This project will identify the barriers to women entering & progressing within the transport sector workforce, & audit/evaluate government initiatives in this area.
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.
This PhD project will focus on smart adoption of MaaS with the purpose of providing vulnerable travellers with equitable mobility services.
Development/delivery of a low-cost IoT-based system for live tracking & condition monitoring of freight consignments across multiple carriers & transport modes.
This project will look at views and perceptions of seafarers’ challenges and training requirements as a critical driver of ITS in the autonomous shipping domain.
This project aims to update and expand the TRavel, Environment and Kids study (TREK) conducted in Perth in 2005.
This project will offer a real-time decision support tool for traffic operations centres to predict network congestion, and evaluate the possible responses.
This project explores new/emerging technologies offering a true frictionless ticketing experience across multiple modes for disabled public transport customers.
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.
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.
Find out about outcomes from Australian C-ITS projects run through iMOVE, presented by leaders and researchers on the projects.
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.