further modelling of some major factors influencing road trauma

MONASH UNIVERSITY

AUSTRALIA

FURTHER MODELLING OFSOME MAJOR FACTORS

INFLUENCING ROAD TRAUMATRENDS IN VICTORIA: 1990-96

FURTHER MODElliNG OF SOME MAJORFACTORS INFLUENCINGROAD TRAUMA TRENDS

IN VICTORIA: 1990-96

by

Stuart NewsteadMax Cameron

Sanjeev Narayan

Report No. 129April 1998

Il MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE

MONASH UNIVERSITY RESEARCH CENTREREPORT DOCUMENTATION PAGE

Report No.129

Report DateApri11998

ISBN0732607094

Pages23 + Appendices

Title and sub-title:

Further modelling of some major factors influencing road trauma trends in Victoria: 1990-96

Author(s)Newstead, S.V.,Cameron, M.H. & Narayan, S.

Type of Report & Period CoveredGE~RJ\L, 1990-1996

Sponsoring Organisation - This project was funded through the Centre's baseline research programfor which grants have been received from:

Department of JusticeRoyal 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 roadtrauma in Victoria over the period 1990-1993, this project has made use of the statistical analysismethods developed to extend these estimates to 1996. The major factors considered in the studyhave 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 inroad 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 toeach factor has been estimated for each year over the period 1990-1996.

Models linking variations in serious casualty crashes to various factors were computed usingmonthly crash data from the years 1983 to 1996. Subsequently, the contributions of random breathtesting, speed camera tickets issued, levels of road safety television publicity, unemployment ratesand alcohol sales to the reduction in the number of serious casualty crashes were estimated for theperiod 1990-96. A method of separately estimating the effect of accident blackspot treatments anddesegregating 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

Contents1. INTRODUCTION 1

1.1 REVIEW OF WORK COMPLETED TO DATE 1

2. INCLUSION OF CRASH DATA TO THE END OF 1996 IN THE MODELS ..•................................3

2.1 FACTORS INCLUDED IN THE MODELS 32.1.1 Economic Measure 32.1.2 Factors Relevant to High Alcohol Hour Crashes 42.1.3 Factors Relevant to Low Alcohol Hour Crashes 12

2.2 DISAGREGGATION AND COMBINATION OF THE RESULTS 142.3 EFFECT OF ACCIDENT BLACK SPOT TREATMENTS AND OTHER FACTORS 142.4 SUMMARY OF ESTIMATED CONTRlBUTIONS 16

3. DISCUSSION .....................................................•................•......•..•...........•............•.........•..........•.....•.....18

4. CONCLUSION .........................................................................................................................•...•.........19

5. FURTHER WORK RECOMMENDED ..................................................•.........•.......•................•.....•...20

6. ACKN0WLEDG MENTS ..........................•.........•...•...............••............................................................21

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 ONSERIOUS CASUALTY CRASHES DURING HIGH ALCOHOL HOURS OF THE WEEK. MELBOURNEAND 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 SERIOUSCASUALTY CRASHES DURING LOW ALCOHOL HOURS OF THE WEEK. MELBOURNE ANDCOUNTRY VICTORIA 1983 - 1996 13

TABLE 3: ESTIMATED REDUCTIONS IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE TO

VARIOUS FACTORS - VICTORIA, ALL HOURS, 1990-96 14

TABLE 4 : ESTIMATED REDUCTIONS IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE TOMAJOR FACTORS: VICTORIA 1990-96 17

VI MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE

List Of Figures

FIGURE 1: PERCENTAGE CHANGE FROM 1987, VICTORIA.FATALITIES AND SERIOUS INJURIES 1

FIGURE 2: UNEMPLOYMENT RATE IN MELBOURNE AND THE REST OF VICTORIA 1983-96 3

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 BYTHEME: MELBOURNE REGION 1989 - 1996 6

FIGURE 4A(II) : TAC ROAD SAFETY TELEVISION ADVERTISING - MONTHLY ADSTOCK BYTHEME: COUNTRY VICTORIA 1989 - 1996 6

FIGURE 4B(I) : DRINK-DRIVING ADSTOCK: MELBOURNE REGION 1989 - 1996 7

FIGURE 4B(II) : DRINK-DRIVING 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 8

FIGURE 5: INDEX OF ALCOHOL SALES IN VICTORIA, 1983 - 1996 9

FIGURE 6A : MONTHLY NUMBER OF TRAFFIC INFRINGEMENT NOTICES ISSUED FORSPEEDING OFFENCES DETECTED BY SPEED CAMERAS. MELBOURNE 1989 - 1996 10

FIGURE 6B : MONTHLY NUMBER OF TRAFFIC INFRINGEMENT NOTICES ISSUED FORSPEEDING OFFENCES DETECTED BY SPEED CAMERAS. REST OF VICTORIA 1989 - 1996 10

FIGURE 7 : ESTIMATED REDUCTIONS IN SERIOUS CASUALTY CRASHES ATTRIBUTABLE TOMAJOR FACTORS: VICTORIA 1990-96 17

FURTHER MODELLING OF SOME FACTORS INFLUENCING ROAD TRAUMA TRENDS IN VICTORIA VII

List of AppendicesAPPENDIX A: PERCENTAGE REDUCTIONS IN SERIOUS CASUALTY CRASHES ATTRIBUTABLETO VARIOUS SOURCES. VICTORIA 1990-1993 RELATIVE TO 1988

APPENDIX B : ESTIMATION OF SERIOUS CASUALTY CRASH REDUCTIONS DUE TO ACCIDENTBLACKSPOT TREATMENTS

VIII MONASH UNIVERSITY ACCIDENT RESEARCH CENTRE

EXECUTIVE SUMMARY

Based on previous work that has estimated the contribution of some major factors inreducing road trauma in Victoria over the period 1990-1993, this project has made use of thestatistical analysis methods developed to extend these estimates to 1996. The major factorsconsidered in the study have stemmed from the results of a number of studies in Victoriawhich have evaluated the effects of countermeasures and other factors which appear to beresponsible for the substantial reduction in road trauma since 1989. The factors for whichcontributions 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


The percentage change in road trauma levels, as measured by serious casualty crashnumbers, 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 usingmonthly crash data from the years 1983 to 1996. Subsequently, the contributions of randombreath 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 casualtycrashes were estimated for the period 1990-96. A method of separately estimating the effectof accident blackspot treatments and desegregating this from the trend was described andapplied.

