ITS-Norway is the leading partner in a Norwegian Peer Learning Network called Maas-Peer. Maas-Peer is one of six international Peer Learning Networks for data collaborations hosted by the Open Data Institute (ODI) and Microsoft through Microsoft’s Open Data Campaign.
The Peer Learning Network’s goal is to arrange data collaborations networks of all sizes to enable peer-to-peer learning with access to expert guidance and support to address the challenges they face more effectively.
The Norwegian MaaS-Peer collaboration consists of entities that make up the whole Maas value chain from public and private transportations providers, orchestrators, mobility providers, and the traveler segment as well as supporting knowledge providers: ITS-Norway, Ruter, Nivel, Entur, FourC, SINTEF, Bouvet and Strandli Consulting.
For a Maas (Mobility as a Service) system to operate successfully, access to shareable data in trusted environments is essential to achieve fair business models. Today, this is not the case.
The Peer-Maas collaboration’s primary goal is to identify and understand the real barriers for a viable MaaS related to data sharing and trust. The main questions are: Under which conditions and criteria can data be shared to make MaaS viable for all actors at the same time? What existing or emerging measures and technologies might be useful? The Maas-Peer collaboration network intends to explore issues associated with:
- Technical approaches and emerging technology that may help build trust between participants in the collaboration
- New governance structures to help demonstrate trustworthiness and to build trust between participants in the collaboration
In the first 2,5 months of the collaboration project, the Maas-Peer partners have attended an international kick-off meeting, international workshops with all six peer-learning networks, 1:1-meetings with ODI/Microsoft, and two national Maas-Peer workshops. ODI has introduced the networks to tools like Data Ecosystem Mapping. This method is helping the networks to focus on value chains, trust and trustworthiness. Furthermore, we’ve learned about new methods and technology like Anonymization, Differential Privacy, Federated Learning, Confidential Computing, and Confidential Machine Learning.
In the first Maas-Peer national workshop, we worked with data ecosystem maps for Micro-mobility identifying potential trust elements. A second workshop identified the value of shared data and services between prominent actors in the ecosystem, where we utilized the concept of Value Proposition Canvas.
The next months’ challenges are to continue the value exercises to identify and characterize the trust-elements and search for suitable methods and techniques to handle these. We intend to use the available ODI/Microsoft expert guidance, knowledge, and support and learn from the other five Peer-Learning networks with their trust handling experience.
Author: Øystein Strandli, ITS Norway