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Short-term prediction of travel time is a central topic in contemporary intelligent transportation system (ITS) research and practice. Given the vast number of options, selecting the most reliable and accurate prediction model for one particular scientific or commercial application is far from a trivial task. One possible way to address this problem is to develop a generic framework that can automatically combine multiple models running in parallel. Existing combination frameworks use the error in the previous time steps. However, this method is not feasible in online applications because travel times are available only after they are realized; it implies that errors on previous predictions are unknown. A Bayesian combination framework is proposed instead. The method assesses whether a model is likely to produce good results from the current inputs given the data with which it was calibrated. A powerful feature of this …