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Transport for NSW Uses AI to Tackle Congestion

Transport for NSW is conducting a new trial, believed to be world-first, involving using artificial intelligence and edge computing technology to reduce congestion. The agency has partnered with a US-based networking hardware company to explore ways to use technology to improve the experience for commuters, travellers and public transport users.

The Minister for Transport and Roads of NSW stated that the partnership aims to investigate how a real-time view of vehicle supply and customer demand, and performance, can guide future network decisions and monitor road conditions to identify where repair work is needed.

As part of one trial, Transport for NSW is using AI, Wi-Fi and edge computing on Pitt St near Central Station to capture real-time data and identify high-risk events. Road user movements are also being tracked at several intersections in Newcastle, using intelligent sensors to help improve overall road safety.

Another trial involves connecting several buses, ferries and light rail vehicles and then using real-time data to help identify ways to improve the services. The Minister noted that buses fitted with this technology can also monitor asset and road conditions, and provide us with real-time information on vehicles.

The networking hardware company is providing several technologies for the trial, including IoT, edge computing, AI and other capabilities. Transport for NSW and the company have an existing partnership aimed at using technology to solve pressing and common transport problems.

According to an earlier OpenGov Asia article, Transport for NSW is hoping that aggregated data collected by a Dutch consumer electronics company and LiDAR systems might provide it with more timely insight into conditions and hazards on the state’s road network.

The agency, in collaboration with iMOVE Cooperative Research Centre (CRC), currently relies on videos taken by crews for safety assessments, from which certain road attributes are extracted. However, TfNSW wants to speed up the process, and has embarked on a project that will “convert raw data… into an international standard five-star rating system”. The project will deliver 20,000 km of road attributes in NSW using TomTom’s MN-R map data, as well as prove feature extraction techniques and machine learning for LiDAR data.

MN-R is the model that the consumer electronics company uses to keep its mapping data up-to-date. It combines several layers of data collection techniques, including from the use of its navigation systems and from sensors. In addition to understanding road conditions and hazards, TfNSW hopes the project could also lead to the development of predictive algorithms around injuries and fatalities in the future. The project will feed into a global ‘AiRAP’ initiative from a non-profit roads rating agency, the International Road Assessment Programme (iRAP).

TfNSW is also working with the University of Technology Sydney and geospatial data experts an NSW software company on the project. The local company has previously partnered with the consumer electronics company to extract more than 50 road assets and safety features such as road markings, safety barriers and trees from LiDAR data.

The IRAP global innovation manager, who is overseeing the project, said AI had the “potential to reduce costs and increase the frequency and accuracy of data”. She noted that making faster and more affordable data collection possible means that safety assessments can be done on an annual basis across the whole road network.

The project comes at a time when the federal government is planning to tie infrastructure funding to “measurable improvements in safety”, according to the draft national road safety strategy 2021-30. Canberra has previously set targets for 90% of national highways and 80% of state highways to meet a three-star or better safety standard.

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