Main Roads WA

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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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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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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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: