Statistics can be seen as our bread and butter in the road safety world. Unfortunately, though, not all stats are created equal. So, how do we understand road safety statistics in Australia in a way that makes sense and represents their value?
Below, we share some of the latest statistics surrounding road fatalities and serious injuries and essential factors to consider when reviewing such information.
Key road safety statistics
Discover the latest numbers surrounding severe and fatal injury incidents on Australian roads.
Australian road crash statistics
- 64% of surveyed Australians over 18 with driver’s licenses have experienced at least one car crash 1.
- With 39,755 people hospitalised due to car crashes in 2018-19 2 , the number of hospitalised injuries increased by 16.2% between 2012 and 2018 3. In 2019, this figure rose to 39,866 4.
- Speeding is the number one cause of car crashes 5, followed closely by distracted, tired and drunk driving.
Male drivers are three times more likely to be involved in a car crash 6.
Australian road fatality statistics
- In 2022, there were 1,192 road fatalities in Australia 7. This is 5.6% more than in 2021; fatalities have generally decreased over the past decade, from about 1,300 to 1,100 per year.
- In March 2023 alone, there were 107 deaths on our roads – a figure 2.3% greater than the average for March over the prior five years 8.
- On a global level, the annual road fatality rate for 15-24-year-olds is 50% higher than for other age groups 9.
- Drink driving causes approximately 30% of fatal crashes in Australia, with over 1 in 4 drivers and passengers killed showing a Blood Alcohol Content (BAC) over the legal limit 10.
- Police crash reports indicate that speeding contributes to approximately 1 in 3 fatal crashes, likely a conservative figure 11.
- In 2021, 163 people were killed in heavy vehicle road crashes. This equates to 15.4% of total road fatalities12.
Image credit: LHD Lawyers
What to consider when looking at road safety statistics
When looking at data such as road fatality and crash statistics, you should always be aware of the following six factors.
1. Is the source biased?
The main starting point is the source of the information. We can’t get anywhere without someone providing some kind of data. This source must be credible and reliable and, in an ideal world, have no skin in the game. However, that isn’t possible, so a transparent source with potential bias is the next best thing.
It also needs to be considered if the source can gather the information appropriately. Not every organisation has enough funds, space, and manpower to conduct or gather information on a large enough scale for the results to have any relevance.
However, even if the source meets all of these criteria and is as transparent as possible, we still need to be aware that researchers, or anyone, cannot be fully aware of their biases.
This brings us to implicit bias. We interpret everything we see, primarily based on concepts and ideas that we take for granted. 13
Image credit: Santa Clara University
For example, one study may examine the average strength of university students by asking them to lift 100kg and recording how much they struggled and how high they could lift the weight.
The results showed that all the males exceeded the females’ strength by two factors. The researchers accept this without question due to a common innate belief that men are stronger than women.
However, it turns out that 80% of the females who participated also use the female sauna at the university’s gym, which has been the recent epicentre of a pretty nasty flu outbreak. Over 50% of female participants were unwell on the study day, most likely harming their results.
The same study is done in a parallel universe, except the women outperform the men by two factors. The researchers question this because the findings contradict their innate belief that men are stronger than women. Once it’s been discovered that there was a flu outbreak in the male sauna at the university’s gym and 50% of the male participants were unwell, the study is re-done.
A key thing to remember here is that implicit biases do not have to be false or negative beliefs, but they are still problematic because they can lead to oversight. 14
2. Is the data objective and transferable?
One way to try and counteract this is by using objective data, such as the number of cars registered, rather than subjective and how the driver’s emotional state appeared, which also helps to ensure the data is as accurate and transferable as possible. 15
That brings us to the next topic: for data to be meaningful, it needs to be transferable, as it is reasonable to assume that similar results would occur. 16 This helps us understand the impact of the numbers.
For example, factors influencing driving behaviour in Brazil cannot reasonably be assumed to transfer to factors influencing driving behaviour in Australia due to cultural and environmental differences. This is not to say that the findings can’t be transferred, just that further studies need to be conducted to determine the transferability.
However, more objective elements, such as the speed at which a car must deploy the airbags when colliding with a solid wall, can be transferred without concern because cultural differences aren’t relevant and environmental factors have already been accounted for.
3. Correlation vs causation
Another aspect that needs to be considered is correlation vs causation. For example, if 70% of people who were sunburned over the weekend also consumed an ice cream 24 hours before showing symptoms, it does not mean that ice cream causes sunburn, but rather is a correlation.
This correlation is most likely due to both being caused by exposure to warm sunny days. 17 To further identify the causation of weather on ice cream consumption meaningfully, you would need to see how many people consume ice cream outside on a cold overcast day.
So, if 25% of people consume ice cream in the opposite conditions, warm sunny weather increases ice cream consumption by 180%, or cold overcast weather reduces ice cream consumption by 100%.
So, what can we extrapolate? The weather influences the consumption rate of ice cream and therefore is most likely a causation factor.
Image credit: YouTube
If you want to be certain, you would need to do repeated tests with varying factors to determine that they aren’t the influence, such as the day of the week or different stages of the moon cycle to determine that weather wasn’t just another correlation. 18
Here’s where it gets further complicated. Other contributing factors, like cost, variety, and cravings, influence people’s decisions to get ice cream, and these can never be entirely removed or accounted for.
