Many public systems face the serious problem of fare dodging, otherwise known as fare evasion. This occurs when travellers do not buy or validate their travel ticket, which in turn impacts negatively upon the operator’s economic sustainability and fosters a feeling of unfairness among paying commuters. To overcome this issue, solutions such as mass ticket inspections and ticket barriers have been put in place, however they are unable to stop fare evaders.
Stopping fare evaders in their tracks
Under the TRAINSFARE project framework, coordinating company AWAAIT worked towards developing DETECTOR, an automatic real-time video analytics system that enables selective controls for tackling fare evasion. Using AI algorithms, the system analyses the images captured by a camera above ticket barriers and sends an alert to the smartphones of ticket inspectors when it detects fare evasion. This way, passenger flow is not interrupted by checks. The technology and business opportunity were realised in Phase 1 of the project. Now in Phase 2, “AWAAIT’s main objective in TRAINSFARE is to achieve a scalable, internationalised and fully marketable version of DETECTOR,” confirms project coordinator Fransje Portegies. The coordinator adds: “The initial prototype was a working prototype fine-tuned for AWAAIT’s first client’s requirements and operational conditions. The aim of the TRAINSFARE project is to evolve this prototype to a new generation with state-of-the-art AI techniques and methodologies and to commercialise the system across public transport operators worldwide.”
The road to market penetration
“Being a small and young company, AWAAIT’s main initial challenges were to enhance the company’s brand image, expand the awareness of its product offering, and devise and execute the right market development strategy for sustainable growth,” reports Portegies. Market penetration is always harder than expected, especially for pioneering solutions and in markets like public transport. For these reasons, the project activated its presence in social networks, redesigned its website and created a blog on fare evasion in public transport. At the same time, it has participated in professional forums and exhibitions focused on public transport. “Our presence at such events was essential for increasing our brand awareness, for extending our contact network in the public transport sector, and for the generation and follow-up of market development opportunities,” emphasises Portegies. Despite the existence of challenges, “the project’s efforts to date have resulted in the generation of market development opportunities and the evolution of the subjacent technology,” reports Portegies. DETECTOR has already been tested in five cities outside of Spain, and two additional non-Spanish operators are in line to perform pilot tests at their premises in 2020. Portegies notes: “From the technological point of view, we have strongly focused on extending the analytical reach and the maintainability and generalisability of the system.” DETECTOR is increasingly able to work in different environments with a higher precision and execution speed.
“We expect to contribute to the economic sustainability of public transport globally and in creating a fairer and a more secure environment in the places where DETECTOR is deployed,” highlights Portegies. The project also envisions being a successful example of the introduction of spearheading technology, AI machine learning in this case, in the field of public transport, to promote the consideration and adoption of subsequent innovation in this field.
TRAINSFARE, public transport, fare evasion, AI, AWAAIT