Modeling Continuous and Heterogeneous Spatio-Temporal Dependencies for Accurate Traffic Forecasting

Accurate traffic flow prediction is fundamental to intelligent transportation systems, yet it remains challenging due to the complex spatio-temporal dynamics of road networks. Most existing approaches model traffic conditions as discrete snapshots, which fail to capture the underlying continuous evolution of traffic states and lead to significant information loss. Moreover, many methods insufficiently model spatial heterogeneity, inadequately distinguishing between local geographic proximity and global semantic correlations. To address these limitations, we propose Spatio-Temporal Forecasting with Adaptive Continuous learning and Heterogeneity awareness (STFACH), a novel framework for traffic forecasting. First, we introduce an Adaptive Continuous Learning module that bridges discrete observations and continuous dynamics, while capturing intrinsic volatility often filtered out by deterministic interpolation. Driven by a learnable control path derived from intrinsic traffic volatility, this module leverages attentive neural differential equations to reconstruct continuous latent trajectories and explicitly model fine-grained temporal evolution. Second, we design a Heterogeneity-Aware module to resolve spatial ambiguity by disentangling spatial dependencies into dual branches, constructing adaptive adjacency matrices that jointly capture local geometric relations and global semantic affinities. Extensive experiments on ten real-world benchmark datasets spanning diverse traffic scenarios demonstrate that STFACH significantly outperforms state-of-the-art baselines, validating its effectiveness and robustness in modeling both continuous dynamics and structural heterogeneity. The code is publicly available at https://github.com/MaxRubby/STFACH_2026.