Smart mobility start-up Skialabs launched by CWI researchers

Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users.

Publication date: 28-11-2019

Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users. Today, Skialabs was launched by CWI researchers.

Moving around freely and efficiently in crowded urban areas can become increasingly challenging, as cities around the world are growing at a fast pace. Instead of just letting traffic clog up, city dwellers and municipal planners could benefit from software that manages traffic based on real-time data. Now, the new start-up Skialabs offers a platform that instantly incorporates current traffic flows, and streamlines the mobility services by connecting citizens, municipal planners and mobility operators.

Digital twin
The Skialabs platform consists of two elements: a digital twin (or virtual copy) of the city and mobile applications for the end-users. The digital twin exploits data and machine learning to replicate the dynamic interactions between pedestrians, bicycles and vehicles. This allows the platform to analyse current traffic situations and predict futures scenarios.

Mobile application
Instead of simply imposing traffic scenarios, the platform takes into account the many preferences users can have. The accompanying mobile applications register real-time demands and preferences of all end-users. These preferences may include, for instance, a request of a citizen for a garbage pick-up, or the lunch break preference of the driver of a parcel delivery company. Combining this information with the digital twin, together with optimisation and reinforcement learning algorithms, then allows finding cost-effective and sustainable policies for mobility planning.

Machine learning and mathematics
Skialabs was founded by PhDs in machine learning and mathematics: Anastasia Borovykh, Prashant Kumar, and Yous van Halder, who are associated with CWI’s Scientific Computing group. It is one of the first companies the Netherlands to provide a highly accurate digital twin of the mobility flows in the city, by exploiting previously unused dataflows.

Collecting industrial waste
Skialabs welcomed the city of Amsterdam as one of its first customers. In Amsterdam, the Skialabs team aims to optimise the way in which industrial waste is collected from companies. Several municipalities and logistics enterprises have expressed their interest in exploring the Skialabs technology, says Borovykh. “We have noticed a growing demand for data-driven mobility planning, not just for road traffic management such as garbage collection, but also for logistics over the water. With the increasing urbanisation the need for a platform like ours will only grow and we are very excited to be able to contribute to the development of more sustainable and streamlined transportation solutions.”