Road casualties, Queensland
liveAggregate counts of people killed or injured in reported Queensland road crashes from 1 January 2001. Each row is one combination of year, police region, casualty severity, age group, gender and road user type, with the number of casualties.
Also called: Queensland road crash casualties, Queensland road injuries and deaths by year, TMR casualty data.
Part of Crash data from Queensland roads, 6 tables the publisher releases together: Road crash locations, Queensland, Driver demographics in road crashes, Queensland, Seatbelt and helmet use in road crash casualties, Queensland, Vehicle types in road crashes, Queensland, Factors in road crashes, Queensland.
- What does one row count?
- The number of people killed or injured in one combination of year, police region, severity, age group, gender and road user type. Add rows to get a total. A crash with three injured people appears as three casualties, not one.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 7 fields, count and filter across all 38,893 rows and diff its versions. Every answer names the version and carries Transport and Main Roads's attribution. No key.
Then ask it: What does Road casualties, Queensland hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcp One command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
Open https://chatgpt.com/plugins/plugin_asdk_app_6abbc4cec1a08191b61eaccfe2091100 and connect the publicdata.au plugin.
Listed in ChatGPT's plugin directory. publicdata.au asks for no key and no account.
https://publicdata.au/mcp Streamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month? For an assistant that reads the web and has no connector.
The dashboard above, live. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=casualty_road_user_type&metric=sum.casualty_count&casualty_severity=eq.Hospitalised
Casualties by road user where severity is Hospitalised: 89,130 Driver, 33,055 Passenger, 23,245 Motorcyclist, 9,663 Pedestrian.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version keeps its content.
One workbook with a records sheet of 38,893 typed rows, a fields sheet and a publicdata sheet with the provenance. Dates are real Excel dates. Opens in Excel, Numbers, LibreOffice and Sheets.
UTF-8 without a byte-order mark, RFC 4180 quoting, one header row using the normalised field names. Opens in Excel, Numbers and Sheets.
One object with a publicdata header (publisher, licence, attribution, version, source hash), a fields list and a records array of 38,893 objects with typed values. Blank cells are null.
Columnar and typed. The smallest download and the fastest to query. Reads directly in DuckDB, pandas, Polars, Arrow and Spark. The provenance header is in the file metadata.
One database with a records table, a fields table and a publicdata metadata table. Open it with any SQLite client.
One DuckDB database with a records table of typed columns, a fields table and a publicdata metadata table. It attaches read-only over HTTPS, so DuckDB, Python and R can query it without downloading it.
The first line is the provenance header, then one JSON object per line. For streaming and for tools that read line by line.
Arrow IPC file (Feather v2), zstd compressed, with the provenance in the schema metadata. Memory-maps in pyarrow, Polars, Arrow JS and R without parsing.
The same CSV, gzipped, at about a tenth of the size. curl, pandas, DuckDB and R read it directly.
One JSON file per value of crash_year, 25 files. The example is by/crash_year/2024.json. Every file carries the same header and a partition note.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/qld-road-casualties/latest/ redirects to the newest version.
https://publicdata.au/d/qld-road-casualties/latest/data.csv In Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/qld-road-casualties/latest/data.csv In Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("qld-road-casualties")
pd_attribution(df) install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("qld-road-casualties")
df.attrs["publicdata"]["attribution"] pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/qld-road-casualties/v/2026-04-24/data.duckdb' AS qld_road_casualties (READ_ONLY);
SELECT crash_year, count(*) FROM qld_road_casualties.records GROUP BY 1 ORDER BY 2 DESC; The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/qld-road-casualties/aggregate?group=casualty_road_user_type&metric=sum.casualty_count&casualty_severity=eq.Hospitalised");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution); The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| crash_year | integer | Crash_Year | |
| crash_police_region | string | Crash_Police_Region | Queensland Police Service region, or Unknown. |
| casualty_severity | string | Casualty_Severity | Fatality, Hospitalised, Medically treated or Minor injury. |
| casualty_age_group | string | Casualty_AgeGroup | Age band such as 17 to 24. |
| casualty_gender | string | Casualty_Gender | Female, Male or Unknown. |
| casualty_road_user_type | string | Casualty_RoadUserType | Driver, Passenger, Motorcyclist, Bicyclist, Pedestrian or Other. |
| casualty_count | integer | Casualty_Count | Number of casualties in this group. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is crash_year, crash_police_region, casualty_severity, casualty_age_group, casualty_gender, casualty_road_user_type.
A sample of 10 rows
From the latest version, newest first by year, then in the publisher's order, with every field. A blank cell is shown as null.
