The Highway Safety Information System 

(HSIS) is a multi-State safety database that 

contains crash, roadway inventory, and traffic

volume data for a select group of States. The 

participating States—California, Illinois, Maine, 

Michigan, Minnesota, North Carolina, Ohio, 

Utah, and Washington—were selected based on 

the quality of their data, the range of data available,

and their ability to merge the data from the various

files. The HSIS is used by FHWA staff, contractors,

university researchers, and others to study current 

highway safety issues, direct research efforts, and 

evaluate the effectivness of accident countermeasures.

Development of a Speeding-Related 

Crash Typology

Summary Report

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike . McLean, VA 22101-2296

Literature Review

Speeding, the driver behavior of exceeding the posted speed limit or driving too fast 

for conditions, has consistently been shown to be a contributing factor to a significant percentage of fatal and nonfatal crashes. Between 1990 and 2006, the frequency 

of speeding-related (SR) fatal crashes ranged from 11,000 to 13,000 each year, and the 

percentage of SR total fatal crashes ranged between 30 and 33 percent according to 

data observed in the Fatal Analysis Reporting System (FARS).(1) Thus, speeding is a 

significant safety issue warranting attention based on its size and impact on society.

While the United States has seen progress in other major safety issues such as 

occupant restraint use and driving under the influence of alcohol, little if any 

progress has been made with speeding. In response to this issue, the United States 

Department of Transportation has instituted the Speed Management Strategic 

Initiative, seeking more effective ways to manage the crash-related effects of 

speeding.(2) In support of this initiative, this study examined recent crash data 

through the development of an SR crash typology. Such a typology can help 

define the crash, vehicle, and driver characteristics that seem to result in a higher 

probability of SR crashes. Thus, the goal of this study was to determine the 

“what,” “where,” “when,” and “who” descriptors of SR crashes in order to provide guidance to the future development of new treatments and to better target 

new and existing treatments to subgroups of drivers and types of roadways 

(e.g., two-lane rural) or roadway locations (e.g., unsignalized intersections).

While numerous research studies have explored the effects of speed 

on crash frequency and severity and on the effect of treatments aimed 

at managing speed (e.g., TRB Special Report 254), two studies have 

developed typologies similar to those developed in this effort.(3) Bowie

and Walz used data from the 1986 Crash Avoidance Research Data 

(CARDfile) from six States as well as from the Indiana Tri-Level Study 

and the 1989 FARS data. (See references 4–6 and 1.) Based on CARDfile,

speed was a factor in about 12 percent of all crashes.(5) Data from the 

Indiana Tri-Level Study indicated that excessive speed was a factor 

in 7.1 to 16.9 percent of crashes.(6) Key findings from the FARS data 

indicate that fatal SR crashes are usually single-vehicle and that there 

is a higher percentage of SR crashes on rural roads, on curves, and at 

night.(1) In these fatal crashes, males were more likely than females 

to be speeding, and drivers under the influence of alcohol were 

more likely to be speeding than those who were not drinking. 

Occupant restraint usage was lower for SR drivers. In addition, 

more than 45 percent of all motorcycle drivers involved in fatal 

crashes were speeding.

Hendricks, et al. examined data from in-depth investigations 

and driver interviews from a sample of 723 relatively severe 

crashes involving 1,284 drivers collected in a special study 

as part of the National Automotive Sampling System Crashworthiness Data System (NASS CDS) program.(7,8) Researchers 
Methodology

2

determined the specific driver behaviors and unsafe driving acts that led to the crashes as 

well as the situational-, driver-, and vehicle-based characteristics associated with these 

behaviors. Where cause could be assessed, excessive vehicle speed was the second leading 

causal factor (i.e., second to inattention). Excessive speed typically involved drivers 

exceeding the speed limit, but it sometimes included drivers operating at inappropriate 

speeds for prevailing weather or roadway conditions (i.e., too fast for conditions). For 

drivers who were assessed to have partially or fully caused the crash, excessive speed was 

the sole primary cause of the crash for 6.8 percent of these drivers. Excessive speeding 

was also a primary cause in combination with other causes for an additional 3.8 percent 

of the contributing drivers and was a contributory cause for an additional 8.1 percent 

of the drivers. When crash types were examined for these drivers excessively speeding,

researchers found that speeding was the leading cause of single-driver right- or leftroadside departure with traction loss (i.e., part of run-off-road crashes) and the third 

leading cause of head-on crashes. The researchers then examined the characteristics

of the drivers and roadways in these excessive speed roadway departure crashes.

