Impact of Speed Limit Increases on Crash Injury Severity

7/27/2019 Impact of Speed Limit Increases on Crash Injury Severity

1/21

Paper No. 990975

PREPRINT

Duplication of this preprint for publication or sale is strictly prohibited without priorwritten permission of the Transportation Research Board

Impact of Speed Limit Increases on Crash Injury Severity:

Analysis of Single-Vehicle Crashes on North Carolina Interstate

Highways

Henry Renski

Department of City and Regional Planning, The University of North CarolinaChapel Hill, North Carolina 27599-3140

Tel (919) 962-4760Email: [email protected]

Asad J. Khattak

Department of City and Regional Planning, The University of North CarolinaChapel Hill, North Carolina 27599-3140

Tel: (919) 962-4760Email: [email protected]

Forrest M. Council

Highway Safety Research Center, The University of North CarolinaChapel Hill, NC 27599-3430

Tel: (919) 962-0454Email: [email protected]

November 15, 1998_____________________________________________________________________

Transportation Research Board

78th

Annual Meeting

January 10 14, 1999


2/21

Renski, Khattak & Council 1

Washington, D. C.

INTRODUCTION

The United States first established National Maximum Speed Limits (NMSLs) in 1974 to promote

energy conservation and offset fuel shortages caused by the Arab oil embargo. The original

NMSLs for Interstate highways were set at 55 mph. Between 1973 and 1974 the average speedson the nations highways dropped from 65 to 57.6 while the proportion of vehicles exceeding 65

mph fell from 50% to 9% along the nations rural Interstate highways (1). Aside from the energy

savings, the decreased roadway speeds were estimated to have saved from 3,000 to 5,000 lives in

1974 (2). However, it is unclear how much the reduction in fatalities was due to the speed limit

change alone, as compared to the reductions in travel demand following the oil embargo.

Meanwhile, the notion that speed kills became firmly established among the American public

and lawmakers, and the NMSLs outlived the oil crisis based upon these perceived safety benefits.

In April 1987, the US Congress allowed states to increase speed limits to 65 mph on

segments of rural Interstate highways and 55 mph on urban Interstate highways. In November

1995, Congress repealed the NMSL, allowing states to set their own speed limits on both rural

and urban Interstates and non-Interstate routes. By the end of 1996, 24 states had raised speedlimits on rural Interstates to at least 70 mph. North Carolina, proceeding with some caution,

asked the North Carolina Department of Transportation (NCDOT) to identify the safest

roadway segments for possible speed limit increases, as determined primarily by roadway design

and crash history. The state legislature also granted the NCDOT the authority to raise the limits

on roads that were deemed safe. In October 1996, North Carolina raised the maximum speed

limits on 376 miles of Interstate highways. In the following May, the speed limits were raised on

an additional 316 miles of non-Interstate roadways. In most cases, speed limits increased by

either 5 or 10 mph.

The purpose of this study is to understand the impact of speed limit on crash injury

severity on Interstate highways. North Carolina was chosen for analysis due to the availability of

good quality data and sufficient variation in speed limit increases, terrain and weather. Single-vehicle crashes are examined because they constitute a large share of injurious and total crashes

and are likely to be affected by speed limit increases. The study compares crash information

collected on highway segments where speed limits were raised against similar highway segments

where speed limits did not increase.

LITERATURE REVIEW

Do speed limits matter in drivers speed choice?

Speed limits only have safety implications in their relationship to actual driving speed. To

understand how the speed limits can affect injury severity, it is important to understand how

drivers make their speed choices and whether speed limits are a consideration.The posted speed limit is one of the many factors that feed into a drivers speed choice.

Other factors include highway and vehicle design, speed enforcement, environmental attributes

and characteristics of the driving population (3). Shinar (4) states that speed limits and travel

speeds only overlap in the presence of at least one of the following: intense enforcement,

environmental constraints (e.g., roadway design or visibility) or vehicular limitations that force

drivers to drive at or below the speed limit. He concludes that speed limits well below design


3/21


limits are more likely to be violated than higher limits, all else being equal. As speed limits are

raised to more closely reflect design speeds, drivers are less likely to violate them. As a

consequence, the overall speed variance of the roadway may decrease.

A majority of studies reviewed suggest that speed limits influence roadway speeds, yet the

actual influence of speed limits on speed choice is uncertain due to difficulty in isolating the

effects of speed limits from contextual factors. Freedman and Esterlitz (5) estimated an increaseof nearly 3 mph for states raising speed limits after the 1987 NMSL changes. They found the

proportion of cars exceeding 70 mph nearly doubled, and that speeds continued to rise during the

years following speed limit increases. Using a more case-oriented approach, Retting and Greene

(6) sampled speeds on urban freeways in Riverside California and Houston Texas before and after

speed limit increases and found the percentage of drivers exceeding 70 mph, increased from 29%

to 41% in Riverside and from 15% to 50% in Houston. Therefore, statistical evidence points

toward increase in actual speed with speed limit increase.

Do higher speeds increase crash severity?

Research clearly indicates that increases in speed (both absolute and relative between vehicles)leads to an increase in crash severity. The faster a vehicle is moving prior to contact with another

vehicle or a stationary object, the greater the exchange of energy resulting in higher crash severity

(4). Solomon (7) measured the relationship between crash and severity by measuring injury rates

(numbers of people injured relative to number of vehicles involved in a crash) and property

damage per crash involved vehicle. In both cases, higher speeds implied greater costs. He also

calculated the fatality rate associated with speeds: which ranged from 1-2 crash/fatality odds for

speeds below 55 mph to over 20 crash/fatality odds for speeds of 70 mph and above. Joksch (8)

found that higher speeds increase injury severity at a rate faster than the increase in speed.

Speed Limits and Roadway Crash Severity

Numerous studies attempt to measure the impact of speed limit changes on fatalities, but few, if

any, attempt to measure the impact of speed limit changes across the entire injury spectrum. Even

among the studies that only examine fatalities, there is a lack of consensus.

Higher speed limits increase fatalities when compared at the state level. The National

Highway Traffic Safety Administration (9) estimated that between 1986 and 1990 the states

which raised their maximum speed limit to 65 mph experienced a 27% increase in fatalities

compared to a 3% increase in states which did not. Ironically, states that raised speed limits to 65

mph experienced a decrease of 4% fatalities between 1989 and 1990. The NHTSA study did not

adequately control for many possible external factors. Controlling for Vehicle Miles traveled

(VMT) and occupancy rates, Baum, Wells and Lund (10) found a net 15% increase in fatalities

on the rural Interstates of 65mph states over 55 mph states. More recently, Farmer, Retting and

Lund (11) compared changes in fatalities on Interstates and freeways for 12 states raising speed

limits before April 1996, to 18 states that did not raise limits or did so on less than 10% of urban

Interstate mileage. They found that Interstate fatalities increased by 12% in the states where

speed limits were raised.

Using a time series regression model and controlling for unemployment, seat belt laws,

linear time trends, and monthly and weekday/weekend traffic patterns, Garber and Graham (12)


4/21


estimated a median increase of 15% in fatalities on rural Interstate highways and a median

increase of 5% on non-Interstate roads where speed limits were raised. Their study demonstrated

inconsistency across states, showing increased fatalities in some states, reduced fatalities in others,

and no discernible change in the remainder. Chang et al. (13) estimated fatality models for thirty

two 65 mph states and 6 55 mph states using a time series interruption analysis between January

1975 and December 1989. Overall, the authors concluded that the 65 mph speed limit had astatistically significant impact on fatalities, but this increase decayed over time following an initial

adjustment period.

Lave and Elias (14) argue that previous research has ignored system-wide effects of speed

limit changes by only measuring localized impacts. By comparing fatalities per VMT for states

that increased rural Interstate speeds against states which did not increase speeds they found that

the former states achieved an overall 3.62% reduction in fatalities. They hypothesize that

relaxation of speed limit enforcement rules in 1987 allowed police to re-direct limited resources

from speed enforcement to more beneficial safety activities. They also hypothesize that increasing

speeds along the safest roads in the system (i.e. the rural Interstate highways), encourages

speeding drivers to shift from more dangerous rural routes to realize travel time savings. This

study has been criticized as being too aggregated to support its explanations. The authors did notdemonstrate that fatality rates declined on alternative routes (11), nor did they show a change in

enforcement patterns after speed limits were increased.

Based upon our review of previous research, it is clear that significant gaps remain in

understanding the impact of changing speed limits on injury severity. Previous speed limit crash

severity studies have concentrated on fatalities, and not on potential changes in the entire injury

spectrum. Since shifts in non-fatal injuries can result in significant economic costs, knowledge of

the effects of the policy change on the entire spectrum is important. In addition, most past

research has concentrated on using one methodology, a comparison of crashes or crash rates

before and after the change in the speed limits policy. Few studies provide adequate control of

the many confounding factors which can influence the findings of a before and after evaluation.

This paper introduces the ordered probit model as a new powerful technique for studying theimpacts of speed limit changes on crash injury severity and compares the findings of this model

with a paired-comparison before/after evaluation.

METHODOLOGY

This study explores the relationship between speed limits and the entire spectrum of injury

severity. It focuses on single-vehicle crashes occurring on Interstate roadways in North Carolina.

The hypothesis is that speed limit increases lead to increased driving speeds and result in higher

crash injury severity. Similarly, highway segments with a larger speed limit increase (that is, from

55 mph to 65 mph as opposed to from 55 mph to 60 mph) should experience a greater increase in

injury severity. It is also possible that a higher absolute speed limit after the policy change (thatis, 70 mph as opposed to 60 mph) leads to a greater injury severity. This analysis uses a quasi-

experimental research design and involves several different data analysis techniques, including

simple frequencies, odds-ratio tests, and ordered probit models.

Data Collection


5/21


The Highway Safety Research Center (HSRC) of North Carolina developed the data set for this

analysis from crash inventory information and North Carolina police accident reports. The dataset

includes roughly two years of accident report data (from 1995 to 1997) collected one year before

and after the speed limit change. Each crash is coded by whether it occurred before or after the

date of the policy change.

Each road segment where speed limits were increased (study segments) was identified andpaired with a comparison road segment where the speed limit was not raised. Comparison sites

were selected taking into consideration the similarity to study sites in Average Daily Traffic

(ADT), road type, rural/urban environment, and geographic proximity. Only crashes occurring on

either study or comparison sites were included in the analysis.

When choosing candidate highways for increased speed limits, the NCDOT only selected

road segments with proven safety records, as determined by crash frequency, rate and roadway

characteristics. Therefore, it is likely that the comparison of speed limit policy changes may suffer

from selectivity bias. If injuries are found to increase significantly, these may represent a

conservative estimate of the possible effects of increasing speed limits.

A further possible complication is that North Carolina does not require an accident report

for crashes with no injury and damage estimated at below $1,000. The sample of non-injurycrashes analyzed is smaller than in other states with lower thresholds. However, since only two

years of crash data is included and during this time the report threshold was constant for these

data, because a significant portion of the crashes are still non-injury, this higher threshold should

not significantly affect the results.

Data Compilation

Driver, vehicle, roadway and crash characteristics combine to influence the ultimate severity of

highway crashes. The complicated relationship between these factors can produce inconclusive

and inconsistent modeling results, unless the individual and combined effects of these factors can

be properly identified and controlled. Therefore, several restrictions on the scope on this analysiswere needed to keep the project reasonable and ensure the accuracy of the findings. To control

for possible injury severity variance due to roadway design, only crashes occurring on Interstate

highways were analyzed. There were 14,745 vehicles involved in crashes one year before the

speed limit change on these segments. To eliminate complicating factors such as conflicting

vehicle speed differentials and vehicle masses, this analysis is limited to only single-vehicle crashes

and excludes crashes involving pedestrians, bicyclists, moped, or motorcycles. When these

crashes are removed 3,272 crashes remain in the database.

Not all single-vehicle crashes are similarly impacted by changes in the maximum speed

limit. Maximum speed limit increases are likely to be less influential on single-vehicle crashes

where the driving speed was far below the speed limit. Therefore, a process was needed to

identify and remove these observations. Since actual pre-crash vehicle speeds cannot bemeasured, a post-crash estimate of the speed prior to the crash is provided by the investigating

police officer. At best the police officers estimate can only be used as a rough approximation of

actual traveling speed. To provide a cleaner estimate of speed limit changes on crash severity,

211 crashes where the estimated traveling speed is 45 mph or below were dropped, leaving 3061

crashes in the data set.


6/21


Based upon the coding scheme crashes occurring on the highway entrance/exit ramps

could not be distinguished from non-Interstate crashes occurring at the junction of a secondary

road and Interstate ramps, therefore crashes occurring at intersection entrance and exit ramps

were also eliminated. One hundred and sixteen other cases were dropped due to suspicion of

improper coding of important variables. The eliminated cases were not any more likely to occur

on the study or comparison segments before or after the policy change. The final data set used infor analysis contained 2,729 observations.

Variable Definitions

The dependent variable in this analysis is the most severe injury of any vehicle occupant involved

in a crash, and is rated on the KABCO scale of injury severity: Killed (fatal), Class A

(incapacitating), Class B (evident), Class C (possible), and Property Damage Only (PDOs).

The independent variables in this analysis are divided into two types: policy variables and

external variables. Policy variables classify crashes by whether they occurred either on a study or

comparison road segment and whether or not the crash occurred before or after the date of the

speed limit policy change. Separate policy variables were created for each of the three types ofstudy segments (where limits increased from either 55 to 60 mph, from 55 to 65 mph, or from 65

to 70 mph) and the two types of control segments (speed limits at 55 or 65 mph which did not

change). This resulted in 10 policy dummy variables:

Study Segment Before 55 to 60 mph Study Segment After 55 to 60 mph

Study Segment Before 55 to 65 mph Study Segment After, 55 to 65 mph

Study Segment Before 65 to 70 mph Study Segment After 65 to 70 mph

Comparison Segment Before 55 mph Comparison Segment After 55 mph

Comparison Segment Before 65 mph Comparison Segment After 65 mph

Other factors may have contributed to changes in crash severity, external from the

speed limit change. Possibilities include:

1. occupant/driver characteristics and behavior (e.g., age, gender, use of occupant

restraints devices, and seat position),

2. vehicle characteristics (e.g., car or truck, number of occupants in vehicle),3. road characteristics (e.g., road surface conditions, road geometry, and exit or entry

ramp),

4. environmental and temporal factors (e.g., weather and visibility, traffic conditions,other policy changes),

5. crash characteristics (e.g., run-off the road or hit object, vehicle roll-overs),6. post-crash factors (e.g., time it took to get severely injured persons to health care,

level of emergency management and hospital health care).

The existence of these factors can confound potential measurement of the true effects of a

policy change unless either explicitly controlled through the model specification or implicitly

through the research design (15). Restricting the analysis to only single-vehicle Interstate crashes

and to a short before-after period controls for many possible external factors. Yet there are many


7/21


avenues for the influence of external factors. This study involved several different data analysis

strategies to measure the impacts of speed limit changes on crash severity. The different methods

used in this analysis make separate assumptions regarding the influence of external factors. The

paired-comparison approach controls for these factors implicitly through the use of comparison

sites, which are assumed to be similar to the study segments apart from the policy change. The

ordered probit model explicitly controls for some of these factors by including them asexplanatory variables in the model.

DATA ANALYSIS

Overview of Data

The frequency of crashes decreases as injury severity increases. By far the largest share of crashes

(62%, N = 1701) were property damage only crashes, followed by Class C injuries (19%, N =

517), Class B injuries (14%, N = 374), and Class A injuries (4%, N = 103). Fatal crashes

constitute only 1% of all crashes (N = 34). On treatment segments where speed limits were raised

from 55 to 60 mph no injury crashes declined from 80 to 60 before and after the change, while

class C crashes increased from 24 to 41, class B crashes increased from 11 to 18, class A crashesdecline from 3 to 2, while there were no fatal crashes on these segments either before or after the

speed limit increase. On the segments where speeds were increased from 55 to 65 mph there was

a slight increase from 34 to 40 no injury crashes, class C crashes increased from 11 to 18, class B

crashes increased from 5 to 13, class A crashes increased from 1 to 6, while there no fatalities

before and one fatality after the speed limit increase on these segments. On the segments where

speed limits were increased from 65 to 70 mph there no injury crashes increased slightly from 356

to 400, class C crashes increased from 91 to 109, class B injuries increased from 83 to 85, class A

injuries declined slightly from 28 to 35 and fatalities increased from 9 to 11 before and after the

speed limit increase. On the 55 mph comparison segments, there was a slight increase in crashes

and on the 65 mph comparison segments, there was a decrease in crashes.

Similar distributions were examined for several of the potential independent variables:

including crash type, impact region, road features, road conditions and the object struck in the

crash. As expected, most single-vehicle Interstate crashes involve the vehicle running off the road

(72%). Other common crash types included the vehicle hitting an animal (11%) or some other

object (7%). Most vehicle impacts occurred to the front region of the vehicle (52%). The

remaining crashes were near evenly split between left side impacts (13%), right side impacts

(12%), rear impacts (9%) and others (14%). Almost all crashes occurred where there were no

specific roadway features involved (92%), while 5% occurred at bridges, and 3% at underpasses.

Most crashes occurred on a dry roadway surfaces (62%) although there were a significant number

of crashes occurring during wet (30%) icy (7%), and snowy (1%) road conditions. Vehicle

rollovers occurred in 25% of all crashes. The most commonly struck fixed objects include the faceof guardrail (21%), a tree or pole (13%), a roadside or median barrier (13%), a ditch or basin

(11%), an animal (10%), a sign or fence (5%), the end of a guardrail (4%), or striking some other

type of fixed object (11%). Twelve percent of all crashes did not strike a fixed object, indicating

that the vehicle either hit a non-fixed object, rolled over without striking a fixed object, or was

involved in another type of hazardous crash, which did not involve striking a fixed object. The

types of vehicles in the crash file include: Sedans (62%), pick-up trucks (11%), station wagons

(8%), vans and buses (8%), heavy trucks (8%), and sport utility vehicles (3%). Of all crashes,


8/21


55% were single occupant vehicles, 27% included two passengers, 9% included three passengers,

6% included four passengers, while the remaining 3% included five or more passengers. Only 5%

of all crashes in the database were alcohol related.

For further insight into the occurrence of different crash types on the study and

comparison segments before and after the speed limit increase we cross-tabulated the 10 policy

variables with the dependent variable (crash injury severity), and key explanatory variables (Table1). Each cell contains both the crash frequency and the percentage of total crashes for each policy

variable. The contingency table analysis identifies potential confounding factors. If the external

factors vary across the before and after periods in the study and control segments then they may

impact the severity of crashes independent of the policy change. A brief examination of the

percentages in Table 1 does not suggest the existence of many possible confounders among the

selected set of external variables. Very few external factors were found to change by more than

10% between the before and after period for the particular type of road segment. The occurrence

of front-end impacts decreased by 15% on 55-60 mph study segments, these percentage were

partially offset by a 12% increase in rear impacts. The percentage of front-end impacts increased

by 24% on 55-65 mph road segments.

Contingency table analysis can identify possible confounders, but this method cannotidentify their existence with certainty. An external factor which does not change in proportion

(identified through the contingency table) across the before and after periods may still have

confounding effects if its impact on crash severity has changed in intensity. It is also possible that

changes in the external factors are the result of changes in the speed limits, essentially becoming

an agent through which the change in speed limit influences injury severity (e.g., rollovers might

increase with speed increases while a small sign impact or animal hit may not). These interactions

between external factors and the policy variables are tested and discussed in the section describing

the ordered probit model.

Paired-comparison Analysis

Paired-comparison analysis is a commonly used method for evaluating the impact of a policy

change in safety research (3). This paired-comparison analysis employs a quasi-experimental

design methodology, where study cases are paired with comparison cases. Ideally, comparison

sites are similar to the study sites in all respects except for the change in speed limits. If the

similarity condition is reasonably met and the sample size is large enough, then the divergence in

crash severity between the study and comparison sites is attributed to the treatment.

The validity of the paired-comparison approach relies upon the degree of similarity

between the study and comparison groups. If the study and comparison segments are indeed

comparable, we should expect a similar distribution of crash occurrence between treatment and

comparison segments before the policy change. This comparison was conducted by examining the

percentage cross-tabulations appearing in Table 1 for the before treatment segments to the

before comparison segments. Overall, the study and comparison sites before the policy change

are similar based upon our percentage comparisons. In all but a few cases the study and

comparison cases are within an absolute difference of 5%. In no cases did the absolute

percentage difference differ between the study and comparison segment by more than 10%.

Assuming that the study and comparison sites are comparable, the next step in the paired-

comparison analysis tests the change in the study segments after the speed limit change, against


9/21


the changes in the comparison group. Cross-tabulating the policy variables with the dependent

variable (injury severity) produced a matrix of crash occurrences by crash severity for the control

and study variables for both the before and after periods. From this information, an odds-ratio

was calculated for each type of study segment at each level of injury severity. The odds-ratios are

calculated by:

Odds Ratio = (ISA/ ISB) / (ICA/ ICB)

where:

ISA = Injuries of severityI- on the study segments After Speed Limit Policy Change

ICA = Injuries of severityI- on the comparison segments After Speed Limit Policy Change

ISB = Injuries of severityI- on the study segments Before Speed Limit Policy Change

ICB = Injuries of severity I- on the comparison segments Before Speed Limit Policy Change

If the speed limit increase had no effect on the number of injuries of severityIon the study

segments the odds ratio will be 1. Alternately, if the speed increase significantly increased

(decreased) injuries, the odds-ratio will be greater (smaller) than one.

The odds-ratio analysis is presented in Table 2. An increase in the odds of greater crashseverity for all study segments after the policy change is expected. When summarized for all

segments regardless of speed change the odds-ratios for all injury types are consistently greater

than one, suggesting an increased likelihood in the study segments after the policy change

compared to the comparison group. In many cases the odds ratio exceed one by a small amount.

Examining odds ratios classified by the different speed changes shows some important

insights. Where study segments where speeds were increased from 55 to 60 mph, property

damage only crashes became less likely on and Class C and Class B injuries became more likely

over the comparison group . We also see similar increased odds for Class B and Class C injuries

in study segments where speeds were raised from 55 to 65 mph. It is possible that higher speeds

have increased the chances of lower injury crash after the policy change. The fastest study

segments (65 to 70 mph) saw a minor odds increase across the entire injury spectrum, but had nooutstanding increases or decreases.

The estimated odds ratios are less reliable when they are calculated based upon few

crashes. This limitation applies to the Class A and Fatal injury classes. The 55 mph study and

comparison segments consistently had less than ten Class A and Fatal crashes per group. In some

cases there were no fatal crashes in either the study or comparison segments and thus the odds

ratios could not be calculated (division by zero). There were also very few fatal crashes among

the separate study and comparison segments. Even when fatal and Class A injuries are combined,

the numbers are still low for proper interpretation.

The paired-comparison technique has several weaknesses. Firstly, the assumption that

study and comparison sites are sufficiently similar and that changes in the treatment groups are

entirely attributable to the policy change. The paired-comparison technique does not control for

external factors that may influence crash severity differently in the before and after periods

between the study and comparison groups. This approach also does not isolate the effects of the

policy change from the external factors to estimate the influence of policy change on the

probability of sustaining a more severe injury, nor can the presence of interaction between the

external factors and policy change be adequately tested.


10/21


Ordered Probit Analysis

To explore the effects of policy variables, while controlling for external factors, ordered probit

models were estimated. Ordered probit models have been used to analyze crash data in several

recent studies (16, 17). The model provides a more accurate estimation of the impact of the

policy change on the probability of crash severity.

Model Specification

Ordered probit models are appropriate when the dependent variable involves an categorical

dependent variable that is classified on an ordinal scale. The dependent variable in this study,

crash severity, is measured on the KABCO injury scale that orders injury severity from property

damage only (lowest) to fatal crashes (highest). Unlike ordinary least squares regression which

assumes that the ordering between categories are of equal distance, ordered probit models are

able to account for unequal difference between categories in the dependent variable. The ordered

form is also preferred to other maximum likelihood models, such as logit and probit, which treat

categories in the dependent variable as independent alternatives and do not account for theordered classification scale of the dependent variable (20).

The ordered probit model has the following form:

y* = x + Where:

y* is the dependent variable (injury severity that is unobserved),

is the vector of estimated parameters,x are the explanatory variables, and

is the normally distributed error term

Parameter estimates () represent the effect of explanatory variables on the underlying injuryscale. Only the signs, relative magnitudes and significance of the parameter estimates can be

interpreted directly; separate computation of the marginal effects for each independent variable is

needed to understand the effect of a unit change in the independent variable. Based upon this

specification, the probability of the dependent variable falling in any ordered category is:

Prob (y=n) = (n - x) - (n-1 - x)

has a cumulative distribution denoted by (.) and density function denoted by (.). An individual

falls in category n ifn-1 < y* < n; the injury data, y, are related to the underlying latent variable,

y*, through thresholds n, wheren=1...4. We have the following probabilities:Prob(y=n) = (n - 'x) - (n-1 - 'x), n=1...4

where, 0 = 0 and 3 = + and where 1


11/21


explanatory variables on the underlying scale. The marginal impacts of factors x on the

underlying injury propensity can be evaluated as:

Prob(y=n)/ x = -[(n - 'x) - (n-1 - 'x)], n=1...4.Computation of marginal effects is particularly meaningful for the ordered probit model

where the effect of variables x on the intermediate categories is ambiguous if only the parameter

estimates are available.A measure of model goodness of fit (2) can be calculated as:

2 = 1 - [ln Lb / ln L0]

Where ln Lb is the log likelihood at convergence and ln L0 is the restricted log likelihood. The 2

measure is bounded by 0 and 1. As 2 approaches 1, the better the fit of the model. This

goodness of fit measure can also be modified to account for more independent variables by:

2 = 1 - [(ln Lb - K)/(ln L0)]

Where: K is the degrees of freedom of the model.

Description of Variables

Separate dummy variables were created to represent each of the policy variables. The threepolicy variables representing study segments after the policy change are expected to significantly

increase the probability of severe injuries. To estimate a model with the policy dummy variables,

it is necessary to withhold a subset of the policy variables, i.e. the base. Because we are

interested in measuring the change attributable to the speed limit change, the three variables

representing study segments in the before period were withheld as the base.

The coefficient estimates of the external variables are included as controls. The inclusion

of external variables in the model was based upon theory and statistical significance. Table 3

provides a list of tested external factors and their expected impacts on injury severity.

Modeling Results

The final model reported in Table 4 included the policy variables (except those withheld as the

base), external factor variables, and the thresholds associated with the ordered probit model. The

model has a 2 of 0.116 and an adjusted 2 of 0.107, indicating a relatively good model fit

considering the complicated nature of most crash injuries. To test the normality assumption an

equivalent ordered logit model, where the error term has a logistic distribution, was estimated.

However the results were identical to the ordered probit model. Furthermore, an graphical

analysis of the residuals of the ordered probit residuals did not trigger warnings indicating mis-

specification.

The policy variables representing study segments where speed limits were raised from 55

mph to 60 mph and from 55 mph to 65 mph are both statistically significant and positive at the

5% significance level. The increase in injury severity is relative to the base, i.e., the study

segments before the policy change. As expected, the increase in crash severity is greater for

segments where speed limits were raised by 10 mph over those where speeds were raised by 5

mph, as indicated by the magnitude of the coefficient estimates. The policy variable representing

65 to 70 mph segments was not statistically significant. None of the other policy variables

included were found to be statistically significant at the 95% confidence level. The magnitudes of


12/21


these and their insignificance indicates no statistically significant change occurred on the

comparison segments during the before and after periods.

Examining the marginal effects for the significant policy variables enables us to determine

the influence of the policy change on the probability change of injury levels when all other

variables are held at their means. Figure 1 demonstrates the marginal effects graphically. There is

an increase in the probability of a Class C and Class B level injury crash in the study segmentsover a property damage only crash. This increase in more pronounced for the segments where

speed limits were raised by 10 mph compared to those where the increase was 5 mph. After a

peaking at Class B injury severity the probability change of a serious injury declines for the study

segments, although this probability remains positive for Class A injuries. The small number of

fatal crashes limits the value of marginal effects in this category.

The external variables that significantly increase the level of most severe occupant injury

include more vehicle occupants (an important exposure variable), if the vehicle overturned, if

alcohol was involved, and if certain fixed objects were stuck including guardrails (both face and

end) barriers (bridge railing included), trees and poles (relative to striking no specified object).

The vehicle occupant variable was found to have a non-linear relationship to crash severity. As

the number of occupants increases the crash severity also increases; the increase gets largermoving from two to three occupants, after which the increase in severity drops slightly and then

stabilizes relative to the base of single occupant vehicles. The variables describing vehicle type

(station wagon, trucks, etc.) were not statistically significant and were not included in the final

specification. The variables representing crash type, although significant, were found to be highly

correlated with the object stuck variables. Therefore, the crash type variables were withheld

from the model.

Interactive Effects

Several external variables were tested for possible interactive effects with the policy variables. A

speed increase can make certain types of crashes more injurious. For example, if striking a treeincreases injury severity, but more so in the after period, then the presence of roadside trees will

be an important consideration in the decision to raise speed limits. The only interaction variable

found statistically significant is striking the face of a guardrail. Table 5 shows that striking a

guardrail face increases injury severity, but not statistically significantly (10% level). However,

striking a guardrail face in the after period significantly increases injury severity. The marginal

effects show that the increase in probability of C injuries is 0.039 (3.9%), B injuries is 0.044

(4.4%), A injuries is 0.01 (1%), and fatal injuries is 0.003 (0.3%). The inclusion of the interactive

variable also reduces the significance and the probability change of the policy variables

representing the 55 to 60 mph and 55 to 65 mph segments after the policy change. It appears that

some of the probability increase attributed to increased speed limit in the original (non-interactive)

model is explained by the more severe guardrail face crashes. However, it is beyond the scope of

this analysis to determine if these crashes would have been even more severe if no guardrails were

present.

CONCLUSION


13/21


The paired-comparison method and the ordered probit model show an increased likelihood of

Class B and Class C injuries on study segments where speed limits were increased from 55 mph to

either 60 mph or 65 mph. The highway segments where speed limits were raised by 10 mph

resulted in a higher probability of increased severity than those raised by 5 mph. No significant

changes in injury severity were found for the comparison segments or for highway segments

where speed limits were raised from 65 to 70 mph. Higher crash severity is observed whenvehicles strike the face of a guardrail after the speed limits were raised. One implication is that

decision-makers consider the presence of guardrails, in addition to existing criteria, for evaluating

whether speed limits should be raised on a road segment.

In exploring crash injury spectrum, the ordered probit model was valuable. Most

previous studies have only examined the impacts of speed limit changes on fatalities. This study

extends the analysis of speed limit changes to the entire spectrum of injury severity for single

vehicle crashes. Our findings show that although the impact of speed limit increases on fatalities

is relatively small, given the segments analyzed, there is a significant increase in the probability of

sustaining minor and non-incapacitating injuries. These previously unexplored injuries can

amount to sizable safety costs for the society and should be weighed against the benefits of

increased travel time savings from the speed limit increases. The impacts of speed limit changeson fatalities could not be conclusively measured, due to a small sample of fatal crashes. The use

of policy variables in the ordered probit model allows the analyst to emulate a before-after paired-

comparison evaluation while explicitly (and implicitly through comparison sites) controlling for

the key external factors which can confound the findings of a traditional paired-comparison

analysis.

Given that this analysis deals with real-life data in a natural experiment, there is the

possibility of selectivity bias. NCDOT selected highway segments with relatively good safety

records for raising the speed limits. If selectivity bias is present, then it is possible that these

findings are relatively conservative than if the study segments were selected randomly. A further

limitation of this study is that it examines only one crash type (single-vehicle crashes) on one type

of roadway (Interstates) over a limited time period (two years) in one state (North Carolina).Therefore, the results should be interpreted with caution. Studies have shown differential impacts

of speed limits on crash types across states (16). Others have found that the safety implications of

speed limits evolve as the policy matures (17). As more data becomes available, future research

should examine crash effects extending beyond one year and perhaps exclude the periods

immediately before and after the speed limit changes. However, given that driver behavior often

changes as a result of future safety changes (e.g. seatbelt laws or speed limit changes) it is

difficult to clearly define a transition period. It is also possible that higher speed limits may have

completely unique effects for different crash and road types. This study also did not examine the

system-wide effects of speed limit changes as hypothesized by Lave (18). Further study is needed

to determine the impact of speed limits on other types of crashes, different vehicle occupants, and

on different road types.

ACKNOWLEDGMENT

The North Carolina data were jointly provided by the Highway Safety Information System (HSIS)

and the UNC Highway Safety Research Center under efforts funded by the NC Governor's


14/21


Highway Safety Program. We greatly appreciate the efforts of Dr. Donald Reinfurt, Ms. Subha

Jamburajan and Ms. Carolyn Williams in the manual extraction of data that identified and matched

the treatment and comparison sites. LIMDEP statistical software was used for estimation of the

ordered probit models.


15/21


REFERENCES

1) Clotfelter, C.T. and J.C. Hahn. (1978) Assessing the National 55 MPH speed limit Policy

Sciences 9.

2) Transportation Research Board. (1984) 55: A decade of experience (Special Report 204).

National Research Council. Washington DC.3) McCarthy, P. (1997) The Effects of Speed Limits on Speed Distribution and Highway

Safety: A Survey of Literature. Report prepared for the National Research Council,

Transportation Research Board.

4) Shinar, D. (1997) Speed and Crashes: a Heated Topic and an Elusive Relationship Report

submitted to the TRB Special Policy Committee on Speed Management.

5) Freedman, M. and J.R. Esterlitz. (1990) The effects of the 65 mph speed limit on speeds in

three states. Transportation Research Record1281.

6) Retting, W.J. and M.A. Greene. (1997) Traffic speeds following repeal of the national

maximum speed limit. ITE Journal67.

7) Soloman, D. (1964) Accidents on main rural highways related to speed, driver and vehicle.

Washington D.C.: U.S. Department of Commerce, Bureau of Public Roads.8) Joksch, H.C. (1993) Velocity change and fatality risk in a crash. Accident Analysis and

Prevention 25.

9) NHTSA. (1992) The effects of the 65 mph speed limit through 1990: A report to Congress. :

National Highway Traffic Safety Administration, U.S. Department of Transportation.

Washington DC.

10) Baum, H.M., J.K. Wells and A.K. Lund. (1990) Motor Vehicle Crash fatalities in the

second year of the 65 MPH speed limits. Journal of Safety Research 21.

11) Farmer, C., R. Retting and A. Lund. (1997) Effects of 1996 Speed Limit Changes on Motor

Vehicle Occupant Fatalities. Insurance Institute for Highway Safety.

12) Garber, S., and J. Graham. (1990) The effects of the new 65 mile per hour speed limit on

rural highway fatalities: A state-by-state analysis.Accident Analysis and Prevention 22.13) Chang, Gang-Len. et al. (1991) Safety Impacts of the 65 mph Speed Limit on Interstate

Highways. AAA Foundation for Traffic Safety, August.

14) Lave, C. and P. Elias, (1994) Did the 65 MPH limit save lives? Accident Analysis and

Prevention 26.

15) Hauer, E. (1997) Observational Before-After Studies in Road Safety. Pergamon Press.

Tarrytown, New York.

16) Duncan, Chandler, A. Khattak and F. Council. (1998). Applying the Ordered Probit Model

to Injury Severity in Truck-Passenger Car Rear-End Collisions. Forthcoming in

Transportation Research Record, TRB, Washington, D.C.

17) ODonnell C. and D. Connor. (1996) Predicting the severity of motor vehicle accident

injuries using models of ordered multiple choice. Accident Analysis and Prevention 28.


16/21


Table 1:

Cross Tabulation of Policy Variables with Crash Injury Severity and Several External Factors

Comparison segments Treatment segments

55 mph 65 mph 55 - 60 mph 55 - 65 mph 65 - 70 mph

Before After Before After Before After Before After Before After Total

Crash Injury Severity

No Injury 129 (58%) 133 (58%) 238 (65%) 225 (66%) 80 (68%) 66 (52%) 34 (67%) 40 (51%) 356 (63%) 400 (63%) 1701

Class C 49 (22%) 60 (26%) 58 (16%) 56 (16%) 24 (20%) 41 (32%) 11 (22%) 18 (23%) 91 (16%) 109 (17%) 517

Class B 38 (17%) 25 (11%) 51 (14%) 45 (13%) 11 (9%) 18 (14%) 5 (10%) 13 (17%) 83 (15%) 85 (13%) 374

Class A 5 (2%) 9 (4%) 14 (4%) 10 (3%) 3 (3%) 2 (2%) 1 (2%) 6 (8%) 28 (5%) 25 (4%) 103

Fatal 1 (0%) 1 (0%) 5 (1%) 6 (2%) 0 (0%) 0 (0%) 0 (0%) 1 (1%) 9 (2%) 11 (2%) 34

Total 222 228 366 342 118 127 51 78 567 630 2729

External variables

Type of Crash

Ran-off right 79 (36%) 68 (30%) 143 (39%) 114 (33%) 31 (26%) 28 (22%) 20 (39%) 28 (36%) 219 (39%) 220 (35%) 950

Ran-off left 81 (36%) 82 (36%) 148 (40%) 134 (39%) 55 (47%) 55 (43%) 18 (35%) 34 (44%) 189 (33%) 219 (35%) 1015

Ran-off straight 1 (0%) 0 (0%) 0 (0%) 2 (1%) 0 (0%) 1 (1%) 0 (0%) 0 (0%) 1 (0%) 2 (0%) 7

Overturn 6 (3%) 1 (0%) 2 (1%) 2 (1%) 2 (2%) 0 (0%) 1 (2%) 0 (0%) 7 (1%) 7 (1%) 28

In road, other 3 (1%) 13 (6%) 12 (3%) 25 (7%) 5 (4%) 7 (6%) 2 (4%) 1 (1%) 21 (4%) 41 (7%) 130

Hit animal 5 (2%) 14 (6%) 36 (10%) 36 (11%) 0 (0%) 3 (2%) 2 (4%) 7 (9%) 98 (17%) 88 (14%) 289

Hit fixed roadway

object

33 (15%) 34 (15%) 8 (2%) 4 (1%) 9 (8%) 22 (17%) 3 (6%) 2 (3%) 5 (1%) 6 (1%) 126

Hit other 14 (6%) 16 (7%) 17 (5%) 25 (7%) 16 (14%) 11 (9%) 5 (10%) 6 (8%) 27 (5%) 47 (7%) 184Total 222 228 366 342 118 127 51 78 567 630 2729

Impact Region

Front Impact 129 (58%) 128 (56%) 186 (51%) 180 (53%) 78 (66%) 65 (51%) 23 (45%) 54 (69%) 280 (49%) 286 (45%) 1409

Right Side Impact 21 (9%) 30 (13%) 35 (10%) 35 (10%) 10 (8%) 15 (12%) 6 (12%) 6 (8%) 71 (13%) 81 (13%) 310

