Fare collection gates play a vital role in keeping public transport flowing smoothly, handling millions of passenger journeys every day. To maintain this performance, transit operators rely on timely repairs and efficient maintenance.
This white paper introduces a new AI-powered approach to predictive maintenance. Developed in partnership with the AIDA Lab at Imperial College London, the system uses real-world data to help transit operators identify replacement parts faster, before engineers even arrive on site.
Key Highlights:
- AI-Powered Maintenance: This approach uses a large language model (LLM) trained efficiently on real maintenance logs to predict which parts are needed for replacement, helping transit teams reduce delays and improve service reliability.
- Smarter Repairs: By learning from over 1 million incident reports, the system can help transit teams act faster and more accurately, cutting unnecessary site visits by up to 70%.
- Built for Transit: Designed to work with both human-written and machine-generated reports, the model is ready to support diverse transit networks and existing workflows.
