Project Focus Area

Intelligent Transport Systems & AI-Driven Mobility Analytics

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Realtime model to estimate delays at traffic signals

This project will develop a pilot model that utilises secondary datasets (e.g. signal timing data) within Main Roads Western Australia to estimate overall delay at intersections in real-time.
Real-time information, especially delay time at intersections, is valuable for traffic operations but is not readily available and costly to procure. Existing data sources that Main Roads has access to do not currently provide this information at a useful level of accuracy.

Such a model would allow Main Roads to determine the delay at a network, intersection, or at an approach level, while not requiring any additional sensor equipment or expensive data licensing agreements. It would inform decisions relating to network operational strategies and road project development.

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Roundabout safety review using drone video analytics

This project leverages drone video analytics data collected at over 50 roundabouts in Perth to conduct comprehensive safety analyses. Building on existing footage from the previous iMOVE project, the research will focus on analysing vehicle trajectories, speeds, and interactions with vulnerable road users.

It also aims to improve vehicle detection algorithms, implement safety surrogate measures, and develop evidence-based assessment tools for roundabout safety. By examining real-world driver behaviours and reactions to geometric design features, this work will help create more effective, proactive safety measures rather than relying solely on accident data.

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Smart bridge health monitoring and maintenance prediction

This project aims to investigate the feasibility of using an integrated package of IoT, computer vision, and machine learning technologies to support smart bridge health monitoring and prediction.
Integrated IoT, computer vision, and machine learning technologies offer a promising supplement to physical bridge health assessment particularly in remote regional contexts which can be costly, time consuming and unsafe to inspect. Conducting regular, efficient, and reliable bridge health monitoring is essential for the long-term protection of valuable road assets through timely maintenance responses.

The research from this project will produce a proof-of-concept to demonstrate the efficacy and feasibility of an integrated package of technologies for first-level bridge health screening and early warning system, reducing the need for traditional physical inspections and instrumentation.

The benefits of the project include contributing to reducing maintenance, operation costs and risk, and achieving a safe transport infrastructure network, ultimately, increasing productivity.

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Transport predictive solution Stage 2: AI and real-time simulation

This project aims to offer a real-time decision support tool for traffic operations centres to predict traffic congestion on the network, quickly assess the impact of unplanned events and evaluate the mitigation potential of several possible responses.
Such a solution will help reduce congestion, especially in non-recurrent situations, and significantly increase travel time reliability.

The use of tools to facilitate longer-term prediction of how transportation networks will perform in the future is a well-established practice in strategic planning by transport authorities. Tools to support day-to-day operations, relying on short-term predictions, are in their infancy, especially in Australia.

Particular objectives to enhance short-term prediction performance are:

  1. Smart sensing for enhanced travel demand estimation; and
  2. Artificial intelligence (AI) and machine learning (ML) for calibration against much larger real-time datasets

The WA node will focus on (2) developing and testing improved model calibration capability for both live and offline models, ensuring prediction accuracy for any hour of the day, seven days a week.

This research proposes to improve model calibration and the accuracy of 24 hour/ 7-day models (live and offline) for not just the AM and PM peaks but any hour of any day. The research results will be tested in a WA Aimsun Live network pilot model, developed as part of the more comprehensive project. Further evaluation and performance accessibility of tools developed in this research will be performed in QLD Aimsun Live network model.

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Using a data-driven approach to improve intersection modelling

Accurate traffic models are essential to test the effectiveness of road and infrastructure designs. In the absence of site-specific data, traffic modellers often use default parameters or apply rules of thumb. As a result, model predictions often deviate from reality and subsequent costly project reworks are needed.
This PhD project investigates the use of big data and advanced mathematical techniques to better model the traffic flow at intersections. Based on high-quality trajectory data extracted with modern video content analytic techniques, it aims to improve parameters estimation for existing commercial modelling packages and to develop a novel data-driven model.

It also looks to obtain deeper insights about the complex traffic dynamics at intersections through a comparison study between the different models.

Find more information on iMOVE website: