Origin–Destination matrices (ODms) are a fundamental input for public transport planning and optimization, as they characterize travel demand across a network. Traditionally estimated from user surveys, ODms are now increasingly inferred from large-scale automatically collected data, such as Automated Fare Collection (AFC), Automated Passenger Counting (APC), and Automated Vehicle Location data (AVL). This review focuses on the reconstruction of static ODms in public transport systems, while accounting for studies that exploit dynamic or short-term observations when these are used to infer static or quasi-static demand patterns. We provide a transversal synthesis of OD estimation approaches by jointly analyzing data sources, modeling assumptions, uncertainty handling, and validation strategies. A structured comparative table summarizes representative case studies across different data contexts, objectives, and methodological families. Beyond a descriptive overview, this review identifies key research gaps, including the lack of uncertainty-aware benchmarking frameworks, the limited propagation of uncertainty across modeling stages, and the strong dependence of reported performance on data quality and validation references. These findings highlight that OD estimation performance is context-dependent and that methodological choices should be aligned with data availability, modeling objectives, and acceptable assumptions rather than with reported accuracy alone.
Burgalat et al. (Thu,) studied this question.