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We propose a Bayesian inference approach for static Origin-Destination(OD)-estimation in large-scale networked transit systems. The approach findsposterior distribution estimates of the OD-coefficients, which describe therelative proportions of passengers travelling between origin and destinationlocations, via a Hamiltonian Monte Carlo sampling procedure. We suggest twodifferent inference model formulations: the instantaneous-balance andaverage-delay model. We discuss both models' sensitivity to various countobservation properties, and establish that the average-delay model is generallymore robust in determining the coefficient posteriors. Theinstantaneous-balance model, however, requires lower resolution countobservations and produces comparably accurate estimates as the average-delaymodel, pending that count observations are only moderately interfered by trendfluctuations or the truncation of the observation window, and sufficient numberof dispersed data records are available. We demonstrate that the Bayesianposterior distribution estimates provide quantifiable measures of theestimation uncertainty and prediction quality of the model, whereas the pointestimates obtained from an alternative constrained quadratic programmingoptimisation approach only provide the residual errors between the predictionsand observations. Moreover, the Bayesian approach proves more robust in scalingto high-dimensional underdetermined problems. The Bayesianinstantaneous-balance OD-coefficient posteriors are determined for the New YorkCity (NYC) subway network, based on several years of entry and exit countobservations recorded at station turnstiles across the network. Theaverage-delay model proves intractable on the real-world test scenario, givenits computational time complexity and the incompleteness as well as coarsenessof the turnstile records.

Bayesian Origin-Destination Estimation in Networked Transit Systems using Nodal In- and Outflow Counts