TransportaTion

ROAD MORTALITY IN EUROPE– How Sensitive Is It to Demographic Structure and Population Dynamics? –Vojtech Eksler

Senior Researcher
 CDV (Transport Research Centre)
Brno, Czech Republic(Received August 23, 2006)

This paper investigates the influence of demographic structure on the level of road safety as measured by mortality rates in 23
European countries and their regions. Standardised mortality ratios (SMRs) for IRTAD age-gender groups computed from fatality
counts recorded in 2002 were compared with crude mortality ratios. This analysis shows that the SMRs differ only slightly from crude
mortality ratios: the difference between the two varies from -4.3% to 2.8% for countries as a whole, but it is sometimes higher forregions within a single country (e.g. in Greece it ranges from -2.3% to 10.8%), suggesting that demographic structure could be omitted in international comparisons but not always in regional ones. The demographic structure explains up to 12% of regional heterogeneities in terms of road mortality in some countries. Furthermore, the development of the number of expected road fatalities wasestimated for three demographic structure scenarios for the years 2020, 2035 and 2050. The aging of the population alone will not
result in significant changes in the expected number of fatalities in Europe, but the impact of population aging on expected road mortality in particular countries could be high.
Key Words: Road mortality, NUTS, SMR, Population aging, Demographic structure

1. Introduction
Road safety practitioners and researchers have traditionally evaluated the road safety performance of acountry by comparing its road safety risk indicators withthose of other countries and by the analysis of their development in time. In an international context, three (different) risk indicators are used1. These indicators are theratios between the number of persons killed in road traffic (nominator) and their exposure to traffic risk (denominator). The most commonly used denominator - and theone preferred by the health sector - is the population count.
This risk indicator is considered to be a measure of mortality, the mortality rate. It reflects the degree to whichroad accidents affect the safety and health of the population. The number of road fatalities divided by the numberof registered motor vehicles is the second most commonly used indicator of road safety, corresponding to the degree of traffic safety. Finally, the most reliable indicator,
fatality risk, uses the exposure to risk expressed as thenumber of kilometres travelled by motorised traffic (traffic volume) as a denominator (see, for example: Kopits2,
Lassarre3). The extent to which these three indicators areused in international comparisons and national time-series analysis depends on the availability of exposure data.

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It is generally understood that in any road safety analysis
at least two of these indicators should be used simultaneously to prevent the wrong conclusions being drawn
about a country’s road safety status. In this exploratory
paper, the mortality rate, originally being a measure of
personal safety is used to evaluate the level of safety inroad traffic. (As a reminder, this indicator does not reflect
the exposure of the population to risk in road traffic, since
it is not related to the level of motorisation of a country
and other road traffic determining characteristics.)
The mortality risk faced by people in road traffic is
not equal for men and women, and the degree of risk also
varies significantly with age4-7. The difference in risk
for men and women is thought to be a result of various
behavioural and physiological aspects related to genderand differences in their exposure in road traffic (and toa different modal-split pattern). The difference in riskfor different age groups is then related, among else, tothe development of driving skills over time and to theprogressively greater vulnerability of the human body.
For example, young men aged 18-24 have a mortality
risk in road traffic that is three times higher than that ofother road users. The elderly are more vulnerable to trauma than other groups, as shown by McCoy8 and Evans5.
The latter found that the fatality risk from the same phys-ROAD MORTALITY IN EUROPE – How Sensitive Is It to Demographic Structure and Population Dynamics? –

+

4

65
F

9

–6
60

F

4

–5
55

F

4

–5
45

–4
F

4
–3

35
F

4

25
F

7

18
F

4

15
F

9

–1
10

F

6–

5
0–

60
M

F

55
M

F

45
M

+

35
M

4

25
M

65

18
M

–6

15
M

M

4
–1

10
M

9

9
6–

M

4

0
–5

0
4

2

–5

2

–4

4

4

4

4

6

–3

6

7

8

–2

8

–1

10

5

10

0–

12

M

12

–2

values for the countries with the minimum and maximum
percentages of population in particular groups, giving anidea of the variations between countries.
At the national level, data on the age and gender ofroad accident victims are available in all European countries (although the proportion of those with unknown
gender-age characteristics can be appreciable in somecountries). For practical reasons, however, it is usual towork with age bands, as it leads to higher counts having
statistically higher value in different comparisons. For
the purposes of this paper, the IRTAD (International Road
Traffic Accidents Database) age groups were taken,
which allowed easy access to national data. It can be assumed that the aggregation used in the IRTAD databaseresults in relatively minor differences in risk within particular age/gender groups, since the age groups are designed in such a way as to take account of all the majordifferences between ages in respect to risk, being related
to the differences in traffic mode use (national legislation
setting).
Beside the differences discussed above, one should
not forget demographic shifts over time. From a demographic point of view, Europe’s population structure ischanging rapidly due to the aging of its population10.
There are three different factors behind the aging of the
population: the decline in fertility rates, the increase inlife expectancy and the maturing of the baby-boom generation. Population aging affects society as a whole andhas repercussions on all generations. According to theEurostat baseline scenario for population changes, the total population in the age group 15-24 years will fall by
25% between 2004 and 2050, while the elderly population (65-79 years-old) is expected to increase by 44%. As
for very elderly people, those aged over 80 years, their–1

ical impact is (28 3)% greater for females than formales, and increases with age from 20 onwards at compound annual rates of (2.52 0.08)% for males and (2.160.10)% for females5.
There are not only substantial differences in demographic structure from one European country to another,
but also significant demographic differences between regions in each country. These differences are attributable
to a variety of factors. For example, the fertility rate inmost Central and Eastern European countries has beenvery low since the early 1990s, and life expectancy inthese countries is some 10 years less than in Western European countries9-11. These demographic differencesamong European countries are mostly due to differencesin the economic, sociological and political conditions
that determine longevity and quality of life. The demographic differences between regions of the same country
usually reflect the migration of some groups of the population from poor, scarcely populated regions to highly
urbanized regions, and vice-versa due to different economic potential of regions.
Demographic differences do not relate solely to theage distribution of the population, but also to the relative
numbers of men and women. Since the life expectancy ofmen is significantly lower than that of women12, andsince men are more likely to die prematurely, the elderly
population is characterised by some specific structuraldifferences. Demographic differences between 25 European countries are shown in Figure 1, which gives a percentage breakdown of each country’s population by age
and gender in 2002. Besides the reference value of EU25
(corresponding to the percentage share of a country’s
population in each age/gender group in relation to the total population of the 25 European countries), it shows theV. Eksler

Fig. 1 Population distribution (as percentage of group in total population) by age/gender groups(M = male, F = female)
IATSS Research Vol.31 No.1, 2007

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number will soar by 180%10.
The questions to be answered in this paper are:
How do demographic differences influence the level ofmortality risk and how will changes in population structure affect road fatality records in the future?

