MONASH
UNIVERSITY
AUSTRALIA
FURTHER MODELLING OF
SOME MAJOR FACTORS
INFLUENCING ROAD TRAUMA
TRENDS IN VICTORIA: 1990-96
FURTHER MODElliNG OF SOME MAJOR
FACTORS INFLUENCING
ROAD TRAUMA TRENDS
IN VICTORIA: 1990-96
by
Stuart Newstead
Max Cameron
Sanjeev Narayan
Report No. 129
April 1998
Il
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
MONASH UNIVERSITY RESEARCH CENTRE
REPORT DOCUMENTATION PAGE
Report No.
129
Report Date
Apri11998
ISBN
0732607094
Pages
23
+ Appendices
Title and sub-title:
Further modelling of some major factors influencing road trauma trends in Victoria:
Author(s)
Newstead, S.V.,Cameron, M.H.
& Narayan, S.
1990-96
Type of Report & Period Covered
GE~RJ\L,
1990-1996
Sponsoring Organisation - This project was funded through the Centre's baseline research program
for which grants have been received from:
Department of Justice
Royal Automobile Club of Victoria Ltd.
VicRoads
Transport Accident Commission
Abstract:
Based on previous work that has estimated the contribution of some major factors in reducing road
trauma in Victoria over the period 1990-1993, this project has made use of the statistical analysis
methods developed to extend these estimates to 1996. The major factors considered in the study
have stemmed from the results of a number of studies in Victoria which have evaluated the effects
of countermeasures and other factors which appear to be responsible for the substantial reduction in
road trauma since 1989. The factors for which contributions have been estimated were:
•
•
•
•
Increased random breath testing, supported by mass media publicity
Speed cameras, supported by mass media publicity
Reduced economic activity
Reduced alcohol sales
•
Improvements to the road system through treatment of accident black spots
The percentage change in road trauma levels, as measured by serious casualty crash numbers, due to
each factor has been estimated for each year over the period 1990-1996.
Models linking variations in serious casualty crashes to various factors were computed using
monthly crash data from the years 1983 to 1996. Subsequently, the contributions of random breath
testing, speed camera tickets issued, levels of road safety television publicity, unemployment rates
and alcohol sales to the reduction in the number of serious casualty crashes were estimated for the
period 1990-96. A method of separately estimating the effect of accident blackspot treatments and
desegregating this from the trend was described and applied.
Key Words: (IRRD except where marked*)
statistical analysis, accident frequency, breath test, speed camera, advertising, economics, alcohol usage,
accident black spot
Reproduction of this page is authorised.
FURTHER MODELLING OF SOME FACTORS INFLUENCING ROAD TRAUMA TRENDS IN VICTORIA
III
IV
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
Contents
1.
INTRODUCTION
1.1
2.
1
REVIEW OF WORK COMPLETED TO DATE
1
INCLUSION OF CRASH DATA TO THE END OF 1996 IN THE MODELS ..•................................3
2.1
FACTORS INCLUDED IN THE MODELS
2.1.1 Economic Measure
3
3
2.1.2 Factors Relevant to High Alcohol Hour Crashes
2.1.3 Factors Relevant to Low Alcohol Hour Crashes
2.2 DISAGREGGATION AND COMBINATION OF THE RESULTS
2.3 EFFECT OF ACCIDENT BLACK SPOT TREATMENTS AND OTHER FACTORS
2.4 SUMMARY OF ESTIMATED CONTRlBUTIONS
4
12
14
14
16
3.
DISCUSSION .....................................................•................•......•..•...........•............•.........•..........•.....•.....
18
4.
CONCLUSION .........................................................................................................................•...•.........19
5.
FURTHER WORK RECOMMENDED ..................................................•.........•.......•................•.....•...
20
6.
21
ACKN 0WLEDG MENTS ..........................•.........•...•...............••............................................................
7.
REFERENCES .........••.........•.....••.......................................••.........................................•.........................
21
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
V
List Of Tables
TABLE 1 : EFFECTS OF UNEMPLOYMENT RATE, NUMBER OF RANDOM BREATH TESTS,
ALCOHOL SALES (ALL VICTORIA), AND ADSTOCK OF TAC DRINK-DRIVING PUBLICITY ON
SERIOUS CASUALTY CRASHES DURING HIGH ALCOHOL HOURS OF THE WEEK. MELBOURNE
AND COUNTRY VICTORIA 1983 - 1992
12
TABLE 2 : EFFECTS OF UNEMPLOYMENT RATE, NUMBER OF SPEED CAMERA TINS ISSUED,
AND ADSTOCK OF TAC "SPEEDING" AND "CONCENTRATION" PUBLICITY ON SERIOUS
CASUALTY CRASHES DURING LOW ALCOHOL HOURS OF THE WEEK. MELBOURNE AND
COUNTRY VICTORIA 1983 - 1996
TABLE 3: ESTIMATED
REDUCTIONS
IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE
TO
VARIOUS FACTORS - VICTORIA, ALL HOURS, 1990-96
TABLE 4 : ESTIMATED REDUCTIONS
MAJOR FACTORS: VICTORIA 1990-96
VI
IN SERIOUS CASUALTY
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
13
14
CRASHES
ATTRIBUTABLE
TO
17
List Of Figures
FIGURE 1: PERCENTAGE CHANGE FROM 1987, VICTORIA.
FATALITIES AND SERIOUS INJURIES
1
FIGURE 2: UNEMPLOYMENT
3
RATE IN MELBOURNE
AND THE REST OF VICTORIA 1983-96
FIGURE 3A : NUMBER OF BUS-BASED RANDOM BREATH TESTS PER MONTH. MELBOURNE
(MSD) 1989-1996
4
FIGURE 3B : NUMBER OF BUS-BASED RANDOM BREATH TESTS PER MONTH. REST OF
VICTORIA (ROV) 1986-1996
5
FIGURE 4A(I) : TAC ROAD SAFETY TELEVISION ADVERTISING - MONTHLY ADSTOCK BY
THEME: MELBOURNE REGION 1989 - 1996
6
FIGURE 4A(II) : TAC ROAD SAFETY TELEVISION ADVERTISING - MONTHLY ADSTOCK BY
THEME: COUNTRY VICTORIA 1989 - 1996
6
FIGURE 4B(I) : DRINK-DRIVING
7
FIGURE 4B(II) : DRINK-DRIVING
ADSTOCK:
MELBOURNE REGION 1989 - 1996
ADSTOCK: REST OF VICTORIA 1989 - 1996
7
FIGURE 4C(I): SPEED AND CONCENTRATION AD STOCK : MELBOURNE REGION 1989 - 1996 .... 8
FIGURE 4C(II):
SPEED AND CONCENTRATION ADSTOCK: REST OF VICTORIA 1989 - 1996
FIGURE 5: INDEX OF ALCOHOL SALES IN VICTORIA, 1983 - 1996
8
9
FIGURE 6A : MONTHLY NUMBER OF TRAFFIC INFRINGEMENT NOTICES ISSUED FOR
SPEEDING OFFENCES DETECTED BY SPEED CAMERAS. MELBOURNE 1989 - 1996
10
FIGURE 6B : MONTHLY NUMBER OF TRAFFIC INFRINGEMENT NOTICES ISSUED FOR
SPEEDING OFFENCES DETECTED BY SPEED CAMERAS. REST OF VICTORIA 1989 - 1996
10
FIGURE 7 : ESTIMATED REDUCTIONS
MAJOR FACTORS: VICTORIA 1990-96
FURTHER MODELLING
IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
TO
17
VII
List of Appendices
APPENDIX A: PERCENTAGE REDUCTIONS IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE
TO VARIOUS SOURCES. VICTORIA 1990-1993 RELATIVE TO 1988
APPENDIX B : ESTIMATION
BLACKSPOT TREATMENTS
OF SERIOUS CASUALTY CRASH REDUCTIONS
VIII MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
DUE TO ACCIDENT
EXECUTIVE SUMMARY
Based on previous work that has estimated the contribution of some major factors in
reducing road trauma in Victoria over the period 1990-1993, this project has made use of the
statistical analysis methods developed to extend these estimates to 1996. The major factors
considered in the study have stemmed from the results of a number of studies in Victoria
which have evaluated the effects of countermeasures and other factors which appear to be
responsible for the substantial reduction in road trauma since 1989. The factors for which
contributions have been estimated were:
•
•
•
•
Increased random breath testing, supported by mass media publicity
Speed cameras, supported by mass media publicity
Reduced economic activity
Reduced alcohol sales
•
Improvements to the road system through treatment of accident black spots
The percentage change in road trauma levels, as measured by serious casualty crash
numbers, due to each factor has been estimated'for each year over the period 1990-1996.
Models linking variations in serious casualty crashes to various factors were computed using
monthly crash data from the years 1983 to 1996. Subsequently, the contributions of random
breath testing (RBT), speed camera tickets issued, levels of road safety television publicity,
unemployment rates and alcohol sales to the reduction in the number of serious casualty
crashes were estimated for the period 1990-96. A method of separately estimating the effect
of accident blackspot treatments and desegregating this from the trend was described and
applied.
The major contributors and the apparent percentage reduction in serious casualty crashes due
to each measure/factor were estimated as:
• Speed camera operations (principally speeding tickets issued):
10-11% each year
• "Speeding" and "concentration" television advertising:
5-7% each year
•
Drink-driving program (bus-based RBT together with
"drink-driving" publicity campaigns)
• Reduced alcohol sales:
9-10% each year
3% in 1990
6% in 1991
7% in 1992
9% in 1993
8% in 1994
9% in 1995
10% in 1996
FURTHER MODELLING OF SOME FACTORS INFLUENCING ROAD TRAUMA TRENDS IN VICTORIA
IX
• Reduced economic activity (measured by unemployment rates):
2% in
12% in
15% in
16% in
14% in
10% in
10% in
1990
1991
1992
1993
1994
1995
1996
• Accident Black Spot treatments
1.6% in
2.5% in
3.2% in
5.3% in
6.2% in
6.2% in
5.6% in
1990
1991
1992
1993
1994
1995
1996
The anti-speeding and drink-driving programs together are estimated to have contributed
reductions in serious casualty crashes of at least 22-25% during these seven years. Including
the accident blackspot treatments, the overall contribution of road safety initiatives is
estimated to have risen from 23% reduction in 1990 to nearly 30% reduction in 1993-1996.
X
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
1.
INTRODUCTION
Since 1989 there has been a remarkable reduction in deaths and injuries on Victorian roads.
Figure 1 shows a decline in fatalities in Victoria from a peak of 776 in 1989 to 417 in 1996.
As well as the substantial decrease in road fatalities in Victoria since 1989, there have also
been large reductions in serious injuries (Figure 1). A relative plateau in the levels of both
fatalities and serious injuries has, however, been observed from around 1992 through to
1996.
FIGURE 1:
Percentage change from 1987.
Victoria, fatalities and serious injuries
120
100
'0
~
'ii
•••
••
lL
.!!
>
l!••t••
C
60
80
40
20
o
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Ve.r
I_Fatal~ies
SeriousinjuriesI
These initial large reductions in deaths and injuries after 1988 have been attributed to the
implementation of various safety programs which were introduced commencing in
September 1989, and to the downturn in the economy which occurred during the period
under consideration. It is important to estimate the contribution of each of these programs
and if possible the mechanism by which they achieved their reductions, so that they can be
fine tuned for further gains and allocation of future resources can be made on the basis of the
best available information.
This project aims to build on previous work which has estimated the contribution of some of
major road safety programs, along with economic factors, which have lead to the dramatic
reduction in road trauma in Victoria since 1989.
1.1
REVIEW OF WORK COMPLETED TO DATE
A study by Newstead et al (1996) has estimated the contribution of some major factors in
reducing road trauma, as measured by serious casualty crashes (SCCs), over the period
1989-94. This updated previous work covering the period 1989-93 (Newstead et al 1995)
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
1
which developed a method of combining the results of a number of studies in Victoria which
have evaluated the effects of countermeasures and the factors which appear to be responsible
for the substantial reduction in road trauma since 1988. The factors for which contributions
were estimated were:
•
•
•
•
Increased random breath testing, supported by mass media publicity
New speed cameras, supported by mass media publicity
Reduced economic activity (as measured by unemployment rate)
Reduced alcohol sales
• Improvements to the road system through treatment of accident black spots
The original studies upon which the work to date has been based have modelled Victorian
road trauma trends for various road user groups at various times of the week (Cameron et al
1992a, Cameron et al 1992b, Cameron et al 1993). These studies have defined four strata in
terms of area and time into which crashes in Victoria can be divided for modelling road
trauma trends. These four strata are; the Melbourne metropolitan area (the capital city of the
State of Victoria) and the rest of Victoria, with each area being considered in two time
divisions, "high alcohol hours" (HAH) and "low alcohol hours" (LAH) (HAH and LAH are
defined by Harrison 1990).
The method of analysis used has been that of multivariate log-linear regression, which
involves relating measures of road safety programs and economic effects, along with general
trend and monthly variation, to the observed road trauma series via a regression equation.
This method has proved useful in being able to establish the particular influence each factor
in the regression equation has on the outcome measure (vis. road trauma). The general form
of the fitted regression equation is
where SCC; is the number of serious casualty crashes observed in month i, TREND is a
linear trend factor, FEB, MAR, ... ,DEC are monthly dummy variables to account for regular
seasonal variation, FACTOR1,FACTOR2, ... are measures of the road safety program,
economic or social factors of interest and (a,b, ...,g) are parameters of the model which
were estimated by multivariate log-linear regression. A separate regression equation of the
form of equation (1) was fitted to the monthly serious casualty crash series each of the four
strata defined above. Factors included in each regression equation were chosen based on the
considerable experience built up from detailed analysis in past evaluation studies.
This project aims to re-assess the contribution these factors have had on Victorian road
trauma trends up to 1996. This has been achieved by extending the results of New stead et al
(1996) to cover the period 1989 to 1996 in order to estimate the contribution of each major
factor to reducing serious casualty crashes over this time.
2
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
2.
INCLUSION OF CRASH DATA TO THE END OF 1996 IN THE
MODELS
This section begins by detailing the trends in the factors considered in the models through to
1996. These factors were then used to model the monthly number of SCCs over the period
1983 to 1996 using the model structures of New stead et al (1995).
2.1
FACTORS INCLUDED IN THE MODELS
The following section describes the factors, both road safety and other, which have been
considered in modelling road trauma in Victoria over the time span under consideration.
Whilst these factors are identical to those used in the work of Newstead et al (1996), it is
considered of value to again present an overview of these in this report to show their
continuing trends to the end of 1996. Knowledge of these trends will be useful in
interpreting the results of the analysis presented further on.
2.1.1
Economic Measure
A selection between the economic measure used by Newstead et al (1996), number
employed, and the one used by Newstead et al (1995), unemployment rate, had to be made
to be included in the main model. It was discovered using residual analysis for the estimated
models that unemployment rate and the number employed showed similar random patterns
and dispersion. Consequently, it was decided to use unemployment rate as the measure of
economic activity since its interpretation is easier in the context of road trauma. Figure 2
shows unemployment rates for both Melbourne and the rest of Victoria over the period 1983
to 1996. Of note in Figure 5 is that, whilst the unemployment has risen sharply and peaked
over the 1990-93, 1994-1996 has seen a recovery in the economy with unemployment rates
falling during these years.
FIGURE 2:
Unemployment Rates in Melbourne (MSD) and
the rest of Victoria (ROV), 1983-96
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0.12
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FURTHER MODELLING
"
~
..,
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
3
2.1.2
Factors Relevant to High Alcohol Hour Crashes
The major road safety initiative targeted at crashes in high alcohol hours (HAH) in Victoria
since 1989 has been the random breath testing program (RBT) supported by a high profile
advertising with drink-driving themes sponsored by the Transport Accident Commission
(TAC). The program of increased RBT using highly visible booze-buses commenced in
September 1989 with car based testing being predominant form of RBT before this time.
With the introduction of the bus-based RBT program, a corresponding decline in car-based
operation was observed. The Victoria Police discontinued recording activity in the car-based
RBT program from January 1sI 1996 but rather, recorded this information under Preliminary
Breath Test (PBT) statistics. Accordingly, it is unknown to what extent car based RBT
activities have been conducted since this date. Hence, in this work, only the bus-based
testing is considered in modelling HAH crashes.
Past studies (Newstead et al 1995, Cameron et al 1992a, 1993a,b) have found the most
appropriate measure of the RBT enforcement activity in relation SCC numbers to be the
number of tests conducted. Figures 3a and 3b shows the number of bus-based RBTs
conducted per month from 1983 to 1996 in metropolitan Melbourne and in the rest of
Victoria respectively. In metropolitan Melbourne during 1996, the level ofRBT activity has
remained similar to that during 1994 and 1995, with around 100,000 tests conducted per
month, except for the final months in 1995 and initial months in 1996 and where the level of
RBT aCtivity had gone beyond 120,000 tests per month. In the country Victoria the total
number of RBTs have fallen to around 30,000 per month during 1996, which is around 2025,000 tests per month less than in previous years.
FIGURE 3a : Number of Bus-based Random Breath Tests per month
Melbourne (MSD) 1989 - 1996
I
140000
120000
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80000
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4
~
~
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
FIGURE 3b : Number of Bus-based Random Breath Tests per month
Rest of Victoria (ROV) 1989 - 1996
50000
45000
40000
35000
{!!.
m
30000
a:
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Supporting the RBT program in Victoria has been an intense publicity campaign sponsored
by the TAC, of which one of the main themes has been drink-driving. Until the end of 1993,
the levels of Adstock generated in metropolitan Melbourne and the rest of Victoria were
reported as the same. During 1994, different levels of advertising were reported as placed in
each region reflecting the Police campaign on drink-driving in Country areas and a series of
advertisements specifically aimed at country people. Figures 4a, 4b and 4c show the levels
of all TAC road safety advertising Adstock, TAC drink-driving Adstock and TAC speed and
concentration Adstock respectively current in each month from 1989 to December 1994 in
Melbourne and the rest of Victoria (for a definition of Adstock, see Broadbent 1979). Both
in metropolitan Melbourne and country Victoria the level of awareness of TAC road safety
advertising to have been maintained at significant levels throughout 1995 and 1996,
although at slightly lower levels than the previous years. The levels of advertising
specifically on the drink-driving theme in both regions have risen marginally in 1996
periods as compared to the other years.
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
5
FIGURE 4a(i) :TAC Road Safety Television Advertising - Monthly Adstock by theme
Melbourne region
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FIGURE 4a(ii) :TAC Road Safety Television Advertising - Monthly Adstock by theme
Country Victoria region
18000
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4000
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14000
16000
%
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I
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
FIGURE 4b(i) :Drink-driving Adstock : Melbourne region 1989-1996
8000
7000
6000
5000
4000
::i 3000
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FIGURE 4b(ii) :Drink-driving Adstock: Country Victoria region 1989-1996
i
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1000
5000
4000
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
7
FIGURE 4c(i) :Speed and Concentration Adstock: Melbourne region 1989-1996
III
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FIGURE 4c(ii) :Speed and Concentration Adstock : Country Victoria region 1989-1996
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8
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
An index alcohol sales in Victoria has been included as an explanatory variable and
represents an exposure measure of the potential to drink-drive. Figure 5 shows the monthly
index of alcohol sales for the period 1983-1996. Whilst the index shows a downward trend
over the years 1990-1996 there is perhaps some evidence of the series leveling from about
1994 onwards.
FIGURE 5:
Index of Alcohol Sales on Victoria, 1983-96
400
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The most recent modelling of SCC trends in HAH (Newstead et al 1996) also considered the
possible effects of the speed camera program on HAH crashes based on the results of work
by Rogerson et al (1994) which examined the localised effects of the speed camera program
during both high and low alcohol hours. The main findings of this study were observed
reductions in casualty crash frequency due to localised speed camera effects during high
alcohol hours. Whilst these effects were localised, being examined only within 1 km radius
of speed camera sites, it is possible that more generalised speed camera effects were also
present in HAH, similar to those measured in LAH by Cameron et al (1992b). Given the
established presence of speed camera effects in HAH, it was considered necessary to again
allow for the expression of these in the models describing HAH crashes in both Melbourne
and the rest of Victoria. Figure 6 shows the number of speed camera TINs issued per month
for the period January 1989 to December 1996. Examination of Figure 6 reveals the number
of speed camera TINS issued through 1996 to be similar to the two previous years,
averaging around 40,000 TINs per month.
