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Two of the most basic problems encountered in numerical optimization are least-squares problems and systems of nonlinear equations. The use of more and more complex simulation tools on high performance computers requires solving problems involving an increasingly large number of variables. The main thrust of this thesis the design of new algorithmic methods for solving large-scale instances of these two problems. Although they are relevant in many different applications, we concentrate specifically on real applications encountered in the context of Intelligent Transportation Systems to illustrate their performances. First we propose a new approach for the estimation and prediction of OriginDestination tables. This problem is usually solved using a Kalman filter approach, which refers to both formulation and resolution algorithm. We prefer to consider a explicit least-squares formulation. It offers convenient and flexible algorithms especially designed to solve largescale problems. Numerical results provide evidence that this approach requires significantly less computation effort than the Kalman filter algorithm. Moreover it allows to consider larger problems, likely to occur in real applications. Second a new class of quasi-Newton methods for solving systems of nonlinear equations is presented. The main idea is to generalize classical methods by building a model using more than two previous iterates. We use a least-squares approach to calibrate this model, as exact interpolation requires a fixed number of iterates, and may be numerically problematic. Based on classical assumptions we give a proof of local convergence of this class of methods. Computational comparisons with standard quasi-Newton methods highlight substantial improvements in terms of robustness and number of function evaluations. We derive from this class of methods a matrix-free algorithm designed to solve large-scale systems of nonlinear equations without assuming any particular structure on the problems. We have successfully tried out the method on problems with up to one million variables. Computational experiments on standard problems show that this algorithm outperforms classical large-scale quasi-Newton methods in terms of efficiency and robustness. Moreover, its numerical performances are similar to Newton-Krylov methods, currently considered as the best to solve large-scale systems of equations. In addition, we provide numerical evidence of the superiority of our method for solving noisy systems of nonlinear equations. This method is then applied to the consistent anticipatory route guidance generation. Route guidance refers to information provided to travelers in an attempt to facilitate their decisions relative to departure time, travel mode and route. We are specifically interested in consistent anticipatory route guidance, in which real-time traffic measurements are used to make short-term predictions, involving complex simulation tools, of future traffic conditions. These predictions are the basis of the guidance information that is provided to users. By consistent, we mean that the anticipated traffic conditions used to generate the guidance must be similar to the traffic conditions that the travelers are going to experience on the network. The problem is tricky because, contrarily to weather forecast where the real system under consideration is not affected by information provision, the very fact of providing travel information may modify the future traffic conditions and, therefore, invalidate the prediction that has been used to generate it. Bottom (2000) has proposed a general fixed point formulation of this problem with the following characteristics. First, as guidance generation involves considerable amounts of computation, this fixed point problem must be solved quickly and accurately enough for the results to be timely and of use to drivers. Secondly the unavailability of a closed-form objective function and the presence of noise due to the use of simulation tools prevent from using classical algorithms. A number of simulation experiments based on two system software including DynaMIT a state-of-the-art, real-time computer system for traffic estimation and prediction, developed at the Intelligent Transportation Systems Program of the Massachusetts Institute of Technology (MIT), have been run. These numerical results underline the good behavior of our large-scale method compared to classical fixed point methods for solving the consistent anticipatory route guidance problem. We close with some comments about future promising directions of research.