In this paper, a multi-step sensitivity analysis approach for model calibration is proposed and applied to a complex traffic simulation model with more than one hundred parameters. Throughout this paper it is argued that the application of sensitivity analysis (SA) is crucial for a true comprehension and the correct use of traffic simulation models, but it is also acknowledged that the main obstacle towards an extensive use of the most sophisticated techniques is the high number of model runs usually required.For this reason we have tested the possibility to perform a multi-step sensitivity analysis, where at each step model parameters are grouped on the basis of possible common features, and a final sensitivity analysis on the parameters pertaining to the most influential groups is then performed.The proposed methodology was applied to an urban motorway case study simulated using MITSIMLab, a complex microscopic traffic simulator. The method allowed the analysis of the role played by all parameters and by the model stochasticity itself with 80% fewer model evaluations than the standard variance-based approach. Ten model parameters accounted for a big share in the output variance for the specific case study. A Kriging meta-model was then estimated and integrated with the multi-step SA results for a global calibration framework in the presence of uncertainty. Results confirm the great potential of this approach and open up to a novel view for the calibration of a traffic simulation model.
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