This short paper outlines an innovative pipeline for estimating dynamic Origin-Destination (OD) matrices in railway transportation networks. By integrating ticket sales data, subscription information, and passenger counts from Automated Passenger Counting systems, the pipeline leverages the Iterative Proportional Fitting algorithm to generate accurate weekly OD matrices. The study emphasizes the significance of dynamic OD matrices in studying the impact of external events on transportation patterns, such as pandemics, strikes, or major public events, to inform decision-making and enhance the resilience and adaptability of transportation systems. By bridging the gap between available data sources and the demand for precise and timely OD matrices, the study contributes to a more informed approach to urban mobility planning, supporting sustainable development and community resilience. The paper showcases a real motivating problem, by focusing on mobility data provided by Trenord, the local transport company in Lombardy, Italy.