Science and Technology

Transport predictive solution Stage 1: Aimsun WA Live pilot

Aimsun Live

This project will see the installation of a Aimsun Live pilot system for Perth CBD, providing real-time simulation-based prediction for the next 60 minutes of traffic at 15-minute intervals.

Actively and efficiently managing a transportation network requires the capability to predict its status in the upcoming hours for a better situational awareness and have access to decision support capabilities.

This prediction relies on the diversity and quality of real-time data available to monitor the network as well as the use of the most advanced computation methods.

While current solutions already exist on the market, some fusing data driven and simulation-based approaches like Aimsun Live, improvements on prediction quality and computational speed are necessary.

Such improvements are being developed within a separate project agreement while this particular project focuses on deploying an Aimsun Live operational pilot for Western Australia to serve as a base for these R&D activities and make sure they are applicable to real life deployment.

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Participants

Project background

Conventionally, transport authorities across the world are responsible for both transport planning and operation for medium- to long-term horizon as well as managing the network as efficiently as possible in its day-to-day operation. Historically, due to technological limitations, there are only limited / no real-time data available to assist and support the operators in making informed decisions.

With the recent advancement of technology particularly for real-time data exchange, software such as Aimsun Live has continued to build on its expertise, and leverage its existing expertise in simulation-based prediction for medium- to long-term transport planning and network operation optimisation, to provide real-time data analysis and short-term simulation prediction. This allows transport operators to gain critical insights into the real-time conditions of the network, making informed decisions, and manage the transport network pro-actively to ensure a better driving experience to the general public.

In comparison to the conventional offline models used for planning, (which are typically calibrated to a typical day and limited to peak periods) a solution like Aimsun Live is required to be calibrated against much larger dataset to handle 365 days 24 hours high quality prediction.

As the difficulty in calibrating such a model increases, it is expected that Artificial Intelligence (AI) and Machine Learning (ML) techniques can assist human modelers by spotting disparity between predicted results and field observation and help identify parts of the model that require special attention. This solution would also suggest possible changes to the simulation and analytical prediction model parameters. It aims at assisting rather than replacing human modellers.

In order to facilitate the above AI and ML study (which will be completed as a separate R&D project), Aimsun has been engaged to install an Aimsun Live pilot system for Perth CBD as the testbed for the study.

The program of works will be delivered in two stages and will form several project agreements (number to be determined):

  • Stage 1 is the Aimsun Live pilot project (this project, 1-024) between Aimsun and Main Roads Western Australia
  • Stage 2 is the Research and Development project (upcoming, project 1-025)

It is planned to involve other jurisdictions into the program to provide more robust and verified research into the product in a multi-state environment. Each jurisdiction will have a stage 1 pilot project when applicable and ideally, all jurisdictions will form part of stage 2 including the associated local university for each R&D topic.

Project objectives

Provide and install an Aimsun Live pilot system for Perth CBD. The system will provide real-time simulation-based prediction for the next 60 minutes of traffic at 15-minute intervals. The simulation-based prediction will be updated at every 5 minutes frequency.

The quality of the prediction will be assessed at the end of each hour post prediction and will be displayed by the Quality Manager in the quality control module in Aimsun Live.

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