Jul 2026
DRIFT: Regime-Aware Self-Supervised Learning, Zero-Inflated Forecasting, and Inference Fusion for Metro OD Demand under Temporal Distribution Shift
Results indicate that jointly modeling regime consistency and sparsity is effective for long-horizon OD forecasting, and that the core representation and sparsity-aware designs alone, without the inference-time fusion, already surpass all baselines.
Hsiang-Chuan Chang, Tzu-Chia Huang, Chih-Yung Chang et al.
· Journal of Internet Technolo... · 0 citations