# Using statistical modelling to predict crash risks, injury outcomes and compensation costs in Victoria.

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4 presents more detailed results on the Injury Severity Model. High level results only from the other models are summarised thereafter. Injury Severity Modelling Results The Injury Severity Models predict the probability of a TAC Claim being minor, moderate, serious and severe injury severities; using Maximum Abbreviated Injury Score (AIS). AIS is an anatomical-based coding system to classify and describe the severity of specific individual injuries. A score of between 1 and 6 (labelled as minor, moderate, serious, severe, critical and maximum) is assigned to each individual injury. Maximum AIS is the score of the person s most severe injury. Due to the very small number of high severity claims in the input datasets, claimants with a Maximum AIS score of 4 and above were grouped into the severe injury group for the purpose of these models. Table 2: Injury Severity Models - Significant Variables Age Gender Person Licence Type Seatbelt / Child Restraint / Helmet Seating Position Geo-demographic Proportion with at least a bachelors degree (in local area) Crash Date Crash Speed Zone Vehicle Intent Vehicle Offending Vehicle Vehicle Type Year of Manufacture The following charts present raw probabilities and pure effect relativities for selected significant variables in the Injury Severity Models. The probability charts on the left show the actual relationship between the predictor variable (e.g. age) and the modelled variable (in this case, injury severity). This is equivalent to the raw, un-modelled data without controlling for other predictor variables. The relativity charts on the right hand side show the pure effect on the model (after controlling for other significant predictors) for values of the predictor variable. For continuous variables, a value of 10 translates to no effect, a value of less than 10 translates to a lower severity and values greater than 10 translate to a higher severity. For categorical variables, one factor was chosen as a base which would obtain 10 relativity, and the other factors would be given a relativity score in relation to it.

5 Figure 1: Injury Severity Models The Effect of Age at Crash Pure Effect Relativities Age at Crash Minor Injury Moderate Injury Serious Injury Severe Injury Age at Crash Minor Injury Moderate Injury Serious Injury Severe Injury Figure 2: Injury Severity Models The Effect of Gender Male Gender Female Pure Effect Relativities Male Gender Female Figure 3: Injury Severity Models The Effect of Licence Type Pure Effect Relativities Other (licenced) Learner Unlicenced Licence Type Other (licenced) Learner Unlicenced Licence Type

6 Figure 4: Injury Severity Models The Effect of Seating Position and Seatbelt/Helmet Pure Effect Relativites Figure 5: Injury Severity Models The Effect of Speed Zone Speed Zone Minor Injury Moderate Injury Serious Injury Severe Injury Pure Effect Relativities Speed Zone Minor Injury Moderate Injury Serious Injury Severe Injury Figure 6: Injury Severity Models The Effect of Truck Involvement Pure Effect Relativities Yes No Truck Involvement Yes No Truck Involvement Selected observations from the results of the Injury Severity Modelling include: Severity of injury increases with age. Males were at a significantly higher risk of serious injury than females. Learner drivers have relatively more severe crashes, but unlicensed drivers are far more likely to have serious or severe crashes. Passengers in general are worse off than drivers. Rear seat occupants specifically were at 5 higher risk of severe injury than drivers. Seat belt use and helmet use is extremely protective, particularly for children. Motorcyclists have more severe injuries, particularly if a helmet is not worn. Pedestrians are slightly worse off than a motorcyclist not wearing a helmet. The faster the speed, the more severe the injuries.

7 Risk of serious injury was 4.5 times higher in truck involved crashes compared to standard vehicles. Overall Project Findings Injury severity was found to be the most significant variable at predicting TAC compensation costs. As expected, the cost of a claim grows with increasing injury severity. 18 year olds are by far the most at risk. Males are worse than females in terms of probability of single vehicle crashes and severity of all crashes. Motorcyclists contribute significantly to the probability of a claim for males, especially in the age group. When motorcyclists are excluded, males and females have a similar claim probability distribution when single and multiple vehicle crashes are combined. Motorcyclists in general have a higher claim probability, are more likely to be involved in single vehicle crashes, have more severe injuries (which are exacerbated if a helmet is not worn) and have slightly higher compensation costs for similar severity of injury. Geo-demographic variables, and particularly socio-economic variables, have a significant influence on injury severity, claim probability and compensation costs. Language barriers tend to increase claim costs but potentially lead to a lower probability. Increased income and education in the area where a claimant lives lead to lower probability, lower severity and lower costs for injuries of the same severity. Newer cars are less likely to be involved in a serious crash, and when they are, the compensation costs are relatively lower. Some vehicle makes, models and types are more prone to single or multiple vehicle crashes. For example, Commodores and Falcons are more likely to be involved in single vehicle crashes than small or expensive cars. The impact of different vehicles is typically watered down in multiple vehicle crashes given that fault was not considered. It is important to note the potential bias in interpreting the results of the vehicle probability model and the impact of different vehicles given that driver characteristics were not included. For example, the results indicating Commodores and Falcons are high risk vehicles may be caused by the types of drivers of these vehicles, rather than the vehicles themselves. Conclusions The established database provides a benchmark for future Road Safety policy analysis, particularly with consideration given to the cost of injury to society. The TAC has begun to update the linked dataset with updated data, and some new and improved data acquisitions. This includes updated TAC lifetime compensation cost estimates, updated ABS Census data, more detailed vehicle specification data and vehicle crash worthiness ratings. The TAC now also has more regular snapshots and a wider range of data on Victorian licence holders from VicRoads, including demerit point history and licence conditions. Future work will also explore utilising self-reported injury data collected by the TAC claims department, in addition to injury codes obtained from the hospitals, to create a richer dataset of injury severity. Future work may also entail sourcing private car insurance data to better understand the population of road users that are not involved in crashes. Although a large suite of variables were tested in the initial models, many more will be tested in future. These include

8 whether the claim had previous psychology, chiropractic or physiotherapy treatment and whether they had pre-existing drug or alcohol issues. Limitations of previous models will be explored and many aspects of the models will be reviewed and refreshed. This will ensure the models reflect the current state of play and new relationships are identified.

9 1 Appendix 1

10 Crash Incidents Population / Exposure Output Files Data Enhancements Administrative Datasets Medical Treatment Provider Data MUARC Injury Severity Coding TAC Claims Data TAC* Lifetime Claim Costs Merged Crash Dataset Victoria Police Crash Database Geocoding Software Enhanced Merged Crash Dataset VicRoads Crash Database ABS Census Data TAC Claimants Non Claimants Crash Vehicle VicRoads Vehicle Registration Data Geocoding Software and ABS Census Data Enhanced Vehicle Registration Data VicRoads Licence Holder Data Enhanced Licence Holder Data 2

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