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Accident Analysis and Prevention 43 (2011) 11481159

Contents lists available at ScienceDirect

Accident Analysis and Prevention

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a a p

The effects of Electronic Stability Control (ESC) on crashesAn update

Alena Hye

Institute of Transport Economics, Department of Environment and Safety, Gaustadalleen 21, 0349 Oslo, Norway

a r t i c l e i n f o

Article history:

Received 30 August 2010

Received in revised form 8 December 2010

Accepted 19 December 2010

Keywords:

Electronic Stability Control (ESC)

Crash

Meta-analysis

Meta-regression

a b s t r a c t

The present study is an update of the meta-analysis by Erke (Erke, A., 2008. Effects of Electronic Stabil-

ity Control (ESC) on accidents: a review of empirical evidence. Accident Analysis & Prevention, 40 (1),

167173). Results from 12 studies of the effects of Electronic Stability Control (ESC) on the number of

differenttypesof crashes were summarized by meansof meta-analysis.The results indicate that ESCpre-vents about 40% of all crashes involving loss of control. The greatest reductions were found for rollover

crashes (50%), followed by run-off-road (40%) and single vehicle crashes (25%). These results are

however likely to be somewhat overestimated, especially for non-fatal crashes. Multiple vehicle crashes

were found to be largely unchanged. Reductions were found for some types of multiple vehicle crashes.

Rear-end collisions are unchanged or may increase. Fatal crashes involving pedestrians, bicycles or ani-

mals were found to increase as well. ESC was found to be more effective in preventing fatal crashes than

non-fatal crashes. ESC is often found to be more effective in Sports Utility Vehicles (SUVs) than in pas-

senger cars. This may be due to differences between drivers of SUVs and passenger cars. Theresults from

meta-analysis indicate that drivers of ESC-equipped vehicles are likely to be safer drivers than other

drivers. All the same, ESC may lead to behavioural adaptation in some cases, but it is not likely that

behavioural adaptation offsets the positive safety effects. This may be due to a lack of knowledge about

ESC.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

Thepresent study is an updateof thestudyby Erke (2008) which

summarizes empirical evidence of the effects of Electronic Stability

Control (ESC) on crashes bymeansof meta-analysis. ESCis an active

safety device for motor vehicles which aims at improving driving

dynamics andat preventing crashes that resultfrom loss of control.

The aims of the study are to investigate the size of the effects of

ESC on different types of crashes and in different types of vehicles,

whether the effects that are found in empirical studies are likely to

be affected by methodological weaknesses or publication bias, and

whether behavioural adaptation is likely to occur.

The study by Erke (2008) was based on 38 effect estimates

from eight studies from the years 2003 to 2006 that had empir-ically investigated the effects of ESC on crash involvement. Most

of these studies were based only on limited numbers of vehi-

cles and for the most part on luxury vehicles. Since 2006, ESC

has become more common and more vehicles that are not lux-

ury vehicles have become equipped with ESC. ESC has also become

more common in Sports Utility Vehicles (SUVs). A number of new

studies have been published since 2006 and it is now possible

Tel.: +47 22573863.

E-mail address: [email protected]

to conduct more detailed analyses, based on a much larger num-

ber of effect estimates, of the effects of ESC on different types of

crashes and for different types of vehicles. The present analysis is

based on 207 effect estimates from 12 studies, seven of which are

updates or new studies that were not included in the study by Erke

(2008).

ESC was first introduced as optional safety equipment in pas-

senger cars on the European market in 1995, and was increasingly

installed in passenger cars from 1998. By 2008 almost all new cars

were equipped with ESCin some countries(e.g. Sweden). However,

there is still large variation in penetration rates between different

countries. The proportions of new cars that are equipped with ESC

(either optional or standard equipment) in some countries are as

follows:

Europe: 36% in 2004 (Deutscher Verkehrssicherheitsrat, 2006);

43% in 2006 and 50% in 2007 (Bosch, undated). Germany: 5% in 1995; 67% in 2004 (Deutscher

Verkehrssicherheitsrat, 2006); 77% in 2006 and 79% in 2007

(Bosch, undated). Sweden: 15%in 2003; 67% in 2006; 96% in December 2007; 97.9%

in December 2008 (Krafft et al., 2009). USA: 9.7% in 2003 and 15.9% in 2004 (cars and trucks; Dang,

2007); 40% in 2006 (standard equipment; Insurance Institute for

Highway Safety, 2006).

0001-4575/$ see front matter 2010 Elsevier Ltd. All rights reserved.

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A. Hye / Accident Analysis and Prevention 43 (2011) 11481159 1149

Australia: 25% in 2007 (standard equipment; Scully and

Newstead, 2008). ESC will be mandatory from 1 November 2011

for all new passenger vehicles and SUVs. Norway: About 96% in 2009 (standardequipment in about 93%of

all new cars; Haldorsen, 2010).

There is large variation in the penetration rates for different

types of vehicles. The proportion of smaller cars that are equipped

with ESC is smaller, and ESC is mostly only optional, not standardequipment (Deutscher Verkehrssicherheitsrat, 2006). In Europe in

2007, only 17% of all small cars had ESC as optional or standard

equipment, while 74% of all compact cars, 94% of all medium size

cars and 100% of all cars in the upper and luxury sector had ESC as

either standard or optional equipment (Bosch, undated). In 2004

nearly 70% of all new SUVs on the US-market were equipped with

ESC (Motor & Equipment Manufacturers Association, 2006).

2. Effects of ESC on crashes

ESC affects crashrisk by enhancing the controllability of vehicles

and by preventing skidding andloss of control. Crash types that are

typicallyassociatedwithESC arecrashes that arecaused bytoo high

speed in curves, collision avoidance manoeuvres, low friction con-ditions, or combinations of these (Sferco et al., 2001). These factors

are often present in single vehicle crashes and in crashes involv-

ing rollover. It is well established in the literature that ESC reduces

crashes, especially crashes involving loss of control (e.g. all stud-

ies included in the meta-analysis; Sferco et al., 2001; Langwieder

et al., 2003). The types of crashes that are typically affected by ESC

are often more serious than other crashes. Effects of ESC are there-

fore assumed to be greater for more serious crashes (e.g. Farmer,

2006; Frampton and Thomas, 2007). It has frequently been shown

that effects of ESC are greater among SUVs than among passenger

cars (e.g. Dang, 2007; MacLennanet al., 2008; Scullyand Newstead,

2008). SUVs have a higher centre of gravity and are therefore more

prone to rollover than passenger cars (Khattak and Rocha, 2003).

The effects of safety measures can generally be explained interms of engineering effects and behavioural effects (Elvik, 2004).

Engineering effects refer to the intended effects of a measure on

one or more risk factors. In the case of ESC, the controllability of

the vehicle is improved. Behavioural effects refer to changes in

driver behaviour that may either offset or strengthen the engineer-

ing effect. Behavioural adaptation may occur when drivers have so

much trustin thesafetyeffects ofthe measure that they forexample

become inattentive, drive fasteror drive more aggressively(Rudin-

Brown and Burns, 2007; Rudin-Brown et al., 2009). Behavioural

adaptation is according to Elvik (2004) more likely to occur if a

measure is easilynoticed and when additional utility can be gained

from behavioural adaptation.

Only two studies were found that have investigated knowl-

edge about and attitude towards ESC and whether ESC may lead tobehavioural adaptation. The results from these studies show that

farfromall drivers areawareof whether or nottheircar is equipped

with ESC. In a telephone survey that was conducted in Canada in

2006 by Rudin-Brown and Burns (2007) only 1% of respondents

mentioned ESC when asked about vehicle safety features and 60%

of respondents had never heard of ESC. In a survey conducted in

Canada 2008 by Rudin-Brown et al. (2008) only 63% of all drivers

of a car that was equipped with ESC were aware of that fact, and

23% of all drivers of ESC-equipped cars had never heard about ESC.

The study results indicate that behavioural adaptation may occur

among some drivers, mainly among young men without univer-

sity education. Behavioural adaptation will most likely result in

driving faster, driving more and faster in bad weather and driving

more aggressively (Rudin-Brown et al., 2008). In other words, only

few drivers are aware of ESC, which makes behavioural adaptation

unlikely. However, once a driver is aware ofESC,he orshe may gain

additional utility, which increases the likelihood of behavioural

adaptation to occur.

Theresults of thestudyby Erke (2008) suggested that the effects

that were found on single vehicle crashes are greater than might

be expected considering the proportions of single vehicle crashes

involving loss of control. Possible explanations are methodologi-

cal weaknesses of the studies or publication bias. E.g. if there are

differences between drivers of ESC- and non-ESC cars that are not

controlled for in the studies, effects may be exaggerated if drivers

of ESC-equipped cars drive more carefully than other drivers. If

ESC leads to behavioural adaptation in the sense of faster or more

aggressive driving, this would be expected to offset, rather than

increase, the engineering effect.

3. Method

3.1. Studies included in the meta-analysis

Twelve studies are included in the present meta-analysis, all

of which have empirically investigated the effect of ESC on crash

involvement. These studies include five studies that were includedinthe study by Erke (2008): Agaand Okada (2003), Bahouth (2005),

Farmer (2006), Kreiss et al.(2006) and Page andCuny (2006). Three

studies are updates of studies that were included in the study by

Erke (2008): Dang (2007), Frampton and Thomas (2007) and Lie

et al. (2006). Additionally, four studies that were not included in

the study by Erke are included in the meta-analysis: Green and

Woodrooffe (2006), MacLennan et al. (2008), Padmanaban et al.

(2008) and Scully and Newstead (2008).

New studies were identified by conducting searches on the

Silverplatter TRANSPORT literature database which combines dif-

ferent international transport related databases, Sciencedirect

(Elseviers online database of over 2500 peer reviewed scientific

journals from before 1900 to 2010), PubMed (the online database of

the U.S.NationalLibraryof Medicine,includingcitationsfrom MED-LINE and other life science journals for biomedical articles from

1948 to 2009), the ISI Web of Knowledge (a database of interna-

tional journals and conference proceedings) and Google Scholar.

Search terms were Electronic Stability Control or Stability Con-

trol or Electronic Stability Program and crash, accident,

fatality or injury.

Studies were retrieved when they were, based on title and

abstract, likely to be empirical studies and likely to have investi-

gatedeffectson crashes. Studies wereincludedin the meta-analysis

if they provided sufficient information to compute estimates of

effect, based on numbers of crashes, and statistical weights. Sta-

tistical weights were computed either by the number of crashes or

confidence intervals of the effect estimates. Studies that have esti-

mated how likely certain types of accidents are to be avoided oraffected by ESC and studies that were not basedon crash data were

not included in the meta-analysis. The studies that are included in

the meta-analysis are listed alphabetically in Table 1.

The methods by which the effects of ESC on crashes were inves-

tigated are quite similar in most studies. The crash involvement of

ESC-equipped vehicles is compared to crash involvement of non-

ESC equipped vehicles. Comparison vehicles are in most studies

earlier make modelsof theESC-vehicles andin some studies similar

models. One study has simply compared crash rates without con-

trolling for any potential confounding variables (Aga and Okada,

2003). The other studies have controlled for potential confounding

variablesby using a comparison groupsof crashes that areassumed

not to be affected by ESC. Comparison groups of crashes are rear-

endcollisions, crashes notinvolving loss of control or other crashes

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1150 A. Hye / Accident Analysis and Prevention 43 (2011) 11481159

Table 1

Studies included in the meta-analysis.

Authors (year) Country Number of

estimates

Statistical

weightsaVehicle types Method

Aga and Okada (2003) Japan 4 139.1 Cars No comparison group

Bahouth (2005) USA 2 957.1 All light vehicles Comparison rear-end crashes

Statistically controlled for vehicle age

Dang (2007) USA 106 10,367.4 Cars, LTVs Comparison non-ESC crashes (several

crash types)

Farmer (2006) USA 32 19,291.7 Cars, LTVs Statistically controlled for vehicle ageFrampton and Thomas (2007) UK 12 4000.7 Cars Comparison non-ESC crashes (several

crash types)

Green and Woodrooffe (2006) USA 3 85.2 LTVs Comparison non-ESC crashes (several

crash types)

Kreiss et al. (2006) Germany 1 1853.5 Cars Comparison non-ESC crashes (several

crash types)

Lie et al. (2006) Sweden 6 326.9 Cars Comparison rear-end crashes on dry

roads

MacLennan et al. (2008) USA 4 120.7 Cars, LTVs Statistically controlled for vehicle and

driver characteristics

Padmanaban et al. (2008) USA 18 273.8 Cars, LTVs Statistically controlled for vehicle and

driver characteristics

Page and Cuny (2006) France 1 14.6 Cars Comparison non-ESC crashes (several

crash types) Statistically controlled for

vehicle and driver characteristics

Scully and Newstead (2008) Australia/New

Zealand

18 9065.9 Cars, LTVs Comparison rear-end crashes

Statistically controlled for vehicle

characteristics

a Sum of statistical weights in fixed effects models of meta-analysis (only effects that are included in the meta-analysis).

that are assumed not to be affected by ESC, such as crashes not

involving a forward movement of the vehicle in question. Some

studies have statistically controlled for potential confounding vari-

ables, either instead of or in addition to using a comparison group.

3.2. Exploratory analyses and test for publication bias

In the exploratory analyses the distributions of effect estimates

andtheirstatistical weights areinvestigated visually andit is inves-

tigated whether the results may be affected by publication bias.

Publication bias occurs when statistically significant results aremore likely to be submitted and published than non-significant

results or results that are contrary to the expectation (Sutton et al.,

2000). If results are affected by publication bias, an asymmetric

distribution of effect estimates and statistical weights is expected,

in which the greatest effects (in the expected direction) have the

smallest weights, while there are no results with equally small

weights for small effects or for effects in the opposite direction.

Publication bias is additionally tested by conducting trim-and-fill

analyses. In a trim-and-fill analysis it is first tested if the distribu-

tion of effect estimates is asymmetric. If it is, new effect estimates

are generated until the distribution is symmetric. A new summary

effect is then calculated based on both the original effect esti-

mates and those that were generated in the trim-and-fill analysis.

Trim-and-fill analyses were conducted according to the proceduredescribed by Duval and Tweedie (2000).

3.3. Meta-regression analysis

In meta-regression analysis the effects of a number of potential

moderator variables are investigated simultaneously. The aim of

the meta-regression analyses is mainly to identify relevant mod-

erator variables. A moderator variable is a variable that affects the

relationship between an independent and a dependent variable, in

this case between the presence of ESC and crash involvement. In

meta-regression analysis the effects of several moderator variables

are investigated simultaneously. Thereby, confounding effects are

controlled for, which is not the case in the meta-analysis in which

only one moderator variable is investigated at a time.

The dependent variable in meta-regression is the natural loga-

rithm of the effect estimate. Each effect estimate is weighted with

the statistical weight in a fixed effects meta-analysis (see Section

3.4). This corresponds to a random effects meta-regression analysis

(Thompson and Higgins, 2002). The meta-regression analyses were

conducted with the programme Limdep (Greene, 1998).

The potential moderator variables in the present study include

crash type, crash severity, vehicle type, and method. These are the

predictorvariables in the meta-regressionanalysis. All variablesare

categorial variables and were coded as dummy variables. For each

predictor variable the number of dummy variables is equal to the

number of categories minus 1; the one variable that is not coded

serves as the reference category (Hardy, 1993). The variables were

coded as follows:

Crash type (multiple vehicle crashes as reference category): All

crashes, single vehicle crashes, rollover crashes, ESC-crashes(sev-

eral types of crashes that are assumed to be affected by ESC),

pedestrian/cyclist/animal crashes. Results are available for sev-

eral more crash types. However, for the most specific crash types

only few results are available and these were not included in

meta-regression. Injury severity (injury/unspecified as reference category): Fatal

crashes. An additional dummy variable for injury crashes was

coded in the initial model. In this model, crashes of unspecifiedseverity are the reference category.

Vehicle type(cars are reference category):LTVs (LightTruck Vehi-

cles);most LTVs areSUVs.Pickup trucksand vans areonlyseldom

equipped with ESC (Dang, 2007). Method: Two different variables were coded for method. Only

one method variable is included in the regression models at

a time. The variable Method A divides studies into those that

have applied a comparison group and that have not additionally

controlled for potential confounding variables with multivari-

ate methods (reference category A0), the one study that has not

applied a comparison group (A1), and those studies that have

controlled for vehicle characteristics with multivariate methods

(A2).The variableMethod B divides studies intosimilarcategories

as Method A. Studies that have controlled for vehicle, but not for

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Fig. 1. Effect estimates and statistical weights, injury/unspecified severity crashes

with cars: all, multiple vehicle and ESC-crashes.

driver characteristics with multivariate methods are included in

the reference category B0. B1 is identical to A1. The category B2

consists of studies that have statistically controlled for driver and

vehicle characteristics. These studies have controlled for morepotential confounding variables than the other studies.

3.4. Statistical analyses in meta-analysis

The statistical analyses that were conducted in meta-analysis

are the same as in the study by Erke (2008). A short description

is given in the following. For a more detailed description of the

methods and the formulae that were used it is referred to Erke

(2008).

Estimates of effect were computed from all studies as odds

ratios. In total 207 effect estimates were calculated from the 12

studies included in the analysis. Summary effects were calculated

by means of the log-odds method of meta-analysis (Christensen,

2003; Elvik, 2005; Fleiss, 1981). Heterogeneity is tested for allresults with the Cochrans Q statistic. When there is significant

heterogeneity moderator variables are likely to be present. If het-

erogeneityis reduced in results for subgroupsthe grouping variable

may be a relevant moderator.

Whenever possible, a random effects model of meta-analysis

was applied when calculating summary effects and confidence

intervals. A random effects model is more appropriate than a

fixed effects model when there is heterogeneity in the results, i.e.

when results are not representatives of one fixed effect but when

moderator variables are present that are not controlled for. More

appropriate means that confidence intervals are not exaggerated

and that larger studies are given less weight than smaller studies

(Hardy and Thompson, 1996). Without heterogeneity, a random

effects model is not inappropriate and the results will be equal to

those from a fixed effects model.

Results are available for a number of different crash types, for

fatal, injury and unspecified severity crashes, and for cars and LTVs

(the majority of which are SUVs). Summary effects are calculated

for each crash type, type of vehicle and severity level separately.

The rationale for combining or not combining results is explored in

meta-regression analysis.

4. Results

4.1. Exploratory analyses and test for publication bias

Distributions of effect estimates are shown in Figs. 15. The sta-

tistical weights are shown in relation to the logarithms of the effect


with cars: pedestrian, single vehicle and rollover crashes.


with LTVs: all, multiple vehicle and ESC-crashes.


with LTVs: pedestrian, single vehicle and rollover crashes.

estimates. Separate figures were made for cars and LTVs and it is

indicated in the figures which crash types the data points refer

to. Note the different ranges of the X- and Y-axes in the figures.

Figs. 14 show only results that refer to injurycrashes or to crashes

with unspecified severity; Fig. 5 shows results that refer to fatal car

crashes.

An asymmetric pattern which may indicate thepresenceof pub-

lication bias can be seen in the distributions of ESC-crashes and

rollover crashes with cars andLTVs, andin thedistribution of multi-

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Fig. 5. Effect estimates and statistical weights, fatal crashes with cars: multiple

vehicle and rollover crashes.

vehicle crashes with cars. In the results for fatal car crashes there

may be an asymmetric pattern as well. Trim and fill analyses were

conductedfor allresultsthat arebasedon at least 9 effectestimates.

These are multiple vehicle and rollover crashes with cars and LTVsandESC-crasheswith cars. Neweffect estimateswere generatedfor

rollover crashes with cars and LTVs and for ESC-crashes with cars.

For these results, summary effects will be presented both with and

without the effect estimates that were generated in the trim-and-

fill analysis in Section 4.3. Formultiplevehiclecrashes nonew effect

estimates were generated. These results appear not to be affected

by publication bias. When trim-and-fill analyses are conducted for

fatal car crashes (multivehicle and rollover), new effect estimates

are not generated either. However, only few effect estimates are

available for these results.

4.2. Results from meta-regression analysis

The results from meta-regression analysis are summarized in

Table 2, the model summaries are shown in Table 3. In each of

the initial models (models 1 and 2) one of the method variables

is included, each represented by two dummy variables. In model 1

both method dummy variables are non-significant.

When the methodvariable B is included in the model (model 2),

the fit of the model improves by 2.3 percentage points (adjusted

R2), the dummy variable for the study without a comparison

group is almost statistically significant (p < .10) and the dummy

variable for studies that have controlled for vehicle and driver

characteristics with multivariate methods is statistically signifi-

cant (p < .01). The positive coefficient indicates that studies that

have controlled for vehicle and driver characteristics with multi-

variate methods (group B2) found smaller crash reductions than

other studies.

In model 3 the method variable B is kept and the dummy vari-

able for injury crashes is omitted. This improves the model fit only

slightly, and none of the coefficients for any of the other variables

changes noticeably. The coefficient for injury crashes is only small

and non-significant in all previous models. These results indicate

that effect estimates for injury crashes do not significantly differ

from results for crashes with unspecified severity. This result isconfirmed when one actually compares results for injury crashes

and crashes with unspecified severity for several types of crashes

(not shown in Table 4, results from meta-analysis). So far, model 3

is the preferred model of meta-regression.

In model 4 the method variable B is omitted from model 3. This

does not change any of the coefficients of the other variables and

model fit is reduced by 2.2 percentage points compared to model

3. Even if Method B is statistically significant in model 3 it does not

seem to contribute to a large degree to the model fit. In model 5

Table 2

Results from meta-regression analyses. Significant values (p > .05) in bold.

Model 1 Model 2 Model 3 Model 4 Model 5

Coeff. p Coeff. p Coeff. p Coeff. p Coeff. p

Constant 0.030 .454 0.035 .333 0.036 .308 0.026 .486 0.028 .556

Fatality 0.153 .029 0.182 .005 0.181 .005 0.112 .054 0.248 .002

Injury 0.012 .884 0.015 .845

All crashes 0.011 .884 0.063 .369 0.064 .357 0.020 .778 0.068 .477

ESC-crash 0.188 .001 0.193 .000 0.195 .000 0.188 .001

Single vehicle crash 0.450 .000 0.498 .000 0.500 .000 0.454 .000

Rollover 0.845 .000 0.834 .000 0.837 .000 0.855 .000

Single/rollover/ESC-crash 0.416 .000

Pedestrian/cycle/animal crashes 0.014 .870 0.027 .743 0.027 .745 0.011 .895 0.012 .908

LTV 0.088 .055 0.082 .058 0.081 .056 0.098 .023 0.084 .124

Method A1 0.165 .219

Method A2 0.001 .985

Method B1 0.212 .095 0.213 .092 0.271 .081

Method B2 0.179 .009 0.179 .009 0.184 .035

Method A0 (reference):studiesthathave applieda comparison group andthat havenot additionallycontrolled forpotential confounding variables withmultivariate methods.

Method A1: studies that have not applied a comparison group (same as A1).

Method A2: studies that have controlled for vehicle characteristics with multivariate methods.

Method B0 (reference): Studies not included in C1 and C2.

Method B1: studies that have not applied a comparison group (same as A1 and B1).

Method B2: studies that have controlled for vehicle and driver characteristics with multivariate methods (the strongest study designs).

Table 3

Meta-regression analyses, model summaries.

df F R2 Adj. R2 Tau2

Model 1 131 15.36 0.54 0.505 0.23

Model 2 131 16.79 0.56 0.528 0.20

Model 3 132 18.79 0.56 0.532 0.20

Model 4 134 21.96 0.53 0.510 0.24

Model 5 134 10.87 0.362 0.329 0.38

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Table 4

Effects of ESC on crash involvement, results from meta-analysis (RE models if not indicated otherwise). Significant values (p > .05) in bold.

All light vehicles Passenger cars LTVs

Test of

heterogeneity

Change in number of

crashes (%)

Test of

heterogeneity

Change in number of

crashes (%)

Test of

heterogeneity

Change in number of

crashes (%)

Q df p Summary

effect

95%

confidence

interval

Q df p Summary

effect

95%

confidence

interval

Q df p Summary

effect

95%

confidence

interval

All crashesFatal 0.75 1 .385 43 (50; 35) 0 38a (51; 22) 0 46a (56; 34)

Injury/unspecified 21.15 4 .000 1 (3; +1) 18.58 3 .000 0 (8; +8) .000 1 .956 3 (6; +0)

ESC-crashes

Fatal 9.70 5 .084 38 (55; 15) 3.84 3 .280 27 (49; +4) 2.88 1 .089 53 (76; 6)

Injury/unspecified 159.85 19 .000 21 (27; 16) 132.29 11 .000 19 (26; 11) 26.02 7 .000 26 (35; 17)

Injury/unspecified; results from tri m-and-fill analysis 239.14 1 4 .000 15 (23; 6)

Single vehicle crashes

Fatal 13.05 6 .042 44 (56; 30) 2.97 3 .396 38 (49; 25) 7.64 2 .022 51 (69; 23)

Injury/unspecified 26.02 7 .000 42 (49; 33) 7.69 5 .174 32 (39; 25) 0.47 1 .492 50a (55; 44)

Rollover crashes

Fatal 3.08 9 .000 67 (78; 51) 11.11 5 .049 57 (73; 32) 8.38 3 .039 75 (86; 57)


Injury/unspecified; results from tri m-and-fill analysis 43.108 16 .000 36 (53; 13) 29.25 13 .006 71 (80; 58)

Run-off-road crashes

Fatal 8.08 3 .044 49 (64; 28) 0.30 1 .583 38a (52; 21) 3.60 1 .058 58 (78; 20)


Side impact fixed object crashesFatal 0.63 3 .888 59 (69; 46) 0.30 1 .586 53a (70; 26) 0.06 1 .806 40a (74; +38)

Injury/unspecified 4.78 5 .443 20 (36; +0) 2.64 2 .267 16 (39; +16) 1.44 2 .487 34 (50; 13)

Multiple vehicle crashes

Fatal 12.92 8 .115 17 (29; 3) 5.43 4 .246 12 (27; +7) 5.54 3 .136 23 (40; 1)

I nj ur y/ uns pecifi ed 20 1. 20 3 2 .0 00 7 (12; 2) 103.59 16 .000 6 (13; +0) 46.90 15 .000 7 (14; +1)

Injury/unspecified; results from trim-and-fill analysis (No new effect estimates with trim-and-fill) (No new effect estimates with trim-and-fill)

Multiple vehicle culpable crashes

Fatal 3.58 3 .311 14 (30; +6) 1.67 1 .196 7 (31; +25) 1.04 1 .308 23 (45; +6)

I nj ur y/ uns pecifi ed 25. 70 1 3 .0 19 13 (18; 8) 14.04 6 .029 12 (18; 6) 11.10 6 .085 16 (24; 6)

Multiple vehicle crashes under adverse conditions

Fatal 1.60 1 .206 33 (58; +7) 0 48a (70; 10) 0 16a (49; +38)

Injury/unspecified 0.80 1 .370 +1 (3; +5) 0 +3 (3; +9) 0 1 (7; +5)

Multiple vehicle high speed crashes

Fatal 2.53 1 .111 45 (66; 11) 0 28a (55; +15) 0 56a (70; 35)

Multiple vehicle rollover

Fatal 1.27 1 .259 32 (68; +45) 0 +8a (63; +216) 0 51a (79; +15)

Head-on collisions

Fatal 0 79a (97; +61)

Injury/unspecified 4.28 1 .038 21 (38; +2)

Pedestrian/bicycle/animal crashes

Fatal 1.06 3 .787 +22 (+8; +38) 0.90 1 .342 +19 (12; +62) 0.10 1 .750 +12a (23; +62)

I nj ury/uns pecifi ed 29. 51 1 3 .0 06 14 (30; +4) 16.67 6 .011 27 (44; 6) 3.53 6 .740 +11a (11; +38)

a Fixed effects model.

single vehicle, rollover and ESC crashes are combined in only one

dummy variable. This reduced the model fit by 20.3 percentage

points.

Thecoefficientsfor injuryseverity, crashtypeand type ofvehicle

are similar in all models. The coefficients for fatal crashes indicate

that effects of ESC on fatal crashes are greater than the effects on

injury or unspecified severity crashes. These coefficients are statis-tically significant in models 1, 2, 3 and 5, and not far from being so

in model 4.

The crash types that are affected most by ESC are rollover, sin-

gle vehicle and ESC-crashes (crashes that are assumed to be affected

by ESC). The effects are highly statistically significant in all mod-

els. The negative coefficients indicate that effects are greater for

rollover crashes than for single vehicle crashes and greater for

single vehicle crashes than for ESC-relevant crashes. This is some-

what unexpected, since ESC-crashes per definition should be

those crashes that are most affected by ESC (ESC-crashes are dis-

cussed in more detail below). When a new regression model is

calculated based on model 3, in which the three dummy vari-

ables for these crash types are combined into a single variable

(rollover/single/ESC-crashes; model 5), this variable is still highly

statistically significant, but the model fit is reduced to an adjusted

R2 of 0.329. When the method variable is omitted from model

5, all coefficients remain almost identical and the model fit is

reduced to 0.310 (not shown in Tables 2 and 3). These results

indicate that the effects on these three types of crashes are

different.

The coefficients for pedestrian crashes and all crashes indicatethat theeffectsof ESCon these crashes arenotsignificantly different

from the effect on multiple vehicle crashes.

The coefficient for LTVs is negative in allmodels. It is statistically

significant only in models 1 and 5, but in the remaining modelsp is

below .1. This result indicates that ESC has greater effects for LTVs

than for cars.

The results from meta-regression have the following implica-

tions for the analyses that are conducted in the next section:

Results are presented from all studies without selecting studies

based on methodological aspects. An additional analysis is made

in order to investigate results from studies that have controlled

for driver and vehicle characteristics with multivariate methods.

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Results for injury crashes and crashes with unspecified severity

are combined. Most of these results refer to unspecified sever-

ity. Only few effect estimates are available for injury crashes

and these do not systematically differ from those for unspecified

severity. Results are not combined for different types of crashes. Results are presented for cars and LTVs separately. Additionally,

combinedresults for cars and LTVsare presented(mainly because

one study only has reported results for both types of vehicles

combined; Bahouth, 2005).

4.3. Results from meta-analysis

Summary effects for the effects of ESC on different types of

crashes in cars, LTVs and all light vehicles are shown in Table 4.

Table 5 shows the results based only on those studies that have

statistically controlledfor vehicle and driver characteristics.Table 6

shows additional analyses which were made for some subgroups

of the results in Table 4. Most results in Tables 46 are based on a

random effects model. In some cases not enough effect estimates

were available and the results are based on a fixed effects model.

Results from trim-and-fillanalysesare shown forall results that are

based on at least 9 effect estimates (at least 8 degrees of freedom).

The results are discussedbelow for each of the moderatorvariablesthat have been investigated.

4.3.1. Study design

The results from meta-regressionindicate thatstudies that have

controlled statistically for vehicle and driver characteristics found

less favourable effects of ESC than other studies. For most crash

types in Table 5 (the studies that have controlled for driver char-

acteristics) the effects are indeed smaller than the corresponding

effects in Table 4 (all studies). However, for some crash types

greater effects were found in the studies that have controlled for

driver characteristics: for ESC-crashes, side impact fixed object

crashes and run-off-road crashes with cars. In the case of ESC-

crashes, different definitions of crash types included in ESC-crashes

can explain this result (see below under Crash types). For pedes-trian/bicycle/animal crashes no effect was found for cars, and for

LTVs a greater increase of crash numbers was found than in all

studies.

4.3.2. Crash severity

According to the results from meta-regression analysis, ESC has

greater effects on fatal crashes than on other crashes. The results in

Table 4 are for the most part consistent with this finding. However,

no or only small differences between fatal and other crashes were

found for single vehicle, rollover, run-off-road and multiple vehicle

culpable crashes. For ESC-crashes,a greater reductionwas foundfor

fatal crashes compared to less severe crashes, but the difference is

only small when looking at the results in which a wider definition

of ESC-crashes has been used (Table 6). Thus, with the exception ofside impacts with fixed objects, similar effects of ESC were found

on fatal and other crashes in all types of crashes that are assumed

to be most affected by ESC.

4.3.3. Vehicle types

The effects of ESC are greater for LTVs than for cars in most

types of crashes (Table 4). This corresponds to the finding from

meta-regressionanalysis accordingto whichESC has greater effects

for LTVs than for cars. In all crashes and in multiple vehicle

injury/unspecifiedseverity crashes there are only smalldifferences

between the effects for cars and LTVs. For all fatal crashes there is

no statistically significant heterogeneity in the results for all light

vehicles, which indicates that the effects are not different between

cars and LTVs. For all injury/unspecified severity crashes and for

multiple vehicle injury/unspecified severity crashes, there hetero-

geneity is statistically significant in the results for all light vehicles

and remains statistically significant in the results for cars and LTVs.

This indicates either that vehicle type is not a relevant moderator,

or that there are more moderator variables that affect results. For

multiple vehicle fatal crashes under adverse conditions the effect

is greater for cars than for LTVs; however both effects are based on

only one effect estimate.

The results from those studies that have controlled for driver

characteristics do not indicate that ESC is more effective for LTVs

than for cars, with the exception of rollover crashes. The effects

for the two types of vehicles are similar and the tests of hetero-

geneity of the results for all light vehicles are non-significant. For

rollover crashes (all rollover and multiple vehicle rollover crashes)

greater effects were found for LTVs than for cars. However, there

is no statistically significant heterogeneity in the results for these

crash types for all light vehicles combined. This indicates that the

effects are not different between cars and LTVs.

4.3.4. Crash types all crashes

Allfatalcrasheswerefoundtobereducedbyabout40%(Table 4).

No statistically significant effect was found on injury/unspecified

severity crashes. None of the effect estimates for all crashes is from

one of the studies that have controlled for driver characteristics.The effects on all crashes have been investigated in four studies. In

thestudies by Agaand Okada (2003)and Farmer (2006) no compar-

ison group was applied and rear-end collisions are included in all

crashes. In the studies by Lie et al. (2006) and Scully and Newstead

(2008) rear end crashes were used as a comparison group and are

excluded from all crashes. The results for these two groups of stud-

ies are shown in Table 6. Studies in which rear-end collisions are

excluded from all crashes found a somewhat greater reduction of

all crashes when the results for all light vehicles are combined and

there is no statistically significant heterogeneity in these results.

When the results from Aga and Okada (2003) are omitted from

the analyses, all remaining studies have statistically controlled for

vehicle age, but not for driver characteristics. The results remain

largely unchanged (Table 6).The results indicate that excluding rear-end crashes from all

crashes may lead to a slight overestimation of the reduction of

all crashes and that rear-end collisions are most likely either not

affected by ESCor increase. However, all results arenon-significant

and the number of all injury/unspecified severity crashes (includ-

ing rear-end collisions) is most likely to be unchanged or reduced

only slightly.

The result for all fatal crashes is based on the study by Farmer

(2006) in which rear-end collisions are included in all crashes. This

result is not biased by the selection of crash types.

4.3.5. Crash types crashes assumed to be affected by ESC

The results in Table 4 show that the greatest crash reductions

were, as expected,foundin those types of crashes that areassumedto be most strongly affected by ESC. The results correspond to

the findings from meta-regression analysis. The greatest effects

were found in rollover crashes, followed by run-off-road and sin-

gle vehicle crashes. For rollover crashes the results are similar

for fatal and injury/unspecified severity crashes. The results for

injury/unspecified severity are likely to be affected by publication

bias. Moreover, the studies that have controlled for driver charac-

teristics yieldsomewhatless favourableresults thanall studies. The

effects that were found for single vehicle and run-off-road crashes

are similar, most likely because most single vehicle crashes involve

road departure. Even if many of the confidence intervals are wide,

almost all of these results are statistically significant.

Effects on ESC-crashes are somewhat smaller than the effects

on rollover, run-off-road and single vehicle crashes, but still

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Table 5

Effects of ESC on crash involvement, results from meta-analysis (RE models if not indicated otherwise); results from studies that have controlled for driver characteristics. Si

All light vehicles Passenger cars LTVs

Test of

heterogeneity

Change in number of

crashes (%)

Test of

heterogeneity

Change in number of

crashes (%)

Test o

hetero

Q df p Summary

effect

95%

confidence

interval

Q df p Summary

effect

95%

confidence

interval

Q

ESC-crashes

Fatal 0.00 1 .963 70a (84; 44) 0 69a (89; 17)

Injury/unspecified 0 43a (75; +30) 0 43a (75; +30)

Single vehicle crashes

Fatal 0.07 1 .789 25 (43; 1) 0 27a (50; +5)

Rollover crashes

Fatal 7.53 4 .110 52 (67; 30) 3.97 2 .137 44 (66; 10) 0.65 Injury/unspecified 4.37 2 .112 49 (62; 30) 0 32a (51; 6) 0.34

Run-off-road crashes

Fatal 0.04 1 .845 43 (60; 19) 0 45 (65; 13)

Side impact fixed object crashes

Fatal 0.10 1 .749 59a (80; 15) 0 61a (83; 12)

Multiple vehicle crashes

Fatal 0.01 1 .943 +4a (18; +33) 0 +5a (23; +43)

Multiple vehicle culpable crashes

Fatal 0.36 1 .547 +8 (3; +20) 0 +10a (23; +56)

Multiple vehicle rollover

Fatal 1.27 1 .259 32 (68; +45) 0 +8a (63;

+216)

Pedestrian/bicycle/animal crashes

Fatal 0.26 1 .613 +4 (12; +22) 0 1 (40; +63)


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Table 6

Effects of ESC on the involvement in ESC-crashes, results from meta-analysis (RE models if not indicated otherwise); additional analyses. Significant values (p > .05) in bold.

