lbs)

Crossover SUVs (>3600 lbs)

Minivans41 Figure 4.5 shows the danger of using the average risks of groups of individual models, such as by curb weight bins. The filled symbols in the figure represent the relationship between minivan casualty risk and curb weight when the data are grouped into bins of curb weight, from Figure 4.4; this analysis shows a high correlation between risk and curb weight. However, if the relationship between risk and curb weight of the 13 most popular individual minivan models, which represent over 90% of the minivan casualties and crashes, are plotted, the correlation is

m uch lower, with the R 2 dropping to 0.38. 7

Figure 4.5

. Relationship between 13

- state casualty risk per crash and curb weight for minivans, by average risk for 100

- lb increments of curb weight and individual models

It is possible that the relationship bet ween vehicle mass and casualty risk is stronger in certain types of crashes. Figure 4. 6 presents the relationship between 100

- lb increments in curb weight and casualty risk in one

- vehicle crashes with a stationary object, the type of crash in which

vehicl e mass is thought to provide occupants the most protection. For cars and CUVs/minivans, casualty risk in crashes with stationary objects does decline as mass increases; the strength of this relationship is greater in crashes with stationary objects than i n al l types of crashes (Figure 4.1). I n the case of light trucks, the re is no relationship or correlation between casualty risk in crashes with stationary objects and mass

.

Note that, for a given vehicle weight, light trucks have a higher casualty risk per crash in crashes with stationary objects than cars or CUVs/ minivans. Since there are no crash partner casualties in crashes with stationary objects, we suspect that light trucks have a higher risk than cars in Figure

7 Figure 4.5 actually excludes one addition al popular minivan model, which has the highest casualty risk (67 per 1,000 crashes) and weight (4,741 lbs) of the minivan models shown; including this model would make the correlation between casualty risk and curb weight even lower. R ! = 0.95

R ! = 0.38

0 10 20 30 40 50 60 70 80

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 720013-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs) Casualty risk per crash, minivans

Curb weight bins

Individual models42 4. 6 because of their tendency to r oll over, their increased use on more dangerous rural roads, and perhaps more passenger casualties in light trucks than in cars.

Figure 4. 6

. Relationship between 13

- state casualty risk per crash in crashes with stationary objects and curb weight

, by vehicle type

Figures 4.1 through 4. 6 show that grouping vehicles into 100

- l b mass increments indicates that, for some vehicle types, casualty risk per crash decreases as mass increases. Figure 4. 7 shows the relationship between vehicle mass and casualty risk by individual vehicle model. Only 275 models with at least 10 billion VMT, or at least 100 fatalities, based on the NHTSA 2011 analysis, are included (106 car models, 131 light truck models, and 38 CUV/minivan models); these models represent over 90% of all casualties and crashes . Here we see that casualty risk

declines slightly with increasing mass for cars, light trucks, and CUVs / minivans

.

A lthough casualty risk gradually declines with increasing mass , the extremely low R 2 values (all

le ss than 0.10) indicate that there is essentially no relationship between mass and casualty risk per crash, for any of the three types of vehicles, and that there is a very large range in casualty risk for individual vehicle models at a given weight. For e xample, the vehicle model labeled A in the figure , which weighs 2, 579 lbs, has a casualty risk of 86 per 1,000 crashes , while model B

, which weighs slightly less (2, 487 lbs)

, has a casualty risk of only 46

.

Of course, differences in vehicles (footprint, t wo

- 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

- casualty

risk st ates) by vehicle model may explain some of the large range in casualty risk by vehicle R ! = 0.35

R ! = 0.00

R ! = 0.49

0 1 2 3 4 5 6 7

20 24 28 32 36 40 44 48 52 56 60 64 68 7213-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs/100) Fatality risk in crashes with objects per curb weight, by vehicle type

Cars

Light trucks

CUVs and Minivans43 Figure 4. 7

. 13

- state casualty risk per crash and curb weight, by vehicle model

weight. To account for these various variables, we reran the 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 casualty

risk per crash for each vehicle in the 13

- state crash database. We 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 number of casualt ies per vehicle, and

then summed across vehicle make and model. W e then divided the total number of predicted fatalities in each make and model by the number of crashes for that make and model , to obtain predicted risk, the number of predicted casualties per poli ce

- reported crash . We exclude footprint as well as mass in the predicted risks we calculate from the regression coefficient s, as the two vehicle attributes are moderately correlated.

While Figure 4. 7 shows the unadjusted casualty risks by vehicle mode s, Figure 4. 8 shows the risks predicted by the regression model coefficients for all control variables except vehicle mass and footprint. Figure 4. 8 indicates that, after controlling for the all of the driver, crash, and

vehicle variables used in the logisti c regressio n model except vehicle mass and footprint, there still is a large range in casualty risk per crash across vehicle models of similar weight, for all three vehicle types. Figure 4. 9 shows the remaining residual, or unexplaine d, casualty risks aft er accounting for all variables except vehicle mass and footprint. In essence Figure 4.9 shows that there is no relationship between vehicle mass and the remaining, unexplained casualty risk after accounting for driver, crash and all other vehicle attribu tes. R ! = 0.02

R ! = 0.00

R ! = 0.08

0 20 40 60 80 100 120 140 160

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 720013-state casualty risk (casualties per 1,000 crashes)

Curb weight (lbs) Unadjusted US fatality risk per VMT and curb weight, by vehicle type

Cars

Light trucks

CUVs and Minivans A

B44 Figure 4. 8

. Predicted 13

- state casualty risk per crash after accounting for all driver, crash, and vehicle variables except mass and footprint, vs. curb weight

Figure 4. 9 . Residual 13

- state casualty risk per crash after accounting for all driver, crash, and vehicle variables except mass and footprint, vs. curb weight R ! = 0.02

R ! = 0.00

R ! = 0.11

0 20 40 60 80 100 120 140 160

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200Predicted 13-state risk (casualties per 1,000 crashes)

Curb weight (lbs) Casualty risk per crash vs. curb weight, after accounting for all driver, crash, and vehicle variables except weight and footprint

Cars

Light trucks

CUVs and Minivans

R ! = 0.00

R ! = 0.00

R ! = 0.08

-40 -30 -20 -10

0 10 20 30

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200Residual 13-state risk (casualties per 1,000 crashes)

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 Minivans45 Table 4.1 summarizes the relationships between predicted and residual casualty risk and vehicle curb weight that are presented in Figures 4. 7 through 4. 9 . In addition to the correlation between casualty risk and curb weight, the table shows the slope of the linear regression line drawn through the risks by vehicle model, which represents the percent change in casualty risk per 100

- pound reduction in mass or 1

- square foot reduction in footprint. The relationship for the three vehicle types is shown at the top of the table, followed by those for the seven detailed vehicle subtypes (with small, i.e. compact and "

- ton, pickups shown separately from heavy

- duty , i.e. !

- and 1

- ton, pickups), and finally the five vehicle type and weight groups NHTSA used in its regression analyses. Cases where there is a positive relationship between casualty risk and

vehicle mass reduction , i.e. where mass reduction leads to red uction in casualty risk , are shown

in red in the table ; there are no c ases where the correlation between risk and weight by vehicle

model exceeds 0.30.

Table 4.1 indicates that casualty risk consistently increases as mass is reduced for almost all

vehicl e types, although the correlation between casualty risk and weight is negligible in almost

all cases

. The exception is CUVs, where casualty risk increases by 1.6% per 100

- lbs of mass reduction, with the correlation between actual and predicted casualty ri sk and mass is moderate,

with an R 2 of over 0. 21 . Table 4.2 presents the same information as Table 4.1, but for the relationship between casualty risk and vehicle footprint reduction, with the similar results: casualty risk increases slightly as footprint is reduced, with negligible correlation between casualty risk and footprint reduction.

Table 4.1 . Rel ationship between actual, predicted, and residual 13

- state casualty risk per crash , and vehicle mas s reduction, after accounting for all driver, crash, and vehicle variables except mass and footprint, by vehicle type and model

Vehicle type Actual 13

- state casualty risk per crash Predicted risk Residual risk

R 2 Slope R 2 Slope R 2 Slope

Cars 0.02 0.3% 0.02 0.2% 0.00 0.0% Light trucks 0.00 0.1% 0.00 0.1% 0.00 0.0%

CUVs/minivans 0.08 0.9% 0.11 1.2% 0.08

- 0.2%

2

- dr cars 0.02 0.3% 0.02 0.2% 0.00 0.0%

4

- dr cars 0.02 0.3% 0.02 0.2% 0.00 0.1% Small pickups 0.00 0.1% 0.03 0.6% 0.26

- 0.5% Heavy

- duty pickups 0.00 0.1% 0.03 0.9% 0.11

- 0.8%

SUVs 0.00 0.2% 0.00 0.3% 0.01

- 0.1%

CUVs 0.21 1.6% 0.22 1.9% 0.08

- 0.3%

Minivans 0.03

- 0.4% 0.05

- 0.4% 0.00 0.0%

Cars < 3106 0.07 1.0% 0.04 0.6% 0.03 0.4%

Cars > 3106 0.06

- 0.8% 0.04

- 0.6% 0.03

- 0.3%

LTs < 4594 0.00 0.4% 0.01 0.6% 0.02

- 0.2%

LTs > 4594 0.01 0.6% 0.02 0.7% 0.00

- 0.1%

CUVs/ minivans 0.08 0.9% 0.11 1.2% 0.08

- 0.2%46 Table 4. 2 . Rel ationship between actual, predicted, and residual 13

- state casualty risk per crash, and vehicle footprint reduction

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

Vehicle type Actual 13

- state casualty risk per crash Predicted risk Residual risk

R 2 Slope R 2 Slope R 2 Slope

Cars 0.02 0.3% 0.02 0.3% 0.00 0.1% Light trucks 0.01

- 0.3% 0.01

- 0.3% 0.01 0.1%

CUVs/minivans 0.00 0.0% 0.01 0.3% 0.07

- 0.3%

2

- dr cars 0.03 0.4% 0.00 0.1% 0.03 0.4%

4

- dr cars 0.02 0.3% 0.02 0.3% 0.00 0.0% Small pickups 0.03

- 0.5% 0.00

- 0.1% 0.17

- 0.4% Heavy

- duty pickups 0.15

- 1.7% 0.12

- 2.0% 0.01 0.3%

SUVs 0.01

- 0.6% 0.01

- 0.5% 0.02

- 0.2%

CUVs 0.04 1.0% 0.05 1.3% 0.06

- 0.3%

Minivans 0.14 1.2% 0.28 1.2% 0.00 0.0%

Cars < 3106 0.10 1.4% 0.02 0.5% 0.11 0.9%

Cars > 3106 0.05

- 0.8% 0.02

- 0.4% 0.07

- 0.4%

LTs < 4594 0.01

- 0.5% 0.01

- 0.6% 0.00 0.1%

LTs > 4594 0.01

- 0.3% 0.01

- 0.5% 0.02 0.2%

CUVs/ minivans 0.00 0.0% 0.01 0.3% 0.07

- 0.3%47 5. Sensitivity of 13

- state casualty risk per crash results to data used and model specification

In this section we examine the sensitivity of our results on the effect of mass and footprint on 13

- state casualty risk per crash . We examine the effect of different methods of accounting for the

sta te in which the crash occurred, and how the effect of ma ss or footprint reduction changes after accounting for vehicle manufacturer, after excluding the calendar year control variables, and

after adding additional data to the analysis.

