Fare evasion costs UK rail £240 million every year.
Short ticketing, where passengers pay for part of a journey instead of end to end, is one way rail companies are losing money.
This comprehensive report, produced in collaboration with the team from Imperial College London, analysed 6.5million journey records from 100 stations using a multi-expert AI framework to reveal how data can help tackle this issue head on.
Key Highlights:
- AI-Powered Detection: The framework applied four unsupervised machine learning algorithms to detect anomalous travel behaviours without relying on labelled data: Isolation Forest, Local Outlier Factor, One-Class SVM, and Mahalanobis Distance.
- Fraud Identification: The newly introduced AI classification system identified 30 high-risk stations and five distinct short ticketing patterns, including “Ghost Station” and “Black-Hole” behaviours.
- Operational Impact: The framework enables targeted deployment of revenue protection staff, replacing random inspections with data-driven interventions.
- Strategic Value: This research demonstrates how AI can automate short ticketing detection at scale, offering a replicable model for transit authorities globally to combat fare evasion more effectively.
