Assessing uncertainties in traffic simulation : A key component in model calibration and validation

Calibrating and validating a traffic simulation model for use on a transportation network depend on field data that are often limited but essential for determining inputs to the model and for assessing its reliability. Quantification and systemization of the calibration/validation process expose statistical issues inherent in the use of such data. These issues are discussed, and a methodology to address them is described. The formalization of the calibration/validation process leads naturally to the use of Bayesian methodology for assessing uncertainties in model predictions arising from a multiplicity of sources (randomness in the simulator, statistical variability in estimating and calibrating input parameters, inaccurate data, and model discrepancy). The methods and the approach are exhibited on an urban street network with the microsimulator CORSIM, and the demand and turning movement parameters are calibrated. A discussion of how the process can be extended to deal with other model parameters as well as with the possible misspecification of the model is included. Although the methods are described in a specific context, they can be used generally, although they are inhibited at times by computational burdens that must be overcome, often by developing approximations to the simulator.

Duke Scholars

Author James O. Berger Statistical Science