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
9
FIGURE 6a: Monthly number of Traffic Infringement Notices Issued
for Speeding Offences Detected by Speed Cameras
Melbourne 1989 - 1996
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FIGURE 6b : Monthly number of Traffic Infringement Notices Issued
for Speeding Offences Detected by Speed Cameras
Rest of Victoria 1989 - 1996
" z.;E••
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6000
4000
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10
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
The model described by equation (1) was fitted to the serious casualty trends in high alcohol
hours for metropolitan Melbourne and country Victoria. The major road safety campaigns
included in the models were monthly number of RBTs, monthly number of speed camera
TINs and monthly road safety media publicity as measured by Adstock. In considering
which component of the media publicity to include in the models, the drink-driving
component alone proved to be just as strong a predictor of SCC numbers as total publicity,
but with a more targeted theme, and hence was included in the model. A linear trend
component and monthly seasonal dummies were also included in the models along with
unemployment rate and alcohol sales. A summary of the key model parameter estimates for
Melbourne and country Victoria is given in Table 1.
The models fitted to HAH SCC data for Melbourne and country Victoria, summarised in
Table 1, explained 84% and 71% of the monthly variation respectively, based on the fitted
model R-squared vales. A statistically significant linear trend was found in the data for
metropolitan Melbourne, with the effects of unemployment rate and alcohol sales also being
statistically significant. Of the road safety programs, the effects of random breath testing and
speed camera TINs were statistically significant. The effect drink-driving publicity was not
statistically significant however, with the factor having to be removed from the model
because of a high co-linearity between it and the number of RBTs. The high degree of colinearity reflects the fact that the effects of drink driving publicity and number of RBTs are
highly confounded meaning individual effects of each factor cannot be estimated
independently. In practice the estimated effect of RBT tests in the model is more
representative of the combined effect of RBT tests and associated publicity in tandem as a
single program.
Effects of the modelled factors in country Victoria were quite different from those in
Melbourne. Here no statistically significant linear trend was found in the data along with no
statistically significant effects due to unemployment rate or speed camera TINs. The effects
of alcohol sales and number of RBTs were found to be statistically significant, however the
same high co-linearity between RBTs and drink-driving publicity was observed, most likely
causing the latter to show no statistically significant association with SCCs, as for
metropolitan Melbourne.
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
11
Table 1:
of
•
Effects of unemployment rate, number of random breath tests, alcohol sales
(all Victoria), number of speed camera TINs issued, and Adstock of TAC
drink-driving publicity on serious casualty crashes during high alcohol hours
of the week. Melbourne and country Victoria 1983-96.
trend
of random
bus-based
random
breath
AREA
OF
VICTORIA
alcohol
sales
during
month
number
unemployment
no.
drink-driving
of
of
speed
Adstock
breath
camera
tests
inTINs
month
(car
issued
ortests
buslogged
rate
in
month
0.7985
0.7084
0.356
-0.375
0.0092
NS
Tvalue
0.8892
-8.730
0.140
0.0291
-2.203
NS
-2.639
NS++
0.0228
0.0001
2.301
4.940
0.0004
0.0001
3.736
-4.639
-0.782
-0.0204***
0.0025***
0.0003
-0.0155**
level
0.4357
-0.3149***
-0.0109*
0.5795***
0.3067*
Estimated
(two-tailed)
(-0.0293)
NS
variable
Significance
(0.0038)NS
(0.0001)
NS
(-0.0068)
NS
df=151
df=155
* Statistically significant at p<O.05 level; ** Highly statistically significant at p<O.OJ level;
*** Very highly statistically significant at p<O.OOJ level; NS = not significant; ++ No estimate available
2.1.3
Factors Relevant to Low Alcohol Hour Crashes
Past studies have found the speed camera program to be a major factor influencing SCC
numbers in low alcohol hours (LAH), with the most appropriate measure of program activity
being in the number of TINs issued for speeding offences detected by speed cameras each
month. Like the RBT program, the speed camera program in Victoria has also been
supported by a high exposure TAC publicity campaign with the speeding theme. Studies
also have found both speeding and concentration publicity campaigns to be a factor
influencing SCC numbers in LAH. Figures 4a,b and c, depicting TAC television advertising
Adstock, show that during 1996, the level of awareness of advertising with speeding and
concentration themes (Figure 4c) remained at significant levels, although at perhaps slightly
lower levels in magnitude compared to the two previous years
Once again, models of the form of equation (1) were fitted to low alcohol hour SCCs
separately in Melbourne and the rest of Victoria, including linear trend, monthly seasonal
dummies, unemployment and road safety program measures as model covariates. The two
major road safety program measures included in the low alcohol hour models were the
12
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
number of speed cameras TINs issued per month, and the road safety media publicity as
measured by Adstock. Exploratory analysis showed Adstock for both "speeding' and
"concentration" themes together showed greater association with monthly SCCs than did
just speeding alone. Table 3 summarises the parameter estimates for the LAH models for the
two regions of the state.
All four main factors fitted were statistically significant predictors in the model of LAH
SCCs in Melbourne, with the model explaining 78% of the variation in the data. The number
of speed camera TINs issued was a highly statistically significant predictor in the models as
were speeding and concentration Adstock and unemployment rate.
For LAH SCCs in the rest of Victoria, the fitted model explained 65% of the variation in the
data. Unlike the models estimated for HAH SCCs in the rest of Victoria, trend and
unemployment rate showed a statistically significant association with SCCs in low alcohol
hours. Road safety publicity with the speeding and concentration theme also showed a
statistically significant association with LAH SCCs in the rest of Victoria, however the
effect of speed camera TINs was not statistically significant, with the factor being removed
from the model because of co-linearity problems with the Adstock measure.
Table 2:
Effects of unemployment rate, number of speed camera TINs issued, and
Adstock ofTAC "speeding"and "concentration"publicity on serious casualty
crashes during low alcohol hours of the week Melbourne and country
Victoria 1983-96.
trend
AREA
OFspeed
VICTORIA
no.
unemployment
of
rate
month
issued
speeding
and
concentration
Adstock
-0.1475*
NScamera
T
valueinTINs
0.0041
-2.916
-0.0013**
0.0090
0.0022
-3.108
-2.648
-0.0196**
0.0002
3.774
0.0015***
0.0293
-2.546
-4.484
-5.349
-0.2191**
NS
NS++
Estimated
-0.0177**
0.0001
-0.0209***
level logged
(two-tailed)
variable
Significance
df=156
df=152
t of
* Statistically significant at p<O.05 level; ** Highly statistically significant at p<O.Ollevel;
*** Very highly statistically significant at p<O.OOllevel; NS = not significant; ++ No estimate available
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
13
2.2
DISAGREGGATION AND COMBINATION OF THE RESULTS
Using the methods of Newstead et al (1995), each of the fitted models have been
decomposed to estimate the annual effect of each significant factor on road traumas in each
strata for the years 1990-1996. In calculating the influence of each of the factors in the
model, a suitable comparison base had to be chosen. Following the methods of Newstead et
al (1995), the influence of the road safety programs was compared to a zero base, the zero
base representing the situation if the programs had not been in operation (viz. operation
levels were zero). For the variables unemployment rate and alcohol sales, comparison with a
base level of zero is not meaningful as these measures will in practice never attain this level.
