Cooperative perception is an emerging and promising technology for CAVs. Its further development has been the focus of a recently completed iMOVE project.
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 PhD project proposes an urban distribution model based on the combination of a logistics platform and a network of Mobile Depots that use light vehicles.
This PhD project looks to investigate how EV owners use and charge their vehicles, and more broadly, Australian consumers’ willingness to purchase an EV.
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.
The research project will provide evidence of the short to medium-term changes and benefits of the Active Travel Plan, as well as enable longer-term benefits.
This project will provide robust recommendations for suggested initiatives to influence travel behaviours and demand in a university environment.
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.
This PhD project involves integration of multiple data sources for estimating freight origin-destination (OD) activities using video and traffic counts.
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.
This research project will develop and trial end-to-end monitoring and traceability of freight, using medicinal cannabis as its use case.
This research will undertake a comprehensive systematic literature review of international best practice in digitisation in the transport & freight sectors.
A downloadable final report for our ‘Managing transport system investment risk’ project, along with the main findings of that report.
This PhD project investigates the use of big data and advanced mathematical techniques to better model the traffic flow at intersections.
The ODIN PASS MaaS trial will launch in mid-2021 at University of Queensland St Lucia for staff and students over a 12-month initial deployment period.
This project will analyse potential use cases of aerial drone use at the warehouse/delivery interface, and their potential in the 4PL freight context.