2. METHODOLOGY
Epidemiologists are often faced with the need tocompare mortality rates in different geographic areas. Indirect standardisation, producing a ‘standardised mortality ratio’ (SMR), is the most commonly used techniquefor doing this. Standardisation is necessary when comparing several populations that differ with respect to age(and/or gender), because age and gender have such a
powerful influence on health risk. Despite the recent evidence of some epidemiological studies that such indirect
standardisation is inappropriate for small area comparisons (not necessarily in terms of area, or population size),since SMRs for different geographic areas have different
denominators13, this is not a major obstacle for an analysis that includes regions, because fairly similar geographic units are used in this paper.
The analyses contained in this paper relate to thethree levels of spatial aggregation: the country level, andtwo regional levels corresponding to NUTS-1 and NUTS2. The NUTS (Nomenclature of Statistical Territorial
Units) refers to the official Eurostat regional classification. The NUTS classification is hierarchical and subdivides each country (NUTS-0) into NUTS-1 territorial
units, each of which is subdivided into NUTS-2 territorial units and so on, whereas some territorial units areclassified at several different NUTS levels.
The source of demographic data for this analysis is
the Eurostat - Regio database, containing the annual average population counts for 25 European countries in adopted age-gender groups. Furthermore, the same type ofdata for regions, corresponding to the NUTS-2 disaggregation level, was obtained for 23 countries. (Annual average population data in particular age-gender groups were
not available for Slovakia and the United Kingdom).
The source of data on disaggregated national road fatalities is the IRTAD database, which contains fatality
counts in age-gender groups for 13 EU countries (Austria,
Belgium, Czech Republic, Denmark, Germany, Spain,
Finland, Hungary, France, Ireland, Poland, Slovenia,
United Kingdom). As regards the absence of the other 12
countries, that is due in some cases to their absence fromthe IRTAD database and in others to the poor design or
unreliability of their accident reporting systems. For ex-

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ample, in some countries the proportion of road fatalities
with unknown age/gender remains quite significant and it
is inappropriate therefore to use it for accurate analyses.
Aggregated fatality counts for other countries come fromthe CARE database. All road fatality counts are policereported official data adjusted for the official 30-day fatality definition (immediate fatalities and those occurring
within 30 days of the accident). The mortality rates (Mi –number of road fatalities per 100,000 population) were
calculated as the number of fatalities in each age/gendergroup per hundred thousand inhabitants in the samegroup.† As a common standard, mortality rates by gender/
age group in 13 EU countries multiplied by a scaling coefficient were used. The scaling coefficient was obtainedby dividing the mortality rate of 25 European countries
in 2002 (MEU25 = 50,089/4,539.77) by the mortality rate
in 13 European countries (M13EU = 35,939/3,362.92) for
which detailed data are available. The scaling coefficient
was calculated as = 11.032/10.687 = 1.032 and applied
to each age/gender group analysed. Since the 13 European
countries accounted for 35,941 of the 55,089 road fatalities recorded in all EU25 countries in 2002 (71% of thetotal) and were geographically and demographically representative of the EU25 countries, the mortality rates byage/gender group arrived at in this way can be consideredvalid for the EU25.
The road mortality ratio of each spatial unit (country, region) is calculated separately by dividing the mortality rate of the unit by the average mortality rate of the
25 European countries in 2002. (MRi = Mi /11.033)
The following formula is then used to calculate
the SMR of a particular geographical unit (country, region):................................................................(1)where:
Yi is the observed number of fatalities in the unit i,
Yi* is the expected number of fatalities in the same
unit i,..........................................................(2)where:
jk, is the standardised mortality rate of the age-sex

group j, k,Nijk is the population count of this group in the geographical unit i,†

Note that, unlike in epidemiology studies, road fatalities are classified
by the place where the accident occurred and not by the place of residence of the accident victims.

ROAD MORTALITY IN EUROPE – How Sensitive Is It to Demographic Structure and Population Dynamics? –V. Eksler

35
Men

Women

30
25
20
15
10
5
0
0–5

6–9

10 – 1415 – 17

18 – 24

25 – 34

35 – 4445 – 54

55 – 59

60 – 64

65+

Fig. 2 Standardized mortality rate ( jk) by age/gender groups adjusted for EU25 countries(in 2002 per 100,000 inhabitants)

The effect of standardisation can then be expressed
as follows:.................................................................(3)