The major contributors and the apparent percentage reduction in serious casualty crashes dueto each measure/factor were estimated as:

• Speed camera operations (principally speeding tickets issued): 10-11% each year

• "Speeding" and "concentration" television advertising:

• Drink-driving program (bus-based RBT together with"drink-driving" publicity campaigns)

• Reduced alcohol sales:

5-7% each year

9-10% each year

3% in 19906% in 19917% in 19929% in 19938% in 19949% in 199510% in 1996

FURTHER MODELLING OF SOME FACTORS INFLUENCING ROAD TRAUMA TRENDS IN VICTORIA IX

• Reduced economic activity (measured by unemployment rates):

• Accident Black Spot treatments

2% in 199012% in 199115% in 199216% in 199314% in 199410% in 199510% in 1996

1.6% in 19902.5% in 19913.2% in 19925.3% in 19936.2% in 19946.2% in 19955.6% in 1996

The anti-speeding and drink-driving programs together are estimated to have contributedreductions in serious casualty crashes of at least 22-25% during these seven years. Includingthe accident blackspot treatments, the overall contribution of road safety initiatives isestimated 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 alsobeen large reductions in serious injuries (Figure 1). A relative plateau in the levels of bothfatalities and serious injuries has, however, been observed from around 1992 through to1996.

FIGURE 1: Percentage change from 1987.Victoria, fatalities and serious injuries

120

100

80

'ii>.!!t-•••••~ 60

'0 C••l!••lL

40

20

o

1987 1988 1989 1990 1991

Ve.r

1992 1993 1994 1995 1996

I_Fatal~ies SeriousinjuriesI

These initial large reductions in deaths and injuries after 1988 have been attributed to theimplementation of various safety programs which were introduced commencing inSeptember 1989, and to the downturn in the economy which occurred during the periodunder consideration. It is important to estimate the contribution of each of these programsand if possible the mechanism by which they achieved their reductions, so that they can befine tuned for further gains and allocation of future resources can be made on the basis of thebest available information.

This project aims to build on previous work which has estimated the contribution of some ofmajor road safety programs, along with economic factors, which have lead to the dramaticreduction 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 inreducing road trauma, as measured by serious casualty crashes (SCCs), over the period1989-94. 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 whichhave evaluated the effects of countermeasures and the factors which appear to be responsiblefor the substantial reduction in road trauma since 1988. The factors for which contributionswere 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


The original studies upon which the work to date has been based have modelled Victorianroad trauma trends for various road user groups at various times of the week (Cameron et al1992a, Cameron et al 1992b, Cameron et al 1993). These studies have defined four strata interms of area and time into which crashes in Victoria can be divided for modelling roadtrauma trends. These four strata are; the Melbourne metropolitan area (the capital city of theState of Victoria) and the rest of Victoria, with each area being considered in two timedivisions, "high alcohol hours" (HAH) and "low alcohol hours" (LAH) (HAH and LAH aredefined by Harrison 1990).

The method of analysis used has been that of multivariate log-linear regression, whichinvolves relating measures of road safety programs and economic effects, along with generaltrend 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 factorin the regression equation has on the outcome measure (vis. road trauma). The general formof 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 regularseasonal 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 theform of equation (1) was fitted to the monthly serious casualty crash series each of the fourstrata defined above. Factors included in each regression equation were chosen based on theconsiderable experience built up from detailed analysis in past evaluation studies.

This project aims to re-assess the contribution these factors have had on Victorian roadtrauma 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 majorfactor 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 THEMODELS

This section begins by detailing the trends in the factors considered in the models through to1996. These factors were then used to model the monthly number of SCCs over the period1983 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 beenconsidered 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 isconsidered of value to again present an overview of these in this report to show theircontinuing trends to the end of 1996. Knowledge of these trends will be useful ininterpreting 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), numberemployed, and the one used by Newstead et al (1995), unemployment rate, had to be madeto be included in the main model. It was discovered using residual analysis for the estimatedmodels that unemployment rate and the number employed showed similar random patternsand dispersion. Consequently, it was decided to use unemployment rate as the measure ofeconomic activity since its interpretation is easier in the context of road trauma. Figure 2shows unemployment rates for both Melbourne and the rest of Victoria over the period 1983to 1996. Of note in Figure 5 is that, whilst the unemployment has risen sharply and peakedover the 1990-93, 1994-1996 has seen a recovery in the economy with unemployment ratesfalling during these years.

