PATREC Research

iMOVE

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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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Working from home: Changes in transport demand in Perth

Focusing on Perth, WA, this project aims to ascertain the extent to which Working from Home (WFH) has been undertaken and will continue to be. Digging deeper, the project will look at aspects such as:

  • the productivity impact when WFH is compared to the workplace, from the perspectives of individuals, employers, and the economy at large
  • the proportion of reduced travel demand that is attributable to WFH
  • the utility of WFH as a future demand management tool for the mitigation of congestion on all transport networks
  • the potential for higher levels of WFH to enable expansions of the transport network to be deferred or avoided; and the facilitation steps that would be required if it became desirable to expand the level of WFH in the longer term

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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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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.

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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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