All light vehicles Passenger cars LTV

Test of

heterogeneity

Change in number of

crashes (%)

Test of

heterogeneity

Change in number of

crashes (%)

Tes

het

Q df p Summary

effect

95%

confidence

interval

Q df p Summary

effect

95%

confidence

interval

Q

All crashes, including rear-end collisions

Injury/unspecified 3.73 2 0.155 1 (4; +3) 1.86 1 0.172 +1 (5; +7)

All crashes, including rear-end collisions (results from Aga and Okada, 2003 omitted)

Injury/unspecified 0.60 1 0.440 2 (4; 1) 0 1 (5; +3)

All crashes, rear-end collisions excluded

Injury/unspecified 14.57 1 0.000 5 (26; +21) 15.69 1 0.000 4 (26; +24) ESC-crashes wider definition

Fatal 2.24 3 .525 23 (30; 15) 0.37 2 .830 25 (44; 1)

Injury/unspecified 59.98 16 .000 18 (22; 13) 45.94 9 .000 16 (22; 10) 8.9

ESC-crashes rollover first harmful event

Fatal 0.00 1 .963 70a (84; 44) 69a (89; 17)

ESC-crashes loss of control

Injury/unspecified 7.91 2 .019 42 (57; 22) 0.16 1 .685 32a (35; 29)

ESC-crashes on wet roads

Fatal 0 38a (79; +80)

Injury/unspecified 19.20 2 .000 61 (87; +21) 3.40 1 .065 30 (65; +39)

ESC-crashes on dry roads

Fatal 0 17a (64; +90)

Injury/unspecified 9.02 2 .011 26 (51; +10) 1.14 1 .286 7 (17; +5)


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greater than on other types of crashes. According to the results

from trim-and-fill analysis the results for ESC-crashes with cars

(injury/unspecified) may be affected by publication bias, although

the difference between the original summary effect and the sum-

mary effect that was calculated in the trim-and-fill analysis is only

small. Those studies that have controlled statistically for driver

and vehicle characteristics have, somewhat unexpectedly, found

greater effects on ESC-crashes than all studies.

The greater effect on ESC-crashes in the studies that have con-

trolled for driver characteristics compared to all studies and the

smaller effect on ESC-crashes than on single vehicle and rollover

crashes can be explained by different definitions of ESC-crashes.

Some studies have applied a wider definition of ESC-crashes, sum-

marizing all crash types as ESC-crashes that theoretically may be

affected by ESC (Dang, 2007; Frampton and Thomas, 2007; Lie

et al., 2006). Other studies have applied more narrow definitions

of ESC-crashes, including only crashes involving loss of control

(Green and Woodrooffe, 2006; Kreiss et al., 2006; Page and Cuny,

2006) or only single vehicle crashes in which rollover was the first

harmful event (Padmanaban et al., 2008) in ESC-crashes. Table 6

shows effects for ESC-crashes with wider and narrower definitions

of ESC-crashes. The effects are consistently greater when narrower

definitions of ESC-crashes are applied. The results for fatal crashes

in which rollover was the first harmful event are based on one ofthe studies that have controlled for driver characteristics, while

the results for loss of control crashes for the most part are based on

other studies.

Effects on ESC-crashes are greater on wet roads than on dry

roads. These results are based on two studies with a wider and one

study with a narrower definition of ESC-crashes. This may explain

the statistically significant heterogeneity in the results. However,

all studies are consistent in showing that ESC is more effective on

wet roads.

4.3.6. Crash types other crashes

The results that are based on all studies show that all multiple

vehicle crashes are reduced by between 6% and 23%, with greater

effects on fatal crashes andfor LTVs. Alleffects forcars andLTVs arehowever non-significant.Publication bias is not likely to be present.

When looking at theresults from those studies that have controlled

for driver characteristics (Table 5), multiple vehicle crashes, includ-

ing multiple vehicle rollover crashes, were found to increase, but

all results are non-significant.

Multiple vehicle crashes were found to be significantly reduced

by about 17% and 7% (fatal and non-fatal crashes, respectively).

Multiple vehicle crashes for which greater reductions were found

are multiple vehicle fatal crashes under adverse conditions, multi-

ple vehicle high speed crashes, multiple vehicle rollover and fatal

head-on collisions. However, notall of these results arestatistically

significant. The large effect for fatal head-on collisions is based on

theone study that hasnot applied anycomparison group.The stud-

ies that have statistically controlled for driver characteristics foundno significant effects on multiple vehicle crashes.

According to the results from meta-regression, the effects on

crashes involving pedestrians, bicycles or animals are not different

from the effects on multiple vehicle crashes. However, the results

in Table 4 show that fatal crashes involving pedestrians, bicycles

or animals increase, and in the studies that have controlled for

driver characteristics a (non-significant) increase was found for

fatal crashes involving pedestrians, bicycles or animals with LTVs.

5. Summary and discussion

In the present meta-analysis results from 12 studies were anal-

ysed in order to investigate the size of the effects of ESC on crashes,

whether the results are likely to be affected by publication bias or

by methodological aspects of the studies and whether behavioural

adaptation is likely to occur among drivers of ESC-equipped vehi-

cles. The study is based on more and more recent studies than the

previous study by Erke (2008). Because a greater number of effect

estimates is available, moremoderator variables,including method

effects, could be investigated.

Publication bias is likely to be present among those results that

refer to crash types that are commonly assumed to be affected by

ESC, but not among other crashes. It might be assumed that the

effects that are found for crashes where ESC is assumed to be effec-

tive are most relevant to whether or not to publish a study. Even

if publication bias could not be tested for most results, it is likely

that some degree of publication bias is present among the esti-

mated effects on all crash types where ESC is assumed to be most

effective, such as single vehicle, rollover, and run-off-road crashes.

Among results for multiple-vehicle crashes and pedestrian, bicycle

and animal crashes publication bias is less likely to be present.

The results are also likely to be affected by methodological

aspects of the studies. Studies that have statistically controlled for

driver characteristicsfound consistently less favourable of ESC than

other studies. This result is consistent with the assumption that

there are differences between drivers of ESC-equipped cars and

other cars, and that drivers of ESC-equipped vehicles are less likely

tobe involved in crashes than other drivers, allelse being equal.Thetype of comparison group that has been applied (crashes assumed

to be unaffected by ESC) and whether or not vehicle characteristics

were statistically controlled for does not affect the results.

Effects of ESC were in most studies found to be greater for LTVs

(most of which are SUVs) than for passenger cars. Meta-regression

analysis also found greater crash reductions for LTVs than for pas-

senger cars. These results are as expected because LTVs are more

often involved in rollover crashes than cars (Khattak and Rocha,

2003). However, no differences between LTVs and cars were found

in those studies that have controlled for driver characteristics. A

possible explanation is that the greater effects that were found

in many studies for LTVs are due to differences between drivers

of LTVs and passenger cars. Differences between different types

of passenger cars could not be investigated in the meta-analysis.Although there are more different types of cars and more mid-size

cars included in the analyses than in the previous study by Erke

(2008), small passenger cars are still underrepresented.

When all crash types are regarded together, fatal crashes were

found to be reduced significantly by about 40% while less severe

crashes seem to be largely unaffected by ESC when all crash types

are regarded together. Those crash types that are most affected

by ESC are, not surprisingly, rollover crashes, run-off-road, single

vehicle crashes and crashes involving loss of control. Taking into

account methodological aspects of the studies, one may assume

that fatal crashes in which rollover is the first harmful event are

reduced by about 70%, rollover crashes of all severities are reduced

by about 50%, that run-off-road crashes and crashes involving loss

of control are reduced by about 40% and that single vehicle crashesare reduced by about 25%. The reductions may be somewhat less

when also taking into account that there may be some degree

of publication bias. The effects may be greater for fatal crashes

than for non-fatal crashes as indicated by meta-regression analysis.

However, no or only small differences between fatal and non-fatal

crashes were actually found in these types of crashes.

For multiple vehicle crashes no significant effects were found

in those studies that have statistically controlled for driver char-

acteristics, even if other studies found large and/or statistically

significant reductions of several types of multiple vehicle crashes

(under adverse conditions, high speed, rollover and head-on). Fatal

crashes involving pedestrians, bicycles or animals may increase,

but the amount of the increase is difficult to estimate based on

the inconsistent results. Rear-end collisions were not investigated

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directly in anyof thestudies,but a comparison of results from stud-

ies that have and that have not excluded rear-end collisions from

all crashes indicate that rear-end collisions are likely to be either

unchanged or to increase.

When one looks at the size of the effects on different types

of injury/unspecified severity crashes, the results seem somewhat

illogical. Theoretically, if there is no change in the total number

of crashes, and if single vehicle crashes are reduced by 25%, then

other than single vehicle crashes should increase by about 11%

(under the assumption that single vehicle crashes are about 30%

of all crashes). Most types of multiple vehicle crashes were found

to be eitherunchanged or to decrease.Non-fatal pedestrian, bicycle

and animal collisions were not found to increase either. Rear-end

collisions may increase, but the increase is not likely to be large.

The solution to the riddle may be one or more of the following: the

reduction that was found for single vehicle crashes may be exag-

gerated, all crashes may not be unaffected but decrease, rear-end

crashes and/or pedestrian/bicycle and animal crashes may increase

more than indicated by the results, or multiple vehicle crashes in

general increase (as was found in the studies that have controlled

for driver characteristics, even if the result is non-significant). The

results for fatal crashes are less illogical.

Previous studies have estimated that about 4050% of all sin-

gle vehicle crashes involve loss of control (the results refer to allseverities and no distinction is made between cars and other light

vehicles; for a summary see Erke, 2008). If single vehicle crashes

are reduced by 25%, as indicated by the results of studies that have

controlled for driver characteristics, about 56% of all single vehicle

crashes involving loss of control would be avoided by ESC (under

the assumption that 45% of all single vehicle crashes involve loss

of control, and that single vehicle crashes not involving loss of con-

trol are unaffected by ESC). This is more than the 40% reduction of

loss of control crashes that was found in all studies. A similar cal-

culation can be made for rollover crashes: most rollover crashes

involve loss of control (McLean et al., 2005). If rollover crashes

are reduced by 50%, as indicated by the results from those stud-

ies that have controlled for driver characteristics, then about 59%

of all rollovers involving loss of control would be prevented by ESC(under the assumption that 85% of all rollovers involve loss of con-

trol and that rollovers not involving loss of control are unaffected

by ESC). One may conclude that the reductions by 25% and 50%

of single vehicle crashes and rollovers may be overestimated, or

that the 40% reduction of loss-of-control crashes may be under-

estimated. If the reductions of fatal single vehicle and rollover

crashes by 25% and 50%, respectively, that were found in those

studies that have controlled for driver characteristics are assumed

to be realistic (not underestimated), then it is likely that the effects

on injury/unspecifiedcrashes are somewhat smaller. Theoretically,

it is also possible that the reduction of loss-of-control crashes is

overestimated. The effects on single vehicle and rollover crashes

would then be far smaller than indicated by the results (e.g., when

loss-of-control crashes are reduced by 30%, single vehicle crasheswould be reduced by 13% and rollover crashes would be reduced

by 25%).

No empirical results are available that are directly related to

behavioural adaptation. However, several of the results from meta-

analysis may give some indication of whether or not behavioural

adaptation is likely to occur.

The results indicate thata largeproportion of all crashes involving

loss of control are prevented by ESC and that the total number of

crashes is at the worst unchanged. This means that behavioural

adaptation does at least notovercompensatefor thesafety effects

achieved by the engineering effect of ESC. The results indicate thatfatal crashes involving pedestrians, bicy-

cles or animals increase (at least with LTVs). Rear-end crashes

may increase as well. Even if the increases are small and uncer-

tain, these results areconsistent with theassumption that drivers

of ESC-equipped cars drive less carefully than other drivers, at

least in some situations. Effects are greater in fatal multiple vehicle high speed crashes

than in other fatal multiple vehicle crashes. This resultis notcon-

sistent with the assumption that drivers of ESC-vehicles drive

faster than other drivers. When driver characteristicsare controlledfor statistically,results

are less favourable for ESC than otherwise. This result is consis-

tent with the assumption that drivers of ESC-equipped vehicles

are more careful drivers than other drivers, and not consistent

with the assumption that drivers overcompensate for the safety

effects of ESC.

6. Conclusions

The results of the present study show that ESC prevents about

40% of all crashes involving loss of control. Results from studies

that have not controlled for driver characteristics are likely to be

confounded by differences between drivers of ESC-equipped vehi-

cles and other drivers. Results that refer to types of crashes that are

generally assumed to be affected by ESC are likely to be biased by

somedegree of publication bias.When taking intoaccount method-

ological aspects of the studies, the following effects were found:

all fatal crashes are reduced by about 40%, less severe crashes are

unchanged when all types of crashes are regarded together. Fatal

crashes in which rollover is the first harmful event are reduced

by about 70%, rollover crashes of all severities are reduced by

about 50%, run-off-road crashes and crashes involving loss of con-

trol are reduced by about 40%, single vehicle crashes are reduced

by about 25%. The reductions are likely to be somewhat overes-

timated, especially for non-fatal crashes. The number of multiple

vehicle crashes is most likely unchanged. Several types of multiple

vehicle crashes were found to decrease (under adverse condi-

tions, high speed, rollover and head-on), rear-end collisions may

increase. Fatal crashes involving pedestrians, bicycles or animals

may increase as well. The results indicate that behavioural adapta-tion may occur in some situations, but that ESC-equipped vehicles

are notgenerallydriven faster or moreaggressively thanother vehi-

cles. This may at least partly be due to a lack of knowledge about

ESC. Moreover, the results indicate that drivers of ESC-equipped

vehicles may be generally more careful drivers than other drivers.

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