To develop a vision-based, low powered, edge device for traffic survey purposes. Although there are already some commercial products for pedestrian detection, most need to be powered by the grid. Meanwhile, MetroCount’s customer feedback shows a potentially large market demand for an off-the-grid device for pedestrian counting. This gap is addressed by combining expertise in hardware (MetroCount) with the research team’s computer vision software development expertise.
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.
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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:
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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This project aims to update and expand the TRavel, Environment and Kids study (TREK) conducted in Perth in 2005. It will investigate school walkability, parent- and student-reported individual, social and environmental factors influencing school transport modes, and latent demand for walking and cycling to school.
Fewer Australian children walk and bike ride to school than ever before. Increasing the prevalence of active school transport is a public health priority and would result in numerous health, environmental, and economic benefits. In Perth, WA, the declining rate of active school transport has been identified as a problem requiring multiple government agency responses to reverse the decline.
Schools and neighbourhoods with the greatest need for connectivity improvements, safety treatments and programs to address parental concerns, will be identified, as well as any other insights for increasing the rates of walking, riding, and use of other micromobility options to travel to school.
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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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