2.5
2
1.5

or as a percentage change:1.........................................(4)where:
SMRi is the standardised mortality ratio of the unit i,
MRi is the mortality ratio of the unit i.
The average EU25 mortality rate by age/gendergroup is shown in Figure 2. This was obtained by multiplying the mortality rate of 13 European countries byage/gender group by the scaling coefficient . There is asmall difference between the mortality rate of men andwomen in the first three age groups, but in the case of 1517-year-olds the rate for men is twice as high as in age
groups 18-24 years and 35-44 years, and up to three times
higher for men than for women. As regards the over-60
years, the mortality rate for men is only double that for
women. It has to be pointed out here that if the exposureof a particular age/gender group to risk on the road istaken as a denominator, then for any particular group thewomen likely run a lower risk in traffic in comparison
with men.
Since the fatality risk and the proportion of population in particular age/gender groups vary, one can be interested which population group change will result in themost significant change of expected fatalities in Europe.
This is illustrated in Figure 3, presenting the percentagechange in the expected number of fatalities in 23 EU
countries due to a 10% change in the number of inhabitants in particular age/gender groups. The population

0.5
0

0–5

6 – 9 10 – 14 15 – 17 18 – 24 25 – 34 35 – 44 45 – 54 55 – 59 60 – 64

65+

Fig. 3 Percentage change of expected number offatalities due to 10% change of population inage groups in Europechange in wide age groups having higher relative numberof population (see also Figure 1 and Figure 2) results in
the highest change in expected mortality.
The demography can be considered as an explanatory factor when analysing the number of road fatalities
in a given spatial unit. To assess its ability to account forthe differences in road mortality in countries and regions,the following two models can be considered:E(Yi) = Ni· ................................................................(5)E(Yi) = Ni· ·Di ..........................................................(6)Where Yi is the registered number of fatalities inunit i, Ni is the number of inhabitants in this unit, is theaverage level of road risk in 25 EU countries and Di is thevariable standing for the role of demographic structure.
Given that these are multiplicative formulae, multiplicative residuals stand for the difference between the local
unit risk level and the average one ( ).

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MRi = Yi /E(Yi)............................................................(7)By computing SMRi, the variable Di is taken into
account. Since all other variables in the model do notchange, the variance difference obtained by comparing
Yi / Ni and with Yi / Ni Di and shows which proportion ofheterogeneity is due to the different demography. Thiscan be simply computed as follows:= [ 2(MRi) –2(SMR )] /i2(SMR )......................(8)iA positive value of the coefficient indicates a decrease of variance between the mortality ratios (MRi) andstandardized mortality ratios (SMRi), meaning that thedemography accounts for the variations in road mortality
between considered countries (regions) to the extent expressed by the value of the coefficient .
Choropleth maps provide an easy and effective wayof visualising how measurements vary across a geographic area, and they also enable areas with extreme values to be identified. For the purposes of this paper, theSMR and the rate between the SMR and the MR at
NUTS-2 level were mapped. The limits of the five classes
applied were identified by the natural breaks classification technique. The natural breaks classification method(also known variously as optimal breaks and the Jenks’Method) identifies breakpoints by looking for groupingsand patterns inherent in the data.

country in question has a favourable effect on the mortality rate (leading to a higher SMR), and vice versa. For
example, in Western European countries a relatively low
proportion of the population is in the most risky age
groups, resulting in a relatively high SMR (and E  > 1),
while Southern European countries have a relatively “unfavourable” population structure with respect to roadtraffic risk, resulting in higher expected fatality counts
and a lower SMR (E  < 1).
Figure 5 ranks countries according to the value ofthe ratio Ei, indicating the percentage difference between
the standardised mortality ratio SMRi and the mortality
ratio MRi for each country. Note that this ranking contains only 23 European countries, since the average annual population counts for the United Kingdom andSlovakia were not available for this analysis. Moreover,this has little influence on countries’ road mortality rankings, resulting only in a few countries swapping positions(Portugal with Estonia, Hungary with Luxemburg, andFrance with Estonia).
The variance between the SMRi and MRi has been
analyzed for 151 NUTS-1 and 208 NUTS-2 regions in 23
European countries. One (five) NUTS-1 (NUTS-2) Frenchoverseas regions were withdrawn due to their outlying
location and different demographic conditions. The variance of Ei for all 23 European countries is 4.063∙10-4.
The variance of the ratio Ei grows with spatial aggrega-