| crash_year | crash_police_region | casualty_severity | casualty_age_group | casualty_gender | casualty_road_user_type | casualty_count |
|---|---|---|---|---|---|---|
| 2025 | Southern | Hospitalised | 30 to 39 | Female | Driver | 31 |
| 2025 | Northern | Hospitalised | 30 to 39 | Female | Driver | 9 |
| 2025 | Brisbane | Medically treated | 17 to 24 | Female | Driver | 46 |
| 2025 | North Coast | Minor injury | 0 to 16 | Male | Other | 4 |
| 2025 | North Coast | Medically treated | 17 to 24 | Female | Driver | 29 |
| 2025 | Far Northern | Hospitalised | 17 to 24 | Female | Other | 1 |
| 2025 | Far Northern | Hospitalised | 30 to 39 | Male | Driver | 13 |
| 2025 | Brisbane | Hospitalised | 30 to 39 | Female | Driver | 36 |
| 2025 | North Coast | Medically treated | 40 to 49 | Female | Driver | 26 |
| 2025 | South Eastern | Medically treated | 30 to 39 | Male | Driver | 52 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/qld-road-casualties/latest/data.json
{
"crash_year": 2004,
"crash_police_region": "Brisbane",
"casualty_severity": "Minor injury",
"casualty_age_group": "40 to 49",
"casualty_gender": "Male",
"casualty_road_user_type": "Driver",
"casualty_count": 81
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive, except that like and ilike ignore case in the ASCII letters A to Z. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters, ignoring case in the ASCII letters A to Z. |
| ilike.*text* | The same as like. Both ignore case in ASCII letters only, so É and é differ. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. When absent, rows come in the order of the file they are read from. For the newest versions that is the version's own data.parquet, which keeps the publisher's order unless the dataset sorted it. Any other version is read from the Parquet file the answer names in file, and when that file is sorted the answer names its sort in order, which ties follow too. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer from that version alone. Every version with a table can be queried, and /api/v1/datasets/<slug>/versions lists them. A query on a version the database does not hold is read from its Parquet file, and one that would read too much of it is refused with DuckDB SQL that answers it from the file. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. Its row tools and the query API answer every version through one query path, so a tool's answer and the query URL it cites give the same rows. A query that would read too much of a file is refused with DuckDB SQL that answers it from the file. It also has search and fetch, the two tools ChatGPT's deep research and company knowledge call by name, which run search_datasets and read a dataset's record in the shapes those features expect. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, connect the publicdata.au plugin in ChatGPT, or give it to any client that connects to remote MCP servers. Each dataset with a table is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
Smaller files
The same rows split by a field, one JSON file each. Each file carries the same provenance header.
By crash_year (25 files)
by/crash_year/index.json lists every file with its row count.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal. The "as at" date is the publisher's own.
- 2026-04-24as at 30 June 2025 38,893 rows7 fieldsutf-8-sigbc9de78b77cc
Files rebuilt on 25 September 2026 so every one carries the operator and a ready-made cite string in its provenance header, and to add the Excel, Arrow, GeoPackage, gzipped CSV, SQL and CSVW files. Rows unchanged.
versions.json · changes.json · history.tar.zst (105 KB, every version's Parquet and manifest)
Questions
How do I download Road casualties as a CSV file?
Open https://publicdata.au/d/qld-road-casualties/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/qld-road-casualties/v/2026-04-24/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Road casualties in Excel?
Yes. https://publicdata.au/d/qld-road-casualties/v/2026-04-24/data.xlsx is a workbook with the 38,893 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/qld-road-casualties/v/2026-04-24/data.csv.gz is the CSV at about a tenth of the size.
What years does Road casualties cover?
The current version covers 2001 to 2025, as at 30 June 2025. Each release from Transport and Main Roads becomes a new dated version here, and earlier versions stay online.
How often is Road casualties updated?
Transport and Main Roads releases it about twice a year. This site checks the portal every week and adds a dated version when the file changes.
Can I use Road casualties commercially?
Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Road casualties?
No. The publisher is Department of Transport and Main Roads, and its page is https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads. National Digital converted the publisher's file to other formats. It renamed and typed the columns and kept only those the schema lists. National Digital made these changes, and the publisher is not responsible for them. The original sits beside every version as source.csv with its SHA-256, so the two can be compared.
How do I get only the rows for one crash year?
Every version has one JSON file per value of crash_year, 25 files in the current version, listed with row counts at https://publicdata.au/d/qld-road-casualties/v/2026-04-24/by/crash_year/index.json. For example https://publicdata.au/d/qld-road-casualties/v/2026-04-24/by/crash_year/2024.json holds the 1,756 rows where crash_year is 2024.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.csv so the change can be checked.
The publisher releases Crash data from Queensland roads as 6 files at once, so all 6 tables here share a version date.
The licence requires the publisher's attribution. We ask that you also name publicdata.au and link to the version you used.
Department of Transport and Main Roads, Queensland Government, Crash data from Queensland roads, sourced 24 September 2026, https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads, licensed under CC BY 4.0. Serialised and versioned by National Digital at publicdata.au, https://publicdata.au/d/qld-road-casualties/v/2026-04-24/
The attribution alone is inside every file. Harvard is the author-date form a report or a brief asks for. Paste the HTML on a web page so the links carry.
Department of Transport and Main Roads states the licence, CC BY 4.0, on its own page, which was read and recorded here on 25 September 2026.
Every fetch reads the licence again and stops when it has changed. The fetch that made version 2026-04-24 read it on 24 September 2026.
- Hugging Faceload_dataset("National-Digital/qld-road-casualties")
- Kagglewith a starter notebook
- ZenodoDOI 10.5281/zenodo.23076539
Each copy carries the same attribution and links back to this page.
- datapackage.jsonFrictionless data package pointing at the latest version
- schema.jsonTable Schema for every field
- schema.xlsxthe same field list as an Excel data dictionary
- versions.jsonevery version with date, rows, fields and hash
- changes.jsondiff for every consecutive pair
- manifest.jsonwhere the bytes came from and when
- Query APIfiltered rows and counts without a download
- openapi.jsonOpenAPI 3.1 for this dataset's query paths
- explore/charts and dashboards in the browser, shareable and embeddable
- index.mdthis page as Markdown
This page carries a schema.org Dataset record and is listed in /.well-known/ard.json and /catalog.json.
Department of Transport and Main Roads publishes this table on www.data.qld.gov.au. Questions about the content go to the publisher. Questions about the files here go to corrections.
Fetched 24 September 2026. Source SHA-256 bc9de78b77cc67ba…
This page is built from register/qld-road-casualties.yaml on GitHub. Anyone can propose a fix to it, such as a clearer description or a field the publisher renamed, as a pull request.
Report a file that differs from the publisher's · How to contribute