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.
Road Cross-Section Design for Road Stereotypes and a Safe System
Pedestrian Planning and Design for Activity Centres
Pedestrian Planning and Design at Intersections
Midblock Crossings for Pedestrians
Transitions Between Steel Beam and Concrete Barriers
Road Space Allocation for Pedestrians
Opportunities to Build Capacity in Traffic Management
Development of Design Procedures for Lightly Bound Cemented Materials in Flexible Pavements
Improved Guidance on Interrupted Traffic Flow Theory
Best-Practice Approaches to Road Freight and Communities
Outcomes from the Long-Term Monitoring of Austroads Sprayed Seal Trials
Implications of Pavement Markings for Machine Vision
A Holistic Investment Prioritisation Framework for Road Assets
Updated Roadside Design Guidance: Crash Risk Evaluation Procedure Background
Optimising Project Delivery Performance
Updated Roadside Design Guidance: Crash Risk Evaluation
Framework and Tools for Road Freight Access Decisions
Draft National Prequalification Scheme for Temporary Traffic Management
Draft National Training Framework for Temporary Traffic Management
Classifying, Measuring and Valuing the Benefits of Place on the Transport System
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