PGET-Net: Physics-Guided Neural Network with Event-Text Conditioning for Traffic Forecasting

Accurate traffic forecasting during disruptive events is essential for dynamic traffic management and emergency decision-making, yet remains challenging. Disruptive events, such as extreme weather and traffic accidents, can disturb routine urban traffic patterns, thereby limiting the ability of methods that rely primarily on historical observations to accurately capture abrupt changes in traffic states. Although event-related text has been incorporated into forecasting models, it is often treated as auxiliary information rather than explicitly coupled with traffic dynamics. We propose PGET-Net, an event-text-conditioned physics-guided neural network that uses event semantics to modulate traffic-state evolution for multi-step forecasting. PGET-Net learns a nonlinear mapping from observed traffic states to a latent traffic potential field and models its evolution using diffusion-advection dynamics. The diffusion component describes routine propagation over the static road topology. The advection component maps event semantics to a graph representing event-impact propagation, which parameterizes event-induced perturbations to the latent dynamics. Both mechanisms are represented within a graph differential equation network, where a learnable gate adaptively combines their contributions to the evolution of the latent field. The resulting trajectory is decoded into future traffic prediction. Experiments on the Beijing Text-Traffic dataset (BjTT) show that PGET-Net outperforms the evaluated baselines, supporting the effectiveness of coupling event-conditioned perturbations with routine traffic propagation.