They found that the more important characteristics of these crashes included 

that they occurred primarily on curves, at night, on local or collector roadways, 

and during clear weather. In addition, younger male drivers (less than 35 years old) 

were more involved, with males younger than 20 years old comprising 46.2 percent

 of the sample.

Databases Used

Since a goal of this study was to explore the “what,” “where,” “when,” and 

“who” questions as they relate to the United States, the study used 2005 

data from the two major national crash databases: the National Automotive 

Sampling System’s General Estimates System (GES) and FARS.(9,1) GES 

data are derived from a nationally representative sample of police-reported

motor vehicle crashes of all types, from minor to fatal. Approximately 

60,000 police accident reports are included each year. Sample weights are 

assigned to each crash based on a sampling protocol. Using the weight, 

the sample can be extrapolated to represent the approximately 6 million 

U.S. crashes that occur each year. FARS is a census of all fatal crashes in 

the United States, with approximately 40,000 crashes occurring in 2005. 

Because of the definitional issues and lack of data described below that 

arose with the FARS and GES data, data from two States that are part 

of the Federal Highway Administration’s (FHWA) Highway Safety 

Information System (HSIS) were also used: 2002–2004 data from 

North Carolina and 2003–2005 data from Ohio.

Definition of SR

For all databases used in this study, the decision on whether or not 

a crash is SR is based on an examination of each vehicle in the 

crash. First, each vehicle is defined as SR or not based on several 

variables. If any vehicle in the crash is SR, the crash is considered 

SR. In addition, if all of the vehicles involved in a crash are coded 

as non-SR, then the crash is defined as not SR. In all other cases, 

it is unknown whether or not the crash is SR. 

The FHWA was interested in examining speeding using a disaggregated definition where “exceeding posted speed limit” 

(herein referred to as “over speed limit”) and “too fast for conditions” would be analyzed separately since the appropriate 

counter-measures could differ for these two types of SR crashes. 
Table 1. The number and percent of SR crashes in GES and FARS (2005)

and in North Carolina (2002–2004) and Ohio (2003–2005) by definition type.

Definition

Fars GES North Carolina Ohio

Combined Combined Combined Over

Speed Limit

Too fast for

conditions Combined Over

Speed Limit

Too Fast for

conditions

Total crashes 39,189 6,146,907 422,324 461,013

SR crashes 11,553 1,195,570 62,746 12,802 49,944 51,906 30,677 30,677

Percent SR 29.5 19.5 14.9 3.0 11.9 11.3 6.7 4.6

3

However, while it is theoretically possible to distinguish 

between these two definitions in FARS and GES, the 

sample sizes available were not adequate for the more 

restrictive “over speed limit” definition. Thus, only the 

broader definition could be used—exceeding the posted 

speed limit or driving too fast for conditions (referred to 

as the “combined” definition). In order to better examine 

the possible effects of using only this combined definition, North Carolina and Ohio data were analyzed since 

they permit the use of both the over speed limit and the 

combined definitions, allowing comparisons of results 

within the same database. Note that the four databases 

differ; FARS provides a census of all U.S. fatal crashes, GES 

provides an extrapolated estimate of U.S. crashes of all 

severities, and the HSIS North Carolina and Ohio 

databases include crashes on only State system roads 

(i.e., excluding city streets or county roads that are not 

State controlled). Thus, differences in results would be 

expected and were found. Table 1 shows the sample sizes 

and percent SR for each of the databases.

Overview of Analysis Methods

Two analysis methodologies were used: (1) single variable

table analysis and (2) classification and regression tree 

(CART) analysis. In the first methodology—single 

variable table analysis—for a series of both crash-based 

variables (e.g., crash type, weather, etc.) and vehicleand driver-based variables (e.g., vehicle type, age of 

driver, etc.), individual codes within each variable were 

examined to determine which showed an overrepresentation of SR crashes or SR vehicles/drivers (e.g., rear-end 

crashes, 16- to 19-year-old drivers, etc.). The second 

method used CART analyses that automatically defined 

which factors/variables were the most critical with 

regard to SR crashes or drivers and which combinations 

of variables/codes were the most important. Similar 

single variable and CART analyses were also conducted 

for five high-priority subsets of the data (e.g., pedestrian 

crashes, intersection crashes, etc.) for a limited number 

of variables within each subset. Additional detail on each 

of these methods is presented in the following sections.