FIGURE 2: Unemployment Rates in Melbourne (MSD) andthe rest of Victoria (ROV), 1983-96

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2.1.2 Factors Relevant to High Alcohol Hour Crashes

The major road safety initiative targeted at crashes in high alcohol hours (HAH) in Victoriasince 1989 has been the random breath testing program (RBT) supported by a high profileadvertising with drink-driving themes sponsored by the Transport Accident Commission(TAC). The program of increased RBT using highly visible booze-buses commenced inSeptember 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-basedoperation was observed. The Victoria Police discontinued recording activity in the car-basedRBT program from January 1sI 1996 but rather, recorded this information under PreliminaryBreath Test (PBT) statistics. Accordingly, it is unknown to what extent car based RBTactivities have been conducted since this date. Hence, in this work, only the bus-basedtesting is considered in modelling HAH crashes.

Past studies (Newstead et al 1995, Cameron et al 1992a, 1993a,b) have found the mostappropriate measure of the RBT enforcement activity in relation SCC numbers to be thenumber of tests conducted. Figures 3a and 3b shows the number of bus-based RBTsconducted per month from 1983 to 1996 in metropolitan Melbourne and in the rest ofVictoria respectively. In metropolitan Melbourne during 1996, the level ofRBT activity hasremained similar to that during 1994 and 1995, with around 100,000 tests conducted permonth, except for the final months in 1995 and initial months in 1996 and where the level ofRBT aCtivity had gone beyond 120,000 tests per month. In the country Victoria the totalnumber 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 monthMelbourne (MSD) 1989 - 1996

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120000

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FIGURE 3b : Number of Bus-based Random Breath Tests per monthRest of Victoria (ROV) 1989 - 1996

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Supporting the RBT program in Victoria has been an intense publicity campaign sponsoredby 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 werereported as the same. During 1994, different levels of advertising were reported as placed ineach region reflecting the Police campaign on drink-driving in Country areas and a series ofadvertisements specifically aimed at country people. Figures 4a, 4b and 4c show the levelsof all TAC road safety advertising Adstock, TAC drink-driving Adstock and TAC speed andconcentration Adstock respectively current in each month from 1989 to December 1994 inMelbourne and the rest of Victoria (for a definition of Adstock, see Broadbent 1979). Bothin metropolitan Melbourne and country Victoria the level of awareness of TAC road safetyadvertising to have been maintained at significant levels throughout 1995 and 1996,although at slightly lower levels than the previous years. The levels of advertisingspecifically on the drink-driving theme in both regions have risen marginally in 1996periods as compared to the other years.


FIGURE 4a(i) :TAC Road Safety Television Advertising - Monthly Adstock by themeMelbourne region

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FIGURE 4a(ii) :TAC Road Safety Television Advertising - Monthly Adstock by themeCountry Victoria region

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16000140001200010000800060004000

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FIGURE 4b(i) :Drink-driving Adstock : Melbourne region 1989-1996

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FIGURE 4b(ii) :Drink-driving Adstock: Country Victoria region 1989-1996

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FIGURE 4c(i) :Speed and Concentration Adstock: Melbourne region 1989-1996

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FIGURE 4c(ii) :Speed and Concentration Adstock : Country Victoria region 1989-1996

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An index alcohol sales in Victoria has been included as an explanatory variable andrepresents an exposure measure of the potential to drink-drive. Figure 5 shows the monthlyindex of alcohol sales for the period 1983-1996. Whilst the index shows a downward trendover the years 1990-1996 there is perhaps some evidence of the series leveling from about1994 onwards.

FIGURE 5: Index of Alcohol Sales on Victoria, 1983-96

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The most recent modelling of SCC trends in HAH (Newstead et al 1996) also considered thepossible effects of the speed camera program on HAH crashes based on the results of workby Rogerson et al (1994) which examined the localised effects of the speed camera programduring both high and low alcohol hours. The main findings of this study were observedreductions in casualty crash frequency due to localised speed camera effects during highalcohol hours. Whilst these effects were localised, being examined only within 1 km radiusof speed camera sites, it is possible that more generalised speed camera effects were alsopresent in HAH, similar to those measured in LAH by Cameron et al (1992b). Given theestablished presence of speed camera effects in HAH, it was considered necessary to againallow for the expression of these in the models describing HAH crashes in both Melbourneand the rest of Victoria. Figure 6 shows the number of speed camera TINs issued per monthfor the period January 1989 to December 1996. Examination of Figure 6 reveals the numberof speed camera TINS issued through 1996 to be similar to the two previous years,averaging around 40,000 TINs per month.


FIGURE 6a: Monthly number of Traffic Infringement Notices Issuedfor Speeding Offences Detected by Speed CamerasMelbourne 1989 - 1996

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FIGURE 6b : Monthly number of Traffic Infringement Notices Issuedfor Speeding Offences Detected by Speed CamerasRest of Victoria 1989 - 1996

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The model described by equation (1) was fitted to the serious casualty trends in high alcoholhours for metropolitan Melbourne and country Victoria. The major road safety campaignsincluded in the models were monthly number of RBTs, monthly number of speed cameraTINs and monthly road safety media publicity as measured by Adstock. In consideringwhich component of the media publicity to include in the models, the drink-drivingcomponent 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 trendcomponent and monthly seasonal dummies were also included in the models along withunemployment rate and alcohol sales. A summary of the key model parameter estimates forMelbourne and country Victoria is given in Table 1.

The models fitted to HAH SCC data for Melbourne and country Victoria, summarised inTable 1, explained 84% and 71% of the monthly variation respectively, based on the fittedmodel R-squared vales. A statistically significant linear trend was found in the data formetropolitan Melbourne, with the effects of unemployment rate and alcohol sales also beingstatistically significant. Of the road safety programs, the effects of random breath testing andspeed camera TINs were statistically significant. The effect drink-driving publicity was notstatistically significant however, with the factor having to be removed from the modelbecause 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 arehighly confounded meaning individual effects of each factor cannot be estimatedindependently. 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 asingle program.

Effects of the modelled factors in country Victoria were quite different from those inMelbourne. Here no statistically significant linear trend was found in the data along with nostatistically significant effects due to unemployment rate or speed camera TINs. The effectsof alcohol sales and number of RBTs were found to be statistically significant, however thesame high co-linearity between RBTs and drink-driving publicity was observed, most likelycausing the latter to show no statistically significant association with SCCs, as formetropolitan Melbourne.


Table 1: Effects of unemployment rate, number of random breath tests, alcohol sales(all Victoria), number of speed camera TINs issued, and Adstock of TACdrink-driving publicity on serious casualty crashes during high alcohol hoursof the week. Melbourne and country Victoria 1983-96.

AREA OF VICTORIA EstimatedTvalueSignificancecoefficient of

level

Explanatory variablelogged variable(two-tailed)

MELBOURNE CRASHESdf=151

•

trend 0.0025***3.7360.0004•

unemployment rate in month -0.3149***-8.7300.0003•

no. of bus-based random breath tests -0.0204***-4.6390.0001

during month •alcohol sales during month 0.3067*2.3010.0228

•number of speed camera TINs issued -0.0109*-2.2030.0291

•drink-driving Adstock in month (-0.0068) NS-0.7820.4357

CRASHES IN COUNTRY VICTORIA

df=155

•

trend(0.0001) NS0.1400.8892

•unemployment rate in month (-0.0293) NS-0.3750.7084

•no. of random breath tests (car or bus -0.0155**-2.6390.0092

based) during month •alcohol sales during month 0.5795***4.9400.0001

•number of speed camera TINs issued NS++NSNS

•drink-driving Adstock in month (0.0038)NS0.3560.7985

* 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 SCCnumbers in low alcohol hours (LAH), with the most appropriate measure of program activitybeing in the number of TINs issued for speeding offences detected by speed cameras eachmonth. Like the RBT program, the speed camera program in Victoria has also beensupported by a high exposure TAC publicity campaign with the speeding theme. Studiesalso have found both speeding and concentration publicity campaigns to be a factorinfluencing SCC numbers in LAH. Figures 4a,b and c, depicting TAC television advertisingAdstock, show that during 1996, the level of awareness of advertising with speeding andconcentration themes (Figure 4c) remained at significant levels, although at perhaps slightlylower levels in magnitude compared to the two previous years

Once again, models of the form of equation (1) were fitted to low alcohol hour SCCsseparately in Melbourne and the rest of Victoria, including linear trend, monthly seasonaldummies, unemployment and road safety program measures as model covariates. The twomajor road safety program measures included in the low alcohol hour models were the


number of speed cameras TINs issued per month, and the road safety media publicity asmeasured by Adstock. Exploratory analysis showed Adstock for both "speeding' and"concentration" themes together showed greater association with monthly SCCs than didjust speeding alone. Table 3 summarises the parameter estimates for the LAH models for thetwo regions of the state.

All four main factors fitted were statistically significant predictors in the model of LAHSCCs in Melbourne, with the model explaining 78% of the variation in the data. The numberof speed camera TINs issued was a highly statistically significant predictor in the models aswere speeding and concentration Adstock and unemployment rate.

For LAH SCCs in the rest of Victoria, the fitted model explained 65% of the variation in thedata. 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 alcoholhours. Road safety publicity with the speeding and concentration theme also showed astatistically significant association with LAH SCCs in the rest of Victoria, however theeffect of speed camera TINs was not statistically significant, with the factor being removedfrom the model because of co-linearity problems with the Adstock measure.

Table 2: Effects of unemployment rate, number of speed camera TINs issued, andAdstock ofTAC "speeding"and "concentration"publicity on serious casualtycrashes during low alcohol hours of the week Melbourne and countryVictoria 1983-96.

AREA OF VICTORIA EstimatedT valueSignificancecoefficient of

levelExplanatory variable

logged variable(two-tailed)MELBOURNE CRASHES

df=152

•

trend 0.0015***3.7740.0002•

unemployment rate in month -0.2191**-5.3490.0001•

no. of speed camera TINs issued -0.0209***-4.4840.0001•

speeding and concentration Adstock -0.0177**-2.6480.0090

CRASHES IN COUNTRY VICTORIA

df=156

•

trend -0.0013**-2.9160.0041•

unemployment rate in month -0.1475*-2.5460.0293•

no. of speed camera TINs issued NS++NSNS•

speeding and concentration Adstock -0.0196**-3.1080.0022

* 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


2.2 DISAGREGGATION AND COMBINATION OF THE RESULTS

Using the methods of Newstead et al (1995), each of the fitted models have beendecomposed to estimate the annual effect of each significant factor on road traumas in eachstrata for the years 1990-1996. In calculating the influence of each of the factors in themodel, a suitable comparison base had to be chosen. Following the methods of Newstead etal (1995), the influence of the road safety programs was compared to a zero base, the zerobase representing the situation if the programs had not been in operation (viz. operationlevels were zero). For the variables unemployment rate and alcohol sales, comparison with abase 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 factorsduring 1988, the year in which both SCC numbers and unemployment rate were near theirmaximum and minimum levels respectively.

Appendix A details disagreggation of the effects of each of the factors in the models for thefour 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 inVictoria during the years 1990-96. It should be noted that in Table 3, estimates for the effectof RBT and drink driving publicity are not given separately because of the high degree ofco-linearity between these factors in the estimated models, as noted above. Instead, theeffects of these two factors have been combined into a single measure labelled as the drinkdriving program.

