Actively and efficiently managing a transport 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.
Such improvements are being developed within a separate project agreement while this particular project focuses on deploying an Aimsun Live operational pilot for Queensland to serve as a base for these R&D activities and ensure they are applicable to real-life deployment.
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. While there is significant real-time data available to support operations, much of this relies on human operator interpretation and knowledge to support operational outcomes.
With the advancement of technology particularly for real-time data exchange and processing, 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 decision support through short term simulation prediction. This allows transport operators to gain critical insights into the real-time and likely future conditions of the network, making informed decisions, and manage the transport network pro-actively to ensure better travel experience to the public.
In comparison to the conventional offline models used for planning, which are typically calibrated against few snapshots taken from typical days and limited to peak periods, a solution like Aimsun Live requires to be calibrated against much larger dataset to handle 365 days 24 hours high-quality prediction. In order to achieve accurate predictions, the model is required to capture an accurate picture of the supply side (e.g. the infrastructure in place) and the demand side (e.g. the users who would like to travel on a specific time of a day in a year).
However, it is a well-known issue that the day-to-day variation of demand is high and fluctuates throughout year. Dynamic OD demand matrix estimation is, hence, a paramount feature to re-base and re-calibrate the existing OD demand matrices, which are developed based on in-depth data analysis of historical data, to incorporate daily variation and reflect the real-time condition more accurately. Additionally, the more data sources included in the dynamic OD demand matrix estimation, the more accurate picture of the real-time OD trip distribution could be estimated.
In order to facilitate the above Dynamic OD matrix estimation study which will be completed as a separate R&D project, Aimsun has been engaged to install an Aimsun Live pilot system as the testbed for the study.
The program of works will be delivered in 2 stages and will form several project agreements (number to be determined):
It is planned to involve other jurisdictions into the program to provide a 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.
The objectives of the project are as follows:
This project will provide Transport and Main Roads access to an operational instance of the Aimsun Live pilot system for the purposes of better understanding the capabilities of the system (noting constraints due to the pilot nature), and the ability to derive operational benefit from the solution (both in operations and planning areas).