Intelligent Transportation Systems
We use machine learning techniques to make transport systems smart and adaptable to passengers' needs.
To get from points a to b today, we must adapt to the transport system. We believe it is ideal for society to have a transport system that primarily adapts to us. Our mission is to develop cutting-edge research in both the machine learning and transport research fields to contribute to future transport services that are much more customised, adaptive and flexible.
Our belief is that through its complexity and real-world impact, mobility is an excellent area from which to develop new machine learning methodologies that eventually will be applicable to other fields. We also believe that only when one is truly is engaged with the domain (and its theory) it is, possible to advance science and technology consistently.
The two other fundamental machine learning for smart mobility components are related to simulation and optimisation. Simulation plays a key role in transport and urban models because it is the natural way to include behavioural models (demand) explicitly and try new smart mobility services (supply). Optimisation is an essential ingredient for the adaptive transport system that we need in our world. While we also have expertise in the field, we strongly rely on collaboration, towards the ultimate goal of predictive optimisation.
Contact Head of Section
Francisco Camara Pereira Professor, Head of Section Phone: +45 45251496 camara@dtu.dk