Periodic Reporting for period 3 - TRAINSFARE (SMART TOOL TO PROTECT PUBLIC TRANSPORT REVENUES, ASSETS, PASSENGERS AND MOBILITY)
Période du rapport: 2019-05-01 au 2020-10-31
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.
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.
Under the TRAINSFARE project framework, AWAAIT is developing DETECTOR, an automatic real- time video analytics system that enables selective controls for tackling fare evasion at metro and commuter train networks.
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 a passenger crossing the fare gate without paying. This way only fare dodgers are intercepted and checked, without disrupting the passenger flow and without causing unnecessary checks on the vast majority of paying passengers.
Thanks to this European grant AWAAIT has been able to grow its team and activity and attain the project objectives.
Using lean principles, AWAAIT has been able to evolve its initial platform, a simple yet effectively working prototype with some limitations, into a more powerful platform with beyond the state- of-the-art AI techniques and methodologies that make DETECTOR ready to scale up internationally.
AWAAIT has participated in professional forums and exhibitions focused on public transport, while improving its online presence, aiming at introducing DETECTOR across mass transit operators worldwide. The continuous communication efforts resulted in a growing interest in the market and the generation of new leads worldwide.
AWAAIT finally completed 7 pilot tests during the TRAINSFARE project. These pilots were of key importance in iterating, further developing and generalizing the technical platform.
The SME Instrument grant also helped AWAAIT in protecting its intellectual property: its first European patent was granted in May 2019 and more are in the pipeline.
AWAAIT has solid leads in its commercial funnel, prepared to make the company grow strongly worldwide when the Covid-19 impact on public transport starts to recede.
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 a passenger crossing the fare gate without paying. This way only fare dodgers are intercepted and checked, without disrupting the passenger flow and without causing unnecessary checks on the vast majority of paying passengers.
Thanks to this European grant AWAAIT has been able to grow its team and activity and attain the project objectives.
Using lean principles, AWAAIT has been able to evolve its initial platform, a simple yet effectively working prototype with some limitations, into a more powerful platform with beyond the state- of-the-art AI techniques and methodologies that make DETECTOR ready to scale up internationally.
AWAAIT has participated in professional forums and exhibitions focused on public transport, while improving its online presence, aiming at introducing DETECTOR across mass transit operators worldwide. The continuous communication efforts resulted in a growing interest in the market and the generation of new leads worldwide.
AWAAIT finally completed 7 pilot tests during the TRAINSFARE project. These pilots were of key importance in iterating, further developing and generalizing the technical platform.
The SME Instrument grant also helped AWAAIT in protecting its intellectual property: its first European patent was granted in May 2019 and more are in the pipeline.
AWAAIT has solid leads in its commercial funnel, prepared to make the company grow strongly worldwide when the Covid-19 impact on public transport starts to recede.
DETECTOR has already been recognized as a pioneering system in the combination of Artificial intelligence and mobile technologies with the aim of tackling fare evasion in public transport.
DETECTOR can help in the economic sustainability of public transport globally and in creating a fairer and a more secure environment in the places where it is deployed.
We aim to be a successful example of the introduction of spearheading technology (AI-Machine Learning in this case) in the field of public transport, hopefully promoting the consideration and adoption of subsequent innovation in this field.
DETECTOR can help in the economic sustainability of public transport globally and in creating a fairer and a more secure environment in the places where it is deployed.
We aim to be a successful example of the introduction of spearheading technology (AI-Machine Learning in this case) in the field of public transport, hopefully promoting the consideration and adoption of subsequent innovation in this field.