As such, it’s possible to have more than one causation. So, while it does seem like the weather has a considerable influence on when people have ice cream, it cannot be considered in isolation.
4. Does the statistic support the statement?
Now it also needs to be considered that just because a stat has been used doesn’t mean it supports the statement 19
There’s an ad out recently proving their service valuable by using the stat that 95% of people who signed up were still members 12 months later. On the surface, it sounds great; how many paid memberships could have a 95% retention rate? Well, the ad didn’t state, so there’s no way for the average person to determine if this result is particularly good.
However, let’s assume this is the best retention rate. The membership service allows users to sign up for a one-month trial period, with an automatic 12-month renewal at the end of the month unless users opt out.
This means that individuals who forget to opt out after the trial period would automatically be included in the 95% of members who continue the service. Similarly, those who initially enjoyed the first 6 months but found that the service was no longer worth it could also opt out anytime.
So, while the 95% retention rate could be a good indicator, the stat doesn’t strictly support the claim.
5. How is the data visualised?
Data visualisation is another aspect that can alter how people interpret facts. If you’ve seen any of our social media, you will know we love a good infographic. However, they don’t always show the whole picture 20 (did you see what we did there? 😉).
Image credit: Whitebox Analytics
For example, I can show you an aesthetic graph highlighting the factors involved in all the road crashes recorded by the NSW gov. It can show that fatigue is the most common factor involved, and we need to be more aware of this. However, not all collisions get reported, especially single vehicles.
As such, we tend to stick to crashes involving death or serious injury (requiring medical attention) because fewer of them can fly under the radar. So just because something looks good and professional, and the graph is even proportionate to the 0.0001th of an mm, doesn’t mean it is complete 21
6. What is the broader context of this statistic?
Further, the data context can be as important as the number itself 22. For example, an experiment could be done where 200 drivers are asked to complete a skills course that contains various challenges a driver might face on Australian roads.
The results showed that 80% of drivers failed to leave an appropriate gap between them and the object in front when coming to a complete stop. Now that’s a very concerning statistic. However, what they did not tell you is that it had rained an hour before commencing the experiment.
Image credit: NSW Government
So, while there are still too many participants needing to account for the road surface and have the appropriate skills to adjust accordingly, it paints a slightly different picture. 23
Another study was completed on 18-year-old NSW high school students’ concentration levels to determine how they may differ from older age groups and if it may be a factor leading to the high mortality rate of young Australian drivers.
The experiment was conducted in a secluded space without outside noise that was familiar to them so that they wouldn’t be distracted by their environment during the experiment. It showed that their concentration level was, on average, 20% less than that of older age groups.
It would be easy to see this and believe it’s a strong indicator of diminished concentration levels in this age group. However, the experiment was conducted two weeks before the HSC exams began that year, meaning they most likely had higher stress levels than usual, a factor known to reduce concentration levels.
Because of elements like these, while we often pick the stats that we feel best represents the issues we are trying to highlight, we often don’t place them into context other than what the study covered. We do this so readers can interpret the data without our influence. 24
Stay in the know with Road Sense Australia
This obviously isn’t the complete guide to data, statistics, and what makes them meaningful, but we hope this helps provide road users with some insight.
Whether that is gaining a new perspective on how to interpret data, learning a new concept you don’t think you otherwise would have, or even discovering a deep rage at the understanding that all data, including road safety statistics, are tainted in some way. There is no conceivable way around it.
At Road Sense Australia, we aim to provide the most precise and accurate information about road rules and safety. That’s why we support better data collection, education and training wherever possible.
To browse all information on community road safety, discover our latest articles on regulations, program updates, and other important topics here.
Glossary of some terms used with data and statistics
Average: Used to describe an amount you get by adding two or more amounts together and dividing by the number of amounts 25
Causation: The empirical relation between two events, states, or variables such that a change in one (the cause) brings about a change in the other (the effect) 26
Correlation: A statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It’s a standard tool for describing superficial relationships without stating cause and effect 27
Data: Information, especially facts or numbers, is collected to be examined, considered, and used to help decision-making. 28
Extrapolate: To use existing information to discover what is likely to happen or be true in the future. 29
Infographic: A picture or diagram or a group of pictures or diagrams showing or explaining information. 30
Mean: An average number value. 31
Median: The median value is the middle one in a set of values arranged in order of size.32
Mode: The number or value most often appears in a particular set. 33
Outlier: A person, thing, or fact that is very different from other people, things, or facts so that it cannot be used to draw general conclusions. 34
Per cent: For or out of every 100, shown by the symbol %. 35
Qualitative: Based on information that cannot be easily measured, such as people’s opinions and feelings, rather than on information that can be shown in numbers. 36
Quantitative: Relating to an amount that can be measured. 37
Ratio: The relationship between two groups or amounts that expresses how much bigger one is than the other. 38
Raw data: Data that has not been processed for use 39
Statistics: A fact in the form of a number that shows information about something 40
Sum: The whole number or amount when two or more numbers or amounts have been added together. 41
*All statistics in this article are hypotheticals and do not reflect accurate information