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We present an operational framework for the calibration of demand models for dynamic traffic simulations. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, at a likewise individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of time-dependent traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet applicable in a purely simulation-based environment, and, as our experimental results show, capable of handling large scenarios.