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AI-Driven Fare Evasion Prediction for Smarter, Targeted Inspections
Fare evasion remains a significant challenge for transport authorities and operators, and inspection planning often relies more on intuition than insight.
In this session, Vy shares how it developed a machine-learning model that uses historical and live operational data to predict fare-evasion risk up to 30 days in advance and identify where inspections will have the greatest impact. The result is a practical decision-support tool that helps inspection teams deploy resources more effectively and improve overall network performance.