Left Side Impact 23 (10%) 21 (9%) 60 (16%) 44 (13%) 8 (7%) 12 (9%) 7 (14%) 4 (5%) 65 (11%) 95 (15%) 339

Rear Impact 24 (11%) 25 (11%) 27 (7%) 28 (8%) 9 (8%) 26 (20%) 6 (12%) 7 (9%) 36 (6%) 44 (7%) 232

Other 24 (11%) 20 (9%) 49 (13%) 52 (15%) 10 (8%) 8 (6%) 9 (18%) 7 (9%) 104 (18%) 108 (17%) 391

Total 221 224 357 339 115 126 51 78 556 614 2681

Road Condition

Dry 108 (49%) 130 (57%) 236 (64%) 247 (72%) 54 (46%) 59 (46%) 24 (47%) 40 (51%) 335 (59%) 428 (68%) 1661

Wet 97 (44%) 88 (39%) 68 (19%) 85 (25%) 47 (40%) 61 (48%) 20 (39%) 34 (44%) 140 (25%) 187 (30%) 827

Snowy 2 (1%) 0 (0%) 7 (2%) 1 (0%) 3 (3%) 0 (0%) 1 (2%) 2 (3%) 17 (3%) 1 (0%) 34

Icy 15 (7%) 10 (4%) 53 (14%) 9 (3%) 13 (11%) 6 (5%) 6 (12%) 2 (3%) 75 (13%) 13 (2%) 202

Other 0 (0%) 0 (0%) 2 (1%) 0 (0%) 1 (1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 3222 228 366 342 118 126 51 78 567 629 2727

Alcohol Related Incident

No 201 (91%) 203 (89%) 350 (96%) 328 (96%) 111 (94%) 119 (94%) 47 (92%) 73 (94%) 553 (98%) 606 (96%) 2591

Yes 21 (9%) 25 (11%) 16 (4%) 14 (4%) 7 (6%) 8 (6%) 4 (8%) 5 (6%) 14 (2%) 24 (4%) 138

Total 222 228 366 342 118 127 51 78 567 630 2729

Object Struck

None 12 (5%) 21 (9%) 41 (11%) 45 (13%) 8 (7%) 8 (6%) 4 (8%) 5 (6%) 79 (14%) 93 (15%) 316

Animal 5 (2%) 13 (6%) 34 (9%) 35 (10%) 0 (0%) 3 (2%) 2 (4%) 7 (9%) 100 (18%) 87 (14%) 286

Tree & Pole 7 (3%) 6 (3%) 31 (8%) 34 (10%) 3 (3%) 3 (2%) 8 (16%) 8 (10%) 127 (22%) 133 (21%) 360

Sign 8 (4%) 8 (4%) 31 (8%) 19 (6%) 4 (3%) 5 (4%) 2 (4%) 8 (10%) 23 (4%) 18 (3%) 126

Guardrail End 9 (4%) 2 (1%) 18 (5%) 14 (4%) 2 (2%) 4 (3%) 1 (2%) 6 (8%) 18 (3%) 38 (6%) 112

Guardrail Face 47 (21%) 45 (20%) 129 (35%) 110 (32%) 11 (9%) 9 (7%) 11 (22%) 11 (14%) 92 (16%) 112 (18%) 577

Barrier 81 (36%) 71 (31%) 18 (5%) 5 (1%) 66 (56%) 78 (61%) 7 (14%) 17 (22%) 11 (2%) 14 (2%) 368

Ditch and Basin 20 (9%) 26 (11%) 35 (10%) 43 (13%) 3 (3%) 3 (2%) 6 (12%) 7 (9%) 71 (13%) 79 (13%) 293Other Object 33 (15%) 36 (16%) 29 (8%) 37 (11%) 21 (18%) 14 (11%) 10 (20%) 9 (12%) 46 (8%) 56 (9%) 291

Total 222 228 366 342 118 127 51 78 567 630 2729

Vehicle Rollover

Yes 31 (14%) 23 (10%) 71 (19%) 57 (17%) 8 (7%) 7 (6%) 7 (14%) 10 (13%) 132 (23%) 127 (20%) 697

No 191 (86%) 205 (90%) 295 (81%) 285 (83%) 110 (93%) 120 (94%) 44 (86%) 68 (87%) 435 (77%) 503 (80%) 2032

Total 222 228 366 342 118 127 51 78 567 630 2729


17/21


Table 2:

Odds-Ratios for Crash Severity: Comparison versus Study Sites

All

Segments

55-60 mph

Change

55-65 mph

Change

65-70 mph

Change

Property Damage Only 1.088 0.800 1.141 1.189Class C 1.182 1.395 1.336 1.241

Class B 1.566 2.487 3.952 1.161

Class A 0.884 0.370 3.333 1.250

Killed 1.167 - - 1.019

Class A + Killed 0.945 0.400 4.200 1.155

Bold face type denotes an odds ratio greater than 1.5Underlined test indicates odds ratios less than .5Italics denotes cases with very few observations on which to base an odds ratiocalculation


18/21


Table 3:

Hypotheses regarding External variables

Variable Hypothesized Direction

Alcohol Involved Positive - drivers impaired by alcohol may have diminished drivingability and thus more susceptible to serious injury.

Vehicle Rollover Positive - if a vehicle rolls over then the occupants are more likely tosustain serious injury, compared not overturning

Road Condition - Wet Unknown - slippery roadways may diminish vehicle braking ability, butdrivers may compensate with extra caution compared to dry roadconditions

Road Condition - Snowy Same as aboveRoad Condition - Icy Same as aboveCrash Type Ran Off Road Positive - compared to crashes involving a non-fixed roadway object,

ran-off-road crashes are more likely to be severe due to the potentialfor striking a fixed roadside object

Crash Type Hit FixedRoadway Object

Positive - fixed objects are more likely to result in serious injury overnon-fixed objects

Crash Type Hit Animal Negative - compared to other hitting a non-fixed object, striking an

animal would result in less severe passenger injuries.Crash Type Other Unknown - Other crash types could include a variety of crash typesthat did fit one of the pre-existing categories.

Region of Impact - Front End Positive - compared with rear-end impacts, it is expected that front endimpacts are more severe

Region of Impact - Left Side Same as aboveRegion of Impact - Right Side Same as aboveVehicle Type Station Wagon Negative - compared with passenger cars, occupants of station wagons

will be less prone to sustain serious injury due to increased vehiclemass.

