Targeted and timely tools for transport policy makers
Transport operators and policy makers need access to a broad range of targeted and timely information to plan, build and operate road transportation networks.
The ubiquitous data generated by road traffic is an under-utilised source of information for planners and operators, and an opportunity exists to extract new insights from existing transport data.
Smarter analysis of transport networks
We are developing and applying new machine learning techniques to create a range of tools enabling safer, quicker and smarter analysis of transportation networks.
The tools include an integrated traffic database created by fusing multiple private and public sector data sources together, large scale transport modelling platforms, and comprehensive machine learning libraries for traffic data analysis.
Some new possibilities include monitoring the entire traffic network, including suburban streets to provide quantitative measures of operational performance, quantifying and resolving the impact of traffic incidents, influencing the behaviour of travellers through real-time information, and being able to evaluate transport projects in terms of their impact on traffic demand and distribution.
Working with the public and private sectors
Alongside public and private sector organisations we are working to improve the planning, design, operation and maintenance of urban road networks for passenger and freight traffic.
Areas of our current focus are:
- the comprehensive evaluation of transport policy, project and pricing schemes for large-scale metropolitan areas
- the solution-oriented, practical, dynamic traffic pattern analysis & real-time prediction
- multi-modal data fusion, integrating social network feeds to highlight abnormal traffic patterns
We have developed a large scale transport modelling and analytic platform to assist with modeling and simulation in large scale urban networks. Our work in these areas has won many awards, including the Intelligent Transport Systems Australia National Research Awards in 2014 and 2015.