Single Variable Table Analyses

As indicated above, single variable tables were produced from each dataset/definition for a large number of 

variables. The choice of variables to be examined was 

based to some extent on the results of past studies of 

SR crashes, particularly on the earlier National Highway Transportation Safety Administration (NHTSA) 

study.(4) The factors describing the overall nature of 

the crash (e.g., crash type, crash location, etc.) were 

examined using a crash-based file where each crash was 

classified as speeding or not. The vehicle- and driverbased factors were examined in a vehicle-based file 

where each vehicle was also classified as speeding or 

not. In general, a category within a variable is defined 

as being over represented if it is characterized by a 

high percentage of SR crashes in comparison to 

percentages for other categories. In a few cases, the 

high percentage categories were found to have low 

frequencies of SR crashes (or drivers or vehicles) 

compared to the total SR frequency. These would not be 

considered as high-priority categories.

Identification of Critical Factors Using Classification 

Trees

Although the analyses of single variable tables provide

 useful information about SR crashes and vehicles and 

drivers in crashes, they do not automatically indicate 

which factors/variables and which combinations of 

variables are the most critical with regard to SR crashes 

or speeding drivers. The CART methodology provides 

such information and requires fewer statistical assumptions than do other methods (e.g., logistic regression).(10)

CART is able to determine not only the most important

variable and categories within that variable in terms of

the risk of an SR crash, but also the most important 

second-level variable and categories within the most 

important categories of the first level, the third level 

within the second level, etc. Note that the analyst does not 

predefine the categories within a variable which should 

be grouped together based on higher SR percents; instead, 

CART does that automatically. The outputs of CART are

presented as a tree with multiple branches that can be 

traced down to determine most important combinations

(or subsets) of variable categories in terms of predicting 

SR crashes. A more detailed explanation of the CART 

methodology is presented in the main report, Development of a Speeding-Related Crash Typology (FHWAHRT-10-024).(11)
What crash characteristics have a higher SR percentage?

» Single-vehicle, run-off-road crashes have higher SR percentage compared to multivehicle crashes.

» SR percentage increases with speed limit in GES and State data, but it is slightly higher at low speed limits in FARS.

Where are crashes with higher SR percentages found?

» Crashes on curves have much higher SR percentages than crashes on tangents (even with the over speed limit definition). 

» Functional class findings differ by database, with GES and State (combined definitions) showing higher SR percentages

 on interstates, while FARS shows higher SR percentages on minor collectors and local roads.

» No consistent pattern of higher SR percentages in work zone crashes.

When do crashes with higher SR percentages occur?

» Nighttime crashes have higher SR percentages.

Who has higher SR percentages in crashes?

» Motorcycle operators (i.e., two to four times SR percentages of car drivers).

» Younger drivers (younger includes 21–25 year olds).

» Males (but only slightly so in GES).

» Non-users of restraints.

» Drivers under the influence of alcohol (i.e., two to four times higher SR percentage than drivers not under the

 influence of alcohol).

» Drivers with prior speeding convictions (FARS analysis only).

» Drivers with no license or invalid license (FARS analysis only).

» Distracted drivers (GES analysis only).

Figure 1. Chart. Single variable results of interest.

Results

4

Just as with the single variable table analyses, CART 

analyses of factors describing the overall nature of the 

crash were examined using a crash-based file, and CART 

analyses of vehicle- and driver-based factors were examined in a vehicle-based file.

Analyses of Data Subsets

In addition to the two sets of analyses described above, 

additional analyses of subsets of the data were conducted. While the above analyses included both SR and 

non-SR crashes (and vehicles and drivers), these subset 

analyses concerned only SR crashes in FARS and GES 

for five data subsets: (1) pedestrian crashes, (2) intersection crashes, (3) lane departure crashes, (4) rural 

crashes, and (5) urban crashes. Lane departure crashes

were defined using a standard FHWA definition including single-vehicle, run-off-road crashes; multivehicle, 

head-on crashes; multivehicle, opposite direction, frontto-side crashes; and multivehicle, opposite direction, 

sideswipe crashes. In addition, only selected variables 

were examined within each subset.

