Flötteröd, GunnarBierlaire, MichelNagel, Kai2011-02-072011-02-072011-02-07201110.1287/trsc.1100.0367https://infoscience.epfl.ch/handle/20.500.14299/64034WOS:000297208700006We present an operational framework for the calibration of demand models for dynamic traffic simulations, where calibration refers to the estimation of a structurally predefined model's parameters from real data. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, also at an 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 it is applicable in a purely simulation-based environment and, as our experimental results show, is capable of handling large scenarios.disaggregate demand calibrationdynamic traffic assignmentmicrosimulationpath flow estimationBayesian estimationOrigin-Destination MatricesGeneralized Least-SquaresPath Flow EstimatorDay-To-DayTrip MatricesProgramming ApproachCongested NetworksLink VolumesCountsModeltext::journal::journal article::research article