DAKIMO - Data and AI as enablers for sustainable, intermodal mobility

Strengthening intermodal mobility ...

Economic efficiency, sustainability, ecology and individuality: Intermodal mobility - the use of different modes of transport on one route - has great potential. But the complexity must remain manageable. In other words: Getting to your destination intermodally, for example with a combination of (rental) bike, public transport and e-scooter, must be as easy and pleasant as reaching for your own car key.

... by means of intelligent planning tools

Since October 2021, the partners in the DAKIMO project (data and AI as enablers for sustainable, intermodal mobility) have been working on using the numerous data already available, for example from mobile applications, public transport operations, and traffic and weather forecasts, to further develop the existing regiomove app of the Karlsruhe Transport Association (KVV). Using modern artificial intelligence methods, the data is processed and analyzed in such a way that it leads to individually sensible route and transport recommendations. In addition to technical implementation, the focus is on sustainability, user-friendliness and data protection.  

Contribution of Fraunhofer IOSB

In addition to project management, we are driving the development of AI methods to fuse live environment data such as weather, traffic conditions, and mobility offers to create preference and behavior models for system optimization and standards-compliant connectivity to the mobility data space. We are establishing smart stations as living laboratories to enable privacy-compliant, automated sensor perception of traffic volumes and provide valuable real-time data. And we are developing the central DAKIMO fusion server, which is responsible for integrating and storing all relevant data, both historical and real-time. Likewise, the server enables the comprehensive evaluation and connection of machine learning algorithms.