Single Variable Table Analyses

While multiple tables were produced in examining the 

“what,” “where,” “when,” and “who” questions related to 

SR crashes, figure 1 presents the results of greatest interest. While four different databases and two definitions

were used, there were some consistencies across the 

findings. In addition, many of these are very similar to 

results found in the earlier two studies related to crash 

typologies, even though the first of these, the NHTSA 

study, was based on data from the 1980s, and the second

unsafe driving acts study was based on a more limited

sample of detailed crash investigations.(3,4) It is also 

noted that these SR findings point to some of the same 

descriptors as do current national and State emphasis 

areas, even though those areas were chosen based on 

non-SR as well as SR crashes—lane departure crashes, 

motorcycle crashes, and crashes involving drivers under 

the influence of alcohol and those not using restraints.

Single Variable Tables for High-Priority Subsets 

As noted above, in addition to the analyses of a large 

series of individual variables in the four databases, 

additional single variable tables were produced for only 

SR crashes in FARS and GES for five data subsets. It is 

pointed out that these results differed from the original single variable results where the goal was to define 

categories within each critical variable that were more 

likely to be SR when compared to other categories. 

Here, the data are restricted to SR crashes, and the goal 

is to examine the nature of these SR crashes within 

each subset by examining only a selected list of variables. 

Table 2 and table 3 include key results for the intersection and lane departure subsets. Note that the latter 

subset is predominantly run-off-road crashes (i.e., over 

90 percent). Results for the urban and rural subsets 
Table 3. Selected characteristics of SR lane departure crashes.

Variable Category FARS (Percent) GES (Percent)

Night 58 46

Predominant speed limit (mi/h) 50–55 39 27

Straight (as opposed to curve/unknown) 51 55

Level (as opposed to grades/hills/unknown) 64 39

Two lanes 71 55

Dry (as opposed to wet, snowy/icy) 80 49

Predominant driver age (years)

16–19 17 24

20–25 26 25

Alcohol involved 35 13

Total frequency 7,796 415,631

Table 2. Selected characteristics of SR intersection crashes.

Variable Category FARS (Percent) GES (Percent)

Daytime 45 73

Predominant speed limit (mi/h)

30–35 30 33

40–45 30 32

Traffic control

Stop/yield 33 16

Signals 31 55

No control 32 26

Straight (as opposed to curve/unknown) 83 85

Level (as opposed to grades/hills/unknown) 80 43

Two lanes 54 28

Predominant driver age (years)

16–19 15 21

20–25 30 20

36–50 19 22

Alcohol involved 28 6

 Total frequency 1,510 383,900

5

(where only FARS data are available) and the pedestrian subset (which account for 

approximately 4 percent of the fatal SR crashes and less than 1 percent of the total SR 

crashes) are not presented in these tables but can be found in the full report.

CART Analyses

As previously indicated, CART analyses were used to identify the variables and category

combinations within those variables that are most critical in describing SR crashes 

and drivers and the most important combinations of those variables. An example of 

the most important part of the tree produced when CART was used to examine all 

crash-level variables in the FARS database is shown in figure 2. The most important 

SR-predictive variable (the top tree branch) is labeled “First Harmful Event,” and 

the categories with the highest SR percentage include rollovers/overturns, jackknife, 

and collisions with various fixed objects on the roadside. Then, within that branch, 

the next variable is roadway alignment, with the highest SR category being curves. 

Within that category, the next variable is speed limit, with the highest SR categories 

being the lower speed limits between 20 and 45 mi/h. In this selected subsample, 

approximately 60 percent of the crashes are SR. CART did not detect a fourthlevel variable within speed limit. In general, this tree indicates that run-off-road 
Total

Speed Related = 31%

N = 26,311

(SR N = 8,115)

First Harmful Event

Collision with motor vehicles, collision with 

pedestrian, collision with animal, etc.

Speed Related = 20%

N = 2,850

Rollover/overturn, jackknife, collision with

various fixed objects on roadside 

Speed Related = 45%

N = 5,265

Roadway Alignment

Straight 

Speed Related = 39%

N = 2,652

Curve

Speed Related = 54%

N = 2,614

20–45 mi/h

Speed Related = 60%

N = 1,258

50+ mi/h

Speed Related = 49%

N = 1,344

Speed Limit

Figure 2. Chart. CART SR output for 2005 FARS crashes.

