Bayesian demand calibration for dynamic traffic simulations
We 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.
Keywords: disaggregate demand calibration ; dynamic traffic assignment ; microsimulation ; path flow estimation ; Bayesian estimation ; Origin-Destination Matrices ; Generalized Least-Squares ; Path Flow Estimator ; Day-To-Day ; Trip Matrices ; Programming Approach ; Congested Networks ; Link Volumes ; Counts ; Model
Record created on 2011-02-07, modified on 2017-02-16