MaaS blueprint design for regional towns and rural hinterlands
Design of a blueprint for future MaaS initiatives in a rural/regional setting, drawing on an iMOVE trial, international evidence, and new data.
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.
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).
This project aims to improve travel demand calibration and accuracy of 24 hour/ 7 days network simulation models for any hour of any day.
The iMOVE project Australia’s Public Transport Disability Standards and CAVs project has been completed, and final reports are available for download.
Development/delivery of a low-cost IoT-based system for live tracking & condition monitoring of freight consignments across multiple carriers & transport modes.
This project will offer a real-time decision support tool for traffic operations centres to predict network congestion, and evaluate the possible responses.
This project explores new/emerging technologies offering a true frictionless ticketing experience across multiple modes for disabled public transport customers.