Hence for these two factors, their influence was calculated relative to the level of the factors
during 1988, the year in which both SCC numbers and unemployment rate were near their
maximum and minimum levels respectively.
Appendix A details disagreggation of the effects of each of the factors in the models for the
four strata, as well as the combination of these results by time of day and region of Victoria.
Table 3 shows the overall combination of these results to estimate the total annual
contribution of each of the major factors considered to have reduced SCC numbers in
Victoria during the years 1990-96. It should be noted that in Table 3, estimates for the effect
of RBT and drink driving publicity are not given separately because of the high degree of
co-linearity between these factors in the estimated models, as noted above. Instead, the
effects of these two factors have been combined into a single measure labelled as the drinkdriving program.
Table 3 :
8394
7.9%
13.5%
40.5%
25.3%
9.5%
5.5%
10.4%
1994
1992
1993
1991
1995
1996
8790
5111
5233
8522
5025
4950
8654
5192
5211
9075
8931
5371
5432
10.0%
12.1%
10.9%
11.2%
41.0%
25.0%
7.0%
11.1%
6.1%
14.8%
7.1%
15.6%
6.7%
42.6%
25.3%
9.9%
8.9%
9.4%
7.0%
25.0%
9.6%
6.5%
11.0%
37.9%
25.2%
10.0%
8.8%
10.3%
6.2%
39.7%
39.2%
51'84
5475
5196
11.1%
11.2%
10.2%
5286
Estimated reductions in serious casualty crashes attributable to various
factors - Victoria, All Hours, 1990-96
1990
8270
3.0%
9.6%
1.9%
25.8%
8.9%
6219
6136
21.8%
5.0%
on of drink-driving program
s
had remained at 1988 levels
2.3
EFFECT OF ACCIDENT BLACK SPOT TREATMENTS AND OTHER
FACTORS
In producing the above estimates of the effect that various major factors have had on road
trauma in Victoria, monthly serious casualty crash numbers have been modelled as a
function of road safety, economic and social factors. As discussed in Newstead et al (1995),
14
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
the form of the models fitted, shown in equation (1), includes a general linear trend
component and monthly dummies. These are included in order to explain residual variation
in the data not explained by the factors of interest in the model, so as to achieve a
satisfactory fit of the model to the observed data.
The roles of the monthly dummies and general linear trend factor in explaining residual
variation in the fitted models are quite different. Monthly dummy variables are included in
order to explain residual seasonality in the monthly crash data over a year. When included in
the models here, the dummies may represent residual variation in the crash data due to, for
example; environmental effects such as rainfall and daylight hours, the effects of holiday
periods, or the differing number of days in each month. None of these factors were
considered explicitly in the fitted models. When considering effects aggregated on an annual
basis however, seasonal effects represented by monthly dummies will be the same each year
and hence of little interest.
In contrast, the role of the general trend factor is to explain residual variation in the data due
to factors varying slowly over time in a monotonically increasing or decreasing fashion.
Factors not included in the models which may be represented by the included trend
component are, for example, population increases, driver licensing increases, gradual and
continual improvement of the road network system or, in fact, any other time variant, nonseasonal factor. The trend component of the model represents the average effect of all these
other non-seasonal factors not included explicitly in the models. The effect of the general
linear trend component can be seen in the change in expected serious casualty crashes from
year to year shown in Table 3.
One road safety program whose effectiveness is of interest, and is currently represented by a
component of the trend in the analysis presented here, is accident blackspot (ABS)
treatments. The data available on accident blackspot treatments in Victoria is available only
in a highly condensed form from RTA, RCA, VicRoads and Ministry of Transport annual
reports, giving the total number of sites treated and total expenditure for each financial year.
Ideally, this data would be included as a factor in the SCC data models and the model
parameters re-estimated to establish the effectiveness of the ABS program in reducing SCC
frequency. Due to the nature of the ABS data available however, this procedure is not
practical for a number of reasons.
Firstly the data is available only on an annual basis whilst the SCC data is modelled on a
monthly basis. This means that, for inclusion in the modelling process, the ABS data would
need to be proportioned across the 12 months of the year. To achieve this, an average
monthly ABS expenditure or treatment profile would need to be known or assumed. Such a
profile is not known and assuming one would be entirely arbitrary. Secondly, the effects of
ABS treatments are thought to be cumulative meaning that once each site is treated, it
remains effective in reducing crash numbers for many years after. Such a cumulative
process, when represented numerically as a covariate for modelling, has a very smooth
shape, which is similar to the functional form of the trend component used in the models,
fitted in this project. As the trend function and ABS treatment profile are of similar shape, it
is not practical to include both factors together as covariates in a single model due to colinearity problems. Inclusion of the ABS data alone in the model would lead to incorrect
estimates of effectiveness. This is because the ABS series would take on the role of the trend
factor in the model and act as a proxy for all factors not included explicitly in the model, as
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
15
did trend alone. Hence an alternative method of including the effects of ABS treatments in
the estimated overall percentage reductions in SCC numbers is required.
Appendix B details the process by which the estimated reductions in SCC numbers due to
ABS treatments can be obtained. It is assumed firstly that each ABS site treated saves one
casualty crash per year from the year of implementation onwards (Cunningham 1993). This
assumption may be slightly optimistic for the TAC funded black spot treatment program
from 1992 to 1994, however it is felt it still leads to estimates which are generally indicative
of the relative countermeasure effectiveness. As the ABS data is given in financial years, it
is assumed that all sites were, on average, completed mid year, ie. they were fully effective
by the commencement of the calendar year. In this way, a cumulative profile of casualty
crashes saved across the years of interest is built up. Casualty crash numbers are then
converted to serious casualty crash numbers, for use here, by using the average ratio of
serious casualty crashes to all casualty crashes in Victoria for each year. Using the expected
number of serious casualty crashes if road safety and other initiatives had remained at 1988
levels from Table 3, along with the estimated annual SCC savings due to ABS treatments, a
percentage reduction in SCC numbers due to ABS treatments can then be estimated.