Table 3 : Estimated reductions in serious casualty crashes attributable to variousfactors - Victoria, All Hours, 1990-96

1990199119921993199419951996

Modelled serious casualty crashes

6136521150254950523354325475

(actual serious casualty crashes)

621953715111519251'8452865196

Expected* serious casualty crashes)

8270839485228654879089319075

Reduction in serious casualty crashes

25.8%37.9%41.0%42.6%40.5%39.2%39.7%

Contribution of unemployment rate

1.9%12.1%14.8%15.6%13.5%10.4%10.3%Contribution of alcohol sales

3.0%5.5%7.0%8.9%7.9%8.8%9.6%

Contribution of speed camera TINs

9.6%10.9%11.1%11.1%11.2%11.0%11.2%

Contribution of speed and concentration

5.0%7.0%7.1%6.7%6.1%6.5%6.2%

publicity Contribution of drink-driving program

8.9%9.4%9.5%9.9%10.0%10.0%10.2%

Contribution of road safety programs

21.8%25.0%25.3%25.3%25.0%25.2%25.2%

*Expected if the road safety initiatives and other factors had remained at 1988 levels

2.3 EFFECT OF ACCIDENT BLACK SPOT TREATMENTS AND OTHERFACTORS

In producing the above estimates of the effect that various major factors have had on roadtrauma in Victoria, monthly serious casualty crash numbers have been modelled as afunction of road safety, economic and social factors. As discussed in Newstead et al (1995),


the form of the models fitted, shown in equation (1), includes a general linear trendcomponent and monthly dummies. These are included in order to explain residual variationin the data not explained by the factors of interest in the model, so as to achieve asatisfactory fit of the model to the observed data.

The roles of the monthly dummies and general linear trend factor in explaining residualvariation in the fitted models are quite different. Monthly dummy variables are included inorder to explain residual seasonality in the monthly crash data over a year. When included inthe models here, the dummies may represent residual variation in the crash data due to, forexample; environmental effects such as rainfall and daylight hours, the effects of holidayperiods, or the differing number of days in each month. None of these factors wereconsidered explicitly in the fitted models. When considering effects aggregated on an annualbasis however, seasonal effects represented by monthly dummies will be the same each yearand hence of little interest.

In contrast, the role of the general trend factor is to explain residual variation in the data dueto 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 trendcomponent are, for example, population increases, driver licensing increases, gradual andcontinual 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 theseother non-seasonal factors not included explicitly in the models. The effect of the generallinear trend component can be seen in the change in expected serious casualty crashes fromyear to year shown in Table 3.

One road safety program whose effectiveness is of interest, and is currently represented by acomponent of the trend in the analysis presented here, is accident blackspot (ABS)treatments. The data available on accident blackspot treatments in Victoria is available onlyin a highly condensed form from RTA, RCA, VicRoads and Ministry of Transport annualreports, 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 modelparameters re-estimated to establish the effectiveness of the ABS program in reducing SCCfrequency. Due to the nature of the ABS data available however, this procedure is notpractical for a number of reasons.

Firstly the data is available only on an annual basis whilst the SCC data is modelled on amonthly basis. This means that, for inclusion in the modelling process, the ABS data wouldneed to be proportioned across the 12 months of the year. To achieve this, an averagemonthly ABS expenditure or treatment profile would need to be known or assumed. Such aprofile is not known and assuming one would be entirely arbitrary. Secondly, the effects ofABS treatments are thought to be cumulative meaning that once each site is treated, itremains effective in reducing crash numbers for many years after. Such a cumulativeprocess, when represented numerically as a covariate for modelling, has a very smoothshape, 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, itis 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 incorrectestimates 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


did trend alone. Hence an alternative method of including the effects of ABS treatments inthe estimated overall percentage reductions in SCC numbers is required.

Appendix B details the process by which the estimated reductions in SCC numbers due toABS treatments can be obtained. It is assumed firstly that each ABS site treated saves onecasualty crash per year from the year of implementation onwards (Cunningham 1993). Thisassumption may be slightly optimistic for the TAC funded black spot treatment programfrom 1992 to 1994, however it is felt it still leads to estimates which are generally indicativeof the relative countermeasure effectiveness. As the ABS data is given in financial years, itis assumed that all sites were, on average, completed mid year, ie. they were fully effectiveby the commencement of the calendar year. In this way, a cumulative profile of casualtycrashes saved across the years of interest is built up. Casualty crash numbers are thenconverted to serious casualty crash numbers, for use here, by using the average ratio ofserious casualty crashes to all casualty crashes in Victoria for each year. Using the expectednumber of serious casualty crashes if road safety and other initiatives had remained at 1988levels from Table 3, along with the estimated annual SCC savings due to ABS treatments, apercentage reduction in SCC numbers due to ABS treatments can then be estimated.

Using the multiplicative percentage reduction theory of Newstead et al (1995), the estimatedpercentage reductions in SCC numbers in Victoria due to ABS treatments over the period1990-96, relative to 1988, can be factored from the trend component. Sections 8.1 and 8.2 ofNewstead et al (1995) give details of the steps involved in factoring out the ABS treatmenteffects. Appendix B shows that the estimated percentage reductions of ABS treatments onSCC numbers, relative to the 1988 base year, ranged from 1.6% in 1990 to 6.2% in 1995and then fell to 5.6% during 1996. The reason for the fall during 1996 is two fold, being acombination of a dramatic cut in accident blackspot expenditure after the 1993/94 financialyear and a fall in the ratio of the serious casualty crashes to all casualty crashes in Victoria in1996.

