Annual average daily traffic volume on declared roads, 2019, Victoria
liveAnnual average daily traffic (AADT) volumes for 2019 from the Department of Transport and Planning, one row per road segment and direction of travel on the declared road network, with the road name, section description, council area, whether the volume is actual or estimated, and the daily volume of all vehicles and of heavy vehicles. The publisher's historical series runs from 2001, one file a year.
Also called: Victoria AADT 2019, Annual Average Daily Traffic Volume Victoria, Traffic counts on Victorian roads, VicRoads traffic volume data.
- What does each row hold?
- One road segment of the Victorian declared road network in one direction of travel, with the average number of vehicles and of heavy vehicles that used it each day in 2019, as a line in the GeoParquet, GeoJSON, GeoPackage and PMTiles files.
- Are the volumes counted or estimated?
- Both. The publisher marks each row Actual where the volume comes from a traffic count and Estimated where it was worked out by its own data processes. About one row in six is Actual.
- Why only 2019?
- The publisher's historical series has one file for each year from 2001 to 2019. This entry holds the 2019 file, the newest in the series. Local roads that councils manage are not in the network it covers.
- What is the percentage of heavy vehicles?
- The publisher's own share of the daily volume that is heavy vehicles, as a fraction, so 0.07 is 7 per cent. It is published as given.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 10 fields, count and filter across all 14,662 rows and diff its versions. Every answer names the version and carries Transport and Planning's attribution. No key.
Then ask it: What does Annual average daily traffic volume on declared roads, 2019, Victoria hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne 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.
https://publicdata.au/mcpStreamable 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.
A dashboard over every row. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=road_name&metric=max.aadt_all_vehicles
Vehicles per day (busiest segment): 118,458 WEST GATE FREEWAY, 92,800 MONASH FREEWAY, 91,382 WEST GATE OUT-WEST GATE OUT RAMP, 87,084 CITYLINK.
Download it as Excel, CSV, JSON and 9 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 never changes.
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/vic-traffic-volume-aadt-2019/latest/ redirects to the newest version.
https://publicdata.au/d/vic-traffic-volume-aadt-2019/latest/data.csvIn 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/vic-traffic-volume-aadt-2019/latest/data.csvIn 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("vic-traffic-volume-aadt-2019")
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("vic-traffic-volume-aadt-2019")
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/vic-traffic-volume-aadt-2019/v/2025-06-24/data.duckdb' AS vic_traffic_volume_aadt_2019 (READ_ONLY);
SELECT road_segment_id, count(*) FROM vic_traffic_volume_aadt_2019.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/vic-traffic-volume-aadt-2019/aggregate?group=road_name&metric=max.aadt_all_vehicles");
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 |
|---|---|---|---|
| road_segment_id | number | Road Segment ID | The publisher's segment identifier. A segment has one row per direction of travel. |
| road_name | string | Road Name | |
| road_section_description | string | Road Section Description | |
| travel_direction | string | Travel Direction | |
| local_government_area | string | Local Government Area | |
| calendar_year | number | Calendar Year | |
| calculation_methodology | string | Calculation Methodology | |
| aadt_all_vehicles | number | Average Annual Daily Traffic Volume | |
| aadt_heavy_vehicles | number | Average Annual Daily Heavy Vehicle Volume | |
| heavy_vehicle_share | number | Percentage of Heavy Vehicles | The publisher's share of heavy vehicles as a fraction of the daily volume. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json.
The first 10 rows
From the latest version, in the publisher's order, with every field. A blank cell is shown as null.