6

collisions on curves that are on roads with lower speed limits are more likely to be SR. 

Note that this final SR subcategory includes 1,258 of the 26,000 fatal crashes included in 

the sample analyzed—approximately 5 percent.

Similar CART analyses were conducted with the GES data and with both definitions 

in each of the two State data files (results from the State analyses are not shown in this 

summary report). Figure 3 shows the top levels of the resulting crash-based CART tree 

for the GES data. This illustrates the point that the findings from the CART analyses 

were somewhat less consistent across databases than the earlier single variable tables.

In this figure, like the earlier FARS results, the most critical descriptor is again related 

to a crash-type variable, but this time, rear-end crashes are included by CART with the 

single-vehicle, run-off-road crashes (note that Not MV collision is any crash in which 

there is not a collision with another motor-vehicle such as fixed object, pedestrian,

 etc.). While the next most important combinations involved curves and lower 

speed limits in FARS, they involved snowy and icy conditions in GES.

CART was also used to examine driver/vehicle characteristics and combinations 

in all four databases. In general, this analysis of fatal crashes (using the liberal 

combined SR definition) indicated that the fatal crash subset most likely to 

include SR vehicles were those involving drivers under the influence of alcohol 

who were young males (defined as those in the age group 16–35) driving either 

automobiles or motorcycles. Approximately 52 percent of the crashes in this 

final subset were SR, and the subset included approximately 2 percent of the 

total sample and 11 percent of the SR sample.

However, the same pattern does not exist in the GES vehicle/driver tree. 

There, the most critical factor was driver distraction of some type (e.g., inattentive, sleepy, etc.) in contrast with not distracted. The remaining descriptors for the distracted group were not very informative, grouping nonbelt 
Atmospheric Conditions

Total

Speed Related = 21%

N = 4.0 million

(SR N = 838,000)

Manner of Collision

Head-on, angle, sideswipe

Speed Related = 6%

N = 96,397

Not MV collision, rear-end

Speed Related = 30%

N = 741,849

Event 1

 Collisions with ped/bikes, 

collisions with animals

Speed Related = 3%

N = 8,754

Rollovers, collisions

 with objects, rear-ends 

Speed Related = 34%

N = 733,095

Roadside Surface Condition

Dry, sand, dirt, oil

Speed Related = 31%

N = 492,703

Wet, snow and sleet, icy

Speed Related = 45%

N = 240,392

No adverse, rain, sleet, etc.

Speed Related = 41%

N = 173,444

Snow, sleet and fog

Speed Related = 59%

N = 66,948

Figure 3. Chart. CART SR output for 2005 GES crashes.

7

users with belt users (versus unknown and no restraint 

available), certain driver vision obstructions including no 

obstruction versus others, and grouping all ages between 

16 and 70 years of age in the high group. These are neither 

consistent with the FARS findings nor very informative 

due to how CART grouped the categories.

This pattern of somewhat inconsistent findings was 

replicated in the State-based analyses. However, in 

general, there was consistency with respect to the fact 

that the top-level predictor of SR crashes was the crash 

type (first harmful event) and that the categories with 

the highest SR percentage were, in general, single-vehicle, run-off-road crashes. Note that GES also included 

rear-end crashes in this set of most critical crash types. 

Lower-level descriptors included snowy/icy roads using 

the combined definition and dry roads using the over 

speed limit definition.

The findings from the vehicle-based analyses differed 

even more across databases and definitions. Using the 

combined definition, FARS noted driver alcohol use as 

the most important descriptor, GES indicated distracted

drivers as the most important descriptor, and North 

Carolina and Ohio data indicated young drivers as the 

most important descriptor. The over speed limit analysis

in North Carolina indicated young drivers (up to 

age 35) under the influence of alcohol not using 

restraint systems as the most important descriptor, while 

the Ohio data indicated young males not using restraints 

as the most important descriptor. A common theme in 

most (but not all) of the results is perhaps young male.

The above sections described some of the specific findings 

from the multiple analyses conducted in this effort. The 

more general findings are as follows:

• For some variables, there were differences between 

FARS and GES findings. Thus, treatments and 

targets for these treatments (e.g., driver subgroups 

or roadway location types) may differ if one is 

focused on fatal crashes versus total crashes.

