StaLiNO: State-Adaptive Latent Neural Operator for Traffic Forecasting

Accurate traffic forecasting supports congestion management and route guidance, and its errors are most costly when congestion forms or dissolves. However, road sensors report flow, which does not reveal the traffic regime: the same flow occurs in free flow and in congestion, where disturbances travel in opposite directions. This raises two challenges: (1) identifying the regime from flow that is dominated by recurring demand, and (2) sharing information within a regime without erasing the differences between regimes. To tackle these challenges, we propose the State-Adaptive Latent Neural Operator (StaLiNO), which forecasts the deviation of flow from a learned daily and weekly base field and shares information through latent traffic states. Regimes are identified by the Reference-Relative Regime Assignment module, which assigns sensors to latent states by their deviation trajectories and fixes these assignments across the input window, so that each state tracks the same sensors. Sharing within regimes is handled by the Regime-Separated Latent Evolution module, which evolves every latent state independently and returns it to the sensors through the same assignments. We further prove that such sharing lowers reconstruction risk exactly when its distortion is below the noise it removes, and that mixing latent states removes noise that does not grow with the number of sensors while adding distortion that does. Experiments on four real-world traffic datasets demonstrate that StaLiNO achieves state-of-the-art accuracy and remains stable when sensors lose their history. Latent evolution improves accuracy with tracked states and degrades it with states regrouped at every step, and the learned states separate free-flow from congested traffic without access to occupancy.