Transport Technologies at the University of Melbourne is a leading interdisciplinary research and education group serving industry, government, and the public. We are based at the Melbourne School of Engineering. The Transport Engineering program offered by the University is focused on contemporary topics in transportation engineering including connected vehicle and roadways, autonomous vehicles, connected public transport and city logistics, connected travellers and smart stations.
It is recognised that the next important opportunity and challenge will present itself through the availability of live data, low-priced technology and connectivity of transport system to travellers. In the Transport Engineering program we have aligned our research focus and efforts to take advantage of this new leap in mobility as an opportunity to change the way we travel, create sustainable transport, and work toward more liveable cities. The research being done within the University of Melbourne focuses on these areas.
Crowd dynamics modelling and simulations
Pedestrian crowd safety is an important matter as there have been numerous incidents in which crowd panic has resulted in severe injuries and death.
This research focuses on crowd dynamics modelling, experiments and simulation during extreme emergency and panic. This work will enable the development of a modelling tools which can be used in the planning, design, and management of major gatherings in public places. These tools can also be used in a wide range of applications such as designing of major infrastructure, assisting disaster relief agencies, and policing wherever crowd movement is a central concern. In our study of crowd dynamics under panic we blend empirical data collected from human crowds under normal walking conditions and group behaviour of live biological organisms (ants, woodlice, and mice) experimentation under panic conditions.
Multimodal large transport network modelling and optimisation
This research focuses on multimodal transport network modelling, transport network optimisation, and land use and transport interaction modelling.
The utilisation of live data, sensor network, crowd sourcing and technology are the main foundation of this research. We use complex network theory, machine learning, big data analysis and sophisticated mathematical modelling to analyse large multimodal transport networks from operation to management and planning. As part of this research theme we also study road transport network resiliency and vulnerability. The outcome would be tools for determining the most cost efficient schedule of preventative strengthening works for road networks for reducing the disruption and recovery costs after extreme events.
Autonomous vehicle navigation
Autonomous vehicles are fast becoming a reality. The pace of developments in the industry is unprecedented.
The unique characteristics of the AV technologies provide opportunities to push for a more efficient transport system that cater to society as a whole. To this end, we are working on a new navigation model based on connected vehicles pursuing a much more sophisticated travel plan. The outcome of this research can have a wide application in fleet management and freight transport.
The Australian Integrated Multimodal EcoSystem (AIMES) is a new initiative led by the University of Melbourne in conjunction with around 40 businesses, research bodies and government partners.
AIMES is a proposed transport test bed area that covers six square kilometres, comprising 25 kilometres of roadways in Melbourne’s inner-north. Its boundaries are Alexandra Parade in the north, Victoria Street to the south, Hoddle Street to the east and Lygon Street on the western side. The idea behind AIMES is to introduce a network of sensors to the test bed area that will be used to study, test and deploy a variety of innovative connected transport technologies in a real-world setting. This includes vehicle-to-vehicle and vehicle-to-infrastructure communication systems, which can be deployed and tested to ascertain their effectiveness in vehicle fleets and infrastructure/assets. The accumulation of this research data is directed at providing real-time operational management opportunities for the entire multimodal transport system – bikes and pedestrians included.