In today’s public transport landscape, operators are increasingly required to optimise services with limited resources, making data-driven approaches essential for evidence-based decision making. Automated Fare Collection (AFC) systems, installed on public transport vehicles, provide a continuous and reliable source of information on passenger trips, providing the basis for a detailed understanding of mobility patterns at both individual and network levels. The objective of this thesis is to construct Origin–Destination (OD) matrices from AFC data collected in a province of the Piedmont region, and to evaluate the performance of different estimation approaches applied to the same dataset. First, a deterministic method based on the Trip-Chain Model is implemented under the common assumption that only boarding data are available, reflecting the limitations of many real-world AFC systems.