5.1. State control variables

As discussed above, we included 12 control variables to account for the different reporting requirements in each state, as well as other differences in risk per crash among states. Figure 5.1 compares the effect of this approach with two other approaches: not accounting for state at all in

the ana lysis, and using only two control variables, HIINJ and LOINJ, to identify states with relatively high (Alabama, Florida, Maryland, and Wyoming,) and relatively low (Michigan, New Jersey, and Washington) casualty risk per crash (see Figure 2.3 , above )

. Figure 5.1 indicates that mass reduction has a much larger beneficial effect, and footprint reduction a much more detrimental effect in cars and CUVs/minivans, when one excludes the state in which the crash occurred from the regression models (shown in blu e). Including only the two variables to control

for state , HIINJ and LOINJ, substantially reduces the effect of mass and footprint reduction on casualty risk per crash (shown in red) ; including the 12 control variables for individual states

reduces the ef fect a little bit more (shown in orange)

.

Figure 5.1

. Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, under three methods of controlling for the state in which the crash occurred-0.78%-1.91%-0.46%-1.85%-1.95%1.78%-0.84%3.58%-0.06%-1.32%-0.44%-0.79%-0.43%0.55%-0.09%0.46%0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%

-3% -2% -1% 0% 1% 2% 3% 4% 5%

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

minivans Cars LTs CUVs/

minivansPercent change in risk (casualty probability) 

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

No state variables

Including HIINJ and LOINJ

Including 12 state variables

100-lb reduction in weight 1-sq ft reduction in footprint48 5.2. Alternative measure of exposure

Figure 5.2 compares the effect of mass and footprint reduction on the risk of a casualty crash, rather than the risk of all casualties that occurred in the crash. In other words, the casualty crash cases are not weighted by the total number of c asualties, either in the case vehicle or in its crash partner. 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 inde pendent observation, and may serve to increase any under

- estimation of the uncertainty around the parameter estimates. As shown in Figure 5.2, this alternative measure of risk, the risk of a casualty crash per all police

- reported crashes (shown in dark or ange) slightly increases the detrimental effect of mass reduction on risk in cars and light trucks, but slightly decreases the effects on risk in CUVs/minivans. The risk of casualty crash per police

- reported crash results in larger increases in the detrim ental effect of footprint reduction in all three types

of vehicles.

Figure 5.2

. Effect of mass and footprint reduction on 13

- state casualties and casualty crashes per crash, by vehicle type

5.3. Vehicle manufacturer

The analysis by vehicle model in Section 4 indicates that the variables included in the regression models account for only 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 variabil ity in risk. We tested that assumption by adding 18 dummy variables based on the vehicle nameplate manufacturer. GM brands (Buick, Cadillac, Chevrolet, GMC, Oldsmobile, Pontiac, and Saturn) are treated as the default value, since combined they represent t he most vehicles by0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%0.24%-0.85%-0.32%-0.67%-0.25%0.55%0.19%0.74%

-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/

minivansPercent change in risk (casualty probability per crash) 

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

13-state casualties per crash

13-state casualty crashes per crash

100-lb reduction in weight 1-sq ft reduction in footprint49 manufacturer, in terms of both casualties and police

- reported crashes. The six Chrysler brands (Jeep, Chrysler, Dodge, Plymouth, AM General and Sprinter) and the three Ford brands (Ford, Lincoln, Mercury) were combined in a single Chry sler and Ford category. The luxury brands of Toyota, Honda, and Nissan (Lexus, Acura, and Infiniti, respectively) were treated as separate

manufacturers. A small number of low

- volume manufacturers were grouped into a separate Other manufacturer category.

Figure 5. 3 compares the effect of adding variables for each of the 18 manufacturers (shown in red) to the regression model s estimating casualty risk per crash from the thirteen states in Figure

2.5 above (shown in light green ). A ccounting for vehicle manufacturer results in a large increase

in the harmful effect of mass reduction for cars, but increases the beneficial effect of mass reduction in light trucks and CUVs/minivans. Accounting for vehicle manufacturer has the opposite result on the effect of footprint reduction on risk: the effect of footprint reduction becomes beneficial for cars, but more detrimental for light trucks and strongly detrimental

CUVs/minivans.

Figure 5. 3 . Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, after controlling for vehicle manufacturer

5. 4 . Calendar year variables

One interesting effect of the regression models is the reduction in casualty risk per crash over time, as indicated by the coeffi cients for the calendar year control variables. The reduction is

consistent , and of roughly the same magnitude, for each vehicle type, 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 control0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%1.13%0.55%-0.54%-0.76%-0.58%-0.37%0.22%1.35%

-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/

minivansPercent change in risk (casualty probability per crash) 

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

13-state casualties per crash

Including vehicle make

100-lb reduction in weight 1-sq ft reduction in footprint50 variables in the regression models. In its 2011 report 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 shows the effect on light truck casualty risk per crash , by type of crash, ov er time; for the most part, these results are consistent with fatality risk per VMT over time, reported in LBNL 2011b. The figure indicates that the effect of the calendar year variables on light truck risk is

strong for crashes with light cars and lighte r 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 decreases over time in light

truck ris k in crashes with heavy

- duty trucks, and other (mostly multi

- vehicle) crashes , as well as in crashes with heavier cars, although the effect is not as large . NHTSA suspects that the risk associated with light trucks involved in crashes with heavy

- duty trucks has decreased over time because heavy

- duty truck activity decreases as the economy has falter ed . The economic recession in 2008 may have reduced the nu mber of heavy

- duty trucks traveling roadways, and thus available as potential crash partners with light

- duty vehicles.

