machine learning project seta scyfer h2020

Scyfer joins H2020 project “SETA” to create solutions for mobility in metropolitan areas

SETA creates technologies and methodologies set to change the way mobility is organised, monitored and planned in large metropolitan areas. This shows great potential for a machine learning project. The solutions build on large, complex dynamic data. This is collected from millions of citizens, thousands of connected cars, thousands of city sensors and hundreds of distributed databases.

See also the SETA website.

SETA allows us to understand and model mobility with a precision and granularity that is impossible with today’s technologies.  The resulting models will inform decision makers on how to improve town planning and infrastructure. Furthermore the models allow us to provide support for individual citizens to plan their journeys in a more efficient and sustainable way.


SETA will provide effective solutions for the Intelligent and sustainable mobility i.e. the smarter, greener and more efficient movement of people and goods. It will also bring a radical change from transport as a series of separate modal journeys to an integrated, reactive, intelligent, mobility system. It will provide always-on, pervasive services to citizens and business, as well as decision makers to support safe, sustainable, effective, efficient and resilient mobility.

Case studies

SETA is heavily based on real world requirements and data. For this reason the project will implement use cases in three different but complementary European metropolitan areas. Moreover, the cities involved in the project have extensive and intense mobility and transport issues: Birmingham, UK; Turin, Italy; Santander, Spain.

The consortium involves partners from 5 countries, UK, Italy, Spain, Poland and The Netherlands.

Machine learning project by Scyfer

SETA is a great opportunity for Scyfer to develop a computer vision solution that allows for recognition of cars, pedestrians and public transport passengers. This solution can become highly accurate the more examples it receives. That in turn can lead to a better insight in traffic fluidity and mobility issues in cities. See our SETA project page for more on how we solved this challenge.