Publications

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…

This report documents a project designed to update the current Austroads road deterioration (RD) models using the long-term pavement performance and long-term pavement performance maintenance (LTPP/LTPPM) dataset and other data available, such as…

This report documents a project designed to update the current Austroads road deterioration (RD) models using the long-term pavement performance and long-term pavement performance maintenance (LTPP/LTPPM) dataset and other data available, such as…

This report documents a project designed to update the current Austroads road deterioration (RD) models using the long-term pavement performance and long-term pavement performance maintenance (LTPP/LTPPM) dataset and other data available, such as…

This report documents a project designed to update the current Austroads road deterioration (RD) models using the long-term pavement performance and long-term pavement performance maintenance (LTPP/LTPPM) dataset and other data available, such as…

This report explores the use of Artificial Intelligence (AI) and Machine-Learning (ML) in pavement asset management and details a methodology for developing new use-cases. While there are some existing uses of AI and ML technology in pavement asset…

Road agencies increasingly recognise the value of coordination and cooperation with public transport operators in responding to road and transport incidents, as it helps support a more holistic and integrated multimodal approach to minimising…

This report details research into the ways transport agencies manage unplanned incidents that impact the normal operation of more than one transport mode. It includes 10 guiding principles and a capability framework. Road agencies need guidance on…

The Austroads Road Authority Data for Connected and Automated Vehicles (RADCAV) project studied the provision of agency-owned data to connected and automated vehicles (CAVs) addressing six high-priority data sets: roadworks, incidents, variable…