Queensland’s transport system is made up of interacting elements such as motorways, arterial roads, tunnels, vehicles, and public transport services, which are monitored and managed to ensure the system works optimally, with minimal congestion and delays avoided where possible.
An area of interest for the Department of Transport and Main Roads (the department) is the transition from reactive to proactive network management, through the use of technologies such as big data and machine learning to support better transport operations. The department maintains a number of existing applications and business processes that have the potential to be impacted by such technology.
Through 2016/17, the Predictive Solutions trial delivered an operational implementation of the Aimsun Live solution. This trial aimed to assess the accuracy of Aimsun Live’s predictions and simulations, the potential limitations of existing technologies and data sources, and the implications for the department such as maintenance requirements and its likely value to operations.
The goal is to enable the department to better predict likely issues and determine the best strategies to prevent or mitigate issues on the transport network, instead of reacting when they occur.
The Aimsun Live trial is part of a broader program of works which focus on investigating opportunities to better use technology and data to get more out of the existing transport network. This includes opportunities relating to network optimisation, incident management, traveller information, and transport coordination.
The trial ran during weekday morning and afternoon peak-hour periods across a small area on the Gold Coast, and used live data inputs from the STREAMS ITS (Intelligent Transport System) platform, including the SIMS incident management system.
The study area was based on part of an existing model, and covered about 20% of the Gold Coast metropolitan area. The trial period ran from August 2016 to June 2017.
As this was a trial the solution was built and executed online, however there was no change to existing operational systems during this period, and no integration into operations. It was acknowledged that this presented limitations on the output of the solution, however this was assessed and recorded as part of the trial.
Despite limitations in the trial in terms of geographical area, time and technology, it provided significant insight into the opportunity presented by simulation based decision support tools, and the necessary business and system changes that would be required to achieve the best outcomes.
The lessons learnt from this trial will inform future phases, such as implementing a 24 hour monitoring/prediction system, and defining a perimeter area including all the observable perturbations of the response plans applied in the network.
The quantity and quality of data inputs had a large influence on the final results, and on the quality of the predictions and evaluations of the future traffic conditions. These data inputs include both network layout, performance, as well as incidents. For future phases, there would need to be greater focus on availability and accuracy of traffic data collection and definition (including detectors/stations, reliability of the data, quantity of data available), and on the incident feed used to generate events to model into the simulation network.
Despite the limitations presented to the trial, the insights gained were of great value, and are being used to inform further departmental projects.
Ronda Universitat 22 B
Barcelona 08007
Spain
t: +34 933 171 693
80 George St
Edinburgh EH2 3BU
UK
t: +44 7401 977 191
Waterhouse Square
138 Holborn
London EC1N 2ST
UK
t: +44 7401 977 191
t: 020 7193 7103
Paseo de la Castellana 77
Madrid 28046
Spain
t: +34 627 454 880
152 Elizabeth Street
Melbourne VIC 3000
Australia
t: +61 404 122 968
980 6th Avenue
2nd Floor
New York, NY 10018
USA
t: +1 917 267 8534
54 Rue de Clichy
Paris 75009
France
t: +33 (0) 1 86 95 41 52
36 Robinson Road, #02-01
Singapore 068877
t: +65 8186 5589
333 George Street
Level 13
Sydney, NSW 2000
Australia
t: +61 (2) 7208 7869
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.