A generalizable hierarchical Bayesian framework for estimating network-level transit OD from heterogeneous data sources

Accurate knowledge of passenger movement patterns across the transit network is essential for public transportation agencies, particularly through the estimation of network-level Origin-Destination (OD) matrices. Traditional scaling methods can only scale a single seed dataset to marginal totals from count data, making results heavily dependent on seed quality. To address this limitation, this study presents a hierarchical Bayesian framework that estimates network-level OD matrices by extending Bayesian inference beyond route-level OD flows to also estimate transfer flows constrained by Automatic Passenger Counter (APC) data. The framework leverages transfer information from Automated Fare Collection (AFC) systems or portable device data sources, and introduces transfer blocks, defined as groups of nearby stops served by multiple routes, to localize transfer flow estimation and enable scalability to networks of any size. By preserving posterior variability through a random prior and a non-informative hyperprior, the framework provides a robust alternative to scaling methods even when auxiliary data sources are sparse or incomplete. Validation on the simulated Sioux Falls network shows the method consistently outperforms the Itinerary Scaling Factors (ISF) method across all AFC penetration rates, with the largest improvement at low penetration. A real-world application to Calgary Transit’s bus network, where only a fraction of trips are fare-validated through the MyFare mobile ticketing AFC system, demonstrates that the framework scales to large networks and produces results consistent with both observed transfer rates and earliest-arrival travel-time benchmarks. This makes the framework particularly valuable in modern systems where credit card-based fare collection increasingly fragments passenger mobility data.