Cracking the Code on Short Ticketing: Using AI to Tackle a Hidden Fare Evasion Challenge

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Our Mission: A Smarter, Data-Driven Solution

Have you ever wondered how some transport riders manage to beat the system without jumping a gate?

As part of a collaboration between Cubic Transportation Systems and researchers from Imperial’s Artificial Intelligence and Data Analytics (AIDA) Lab, we set out to investigate a subtle, yet costly practice called short ticketing.

Unlike passengers who skip payment altogether by jumping barriers, slithering under the gate, or even pushing through the paddles; short ticketing involves a subtler practice of purchasing a cheaper, shorter distance ticket, but secretly travelling farther than allowed.

For example, a rider on a bus taps off earlier than their intended destination but stays on the bus anyway effectively avoiding their full fare. Think of it as booking half a trip but riding the full route.

Short ticketing is incredibly problematic for transit agencies. In the UK alone, fare evasion costs the UK rail system an estimated £240 million every year, with short ticketing representing an often undetected aspect of the broader issue. Not only does it cost agencies revenue, it also skews ridership data, making it harder for agencies to plan services and set fair fares.

Working alongside experts from Cubic Transportation Systems , we set out to explore how AI could help us detect possible short ticketing hotspots across the network automatically and inform the proactive allocation of resources.