Before looking at individual locations or road conditions, it is useful to begin with the overall picture.
The first visualisation compares fatal and serious injury crashes with other injury crashes from 2012 to 2025.
The 2026 data are excluded because the dataset only contains part of 2026. Comparing a partial year with complete years could create a misleading impression.
A serious crash during weekday traffic may happen under very different circumstances from one late at night on a weekend.
The heatmap below combines day of the week and hour of the day for fatal and serious injury crashes.
The interactive cells allow the exact number of crashes to be viewed by hovering over each time period.
Serious road trauma is not spread evenly across Victoria.
The next chart shows the 15 Local Government Areas with the highest number of recorded fatal or serious injury crashes.
Colour is used to show the broader Department of Transport and Planning region for each LGA.
Serious crashes occur under many different road conditions.
This visualisation compares speed zone and lighting condition at the same time.
Combining these variables gives a more detailed view of the road environments in which fatal and serious injury crashes are recorded.
Road crashes affect different types of road users.
The final chart compares selected groups recorded in fatal and serious injury crashes, including pedestrians, bicyclists, motorcyclists, young drivers, older drivers and heavy vehicles.
These categories should not be interpreted as direct measures of individual risk. Instead, they show the different types of road users and vehicles involved in serious crashes.
Victoria’s serious road-crash problem cannot be understood through one statewide number alone.
The five visualisations show that serious crashes vary across several dimensions, including time, day of the week, location, speed environment, lighting conditions and road-user involvement.
The yearly trend shows that other injury crashes changed substantially over time, including a noticeable decline around 2020 followed by an increase in later years. Fatal and serious injury crashes also changed, but their pattern was less dramatic.
The time-of-day analysis shows that serious crashes are more concentrated during the afternoon, particularly on weekdays. The geographic comparison shows that Geelong, Casey and Melbourne recorded some of the highest numbers of fatal or serious injury crashes among Victorian LGAs.
Road environment also matters. 60 km/hr roads recorded the largest number of fatal or serious injury crashes, while daylight accounted for a large proportion of crashes across several speed zones.
Finally, road-user involvement was not evenly distributed. Young drivers aged 18–25 had the highest recorded involvement among the selected groups, followed by motorcyclists.
Together, these results show that serious road trauma in Victoria is a multidimensional issue. Looking only at an annual statewide total can hide important differences in when crashes happen, where they occur and who is involved.
Here we uses the Victoria Road Crash Data published by the Victorian Department of Transport and Planning.
The dataset includes information about crash date and time, severity, Local Government Area, transport region, speed zone, lighting conditions and selected road-user characteristics.
For this visual story, crashes classified as Fatal accident and Serious injury accident were grouped together as Fatal or serious injury.
The 2026 data were excluded from the yearly trend because the dataset contains only part of 2026. Including a partial year beside complete years could create a misleading comparison.
There are several limitations.
First, the dataset describes recorded road crashes and does not directly measure the personal risk faced by individual road users.
Second, the LGA comparison is based on raw crash counts. It does not adjust for population, road length, traffic volume or the number of journeys made in each local area.
Third, the road-user categories shown in the final visualisation represent different forms of involvement and should not be interpreted as directly comparable measures of individual risk.
Finally, this is observational data. The visualisations can show patterns and associations, but they cannot prove that one factor caused a crash.
The road crash data used in this assignment were published by the Victorian Department of Transport and Planning through the Victorian Government open data platform.
I used openAI like Chatgpt to help find a good topic, refine grammar and wording. I also used it to help resolve an error when importing the CSV file into my R code. i reviewed the sources and final work is from myself.
Department of Transport and Planning. (2026). Victoria road crash data. Victorian Government. https://opendata.transport.vic.gov.au/dataset/victoria-road-crash-data