Figure 5. 4 . Effect of calendar year variables on 13

- state casualty risk per crash, by vehicle
type

-10% -5% 0% 5% 10% 15% 20% 25% 30%CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (casualtyy probability per crash) Effect of control variables on risk, CUVs and minivans

Cars Light Trucks CUVs/Minivans51 Figure 5. 5 . Effect of calendar year variables on light truck risk , by crash type

Note that there is no consistent trend in light truck casualty risk per crash in rollover crashes; NHTSA believes that the decline in light truck rollover fatality risk per VMT over time (reported

in LBN L 2011 b ) may be the result of manufacturers increasing static stability factor or other aspects of light truck design to reduce their likelihood to rollover. These changes would not be expected to reduce risk of casualty (or fatality) in light trucks once a rollover has occurred, and Figure 5. 5 does not show a consistent reduction in rollover risk per crash.

Another difference is that light truck casualty risk per crash consistently increases over time in crashes with heavier light

- duty trucks , wherea s the earlier analysis (of the effect of calendar year on US fatality risk per VMT) showed no consistent increase over time in fatality risk in crashes with heavier light

- duty trucks. Figure 5. 6 indicates that this is a consistent trend across all three types of case vehicles. We have no explanation for why casualty risks per crash are dramatically lower in crashes with heavier light

- duty trucks than in crashes with other types of light

- duty vehicles, or why risks in these types of crashes appear to be increasing over time. -40% -30% -20% -10%

0% 10% 20% 30% 40%CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007CY2002CY2003CY2004CY2005CY2007Percent change in risk (casualty probability per crash) 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 Other52 Figure 5. 6 . Effect of calendar year variables on crashes with heavier light

- duty trucks, by case vehicle type

Figures 5

. 7 through 5. 10 show the effect of removing the calendar year variables from the regression model of 13

- state casualty risk per crash (shown in light green). Figure 5. 7 indicates that excluding the calendar year variables has little effect on the effect of mass reduction in cars, or for footprint red uction in CUVs/minivans. However, removing the calendar year variables

reduces the beneficial effect of mass reduction in trucks and CUVs/minivans ; and increases the detrimental effect of footprint reduction in cars but increases the beneficial effect of footprint reduction in light trucks. -40% -30% -20% -10%

0% 10% 20% 30% 40%CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008CY2002CY2003CY2004CY2005CY2007CY2008Percent change in risk (casualtyy probability per crash) Effect of control variables on risk, CUVs and minivans

Cars Light Trucks CUVs/Minivans53 Figure 5. 7 . Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, including and excluding calendar year variables

Figures 5. 8 through 5. 10 show what effect removing the c alendar year variables ha s on the vehicle control variables; there is little to no effect on the driver or crash control variables (not shown). Figures 5

. 8 through 5. 10 indicate that removing the calendar year variables has a large

effect on the curt ain airbag variable in cars and CUVs/minivans, and the SUV, HD pickup, BLOCKER2, and ESC variables in light trucks. In addition, the figures indicate that removing the calendar year variables lowers the effect of vehicle age , and whether a vehicle is bran d new,

on casualty risk per crash in all three vehicle types. Figures 5

. 7 through 5. 10 suggest that inclusion of the calendar year variables in the regression models dilutes the effect of airbag technologies in cars and CUVs and minivans, the added risk in SUVs and heavy

- duty pickups, and the beneficial effect of ESC in light trucks in gener al, while over

- stating the effect of vehicle age in all three vehicle types.0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%0.12%-1.07%0.07%-0.34%0.29%0.68%-0.24%0.26%

-2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 4.0%

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

minivans Cars LTs CUVs/

minivansPercent change in risk (casualty probability per crash) 

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

13-state casualties per crash

Excluding CY variables

100-lb reduction in weight 1-sq ft reduction in footprint54 Figure 5. 8 . Effect of selected control variables on car risk , including and excluding calendar year variables

Figure 5. 9 . Effect of selected control variables on light truc k risk , including and excluding calendar year variables -25% -20% -15% -10%

-5% 0% 5% 10%CURTAINCOMBOTORSOABSESCVEHAGEBRANDNEWPercent change in risk (casualty probability per crash) Effect of control variables on risk, passenger cars

13-state casualties per crash

Excluding CY variables

-25% -20% -15% -10%

-5% 0% 5% 10%SUVHD_PKPBLOCKER1BLOCKER2ESCAWDVEHAGEBRANDNEWPercent change in risk (casualty probability per crash) Effect of control variables on risk, light trucks

13-state casualties per crash

Excluding CY variables55 Figure 5. 10 . Effect of selected control variables on CUV/minivan risk , including and excluding calendar year variables

5.5. Effect of including sports, police, and all

- wheel drive cars, and full size vans

In its analysis of US fatality risk per VMT, NHTSA excluded three types of cars, model sused as sports cars, police cars, and models with all

- wheel drive, as well as fullsize vans. Including these vehicles in our analysis of 13

- state casualty r isk per crash slightly increases the effect of mass reduction, and has no change on the effect of footprint reduction, on casualty risk for cars, as shown in Figure 5.1 1 . Including fullsize vans has the opposite effect on the results for light

trucks: the effect of mass reduction is slightly reduced, while the effect of footprint reduction is slightly increased. -25% -20% -15% -10%

