MiRRORS
Reliable and complete traffic information is critically important for Intelligent Transportation Systems (ITS). From personal traffic apps to traffic control centers, the efficiency and effectivity of the services delivered require accurate and reliable traffic estimations and predictions. In this project, we develop a new innovative multi-scale framework that can deliver these for large road traffic networks using whatever data sources are available. Our approach is unique in the world. First, we develop hybrid approaches where we combine pattern recognition and classification approaches with state-of-the-art traffic flow models. Second, we develop integrated multi-scale solutions, in which traffic information we estimate on one scale level is used to support and strengthen estimation and prediction on other levels. Third, we develop a distributed approach that is robust and scalable for large-scale road networks.
MiRRORS is led by prof Hans van Lint in close collaboration with prof Alexander Verbraeck.
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