This project will leverage AI algorithms and machine learning to optimise the performance of city roads, predicting future traffic speeds and responding to them.
The development of the Freight and Servicing Last Mile Toolkit, the outcome of a project involving Transport for NSW, University of Melbourne, and iMOVE.
Project aim is to identify/analyse specific aspects of existing & emergent connected & other vehicle & traffic data, to improve existing network management.
Development of systems of classifying street networks & local terrain & delivery density, where a mix of delivery modes may improve on mono-modal delivery.
Victor Deville’s PhD project is Non-invasive vehicle classification solution using tracking radar. Read about the project, and his approach to the research.
Watch the video recording of our ‘C-ITS in Australia:’ webinar, plus an invitation to participate in future C-ITS projects.
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 webinar will look at progress made to date with C-ITS technologies in Australia, and discuss ideas about how we can build on this knowledge.
A downloadable progress report on our Evaluating loading dock capacity in new developments project.
The main aim of this project is to develop a set of effective policy responses for improving the efficiency of delivery operations in the Sydney metropolitan area.
The results of an eight-month study of the benefits of moving to more connected vehicle technology on Australian roads have been released.
R&D of new algorithms to process raw data generated by the current RT4 radar, assigning an Austroads classification to all detected vehicles.
Watch the video of our Insights from COVID-era traffic data webinar Q&A, featuring speakers from Intelematics, TomTom, and the University of Melbourne.
The technology on iMOVE’s project on the AIMES transport test bed is about to have an AI-enabled video system to make its intersections safer.
A PhD research project aiming to build a model/platform using real-time feeds of all mode movements from smart intersections to optimise signal control.
Development of an improved and reliable methodology for forecasting demand to establishing loading dock requirements in new developments.