Learning Continuously Evolving Spatio-Temporal Explanations for Traffic Flow Forecasting

Traffic flow forecasting plays a pivotal role in intelligent transportation systems. However, most existing methods adopt a black-box learning paradigm, resulting in a lack of interpretability in their decision-making processes. Existing explainable methods mostly follow the feature attribution paradigm, leaving explanations at the level of static or discrete feature evidence and making it difficult to reveal the continuous evolution mechanism of spatio-temporal dependencies during dynamic traffic propagation. Meanwhile, prior methods often decouple spatial and temporal explanations, treating spatial structures and temporal dynamics independently and thus failing to capture the intrinsic coupling between spatial dependencies and temporal contexts in model decision-making. To this end, we propose STGIB, a new spatio-temporal graph information bottleneck theory for characterizing continuously evolving explanations in traffic forecasting. STGIB redefines traffic explanation as a time-indexed spatio-temporal explanation trajectory, and jointly learns dynamic key structures and their temporal representations under predictive sufficiency and information compression constraints. Furthermore, we derive tractable recursive variational bounds to make the theoretical objective optimizable, and instantiate a new explainable traffic flow forecasting model via spatio-temporal explanation trajectory generation. Experiments on real-world traffic datasets demonstrate that STGIB maintains competitive predictive performance while generating more faithful and spatio-temporally consistent explanations.