Estimation of origin-destination (OD) vehicle flows and link capacity calibration are fundamental processes in transportation science, especially in the context of transport modelling and simulation. They ensure that transportation models accurately reflect real-world travel behaviour and network conditions.

This thesis develops a simulation-based optimization algorithm for network-wide link capacity calibration. To address the high dimensionality of large-scale networks, the algorithm is integrated with partial least squares (PLS) regression, which reduces the number of variables and enhances computational efficiency. The algorithm is evaluated on an urban road network in Stockholm, Sweden, where it demonstrates feasibility and higher efficiency compared to the simultaneous perturbation stochastic approximation (SPSA) method.

For large-scale OD estimation, this thesis advances the field in two main directions. First, it develops a data fusion framework that integrates multiple heterogeneous data sources, including mobile network data, link count observations, and turning proportion data. Second, it proposes several methods to enhance the computational efficiency of OD estimation in large urban networks. These include: (i) implementing data-driven network assignment (DDNA) using GPS data to construct a fixed OD–to–link mapping, thereby eliminating the need for iterative assignment within a bi-level optimization structure; (ii) applying non-negative matrix factorization (NNMF) for dimensionality reduction, which simplifies the optimization by reducing the number of variables; and (iii) developing a numerical solver based on an interior-point method that exploits structural properties of the assignment matrix, such as sparsity and linearity, to enhance computational performance. The proposed OD estimation methods are evaluated on real-world networks in central Stockholm and Norrköping, Sweden, demonstrating accurate and stable OD and link flow estimates with substantial gains in computational efficiency compared to solving the OD estimation problem without dimensionality reduction techniques or numerical solver improvement.