-5% 0% 5% 10%MINIVANCURTAINCOMBOTORSOABSESCAWDVEHAGEBRANDNEWPercent change in risk (casualty probability per crash) Effect of control variables on risk, CUVs and minivans

13-state casualties per crash

Excluding CY variables56 Figure 5.1 1 . Effect of mass and footprint reduction on 13

- state casualty risk per crash by vehicle type, after including sports, police, and all

- wheel drive cars, and fullsize vans

5. 6

. Effect of adding additional data

As mentioned above, we excluded certain data that NHTSA did not include in their regression analyses:

• vehicles from 2000 to 2007 for three states (Georgia, Illinois, and New Mexico) that

NHTSA did not include in its analysis because those states did not report the posted speed limit at the crash site (Georgia data are available only through 2006, while New

Mexico data are available through 2008) . NHTSA included IL in its 2003 and 2010 analyses, using the fraction of FARS crashes occurring on roads with a posted limit 55 mph or over, by roadway classification; we used this same method to assign posted speed limits to these three states in this analysis; • vehicles whose model year was not reported in the state crash data (except all crash

records from Washington, which were included in the above analysis); • vehicles involved in police

- reported crashes in 2008 (fro m six states), and in 2000 and

2001 (from all states but Michigan, New Jersey, and Washington); • vehicles that had apparent VIN transcription errors that we corrected so that the model

year reported in the state crash data matched the VIN model year . The method to

make these corrections for VIN errors is described in LBNL 2011a

.

We added these data sequentially and analyzed the effect of each on the regression coefficients on vehicle mass and footprint; we also ran regression models using all of the addi tional data. Figure 5.1 2 shows that including all of the additional data increases the number of vehicle records by nearly 40%. Figure 5.1 3 indicates that adding the data, either sequentially or all at0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%0.16%-0.88%-0.50%-0.93%-0.13%0.32%0.10%0.33%

-2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 4.0%

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

minivans Cars LTs CUVs/

minivansPercent change in risk (casualty probability per crash) 

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

13-state casualties per crash

Including all cars and fullsize vans

100-lb reduction in weight 1-sq ft reduction in footprint57 once, results in little change in the effect of mass or footprint reduction on casualty risk per crash, for all vehicle types. The results in Figure 5.13 are from single regression analyses across all crash types, for each vehicle type, and are not adjusted for the effect of full adoption of ESC by 2017. Figure 5.14 compares the first and last cases in Figure 5.13 , after reweighting the coefficients for each type of crash by the anticipated distribution of casualties in 2017, after full adoption of ESC. Note in Figure 5. 14 that the last column (in light o range ), which represents the effect from increasing the sample size by about 40%, reduces the uncertainty of the estimates of the effect of mass or footprint on risk by only a small amount. This suggests that the uncertainty

is a function of the variabili ty in the effect of mass or footprint reduction on risk by vehicle, and not a function of relatively small sample sizes.

Figure 5.1 2 . Number of vehicles involved in casualty crashes by vehicle type, using

additional state crash data

0 50,000 100,000 150,000 200,000

Cars LTs CUVs/ minivansNumber of vehicles involved in casualty crashes 

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

13-states

16-states

16 plus missing model year

16 plus corrected VINs

16 plus CY00, CY01, CY08

16-states plus all

Vehicle type58 Figure 5.1 3 . Effec t of mass and footprint reduction on casualty risk per crash by vehicle type, using additional state crash data

Figure 5.14. Effect of mass and footprint reduction on casualty risk per crash by vehicle type, using additional state crash data, after accou nting for full adoption of ESC0.14%-0.99%-0.34%-0.79%-0.33%0.55%0.10%0.55%0.24%-0.88%-0.25%-0.83%-0.38%0.44%0.04%0.48%0.27%-0.71%-0.24%-0.81%-0.31%0.31%0.02%0.47%0.26%-0.81%-0.23%-0.80%-0.41%0.39%0.03%0.46%0.26%-0.87%-0.01%-0.73%-0.18%0.57%-0.04%0.49%0.28%-0.69%-0.03%-0.70%-0.21%0.41%-0.05%0.51%

-1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5%

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

minivans Cars LTs CUVs/

minivansPercent change in risk (casualty probability) 

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

13-states

16-states 16 plus missing model year 16 plus corrected VINs 16 plus CY00, CY01, CY08 16-states plus all

100-lb reduction in weight 1-sq ft reduction in footprint these are based on single regression for all crash types0.15%-0.91%-0.37%-0.69%-0.13%0.32%-0.04%0.33%0.30%-0.60%-0.06%-0.60%-0.14%0.19%-0.21%0.40%

-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/

minivansPercent change in risk (casualty probability per crash) 

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

13-states

16-states plus missing MY, corrected VINs, and CY00, CY01, CY08

100-lb reduction in weight 1-sq ft reduction in footprint59 6. Conclusions

NHTSA recently completed a logistic regression analysis ( Kahane , 2011) updating its 2003 and 2010 studies of the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT). The new study updates the previous analyses in several ways: updated FARS data for 2002 to 2008 involving MY00 to MY07 vehicles are used; induced exposure data from police reported crashes in several additional states are added; a new vehicle category for car

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

- duty vehicles are divided into two groups based on the crash partner vehicle’s weight, and a category for all other fatal crashes is added; and new control variab les 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.

In a companion report ( Wenzel , 2011b), we use 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 20 07 light

- duty vehicles involved in fatal crashes between 2002 and 2008. The data are examined in slightly different ways, to get a deeper understanding of the relationship between reductions in vehicle mass and footprint, and overall safety.

