When Events Reshape Traffic Graphs: Event-Triggered Dynamic Graph Learning for Traffic Forecasting

With the increasing complexity of urban traffic systems and the growing availability of spatio-temporal observations, accurate traffic forecasting under dynamic and event-driven conditions has become a critical challenge. Most existing dynamic graph forecasting methods update spatial dependencies according to fixed time intervals or historical traffic correlations, allowing them to capture that traffic relationships have changed but making it difficult to identify whether such changes are triggered by accidents, road construction, severe weather, or large-scale events. Moreover, event impacts are rarely isolated to individual locations, but instead propagate through upstream and downstream road segments and may further induce traffic redistribution over alternative routes. Existing methods also tend to model long-term traffic regularities and short-term event disturbances within a unified representation, causing transient anomalies to interfere with stable trends and periodic patterns. To overcome these limitations, we propose ETDGN, an Event-Triggered Disentangled Graph Network for traffic forecasting. ETDGN introduces an event-triggering mechanism that jointly exploits explicit contextual information and implicit traffic anomalies to selectively activate graph updates when event-related changes occur. An event-conditioned dynamic graph is then constructed to capture event-induced spatial dependency shifts and model the propagation of disturbances over the traffic network. To prevent transient events from contaminating stable traffic dynamics, ETDGN further disentangles long-term trend and periodic components from short-term event residuals, and incorporates event-aware residual correction into the routine forecast. Extensive experiments demonstrate that ETDGN outperforms state-of-the-art baselines in both forecasting accuracy and scalability, validating its effectiveness for continual spatio-temporal forecasting. Code is available at https://anonymous.4open.science/r/ETDGN-ICLR-2027-C367.