Using the multiplicative percentage reduction theory of Newstead et al (1995), the estimated
percentage reductions in SCC numbers in Victoria due to ABS treatments over the period
1990-96, relative to 1988, can be factored from the trend component. Sections 8.1 and 8.2 of
Newstead et al (1995) give details of the steps involved in factoring out the ABS treatment
effects. Appendix B shows that the estimated percentage reductions of ABS treatments on
SCC numbers, relative to the 1988 base year, ranged from 1.6% in 1990 to 6.2% in 1995
and then fell to 5.6% during 1996. The reason for the fall during 1996 is two fold, being a
combination of a dramatic cut in accident blackspot expenditure after the 1993/94 financial
year and a fall in the ratio of the serious casualty crashes to all casualty crashes in Victoria in
1996.
2.4
SUMMARY OF ESTIMATED CONTRIBUTIONS
Table 4 gives a summary of the estimated percentage reductions in serious casualty crashes
in Victoria attributable to each of the factors considered in the modelling process, along with
the contribution of the accident black spot treatments. Shown are the observed, expected and
estimated annual percentage reductions in the number of serious casualty crashes over the
period 1990-96, along with the individual contribution of each of the major factors
considered and the total contribution ofthe road safety programs in each year.
16
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
2.5%
7.0%
7.1%
11.1%
9.9%
5.3%
3.2%
10.2%
45.6%
44.0%
4950
1993
1994
1996
1995
5475
10.9%
10.0%
9.4%
26.9%
6.7%
9.5%
29.3%
27.7%
6.2%
6.1%
11.2%
11.0%
6.5%
10.0%
29.4%
5.6% Estimated reductions
1.6%
8.9%
5.0%
1992
5111
42.7%
1991
5025
5211
9099
8770
39.3%
5.5%
15.6%
14.8%
9345
5371
7.9%
8.9%
13.5%
9480
5286
9.6%
5233
9572
5196
42.8%
42.7%
8.8%
10.3%
10.4%
8371
1.9%
29.8%
29.7%
9.6%
23.0%
8585
5192
5184
6219
6136
26.7%
3.0%
Table 412.1%
:5432
in
serious casualty crashes
to major factors: Victoria 1990-96.
ution
of all
drink-driving
program
remained
at 1988
tion
of
road levels
safety attributable
programs
1990
Figure 7 summarises graphically the information given in Table 4. Each column in Figure 7
shows the percentage reduction in serious casualty crashes attributable to each of four road
safety programs considered along with the contributions of the changes in alcohol sales and
unemployment rate. The line on Figure 7 gives the total contribution of all the factors
considered in reducing road trauma, obtained by multiplying the individual factor effects.
Use of multiplicative methods to obtain the total effect was necessary because of the
multiplicative model structure used to obtain the effect estimates.
FIGURE 7:
Estimated reductions in serious casualty crashes
attributable to major factors: Victoria 1990-96.
Year
1990
1991
1992
1993
1994
1995
1996
0.0
IliiiZJAccident Blackspot
-10.0
"
tl Ill:0c••
::J
~
c::::J Unemployment
c::::JAlcohol sales
-20.0
-30.0
-40.0
_
-50.0
Drink-driving
program
IiiiiiilSpeed and
Concentration
Publicity
_Speed
TINs
-+- Total
Camera
Reduction in
SCCs
-60.0
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
17
3.
DISCUSSION
Results of the analysis presented in Table 3 and Appendix A show a few noteworthy
differences in comparison to the results of Newstead et al (1995) covering the years 199093.
One of the most notable differences is the estimated magnitude of the effects of the road
safety programs on SCCs in the rest of Victoria. Newstead et al (1995) estimated the road
safety programs in the rest of Victoria to have lead to reductions in SCCs of the order of
26% in LAH and 20% in HAH. The results presented here, however put these estimates at
around only 15% and 14% respectively. There appears to be two principal reasons for these
lower estimates. In LAH, whilst the estimated effect of speed and concentration publicity is
only slightly lower, speed camera TINs were no longer found to be a significant predictor of
SCC numbers in this stratum. Previously, speed camera TINs were estimated to have
reduced SCC numbers by around 11%. For HAH crashes in the rest of Victoria, the effects
of the drink-driving program has fallen from an estimated 20% reduction to an estimated
12% reduction in the current work. Whilst the influence of alcohol sales on HAH SCCs in
the rest of Victoria is much the same, unemployment rate is no longer a significant predictor
of SCC numbers in the stratum.
The reasons for the differences discussed above are not clear although there are a number of
possibilities. It is possible that the effect of the road safety programs in the rest of Victoria
has declined significantly over the period after 1993 as people become used to the programs.
This may be particularly pertinent to aspects such as media publicity and the speed camera
program. In the case of the drink-driving program in the rest of Victoria, the changing result
may be a reflection of a change in enforcement strategy not being adequately captured by
the program measure currently in use. Rural drink-driving enforcement has changed to the
use of satellite cars in combination with booze buses to counter such effects as the 'bush
telegraph' and the local population's knowledge of back roads whilst the program measure
being used is still only the number of booze-bus tests carried out. Such possibilities would
need to be checked by further specific research into the effects of these programs. This
might be achieved using methods such as inclusion of factors in the models to test for time
trends in the effectiveness of various factors. Another possibility is investigation of the use
of other program measures which potentially better reflect the current methods of program
operation, as in the case of drink-driving enforcement.
Another potential reasons for the differences discussed above is that the modelling
procedure is more unstable for SCCs in the rest of Victoria. This is possible given the
relatively smaller number of SCCs in comparison to metropolitan Melbourne and the
generally lower statistical significance levels of the fitted parameters and R-squared values
of the fitted models. A sensitivity analysis on the fitted models would need to be carried out
to determine this.
Estimates of the relative effects of each modelled factor on SCCs in metropolitan Melbourne
obtained here are closely concordant with those obtained by Newstead et al (1995). A few
differences however exist in the estimates for HAH crashes in this region. Once again,
estimated SCC reductions due to the effects of the drink-driving program are lower here at
20% against 28% previously. This may be partly due to the fact that the effect of speed
camera TINs has been included here, where it was not previously, with the effects of speed
18
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
camera TINs and the drink-driving program not being completely independent. It is
interesting to note that the estimated total combined effect of the road safety programs for
this stratum is similar here to previous estimates. Like the rest of Victoria, the effect of
alcohol sales on HAH crashes in Melbourne is lower here than in previous work.
Another point not clear from the modelling work is the relationship between the index of
alcohol sales and the drink-driving program. Whilst modelling does not indicate any
particular co-linearity between these factors, it is possible a relationship exits in a subtle or
indirect way not reflected in the modelling procedure. Subtle relationships may also exist
between alcohol sales and unemployment rate, with alcohol sales possibly driven to a certain
degree by economic activity which unemployment rate is reflecting.
Despite the differences noted above, the overall estimate of the effect each major factor has
had on road trauma in Victoria as a whole are generally consistent with previous estimates,
particularly in a relative sense.
It is again appropriate in discussing the results of the road trauma trends modelling detailed
in this report to reiterate the purpose and interpretation principles of the work presented. The
general aim of the work presented here is to estimate the relative effectiveness of some
major road safety and socio-economic factors in reducing road trauma in Victoria over the
period 1990-1996. Obviously this work is not all encompassing in assessing every possible
factor which may have contributed to changes in road trauma over this period. Indeed, there
is likely to be many successful programs that have not been considered in this work. There
are number of reasons for not considering programs other than those appearing here.
Firstly, many of the programs not considered are targeted specifically at a narrow range of
crashes and/or injuries and hence, whilst they may be highly effective on their target area,
their effects are not global enough to measure by the techniques considered here. These last
comments highlight the important fact that the methods and results presented here are in no
way to replace formal countermeasure evaluation in the traditional manner. Historically,
development of the methods presented in this report stemmed from a desire to combine
results of a number of formal evaluations to gain an overall picture of the relative effect of
each countermeasure that had been in considered in relation to Victoria as a whole during all
the periods of the week. To this end, the methods developed were very successful and have
remained so in the work presented to date. The methods will remain useful for this purpose
in the future also.
4.
CONCLUSION
Building on the work of a project by Newstead et al. (1995) this project details results of
statistical modelling to asses the influence of some major factors influencing road trauma in
Victoria over the period 1990-1996. Unlike the previous work, the work presented here
considers the effect of the speed camera program on high alcohol hour crashes in Victoria.
A number of road safety measures and other factors contributed to the reductions in road
trauma in Victoria during the years 1990 to 1996. The major contributors and the apparent
percentage reduction in serious casualty crashes due to each measure/factor were:
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
19
• Speed camera operations (principally speeding TINs issued):
10-11 % each year
• "Speeding" and "concentration" television advertising:
5-7% each year
•
Drink-driving program (bus-based RBT together with
"drink-driving" publicity campaigns)
• Reduced alcohol sales:
9-10% each year
3% in 1990
6% in 1991
7% in 1992
9% in 1993
8% in 1994
9% in 1995
10% in 1996
• Reduced economic activity (measured by unemployment rates): 2% in 1990
12% in 1991
15% in 1992
16% in 1993
14% in 1994
10% in 1995
10% in 1996
• Accident Black Spot treatments
1.6%
2.5%
3.2%
5.3%
6.2%
6.2%
5.6%
in
in
in
in
in
in
in
1990
1991
1992
1993
1994
1995
1996
These percentages cannot simply be added up to estimate the total contribution. If more than
one contributor is being considered, the percentage reduction of each must be applied in
turn. The anti-speeding and drink-driving programs together are estimated to have
contributed reductions in serious casualty crashes of at least 22-25% during these seven
years. Including the accident blackspot treatments, the overall contribution of road safety
initiatives is estimated to have risen from 23% reduction in 1990 to nearly 30% reduction in
1993- 1996.
5.
FURTHER WORK RECOMMENDED
The results of this research indicate two general areas of further research which should be
pursued.
(1) Further developments of the methods of road trauma trends modelling.
20
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
Whilst the general framework of modelling which has been developed in this project and its
predecessors has proven extremely valuable in monitoring Victorian road trauma, a number
of opportunities for fine tuning and enhancing these methods exist. Potential enhancements
may include:
• Development of total program measures for countermeasure groups with high
interdependency, such as the drink-driving program factors
• Development of methods to account for program interactions as part of the modelling
process
• Development of methods to place confidence limits on the combined percentage
reductions estimates.
• Detailed study of the relationships between various socio-economic measures
(unemployment rates, employment numbers and alcohol sales) and road trauma
outcomes.
• Sensitivity analysis of the modelling process for road trauma in the rest of Victoria
(2) Investigation of the effects of road safety programs in the rest of Victoria.
Comparison of the results obtained in this research when compared to previous research
indicate a possible reduction has occurred in the effectiveness of the major road safety
programs considered in reducing road trauma in the areas of Victoria outside metropolitan
Melbourne. Further specific research is needed to determine whether this is indeed true and,
if so, to establish the reasons for this diminishing effectiveness and possible ways to counter
these.
6.
ACKNOWLEDGMENTS
David Farrow and Geoff Elston of VicRoads are gratefully acknowledged for supplying the
crash data files used in this project. Ron Cook and Paul Williamson of the Traffic Camera
Office are acknowledged for supplying data on the number of traffic infringement notices.
Grey advertising is thanked for supply of the TARP data for the TAC advertising campaign.
7.
REFERENCES
BROADBENT, S (1979), "One way TV advertisements work". Journal of the Market
Research Society, Vol. 21, No. 3. London.
CAMERON, MH, CAVALLO, A, and SULLIVAN, G (1992a), "Evaluation of the random
breath testing initiative in Victoria 1989-91: Multivariate time series approach". Report No.
38, Monash University Accident Research Centre.
CAMERON, MH, CAVALLO, A, and GILBERT, A (1992b), "Crash-based evaluation of
the speed camera program in Victoria 1990-91. Phase 1: General effects. Phase 2: Effects
of program mechanisms". Report No. 42, Monash University Accident Research Centre.
CAMERON, MH, HAWORTH, N, OXLEY, J, NEWSTEAD, S and LE, T (1993)
"Evaluation of Transport Accident Commission road safety television advertising". Report
No. 52, Monash University Accident Research Centre.
FURTHER MODELLING
OF SOME FACTORS INFLUENCING
ROAD TRAUMA TRENDS IN VICTORIA
21
CAMERON, MH, and NEWSTEAD, SV (1993) "Evaluation of mass media publicity as
support for enforcement". Paper presented at Australasian Drink-Drive Conference,
Melbourne, November 1993.
CAMERON, MH, NEWSTEAD, SV, and VULCAN, AP (1994), " Analysis of reductions in
Victorian road casualties, 1989 to 1992". Proceedings 17th ARRB conference, Part 5,
pp165-182.
CUNNINGHAM, J. (1993) "Accident savings accruing from ABS programs", Memo,
VicRoads, Road Safety Division, 24th May 1993.
HARRISON, WA (1990) "Update of alcohol times as a surrogate measure of alcoholinvolvement in accidents". Research Note, Monash University Accident Research Centre.
NEWSTEAD, S., CAMERON, M., GANTZER, S. and VULCAN, P. (1995) "Modelling of
some major factors influencing road trauma trends in Victoria 1989-93" Report No. 74,
Monash University Accident Research Centre.
NEWSTEAD, S., GANTZER, S. and CAMERON, M. (1996) "Updated modelling of some
major factors influencing road trauma trends in Victoria 1990-94: all crashes and specific
crash sub-groups" Monash University Accident Research Centre.
ROGERSON, P., NEWSTEAD, S. and CAMERON, M. (1994) "Evaluation of the speed
camera program in Victoria 1990-1991. Phase 3: Localised effects on casualty crashes and
crash severity. Phase 4: Generalised effects on speed." Report No. 54, Monash University
Accident Research Centre.