This repor t compares the logistic regression results of the NHTSA analysis of US fatality risk per VMT with an analysis of 13

- state fatality risk and casualty risk per crash. This analysis differs from the NHTSA analysis in two respects: first, it analyzes risk per crash, using data on all police

- reported crashes from thirteen states, rather than risk per estimated VMT; and second, it analyzes casualty (fatality plus serious injury) risk, as opposed to just fatality risk. There are several good reasons to investiga te the effect of mass and footprint reduction on casualty risk per crash. First, risk per VMT, accounts for two effects that influence whether a person is killed or seriously injured in a crash: how well a vehicle can be driven (based on its handling,

acc eleration, and braking capabilities) to avoid being involved in a serious crash (crash avoidance), and, once a serious crash has occurred, how well a vehicle protects its occupants from fatality or serious injury (crashworthiness). By encompassing both of these aspects of vehicle design, risk per VMT gives a complete picture of how vehicle design can promote, or reduce, road user safety. On the other hand, risk per crash isolates the second of these two safety effects, crashworthiness, by examining the ef fect of mass and footprint reduction on how well a vehicle protects its occupants once a crash occurs.

Second, estimating risk on a per crash basis requires using data on police

- reported crashes from states. Because only sixteen states currently record the vehicle identification number of vehicles involved in police

- reported crashes, which is necessary to determine vehicle characteristics, and only thirteen states also report the posted speed limit of the roadway on which the crash occurred, extending th e analysis to casualties (fatalities plus serious/incapacitating injuries) reduces the statistical uncertainty of analyzing just fatalities per crash. Finally, a serious incapacitating injury can be just as traumatic to the victim and her family, and cost ly from an economic perspective, as a fatality. Limiting the analysis to the risk of fatality, which is an extremely rare event, ignores the effect vehicle design may have on reducing the large number of incapacitating injuries that occur each year on the nation’s roadways. All risks in this report are60 societal risk, including fatalities and serious injuries in the case vehicle and any crash partners, and include not only driver casualties but passenger and non

- occupant casualties as well.

Table 6.1 summ arizes the results of our analysis of the effect of vehicle mass or footprint reduction on the two components of risk per VMT, crash frequency (number of crashes per VMT) and crashworthiness (risk per crash), for both fatality and casualty risk, using data from

13 states. We convert the percent change in the log

- odds of casualty or fatality per crash output from the SAS LOGIST procedure to the percent change in the probability of casualty or fatality per crash. This conversion has no effect on the output regression coefficients when the change in the log

- odds of casualty is small , but substantially increases the percent change for explanatory variables that have a large effect on the log

- odds of casualty

. Effects that are statistically significant are sho wn in red in the table; significance is based on the 95% confidence interval derived from the standard error of the output of the SAS LOGIST procedure, converted to a percent probability interval.

Table 6.1 indicates that f or cars and light trucks, the ef fects from the two components , crash frequency and crashworthiness, roughly add together to result in the overall effect on fatality risk per VMT. For example, a 100

- lb reduction in the mass o f lighter cars leads to a 1.84% increase

in crash frequency (columns B) , while mass reduction leads to a 0.7 6 % decrease in the number of fatalities per crash (column C) ; the net effect is only a 1.08% increase in the risk of fatality per

VMT (column D) . For CUVs/minivans, the relationship is different; mass reduction leads to a

0.71% increase in crash frequency, as well as a 0.87% increase in the number of fatalities per crash; however, the net result, a 1.29% increase in the number of fatalities per V MT, is less than the sum of the two components.

Table 6.1 indicates that mass reduction increases crash frequency (columns B and E) in all five vehicle types, with larger increases in lighter

- than

- average cars and light

- duty trucks. As a result, mass r eduction has a more beneficial effect on crashworthiness, or casualty risk per crash (column F), than on casualty risk per VMT (column G), and on fatality risk per crash (column C) than on fatality risk per VMT (column D). Mass reduction actually decreases casualty risk per crash (column F) in all vehicles except light cars, and in three of the four cases these reductions are statistically significant, albeit small. Footprint reduction increases crash frequency (columns B and E) in cars and light trucks, b ut slightly reduces crash frequency in CUVs/minivans; footprint reduction does not have a statistically

- significant effect on casualty risk per crash (column F), and only for fatality risk per crash (column C) for light trucks. In general mass or footprin t reduction has a smaller effect on casualty risk per crash (column F) than on fatality risk per crash (column C).

The effect of mass reduction on 13

- state casualty risk per VMT (column G) is quite consistent with that NHTSA estimated for US fatality risk per VMT (column A), although the effect is estimated to be slightly larger for lighter cars and lighter light trucks, and slightly smaller for heavier cars, heavier light trucks, and CUVs/minivans.

In contrast with NHTSA’s findings on US fatality risk pe r VMT (column A), mass reduction

reduces casualty risk per crash (column F) for four of the five vehicle types, and three of these four reductions are statistically significant. Mass reduction results in a small but insignificant61 increase in casualty risk per crash for lighter cars. And footprint reduction results in much smaller, and not statistically significant, effects on casualty risk per crash (column F) than on US fatality risk per VMT (column A).

Many of the control variables included in the logi stic regressions are statistically significant, and have a large effect on fatality or casualty risk per crash, although not as large an effect as on fatality risk per VMT. For example, a car’s mass could be reduced by 2,300 lbs while adding

ABS without i ncreasing its casualty risk per crash. While the effect of mass reduction may result in a statistically

- significant increase in risk in certain cases, the increase is small and is overwhelmed by other known vehicle, driver, and crash factors.

