This thesis deals with the optimal use of existing models that predict certain phenomena of the road traffic system. Such models are extensively used in Advanced Traffic Information Systems (ATIS), Dynamic Traffic Management (DTM) or Model Predictive Control (MPC) approaches in order to improve the traffic system. As road traffic is the result of human behavior which is ever changing and which varies internationally, for each of these phenomena a multitude of models exist. The scientific literature generally is not conclusive about which of these models should be preferred. One common problem in road traffic science is therefore that for each application a choice has to be made from a set of available models. A second task that always needs to be performed is the calibration of the parameters of the models. A third and last task is the application of the chosen and calibrated model(s) to predict a part of the traffic system. For each of these three steps, generally data (measurements of the traffic system) is required. In this thesis, all three uses of data are summarized into data assimilation, which is defined as “the use of techniques aimed at the treatment of data in coherence with models in order to construct an as accurate and consistent picture of reality as possible. It comprises the use of data for model validation and identification (choosing between models), model calibration and estimation and prediction and specifically deals with the interactions between all these tasks”. In this thesis, a Bayesian framework is used in which these interactions can be treated consistently: solving one of these steps automatically leads to the solution of the other steps. Throughout the thesis, the calibration task is always performed first using standard optimization techniques such as regression or gradient-based algorithms. (Author/publisher)
Samenvatting