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This work proposes a procedure to simplify the demand estimation problem in the dynamic case while guaranteeing reliable solutions and without increasing problem complexity.The procedure does not claim to be an alternative to existing theoretical estimation approaches, but focuses on extending and testing practical solution algorithms based on previous models developed by the authors, in order to improve their applicability in terms of networks sizes and in cases where a reliable a priori estimate of the demand is not available.In fact, the assumption often made by researchers about its availability, or its reliability is not always true.Thus, for dealing with this occurrence a two-step approach is proposed in this paper: it aims at estimating the proper level of demand generated by any traffic zone in the first step, and accurate demand distributions between the different OD in the second one, while preserving the correct traffic regime.Tests carried out show that a more reliable estimation of the demand over time and space is achieved.Main contributions of the new approach are a) a considerable reduction of the variance in calibration parameters, implying that robust and accurate solutions have been obtained; b) its suitability to large-scale networks.The latter point derives from a new variant of the widely adopted Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm proposed in the second step, where an opportune subset of the OD flows is perturbed at once.Performed experiments show that the proposed procedure is able to obtain results as accurate as those of the conventional SPSA reducing the number of variables to be used in each iteration up to 50%.
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