ExoC2T: An exogenous-driven spatio-temporal learning framework for cross-city transfer

Forecasting spatio-temporal human mobility is essential for urban infrastructure optimization, yet dense traffic sensing remains prohibitively expensive for many cities. Existing cross-city transfer methods and recent spatio-temporal foundation models usually rely on historical target-side traffic sequences as the prediction context, which limits their applicability to under-instrumented, graph-missing, or newly monitored cities. We study exogenous-driven cross-city transfer, where target traffic must be predicted from static and dynamic exogenous urban context without historical flow input. This exogenous-only observability removes the direct endogenous state observation used by flow-driven predictors and turns cross-city forecasting into latent traffic-generating mechanism recovery under city shift. The key challenge is to recover traffic-state information from indirect external evidence while organizing cross-city response patterns into a mechanism space that can adapt to target variation without destabilizing transferable structure. To address this challenge, we propose \textbf{ExoC2T}, a plasticity-inspired spatio-temporal learning framework. ExoC2T uses an Exogenous-Infused Spatio-Temporal Network (\textbf{EXIST}) to build an exogenous mechanism substrate, where semantic relations, temporal regimes, and environment-conditioned responses are organized into a shared prototype geometry. On this substrate, Stability-Plasticity Adaptation separates a stable exogenous-to-flow core from an adaptive city-conditioned deviation, infers a support-conditioned latent posterior from limited target evidence, and controls target risk through invariant discrepancy reduction and posterior correction. The resulting model preserves transferable exogenous-to-flow structure while regulating target-specific modulation through posterior-dependent plasticity. Extensive experiments on a multi-city benchmark show consistent improvements over strong forecasting and transfer baselines, demonstrating the value of exogenous-driven transfer for low-cost and scalable urban computing.