THORESEN, T, FRY, T, HEIMAN, L and CAMERON, MH (1992), "Linking economic
activity, road safety countermeasures and other factors with the Victorian road toll". Report
No. 29, Monash University Accident Research Centre.
22
MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE
APPENDIX A
Percentage reductions in serious casualty crashes attributable to various sources
Melbourne Statistical Division (MSD) : 1990 -1996 relative to 1988
1553
22.3%
3154
6170
1618
3287
1777
3061
3450
50.8%
1661
1518
1806
3250
3530
12.2%
1992
3017
1734
2964
1968
1
3181
3125
12.5%
11.0%
16.0%
18.6%
10.8%
20.1%
10.2%
48.5%
29.3%
10.9%
47.1%
6.9%
4.4%
19.4%
10.3%
28.8%
29.1%
15.2%
6025
6792
48.0%
46.7%
43.5%
13.6%
10.6%
13.8%
23.2%
10.7%
20.7%
3284
3813
3745
6.4%
15.0%
15.6%
6631
7.2%
5.6%
3348
3555
50.4%
53.3%
49.2%
20.6%
3387
6.1%
6.7%
6473
6320
43.9%
15.3%
45.5%
15.7%
17.4%
20.0%
2007
40.3%
16.3%
14.2%
42.5%
31.4%
13.1%
19.8%
19.7%
14.0%
11.1%
11.3%
31.1%
13.4%
3070
1869
1766
40.2%
38.0%
30.0%
13.7%
38.1%
14.6%
20.3%
43.8%
29.0%
15.4%
10.8%
29.2%
28.9%
6.3%
14.9%
7.8%
8.6%
13.4%
3238
30.8%
30.3%
1770
19.5%
16.9%
2971
1977
19.7%
30.7%
2912
4.7%
2.4% 17.6%
13.5%
5882
9.6%
3.9%
3.2%
4079
26.0%
27.4%
10.0%
33.4%
25.9%
27.8%
2.6%
2102
5.0%
10.1%
1991
APPENDIXA1
1662
24.9%
1233
793
899
19.7%
15.4%
31.2%
14.9%
33.9%
13.4%
22.2%
37.1%
1996
775
796
815
24.6%
1233
18.7%
873
2352
1136
1101
1666
890
15.0%
2317
1703
1.1%
2283
891
1084
1050
15.5%
5.4%
1687
887
16.5%
2.4%
2300
1067
18.1%
1.1%
924
2387
2056
5.1%
15.7%
27.1%
849
1993
1992
35.7%
1991
1995
1994
35.4%
8.5% -1.5%
0.0%
993
12.0%
19.5%
10.3%
2.8%
20.5%
7.5%
7.3%
2369
1738
26.1%
15.2%
13.2%
7.6%
6.3%
14.0%
3.3%
13.5%
2334
28.6%
1118
11.8%
8.0%
2.5%
26.5%
20.7%
6.9%
7.8%
6.2%
14.5%
27.2%
7.7%
6.7%
1823
14.4%
26.6%
888
4.0%
4.4%
-3.0%
0.0%
1154
1064
11.0%
12.0%
13.9%
6.2%
7.8%
10.6%
10.6%
23.0%
0.0%
7.0%
14.3%
13.8%
8.2%
6.0%
1.9%
..................
Percentage
reductions in serious casualty
crashes
attributable
to various sources
APPENDIXA2
ROY)
1992
1991
1994
14.6%
5475
2402
8522
2517
8394
2757
8790
24.7%
4788
4135
4387
4294
4581
4248
1995
12.1%
13.2%
18.5%
24.9%
45.3%
16.1%
2581
9075
11.2%
11.6%
14.8%
10.3%
6.2%
2623
2694
2894
4950
5025
41.4%
14.9%
19.3%
46.1%
14.7%
37.9%
8654
40.5%
2476
14.3%
5211
18.4%
25.5%
2574
8931
2859
5432
5233
9.6%
8270
2.7%
1.9%
2970
6136
3166
23.1%
17.5%
29.4%
25.8%
5.0%
10.8%
27.0%
13.6%
18.2%
4288
7.8%
8.0%
14.5%
13.5%
9.6%
10.2%
26.1%
4100
4483
15.1%
32.5%
7.7%
5.5%
4209
12.2%
4683
25.0%
26.6%
14.4%
12.8%
7.9%
16.9%
34.3%
32.7%
4171
1996
1993
10.9%
41.0%
7.1%
11.1%
25.7%
39.7%
11.0%
42.8%
10.4%
15.6%
6.5%
6.1%
19.2%
45.0%
46.0%
2311
19.1%
25.4%
48.4%
16.8%
2639
39.2%
10.0%
25.3%
7.0%
9.5%
36.6%
9.0%
26.3%
34.5%
25.2%
9.4%
13.2%
14.0%
8.8%
10.0%
26.5%
17.2%
36.7%
9.1%
5.8% 12.6%
3.0%
4204
4066
6.8%
1.0%
21.8%
21.5%
22.1%
8.9%
10.2%
6.7%
8.9%
9.9%
Percentage
reductions in serious casualty
crashes
attributable
to various sources
APPENDIXA3
AI..I..VICTORIA
APPENDIXB
531
157
0.4515
49
82
375
278
204
257
300
337
6780
246
No.
of sites
118
90TVTV
278
204
300
300
7748
7351
8394
8033
40
40
2027
0.3815
0.3850
0.3154
0.3312
1749
0.3234
0.3433
0.3%
40 REDUCTIONS
109.5
234.1
7460.5
7134.1
416.5
9881.9
636.1
566.5
9158.1
90
118
1989
1995
1994
1993
375
82
531
278mid 77.4
257
337
337VEO (assuming
7084
7476
8150
8654
8522
9075
8931
8270
40
157
246
246
130
1288
137.8
287
0.3601
0.3596
651
405
1.9%
4.9%
6.3%
0.7%
0.3138
296.9
6.9%
9.6%
2933
3064
1.5%
0.3239
508.0
9.7%
4.1%
0.2936
1545
2558
3015
0.3087
560.4
611.8
607.3
0.3227
988
50.1
7621.8
145.8
8960.5
8566.5
9501.2
3.2%
6.2%
847.2
899.6
950.9
9974.6
8746.9
476.9
9736.5
1.6%
339.2
8372.2 157
6798.1
18.1
0.9%
90
118
90
1985
1984
1988
1990
531
8790
2.9%
227.3
9.0%
8.9%
5.5%
2.5%
5.3%
5.6%
946.5
1987
1986
1992
1991
1996
375
ESTIMATION
OF 7982.1
SERIOUS
CASUAL
CRASH
DUE TO 257
ACCIDENT
BLACKSPOT
TREATMENTS
CASUAL
CRASHES
SA
all sites 204
treated
at
financial
year)
Year
1983
TOTAL
CAS
CRASHES
SERIOUS
CAS
CRASHES
EXPECTED*
82
49
APPENOIXB