Discussion
for more information

This research was conducted by Forrest M. Council, Raghavan 

Srinivasan, Scott Masten, and Daniel Carter of the University

of North Carolina Highway Safety Research Center and 

Martine Reurings of the SWOV Institute for Road Safety 

Research in the Netherlands. The full final report by the same 

name can be downloaded from the FHWA Safety Research 

and Development publications Web site related to “Speed 

Management” at http://www.tfhrc.gov/safety/speedm.htm. 

For more information about HSIS, contact Carol Tan, HSIS 

program manager, HRDS, (202) 493-3315, carol.tan@dot.gov.

Visit us on the Web 

at www.tfhrc.gov

REFERENCES

1. National Highway Traffic Safety 

Administration. (2009). Fatality 

Analysis Reporting System (FARS), 

Washington, DC. Obtained from: 

ftp://ftp.nhtsa.dot.gov/FARS/. 

2. National Highway Traffic Safety 

Administration. (2005). Speed 

Management Strategic Initiative,

DOT-HS-809-924, U.S. Department 

of Transportation, Washington, DC.

3. Transportation Research Board. 

(1998). TRB Special Report 254: 

Managing Speed—Review of Current 

Practice for Setting and Enforcing Speed Limits, National Research 

Council, Washington, DC.

4. Bowie, N.N. and Walz, M. (1994). “Data 

Analysis of the Speeding-Related Crash 

Issue,” Auto & Traffic Safety, 31–38.

5. National Highway Traffic Safety 

Administration. (1984–1986). Crash 

Avoidance Research Data File (CARDfile), 

Washington, DC.

6. Treat, J.R., Tumbas, N.S., Mcdonald, S.T., 

Shinar, D., and Hume, R.D. (1979). Tri-Level 

Study of the Causes of Traffic Accidents, 

Executive Summary, Report No. DOTHS-805-099, National Highway Traffic Safety 

Administration, Washington, DC.

7. Hendricks, D.L., Fell, J.C., and Freedman, M. 

(2001). The Relative Frequency of Unsafe Driving 

Acts in Serious Traffic Crashes—Summary 

Technical Report, Report No. DOT-HS-809-205, 

National Highway Traffic Safety Administration, 

Washington, DC. Obtained from: http://ntl.bts.

gov/lib/26000/26000/26077/DOT-HS-809-205.pdf

8. National Highway Traffic Safety Administration. 

(2009). National Automotive Sampling System 

Crashworthiness Data System (NASS CDS), 

Washington, DC. Obtained from http://www.

nhtsa.dot.gov/people/ncsa/nass_cds.html

9. National Highway Traffic Safety Administration. 

(2009). National Automotive Sampling System 

General Estimates System (NASS GES), Washington, 

DC. Obtained from: ftp://ftp.nhtsa.dot.gov/GES/. 

10. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, 

C.J. (1984). Classification and Regression Trees, 

Chapman and Hall/CRC, New York, NY.

11. Council, F.M., et al. (2010). Development of a 

Speeding-Related Crash Typology, FHWA-HRT-10-024, 

Federal Highway Administration, Washington, DC.

FHWA-HRT-10-039

HRDS-05/05–10(1500)E

8

• There were differences between findings from the two States (and between States and 

FARS and GES). Thus, it would appear important for each State to conduct its own 

SR analysis in its Strategic Highway Safety Plan.

• There were few differences in findings between analyses conducted with the combined definition versus the over speed limit definition in the State databases. 

The exception was findings related to weather or road condition where the over 

speed limit definition showed “dry” with higher SR percentages and the combined definition showing “nondry” with higher SR percentages. The fact that 

there were few differences supports the use of the combined definition findings 

in national databases.

In summary, this study produced a large group of findings, with some findings not 

being consistent across all four databases and two definitions. This was not completely 

unexpected because prior studies of other crash types not related to speeding have 

shown that the characteristics of fatal and nonfatal crashes do differ, and States would 

be expected to sometimes differ from each other and from a composite national 

picture. This effort did produce some consistent (and inconsistent) findings that 

can be used in treatment development and targeting. The findings were very 

consistent with those from the one similar SR study conducted in 1994, even 

though the data used there were from the 1980s. In essence, the problem 

characteristics have not changed much, and the problem is still a significant one 

that demands attention. The current focus on the issue is both well justified 

and of critical importance in further reducing the huge cost to society resulting 

from vehicle crashes in the United States.