5G aid in automated mobility for elderly and people with disability
This research program will explore opportunities that 5G offers to improve performance of CAV shuttles and accessibility for elderly and people with disability.
This research program will explore opportunities that 5G offers to improve performance of CAV shuttles and accessibility for elderly and people with disability.
This project’s evidence-based approach will allow Main Roads Western Australia’s improvement of roundabout modelling practice and guidelines.
This project will develop a deeper understanding of the transport workforce implications due to digitalisation and automation in the Australian market.
This project will provide comprehensive driving data and analysis tools for the design and testing of an autonomous vehicles software stack.
Design of a blueprint for future MaaS initiatives in a rural/regional setting, drawing on an iMOVE trial, international evidence, and new data.
A study of the attraction/retention of businesses/households to regional cities, & the long-term impacts of COVID on spatial patterns of employment/settlement.
This R&D project will research existing Australian freight and supply chain geospatial initiatives for a SWOT analysis of national freight mapping.
Download the final report from iMOVE’s ‘Innovative local transport: Community transport of the future’ project.
This project will investigate likely contributing factors for traffic crashes involving traffic signal posts in Queensland, and strategies to mitigate these collisions.
Our ‘Encouraging continuation of work from home post-pandemic’ project has been completed, and the final report is available here.
This research will extend over a three-year period and develop a strategic digital plan to improve the parking experience in Brisbane.
This PhD project will develop traffic management strategies and infrastructure allocation algorithms needed to improve emergency vehicle logistics.
This project will create a model for estimating delays at Perth’s traffic signals, which would inform project decisions and operational strategies.
Using state-of-the-art machine learning algorithms, this study will use a novel modelling approach to accurately predict traffic crashes in real-time.
This project investigates the use of an integrated package of IoT, computer vision and machine learning to support smart bridge health monitoring and prediction.
This project will develop a framework/tool for forecasting future scenarios of urban freight in informing planning and regulation (transport and land use).