3. RESULTS
As mentioned above, the effect of standardisation
on mortality rates is expressed here by comparing the
SMRi with the MRi. The SMRi stands here for the difference between the road mortality experience of the population and the experience it would have if it experienced
the age and gender specific rates of the comparison population (rate between the number of observed fatalities andthose expected in 25 European countries). An SMR
greater than 1.0 indicates that more fatalities have occurred than would have been expected; while a ratio ofless than 1.0 indicates that fewer fatalities occurred. The
decimal fraction shows the percentage comparison.
SMR values for the NUTS-2 regions are mapped in Figure
3, showing the heterogeneity of mortality ratios acrossEurope.
For this paper, the effect of standardisation on themortality rate was assessed through the ratio Ei = SMRi/
MRi. A value greater than 1 means that the standardisedmortality ratio is higher than the mortality ratio, leading
to the conclusion that the population structure of the84

IATSS Research Vol.31 No.1, 2007

Legend0.18−0.58
0.59−0.99
1.00−1.38
1.39−1.86
1.87−2.94

Fig. 4 Standardised mortality ratio (SMR) in NUTS-2
regions of 23 EU countries

ROAD MORTALITY IN EUROPE – How Sensitive Is It to Demographic Structure and Population Dynamics? –V. Eksler

1.05

1.025

1

0.975

0.95
ES GR SI CZ PL PT

IT

IE HU MT DE AT LV BE EE FI FR SE CY NL LT LU DK

Fig. 5 Ranking countries according to the influence of population structure on mortality rate
(Ei = SMRi /MRi)tion, but at NUTS-1 level it is bigger than at NUTS-2
level. The variance of the ratio Ei at NUTS-2 level issmallest for the Czech Republic (9.25∙10-6), Poland(25.77∙10-6) and Sweden (67.05∙10-6), and highest for
Greece (3.46∙10-4), Germany (5.29∙10-4) and France(9.33∙10-4). The average infra-national variance of Ei forthe countries, 2.17∙10-4, is lower than the intra-nationalvariance of 23 countries. Regional variations are thus
smaller than the intra-national ones. Table 1 presents thevariance between all 23 countries and 151 (208) regions.

Legend0.90−0.96
0.97−0.98
0.99−1.00
1.01−1.02
1.03−1.05

Table 1 The variance between SMRi/ MRi values at
the three levels of spatial disaggregation
CountriesN
2(E)Regions(NUTS-1)

Regions(NUTS-2)

23

151

208

4.063∙10-4

12.56∙10-4

5.869∙10-4

The effect of the population structure on the observed road mortality can be captured at several levels of
spatial disaggregation. Figure 6 shows the heterogeneityof SMR/MR values across Europe, while Figure 7 shows
the variance of the SMR/MR rate in 15 countries. Again,
the maximum and minimum values for NUTS-2 regions
are presented around the national value (NUTS-0 level).
Eight of the 10 European countries missing from thisfigure do not have NUTS-2 regions, while detailed population data are not available at NUTS-2 level for theother two (United Kingdom, Slovakia). The countries areranked according to the relative difference of E values:= (Emax – Emin)/Ecountry. The two limits show the extreme values for the countries and create a range of varia-

Fig. 6 Rate between SMR and MR for EU countries for
NUTS-2 regions

tions of 5 among countries. This is generally narrower
than the range of variation of E values among NUTS-2
regions. The observed variations are directly related tothe demographic structure of regions: densely populated
regions (with a higher GDP) usually have a higher shareof young, economically active population with an increased mortality risk in road traffic compared to regionswith low population density.
Now let us compare two different SMRi values, one
standardised by age and gender and the other one standardised by age only. The difference between these two
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1.1

