Australia transport data

Overview The Crash Information (Last 5 Years) dataset provides high-level information about reported crashes recorded in Western Australia during the previous five calendar years. Information includes crash location, date, time, severity and crash characteristics, together with information about the different road user and vehicle types involved. Crash data is recorded in the Integrated Road Information System (IRIS) and is provided for information purposes only. Refresh frequency The dataset is refreshed weekly. Intended use This dataset supports analysis of crash locations, trends, characteristics and outcomes. Refer to the data dictionary when interpreting fields, coded values and summary measures. Interpretation A crash may involve more than one unit type. Unit-type measures must not be added together to calculate the total number of crashes. Heavy trucks are also included within the truck category. Refer to the data dictionary for the definitions of each measure. Documentation The Main Roads Crash Data Dictionary describes the available attributes and provides guidance for understanding, interpreting and analysing the crash information, intersection summary and midblock summary datasets. Main Roads Crash Data Dictionary (PDF) https://mainroads.sharepoint.com/:b:/s/mr-opendata/IQDm4WzPtcQvS5q435Mc-LMlAXTqiR8kbkG3hNlSGl6Rc2Y?e=5UpZaG Related datasets Crash Summary for Midblocks (Last 5 Years) Crash Summary for Intersections (Last 5 Years) Data Governance Roles Data Domain Steward Road Safety Research and Evaluation Manager Data Custodian Asset and Geospatial Information Branch (agi@mainroads.wa.gov.au) Operational Data Steward Information Systems Administrator Licence Notice You are accessing this data pursuant to a Creative Commons Attribution Licence, which includes a disclaimer of warranties and limitation of liability. The data provided under the licence is subject to change. Pursuant to section 3 of the licence, the following notice is to be included when sharing the licensed material: The Commissioner of Main Roads is the creator and owner of the data and licensed material, which is accessed pursuant to a Creative Commons Attribution Licence, which has a disclaimer of warranties and limitation of liability. Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Overview The Crash Summary for Midblocks (Last 5 Years) dataset provides aggregated counts of midblock crashes recorded in Western Australia during the previous five calendar years. A midblock is the section of road between intersections. The dataset includes total crash counts, with summaries by crash severity and different road user and vehicle types involved. Crash data is recorded in the Integrated Road Information System (IRIS) and is provided for information purposes only. Refresh frequency The dataset is refreshed weekly. Intended use This dataset supports analysis and reporting of crashes on road sections between intersections. Refer to the data dictionary when interpreting fields, coded values and summary measures. Interpretation The Total Number of Crashes field represents the number of crashes at the location for the reporting period. Unit-type measures represent the units involved in those crashes and must not be added together to calculate the total number of crashes. Heavy trucks are also included within the truck category. Refer to the data dictionary for the definitions of each measure. Documentation The Main Roads Crash Data Dictionary describes the available attributes and provides guidance for understanding, interpreting and analysing the crash information, intersection summary and midblock summary datasets. Main Roads Crash Data Dictionary (PDF) https://mainroads.sharepoint.com/:b:/s/mr-opendata/IQDm4WzPtcQvS5q435Mc-LMlAXTqiR8kbkG3hNlSGl6Rc2Y?e=5UpZaG Related datasets Crash Information (Last 5 Years) Crash Summary for Intersections (Last 5 Years) Data Governance Roles Data Domain Steward Road Safety Research and Evaluation Manager Data Custodian Asset and Geospatial Information Branch (agi@mainroads.wa.gov.au) Operational Data Steward Information Systems Administrator Licence Notice You are accessing this data pursuant to a Creative Commons Attribution Licence, which includes a disclaimer of warranties and limitation of liability. The data provided under the licence is subject to change. Pursuant to section 3 of the licence, the following notice is to be included when sharing the licensed material: The Commissioner of Main Roads is the creator and owner of the data and licensed material, which is accessed pursuant to a Creative Commons Attribution Licence, which has a disclaimer of warranties and limitation of liability. https://creativecommons.org/licenses/by/4.0/

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Australia **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 1.8314 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.5836 million km (31.8659%) - Unpaved roads: 0.8024 million km (43.8114%) About 0.4455 million km (24.3227%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0086 million km (1.9402%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org