Using real-time train data to improve level crossing safety
A feasibility study, examining the use of real-time train info to inform pedestrians/road users when a train will be passing, and how long the gate will be closed.

A feasibility study, examining the use of real-time train info to inform pedestrians/road users when a train will be passing, and how long the gate will be closed.
An overview of Xiaojie Lin’s completed PhD project, “Strengthening cybersecurity in vehicular networks”, plus expected outcomes and future work.
This project will investigate/develop a data-driven assessment tool for arterial networks, using established/emerging technologies and advanced data analytics.
This project will contribute to improving road safety in WA, and state and national goals of reducing road trauma and enhancing safe mobility for all road users.
This project will allow assessment of C-ITS benefits, deployment, & considerations & provide recommendations that could support adoption by Australian road authorities.
An overview of the completed project ‘Improving worker safety on the M1 Motorway’, including a copy of the project’s final report.
This project will develop a centralised trip/parking generation database that can also facilitate the design, implementation, and validation of Green Travel Plans.
This project will develop a prototype Uncrewed Air System (UAS) for long-range delivery of packages in Australia.
This project will develop guidance materials for CAV and public transport developers/operators to ensure people with disability are considered in design/implementation.
The intent of this project is to better understand active travel demand – people’s choices to use active travel, trip purposes, and their route choices.
This project will test and improve the cost and policy settings of the National Variable Pricing Matrix for community transport providers to ensure sustainability.
Development and deployment of a proof of concept Real-Time Decision Support Tool for the Mitchell Smart Freeway (Southbound).
The overall objective of this project is to be able to provide more realistic and better travel modelling to inform and influence better policy and infrastructure decisions.
This project will use AI and Computational Fluid Dynamics to simulate, model, and analyse flight performance in microgravity conditions.
This project will leverage AI algorithms and machine learning to optimise the performance of city roads, predicting future traffic speeds and responding to them.
This project aims to develop a system to control driver / occupant emotions and mental & physical conditions to enhance road safety & in-vehicle experience.