Control va riables for ABS and ESC in cars, AWD in light trucks and CUVs/minivans, and drivers aged 14 to 30 have a larger effect on crash frequency than fatality risk per crash. On the other hand, whether a vehicle is a two

- door car, SUV, or heavy

- duty pickup, side airbag variables in cars, and middle

- aged drivers have a larger effect on fatality risk per crash than on crash frequency. Whether a driver is male has essentially no effect on crash frequency, but surprisingly causes a statistically significant increase in fatality risk once a crash occurs. Driving at night on high

- speed rural roads, also has a surprisingly much lower effect on crash frequency than on fatality risk given a crash.

In contrast with NHTSA’s results for US fatalities per VMT, allowing foo tprint to vary along with weight results in little change in the effect of mass reduction on casualty risk per crash than when footprint is held constant; however, the detrimental effect of mass reduction on casualty risk per crash in lighter cars is incre ased just enough to make it statistically significant. When mass is allowed to vary along with footprint, footprint reduction slightly reduces the already small detrimental effect on casualty risk per crash (Alternative 1 in Table 6.2). As with

NHTSA’s a nalysis of fatality risk per VMT, mass reduction does not consistently increase casualty risk per crash across all footprint deciles for any combination of vehicle type and crash

type. Mass reduction increases casualty risk per crash in a majority of foot print deciles for 10 of the 27 crash and vehicle combinations, but few of these increases are statistically significant. On the other hand, mass reduction decreases risk in a majority of footprint deciles for 14 of the 27

crash and vehicle combinations.

Similar to our findings on US fatality risk per VMT, after accounting for all of the control variables in the logistic regression model, except for vehicle mass and footprint, we find that the correlation between mass and the casualty risk per crash by ve hicle model is very low. There also is no significant correlation between the residual, unexplained risk and vehicle weight. These results corroborate our earlier finding that, even after accounting for many vehicle, driver, and crash factors, the varian ce in risk by vehicle model is quite large and unrelated to vehicle weight

.

Other changes in the data and variables used in the regression models result in only slight changes in the effect of mass or footprint reductions on casualty risk per crash, as summarized in Table 6.2. For example:62 • Regression analyses using police

- reported crash data from states must use control variables

to account for differences in definitions of “serious” or “incapacitating” injuries, and reporting requirements, across st ates. Removing the 12 state control variables results in a large reduction in casualty risk per crash from mass reduction in all five vehicle types, a large increase in risk from footprint reduction in cars and CUVs/minivans, and a small

reduction in risk from footprint reduction in light trucks (Alternative 2 in Table 6.2). Including only two variables to control for states with high and low casualty risk per crash substantially reduces the effect of mass and footprint reduction on casualty risk (Alterna tive 3 in Table 6.2), while including the 12 control variables for individual states reduces the effect a little bit more. These results indicate that excluding control variables for the state in which a crash occurred from a regression model using state police

- reported crash data can give inaccurate estimates of the effect of mass or footprint reduction on casualty risk per crash.

• Calculating risk as casualty crashes, rather than total casualties, per crash results in little

change in the effect of mass reduction on risk, but does increase the small detrimental effect of footprint reduction in cars on risk, and makes it statistica lly significant (Alternative 4 in Table 6.2).

• Adding control variables for vehicle manufacturer results in mass reduction increasing

casualty risk per crash in cars, but increases the small beneficial effect of mass reduction in light trucks and CUVs/m inivans. Accounting for vehicle manufacturer causes a reduction in footprint to reduce casualty risk per crash in cars, but increase casualty risk per crash in light trucks and CUVs/minivans (Alternative 5 in Table 6.2).

• As we found in our assessment o f NHTSA’s analysis of US fatality risk per VMT,

including calendar year variables in the regression models appears to weaken the benefit of side air bags in cars and CUVs/minivans, and compatibility measures and ESC in light

trucks. These variables also a ppear to minimize the increased risk of SUVs and heavy

- duty pickup trucks. Excluding the calendar year variables from the regression models slightly reduces the beneficial effect of mass reduction on casualty risk per crash in light trucks and

CUVs/miniva ns, and slightly increases the detrimental effect of footprint reduction on casualty risk per crash in cars (Alternative 6 in Table 6.2).

• Including all

- wheel

- drive, sports, and police cars, and fullsize vans results in virtually no

change in the effect of mass or footprint reduction on casualty risk per crash for cars or light trucks (Alternative 7 in Table 6.2).

• Adding data from three additional states, including vehicles with unreported model year,

correcting VIN transcription errors, and expanding the analysis to calendar years 2000, 2001, and 2008, increases the number of state crash records by about 40 percent. Including these data in the regression analyses results in the effect of mass reduction in lighter cars being slightly more detrimental, and the effect of mass reduction in heavier cars, all light trucks, and CUVs/minivans being slightly less beneficial. Including these data slightly reduces the small detrimental effect of footprint reduction on casualty risk per crash for cars, increases the beneficial effect for light trucks, and increases the detrimental effect for63 CUVs/minivans. However, increasing the sample size included in the regression analysis by 40% does not noticeably reduce the confidence intervals around the point estimates (Alternative 8 in Table 6.2).

In conclusion, casualty risk per crash is not necessarily a better metric than fatality risk per VMT for evaluating the effect of mass or footprint reduction on risk; rather, it provides a different perspective in assessing t he benefits or drawbacks of mass and footprint reduction on safety in vehicles. However, it does allow the separation of risk per VMT to be separated into its two components, crash frequency and risk per crash. Our analysis indicates that much of the

det rimental effect of mass or footprint reduction on risk can be attributed to the tendency for mass or footprint reduction to increase crash frequency, rather than to reduce vehicle crashworthiness (risk once a crash has occurred).

