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Bayesian calibration of dynamic traffic simulations

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 sensor data such as 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 estimating practically relevant scenarios in real-time.

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