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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - TRAINSFARE (Transport System with Artificial Intelligence for Safety and Fare Evasion)

Teaser

Summary

One of the concerns of Mass Transportation Operators (MTO) worldwide is the significant amount of users that avoid paying (fare dodging), amount that is increasing nowadays (estimated value of â‚¬1.8 billion/year).
In the case of rail and metro, the installation of mechanical fare gates activated with magnetic cards helps reducing fare dodging but some users take advantage of the gate closing delay (necessary for safety reasons) and pass right behind the previous user, without validating any ticket whatsoever (tailgating). There is currently no fully operational solution for this problem except permanent human surveillance at the gates or the frequent deployment of mass controls: a group of inspectors checks every passing user. These mass controls are cumbersome, inconvenient for the paying user and easily avoidable by the fare dodger.
HAL has developed an artificial vision system that automatically detects tailgating and allows the selective interception of the suspected wrong-doer even before they reach the platform.
The system is being developed in collaboration with a globally respected MTO active in Barcelona, FGC (Ferrocarrils de la Generalitat de Catalunya), within their Smart Train program. While in Phase1, a pilot under regular operating environment has proven the effectiveness of the system, reducing tailgating by 70%.
After successful completion of Phase 1 (proposal 663110), we plan to enter into Phase 2 in order to scale-up and disseminate our system. Some additional development is required to enhance the capabilities of the application validated during Phase1.
The Feasibility Study had the following objectives:
- Design the global growth strategy of the company (market study, customer needs, prioritization, commercial alliances)
- Determine additional R+D efforts needed to cope with global customer requirements. IP strategy
- Design the required business structure to support commercial growth and R+D.
- Perform a Quality & Risk Assessment
- Prepare a Data management and IPR plan
- Define the Industrialization, internationalization an dissemination strategy

Work performed

The Feasibility Plan has been completed as planned. Now we have a clear market strategy, a sound IPR strategy and a plan to obtain the additional resources needed for the success of the project

Final results

We expect that our initial application, a tailgating detection and alert tool, effectively contributes to the economical sustainability of mass transport networks. And we expect to expand the field of application of machine vision, initially in the transportation market