Learning Urban Access Costs from Origin-Destination Flows via Inverse Optimal Transport

Abstract:Cities deliver basic services through mixed public-private facility networks, including schools, clinics, transit providers, and subsidized service points. In these systems, planners often observe where households go, but not the latent cost function through which they trade off factors such as distance, price, and institutional access. We study this urban problem through school choice in the Philippines, where the country's largest national education subsidy is intended to redirect learners from congested public schools to participating private schools. Treating school-to-school enrollment flows as an entropic optimal transport plan, we recover latent choice costs using two complementary inverse optimal transport models: an interpretable distance-banded model with a subsidy term, and a neural cost model trained through a differentiable Sinkhorn forward pass. Applied to 283{,}016 learner trips across 23{,}820 observed flows in the most populated region, the framework estimates a subsidy-equivalent distance, $\lambda^{(k)}$, interpreted as the kilometers of perceived travel cost offset by the subsidy. The case demonstrates how administrative origin-destination data can be transformed into interpretable planning metrics for accessibility-aware subsidy design, facility siting, and urban service allocation.

Oral Presentation. 2026 International Conference on Urban AI

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.14157 [cs.LG]

(or arXiv:2606.14157v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.14157 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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