PATREC Research

2022 – 2023

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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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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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Defining transport disadvantage in Perth

The provision of transport infrastructure and services plays a critical role in connecting communities to essential services, as well as to employment and social activities. A lack of access to transport can lead to disadvantage in many forms and can be influenced by many variables.
To better understand transport disadvantage in Greater Perth this project will involve a literature review and stakeholder interviews to identify and apply locally relevant indicators to guide the estimation of the extent, spatial distribution, and nature of transport disadvantage in the Greater Perth region.

Drawing on the findings, an overview of how transport disadvantage is affecting travel decisions will be provided. Recomendations for further action by all levels of government and other key service providers will be developed, with the aim of building upon existing approaches to address areas of need.

The recommendations will identify the potential for new and research-informed initiatives that builds upon existing approaches and local experience contributing to addressing the needs of the beneficiaries (i.e. transport users, governments and community).

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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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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.

Find more information on iMOVE website: