Austroads: Transport Research and Trends
Government
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 explore whether ML can learn to reproduce expert treatment decisions and automatically identify candidate projects, using historic condition, treatment and inventory data.
In the second study, we explore extensions to conventional Pavement Management System (PMS) optimisation tools which provide insight into the network-wide implications of various multi-criteria funding allocation scenarios, and the levels of service that can potentially be realised.
The webinar is presented by Tim Cross and David Rawlinson.
National View on Regional and Remote Road Safety
Dangerous Goods in Tunnels
Opportunities in Mobility as a Service (MaaS)
Guide to Road Tunnels Part 4: Retrofitting Tunnels
Guide to Pavement Technology Part 5: Pavement Evaluation and Treatment Design
Development of a Sprayed Seal Binder Cracking Test
General Conditions of Contract for Construction - National Capital Works 4 (NCW4)
Opportunities in Artificial Intelligence Applied to Road Network Operations
Relationships Between Cutter Oil Properties and Sprayed Seal Performance
Austroads Guide to Asset Management
Lean Construction: Responding to the Productivity Challenge Faced by the Construction Industry
Updated Guidance for the Design of New Pavement for Road Tunnels
Guide to Pavement Technology Part 4D: Stabilised Materials
A National Approach to Measuring Non-fatal Crash Outcomes
Road Risk Assessment, Case Studies and Engagement Guidance for Speed Management
Guide to Road Safety Part 6: Managing Road Safety Audits
Developing the Data to Support Heavy Vehicle Road Reforms
Long-term Pavement Performance Study
Passing Lanes: Safety and Performance
Improving Driver Licensing Programs for Indigenous Road Users and Transitioning Learnings to Other User Groups
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