The Australian Road Deaths Database provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities. Road deaths from recent months are preliminary and the series is subject to revision.

Context

The Australian Road Deaths Database provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities. Road deaths from recent months are preliminary and the series is subject to revision.

Content

The data is collected from the year 1989 to 2021, Click here to lear more about the dataset.

Acknowledgements

Bureau of Infrastructure and Transport Research Economics: The Bureau of Infrastructure and Transport Research Economics (BITRE) provides economic analysis, research and statistics on infrastructure, transport and cities issues to inform both Australian Government policy development and wider community understanding. click here

Data Removal

Please contact me if any part of the data is to be removed.

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 30 June 2024.

Fatal, Hospitalisation, Medical treatment and Minor injury:

This dataset contains information on crashes reported to the police which resulted from the movement of at least 1 road vehicle on a road or road related area. Crashes listed in this resource have occurred on a public road and meet one of the following criteria:

• a person is killed or injured, or
• at least 1 vehicle was towed away, or
• the value of the property damage meets the appropriate criteria listed below.

Property damage:

1. $2500 or more damage to property other than vehicles (after 1 December 1999)
2. $2500 or more damage to vehicle and/or other property (after 1 December 1991 and before 1 December 1999)
3. value of property damage is greater than $1000 (before December 1991).

Please note:

• This data has been extracted from the Queensland Road Crash Database.
• Information held in the Road Crash Database on events occurring within the last 12 months is considered preliminary as investigations into crashes can take up to 1 year to finalise.
• Property damage only crashes ceased to be reported/recorded by Queensland Police Service after 31 December 2010.
• These crash location coordinates reference the current Australian geodetic datum is GDA2020 (previously it was GDA94).

Details of reported road crashes and casualties in South Australia.

Sites of Road Crashes in South Australia.

This is a link to the NSW Toll Road Data website. The datasets on this website contain traffic data for the following toll roads in Sydney, New South Wales, Australia that are wholly or partly owned by Transurban:

• Cross City Tunnel (CCT)
• Hills M2 (M2)
• Lane Cove Tunnel/Military Road E-Ramp (LCT)
• M1 Eastern Distributor (ED)
• M4 (M4)
• M5 South West Motorway (M5)
• M5East & M8 (M5E)
• NorthConnex (NCX)
• Westlink M7 (M7)

Data available is grouped by quarter for each year starting 2009.

Road safety systems are essential for planning, managing, and improving road infrastructure and decreasing road accidents. Manual systems used for road safety assessments are inefficient, time consuming, and prone to error. Some automated systems using sensors, cameras, lidar, and radar to detect nearby obstacles such as vehicles, pedestrians, lane lines, some traffic signs and parking slots have been introduced to reduce road fatalities by minimizing human error. However, the existing road safety systems available in industry are unable to accurately detect all road safety attributes required by the Australian Road Assessment Program (AusRAP), a program launched to establish a safer road system through high-risk roads inspection, developing star ratings and safer roads investment plans to mitigate the possibility of meeting with accidents. Therefore, it is important to explore novel techniques and develop better automated systems which can accurately detect and classify all road safety attributes.

This research focuses on the development of a novel deep learning technique for the analysis of road safety attributes. Various architectures, learning and optimisation techniques have been investigated to develop an appropriate deep learning-based technique that can detect road safety attributes with high accuracy. Firstly, a single-stage segmentation and classification technique to automatically identify AusRAP attributes has been investigated. Secondly, multi-stage segmentation and classification techniques using various classifiers have been investigated. Finally, Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO)-based techniques have been investigated to optimise the proposed deep learning techniques.

The proposed techniques were evaluated on a real-world dataset using roadside videos provided by the Department of Transport and Main Roads (DTMR), Queensland, Australia, and Australian Road Research Board (ARRB). The classification accuracy was used as a metric to measure the performance, and to further validate the efficacy, different diversity measures such as specificity, sensitivity, and f1-score were used. An appropriate analysis and a comparison with existing techniques were conducted and presented. The results and analysis show that the proposed single-stage and multi-stage deep learning-based techniques achieve classification accuracy and misclassifications better than the existing state-of-the-art segmentation and classification techniques. It was found through experimentation that proposed single stage technique can avoid re-training the whole model using all training samples which requires a lot of time when a new attribute is introduced. Moreover, through extensive experimentation, it was found that it is not always necessarily required to have a large dataset for training. Effective solutions were found to eliminate the requirement to annotate large number of samples for each attribute to produce acceptable accuracy for industry. Both single-stage and multi-stage deep learning-based techniques were also validated using real world test data without cropping and pixel wise prediction was obtained for each object. The accurate location of the predicted object was known in predictions and hence, bounding box problem was avoided. Through the incorporation of optimisation techniques, optimum parameters suitable for road safety attributes were determined. The optimum parameters proved to be effective in terms of classification accuracy and time to achieve minimum error.

The Australian Roadside Drug Testing series presents annual counts of roadside drug tests in Australia, proportion of positive results and where available, the number of annual road deaths involving an illegal drug.

This dataset, released February 2021, contains the potential years of life for all death by various causes over the years 2014 to 2018. Potential years of life lost represents the sum of the actual age at death and 75 years of age. Causes include cancer, diabetes, circulatory system diseases, respiratory system diseases, and external causes(road traffic injuries, suicide and self-inflicted injuries)

The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP) for Australia, 30 June 2014 to 30 June 2018.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released February 2021, contains the statistics of premature mortality by various causes for people below 75 years, over the years 2014 to 2018. Causes for death include cancer (colorectal, lung, breast), diabetes, circulatory system diseases (ischaemic heart disease, cerebrovascular disease), respiratory system diseases (chronic obstructive pulmonary disease), and external causes (road traffic injuries, suicide and self-inflicted injuries) The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP) for Australia, 30 June 2014 to 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.