1

0.9

CZ

IE

PL

AT

FI

SE

HU

PT

FR

IT

BE

ES

NL

GR

DE

Fig. 7 Rate between SMR and MR for EU countries at NUTS-2 level in 2002, minimum,
maximum values at NUTS-2 level around the value for country level

ratios (F) varies from -1.0% to 2.5%, whereas the average absolute change is about 0.7%. The standard deviation for country level values ranges from 0.0003 to 0.04
and is lowest for Italy, Ireland and the Czech Republicand highest for the three Baltic countries. These differences are relatively small and would not significantly influence presented conclusions.
After drawing some conclusions about the influence of structural demographic differences in populations
on the level of road safety as measured by the mortality
ratio and SMRs, one may want to look at how futurechanges in population structure will influence expectedroad safety records. In the following exercise, expectedfatality counts are calculated for the national demographic patterns of 23 EU countries. The latest demographic
forecasts published by Eurostat provide the data needed
to calculate expected fatality counts for the three years
2020, 2035 and 2050. As we are seeking to analyse thestructural changes in population (percentage distribution
in each age/sexgender group) only, changes in the total
population were not taken into account. Using road mortality rates as calculated above for age/gender groups in
2002, the expected number of fatalities can be calculated
for the three years. The expected number of fatalities foreach year is then divided by the expected number of fatalities for 2002, so the ratio between the two years is
obtained. This ratio shows whether the demographic
changes will result in a rise or a fall in the number of road
fatalities.
For all 23 EU countries, the expected number offatalities decreases from 43,201 in 2002 to 41,856 in
2020, but then rises to 42,498 in 2035 and 42,730 in
2050. But for some other countries, the changes are more

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significant, as shown in Figure 8: For example, in Spainthe expected number of road fatalities falls from 4,754
in 2002 to 4,418 in 2020 and then rises to 4,576 in 2035
and 4,595 in 2050 respectively. In other words, the number is 7.6% smaller in 2020, but 3.5% (0.4%) higher in2035 (2050). Spain, Greece, Latvia and Denmark are
the countries whose expected mortality is most sensitive to structural demographic changes, while in the
Netherlands, Cyprus and Latvia expected mortality rates
are stable over the time.
Last, but not least, the statistical evidence on therole of demography as an explanatory factor for various
fatality records was studied. The value of the variance
between mortality ratios (MRi) and standardized mortality ratios (SMRi) of countries and regions within particular countries are summarized in Table 2 and Table 3.
The statistical significance of the difference between thevariance of the two ratios can be assessed by the so called
F-concept16. The significance at the 95% level is reached
if the rate of the two ratios (F0) is smaller than the F critical value, which depends on the degrees of freedom in
the numerator variance (a-1) and in the denominator variance [a(n-1)].
While there is no evidence of the explanatory effectof the demography at a country level (NUTS-0), the figures suggest that demography explains 10%, respectively
5% of the variation in road mortality between 141 NUTS1 (208 NUTS-2) regions of 23 EU countries, but the estimates is no significant at NUTS-3 level.
Similarly, the demography accounts for up to 10%of the variance in road mortality registered within NUTS2 regions of some EU countries, but estimates are notsignificant.

ROAD MORTALITY IN EUROPE – How Sensitive Is It to Demographic Structure and Population Dynamics? –1.05

V. Eksler

IE
ES
LV

1.025

DK
PL
HU

DK
CY
NL

1

IE
EE
LV

HU
LU
DK

0.975
LV
GR
ES

0.95

0.925

2020

2035

2050

Fig. 8 Ratio between the expected number of road fatalities in 2020 (2035, 2050) and 2002 in
23 EU countries

Table 2 The variance of MR and SMR at the three levels of spatial disaggregation
Nr

MR

SMR

F0 =MR/SMR

DF

Fcrit (95%)

NUTS-0

23

0.168

0.168

1.000

0.000

22

2.12

-

NUTS-1

151

0.207

0.185

1.119

11.892

150

1.10

**

NUTS-2

208

0.239

0.226

1.058

5.752

207

1.10

-

DF

Fcrit (95%)