As with our analysis of U S fatalities per VMT, this report concludes that the effect of mass reduction on casualty risk per crash is small, and tends to be overwhelmed by other control variables, such as vehicle type, specific safety technologies, and crash conditions such as

whet her the crash occurred at night, in a rural county, or on a high

- speed road. This report indicates that the effects are only slightly sensitive to what variables and data are included in the regression analysis. Finally, as in our analysis of US fatality risk per VMT, this report shows that after accounting for many vehicle, driver, and crash variables there remains a wide variation in casualty risk per crash by vehicle make and model, and this variation is unrelated to vehicle

mass.

It is important to re cognize that the results of this study 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 vehic le designs and incorporate new technologies, such as more extensive use of strong lightweight materials and specific safety technologies.64 Table 6 .1. Effect of mass and footprint reduction on two components of fatality and casualty risk per VMT, crash frequency (crashes per VMT) and crashworthiness (risk per crash) ; effects in red are statistically significant (see text)

Variable Case vehicle type A. NHTSA

US fatalities

per VMT B. 13

- state
crashes per VMT C. 13

- state
fa talities per crash D. 13

- state
fatalities per VMT E. 13

- state
crashes per VMT F. 13

- state
casualties per crash G. 13

- state
casualties per VMT

Mass

reduction Cars < 3106 lbs 1.43%* 1.84%

- 0.76% 1.08% 1.84% 0.15% 1.76%

Cars > 3106 lbs 0.48% 1.23%

- 2.44%

- 1.42% 1.23%

- 0.91% 0.32%

LTs < 4594 lbs 0.52% 1.42%

- 1.98%

- 0.43% 1.42%

- 0.37% 1.41%

LTs > 4594 lbs

- 0.40% 0.93%

- 1.22%

- 0.46% 0.93%

- 0.69%

- 0.22%

CUV/ minivan

- 0.47% 0.71% 0.87% 1.29% 0.71%

- 0.13%

- 0.02% Footprint

reduction Cars 1.89% 1.05% 1.24% 2.80% 1.05% 0.32% 2.00%

LTs

- 0.02% 1.15% 0.73% 1.92% 1.15%

- 0.04% 1.13%

CUV/ minivan 1.73%

- 0.37%

- 2.25%

- 1.54%

- 0.37% 0.33% 1.52% * Based on NHTSA’s estimation of uncertainty using a jack

- knife method, only mass reduction in cars less than 3,106 lbs has a statistically significant effect on US fatality risk.

Table 6 .2. Effect of mass and footprint reduction on 13

- state casualty risk, under alternative regression model specifications and using additional data ; effects in red are statistically significant (see text)

Variable Case vehicle type13-state casualtiesper police-reportedcrash1. Excluding. massor footprint variable2. Excludingstatecontrol variables3.Including onlytwo state controlvariables4. Casualty crashesper crash5.Including vehiclemake variables6.Excluding CYvariables7. Includingsports,squad, AWD carsand fullsize vans8. Includingalladditional data

Mass

reduction Cars < 3106 lbs 0.15% 0.36%

- 0.78%

- 0.06% 0.24% 1.13% 0.12% 0.16% 0.30%

Cars > 3106 lbs

- 0.91%

- 0.67%

- 1.91%

- 1.32%

- 0.85% 0.55%

- 1.07%

- 0.88%

- 0.60%

LTs < 4594 lbs

- 0.37%

- 0.41%

- 0.46%

- 0.44%

- 0.32%

- 0.54% 0.07%

- 0.50%

- 0.06%

LTs > 4594 lbs

- 0.69%

- 0.72%

- 1.85%

- 0.79%

- 0.67%

- 0.76%

- 0.34%

- 0.93%

- 0.60%

CUV/ minivan

- 0.13% 0.02%

- 1.95%

- 0.43%

- 0.25%

- 0.58% 0.29%

- 0.13%

- 0.14% Footprint

reduction Cars 0.32%

- 0.05% 1.78% 0.55% 0.55%

- 0.37% 0.68% 0.32% 0.19%

LTs

- 0.04%

- 0.44%

- 0.84%

- 0.09% 0.19% 0.22%

- 0.24% 0.10%

- 0.21%

CUV/ minivan 0.33% 0.20% 3.58% 0.46% 0.74% 1.35% 0.26% 0.33% 0.40%65 7 . References

Kahane, C.J. 2003. Vehicle Weight, Fatality Risk and Crash Compatibility of Model Year 1991

- 99 Passenger Cars and Light Trucks. National Highway Traffic Safety Administration Technical Report, DO T HS 809

- 662, Oct ober

.

Kahane C.J. 2010. Relationships Between Fatality Risk, Mass, and Footprint in Model Year

1991

- 1999 and Other Passenger Cars and LTVs. Final report included in the Corporate Average Fuel Economy for MY 2012

- MY2016 Passenger Cars and Light Trucks Final

R egulatory Impact Analysis , NHTSA, March

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Kahane, C.J. 2011. Relationships Between Fatality Risk, Mass, and Footprint in Model Year

2000

- 2007 Passenger Cars and LTVs. Draft final report prepared for the Nationa l Center for Statistics and Analysis, Natio nal Highway Traffic Safety Administration, July 14.

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March

. LBNL

- 489 7E.

Wenzel, T.P. 2011b. Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000

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. Draft final report prepared for EERE, US DOE, November

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