Inferring Behavioral Regimes in Urban Mobility via Spatio-Temporal Optimal Transport

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes.