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. Once all available models are calibrated, a choice can be made between them. The selected model(s) can then be used to make an as accurate prediction as possible. One very important feature of the Bayesian framework is that it takes the complexity of models into account in the model comparison step. More complex models generally show a lower calibration error than more simple models, but they do not necessarily make better predictions. This is known as the problem of overfitting. The Bayesian framework deals with overfitting by penalizing models which contain more parameters and are thus more complex. The Bayesian assessment of models produces a measure called the evidence, which balances between a goodness of fit to the calibration data set and the complexity of the model. Besides this, the framework has more benefits. First, prior information can easily be included in each step of data assimilation. Second, error bars can be constructed on the predictions. This may be beneficial to the performance or public acceptance of ATIS, DTM or MPC systems. Third, a committee can be constructed, in which predictions of multiple models are combined. Committees generally produce more accurate predictions than individual models. The Bayesian framework for data assimilation is applied to three different phenomena: (1) car-following modeling, (2) travel time prediction and (3) traffic state estimation using a first order traffic flowmodel (the LWR model) and an Extended Kalman Filter. Finally, a part of the research is devoted to speeding up the EKF such that it can be applied together with the LWR model in real time to large networks. Car-following behavior Recent research has revealed that there exists large heterogeneity in car-following behavior such that different car-following models best describe different drivers behavior. The choice of a car …