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This thesis presents a novel method for calibrating urban road network capacities using Partial Least Squares (PLS) regression. The method aims to improve the accuracy and efficiency of congestion estimation by effectively determining road capacities without heavily relying on initial input variables. It explores the influence of parameters such as wiggling amplitude and number of loading vectors, demonstrating its feasibility in urban settings like Stockholm. The research highlights the method's potential applications in origin-destination calibration and other inverse problems, offering a promising approach for urban traffic modeling. The intended audience includes researchers and professionals in traffic and urban planning.

Traffic congestion. --- Urban transportation.