A model-based approach for state estimation for networks.

The aim of this thesis is to develop a model that is able to estimate the traffic state of a network (focusing on urban networks) in real time, by taking into account and fusing traffic data from various sources (e.g. VLOG and Floating Car Data) as they arrive. Most approaches in traffic state estimation focus on freeways and mainly use one source of traffic data. In addition, methodologies designed for use in urban networks usually incorporate a simple node model instead of full modelling of junctions, although the influence of junctions on the traffic state in urban networks is significant. The model developed in this thesis uses Streamline by DAT.Mobility as the process model, which includes detailed modelling of junctions. Data fusion of flow (VLOG) and speed (FCD) data is achieved using Extended Kalman Filtering (EKF). The model is validated through a series of tests using artificial ground truth and measurements.