This PhD project involves integration of multiple data sources for estimating freight origin-destination (OD) activities using video and traffic counts.
This PhD project investigates the use of big data and advanced mathematical techniques to better model the traffic flow at intersections.
This project will analyse potential use cases of aerial drone use at the warehouse/delivery interface, and their potential in the 4PL freight context.
This PhD project will develop an end-to-end food shipping quality monitoring and assurance system based on a wide area IoT network architecture.
This PhD project will design, implement, & evaluate a novel distributed publish-process-subscribe framework for supply chains using blockchain technology.
This project will investigate AV/pedestrian interaction and AV/cyclist interaction to help introduce AVs to Australian roads safely and effectively.
This PhD research project aims to assist public transport service providers improve quality, operational efficiency, and farebox recovery ratios.
This project will develop new highly efficient cybersecurity schemes to reverse engineer vehicular network communications.
This project will examine if and how CAV modes and services can be incorporated into the Disability Standards for Accessible Public Transport 2002.
Does the formation of traffic congestion resemble the spread of a virus? This PhD project with the University of NSW is investigating.
PhD research, developing a new algorithm for integrated traffic network control, with ramp metering, variable speed limit and arterial intersection control.
R&D of new algorithms to process raw data generated by the current RT4 radar, assigning an Austroads classification to all detected vehicles.
This PhD looks at prediction of traffic disruptions in cities using AI and synergising traffic simulation modelling to help traffic authorities respond.
This project investigates a novel method of using a long tether and a medium fixed-wing drone to deliver multiple packages at various weights.
This PhD project aims to explore how berry supply chain decision-makers may use real-time data and make in-transit interventions to preserve quality.
Development of simulation/optimisation framework to generate the trade-offs derived from the interaction of the optimal number and location of loading bays.