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SMART TOOL TO PROTECT PUBLIC TRANSPORT REVENUES, ASSETS, PASSENGERS AND MOBILITY

Project description

The battle against fare evasion begins

With a shocking EUR 2.9 billion lost annually in Europe due to fare evasion, the battle against this widespread problem has reached a critical point. Random ticket inspections have proven futile, causing inconvenience for honest passengers, while sly culprits continue to exploit the system. Enter the EU-funded TRAINSFARE project, set to revolutionise the game. Answering the plea from Barcelona’s FGC metro and commuter train operator, TRAINSFARE proposes DETECTOR. This precision tool leverages the transport network’s geometry to expose fraud at barriers. Equipped with real-time alerts sent directly to ticket inspectors through a mobile app, DETECTOR confronts fare evasion head-on. Furthermore, it possesses the ability to identify acts of vandalism and unattended baggage and even analyse passenger behaviour.

Objective

TRAINSFARE’s main objective is to help reduce fare evasion on public transport, which causes revenue losses in excess of €2.9bn/year in Europe alone.

To tackle fare evasion public transport operators use random ticket inspections with variable success. This method inconveniently disrupts the natural flow of paying passengers, while fare dodgers all too often find ways to elude spot checks. Therefore, harnessing the geometry of their networks, many metro and commuter train operators (MTOs) have installed fare gates (or ticket barriers) at access/exit points. However, even with fare gates, fraud rates are typically 2-7% of passengers, rising to double digits on some networks.

At the request of FGC (an innovative MTO in Barcelona that could not find an adequate system on the market to step up fraud prevention), we developed DETECTOR, a highly precise and effective support tool that detects fraud at barriers and alerts ticket inspectors in real time via a mobile app so that they can act immediately.

Because the DETECTOR system uses video streams, it can also be used to detect vandalism, unattended baggage, equipment malfunction and behavioural analysis based on passenger trajectories. Our patent pending methodologies have enabled DETECTOR to resolve issues that previously limited the effectiveness of video analytics such as shadows, reflections, occlusions and variable sunlight exposure.

DETECTOR 1.0 has been tested and approved by FGC, is successfully operating in four of FGC’s main stations and further rollout is planned. Tests have also been agreed with other European MTOs.

The UITP (International Association for Public Transport) also recognised DETECTOR’s innovative approach when it was nominated as a global finalist for the Operational and Technical Excellence Award (Milan 2015).

With a feasibility plan developed under a SMEInst Phase 1 grant, TRAINSFARE is now seeking Phase 2 support to scale up and market replicate the system.

Call for proposal

H2020-SMEInst-2016-2017

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Sub call

H2020-SMEINST-2-2016-2017

Coordinator

AWAAIT ARTIFICIAL INTELLIGENCE SL
Net EU contribution
€ 728 703,50
Address
AVENIDA DIAGONAL NUM 601, PISO 8A
08028 BARCELONA
Spain

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Este Cataluña Barcelona
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost
€ 1 585 883,75

Participants (1)