Public transport is crucial to the liveability of any city. It plays a key role in the reduction of congestion and pollution and in the increase and improvement of mobility.
Most transport operators depend on ticket revenue to sustain their operations. This pay-per-use method is commonly guaranteed with the usage of travel tickets that are canceled or validated when accessing the transport network. However, some passengers do not buy or validate a travel ticket. This practice, known as fare-dodging or fare evasion, is a serious problem in many public transport systems around the world. Not only does fare evasion have a negative impact on operators’ economic sustainability, but it also creates a feeling of unfairness among paying commuters.
A common way of reducing this problem is the deployment of random mass ticket inspections: stopping every single passenger in the vehicles or in the premises of the transport company, to check whether they carry a valid pass. These mass inspections interfere with the overall passenger flow and affect the customer experience, which makes them an inconvenient solution especially during rush hours. Many operators rely on the installation of ticket barriers, also called fare gates, at the entrance and/or exit of their premises, to keep fare evasion low. Yet fare evaders have learned to dodge fare gates with practices like tailgating (passing close behind a paying passenger), jumping over fare gates, entering through exit doors and other methods to avoid paying.
Under the TRAINSFARE project framework, AWAAIT is developing DETECTOR, an automatic real-time video analytics system that enables selective controls for tackling fare evasion.
Using Artificial Intelligence (AI) algorithms, DETECTOR 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 only fare dodgers are intercepted, without disrupting the passenger flow and causing unnecessary checks, even during the busiest hours.
This pioneering system exerts a strong deterrent effect (offenders intercepted shortly after entry, for other passengers to see), effectively reduces fare evasion and facilitates the job of ticket inspectors.
DETECTOR enables the use of leaner ticket inspection teams that can move faster around the network, as well as a better traveling experience for paying passengers. Furthermore, ticket inspectors that have tested the system are eager to adopt it.
After successfully proving the technology and business opportunity in the earlier Phase 1 project, AWAAIT’s main objective in TRAINSFARE is to scale up and internationalize DETECTOR. Firstly, the company aims to evolve the current platform to a new generation with beyond the state-of-the-art AI techniques and methodologies. Secondly, AWAAIT aims at introducing the system across public transport operators worldwide. Finally, AWAAIT pursues to become a world class player in the development of industrial solutions that use AI.
