Origin-destination inference in public transportation systems: A comprehensive review

Origin-destination (OD) modeling facilitates effective demand-responsive public transportation planning in order to meet emergent needs. Given recent advances in transit information and personal communications technology, transit OD estimation methods have evolved from relying on limited survey sources to automated big data sources. Innovative modeling approaches have also been developed over several decades to estimate trip ODs, not only for single routes, but also for full networks, including transfers. In this paper, we synthesize a review of the state of the art in research and practice, along with descriptions of key data types and methodological approaches, indicating how they interact. We also discuss current research gaps and opportunities for further innovation. This review provides a comprehensive resource that should facilitate the application of these methods to various transit systems, thus enabling planners and policymakers to gain insights from new and improved model estimates in various transit systems.