Vehicle Type Heavy Truck Same as aboveVehicle Type Sports Utility Same as aboveVehicle Type Pickup Same as aboveObject Struck Guardrail End Positive - striking the end of a guardrail will result in a more severe

injury compared to not striking a fixed objectObject Struck Guardrail Face Same as aboveObject Struck Sign Same as aboveObject Struck Barrier (incl.bridge)

Same as above

Object Struck AnimalObject Struck Ditch Same as aboveObject Struck Tree or Pole Same as aboveObject Struck Other Same as aboveNumber of Occupants Positive - more vehicle passengers increases the chances of more

severe injuries.


19/21


Table 4:

Ordered Probit Model for Injury Severity Measured on the KABCO scale

Number of Observations 2729Degrees of Freedom 24Log likelihood -2559.759

Restricted log likelihood -2894.130

2

.116

Adjusted 2

.107

Marginal Effects

Variable Coefficient z-Stat PDO Class C Class B Class A Killed

Constant -0.743 -9.470 0.277 -0.109 -0.127 -0.033 -0.009Study Segment - After 55 to 60 mph 0.279 2.116 -0.097 0.044 0.042 0.009 0.002

Study Segment - After 55 to 65 mph 0.376 2.351 -0.127 0.059 0.054 0.012 0.003

Study Segment - After 65 to 70 mph -0.051 -0.730 0.019 -0.007 -0.009 -0.002 -0.001

Comparison Segment - After 55 mph 0.201 2.014 -0.071 0.031 0.031 0.007 0.002Comparison Segment - After 65 mph -0.034 -0.400 0.013 -0.005 -0.006 -0.002 0.000Comparison Segment - Before 55 mph 0.127 1.297 -0.046 0.019 0.020 0.005 0.001Comparison Segment - Before 65mph

-0.038 -0.475 0.014 -0.005 -0.007 -0.002 0.000

Alcohol Involved 0.374 3.853 -0.126 0.059 0.053 0.012 0.003Vehicle Rollover 1.141 17.348 -0.242 0.146 0.081 0.012 0.002Road Condition - Wet -0.264 -4.666 0.104 -0.031 -0.051 -0.017 -0.005Road Condition - Snowy -0.996 -3.963 0.378 -0.046 -0.190 -0.096 -0.045Road Condition - Icy -0.263 -2.728 0.102 -0.033 -0.049 -0.015 -0.005Two Occupants 0.101 1.748 -0.036 0.015 0.016 0.004 0.001Three Occupants 0.379 4.486 -0.127 0.060 0.053 0.011 0.002Four Occupants 0.298 2.886 -0.103 0.047 0.044 0.010 0.002Five or more Occupants 0.310 2.180 -0.107 0.049 0.046 0.010 0.002Object Struck - Guardrail End 0.892 7.914 -0.245 0.133 0.092 0.017 0.003Object Struck - Guardrail Face 0.241 3.140 -0.083 0.038 0.035 0.008 0.002Object Struck - Sign 0.169 1.305 -0.061 0.026 0.027 0.006 0.002Object Struck - Barrier (incl. bridge) 0.361 4.111 -0.120 0.057 0.050 0.011 0.002

Object Struck - Animal -0.889 -6.642 0.339 -0.037 -0.172 -0.088 -0.042Object Struck - Ditch 0.178 1.973 -0.063 0.028 0.028 0.007 0.002Object Struck - Tree or Pole 0.662 8.071 -0.192 0.102 0.074 0.014 0.003Object Struck - Other -1.657 -2.388 0.545 0.035 -0.229 -0.196 -0.154

1 0.695 24.696

2 1.609 33.696

3 2.291 31.346


20/21


Table 5:

Ordered Probit Model for Injury Severity Measured on the KABCO scaleincluding Interactive Variable Effects

Number of Observations 2729Degrees of Freedom 25Log likelihood -2555.265

Restricted log likelihood -2894.130

2

.117

Adjusted 2

.108

Marginal Effects

Variable Coefficient z-Stat PDO Class C Class B Class A Killed

Constant -0.731 -9.262 0.272 -0.107 -0.124 -0.033 -0.008



Study Segment - After 65 to 70 mph -0.125 -1.683 0.047 -0.018 -0.021 -0.006 -0.002Comparison Segment - After 55 mph 0.212 2.128 -0.079 0.031 0.036 0.010 0.002Comparison Segment - After 65 mph -0.017 -0.197 0.006 -0.002 -0.003 -0.001 -0.000Comparison Segment - Before 55 mph 0.139 1.417 -0.052 0.020 0.024 0.006 0.002Comparison Segment - Before 65 mph -0.019 -0.235 0.007 -0.003 -0.003 -0.001 -0.000

Alcohol Involved 0.382 3.949 -0.142 0.056 0.065 0.017 0.004Vehicle Rollover 1.141 17.252 -0.425 0.167 0.194 0.051 0.013Road Condition - Wet -0.273 -4.793 0.102 -0.040 -0.046 -0.012 -0.003

Road Condition - Snowy -0.984 -3.865 0.367 -0.144 -0.168 -0.044 -0.011Road Condition - Icy -0.264 -2.745 0.099 -0.039 -0.045 -0.012 -0.003Two Occupants 0.105 1.823 -0.039 0.015 0.018 0.005 0.001Three Occupants 0.381 4.512 -0.142 0.056 0.065 0.017 0.004Four Occupants 0.311 3.033 -0.116 0.046 0.053 0.014 0.004Five + Occupants 0.308 2.141 -0.115 0.045 0.052 0.014 0.004Object Struck - Guardrail End 0.903 8.018 -0.337 0.132 0.154 0.040 0.010

Object Struck - Guardrail Face 0.139 1.628 -0.052 0.020 0.024 0.006 0.002Object Struck - Sign 0.174 1.351 -0.065 0.026 0.030 0.008 0.002Object Struck - Barrier (incl. bridge) 0.356 4.057 -0.133 0.052 0.061 0.020 0.004Object Struck - Animal -0.885 -6.619 0.330 -0.130 -0.151 -0.040 -0.011Object Struck - Ditch 0.183 2.029 -0.068 0.027 0.031 0.008 0.002Object Struck - Tree or Pole 0.675 8.209 -0.252 0.100 0.115 0.030 0.008Object Struck - Other -1.675 -2.407 0.624 -0.245 -0.285 -0.075 -0.019

Interactive - Guardrail Face*StudyAfter

0.400 2.996 -0.149 0.059 0.068 0.018 0.005

1 0.697 24.685

2 1.613 33.720

3 2.299 31.096


21/21


Figure 1

Marginal Effects of Speed Lim it Changes on Crash Severity

-0.150

-0.100

-0.050

0.000

0.050

0.100

PDO Class C Class B Class A Killed

ProbabilityChange

Study 55 to 60 mph

Study 55 to 65 mph

Documents

Impact of Speed Limit Increases on Crash Injury Severity