Intelligent Transportation with AI-driven Digital Twins
Contact person: Tor Skeie
Keywords: Intelligent Transportation, Environmental Monitoring, Digital Twins, Cognitive Services
Research groups: Network and Distributed Systems (ND)
Department of Informatics
AI-driven digital twin technology maximizes the application of urban big data in the digital information space, optimizing smart city management and service business processes. For instance, in urban traffic systems, real-time simulation, optimization and comprehensive perception of city traffic can be achieved. By deploying monitoring devices and roadside units, collecting real-time traffic data, and analyzing and applying it extensively, real-time conditions can be highly restored, offering more intelligent and adaptive solutions for optimized resource utilization, smart traffic management, and environmental monitoring (i.e., Cognitive Smart City Services). Simultaneously, leveraging historical and real-time monitoring data through AI-driven digital twins, real-time vehicle tracking and traffic management can be realized, enabling swift fault response, monitoring urban traffic operation, intelligent traffic flow dispatching, and predicting future traffic demands, thereby enhancing overall traffic efficiency. Cooperation with companies such as Cognata, Nornir, and Meshcrafts can also be considered, in addition to those listed below as potential partners.
Methodological research topics:
- Self-adaptation in digital twins
- Digital twin life-cycle
- Decision-making with digital twins
Relevance of natural sciences or technology:
- Traffic engineering (city traffic monitoring, analysis and planning)
- Environmental sustainability (traffic digital twining for smart city services planning and optimization)
External partners:
- Avinor Group AS
- Statkraft AS
- Institute for Energy Technology (IFE)