Accurately estimating Origin-Destination (OD) matrices is a topic of increasing interest for efficient transportation network management and sustainable urban planning. Traditionally, travel surveys have supported this process; however, their availability and comprehensiveness can be limited. Moreover, the recent COVID-19 pandemic has triggered unprecedented shifts in mobility patterns, underscoring the urgency of accurate and dynamic mobility data supporting policies and decisions with data-driven evidence. In this study, we tackle these challenges by introducing an innovative pipeline for estimating dynamic OD matrices. The real motivating problem behind this is based on the Trenord railway transportation network in Lombardy, Italy. We apply a novel approach that integrates ticket and subscription sales data with passenger counts obtained from Automated Passenger Counting (APC) systems, making use of the Iterative Proportional Fitting (IPF) algorithm. Our work effectively addresses the complexities posed by incomplete and diverse data sources, showcasing the adaptability of our pipeline across various transportation contexts. Ultimately, this research bridges the gap between available data sources and the escalating need for precise OD matrices. The proposed pipeline fosters a comprehensive grasp of transportation network dynamics, providing a valuable tool for transportation operators, policymakers, and researchers. Indeed, to highlight the potentiality of dynamic OD matrices, we showcase some methods to perform anomaly detection of mobility trends in the network through such matrices and interpret them in light of events that happened in the last months of 2022.

翻译：精确估计起讫矩阵对于高效交通网络管理与可持续城市规划日益重要。传统上，出行调查支撑了这一过程，但其可用性和全面性可能受限。此外，近期COVID-19大流行引发了出行模式前所未有的转变，凸显了准确且动态的出行数据对以数据驱动证据支持政策与决策的迫切性。本研究通过引入动态起讫矩阵估计的创新流程应对这些挑战。其背后的实际驱动问题基于意大利伦巴第大区Trenord铁路交通网络。我们采用了一种新颖方法，整合车票及订阅销售数据与自动乘客计数系统获取的客流数据，并运用迭代比例拟合算法。本工作有效解决了不完整与多样化数据源带来的复杂性，展示了流程在不同交通情境下的适应性。最终，本研究弥合了现有数据源与日益增长的精确起讫矩阵需求之间的鸿沟。所提流程促进了对交通网络动态的全面理解，为交通运营商、政策制定者及研究人员提供了宝贵工具。为凸显动态起讫矩阵的潜力，我们展示了一些通过该矩阵进行网络出行趋势异常检测的方法，并结合2022年最后数月发生的事件进行解读。