Optimising motorway control algorithms: New technologies/data
This project aims to perform a comprehensive diagnostic of TMR’s motorway control algorithms and opportunities for optimisation using emerging technologies.
This project aims to perform a comprehensive diagnostic of TMR’s motorway control algorithms and opportunities for optimisation using emerging technologies.
This project will complete an investigation of train horn effectiveness, & inform transport industry orgs and policymakers of future procedures and applications.
Demonstrating the use of historic crowdsourced data to create value in road management process by prototyping tools supporting road network management.
This project will identify functional safety risks of selected scenarios of the remote operation of Highly Automated Vehicles, and provide recommendations.
Attendees at the upcoming ITS Australia 2022 Summit event in Brisbane will be able to try Mobility as a Service, with the ODIN Pass.
Chintan Advani’s PhD topic is ‘Empirical modelling of traffic states and route choice behaviour’. This profile outlines his work, lessons learnt, and more.
This research will address key issues regarding perceptions of ADAS and AVs in older adults, the extent to which this tech can improve their safety and mobility.
Download the three final reports from iMOVE’s ‘Ipswich Connected Vehicle Pilot (ICVP) project.
The National Forum on Urban Freight Logistics Education will take place on 17 June, 2022, at QUT and online.
An overview of the research capability, staff, and iMOVE projects involving Queensland University of Technology’s CARRS-Q.
This project will investigate likely contributing factors for traffic crashes involving traffic signal posts in Queensland, and strategies to mitigate these collisions.
A scoping study to identify opportunities to improve urban freight planning tertiary education in Australia, improving education outcomes for freight logistics.
This research will extend over a three-year period and develop a strategic digital plan to improve the parking experience in Brisbane.
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 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.