Connected vehicles: Data and behavioural analysis insights
This collaborative research project explores how connected vehicle (CV) data can inform insurance risk assessment while addressing customer privacy concerns.

This collaborative research project explores how connected vehicle (CV) data can inform insurance risk assessment while addressing customer privacy concerns.
Test to what extent C-ITS as a new perception layer of ADAS can increase the awareness of motorcycles among car drivers in common road scenarios.
This project will model key segments of Australia’s retail fuel market to assess impacts of fuel pricing schemes and regulatory options in a changing market.
A wrap-up of the completed Streamlining and integrating incident data project, in which an AI-powered solution was developed to manage information about road transport incidents in real time.
This project aims to investigate ADAS to improve driver behaviour and deliver safer roads, lower claims costs and reduce e-waste.
This project aims to develop a robust, data-driven framework to assess the performance of key suburban corridors in Brisbane.
This project will use advanced AI approaches that fuse incident records with contextual data to predict long-term incident hotspots.
This project will continue to develop and test a methodology to generate and evaluate strategies to improve the flow of buses in Brisbane.
Overview and final reports from our completed project, “Motorcyclist safety: Connected motorcycle pilot”. Background, recommendations, and more.
Overview and final reports from our completed project, “Environmental impacts of Connected and Automated Vehicles”. Background, key findings and more.
An overview of the completed “Mitchell Smart Freeway (Southbound): Live decision support” project, enabling MRWA to exhaustively test response plans.
Using sensors to detect unsafe following distances, the system aims to reduce tailgating by encouraging better behaviour through targeted messages.
Deployment and testing the utility of this new Local Positioning System (LPS) across multiple high-value transport use cases.
This project will use transport and logistics models to anticipate & design next-gen delivery models for emergency medical services across Australian capital cities.
Development of an interactive bussing tool to accurately visualise disruptions, quantify customer impacts, and recommend actionable and right size responses.
Use of AI-based deep learning analytics and high-resolution time-series satellite and aerial imagery to enhance current dwelling yield forecasting approaches.