Sign.(95%)

10

2.98

-

2

Sign. (95%)

Table 3 The variance of MR and SMR at regional level (NUTS-2)
2

Nr

MR

SMR

F0 =MR/SMR

BE

11

0.332

0.350

0.948

-5.202

CZ

8

0.101

0.096

1.056

5.598

7

3.73

-

DE

42

0.102

0.097

1.046

4.565

41

1.69

-

GR

13

0.271

0.248

1.093

9.307

12

2.75

-

ES

19

0.241

0.214

1.126

12.623

18

2.12

-

FR

22

0.104

0.106

0.981

-1.879

21

2.12

-

IT

21

0.121

0.119

1.022

2.225

20

2.12

-

HU

8

0.066

0.063

1.047

4.694

7

3.73

-

NL

12

0.033

0.034

0.978

-2.246

11

2.75

-

AT

9

0.138

0.141

0.983

-1.653

8

3.44

-

PL

16

0.110

0.105

1.047

4.684

15

2.49

-

PT

7

0.598

0.539

1.110

11.047

6

4.28

-

FI

5

0.106

0.104

1.012

1.171

4

6.39

-

SE

8

0.036

0.036

1.017

1.708

7

3.73

-

4. CONCLUSIONS
The differing demographic structures of 25 European countries have only a minor influence on roadmortality rates (ratios). Differences in the mortality ra-

tio and the SMR between countries vary only slightly,
from -4.3 to 2.9%. However, the corresponding ratios
calculated for the NUTS-2 regions of the same countries
show greater variations, the largest ones being found inFrance, Germany, Greece and the Netherlands, which
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are countries with a high number of NUTS-2 regions ingeneral. But the variations are relatively small in thecase of small countries such as the Czech Republic andHungary. (In Greece, for example, the ratios vary from
-2.3% to -10.8 %, while in the Czech Republic they vary
only from -1.9% to -2.8%).
The interpretation of the SMR has to take full account of the possible effects of random variations due tothe low number of fatalities in some of the age/gendergroups used, especially in the case of small geographicalunits. The limitations of the approach outlined here are
due to the use of a relatively small number of age/gendergroups and 12-month data, while forecasting the SMR
also suffers from the absence of a more complex prediction model that enables the number of road fatalities to be
estimated. A further disaggregation of some age groups,
especially the relatively wide ones (18-24, 65+), is very
likely to result in minor changes in computed SMRi, andhence slightly different conclusions.
Forecasting how the (relative) mortality risk intraffic will develop for particular age groups is a very
complex business, since it depends so heavily on how
these groups perform in traffic and on other factors14. An
increased proportion of elderly drivers in traffic will result in an increased number of fatalities among them, but
it will not be as great15. This leads to the conclusion thatlinear predictions of older drivers’ accident involvementthat is directly based on their increasing numbers arelikely to be overly pessimistic. Greater longevity willbring a significant increase in the number of very old
drivers who are at greater risk of being involved in roadaccidents14. As the size of this age group is set to double
between now and the year 2050, when it is expected torepresent about 30% of the total population in Europe12,
there is a strong need to determine the road risk faced byseveral age groups above this age. Road safety administrators are likely to target the elderly more closely in theirstatistics, and road safety researchers should make a contribution to a better understanding of the risk faced by theelderly.
The aging of the population, together with changes
in total population, will result in relatively minor changes
in the expected number of road fatalities in Europe, but
aging could have a major impact in some countries, since
there might be a shift in the SMRi calculated by country,with values by 2050 showing differences of 30% relative to those calculated for the year 2002.

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IATSS Research Vol.31 No.1, 2007

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ACKNOWLEDGEMENTS
This analysis is part of a broader research project dealing
with the spatial variation of road risk in European regions. The
author is particularly indebted to Isabelle Thomas and Sylvain
Lassarre for their valuable comments and suggestions.