Brisbane parking management: An integrated, strategic approach
This research will extend over a three-year period and develop a strategic digital plan to improve the parking experience in Brisbane.
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.
Cooperative perception is an emerging and promising technology for CAVs. Its further development has been the focus of a recently completed iMOVE project.
Read about the findings from our ‘Conceptual architecture for future transport and mobility environment’ project, and download the final report.
This project will identify and trial suitable systems for automatic real-time patronage counting for replacement bus services during Melbourne rail disruptions.
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.