The Barcelona-based start-up has developed DETECTOR, a smart tool based on Artificial Intelligence-Machine Learning algorithms that protects public transport reveneues and mobility

Barcelona, May 2014 – Some metro and commuter train operators deploy frequent random ticket inspections to control fare dodging. Others combine ticket inspections with fare gates at entrance and exit points. These are reasonably effective, but have a major weakness: they do not fully prevent fare evasion, which not only has a direct negative impact on revenue, but also creates a feeling of unfairness and insecurity among paying passengers.

There are systems on the market that can detect fare evasion at the access gates; however, they have not been successfully applied to apprehend offenders on rail networks. Awaait Artificial Intelligence S.L. puts an end to it with DETECTOR.

Via Artificial Intelligence-Machine Learning algorithms, a camera over the ticket barrier observes the ticket validation process. The system sends an alert to mobile terminals when detecting a fare dodging incident. A sequence of images is sent within 3 seconds to inspector’s smartphones, enabling them to react rapidly and intercept offenders before they reach the platform. The system also feeds back the outcome of each inspection to be seen in real time by other inspectors, allowing coordinated ticket control deployments.

Since ticket controls using DETECTOR are very selective, checks on paying passengers are drastically reduced and overall mobility remains undisturbed. Moreover, fare evasion reduction helps to improve public transport’s economic sustainability.

DETECTOR has been developed in collaboration with the regional and commuter train operator Ferrocarrils de la Generalitat de Catalunya (FGC) as part of their “Smart Train programme”. The system has proven its effectiveness with pilot tests in FGC’s Provença station (Barcelona).