Keoride Mobility-as-a-Service Project Committee
Keolis Downer, Data61, AECOM, GoGet, UNSW and Via
The Keoride team, led by Keolis Downer, won the Best Industry Led Partnership and the overall Smart City Project of the year. This is a great outcome for the high-achieving partnership between Keolis Downer, Data61, AECOM, GoGet, UNSW and Via.
Industrial & Primary Industries of the Year: Predictive Analytics for Water Pipe Maintenance
Data61, Western Water, UTS
As a leading research group in the current age of big data and data economy, we have developed advanced machine learning techniques on predictive analytics and successfully applied these cutting-edge research outcomes to various areas, e.g., finance, smart infrastructure and asset management. These techniques have well demonstrated their capability and effectiveness, especially in water asset analysis and management.
The collaborative efforts among Western Water, Data61 and UTS, led by Dr Fang Chen have aligned modern data-driven technology and used machine learning to predict the likelihood of failures for pipes in water networks for Western Water. The developed model is able to provide comprehensive outcomes at various spatio-temporal levels, i.e., from short term to long term, from pipe level to suburb level. Specifically, the developed failure prediction model involves specialized algorithms to handle the large amount of numerical calculations and data for prediction. The model is able to identify long-term pipe failure trends which is important as in reality, not all older pipes necessarily require replacement. The prediction performance significantly outperforms current state-of-the-art methods. The evaluation results demonstrate that more than 40% of failures can be predicted if the top 10% of the pipe length is inspected, and approximately 75% of failures can be predicted when the top 10 suburbs are inspected. Importantly for the asset manager, end-to-end failure prediction is automated. Although the research outcomes have demonstrated that the issues can be well addressed by applying machine learning techniques, it remains challenging to incorporate them into the daily practice of water utilities.
Water authorities in Australia will be the first users of the research. The model provides the most accurate prediction method in the world for urban water infrastructure pipe failure prediction. It has been validated worldwide through datasets from more than 20 global water utilities. When inspecting prioritised assets of the network, the developed model identifies more than double breakages than the existing technology. Our pipeline failure prediction models have been deployed in three Australian states: Victoria, New South wales and Queensland.
Advanced Data Analytics in Transport (ADAIT)
Data61 (CSIRO) and Transport for NSW
Data61 and Transport for NSW jointly developed a prototype AI engine for congestion management. The AI engine trains machine-learning models that complement traffic simulation for network prediction. The development is the first of its kind in Australia to make use of the power of deeplearning and big-data analytics to provide at realtime the foresight of transport network situations and the insight on the impact of major disruptions to multi-modal, end-to-end journeys in NSW.
AI is transforming the way of future transport. Under the NSW Premier’s Innovation Initiative, now an innovation pilot of the Intelligent Congestion Management Program, the AI engine complements the existing solutions by integrating machine learning with traffic simulation for short-term network prediction. The AI engine is generic and agnostic to transport networks. It ingests transport datasets for the training of special purpose predictive models. The models are used for predicting network demand and speed for the next 30-minutes and initiating action in 5-minutes. The AI engine forms a closed-loop with simulation models by continuously learning the causal relationship and calibrating the simulation model for improved modeling fidelity.
The engine is currently being refined and is scheduled for deployment to the Advanced Data Analytics in Transport (ADAIT) cloud platform by mid-2019. The first user is the NSW Traffic Management Centre (TMC). The use cases at TMC can be highlighted as: a) continuous improvement in predictive analytics by using the AI engine to build and train predictive models for changes in networks; b) real-time rolling prediction of the network demand and speed; c) autonomous network anomaly detection and verification; d) modeling of multi-modal journeys and the impact of major network disruptions.
Further expansion of the AI engine may include the feedback control to traffic signal systems, VMS and smart motorway systems.
Advanced Data Analytics in Transport (ADAIT)
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.