DTU - Intelligent Transportation Systems

Our mission is to develop cutting edge research in both Machine Learning and Transport Research fields. Our belief is that, by its complexity and real-world impact, mobility is an excellent area to develop new ML methodologies, eventually applicable to other fields. We also believe that, only when one is truly engaged with the domain (and its theory), it is possible to advance science and technology in a consistent way. It is for this reason that our publication record combines articles in top Machine Learning venues (such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, AAAI, ICML, and many more to come) as well as in the main Transport Research ones (IEEE Transactions on ITS, Transport Research Part C, Journal of ITS and others). The two other fundamental MLSM components to mention relate with Simulation and Optimization. The former plays a key role in transport and urban models, because it is the natural way to explicitly include behavior models (demand) and try new smart mobility services (supply). Finally, optimization 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 optimization.