| road_segment_id | road_name | road_section_description | travel_direction | local_government_area | calendar_year | calculation_methodology | aadt_all_vehicles | aadt_heavy_vehicles | heavy_vehicle_share |
|---|---|---|---|---|---|---|---|---|---|
| 1024 | VINCENT STREET | CAMPBELL STREET NEBd btwn HIGH STREET & PYRENEES HIGHWAY | NORTH EAST BOUND | ARARAT | 2019 | Estimated | 3521 | 230 | 0.07 |
| 1027 | HIGH STREET | HIGH STREET NEBd btwn DERBY ROAD & MARYBOROUGH-MAJORCA ROAD | NORTH EAST BOUND | MARYBOROUGH | 2019 | Estimated | 2756 | 355 | 0.13 |
| 845 | PYRENEES HIGHWAY | LYONS STREET E Bd btwn PYRENEES HIGHWAY & HILLIERS STREET | EAST BOUND | NEWSTEAD | 2019 | Estimated | 1067 | 103 | 0.1 |
| 847 | TOORAK ROAD | TOORAK RD E BD BTWN MONASH FWY & TOORONGA RD | EAST BOUND | MALVERN | 2019 | Estimated | 19131 | 414 | 0.02 |
| 850 | BURWOOD HIGHWAY | BURWOOD HWY E BD BTWN ELGAR RD & STATION ST | EAST BOUND | BURWOOD | 2019 | Actual | 19191 | 1017 | 0.05 |
| 853 | BURWOOD HIGHWAY | BURWOOD HWY SE BD BTWN SCORESBY RD & FERNTREE GULLY RD | EAST BOUND | FERNTREE GULLY | 2019 | Actual | 13454 | 498 | 0.04 |
| 1231 | MONARO HIGHWAY | CANN VALLEY HIGHWAY S Bd btwn BENNETT STREET & PRINCES HIGHWAY EAST | SOUTH BOUND | CANN RIVER | 2019 | Estimated | 210 | 85 | 0.4 |
| 1235 | HAMILTON HIGHWAY | HAMILTON HIGHWAY E Bd btwn PETSCHELS LANE & UNNAMED | EAST BOUND | HAMILTON | 2019 | Estimated | 956 | 157 | 0.16 |
| 1043 | HAMILTON HIGHWAY | HAMILTON HIGHWAY SEBd btwn CONNEWARREN LANE & DUNLOP STREET | SOUTH EAST BOUND | MORTLAKE | 2019 | Estimated | 1076 | 214 | 0.2 |
| 1240 | HIGH STREET | HIGH STREET E Bd btwn BROWN STREET & WEST STREET | EAST BOUND | LISMORE | 2019 | Estimated | 1188 | 227 | 0.19 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/vic-traffic-volume-aadt-2019/latest/data.json
{
"road_segment_id": 1024.0,
"road_name": "VINCENT STREET",
"road_section_description": "CAMPBELL STREET NEBd btwn HIGH STREET & PYRENEES HIGHWAY",
"travel_direction": "NORTH EAST BOUND",
"local_government_area": "ARARAT",
"calendar_year": 2019.0,
"calculation_methodology": "Estimated",
"aadt_all_vehicles": 3521.0,
"aadt_heavy_vehicles": 230.0,
"heavy_vehicle_share": 0.07
}
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 with ilike. 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. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| 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. The publisher's row order when absent. |
| 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 loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. 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. 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, or give it to any client that connects to remote MCP servers. Each queryable dataset 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.
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.
- 2025-06-24 14,662 rows10 fieldsutf-8-sigaa310b3f6488
versions.json · changes.json · history.tar.zst (9.1 MB, every version's Parquet and manifest)
Questions
How do I download Annual average daily traffic volume on declared roads, 2019 as a CSV file?
Open https://publicdata.au/d/vic-traffic-volume-aadt-2019/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/vic-traffic-volume-aadt-2019/v/2025-06-24/data.csv today. The same path serves Excel, JSON, GeoJSON, Parquet, SQLite, DuckDB, GeoPackage, PMTiles, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Annual average daily traffic volume on declared roads, 2019 in Excel?
Yes. https://publicdata.au/d/vic-traffic-volume-aadt-2019/v/2025-06-24/data.xlsx is a workbook with the 14,662 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/vic-traffic-volume-aadt-2019/v/2025-06-24/data.csv.gz is the CSV at about a tenth of the size.
What years does Annual average daily traffic volume on declared roads, 2019 cover?
The current version covers the years since 2019. Each release from Transport and Planning becomes a new dated version here, and earlier versions stay online.
How often is Annual average daily traffic volume on declared roads, 2019 updated?
Transport and Planning no longer updates it. This site checks the portal every week and adds a dated version when the file changes.
Can I use Annual average daily traffic volume on declared roads, 2019 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 Annual average daily traffic volume on declared roads, 2019?
No. The publisher is Department of Transport and Planning, and its page is https://discover.data.vic.gov.au/dataset/historical-annual-average-daily-traffic-volume. This site republishes the publisher's file without changing its content. The original sits beside every version as source.geojson with its SHA-256, so the two can be compared.
What coordinate system does Annual average daily traffic volume on declared roads, 2019 use?
The publisher's GeoJSON names no datum, so it is read as WGS84 (EPSG:4326) as the GeoJSON standard sets, and moved to GDA2020 for publication. The GeoJSON file uses GDA2020 as well, which differs from the WGS84 GeoJSON expects by well under a metre.
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.geojson so the change can be checked.