Smart bridge health monitoring and maintenance prediction
This project investigates the use of an integrated package of IoT, computer vision and machine learning to support smart bridge health monitoring and prediction.
Advanced data analytics: Real-time demand calibration/prediction
This project aims to improve travel demand calibration and accuracy of 24 hour/ 7 days network simulation models for any hour of any day.
Empirical modelling of traffic states and route choice behaviour
In this PhD project data analytics on Bluetooth trajectories and traffic states will be applied to empirically estimate the assignment matrix for the network.
Advanced driver state monitoring system
This PhD project aims to develop a system with a data-driven human driver behaviour model that can help detect the attention level of drivers.
Transport predictive solution Stage 2: AI and real-time simulation
This project will offer a real-time decision support tool for traffic operations centres to predict network congestion, and evaluate the possible responses.
Co-operative eco-driving system for mixed traffic on urban roads
The objective of this PhD study is to develop an eco-driving system for a mixed traffic consisting of CAVs and human-driven vehicles (HVs) on urban roads.
Development and use of cooperative perception for CAVs
Cooperative perception is an emerging and promising technology for CAVs. Its further development has been the focus of a recently completed iMOVE project.
Replacement bus patronage counting and wait time measurement
This project will identify and trial suitable systems for automatic real-time patronage counting for replacement bus services during Melbourne rail disruptions.
Melbourne tram load estimation and real-time load prediction
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.
Accelerated and intelligent RAP data collection
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.
Incident response vehicles and truck-mounted attenuators
This project will provide guidance on best practice for the procurement and safe use of incident response vehicles and truck-mounted attenuators.
Australian CAV readiness: Integrating a data probe vehicle
The objective of this project is to establish a vehicle as a test bed to enable applied research by the ARRB FTS team into deployment of CAVs in Australia.
Recalibrating Adelaide’s strategic transport model
This study will develop a methodology for the recalibration of the South Australian strategic transport demand model using new data collection methods.
Big data for strategic transport planning
An exploratory study investigating the ability of new sources of passively-collected transport data as collection methods for strategic transport planning.
Real-time network prediction and response simulation
This project will see an Aimsun Live pilot system installed in Queensland, providing real-time simulation-based prediction, projecting 60 minutes ahead.
HD mapping Australia’s CAV future: Final report
A new report from iMOVE, TMR, QUT, and RACQ, to investigate exactly what is needed for maps to aid in the safe introduction of CAVs on Australian roads.