2.4 SUMMARY OF ESTIMATED CONTRIBUTIONS

Table 4 gives a summary of the estimated percentage reductions in serious casualty crashesin Victoria attributable to each of the factors considered in the modelling process, along withthe contribution of the accident black spot treatments. Shown are the observed, expected andestimated annual percentage reductions in the number of serious casualty crashes over theperiod 1990-96, along with the individual contribution of each of the major factorsconsidered and the total contribution ofthe road safety programs in each year.


Table 4 : Estimated reductions in serious casualty crashesattributable to major factors: Victoria 1990-96.

1990

199119921993199419951996

Modelled serious casualty crashes

6136521150254950523354325475

(actual serious casualty crashes)

6219537151115192518452865196

Expected* serious casualty crashes

8371858587709099934594809572


26.7%39.3%42.7%45.6%44.0%42.7%42.8%



3.0%5.5%7.0%8.9%7.9%8.8%9.6%


9.6%10.9%11.1%11.1%11.2%11.0%11.2%

Contribution of speed and concentration

5.0%7.0%7.1%6.7%6.1%6.5%6.2%

publicity Contribution of drink-driving program

8.9%9.4%9.5%9.9%10.0%10.0%10.2%

Contribution of accident b1ackspot

1.6%2.5%3.2%5.3%6.2%6.2%5.6%treatments

Contribution of all road safety programs

23.0%26.9%27.7%29.3%29.7%29.8%29.4%

• Expected if the road safety initiatives and other factors had remained at 1988 levels

Figure 7 summarises graphically the information given in Table 4. Each column in Figure 7shows the percentage reduction in serious casualty crashes attributable to each of four roadsafety programs considered along with the contributions of the changes in alcohol sales andunemployment rate. The line on Figure 7 gives the total contribution of all the factorsconsidered in reducing road trauma, obtained by multiplying the individual factor effects.Use of multiplicative methods to obtain the total effect was necessary because of themultiplicative model structure used to obtain the effect estimates.

FIGURE 7: Estimated reductions in serious casualty crashesattributable to major factors: Victoria 1990-96.

Year

1990 1991 1992 1993 1994 1995 1996

0.0

-10.0

-20.0

~c0 -30.0tl::J"••Ill:

-40.0

-50.0

-60.0

IliiiZJAccident Blackspot

c::::JUnemployment

c::::JAlcohol sales

_ Drink-drivingprogram

IiiiiiilSpeed andConcentrationPublicity

_Speed CameraTINs

-+- Total Reduction inSCCs


3. DISCUSSION

Results of the analysis presented in Table 3 and Appendix A show a few noteworthydifferences 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 roadsafety programs on SCCs in the rest of Victoria. Newstead et al (1995) estimated the roadsafety programs in the rest of Victoria to have lead to reductions in SCCs of the order of26% in LAH and 20% in HAH. The results presented here, however put these estimates ataround only 15% and 14% respectively. There appears to be two principal reasons for theselower estimates. In LAH, whilst the estimated effect of speed and concentration publicity isonly slightly lower, speed camera TINs were no longer found to be a significant predictor ofSCC numbers in this stratum. Previously, speed camera TINs were estimated to havereduced SCC numbers by around 11%. For HAH crashes in the rest of Victoria, the effectsof the drink-driving program has fallen from an estimated 20% reduction to an estimated12% 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 predictorof SCC numbers in the stratum.

The reasons for the differences discussed above are not clear although there are a number ofpossibilities. It is possible that the effect of the road safety programs in the rest of Victoriahas 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 cameraprogram. In the case of the drink-driving program in the rest of Victoria, the changing resultmay be a reflection of a change in enforcement strategy not being adequately captured bythe program measure currently in use. Rural drink-driving enforcement has changed to theuse 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 measurebeing used is still only the number of booze-bus tests carried out. Such possibilities wouldneed to be checked by further specific research into the effects of these programs. Thismight be achieved using methods such as inclusion of factors in the models to test for timetrends in the effectiveness of various factors. Another possibility is investigation of the useof other program measures which potentially better reflect the current methods of programoperation, as in the case of drink-driving enforcement.

Another potential reasons for the differences discussed above is that the modellingprocedure is more unstable for SCCs in the rest of Victoria. This is possible given therelatively smaller number of SCCs in comparison to metropolitan Melbourne and thegenerally lower statistical significance levels of the fitted parameters and R-squared valuesof the fitted models. A sensitivity analysis on the fitted models would need to be carried outto determine this.

Estimates of the relative effects of each modelled factor on SCCs in metropolitan Melbourneobtained here are closely concordant with those obtained by Newstead et al (1995). A fewdifferences 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 at20% against 28% previously. This may be partly due to the fact that the effect of speedcamera TINs has been included here, where it was not previously, with the effects of speed


camera TINs and the drink-driving program not being completely independent. It isinteresting to note that the estimated total combined effect of the road safety programs forthis stratum is similar here to previous estimates. Like the rest of Victoria, the effect ofalcohol 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 ofalcohol sales and the drink-driving program. Whilst modelling does not indicate anyparticular co-linearity between these factors, it is possible a relationship exits in a subtle orindirect way not reflected in the modelling procedure. Subtle relationships may also existbetween alcohol sales and unemployment rate, with alcohol sales possibly driven to a certaindegree by economic activity which unemployment rate is reflecting.

Despite the differences noted above, the overall estimate of the effect each major factor hashad 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 detailedin this report to reiterate the purpose and interpretation principles of the work presented. Thegeneral aim of the work presented here is to estimate the relative effectiveness of somemajor road safety and socio-economic factors in reducing road trauma in Victoria over theperiod 1990-1996. Obviously this work is not all encompassing in assessing every possiblefactor which may have contributed to changes in road trauma over this period. Indeed, thereis likely to be many successful programs that have not been considered in this work. Thereare 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 ofcrashes 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 lastcomments highlight the important fact that the methods and results presented here are in noway to replace formal countermeasure evaluation in the traditional manner. Historically,development of the methods presented in this report stemmed from a desire to combineresults of a number of formal evaluations to gain an overall picture of the relative effect ofeach countermeasure that had been in considered in relation to Victoria as a whole during allthe periods of the week. To this end, the methods developed were very successful and haveremained so in the work presented to date. The methods will remain useful for this purposein the future also.

4. CONCLUSION

Building on the work of a project by Newstead et al. (1995) this project details results ofstatistical modelling to asses the influence of some major factors influencing road trauma inVictoria over the period 1990-1996. Unlike the previous work, the work presented hereconsiders 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 roadtrauma in Victoria during the years 1990 to 1996. The major contributors and the apparentpercentage reduction in serious casualty crashes due to each measure/factor were:


• Speed camera operations (principally speeding TINs issued):

• "Speeding" and "concentration" television advertising:

• Drink-driving program (bus-based RBT together with"drink-driving" publicity campaigns)

• Reduced alcohol sales:

10-11 % each year

5-7% each year

9-10% each year

3% in 19906% in 19917% in 19929% in 19938% in 19949% in 199510% in 1996

• Reduced economic activity (measured by unemployment rates): 2% in 199012% in 199115% in 199216% in 199314% in 199410% in 199510% in 1996

• Accident Black Spot treatments 1.6% in 19902.5% in 19913.2% in 19925.3% in 19936.2% in 19946.2% in 19955.6% in 1996

These percentages cannot simply be added up to estimate the total contribution. If more thanone contributor is being considered, the percentage reduction of each must be applied inturn. The anti-speeding and drink-driving programs together are estimated to havecontributed reductions in serious casualty crashes of at least 22-25% during these sevenyears. Including the accident blackspot treatments, the overall contribution of road safetyinitiatives is estimated to have risen from 23% reduction in 1990 to nearly 30% reduction in1993- 1996.

5. FURTHER WORK RECOMMENDED

The results of this research indicate two general areas of further research which should bepursued.

(1) Further developments of the methods of road trauma trends modelling.


Whilst the general framework of modelling which has been developed in this project and itspredecessors has proven extremely valuable in monitoring Victorian road trauma, a numberof opportunities for fine tuning and enhancing these methods exist. Potential enhancementsmay 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 traumaoutcomes.

• 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 researchindicate a possible reduction has occurred in the effectiveness of the major road safetyprograms considered in reducing road trauma in the areas of Victoria outside metropolitanMelbourne. 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 counterthese.

6. ACKNOWLEDGMENTS

David Farrow and Geoff Elston of VicRoads are gratefully acknowledged for supplying thecrash data files used in this project. Ron Cook and Paul Williamson of the Traffic CameraOffice 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 MarketResearch Society, Vol. 21, No. 3. London.

CAMERON, MH, CAVALLO, A, and SULLIVAN, G (1992a), "Evaluation of the randombreath 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 ofthe speed camera program in Victoria 1990-91. Phase 1: General effects. Phase 2: Effectsof 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". ReportNo. 52, Monash University Accident Research Centre.


CAMERON, MH, and NEWSTEAD, SV (1993) "Evaluation of mass media publicity assupport for enforcement". Paper presented at Australasian Drink-Drive Conference,Melbourne, November 1993.

CAMERON, MH, NEWSTEAD, SV, and VULCAN, AP (1994), " Analysis of reductions inVictorian 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 ofsome 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 somemajor factors influencing road trauma trends in Victoria 1990-94: all crashes and specificcrash sub-groups" Monash University Accident Research Centre.

ROGERSON, P., NEWSTEAD, S. and CAMERON, M. (1994) "Evaluation of the speedcamera program in Victoria 1990-1991. Phase 3: Localised effects on casualty crashes andcrash severity. Phase 4: Generalised effects on speed." Report No. 54, Monash UniversityAccident Research Centre.

THORESEN, T, FRY, T, HEIMAN, L and CAMERON, MH (1992), "Linking economicactivity, road safety countermeasures and other factors with the Victorian road toll". ReportNo. 29, Monash University Accident Research Centre.


APPENDIX A

Percentage reductions in serious casualty crashes attributable to various sourcesMelbourne Statistical Division (MSD) : 1990 -1996 relative to 1988

APPENDIXA1

199119921Observed serious casualty crashes

2102177017341766186919682007

Expected serious casualty crashes

2912296430173070312531813238


27.8%40.3%42.5%42.5%40.2%38.1%38.0%


2.6%13.4%16.3%16.9%14.6%11.3%11.1%


17.6%19.8%20.0%19.8%20.0%19.5%19.7%

Contribution of speed & concentration publicity

10.1%14.0%14.2%13.7%12.5%13.4%13.1%


25.9%31.1%31.4%30.8%30.0%30.3%30.3%

Observed serious casualty crashes

1977161815531518166117771806


2971306131543250334834503555


33.4%47.1%50.8%53.3%50.4%48.5%49.2%



4.7%8.6%11.0%14.0%12.2%13.8%14.9%

Contribution of speed camera TINS

9.6%10.8%10.9%10.8%10.9%10.6%10.7%

Contribution of drink-driving program

19.7%20.3%20.0%20.7%20.6%20.7%20.7%


27.4%28.9%28.8%29.3%29.3%29.1%29.3%


4079338732873284353037453813


5882602561706320647366316792


30.7%43.8%46.7%48.0%45.5%43.5%43.9%



2.4%4.4%5.6%7.2%6.3%7.2%7.8%


13.5%15.2%15.4%15.2%15.3%14.9%15.0%


5.0%6.9%6.9%6.7%6.1%6.4%6.3%


10.0%10.3%10.2%10.6%10.7%10.8%10.9%


26.0%29.2%29.3%29.2%28.9%28.9%29.0%

Percentage reductions in serious casualty crashes attributable to various sources APPENDIXA2

