Australian iMOVE project to study big data’s role in improving transport management


The Australian iMOVE Cooperative Research Center (CRC) consortium has launched a new project that will study and evaluate the use of multiple data sources for improved transport management and journey reliability.

At present, transport is managed using a suite of operating regimes based on technology, user behaviors, service offerings and management approaches, that have been around for a number of years. To get a better understanding of how the transport network is operating in real time, and identify the true impacts resulting from changes in operating regimes, an increase in both the detail and accuracy of current monitoring data is required. Emerging technology such as connectivity through the use of dedicated short-range communications (DSRC) will play a crucial role in delivering this greater depth of operational information.

The objective of the new iMOVE project is to understand how to use data to better monitor and hence provide better situational awareness for those tasked with managing the network in a connected environment. Using data available from monitoring the current condition of the transport network, combined with historic data it will be able to deliver improved predictive congestion modeling to better support operating regimes and network interventions, particularly during network disruptions.

The project seeks to make use of the data that is available within the Australian Integrated Multimodal Eco-System (AIMES) to develop this understanding and demonstrate that improved journey reliability can be delivered in a connected environment.

AIMES is a transport testbed area in the city of Melbourne, Victoria, incorporating over 60 miles (100km) of roads and motorway, which uses around 1,000 sensors to collect data on vehicle and pedestrian movement and public transport use. Using the connected infrastructure and architecture already established in AIMES, the new project will have access to a wealth of detailed data covering multiple modes of transport in a live real-world environment.

These datasets will be combined with other internal and external data sources from across the transport portfolio and assessed to identify their added value and application in delivering situational awareness and enabling the prediction of upcoming network events such as congestion. Analysis of the detailed data will help develop, implement and validate advanced algorithms that will support the early identification of the onset of congestion, and identify how best to mitigate its potential impact.

These updated/new management strategies, such as mode prioritization, changing traffic signal timings dynamically, and slowing the approach of vehicles to a junction to reduce pollution, will then be tested and evaluated in AIMES. With safety a key concern when changing operating regimes, all network user types will continuously be evaluated to ensure no detrimental impact on safety.

The project will also use the additional data from the planned introduction of connected vehicles within the AIMES testbed to understand their impact and how to further update current operating regimes to best accommodate the new technology in a safe and effective manner. The participants in the new project are: Cubic Transportation Systems (CTS), the University of Melbourne, VicRoads, Transport for Victoria, and the Transport Accident Commission.

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About Author


Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).