Streamlining and integrating incident data
Transport for NSW (Transport) is partnering with the University of Technology Sydney to explore the use of advanced AI methods in streamlining incident data to support better decision making.
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This project is being undertaken through the iMOVE CRC.
Background
Road incident data is manually recorded and updated in various systems for state roadways, motorways, and local roads by stakeholders such as the police, area operators, private motorway operators, and traffic controllers. These records typically include key information such as incident start and end times, descriptions, event type and subtype, lane details, and location data.
Because this data is received from multiple sources, incident reporting can be duplicative or unnecessarily complicated. Incidents can be reported twice, data is not always standardised, and this inconsistency can hinder decision making.
Objectives
This research project aims to leverage advanced AI methods, such as Large Language Models and Fuzzy Machine Learning to streamline and integrate incident reporting, resolve data conflicts and improve sources of information. The research will help to improve the reliability and consistency of incident recording and provide accurate statistics and better insights for enhanced decision making.
Further updates will be provided as this project progresses.
This research is being delivered in partnership with iMOVE CRC and supported by the Cooperative Research Centres program, an Australian Government initiative.