Operator Learning with Branch–Trunk Factorization for Macroscopic Short-Term Speed Forecasting

Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepONet)-based framework that treats traffic prediction as learning a mapping from historical states and boundary conditions to future speed states, enabling robust forecasting under changing scenarios. We project logistics demand onto a road network to generate diverse congestion scenarios and employ a branch–trunk architecture to decouple historical dynamics from exogenous contexts. Experiments on both a controlled simulation dataset and the real-world Metropolitan Los Angeles (METR-LA) benchmark demonstrate that the proposed method outperforms classical regression and deep learning baselines in cross-scenario generalization. Specifically, the operator learning approach effectively adapts to unseen boundary conditions without retraining, establishing a promising direction for resilient and adaptive logistics forecasting.