Project Focus Area

Intelligent Transport Systems & AI-Driven Mobility Analytics

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A Smart Transport Technology Roadmap for Perth

This project aims to identify promising technologies that can best address key transport and mobility challenges in Perth, Western Australia (WA) and outline a Smart Transport Technology Roadmap for the next three to five years.

Reduced traffic throughput, higher crash rates, reduced public transport reliability, reduced walkability and increased fuel consumption and emissions are features of WA’s increasingly congested roads. The timely development and implementation of technology solutions (or Intelligent Transport Systems, ITS) to enable a safe, efficient and seamless transport system is essential to supporting the State’s future productivity and liveability.

For several years, RAC has been calling on the Federal and State Governments to commit funding for the planning and deployment of smart transport technologies to improve safety, efficiency and reliability for all road users.

This project will support RAC’s social impact activities by recommending the most beneficial Roadmap option for Perth through Strategic Analysis (Stage 1), and Options Identification (Stage 2).

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Developing a low-powered edge camera system for pedestrian and cyclist surveys

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.

Enhanced vehicle detection at traffic signals and on smart freeways

The project will investigate alternative vehicle detection technologies for traffic signal control and smart freeway operations through a comparative desktop analysis and field trials of shortlisted technologies at two locations (intersection and freeway). This research will be used to inform decisions on future traffic network investments, primarily through the future enhanced detection installation business case and delivery strategy, particularly for future smart freeway projects.

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Improving roundabout modelling using drone video analytics

This project proposes the development of evidence-based parameter estimation methods to improve Main Roads Western Australia’s roundabout modelling practice and operational guidelines by accounting for various local conditions such as geometry, topography, location type (residential, industrial, rural etc.), traffic mix, and seasonality, as well as driving behaviour. The data will be used to develop dedicated roundabout models for Aimsun at micro-, meso- and macroscopic levels.

Models play a vital role in supporting decision-making at both strategic and operational levels in the transport industry. In this project, we focus on roundabouts, where significant delays on arterial roads occur. Designers rely on traffic models to test design performance, so the quality of model predications directly affects the quality of roundabout design. Data is the foundation of modelling but conventional manual traffic surveys are deficient in both quality and quantity.

Although a wide range of sophisticated software tools for traffic modelling have been developed over the years, the lack of abundant high-quality data hinders model calibration, validation, and continuous development to account for changing driving behaviour and local conditions.

This project addresses both quality and quantity problems in traffic data by applying the latest drone video analytics technology developed by University of Western Australia (UWA) researchers to inform and improve roundabout modelling.

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Modelling perimeter controls: Detailed simulation

In our previous project (Improved network performance prediction through data-driven analytics and simulation), we have numerically simulated perimeter control (gating) based on macroscopic fundamental diagrams (MFDs). The results demonstrated the benefit of gating and how the Perth road network could be optimally divided into multiple zones for this purpose.
As the next step towards operationalising it in Perth, this proposed project aims to extend the work by more detailed simulation of traffic behaviour and gating strategies. It will produce better estimates of the potential benefits and effectiveness of the MFD-based controllers and enable Main Roads to make informed decisions. It will pave the way for an actual trial if the estimated benefits are significant.

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Optimising video analytics for traffic data collection and calibration incorporating fixed camera videos

Main Roads Western Australia has been working with the University of Western Australia (UWA) to develop video analytics (VA) software for processing and analysing drone videos to gather and auto-calibrate critical traffic data for network optimisation, such as vehicle counts and trajectories, delay, saturation flow, queue length, back-of-queue arrival rate, and gap acceptance. The evolving research has been supported by Main Roads through a series of projects.

This project will further develop the capability by integrating processing of videos recorded by fixed cameras, already in place and in use on the road network. Fixed cameras can complement drones in areas with flight restrictions or severe occlusions caused by the environment. They can also record videos with much longer duration. The main objectives are faster processing time, more robust algorithms to deal with occlusions, and more accurate data.

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