This PhD project proposes a methodology to estimate real-time queue lengths by using high-resolution detector data and signal timing data.
Overviews, progress reports, and general info for research and development projects carried out by iMOVE and its partners, in the areas of: Intelligent Transport Systems, Freight and Logistics, and Personal and Public Mobility.
This project will see the operation and validation of a developed decision support system in real-time on Calypso Mango supply chains in QLD and the NT.
A scoping study to identify opportunities to improve urban freight planning tertiary education in Australia, improving education outcomes for freight logistics.
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 aims to help parcel delivery companies thrive in a competitive environment with a strategic approach to last mile network, & fleet composition.
This PhD project explores ways for airlines to engage with customers, industry and more to incorporate new insights into their sustainability strategies.
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.
This project will provide a comprehensive review of currently available Australian anthropometric data and its applicability to the Australian Transport industry.
In this PhD project multiple trucks, each of which is equipped with drones, will via simulation deliver grocery purchases in parallel to customers.
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 PhD project will, at its conclusion, demonstrate how the roles and responsibilities of different stakeholders impact building a collaborative MaaS environment.
This PhD project explores cycle lane implementation from both a policymaker’s and user’s perspective, and flexible transport solutions for rural users.
This project will develop a framework/tool for forecasting future scenarios of urban freight in informing planning and regulation (transport and land use).