This project will develop data fusion and machine learning models to estimate service use for Melbourne trams and make real-time prediction of tram loads.
Use of AI and machine learning techniques in collecting AusRAP data has potential to reduce costs and increase the frequency and accuracy of its information.
This project will provide guidance on best practice for the procurement and safe use of incident response vehicles and truck-mounted attenuators.
The objective of this project is to establish a vehicle as a test bed to enable applied research by the ARRB FTS team into deployment of CAVs in Australia.
This study will develop a methodology for the recalibration of the South Australian strategic transport demand model using new data collection methods.
An exploratory study investigating the ability of new sources of passively-collected transport data as collection methods for strategic transport planning.
This project will see an Aimsun Live pilot system installed in Queensland, providing real-time simulation-based prediction, projecting 60 minutes ahead.
A new report from iMOVE, TMR, QUT, and RACQ, to investigate exactly what is needed for maps to aid in the safe introduction of CAVs on Australian roads.
Australian scientists have made a breakthrough in cooperative perception tests, showing that CAVs can now see hidden pedestrians, even through buildings.
This project will build on simulations of gating, and use MFDs to demonstrate its benefits in enabling better control of the Perth road network.
Project to build new knowledge and develop approaches to perpetuate and mainstream some of the more positive behaviours and outcomes of working from home.
A project examining the benefits/drawbacks of working from home, and the impacts of different arrangements on productivity, transport, health, & wellbeing.
The results of an eight-month study of the benefits of moving to more connected vehicle technology on Australian roads have been released.
Research project to develop empirical understanding of the skills gaps, shortages, and the necessary training needs in transport/mobility to inform policy.
The build of standardised analysis methodology to evaluate the safety benefits of C-ITS, guiding transport agencies in regard to supporting its development.
R&D of new algorithms to process raw data generated by the current RT4 radar, assigning an Austroads classification to all detected vehicles.