Inductive Transportation Origin-Destination Demand Forecasting via Bayesian Destination-Choice Modeling
TL;DR
A Bayesian spatiotemporal hierarchical model designed specifically for inductive forecasting that is able to accurately estimate O-D flows and provide robust inductive forecasts with full uncertainty quantification, which is essential for robust decision making in downstream applications, such as stochastic network optimization and facility location problems.
Abstract
Accurate forecasting of origin-destination (O-D) demand is critical for transportation network design, planning, and operational management. An underexplored challenge is inductive forecasting: predicting O-D flows for new or unobserved locations, which is essential for evaluating network expansions and new facility placements. However, most existing approaches either are transductive (limited to fixed observed networks) or are deep learning models that lack interpretability, fail to provide uncertainty quantification, and often overlook critical data constraints such as structural zeros. To this end, we develop a Bayesian spatiotemporal hierarchical model for probabilistic O-D demand estimation, designed specifically for inductive forecasting. We assume total trip generation is given and model the destination-choice counts using a multinomial distribution. To handle the high-dimensional probability tensor, which features both simplex and structural zero constraints, we parameterize it via a masked-centered softmax transformation of a latent utility tensor. We then model this latent utility tensor using a CANDECOMP/PARAFAC (CP) tensor factorization to parsimoniously capture spatiotemporal patterns. Crucially, we impose Gaussian process (GP) priors on the spatial and temporal latent factors; the GP over the network provides the principled statistical mechanism to make inductive predictions for new locations. For posterior inference, we propose an Markov chain Monte Carlo algorithm. We validate the proposed model on synthetic data and two real-world O-D data sets. Results confirm our model’s ability to accurately estimate O-D flows and provide robust inductive forecasts with full uncertainty quantification, which is essential for robust decision making in downstream applications, such as stochastic network optimization and facility location problems. Funding: This research is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada [Discovery Grant RGPIN-2025-04479]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2026.0063 .