Data use for improved transport management and journey reliability
The application of emerging technology to transport, in particular connectivity between assets and people, can provide a more comprehensive understanding of how people, goods and vehicles are moving and interacting across the transport network.
Combining this data with additional sources of data in an existing operational environment provides the opportunity to evaluate in live operations how to transform the way the transport network is both used and managed to bring about safety, efficiency and environmental benefits for all users.
With the increasing volume and higher accuracy of information becoming more readily available from new technologies, it is critical that we develop greater insights into how best to release the value from this data. The project will evaluate how the application of this data can be used to better inform future strategic, development, investment and implementation decisions.
This project seeks to leverage 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. It will undertake the collection and analysis of the detailed data available to implement and validate advanced algorithms. These algorithms will support the early identification of the onset of congestion and identify how best to mitigate its potential impact. The project will evaluate the impact of improvements in data coverage and quality as a key part of delivering improved management capabilities for the future.
At present, transport is managed using a suite of operating regimes based on technology, user behaviours, 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 this iMOVE project is to understand how to utilise 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 modelling to better support operating regimes and network interventions, in particular during network disruption.
Leveraging the infrastructure and architecture already established in AIMES, the project will have access to a wealth of detailed data covering multiple modes of transport in a live environment. These data-sets 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). Advanced algorithms will be developed, along with a suite of operating regimes/network interventions, all with the aim of improving the management of the transport network.
These updated/new management strategies (for example mode prioritisation, 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 to where appropriate further refined. 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 AIMES to understand their impact and how to further update current operating regimes to best accommodate this technology in a safe and effective manner.