Rest of Victoria (ROV) : 1990 - 1996 relative to 1988ROY)

..................1990 199119921993199419951996


1064924890873888891887


1154113611181101108410671050


7.8%18.7%20.5%20.7%18.1%16.5%15.5%


-3.0%4.0%6.0%6.9%5.4%2.5%2.4%


0.0%0.0%0.0%0.0%0.0%0.0%0.0%


10.6%15.2%15.4%14.9%13.4%14.4%13.4%


10.6%15.2%15.4%14.9%13.4%14.4%13.4%


993899849793815796775


1233123312331233123312331233


19.5%27.1%31.2%35.7%33.9%35.4%37.1%



8.5%15.7%19.7%24.9%22.2%24.6%26.5%

Contribution of speed camera TINS

0.0%0.0%0.0%0.0%0.0%0.0%0.0%


12.0%13.5%14.3%14.3%15.0%14.4%14.5%


12.0%13.5%14.3%14.3%15.0%14.4%14.5%


2056182317381666170316871662


2387236923522334231723002283


13.9%23.0%26.1%28.6%26.5%26.6%27.2%


-1.5%1.9%2.8%3.3%2.5%1.1%1.1%Contribution of alcohol sales

4.4%8.2%10.3%13.2%11.8%13.2%14.3%


0.0%0.0%0.0%0.0%0.0%0.0%0.0%


5.1%7.3%7.3%7.0%6.3%6.7%6.2%


6.2%7.0%7.5%7.6%8.0%7.7%7.8%


11.0%13.8%14.3%14.0%13.8%13.8%13.5%

Percentage reductions in serious casualty crashes attributable to various sources APPENDIXA3All Victoria: 1990 - 1996 relative to 1988

AI..I..VICTORIA

1990199119921993199419951996


3166269426232639275728592894

Expected serious casualty crashes4066410041354171420942484288

Reduction in serious casualty crashes22.1%34.3%36.6%36.7%34.5%32.7%32.5%

Contribution of unemployment rate1.0%10.8%13.5%14.3%12.2%9.1%9.0%


12.6%14.3%14.6%14.6%14.8%14.6%14.9%

Contribution of speed & concentration publicity10.2%14.4%14.5%14.0%12.8%13.6%13.2%

Contribution of road safety programs21.5%26.6%27.0%26.5%25.7%26.3%26.1%


2970251724022311247625742581

Expected serious casualty crashes4204429443874483458146834788

Reduction in serious casualty crashes29.4%41.4%45.3%48.4%46.0%45.0%46.1%

Contribution of unemployment rate2.7%13.2%16.1%16.8%14.7%11.6%11.6%

Contribution of alcohol sales5.8%10.8%13.6%17.2%15.1%16.9%18.2%

Contribution of speed camera TINS6.8%7.7%7.8%7.8%8.0%7.8%8.0%


17.5%18.4%18.5%19.1%19.3%19.2%19.3%



6136521150254950523354325475


8270839485228654879089319075


25.8%37.9%41.0%42.8%40.5%39.2%39.7%



3.0%5.5%7.0%8.9%7.9%8.8%9.6%

Contribution of speed camera TINs9.6%10.9%11.1%11.1%11.2%11.0%11.2%


5.0%7.0%7.1%6.7%6.1%6.5%6.2%

Contribution of drink-driving program8.9%9.4%9.5%9.9%10.0%10.0%10.2%


APPENDIXB

ESTIMATION OF SERIOUS CASUAL TV CRASH REDUCTIONS DUE TO ACCIDENT BLACKSPOT TREATMENTS APPENOIXB

Implementation YearNo. of sitesCASUAL TV CRASHES SAVEO (assuming all sites treated at mid financial year)

Treated 19831984198519861987198819891990199119921993199419951996

82/83404040404040404040404040404040

83/849090909090909090909090909090

84/85157157157157157157157157157157157157157

85/86118118118118118118118118118118118118

86/87246 246246246246246246246246246246

87/88337 337337337337337337337337337

88/89300 300300300300300300300300

89/90257 257257257257257257257

90/91204 204204204204204204

91/92278 278278278278278

92/93531 531531531531

93/94375 375375375

94/9582 8282

95/9649 49

CRASH SAVINGS TOTAL CAS CRASHES

4013028740565198812881545174920272558293330153064SCC/CC

0.45150.38500.38150.36010.35960.34330.32340.30870.32390.31380.33120.32270.31540.2936SERIOUS CAS CRASHES

18.150.1109.5145.8234.1339.2416.5476.9566.5636.1847.2946.5950.9899.6

TOTAL S.C.CRASHES EXPECTED*

67807084735174767748803381508270839485228654879089319075EXPECTED+SA VING

6798.17134.17460.57621.8 7982.18372.28566.58746.98960.59158.19501.29736.59881.99974.6PERCENT SAVING

0.3%0.7%1.5%1.9%2.9%4.1%4.9%5.5%6.3%6.9%8.9%9.7%9.6%9.0%

SAVINGS SINCE 1988 SERIOUS CAS CRASHES

77.4137.8227.3296.9508.0607.3611.8560.4PERCENT SAVING

0.9%1.6%2.5%3.2%5.3%6.2%6.2%5.6%

*Expected if the road safety initiatives and other factors had remained at 1988/evels

further modelling of some major factors influencing road trauma

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