Publications

This report is an output of a project that incorporated both consultative and desktop research components to establish supportable national policy directions to inform the development of a national mobile driver licences (mDL) implementation roadmap.…

This document provides a hyperlinked list of the Austroads Test Methods and Specifications.

This method has been developed for extracting bituminous binders from bituminous mixtures i.e. asphalt field samples, loose plant mix or reclaimed asphalt pavement.

The test method sets out the procedure for the determination of viscosity of binders recovered from RAP source materials, as well as of virgin binders and rejuvenators.

Join us for a webinar that delves into the latest on connected and automated vehicles (CAVs) and the pivotal role that interaction with physical infrastructure plays in their integration into our transport system. In this session, we will present a…

Austroads extended vehicle classification scheme builds on the influential 1994 scheme. The extended scheme provides greater granularity across the 12 classes in the 1994 scheme. It also provides a new classes for: active transport to address the…

In this webinar, Chris Koniditsiotis, Lead Implementation Consultant and Patsy Thomas, National Temporary Traffic Management (TTM) Operations Manager provide an update on the implementation and operational commencement of the Austroads National…

This report presents a set of reference architecture models, content and guidance for use by road agencies to identify, plan and improve their Multimodal Incident Management (MMIM) capabilities. MMIM is defined as the resolution of an unplanned…

This is the second webinar in a two-webinar series. The session describes two case studies in the use of machine learning (ML) and artificial intelligence (AI) to create decision-support tools for pavement asset management. In the first study, we…

Austroads has published practical guidance to help expand the use of artificial intelligence (AI) and machine learning (ML) in pavement asset management. AI/ML projects are notoriously prone to failure, including late or incomplete delivery, or even…