Advanced Data Analytics in Transport (ADAIT)
Data61 (formerly NICTA)
This research provides a new approach to one of the central problems in urban transportation: reliable estimation of origin-destination traffic flows (that is, flows that specify where traffic comes from, and where traffic goes to). The research operates on two key insights. First, in most large-scale urban networks, most traffic is concentrated around a few dominant origins and destinations. This allows one to automatically select a parsimonious, interpretable set of origin-destination flows that explain the observed traffic on a network. Second, it is possible to automatically determine a finegrained zoning of the traffic network purely from observed traffic data. This allows one to zone the transport network in a way that facilitates tractable analyses, without undue sacrifice in accuracy.
According to Data61, the above insights are novel in the analysis of transportation networks. Further, the research conducted is the first to present a unified analysis of both the traffic zoning and origin-destination estimation problems. A range of experiments and analyses on a real-world traffic network have validated the efficacy of the proposed approach.
The likely first users of the research are transportation scientists and engineers interested in modelling and simulation of traffic flows in large-scale urban networks, as it allows them to reliably analyse real world traffic patterns with minimal manual intervention. The research has already proven useful in the modelling of a large motorway in Sydney with the proposed algorithm predicting traffic flows on the motorway to a very high (>99%) degree of accuracy. Efforts are currently underway within Data61 to integrate the research into the existing workflow of largescale simulation.
Machine Learning Research Group
NICTA (National ICT Australia)
Dr Fang Chen
The main focus of the Advanced Data Analytics in Transportation (ADAIT) Project is to apply modern machine learning techniques to transport science, specifically transport modelling. By utilising everincreasing volume of transport data in an advanced computing framework it will be possible to produce transport models that are more accurate and accountable than ever before. ADAIT team members: Fang Chen, Chen Cai, Hoang Nguyen, Weihong Wang, Aditya Menon, Tao Wen (Student), Tuo Mao (student).
The scientific underpinning is Machine Learning: a branch of computer science and mathematics that uses computer algorithms to discover recurrent patterns in data. By 'training' the algorithms on historical data these algorithms are able to rapidly identify 'learnt' patterns in previously unseen data. By combing (fusing) data from multiple sources in a machine learning framework it is possible to greatly improve the data inputs required for fundamental transport models, increasing their reliability and robustness enormously, and making them even more useful for designing and planning transport systems.
When used in a high-performance distributed computing environment the same techniques may be used to make real-time predictions thereby bridging the gap between planning and operations, a long sought-after goal in computational transportation science.
Transport authorities in Australia will be the first users of the research. The ideas embodied in this application have already been applied to assist NSW Roads and Maritime Services identify and address mobility issues that will arise in the Sydney CBD during the construction of the South East Light Rail Project. The machine learning techniques established event-to-impact causality from traffic data, and used it to predict the impact on traffic during the proposed construction.