View Interactive Dashboard on Tableau Public
This project analyses Victorian road crash data between 2018 and 2023 to identify crash trends, severity factors, high-risk locations, and demographic risk patterns. The analysis combines Python-based data cleaning with Tableau dashboard development to generate interactive business intelligence style visualisations for road safety analysis.
The project focuses on:
- Monthly crash and severity trends
- Geographic crash hotspots across Victoria
- Crash severity across speed zones
- Vehicle body style risk analysis
- Demographic and behavioural risk factors
- COVID vs Post-COVID crash behaviour analysis
The findings support data-driven road safety planning, behavioural awareness campaigns, and transport policy decisions.
Road crashes create major social, economic, and public safety challenges across Victoria. Understanding where crashes occur, who is most affected, and which environmental or behavioural factors contribute to severe outcomes is critical for improving road safety strategies.
This project aims to answer key analytical questions such as:
- Which regions experience the highest crash density?
- How did COVID restrictions impact crash frequency and severity?
- Which speed zones are associated with more severe crashes?
- Which demographic groups face the highest fatality risk?
- Which vehicle body styles appear most frequently in fatal crashes?
- Tableau Desktop
- Python
- Pandas
- NumPy
- Jupyter Notebook
- Geospatial Mapping
- Data Cleaning & Transformation
- Business Intelligence & Dashboarding
The analysis used three cleaned datasets linked through the common field Accident No.
vic_road_crash_data_cleaned.csvvehicle_cleaned.csvperson_cleaned.csv
The repository does not include the original raw datasets due to repository organisation and file management considerations.
The project includes:
- Cleaned data processing notebooks
- Tableau dashboard workbook
- Final analytical report
- Dashboard visualisations
- Data dictionary explaining dataset fields and attributes
This dashboard analyses monthly crash counts and severe crash percentages between 2018 and 2023. The visual highlights the significant decline in crash activity during COVID lockdown periods and the recovery in post-pandemic years.
- Crash frequency declined sharply during 2020 lockdown periods while severe crash proportions remained relatively stable.
This geospatial dashboard maps crash hotspots across Victoria using latitude and longitude coordinates.
- Melbourne metropolitan regions recorded the highest crash density, while rural corridors experienced lower crash frequency but higher fatality risk.
This dashboard examines how crash severity changes across different speed environments.
- High-speed rural zones showed fewer crashes overall but significantly higher fatal and serious injury proportions compared with urban zones.
This dashboard analyses fatal crash distribution across different vehicle body styles.
- Sedans, utility vehicles, vans, and motorcycles appeared disproportionately in fatal crash outcomes.
Python was used extensively for preprocessing and preparing the datasets before importing them into Tableau.
- Date and time standardisation
- Missing value handling
- Invalid coordinate correction
- Feature engineering for year, month, and hour
- Removal of inconsistent records
- Text category standardisation
- Dataset relationship validation
The datasets were then linked in Tableau using Accident No to support multi-table analysis.
- Crash counts declined significantly during COVID lockdown periods.
- Melbourne and Geelong corridors showed the highest crash concentrations.
- Rural crashes were less frequent but more severe.
- Male drivers aged 22–39 represented a large share of fatal crashes.
- Failure to wear helmets or seatbelts strongly correlated with fatal outcomes.
- High-speed zones were associated with higher fatality rates.
- Sedans, utility vehicles, and motorcycles showed elevated fatal crash representation.
victoria-road-crash-analytics-dashboard/
│
├── dashboards/
│ └── victoria_road_crash_dashboard.twbx
│
├── visuals/
│ ├── monthly_crash_severity_trends_victoria.png
│ ├── victoria_crash_hotspots_geospatial_analysis.png
│ ├── victoria_speed_zone_crash_severity_analysis.png
│ └── victoria_vehicle_body_style_crash_severity_analysis.png
│
├── notebooks/
│ ├── person_data_cleaning.ipynb
│ ├── vehicle_data_cleaning.ipynb
│ ├── road_crash_data_cleaning.ipynb
│ ├── person_data_cleaning.pdf
│ ├── vehicle_data_cleaning.pdf
│ └── road_crash_data_cleaning.pdf
│
├── reports/
│ └── victoria_road_crash_analytics_report.pdf
│
├── data/
│ ├── README.md
│ └── data_dictionary.xlsx
│
└── README.md
This project follows ethical data visualisation principles focused on:
- Accuracy
- Fair representation
- Transparency
- Public benefit
- Responsible interpretation of crash data
The dashboards were designed to support evidence-based road safety insights without misleading or biased representation.
Potential future enhancements include:
- Predictive crash severity modelling
- Real-time dashboard integration
- Machine learning risk prediction
- Weather and traffic condition integration
- Interactive web dashboard deployment
- Time-of-day crash forecasting
Master of Business Analytics
Macquarie University
- Data Analytics
- Business Intelligence
- Tableau Dashboarding
- Predictive Analytics
- Transport & Operations Analytics
- Geospatial Data Analysis
This project demonstrates end-to-end analytics workflow capabilities including:
- Data cleaning and preprocessing
- Exploratory data analysis
- Geospatial visualisation
- Tableau dashboard development
- Business intelligence reporting
- Public safety analytics
The project showcases practical skills relevant to data analytics, business intelligence, transport analytics, and dashboard development roles.