It was recently reported that Transport for NSW (TfNSW) in Australia is using AI to develop predictive algorithms in order to help national, state, and local governments manage their road safety performance.
Indeed, TfNSW is aiming to create a faster and more automated solution to extract raw road data. The TfNSW is thus planning to meet the 2018-2020 National Road Safety Action Plan that requires 90% of travel on national highways and 80% on state highways to be three-star or better safety standards.
Previously, in order to assess the standards of roads, the company has to collect video survey footage and manual recording methods. Now, the accelerated and intelligent road assessment program data collection (AiRAP) project can deliver usable data for 20,000km of NSW roads using TomTom’s MN-R next-generation map data, as well as extraction techniques and machine learning for Lidar data.
By using artificial intelligence and machine learning to collect the data, it will be easier to reduce costs and increase the frequency and accuracy of data. Besides, this project will lead to the opening of existing and emerging data sources that could help with improving road safety assessments.
In order to make this possible. TfNSW has teamed up with iMove Cooperative Research Centre (CRC), the University of Technology Sydney, the International Road Assessment Programme (iRAP), and Anditi.